Golgi apparatus is the central component of the mammalian secretory pathway and it regulates the biosynthesis of the plasma membrane through three distinct but interacting processes: (a) processing of protein and lipid cargoes; (b) creation of a sharp transition in membrane lipid composition by non-vesicular transport of lipids; and (c) vesicular sorting of proteins and lipids at the trans-Golgi network to target them to appropriate compartments. We discuss the molecules involved in these processes and their importance in physiology and development. We also discuss how mutations in these molecules affect plasma membrane composition and signaling leading to genetic diseases and cancer. The plasma membrane (PM) plays an important role in being the interface of a cell with its environment. Receptors localized to the PM sense changes in the environment and transmit signals to the inside of the cells so as to appropriately modify the state of the cell. The PM interface not only receives signals but also sends them by exposing several important molecules on the cell surface, which advertise the cell identity and state to the extracellular environment. This bidirectional signaling has important roles in both unicellular and multicellular organisms promoting cell division and differentiation, cell migration, cell death, infection, etc. Given the enormous importance of PM signaling, understanding the processes that contribute to achieving and maintaining appropriate PM composition in terms of lipids and proteins is vital. Secretory pathway processes play an important role in this by synthesizing the PM localized molecules and transporting and targeting them to appropriate membrane domains. The endocytic pathway also plays an important role in regulating the levels and localization of the PM receptors. This has been extensively reviewed elsewhere ,2,3,4. TThe Golgi apparatus is the central organelle of the secretory pathway that receives cargoes (lipids and proteins) synthesized in the endoplasmic reticulum (ER), processes them , and then sorts them to the correct intracellular compartment. The organelle consists of a stack of flat cisternae which vary from 3–12 in number depending on the cell type and organism . In mammThe cis and trans-poles of the Golgi, while structurally similar, show differences in terms of their lipid composition, the proteins localized to these structures, and also in the luminal environment of the cisternae ,9. SimilIn this review, we will discuss three distinct processes that happen at the Golgi that together build the cellular PM. These are: (1) Processing of protein and lipid cargoes in the Golgi; (2) Non-vesicular lipid transport through membrane contact sites (MCSs) at the TGN; and (3) Sorting of proteins and lipids at the TGN to the appropriate domain of the PM.The cargoes synthesized in the ER are transported to the Golgi apparatus where they are processed further by several post-translational modifications, of which the major ones are glycosylation and palmitoylation. There are also other cargo processing reactions in the Golgi, including phosphorylation and protease cleavage that are not discussed here. The processing reactions play an important role in determining the form, levels, and activity of the molecules in the PM.Glycosylation is one of the most abundant post-translational modifications . GlycansN-glycosylation starts in the ER with the addition of a 14-sugar mannose-rich glycan onto the asparagine residue present within the consensus sequence Asn-X-S/T . This maThe typical O-glycosylation, the mucin-type O-glycosylation, starts in the Golgi with the addition of GalNAc to the O-atom of a Ser/Thr or rarely Tyr residue . The conGAGs are long polymers of repeating disaccharide units, one of which is an amino sugar  and the other a galactose or uronic acid . GAG chaGSLs are key signaling molecules in the PM that regulate several physiological processes . GSL bioThe built glycans can also be further modified by phosphorylation, acetylation, sulfation, etc. creating several different glycoforms of a protein or lipid. The variations that are possible with glycans makes them information-rich and it is not surprising that they are used to communicate distinct cell states to the exterior, and as such, can act as cellular identity cards .The Golgi apparatus is also the major location of another post-translational modification called S-Palmitoylation , which iThus, the Golgi apparatus receives cargoes synthesized in the ER and processes them to produce a variety of modified gene products that are not directly encoded in the genome.A distinguishing feature of membranous compartments is their differential lipid composition in addition to their characteristic protein profile. For instance, the PM is highly enriched in cholesterol and sphingolipids while the ER where the biosynthesis of the lipids happens or at least begins, is depleted in these lipids . There iA recent study on the ER-TGN contact sites has shown that nearly half of the Golgi stacks in a mammalian cell have these MCSs, and about a quarter of the TGN surface area is engaged in contact site formation . Thus, wThe cargoes that have been processed in the Golgi apparatus reach the TGN where some capping reactions that terminate the glycan processing happen before the sorting of cargoes to appropriate compartments ,44. WhilThe promotion of the interaction between adaptors and cargoes is regulated by Arf GTPases and PI4P present on the TGN membrane. The recruitment of adaptor complexes by Arf GTPase can also lead to their activation by a conformational change that promotes cargo binding as in the case of AP-1 . Once reWhile this represents the classical understanding of how cargo is sorted at the TGN, there are also alternate ways to form cargo laden carriers at the TGN. A well-known example is the calcium-mediated cargo sorting at the TGN. The actin-cofilin machinery regulates the activity of the calcium pump SPCA to pump calcium into the lumen of the TGN, which then promotes the binding of cargoes to CAB45 and their aggregation . The aggWhile distinct signals that transport cargoes to basolateral PM have been identified, the signals for the transport to apical membrane are still unclear. An exception is the apical sorting signal on Rhodopsin A, the VXPX motif that binds Arf4, which then organizes a complex of Rab11, FIP3, and ASAP1 to promote the transport of rhodopsin from TGN to the ciliary membrane present on the apical PM . While aThe glycosylation reactions happening at the Golgi determine the glycoform of a protein that is present at the PM. A change in the glycoform influences several aspects of a protein function, including its interactions with the other proteins/molecules, its association with lipid rafts, its ability to polymerize, and its half-life . A pertiChanges in the glycoform can also affect protein–protein interactions. For instance, the epidermal growth factor receptor (EGFR) is known to have 12 N-glycans on the extracellular domain that are processed as the receptor passes through the Golgi. Sialylation and/or fucosylation of the N-glycan branches prevents EGFR dimerization and subsequently reduces receptor kinase activity  60]. On. On60]. Receptor activities can also be influenced by changes in the glycosylation of PM-associated lipids. The amount of GSLs and their glycosylation is known to affect the activities of PM receptors by direct interaction of lipids with receptors or indirectly by sorting them into or away from lipid nanodomains/rafts. An example of the former is the regulation of EGFR activity by the ganglioside GM3 . The interaction of the sialyl group of GM3 with a lysine at a membrane proximal position on EGFR prevents receptor dimerization, while an interaction between the sialyl group of GM3 with the GlcNAc on the N-glycans of the EGFR prevents its autophosphorylation and activation . GSLs acSimilar to glycosylation, palmitoylation can also influence the localization and consequently the function of a protein. Analysis of palmitoyl-proteomes reveals that almost 20% of the human proteome including both transmembrane and peripheral membrane proteins is S-palmitoylated . S-palmiCycles of palmitoylation-depalmitoylation are also crucial in controlling the localization and function of various peripheral membrane proteins . For insTransport of lipids against a concentration gradient at the MCS contributes significantly to the concentration of cholesterol and sphingolipids at the TGN, which is essential for the assembly of carriers with a lipid composition mimicking that of the PM. The importance of these processes in determining the PM lipid composition was shown by cell lines mutated for CERT. As mentioned earlier, CERT transports ceramide in a non-vesicular fashion from ER to the TGN where it is converted to sphingomyelin by the action of SMS1 . ImpairmAfter the cargoes are processed at the Golgi apparatus, they are sorted to their correct destination at the TGN. This sorting step is a key player in determining the differential PM composition in polarized cells like epithelial and neuronal cells. Mutation of targeting signals present in the cargoes targeted to the basolateral PM has been shown to result either in their targeting to apical PM or a loss in their differential localization to the basolateral PM . The expC. elegans and zebrafish [The importance of the TGN sorting machinery in determining the PM composition is also evident from mice KO experiments. Mice KO for μ1B show impaired basolateral sorting of several cargoes like LDLR, Ephrin receptor, E-Cadherin, and IL-6 signal transducer . Missortebrafish .In addition to epithelial cells, neuronal cells also have polarized PM domains with the basolateral PM of an epithelial cell most likely corresponding to the somatodendritic PM and the apical PM to the axonal PM. Impairment of AP-1 subunits or clathrin led to impaired targeting of somatodendritic proteins like transferrin receptor and NMDA-type glutamate receptor protein . ImpairiThus, processing and sorting of proteins and lipids at the Golgi apparatus can influence their localization to the correct PM.Changes in Golgi processing reactions result in inappropriate forms of proteins and lipids being displayed on the PM, which influences several aspects of the interaction of a cell with its environment. These changes when detrimental to development and/or physiology result in diseases, including genetic diseases and cancer. A group of genetic diseases resulting from impaired Golgi glycosylation reactions resulting in developmental problems is known as congenital disorders of glycosylation (CDG) ,88. TherA well-known biomarker of cancer is the increase in Tn antigens on the cancer cell surface . Tn antiGOLPH3 has shown how glycosylation can indirectly affect the activity of PM receptors. GOLPH3 was recognized to be an oncogene but its mode of action was not clear [While O-glycosylation of MMPs shows how glycosylation of a protein can affect its activity directly, the dissection of the pathogenic pathway of the oncogene ot clear ,99. Receot clear . Increasot clear . It was ot clear . WhetherSimilar to glycosylation, aberrant palmitoylation is also associated with several pathologies, including neurological diseases and cancer . For insThe activity of ER-TGN MCS has bearing on the physiology of the cell and the organism. Recently a mutation in CERT (S135P) was described that is associated with intellectual disability . It was Impaired TGN sorting leads to improper PM composition that in several instances results in pathological consequences. For example, the distal determinant for basolateral targeting of LDLR is mutated (G823D) in patients with familial hypercholesterolemia–Turku variant . This muThe discussions above point to the fact that the Golgi apparatus plays an important role in determining the composition and organization of the PM. Through this, the Golgi apparatus controls how the cell interacts with the external environment—by regulating the signals sent out by the cell and also the signals received by it from the outside. Though not discussed in this paper, the Golgi apparatus also regulates the composition of the extracellular matrix in multicellular organisms, which have important physiological and pathological consequences . While d1. How is the transport and processing of cargoes in the Golgi apparatus coordinated to achieve faithful processing of the cargoes?2. What are the molecular mechanisms that organize the Golgi apparatus and determine the sub-Golgi localization of the glycosylation machinery?3. How many transport pathways exit from the TGN?4. How are the cargoes destined for apical transport recognized? What are the molecular machineries involved in apical transport?5. How are all these processes regulated? What are the autoregulatory pathways maintaining homeostasis of the compartments? How does the Golgi apparatus respond to external signals to coordinate its functions efficiently?6. There are still numerous proteins localized to the Golgi apparatus whose function is not completely understood, such as several TMEM proteins and Golgins. How do they contribute to the functioning of the Golgi?7. While we do have a model for how each member of the trafficking machinery contribute to transport across the organelle, the complications arising from studies of CDG—different mutations in the same gene causing different phenotypes, mutations in genes encoding proteins that belong to the same complex having varied phenotypes—have shown that our understanding of the system is far from complete and we still need to build a model of Golgi transport that explains all these divergent phenotypes.In the coming years, a combination of systems level analysis of Golgi functions along with the classic methods of focused and reductive analysis of individual components will likely lead to better understanding of Golgi functions and its contribution to physiology and pathology.


