bluuebunny/crossref_metadata_2025 huggingface dataset:
Depression, stigma and social isolation: the psychosocial trifecta of primary chronic cutaneous lupus erythematosus, a cross-sectional and path analysis
Authors: Drenkard Cristina , Theis Kristina A , Daugherty Timothy T , Helmick Charles G , Dunlop-Thomas Charmayne , Bao Gaobin , Aspey Laura , Lewis Tené T , Lim S Sam
Published: August 2022

Objective

Depression is common in individuals with chronic cutaneous lupus erythematosus (CCLE). However, how CCLE may impact patients’ psychological well-being is poorly understood, particularly among disproportionally affected populations. We examined the relationships between depression and psychosocial factors in a cohort of predominantly Black patients with primary CCLE (CCLE without systemic manifestations).

Methods

Cross-sectional assessment of individuals with dermatologist-validated diagnosis of primary CCLE. NIH-PROMIS short-forms were used to measure depression, disease-related stigma, social isolation and emotional support. Linear regression analyses (ɑ=0.05) were used to test an a priori conceptual model of the relationship between stigma and depression and the effect of social isolation and emotional support on that association.

Results

Among 121 participants (87.6% women; 85.1% Black), 37 (30.6%) reported moderate to severe depression. Distributions of examined variables divided equally among those which did (eg, work status, stigma (more), social isolation (more), emotional support (less)) and did not (eg, age, sex, race, marital status) significantly differ by depression. Stigma was significantly associated with depression (b=0.77; 95% CI0.65 to 0.90), whereas social isolation was associated with both stigma (b=0.85; 95% CI 0.72 to 0.97) and depression (b=0.70; 95% CI0.58 to 0.92). After controlling for confounders, stigma remained associated with depression (b=0.44; 95% CI0.23 to 0.66) but lost significance (b=0.12; 95% CI −0.14 to 0.39) when social isolation (b=0.40; 95% CI 0.19 to 0.62) was added to the model. Social isolation explained 72% of the total effect of stigma on depression. Emotional support was inversely associated with depression in the univariate analysis; however, no buffer effect was found when it was added to the multivariate model.

Conclusion

Our findings emphasise the psychosocial challenges faced by individuals living with primary CCLE. The path analysis suggests that stigmatisation and social isolation might lead to depressive symptoms. Early clinical identification of social isolation and public education demystifying CCLE could help reduce depression in patients with CCLE.
Arctic roads and railways: social and environmental consequences of transport infrastructure in the circumpolar North
Authors: Povoroznyuk Olga , Vincent Warwick F. , Schweitzer Peter , Laptander Roza , Bennett Mia , Calmels Fabrice , Sergeev Dmitrii , Arp Christopher , Forbes Bruce C. , Roy-Léveillée Pascale , Walker Donald A.
Published: August 2022

Land-based transport corridors and related infrastructure are increasingly extending into and across the Arctic in support of resource development and population growth, causing large-scale cumulative changes to northern socio-ecological systems. These changes include the increased mobility of people, goods and resources, and environmental impacts on landscapes and ecosystems as the human footprint reaches remote, unindustrialized regions. Arctic climate change is also generating new challenges for the construction and maintenance of these transport systems, requiring adaptive engineering solutions as well as community resilience. In this review article, we consider the complex entanglements between humans, the environment, and land transportation infrastructure in the North and illustrate these interrelations by way of seven case studies: the Baikal–Amur Mainline, Bovanenkovo Railway, Alaska–Canada Highway, Inuvik–Tuktoyatuk Highway, Alaska Railroad, Hudson Bay Railway, and proposed railways on Baffin Island, Canada. As new infrastructure is built and anticipated across the circumpolar North, there is an urgent need for an integrated socio-ecological approach to impact assessment. This would include full consideration of Indigenous knowledge and concerns, collaboration with local communities and user groups in assessment, planning and monitoring, and evaluation of alternative engineering designs to contend with the impacts of climate change in the decades ahead.
Elemental composition of particles emitted from aircraft: High-sensitivity elemental analysis by using micro-PIXE
Authors: Saitoh Katsumi , Matsuyama Shigeo , Takai Yuta , Miwa Misako , Toyama Sho , Kikuchi Yohei , Nakazawa Koyomi , Nagafuchi Osamu
Published: August 2022

