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Breast cancer is a highly prevalent cancer. Triple-negative breast cancer (TNBC) is more likely to recur and metastasize than other subtypes of breast cancer. Research on the treatment of TNBC is of great importance, and accurate segmentation of the breast lesion area is an important step in the treatment of TNBC. Currently, the gold standard for tumor segmentation is still sketched manually by doctors, which requires expertise in the field of medical imaging and consumes a great deal of doctors' time and energy. Automatic segmentation of breast cancer not only reduces the burden of doctors but also improves work efficiency. Therefore, it is of great significance to study the automatic segmentation technique for breast cancer lesion regions. In this paper, a deep-learning-based automatic segmentation algorithm for TNBC images is proposed. The experimental data were dynamic contrast-enhanced magnetic resonance imaging TNBC dataset provided by the Cancer Hospital of Zhengzhou University. The experiments were analyzed by comparing several models with UNet, Attention-UNet, ResUNet, and SegNet and using evaluation indexes such as Dice score and Iou. Compared to UNet, Attention-UNet, ResUNet, and SegNet, the proposed method improved the Dice score by 2.1%, 1.54%, 0.88%, and 9.65%, respectively. The experimental results show that the proposed deep-learning-based TNBC image segmentation model can effectively improve the segmentation performance of TNBC tumors. [ABSTRACT FROM AUTHOR]

The replacement of the loading zone is not considered in the active calculation method on fatigue life of remanufactured bearings. In practical application, when the radial bearings are reinstalled after remanufacturing, it is required to replace the loading zone, which results in a large deviation between the calculated fatigue life according to the active calculation method and the actual life. In this paper, the fatigue life factors of radial bearings with different remanufacturing levels are calculated according to the actual application condition. The results of case studies show that with the improvement of the remanufacturing level, regardless of whether the loading zone is replaced or not, the remanufacturing bearing life factor is gradually increased and the maximum can reach 1. Considering the replacement of the loading zone, the life factors of remanufactured bearings with different fixed rings are also very different, among which the remanufacturing deviation of fixed outer-ring level II is the largest, reaching 11.3%. However, with the increase of the remanufacturing level, the deviation decreases gradually. The life factors by the method presented in this paper of remanufactured radial bearings with the replaced loading zone are significantly higher than those of the active calculation method. The research results of this paper provides a more accurate calculation scheme for the fatigue life of remanufactured radial rolling bearings, which is a supplement to the active calculation method and has important practical significance for the practice of bearing remanufacturing engineering. [ABSTRACT FROM AUTHOR]

Measuring the systemic impact of major public health emergencies on the light industry and preventing various uncertain future external risks have become the key challenges to ensuring the stability of the light industry. This paper takes the occurrence of major public health emergencies as the background and focuses on environmental issues such as air pollution and indoor air quality degradation during emergencies. And to explore the multiscale impact of major public health emergencies on the light industry, typical light industry subsectors, and light industry enterprises. The findings of our study reveal that major public health emergencies have a negative impact on the light industry, particularly in the form of a short-term decline in exports, which tends to converge in the long run. Further, it is also revealed that there is heterogeneity in the impact on environmentally sensitive industries, labor-intensive industries, and others. At the microfirm level, major public health emergencies have shown a negative effect, especially the recent pandemic, which has a longer duration and a wider reach. Through multiscale research, this paper provides policy suggestions to improve the macrogovernance mechanism and risk prevention system for the light industry. [ABSTRACT FROM AUTHOR]

Affected by the movement of drones, missiles, and other aircraft platforms and the limitation of the accuracy of image sensors, the obtained images have low-resolution and serious loss of image details. Aiming at these problems, this paper studies the image super-resolution reconstruction technology. Firstly, a natural image degradation model based on a generative adversarial network is designed to learn the degradation relationship between image blocks within the image; then, an unsupervised learning residual network is designed based on the idea of image self-similarity to complete image super-resolution reconstruction. The experimental results show that the unsupervised super-resolution reconstruction algorithm is equivalent to the mainstream supervised learning algorithm under ideal conditions. Compared to mainstream algorithms, this algorithm has significantly improved its various indicators in real-world environments under nonideal conditions. [ABSTRACT FROM AUTHOR]

This paper presents a low-carbon vehicle routing optimization model to reduce energy consumption and carbon emissions in logistics and distribution. The model is solved using a hybrid algorithm of simulated annealing and ant colony optimization. It enhances the information pheromone concentration update process and directionality by introducing a carbon emission factor and a multifactor operator. Additionally, an adaptive elite individual reproduction strategy is employed to improve algorithm efficiency. In this case study focusing on cold chain logistics distribution, both the model and algorithm under consideration were evaluated. The findings affirm the effectiveness of the model in reducing carbon emissions and demonstrate the efficiency and robustness of the algorithm. Through this analysis, the paper sheds light on environmentally sustainable practices in logistics distribution. [ABSTRACT FROM AUTHOR]

Failure of vulnerable lines in the power system often results in tidal shifts, and triggering chain failures and their corresponding transmission sections are concentrated manifestations of the weak links in the power system. It is very important to identify the vulnerable lines and search the transmission section to prevent the chain faults as well as to analyze the stability of the power system. Aiming at the problems of inaccurate search of vulnerable lines, difficulties adapting to the complex and changing power system as well as wrong selection and omission of transmission section search in the existing references, this paper proposes an algorithm for searching vulnerable lines and their key transmission sections based on the graph theory and PMU (phasor measurement unit) data. First, the method combines with the graph theory and PMU data to construct the grid topology map. Second, the comprehensive indicators for screening vulnerable lines are proposed by fully considering the network topology and line capacity, which combines with power exchange efficiency and energy fluctuation probability. Third, the distance matrix in the Floyd algorithm is transformed into a unit group that can store more elements, which reduces the traversal times of the algorithm and improves computational efficiency. The fast localization of transmission cross sections associated with vulnerable lines is realized. Finally, the critical transmission cross sections are screened according to the line outage distribution factor and line safety margin. The IEEE 39-bus system is selected for simulation experiments, and the simulation results show that the key transmission section search method proposed in this paper can better adapt to the variable power grid and is faster and more accurate than the other common method. [ABSTRACT FROM AUTHOR]

Introduction. The placenta, or afterbirth, plays a vital role in supplying nutrients and oxygen via the umbilical cord. Western medicine sees the placenta as a medical waste and discards it after delivery. Meanwhile, indigenous groups observe rituals or ceremonies prior to their disposal since it bears sacred importance. Aim. The aim of the literature review is to review the current literature on indigenous methods of disposing placenta. Methods. Through the EBSCOhost search engine, the authors had access to the following databases: CINAHL; MEDLINE; E-Journals; Health Sources: Nursing/Academic Edition; Scopus; and African Journals Online. A manual search of the grey literature through Google Scholar and Google Search engines, as well as citation searching using reference lists, was also used. The following keyword searches came up: placental disposal, placental waste, placental release, indigenous placental disposal, traditional placental disposal, cultural placenta, and placental rituals. The authors followed the inclusion criteria of qualitative, quantitative, or mixed research articles or reports from experts and different organisations published between 2013 and 2022 in English. Findings. The following three themes with subthemes emerged in the context of this review paper: (1) placental consumption (increases milk production, prevents postpartum depression, and prevents postpartum bleeding); (2) placental burial (burial site determines the child's fate, protection of the child, and fertility); and 3). artifacts (memorabilia). Conclusion. Indigenous placental disposal methods have a significant value to Indigenous women globally. The rituals performed have a special meaning attached to them. It is important for Western medicine to respect and support indigenous placental disposal methods and ensure safe handling from the healthcare facilities to their homes. [ABSTRACT FROM AUTHOR]

Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. Firstly, the upper stream divides the input garbage image into category MPP (metal, paper, and plastic class) or category CGT (cardboard, glass, and trash class). Then, the lower stream predicts the exact class of trash according to the results of the upper stream. The proposed hybrid deep learning model achieves the best result with 98.5 % than that of the state-of-the-art approaches. Through the verification of CAM (class activation map), the proposed model can reasonably use the features of the image for classification, which explains the reason for the superior performance of this model. [ABSTRACT FROM AUTHOR]

Adding or mixing CNTs to different matrix materials may change and enhance the functionality in particular, the electrical properties of the formed CNTs nanocomposite. Keywords: nanoparticles; multiwalled carbon nanotubes; graphene; 2D layered materials; perovskites; surface plasmon resonance; 2D photonic crystals; sensors EN nanoparticles multiwalled carbon nanotubes graphene 2D layered materials perovskites surface plasmon resonance 2D photonic crystals sensors 1 4 4 12/23/22 20220611 NES 220611 This Special Collection on I Nanomaterials and Nanotechnology i is devoted to the collection of articles from several different research fields and applications of nanotechnology which have been presented at the NanoMITe Annual Symposium and Nanotechnology Malaysia Annual Symposium (NanoSym2019) in Putrajaya during 21 SP st sp -22 SP nd sp August 2019. Another interesting topic, the multiwalled carbon nanotubes (CNTs), unique one-dimensional carbon structures, is the object of the third selected paper in this field by P.Y. Foong. In this paper, multiwalled carbon nanotubes were proposed as the susceptor for the microwave welding of high-density polyethylene, considering multiwalled carbon nanotube is a good microwave absorber. [Extracted from the article]

The traditional home product design system mainly depends on relatively shallow learning network, relatively simple embedded technology and Internet of things technology. The traditional home design system mainly depends on the traditional self-encoder technology. When combined with the deep neural network, this technology has serious defects in the computer vision algorithm, resulting in the serious waste of the corresponding system storage and computing resources, the corresponding system learning efficiency is relatively poor and the learning ability is weak. Based on this, this paper will build a home product design system based on the deep neural network of self-encoder. By improving the sparsity of self-encoder in the process of learning and training, we can further improve the sparsity of the system and further optimize the structure of self-encoder in the design system, The performance of the deep learning model of the design system is further improved through the hierarchical features continuously learned by the self-encoder in the process of home case design. Based on the optimization of the home product design system in this paper, the system effectively improves and improves the accuracy and stability of the internal feature classifier of the system, and improves the overall performance of the furniture design system. In the specific system construction part, based on ZigBee technology and embedded technology as the design carrier, and adhering to the goal of simplicity, intelligence and convenience, this paper designs and constructs the home product design system. The experimental results show that the noise processing level of the proposed home product design system is lower than 4-5db compared with the traditional design system, and the corresponding image classification accuracy is about 4% higher than the traditional design system. Therefore, the experimental results show that the home design system proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

Dramatic short videos have quickly gained a huge number of user views in the current short video boom. The information presentation dimension of short videos is higher, and it is easier to be accepted and spread by people. At present, there are a large number of drama short video messages on the Internet. These short video messages have brought serious information overload to users and also brought great challenges to short video operators and video editors. Therefore, how to process short videos quickly has become a research hotspot. The traditional episode recommendation process often adopts collaborative filtering recommendation or content-based recommendation to users, but these methods have certain limitations. Short videos have fast dissemination speed, strong timeliness, and fast hot search speed. These have become the characteristics of short video dissemination. Traditional recommendation methods cannot recommend short videos with high attention and high popularity. To this end, this paper adds external index features to extract short video features and proposes a short video recommendation method based on index features. Using external features to classify and recommend TV series videos, this method can quickly and accurately make recommendations to target customers. Through the experimental analysis, it can be seen that the method in this paper has a good effect. [ABSTRACT FROM AUTHOR]

In the paper entitled "Spam Detection Approach for Secure Mobile Message Communication Using Machine Learning Algorithms", L. G. Jun et al. proposed the applications of the machine learning-based spam detection method for accurate detection. The data extraction process of the review showed that (1) latent Dirichlet allocation topic modeling is among the widely used machine learning algorithms in the selected studies and (2) precision and recall are amongst the most utilized evaluation methods for measuring the performance of these machine learning algorithms. Machine learning (ML) and cryptography have many things in common, for instance, the amount of data to be handled and large search spaces. [Extracted from the article]

The object detection task is to locate and classify objects in an image. The current state‐of‐the‐art high‐accuracy object detection algorithms rely on complex networks and high computational cost. These algorithms have high requirements on the memory resource and computing capability of the deployed device, and are difficult to apply to mobile and embedded devices. Through the depthwise separable convolution and multiple efficient network structures, this paper designs a lightweight backbone network and two different multiscale feature fusion structures, and proposes a lightweight one‐stage object detection algorithm—MiniYOLO. With the model size of only 4.2 MB, MiniYOLO still maintains a high detection accuracy, realizing the trade‐off between the model size and detection accuracy. Experimental results on MS COCO 2017 data set show that compared to the state‐of‐the‐art PP‐YOLO‐tiny, MiniYOLO achieves higher mAP with the same model size. Compared with other lightweight object detection algorithms, MiniYOLO has certain advantages in detection accuracy or model size. The code associated with this paper can be downloaded from https://github.com/CaedmonLY/MiniYOLO/. [ABSTRACT FROM AUTHOR]

In order to solve the problem of online inspection of students' theoretical knowledge of Japanese, this paper further optimizes and adjusts the design of the online Japanese examination system and presents an online Japanese examination system based on genetic algorithm. Taking the Japanese test as the research object, on the basis of comprehensively analyzing the problems of slow test paper composition, low success rate, and low quality of traditional online test systems, an intelligent test composition model based on genetic algorithm is proposed, and the implementation process of genetic algorithm and the key steps are described in detail. The results show that the online Japanese examination system based on genetic algorithm can meet the needs of test paper generation in more complex situations. After a long time of operation and continuous improvement, the online Japanese examination system has obtained the adaptability of the best solution. The value of the fitness is 99.666667; when the fitness is at this value, the error of the question type score of the test paper is 0, the average difficulty error on the test paper is 0, and the error of the section test point distribution is 0.666667. This fully illustrates the stability and effectiveness of the Japanese online examination system, which can meet the needs of daily Japanese majors and improve the efficiency of Japanese teaching. [ABSTRACT FROM AUTHOR]