The selection of MOOC teaching resources is influenced by diversified resource positioning methods, which leads to low index efficiency of resource mining. Therefore, this paper proposes a multiresource mining method based on association rules to collect the learning behavior data of MOOC users and establish the MOOC teaching resource warehouse. Aiming at the attribute set of information association positioning, the association rules of teaching resources are designed. In addition, the association rules are combined with the shortest path scheduling scheme of teaching resources to establish the location and mining of diversified MOOC teaching-associated resources. Finally, the clustering method is used to process the results of teaching resource mining and complete the clustering of diversified teaching resources. Experimental results show that the index time required by the proposed mining method is 0.1 s, which is only 1/6 of other resource mining methods. Driven by the internet, MOOC emerges as a large-scale open online teaching model. The use of the internet and modern communication technology to collect useful teaching resources from all over the world, with the help of the internet for real-time dissemination, ensures that every learner can access resources and learn effectively anytime and anywhere . As educIn the age of the knowledge economy, the online teaching resource library, as a digital teaching resource center, is a key component of creative education and training of college students with professional knowledge and information literacy . The resUsers' learning behavior data is in a variety of forms, including structured and semistructured information, due to the complexity of MOOC teaching situations . The serBased on the aforementioned gathered data, the creation of an object-oriented, integrated, nonvolatile, and time-variable data warehouse is beneficial to management decision support . The datThe process of constructing a teaching resource data warehouse entails switching from a standard database-centered operational system structure to a data warehouse-centered system structure . The firThe educational process must follow the necessary educational conduct regulations, and educational resource mining must also follow specific guidelines. The article should be based on resource mining ideas before establishing the association rules for teaching resources . We mustIn order to better define the field and focus of resource mining, it is necessary to combine the current situation of current college students' ecological values acquisition with the new requirements put forward by social progress for the majority of young students in the process of mining ecological values education resources . To achiBased on the above principles, an association rule is established for the attribute set of information association positioning in MOOC teaching resource warehouse. Concept lattice and association rule attributes and the reduced concept lattice between them are used to generate frequent item sets of information association positioning and extract implication rules. The support vector set is a subset of interesting rules in the data mining transaction set, and the concept lattice on FP-tree is used to represent the support degree of the two sets, so as to obtain the probability set of redundant association rules, and the calculation formula is as follows:X and Y represent the attribute set, S represents the support between two attribute sets, and P represents the probability of redundant association rules. Considering that the associated data of MOOC teaching resource information has the characteristics of regular state distribution, the probability formula of distribution condition is as follows:In formula , X and YC represents confidence degree, and the characteristic scale of associated data of MOOC teaching resources under association rule training is called minimum confidence minconf. When the support of associated data is greater than the given minimum support minsup, the synthesis rule in the joint probability dense function set D is a nonempty subset, called frequent item set. Using frequent item sets to design association rules, the closed frequent item mathematical model of mining associated data of MOOC teaching resources is as follows:In formula , C repreIn formula ,(4)Nk=xW represents closed frequent item set, l represents the label of mining node, k represents the feature vector of teaching resource, η represents the test time window of data, N represents the data scale, x represents the constraint condition, and r represents the constraint set. Through the adaptive configuration of the fitness of the difference features and under the guidance of the synthesis rule of associated data mining, the association rules of association positioning are designed as follows:In formula , W repreAnd get the formula as follows:m represents associated data, n represents the total number of training samples, A represents constraint concept lattice, and K represents association rule. Pheromone guiding of information association location is carried out by obtaining training samples of network education information association data from the information database and developing the association rules of information association location.In formulas  and 6),,6), m reAccording to the above information association positioning rules of MOOC teaching resources, the improved design of information association positioning mining method is carried out. This paper proposes a positioning mining method for MOOC teaching-associated resources based on shortest path scheduling of educational information resources. The shortest path scheduling of educational information resources adopts the constraint concept lattice interval scheduling model, and the constraint set of shortest path relationship of educational information resources is as follows:In formula , the minJ represents the constrained evolution model, and e represents the number of mining training. The teaching source shortest route scheduling is done using a restricted idea lattice and the best combination of candidate nodes. The instructional resource diversity selection gene then defines the shortest route scheduling procedure on the limited data set at the frequent nodes that match the conditions, given the degree of support.In formula , J repreThe constraint requirements of network education resource shortest route scheduling are established using the extracted constraint association rule database and the support threshold of frequent item set. The restricted idea lattice Hasse diagram of MOOC teaching resource association placement is produced based on the given requirements. The frequent item set characteristics of every object information in the network education information system are retrieved using the shortest route scheduling results of educational information resources. The priority attribute list of association rules of the network education information system sample database is provided in The core of the association rule mining algorithm is to find frequently occurring item sets, which determines the algorithm's overall performance. Multiple database scans are frequently required to solve this problem, implying that a significant amount of time will be spent on database scans and IIO operations. As a result, the main problem that all types of association rule mining algorithms must solve is how to find all frequent item sets quickly and efficiently, and it is also the standard by which to compare the benefits and drawbacks of various association rule mining algorithms. The frequent pattern tree of information association location mining is constructed as shown in CP), whereas, Р is the infrequent item in the transaction item set, A is the minimum expected support number, and B is the correlation positioning connotation after the transaction item set is processed. The abovementioned constraint criteria are used to carry out the information association location mining. The input initialization parameter value, the new head table, and the constraint index parameter set of data mining are all input when the resource diversification mining method is used. Obtain information about an association's location by mining the two-dimensional table's predicted support number. Frequent patterns are mined in the global tree according to the new head table, and the result is a two-dimensional table form with 0, 1 values, where 0 indicates that the mined information data does not meet the minimum expected support set and 1 indicates that the mined location information results meet the minimum expected support set. The diverse mining results of MOOC teaching materials are achieved using the following mining approaches.The frequent item set features of diversified object information in the teaching resource warehouse are extracted from the frequent pattern tree, and the node of concept cell is expressed as One of the essential technologies of the association rule mining technique described in this research is whether the data can be successfully clustered, and one of the core difficulties of clustering is the selection of the correlation function. The clustering approach is used to deal with items in categorised data after mining instructional resources based on association criteria. A hierarchical clustering approach based on item correlation function is designed to suit the clustering needs of diverse instructional materials. Starting clustering from the items of the first or final layer (leaf layer) of the classification data to the higher or first layer of the leaf layer, a top-down or bottom-up technique is used. The minimal support for each layer of clustering operation is lowering in the hierarchical clustering process of the items at the first layer. The following is the particular operation:To begin, each item in the frequently used item set is separated into classes. To decrease computational complexity, the classes with the highest similarity are joined using the correlation between them. Finally, using the concept of hierarchical clustering, the two classes with the highest degree of similarity are explored further to see whether they may be combined. The first-level item clustering of MOOC instructional materials is complete at this stage. The same resource clustering strategy is utilized for successive clustering after the first clustering. Clustering categorised data pieces has the primary