This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance. Anti-counterfeiting techniques can prevent or identify counterfeits to a certain extent, and traditional techniques can be classified into the following categories: visual anti-counterfeiting, electronic identification anti-counterfeiting, electronic code anti-counterfeiting, and texture anti-counterfeiting. Visual anti-counterfeiting mainly uses laser holography ,2,3,4, sDifferent degrees of random toner adsorption occur in a digital graphic during printing, ensuring its physical non-replicability. The special design of the graphic can further amplify the distortion phenomenon, which can be identified by the algorithm. QR codes are widely used due to their strong coding ability and large information capacity. As QR codes are composed of a large number of black and white blocks, they are insensitive to the noise and distortion generated during printing. Therefore, the fusion of anti-counterfeiting graphics with QR codes is gaining increasing attention. At present, the fusion can be summarized as physical unclonable function (PUF) ,17,18,19For example,  utilizes(1)This paper designs a texture-hidden anti-counterfeiting QR code to solve the problem of the easily illegally copied QR code.(2)An effective quality assessment algorithm is proposed to judge the type and degree of blur.(3)The proposed dual-feature detection algorithm is shown to cope with different forgery means, capture devices, and attack scenarios.To address the above issues, this paper designs a texture-hidden QR code, based on the codec mechanism of QR codes, using Gaussian distribution and information-hiding technology. The licensee generates a digital image of the code and then uses an officially authorized printer to legally print the authentic product and paste it on the goods or documents for circulation. The forger does not have access to the original code digital image, so the process involves scanning, reprinting, and pasting the fake product or document on top of the original. Users upload codes by taking photos on their mobile phones, which can be used to verify authenticity. The code has abundant texture details and specific frequency characteristics which improve the anti-replication capability while maintaining the generality of the QR codes. An efficient quality assessment algorithm is also proposed to address possible blur in the authentication processing. The proposed method describes the DFT low-frequency region of a code and computes and compares its magnitude and azimuthal features to determine the presence and degree of blur. Finally, this paper proposes a dual feature detection algorithm that includes both a decodability analysis and DFT spectral features. Specially designed anti-counterfeiting textures will show different diffusion patterns after printing. The details of fake codes will be glued together in large quantities, the code points will be destroyed, and the decoding will fail. To ensure accuracy, this paper calculates the spectral similarity between the tested code and the sample code and further compares the eigenvalues in four feature regions. The contributions of this paper are as follows.The paper is structured as follows: During the printing process of a digital pattern, the toner will be scattered randomly. Genuine codes only need to be printed once, while forged codes need to be printed at least twice, which can cause severe distortion. Realizing the above mechanism, this paper designs a texture-hidden QR code based on Gaussian distribution and information hiding to achieve an anti-copying effect. The code generation process is shown in Step 1: Generate Gaussian random texturesA random matrix is built from a Gaussian random generation function and visualized as a fine random texture pattern. The pattern and its enlarged detail are shown in Step 2: Use bilinear interpolation operationBilinear interpolation is to perform linear interpolation in both directions separately. Interpolation has the effect of low-pass filtering which can be anti-aliasing and effectively reduce some of the visual distortions caused by image scaling. Step 3: Adopt halftone treatmentSince the printing device can only print black and white, the continuous gray tone image must be processed into a binary halftone image in the printing process, and the unit coverage area of small black and white dots that human eyes cannot distinguish is used to simulate the gray level change of the image so that the binary image looks as close as possible to the original image. In this paper, the error diffusion method proposed by  is adoptStep 4: Refine QR codeStep 5: Combine texture patterns and refined QR codeAs shown in The captured anti-counterfeiting codes may be blurred due to various factors such as environment, equipment, habit, etc. which typically manifest as defocus blur and motion blur. A clear code is particularly crucial for subsequent authentication. This paper transforms the code in the DFT frequency domain and calculates the magnitude and azimuthal characteristics of the low-frequency region to determine the type and degree of blur. The defocus blur is due to the incomplete coincidence of the image plane with the detector’s receptive plane, resulting in the lens not being clustered. Motion blur is caused by the relative motion between the image and the camera that captured it. The texture and edges of the image will become blurred, and its general expression is:The main difference between the two blurs is For defocus blur, For motion blur, The proposed anti-counterfeiting code is characterized by slow and continuous grayscale variations between pixels, as well as rich low-frequency information. Therefore, it is used to evaluate the quality of the anti-counterfeiting code based on the DFT spectral image features. It can be translated into a frequency domain operation and can be written, regardless of noise, as:X-axis, Y-axis, X’-axis, and Y’-axis.Step 1: Transform to the DFT domain and calculate low-frequency values in four directions. (1) Perform DFT frequency domain transformation on the anti-counterfeiting code, (2) move the DC component to the center of the spectrum, (3) calculate the normalization of the spectrum amplitude, (4) perform threshold process to obtain a binarized spectrogram, and (5) calculate Step 2: Determine how blurred the image is and calculate the quality score of the code according to the following formula:Step 3: Determine the type of blurred code by using the following relation:In this paper, random texture patterns are combined with refined QR codes to generate anti-counterfeiting codes. Due to the integration of QR codes, anti-counterfeiting codes do not require other special position symbols or marked information and can be directly decoded by a decoding program.Printing or capturing comes with channel distortion and noise, and the decodability varies after the code is passed through different channels. For genuine codes that have been printed and captured only once, the resulting distortion and noise are small, and codes can still be recognized by the decoding device. For illegal channels that undergo multiple replicas, more severe distortions and noise are generated, with obvious forgery traces, and substantial texture details are lost. Stochastic diffusion phenomena exhibit different diffusion patterns, which can cause decoding to fail. Therefore, the decodability of the code to be tested can be used as an evaluation metric, and those codes that cannot be decoded are identified as fakes.In the actual process of counterfeiting, the counterfeiters resort to superior-quality enlarged copies. On the one hand, consumers generally do not know the size of the code. The original size of the code is generally small, and a copy enlarged within two times will not make a particularly noticeable difference. On the other hand, product packaging includes not only a single outer package but also a boxed package. The use of enlarged anti-counterfeiting codes on the whole box packaging is not against the law and will deceive consumers. As shown in M − 1, N − 1; and The code can be regarded as a 2D discrete signal. The Discrete Fourier Transform (DFT) decomposes the signal into several sinusoidal plane waves and converts the code from the spatial domain to the frequency domain. It can be represented by the following equation:The frequency of an image represents the grayscale gradient, which represents the difference between a point and its neighborhood on a grayscale image. NC) can be used to measure the overall spectral similarity between the code to be tested In this paper, the macro structure information and micro structure information of the code are extracted for authentication. The Normalized Correlation Genuine and counterfeit codes datasetGenerate genuine and counterfeit paper codes: a computer generates 10 electronic codes with different semantics, and then prints out 10 paper codes. After that, ten printers of different brands or models, including the official one, are used to make an equal copy. Then, eight printers are used to make 1.5 times and 2 times enlarged copies of the original code, resulting in 260 counterfeit paper codes. Collect sample codes: the portable microscope camera, Anyty, is used to collect 10 genuine paper codes. Collect genuine and counterfeit codes: six mobile phones of different brands or models are used to photograph each paper genuine code five times in different environments. In addition, the mobiles are used to take pictures of each paper counterfeit code one time. The dataset consists of 10 groups, each with a different 16-bit random number for the semantics of the codes. The data of each group includes 1 sample code, 30 genuine codes, and 156 counterfeit codes . The dataset information is shown in (2)Attacked codes datasetSince counterfeit codes will not be recognized as genuine after the attack, only an attack on the genuine codes is performed to check the authenticity algorithm. The size of the 300 genuine codes is reduced in various proportions to 12%, 24%, 36%, and 48% of the original code size. The 300 genuine codes are cut to different degrees, with cut areas of 1%, 3%, 5%, 7%, and 9% of the original code. (3)Quality assessment codes datasetDifferent degrees of defocus blur  and motion blur  are added to 300 clear digital genuine codes to test the performance of the quality assessment algorithm in this paper. The dataset is presented in To evaluate the performance of the anti-counterfeiting codes and algorithms, three evaluation metrics are formulated as follows:For the methods that need to be trained and tested, the following experiments randomly select half of the The dataset shown in Meanwhile, the quality evaluation method in this paper is able to further distinguish the blur types with 95% accuracy for blurred codes. The specific results are shown in Judging whether the code can be decoded correctly is the first part of the authenticity process which can quickly screen out counterfeit codes. In this paper, the genuine and counterfeit codes dataset  is decodQR codes have some error correction capability and up to 30% of the area covered can still be correctly decoded. In this paper, patterns with anti-counterfeiting capability are incorporated into QR codes to ensure some degree of error correction capability. The comparison of DFT spectral features is the second part of the discrimination procedure to ensure accuracy. The test consists of two metrics: NC and PR, and a code is identified as genuine only if both metrics reach a threshold. (1)No attacked testTo further verify the superiority of the proposed dual feature algorithm, the genuine and counterfeit codes dataset shown in (2)Attacked testThis paper experimentally verifies the robustness of the proposed anti-counterfeiting code and the corresponding algorithm in view of the possible abrasion phenomenon during the use of the code. The dataset in In this paper, a texture-hidden anti-counterfeiting QR code and a related authentication scheme for mobile devices are proposed. The authentication scheme includes a quality assessment algorithm and dual feature detection. In the test of quality assessment algorithms, the proposed algorithm is compared with several current common algorithms without reference and the superiority of the proposed algorithm is verified. In the authenticity test, the proposed dual feature method is compared with various texture and corner methods. The proposed method achieves an accuracy, precision, and recall of up to 100%, and it also performs well on attacked datasets with reduction and cut. In the following research work, it is necessary to expand the data set  to more comprehensively evaluate the practicality of the codes and authentication schemes to achieve convenient pre-sale anti-counterfeit measures.The dataset used in this paper is obtained by smartphones shooting under indoor conditions, but the collection of 2D codes will also encounter some abnormal conditions, such as insufficient illumination, severe shaking, etc. How to efficiently recover codes and authenticate them is a problem that requires further work. In addition, with multiple forgery devices available, how to authenticate codes using only genuine samples is an important research direction.