To achieve the elemental characterization of particles emitted from the aircraft, we performed elemental analysis using micro-PIXE for those emitted from carbon disc brakes and tires in addition to the turbine blade particles and engine reverser particles of the exhaust produced by jet engines, and identified the characteristics of individual particles from the elemental maps. As for the elemental characteristics of aircraft source particles, the turbine blade particles contained 16 elements (Na, Mg, Al, Si, S, K, Ca, Se, Ti, Cr, Mn, Fe, Co, Ni, Cu and W), and were considered to be those of the same origin. In the case of the engine reverser particles, in addition to turbine blade particles and turbine blade wear particles (nickel alloy), atmospheric particles were found. As one hypothesis, particles containing Ti–V, Mn–W and Cu–Zn may have got altered or aggregated in the process of particles moving from the turbine blade to the engine reverser. Clumps of particles were exhibited only in the elemental maps of tire particles. These clumps of particles were shaped like they had been scraped away, and were believed to have been caused by friction between the tire and the runway. These results aid in determining the component features of particles emitted from an aircraft.
Global H4 solution for the fifth-order Kudryashov–Sinelshchikov–Olver equation
Authors: Coclite Giuseppe Maria , Di Ruvo Lorenzo
Published: August 2022

The fifth-order Kudryashov–Sinelshchikov–Olver equation is a nonlinear partial differential equation, which describes the interactions between short waves and long waves. Here, we prove the global existence of solutions for the Cauchy problem associated with this equation.
On vanishing pressure limit of continuous solutions to the isentropic Euler equations
Authors: Peng Wen-Jian , Wang Tian-Yi
Published: August 2022

The vanishing pressure limit of continuous solutions isentropic Euler equations is analyzed, which is formulated as small parameter [Formula: see text] goes to [Formula: see text]. Due to the characteristics being degenerated in the limiting process, the resonance may cause the mass concentration. It is shown that in the pressure vanishing process, for the isentropic Euler equations, the continuous solutions with compressive initial data converge to the mass concentration solution of pressureless Euler equations, and with rarefaction initial data converge to the continuous solutions globally. It is worth to point out: [Formula: see text] converges in [Formula: see text], while [Formula: see text] converges in [Formula: see text], due to the structure of pressureless Euler equations. To handle the blow-up of density [Formula: see text] and spatial derivatives of velocity [Formula: see text], a new level set argument is introduced. Furthermore, we consider the convergence rate with respect to [Formula: see text], both [Formula: see text] and the area of characteristic triangle are [Formula: see text] order, while the rates of [Formula: see text] and [Formula: see text] depend on the further regularity of the initial data of [Formula: see text].
Nonequilibrium Transport Induced by Biological Nanomachines
Authors: Hosaka Yuto , Komura Shigeyuki
Published: August 2022

Biological nanomachines are nanometer-size macromolecular complexes that catalyze chemical reactions in the presence of substrate molecules. The catalytic functions carried out by such nanomachines in the cytoplasm, and biological membranes are essential for cellular metabolism and homeostasis. During catalytic reactions, enzymes undergo conformational changes induced by substrate binding and product release. In recent years, these conformational dynamics have been considered to account for the nonequilibrium transport phenomena such as diffusion enhancement, chemotaxis, and substantial change in rheological properties, which are observed in biological systems. In this paper, we shall give an overview of the recent theoretical and experimental investigations that deal with nonequilibrium transport phenomena induced by biological nanomachines such as enzymes or proteins.
AN OPTIMAL CONTROL OF SMUGGLING
Authors: NAWAZ NASREEN
Published: August 2022

Existing literature does not capture efficiency losses on the dynamic adjustment path of smuggling control market from initial to final equilibrium after a shock in order to formulate an optimal smuggling control policy. Furthermore, a number of public service units and smuggling control rate are major determinants of smuggling cases controlled in a society, and a policy without taking into consideration such vital determinants cannot ensure adjustment of a number of smuggling cases controlled as a result of cost movement in desired time, which may lead to extra efficiency losses than those envisaged during policy formulation for an optimal level of smuggling control in a society. This article designs a comprehensive optimal smuggling control policy mechanism by modeling a three-dimensional smuggling control system in society capturing the number of public service units, smuggling control rate, and cost, while taking into account efficiency losses during adjustment of smuggling control market, smuggling control rate and the number of public service units in addition to those which result due to movements from initial to final equilibriums.
IoT-Oriented 6G Wireless Network System for Smart Cities
Authors: Kamruzzaman M.M. , Hossin Md Altab , Alruwaili Omar , Alanazi Saad , Alruwaili Madallah , Alshammari Nasser , Alaerjan Alaa , Zaman Roksana
Published: August 2022

