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Abstract Background Metarhizium rileyi is an important entomopathogenic fungus which has a wide range of host pests. In this work, the virulence of M. rileyi EFNL-08 against 3rd instar larvae of the fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), was quantified. Additionally, the infection process and aspects of the host immunological response were described in the laboratory. Results Bioassay results proved that M. rileyi was highly virulent to 3rd instar S. frugiperda larvae with 98.33% mortality at 1 × 108 conidia/ml. Scanning electron microscopy was used to monitor morphological aspects of the infection process. When hyphal bodies (Hbs) of M. rileyi were injected into the hemocoel of host, they were not recognized by the host’s immune system as invaders. There were nonsignificant differences in the hemocytes and phenoloxidase activity between the infected and control larvae at the initial 30 h, indicating that fungus successfully avoids the attack from the immune system. However, it inhibited the enzyme activity at the latest stage when Hbs reached a high density. After infection with M. rileyi, the activity of glutathione-S-transferase (GST) of S. frugiperda larvae presented a significant upward trend, while the activity of acetylcholinesterase (AchE) initially increased, followed by a decrease, and eventually stabilized. However, the activity of both detoxification enzymes in the control group showed slight fluctuations in the initial stage, and then stabilized at a low level. Conclusion Obtained results provided new insights into the virulence and potential physiological responses of M. rileyi. This provides ideas for further development of novel and efficient entomopathogenic fungi (EPF) for pest control.

As one of the essential topological structures in complex networks, community structure has significant theoretical and application value and has attracted the attention of researchers in many fields. In a social network, individuals may belong to different communities simultaneously, such as a workgroup and a hobby group. Therefore, overlapping community discovery can help us understand and model the network structure of these multiple relationships more accurately. This article proposes a two-stage multi-objective evolutionary algorithm for overlapping community discovery problem. First, using the initialization method to divide the central node based on node degree, combined with the cross-mutation evolution strategy of the genome matrix, the first stage of non-overlapping community division is completed on the decomposition-based multi-objective optimization framework. Then, based on the result set of the first stage, appropriate nodes are selected from each individual’s community as the central node of the initial population in the second stage, and the fuzzy threshold is optimized through the fuzzy clustering method based on evolutionary calculation and the feedback model, to find reasonable overlapping nodes. Finally, tests are conducted on synthetic datasets and real datasets. The statistical results demonstrate that compared with other representative algorithms, this algorithm performs optimally on test instances and has better results.

The article proposes an optimization algorithm using a hierarchical environment selection strategyto solve the deficiencies of current multimodal multi-objective optimization algorithms in obtaining the completeness and convergence of Pareto optimal Sets (PSs). Firstly, the algorithm in this article is framed by a differential evolutionary algorithm (DE) and uses a special crowding distance to design a neighborhood-based individual variation strategy, which also ensures the diversity, and then special crowding distance is used to help populations with non-dominated sorting. In the stage of environmental selection, a strategy of hierarchical selection of individuals was designed, which selects sorted non-dominant ranked individual layer by layer according to the ratio, which allows potential individuals tobe explored. Finally, in the stage of evolution of individuals, the convergence and diversity of populations were investigated, anddifferent mutation strategies were selectedaccording to the characteristics of individuals. DE reproduction strategies are used for iteration, preventing individuals from avoiding premature convergence and ensuring the algorithm’s searchability. These strategies help the algorithm to obtain more diverse and uniformly distributed PSs and Pareto Front (PF). The algorithm of this article compares with several other excellent algorithms on 13 test problems, and the test results show that all the algorithms of this article exhibit superior performance.

The maximum clique problem in graph theory is a well-known challenge that involves identifying the complete subgraph with the highest number of nodes in a given graph, which is a problem that is hard for nondeterministic polynomial time (NP-hard problem). While finding the exact application of the maximum clique problem in the real world is difficult, the relaxed clique model quasi-clique has emerged and is widely applied in fields such as bioinformatics and social network analysis. This study focuses on the maximum quasi-clique problem and introduces two algorithms, NF1 and NR1. These algorithms make use of previous iteration information through an information feedback model, calculate the information feedback score using fitness weighting, and update individuals in the current iteration based on the benchmark algorithm and selected previous individuals. The experimental results from a significant number of composite and real-world graphs indicate that both algorithms outperform the original benchmark algorithm in dense instances, while also achieving comparable results in sparse instances.

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods.