Road project sites are dangerous and crash-prone, with many hazards that can cause injuries and can result in the deaths of road users or site-workers. Work zones for road construction or maintenance can potentially contribute to increasing these crashes. Many studies have addressed this issue; however, there is a lack of similar studies in Saudi Arabia. Therefore, this study contributes to developing safety practices for road work zones in Saudi Arabia by identifying, analyzing, and controlling the main risk factors. A survey approach was used to identify risk factors and potential countermeasures from road users’ and civil engineering experts’ perceptions. The main findings showed that most participants believed that the presence of work zones on the road might increase the probability of crash occurrence and that the highest risk factor that could cause a crash in a work zone is related to driver behavior. Both groups agreed that strict action against contractors or consultants who have safety violations would enhance road safety in work zones. Considering the findings of this study, decision-makers should take strong action to implement and improve road safety practices. The increasing demand for road construction, maintenance, or rehabilitation activities increases the number of work zones (WZs) on streets and highways. This is particularly hazardous and could cause significant safety concerns for road users and site-workers and affect the traffic flow. The presence of WZs for road construction, maintenance, or rehabilitation activities could potentially contribute to an increase in the number of traffic crashes or their severity. This has led to many negative impacts on society and the economy. Road crashes are a severe public health and development issue globally. The Global Status Report on Road Safety 2018 shows that traffic accidents lead to roughly 1.35 million lives lost around the world every year [The Kingdom of Saudi Arabia (KSA) is considered one of the countries with the highest number of road traffic crashes. It recorded the highest number of deaths caused by road traffic crashes among the G20 countries in 2016. Additionally, the official statistics of Saudi Arabia indicate that there were 11,929,839 traffic crashes between 1970 and 2019, of which 208,949 were fatal [Safety in road work zones (RWZs) remains a high-priority issue for roadway agencies mainly due to the limited understanding of the contributing factors of the crashes and the lack of perception of road works’ importance globally and locally. Accordingly, much research has been conducted to investigate the influences of many aspects of road work zone characteristics on RWZ safety ,28,29,30Moreover, the majority of studies agree that the most common crash type is the rear-end crash, which is the most predominant crash in RWZs ,15,16,27Studies conducted by Li and Bai (2008) and Zhang and Hassan (2019) concluded that younger drivers had a higher probability of being involved in severe work zone crashes ,24. In aIn addition, previous studies have made many recommendations that could eliminate the risks of the road work zone and promote safety and mobility in the work zone, for example, the awareness of drivers about road work hazards and how this may affect their daily commute. Direct communication with motorists about precautions that should be taken when driving through WZs using social media campaigns, radio stations, effective educational programs, and seminars for the general public is recommended ,24,29,30These factors are highly sensitive to the study region, and there is a lack of understanding of the magnitude of the crashes’ causes and their consequences on Saudi Arabia’s road projects. Therefore, this study aims to improve road projects’ health and safety practices in Saudi Arabia by identifying and analyzing the main risk factors that cause crashes in RWZs and addressing the potential suggestions and recommendations to minimize these risks. This study could be used as a guide for successful construction safety program implementation in road projects as well as safety policy development in the Saudi construction industry.In order to achieve the goals of this study, a survey approach that addresses RWZs’ potential sources of risk and the suggested solutions, including the findings from previous similar research, was used. This survey was designed to cover both road users and experts, represented by professionals from different cities, to increase the accuracy and reliability of the results. This is important to understand the safety perception of road users toward WZs. The safety perception affects road user decisions yet is not easily understood by looking at crash data . Civil eThe survey consists of three parts addressed to both groups. The first part of the survey consists of several questions related to demographic data . The second section focuses on crash causes in RWZs and measures drivers’ risk perception. The second part of the survey is divided into two subsections. The first subsection contains six general questions regarding the respondent’s evaluation of the influences of the presence of RWZs, number of existing RWZs, area of RWZs as shown in The sample size of the study was obtained using the formula given below (Equation (1)). Using this formula, the sample size of the current study was 225, assuming that the population size of the sample is vehicles registered in Saudi Arabia until the end of 2019. The number of vehicles registered until 2019 was approximately 12 million . A web sConfidence Level (%) = 90e = Margin of Error (%) = 5.5z = z-score = 1.65p = Sample proportion (50%).Before analyzing the data, a survey data cleaning method was implemented to obtain the most accurate survey data after collecting the survey responses. Survey data cleaning involves identifying and removing responses from individuals who did not answer the questions thoughtfully using three criteria or filters. The first filter excluded those who did not complete the whole survey. The second filter excluded respondents who blindly selected the same answer/response for each question throughout the entire survey . Finally, the third filter excluded inconsistent responses . After defining and cleaning the data, they were inspected, and the nature of the variables was explored. One-way and two-way contingency table analyses were performed. The former approach is fundamental analysis, and it helps to obtain the basic information about the survey participants and crash causes data. The latter approach was performed to examine the association between the observed variables and identify any specific data trends; it is commonly used to deal with survey questions.The appropriate statistical analyses were used according to the data type from a question. Descriptive statistics such as frequencies, percentages, averages, and standard deviations were used to present an overview of findings from the study population. Additionally, inferential statistics were used to explore the statistically significant differences among groups and the association between variables, for example, the chi-square test and the Independent-samples T-test. It is worth mentioning that because of violating assumptions of the Pearson chi-square, a likelihood-ratio chi-square was selected to determine the association between the variables.The data were analyzed using Statistical Package for Social Science .Prior to distributing the survey, a pilot study was conducted to check its reliability. Reliability analysis was calculated using Cronbach’s alpha coefficient. Cronbach’s alpha coefficients for all parts of the survey ranged from (0.878–0.948), which is acceptable and refers to the high reliability of the study tool.A total of 410 responses were received before the data cleaning process. In the end, 223 road users’ and 98 civil engineers’ responses were used in this study. The percentage completion rate of the survey was 78%. A chi-square test for independence was used to test hypotheses that answer the study question of whether there is a relationship between two categorical variables. It was performed for the two groups separately. The test was performed between the observed six general questions (listed in the previous section). It covers the number of times the participants were involved in a traffic crash generally, or particularly in an RWZ, with the socio-economic information to determine the association at a significance level of 0.05. Regarding the road users’ data, the results show that none of the road users who had comprehensive vehicle insurance disagreed with the statement that the presence of a WZ on the road would increase the probability of crash occurrence. Additionally, 84.1% of those with third-party insurance agreed with the statement. This safety perception of road users toward WZs should be taken into consideration by each entity to improve safety in WZs. Further, the majority of the road user participants, regardless of their education levels, agreed that the transition area is the riskiest area that could cause a crash in WZs. Thus, there must be more precautions in the transition area. Concerning involvement in