The Experimental Zooprophylactic Institute of Southern Italy  is a public health institution operating within the Italian National Health Service. Over the past 5 years [IZSM] has promoted several research studies and interventions in an effort to tackle the ‘Land of Fires’ phenomenon, caused by the continued trafficking and uncontrolled incineration of waste that has affected some areas of Campania for decades. In this article, a mathematical model that generates a municipality index of environmental pressure is presented. The model was developed by a multidisciplinary team led by an environmental engineer and included researchers in the fields of veterinary and human medicine, biology and computer science. This model may serve as a geostratification tool useful for the design of human biomonitoring studies, although it may also be employed for strategic planning of remediation programs and public health interventions. The complex environmental scenario of the ‘Land of Fires’ phenomenon is caused by the continued trafficking and uncontrolled incineration of waste, and has affected some areas of Campania in Italy for decades. In this article a mathematical model that generates a municipality index of environmental pressure is presented. This index may be used for the design of biomonitoring studies as well as for planning of remediation interventions. Since the 1980s, organized crime has been responsible for the continued illegal trafficking of industrial waste and toxic materials in the so-called ‘Land of Fires’ (Terra dei Fuochi [TdF]), a territory mostly located in the provinces of Naples and Caserta in the Campania region of southern Italy. The term ‘Terra dei Fuochi’ was introduced by the Italian environmental association Legambiente and refers to the fact that waste was abandoned and illicitly disposed through uncontrolled combustion [In the early 1990s the Campania region suffered from a prolonged ‘waste crisis’ that lasted roughly 15 years and was caused by the inability of institutions to provide for the proper management of urban solid waste. Waste that accumulated in municipal areas was often set on fire by citizens exasperated by the nauseating smell , which gPublic concern about the threats posed to human health by environmental contamination grew in 2004, when Mazza and Senior used the expression ‘Triangle of Death’ to indicate a geographical area contained within the municipalities of Acerra, Nola and Marigliano of the province of Naples  were larWhile a growing body of scientific evidence suggested that citizens dwelling in the provinces of Naples and Caserta could be affected by an increased risk of death, cancer, cancer-related mortality and congenital abnormalities , with an2 and has over 5.5 million inhabitants residing in 550 municipalities divided into five provinces [Although human biomonitoring studies play a key role in assessing the threats posed by environmental pollution, only a few such studies have been conducted in the Campania region ,17. In arovinces ,19, a syIn this original work, we constructed a mathematical model that computes a synthetic index of environmental pressure at a municipality level . We computed the MIEP for all municipalities of the Campania region and then used it as a geostratification tool for the recruitment plan of a human biomonitoring survey at a regional level .The MIEP is defined based on a pairwise comparison process between variables  to whichi is the single source of contamination considered and aij is the numerical value resulting from the comparison between criteria i and j, which can vary from 1 to 9, where each value of the scale is assigned according to the criteria proposed in ij values of matrix A have the following properties:ij = a, then aji = 1/a, with a >0; If ai is judged to be of equal intensity relative to Aj, then aij = aji = 1. If the variable AMatrix A is an 8 × 8 square matrix in which the values resulting from pairwise comparisons are reported above the main diagonal, while the reciprocals of these values appear in the lower part. The aThe last row in matrix A shows the sum of the individual elements that make up each column.ij by the sum relative to the j-th column. Subsequently, the average value of each i-th row of the matrix was calculated, defining the ‘priority vector’ as shown in Matrix A was normalized, dividing each element ai, the model gave its percentage weight; in For each source of contamination ATo evaluate whether matrix A was consistent, or that the requirements of consistency and significance in the judgments expressed by the ‘preference indices’ were met, all the cells belonging to the i-th row of the non-normalized matrix were added together and multiplied vectorially by the sum of the priority vector and divided by the weight of the criterion relating to that row. In this way it was possible to quantify the consistency of each priority, as shown in λ represents the maximum eigenvalue of matrix A and n the dimension of the matrix itself (The consistency index (CI) of the entire matrix A was calculated using the following relation, where x itself :(Eq. 1)Cn, then the matrix is not perfectly consistent, although the methodology used accepts a low degree of inconsistency because this does not affect the validity of the result obtained. As a first approximation, the maximum eigenvalue of matrix A can be evaluated by referring to the average of the consistencies relating to the individual variables; the result is a maximum eigenvalue equal to 8.70, which is close to the dimension n of matrix A.In n = 8) the value of this index is equal to 1.41  of matrix A, defined by the following equation :(Eq. 2)CFor matrix A to be consistent, the value of CR must be less than 0.1. In the specific case, i’ weights to be assigned to each pressure variable were determined, the MIEP values were determined. Specifically, for each municipality, MIEP was calculated by a linear combination of the set of pressure variables considered, multiplied in turn by specific amplification coefficients as functions of the number, type, extent, hazard, environmental status and impact of the variable itself. These coefficients were introduced in such a way as to be able to define the model on the environmental and territorial characteristics of each municipality in the Campania region. The MIEP relating to the i-th municipality of Campania region is expressed by the following relationship:Once the ‘PIn order to make a comparison between the environmental pressure indices determined, the variable was normalized in such a way as to have values between 0 and 100. The normalization operation was carried out through application of the following relationship:i to refer to normalized MIEPi from now on in text unless otherwise specified.We use the abbreviation MIEPThe ‘contaminated sites’ variable includes contaminated landfills as defined by Legislative Decree 152/2006 and plots of land of classes 3, 4 and 5 as indicated in Decree No. 56 of 9 March 2015 and No. 191 of 19 August 2015 relating to TdF. Following investigations by the Campania Regional Environmental Protection Agency (ARPAC), the plots of land defined by decree were further classified as shown in In determining the environmental pressure index, only types B, NC and D of class 3, 4 and 5 plots of land were considered. A further p″ score was attributed according to the specific spatial extension of these sites, assuming that the degree of pressure is directly proportional to the extent of the site, as shown in The pressure index relating to the variable in question is expressed by the following mathematical relationship, where the sum is extended to all the contaminated sites present in a municipality:This variable includes sites of national and regional interest present in the area, illegal landfills awaiting investigation, as well as potentially contaminated sites investigated. For each area of interest considered, a preliminary score p′ is assigned based on the different degrees of presumed pressure. In the specific case, sites of national interest are assigned the highest score, followed by illegal landfills, areas awaiting characterization and, finally, potentially contaminated sites. The potential risk index relating to the ‘areas of particular interest’ variable was evaluated by assigning the score to the single element of the variable, as shown in The ‘zoning’ variable considers the different impacts, both direct and indirect, exerted by different land uses . To define the environmental pressure index relating to the ‘zoning’ variable, scores (p′) were assigned to the different land use destinations present in the 2012 edition of the ‘Corine Land Cover’ map, based on qualitative assessments of pressures exerted in each of them. Based on the available data, the criteria followed consisted of attributing a greater weight, in terms of environmental hazard, to residential areas, followed by industrial areas and then agricultural and wooded areas . Residential area/municipal area Industrial area/municipal area Wooded area/municipal area Agricultural area/municipal areaTo represent the relative extension of each intended use over the entire municipal area, a specific p′′ indicator was introduced, divided into the following parameters:The environmental pressure index associated with the zoning variable is expressed analytically by the following expression:where the sum is extended to the n land uses present in a municipality.https://www.smacampania.info/chi-siamo/) was taken into consideration. The environmental pressure index relating to the ‘illegal waste spills and fires’ variable