Water is a critical resource globally, covering approximately 71% of the Earth’s surface. Employing analytical models to forecast water quality parameters based on historical data is a key strategy in the field of water quality monitoring and treatment. By using a forecasting model, potential changes in water quality can be understood over time. In this study, the gated recurrent unit (GRU) neural network was utilized to forecast dissolved oxygen levels following variational mode decomposition (VMD). The GRU neural network’s parameters were optimized using the grey wolf optimizer (GWO), leading to the development of a VMD–GWO–GRU model for forecasting water quality parameters. The results indicate that this model outperforms both the standalone GRU model and the GWO–GRU model in capturing key information related to water quality parameters. Additionally, it shows improved accuracy in forecasting medium to long-term water quality changes, resulting in reduced root mean square error (RMSE) and mean absolute percentage error (MAPE). The model demonstrates a significant improvement in the lag of forecasting water quality parameters, ultimately boosting forecasting accuracy. This approach can be applied effectively in both monitoring and forecasting water quality parameters, serving as a solid foundation for future water quality treatment strategies.

Abstract To lower operational costs as well as emissions when wind and solar resources are available in a microgrid (MG), this study discusses the scheduling of electric vehicles (EVs) and responsive demands simultaneously. To mitigate the effects associated with undispatchable energy sources such as wind and solar, the proposed system makes use of EVs for peak shaving and load curve changes, while responsive demands provide the reserves required to do so. In addition, a two‐stage model is provided to evaluate MG's planned running costs (energy and reserve). Costs related to generating and reserving electricity are minimized in Stage 1, while costs related to adjusting unit scheduling to account for fluctuations in wind and photovoltaic output are minimized in Stage 2. Converged barnacles mating optimizer (CBMO) is a highly effective and powerful optimization tool that is used to handle the resultant objective optimization issue. An MG consisting of multiple dispersed generations is used to implement the proposed model. It is worth mentioning that three scenarios have been defined to analyze the impact of joint scheduling of EVs and controllable loads on the MG's day‐ahead operation. The three cost terms, that is, the generation cost, the reserve cost, and the startup cost of units in this scenario, are derived as $745.6913, $10.5278, and $6.35, respectively, remarkably less than the values reported in Scenarios 1 and 2. In Scenario 1, the CBMO algorithm yielded a lower MG operational cost than methods by a margin of 843.2 $/day. Costs per day of operation in Scenario 2 are derived to be $819.3 using the CBMO technique, whereas in Scenario 3, they are determined to be $743.1.

Abstract Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

This study investigates and describes a novel method for using biogas to run a cogeneration system that generates electricity/cooling and cools with recovered heat from liquefied natural gas (LNG). The investigated setup comprises an open-loop Brayton cycle fed by biogas, a close-loop Brayton cycle (CLBC), an LNG open power generation cycle, and a dual-stage combined cooling and power (CCP) unit composed of an organic Rankine cycle (ORC) integrated with an ejector refrigeration cycle (ERC). The performance of the system is scrutinized from many perspectives, such as the ones of energy, exergy, exergoeconomic, CO2 emmision rate, payback, and multi-objective optimization. It is deduced that TGT1 is the most influential decision variable from exergy and cost outlooks, while energy efficiency undergoes considerable changes with variation in Teva. Based on optimization, the developed unit generated 424.1 kW of cooling and 1,864 kW of net electricity, corresponding to 80.4% energy efficiency, 41.24% exergy efficiency, and 10.07 $/GJ for the unit’s overall product cost. In base and optimum design modes, the combustion chamber (1058 kW) and condenser (1023 kW) are responsible for the high levels of total exergy destruction. At a selling price of 0.27 $/kWh for cooling and 0.06 $/kWh for electricity, payback period equals 12.13 years. Furthermore, it is also expected that over the course of 20 years, the system can earn 3.5 million dollars in profits. Based on environmental aspect, the cogeneration system emits 6091 kg/ MWh of CO2, which decreased to 3913 kg/MWh after optimization.

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard.

Precisely forecasting air temperature as a significant meteorological parameter has a critical role in environment quality management. Hence, this study employs a hybrid intelligent model for accurately monthly temperature forecasting for one to three times ahead in the hottest and coldest regions of the world. The hybrid model contains the artificial neural network (ANN) hybridized with the powerful hetaeristic Honey Badger Algorithm (HBA-ANN). The average mutual information (AMI) technique is employed to find the optimal time delay values for the temperature variable for different time horizons. Finally, the performance of the developed hybrid model is compared with the classical ANN and the Gene Expression Programming (GEP) using some statistical criteria, and the Taylor and scatter diagrams. Results indicated that in each time horizon, the HBA-ANN model with the lowest distance from observation points based on Taylor diagram, high values for NSE and R2, and low values for RMSE, MAE, and RSR outperformed the ANN and GEP models in both training and testing phases. Hence, using the Honey Badger Algorithm could increase the accuracy of the model. This model's precise performance supports the case for it to be employed to forecast other environmental parameters.