goal of reducing and dividing the database. This article proposes a database reduction strategy that is basically a specific “partitioning” method. The reduced method, as opposed to the traditional method of dividing transaction databases by transaction records, divides the items in transaction records, allowing high-correlation items to be classified into the same transaction database and diversified clustering of teaching resources to be realized.Finally, the AFOPT-tree compression structure used in this study is appropriate for varied clustering of instructional materials, and the size of the AFOPT-tree is further reduced by subtree merging. The AFOPT-tree is further lowered when each resource item is examined by merging its relevant subtrees. After establishing all frequent item sets with the prefix of the item created in each inspection, the node X corresponding to the item generated may be pruned from AFOPT-tree, and a merger operation of the node idea tree can be added using the top-down and depth-first traversal approach. The so-called subtree merging procedure involves comparing all child nodes of X node with all sibling nodes of X node, merging the node if an item with the same name exists, and adding up the node count. If this is not the case, a new child node is placed as the parent node of X node. In general, even though this step adds overhead, it is still preferable to enhance the algorithm's efficiency after AFOPT-tree compression. In addition, two auxiliary data structures, a head table and a subhead table of items, are introduced in this study. Based on the AFOPT-tree structure, all frequent item sets with X as the prefix are generated. Subhead table pointers may be used directly to identify nodes in the AFOPT-tree, allowing the original AFOPT-tree to be reused. Reconstructing the conditional sub-AFOPT-tree adds unnecessary overhead. Filtering pruning is accomplished using classification features, and filtering pruning optimization strategies are also presented based on classification data characteristics. If an extension item set has an ancestor or descendant connection with item X, it is not entered into the child header of item X, and the search for following extension items is not resumed. The optimization rule can effectively reduce the candidate item set size of all frequent item sets prefixed with this item and improve the clustering efficiency of diversified educational resources. By clustering diversified teaching resources through the above operations, diversified mining of English MOOC teaching resources based on association rules is realized and the MOOC teaching resources are enriched.Experiments are carried out using this method; the experimental operation is carried out, so as to clarify the superiority of the resource mining method designed in this paper.In order to enhance the authenticity of experimental results, an English MOOC teaching software was selected to collect experimental data. The MOOC software has 10TB of English teaching resources, with more than 4,500 registered users and more than 300 visitors per day. Based on the MOOC software, the experimental data are obtained as shown in Based on the above experimental data, it is divided into four groups of different numbers of data sets. The mining method designed in this paper, BP neural network mining method, decision tree mining method, and particle swarm mining method are used to conduct the simulation experiment of diversified mining of MOOC teaching resources.Simulation experiments were conducted according to different resource mining methods. Considering the differences of English MOOC teaching courses, the class clusters were divided into 3 to obtain the teaching resource mining results shown in According to The experimental data were directed to a grid that was separated into horizontal and vertical coordinates, with the vertical representing the quantity of data and the horizontal representing the time. According to In order to clarify the advantages of the design method in this paper, before the software is used to complete resource mining, the time function is added in the front and the back of the program, respectively, and the time difference obtained is the time of the method to execute the class. Through the above operations, the index time of different mining methods is calculated and obtained.Set the number of index words to 3 to analyze the index efficiency of four different resource mining methods. According to Aiming at the problem of low mining efficiency of traditional methods, this paper proposes a mining method of information association location based on association rules, applies association rule mining to English MOOC teaching resource mining, analyzes the association relationship between resource information, and realizes efficient resource mining. In this paper, a method of English MOOC teaching resource information association location mining is proposed, and the performance of information location mining is improved through the optimization algorithm design of the location mining method. The results show that the index efficiency of information association location mining in online education information system by using this method is greatly improved, and it has good application reliability, which shows the good application value of the method presented in this paper. Although the multimining method of teaching resources in this design has carried out a large number of experiments and engineering usability research and has made some achievements and progress, there are still some problems. The following are the author's suggestions for the next research work; only by using heuristic conditions and analysis of hyperlink anchor text can we obtain the links of teaching resources and recycle the web pages to expand the amount of teaching resource collection.


Flood depth monitoring is crucial for flood warning systems and damage control, especially in the event of an urban flood. Existing gauge station data and remote sensing data still has limited spatial and temporal resolution and coverage. Therefore, to expand flood depth data source taking use of online image resources in an efficient manner, an automated, low-cost, and real-time working frame called FloodMask was developed to obtain flood depth from online images containing flooded traffic signs. The method was built on the deep learning framework of Mask R-CNN , trained by collected and manually annotated traffic sign images. Following further the proposed image processing frame, flood depth data were retrieved more efficiently than manual estimations. As the main results, the flood depth estimates from images (without any mirror reflection and other inference problems) have an average error of 0.11 m, when compared to human visual inspection measurements. This developed method can be further coupled with street CCTV cameras, social media photos, and on-board vehicle cameras to facilitate the development of a smart city with a prompt and efficient flood monitoring system. In future studies, distortion and mirror reflection should be tackled properly to increase the quality of the flood depth estimates. Flood is a natural disaster that brings major loss to human society and the environment . VariousIn flood modeling, the water level is one of the most important parameters for precisely assessing the flood impact and flood forecast, which are obtained by remote sensing but with limitations in water level data sources ,8. Cian Other than remote sensing data, sensors and other hardware are utilized for more data sources to improve the spatial and temporal resolutions of water level data ,14. MariNovel approaches to extract water depth are forthcoming in academic research as artificial intelligence algorithms develop . Event rThus, this study set out to expand the number of limited flood depth data sources using deep learning methods in an efficient and rather accurate manner as an alternative to remote sensing and on-site sensor network. Here, stop signs were utilized as an example type of the traffic signs to develop and evaluate the flood depth extraction method (FloodMask) based on online image resources. The FloodMask is developed based on Mask R-CNN. It was first trained by annotated stop sign images to learn to segment the stop sign out in an image, then images highlighting the masked targets from the collected stop sign images with flood were produced. In the end, flood depth data were estimated based on the local traffic sign dimension. To evaluate the method, manual measurements were obtained by visual observation on raw input traffic sign-containing flood images. The comparison of the automated method with manual measurements completed our objectives of (1) testifying the feasibility of water level extraction with Mask R-CNN; (2) evaluating the precision of FloodMask, as well as post image processing; and (3) investigating the application scenarios of the FloodMask method.The overall architecture of FloodMask is illustrated in Mask R-CNN is a two-stage deep learning framework used for identification of the object contour at the pixel level in an image and is currently widely used in the image detection field . Figure The first stage involves two convolutional neural networks, namely the feature pyramid network (FPN) for image representation, and the region proposal network (RPN) to scan FPN. The FPN was built on the feature extractor, which is a standard convolutional neural network. The backbone network can be illustrated in a pyramid shape, which starts with the input image at the bottom . The earAt the second stage, another convolutional neural network mcnn in  generateSigns stand still in the flood; andThe stop signs in input images have a standard size.To measure flood water depth, it is essential to correctly detect and segment the object, as well as the feasibility to interpret the detection results into the ultimate water level data. With one of the effective frameworks in the instance segmentation field, the Mask R-CNN, the first goal can be reached. Such a deep learning method can recognize and detect the object of its type and distinguish one from another even overlapped by each other. In other words, the above-water part of the traffic signs can be segmented in the average flood scenario. We make the following two assumptions:Why do we assume that the images we use only contain signs that stand still? In this study, we focus on the basic feasibility