coincides with the number of spills detected on a municipal basis. It can be mathematically formalized by the following formula:The ‘illegal waste spills and fires’ variable indicates the presence of abandoned waste and uncontrolled fires. To determine the pressure index relating to this variable, the number of waste spills detected in the municipalities by the monitoring activity carried out by SMA Campania  and those of classes 5, 4 and 3 which, following investigations, are not contaminated  were takThe environmental pressure index relating to the variable ‘TdF decree plots’ was assessed by assigning the score p′ to the single element of the variable according to the formula presented below.The potential hazard variable was created starting from analysis of the spatial distribution of the concentration values of contaminants using spatial statistics models, which made it possible to reconstruct continuous concentration areas on the entire regional territory and to estimate the probability of exceeding the legal limits or reference values in areas not covered by sampling . The ‘poa represents the number of analytes the concentrations of which have exceeded regulatory limits and the sum present is extended to the n areas potentially at risk, with reference to the municipal territory:The environmental pressure index relating to this variable is determined through the relationship shown below, where This variable takes into account the quality status of groundwater bodies. To fully define the environmental pressure index relating to this variable, a series of attributes were introduced that indicate the qualitative status  and a series of indicators that take into account the percentage municipal coverage of the underground aquifer. In fact, the chemical status of groundwater index summarizes the qualitative state of groundwater based on a comparison of the average annual concentrations of the chemical parameters analyzed with the relative quality standards and threshold values defined at national level by Legislative Decree 30/09 , also taSubsequently, for each municipality, the indicator ‘groundwater body area/municipal area’ was introduced to take into account the extension of the groundwater body in relation to the municipal area.The values of these indicators were divided into a series of intervals (bands), established according to a classification of the ‘natural breaks’ type. A second p″ score was then assigned to each interval thus defined. where the sum is extended to all groundwater bodies within a municipality.Impact areas are made up of an aggregation of municipalities, chosen in an arbitrary manner according to criteria of spatial contiguity and technical/logistical needs. The Impact Area Pressure Index is calculated as the average of the municipalities that make it up, weighted with respect to the resident municipal population . RelativFollowing the application of multicriteria analysis, the contaminated sites variable assumes greater significance than the others considered, because health risk has been ascertained (exceeding risk threshold concentrations) for the potentially exposed population. With a difference of about 9%, it follows the ‘areas of particular interest’ variable, which includes all those territorial circumstances in which there has been an exceeding of contamination threshold concentrations in one or more environmental compartments investigated through sampling and analytical tests, thus denoting phenomena of potential contamination in progress. This is followed on a par by the ‘zoning’ variable, which directly considers the different land uses , with particular reference to the set of activities present and potential pressures exercised on environmental sectors. ‘Status of water bodies’ and ‘potential hazard’ come next, with a difference of about 8%; these two variables indicate the degree of pressure determined by the quality status of the underground/surface water bodies that pass through and by the presence of soil contamination phenomena, also attributable to natural factors.The ‘illegal waste spills and fires’ variable was considered more important than the last two variables (waste management plants and plots of land of the TdF Decree) because the former considers mainly authorized plants with controlled management, while the latter considers the TdF plots for which a site-specific survey has not yet been carried out (classes 2A and 2B) and those which, following the investigations, are not polluted. Supplementary Table 1 & For each municipality in the Campania region, the model gave an MIEP value ranging from 0 to 100  based on geographical contiguity, and were classified at high, medium and low environmental pressure , an area characterized by the massive presence of chemical and textile industries which have strongly modified the entire territorial and social structure since the early 1970s [et al. is based on a score and weight system which takes into account 24 analysis factors, grouped into three main categories: characteristics of the waste, migration routes of contaminants and typology of receptors. Each factor is measured by an index score with a range of variability from 0 to 10 proportional to the incidence of the factor itself on the risk analysis. The score obtained is then multiplied by a weight (Pesoi), which varies from 1 to 3 depending on the significance of the factor’s contribution to overall risk conditions. In accordance with the risk indices obtained, three priority areas were identified , in a manner similar to the methodology we used.Existing literature provides several examples of synthetic environmental pressure/risk indices, sometimes for purposes that differ substantially from those pursued by our research group. Vacca ly 1970s . In thiset al., the authors present a risk assessment model applied to a large number of brownfield sites in large areas , which is useful for planning reclamation and redevelopment actions [In the study by Chrysochoou  actions . The modet al., which evaluated the impact on public health of the waste emergency in the provinces of Naples and Caserta, the authors developed a municipal index of environmental pressure from waste disposal, which was used in the analysis of geographical correlation with epidemiological data [In the study by Martuzzi cal data . Startincal data . The groThe Experimental Zooprophylactic Institute of Southern Italy, with headquarters in Portici, is one of the ten Zooprophylactic Institutes in Italy operating within the National Health Service on Hygiene and Veterinary Public Health as a technical and scientific tool for the State and of the Campania and Calabria regions. In response to the complex social, environmental and economic situation caused by the TdF phenomenon, the Experimental Zooprophylactic Institute of Southern Italy collaborated closely with the TdF working group, carrying out additional monitoring surveys both on food (QR Code Campania project ) and on The model proposed here is useful for the global and synthetic assessment of environmental pressure on a municipal basis. As shown, it can also be applied to aggregations of municipalities. Furthermore, it can be used in the context of institutional actions for the planning and monitoring of improvements on a local or regional scale. Finally, the proposed MIEP represents a basis for geostratification of the sample in the context of population biomonitoring studies on a regional scale, as in the described biomonitoring study design applicable to the Campania region.In the future, the threats posed by widespread pollution to human health are going to increase. In the peculiar context of the TdF, environmental contamination has not been the result of industrial activities, but of illegal activities. Little progress has been made in Campania regarding remedial interventions. Mathematical models capable of assessing the levels of pollution in a certain area represent a valuable tool to identify areas in need of remediation. Conversely, the need for biomonitoring studies is going to become more and more compelling, and the implications of biomarkers of exposure for the health of the individual citizen remain unknown. More studies are required to interpret the results of biomonitoring studies at an individual level.We constructed a mathematical model that computes a synthetic index of environmental pressure at a municipality level .We computed the MIEP for all municipalities of the Campania region and used it as a geostratification tool for the recruitment plan of a human biomonitoring survey at a regional level.The MIEP is defined based on a pairwise comparison process between variables to which scores of relative significance are assigned through a multicriteria approach based on the analytic hierarchy process method.For each municipality in the Campania region, the model gave a MIEP value ranging from 0 to 100.It was observed that the municipalities with the highest MIEPs are concentrated mainly in the provinces of Naples and Caserta.The model proposed here is useful for the global and synthetic assessment of environmental pressure on a municipal basis.The model can also be applied to aggregations of municipalities.The model can be used in the context of institutional actions for the planning and monitoring of improvements on a local or regional scale.Finally, the proposed MIEP represents a basis for geostratification of the sample in the context of population biomonitoring studies on a regional scale, as in the described biomonitoring study design applicable to the Campania region.Click here for additional data file.