Abstract Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%.

Abstract Cloud Mobile Ad-hoc Networks (Cloud-MANETs) is a framework that can access and deliver cloud services to MANET users through their smart devices. MANETs is a pool of self-organized mobile gadgets that can communicate with each other with no support from a central authority or infrastructure. The main advantage of MANETs is its ability to manage mobility while data communication between different users in the system occurs. In MANETs, clustering is an active technique used to manage mobile nodes. The security of MANETs is a key aspect for the fundamental functionality of the network. Addressing the security-related problems ensures that the confidentiality and integrity of the data transmission is secure. MANETs are highly prone to attacks because of their properties.In clustering schemes, the network is broken down to sub-networks called clusters. These clusters can have overlapping nodes or be disjointed. An enhanced node referred to asthe Cluster Head (CH) is chosen from each set to overseetasks related to routing. It decreases the member nodes’ overhead and improvesthe performance of the system. The relationship between the nodes and CH may vary randomly, leading to re-associations and re-clustering in a MANET that is clustered. An efficient and effective routing protocol is required to allow networking and to find the most suitable paths between the nodes. The networking must be spontaneous, infrastructure-less, and provide end-to-end interactions. The aim of routing is the provision of maximum network load distribution and robust networks. This study focused on the creation of a maximal route between a pair of nodes, and to ensure the appropriate and accurate delivery of the packet. The proposed solution ensured that routing can be carried out with the lowest bandwidth consumption. Compared to existing protocols, the proposed solution had a control overhead of 24, packet delivery ratio of 81, the lowest average end-to-end delay of 6, and an improved throughput of 80,000, thereby enhancing the output of the network. Our result shows that multipath routing enables the network to identify alternate paths connecting the destination and source. Routing is required to conserve energy and for optimum bandwidth utilization.

Abstract Broadcasting more educating and language-reviving contents are ways radio stations can help revitalize the use of the English language in the Hunan province of China. The challenges faced in communicating in English in Chinese radio stations are majorly caused by the lack of language professionals and linguists in the broadcast stations. The absence of these professionals is a major constraint to the development of the community. The broadcast media can help manage multilingualism through the introduction of new words which would give little or no room for lexicon dearth but would expand the language lexicon. Using the English language during broadcast reduces language dearth, and helps reach a much larger audience, even those not in China. Programmes anchored in English in places where the language is barely spoken enhances the vocabulary, comprehension and language vitality of the listeners. This study examined the impact of the English language used in radio broadcasting using a descriptive Big Data survey research design. The study’s population comprises of the inhabitants of the Hunan province in China, from which a sample of 50 broadcast staff and 150 regular inhabitants was drawn using a stratified random sampling technique. The instrument of data collection was a structured questionnaire with closed questions and a self-structured interview. The sample employed frequency distribution tables, percentages, and charts in the presentation and analysis of data. The results revealed that majority of the respondents in Hunan listened to radio broadcast indicating that the use of English language can have massive impact on the people. The study also found that majority of the respondents use their indigenous languages in their day-to-day activities as well as their schools with English being used majorly only in schools with only English-speaking students. The study recommends, amongst others, that the Broadcasting Corporation of China (BCC) review their policy on the allocated time of broadcast in English languages, and that more English language experts and linguists should be incorporated into the broadcast system.

In the third millennium, developing nations face the most pressing challenges involving climate change, fossil fuel scarcity, ozone depletion, global warming, and the production of greenhouse gases. To mitigate these challenges, renewable energy-driven power generation systems are of paramount importance. Present paper aims at proposal of two novel configurations for clean electricity generation with high-efficiency. The first system (standard system) is composed of an OFWH-assisted steam Rankine cycle and a gas turbine cycle which is driven solely by biomass energy. On the other hand, the second system (hybrid system) uses biomass and geothermal energy to drive the system, where the geothermal energy heats the water entering the OFWH and augments the power generation. Detailed analyses and comparisons between two systems are conducted using thermodynamics and exergoenvironmental methods. After completing the parametric analysis, a Pareto front is obtained for highest efficiency and lowest exergoenvironmental index (EEI). A notable finding of the parametric study is that the hybrid system is less energy efficient than the standard system, however it has higher exergetic efficiency and lower negative impacts on environment. Furthermore, increasing the pressure ratio of the air compressor leads to a consistently increasing EEI, while output electricity and exergy and energy efficiencies show an increasing-decreasing trend. Based on the bi-objective optimization, the gasifier with 3569 kW is the most exergy destructive component of the system in the base case and optimal case. Finally, the optimum values are obtained as 0.4734 and 42.63% for the EEI and exergy efficiency, respectively.