The smart city is an emerging concept that is based on the integration of various electronic devices and citizens that enhance the flow of information. IoT is an integral part for next generation wireless network infrastructure for acting as an interface of collecting data and controlling delivery of message which are using in smart cities. In this paper, an IoT-oriented relay assisted MIMO for beyond the fifth-generation wireless network system is proposed. The proposed system provides higher capacity and lower BER. The proposed system’s BER results are compared with various combinations of transmission and receiving antennas at source, relay, and destination. It is found from BER performance that the developed scheme with relay does provide 1–17 dB gain with respect to direct connection. It is also found from mathematical analysis and simulation results that this scheme provides 3 to 9 b/s/Hz improvement in performance of capacity at 5 to 10 dB by adding a different combination of STBC and VBLAST. Simulation results are also presented to demonstrate the diversity and multiplexing gain that is a key to providing high data rates with reliable communication with many interferences for the IoT system. This system can also be used for massive antennas-based IoT system by raising the number of transmitting and receiving antennas with proposed encoding and decoding techniques explained in this paper.
Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction
Authors: Kishore Bhamidipati , Yasar Ali , Taspinar Yavuz Selim , Kursun Ramazan , Cinar Ilkay , Shankar Venkatesh Gauri , Koklu Murat , Ofori Isaac
Published: August 2022

Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.
New Energy Power Generation Enterprise Credit Evaluation Based on Fuzzy Best-Worst and Improved Matter-Element Extension Method
Authors: Liu Wei , Guo Liang , Kou Yan , Wang Yuan , Li Bingkang , Zhao Huiru , Li Chenhui
Published: August 2022

Under the background of the new power system, the proportion of new energy power generation enterprises in the power market is gradually increasing. With the further expansion of China’s power trading scale, the increasingly fierce market competition, and the instability of new energy output, the credit problems of new energy power generation enterprises in the trading process cannot be ignored. Therefore, improving and perfecting the credit system of new energy power generation enterprises is necessary for building a modern market system. Firstly, the credit indexes of new energy power generation enterprises are constructed from the three dimensions of performance ability, performance behavior, and performance willingness. Then, a credit index evaluation model of new energy power generation enterprises is proposed based on the fuzzy best-worst and improved matter-element extension method. Finally, an empirical study is carried out. The analysis results show that scheduling discipline compliance, historical credit, and participation rate of the market-oriented transaction have a more significant impact on the recognition of new energy power generation enterprises. They should focus on market transactions. The model proposed in this paper can effectively deal with the ambiguity of indexes in the credit evaluation of new energy power companies. Through comparison with other models, the effectiveness of the model proposed in this paper in the credit evaluation of power companies is further verified. Construction provides method support.
The Silver Ion and Nanocrystalline Pattern in the Glass Substrate, the Electric Field-Induced Thermal Transfer and Ink Absorption Layer Structure and Printing Performance
Authors: Dong Cong
Published: August 2022

People’s research on nanocrystals is getting more in-depth with the development of science and technology, and the patterned arrangement of nanocrystals can greatly improve the performance of our equipment in related fields, allowing people to control the patterning of nanocrystals. Research on thermal transfer is also increasing. Glass materials doped with patterned metal nanocrystals have great application potential, and the search for a simple and efficient patterned preparation method has attracted great attention of many researchers. Using the directional induced migration effect of the high temperature and high voltage DC electric field, combined with the subsequent heat treatment process, the distribution of silver nanocrystals corresponding to the surface silver film pattern can be formed in the silicate glass substrate, to realize the electric field-induced thermal transfer of the nanocrystal pattern print. This article aims to study the patterned thermal transfer of silver ions and nanocrystals on the glass substrate by applying an electric field to induce and analyze the ink absorption layer structure and printing performance. On this basis, an electron beam-induced thermal transfer method and Maxwell’s equation are proposed to investigate and calculate the structure of the ink absorption layer. The experimental structure shows that using this method increases the success rate of the preparation of silver ions and nanocrystal patterns on the glass substrate by 30%, which improves the ink absorption layer and printing performance to different degrees.
Real-Time Recognition of Loading Cycles’ Process Based on Electric Mining Shovel Monitoring
Authors: Wang Bonan , Duan Yun , Xu Wei
Published: August 2022

An automatic recognition algorithm based on the feature extraction of working parameters to recognize each state in the loading cycle process of an electric mining shovel was proposed. The working parameters were collected using the electric shovel’s online monitoring system. The swing angle of the shovel boom and motor operating signals were used as key recognition objects; the waveform features of each stage were extracted as recognition marks in the loading cycle of an electric shovel using the time domain characteristic analysis method. The algorithm was developed to recognize the loading cycles in real time. Moreover, the dynamic time warping (DTW) algorithm was used to detect and classify the preliminary recognition results by optimizing its distance threshold parameters, reducing the error rate of the model. The method was validated by comparing synchronous video-recordings with the results of the algorithm. Results showed that the proposed recognition method of the shovel loading cycle process exhibited real-time performance and high accuracy in understanding the different work tasks, providing effective data support for mining and the analysis of shovel working parameters, helping to improve the energy efficiency of electric mining shovel.
A Case Retrieval Strategy for Traffic Congestion Based on Cluster Analysis
Authors: Zhang Hao , Yang Jing
Published: August 2022