a traffic crash, the office public employees had the highest number of crashes among other occupations.For the civil engineers’ group, 83% of the participants who have driving experience of more than 10 years believed that the presence of a WZ on the road would increase the probability of crash occurrence. In contrast, 33.3% of them with driving experience between 1–3 years believed that the presence of a WZ on the road would not increase the probability of crash occurrence. This indicates that those with more years of driving experience perceive that the WZ is risky. In addition, none of the civil engineers with postgraduate degrees had been involved in RWZ-related crashes.After reviewing the previous research related to the safety of RWZs, there were 17 potential risk factors identified to be asked of the respondents in order to evaluate them. These risk factors generally represent three categories, driver behavior, WZ setup, and worksite management. It is crucial to assess those factors to identify potential hazards that could face the drivers and relate them to the road work zone.t-test has been employed. Thus, for risk factors that had significant differences in mean and values less than p < 0.05, the null hypothesis would be rejected and conclude that the mean of the four risk factors for road users and civil engineers are significantly different for some risk factors. There were only four risk factors that had a statistically significant difference in the mean, namely, a poor design of the detour, a large speed variance between the posted temporary speed limit and permanent speed limit within short distances, no longitudinal and transverse grades for rain drainage in the RWZ or the detour, and drivers not responding to temporary warning signs in RWZs to reduce vehicle speed. However, that could assess the importance of the finding by calculating the effect size. Based on these results, Eta squared was employed to estimate the size effect of the difference. Eta squared effect size statistics indicate the proportion of variance of the dependent variable explained by the independent variable, and its values range from 0 to 1. The interpretation guideline of values is 0.01 = small effect, 0.06 = moderate effect, and 0.14 = large effect, as proposed by Cohen (1988) [In order to check if there was a statistically significant difference in the mean scores of risk factors to the RWZ for the two groups regarding their opinion on potential risk factors, an independent-samples n (1988) . The effThat gives an indicator of which most dangerous risk factor has the highest probability of causing a crash in RWZs. Hence, the responsible agency or decision-makers should take urgent action to resolve the issue and improve road safety on the site. By sorted risk factors, the highest factor’s mean indicates the most probable risk factor that could cause a crash in the RWZ, as well as other factors sequentially ordered based on the most probable risk factor to the lowest probable that could cause a crash on the RWZ. The results show that both groups agreed that the highest risk factor is reckless or aggressive driving through the WZ. This implies the importance of accommodating human behavior when designing RWZs. This finding agrees with the findings of Akepati and Dissanayake (2011), which stated that the primary contributing factors in RWZ crashes were inattentive driving and following too close . In addiThe last part of the survey contained suggestions to improve road safety in WZs. Based on previous literature, many researchers have proposed various suggestions and recommendations that they believe will enhance safety and mobility in work zones. Some of those suggestions and recommendations were selected, new suggestions were added, and the respondents were asked to evaluate them. This will help decision-makers to choose the most effective suggestions to take corrective action to eliminate the risk factors.The results show that five suggestions that had a significant difference in the mean scores between the two groups were (1) awareness among drivers about road work hazards through social media campaigns, (2) stronger collaboration between government agencies in terms of improving road safety, 3) better and updated work zone standards that reflect local conditions, (4) provide a database system for documenting and reporting the crashes causes, and 5) having a certificated safety engineer or professional on the project for road risk assessment. The magnitude of rate differences of suggestions between road users and civil engineers are small . Because of the small differences, both groups’ opinions were combined to get the overall mean of suggestions representing both opinions. By sorted suggestions, the highest suggestion’s mean indicates the most probable suggestion that will enhance the road works’ health and safety practices and could lead to minimizing road crashes. Sequentially mean ordered based on the most probable suggestion to lowest probable are shown in  better a having aLastly, as shown below in the list, the lowest suggestion is to consult with road users on matters affecting their health and safety in RWZs and get their feedback. This could mean the risk factors were apparent and only needed the decision-makers to take corrective action to eliminate them.These suggestions can be described as those that the road users and civil engineers perceive to need more focus and apply by decision-makers to improve road health and safety practices.Applying those suggestions would help reduce traffic issues in the work zone area and make the traffic flow smoothly without disruption. Additionally, it will reduce the loss of road users’ and workers’ lives and mitigate safety costs arising from severe injuries, fatalities, legal obligations, and expenses. There is consensus that the effects of these countermeasures have positive impacts on WZ safety.Saudi Arabia has many kilometers of roads due to its vast area. In order to achieve the Vision of KSA 2030 of reducing the number of fatalities due to traffic crashes and improving quality of life, it is vital to investigate the health and safety of road project sites during construction and maintenance. The presence of WZs on the road raises numerous risk factors. Moreover, those risk factors could potentially contribute to increasing crashes in WZs. The main objectives of this study were to identify the potential risk factors and the corresponding suggestions to mitigate those risks. Detailed literature was reviewed to get a better understanding of similar conditions worldwide. Additionally, a multiple survey approach was carried out to obtain data from road users and civil engineering experts. The survey addresses RWZ issues of potential factors and corresponding suggestions to mitigate those risks. Lastly, one-way analysis and two-way contingency table analysis were applied.The results show that most road users and civil engineers find RWZs in their daily trips. Most participants believed that the WZs on the road might increase the probability of crash occurrence. Therefore, there is an issue facing drivers every day while driving. The majority of the participants believed that the transition area is the highest risk area in an RWZ. Regarding exploring associations, relationships were found between some variables with factors related to socio-economic information.In addition, several risk factors with respect to driver behavior, WZ setup, and worksite management were identified and ranked. Similarly, several suggestions were identified to reduce the risk in WZs.Reckless or aggressive driving through the work zones;Lack of lighting in RWZs;Lack of temporary road signs in RWZs.Both groups agreed that the most contributing factors for crashes are:Strict action against contractors or consultants who commit safety violations;Stronger collaboration between government agencies in terms of improving road safety;Have a certificated safety engineer or professional on the project for road risk assessment.The probable suggestions that would improve road safety in the WZs based on the participants’ opinions are:The main findings of this study addressed the main potential risk factors of RWZs that could cause crashes in RWZs. Additionally, it suggested appropriate actions for decision-makers that will enhance the road works safety practices and could lead to minimizing road risks. It is highly recommended to consider these findings, including providing a database system for documenting and reporting the crashes caused by government agencies for more efficient management and analysis in the future. Based on the research outcomes, the authors suggested focusing on intelligent transport systems (ITSs) to be considered in future studies, such as testing and measuring how ITSs technologies could affect driver behavior in the road work zone. Additionally, it could quantify the risk factors and the suggestions by using a regression model to describe the relationship between them and, locally, study the overlapping work between the government agencies in terms of traffic safety in Saudia Arabia and the reasons for the reckless and aggressive driving behavior.