Abstract Cloud computing is an extremely important infrastructure used to perform tasks over processing units. Despite its numerous benefits, a cloud platform has several challenges preventing it from carrying out an efficient workflow submission. One of these is linked to task scheduling. An optimization problem related to this is the maximal determination of cloud computing scheduling criteria. Existing methods have been unable to find the quality of service (QoS) limits of users- like meeting the economic restrictions and reduction of the makespan. Of all these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm produces the maximum outcomes for scheduling tasks in a heterogeneous environment in a reduced time. Reviewed literature proves that HEFT is efficient in terms of execution time and quality of schedule. The HEFT algorithm makes use of average communication and computation costs as weights in the DAG. In some cases, however, the average cost of computation and selecting the first empty slot may not be enough for a good solution to be produced. In this paper, we propose different HEFT algorithm versions altered to produce improved results. In the first stage (rank generation), we execute several methodologies to calculate the ranks, and in the second stage, we alter how the empty slots are selected for the task scheduling. These alterations do not add any cost to the primary HEFT algorithm, and reduce the makespan of the virtual machines’ workflow submissions. Our findings suggest that the altered versions of the HEFT algorithm have a better performance than the basic HEFT algorithm regarding decreased schedule length of the workflow problems.

Abstract At both clinical and diagnostic levels, machine learning technologies could help facilitate medical decision-making. Prediction of sports injuries, for instance, is a key component of avoiding and minimizing injury in motion. Despite significant attempts to forecast sports injuries, the present method is limited by its inability to identify predictors. When designing measures for the avoidance of work-related accidents and the reduction of associated risks, the risk of injury to athletes is a crucial consideration. Various indicators are being evaluated to identify injury risk factors in a number of different methods. Consequently, this paper proposes a Deep Learning-assisted System (DLS) for diagnosing sports injuries using the Internet of Things (IoT) and the concept of cloud computing. The IoT sensors that compose the body area network collect crucial data for the diagnosis of sports injuries, while cloud computing makes available flexible computer system resources and computing power. This research examines the brain injury monitoring framework, uses an optimal neural network to forecast brain injury, and enhances the medical rehabilitation system for sports. Using the metrics accuracy, precision, recall, and F1-score, the performance of the proposed model is assessed and compared with current models.

Abstract The rapid growth and development of financial technological advancement (Fintech) services and innovations have attracted the attention of scholars who are now on a quest to analyse their impact on the banking sector. This study conducts several kinds of analyses to measure the effect of the fintech era on the stability of the Chinese banking sector. It uses Big Data and performs Pearson correlation and regression analysis on the fintech era’s transition period to measure the impact of several explanatory variables— institutional regulation, government stability, bank credit to deposit ratio, and economic growth— on the outcome variables, which includes Nonperforming loans (NPLs) and its numerical measurement in relation to the mean score of the Big Data (Z-score). This study uses yearly Big Data from 1995–2018 and revealed that compared to the first wave of the fintech era, the second wave helped in the reduction of NPLs and the enhancement of financial stability in China. This study concludes that in the second wave of the fintech era, the explanatory variables mentioned above had a positive impact on NPLs and banking stability. This work helps comprehend fintech development in modern society and the importance of its disruptive forces in developing and developed countries.

Abstract In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users’ voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.

Abstract This study investigates the possibility of supporting tourists in a foreign land intelligently by using the Tourism Cloud Management System (TCMS) to enhance and better their tourism experience. Some technologies allow tourists to highlight popular tourist routes and circuits through the visualisation of data and sensor clustering approaches. With this, a tourist can access the shared data on a specific location to know the sites of famous local attractions, how other tourists feel about them, and how to participate in local festivities through a smart tourism model. This study surveyed the potential of smart tourism among tourists and how such technologies have developed over time while proposing a TCMS. Its goals were to make physical/paper tickets redundant via the introduction of a mobile app with eTickets that can be validated using camera and QR code technologies and to enhance the transport network using Bluetooth and GPS for real-time identification of tourists’ presence. The results show that a significant number of participants engage in tourist travels, hence the need for smart tourism and tourist management. It was concluded that smart tourism is very appealing to tourists and can improve the appeal of the destination if smart solutions are implemented. This study gives a first-hand review of the preference of tourists and the potential of smart tourism.