In order to improve the retrieval efficiency, this paper uses case-based reasoning (CBR) in the retrieval of traffic congestion cases and tries to adopt the strategy of clustering case databases before retrieval so as to narrow the scope of case retrieval. In terms of case clustering, the k-means algorithm, with excellent performance in text clustering, is selected to cluster traffic congestion edge cases. At the same time, considering that there is a certain similarity among the descriptions of traffic congestion, the K-means algorithm is optimized to generate an accurate clustering. Those edge cases are clustered into microcase clusters of traffic congestion and then divided into different traffic congestion categories according to the distance of cluster center. Experimental results show that the clustered case base is divided into several microcase bases, which improves the accuracy and shortens the retrieval time in the process of retrieval and provides a new idea for the retrieval method in the process of case-based reasoning.
Fuzzy Logic and Machine Learning-Enabled Recommendation System to Predict Suitable Academic Program for Students
Authors: Kumar Tribhuwan , Sankaran K. Sakthidasan , Ritonga Mahyudin , Asif Shazia , Sathiya Kumar C. , Mohammad Shoaib , Sengan Sudhakar , Asenso Evans
Published: August 2022

In recent years, educational data mining has gained a considerable lot of interest as a consequence of the large number of pedagogical content that can be gathered from a range of sources. This is because there is a lot of instructional information that can be obtained. The data mining tools collaborate with academics to improve students’ learning strategies by analyzing, sifting through, and estimating components that are pertinent to students’ characteristics or patterns of behavior. This is accomplished through the following steps: EDM is utilized in the vast majority of instances to develop the classification model, which then assigns a certain class to each student based on the known properties of the training dataset. Before putting the classification model into use, it is possible to utilize a test dataset to verify that the model is accurate. This article provides a description of a recommendation system that determines the most beneficial academic program for students by utilizing fuzzy logic and machine learning. The compilation of a student dataset has begun. It includes a total of 21 features and 1000 individual cases. The initial step is to employ the CFS attribute selection method. This methodology selects 15 of the initial set of 21 characteristics. Following the completion of the data gathering, it is put through various machine learning methods such fuzzy SVM, random forest, and C4.5. This methodology that has been offered makes predictions about the academic program that is best suitable for students.
Future of Water/Wastewater Treatment and Management by Industry 4.0 Integrated Nanocomposite Manufacturing
Authors: Uwamungu Jean Yves , Kumar Pawan , Alkhayyat Ahmed , Younas Tasaddaq , Capangpangan Rey Y. , Alguno Arnold C. , Ofori Isaac
Published: August 2022

Innovative technologies enable businesses to stay competitive in marketplace while also increasing profits in manufacturing of nanocomposites for water treatment. Aforementioned driving factors resulted in adoption of a number of innovative technologies, and no other trend has had a greater impact in recent years than Industry 4.0. Industry 4.0 is a comprehensive term that encompasses data management, manufacturing competitiveness, manufacturing processes, and efficiency. The term “Industry 4.0” refers to a group of key enabler technologies, such as cyber physical models, Internet of Things (IoT), artificial intelligence (AI), and big data analytics, including embedded devices which are all significant components to the mechanized and digitized industrial environments. AI approaches have been used for water treatment processes as well as desalination in recent years for optimizing the process along with providing realistic answers to water scarcity and water pollution‐related issues. AI applications have been used to predict and minimize water treatment process operational costs by lowering costs and optimizing chemical utilization. Several AI models are successful and accurate in predicting effectiveness of various adsorbents used in the removal process of a variety of contaminants from water. To identify the current level of research and future development prospects of smart manufacturing, this study uses a comprehensive literature review technique for manufacturing sustainability of nanocomposite fabrication for water treatment applications. The model provided will help to create a baseline for AI and hybrid models in the water treatment and wastewater management sectors, allowing for the increased performance and innovative growth. It will serve to provide the framework and give guidance for researchers interested in creating superior nanocomposites for waste and fresh water treatment and management using Industry 4.0. This study looks at a variety of AI approaches as well as how they may be used in water treatment, with an emphasis on pollutant adsorption. This assessment also identified certain obstacles and research gaps in the field of AI applications in water treatment processes.
Reliability Modeling and Evaluation for Complex Systems Subject to New Dependent Competing Failure Process
Authors: Hao Huibing , Li Chunping
Published: August 2022