The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed.This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant  Water distribution networks are complex systems that are difficult to manage and monitor with extreme importance nowadays. The detection and location of leaks have become crucial for water distribution because when there are bursts or leaks, this can generate not only economic losses but also an environmental issue and represents a potential risk to public health with contaminated water . AnotherThe infrastructure in a medium-sized city can have pipes that span hundreds of kilometers connected to hundreds of nodes (pipe junctions or customers that connect to the network). Therefore, several factors can generate water loss during transport between the treatment plants and the reservoir for consumers, usually attributed to several causes, including leaks, measurement errors, and theft. Water loss can be divided into two terms, “real losses” and “apparent losses”. Apparent losses are constituted by badly read measurements, data handling errors, and illegal water tapping. In contrast, the real losses comprise leakage from all system parts and overflow at storage tanks. Real losses are divided into “background leakage” made up of small undetectable and into detectable leaks relevant for detection as they represent significant losses for the water distribution company.Effective leak management is vital for all of the factors mentioned above to save financial resources and water. The methods of leak localization can be classified into two categories: Hardware-based system and Software-based.The Hardware-based utilizes hardware sensors to detect a leak directly and help the localization of the leak. As there are various types of sensors and instruments available, they can be further subclassified as: acoustic ,5 and noSoftware-based methods generally rely on an algorithm or model for detecting leaks. Unlike hardware-based methods, these methods do not seek to locate the leak point accurately but minimize possible leakage areas. Since these methods are based on information, such as the pressure of the pipe network, flow data, and so forth, they work well on any type of pipe. These methods can be divided into physical modeling methods and data-driven methods. The physical modeling methods or model-based methods identify the leak using a numerical model and compare the results with the field data, for example, Ref.  which usLike other recent works ,19, it rThe remainder of this paper is organized as follows: m pipes, n internal consumer nodes and can be described by a directed graph m pipes in the network.A water distribution network is composed of  G={V,E},, with V=The graph The direction of the edge represents a reference direction for the flow in the corresponding pipe. The incidence matrix is composed of i, and The WDN must fulfill mass conservation law, which expresses the conservation of mass in each vertex, described by:jth edge. The relationship between pipe flow and energy loss caused by friction in individual pipes can be computed using the Hazen–Williams formula )Notice that the result of the leak localization method (10) is one of the j).Then, the leak localization results in Considering the Hazen–Willians Equation (3) for every pipe  and determine which is the one with the minimum resistance among the On the other hand, the minimum path from the j, j to sensor j and the node where the sensor is located c refers to the common path between node and sensors, and j to the different sensors:When a leak is produced in node j, j to sensor The For every node l:The k can be taken into account applying the Bayes’ rule as:In order to improve the performance of the leak localization method, the information of the residuals at different time instants When a leak is not detected by the leak detection method, anomalous values of pressure residuals The accuracy of this fault detection method depends on the length of residual bounds esiduals  between In the same way as the pressure residuals, spatial residual bounds As model errors will affect in a similar way as close pressure sensors, it is expected that some spatial residual bounds will be smaller than pressure residual bounds. Therefore, fault detection defined by (24) will be more sensitive to pressure sensor faults than the one defined by (22). The accuracy of the sensor fault detection can be increased by means of average computing residuals in a time window leading to smaller residual bounds.Algorithm 1 Sensor validation search for sensor faultStage 1: In the case of the activation of one or more sensor faulty signals Stage 2: Only considers Spatial faulty signals fordo1: fordo2:  forthen3:   4:5:    Discard sensor end if6:   end for7:  end for8: Once a residual has been violated, that is, at least one of the sensor faulty signals In the case that two or more sensors obtain the same cost function in (25) and less than the maximum possible value The Network used for the case study is a reduced city’s network model from Hanoi’s WDN (Vietnam). It is composed of one inlet (reservoir), 34 pipes, and 31 nodes, represented by To analyze the performance of the proposed approach, data with different conditions have been generated artificially using the EPANET hydraulic simulator . In ordeThe daily water consumption pattern used for the calibration of Equation (6) is shown in The sample rate is 10 min, but average hourly measurements are calculated to reduce uncertainties on the diagnostic stage.The evaluation of the performance of the proposed leak localization method at node level defined in Equation (21) will be analyzed utilizing Average Topological Distance (ATD) . The ATDFinally, the confusion matrix fined in  and depiConsidering the confusion matrix Four cases have been considered with different quantities of sensors in the network to analyze how this affects the final result. Using the inlet flow data and non-leak historical pressure measurements of the selected sensors, the For every sensor configuration, normalized incidence factors (14) have been computed with topological information: node connections and pipe characteristics .  in Ref. . FinallyIn order to illustrate the performance of the proposed sensor validation method, Case 1 (four sensors) will be considered. The four-sensor residuals computed by Equation (7) have been considered in a time window of 24 h using leak-free data leading to upper residual bounds equal to:In the same way, the six spatial residuals defined by (23) have been computed in the same conditions as sensor residuals leading to spatial residual bounds:The second case study selected to test the performance is the reduced model of the real water distribution network of the Italian city Modena. This large-scale network is comprised of 268 junctions (nodes) connected through 317 pipes and served by four reservoirs. There are no pumps in the network since it is entirely gravity-fed ,33.The leak scenario consists of data samples collected every 10 min and filtered to hourly values to reduce the uncertainty in the data;The uncertainty of demand is considered by introducing the uncertainty of 10 [%]  of the nominal demand value. In addition, white noise is deemed to emulate the noise in the measurements;The leak size is randomly selected with a range of 3 to 6 [l/s], representing 1% to 2.5% of the network consumption.The EPANET hydraulic simulator was used to generate artificial data to analyze the performance of the proposed method. The following simulation conditions were considered:The sensor bias, sensor drift, and abrupt sensor failure of sensor faults were proposed to analyze the sensor validation method. The sensor bias fault was simulated as a step change, and the drift fault was given as a time-varying ramp signal. In both cases, the fault magnitude was randomly chosen with a range of As applied in the previous case study, the Average Topological Distance (ATD) was used to assess the performance of the proposed leak localization method at the node level defined in (19). Two scenarios have been considered with five and ten pressure sensors that are presented in On the other hand, a total of 6000 scenarios were simulated with 10 days each to evaluate the sensor validation method for the five sensor configurations depicted in To calculate the residual and spatial residuals bounds, a 6-month leak-free scenario was generated. The five sensor residuals computed by Equation (8) considering the time window of 24 h and increasing Following, the ten spatial residuals defined by (23) were computed in the same conditions as sensor residuals leading to spatial residual bounds:For this study, the evaluation metric applied was classification accuracy. To this purpose, the confusion matrix was used, which presents the classification accuracy and the misclassification error, and the horizontal axis of the confusion matrix describes the predicted labels of samples, while the longitudinal axis depicts the true labels of samples. The right side shows the percentages of correctly and incorrectly classified observations for each true class.A new data-driven method for leak localization in WDN based on historical non-leak data and the topological information of the network is proposed. The proposed method is triggered when a leak is detected, and it is based on the evaluation of residuals generated by leak pressure measurements in some inner nodes and the estimation of leak-free pressures in these nodes utilizing a reduced-order model and historical data. Topological information is used to compute a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. The proposed incidence factor combined with residual information generates a likelihood index that allows leak localization at the node level. In addition, a sensor validation method based on the sensor pressure residuals, which is able to detect and isolate pressure sensor faults, is proposed.The proposed method’s general performance for leak location and sensor validation is evaluated in reduced models of the Hanoi and Modena water distribution networks. The results of the leak localization are compared to another technique published with satisfactory results. Future works can be developed to improve the leak localization and sensor validation performances, with a study of an algorithm to automatically determine the optimal sensors required to maximize the leak localization performance.