Abstract The quality assurance of higher education in China is an issue of vital international interest. To improve the international reputation of the nation’s universities, steps must be taken to ensure a sustained focus on the quality assurance within its ranks. This paper is primarily focused on the quality assurance models operational in Chinese universities, the Big data challenges and the legal framework backing them. The paper also discusses the implementation of the models, the extent to which they meet international standards, and how they adhere to prevailing laws. The degree of success in attaining and maintaining quality and evaluation of quality improvement opportunities are also discussed. Some of the solutions recommended in this study are the participation of more teachers and students in quality management, more emphasis of Higher Education Institution (HEI) quality assurance on self-regulation and a learning-oriented approach and conducting sessions to collect anonymous feedback from students to reward staff with best practices. Some of the Quality Assurance practices/models adopted in Chinese Universities are the Ministry of Education (MOE) reviews; the Academic Degree Committee oversight; Higher Education Evaluation Center (HEEC) overview, University self-evaluation according to HEEC Indicators, and the Webometric Ranking Model.

Abstract In order to adapt to the development of modern education and provide education practitioners with decision‐making suggestions, it is necessary to understand the relationship between the public's attention to basic chemistry education (BCE) and academic attention. However, many existing research is based on a single platform to study social hot information, such as Google Index, Baidu Index, Web of Science, and so on. But they ignore the relationship that exists between the Baidu Index and related academic platforms, and ignore the common information reflected between them. This paper takes advantage of the big data method, through the big data of Baidu Index and the big data obtained from China National Knowledge Internet (CNKI) database, to study the network attention and academic attention of BCE, and propose a CVS‐LSP‐GP framework. It first selects keywords through correlation analysis, secondly uses the data obtained from the first step to construct a nonlinear regression model, and finally combines the results of gray prediction to predict the academic attention of CNKI related to BCE. The research results show that the BCE is mainly affected by the micro‐lecture teaching mode, and relevant education practitioners should integrate the micro‐lecture mode into teaching for more further research and practice.

The goal of dynamic community discovery is to quickly and accurately mine the network structure for individuals with similar attributes for classification. Correct classification can effectively help us screen out more desired results, and it also reveals the laws of dynamic network changes. We propose a dynamic community discovery algorithm, NOME, based on node occupancy assignment and multi-objective evolutionary clustering. NOME adopts the multi-objective evolutionary algorithm MOEA/D framework based on decomposition, which can simultaneously decompose the two objective functions of modularization and normalized mutual information into multiple single-objective problems. In this algorithm, we use a Physarum-based network model to initialize populations, and each population represents a group of community-divided solutions. The evolution of the population uses the crossover and mutation operations of the genome matrix. To make the population in the evolution process closer to a better community division result, we develop a new strategy for node occupancy assignment and cooperate with mutation operators, aiming at the boundary nodes in the connection between the community and the connection between communities, by calculating the comparison node. The occupancy rate of the community with the neighbor node, the node is assigned to the community with the highest occupancy rate, and the authenticity of the community division is improved. In addition, to select high-quality final solutions from candidate solutions, we use a rationalized selection strategy from the external population size to obtain better time costs through smaller snapshot quality loss. Finally, comparative experiments with other representative dynamic community detection algorithms on synthetic and real datasets show that our proposed method has a better balance between snapshot quality and time cost.

Abstract Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.

Abstract Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.

Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network’s data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

Abstract Analyzing the relationship between Internet attention and teaching research can provide a reference for extracting and identifying research hot spots in the discipline. Currently, many kinds of literature obtain social hot‐spot information based on a single platform, such as the Baidu Index, China National Knowledge Infrastructure (CNKI), and Web of Science. Still, they ignore the common information reflected between the Baidu Index platform and the professional academic platform and the lack of relationship demonstration between them. Therefore, based on the CNKI database and Baidu Index platform, the most critical data statistical analysis service platform in the Internet era, this article proposes a relationship modeling and prediction framework (RMPF). First, RMPF analyzes the relationship between public attention and teaching research from the perspective of “high school information technology” by word frequency analysis and Pearson. Second, RMPF constructs the relationship model between them using leave‐one‐out cross‐validation. Third, RMPF predicts the development of subject teaching and research using the autoregressive integrated moving average model and multiple regressions. The results show that the correlation coefficient between public attention and teaching research is more than 0.65 from the perspective of “high school information technology,” showing a strong positive correlation. In recent years, the focus of high school information technology discipline tends to be integrating multimedia technology, cloud classroom, artificial intelligence, and other technologies and disciplines. In addition, discipline literature volume growth will slow in the next 2 years, with an average annual publication of 350 articles. The overall number of literature decreased by about 120 compared with 2020. Based on the proposed RMPF, this study clarifies the relationship between subject attention and teaching research through experimental demonstration and provides an implementation framework reference for researchers of relevant literature.