In medicine, the count of different types of white blood cells can be used as the basis for diagnosing certain diseases or evaluating the treatment effects of diseases. The recognition and counting of white blood cells have important clinical significance. But the effect of recognition based on machine learning is affected by the size of the training set. At present, researchers mainly rely on image rotation and cropping to expand the dataset. These methods either add features to the white blood cell image or require manual intervention and are inefficient. In this paper, a method for expanding the training set of white blood cell images is proposed. After rotating the image at any angle, Canny is used to extract the edge of the black area caused by the rotation and then fill the black area to achieve the purpose of expanding the training set. The experimental results show that after using the method proposed in this paper to expand the training set to train the three models of ResNet, MobileNet, and ShuffleNet, and comparing the original dataset and the method trained by the simple rotated image expanded dataset, the recognition accuracy of the three models is obviously improved without manual intervention. [ABSTRACT FROM AUTHOR]

This paper reports the findings of a thematic narrative review of peer‐reviewed articles exploring innovation in UK independent homecare services published between January 2009–August 2021. Our analysis of 15 papers reveals four broad innovation types: personalised funding, operational models, workforce development and assistive technology. We conclude that research focused on innovation in independent homecare offers important insights into the positive and negative outcomes of different types of innovation for providers, care workers and people receiving care. There are, however, also areas which are neglected and need further elaboration, including more robust evidence of outcomes and clearer articulation of innovation processes. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to identify high‐impact papers on global nursing to determine and analyse the publication of articles on core competencies in nursing‐related journals and the research trends in the era of globalization. Background: Bibliometrics has been shown to be an effective method for analysing publications. Through bibliometrics, nursing managers and researchers can understand the trends of high‐impact international nursing core competencies research, identify mainstream research directions and obtain relevant knowledge and information, thereby facilitating the translation of research outcomes into nursing management practice. Evaluation: The study adopted bibliometric analysis and the VOSviewer software to explore dynamic publication trends and analyse the current situation of nursing research from a comprehensive development perspective, which was realized by searching for nursing core competencies papers in the Web of Science (WoS) database, calculating citations and determining the trends of the most influential papers. Key issues: Nursing core competencies research grew rapidly between 1997 and 2022. Countries with the most core competencies publications were the United States, England, Australia and Canada. The Journal of Nursing Management has attracted substantial attention from researchers worldwide. Education, Management and Nurses were the most frequently used keywords in the study. A total of 534 papers were retrieved from the WoS database with the main research fields, including nursing, business economics, public environmental occupational health and health care science services. Conclusion: Equipping nursing graduates with core competencies has always been an important goal of global medical and nursing education. This study analysed papers across 35 years, most of which were published in the Journal of Nursing Management. In addition, the study identified some of the main research topics of nursing management, such as the integration of education with nursing management and the cultivation of nurses' core competencies. The study also provides a fresh review of highly cited articles. The results of the study show that high‐quality articles play the role of improving both the quality and the quantity of related research. By analysing the trends of the research on core competencies, this study lays a bibliometric foundation for researchers regarding international journals, hot topics and relevant fields. In addition, the highly cited articles reveal new perspectives for the nursing field, providing inspiration for nursing management and education researchers. Implications for Nursing Management: This study provides scholars and managers with an overview of the current situation of nursing management research and the development of benchmark journals. The study provides researchers not only with a better understanding of various international journals, allowing them to transition out of traditional thinking in the era of science and technology, but also with innovative thinking by combining research with nursing management. The results of this study invite nursing managers to study relevant topics of core competencies and integrate information technology to education, management and nurses, thereby contributing to nursing management and educational research. [ABSTRACT FROM AUTHOR]

As an important part of the bridge structure system, the underwater pile-pier structure usually occurs various defects on its surfaces due to its complex hydrological environment. The existing conventional defect detection approaches exist two aspects of problems: (1) insufficient definition and color distortion of the underwater images, and (2) low efficiency and error-prone. To solve these problems, this paper proposed the target defect detection model by integrating the image-fusion enhancement algorithm and the deep learning algorithm. Firstly, by analyzing the reasons for the degradation of the underwater images, the ACE (automatic color equalization) and CLAHE (contrast limited adaptive histogram equalization) algorithms are selected to enhance the image, respectively. Secondly, the two enhanced images are fused based on the point sharpness weight, and then the fusion results are further sharpened by the USM (unsharp mask) algorithm, thus obtaining the final fused images. Thirdly, 3,200 fused images are taken as the training set, by adopting the YOLOv3 algorithm to train the detection model, and then the training model is validated and tested by the other each 400 fused images, thus building up the target automatic detection model of underwater pile-pier surface defects. Finally, a series of comparison and discussion were conducted to validate the effectiveness of image-fusion and the robustness and effectiveness of the target detection model. The results found that the target detection model has excellent robustness against noise and effectiveness in the surface defect detection. This indicates that the image-fusion approach proposed in this paper can effectively enhance the image features, and the target detection model is feasible, robust, and effective in the automatic detection of surface defects on underwater pile-pier structures. [ABSTRACT FROM AUTHOR]

In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process. [ABSTRACT FROM AUTHOR]

Debt restructuring is an effective means to solve the financial difficulties of enterprises. At present, the operation methods and means of enterprises with different characteristics to participate in debt restructuring are different, and no universal debt restructuring scheme has been formed. In order to solve this problem, this paper uses the method of fuzzy set qualitative comparative analysis. This paper discusses the influence mechanism of multiple concurrent factors on enterprise debt restructuring and summarizes the selection rules of debt restructuring strategies for enterprises with different characteristics of debt. The research shows that there are five kinds of allocation for enterprises to realize debt restructuring. According to the different core conditions, enterprise debt restructuring can be divided into high-quality operation and management mode, technology leading mode, and debt-to-equity mode. The research results reveal the mechanism behind enterprise debt restructuring and provide a reference basis for the government and creditors to make corresponding decisions in the debt restructuring process. [ABSTRACT FROM AUTHOR]

Domestic abuse is a worldwide public health and social issue which impacts the health and wellbeing of those experiencing abuse and their families. People living with a life-limiting illness are at increased risk of domestic abuse because they are often frail, isolated, and dependent on others. Little is known about domestic abuse and the coexistence of life-limiting illness and existing support for those experiencing or at risk of abuse. A scoping review was conducted to identify factors that shape and characterise experiences of adults with a life-limiting illness affected by domestic abuse and the health and social care practitioners and other organisations that support them. The Joanna Briggs Institute (JBI) methodology and checklist for Preferred Reporting Items for Systematic Reviews and Meta Analyses-Extension for Scoping Reviews (PRISMA-ScR) were applied. Five databases were systematically searched from 2000 to 2021: MEDLINE; CINAHL; PsycINFO; Social Sciences Citation Index (Web of Science); and ProQuest Dissertations and Global. Twenty-one papers met the inclusion criteria. Most studies were conducted in North America, with female participants living with cancer, and conducted in health and community settings. There were no studies involving third sector organisations. A range of abusive behaviours was reported resulting in missed medical appointments, delays in screening leading to late diagnosis, and palliative rather than curable treatment. Abuse also impacted on the physical, emotional, and psychological wellbeing of the person experiencing the abuse, which increased stress levels and could have a detrimental effect on their health. Identifying domestic abuse within the context of life-limiting illness was reported to be challenging for health and social care professionals, as was responding to and managing a disclosure. Further research is needed to address existing knowledge in order to inform policy and practice to identify and manage domestic abuse where it coexists with life-limiting illness. [ABSTRACT FROM AUTHOR]