The rapid spread of the novel coronavirus disease 2019 (COVID‐19) causes a significant impact on public health. It is critical to diagnose COVID‐19 patients so that they can receive reasonable treatments quickly. The doctors can obtain a precise estimate of the infection's progression and decide more effective treatment options by segmenting the CT images of COVID‐19 patients. However, it is challenging to segment infected regions in CT slices because the infected regions are multi‐scale, and the boundary is not clear due to the low contrast between the infected area and the normal area. In this paper, a coarse‐refine segmentation network is proposed to address these challenges. The coarse‐refine architecture and hybrid loss is used to guide the model to predict the delicate structures with clear boundaries to address the problem of unclear boundaries. The atrous spatial pyramid pooling module in the network is added to improve the performance in detecting infected regions with different scales. Experimental results show that the model in the segmentation of COVID‐19 CT images outperforms other familiar medical segmentation models, enabling the doctor to get a more accurate estimate on the progression of the infection and thus can provide more reasonable treatment options. More than ten million people were infected, among whom hundreds of thousands died since 2019 [Chest computed tomography (CT) is a convenient diagnostic tool for pneumonia, and it is widely used for detecting typical imaging features of COVID‐19 , 3, 4, 5A recent review  concludeThe development of deep learning techniques provides strong supports for medical image segmentation. Recently, the residual attention U‐Net  is propoTo address the above problems, in this paper, we proposed a coarse‐refine network for capturing both coarse and fine contexts, which can achieve accurate object segmentation with high‐quality boundaries. Specifically, we use the coarse‐refine architecture and hybrid loss to guide the model to predict the delicate structures with clear boundaries to address the problem of unclear boundaries. We add the atrous spatial pyramid pooling module (ASPP)  in the n1)We propose a network for the segmentation of COVID‐19 Chest CT slices. In this network, we use a coarse segmentation module to obtain a rough segment result from input images. We employ a refined module to learn the residuals between the rough segment result and the ground truth to adjust the erroneous segmentation output, especially the edge. Moreover, we add an atrous spatial pyramid pooling module to both the coarse segmentation module and the refine module so that the network can accurately detect infected regions with different scales. We adopt the hybrid loss  as train2)We regard the encoder of the coarse segmentation module as a classifier. We put all the CT slides of a person into the classifier to determine whether the person is infected with COVID‐19. We only segment the images that are judged with COVID‐19 lesions, so the classifier can considerably speed up the network.3)We analyse the statistics of the segmentation results and show that the segmentation results provide strong evidence for doctors to make a much more accurate estimate of the infection severity. Subsequently, the patients with COVID‐19 can receive more reasonable treatment options.The contributions of this paper are as follows:2This section starts with the dataset introduction. We describe the dataset in Section 2.1We collected the CT images of patients with COVID‐19 manually labelled from Renmin Hospital of Wuhan University and Sun Yat‐Sen Memorial Hospital. The manual labelling of the lesion area dataset includes 1060 CT slices from 226 patients with COVID‐19. The images were annotated by five experienced physicians and checked by two senior doctors, leading to 1060 images from 226 patients with COVID‐19. These data are used for the segmentation task. The annotations include three types of lesions: fibrosis, consolidation, and ground‐glass opacity (GGO). The severity of the illnesses is independently diagnosed to mild, moderate, and severe according to CT images and clinical symptoms. Table  To test the performance of the classifier, We collected another classification dataset. It is shown in Table  2.2Human‐level results are more meaningful than image‐level in conducting diagnosis and therapy. Hence, we split the dataset at the human level. We use the data of 226 patients with COVID‐19 that labelled lesion areas as the positive samples of the segmentation model. We regard the 78 healthy persons' CT slices, 100 patients' data with bacterial pneumonia and 219 patients' data with typical viral pneumonia as the segmentation model's negative samples because their images do not contain the infected region of COVID‐19. We get the data for the segmentation model by combining the positive and negative samples. We divide the segmentation dataset by splits of 70%/10%/20% as training, validation, and test data at the human level and carry out five‐fold cross‐validation. The number of COVID‐19 patients, the number of CT slices, and the number of various types of lesions in a fold of training set, validation set, and test set is shown in Table  We regard patients with COVID‐19 as a class. The CT images of healthy people and bacterial pneumonia and typical viral pneumonia as one category. Those data are used to recognise whether a person is infected with COVID‐19 from their CT slices. We divide the classification dataset by splits of 70%/10%/20% as training, validate and test data at the human level and also carry out five‐fold cross‐validation.2.3As our deep learning methods are affected by the amount of data, we do data augmentation for our training data. In this data augmentation, all images are rotated by 90, 180, and 270 degrees to obtain three times more images. We also flipped the original images horizontally to produce another copy of the images. As a result, the data augmentation expands the number of images to five times, so we get 15,060 training images in every fold.2.4In the design of our network, we consider the fact that doctors commonly locate infected regions roughly and then adjust contours to get fine annotations. Rough segmentation and the refine operation are all very important. Inspired by this, we design the network as a coarse segmentation module and a segmentation refinement module. As shown in Figure  The coarse segmentation module is used to obtain rough segmentations from input images through an encoder–decoder network based on U‐Net . In this2.5The coarse segmentation module is designed as an encoder–decoder network based on the U‐Net . In thisIt is challenging to make accurate segmentation , 23. In ×1 convolution, an image pooling, and three atrous convolutions with different rates in parallel. All convolution layers (the 1×1 and three atrous convolution layers) contain 64 convolution filters. The different rates  of three atrous convolutions are used for combining high‐level semantics with low‐level details.ASPP is robust to fuse semantic information of different levels . As the The structure of our decoder is symmetrical to the encoder. Each stage comprises three convolution layers and is followed by a batch normalisation and a ReLU activation function, finally followed by a bilinear up‐sampling  and a si2.6In our network, a segmentation refinement module is designed to refine the coarse predictions by learning its difference from the ground truth, similar to previous studies , 29.It has been proved that the residual refinement module has good detection ability on the edge part , 29. Res×3. The ASPP bridge is the same as the coarse segmentation module. Each stage of encoder and decoder is followed by a max‐pool layer and a bilinear up‐sample layer, respectively. Add the rough segmentation result and the refined result and then input the sum into a sigmoid function as the result of our model.To improve the accurate boundary and region in coarse segmentation, we developed a segmentation refinement module based on RRM . As show2.7The hybrid loss was proposed in BASNet , which olbce,lssim and liou denote BCE loss [The hybrid loss  is definBCE loss , SSIM loBCE loss  and IoU BCE loss , respectBCE loss is pixel‐level because it does not consider the local neighbourhood's labels and the weights of all pixels are equal. It is helpful for the convergence of all pixels. SSIM loss is patch‐level because it considers an adjacent neighbourhood for each pixel. It assigns a higher weight to the edge so that it helps to optimise the boundary segmentation. IoU loss is a map‐level measure because as the foreground's forecast confidence grows higher, the foreground's loss will gradually approach zero. We use BCE loss to maintain a smooth gradient for all pixels by combing those losses, and we utilise IoU loss to focus on the foreground. We use SSIM loss to provide further weight for the boundary to obtain a more accurate boundary prediction. In our model, we use hybrid loss at intermediate coarse segment output and the final output and add them up, then use backpropagation to optimise the parameters.2.8To enable the model to distinguish the person with COVID‐19 from a person without COVID‐19. For the propose to verify that the segmentation labelled data can improve the ability of network feature extraction in COVID‐19. We regard the encoder part of the coarse segmentation module as a classifier. The encoder part of our coarse segmentation module has an input layer and four stages comprised of basic se‐res‐blocks, and the input layer and four stages are adopted from SE‐ResNet‐34 , 28. Whe33.1×512. We use Adam optimiser to optimise all methods, and the learning rate is set to 3e‐4 by default. We train all models with a mini‐batch size of 8. We run up to 200 epochs for each method. The training monitors the loss on the validation dataset, and it will early stop if the loss increases too much. All those methods had the same training strategy and made the same data augmentation mention before.We ran the experiment on Ubuntu 16 with an NVIDIA TITAN RTX 1080 GPU with 11 GB of memory. We use Python 3.6 and PyTorch 1.0 to implement those algorithms. The size of all CT slices is 512Firstly, we train the whole segmentation network, and when the segmentation network converges, we freeze all parameters of the segmentation model. Later, we train the classification part of the coarse segmentation module except for the input layer and the first four stages until the classification model convergence.In the test process, for each image of a patient, we first input the image into the encoder of the segmentation network to determine whether it has lesions. If it has some lesion of COVID‐19, we continue to segment it. When all the images of a patient are tested, if no CT image contained the lesion area, the model will predict that this person is not infected with COVID‐19. Otherwise, it will indicate the person is infected with COVID‐19.3.2G is the set of ground truth pixels of the lesions and P is the set of predicted pixels. |G∩P| is the number of elements of the intersection between ground truth pixels and predicted pixels, and |G| and |P| are the number of elements of the set of ground truth pixels and predicted pixels respectively.For the segmentation task, we use Dice coefficient , IOU 2626, pixelTP,TN,FP, and FN are the numbers of true positive, true negative, false positive, and false negative, respectively.In the classification task, we computed accuracy , precisi3.3Table  From Table  3.4In addition to locating various types of lesions accurately, we can eventually provide the proportion of different types of lesions in the lung area to get a preliminary diagnosis of the severity. The statistics can be seen in Figure  p‐value <0.01), which means there is a strong correlation between the predicted proportion of lesion area and severity of COVID‐19. As we can see from Figure  As can be seen from Figure  3.5As shown in Table  By comparing the results of our classifier with the SE‐ResNet34 model, we can find that encoder of coarse segmentation module