Abstract Identifying cohesive subgraphs is an important topic in graph theory and complex network analysis. The quasi‐clique, as a generalization of clique, can be used to identify the functional and structural properties of various networks. In this paper, the authors study the maximum weighted quasi‐clique problem and propose a local search algorithm for solving the problem. In the algorithm, an iterated local search method is used as the search framework. To find the quasi‐clique with the maximum total weights, hybrid vertex selection strategies are proposed and incorporated into the authors’ algorithm. The hybrid strategies utilize a probability‐based mechanism for choosing sub‐strategies in each round of the local search. The authors conduct experiments on synthetic networks and real‐world networks to show the effectiveness of the authors’ algorithm. The results indicate that hybrid strategies perform better than existing methods, and thus the authors’ algorithm has a good ability to tackle various networks.

To better understand the latest developments in global science, technology, engineering, and mathematics (STEM) education research, this study collected STEM education research materials to sort out the development of STEM education as a whole, so as to get a clearer path and trend of STEM education development. This study conducted a visualization and quantitative analysis of the literature on STEM education research in Science Citation Index Extended (SCI-E) and Social Science Citation Index (SSCI) using the CiteSpace (5.8.R3) tool. First, the basic information of STEM education was analyzed in terms of annual publication volume, authors, countries, and research institutions. Secondly, the main fields, basic contents and research hotspots of this research were analyzed by keyword co-occurrence and keyword time zone mapping. Finally, the research frontiers and development trends are presented through co-citation clustering and high-frequency keyword bursts. The research hotspots are focused on engineering education, teachers’ professional development, and gender differences. The research frontiers are mainly related to teacher professional development, 21st century skills, early childhood creativity, and gender differences. This study systematically analyzes the latest developments in global STEM education research, which is beneficial for readers to understand the full picture of STEM education research so that researchers can conduct more in-depth studies and promote better development of STEM education. The number of analyzed literature is limited. We only analyzed articles from SSCI and SCI-E databases, and the articles were written in English. In addition, we only analyzed the literature and lacked empirical studies on the findings of the literature.

Abstract Background There are over 16.8 million rare disease patients in China, representing a large community that should not be neglected. While the public lack the awareness of their existence and difficult status quo, for one reason that they exist as a rare and special group in our society, for another reason that all sectors of the community haven’t introduced and propagandized them suitably. However, as a special group with more difficulties in all aspects than normal healthy persons, they need enough care and love from us. To provide a basis for policy-makers to better understand the status quo of rare disease patients and care-givers in China and to devise some new policies to improve their quality of life, a comprehensive analysis of the status quo, unmet needs, difficulty caused by the rare disease is essential. Methods A questionnaire-based online study of patients and care-givers (usually family members) was performed. The questionnaire was composed of 116 questions, such as the diagnosis process, treatment access, financial burden, views on patients’ organizations, and a series of standardized tests to assess the quality of their life, including the SF-36, PHQ-9, PHQ-15, GAD-7, and PSQI. To examine the influence of age, disease type, and relationship to patients on the scores in these tests, statistical analysis with a general linear model was conducted. Findings A total of 1959 patients and care-givers participated in the survey, representing 104 rare diseases, such as lysosomal storage diseases, hemophilia, and muscular dystrophy diseases. The diagnosis was delayed for 1.4 ± 3.0 years, and patients experienced 1.6 ± 3.8 misdiagnoses between 3.2 ± 2.4 hospitals. The hospitals where diagnoses were made were highly concentrated in 10 large hospitals (43.8%) and 5 big cities (42.1%), indicating a significant inequality of medical resources. The disease often led to difficulty in social life, education, and employment, as well as financial burden that was seldom covered by medical insurance. A battery of standardized tests demonstrated poor health status, depression, somatization, anxiety, and sleeping issues among both patients and care-givers (p

