Climate change and its respective environmental impacts, such as dying lakes, is widely acknowledged. Studies on the impact of shrinking hyper-saline lakes suggest severe negative consequences for the health of the affected population. The primary aim was to investigate the relationship between changes in the water level of the hyper-saline Lake Urmia, along with the associated salt release, and the prevalence of hypertension and the general state of health of the local population in Shabestar County north of the lake. Moreover, we sought to map the vulnerability of the local population to the health risks associated with salt-dust scatter using multiple environmental and demographic characteristics. We applied a spatiotemporal analysis of the environmental parameters of Lake Urmia and the health of the local population. We analyzed health survey data from local health care centers and a national STEPS study in Shabestar County, Iran. We used a time-series of remote sensing images to monitor the trend of occurrence and extent of salt-dust storms between 2012 and 2020. To evaluate the impacts of lake drought on the health of the residences, we investigated the spatiotemporal correlation of the lake drought and the state of health of local residents. We applied a GIScience multiple decision analysis to identify areas affected by salt-dust particles and related these to the health status of the residents. According to our results, the lake drought has significantly contributed to the increasing cases of hypertension in local patients. The number of hypertensive patients has increased from 2.09% in 2012 to 19.5% in 2019 before decreasing slightly to 16.05% in 2020. Detailed results showed that adults, and particularly females, were affected most by the effects of the salt-dust scatter in the residential areas close to the lake. The results of this study provide critical insights into the environmental impacts of the Lake Urmia drought on the human health of the residents. Based on the results we suggest that detailed socioeconomic studies might be required for a comprehensive analysis of the human health issues in this area. Nonetheless, the proposed methods can be applied to monitor the environmental impacts of climate change on human health. Environmental risks have been shown to significantly impact human health, either directly, by exposing individuals to toxic agents, or indirectly, by affecting life-sustaining ecosystems2. According to the World Health Organization3, the environmental risk of air pollution is one of the leading universal public health issues and is responsible for about 9 million deaths per year. Environmental effects contribute to about 25% of diseases worldwide4. Environmental risks can take many forms. In recent decades, the environmental challenges associated with climate change, such as drought, deforestation, and desert extension, are increasingly widespread and severe and have significantly impacted the ecology of most of the lakes around the world.The significance of healthcare is undeniable. The prevalence of communicable and non-communicable diseases is rooted in various and sometimes invisible patterns of environmental phenomena. Some diseases, such as hypertension, can be caused by environmental phenomena such as the release of salt-dust scatter or air pollution5. Some noteworthy examples include Lake Urmia, Lake Chad, the Sea of Galilee, and Aral6. Salt-dust scattering occurs in areas where saline lakes are drying up and this can have severe negative impacts on the health of the residents in the area7. Salt-dust scatters are a type of atmospheric phenomenon linked to various environmental and climate impacts8. Dust scatters, which generally affect semi-arid and arid environments, are generated by thunderstorms or significant air pressure gradients that increase the wind speed across a large area9.Drought is one of the most tangible impacts of climate change. It leads to global environmental challenges such as water scarcity, decreased food production, and the drying of large lakes. Large lakes are significant freshwater sources that now face critical consequences due to climate change and associated phenomena11. From the centers of salt diffusion, salt-dust is scattered to the surrounding environment, including residential areas, causing significant air pollution. Particulate matter (PM), particles of variable but very small diameter, penetrate the respiratory system via inhalation, causing respiratory and cardiovascular diseases, reproductive and central nervous system dysfunctions, and cancer1. Therefore, the survey and monitoring of environmental phenomena that may endanger the health of the community should be prioritized.Salt-dust scatters have been occurring more frequently in temperate regions in recent years as a result of the effects of climate change, such as growing air pressure differences between the polar and tropic zones or prolonged drought periods12, with many studies emphasizing the relationship between environmental changes and human health3. In a fragile ecosystem, such as that of a dying lake, the manifestation of various diseases can be expected14. These diseases can include tuberculosis, respiratory diseases, hypertension, asthma, eye diseases, pharynx and larynx diseases, kidney and liver diseases, hepatitis, and many more13. Exposure to air pollution causes various acute and severe health effects, such as respiratory and cardiovascular diseases15. As confirmed by earlier studies, air pollution and dusts scatters lead to tangible environmental issues associated with hypertension and a decline in general health of the affected population19. In addition, heavy metals, bacteria, viruses, and dangerous minerals may be carried by salt-dust scatters and inhaled by individuals, causing health problems8.In this context, earlier studies emphasized the determinative role of the environment on human health20. In this context, vulnerability is an all-pervasive phenomenon in healthcare21. Geospatial health vulnerability mapping enables us to define and quantify vulnerability in geographic space and thus identify areas most in need of intervention22. Geographic Information System (GIS)-based spatial analysis allows us to model the spatial distribution of salt-dust scatters and their effects on human health. Due to the technological advancements over the past decade, the implementation of GIS spatial analysis for human health modeling and assessment has increased markedly. GIS spatial analysis aims to support the treatment of health problems in various geographic areas7. The ability of GIS to complement a range of different information sources associated with people and their locations also makes them particularly beneficial to human health specialists23. As GIS techniques support health planning, mapping of the environmental hazard, management of produced data, and spatial identification of human health vulnerability and risks, governments and organizations can effortlessly recognize the distribution and spread of disease across areas, and thus optimize the planning of intervention measures and monitor their effectiveness25.It is widely acknowledged that human health can be impacted by environmental factors, some of which can be catastrophic. Therefore, developing appropriate mitigation plans based on health vulnerability and risk assessments is critical to minimize the environmental impacts on human health14. Therefore, this research about the health of the local population in Shabestar County in relation to the dying lake Urmia has to main objectives: (1) To identify the relationship between changes in the water level of the hyper-saline Lake Urmia and associated salt release with the prevalence of hypertension in the local population. (2) To assess and map the risk of salt dust effects on human health.Lake Urmia, one of the world’s largest dying lakes, has been facing the impacts of climate change for decades. Due to its hyper-salinity, it is expected to significantly impact its surrounding ecosystems. The salinity of the marginal lake, the increase in total salinity, the reduction of conventional agriculture, and salt-dust scatter are critical aspects of the dying lake2, lies north of Lake Urmia in Iran. The county borders Mishow Mountain to the north and Lake Urmia to the south. The elevation increases from 1286 m at the lake bed to 3126 m in the Mishow mountain range  project of the East Azerbaijan Province provided climatology, topography, and demography datasets of the study area at the scale of 1:25,000  under the ethics code IR.SBMU.RETECH.REC.1394.12144 The STEPS projects already considered the demography of the 12,000 participants, but as we intended to provide the population density of the study area for the task of vulnerability and health risk mapping, we considered the detailed demographic information obtained using the latest census in 2016 for all 135,421 people in Shabestar County. We categorized the population into different classes based on the vulnerability to the salt-dust scatter hazard. Therefore, population data were classified from 15–25, 26–35, 36–45, 46–55, 56–65 based on the health STEPS survey dataset45. We also developed a population density map of the study area based on the census data.Demographic characteristics are one of the major aspects of the health and disease patterns within a society. It is well-known that the population characteristics such as age and gender, along with the anthropometric indices such as the lipid profile and the systolic and diastolic blood pressure, factor into the most common non-communicable diseases such as hypertension, type 2 diabetes, cancer, and chronic respiratory diseases, which affect the health-related quality of life of the susceptible population35. Aspects facing the winds that blow from the lake in the east are more at risk, including western, southern, and southwestern slopes (180 to 360 degrees).We developed a digital elevation model (DEM) based on a 1:25,000 topographic map of the study area. We then used the DEM to generate slope and aspect maps. The slope and aspect were used as geographical indicators as they can affect the extent and distribution paths of salt and dust scatter46. We also used the vegetation continuous fields (VCF) product to analyze the vegetation cover of the study area. The VCF represents the global surface coverage from 2000 to 2020, comprising data on tree cover, non-vegetative cover, and cloud cover. Given its global span and two-decade timeframe, the VCF has proven to be a valuable tool for researchers across disciplines. The VCF dataset is derived from the Global Forest Cover Change-Surface Reflectance product, which is based on the enhanced Global Land Survey (GLS) datasets. The GLS datasets are composed of high-resolution Landsat 5 and Landsat 7 images at a 30-m resolution from Google Earth Engine47.Land use/cover can affect the spread of dust and salt particles around the lake. For instance, vegetated areas, especially with a dense tree cover, can prevent the spread of salt to residential areas, while bare lands or salt lands around the lake are the hot spots of the distribution of dust and salt particles. The land use/cover data for the year 2020 was obtained from our earlier studies regarding the Lake Urmia drought and its respective environmental impacts47. It is well understood that proximity to these saline diffusion centers both directly and indirectly affects the prevalence of respiratory diseases and hypertension. We thus considered the areas within 80 km of saline diffusion sources.Since exposure to the saline sources is expected to significantly impact the residents’ health in the study area, we considered the distance to saline flows as one of the causal criteria. We obtained the saline diffusion center data from our earlier studiesThe spatial distribution of salt-dust scatters is closely correlated