The objective of this study is to construct some system-level reliability models subject to new dependent competing failure process of the soft failure process and hard failure process. In those complex system-level models, the soft failure caused by continuous degradation and sudden degradation increases by random shocks and hard failure due to the same shock process; in addition, the bidirectional effects between natural degradation and random shock are considered in this study; in other words, the random shock can bring additional degradation, and the natural degradation may also impact the shock process. Therefore, the soft failure process and hard failure process are dependent. By using the cumulative shock model and Gaussian stochastic degradation process with line mean value and line standard deviation, five types of reliability models were established: (1) single component model; (2) series system model; (3) parallel system model; (4) series-parallel system model; and (5) parallel-series system model. Finally, an example of fatigue crack growth dataset is taken to verify the effectiveness of the established model and method, and some sensitivity analyses are given. The results show that the bidirectional effects can significantly accelerate the system failure, and the impact of natural degradation process to random shocks should not be ignored. In addition, the results also show that random shock and failure threshold have significant effects on the different complex systems.
The Path Evaluation of Integrated Development of Leisure Sports and Rural Ecological Environment in Guangxi Based on Fuzzy Comprehensive Evaluation Model
Authors: Li Qianying , Zhang Dayao , Han Yu , Xie Youchang
Published: August 2022

Sports tourism is a new form of tourism based on sports resources, which attracts people to participate and feel the interest of sports activities and nature, and is an important part of sports industry. Through the field survey method and logical analysis method, we make a comparative analysis of the five existing rural sports tourism integration development models in Guangxi, analyze the problems of the existing rural sports tourism integration development models in Guangxi from macro-, meso-, and microdimensions, propose corresponding development countermeasures, provide reference for promoting the national strategy of building important tourism bases in Guangxi and theoretical system construction, use the method of fuzzy mathematics to construct a fuzzy comprehensive evaluation model, and apply this model to objectively evaluate the sports tourism resources in Guangxi Province. Finally, in response to the evaluation results, it is proposed to deeply develop characteristic advantageous sports tourism resources, focus on breakthroughs, develop fitness and leisure participation sports tourism industry, and reasonably lay out and cultivate some attractive sports tourism products.
Unmet Medical Needs and Early Referral of Pediatric Atopic Dermatitis: An Expert Modified Delphi Consensus from Saudi Arabia
Authors: Alradaddi A. , Al Twaim A. , Abu-aliat A. , Al-Atass K. , Alogayell L. , Aldayil M. , AlBreiki S. , Abed S. , Fatani M. , Alsharif O. , Darwesh B. , Gunay Levent M. , Al-Khenaizan S.
Published: August 2022

Atopic dermatitis (AD) is a chronic skin disease with increasing prevalence worldwide. It is characterized by pruritic eczematous lesions, affecting up to 20% of the children and negatively impacting their quality of life. Guidelines for AD management are available worldwide, but specific guidelines for pediatric AD in Saudi Arabia are lacking. This consensus document aims to identify the needs for the diagnosis and management of pediatric AD in Saudi Arabia by gathering the opinions and recommendations of key experts. We conducted a three‐step modified Delphi method to develop the present consensus. The experts agreed that pediatricians and dermatologists commonly encounter AD; however, it is still under‐recognized in its early stage in Saudi Arabia. The family physicians should be involved in assessing suspected children with a family history of atopy, particularly in patients with isolated lesions. Further, the experts confirmed that AD diagnosis should be documented, showing assessment criteria used, key morphological characteristics, and features used to ascertain the severity of the disease. There is still a need for simple validated diagnostic criteria suitable for daily practice for pediatric AD. The experts highlighted several medical conditions that pertain to the diagnosis and management of AD in Saudi Arabia.
Effect of Pulmonary Function Training with a Respirator on Functional Recovery and Quality of Life of Patients with Stroke
Authors: Wei Haitang , Sheng Yang , Peng Tao , Yang Dan , Zhao Qiang , Xie Liang , Liu Zhixia
Published: August 2022