As a popular research subject in the field of computer vision, knowledge distillation (KD) is widely used in semantic segmentation (SS). However, based on the learning paradigm of the teacher–student model, the poor quality of teacher network feature knowledge still hinders the development of KD technology. In this paper, we investigate the output features of the teacher–student network and propose a feature condensation-based KD network (FCKDNet), which reduces pseudo-knowledge transfer in the teacher–student network. First, combined with the pixel information entropy calculation rule, we design a feature condensation method to separate the foreground feature knowledge from the background noise of the teacher network outputs. Then, the obtained feature condensation matrix is applied to the original outputs of the teacher and student networks to improve the feature representation capability. In addition, after performing feature condensation on the teacher network, we propose a soft enhancement method of features based on spatial and channel dimensions to improve the dependency of pixels in the feature maps. Finally, we divide the outputs of the teacher network into spatial condensation features and channel condensation features and perform distillation loss calculation with the student network separately to assist the student network to converge faster. Extensive experiments on the public datasets Pascal VOC and Cityscapes demonstrate that our proposed method improves the baseline by 3.16% and 2.98% in terms of mAcc, and 2.03% and 2.30% in terms of mIoU, respectively, and has better segmentation performance and robustness than the mainstream methods. In the field of computer vision, SS tasks occupy a pivotal position . The SS KD is one of the effective methods to improve the performance of lightweight models, which has been confirmed by many studies. The classical paradigm of the teacher–student model aims to use a deeper or wider network (teacher) to learn more knowledge and to guide and correct another compact network (student). According to the way of knowledge transfer, KD can be categorized as output feature knowledge, intermediate feature knowledge, relational feature knowledge, and structural feature knowledge. The concept of KD was first proposed by Hinton et al.  in 2015 However, although the above KD methods perform well in many computer vision tasks, they still have limitations for dense prediction tasks such as SS. The KD approach applied to SS requires the teacher network to output feature mapping with more pixel feature representation. Therefore, in the process of teacher network guidance, in addition to knowledge transfer, how to enhance the feature representation capability of the teacher network output is also the key to improving the performance of the student network.This paper applies an output-feature-based KD to the SS task. We propose a feature condensation method to extract critical foreground knowledge and reduce background noise in the output feature maps to improve the representation of the output features and the difficulty of fitting the student network to the teacher network. To reduce the weakened feature correlation caused by the feature condensation process, we propose a feature soft enhancement method to further intensify the feature representation capability of the teacher network output by improving the elemental correlation of salient regions from the spatial and channel domains. Specific contributions are as follows.1. According to the calculation rule of pixel information entropy, we designed feature condensation to separate the foreground feature knowledge and background noise of the teacher network outputs. With the help of feature condensation, the teacher network filters the pseudo-knowledge and transfers more accurate foreground feature knowledge to the student network.2. After feature condensation, we design a feature soft enhancement method, which utilizes the softmax function to calculate the pixel information distribution from the spatial and channel dimensions and converts it into a weight matrix to act on the original feature map, thus enhancing the pixel dependency.3. Finally, multi-path KD is performed according to the network structure. Extensive experiments using the public Pascal VOC and Cityscapes datasets demonstrate that our proposed method has better performance and robustness than the current mainstream methods. Additionally, the generalization ability test on the fundus retinal vessel segmentation task demonstrates that our method has good cross-domain generalization performance.The rest of this paper is organized as follows: In recent studies, KD-based methods have been widely used to improve the accuracy of lightweight models in SS tasks. Due to different SS scenes and model defects, various pertinent KD methods are proposed. Liu et al.  considerContrary to the above methods, our work is more concerned with the quality of the output features of the teacher network. The knowledge value contained in a pixel is evaluated by calculating the information entropy of each pixel. Then a threshold is set to filter pixels with low knowledge values and only pixels with high knowledge values are retained for KD operation. Such an approach can well suppress the influence of useless pseudo-knowledge from the original features transferred to the student network.Feature enhancement is an important tool to improve model performance. Unlike data augmentation, feature enhancement is to perform pixel-level processing on the middle layer feature maps or output feature maps, and improves the representation ability of image features by improving the dependence between pixels. Hou et al.  proposedDifferent from the design concept of these networks, our method is proposed to further improve the fitting degree of the teacher–student network in the KD process. According to the research of Mirzadeh et al. , it is nIn this paper, we propose a feature condensation KD (FCKDNet) based on the teacher–student model. As shown in Specifically, we feed images into both teacher and student networks by batch for regularization training and obtain the respective output feature maps. To reduce the impact of background noise on foreground pixel prediction, we propose a feature condensation method to separate the foreground pixels from the background pixels in the feature maps output by the teacher network. In the separation phase, the information quantity of each pixel is calculated using the calculation rule of pixel information entropy and the threshold is set to filter the foreground pixels with high information quantity. Then the feature condensation matrix is obtained to multiply with the original feature maps of the teacher and student networks, respectively. The feature maps containing only foreground feature knowledge are obtained. After that, the proposed feature soft enhancement method is used to intensify the foreground features from the spatial and channel dimensions. In the distillation phase, we calculate the similar error between the spatial and channel soft enhancement features of the teacher network and the outputs of the student network as the distillation loss. While the original outputs of the student network calculate the cross-entropy loss with the ground truth and then back-propagating together.Feature condensation is a process of filtering useless information. In this paper, we separate the foreground feature knowledge from the background noise in the feature maps and select only the real and rich foreground feature knowledge for distillation. It will reduce the unnecessary effect of background noise on the student network during distillation. Before that, we need to clarify two issues: (1) how to evaluate the usefulness of a feature element and (2) how to filter out useless information from the output feature map of the teacher network. According to , the essAfter obtaining the prediction information entropy of pixel points, we can filter out the pixel points with high knowledge contribution by setting a threshold and retaining the position index of these points. Finally, the position index is used to find the pixel value of this position from the original feature maps and retain it as knowledge.c denotes the number of output channels and H and W denote the spatial dimensions, firstly, we flatten it into the two-dimensional form of x represents the index in the matrix, and For the output feature map x is the element value corresponding to pixel index i in In order to intensify the representation ability of the teacher network after obtaining the foreground feature map eference , we desiith row in the matrix. Then, the spatial and channel-based KD loss can be defined as: b represents the number of batches in the feature map, i represents the ith element in the matrix. i can index all elements in the teacher–student matrix and calculate the mean element-wise squared difference. Finally, we define the total loss function as: y is the ground truth, and For the KD phase, we use a distillation method similar to similarity-preserving KD . This meDataset. We perform all experiments on three public datasets with different application scenes. (1) Pascal VOC . To fit the input size of the network, the images were cropped to Our proposed method is to optimize the output features of the teacher network. It is necessary to verify the effectiveness of our method using different types of output features. Therefore, we use the deep network (Res101) and wide network (WRes50) as the backbone of the teacher network namely DeepLabV3+-Res101 and DeepLabV3+-WRes50. Additionally, we add the mainstream distillation methods SKD , CWD 2121, and IWe measure the number of parameters, computational complexity of the teacher and student networks, and the computational time required for each KD method. Experiments were performed using the Pascal VOC and Cityscapes datasets, and the results are shown in To show the segmentation performance difference more visually, we visualize the segmentation results of the student network, CWD, and the proposed FCKDNet, as shown in The wide teacher network ensures that rich features, e.g., texture features in different orientations and frequencies, are learned at each layer. Then, our proposed feature condensation operation and feature soft enhancement method are used to further highlight feature information from the pixel level. In this section, we conduct detailed ablation experiments for each module in the network. We use the student network as a baseline on which we add the teacher network, feature condensation, spatial soft enhancement, and channel soft enhancement successively. All experiments are performed on Pascal VOC. As shown in Adjusting the feature condensation threshold Finally, we discuss the cross-domain generalization ability of FCKDNet. Different from the natural-environment-style dataset used in the above section, we use a medical image dataset to illustrate the cross-domain segmentation performance of the proposed method. This is a fundus retinal vessel segmentation task, and since the DRIVE dataset used does not contain a validation set, we divide the 20 images of the training set into 16 for training and 4 for validation. Finally, the allocation ratio of the training set, validation set, and test set for the experiment is 16:4:20. Moreover, we choose the classic segmentation network SegNet  and the The experiments are performed according to the training details in In this paper, a novel feature condensation KD method is proposed for SS. The method is able to separate foreground feature knowledge and background noise at the pixel level in the output features of the teacher network. Then, a feature soft enhancement method based on spatial and channel dimensions is used for the foreground feature knowledge to further improve the feature representation ability of the network. Finally, the enhanced features of the teacher network are used to distill knowledge with the student network. Compared with the current mainstream KD methods, our method can effectively help the teacher network filter pseudo-knowledge and improve student network performance. Experiments on public datasets demonstrate the effectiveness and good cross-domain generalization performance of our FCKDNet. In addition, our method still suffers from shortcomings such as the inability to adaptively find the optimal solution of the threshold during the feature condensation process, which may cause over-separation of effective knowledge. In the future, we will continue to optimize our network, and we hope our work will inspire more researchers to investigate feature filtering and apply it to segmentation KD.