with the climate characteristics. The following criteria were considered to represent the climate characteristics of the study area.48.Soil moisture plays an important role in releasing salt particles and dust into the air. Particles are transported by the wind more easily in areas where the soil moisture is low. We, therefore, employed the NASA-USDA Global Soil Moisture Dataset to represent the soil moisture of the study area. The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil moisture dataset provide soil moisture information across the globe at a 0.25° × 0.25° spatial resolution. Values around 0 indicate typical moisture conditions, while very positive and very negative values indicate extreme moisture (soil moisture conditions above average) and dryness (soil moisture conditions below average), respectivelyWind speed is another main factor affecting the impact of salt-dust on the health of the residents. Thus, the wind speed data of meteorology stations were collected from the SDI project of the EAP, and wind speed and direction maps were obtained based on interpolation techniques. Based on this dataset, we consider winds speed of 14–17 knots (7–8.5 m/s) to be causal indicators in the study area.The wind direction determines the distribution of dust and salt particles in the areas around Lake Urmia. The wind ordinarily blows from north to south and from east to west in the study area. In some cases, in the north of Lake Urmia, the wind direction changes from west to east and affects the study area.The research methodology was established based on GIScience's geospatial analysis and spatiotemporal modeling techniques. Landsat earth observation satellite images were used to obtain the trend of exposed salt lands in the area north of Lake Urmia. For this goal, we employed timeseries Landsat satellite images from July 2012 to 2020 provided by NASA/USGS. All image processing performed in geospatial platform of Google Earth Engine. In the second step, a GIS spatial analysis was applied to map the hotspots of hypertension using Moran's I Index. In addition, a GIS-based multi-criteria decision analysis (GIS-MCDA) approach was also used to develop a public health risk susceptibility map based on the relevant indicators. Figure 7.Our first step was to investigate the relationship between changes in the water level of Lake Urmia and associated salt release and the prevalence of hypertension. Therefore, we applied remote sensing time-series satellite imagery to monitor the multi-temporal trend of lake changes. An integrated deep learning and machine learning data-driven algorithm was applied to determine the saline flow sources and salt-dust scatter sources around the lake. We refer to our earlier work in his area for the detailed approach, results, and their validation50. The GMI is frequently used to depict a spatial relationship based on spatial attributes, which can be used to analyze the location of impacting criteria such as land characteristics. We employed the GMI method to investigate the spatial correlation of the hypertension hotspots with environmental indicators . The geographical distribution highlights a complicated distribution and spatial patterns. Thus, the GIS-based GMI can be used to investigate the spatial correlation to determine whether nearby areas have similar or dissimilar values51. The spatial correlation measures the spatial dependency between the values of random variables in different geographical regions52. The GMI spatial correlation index is calculated using Eq. , whereas a value around − 1 indicates dispersed patterns. Random patterns are indicated by a value close to zero53.GIS-based spatial analysis provides unique capabilities for hotspot mapping. Since the survey data was recorded in the GPS points format, we were able to apply a GIS-based point pattern analysis to determine the spatiotemporal pattern of hypertension. Therefore, in the first step, the Kernel Density Estimation technique was applied to obtain the spatial pattern of the hypertension dataset. In the second step, the Global Moran’s Index (GMI) was applied to detect the hypertension hotspots in the study area. The GMI is one of the most efficient spatial autocorrelation assessment methods in GIS54. Of the spatial decision-making methods, the MCDA produces the most consistent judgement as it has a better structure and methods for obtaining and translating human judgements into priorities55. GIS-MCDA methods support meaningful spatial decisions by integrating multiple criteria from various spatial data sources57. Leveraging this capability, we used 11 criteria in three major groups to analyze and develop human vulnerability and health risk maps. The selection of the relevant criteria was based on the data availability and research literature62. Table GIS-based Multi-Criteria Decision Analysis (GIS-MCDA) methods have become prevalent in environmental modeling and natural hazard prediction56. As Table In GIS-MCDA, standardization is critical due to the variety of data sources and the different measurement scales. To facilitate standardization, fuzzy logic can be employed as a mathematical basis for incorporating different variables and decision systems with varying levels of ambiguity54. We employed the integrated fuzzy analytical network process (FANP) approach for criterion weighting. FANP is one of the most widely used methods for calculating criteria weights66, and previous studies attest to its effectiveness57. The integrated FANP technique can be used to calculate the intrinsic weight of each criterion. We applied the FANP to the initial criteria ranking obtained in the pairwise comparison matrix based on the expert knowledge of 30 specialists from the department of environmental health at the Medicine University of Tabriz, who ranked the criteria from 1 to 9 based on the significance of each criterion for vulnerability and health risk. Then, the pairwise comparison matrix was developed using the Super Decisions software, and the criteria weights were computed  and a global sensitivity analysis (GSA) to calculate the uncertainty of the criteria weights. The MCS was computed as follows:T is the simulation, wi is the random number and N indicates the simulation number. The GSA can also be computed using the following equations:Due to the various data sources, expert knowledge for criterion ranking, and modeling faults, uncertainty in GIS-MCDA is inevitablek factors the total number of indices (including the Sis) that should be estimated is as high as 2k − 1. For this reason, a more compact sensitivity measure is used. This is the total effect sensitivity index, which concentrates all the interactions involving a given factor Xi in one single term. For example, for a model of k = 3 independent factors, the three total sensitivity indices would be as follows73.Equation  is knownAnalogously:73. It expresses the total contribution to the variance of Y due to non-Xi, i.e. to the k − 1 remaining factors, hence Xi. In general, Xi a notable difference between STi and Si flags an important role of interactions for that factor in Y. Highlighting interactions between input factors helps us to improve our understanding of the model structure73. In the context of implementation variance-based GSA we continued the analysis from calculating the importance of spatial bias in determining option rank order by means of Average Shift in Ranks (ASR) as follows69:a_rankref is the rank of option A in the reference ranking , and a_rank is the current rank of option A. ASR captures the relative shift in the position of the entire set of options and quantifies it as the sum of absolute differences between the current option rank (a_rank) and the reference rank (a_rankref), divided by the number of all options69. In this regard, the original FANP weights can be applied as input weights for MCS and GSA-based sensitivity analysis. Thus, the MCS-GSA implementation consists of four essential steps, namely: (a) obtaining training data from the results of the hypertension hotspot analysis, as discussed above, (b) using the FANP's weights as reference weights, (c) running the MCS-based simulation 10,000 times, and (d) computing the spatial distribution of ranks  results using the inverse weighted distance spatial interpolation method. As part of the GSA implementation, we also computed the two critical indices of S (first-order) and St (second-order) using the GSA approach . Technically, the S and St stand for the FANP's weights, which are indicated semantically. The uncertainty associated with the criteria weights can be defined as any difference between the value and order of the S and St indexes and the reference weights . A considerable disparity in the values of S and St suggests that the reference weights  are questionable, which could lead to erroneous conclusions. Table S and St is not significant, indicating that the computed FANP weights can be used in data aggregation.In other words, the conditional variance in Eq.  can be gs in Eq. , which i57]. In this numerical technique, the geographical criteria from one map layer are integrated with the attributes  of the other criteria72. The OWA is one of the most efficient data aggregation models in GIS that uses fuzzy decision rules for data aggregation and provides a parameterized class of MCDA aggregating operators. OWA provides a parameterized class of multi-criteria aggregation operators between the minimum and the maximum. For a given set of n criterion (attribute) aps, an OWA operator can be defined as the following function 46. The OWA combination operator in Eq.  is a popular spatial aggregation method in GIS that has been supported by research [e.g.,Accordingly, given the sets of criterion weights, W, and order weights, 72. We refer to Feizizadeh and Kienberger73, for detailed information and the mathematical background of the OWA method. In order to apply the OWA as a spatial aggregation method, the standardized criteria were aggregated based on the computed FANP weights.OWA provides a tool for generating a wide range of decision strategies in a decision strategy space, by applying a set of order weights to criteria that are ranked in ascending order on a pixel-by-pixel basis. Using the fuzzy decision rules for spatial aggregation provides further flexibility for the aggregation technique and improves the method's functionalityThe study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki, and the Ethics Committee of Shahid Beheshti University of Medical Sciences under the ethics code IR.SBMU.RETECH.REC.1394.121 approved it. Researches considered all the principles of medical ethics, including the consent of the participants, the confidentiality of personal data and free measurements.We state that the use of experimental human participants is not relevant to this study. We employed a secondary dataset obtained from a health survey of the national STEPS project which is carried out by the Iranian Ministry of Health and Medical Education based on the international standards to reserve all human rights of the participants.2, which increased to 229, 345, and 343.6 km2 in 2016, 2017, and 2018, respectively. The salt-dust scatter sources reduced in 2019 and 2020 to 189.70 and 111.51 km2, respectively  in the vicinity of lake Urmia for the year of 2013. According to their study, the highest concentration of PM was observed during the summer season 81. According to Alizade Govarchin Ghale et al.85, there was an inverse relationship between water level fluctuations of the lake and aerosol pollution. They also observed that the mean of aerosol optical depth increased to 0.42 from the period of 2010 to 2019. Based on this study, the annual mean PM10 concentration has increased after 2013 significantly. They pointed out the maximum 24-h mean PM10 concentration to be in December of 2015 to be about 876.13 μg/m385. As our study as well