trained with segmentation annotation data has a stronger ability to extract features, which can significantly improve the classification performance of the encoder. As a kind of supervision information, the segmentation annotation is also passed to the encoder through the loss function of segmentation. The classifier trained with segmentation annotation data enhances the accuracy and recalls by nearly ten percentage points, while precision also improves by nearly seventeen percentage points. The confusion matrix is shown in Figure  p<0.05 for the data with a sample size of 936 and the number of attributes of 2 is 59.97. Obviously, the Friedman test results on SE‐ResNet34 and our method are much smaller than the critical value, which illustrates only little difference between the predicted results and the truth label.We adopt the Friedman test  for the 3.6In addition to the mentioned results above, we are also interested in how each component in the model affects the result. Accordingly, we quantified how each module improved the results. Therefore, we added a submodule to the original model and observed the effect of each module. The ablation studies were conducted in the same experimental environment to make sure a fair experimental comparison. Here, we grouped all lesions into one category for calculation and judged the quality of results by comparing dice to facilitate the comparison of results. From Table  The result improved by residual refine module proved that the coarse‐refine network is available. The result enhanced by the ASPP also indicates that ASPP can enhance the global information extraction ability and strengthen the ability to detect objects of different scales in the COVID‐19 segmentation task. The result improved by hybrid loss shows hybrid loss can accurately predict the delicate structures and clear boundaries. At the same time, each module has excellent help for the improvement of classification accuracy.3.7As an example, Figure  It can be found from the comparison of segmentation results in Figure  4In this paper, we propose a coarse‐refine network for the CT lesion segmentation of the COVID‐19. Experimental results show that our model in the segmentation of COVID‐19 CT images outperforms other familiar medical segmentation models, enabling the doctor to get a more accurate estimate on the progression of the infection and thus can provide more reasonable treatment options. In the previous work, various U‐Net and their variants have been developed, achieving reasonable segmentation results in COVID‐19 applications. Military et al.  propose


When laser beams propagate through media with non-vanishing absorption, the media is heated resulting in a change of the refractive index, which can lead to thermal lensing and thermal blooming. However, experimental details about both phenomena for propagations in water are lacking, especially for high-power lasers in the kilowatt range. We show that significant thermal lensing occurs only for high input powers before the onset of convective flow, while for low input powers, no strong thermal lens arises. After the onset of water flow, thermal blooming occurs at low input powers comparable to that known for propagations over kilometres in the air. However, for high input powers a thermal blooming on a qualitatively higher level is shown. By wavefront sensing, the change of refractive index distribution in water is investigated. This clearly shows the fast development of a strong thermal lens for high input powers and the onset of convection. Furthermore, a qualitatively good agreement of the accompanying simulations is observed. It is found that the absorption coefficient is linear with a value of  Due to the deposited energy, the media temperature increases resulting in a locally changed refractive index. Since the refractive index in water decreases with increasing temperature5, a diverging lens is formed. As long as there is no movement of the medium, the assumption of a symmetrical beam profile results in a constant symmetrical distribution of the refractive index and thus also of the thermal lens6.Thermal lensing is known to occur for laser beams that propagate in transparent media without vanishing absorption7 or internally by the onset of a convective flow8. Then, the thermal lens is distorted, resulting in non-symmetric transmitted beam shapes. This is called thermal blooming and is mainly investigated for beam propagation in air11. Many investigations have been carried out using simulation in air13 and have mainly been driven by the goal to overcome the beam distortion by compensation with adaptive optics15. For laser beam propagation in liquids, only minor investigations do exist in experiment16 and simulation17. Investigations for water and high-power lasers in the kilowatt range are lacking completely. Compared to propagation in air, the absorption coefficient in water is much higher18 resulting in an increased thermal blooming effect. Therefore, the required propagation distance to obtain a similar beam distortion is strongly decreased.A medium movement occurs in fluid or gaseous media and is either driven externally by e.g. wind19. However, most techniques require multiple partial measurements to determine a wavefront distortion. This is inappropriate for fast processes. Techniques based on the Hartmann-mask principle20 can be single-exposure measurement methods21. In this case, a reference image must be acquired before starting the experiment, and then only one image per acquisition is needed to determine the wavefront distortion. From the local shift of the Hartmann-mask structures, the wavefront distortion due to refractive index inhomogeneities can be calculated. For investigations with a large field of view, wavefront sensors that image directly onto a focal plane array are not convincing because large detectors are required23.A direct investigation of the propagation medium in the path of high-power lasers is not possible, since all devices could interact with the laser and be destroyed by the high thermal load. Therefore, indirect techniques or suitable simulations are required. The refractive index of an optical element can be measured indirectly with a wavefront sensor24. In this context, a certain working distance must be maintained for safe laser processing. Therefore, knowledge about opportunities and restrictions of laser processing with high powers through water columns is necessary. By imaging the intensity distribution of the laser spot after propagation through a water layer, we show the occurrence of thermal blooming for lower input powers. For high laser powers, the establishment of the thermal lens is faster than the onset of water movement. Thus, initially a strong thermal lens is created followed by a strong irregular beam distortion. While for low laser powers the transmitted intensity distribution is similar to the well known thermal blooming8, the distortions for high laser powers are much stronger showing a new level of thermal blooming above the known moderate levels.With this work, we aim to fill a gap in our knowledge about the physical basis of the occurrence of thermal lensing and thermal blooming in water, especially at high laser intensities. Possible applications arise in the field of defusing of explosives by deflagration under waterThe change of the refractive index distribution in water is determined by means of Hartmann-mask wavefront sensing. Due to the perpendicular orientation of the wavefront sensor, both the thermal lens evolution and the convective flow are detected. The accompanying simulations show comparable results, confirming their validity. This provides a more complete picture of the changing parameters such as refractive index distribution and temperature in the water.26 behind a water layer. The absorption coefficient for the used wavelength of 1070 nm is reported to be between 12 29. The absorption only decreases slightly when the water is heated30. However, these absorption coefficient values are normally determined at low intensities. Until now, it has not yet been investigated whether these values are still valid at high intensities which are typical for high-power lasers. Here we describe the absorption of cw laser light in the kilowatt range with intensities of up to 8 In addition to the beam distortion, the effect of the absorption of the laser energy in the water is analysed. The transmitted output power and intensity distribution ultimately is of interest to understand the effects of high-power lasers to target materialsIn Fig. The laser used was an ytterbium fiber-laser  with a wavelength of 1070 nm, a line width of 5nm and a maximum average power of 10 kW (1% power stability). It was operated in continuous wave mode. It consists of 18 single-mode laser modules, which are spliced into a feeding fiber with a 100 The temperature of the water was measured with a type K thermocouple  with a signal conditioning unit TSA-TC2-K-T12-1k/30-BW-V2/A3 . The tip of the thermocouple was placed at the beginning of the vessel next to the laser spot to not irradiate the thermocouple. The signals of the laser control output, the power meter and the thermocouple were recorded with a transient recorder TransCom-CompactE-XL  with a 10 kHz frame rate.The local distribution of the transmitted laser beam intensity was measured in a separate experiment by imaging the laser light scattered from an aluminium plate with a “PCO edge 5.5”  with a “Nikkor AF 24-85mm” lens , operated in gating mode with an internal frame rate of 100 Hz and an exposure time of Lasing time was modulated in two ways: First, in order to determine the transmitted laser power, pulse trains of 5 pulses of 0.1 s duration and 10 Hz repetition rate were used. The primary laser power was changed from 1 to 10 kW. To account for the reduced transmission inside the zoom optics and the water vessel windows, the transmission without water was determined with the same pulse trains for primary laser powers of 1, 2 and 3 kW as well. For analysis purposes, either the average transmission values of the complete laser-on times or only a single point in time at the beginning of the first pulse were used. Second, in order to further investigate the transmission, temperature increase, refractive index changes and particularly the temporal intensity distribution of the transmitted laser beam, single pulses of 5 s duration with primary laser powers of 1, 2, 5, 7 and 10 kW were used. For the results shown, the transmission corrected input power at the beginning and the output power at the end of the water phase are always stated. Due to the partly strong heating of the water, it was replaced after each laser pulse series and 5 s long single pulse.20, three contrast types can be obtained in single exposure acquisition: absorption, scattering and differential phase. The latter is of importance to investigate fast changing systems. The broad beam of a continuous wave LED light  is split into a bunch of beamlets when passing the Hartmann-mask. The latter was realized with a printed black paper with white dots on it  and the measurement images.To investigate the change of the refractive index distribution, a wavefront measurement with a probe beam transversal to the high-power