A stroke is a sudden onset cerebral blood circulation disorder. It occurs in patients with cerebrovascular disease due to various predisposing factors causing stenosis, occlusion, or rupture of intracerebral arteries, which, in turn, causes acute cerebral blood circulation disturbance and clinically manifests as symptoms and signs of excessive or permanent cerebral dysfunction. It can cause serious harm to patients’ physical and mental health. This study aimed to evaluate the effect of Breathe‐Link breathing trainers on lung function and the ability to perform activities of daily living in patients with stroke. Sixty patients with stroke were randomly divided into two groups. One group was set as the control group and received routine breathing training. The experimental group received a Breathe‐Link trainer based on regular training, with rehabilitation training for 12 weeks as the time node. Respiratory muscle strength, respiratory velocity, respiratory capacity, forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and rate in the first second (FEV1/FVC) were used to evaluate the respiratory function of patients, and the Barthel index was used to evaluate the ability to perform activities of daily living. Improvements in respiratory function and daily living ability were compared between the two groups. After 12 weeks of training, respiratory muscle strength, respiratory velocity, respiratory volume, FVC, FEV1, FEV1/FVC, and Barthel index of patients in the two groups improved compared with those before training (P < 0.05), and the improvement in the treatment group was better than that in the control group (P < 0.05). Breathe‐Link breathing trainers can improve lung function and the ability to perform activities of daily living in patients with stroke, and its effect is acceptable. It can be recommended for clinical use.
A Deep Learning Algorithm for Special Action Recognition of Football
Authors: Wang Sheng
Published: August 2022

Soccer (football) is a popular form of exercise on the planet. There are a lot of individuals who tune into football matches in real time on television or the Internet. A game of American football lasts 90 minutes, but to save time, spectators may simply want to see a few highlights. As far as we know, no such tool exists that can be used to extract intelligent highlights from a football match. In this research, we present a technique for clever editing of live football matches. Our technology allows for the automatic extraction of key players’ goals, shots, corner kicks, red and yellow cards, and the presence of key players from a football match’s live stream. During the 2018 FIFA World Cup, our solution was integrated into live streaming platforms and it functioned admirably.
Framework for Classification of Chest X-Rays into Normal/COVID-19 Using Brownian-Mayfly-Algorithm Selected Hybrid Features
Authors: Biju Roshima , Patel Warish , Suresh Manic K. , Rajinikanth Venkatesan
Published: August 2022

The improvements in computation facility and technology support the development and implementation of automatic methods for medical data assessment. This study tries to extend a framework for efficiently classifying chest radiographs (X-rays) into normal/COVID-19 class. The proposed framework consists subsequent phases: (i) image resizing, (ii) deep features extraction using a pretrained deep learning method (PDLM), (iii) handcrafted feature extraction, (iv) feature optimization with Brownian Mayfly-Algorithm (BMA), (v) serial integration of optimized features, and (vi) binary classification with 10-fold cross validation. In addition, this work implements two methodologies: (i) performance evaluation of the existing PDLM in the literature and (ii) improving the COVID-19 detection performance of chosen PDLM with this proposal. The experimental investigation of this study authenticates that the effort performed using pretrained VGG16 with SoftMax helped get a classification accuracy of >94%. Further, the research performed using the proposed framework with BMA selected features (VGG16 + handcrafted features) helps achieve a classification accuracy of 99.17% on the chosen X-ray image database. This outcome proves the scientific importance of the implemented framework, and in the future, this proposal can be adopted to inspect the clinically collected X-rays.
Internal Resource Allocation to Information Security of Smart Cities Using Evolutionary Game Model
Authors: Li Jun , Zou Kai , Xing Lining
Published: August 2022

The internal resource allocation approach is proposed for the information security of smart cities using the evolutionary game model. Focusing on the information resource allocation of smart cities, the relationship among information security factors is first analyzed, then the directed connection graph and adjacency matrix are constructed to obtain its directed hierarchical structure diagram by calculating the reachable matrix, thereby developing an explanatory structure model for information security resources in smart cities. Furthermore, an evolutionary game model is established, and the replicator dynamics are used to adjust the model. Finally, three types of typical problems are investigated through simulation experiments to verify and explain the structural model.
Node Injection Control Logic Design for Intelligent Home System Based on Wireless Sensor Network
Authors: Zhou Zhenggui
Published: August 2022

Smart home is a building, network, communication, automation, set management, and service, as one of the efficient, comfortable, safe, and environmental protection of the living environment, and can provide personalized living space according to the needs of different users. Due to the advantages of wireless sensor network (WSN), such as strong expansion ability, strong communication ability, and strong self-organization ability, the smart home system based on WSN has become the main development direction in the field of smart home. This paper analyzes the different functions and features of wireless sensor network routing protocol and use scope, aiming at the application layer, using the MQ queue enhance server concurrent processing capacity and system scalability and design based on application layer gateway and terminal node “data matching list,” which are introduced in the design of smart home system based on geographic information location energy-efficient routing protocols. The solution of routing void problem is improved by using information feedback mechanism and alternate nodes. Simulation results show that this method can improve the energy saving of energy-efficient routing protocol. Finally, the intelligent home system experiment platform was built to verify the overall function of the system, and the communication function of the WiFi network and ZigBee network was tested. Based on the analysis of the interference between ZigBee and WiFi network, frequency agility algorithm is used to solve the problem of the same frequency interference, so as to ensure the performance of the system to meet the needs of the smart home system.
A Reputation-Based Approach Using Consortium Blockchain for Cyber Threat Intelligence Sharing
Authors: Zhang Xiaohui , Miao Xianghua , Xue Mingying
Published: August 2022