as the related studies also indicated, the water level of Lake Urmia dramatically dropped from 2013 to 2015 which accordingly exposed incisive saline flow sources.Review of research laterite regarding the air pollution resulted from the saline flow resources of the lake Urmia indicated the significant increase in aerosol pollution around the lake over the last 10 years. The significance concern of harvesting deposited salts from the exposed saline flows from the lake bed. Based on these studies, the dust emission phenomena could become more severe and widespread because of uncovering and drying of sludge existing under the salt layers78. The field of epidemiology makes use of the capabilities of GIS, which has been one of the most significant investigation domains in the past two decades88. Considering that epidemiology is completely related to the spatial dimension, questions about the origin and prevalence patterns of diseases can thus be answered based on a GIS spatial analysis47. Access to accurate data can help identify and treat disease and prevent the occurrence and spread of disease using screening and prevention measures. In this regard, strong statistical approaches were developed to identify spatial or spatiotemporal clusters88.From the methodological perspective, results of this study pointed out that remote sensing enables us to represent the spatial and temporal relationships of various phenomena, which is critical for monitoring the environmental impacts on human health. Our results indicated that the integrated GIS and remote sensing (or Geoinformation) approach could be applied for human health monitoring and vulnerability and risk mapping through forecasting the future environmental conditions and their impacts. Healthcare planning and GIS are two related fields that both depend on spatial data. Spatial features in healthcare include the geographical location of the patient and the distribution of healthcare facilities, the management of which is supported by various GIS functions and models. One of the issues that justifies the existence of an appropriate GIS system in the healthcare field, is less reliable population data, sensitivity to lost data, and data elimination in sparsely populated areas24. Planning the allocation of healthcare resources is critical to assure maximum equality in healthcare access90. GIS has played a crucial role in managing various health issues , which has led researchers to consider the spatial relationships between health and various influencing factors affecting it. In this context, GIS-based analysis plays a principal role in the spatial modeling of pandemics to support epidemic prevention and control measures, determine the spatial allocation of health resources and recognize social behavior patterns89. GIS has played a significant role in analyzing health issues by applying analytical approaches and evaluating the spatial inequality of access to healthcare facilities. The use of GIS for surveying healthcare planning problems is vital to relevant studies65. It is vital that all patients are able to access healthcare facilities and political policy should ensure that access to adequate healthcare is available to all. In this regard, to define a unique theoretical geographic unit for the healthcare market , it is necessary for researchers and policy-makers to make use of a GIS spatial analysis.The combination of public health and informatics shows how numerous health problems affect certain populations and identifies the trends of this impactOur study faces some limitations in terms of data availability. The health data we have is of good quality (reference), but it only represents a small sample of the total population. Not all age groups are covered, for example. There could also be a temporal lag between exposure to salt dust, or other variables that affect health, and suffering the negative health effects of these impacts. Long-term analyses should be established to address this effect in more detail. Moreover, we had to approximate the determination factors of hypertension using indicators because of missing modeling results on salt dust distribution patterns, e.g., wind speed, land use. A salt-dust diffusion and impact model is a necessary next step to better understand the actual spatial and temporal effects of salt dust. Finally, in several steps of the analysis, expert knowledge was used to inform weight adjustments, our choice of indicators, and to define parameters. We took care to do this with transparency towards the experts and leveraging the current body of knowledge. Monitoring future health outcomes in relation to the proposed risk map and transferring the presented approach to other regions may be useful to further develop the approach.The main objective of this research was to monitor the spatiotemporal impacts of the Lake Urmia drought on the human health of the local population in Shabestar County. As indicated in the research methodology, we also carried out GIS-MCDA-based vulnerability and health risk mapping (VHRM) to ascertain the future health risks in the study area. We compared the resulting average hypertensive map against the selected criteria to analyze the spatial correlation of the number of hypertensive patients in the study area with selected criteria for VHRM. According to our results, the main conclusion to be drawn from this study was the direct association between Lake Urmia drought, the expansion of salt diffusion centers and their respective salt-dust scatters, and the increase in hypertension risk in Shabestar County. The study indicated that the appearance of salt diffusion centers around the lake, coupled with certain environmental criteria , can significantly contribute to the increase in hypertension in all area of Lake Urmia basin which lead us to conclude that the lake drought will further impact the health of the residents around the lake in years to come. It must be highlighted that about 7.3 million people live around Lake Urmia, and we only evaluated a small fraction of Shabestar County based on the data available through the health care centers and STEPS survey. Thus, we expect the actual number of hypertension patients to be much higher than the number of patients identified in this study, although the percentages certainly indicate a general trend. Therefore, the results of this study are critical for decision-makers and authorities because they provide detailed information regarding the environmental impacts of the lake drought on salt-dust-scatter and its respective impacts on the health of the residents in Shabestar County.90. Therefore, we conclude that a comprehensive study that considers lifestyle and nutrition might be required to provide further details to thoroughly analyze public health in this area. The results of this initial research could already be used in planning measures to allocate services to areas with a high risk of hypertension to identify and mitigate possible damage caused by consequences of the lake drought through strategies such as preventing the destruction of halophyte plants around the lake. This study clearly indicates the significant impacts of environmental issues associated with dying lakes on the health of the residents. In considering the extensive impacts of global climate change and the issues associated with dying lakes, we conclude that the proposed integrated Geoinformation approach can be efficiently applied to investigate the relationship between environmental degradation and scenario-based human health vulnerability and risk mapping in other areas with dying lakes around the world.Even though the results show a significant correlation between the lake drought and increasing numbers of hypertension patients in Shabestar County, it must be said that hypertension can also be impacted by lifestyle choices, nutrition, and socioeconomic status. It might even be worthwhile considering the impacts of the current economic situation in Iran that led to a drop-in revenue, a depreciation of the national currency, an increase in inflation, and unemployment. These issues significantly impact the quality of life by reducing people's overall wellbeing and their capacity to acquire basic necessities such as nutritious food, healthcare, and medicine


It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM) model for surface temperature prediction of the billet, which is further improved by Logistic Chaotic Mapping and Golden Sine Algorithm . Using the Improved Logistic Chaos Mapping and Golden Sine Algorithm to find the optimal initial sparrow population, the value of penalty factor  During the process of continuous casting production, the secondary cooling control, which is the essential part of the billet solidification process, largely determines the billet structure and defects. The use of a reasonable secondary cooling system to obtain a suitable solidification rate is the key to ensure an efficient continuous casting process and thus the good quality for billets.. The billet surface temperature, as an important feedback parameter reflecting the intensity of secondary cooling is a prerequisite for the dynamic control of secondary cooling water. Oftentimes, unreasonable pouring process and secondary cooling water distribution system could lead to quality defects inside the billet. In order to minimize the billet defects, the solidification process must be controlled. If the surface temperature of billet can be accurately obtained and feed back to the secondary cooling control system for process optimization, then the dynamic optimization of the secondary cooling water distribution and the closed-loop control of the billet temperature field can be accomplished, which is of great significance for the secondary cooling control, improving the quality of the billet, reducing the internal cracks of the billet and improving the automation level of the continuous casting process.1 using conventional contact temperature measurement methods. The presence of water film and randomly peeled iron oxide on the surface of the billet, as well as the varying surface emissivity of the billet would all make the single-point thermometer and thermal imaging camera (based on the infrared temperature measurement mechanism) unable to functionally and accurately operate during a lasting time period4 The temperature field based on mechanism model is an important research topic of billet temperature analysis. N. K. Nath, et al.10 achieved the acquisition of billet temperature by establishing the heat transfer mechanism models of billet in metallurgical process. However, continuous casting process is a complex process both physically and chemically, and it has been a difficult task to obtain model parameters during actual production process. Theoretical models are sophisticated on paper and yet cannot be guaranteed in real-life application. Shu Fuhua, et al.11 used the Least Squares Support Vector Machine(LSSVM) to predict the billet temperature, but the prediction still has room for improvement. Sun Jie, et al.12 established the billet surface temperature prediction model with the Ant Colony Algorithm, but also carries the drawback from this algorithm of easily falling into the local optimal.Due to the influence from the high temperature as well as the water mist environment of the secondary cooling zone, it is difficult to measure temperature of the moving billetAiming the the applicability and accuracy of billet surface temperature analysis and prediction, our billet surface temperature prediction model based on ILGSSA-LSSVM is proposed in this paper. In our proposed method, first a Least Square Support Vector Machine (LSSVM) regression model is established to predict the billet surface temperature; secondly, the initial weights and thresholds of the model are optimized by using the sparrow search algorithm (SSA);finally, the initialization