The present microstructural evaluation was carried out on the woods of three ethnobotanically important local fruit trees, namely, Ziziphus mauritiana Lam., Z. spina-christi (L.) Willd., and Z. nummularia (Burm.f.) Wight and Arn., of family Rhamnaceae from Cholistan Desert of Pakistan. Wood samples were sectioned with sliding sledge microtome to make permanent slides for observing different anatomical parameters under the light microscope. All selected species were observed to have diffuse‐porous wood with indistinct growth rings. The vessels were rounded in outline in all the species studied and found mostly solitary or in radial multiples of 2 in Ziziphus mauritiana and Z. nummularia, while in radial multiples of 2 to 5 in Z. spina-christi. The intervessel pits were scalariform to opposite. The rays were uniseriate in Ziziphus mauritiana, while mostly were biseriate in Ziziphus spina-christi. Simple perforation plates and diffuse, confluent, and vasicentric types of axial parenchyma were present in all the selected species. The fibers were thin‐walled and nonseptate. One‐way ANOVA followed by the Tukey test was conducted to observe different anatomical variations within selected species. Principal component analysis revealed correlations among studied anatomical parameters. The number of rays per mm was comparatively larger in Ziziphus nummularia, showing its greater susceptibility to wood‐deteriorating agents than in other selected species. The Runkel ratio indicated the selected species suitable for making paper. [ABSTRACT FROM AUTHOR]

Concrete dam deformation prediction plays a crucial role in ensuring dam safety monitoring. Machine learning, especially recurrent neural networks, plays an important role in this, yet its predictive accuracy is not high. This paper first preprocesses time series data using successive variational mode decomposition (SVMD). Then, it combines residual network (ResNet) and gate recurrent unit (GRU), fully leveraging the excellent information extraction capability of the former and the powerful sequential data processing capability of the latter, proposing the ResNet‐GRU model for deformation prediction analysis. During the prediction process, the sanitized gray wolf optimizer (SGWO) algorithm adjusts the parameters, further enhancing the prediction accuracy and performance of the model. The model has also been successfully applied in practical cases, validating its effectiveness. The results indicate that the ResNet‐GRU‐SGWO model can accurately simulate the dynamic deformation process of concrete dams. Compared with various intelligent prediction models, it achieves the highest prediction accuracy and outstanding performance, providing a reference for the prediction of concrete dam deformation. [ABSTRACT FROM AUTHOR]

Indoor air quality is becoming one of the most important factors for public health, as people tend to spend more time indoors, either at home or at work. This paper combines the use of calibrated low‐cost particulate matter (PM) sensors with source apportionment algorithms to understand the factors that affect the indoor air quality within a typical UK school. Low‐cost optical particle counter (OPC) sensors were placed in three different classrooms within the school, measuring PM concentrations during a typical school week and the subsequent holiday period, to understand the role of occupancy within schools for air quality. When students were in attendance during school hours (09:00–15:30), the classroom with the greatest average PM levels had PM2.5 and PM10 mass concentrations of 14.4 and 51.0 μg/m3, respectively. During school hours, when students were present, all classrooms had average PM2.5 concentrations greater than 8.1 μg m−3 and average PM10 concentrations greater than 13.1 μg m−3. Notably, the English studies classroom exceeded the 24‐h WHO ambient PM10 guideline (45 μg m−3). Employing the non‐negative matrix factorization (NMF) algorithm for source apportionment revealed that between 93% and 98% of PM1 observed within classrooms derived from outdoor sources. This contribution diminished as particle size increased, with outdoor sources accounting for 74%–89% of PM2.5, and 19%–40% of PM10, respectively. The differences in classroom PM concentration and source percentages are attributed to differences in lesson activities, lesson frequency, flooring (carpeted versus hard flooring), location within the school, and proximity to outdoor sources such as roads. The approach described within the paper is easily translated to other indoor locations and could also be straightforwardly scaled due to its relatively low cost. Thereby, it allows for air quality management in locations crucial for the public health and educational outcomes of children. [ABSTRACT FROM AUTHOR]

Transmission towers serve as crucial safety pillars within the power transmission system, and their damage can lead to severe consequences. The structural failure of a tower undergoes a process from the initiation of local damage to overall failure, emphasizing the importance of conducting detailed local safety research. This paper introduces a nonlinear damage analysis method rooted in the continuous damage theory, specifically designed for critical areas of transmission towers. A material subroutine for elastic‐plastic‐damage constitutive equations is developed using commercial software, and thorough verification ensures the accuracy of both the subroutine and the algorithm. The proposed algorithm is then applied to analyze the damage in critical areas of a tower, simulating the plasticity‐damage coupling evolution of the main leg during the collapse of the transmission tower. Regarding the treatment of bolt connections in the local model, it indicates that there is a small difference between the contact model and the rigid‐joint model results. Taking computational efficiency into consideration, it is recommended to employ rigid‐joint model to simulate the evolution of damage. The presented example illustrates damage occurring on the outer side of the main leg, ultimately leading to lateral damage under the combined influence of bending and torsion. This research offers a novel method for investigating the failure mechanisms of transmission towers under extreme weather conditions and proposes precise reinforcement strategies. [ABSTRACT FROM AUTHOR]

Background. Phytochemicals are compounds that are naturally found in plants and are known to have various health benefits. However, phytochemicals are structurally complex, inherently unstable, and have low bioavailability. Unfortunately, research on phytochemicals lags behind that of essential nutrients. This paper focuses on a bibliometric analysis to understand citation patterns in phytochemical research related to chronic diseases. It examines the current state of research, research focal points, and anticipated trends in the field. Methods. We analyzed published literature on phytochemicals and chronic diseases using the Web of Science database. Our search included only English‐language publications until April 1, 2023. Visual Metrics software was used to examine data on countries, institutions, authors, journals, and citations. Results. For this study, a total of 2,297 articles were retrieved from 2008 to the present, with a significant increase in citations starting in 2017. China was found to be the leading country in paper production, while the United States had the highest H‐index, placing both at the forefront of research in this field. King Saud University published the most, and Liu RH emerged as the most influential author. The analysis showed limited collaboration between institutions across different countries. Molecules were the primary source for phytochemical‐related papers. The top 20 keywords highlighted flavonoids and their association with cardiovascular diseases, indicating them as prominent themes in recent phytochemical research with the potential to remain relevant. Conclusions. The study highlights the increasing interest in phytochemical research related to chronic diseases. It provides a thorough review and analysis of the present situation, the latest research topics, and academic trends in this field. This information is precious for researchers and healthcare professionals. It helps them to comprehend significant literature and keep up‐to‐date with the latest advancements in the field, eventually leading to progress in this field. [ABSTRACT FROM AUTHOR]

