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Increased issues in the health of urban migrant children induced by mental stress have attracted worldwide attention. The stable growth of migrant children, which affects the sustainable stability of the community, is adversely impacted by sustained pressures without adequate assistance and advice. Therefore, psychological stress from each post on microblog has been established through research focused on detecting migrant children. Nevertheless, after stressful times, it is more important to consider stress from aspect to recognize stressful periods and stressful incidents that cause each stressful period. This paper identifies the problem of the recognition of migrant children in the open social network microblog stressful periods and stressor events. Besides, the improved Poisson-dependent probability model has been developed and focused on the study of the postings of migrant children during stressful activities in schools to compare stressors with a set of postings on Tencent Weibo (microblog). Through this model the average stressed times of migrant children which obtains more information regarding potential stressors affecting stressed periods., Author(s): Fang Ji [sup.1] , Ching-Hsien Hsu [sup.2] , Carlos Enrique Montenegro-Marin [sup.3] Author Affiliations: (1) grid.440661.1, 0000 0000 9225 5078, School of Literature, Art and Communication, Chang'an University, , [...]

Nowadays, economic growth is influenced by many domestic and foreign global issues, concerning the decrease of industrial output for the rise in economic inequality and the weakening of the overall condition in the countries. The role of economic growth is based on human capital and investment. Therefore, in this paper, the human capital and economic management system (HC‐EMS) has been proposed to enhance the country's quality based on the capital structure investment. HC explores the role of command and its significance in the production and creation aspects of human capital such that an area can grow creatively. HC‐EMS analysed critical monitoring systems for developing human resources and the high‐tech and creative economic sectors and presented suggestions for change. Besides, there is a need to prove the shortcomings and benefits of human resource development systems and statistical approaches for gathering and analysing information. The impact of financial services and financial transactions may vary; therefore, it is critically important for fostering economic growth. The experimental result suggests that the proposed HC‐EMS achieves the highest growth development ratio than other existing methods. [ABSTRACT FROM AUTHOR]

Open data has been improving both publishing platforms and the consumers-oriented process over the years, providing better openness policies and transparency. Although organizations have tried to open their data, the enrichment of their resources through the Web of Data has been decreasing. Linked data has been suffering from notable difficulties in different stages of its life cycle, becoming over the years less attractive to users. According to that, we decided to explore how the lack of some opening requirements affects the decline of the Web of Data. This paper presents the Web of Data radiography, analyzing the governmental domain as a case study. The results indicate that it is necessary to strengthen the data opening process to improve resource enrichment on the Web and have better datasets. These improvements describe that open data must be public, accessible (in machine-readable formats), described (use of robust, granular metadata), reusable (made available under an open license), complete (published in primary forms), and timely (preserve the value of the data). The implementation of these characteristics would enhance the availability and reuse of datasets. Besides, organizations must understand that opening and enriching their data require a completely new approach, and they have to pay special attention and control to this project, generally by putting money, the commitment by management at all levels, and lots of time. On the contrary, given the magnitude of availability and reuse problems identified in the opening and enrichment data process, it is believed that the Web of Data model would inevitably lose the interest it aroused at the beginning if not addressed immediately by data quality, openness, and enrichment issues. Besides, its use would be restricted to a few particular niches or would even disappear altogether. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technologies allow building a digital representation of people, objects, or physical phenomena to be available on the Internet. Thus, stakeholders can access this information from remote places or computational systems could analyze this data to find patterns, make decisions, or execute actions. For instance, a doctor could diagnose patients by analyzing the received data from an IoT system even when patients are located in a remote place. This article proposes an IoT system for monitoring electrocardiogram (ECG) signal and processing heart data in order to generate an alert when an arrhythmia is present. This system involves a Polar H10 heart sensor, machine-learning models to classify heart events, and communication technology to share and store patient's information. In the first place, the architecture of the IoT monitoring system and the communication between the components are described by discussing the designing criteria. Second, the experimentation process performs the training and the assessment of three classification algorithms, random forest, convolutional neural network, and k-nearest neighbors. The results show that k-nearest neighbor has the best accuracy percentage classifying the arrhythmias under study (premature ventricular contraction 94%, fusion of ventricular beat 81%, and supraventricular premature beat 82%); also, it is able to discern normal and unclassifiable beats with 93% and 97%, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing and Internet of Things (IoT) are popularly intertwined to create a network of smart systems, especially in the field of Agriculture. As agricultural industries rise, augmented use of heterogeneous communication and sensing on the field would be needed. For all this data to be transferred and processed on the cloud efficiently, low latency rates, consistently high bandwidths, minimal congestion, etc. would be needed, which, is still a complication with the current cloud computing models. This paper proposes a multi-layered fog architecture that detects outliers in the data received from the sensing environment based on three categories: Classification, Isolation, and Clustering and then aggregates it before sending it to the cloud. The architecture works closely in a user-centered design approach that connects the farmers and analysts to the fog allowing them to create an automated agricultural system. With the help of Fog, processing abilities are brought closer to the data source which reduces the load on cloud resources, thereby making the overall system a lot more efficient and secured. This paper also presents a prototype of the interface that can be used to monitor and control IoT devices on the field as well as define fuzzy rules for the agricultural system. [ABSTRACT FROM AUTHOR]