quality of SSA is improved by using an improved logistic chaos mapping, and the global search capability of SSA is improved by using the golden sine algorithm. This paper is organized as follows: Section "13 in 1995 is a machine learning model that seeks the best compromise between the learning accuracy of training samples and the ability to identify arbitrary samples based on the VC dimension theory and the principle of minimum structural risk. SVM has strong generalization, compatible to solve nonlinear problems and can avoid local minima in solving small sample training set.The support vector machine (SVM) theory proposed by C. Cortes14 proposed the least squares support vector machine (LSSVM). Based on the original method, the two norms are used and the inequality constraint is changed into equality constraint, so that solving the convex quadratic programming problem is transformed into solving linear equations, and the efficiency is improved.In 1999, J.A.K. SuykensThe mathematical description of LSSVM is as follows: Suppose there is a training data set composed of In order to solve the problem of partial specific points, the error variable Lagrange multiplier is introduced to solve the optimization problem:According to the KKT Conditions (Karush–Kuhn–Tucker Conditions), take the derivative of each variable to solve the values of For the new sample In LSSVM regression modeling, the prediction accuracy depends on the value of penalty factor 15. In foraging behavior, the population of sparrows is divided into the finder population and follower population. After foraging, those randomly selected individuals in the population turn to be the guard population. The finder population is responsible for finding the feeding area and direction, and the follower population forages with the finder population. Each sparrow could be a finder, but the ratio of finders to followers remains constant throughout the population. When the alarm value is greater than the safe value, the sparrow population will give up the current position and fly to the safe area.Sparrow search algorithm is a population optimization algorithm based on swarm intelligence, specifically the foraging behavior and anti-predation behavior of sparrowsIn this paper, SSA is adopted to optimize the penalty factor value and kernel parameters of LSSVM, which solves the problem of low prediction accuracy due to the limitation of parameter selection.Assume that there is a sparrow population with a number of The fitness value of the sparrows can be expressed as:21. However, in practical applications, the point distribution of the logistic map is more concentrated in the upper half and less common in the lower half, as shown in Fig. Since the initial solution is generated randomly in SSA, it will cause the initial solution to be aggregated, resulting in uneven distribution of solution space. Logistic chaotic map has the advantage of decent randomness, so we use Logistic chaotic map to generate initial solution In order to compare the improvement effect clearly and intuitively, we set the number of iteration times to 2000, the map points distribution after improvement is shown in Fig. Observing Fig. 25 can obtain high quality area. In this paper, the updating rule of finders' position of sparrow population is defined as:Golden sine algorithm simulating the searching process of unit circle by sine functionWhen The follower population position updating rule in the population is defined as:When The initial population randomly selects 10% to 20% of individuals as guards who are responsible for early warning and detection of the surrounding environment:If When the sparrow population reaches the minimum fitness or the maximum number of iterations, the population stops updating.The improved Logistic chaotic mapping initializes sparrow population.Calculate and sort the individual fitness of the population, and mark the best fitness and the worst fitness.Update the position of the finder population, follower population and guard population.Determine whether the minimum fitness or the maximum number of iterations has been achieved. If not, go to Step 2). If yes, proceed forward.Assign the optimal individual position of output to the Predict the billet surface temperature using LSSVM regression model.In this paper, the fitness  is used to evaluate the global optimization results. When the sparrow population reaches the minimum fitness or the maximum number of iterations, the population stops updating, and the optimal position information output is the optimal value of A comparison of the fitness curves between ILGSSA-LSSVM model and SSA-LSSVM model over the course of 20 iterations is shown in Fig. 26 and SSA are both bioheuristic algorithms that emerged in recent years.. The GWO algorithm is compared with the ILGSSA algorithm. BP network is one of the most widely used neural network modelsthat can learn and store a large number of mapping relations. Here BP neural network is compared with LSSVM. In order to verify the accuracy of this soft sensing method for billet surface temperature, five soft sensing methods including ILGSSA-BP model, SSA-LSSVM model, LSSVM model, GMO-LSSVM model and BP model, will be compared respectively with ILGSSA-LSSVM model.Figure The parameters of ILGSSA-LSSVM model, ILGSSA-BP model and SSA-LSSVM model are set to be consistent. The parameter values of other control models are: in the SSA-LSSVM model, Suppose the warning value of sparrow population is 0.6, the finders take 70% of the population, the followers take 30%, and the guards take 40%. The optimization range of parameters The comparison results of the above six methods are shown in Table The mean square deviation (Evaluation indexes of different models are shown in Table In summary, the ILGSA-LSSVM soft sensing method has the highest accuracy, the best fitting degree and the smallest error fluctuation. In real life production process, the soft temperature sensing method and the infrared colorimetric thermometer verify each other, which aids and improve the calibration of temperature measurementand provides valuable reference for the actual production of steel mills.With the improved Logistic chaotic mapping, the stability and uniformity of the initial solution distribution of sparrow population is improved. Golden sine algorithm is used to obtain better high-quality solution region of sparrow population scanning, which improvesthe global searching ability of ILGSSA-LSSVM.Global optimization method is used to find the optimal parameter combination of In this paper, we proposed a hybrid improved billet surface temperature soft sensing method based on ILGSSA-LSSVM, which achieved good prediction for the surface temperature of billet and provided accurate parameter feedback for secondary cooling water distribution process of continuous casting. Conclusions are as follows:Compared with ILGSSA-BP model, SSA-LSSVM model, LSSVM model, GMO-LSSVM model and BP model, ILGSSA- LSSVM model has the superiority of higher convergence accuracy with fewer iterations, it is able to obtain the surface temperature of billet more efficiently and accurately. We are inclined to believe that our proposed ILGSSA-LSSVM soft sensing method lays a reliable and inspiring foundation for the research of developing techniques for controlling and optimizing secondary cooling water distribution process in continuous casting of billet.


The transformation of an hydrogenated amorphous silicon solar cell (aSiH) into an optoelectronic refratometric sensor has been possible through the addition of dielectric bow-tie resonant structures. The indium transparent oxide top electrode is replaced by a thin metallic layer to selectively prevent the direct transmission of light to the active layer of the cell. Then, an array of dielectric bow-tie structures is placed on top of this electrode, to activate the optical absorption through surface plasmon resonance (SPR). The whole device is exposed to the analyte under measure, which is the surrounding medium. Three different dielectric materials with low, medium, and high refractive index were selected for the bow-ties, namely magnesium fluoride (MgF This technology has been demonstrated for material identification3, food quality assurance4, colorimetry5, environmental quality tests6, or biosensing applications7. Focusing on environmental applications, high resolution and sensitivity devices are required to detect very small quantities of atmospheric pollutants like hydrocarbons, volatile organic compounds, microbiological hazards, etc.14. The monitoring of air quality and composition can be made through the measurement of its index of refraction16, that also depends on other physical parameters (temperature and pressure), and the chemical composition of the atmosphere 17. As a consequence, a refractometric sensor can check if some preset conditions are fulfilled, or a known specimen varies its concentration.Optical sensing based on plasmonic effect has been applied in many areas over the last three decades19. This technology can be applied to gas sensing20, refractive index sensing24, and chemical sensing25. They can also be included in multi-functional and multiparametric sensors26. Plasmonic sensor can be interrogated angularly, spectrally, and opto-electronically. When using the angular dependence of the plasmonic response, the system usually requires moving parts and a high-precision goniometer or expensive integrated readout systems27. The same happens with the spectral interrogation: it needs high-resolution monochromators in the illumination and/or the detection arms28. The pure opto-electronic interrogation method benefits from the electric signal delivered by the sensor itself without needing moving parts and/or monochromators. This fact eases the interrogation subsystems and makes a more compact and more reliable sensor29.Due to their narrow selective response, opto-electronic devices based on surface plasmon resonances (SPR) are one of the solutions for environmental monitoring and sensing30. As a whole, the customized device becomes a self-powered optoelectronic sensor20. Organic and inorganic thin film solar cells are the best candidates for low-cost, light-weight and compact sensing devices31. Among them, hydrogenated amorphous silicon (aSiH) cells are commercial devices that employ an abundant, non-toxic and stable material33 at a quite affordable price. Then, aSiH will be considered in this paper as the base device that will be transformed to work as a refractometric gas sensor interrogated opto-electronically34.A solar cell can be seen as an already-made and low-cost light detector. Although designed as a wide spectral photovoltaic detector, it can be easily transformed to respond selectively through the excitation of SPRs generated by nanostructured metasurfaces16, most of them are expensive and complex to operate. Our system is based on the selective opto-electronic response of a solar cell where its exposed surface is populated with dielectric bow-tie resonant structures sitting on a thin-film metallic front electrode that replace the classical transparent ITO layer.In this contribution, we numerically analyze and quantitatively describe how to modify an aSiH cell to sense changes in controlled atmospheres. Although several optical technologies have been applied to sense refractometric changes in gases18. The modeling domain is a small unit cell or the building block of a large array, which is repeated transversely by applying periodic boundary conditions. An excitation port with a customized wavelength, intensity, and orientation is placed on top of the whole structure as illumination source. At the top of the illumination source we place a perfect matching layer (PML) to absorb reflected waves, and prevent them from falsely interfering with the incoming light. At the bottom, the cell layers are stacked successively