Environmental contaminants in ambient air pollution pose a serious risk to long-term metabolic health. Strong evidence shows that prenatal exposure to pollutants can significantly increase the risk of Type II Diabetes (T2DM) in children and all ethnicities, even without the prevalence of obesity. The central nervous system (CNS) is critical in regulating whole-body metabolism. Within the CNS, the hypothalamus lies at the intersection of the neuroendocrine and autonomic systems and is primarily responsible for the regulation of energy homeostasis and satiety signals. The hypothalamus is particularly sensitive to insults during early neurodevelopmental periods and may be susceptible to alterations in the formation of neural metabolic circuitry. Although the precise molecular mechanism is not yet defined, alterations in hypothalamic developmental circuits may represent a leading cause of impaired metabolic programming. In this review, we present the current knowledge on the links between prenatal pollutant exposure and the hypothalamic programming of metabolism. Air pollution is one of the leading environmental concerns and poses a significant risk to the health of people around the world, despite advancements in medicine and technology. According to the World Health Organization, around 7 million deaths were prematurely caused by air pollution per year, including both ambient outdoor pollution and household pollution , 2. Of tin utero (around mid-gestation to birth) and the other extra utero  contains pro-opiomelanocortin (POMC) neurons that produce the anorexigenic peptide melanocyte-stimulating hormone (MSH) and neurons that co-express the orexigenic peptides neuropeptide Y (NPY) and agouti-related protein (AgRP), which regulate food intake and energy expenditure –32. The via microglial TAM receptors  are known to be harmful to a developing fetus , 92–96. via inflammation or hypoxia , and NIEHS R01DK129681 for MS.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Object detection plays a vital role in autonomous driving systems, and the accurate detection of surrounding objects can ensure the safe driving of vehicles. This paper proposes a category-assisted transformer object detector called DetectFormer for autonomous driving. The proposed object detector can achieve better accuracy compared with the baseline. Specifically, ClassDecoder is assisted by proposal categories and global information from the Global Extract Encoder (GEE) to improve the category sensitivity and detection performance. This fits the distribution of object categories in specific scene backgrounds and the connection between objects and the image context. Data augmentation is used to improve robustness and attention mechanism added in backbone network to extract channel-wise spatial features and direction information. The results obtained by benchmark experiment reveal that the proposed method can achieve higher real-time detection performance in traffic scenes compared with RetinaNet and FCOS. The proposed method achieved a detection performance of 97.6% and 91.4% in AP50 and AP75 on the BCTSDB dataset, respectively. Vision-based object detection in traffic scenes plays a crucial role in autonomous driving systems. With the rapid development of autonomous driving, the performance of object detection has made significant progress. The traffic object  can be detected automatically by extracting the features. The result of perceiving the traffic scenario can ensure the safety of the autonomous vehicle. This kind of method can be divided into anchor-based and anchor-free.Deep-learning-based object detection can be divided into single-stage and multi-stage object detection. The multi-stage algorithms extract the region of interest first, and then the location of the object is determined in these candidate areas. The single-stage algorithm’s output the location and category with dense bounding boxes directly on the original image. These detection algorithms classify each anchor box or key point and detect different categories independently, while ignoring the relationships between categories. There exists a specific relationship between other objects, such as probability, location, and scale of different objects in a particular environment, which is essential for object detection and can improve object detection accuracy.This relationship between categories exists in many cases in traffic scenarios. For example, pedestrians appearing in highway scenes and vehicles appearing on the pedestrian path are low-probability events, which indicates the connection between object categories and scenarios. Secondly, the signs “Passing” and “No Passing” should not appear in the same scene, which indicates the connection between different object categories. There exist specific implicit relationships between object categories and the background of traffic scenes. Existing object detection methods do not consider this relationship in scenes, and their classification subnetwork is trained to independently classify different objects as individuals without the objects knowing each other, which results in the model underperforming in terms of fitting the distribution of objects and the scene background. Additionally, the model does not thoroughly learn the features required by the detection task and will cause a gap in the classification confidence between categories, which influences the detection performance.Based on the above-mentioned assumptions, this paper proposes a category-assisted transformer object detector to learn the relationships between different objects called DetectFormer, based on the single-stage method. The motivation of this study was to allow the classification subnetwork to fit better the distribution of object categories with specific scene backgrounds and ensure that the network model is more focused on this relationship.Transformer  is widel(1)The Global Extract Encoder (GEE) is proposed to extract the global information of the image features output by the backbone network, enhancing the model’s global perception ability.(2)A novel category-assisted transformer called ClassDecoder is proposed. It can learn the object category relationships and improve the model’s sensitivity by implicitly learning the relationships between objects.(3)The attention mechanism is added to the backbone network to capture cross-channel, direction-aware and position-sensitive information during feature extraction.(4)Efficient data augmentation methods are proposed to enhance the diversity of the dataset and improve the robustness of model detection.The contributions of this study are as follows:The rest of this paper is organized as follows. In Traditional object detection uses HOG  or DPM  to extraThe transformer is a new encoder–decoder architecture introduced by Vaswani et al.  first usAlthough the transformer structure shows strong performance, the training based on the transformer takes a long time, and requires a large amount of data sets and ideal pre-training. This paper proposes a learnable object relationship module based on a transformer with self-attention, and a single-stage detector was designed to complete the task of traffic scene object detection. Compared with other methods, the proposed method achieves better detection performance in a shorter training time.The overall pipeline of our proposed method is shown in The convolutional neural network is usually affected by the kernel size, network depth, and other factors, causing the receptive field cannot cover the whole area of the image, which is challenging to learn the relationship between long-distant regions or pixels. When extracting the features of the object, the network cannot obtain global information.Inspired by the transformer architecture and the vision transformer, this study designed the Global Extract Encoder (GEE) to enhance the model’s global perception ability. As shown in n donates the number of heads. The feedforward network (FFN) enables GEE the ability of nonlinear fitting. After global feature extraction, We split the feature maps into patches, and collapsed the spatial dimensions of F(.) means collapse operator, which transforms two-dimensional feature matrices into feature sequences.To learn the object category relationships and improve the model’s sensitivity to the categories by implicitly learning the relationships between objects, a novel learnable object relationship module called ClassDecoder is proposed. The structure of ClassDecoder is shown in G  and AP for different scale objects , which consider both recall and precision. The top-n accuracy was used to evaluate the classification ability of different methods. Top-n represents the truth value of the object in the first n confidence results of the model. We also use parameters and FLOPs (floating-point operations per second) to measure the volume and computation of different models.The average precision (AP) metrics were used to evaluate the detection performance, including AP at different IoU thresholds  to highThe convergence curves among the DetectFormer and other SOTA (state-of-the-art) methods, including RetinaNet, DETR, Faster R-CNN, FCOS, and YOLOv5, are shown in The proposed method was also evaluated on the KITTI dataset. As shown in The detection results are shown in Why can ClassDecoder improve the classification ability of models? In this paper, we propose ClassDecoder to improve the classification ability, which is designed based on the transformer architecture without any convolution operations. The model interacts with different background feature maps in scaled dot-product attention and multi-head attention by using proposal categories, and learns the implicit relationship between the background and the category by using the key-value pair idea in the Transformer. The number of proposal categories is equal to the number of object categories, and the parameters of proposal categories are learnable. The input of ClassDecoder is the feature maps, and proposal categories, and the output is the prediction category of the current bounding box. The output dimensions are the same as those of the proposal categories, and the proposal categories are associated with the output in the role of Query . It can be understood that the proposal categories are vectors that can be learned, and their quantity represents the confidence vectors corresponding to different categories of the current bounding box. Then, the model converts the confidence vector into category confidence through feed-forward network. The category with the highest confidence is the category of the predicted bounding box.This paper proposes a novel object detector called DetectFormer, which is assisted by a transformer to learn the relationship between objects in traffic scenes. By introducing the GEE and ClassDecoder, this study focused on fitting the distribution of object categories to specific scene backgrounds and implicitly learning the object category relationships to improve the sensitivity of the model to the categories. The results obtained by experiments on the KITTI and BCTSDB datasets reveal that the proposed method can improve the classification ability and achieve outstanding performance in complex traffic scenes. The AP50 and AP75 of the proposed method are 97.6% and 91.4% on BCTSDB, and the average accuracies of car, pedestrian, and cyclist are 86.6%, 79.5%, and 81.7% on KITTI, respectively, which indicates that the proposed method achieves better results compared to other methods. The proposed method improved detection accuracy, but it still encountered many challenges when applied to natural traffic scenarios. The experiment in this paper is trained on public datasets and real traffic scenes facing challenges with complex lighting and weather factors. Our future work is focused on object detection in an open environment and the deployment of models to vehicles.