Social networks on the dark web are rich in data that provides valuable insight into the nature of the activities on the dark web and human behaviors related to these activities. It also encompasses a diversity of ideologies, interests, and thought patterns associated with illicit activities and businesses on the dark web. For this reason, social networks on the dark web constitute a powerful tool and a profuse data source for various investigative work. However, such investigations encounter considerable challenges related to the massive volumes of textual data, analyzing it effectively, and extracting knowledge from it. This knowledge can be used in various investigations and studies when representing it in ontologies as a unified and integrative data source. In this paper, we introduce a novel approach for extracting and representing knowledge hidden in dark web communities through topic modeling and ontology learning methods. We start from the conceptual design of the ontology and employ several stages of text processing and analysis to achieve the desired knowledge graph, DarkOnto. These stages include data cleaning and preprocessing, topic modeling using correlated topic model (CTM), class‐topic similarity estimation, ontology construction, ontology population, and ontology evaluation, where the proposed approach achieved high results. Furthermore, we discuss the results, limitations, challenges, and future work. This paper presents a promising approach for extracting hidden valuable knowledge from dark web communities where investigating and conceptualizing criminal communities can be conducted efficiently. [ABSTRACT FROM AUTHOR]

This paper offers an in‐depth investigation into pure ideals within the Hurwitz series ring. Specifically, by focusing on the Hurwitz series ring, denoted as HR over a ring R, we present a comprehensive characterization of differential ideals. In this paper, we prove that these differential ideals can be expressed in the form HI, where I represents an ideal in the underlying ring R. Through this analysis, a comprehensive understanding of the structure and properties of pure ideals within the Hurwitz series ring is achieved. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator (PMSG). This hybrid controller combines the adaptability of an adaptive neuro‐fuzzy inference system (ANFIS) with the simplicity of a proportional‐integral (PI) controller. The PI controllers are traditionally used for stability and noise handling. ANFIS adds adaptability, making it more suitable to cope with the variable nature of wind energy. The primary objective of this hybrid strategy is to augment the overall control performance and reliability of PMSG‐based WTPGS when encountered with continuous variable wind conditions. However, implementing the PI controller alone with the WTPGS often suffers from high overshoot and sluggish response in nonlinear systems like WTPGS. In contrast, ANFIS controllers offer superior performance to PI and other artificial intelligence controllers but are still susceptible to noise issues. In this paper, the proposed WTPGS system is designed in MATLAB/Simulink software where a hybrid controller (ANFIS‐PI) is implemented in the machine‐side converter (MSC) and grid‐side converter (GSC) of a variable speed PMSG‐based wind turbine to enhance its performance subjected to wind variations. The hybrid controller is implemented in such a way that the ANFIS controller is implemented in the outer layers while the PI controller is applied in the inner layers of both MSC and GSC. The simulation results for this hybrid controller in the MSC outperform those of the conventional PI controller. They demonstrate minimal overshooting and settling time, maintaining consistent stability even when subjected to various test signals at different intervals. Similarly, the GSC also surpasses conventional PI controllers, achieving a significant 6.4% reduction in maximum overshoot and a decrease of 4.36 seconds in settling time. This highlights its strong suitability for wind turbine applications. [ABSTRACT FROM AUTHOR]

With the rapid development of China's construction industry and the acceleration of urbanization, large‐scale public building projects are becoming increasingly important in urban development, and the risk management problems of them should be pay more attention to. Based on the integration of back propagation (BP) neural network and building information model (BIM) technology, this paper carries out the research on risk management process of the whole life cycle of large public buildings and identifies the risk factors of large public buildings from the application dimension and the management dimension. The risk management evaluation index system is constructed and identified, and assessment, early warning, prevention, and control of risk management are applied and analyzed throughout the process. The international large public sports center project is used as a case study to establish a BIM model, while the BP neural network risk management model is used for prediction and calculation. The results of this study show that, first, the maximum deviation rate of the output indicators of the BP neural network risk model is 3.57% in the design period (B2) and the minimum deviation rate is 0.00% in the commissioning period (B4), which verifies the reliability of the training results of the model. Second, the best effect of risk management in the whole life cycle of the building is in the investment period (B1) and the highest risk is in the construction period (B3). Last, this paper constructs a new risk management framework to realise the risk management of the whole cycle of construction projects from design to operation, which helps to improve the management level and risk response ability of construction projects and ensure the smooth and sustainable development of the whole life cycle of construction. [ABSTRACT FROM AUTHOR]

Energy poverty (EP) has emerged as a major challenge to achieving sustainable development goals, and its significance in social development has increased over time. This paper aims to analyze the spatial autocorrelation between EP and social factors on a global scale. Utilizing the panel data of 116 countries from 2012 to 2019, the Bivariate local Moran index, a representative spatial econometrics tool, has been employed to examine temporal changes and spatial differences of transboundary synergy and tradeoff relations between EP and social factors. The results indicate that EP has synergy relationships with social factors, including life expectancy at birth, access to immunization, CO2 emission, and forest area, and tradeoff relationship with social factors, such as infant mortality rate, prevalence of undernourishment, forest rents, and gender inequality. Significant spatial differences have been observed that clusters of high‐income countries, particularly those in the Global North, tend to have better energy access and are surrounded by areas with favorable social conditions, and clusters of lower‐income countries, especially those in South Africa and Southeast Asia, have lower energy access and are surrounded by areas with more severe social conditions. The robustness analysis has been conducted to verify the reliability of the results. The spatial imbalance of findings offers robust evidence by emphasizing the importance of key areas, such as Southeast Asia and South Africa, that should be prioritized to take essential policy measures to address the EP and social issues. [ABSTRACT FROM AUTHOR]

Accurately forecasting electricity demand is crucial for maintaining the balance between supply and demand of electric energy in real‐time, ensuring the reliability and cost‐efficiency of power system operations. The integration of numerous active loads and distributed renewable energy sources into the grid has led to increased load variability, rendering the traditional point forecasting approach inadequate for meeting the evolving needs of the power system. Probabilistic forecasting, which predicts the complete probability distribution of loads and provides more extensive information on load uncertainty, has emerged as a key solution to address these challenges. The long short‐term memory (LSTM) model, known for its strong performance in modeling long series, is commonly utilized in load forecasting. Therefore, this study focuses on short‐term electric load probability forecasting for users in a specific park in Yantai. We propose a short‐term load probability forecasting model based on integrated feature selection (IFS), genetic algorithm (GA) optimization of LSTM, and quantile regression (QR), referred to as the IFS‐GA‐QRLSTM model. Initially, the integrated feature selection method is employed to identify the most influential factors affecting electric load, optimizing the model's input features and reducing data redundancy. To address the subjective nature of parameter selection in the LSTM model, we use a GA to optimize model parameters. The combination of optimized LSTM with QR enables direct generation of quantile load predictions, which are further used in kernel density estimation to construct the probability density distribution. We compare the proposed method with five basic models, QRLSTM, IFS‐QRCNN, IFS‐QRRNN, IFS‐QRLSTM, and IFS‐QRGRU, for point prediction, interval prediction, and probability prediction. Experimental results demonstrate that the proposed method in this paper exhibits better prediction performance, smaller prediction errors, and greater effectiveness compared to the aforementioned models. [ABSTRACT FROM AUTHOR]