laser was performed. With the multi-contrast imaging technique based on a Hartmann-maskIn the experiment, only minor information about the temperature distribution and hence distribution of the refractive index as well as the convective flow during the laser irradiation can be obtained. For example, the temperature can only be measured outside the laser beam path. Furthermore, by the phase contrast imaging, only the effect of a cumulative change in the refractive index along the beamlet path can be monitored. To obtain more information about the laser induced changes of temperature distribution and flow velocity in the water, the experiments were simulated with COMSOL Multiphysics 5.6 (Build: 280) . The simulation consisted of two parts: first, the absorption of the laser in the water with consequent heating up and water flow. In the second step, ray tracing was applied to determine beamlet deflections due to the local inhomogeneous refractive index distribution.The modules COMSOL Multiphysics, CFD, Heat Transfer and Ray Optics with the physic modules “radiative beam in absorbing media”, “heat transfer in fluids”, “laminar flow” and “geometrical optics” were used. Furthermore, the multiphysics modules “heat transfer with radiative beam in absorbing media” for coupling the “heat transfer in fluids” with “radiative beam in absorbing media” and “nonisothermal flow” to couple the “laminar flow” with the “heat transfer in fluids” were used. The laminar flow was done with a “weakly compressible flow”, inclusion of gravity and no turbulences. The wall condition was “no slip”. The choice of the laminar flow solver was motivated by a first set of simulations, where laminar and turbulent flow models were compared. It was found that there was no significant difference between the different flow models. Therefore, the laminar flow model was used for further data analysis, since it requires less computational time. Simulating the turbulence observed in some of the experimental data would require a more complex analysis and is beyond the scope of this paper.3 for the width, height and depth from the perspective of the laser. The meshing of the water was done with the predefined size “extremely fine”. The walls of the water were set as thermally isolated. The distance from the end of the water to the ray detecting wall of the ray tracing was 15 cm. This is a similar distance compared to the imaging of the transmitted intensity distribution. To simulate the laser beam shape after transmission, a ray tracing parallel to the laser beam over the complete water vessel area was performed with a rectangular ray grid of spacing 0.5 mm. The intensity distribution was propagated from the starting points of the rays to the endpoints to calculate the intensity distribution after water transmission.The water geometry was similar to the one in the experiments with a rectangular box of the size The axis of the incident laser beam was oriented horizontally and was placed through the center of the simulation volume at a height of 3 cm. The beam shape used was a Gaussian distribution with standard deviation n(T) was calculated by: T was in units of Kelvin. The parameters were determined by fitting the tabulated values for a wavelength of 404nm between 05. Although this formula does not represent the absolute value of the laser at a wavelength of 1070 nm, it is noted that here only the change of the refractive index and not the absolute value is of interest. Therefore, the differences for the absolute values compared to the laser wavelength of 1070 nm is irrelevant. The change of the refractive index with temperature is almost independent of the wavelength in the region of interest of this paper. The starting value for the temperature was 293.15 K.As material parameters, the build-in parameters for “H2O (water) [liquid]” were used except for the absorption coefficient, the refractive index and the ratio of specific heats, which was set to 1. The absorption coefficient of water was To simulate the Hartmann-mask imaging of the experiments, a ray tracing with a rectangular grid of initially parallel rays was used. Therefore, the refractive index distribution in the water was calculated from the temperature map as stated above. A grid of rays was released at a spacing of 2 mm, the Hartmann-mask spot distance, over the complete area of the water vessel. The starting point of the rays was the one vessel side with a perpendicular direction into the water. After penetration through the water, the rays were stopped at a detector wall placed 15 cm behind the water. Since the beam deflection was calculated, the absolute value of the free propagation between the water and the detector wall is not relevant.The simulation was done in two consecutive studies. First, the absorption of the laser beam in the water phase with the included thermal liquid changes was studied. The time was changed from 0 to 5 s in steps of 0.1 s. The study time steps were 0.005 s. In the second step, the ray tracing was performed on basis of the results of each time step of the first study. The time steps for the ray tracing were 0.002 ns. The analysed time steps were 0 ns and 2 ns representing the start at the Hartmann-mask and the end after detection in the camera. However, for the second time step, all rays were already frozen at the walls acting as detectors.Both the zoom optics and the windows of the water vessel have a finite transmission. To take this into account, the setup transmission was determined by detecting the transmitted power of only the zoom optics and the zoom optics together with the empty water vessel. The zoom optics has a transmission of 80% and each of the two vessel windows one of 93.5%. The primary laser power and the measured power with the power meter are thus corrected for the transmission of the zoom optics and the vessel windows. In the following, the laser power at the beginning and at the end of the water phase are always given as “input power” and “output power”. The input power thereby represents 75% of the primary laser power. The output power before the exit window is 106.5% of the power measured by the power meter.One further aspect of the calibration of the setup was to exclude the fact that the glass windows alone cause a significant thermal lensing effect. Therefore, the intensity distribution behind the empty vessel with only the glass windows was recorded analogously to the investigations with the water see Fig. . No chan34. It originates from refractive index variations due to medium heating by the laser in conjunction with a movement of this heated medium. This movement results either from external conditions like wind in air or from internal heat-induced convection. The latter is taking place here, as no external flow exists. The water efficiently absorbs the laser light, resulting in rapid heating of the water and consequent convective flow.For laser beams transmitted through fluid media with non-vanishing absorption, it is known that thermal blooming occurs35. As air has got a much smaller absorption coefficient compared to water, the propagation distances are much larger in these cases. However, the presented low input power intensity distributions after the short propagation distance in water look very similar.In Fig. entclass1pt{minima5 leading to a diverging lens. In the high-power range, this process is so fast and strong that within 200 ms a thermal lens is formed that is so strong that it displaces almost all of the radiation from the primary spot region. A similar behaviour was previously found for laser propagation in ethanol4. No significant water flow has formed during this time, resulting in the nearly round intensity distribution. However, due to the high temperature gradients a significant water flow forms afterwards. The simulations specify a mean velocity of 2.5 cm/s within the initial beam diameter and a velocity of 4 cm/s in the initial center of the spot as maximum value (data not shown). This constant flow forms within 1 s. Then, a more or less stationary distribution of the temperature and refractive index arises within the laser path. From below, cold water flows into the region of the laser path, heats up and continues to flow upward. Therefore, the shape of the transmitted intensity varies only slightly after about 0.5 s. A laying half moon is observed at the bottom with fast changing but low intensity parts at the top.A much faster and different change of the intensity distribution is observed for the high input power, as can be seen in Fig. The simulations of the experiment show a qualitatively good agreement. The intensity distribution after water penetration changes slightly for low input powers and strongly for high input powers see Fig. . The simWith the differential phase contrast of the multi-contrast imaging by a Hartmann-mask, the change of the refractive index distribution can be visualised. The beamlets of the Hartmann mask get deflected by refractive index inhomogeneities as they pass through the water. Note that only continuous changes and no step function of the refractive index can be measured with this technique, which is valid for these experiments. Furthermore, it has to be mentioned that the beamlet deflection is the integrated refraction power over the complete penetration path of the beamlets. In Fig. For the high input powers, a much stronger thermal lensing is observed in Fig. As in the case of the intensity distribution, the simulations of the Hartmann-mask measurements also show good agreement with the experimental results. The comparison of the various differential phase maps in Fig. As discussed before, the intensity distribution of a high-power laser beam radiating through a water column changes with time. The water absorbs light of a wavelength of 1070 nm comparatively strongly, which leads to thermal lensing and thermal blooming due to water heating. For a technical usage of high-power lasers for under water processing, the deposited energy at a final target is of central interest. In Fig. For the largest spot diameter, plotted in Fig. This variation of the power in the bucket must be attributed mainly to the spatial distortion of the laser beam in the water. As discussed above, thermal lensing and thermal blooming do occur very fast. Only a small uncertainty of less than 2% was observed in the transmission calibration of the setup. For the small spot size, the intensity limit of the used power meter is already reached, which may have caused this small uncertainty. Moreover, a significant change in the power in the bucket at the beginning of each pulse within a pulse train is only observed in the experiments with water, but not in the transmission calibration.As already shown, the intensity distribution of the transmitted laser beam changes very fast for high input powers. In Fig. 29.In Fig. 30.For the larger spot diameters, the output power of the beginning of the first pulse is slightly smaller compared to the mean values over several pulses, showing that the power in the bucket increases within the pulses. This can be seen in Fig. In conclusion of the experimental observation of the absorption in water it can be stated that the transmission is linear for the used parameters here with an absorption