Increased issues in the health of urban migrant children induced by mental stress have attracted worldwide attention. The stable growth of migrant children, which affects the sustainable stability of the community, is adversely impacted by sustained pressures without adequate assistance and advice. Therefore, psychological stress from each post on microblog has been established through research focused on detecting migrant children. Nevertheless, after stressful times, it is more important to consider stress from aspect to recognize stressful periods and stressful incidents that cause each stressful period. This paper identifies the problem of the recognition of migrant children in the open social network microblog stressful periods and stressor events. Besides, the improved Poisson-dependent probability model has been developed and focused on the study of the postings of migrant children during stressful activities in schools to compare stressors with a set of postings on Tencent Weibo (microblog). Through this model the average stressed times of migrant children which obtains more information regarding potential stressors affecting stressed periods. [ABSTRACT FROM AUTHOR]

Background: Internet of Things (IoT) is a hopeful advancement that is an accurate international link for smart devices for total initiatives. Physical Education (PE) builds students' abilities and trust to engage in various physical activities, both within and outside their classrooms. The challenging characteristics in the learning management system include lack of setting a clear goal, lack of system integration, and failure to find an implementation team is considered as an essential factor.Objective: In this paper, an IoT-based technological acceptance learning management framework (IoT-TALMF) has been proposed to identify the objectives, resource allocation, and effective team for group work in the physical education system.Method: Physical Educators primarily use the learning management framework as databases of increased management components, choosing to interact with students, teammates, organizations. Statistical course content analysis is introduced to identify and set clear goals that motivate students for the physical education system. The course instructor learning technique is incorporated with IoT-TALMF to improve system integration based on accuracy and implement an effective team to handle unexpected cost delays in the physical education system.Results: The numerical results show that the IoT-TALMF framework enhances the identity accuracy ratio of 97.33%, the performance ratio of students 96.2%, and the reliability ratio of 97.12%, proving the proposed framework's reliability. [ABSTRACT FROM AUTHOR]

The Pacific Alliance is an economic and development initiative where four Latin American nations: Chile, Colombia, Mexico, and Peru, seek the growth in the economics of the four nations. It is in this way that agriculture plays an important role in this union since it is one of the greatest sources of income in each nation. However, the internal conflict that afflicts each country generates stagnation in economic growth, affecting its exports. Forced displacement is one of the main consequences of the internal conflict within the countries that make up the Pacific Alliance. Therefore, a model of Population Dynamics System P (PDP System) is proposed, viewing this phenomenon as a classic predator-prey model, where one 'predator' resources that in this case are the territories, and in this struggle one species predominates over the other. The simulations showed how the populations are being affected and how the analogy between the social conflict described above and the Lokta-Volterra model can be modelled with a topic that has always been considered to be social. Although much more complex scenarios can be simulated by adding other variables, this study is an approach that yields important results that can be considered in other studies [ABSTRACT FROM AUTHOR]

Connected vehicles in intelligent transportation system (ITS) scenario rely on environmental data for supporting user-centric applications along the driving time. Sensors equipped in the vehicles are responsible for accumulating data from the environment, followed by the fusion process. Such fusion process provides accurate and stable data required for the applications in a recurrent manner. In order to enhance the data fusion of connected vehicles, this article introduces multi-variate data fusion (MVDF) technique. This technique is competent in handling asynchronous and discrete data from the environment and streamlining them into continuous and delay-less inputs for the applications. The process of data fusion is aided through least square regression learning to determine the errors in different time instances. The indefinite and definite data fusion instances are differentiated using this regression model to identify the errors in fore-hand. Besides, the differentiation relies on the application run-time interval to progress data fusion within the same or extended time instance and data slots. In this manner the differentiation along with the error identification is regular until the application required data is met. The performance of this technique is verified using network simulator experiments for the metrics error, data utilization ratio, and computation time. The results show that this technique improves data utilization under controlled time and fewer errors. [ABSTRACT FROM AUTHOR]