The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method’s root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest. Target tracking in the presence of clutter has received much attention in recent times due to proposing improvements in the tracking algorithms. The tracking procedure should incorporate different problems while tracking a moving target in the presence of clutter . Among tA number of single target non-maneuvering tracking algorithms are available in the literature, which can track a target moving without any maneuver during their movement ,13,14, bThe maneuvering targets are tracked more efficiently by using multiple model tracking approaches such as the interacting multiple model (IMM) tracking algorithm. In Ref. , the autSmoothing algorithms are used in a number of applications in target tracking algorithms to improve their performance. The smoothing algorithms increase the computational time and tend to offer delays in any tracking algorithm . HoweverThis work has enhanced the earlier work proposed in Ref.  by addinMathematical formulation for the FLs IMM-IPDA in terms of smoothed target trajectory state estimation, smoothed target existence state update, and smoothed mode probabilities;Utilization of the fixed lag smoothing algorithm to improve the tracking performance of IMM-IPDA;Improvement in the RMSE, TTR, and mode probabilities using the fixed lag smoothing algorithm;A complete set of simulations are performed in MATLAB to prove the above contributions.In this work, a fixed lag smoothing algorithm is devised for the IMM-IPDA algorithm to improve the performance of the IMM-IPDA algorithm in a cluttered environment. The main contributions of proposed study are:This paper is divided into different sections to properly describe the principal of proposed work. The target motion model and related concepts are addressed in k. k conditioned on its existence at scan In this work, the target is assumed to switch in-between the target existence and non-existence state randomly during its motion. It is assumed that the target follows the Markov chain model for its propagation. The probability of the target existence is defined asq is the plant noise parameter, m is the dimension of measurement vector. The k and it is defined asThe target follows the state transition model defined in .3)xk=Fxk=F(3)xkfined asQ=q0.25T4k are defined ask, and R is assumed to bex-axis and y-axis, respectively, and are assumed to 5 m in this study equally. In , while the other one is the constant acceleration model (CA). For the CA model, the state trajectory isT is the sampling time. On the other hand, error covariance matrix q denotes the plant noise parameter and The variable ajectory  becomes ajectory . The CV The state transition matrix The error covariance matrix fined in  and qa im-dimensional zeros column.For constant acceleration state vector , the H mIn this section, a step-by-step approach towards the working of the proposed algorithm is provided. Tracks are initialized using the two-point difference  method. k with target initial augmented trajectory state for the jth model asA’ in the superscript is the significance of the augmented state while the j denotes the jth model in progress.Tracks are initialized at any scan k for constant velocity and constant acceleration model are defined asThe augmented state transition matrix fined in  and 14)FkA,j at The process noise covariance matrix ioned on  and 16)QkA,j isk. For any scan k, it is assumed that the transition probability, which defines the switching from mode (model) to mode (model), is already known. The mode transition probabilities can be modeled in the form of matrixThe mixing step is key to the IMM initialization at any current scan The selection procedure for the values in  is discuThe mixing steps in IMM algorithm are summarized below:1: Mixing mode probabilityi and j represents the previous and current scans index, respectively. jth scan, while ith scan. The denominator in 28), the All parameters used in  and 31)31) are d4: Update Processrth measurement information k. The measurement selection is carried out using the following selection criteriag is the gating threshold. Its value is selected as the In this step, the mode probability, target trajectory state and its associated error covariance, and the target existence state is updated using each The measurements from the measurement set Equation , are sel4.1: Calculation of data association probabilities at the current scanEach measurement pothesis .(34)βk|The .4.2: Mode probability update at current scanjth mode, conditioned on the rth measurement The mode probability at the current scan for For the non-detection event r=0,  will be jth mode at current scan, we have to use Pk, as defined in , , , and 7474 are avfined in . The norPMk−21|Ykjth mode at fixed lag N using the measurement information till the current scan k, the following set of equations can be used,In general, to obtain the smoothed mode probability for N.With the use of  and 78)78), one 5.4: Merged Smoothed Target Trajectory State Updatejth model smoothed state is probabilistically weighted and merged to calculate the smoothed target trajectory state at fixed lag N for the jth model. For this purpose, the smoothed target trajectory state N from the augmented state smoothed target trajectory state , true track rate (TTR), and mode probabilities are presented in this section to compare the performance of the proposed algorithm with the other algorithms available in the literature, which also assumed the target existence state as an event .In In In addition to the above experiments, the results are compared for high clutter density in the same target trajectory environment. The results show the performance of the proposed algorithm in In this paper, a fixed lag smoothing technique is suggested to enhance the trajectory state and existence state of the target while it is being maneuvered in the presence of clutter. In addition, the proposed algorithm has provided a complete and generalize mathematical formula to smooth the target mode probabilities while it maneuvers during its motion in the presence of clutter.The target hybrid state and mode probabilities at fixed lag N are not smoothed using the standard IMM-IPDA technique due to the lack of a formal mathematical foundation. The suggested smoothing framework outperformed other methods in the literature, according to simulation findings for RMSE, CTTR, and mode probabilities.