on top of each others with the bow-tie on top of them. This later structure excites surface plasmon waves on the metal layer surface35. This excitation happens when a plane wave having its magnetic vector along the 37. Then, we can optimize the period of the design to work for the selected wavelength.A simulation package COMSOL Multiphysics based on finite element method is used to solve Maxwell equations for the optical fields distribution inside every layer in the three dimensional model of the device. These fields are used to calculate absorption at each layer. The extracted current from the device is directly proportional to the absorption in the active layer of the device. Finally, we weight this current to the incident optical power to obtain the responsivity39. A finer mesh leads to better convergence of the solution at a cost of increasing computational resources. Having these criteria into account and considering that our unit cell does not contain extreme tiny structures, we have selected a minimum mesh size around In computational electromagnetism, the selection of an appropriate meshing is a key factor to assure reliable and robust results. In our case, physical domains use tetrahedral elements while perfect matching layer (PMLs) use a prismatic mesh. The density of the meshing elements is related to the dimension of the smallest structure, and should be equal or less than 40. This device has a broadband absorption at the iaSiH active layer in the spectral range (300–720) nm, combined with a total low reflectance due to the transparency of the ITO electrode.The layered structure of a conventional aSiH thin film solar cell is presented in Fig. 41. The response of the device with layer structure Fig. 33. However, for the device in Fig. 44. From a fabrication point of view, the design is flipped, being the Al back electrode directly deposited on the substrate  and bottom (substrate) domains are defined as semi-infinite. In Fig. mentclass2pt{minimmentclass2pt{minimBW, and S for different bow-tie materials.Every optimization begins with the definition of a meaningful merit function that best describes the desired performance of the device. In our case, we want to maximize the short circuit current delivered by the detector, mentclass2pt{minimS and BW as free parameters to present two-dimensional maps of the merit function. This analysis is made for three dielectric materials of the bow-tie. These materials are MgFFor our first optimization step we set the height of the bow-tie to BW tunes its spectral scattering, meanwhile the separation, S, determines the strength of the coupling between adjacent bow-ties. Both effects have a direct impact on the spectral location and shape of the SPRs excited by the structure. Figure BW and S for the three dielectric materials under analysis  and materials, we have analyzed the role of the remaining geometric parameters. When the gap separation between the portions of the dielectric bow-tie, g, increases, we observe a slight, but consistent, decrease in the absorption at the active layer (see g larger than 40 nm. On the other hand, this absorption shows a maximum for a value of After optimizing the absorption at the active layer with an appropriate choice of the geometry (considering ayer see e, especisee Fig. f, allowiz-axis passing through all layers of the device at the resonance wavelength, xz plane for the device using each bow-tie material, showing the SPR characteristic decaying maxima, and the corresponding field in the active layer. The case of the planar cell structure, without resonant elements is also plotted in the figure, showing how the field is enhanced when including the bow-tie arrangement.To better understand the electromagnetic behavior of the device, we have plotted the modulus of the magnetic field along the 18 for the geometrical parameters and material choice already discussed previously at wavelength of The sensing performance of a refractometric sensor is evaluated through the sensitivity, Our design is interrogated electronically, then its responsivity, 49.49 as:The sensitivity of this sensor can be obtained form the variation of 50. The detection of those tiny variations requires high-resolution devices50.A gas under atmospheric pressure shows a very little change in the index of refraction (with an order of magnitude of 10For our device, the analyte is in contact with the dielectric bow-tie and the front metal layer. The electric signal delivered by the system is generated by the absorbed optical power at the active layer, that is enhanced through the excitation of SPR. The dependence of the plasmonic resonance with the index of refraction of the analyte provides the desired functionality of the device. The device is highly sensitive to the presence of gas contentment in air. for example, using the device with bow-tie made of SiOIn this contribution we have taken advantage of the maturity of aSiH photovoltaic solar cells. The addition of a customized metasurface made of an array of dielectric bow-ties on top of a metallic layer, has transformed the energy harvester device into a refractometric sensor. In this transformation, we have also replaced the transparent electrode by a thin-film metallic one. This metal layer is thick enough to excite plasmon resonances that are strongly dependent on the index of refraction of the analyte. The main advantage of this approach is that the change of the index of refraction of the analyte is translated to the response of the device, allowing a simpler design and operation. Then, the sensor becomes opto-electronically interrogated. The optical absorption is maximized by the geometrical parameters of the bow-tie arrangement through the excitation of the SPR. Therefore, we have analyzed in detail how the geometry can be tailored to enhance absorption at the active layer and generate a quite selective response in wavelength and index of refraction related with the conditions for the excitation of SPR. The device can be operated by exposing the bow-tie array and the metallic front thin-film to the atmosphere. As a sensor, its performance is quantitatively given by the value of its sensitivity of As a summary of this contribution, our proposed design opens the way for plasmonic refractometric sensor to be incorporated in simpler and more compact designs, just by exploiting the advantages of photovoltaic technologies and metasurfaces.


We applied a polarization filter array and high-speed camera to the imaging of biological tissues during large, dynamic deformations at 7000 frames per second. The results are compared to previous measurements of similar specimens using a rotating polarizer imaging system. The polarization filter eliminates motion blur and temporal bias from the reconstructed collagen fiber alignment angle and retardation images. The polarization imaging configuration dose pose additional challenges due to the need for calibration of the polarization filter array for a given sample in the same lighting conditions as during the measurement. Recent advances in high-speed cameras have created new possibilities to apply polarization state imaging to dynamic loading cases ,2,3,4,5.In general, there are two different strategies to increasing the acquisition speed of the polarization state data at each pixel. The first is to use a classical optical configuration with a polarization state generator in front of the specimen and a polarization state analyzer between the specimen and the camera. The rotating element of the polarization state generator is then rotated sufficiently fast, generating two polarization and retardation maps per rotation, and the camera frame rate set sufficiently high to collect multiple images per rotation of 180surement ,10,11, asurement .The second approach is to split the camera pixel array into different regions, divided into different polarization angles. Early versions of this method split the field of view into different beam paths with beamsplitters and projected them onto to distinct cameras . Later an images ,3,4,5. On images  developeN polarization states are used, the temporal resolution is reduced by a factor of N. In addition, since the multiple polarization states are used to calculate a single alignment and retardation image, rapid changes in these properties in time can create a phase-lag in the images [N. The second approach is simpler to implement in a laboratory environment, since no rotating elements are required; however, it can be more complex in manufacturing because of the polarizing filter array.The rotating polarization state generator approach has a higher spatial resolution, as the full pixel resolution is preserved; however, the temporal resolution is lower than the camera’s frame rate, since multiple images are obtained per pixel to resolve the polarization and retardation images. When e images ,13. The Both approaches to dynamic polarization imaging have been successfully implemented by researchers for biological tissue samples. Photoelasticity, combined with phase-stepping methods, have been applied to these materials to determine the state of collagen fiber alignment (polarization microscopy). For these applications, the principal material angle and retardation correspond to the orientation and strength of the collagen fiber alignment. Quinn and Winkelstein  developeto 40 Hz ,4,5.At relatively high imaging speeds, the need for uniform lighting at intensity levels, sufficient for the short image acquisition times, becomes a significant challenge. Many biological tissues display a high level of birefringence during loading, for example, due to aligned collagen fibers, but also nontrivial amounts of inherent scattering. The inherent material scattering in tissues, combined with the short exposure time at high-speed imaging rates, can lead to low signal-to-noise ratios in the images. In addition, wide variations in material properties can exist over the region of interest, further degrading the imaging performance. Finally, these materials often deform considerably during dynamic loading, which makes tracking of material pixels between two consecutive images difficult.In this study, we applied the polarization filter approach to dynamic polarization imaging of polymer (low-scattering) and tendon-to-bone insertion (high-scattering) specimens during dynamic, tensile loading at 7000 frames per second (fps). The results are compared to previous measurements (at similar frame rates) of similar specimens using a rotating polarizer imaging system. By comparing the results of the reconstruction of the collagen fiber alignment and retardation , we evaluate the relative merits and issues between the polarization filter and rotating polarization state generator approaches for high-speed imaging of these materials under high deformation.A schematic of the optical train used to measure the specimen birefringence parameters is shown in In the high-speed polarization camera , the image sensor is integrated with an array of 2 × 2 photonic crystal polarization filters at four different orientation states, 0The light intensities in each 2 by 2 array of polarization filters were used to calculate the alignment angle, The high-speed polarization camera provides a resolution of 1024 by 1024 pixels; hence, the resolution of the generated alignment and retardation images is 512 by 512 pixels. For the tests on the polycarbonate specimen, the high speed camera was operated at 3000 fps. However, for tests on the tendon-to-bone insertion specimens, the material displacement at each time step was greater, so the high-speed camera was operated at 7000 fps.Prior to testing, it was necessary to calibrate the imaging sensor array for the specific optical train. This was performed prior to inserting the specimen. The calibration determines the range of expected intensities for each pixel and is performed by using retarders with