coefficient of In comparison to the measurements of the transmitted intensity distribution and the refractive index changes, in Fig. For the higher input powers, on the other hand, significant changes do occur. For an input power of 7.5 kW, the power in the bucket within the first second first increases slightly before it shows a V-shaped valley. Subsequently, it decreases again until it stabilizes at a lower level than at the beginning. A similar behaviour is observed for 5.2 kW and 3.7 kW, however with a reduced amplitude and slower course. This behaviour can be explained with the thermal lensing as already discussed. With increasing input power, a stronger thermal lens arises, resulting in a stronger deflection of the laser beam from the primary path. For 7.5 kW, the Gaussian shape deforms to a ring within 0.1 s and then enlarges. After 0.2 s a reduced energy deposition  in the rear part of the water is observed. Hence, when the power in the bucket starts to decrease drastically, the main part of the laser beam gets deflected outside of the aperture of the detector by the thermal lens. At the minimum (at 0.5 s), the first macroscopic vapour bubbles also emerge (data not shown) resulting in additional scattering of the laser beam. After that, the water flow sets in, leading to a partial recovery of the power in the bucket, as at least the lower part of the laser path comes to a steady state. New cold water enters the laser path from the bottom getting heated up while flowing upwards. This also explains the bottom half moon shape of the transmitted intensity distribution.The temporal evolution of the power in the bucket measured by the power meter see Fig.  can be pIn this paper we showed that the absorption coefficient of tap water is


Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package it is possible to model distance restraints, dihedral restraints and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the  If input files are indeed correct, a GROMACS energy minimization is run and the output structure compared to the PDB structure. Since the energy minimization is performed Several short polypeptides were taken from the Protein Data Bank to make a full MD run with and without restraints and thus verify the compatibility of the output from the program with the GROMACS software. The peptides are of different lengths and have different types of restraints. Table The Charmm force field version 27  from the simulation trajectories as follows. All the atom-atom distances entclass1pt{minimaRestrained simulations require a number of parameters like the force constant mentclass2pt{minimTable do novo refinement was attempted where the initial conformation for the simulation is completely extended. The folding of 1lb0 in simulations with and without restraints is shown in Figure For one of the peptides a In this contribution, we present a Python package for importing data from nuclear magnetic resonance files NMR-STAR files into the GROMACS software. We have examined 8 different polypeptides with distance, dihedral and orientation restraints Table . From a For distance restraints we suggest the following parameters for force constant and the averaging time: Finally, we have used GROMACS to refine the 1lb0 peptide structure from an extended conformation. Simulations with and without restraints were run and it was found (Figure


Average biodegradation rate of newly filled municipal solid waste (MSW) in landfills is relatively fast, and the landfill gas produced by the new MSW biodegradation can cause great variations in gas pressure. To predict the gas pressure distribution in the MSW layer, a one-dimensional gas transport model is established in this study. The following factors are considered in this model: (1) the variation of gas permeability with depth; (2) the anisotropy ratio of gas permeability; (3) the settlement caused by waste biodegradation. Furthermore, a single peak model for gas production is applied as the source term of gas production. The equation for settlement caused by waste biodegradation is presented, and the time of peak gas production rate is obtained by fitting the settlement of the newly filled layer. The stratification of the unsaturated and saturated regions is taken into account by distinguishing the difference in gas saturation. The layering of the new and old waste layers is considered by distinguishing the difference in the length of time that waste has been degraded to produce gas. Based on the method of numerical calculation, the gas pressure distribution in the landfill with layered new and old MSW is well simulated. The position where the maximum gas pressure occurs is found. The sensitivity analysis shows that the influence of the anisotropy ratio on gas pressure distribution is more significant. Additionally, slope instability caused by changes in landfill gas pressure often occurs7. Therefore, the exploration of gas pressure and its distribution in landfills has become an important research area. Kjeldsen and Fischer8 monitored the gas pressure in the old waste layer of Skellingsted landfill for 35 days, and results show that the variation of gas pressure in the landfill has a great influence on the composition of landfill gas. Spokas and Bogner9 and Bentley et al.10 measured gas pressure in the Olinda and Louisiana landfills for 3 and 5 days, respectively, and found that the measured pressure is influenced by fluctuations in atmospheric pressure. Gebert and Groengroft11 found that the amplitude of the gas pressure measured in two gas collection wells in an old German landfill exhibits a linear correlation with the amplitude of atmospheric pressure. Zhang et al.12 observed the gas pressure in the newly filled municipal solid waste (MSW) layer of the Wuxi landfill for more than 500 days, with results showing that the gas pressure varies with time, showing a single peak curve. The stratification of new and old waste layers is constantly occurring in operating landfills14. The variation of gas pressure over time in this landfill needs to be estimated by theoretical calculation.The continuous generation of gas in landfills causes landfill gas to escape into the atmosphere due to pressure differences24 and analytical29 calculations. Current research is mainly based on the theory to estimate the gas pressure in landfills as the data of gas pressure distribution in the field is rarely reported, and estimation of gas pressure through the combination of the field data and theoretical analysis is relatively scarce. Moreover, landfills in these studies are generally regarded as landfills with a homogeneously unsaturated waste layer29 or a continuously placed waste layer29. Findikakis and Leckie15, Findikakis et al.16, and Liu et al.27 considered the increasing stage of gas production rate, and Lu et al.23 considered the anisotropic ratio of gas permeability. The working conditions for the stratification of new and old waste and the stratification of saturated and unsaturated waste in an operating landfill were investigated by Zhang et al.12, but current research methods have not considered these two working conditions. If the gas pressure distribution in the landfill needs to be estimated, it is necessary to consider the increasing stage of gas production rate and the anisotropy ratio of gas permeability. In addition, the stratification of new and old waste and the stratification of saturated and unsaturated waste should also be considered.At present, many scholars have obtained the gas pressure distribution in landfills through numerical12. The validity of the calculation method and the theoretical model in simulating the gas pressure distribution in the newly filled waste layer is determined, and the evolution of the gas pressure distribution in the landfill with layered new and old waste layers is analyzed. The position of the maximum gas pressure and the significant sensitive parameter of the gas pressure distribution are found.Based on numerical methods, a one-dimensional gas transport model for estimating the gas pressure in a landfill with layered new and old waste and layered saturated and unsaturated waste is presented in this study. The factors considered in this model are as follow: firstly, the stratification of the length of time that waste has been degraded, gas permeability, and porosity; secondly, the anisotropy ratio of gas permeability; thirdly, the settlement caused by waste biodegradation; fourthly, the source term of gas production. The equation for quantifying settlement induced by waste biodegradation is presented, and the time of peak gas production rate is obtained by fitting the settlement of the new waste layer with this equation. The stratification of the unsaturated and saturated zones is considered by distinguishing the gas saturation in the different zones. The stratification of the new and old waste layers is considered by distinguishing the length of time that the waste has been degraded to produce gas in the different waste layers. The results of the numerical calculation are then compared with the gas pressure in the newly filled waste layer measured by Zhang et al.ρg is the density of landfill gas (kg m-3); Vx, Vy and Vz are the volumes of landfill gas entering the unit body along directions ox, oy and oz per unit time (m3 d−1), respectively; QG is the volume of gas produced by biodegradation per unit volume of waste per unit time (d−1); n is the porosity of waste; Sg is the gas saturation.To establish a model for predicting gas pressure in landfills, it is assumed that gas transport in the MSW layer follows Darcy’s law, and settlement only occurs in the vertical direction. According to the mass conservation law, the net mass of gas flowing into and out of the unit body plus the mass of gas production equal to the variation of gas mass in the unit body, which can be represented by the following equation:i, j or k = x, y, or z (i ≠ j ≠ k); Kgi is the gas permeability in the direction i (m2); μg is the gas dynamic viscosity (Pa s); Pg is gas pressure (kPa).Following Darcy”s law, the following equation is obtained:εt is the biodegradation settlement strain of the landfill.When the settlement caused by waste biodegradation in landfills is considered, the following equation can be obtained:Patm is atmospheric pressure (kPa); Mg is the molar mass of landfill gas (g mol−1); R is the gas constant (J (mol K)−1); T is waste temperature (℃).According to the ideal gas law, the following equation can be obtained:27, and waste can be divided into easily degraded, moderately degraded, and difficultly degraded according to the biodegradability of the waste31. The volume of landfill gas generated by biodegradation per unit volume of waste per unit time can be presented, as is shown in the following equation:ρ is the density of waste (kg m-3); L0 is the volume of landfill gas generated by biodegradable per unit mass of waste (m3 kg−1); i = 1, i = 2, and i = 3 represent easily degraded, moderately degraded and difficultly degraded component in waste, respectively; ωi is the proportion of component; AGi is the parameter related to gas production rate (d−1); BGi is the time of peak gas production rate (d); DGi is the length of time that the waste has been degraded to produce gas (d).According to the equation of waste biodegradation rate presented by Liu et al.