of the method and its possibility to produce flood depth data, which means extreme cases of flood events occurring in urban areas are not considered. Here, extreme cases can refer to floods during which the water level exceeds the total height of stop traffic signs. This can result in images where the stop signs are leaning too much as if they were bent or captured obliquely by the camera. These extreme scenarios in the application phase could be avoided by asking social media users or photographers to follow certain photo shooting guidelines for citizen-uploaded images and excluding the images where stop signs have large displacement from before the flood. Photos are usually used by news media to indicate the flood depth with the intuitive illustration of traffic signs being submerged. Therefore, news photos are mostly not in the extreme flood scenario mentioned above and they are the ideal input data to validate our method.The second assumption is made because the interpretation of segmented traffic sign images requires knowledge of their real size. Usually, one type of traffic sign has a universal size within a certain administrative region . For exaDue to constraints from the assumptions and existing resources, only a limited number of images containing stop signs with and without flood were collected from internet as input data. In this study, 80 stop sign images without flood were annotated as the input images for Mask R-CNN training, half of which were used for validation. As shown in The masked images were used as input in this step to interpret flood water levels with computer vision and basic data processing functions. Generally, the flood depth data retrieval can be concluded in two parts.The first part deals with obtaining the width and height of the masked sign on the pixel level, as in The second part of water level retrieval was a final calculation with all the parameters from the first part to acquire the flood level in a metric unit. This part was applied in both image processing methods (A and B) and the manual retrieval process. For each masked image, the calculation process and relation between the parameters are illustrated in gn SW in . FurtherIn the following two subsections, we describe the methodology of image processing and manual extraction in more detail.In the field study of computer vision, the OpenCV (Open Source Computer Vision Library) library has long been established to carry out image processing . Recent After obtaining a grayscale image, image pre-processing was carried out to acquire a better image quality for later image manipulation. In this study, Gaussian blur  was applied to enhance the image structure, which is a widely used procedure in computer vision algorithms. Since the grayscale masked images could contain noise, random variation in the brightness, or hue among pixels, the grayscale values for pixels showed a high standard deviation. Therefore, a hazier image with a normal distribution of grayscale values provided less random values for later calculation. The Canny edge detector was also applied to detect the edges in images. Such pre-processing procedures were adopted to improve the relative coarse masking and produce output data as precisely as possible. This is especially relevant because the background and flood water surface of stop signs in the image may affect the mask results, incorrectly producing a masked object area with a longer pole, which is a reflection from the water surface.After the pre-processing, two uncomplicated representation methods were operated to calculate the height and width parameter in the pixel level, which is introduced in (1)Method A: numbers of the non-zero sum values of the column and row, respectively(2)p and Hp were counted as one plus the maximum difference of indices of non-zero sum values in the column and row, respectively.Method B: WIn Once the pixel height and pixel width were acquired, flood depth data were then computed using Equations (1)–(3) in As a validation step to output error with image processing data, manual retrieval by human visual inspection was carried out, using unmasked stop sign images with flood. Human visual inspection started with pinning points on the essential location that shaped the object. In After obtaining the coordinates, the parameters of the masked area mentioned in Equations (1)–(3) were calculated to obtain the flood depth results. At first, Finally, the flood depth for each image was calculated using Equations (1)–(3) with manually measured parameters on the pixel level. Thus, the flood depth data were retrieved for later analysis. For evaluation of FloodMask, manually retrieved data were used to compare and analyze the error.One noticeable limitation of projection distortion arises from the parameter calculation methods (in Equations (1)–(6)): the signs might distort when the input images in flood are not in orthographic projection in the camera, resulting in the calculated flood depth data being larger or smaller than the ground truth for each case. Perspective transform of the traffic sign could be achieved using another convolutional neural network with boundary estimation of the detected object . This meIn In reviewing the literature, no such quantitative method was proposed for the collection of flood depth data. Previous studies using a similar machine learning algorithm only estimated the flood level based on the flooded objects, including humans walking through the flood and house, which is affected by various uncertainty sources . In our First, to establish the training materials, 80 stop sign images without flood were annotated and a JSON file containing the annotation and category information was produced. Then, in the main Python script of execution, the JSON file was used to train the convolutional network. Second, the network learned from the training images and then was established after training and validation steps. Third, the network segmented and masked the input images with flood and finally, produced an PNG image  networks, in flood-sensitive regions. Individual parameterization of post-processing could be set up for each monitoring location by the user, which would improve the accuracy of the depth estimates due to the relatively static backgrounds. Besides, business entities, like vehicle companies and map service companies, could collect on-board vehicle camera information with consent and provide a flood warning service. Moreover, compared to other traditional measuring sensors like remote sensing, gauge stations, and onsite sensors, this image-based method has serval advantages: it works faster, records the evidence  for double checking, and uses traffic CCTV resources without the cost of installation of extra monitoring devices.This study set out to provide an automated methodology to train a convolutional neural network to be able to segment stop signs and retrieve flood depth data from flood images that include such signs. The main goal was to examine the feasibility and performance of this methodology, investigating the extent to which it can be applied to different scenarios of input images. Based on the results of this study, this method can detect the target objects and interpret the flood depth with a satisfying precision of error of 0.11 m for images without mirror reflection and multi-object scenarios. One of the strengths of this study is that it represents an investigation of the potential to apply machine learning algorithms and computer vision in the field of hydrological data acquisition. It offers an exploration of such a framework and yields an affirmatory answer. Another strength of FloodMask lies in its potential to automate an expansion of the flood depth data source. Here, the empirical findings provided a new understanding of the potential resources of water level data for hydrological modeling. It expanded the water level data resources to social media photos, surveillance footage, and even on-board vehicle cameras other than those generally utilized data sources, including remote sensing data, gauge station records, and ground sensors. Moreover, this method established an automated way, which could save labor costs to some degree and increase the efficiency of the data processing procedure.The generalizability of these results is subject to certain limitations. For instance, the sample was regionally representative of the stop sign but gave no guarantee that all other categories of traffic signs in practice have a standard size in reality. Furthermore, with a small training sample size, caution must be applied, as the instance segmentation results may not be transferable to other training samples. However, an increasing number of datasets will be available for application. For example, an open-access traffic sign dataset of German road signs was prepared with more than 50,000 images and in 43 classes, and on which CNNs produced high accuracies for classification . Additio