Monitoring the nitrate concentration in soil is crucial to guide the use of nitrate-based fertilizers. This study presents the characteristics of an impedance sensor used to estimate the nitrate concentration in soil based on the sensitivity of the soil dielectric constant to ion conductivity and on electrical double layer effects at electrodes. The impedance of synthetic sandy soil samples with nitrate nitrogen concentrations ranging from 0 to 15 mg/L was measured at frequencies between 20 Hz and 5 kHz and noticeable conductance and susceptance effects were observed. Long short-term memory (LSTM), a variant of recurrent artificial neural networks (RNN), was investigated with respect to its suitability to extract nitrate concentrations from the measured impedance spectra and additional physical properties of the soils, such as mass density and water content. Both random forest and LSTM were tested as feature selection methods. Then, numerous LSTMs were trained to estimate the nitrate concentrations in the soils. To increase estimation accuracy, hyperparameters were optimized with Bayesian optimization. The resulting optimal regression model showed coefficients of determination between true and predicted nitrate concentrations as high as 0.95. Thus, it could be demonstrated that the system has the potential to monitor nitrate concentrations in soils in real time and in situ. Nitrate nitrogen (nitrate-N) is one of the most important nutrients for securing the yield and quality of harvested products. However, excessive content of nitrate in soil leads to environmental problems. In Germany, nitrate is the main source of pollution for the groundwater. Nationwide more than a quarter of all near-surface groundwater fails to meet the nitrate target 50 mg/L) of the European Water Framework Directive . Current0 mg/L ofA method that could eventually lead to such a monitoring system is electrical impedance spectroscopy (EIS). EIS is a powerful measurement technique used to measure the complex impedance of materials and systems over a range of frequencies. A small A-C signal stimulus is applied to a conductive electrode, thus obtaining a characteristic response that can be observed on the system surface. It has a high potential for fast, inexpensive, on-site measurements and is therefore used in many fields, such as material characterization , bioimpeIn an electric A–C field, energy is absorbed and stored in soil solution mixtures. The effect of energy storage is connected with the real part When there is more than one ion species present in the soil, they all contribute to the impedance spectrum. The mixture problem is always a challenge in chemical sensing . Here weWith this assumption, in principle, these relationships in In this work, machine learning and neural networks are used to extract information from impedance spectra to explore how well they are able to untangle the information mess. This is consistent with the reasoning that we only want to gain information on the nitrate concentration in the soil, despite the many influence effects, but do not require physical cause-and-effect relationships .To be more concrete, we consider long short-term memory (LSTM), a variant of recurrent artificial neural networks (RNN) , to estiOften, it is not clear a priori which measurement data features are best passed to a neural network to optimize its prediction accuracy. Before training the network (here: the LSTM), the most suitable feature subset must be identified by a feature selection algorithm. The resulting removal of redundant and irrelevant data facilitates the design and the training of the neural network without incurring much loss of information . RedundaAll these feature selection methods treat the series data first as discrete input data and then estimate the importance of each feature. The dependency between data points in the series is not considered. The selected features are delivered as sequence of data to LSTM RNNs in which the dependencies between data points play an important role . TherefoTo validate the performance of this new method and compare the evaluation result of nitrate concentration in synthetic soils with LSTM, feature selection and evaluation with random forest (RF) is applied for comparison, all in the context of processing and interpreting impedance spectra of soil samples.RF is a known ensemble learning method that constructs a multitude of decision trees independently at training time. In particular, each RF decision tree is trained using a bootstrap set, which is randomly sampled from the input data with replacement . In this way, about two thirds of the instances in the bootstrap set are unique and used to build a decision tree, and about one third are left out as out-of-bag (OOB) instances used to validate the decision tree . The ensAs all decision trees are built over a random extraction of the instances from the database and a random extraction of the variables, RF is also widely used to measure variable importance. A suitable metric for this is the mean decrease in impurity (MDI), also known as Gini importance, defined as the sum of all decreases in Gini impurity  due to aAnother advantage of RF is the ability to measure variable association, which is an indication of correspondence between variables. Variable correspondence can increase the standard errors of a machine learning model . It is aThe method of measuring nitrate concentration in soil based on the combination of EIS with LSTM networks is characterized for the first time.The advantage of a new feature selection method over conventional methods, especially random forest, is demonstrated for the first time, the advantage being a higher coefficient of determination between the actual nitrate concentration and the concentration predicted by the LSTM network fed with the selected features.The results presented improve the quantitative understanding of the potential of the described approach in the context of the low-cost, robust, fast, in situ, and soil property-independent measurement of nitrate concentrations in soils.To the best of the authors’ knowledge, this work enhances the state of the art in the following respects:V, and As material under test (MUT), dry quartz sand from Karl Inzelsberger Sand and Kaolinwerk in Creußen, Germany, prepared with six different nitrate-N concentrations, viz., 0, 2, 4, 6, 8, and 10 mg/kg, was used as synthetic soil. These nitrate-N concentrations are below the recommended levels of 20 to 40 mg/kg in soils depending on crop . A totalTo investigate various synthetic soil mixtures, the measuring cell shown in Z| and the impedance phase φ were recorded and then evaluated in Matlab R2021a. Each MUT was repeatedly measured 20 times.The impedance G increases with increasing nitrate-N and water contents, the nitrate-N sensitivity being significantly greater than the moisture sensitivity. This demonstrates the potential for monitoring the nitrate-N concentration in soil with water as influence quantity, but also emphasizes the need for prior calibration.B increases with increasing nitrate-N and water contents. This can be explained by the electrical double-layer effect, which is strongly related to the ion concentration [Likewise, ntration . It may The data in G and susceptance B to provide a better view of the electrochemical changes in the measuring cell. As shown in G and B are functions of frequency and the derivatives of them may contain useful information. Therefore discrete approximations of G and B (vectors of length 98), Before the analysis using machine learning, it is an important step to prepare data, which involves reformatting data, making corrections to data and enriching data. The raw data sampled from our experiments consist of measured impedances In the following, feature selection by RF and by a newly developed method based on LSTM is evaluated with respect to the requirements for nitrate concentration measurement.RF provides estimates both of the association between various features and of the feature importance. The association and importance of the eight features available in this work are respectively presented in G is the most important feature, followed by the volumetric water content Since feature selection with RF does not consider the dependencies between different frequency points, it may not provide the optimal result when training LSTM RNN. We therefore developed a new feature selection method combining SFS with LSTM to select proper features of impedance spectra and their derivatives.SFS selects features sequentially based on a criterion to be minimized over all feasible feature subsets. In our case, the validation root mean square error (RMSE) of the LSTM network output was used as criterion. In SFS the features are sequentially added to an empty candidate set until the addition of further features no longer improves  the RMSE by more than a preselected minimum value, set to 0.1 mg/L in the given context.The difficulty of using the RMSE of the LSTM output as a criterion for SFS is that the optimum hyperparameters of the LSTM network vary with the feature subsets. To solve this problem, Bayesian hyperparameter optimization was used to find out the optimum hyperparameters for the LSTM RNNs of all feature subsets. Bayesian optimization calculates the optimum hyperparameters in a specified range of values and uses a metric  to optimize. In this way, not only the optimum hyperparameters are determined, but also the RMSE for the SFS process. For every subset, an LSTM RNN with optimized hyperparameters and minimized RMSE was trained. The one feature that decreases the RMSE most at each step is retained in the further process.The main structure of an LSTM RNN is shown in B decreases the RMSE most and thus is the most important feature for a successful LSTM estimator. Adding the volumetric water content G and the soil density G and B are not needed to estimate nitrate concentrations in soils.The hyperparameters listed in B, had little or no effect according to the former. Conversely, the most important feature by RF, the conductance G , one obtains results between rather unconvincing, B, G . However, the LSTM method takes the whole frequency spectrum as input, and as B(f) depends more strongly on f than G(f) and thus contains more information as sequence input, LSTM can estimate the nitrate concentration better from B than from G and better than the RF-based regression model.It is obvious that the RF-selected features are not the optimal choice for the LSTM RNN. In fact, a regression LSTM RNN trained with the top three RF-favored features  and with additional influence quantities. Our further goal is to conduct more experiments to expand the measuring range and include more soil types, such as potting soils, brown soils, and clay mixtures. In this way, we hope to implement a method based on machine learning that is capable of measuring nitrate-N concentrations virtually independently of soil properties and types. True field measurements cannot rely on bulky and expensive laboratory equipment but will require application-specific printed-circuit-board impedance meters.