Abstract Background It is estimated that there are over 16.8 million rare disease patients in China, representing a significant challenge for the healthcare system and society. Rare disease patients often experience delayed diagnosis, misdiagnosis, or improper treatment, which may be due to the lack of rare disease awareness among physicians. Materials and methods A total of 224 physicians from different hospitals in China participated in the questionnaire, and 9 rare disease experts were interviewed with open-ended questions. Results Most physicians (83.5%) were from Tertiary hospitals, which have over 500 beds. Only 5.3% of physicians were moderately or well aware of rare diseases. Most physicians (80.1%) had suspected their patients to have rare diseases less than 3 times. There was a strong support for special legislations for rare diseases and orphan drugs. Further, multinomial logistic regression (MLR) was used to determine whether hospitals, gender, and career length has an impact on perspectives and awareness. It was shown that male physicians were more likely to think newborn screening is important (p

The sweetpotato whitefly, Bemisia tabaci species complex, comprises at least 44 morphologically indistinguishable cryptic species, whose endosymbiont infection patterns often varied at the spatial and temporal dimension. However, the effects of ecological factors (e.g., climatic or geographical factors) on the distribution of whitefly and the infection frequencies of their endosymbionts have not been fully elucidated. We, here, analyzed the associations between ecological factors and the distribution of whitefly and their three facultative endosymbionts (Candidatus Cardinium hertigii, Candidatus Hamiltonella defensa, and Rickettsia sp.) by screening 665 individuals collected from 29 geographical localities across China. The study identified eight B. tabaci species via mitochondrial cytochrome oxidase I (mtCOI) gene sequence alignment: two invasive species, MED (66.9%) and MEAM1 (12.2%), and six native cryptic species (20.9%), which differed in distribution patterns, ecological niches, and high suitability areas. The infection frequencies of the three endosymbionts in different cryptic species were distinct and multiple infections were relatively common in B. tabaci MED populations. Furthermore, the annual mean temperature positively affected Cardinium sp. and Rickettsia sp. infection frequencies in B. tabaci MED but negatively affected the quantitative distribution of B. tabaci MED, which indicates that Cardinium sp. and Rickettsia sp. maybe play a crucial role in the thermotolerance of B. tabaci MED, although the host whitefly per se exhibits no resistance to high temperature. Our findings revealed the complex effects of ecological factors on the expansion of the invasive whitefly.

Abstract Background Each rare disease only affects a small number of population. However, a total of 7000 rare diseases may affect 10% of the population. Due to the severity and lack of rare disease awareness, rare disease represents a huge challenge for the healthcare system. In Western countries, patient organizations have been playing an integral role in raising awareness, advocating legislation, and supporting drug development. This study aims to assess the unmet needs of rare disease patient organizations in China, and identify their unmet needs, providing essential information for the government and legislators. Results A total of 28 individuals representing 28 patient organizations in China were interviewed. Most organizations do not have official registration, employees, written standard operation protocol, or reliable financial resources. Misdiagnosis or delayed diagnosis is common, and treatment is often lacking. Due to the lack of financial resources, no organizations have been able to sponsor academic research, unlike their counterparts in Western countries. As to challenges, 71.4% of interviewees listed lack of rare disease awareness among the general public, while 67.9% selected lack of financial resources. Further, only 7.3% of these organizations received support from the government, and 28.6% received support from the general public. As to recommendations to the government, 82.1% of interviewees selected special insurance programs for rare diseases because rare diseases have been generally excluded from the national medical insurance programs. In addition, 78.6% of interviewees recommended to stimulate rare disease research, 75% recommended to import orphan drugs, and 71.4% recommended legislation of an orphan drug act, highlighting the urgent need of therapies. Conclusions Due to lack of support and rare disease awareness, patient organizations in China are still in the early phase. To empower these patient organizations, the interviewees’ recommendations, including legislating orphan drug act and releasing official definition of rare diseases, should be considered by the government and legislators.

The (weighted) partial maximum satisfiability ((W)PMS) problem is an important generalization of the classic problem of propositional (Boolean) satisfiability with a wide range of real-world applications. In this paper, we propose an initialization and a diversification strategy to improve local search for the (W)PMS problem. Our initialization strategy is based on a novel definition of variables’ structural entropy, and it aims to generate a solution that is close to a high-quality feasible one. Then, our diversification strategy picks a variable in two possible ways, depending on a parameter: continuing to pick variables with the best benefits or focusing on a clause with the greatest penalty and then selecting variables probabilistically. Based on these strategies, we developed a local search solver dubbed ImSATLike, as well as a hybrid solver ImSATLike-TT, and experimental results on (weighted) partial MaxSAT instances in recent MaxSAT Evaluations show that they outperform or have nearly the same performances as state-of-the-art local search and hybrid competitors, respectively, in general. Furthermore, we carried out experiments to confirm the individual impacts of each proposed strategy.