The CTI (Cyber Threat Intelligence) sharing and exchange is an effective method to improve the responsiveness of the protection party. Blockchain technology enables sharing collaboration consortium to conduct a trusted CTI sharing and exchange without a centralized institution. However, the distributed connectivity of the blockchain-based CTI sharing model proposed before exposes the systems to byzantine attacks. The compromised members of partner organizations will further decrease the accuracy and trust level of CTI by generating false reporting. This paper proposes a new blockchain-based CTI model to address the unbalance issues of performance in speed, scalability, and security, which combines consortium blockchain and distributed reputation management systems to achieve automated analysis and response of tactical threat intelligence. In addition, the novel consensus algorithm of consortium blockchain that is fit for CTI sharing and exchange is introduced in this paper. The new consensus algorithm is called “Proof-of Reputation” (PoR) consensus, which meets the requirements of transaction rate and makes the consensus in a creditable network environment through constructing a reputation model. Finally, the effectiveness and security performance of the proposed model and consensus algorithm is verified by experiments.
A Case of Peritoneal Encapsulation Presented as Acute Mechanical Small Bowel Obstruction: A Case Report and a Brief Literature Review
Authors: Abuzaina Khalil , Abuayash Ahmad , Al-Shweiki Hidaya , Hroub Mohammad O. M. , Jabari Anwar Yousef , Hafiz Shayma , Ihlayel Tuqa Abu , Mohsen Balqees , Fakhouri Sulaiman Naji , Jaa'freh Murad
Published: August 2022

Peritoneal encapsulation (PE) is a rare congenital malformation in which the small intestine is partially or totally encased in a supplementary peritoneal sac. PE is usually asymptomatic; therefore, it is one of the rarest etiologies of bowel obstruction. Our patient presented at the age of 55 with no prior surgical history and a 3-day history of abdominal pain associated with nausea, vomiting, belching, and constipation. An obstruction secondary to an internal hernia—visualized on a CT scan—was suspected as the initial etiology. On exploratory laparotomy, the small bowel was covered by a thick adherent sac. These findings are consistent with PE, a condition that deserves recognition among clinicians worldwide. Intraoperatively, the sac was excised, and the small bowel was pulled up to the peritoneal cavity starting from the ileocecal valve to the duodenojejunal junction. In the postoperative period, the patient was managed with intravenous fluids, analgesics, and antibiotics. Wound infection was the only postoperative complication. Otherwise, all symptoms subsided, and the patient improved and was discharged home on the 8th postoperative day.
Energy Efficiency Strategy for Big Data in Cloud Environment Using Deep Reinforcement Learning
Authors: Pandey Neeraj Kumar , Diwakar Manoj , Shankar Achyut , Singh Prabhishek , Khosravi Mohammad R. , Kumar Vivek
Published: August 2022

Big data entails massive cloud resources for data processing and analysis, which consumes more energy to run. The resources and tasks are increasing exponentially in the cloud environment for the processing of big data, which results in an increment in power consumption to run the cloud data center. So, there is always a scope for optimizing the energy utilization in cloud data centers. This paper presents a visionary architecture in a cloud environment for big data with a proposed energy-efficient strategy based on LSTM-DQN (long-short-term memory-deep Q network) using reinforcement learning (RL). The traditional techniques are not so efficient when the tasks are allocated dynamically, and the generic RL strategies are not able to store the data iterated in the last cycles of processing, so the LSTM is considered for this purpose. In the proposed model, integration of DPSO and DQN is used for better estimation and rectification of the curse of dimensionality. The proposed strategy is compared with different variants of PSO (particle swarm optimization) such as DPSO and QoS-PSO. The improvement in results through proposed model is recoded over the algorithm such as load aware (8.01%), DQN (13.36%), EA-DQN (34.16%), L-No-DEAF (15.62%), DPSO (62.68%), QoS-PSO (72.69%), FFO-EVSM (75.42%), and MIMT (76.39%) on the parameter of energy efficiency, tasks completion time, and energy consumption over the timeline. So, the proposed model is encouraging in the energy-efficient cloud environment for big data with the challenges that the technological world is facing and the emergence of deep learning as one propitious field.
Research on Modular Management of Railway Bridge Technology Innovation in Complex and Difficult Mountainous Areas
Authors: Chen Huihua , Cheng Qintao , Xie Yingxue , Cai Chaoxun , Ban Xinlin , Hu Xiaodong
Published: August 2022