λt is waste biodegradation rate; ms0 is the waste mass at the initial moment; mst is the waste mass at the moment t; Vs0 is the waste volume at the initial moment; Vst is the waste volume at the moment t; h0 is the initial height of landfill; ht is the height of landfill at time t; e0 is the initial void ratio; et is the void ratio at time t.To facilitate the calculation of the settlement caused by waste biodegradation, the landfill is simplified into the schematic diagram shown in Fig. 33. The gas pressure distribution in the landfill is a one-dimensional problem, because the landfill gas is only considered to migrate to the outside and into the old waste layer. Therefore, the following equation can be obtained:Substituting Eqs. –6) into into6) iEquation  is the ohz is the step length in the vertical direction; τ is the step length of time.The following equation can be obtained by differencing Eq. :8\documeEquation  is the d29 is calculated with new method, and the numerical results are compared with the original analytical results. The mathematical model of gas transport presented by Li et al.29 is shown in Eq. (Pg is gas pressure (kPa); Patm is atmospheric pressure (101.3 kPa); KG is gas permeability (6.9 × 10–13 m2); ng is gas content (0.5); μL is gas dynamic viscosity (1.37 × 10–5 kg (m s) −1); R is gas constant (8.31 J (K mol) −1); TL is gas temperature (298 ℃); ωL is the molar mass of gas (0.03 g mol−1); Lg is the mass of gas produced by the waste of unit volume (230 kg m-3); kL is the constant of gas production rate (0.1 year−1); rL is the waste placing rate (3 m year−1); HL is the thickness of landfill (30 m).In this study, the gas pressure in the new and old waste layers is calculated based on the defined numerical calculation method. In order to verify the reliability of this method, the mathematical model of gas transport presented by Li et al.29 can be obtained as follow:hz is the step length in the vertical direction; τ is the step length of time; oz(z) and ot(t) are the node functions in the vertical direction and time, respectively.When Eq.  is diffe29 is obtained by the numerical calculation method, as is shown in Fig. 29 using their analytical method, illustrating the reliability of the numerical method used in this study.The gas pressure distribution in the landfill described by Li et al.12 conducted their field test, is located in the city of Wuxi, China, approximately 100 km northwest of Shanghai. The average annual rainfall is approximately 1900 mm, and the atmospheric temperature ranges from − 9 to 39°C12. Wuxi landfill is a typical valley-type landfill, constructed in 1995. Designed to accommodate 4.20 million m3 of MSW. During the year 2016, 2200 t d−1 of MSW were placed in this landfill. The organic waste mainly includes food, textile, paper and wood, their total content is approximately 48.9%12. Zhang et al.12 monitored the gas pressure in the new waste layer from January 18, 2016 to June 18, 2017. The gas pressure is obtained by measuring the pressure at the head of the monitoring well using a micromanometer.Wuxi landfill, where Zhang et al.29. However, in order to reasonably simulate the upper boundary of the Wuxi landfill, the gas pressure at the shallowest depth (depth: 1.3 m) observed by Zhang et al.12 is used as the upper boundary (Dirichlet boundary condition). The gas pressure at this depth is fitted with a single peak function, and the fitting result is shown in the top of Fig. 12. The physical model for simulating gas transport in the Wuxi landfill is shown in Fig. The leachate drainage and liner systems were built at the bottom of the Wuxi landfill. However, there is not a landfill gas extraction system at the bottom. Therefore, the boundary condition at the bottom of the landfill can be considered as an impermeable boundary condition (Neumann boundary condition) in the numerical calculation. Generally, when the gas pressure in an uncovered landfill is calculated, the upper boundary is regarded as the atmospheric pressure, that is, the relative gas pressure is zerodepth: 1. m observdepth: 1. m observAccording to Eq.  and Fig.When Eq.  is diffeτ is the step length of time; ot(t) and oz(z) are the node functions in time and the vertical direction, respectively.where 12 is studied, and the layout of the field test site can be viewed in the paper of Zhang et al.12. In order to select reasonable calculation parameters, the parameters which are suitable for the Wuxi landfill are selected. The parameters used in the numerical calculation are shown in Table The gas pressure distribution in the monitoring test conducted by Zhang et al.27. It is worth noting that the gas pressure in #0 well (extraction well) is the mixed gas pressure within the entire perforated pipe (length: 7.7 m). In order to calculate the gas pressure in the gas collection well, the gas pressure in the waste body is integrated into the direction of the length of the perforated pipe. Then the result obtained by integration is divided by the length of the perforated pipe, and the average gas pressure in the gas collection well is obtained, as is shown in Fig. 12. However, the gas pressure below the leachate level in the landfill can be theoretically calculated, as is shown in Fig. The variation of gas pressure with time in the newly filled MSW layer of the Wuxi landfill was obtained by numerical calculation, as is shown in Fig. 46 and gas permeability35 decrease with depth in landfills, the pore volume and migratory ability of the gas, respectively, also decrease with depth, which ultimately increases gas pressure with depth. According to the ideal gas law, the gas pressure increases with depth under the assumption that the gas production yield of new waste is constant.The variation of gas pressure with height in the Wuxi landfill is shown in Fig. The waste in the old MSW layer has been buried for a longer period time, and the gas production rate decreases with depth. The gas pressure in the old MSW layer decreases with depth in the earlier period. Therefore, it can be inferred that the maximum gas pressure at this stage occurs where the waste biodegradation rate is not high. According to the result of calculation, this position is in the old MSW layer close to the boundary of the new and old waste layers. At the same time, as the landfill gas in the newly filled MSW layer migrates to the old MSW layer, the gas pressure in the old waste layer begins to increase.12. However, the gas pressure below the leachate level can be obtained by theoretical calculation. It can be also found from Fig. As the waste biodegradation rate in the newly filled waste later begins to decrease, the rate of gas pressure increase begins to slow until it ultimately reaches its peak. Due to the migration of landfill gas from the position of high gas pressure to the old MSW layer with low gas pressure, the gas pressure in this layer begins to increase with depth. Therefore, the maximum gas pressure in the later period appears at the bottom of landfill. However, the maximum gas pressure in this period is less than that in the earlier period. Because the landfill gas slowly migrates from the position of higher pressure to the position of lower pressure in the later period, resulting in the gas pressure in the landfill to slowly decrease. The gas pressure gradient in the old waste layer is less than that in the new waste layer, because the waste biodegradation rate in the old waste layer is lower than that in the new waste layer. The gas pressure below the leachate level was not monitored in the field test conducted by Zhang et al.Applying the upper and lower limits of gas permeability relative to porosity, and the equation relating porosity to depth in Table 41 and Zhan et al.47 summarized the range of gas production potential, and found this to range from 43.3 to 469 L kg−1. The comparison of the gas pressure distribution under the conditions of different gas production potentials is shown in Fig. The gas production potential of waste is closely related to gas production, Gao et al.32. The range of the reported anisotropy ratio of gas permeability is between 1.5 and 2049. The comparison of the gas pressure distribution under the conditions of different anisotropy ratios is shown in Fig. The anisotropy ratio of gas permeability  has a great influence on gas pressureUsing the mathematical model of gas transport developed in this study, the main sensitive parameters to the quantification of gas pressure were analyzed. A larger gas production potential, larger anisotropy ratio, and smaller gas permeability make the gas pressure in a landfill larger, which is beneficial for the collection of landfill gas. However, greater gas pressures adversely affect the stability of landfills and increase the possibility of landfill gas emissions. When gas permeability, gas production potential, and anisotropy ratio increase by 1%, the maximum gas pressures increase by − 0.02%, 0.76%, and 1.22%, respectively. Therefore, the gas pressure is most sensitive to the anisotropy ratio of gas permeability. It is very important to select the parameters that are suitable for the specific landfill when the gas pressure distribution is estimated. In particular, the irrationality of the anisotropy ratio will cause a large error in the estimation of gas pressure.A one-dimensional gas transport model was established for gas response in a landfill with layered new and old waste. The model takes into account the variation of gas permeability with depth, the anisotropy ratio of gas permeability, and the settlement caused by waste biodegradation. Besides, a single peak model for gas generation is used as the source term of gas production in this model. The stratification of the unsaturated and saturated zones is taken into account by distinguishing the difference in gas saturation. The layering of the new and old waste layers is considered by distinguishing the different length of time that waste has been degraded to produce gas. The time of peak gas production rate is obtained by fitting the settlement of the newly filled MSW layer with the biodegradation settlement equation presented in this study.Based on the numerical calculation method, the gas pressure distribution in the new and old waste layers is obtained. After the results of calculation are compared with the test result, the following conclusions are found: (1) the gas pressure distribution in the landfill with layered new and old waste is better simulated using the gas transport model and the calculation method developed in this study; (2) the time of peak gas production rate fitted by biodegradation settlement is relatively reliable; (3) the maximum gas pressure occurs in the old waste layer near the boundary between new and old waste layers in the earlier period, but moves to the bottom of landfill in the later period. (4) The anisotropy ratio is a more sensitive parameter influencing the gas pressure distribution. In this study, the gas transport model, the calculation method, and the selection method of parameters provide a theoretical basis for evaluating the variation of gas pressure in a landfill with layered new and old waste.Supplementary Information