In recent years, the low Earth orbit microsatellite network technology has experienced rapid development due to its advantages of low latency, low cost, and short development cycles. However, building an efficient and reliable satellite network routing system faces many challenges due to the characteristics of satellite networks, such as fast‐changing topology, large variations in link latency, higher probabilities of node and link failures, and limited resources. Routing algorithms have a significant impact on intersatellite communication and have become a research hotspot. The current mainstream algorithms focus on reducing information propagation delays between satellites to enable faster transmission. However, many satellite networks, such as meteorological observation satellite networks, are not sensitive to propagation delays but emphasize reducing hardware costs, especially for the receiving and transmitting signal systems of satellites. This means minimizing the single‐step signal transmission distance of satellites. This article proposes a routing algorithm based on time slot planning and the shortest path step length. Experimental simulation results demonstrate that this algorithm significantly reduces the step length of signal transmission and lowers hardware costs. [ABSTRACT FROM AUTHOR]

Currently, regardless of the algorithm used, motion planners for dealing with dynamic obstructions need to rely on high‐precision sensors and high performance processors. The requirements for hardware increase as the density of dynamic obstructions in an area becomes higher. Additionally, motion planners are more prone to errors in complex environments. The Rapidly‐exploring Random Tree (RRT ∗) algorithm only considers static obstructions and cannot effectively avoid densely populated regions of dynamic obstructions. This paper develops an improved RRT ∗ algorithm that is capable of avoiding densely populated regions of dynamic obstructions. In this algorithm, the cost function of the traditional RRT ∗ algorithm is modified based on the density of dynamic obstructions, allowing the planned path to bypass densely populated regions. The algorithm also introduces reasonable penalty terms to penalize segments that pass through densely populated regions, while maintaining asymptotic optimality of the traditional RRT ∗ algorithm. Numerical experiments reveal that the improved RRT ∗ algorithm is able to successfully avoid densely populated regions of dynamic obstructions with minimal time cost and exhibits better robustness during the path search process in comparison to the traditional RRT ∗ algorithm. Thus, the improved RRT ∗ algorithm possesses the ability to adapt to more complex areas for path planning. [ABSTRACT FROM AUTHOR]

This paper reviews the current state of microbial fuel cell (MFC) technology for energy generation. It begins by exploring clean energy alternatives, focusing on waste‐to‐energy solutions, and introduces the concept, applications, and advantages of MFCs. The biochemical processes within MFCs are explained, highlighting how microorganisms metabolize substrates through glycolysis, the Krebs cycle, and the electron transport chain to generate electrons. These electrons flow through an external circuit and combine with protons and oxygen at the cathode to produce water or reduced forms of nitrogen and sulfur. This paper also analyzes 10 key parameters affecting MFC performance: coulombic efficiency, pH, temperature, substrates, organic loading rate, electrode potential, open circuit voltage, treatment efficiency, organic removal rate, and hydraulic retention time. Recent advancements in MFC technology are also discussed, including innovations in reactor configuration and scaling, the development of new membrane materials like earthen and ceramic, and improvements in wastewater treatment methods. The advancements also extend to genetic engineering techniques to enhance microbial efficiency and component modifications, such as the use of carbon‐based nanomaterials and metal catalysts for improved performance, innovations in proton transfer membranes, and mediator‐less MFCs utilizing metal‐reducing bacteria. Challenges facing MFC technology, such as cost, scalability, and environmental sensitivity, are mentioned. The paper concludes with future directions, including the use of advanced materials, integration with wastewater treatment infrastructure, and the potential for nutrient recovery and chemical synthesis. This comprehensive review aims to provide knowledge into optimizing MFCs for sustainable energy generation and environmental benefits. [ABSTRACT FROM AUTHOR]

Upper limb exoskeleton rehabilitation devices can improve the quality of rehabilitation and relieve the pressure of rehabilitation medical treatment, which is a research hotspot in the field of medical robots. Aiming at the problems such as large volume, high cost, low comfort, and difficulty in promotion of traditional exoskeleton rehabilitation devices, and considering the lightweight, discontinuous, high flexibility, and high biomimetic characteristics of tensegrity structure, we designed an upper limb bionic exoskeleton rehabilitation device based on tensegrity structure. First, this article uses mapping methods to establish a mapping model for upper limb exoskeletons based on the tensegrity structure and designs the overall structure of upper limb exoskeletons based on the mapping model. Second, a bionic elbow joint device based on gear and rack was designed, and the stability of the bionic elbow joint was proved using the positive definite matrix method. This device can simulate the micro displacement between bones of the human elbow joint, improve the axial matching ability between the upper limbs and the rehabilitation device, and enhance the comfort of rehabilitation. Third, an impedance control scheme based on back propagation (BP) neural network was designed to address the low control accuracy of flexible structures and patient spasms. Finally, we designed the impedance control scheme of the PSO–BP neural network based on a fuzzy rehabilitation state evaluator. The experimental results show that the exoskeleton rehabilitation device has good flexion motion stability and assist ability and has significant advantages in volume and mobility. The control strategy proposed in this paper has high control precision and adaptive ability and has potential application value in the field of medical rehabilitation. [ABSTRACT FROM AUTHOR]

With high aerodynamic efficiency and low observable design for flying wing configuration, the control ability decreases and the flying risk increases, especially during the takeoff and landing processes for unmanned aerial vehicle (UAV). To solve the nonlinear characteristic problem of the flying wing configuration at a high angle of attack (AoA) for a low aspect ratio flying wing UAV, a controllable region and stability analysis method is proposed in the paper. Firstly, the aerodynamic characteristics and static stability of a flying wing UAV are analyzed. Secondly, according to the characteristics of aerodynamic data, an efficient longitudinal trim state calculation method under the low‐speed range is proposed. The trim state contour map can be used effectively with the proposed trim state calculation method, which can further analyze the constraint relationship between different trim states and the controllable region of the UAV. Thirdly, the stability of different trim states is analyzed through the phase portrait between AoA and airspeed. With the analysis result, longitudinal attitude and AoA decoupling control laws are designed based on the incremental nonlinear dynamic inversion (INDI) method. The effectiveness and adaptability of the designed control methods are verified through simulation at high AoA. [ABSTRACT FROM AUTHOR]

Traditional short‐term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short‐time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO‐VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short‐time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep‐learning traffic prediction framework based on the stacking of multiple temporal trend‐aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend‐aware temporal graph neural network (TTGAN). TGA dynamically models the space‐time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR). [ABSTRACT FROM AUTHOR]