known retardation values.The high-speed polarization camera measurement system was first applied to specimens with low levels of inherent material scatter (polycarbonate) and then to highly scattering biological specimens (tendon-to-bone insertion), both under rapid tensile loading. These two specimens types were chosen because their visualization with high-speed polarization imaging had been previously demonstrated by the authors using a rotating polarizer system ,13.The uniform-thickness polycarbonate dogbone specimens were cut using a laser cutter. The dimensions of the specimens are shown in Next, biological specimens with much larger amounts of inherent material scattering were tested. Tendon-to-bone insertion specimens, shown in Digital flexor tendon-bone units were removed from porcine forelimbs procured from a local abattoir. The raw tendon-to-bone samples were first frozen to −20 authors .Both specimen types were mounted in a drop tower to apply dynamic tensile loads to the specimen. For the polycarbonate specimens, standard flat grips were used to hold them. For the tendon-to-bone insertion specimens, specially designed curved, textured grips were used to clamp the tendon end of the specimen, which were attached to the frame of the drop tower. The tendon end was first threaded through a hole in the crosshead, so that the crosshead pushed down on the bone end of the specimen under tension to load it during the impact.t = 0 is the start of an impact event. The displacement of the impactor, K = 20.458 kN/m is the stiffness of each of the two springs to decelerate the crosshead [Since the position sensor’s data were not correlated with the high-speed polarization camera images during testing, the first frame in the camera video that showed specimen motion was set to be 0 ms as the time reference. Therefore, rosshead .Eight samples were tested to capture the responses of the polycarbonate samples. The results were comparable between samples. The data from one representative test are presented in this section. Selected video images, alignment angle maps and retardation maps for this specimen are shown in The loading began at 0 ms with increased tensile loading of the specimen until failure at approximately 11 ms. The specimen was loaded under tension along the vertical axis. Generally, there are three different periods that can be seen in the high-speed images. For 0 to 4 ms, the alignment angle images and retardation images show vertical line patterns from right to left. Prior to failure of the specimen, the alignment images were expected to show a uniform angle aligned with the horizontal direction (the slow axis of the tensile specimen). The vertical lines in the initial loading frames were due to the misalignment of the sample in the grips. The specimen was placed in the grips without pre-loading and was rotated relative to the light beam, in the order of a few tens of nanometers of displacement. Once loading was applied to the specimen, it underwent rigid body rotation to align with the loading axis. This rotation appeared as an apparent alignment angle retardation change until the stress induced birefringence was greater than this effect. As the load was increased, the specimen rotated until it was aligned, and the loading was pure tension, by 4.0 ms. A similar phenomenon was observed using the rotating polarizer method on similar specimens [After 4 ms, the specimen was under a uniform state of stress. However, the alignment images from 4 to 8 ms appear faded to a primarily white color. This fading was due to the fact that for the majority of sensor pixels, the calculation of alignment angle was unresolved, and therefore, they were plotted as white. These points can represent alignment angles of 0To test this assumption, the maximum range of the retardation calculation was varied for a similar specimen. We adjusted the maximum limit of retardation and considered only the pixels for which the calculated retardation was below the set maximum limit. At 9 ms, we could see the sample start to neck at the top. This caused the lower portion of the specimen to move down in the images. The stress field also became a much more complicated distribution of alignment angle and retardation. The expected stress concentration lobes near the notch were visible in the bottom-right hand side of the sample. These lobes were identical to those seen using the rotating polarizer . The plaIn summary, the use of the polarization filter array did permit high-speed polarization imaging of the polycarbonate specimen under dynamic loading conditions. The alignment angle and retardation value images were consistent with the expected behavior of the material and previous measurements using a rotating polarizer. Importantly, there is not a temporal bias in the images, since all four polarization states were collected at the same time. Temporal bias was a significant issue with the previous measurements . NumericNext, we discuss the imaging of the biological tendon-to-bone specimens under the same dynamic loading. As mentioned earlier, the imaging parameters were not the same for these specimens as for the polycarbonate specimens; therefore, we cannot compare the noise levels quantitatively. Instead, we consider the advantages and disadvantages of the polarizing filter array high-speed camera for testing of these materials. As compared to the polycarbonate, biological tissue acts as a birefringent medium due to the presence of aligned collagen fibers. The medium is partially scattering and partially transparent; therefore, the alignment angle corresponds to the alignment angle of the collagen fibers, and the retardation corresponds to the strength of the alignment . Figure The dotted blue line in each of the images in The unusual shape of biological specimens means that it was difficult to match the geometry of the camera’s field of view to the region of interest. Therefore, it is common to have areas such as as the bright region in Throughout the loading, we again observed a significant number of white pixels  in the alignment angle images. In this case, the white pixels were due to the high scattering coefficient of the tissue, increasing the noise associated with the calculation of the alignment angle. However, for most of the insertion region, data were collected throughout loading. Additionally, an unsharp mask (radius = 2At the start of loading, the collagen fibers are roughly aligned in the vertical direction. Starting at the initial tension on the specimen at 0 ms, we could see the presence of a stress concentration in the left section, which caused rotation of the fibers to approximately −45The alignment maps and retardation images for the second specimen, shown in Finally, some comments should be made on the calibration procedure for the tendon-to-bone specimens. The calibration of the camera system with the tendon-to-bone insertion was difficult due to the high level of scatter in the tissue; historically, high-speed camera imaging tests have been performed on optically clear specimens. The goal of the calibration procedure is to provide a baseline intensity for each pixel in the camera polarization filter array, from which the relative polarization states will be calculated for later images. For the polycarbonate specimens, a single calibration was performed for all the specimens. The challenge with calibrating the camera polarization array for specimens with high levels of random scattering is that there are large local variations between specimens. This effect could be further exacerbated by the specimen’s movement during the dynamic loading.Therefore, we collected calibration-polarization-state images using three different approaches: illuminating the camera imaging area with the same camera settings as during the dynamic tests; illuminating the camera imaging area while reducing the camera shutter speed to reduce the light intensity closer to that transmitted through the tendon-to-bone insertion specimen; and placing a woven fabric breather sheet between the laser illumination and the camera imaging array to better replicate a high-scattering material and using the original camera settings. These experimental results demonstrated the expected advantages of polarization-filter-array methods over the rotating polarizer approach for high-speed imaging of biological tissues under large, dynamic deformations. There was no time lag within a polarization state calculation, as we obtained instantaneous data for each image at that point in time. We were also able to eliminate any motion blur, as there were no moving parts so as long as the shutter speed was short relative to the motion. The method did reduce the overall image resolution by a factor of four. There are software methods to overlap the images using each pixel multiple times and retain the original resolution, only losing information at the borders . HoweverThe polarization imaging configuration did pose additional challenges due to the need for a calibration of the polarization filter array for a given sample in the same lighting conditions as during the measurement. These experiments showed that the calibration procedure strongly affects the results of the alignment and retardation maps. They also highlighted the potential measurement errors by using a calibration procedure based on a relatively uniform distribution of light intensity. Therefore, the calibration procedure may reduce the sensitivity of the camera to the measurements being obtained in the specimen, and must be thoroughly explored prior to testing of the sample.Overall, the polarization filter array camera offers some benefits over the rotating polarization camera; however, there are still some other factors, such as alignment, reduction of image size and calibration methods that could use further development.


Edge computing (EC) gets the Internet of Things (IoT)-based face recognition systems out of trouble caused by limited storage and computing resources of local or mobile terminals. However, data privacy leak remains a concerning problem. Previous studies only focused on some stages of face data processing, while this study focuses on the privacy protection of face data throughout its entire life cycle. Therefore, we propose a general privacy protection framework for edge-based face recognition (EFR) systems. To protect the privacy of face images and training models transmitted between edges and the remote cloud, we design a local differential privacy (LDP) algorithm based on the proportion difference of feature information. In addition, we also introduced identity authentication and hash technology to ensure the legitimacy of the terminal device and the integrity of the face image in the data acquisition phase. Theoretical analysis proves the rationality and feasibility of the scheme. Compared with the non-privacy protection situation and the equal privacy budget allocation method, our method achieves the best balance between availability and privacy protection in the numerical experiment. The advantages of FR technology in terms of non-contact and convenience have applied themselves to a greater extent in applications such as health testing, mobile payment, personal information collection, and identity authentication, especially since the outbreak of COVID-19. The vigorous development of machine learning (ML)  has greaEC solves the above difficulties in a new way. It analyzes and processes data near the source of data, and there is no data circulation . In EFR Do not rely on the existence of a trusted third-party server;The attacker cannot associate the acquired facial features with other sensitive data;Facial biometrics should be irreversible one-way conversion;Computational complexity shows friendliness to resource-constrained equipment and can be extended to large-scale data processing occasions.Starting with secure data transmission