With China’s government facilitating railway projects, more railway bridges (RBs) are gradually built in complex and difficult mountainous areas (CDMAs). The construction activities of RB in CMDAs are facing formidable challenges in terms of the natural environment, technology, and organization management, which are hardly solved by traditional bridge design and construction techniques. Therefore, there is an urgent need for technology innovation (TI). Studies on the management of RB-TI in CMDAs are limited. As such, this study aims to offer an effective modular management approach to RB-TI in CMDAs. A system of demand and obstacle factors for RB-TI in CMDAs was identified firstly based on the literature review and the grounded theory, including seven intermediate codings and 29 initial codings. Then these factors were regarded as the system requirements for modular decomposition, to establish a “cut-to-fit” modular management approach to RB-TI in CMDAs. A case (i.e., the LD bridge project of the CZ railway) was selected to demonstrate and validate the developed approach. The results show that the proposed approach can be applied to manage RB-TI in CMDAs. The innovation of this study lies in the integration of grounded theory and modular theory and provides modular management ideas and measures for bridge engineering technology innovation. Findings from this study enrich the knowledge body of RB-TI and guide innovation subjects in the practical management of RB-TI in CMDAs.
Simulation Study on Opinion Evolution of Collaborative Shopping
Authors: Liang Shulin , Hu Wang
Published: August 2022

DW model is improved based on the unique characters of group consumption. At first, using social experiment, we determine the categories and attributes of agents, and then based on the DW model, participants’ interaction rules are established. Finally, a mass of numerical simulation experiments show that in the form of collaborative online shopping, merchants can reverse opinions, who could persuade consumers with negative attitudes, changing opinions, and supporting collaborative online shopping, and the level of characteristics is closely related to the number of consumers changing opinions; opinion leaders can differentiate group opinions, neither accelerating effect nor destructive effect; the characteristics level of individual consumers has close relation with positive group polarization effect; when an individual consumer has high conformity or trust propensity, the opinions of some customers with supporting collaborative online shopping will be strengthened, they accept collaborative online shopping more. In addition, for group interaction of collaborative online shopping context, there is no negative group polarization effect.
Coupling of Photonic Crystal Surface Modes
Authors: Yücel Melike Behiye
Published: August 2022

Guiding and evanescent coupling properties of surface modes bound to the interfaces of two-dimensional photonic crystals in close proximity are numerically demonstrated. Interacting photonic crystals are composed of silicon pillars in air, where their outermost layers facing each other are annular. Surface modes are identified through supercell band structure computations, while their excitation by the electromagnetic waves through a perpendicular insertion waveguide is demonstrated using finite-difference time-domain simulations. Lifting the degeneracy between the surface modes as a consequence of bringing two identical photonic crystal surfaces to a sufficient distance results in evanescent coupling in a beating manner whose beat length linearly varies between 10 and 20 periods up to a frequency at which both surface modes travel with the same group velocity. The surface mode coupling phenomenon could be employed either to enhance sensitivity or to reduce device size in bio/chemical sensor applications since the effective travelling length of surface waves increases by about 3.5 times due to evanescent coupling.
Systematic comparison of pancreatic ductal adenocarcinoma
models identifies a conserved highly plastic basal cell state
Authors: Pitter Kenneth L. , Grbovic-Huezo Olivera , Joost Simon , Singhal Anupriya , Blum Melissa , Wu Katherine , Holm Matilda , Ferrena Alexander , Bhutkar Arjun , Hudson Anna , Lecomte Nicolas , de Stanchina Elisa , Chaligne Ronan , Iacobuzio-Donahue Christine A. , Pe'er Dana , Tammela Tuomas
Published: August 2022

Intra-tumoral heterogeneity and cellular plasticity have emerged as hallmarks of cancer, including pancreatic ductal adenocarcinoma (PDAC). As PDAC portends a dire prognosis, a better understanding of the mechanisms underpinning cellular diversity in PDAC is crucial. Here, we investigated the cellular heterogeneity of PDAC cancer cells across a range of in vitro and in vivo growth conditions using single-cell genomics. Heterogeneity contracted significantly in 2D and 3D cell culture models but was restored upon orthotopic transplantation. Orthotopic transplants reproducibly acquired cell states identified in autochthonous PDAC tumors, including a basal state exhibiting co-expression and co-accessibility of epithelial and mesenchymal genes. Lineage-tracing combined with single-cell transcriptomics revealed that basal cells display high plasticity in situ. This work defines the impact of cellular growth conditions on phenotypic diversity and uncovers a highly plastic cell state with the capacity to facilitate state transitions and promote intra-tumoral heterogeneity in PDAC.