The satisfiability (SAT) problem is a core problem in computer science. Existing studies have shown that most industrial SAT instances can be effectively solved by modern SAT solvers while random SAT instances cannot. It is believed that the structural characteristics of different SAT formula classes are the reasons behind this difference. In this paper, we study the structural properties of propositional formulas in conjunctive normal form (CNF) by the principle of structural entropy of formulas. First, we used structural entropy to measure the complex structure of a formula and found that the difficulty solving the formula is related to the structural entropy of the formula. The smaller the compressing information of a formula, the more difficult it is to solve the formula. Secondly, we proposed a λ-approximation strategy to approximate the structural entropy of large formulas. The experimental results showed that the proposed strategy can effectively approximate the structural entropy of the original formula and that the approximation ratio is more than 92%. Finally, we analyzed the structural properties of a formula in the solution process and found that a local search solver tends to select variables in different communities to perform the next round of searches during a search and that the structural entropy of a variable affects the probability of the variable being flipped. By using these conclusions, we also proposed an initial candidate solution generation strategy for a local search for SAT, and the experimental results showed that this strategy effectively improves the performance of the solvers CCAsat and Sparrow2011 when incorporated into these two solvers.

This study examined defensive behaviors of Mythimna separata (Lepidoptera: Noctuidae) larvae varying in body size in response to two parasitoids varying in oviposition behavior; Microplitis mediator females sting the host with the ovipositor after climbing onto it while Meteorus pulchricornis females make the sting by standing at a close distance from the host. Mythimna separata larvae exhibited evasive (escaping and dropping) and aggressive (thrashing) behaviors to defend themselves against parasitoids M. mediator and M. pulchricornis. Escaping and dropping did not change in probability with host body size or parasitoid species. Thrashing did not vary in frequency with host body size, yet performed more frequently in response to M. mediator than to M. pulchricornis. Parasitoid handling time and stinging likelihood varied depending not only on host body size but also on parasitoid species. Parasitoid handling time increased with host thrashing frequency, similar in slope for both parasitoids yet on a higher intercept for M. mediator than for M. pulchricornis. Handling time decreased with host size for M. pulchricornis but not for M. mediator. The likelihood of realizing an ovipositor sting decreased with thrashing frequency of both small and large hosts for M. pulchricornis, while this was true only for large hosts for M. mediator. Our results suggest that the thrashing behavior of M. separata larvae has a defensive effect on parasitism, depending on host body size and parasitoid species with different oviposition behaviors.

Concerns persist about adverse reactions to intravenous (IV) iron. We aimed to determine the relative safety of ferumoxytol compared to other IV iron compounds. This retrospective cohort study with propensity-score matching for patients and drug doses used the Medicare 20% random sample to identify patients (1) without chronic kidney disease (non-CKD) and (2) with non-dialysis-dependent chronic kidney disease (NDD-CKD) who received a first dose of IV iron in 2010-2012. Exposures were ferumoxytol, iron sucrose, sodium ferric gluconate, or iron dextran. Outcomes were hypersensitivity symptoms, anaphylaxis, emergency department (ED) encounters, hospitalizations, and death after acute IV iron exposure. In the primary analysis for reactions on the day of or following exposure, there was no difference in hypersensitivity symptoms (hazard ratio 1.04, 95% confidence interval 0.94-1.16) or hypotension (0.83, 0.52-1.34) between 4289 non-CKD ferumoxytol users and an equal number of users of other compounds; results were similar for 7358 NDD-CKD patients and an equal number of controls. All-cause ED encounters or hospitalizations were less common in both the non-CKD (0.56, 0.45-0.70) and NDD-CKD ferumoxytol-treated patients (0.83, 0.71-0.95). Fewer than 10 deaths occurred in both the non-CKD and NDD-CKD ferumoxytol users and in matched controls; the hazard for death did not differ significantly between ferumoxytol users and controls in the non-CKD patients (2.00, 0.33-11.97) or in the NDD-CKD patients (0.25, 0.04-1.52). Multiple sensitivity analyses showed similar results. Ferumoxytol did not appear to be associated with more adverse reactions than other compounds for the treatment of iron-deficiency anemia in both non-CKD and NDD-CKD patients.