Brain–computer interface (BCI) research has attracted worldwide attention and has been rapidly developed. As one well-known non-invasive BCI technique, electroencephalography (EEG) records the brain’s electrical signals from the scalp surface area. However, due to the non-stationary nature of the EEG signal, the distribution of the data collected at different times or from different subjects may be different. These problems affect the performance of the BCI system and limit the scope of its practical application. In this study, an unsupervised deep-transfer-learning-based method was proposed to deal with the current limitations of BCI systems by applying the idea of transfer learning to the classification of motor imagery EEG signals. The Euclidean space data alignment (EA) approach was adopted to align the covariance matrix of source and target domain EEG data in Euclidean space. Then, the common spatial pattern (CSP) was used to extract features from the aligned data matrix, and the deep convolutional neural network (CNN) was applied for EEG classification. The effectiveness of the proposed method has been verified through the experiment results based on public EEG datasets by comparing with the other four methods. The brain is closely related to human physiological functions and psychological activities, so brain science research plays a significant role in promoting human health and well-being. In the past decades, human enthusiasm for brain research has been gradually increasing. The US government named the last decade of the twentieth century “the decade of the brain” , and theSince the German expert Hans Berger first discovered the electrical activity of the cerebral cortex and recorded it in the form of electroencephalograph (EEG) in 1929 , scientiA complete EEG-based BCI system consists of four parts: EEG signal acquisition, signal preprocessing, feature extraction, and pattern classification . The sigFor feature extraction methods, there are four commonly used algorithms: power spectrum density (PSD), sample entropy, wavelet transform, and common spatial pattern (CSP). PSD analyzes the signal from the perspective of frequency domain, and the purpose is to transform the EEG signal into a power spectrum and analyze the properties in the frequency domain such as power spectral density, energy, and amplitude. The PSA algorithm is simple and easy to operate, but cannot reflect the latent features. Sample entropy is a signal complexity analysis method and has the advantages of short analysis data, light computational load, and strong anti-interference . As a noThe current motor imagery EEG-based BCI system mostly uses conventional signal processing or machine learning methods for feature extraction and classification ,22,23. T(1) Strong individual difference: The different motor imagery habits of individuals can lead to different motor imagery signals. Even for the same person performing the same motor imagery task at a different time, the incurred motor imagery signals will have some fluctuation and the interference from environment will intensify the difference. Thus, the abovementioned factors require the motor imagery signal processing algorithm to have strong adaptability and universality to ensure an accurate interpretation of the human consciousness and a successful human–computer interaction .(2) Event-related synchronization and desynchronization: When persons make different limb motions, their active brain regions change, as do the potential frequencies generated. The amplitude of the beta rhythms (13~22Hz) and the mu rhythm (8~12Hz) of the sensorimotor cortex on the left side of the brain rises dramatically while envisioning the movement of the left limb, resulting in event-related synchronization (ERS); while the two rhythms of the right side sensorimotor cortex are significantly reduced, resulting in event-related desynchronization (ERD) ,26. AccoBased on the above analysis, it is often difficult and costly to obtain a large amount of labeled training data for the same subject in motor imagery EEG classification task. Moreover, due to the individual differences, it is infeasible to directly use the EEG data of other subjects to train the classification model for new target subjects . TherefoThe main contributions of this paper areas follows: (1) an unsupervised feature extraction approach based on Euclidean space data alignment (EA) and CSP are proposed; (2) a deep transfer learning scheme based on the unsupervised feature extraction and the deep convolutional neural network classifier is established for motor imagery EEG classification. The rest of this paper is organized as follows: The EEG signal collected from the scalp is usually in microvolt level; thus, the acquisition process is prone to electromagnetic interference in the environment and the subject’s own electronystagmogram (ENG) and electromyography (EMG) signals. Therefore, the signal–noise ratio (SNR) of EEG signals is low, and improving the SNR is a prerequisite to guarantee the effective and reliable usage of EEG signal.In this study, the wavelet analysis method was adopted to denoise and reconstruct EEG signals. Wavelet analysis is based on Fourier analysis with better time–frequency window characteristics and is more suitable for the analysis of non-stationary signals such as EEG signals than Fourier analysis.As a type of transfer learning method, EA can realize similar distribution between the source domain data and the target domain data after Euclidean space transformation . EA is aM is the signal channel number and N is the sampling number. The spatial distribution is encoded by the covariance matrix The signal matrix of n is the subject trial number.Then, the following reference matrix Then, the alignment process is conducted based on the following equation:n aligned trials isThe mean covariance matrix of all From the above analysis, the mean covariance matrices will become the identity matrix after data alignment for all subjects. The same mean covariance matrices of all subjects indicate that the spatial distributions are becoming the same, which is an important prerequisite in transfer learning. Then, the aligned signal matrix can be used for further data processing such as feature extraction.CSP is a supervised two-class spatial filtering algorithm, which was first proposed by Koles et al. in 1990 . The basX, where M is the number of EEG channels and T is the number of sampling points for each channel. The average covariance of the two categories can be derived as follows:ith sample, y, and y. Then, the covariance of the mixed space is obtained by superposing the average covariance of different classes:Suppose the EEG signal of a certain motor imagery trial is expressed as an P is the whitening matrix for spatial decorrelation to transform the covariance matrix of We perform eigenvalue decomposition on the mixed space covariance matrix:The principal component decomposition of the matrix after whitening isB, and the sum of the eigenvalue matrices of two classes obtained by the decomposition is the identity matrix I. The eigenvector corresponding to the largest eigenvalue of P and the decomposed eigenvector matrix B, the projection matrix can be obtained asAfter decomposition, The original signal r represents the rth row of signal, m lines, and rth row.Finally, the features can be obtained as follows:Deep learning algorithms have developed rapidly in recent years, and their powerful feature extraction capabilities have made them favored by more and more researchers ,31. Throkth convolution kernel is shown below:f is the activation function of the neuron, b is the bias, kth convolution kernel of lth layer, and x is the input of the unit.The convolution layer is the crucial layer in CNN, and the specific operation is to perform convolution operations on different kernels with input data through forwarding operations. Therefore, different feature maps of the same input data can be obtained. The value of the feature map coordinates In order to reduce the dimension of the outputs and weights network and avoid overfitting, the pooling layer is adopted to downsample the obtained feature maps f is the activation function of the neuron, l and b is the bias.The fully connected layer is used to classify the extracted features after the last pooling layer. The output of the neuron in the fully connected layer is computed as follows:In this study, a deep transfer learning scheme based on the unsupervised feature extraction and deep convolutional neural network classifier was proposed for motor imagery EEG classification. To illustrate the effectiveness of the proposed method, experiments were conducted based on motor imagery EEG datasets with two comparison approaches, support-vector-machine (SVM)-based approach and fine-tuning CNN approach. This section introduces the experiment paradigm and the experiment results with discussion. The flowchart of the proposed unsupervised deep transfer learning method and two comparison methods is shown in The dataset of “two class motor imagery (002-2014)” from BNCI Horizon 2020 was used in the experiments . Figure The cue-guided Graz-BCI training paradigm was used as the guideline in this study . In thisTo obtain the original signal matrix, the sampling points were selected from 0.5 s after the cue of each trial with time duration of 4 s. For each subject, the signal matrix of The signal denoise for both source and target domain signals were conducted using wavelet decomposition algorithm. Taking the C3 electrode located in the left hemisphere motor area as an example, the respective electrode position is shown in In this study, the Daubechies wavelet db6 function was adopted to decompose the collected motor imagery EEG signals into four frequency bands: 0–6.5 Hz, 6.5–12.5 Hz, 12.5–25 Hz, and 25–50 Hz. Then, only the two main frequency bands 6.5–12.5 Hz and 12.5–25 Hz were retained during the EEG signal reconstruction. The EEG signal preprocessed by wavelet analysis can reduce noise and enhance features for subsequent feature extraction and classification.To illustrate the effect of of denoise and enhancement of the original signal, we take the C3 original signal when performing right-hand MI as an example to show the process of wavelet denoise. The EA algorithm was adopted to realize the data alignment of the source domain and target domain. To illustrate the effect of data alignment, we performed t-stochastic neighbor embedding (t-SNE)  on the sThe CSP algorithm is used in feature extraction for both aligned source domain and target domain signal matrix. For each For each To illustrate the effectiveness of the proposed method, two comparison approaches were used in this study to compare the classification performances as shown in The overall classification accuracy of the proposed method and the comparison methods on four target subjects are shown in Due to the strong individual difference and the limited correlation in motor imagery EEG signals, an unsupervised deep transfer learning scheme for motor imagery EEG classification was proposed in this paper to achieve reliable and accurate feature extraction and classification and reduce the computation complexity. The deep transfer learning scheme adopts EA and CSP to realize unsupervised feature extraction, and deep CNN is utilized as the classifier. The experiment results based on motor imagery EEG dataset illustrated the effectiveness of the proposed method by comparing with transfer-learning-based SVM method and fine-tuning CNN method. The future work can be conducted on improving the transfer learning algorithm by revising the data alignment method. In the conventional EA algorithm, the alignment of the Euclidean space only considers the alignment of the covariance matrix, but not the alignment of the overall probability distribution. Therefore, the probability distribution adaptation can be added to the alignment of the covariance matrix in the subsequent work.