In many practical communication environments, traditional feature extraction methods in spectrum sensing fail to fully exploit the information of primary users. Additionally, conventional machine learning methods have weak learning capabilities, making it difficult to maintain efficient and stable spectrum sensing performance in complex noise environments. Furthermore, non‐Gaussian noise can significantly affect the detection performance of spectrum sensing. To address these issues, this paper first proposes a feature extraction method based on Hierarchical Fuzzy Dispersion Entropy (HFDE) to better extract high‐frequency and low‐frequency information from signal samples, providing more comprehensive features for subsequent models to optimize feature extraction effectiveness. Then, a parallel model combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) is constructed to enhance learning ability. While CNN extracts local features, GRU processes temporal relationships, and the features output by both are concatenated to achieve effective feature learning and temporal modeling of primary user signal data represented by HFDE. Finally, using the feature vectors output by the CNN‐GRU model, detection statistics and detection thresholds for spectrum sensing are constructed for online detection. Simulation results validate the effectiveness and robustness of this method in spectrum sensing under non‐Gaussian noise. In the presence of significant non‐Gaussian noise intensity and a signal‐to‐noise ratio of −14 dB, the detection probability can reach 97.1%. Additionally, for the detection of unknown signals, the model can still maintain a detection probability of over 90%. [ABSTRACT FROM AUTHOR]

Predicting mortality in COVID‐19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India, Brazil, Russia, France, China, and Bangladesh. Data were gathered between December 31, 2019, when COVID‐19 began, and March 31, 2021. The study used 457 observations with 4 variables: daily confirmed cases, daily deaths, daily mortality rate, and date. To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. The autoregressive integrated moving average (ARIMA) model with the lowest AIC value for each nation is found through time series analysis. By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. Additionally, SVM analyzes every country and calculates its R2 and root‐mean‐square error (RMSE). The lowest RMSE value is used to compare all of the time series and machine learning models. According to the comparison table, SVM provides a more accurate model to predict the mortality rate of the seven countries, with the lowest RMSE value. During the study period, mortality rates increased in Brazil and Russia and decreased in the United States, India, France, China, and Bangladesh, according to the comparison value of RMSE in this study. Furthermore, this paper shows that SVM outperforms all other models in terms of performance. According to the author's analysis of the data, SVM is a machine learning technique that can be used to accurately predict mortality in a pandemic scenario. [ABSTRACT FROM AUTHOR]

Methods of structural health monitoring (SHM) are often challenged by changing environmental and operational conditions (EOC). This paper presents a novel experimental testing field specifically designed for studying the effects of EOC on black box vibration‐based output‐only SHM methods. The experimental testing field consists of two identical mechanical structures that are exposed to mass and stiffness perturbations: one in a controllable laboratory setup and one under the influence of varying EOC in a field setup. The paper demonstrates the feasibility and usefulness of the dual experimental testing field for studies about EOC influences on SHM. The results of a preliminary study of the occurring EOC in the field setup are presented, and a modular measurement system that provides high‐quality data is introduced. By providing the experimental acceleration data, a new experimental benchmark dataset for various studies and future use in the field of SHM is presented. [ABSTRACT FROM AUTHOR]

A novel hybrid algorithm combining the 3‐D vector finite element method‐boundary integral method (FEM‐BIM) with the half‐space Green's function with the rough‐surface interface is developed in this paper to study the scattering from a 3‐D coated target above a perfectly electric conducting (PEC) rough surface. The half‐space Green's function is derived from the Kirchhoff approximation (KA). The 3‐D vector FEM is applied to model the coated object, and the BIM with the half‐space Green's function is selected as the exact radiation boundary conditions. The accuracy of our numerical code is validated by the numerical method. Some numerical results are provided to study the electromagnetic scattering characteristics from a coated sphere above a Gaussian rough surface. [ABSTRACT FROM AUTHOR]

This paper focuses on the finite‐time synchronization issue for reaction‐diffusion competitive neural networks (RCNNs) with different time scales and time‐varying delays. To reduce the waste of network resources, a periodically intermittent control strategy is presented based on two time scales (short and long memory) and time‐varying delay. By constructing the Lyapunov–Krasovskii functional, with the help of Lyapunov stability theory and auxiliary inequality technique, the finite‐time synchronization can be guaranteed and the settling time is exactly estimated. Finally, an exhaustive numerical analysis is presented to illustrate the effectiveness of the controller and the correctness of theoretical results. [ABSTRACT FROM AUTHOR]

In response to the challenges of path planning in complex scenarios, to overcome the influence of optimal path determination by the precision of grid map sizes, and to escape the uncertainty in solving by intelligent algorithms, this paper designs a method for obtaining an adjacency matrix based on node planning of shortest path diagrams with polygonal obstacles and then uses the Dijkstra algorithm to get the shortest path. For irregular curved obstacles, an edge straightening method is proposed. To enhance the applicability of the path, this paper introduces the constraint of minimum turning radius. It researches path smoothing under obstacle conditions based on arcs and straight lines, providing practical solutions for different scenarios. Considering the need to maintain a safe distance due to the size of the moving carrier and the deviation in trajectory tracking, this paper conducts an expansion analysis of obstacles. It obtains the trajectory on the arc after edge straight line fitting, followed by further smoothing treatment. The method proposed in this paper demonstrates excellent accuracy and robustness in path planning through simulation verification, proving its practicality and effectiveness in complex environments. [ABSTRACT FROM AUTHOR]

Modified landscapes could create breeding habitats for mosquitoes and affect their community structure and susceptibility with implications for their management. Hence, in this study, household mosquito control methods in two urbanized landscapes; industrial and residential human settlements, in Ghana and insecticide susceptibility of the inhabiting Anopheles populations were assessed. Household knowledge and usage pattern of mosquito control methods in the modified landscapes were obtained using a questionnaire. Female adult Anopheles mosquitoes were also subjected to susceptibility tests using mosquito coils (0.08% meperfluthrin, 0.03% dimefluthrin, and 0.3% transfluthrin) and World Health Organization (WHO) insecticide test papers (0.05% deltamethrin, 4% DDT, 0.1% bendiocarb, 0.25% pirimiphos‐methyl, and 5% malathion). Although insecticide‐treated nets and aerosols were used for mosquito control, mosquito coils were the most common and widely preferred household method. The Anopheles mosquitoes were resistant to all the insecticides (mosquito coils and WHO test papers) except pirimiphos‐methyl. Land use type did not affect insecticide resistance, but the insecticide type did. The findings indicate the effect of household insecticide usage practices on local mosquito populations and their implications for effective vector management and disease control in modified landscapes. [ABSTRACT FROM AUTHOR]