Although syndromes that cause voice tremor have singular characteristics, the differential diagnosis of these diseases is a challenge because of the overlap of the existing signs and symptoms.To develop a task-specific protocol to assess voice tremor by means of nasofibrolaryngoscopy and to identify those tasks that can distinguish between essential and dystonic tremor syndromes.Cross-sectional study. The transnasal fiberoptic laryngoscopy protocol, which consisted of the assessment of palate, pharynx and larynx tremor during the performance of several vocal and non-vocal tasks with distinct phenomenological characteristics, was applied to 19 patients with voice tremor. Patients were diagnosed with essential or dystonic tremor according to the phenomenological characterization of each group. Once they were classified, the tasks associated with the presence of tremor in each syndrome were identified.The tasks that significantly contributed to the differential diagnosis between essential and dystonic tremor were /s/ production, continuous whistling and reduction of tremor in falsetto. These tasks were phenomenologically different with respect to the presence of tremor in the two syndromes.The protocol of specific tasks by means of transnasal fiberoptic laryngoscopy is a viable method to differentiate between essential and dystonic voice tremor syndromes through the following tasks: /s/ production, continuous whistling and reduction of tremor in falsetto. Voice tremor can be present in defined syndromes when it is associated with other neurological signs , or it is frequently described as an isolated voice tremor when the voice tremor is the only manifestation.Among the neurological syndromes with possible voice tremor manifestations, essential tremor is most prevalent followed by Parkinsonian tremor and dystonic tremor.Essential tremor presents with vocal disorders in 11–30% of cases.Koufman and Blalock (2004)Although there are specific characteristics among the syndromes that cause voice tremor, there is no sufficiently specific diagnostic method by which to differentiate these diseases because of their overlap of signs and symptoms.The objective of this study is to develop a protocol of specific tasks for the evaluation of vocal tremor by nasofibrolaryngoscopy and to identify the tasks that phenomenologically differentiate the syndromes of essential and dystonic tremor.After approval by the Institutional Ethics Committee under protocol number 1853/10 and the free and informed written consent, this cross-sectional study included subjects with voice tremor treated at the neurolaringology sector of a university hospital. Patient assessment included clinical history, general physical and head and neck examination, auditory-perceptual assessment of voice and nasofibrolaryngoscopy evaluation with specific task protocol.Inclusion criteria: (a) patients aged 18 and older; (b) trembling voice in the auditory-perceptual assessment; (c) tremor of the phonation device structures identified at nasofibrolaryngoscopy; (d) voice tremor complaint for over a year; (e) absence of periods of complete remission of voice tremor.Exclusion criteria: (a) treatment of the tremor or use of antihypertensive beta-adrenergic blockers in the past six months; (b) laryngeal paralysis or morphological lesions in the phonatory apparatus; (c) signs of Parkinson's or cerebellar disease; (d) thyroid hormone alteration.The nasofibrolaryngoscopy was performed with the patient in the sitting position with no topical anesthesia. Three anatomic subsites  were observed during the following specific tasks: prolonged production of phonemes , quiet breathing and continuous whistling. The tasks were evaluated for at least 5 s. Phonemes were produced using pitch and intensity that were comfortable for the patient, with the exception of /i/, which was produced in falsetto. The tests lasted an average of 2 min and were carried out with a KayPENTAX FNL-15RP3 flexible endoscope under continuous halogen lighting, connected to a TOSHIBA IK-CU44A camcorder and recorded in digital video system.The video images of nasofibrolaryngoscopy tests were edited in order to identify each specific task performed and remove the audio track to reduce the observation bias. The visual–perceptual analysis of the videos, omitting patient identification, was performed through the consensus of three otorhinolaryngologist examiners with experience in neurolaringology. The evaluators were instructed to: (1) assess the presence or absence of tremor at each subsite evaluated for each of the proposed tasks of the study; (2) identify whether there was a reduction in the intensity or disappearance of tremor in the /i/ task in falsetto in relation to the /é/ task . There were no limits as to the repetition of the videos for the examiner's assessment.The criteria used to consider the presence of tremor were: (a) tremor on the palate – rhythmic oscillation of the soft palate; (b) tremor in pharynx – rhythmic constriction of the pharyngeal wall; (c) vertical laryngeal tremor – rhythmic oscillation of the larynx in the vertical plane in relation to the surrounding aerodigestive tract; (d) horizontal tremor of the larynx – rhythmic oscillation of the vocal folds, vestibular folds or arytenoids in the horizontal plane.To identify specific tasks related to tremor syndromes, initially the patients were classified as having essential and dystonic tremor according to the presence of horizontal tremor of the larynx during the /s/ production, and then this task was combined with others (phonatory and non-phonatory), based on the assessments made by consensus among the three examiners.α significance level of 5% was used for the conclusions obtained through the inferential analyses.In the descriptive analysis of the sample characteristics, the mean, minimum value, maximum value and standard deviation were calculated for numeric variables; for the categorical variables, the absolute and relative frequencies were calculated. Fisher's exact test was used to identify associations between categorical variables of the protocol. The The 19 individuals included in the study performed the protocol tasks adequately, except for one patient who failed to perform the continuous whistling. Through the information collected from the visual–perceptual assessment of the tremor by consensus among the three examiners, 12 patients were classified as having essential tremor and seven as dystonic tremor, according to the presence or absence of horizontal tremor of the larynx in the /s/ production task .Table 2Dp = 0.057).When studying the association between horizontal tremor of the larynx in the /s/ production and non-phonatory tasks , a trendp < 0.001). In the selected sample, the 11 individuals classified as essential tremor (except one who failed to perform the continuous whistling task) had horizontal tremor of the larynx in the continuous whistling task.The horizontal tremor of the larynx in the /s/ production was strongly associated with horizontal tremor of the larynx in the continuous whistling task (p = 0.020), pharynx (p = 0.038) and horizontal larynx (p = 0.002) was more frequent in patients classified as having dystonic tremor. No other phonatory task could differentiate the essential from the dystonic tremor.Comparing the phonatory tasks and the horizontal tremor of the larynx in the /s/ production , we obseDespite the advances in neurolaringology, the distinction between the essential and dystonic voice tremor remains challenging, and this directly impacts the choice among the several types of treatment. For this differentiation, several research methods have been used, among them the auditory-perceptual voice assessment, the transnasal fiberoptic laryngoscopy (nasofibrolaryngoscopy), acoustic measurements and larynx electromyography.The main characteristic of dystonic tremor is that it depends on a specific task to manifest itself, which, in the case of voice tremor, is phonation. In these patients, by definition, the tremor would not be present in non-phonatory situations , even if there is muscle action to maintain the laryngeal posture, as during the /s/ production. However, patients with essential tremor have muscle tremor when it is contracted, even if it is only to maintain the posture. Thus, the tremor in phonatory apparatus would be present during any activity where there is posture maintenance, regardless of phonation and, therefore, during the /s/ production. Thus, the /s/ production task, which represents a non-vocal fricative phoneme without vocal fold vibration, appears to be a watershed between these two syndromes, since during its performance patients with dystonic tremor would not have tremor, while patients with essential tremor would manifest it. Therefore, this task was chosen to assess variations and differentiate patients, classifying them as having essential tremor syndrome or dystonic tremor syndrome, and also to identify associations between the syndromes and the several protocol tasks .There is still no consensus on the muscle activity of the larynx during quiet breathing. According to a neurolaringology committee report of the American Academy of Otolaryngology – Head and Neck Surgery,The continuous whistling task was chosen for inclusion in the protocol due to its resemblance to the /s/ production, that is, it is a non-phonatory task in which there is evident posture maintenance in the larynx. In this sample, the horizontal larynx tremor during the whistling showed to be strongly associated with horizontal tremor of the larynx during the /s/ production, with both tasks appearing always equal in relation to the presence of tremor . This obIn 2006, De Biase et al.The literature regarding adductor laryngeal dystonia has evaluated the change in the intensity of spasms in accordance with the variation of the pitch; phonation in falsetto reduces the stimulus for the occurrence of spasms.The /z/ production is important to verify that the phonatory tasks are not useful to differentiate the types of tremor, as only one patient had no horizontal tremor of the larynx during this activity . DespiteUnlike dystonia, essential voice tremor usually presents as a more widespread disease manifestation, in which the tremor is not restricted to the intrinsic muscles of the larynx and may involve the palate, pharynx, tongue and articulatory muscles.The absence of a gold-standard assessment for vocal tremor analysis makes it difficult to assess the reliability of this proposed protocol. As previously discussed, there is a possibility of diagnostic error in this assessment, for instance, the presence of false negative or false positive results in the examination, and therefore one should avoid establishing a diagnosis based on the tremor representation in a single task. To obtain greater reliability when defining the nosological entity in question, the latter should be determined only when the tremor manifestation in different tasks phenomenologically corresponds to the same tremor syndrome. In cases where the tremor manifestation in the several tasks does not point to the same syndrome, the implementation of this protocol becomes less reliable for diagnostic definition.Although it seems arbitrary to syndromically classify voice tremor by the presence of horizontal tremor of the larynx in the /s/ production, it is justified by the phenomenological basis demonstrated in this study and the associations found in the several tasks, which partly replicate the characteristics traditionally described for each disease. Therefore, the distinction between essential and dystonic voice tremor seems to be feasible, provided that adequate evaluation is performed, including an assessment of the phonatory apparatus by transnasal fiberoptic laryngoscopy, especially during non-phonatory tasks. Moreover, based on higher diagnostic accuracy, it is possible that specific treatments targeted for each type of tremor may allow a better therapeutic response.The protocol of specific tasks during transnasal fiberoptic laryngoscopy is a viable method to differentiate between essential and dystonic voice tremor syndromes. Tasks with significant importance for this differentiation are the /s/ production, continuous whistling and observation of reduced tremor in falsetto.10.13039/501100002322Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES.This study was supported by The authors declare no conflicts of interest.