and access control is the most traditional method to solve the privacy protection problem of the FR system, for example, ML frameworks based on homomorphic encryption technology, SecureML  and then subjected to PCA dimensionality reduction processing. And, a set of feature vectors orthogonal to each other are generated, that is, eigenfaces  provides The training of classification models based on perturbed eigenfaces can be seen as an independent transformation process for the output of  Theorem . Since DThereby, the PEPI (Algorithm 3) satisfies The temporary key provides a guarantee for the establishment of secure communication.A terminal device that wants to access the edge network must register by submitting its own ID in order to obtain a legal identity. Then, in the data preprocessing stage, once the edge node finds that a certain device submits fake data, it immediately prohibits its access, refuses to provide it with subsequent functional services, and conducts identity tracking. In this way, it can resist poisoning attacks and collusion attacks.In the identity authentication phase, the edge node judges the validity of the response time from the terminal device. If it times out, even if the ID and registration key provided by the device are correct, it will still be rejected by the edge node. In this way, the terminal device’s replay attack on the system is avoided.The encrypted transmission based on the AES algorithm further ensures the security of data communication between the terminal device and the edge node.The transmission of terminal data to the edge is secure. During the transmission process from the edge node to the cloud platform, the transmission data stolen by the attacker is the principal component characteristic data protected by LDP, so that the attacker cannot directly obtain and infer the original face image data. Therefore, this process can ensure that the face image data is safe.ID or location. Therefore, even if the cloud server leaks relevant information in the face image processing, the attacker cannot associate the user with the leaked information.User privacy is guaranteed because the edge node submits a face feature data set that meets differential privacy protection, and does not carry any other identifying information associated with the user terminal device, such as The system considers the actual application scenarios, and does not assume that the cloud server is completely credible as a third party. Moreover, as a shared storage space, there are other resource users and visitors, and it is not ruled out that individual attackers want to access the image data information in the cloud space and obtain valuable information from it. The system can ensure the safety of the following aspects: We choose two open source datasets to test the performance of the algorithm, namely a small-scale face image dataset named fetch−olivetti−faces (Olivetti) and a larger-scale face image dataset named lfw−funneled people (LFW). The Olivetti data set is taken by the Cambridge Laboratory in the UK and contains photos of 40 different individuals. The LFW data set is a database compiled by the Computer Vision Laboratory of Massachusetts State University Amherst, and is usually used to study face recognition problems in unrestricted environments. LFW contains more than 13,000 gray-scale face images collecting on the Internet.K.We use PCA to reduce the dimensionality of high-dimensional images, which can drop the complexity of subsequent image processing while maximizing the preservation of the original information. Of course, the more features are retained at the end, the less information is lost, but the computational complexity is higher. We implement parameters tuning using the accumulative contribution threshold K eigenvectors corresponding to the eigenvalues to describe the original face space. This set of orthogonal vectors is called eigenface. Eigenface shows the basic features of the input image, and any face image is a combination of a group of eigenface. Figure Figure The eigenface hides the most important biological characteristics of the face database, and with the help of effective face reconstruction technology, the original face image information can be restored through the eigenface. Therefore, it is necessary to protect the privacy of eigenfaces. Figure We use Tensorflow environment and Python language programming for simulation experiments. The server used has the following resource configuration: CPU Intel Xeon CPU E5-2620 v4 2.10 GHz, Hynix DDR4 16 G memory, hard disk with 300 G (solid state) plus 2 T . The recognition performance test algorithm uses support vector machine (SVM) to perform multi-class recognition of face images after PCA dimensionality reduction.We used the weighted average of training accuracy, recall, and F1 scores to reflect the impact of different levels of privacy budget K is constant, increasing the privacy budget improves the accuracy, because a higher privacy budget imposes less randomized disturbance on the eigenface. In addition, when Figure K values are 20, 50, 100, and 150. Through comparison, we find that the number of principal components that retain the original image data after dimensionality reduction also directly affects the accuracy of the final classification. The smaller the K value, the less the proportion of original image feature information is retained, which will restrict the performance of later image analysis to a certain extent.Figure K value reaches a certain value  and the reduced image can cover more than 95% of the feature information of the original image, the performance of the algorithm will be less affected by changes in privacy budget. There is almost no difference in the amount of information contained in However, when the K directly affects the proportion of these feature components that contain the original complete information, which in turn affects the accuracy of face recognition. We keep the privacy budget K, and then compare the accuracy of face recognition in three different situations: no privacy protection, equal allocate privacy budgets and allocate privacy budgets based on the proportion of feature information.PCA retains the main feature components, and the value of the principal components K reaches a certain size, the increase in accuracy will tend to be flat.Figure K increases from 10 to 20, the performance improves rapidly. After K increases to 20, the performance gradually improves. This is because the first 20–40 principal components represent the most important features of the original face image. Although the performance improvement is not too obvious when K is a value after 40, the overall performance is improved, which also shows that the more principal components retained, the better the performance.Figure In addition, we also compared the changes of face recognition performance with principal components under different Table We propose a user privacy protection scheme suitable for FR application scenarios based on EC. This framework takes into account the privacy protection issues during the entire cycle from collection to recognition of face images. A new mechanism of PEPI using the characteristics of LDP is proposed, which combines the proportion of principal component feature information to perform data disturbance to realize the privacy protection of FR users.Our scheme does not require a trusted third party. The edge center uses localized processing methods to apply randomization before the image reaches the untrusted server. In addition, adjusting the privacy budget parameters according to the proportion of principal component feature information can achieve the maximum balance of privacy protection and data utility compared with existing solutions. Of course, there are still some issues worthy of further research in the future, such as improving the security of the key exchange process, edge-based processing with private multi-dimensional classification information and heterogeneous data.


One of them has a defect consisting of SiO2. The Fabry-Perot cavity (FP cavity) formed by the sandwiched photonic crystal achieves up to 14-fold enhancement of the excitation electric field. We modulate the electric field radiation distribution of the fluorescent material by using photonic forbidden bands. For a 3.18 μm thick layer of the fluorescent solution, the structure achieves up to 60-fold fluorescence enhancement. We also discussed that the reason for the different enhancement abilities in different places is the phase change caused by the optical path difference. This design is expected to have applications in display, imaging, etc.One-dimensional photonic crystal structures have been widely used to enhance fluorescence. However, its fluorescence enhancement is low-fold because of a weak excitation field region. In this paper, we used a genetic algorithm to assist in the design of two photonic crystals based on Al Fluorescence-enhanced structures are widely designed for environmental monitoring, biochemical, medical, and display applications ,3,4,5,6.Some studies have explored considerable area fluorescence enhancement, e.g., Markus Nyman et al.  used surTherefore, we propose to realize fluorescence enhancement using one-dimensional photonic crystals. Since one-dimensional photonic crystals will make the excitation electric field intensity will be precisely the same in one plane, ensuring the same fluorescence excitation enhancement multiplier in the same plane. In addition, we design two photonic crystals with high reflectivity to excitation light and high transmittance to emission light versus high transmittance to excitation light and high reflectivity to emission light through the genetic algorithm-assisted design of photonic crystal parameters and combine the two photonic crystals to achieve an upper limit beyond the excitation enhancement of a single common photonic crystal due to the resonant cavity. Compared to some works with a high Q factor ,32. It iThe finite difference time domain method is one of the effective methods to solve the complex electromagnetic environment generated by the interaction of electromagnetic waves with the matter, and in principle, it can solve the problems of arbitrary forms of electromagnetic fields and electromagnetic wave technology. The finite-time difference method takes a discrete approach to the E and H components of the electromagnetic field with alternate sampling in space and time, and in this way, the Maxwell rotational equations with time variables are transformed into a set of difference equations, and the spatial electromagnetic field is solved step by step in the time axis.The genetic algorithm , as a trc1p of the forward photonic crystal; the transmittance Tc1d of the forward photonic crystal point source; the extreme value Emax of the square of the enhanced electric field mode generated by the combination of the backward photonic crystal and the forward photonic crystal; and the reflectance Rc2d of the back photonic crystal point source. The plane wave approximation represents the value of Rc2d . By adjusting the photonic crystal parameters and considering the introduction of defects through the genetic algorithm-assisted design, the large-volume enhancement of Rhodamine 6G is finally achieved by combining the optical resonant cavity under the action of the forbidden band and the conduction band of the photonic crystal. Through numerical calculations, we can achieve a maximum of 60-fold enhancement under x, y, and z polarizations. We analyzed the enhancement conditions of the excitation electric field in the FP cavity. We also illustrate the periodic enhancement characteristics of the reflective fluorescence enhancement structure at large volume enhanced fluorescence and perform a qualitative analysis. This design can be used in fields such as displays and further improve device lifetime.


Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identification characteristics of cassava diseases such as different color of cassava leaf disease spots, abnormal leaf shape and disease spot area were studied. In this paper, deep convolutional neural network was used to classify cassava leaf diseases, and image classification technology was used to recognize and classify cassava leaf diseases. A lightweight module Multi-scale fusion model (MSFM) based on attention mechanism was proposed to extract disease features of cassava leaves to enhance the classification of disease features. The resulting feature map contained key disease identification information. The study used 22,000 cassava disease leaf images as a data set, including four different cassava leaf disease categories and healthy cassava leaves. The experimental results show that the cassava leaf disease classification model based on multi-scale fusion Convolutional Neural Network (CNN) improves EfficientNet compared with the original model, with the average recognition rate increased by nearly 4% and the average recognition rate up to 88.1%. It provides theoretical support and practical tools for the recognition and early diagnosis of plant disease leaves. With the current climate posing a threat to human health, vegetation and biodiversity , and theThere have been many research achievements in judging plant diseases through traditional machine learning methods. Since they are all based on artificial designs of features, they are inefficient and have a large workload . In addiWith the proposals of AlexNet  and VisuHewitt  only useIn view of the above mentioned contents and problems, in order to improve the efficiency of cassava disease classification and recognition, this paper uses the deep convolutional neural network method according to the characteristics of crop disease images in real scenes, takes cassava leaf disease images as the research object, and designs a cassava disease classification model based on multi-scale fusion CNN. A multi-scale fusion module is proposed to extract multi-scale information features of images. Focal loss was adopted to reduce the emphasis on most categories caused by data imbalance, and to solve the problem of low classification accuracy for categories with few samples. CBAM  module wWe enhance and amplify the existing data images and add these images to the existing data set to form new mixed data for training the model. The effectiveness of the proposed model was verified by designing several comparison experiments and comparing them with network models such as Resnet and VGG.The rest of this article is organized as follows. The Materials and Methods section introduces our proposed cassava disease classification model based on multi-scale fusion Convolutional Neural Network. See the results section for experimental results. Finally, in the conclusion part of the summary of this article.To summarize, the main contributions of this study are as follows:(i)a model is developed to recognize cassava disease based on Convolutional Neural Network deep learning.(ii)the accuracy of cassava disease classification model is evaluated using images taken from nature and artificial enhanced images.(iii)a lightweight module based on attention mechanism to enhance the classification accuracy of the cassava disease model.In this paper, a classification model of cassava disease was proposed based on multi-scale fusion shown in Figure 1We generally expand the network scale by increasing network depth D, receptive field W, and resolution R. Compared with AlexNet, VGG network convolution kernels were all replaced with smaller 3×3 convolution kernels (including a few 1×1 convolution kernels), which achieved better training results through deeper network structure. However, with the gradual deepening of network depth, network training becomes more difficult due to the emergence of Vanishing Gradient, over-fitting, and other problems. Even if the problem of Vanishing Gradient is solved, the low precision return will lead to high calculation cost and low efficiency of increasing network depth blindly. For another example, MobileNet  can adjuThe low-level feature map has a small stride, a large size, and a small receptive field to detect the details of small targets. The high-level feature map has a larger stride, smaller size, larger receptive field, and rich semantic information. The model extracts detailed features such as color and texture from the low-level network and extracts the blade shape feature from the high-level network.The key features in the map can be selectively enhanced and the features can be accurately located by redistributing both channel and spatial weights through the Attention Mechanism. Compared with the previous EfficientNet model, which did not include the MSFM module, the new model adds modules based on the attention mechanism to allocate computing resources to more important tasks. The operations of different pooling layers in channel and space were added to enhance important features and reduce the proportion of unnecessary features. At the same time, the sensitivity field of the low-level feature map augmented by expansion convolution with different expansion rates can not only extract the details of color and texture, but also fully obtain the context information of the image. In the end, the feature map output obtained by different expansion rates is fused to improve the classification effect.In the result part, we can see from the comparison experiment that our model has higher accuracy compared with other models, and in the heat map, we can see that it has more accurate judgment on key points.The attention mechanism is a relatively efficient data processing method developed in machine learning in recent years, and is widely used in various types of machine learning tasks such as image recognition and natural language processing. When people observe things outside, they usually focus on what they think is important. The attention mechanism focuses on local information that allows the network to achieve better results. Therefore, in this paper, Convolutional Block Attention Module is added before each regularization of the pre-training network and improves the features of the selected maps to increase the accuracy of the model shown in Figure 3Channel Attention Mechanism (CAM)2.1.2.1.1.1 SENet , as the The two matrix results are fed into a two-layer multilayer perceptron (MLP), respectively, and the MLP can share parameters.After adding the two feature vectors, the weight coefficients are obtained by the Sigmoid activation function again.The weight coefficient is multiplied by the original input feature to obtain the final output feature.Spatial Attention Mechanism (SAM)2.1.2.1.1.2 Different from the weight of each feature plane of the channel attention allocation, the spatial attention model is to find the most important part of the network for processing shown in Figure 5The input characteristic graph is H×W×C. The Max-Pool and Avg-Pool of one dimension are spliced and dimensionally reduced to generate two one-dimensional feature maps.The weight parameters are generated by the Sigmoid activation function, and then the final output feature is obtained by multiplying the original input feature.First, the MSFM redistributes the channel and spatial information through the Convolutional Block Attention Module to enhance the characteristics of small target information. Then Feature Extraction is carried out using dilated convolution of different dilatation rates, and the context information of feature images is fully extracted by expanding different receptive fields. The convolution kernel size of dilated convolution  is the swhered = Size of the expanded convolution kernelkk = Size of original convolutionr = Dilation rateThe calculation of the receptive field of dilated convolution is as follows:wheref = Receptive fieldrk = Size of original convolutionr = Dilation rateThe multi-scale fusion module designed in this paper is shown in In recent years, transfer learning has been widely used in machine learning, which presents satisfactory application results in deep learning. For multi-classification tasks, the cross-entropy loss function  is generThe most commonly used cross-entropy loss function is:whereCE = Losst = Predictive valuePt = Added parameters that represent weights for different categoriesaCross entropy loss function under multiple classifications:(b) Focal lossTo a certain extent, traditional methods can solve the problem of fewer categories and unbalanced image distribution, but when there are many easily classified samples, the samples will still dominate the training process, so some difficult-to-classify samples have little chance of gaining the attention of the model. The focus function  treats tFocal loss adjusts the weights of the difficult-to-classify and easy-to-classify samples in the formula:γ is a constant, and the magnitude of γ determines the weight of small and difficult samples.When γ<=0, the focusing parameter can be adjusted. When the value of γ is larger, the loss of the sample that is easy to classify is small, and the focus of the model is on the sample that is difficult to classify. This is because when γ is larger, the loss of small samples and difficult samples will be larger, so that they can obtain greater weight.When γ>1, the training loss of large and simple samples can be reduced, while the loss of small and difficult samples will not be reduced much.When γ=1, the equation degenerates into the cross-entropy loss function mentioned above.The focus loss function of multi-classification:The data used in this paper are a cassava leaf dataset manually photographed in Uganda and annotated by experts from the National Crop Resources Institute in collaboration with the Artificial Intelligence (AI) Laboratory at Makerere University, Kampala. The data set contains five kinds of cassava leaf images, and disease images are cassava white leaf blight, brown streak disease, green mottling disease, Mosaic disease and healthy cassava leaf images. It can better reflect the characteristics and symptoms of healthy cassava leaves and diseased cassava leaves in natural environment, and it also represents the real and low diagnostic format that farmers need in real life. The dataset includes images taken under field conditions  is used as an evaluation index for multi-classification problems in the laboratory. The accuracy of the experimental model classification can be obtained by removing the number of labels in the test set according to the evaluation criteria. The calculation formula of indicators is as follows:TP=Positive sample prediction is the number of positive classes.FP=Negative sample prediction is the number of negative classes.In order to verify that the improved model in this paper has better image recognition ability compared with the traditional model, this paper uses multiple groups of comparative experiments. The experimental results are shown in The confusion matrix  is also (i)The percentage of the total that all predictions are correct, as in Equation 1:(ii)The proportion of those correctly predicted to be positive that are actually positive, as in Equation 2:(i)The proportion of correct positive precisions to total positive precisions, as in Equation 3:The column labels of the confusion matrix represent the predicted cassava leaf disease type, and the sum of the corresponding row values represents the sum of the samples of this type. The diagonal line indicates the number of correctly predicted labels. Each value on the diagonal line indicates the number of correctly predicted labels. The value at the intersection of the columns represents the value of the corresponding tag predicted. If it is not on the diagonal, it can be seen as the number of wrongly predicted tags. The darker diagonal suggests the better model. In the classification results, the judgment accuracy is high, and most of the results of the test set are concentrated on the diagonal of the confusion matrix. The identification accuracy of all kinds of blades is greater than 90%. However, the identification accuracy of Cassava Mosaic Disease in the test set is lower than that of other diseases, and it is easy to misjudge it as other cassava leaf diseases. By observing the confusion matrix, it can be found that the pictures of Cassava Mosaic Disease can be easily identified as Cassava Bacterial Bligh and Cassava Green Mottle, because the symptoms of these three diseases are relatively similar, so classification errors are prone to occur. The obfuscation matrix of the improved model presented in this paper has better performance and a higher average recognition rate.Class Activation Mapping (CAM) was used to visualize each trained model  to betteCassava, as one of the three varieties of potato, is an important food. Compared with the diagnosis of cassava disease by human, the identification of cassava disease by computer and deep learning method not only has low cost and higher accuracy than manual diagnosis, but also greatly reduces the efficiency. The multi-scale cassava leaf classification model proposed in this paper can better ensure the safety and efficiency of cassava food production and judge the cassava disease type more accurately. Compared with the diagnosis of cassava leaf disease using the human eye, the identification of cassava leaf disease by computer and deep learning method is characterized by lower cost, higher accuracy, and greatly increased efficiency. In this paper, a multi-scale fusion module was proposed, and the Focal Loss function and CBAM module were introduced. An optimization network model of a multi-scale fusion network based on EfficientNet and attention mechanism was proposed. The model was used to train the cassava leaf disease data set and compare with the EfficientNet, ResNet50, and VGG16 networks. The experimental results show that the improved network proposed in this paper has higher precision and better generalization ability. The problem of uneven data was solved by changing the loss function, the distinguishing ability of cassava leaf diseases was improved through an attention mechanism, and the recognition ability of the model was enhanced by multi-layer fusion. According to the pricing standard of the model, the model proposed in this paper can be used for image recognition of Cassava leaf disease.Since our model adds the lightweight module MSFM based on EfficientNet, our model can be installed on mobile devices, such as microprocessors. Due to the large-scale application of 5th Generation Mobile Communication Technology (5G) , there ihttps://www.kaggle.com/competitions/cassava-leaf-disease-classification/data.Publicly available datasets were analyzed in this study. This data can be found here: ML: Modeling and experimental design of cassava leaf disease; HL: Experimental design and data analysis and arrangement; MH: AI model selection and debugging. All authors contributed to the article and approved the submitted version.