In the dredging of the Yellow River, a common practice involves extracting alluvial fill from the river and depositing it on the rear side of the embankment, serving the dual purpose of river dredging and reinforcing the embankment. Nevertheless, there are few studies on the consolidation of alluvial fill extracted from the Yellow River. In this paper, laboratory experiments were conducted to examine the change in hydraulic conductivity during the consolidation process of the alluvial fill. Besides, a numerical model was established to investigate the dissipation law of excess pore water pressure and the settlement law of layered consolidation of alluvial fill. According to the research, the main conclusions can be drawn as follows: (1) During the consolidation process of the alluvial fill, the hydraulic conductivity exhibited a linear decline as the consolidation pressure increased. (2) The consolidation speed under the variable hydraulic conductivity case is lower than that on the condition when the hydraulic conductivity is constant. (3) In layered construction, the consolidation rate of alluvial fill decelerates with the augmentation of layers. Additionally, the dissipation rate of pore water pressure at the top of the alluvial fill surpasses that at the bottom. (4) Setting a geotextile mat can accelerate the consolidation speed and shorten the consolidation time of alluvial fill by 8.3%–30%. [ABSTRACT FROM AUTHOR]

The China‐Europe Railway Express (C‐ER Express) provides a transcontinental rail container service between China and Europe. As most C‐ER Expresses are affected by frequent natural disasters and public health incidents, it faces the increasing risk of network vulnerability. When previous studies investigated the evolution of network vulnerability through local information, they often overlook the complexity of the network's multidimensional characteristics. The nonlinear load‐capacity (NLC) model proposed in this paper integrates local and global information of the network. This approach enables a detailed investigation into how condition thresholds and different types of nodes influence network vulnerability. Firstly, a feature matrix is constructed for C‐ER Express based on the topological measures, freight information, and external environment scores. Then, the autoencoder is used to extract the low‐dimensional dense information, and the DBSCAN is used to classify C‐ER Express into distinct clusters. Secondly, The NLC model integrates feature coefficient to describe the initial capacity of nodes. Subsequently, the failure load is redistributed proportionally to neighboring nodes and remaining normal nodes based on time‐varying load and initial capacity of nodes. Finally, the improved NLC model is applied to the C‐ER Express under different simulation scenarios. Simulation results show that a reasonable condition threshold can mitigate the impact of small‐scale node failures on the network. The DBSCAN attack strategy can effectively identify the node types and prevent the network from chain reactions brought by different types of node failures. This research study is expected to provide some reference value for relevant research about vulnerability analysis of the C‐ER Express network. [ABSTRACT FROM AUTHOR]

Persistent Scatterer Interferometry (PSI) is a remote sensing measurement technology based on electromagnetic waves, capable of simultaneously monitoring deformations in large urban building complexes with millimeter‐level precision. However, relying solely on the line of sight (LOS) deformation sequence based on a specific permanent scatterer cannot accurately analyze building deformations, particularly in cases where high‐rise buildings may simultaneously experience various deformation components such as temperature‐induced deformation, shrinkage, creep, and tilting. To accurately identify the deformation states of high‐rise buildings, the paper begins by systematically summarizing three typical deformation patterns from the unique perspective of synthetic aperture radar (SAR) satellites. These patterns include Pattern I, characterized by temperature‐induced deformation alone, and Pattern II and Pattern III, which involve a combination of deformation in different directions relative to the SAR satellite in addition to temperature‐induced deformation. To accurately monitor the LOS deformation of high‐rise buildings, the paper introduces the concept of acquiring the evolutionary trends of temperature‐related deformation coefficients and proposes a methodology for recognizing and quantifying deformation in high‐rise buildings. Subsequently, this study utilized freely available Sentinel‐1 satellite data to observe the deformation of nine high‐rise buildings in Changsha, China. The research findings indicate that the thermal expansion coefficients of most high‐rise buildings fall within the range of 6 ∼ 12 × 10−6/°C. High‐rise buildings that have been constructed for more than ten years almost no longer experience significant shrinkage or creep, while new constructions may exhibit an initial shrinkage and creep of up to 1.2 × 10−4 mm/mm. Additionally, the study results demonstrate that super‐tall buildings may exhibit centimeter‐scale lateral deformations at their tops due to uneven shrinkage. Findings from the study indicate that the proposed method can achieve cost‐effective and sustainable deformation monitoring of high‐rise building clusters within a large urban area. [ABSTRACT FROM AUTHOR]

The classification process of MRI (magnetic resonance imaging) is frequently used for making medical diagnoses for conditions including pituitary, glioma, meningioma, and no tumor. For this reason, determining the type of MRI and its quantity are significant and valuable measurements that reveal the brain's state of health. To segment and classify brain analysis, laboratory personnel employ manual examination via screen; this requires a lot of labour and time. On the other hand, the devices used by specialists are not practical or inexpensive for every doctor or institution. In recent years, a variety of computational algorithms for segmentation and classification have been developed with improved results to get around the issue. Artificial neural networks (ANNs) have the capability and promise to classify in this regard. The purpose of this paper is to create and put into practice a system for classifying different types of MRI images of brain tumor samples. As a result, this paper concentrated on the tasks of segmentation, feature extraction, classifier building, and classification into four categories using various machine learning algorithms. The authors used VGG‐16, ResNet‐50, and AlexNet models based on the transfer learning algorithm for three models to classify images as an ensemble model. As a result, MRI brain tumor segmentation is more precise because each spatial feature point can now refer to all other contextual data. In the specifics, our models outperformed every other published modern ensemble model in the official deep learning challenge without any postprocessing. The ensemble model achieved an accuracy of 99.16%, a sensitivity of 98.47%, a specificity of 98.57%, a precision of 98.74%, a recall of 98.49%, and an F1‐score of 98.18%. These results significantly surpass the accuracy of other methods such as Naive Bayes, decision tree classifier, random forest, and DNN models. [ABSTRACT FROM AUTHOR]

This paper explores the role of environmental consequences, perceived barriers, policy interventions, public opinions, and knowledge and awareness in using electric vehicles (EVs). We collected 506 responses about their intention to use the EVs to develop our hypothesis. This study uses knowledge and awareness of the EVs as mediating variables towards adopting the EVs and consumers' residences as moderating variables. It also introduces several control variables in this proposed research model to measure the effect on the intention to use the EVs. In this research study, we test the importance‐performance map analysis and check Cohen's f2 to identify a better output. The measurement and structural equation modelling results show that the environmental consequences are a stronger predictor of intention and policy interventions. The findings suggest that government policies can also have an attractive position in the EV segment. In addition, knowledge and awareness mediate the adoption of the EVs. Perceived barriers do not influence consumers to use an EV in India. We test the moderating role of residence with our construct and find a partial moderation role with policy intervention and public opinion. We introduce three self‐created constructs, i.e., intention to use, knowledge, and public opinion. Public opinion for the EVs supports consumers' intention to use, and knowledge also plays a significant role in using EVs. These newly added constructs will be essential to manufacturers and policymakers while promoting EVs. Also, environmental consequences and policy interventions emerge as significant factors of user behaviour. The paper highlights the critical predictors of consumers' intention to use the EVs. Thus, it helps society, policymakers, and managers formulate and implement schemes to boost EV purchasing. [ABSTRACT FROM AUTHOR]