The exothermic reactor for ammonia synthesis is a primary device determining the performance of the energy storage system. The Braun-type ammonia synthesis reactor is used as the exothermic reactor to improve the heat release rate. Due to the entirely different usage scenarios and design objectives, its parameters need to be redesigned and optimized. Based on finite-time thermodynamics, a one-dimensional model is established to analyze the effects of inlet gas molar flow rate, hydrogen–nitrogen ratio, reactor length and inlet temperature on the total entropy generation rate and the total exothermic rate of the reactor. It’s found that the total exothermic rate mainly depends on the inlet molar flow rate. Furthermore, considering the minimum total entropy generation rate and maximum total exothermic rate, the NSGA-II algorithm is applied to optimize seven reactor parameters including the inlet molar flow rate, lengths and temperatures of the three reactors. Lastly, the optimized reactor is obtained from the Pareto front using three fuzzy decision methods and deviation index. Compared with the reference reactor, the total exothermic rate of the optimized reactor is improved by 12.6% while the total entropy generation rate is reduced by 3.4%. The results in this paper can provide some guidance for the optimal design and application of exothermic reactors in practical engineering. As solar illumination fluctuates greatly with weather conditions, the thermal storage system is pivotal for the stable operation of solar thermal power generation systems. The industrialization of solar thermal power systems equipped with thermal storage systems will effectively improve the global energy supply structure and promote emission peaking and carbon neutrality. Luzzi et al. ,2, LovegExisting studies of exothermic reactors for ammonia synthesis have mainly concentrated on single-tube reactors because their operation and design parameters are more easily controlled. Long and Liao  establisFinite Time Thermodynamics ,16,17,18However, the existing studies of ammonia synthesis exothermic reactors mainly focus on single-tube reactors, with the maximum exothermic rate as well as the minimum exergy destruction rate as their analysis and optimization objectives ,13,14,15Braun ammonia synthesis reactors usually consist of two to four adiabatic reactor towers with one heat exchanger behind each tower. Industrial reactors are designed to save energy and increase ammonia production and outlet ammonia content, so their design and operating parameters are not fully applicable to thermal storage and power generation scenarios. Therefore, this paper applies finite-time thermodynamics to establish a one-dimensional model to analyze the influence of inlet gas molar flow rate, hydrogen to nitrogen ratio, length and inlet temperature of individual reactors on the entropy generation rate and the total exothermic rate. And finally, taking inlet flow rate, lengths and temperatures of three reactor towers as optimization variables, the NSGA-II algorithm is carried out for the multi-objective optimization of the minimum entropy generation rate and the maximum total exothermic rate.Braun three-tower ammonia synthesis system is composed of three adiabatic synthesis towers equipped with one heat exchanger for cooling behind each synthesis tower. The feed gas flows in from one end of the reactor, and the synthesis reaction occurs and exotherms under the action of the catalyst, causing the temperature of the gas mixture to increase. Then, it flows into the tube pass of the counter-current heat exchanger behind the reactor and heats the water vapor in the shell pass. Then, the cooled gas mixture flows into the next reactor, and so on. What should be noted is that there is no mass transfer between system and outside during the process as the Braun synthesis tower is cooled by heat exchangers rather than quench. In other words, there is no new reactants are added and no ammonia is separated between the inlet of reactor 1 and the outlet of exchanger 3. The post-reaction gas mixture from heat exchanger 3 will flow into the gas transport, separate and storage system, where the separation and storage of the gas mixture will take place to allow the energy storage system to operate in a cyclic manner. However, this gas transport, separate and storage system is not within the scope of this paper. This paper uses a one-dimensional model, and the ammonia synthesis reactor tower can be simplified to a tubular filled-bed reactor with a large diameter. The gas is thoroughly mixed uniformly in the radial direction, the radial temperature difference inside the reactor and heat exchanger is ignored, and no back-mixing occurs during the axial flow. A model diagram of a tubular filled-bed ammonia synthesis reactor is shown in Because the reaction pressure of the ammonia synthesis process reaches 15 MPa and the temperature can achieve 600–800 K, the ideal gas equation of state has a large error. So, all the property parameters involved, such as the specific heat capacity at constant pressure, thermal conductivity, density, and kinetic viscosity are calculated by the REFPROP software developed by NIST.The reaction kinetic model for ammonia synthesis obeys the Temkin–Pyzhev reaction rate equation. For real gases, directly calculate the partial pressures of the components by molar fraction is not accurate, so, to keep the equation form simple, activity is introduced into the reaction rate equation :(1)r=KbKis paper .T is the reaction temperature in K; i; i, which can be found in The quations :(2)logKb(T)i, and p is the total reaction pressure in kPa.The partial pressure omponent :(6)αi=γiThe activity coefficients in Equation (6) are calculated by the following Equation :(7)RTlnγWhen the temperature is 653 K and the pressure is 15.6 MPa, each component’s partial pressure calculated by molar fraction and actual gas state are listed in j in Since the ammonia synthesis reactor is adiabatic, all the reaction heat is absorbed by the gas mixture inside the reactor. So the energy conservation equation in each axial micro-element can be represented by the change in temperature of the reacting gas ,40:(10)dThe mass conservation equation is expressed by the differential equation for the rate of conversion as follows:After trial calculations, the Reynolds number of the gas mixture in the reactor ranges from 3000 to 12,000 in the given range of operating parameters, so the momentum conservation in the reactor is characterized by using the Hick’s pressure drop equation as follows ,41:(12)dThe irreversibility losses for irreversible processes can be measured by entropy generation. Entropy generation in the reactor is mainly from the finite rate chemical reaction and the finite pressure-difference fluid flow, so the total entropy generation rate of the microelement length in the reactor is ,43:(13)σThe total entropy generation rate of the whole reactor is the integral of the total entropy generation rate of the microelement length along the tube length.The heat exchanger adopts counter-flow tube bundle heat transfer, with the high-temperature gas mixture from the reactor in the tube process and the water vapor at an initial pressure of 150 kPa and an initial temperature of 150 °C in the shell process. The schematic image of the cooler is shown in The Nusselt number for the heat transfer in the tube flow is calculated using the Gnielinski formula :(15)Nu=(The  formula :(16)f= needs to be changed to 4 times the hydraulic radius when calculating the Nu of the shell process.The pressure drop in the heat exchanger comes from the viscous frictional flow process and is calculated using Darcy’s equation  for the The heat transfer coefficients and heat transfer thermal resistance of the tube and shell processes can be obtained by the defined equation of Nu.The total entropy generation rate in the cooler comes from the finite temperature difference heat transfer and the finite pressure difference flow processes, so the total entropy generation rate of the microelement length is given by ,43(19)σThe application of industrial reactors to thermal storage and power generation requires the analysis and optimal design of its structural and operation parameters. In this paper, we analyzed the effects of eight single variables on the total exothermic rate and system entropy generation rate, and the variables includes the inlet molar flow rate, inlet gas hydrogen to nitrogen ratio, three reactor lengths and three reactor inlet temperatures. After that, considering minimum entropy generation rate and maximum exothermic rate, a non-dominated solution ranking genetic algorithm with elite strategy ,47 , ammonia production rate (AP) and exit ammonia molar fraction (AF) with the increase in inlet molar flow rate is shown in The variation of total exothermic rate (ER), total entropy generation rate , and the total exothermic rate shows a slight decrease with the increase in hydrogen-nitrogen ratio. The variation of the total entropy generation rate of the system with the hydrogen to nitrogen ratio is shown in As shown in The main reason for the above-mentioned rules is that when the inlet flow rate is large and the hydrogen–nitrogen ratio is low (more nitrogen content), the remaining nitrogen content is still high after entering the third tower. The high nitrogen content can effectively promote the ammonia synthesis reaction, making the third reaction tower’s outlet temperature and total entropy generation rate much higher.In this paper, three reactor towers are involved, so the effect of the variation of the third reactor length on the performance index is analyzed when the other two reactor’s lengths are maintained at reference values. The changes of total exothermic rate and total entropy generation rate corresponding to the variation of each reactor’s length are shown in The different lines in Similarly, two reactors’ inlet temperatures are controlled as reference values, and the third reactor inlet temperature varies in the range of 50 K above and below the reference value. The effect of individual reactor inlet temperature variations on the total entropy generation rate is shown in The effect of inlet temperature on the total exothermic rate is also not obvious, but that on total entropy generation rate is significant. As shown in Taking minimum entropy generation rate and maximum exothermic rate as objectives, the NSGA-II algorithm  is appliIn Two sets of structural and operation parameters are selected from the Pareto front by three fuzzy decision methods, i.e., TOPSIS ,62,63, LDideal is the distance from the decision point to the ideal point and the Dnon is the distance from the decision point to the non-ideal point.The The inlet molar flow rate is the key parameter affecting the total exothermic rate of the system. The total exothermic rate basically increases linearly with the increase in the molar flow rate, but the total entropy generation rate climbs sharply.Under the constant inlet molar flow rate, the change of hydrogen to nitrogen ratio has little effect on the total exothermic rate. But when the molar flow rate is large, the hydrogen to nitrogen ratio has an obvious effect on the total entropy generation rate, and a higher hydrogen to nitrogen ratio should be chosen for large molar flow rate.Compared with the reference reactor, the TOPSIS and LINMAP optimal reactor improves the total exothermic rate by 13.1% and the total entropy generation rate by 6.7%; the Shannon entropy optimal reactor improves the total exothermic rate by 12.6% and reduces the total entropy generation rate by 3.4%.According to the deviation index, the Shannon Entropy optimal reactor is choosen as the optimized reactor.In this paper, a one-dimensional model of Braun-type ammonia synthesis exothermic reactor is established based on finite-time thermodynamics, and the effects of parameters such as inlet molar flow rate, hydrogen-nitrogen ratio, reactor length, and reactor inlet temperature on system performance are analyzed. The Pareto front is obtained by NSGA-II algorithm, and three decision methods including TOPSIS, LINMAP and Shannon Entropy, are applied to obtain optimal reactors. The analysis and optimization results show that:


To assist researchers in processing large amounts of bird image data, many algorithms have been proposed, but almost all of them aim at solving the problems of bird identification and counting. We turn our attention to the recognition of habitat elements in bird images, which will help with automatically extracting habitat information from such images. To achieve this goal, we formed a dataset and implemented our proposed method with four kinds of deep convolutional neural networks, and the recognition rate reached a minimum of 89.48% and a maximum of 95.52%. The use of this method will supplement the extraction of bird image information and promote the study of the relationships between birds and habitat elements.With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements. Monitoring the populations and habitats of wild animals and plants is not only very important for protecting biodiversity but also closely related to human survival and development. Because of their wide distribution, great mobility, and high sensitivity to environmental changes , birds hIn recent years, deep learning ,16 has mSuch studies are very helpful for performing bird monitoring research. However, few studies have been conducted on the recognition of habitat elements, which are very important for studying the relationships between birds and the environment. Habitat elements are generally located in image backgrounds. In studies by ,33, an aTherefore, it is necessary to study an algorithm for the automatic recognition of habitat elements from bird images, as this will be of great help for ornithology research. The automatic identification of habitat elements can be regarded as an image classification problem. Given the extraordinary performance of DCNNs in image classification problems, we assume that the use of this technology to identify habitat factors from bird images is also feasible and effective.To our knowledge, this study is the first attempt to identify habitat elements in bird images and to build a database for this kind of research. In summary, the contributions of this article to the field mainly include: We built a dataset, Habitat Elements of Bird Images (HEOBs-10), for identifying habitat elements from bird images; HEOBs-10 contains 2135 images across 10 categories. We used four popular DCNNs to implement automatic identification for habitat elements and achieved good results, which verified the effectiveness of DCNNs in solving the problem of identifying habitat elements from bird images and provided a baseline for future research.Since no public image dataset exists for identifying habitat elements, it was necessary to build an appropriate dataset. For this reason, we built a database called HEOBs-10. The database contains 10 categories, each with approximately 200 images; all images in the database are randomly divided into three parts at a ratio of 3:1:1 , and the distribution of samples for each subset tends to be balanced . The traThe majority of the images in the database were mainly contributed by the citizen science project called BirdFans in China  and befoDeep convolutional neural networks are very similar to artificial neural networks (ANNs) , which aIn image classification tasks, cross entropy (CE)  is oftenn represents the number of categories.The cross-entropy HAlexNet  won the A DCNN can also be regarded as being composed of two parts: a feature extractor and a classifier. The feature extractor can extract low-level features, more complex features, and high-level features from the original image to obtain a feature map, which is then expanded into a high-dimensional feature vector. The high-dimensional feature vector is then fed to the classifier, where the vector undergoes some linear transformations and nonlinear transformations, and finally passes through the softmax function; the output is a vector of the same size as the number of categories to be identified.In this work, we used four DCNN models in the PyTorch  model liTransfer learning is used to improve a model from one domain by transferring information from a related domain  and is wA Linux server with one GV100GL  was used to train all the networks. The networks were implemented using Python 3.7.4 and PyTorch  . We usedAfter the dataset was preprocessed through the method described above, we needed to set the hyperparameters involved in the training process. These hyperparameters have different effects on the model training time, convergence, and equipment load. Therefore, before starting the training process, we employed several pre-experiments and obtained the best combination of hyperparameters using the grid search method . Table 2The learning rate controls how much the model should be changed in response to the loss each time the model’s weights are updated. A large learning rate may cause the model to oscillate during the training process and fail to converge; a learning rate that is too small greatly increases the convergence time. Here, we adopted a policy called step decay, which can adaptively change the learning rate automatically as the training procedure progresses. Given the machine’s capacity and algorithm convergence, usually, an entire dataset  is not passed through a model at once. Instead, the complete dataset is passed to the same model iteratively in batches. The batch size refers to the number of training images utilized in one iteration. One epoch represents that the entire dataset is passed forward and backward through the model only once. To obtain a model with good performance, the model needs to be trained for several epochs.We obtained four DCNN models, which were pretrained on approximately 1.28 million images (1000 object categories) from the ImageNet, from PyTorch’s model management library. We modified the models according to the number of categories in our dataset and used each modified model as the initial network model. During the training phase, to increase the size of the training set and decrease overfitting problems , multiplThe whole experiment in our work was performed in two separate stages. In the first stage, we used the training set and validation set data to fine-tune each pretraining model in turn, recording the training loss and validation accuracy of the model in each epoch. In the training phase, the model with updated parameters after the first epoch was automatically saved as a temporary optimal model. At the end of the new epoch, we compared the verification accuracy of the new model with that of the previous model. If the validation accuracy of the new model was higher than that of the previous model, the new model overwrote the previously saved optimal model. Otherwise, the previous model was retained. This was repeated until the last (50th) epoch was finished. After performing these steps , we obtaIn the second stage, to observe the effect of the size of the training dataset on the effectiveness of the DCNNs in identifying habitat factors, we specifically processed the initial dataset. We kept the validation set and test set unchanged. We copied the four original training datasets and randomly selected 20%, 40%, 60%, and 80% of the images from these copied datasets to generate four new training datasets. Then, we used these subdatasets of different sizes and the original verification set and test set to repeat the procedure of the first stage.For deep learning, generally, it is difficult for the same team in the same location to obtain the exact same results on different experiments with the same precision under the same experimental setup, such as the hardware and software settings used during multiple trials . To obtaWe used the 435 test images to evaluate the retrained models. Note that during the model evaluation stage, each model’s parameters were not updated. All the test images were divided into four categories according to the real labels and the prediction results. There are altogether four basic counts: true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs). The numbers of correctly predicted test images are indicated by the TPs and TNs, and the numbers of incorrectly predicted images are the FNs and FPs. Based on the above definitions, the following four indicators commonly used to evaluate the performance of classification models can be defined: accuracy, precision, recall, and F1-score.Accuracy: This metric is defined as the ratio of correctly classified images to the total number of images in the dataset and can be defined as follows:Precision, Recall, and F1-score: These metrics are used to measure how close the results obtained for each category are to the corresponding real labels. Take the trunk class of test images as an example. The precision refers to the ratio of the number of images correctly predicted as trunks (TPs) to the total number of images predicted as trunks (TP+FP); the recall rate refers to the ratio of the number of images correctly predicted as trunks (TPs) to the number of images with the true label of “Trunk” (TP + FN). The F1-score is computed as the harmonic average of precision and recall. Therefore, the above three indicators can be defined as:In our experiment, during the training phase, the proposed models tended to converge in approximately 20 epochs, and the verification accuracy of each model also tended to stabilize, which indicates that each network was fully trained .On the whole, good results were obtained for the proposed models . The Resp < 0.05, The F1-score of each habitat factor is greater than 0.80 , which iWe utilized AlexNet to make predictions for 10 samples randomly picked from different categories in the test set. We observed that the model was very confident in the predictions of these sample images, and most of the probability values were above 0.90 .We observed that the four DCNN models performed well in terms of the recognition of water, and no errors occurred. When identifying “leafless” and “stalk”, the performances were relatively poor; this was consistent with the F1-score evaluation . For “leAlthough DCNNs have higher requirements than other networks regarding the number of samples, in our research, when using 20% of the training set data, the test accuracies of the four networks exceeded 0.80 . In our As expected, our study verified the feasibility and effectiveness of using DCNNs to automatically identify habitat elements, and the best accuracy rate reached 97.76%. Although we only used ten habitat elements as the objects of recognition in our research, DCNNs have also achieved good results in terms of the recognition of 1000 categories in the ImageNet recognition task , so we hIn our work, when the four network models recognized “leafless” and “stalk”, the recognition rates were relatively low, and the two categories were prone to being confused with one another. However, the training images of these two habitat factors were not the least common. This result may have been caused by the visual similarity of the two types of habitat elements.We can identify habitat elements from bird images, which will help us to understand the characteristics of bird habitats and a given bird’s preferences for habitat elements by analysing the object co-occurrence  relationThe continuous in-depth research and application of image recognition technology in various fields have also caused some concerns among researchers ,69,70,71We used a single-label method to annotate the images in our experiments; this approach could not fully express the information about the habitat elements in the images, although most images in the dataset could be processed in this way. In addition, our algorithm is invalid for unfamiliar data ; this fact is caused by the activation function used by the algorithm. Therefore, for future research, we believe that the use of multi-label learning ,74 methoMining interesting knowledge from bird image data is helpful for promoting bird research. Tryjanowski et al.  used YouIn general, the promising performance of this line of research provides us confidence that DCNNs can be used to automatically identify habitat elements from bird images. The practical application of this technology will promote research on the relationships between birds and habitat elements. At the same time, this technology may also contribute to improving the accuracy of bird recognition because some studies ,78 have In the present study, we demonstrated the effectiveness of DCNNs in automatically identifying habitat elements from bird images. For the needs of research, we established a dataset called HEOBs-10, which is composed of bird images related to 10 categories of habitat elements. Good results were obtained from all the proposed models. ResNet-152-based models yielded the best validation accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). The set of experiments performed in this work provides baseline results for the introduced database, which may minimize the lack of a robust public dataset in the field of automatically identifying habitat elements in bird images, thereby making it possible to conduct future benchmarks and evaluations. An evaluation of the performance of the proposed dataset in the real world requires further research. First, the dataset needs to be supplemented by additional bird images containing new categories of habitat elements. Second, the dataset should encourage the use of multilabel learning methods to identify habitat elements, as such methods will be more suitable for the needs of real scenarios than the single-label method. Third, DCNNs can be used to establish relationship graphs between birds themselves and habitat elements, which will be a meaningful exercise.


Thickness, cross-sectional area (CSA), and hardness of the plantar fascia, abductor hallucis (AbH), and flexor hallucis brevis (FHB) muscles were measured using ultrasonography. Single-leg drop landing and repetitive rebound jumping tests assessed the ground reaction force (GRF) and reactive jump index (RJI), respectively. The CSA of FHB was negatively correlated with maximum vertical GRF  in the single-leg drop landing test. The CSA of AbH was negatively correlated with contact time , and the plantar fascia thickness was positively correlated with jump height  and RJI  in the repetitive bound jump test. In multivariate regression analysis, only the plantar fascia thickness was associated with RJI . The CSA of FHB may contribute to force attenuation during landing. The thickness of the plantar fascia and CSA of AbH may facilitate jumping high with minimal contact time.The plantar fascia and intrinsic foot muscles (IFM) modulate foot stiffness. However, it is unclear whether the corresponding ultrasonography findings reflect it. This study aimed to examine the effect of the plantar fascia and IFM morphologies on force attenuation during landing and reactivity when jumping in healthy adults ( The human foot comprises 28 bones that form three flexible arch structures. The stiffness of these foot arches is passively and actively modulated by the plantar fascia and the intrinsic foot muscles (IFM), respectively . The plaIn sports activities, force attenuation while landing is necessary to prevent injury ; meanwhiIn recent years, ultrasonography has been used to evaluate the morphologies of the plantar fascia and IFM; their relationship with performance has been investigated in gait , runningTwenty-three healthy recreationally active collegiate students were recruited for participation in this study from Hiroshima University. “Recreationally active” was defined as participation in at least 150 min of moderate activity per week for at least 6 months . IndividThe study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee for Epidemiology of Hiroshima University . All participants provided informed consent to study participation.Morphologies of IFM and plantar fascia , and hardness) were imaged by B-mode ultrasonography  and real-time tissue elastography (RTE). An 8-MHz linear array probe was used to obtain a continuous ultrasound image. An acoustic coupler  was put on the transducer with a plastic attachment  and used as reference material. The hardness value of reference material was 22.6 ± 2.2 kPa, which was a relative value based on the acoustic coupler in the material tests conducted by the manufacturer.Thickness of the abductor hallucis (AbH), flexor hallucis brevis (FHB), and flexor digitorum brevis (FDB) muscles;CSA of AbH, FHB, and FDB, and;Thickness of the plantar fascia in the calcaneus.Thickness and CSA values of selected tissue were measured with B-mode ultrasonography, as reported previously ,13; the The position of the probe was marked on the skin surface with semipermanent ink. The probe was placed along a line perpendicular to the long axis of the foot on the anterior aspect of the medial malleolus to record the CSA image of AbH; it was subsequently placed approximately perpendicular to the same line to record the thickness image. Furthermore, the probe was placed perpendicular to a line parallel to FHB to record its CSA image and was subsequently placed along the same line to record the thickness image. Moreover, the probe was placed along a line between the third toe and the medial calcaneal tubercle to record the CSA image of the FDB muscle and was subsequently placed along the same line to record the thickness image. Moreover, the probe was placed along a line between the second toe and the medial calcaneal tubercle to record the thickness image . These mThe head of the probe was coated with a transmission gel to achieve acoustic coupling. The RTE was imaged by manually applying repetitive light compression (rhythmic compression–relaxation cycle) with the transducer in the scan position. To obtain appropriate images for investigation, the examiner responsible for ultrasonography assessment pressed the transducer against the selected tissue with constant repeated pressure (the strain ratio in the range of −0.7 to 0.7) and monitored the pressure indicator incorporated into the ultrasound scanner . For eacThe mean strain in the region of interest (ROI) was monitored on a strain graph to adjust the force and frequency of compression. When the elastogram was successfully superimposed on the ROI, the frequency was adjusted from 2 Hz to 4 Hz to maximize the image quality. The strain rate within each ROI was measured automatically, using built-in software, and the strain ratio  was calculated per image. The RTE in response to the compression force is smaller in harder than in softer tissue; hence, as the tissues become stiffer, the RTE value becomes smaller . RTE meaThe single-leg drop landing test was conducted using the center of pressure (COP) trajectory, and the vertical ground reaction force was measured using a force plate , 10 cm in height. The participants stood one-legged on their right foot, static and barefoot on a box, 30 cm in height. They were asked to jump in a forward direction from the box on a cue given by voice guidance from a personal computer with relevant software . They were instructed to jump with their hands folded in front of their chest, landing with their right foot on the center of the force plate placed in front of the box. The participants were asked to maintain the single-legged standing posture for 5 s after landing, and to continue keeping both hands folded in the same position. A 10-s interval was set between trials. Trials were excluded from the data analysis when a participant (1) could not maintain a single-leg standing posture for 5 s after landing; (2) was disengaged from the force plate during or after landing; (3) moved the base of support by shifting their foot after landing, trying to maintain standing posture; or (4) released their folded arms. The landing test was repeated until three successful trials were performed and used for data analysis.TM system . This system consists of two infrared photocell bars, with one bar acting as a transmitter unit containing 96 light-emitting diodes positioned 0.003 m above the ground, while the other bar acts as a receiver unit. Participants were instructed to keep their hands on their hips to avoid upper-body interference, while jumping and landing repetitively. They were asked to land on the same spot each time with legs initially extended and then flexed, while looking ahead at all times. Additionally, the participants were asked to jump as high as possible and minimize the ground contact time, while jumping again immediately. When a participant performed the RJ within the parallel bars placed on the ground, the light was interrupted by the participant’s foot during the jump, which triggered the sensor in the unit to record a sampling frequency of 1000 Hz. Two sets of seven repetitive rebound jumps were performed with a 5-min rest between sets. Of seven repetitive jumps in the second set, the second through fifth jumps were used in the analysis, as this approach is associated with good inter-day reliability ”).As performed in a previous study , a post Two of 23 prospective participants were excluded from this study due to hallux valgus. The mean, median, IQR, and SD values of ultrasonographic evaluations and repetitive rebound jump and drop landing assessments are presented in p = 0.031) (p = 0.018). In addition, the AbH muscle size and plantar fascia thickness were correlated with increased RJ performance.The CSA of FHB was negatively correlated with VGRFmax in forward drop landing  . The thiThe main finding of this study indicated that the morphology of the FHB muscle and plantar fascia was related to force attenuation in single-leg drop landing; meanwhile, the AbH muscle and the plantar fascia were related to reactivity in the repetitive rebound jump test. Moreover, the thickness of the plantar fascia is associated with the RJI. Since the RJI is considered to be the indicator of an athlete’s ability to efficiently brake and absorb eccentric forces within specific time frames, before subsequently generating a propulsive concentric force, this present study suggests that the thicker plantar fascia may indicate an ability to jump higher with minimal contact time during jumping.Olsen et al.  reportedThe present study showed a significant negative correlation between plantar fascia thickness and COP length in single-leg drop landing. Generally, COP is transferred by postural sway. If its structure is too flexible, postural sway would increase because it needs to be compensated by proximal joint. In other words, the ability to regulate the stiffness of the foot structure is considered to be important for reducing COP displacement. The plantar fascia is a fiber that attaches from the calcaneus to the proximal phalanx, and can change the stiffness of the foot structure through the windlass mechanism . Thus, iTo the best of our knowledge, this is the first study to showing a relationship between the CSA of AbH and RJI. A previous study showed that the RJI is positively correlated with the height of the medial longitudinal arch . This fiThis study has demonstrated an association between the thicker plantar fascia and greater RJI. A previous study explored the relationship between the thickness of the plantar fascia and the RJI and reported likely correlation between them . The RJIPreviously, it was unclear if the morphology assessed by ultrasound of the plantar fascia and IFM could reflect sports performance during the drop landing and repetitive rebound jump. In the present study, these soft tissue morphologies and actual sports performance were correlated. Therefore, this indicated the possibility to assess the plantar fascia and IFM by using ultrasound imaging devices in order to quantify sports performance in the future. In addition, it was also unknown which of plantar fascia or IFM affect attenuating force during landing and repetitive rebound jumping. This study demonstrated the relationship between the plantar fascia, flexor hallucis brevis, and abductor pollicis brevis and sports performances. These results may be important findings for specific training for the foot and consideration of the mechanism of injury in the future.This study has some limitations that need to be considered when interpreting its findings. First, this study only included healthy adults without foot abnormalities. However, previous studies have shown that the plantar fascia is thicker in populations with plantar tendinitis ; thus, aIn conclusion, there were significant correlations between the thickness of the plantar fascia and the CSA of FHB and VGRFmax during landing, and correlations between the plantar fascia and AbH and reactivity during repetitive jumping among healthy adults. Thus, the present findings suggest that the CSA of FHB contributes to force attenuation during landing, and that the thickness of the plantar fascia and the CSA of AbH facilitate jumping high with minimal contact time during repetitive rebound movement performed by healthy adults.