Aim: This paper aims to analyze, prepare, and review the general guidelines and rules that govern the development of key factors influencing the enhancement of emotionally supportive networks and selection models using fuzzy logic theory. The researchers have identified eight important components of the information society (IS), representing the computerized economy's growth to explain a realistic framework for medium-term gauges and proposals. Materials and methods: A discrete-nonstop opportunity paradigm portrays the creation of the general framework, in which the mutual effects of each of the components are spoken to models within the state-space. The software's mechanical quality offers improvement displayed along these lines that may indicate future interest to programing suppliers. The researchers have given supposed to the developments and interests of information technology (IT) professionals in R&D to provide insightful foundations. For example, this study will demonstrate the development of emotionally supportive networks and recommendations of choices for 3D-web-based businesses and their impact on mechanical advancement, examples of use and social behavior. Results: During an IS/IT foreknowledge undertaking completed in Poland in 2019 and sponsored by the Education Research and Development Foundation ERDF, the results were obtained. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has gained significant importance due to its flexibility in integrating communication technologies and smart devices for the ease of service provisioning. IoT services rely on a heterogeneous cloud network for serving user demands ubiquitously. The service data management is a complex task in this heterogeneous environment due to random access and service compositions. In this article, a machine learning aided information management scheme is proposed for handling data to ensure uninterrupted user request service. The neural learning process gains control over service attributes and data response to abruptly assign resources to the incoming requests in the data plane. The learning process operates in the data plane, where requests and responses for service are instantaneous. This facilitates the smoothing of the learning process to decide upon the possible resources and more precise service delivery without duplication. The proposed data management scheme ensures less replication and minimum service response time irrespective of the request and device density. [ABSTRACT FROM AUTHOR]

In recent days, many information and image evidence in the field of medicine is being designed and developed by the advancement of computer technology. Presently, sport medical data is an essential department for medical sector and it is responsible for assuring sports safety based on the recovery level after an injury due to sports activity. The approach to reliably interpretation and valuable data information using a vast number of medical sports data and events has become an important research path in the collection and analysis of medical data. This paper discusses the extraction, study, and lack of training and accuracy of complex algorithms for critical sporting medical data. This paper involves with an optimized convolutional neural network (OCNN) based on deep-learning model to ensure successful detection and risk assessments of sport-medicine diseases and adopts the Self-Adjustment Resizing algorithm (SAR) augmented by the self-coding method of the convolution (SCM). CNN model helps to evaluate sports medicine in multi-dimensional results and suggested OCNN classification constitutes two convolutional layers, two pool layers, a fully connected layer, and a SoftMax structure that can be used for the classification of sport-related medical data. The CNN facilitates multi-dimensional sports medicine data analysis and to conclude a cloud-based loop model to create an advanced medical data network for sports medicine. Experiments illustrate that this approach offers technical support and guide to deploying a specific cloud-based fusion system. [ABSTRACT FROM AUTHOR]

The huge amount of data on the Internet and the diverse list of strategies used to try to link this information with relevant searches through Linked Data have generated a revolution in data treatment and its representation. Nevertheless, the conventional search engines like Google are kept as strategies with good reception to do search processes. The following article presents a study of the development and evolution of search engines, more specifically, to analyze the relevance of findings based on the number of results displayed in paging systems with Google as a case study. Finally, it is intended to contribute to indexing criteria in search results, based on an approach to Semantic Web as a stage in the evolution of the Web. [ABSTRACT FROM AUTHOR]

Through different viewpoints exposed by an ArchiMate Business Architecture Model, a high level of business abstraction is achieved in the development phase of a software system. This allows to carry out the automation of business rules for payroll loan products placement within the receipt of credit applications. Currently the flexibility of information systems plays a key role as a strategy in the decision-making process in financial institutions. This is because they require a timely response to the changing market demand for products and services for financial consumers. Given the above, information technology management in organizations faces the ongoing challenge of developing software solutions that meet the needs of internal and external customers. This is done by using recurring architectural models that provide safety, maintainability and scalability. In order to make this possible, a business rules automation model that dissociates the business logic from the business rules facilitates, subsequently, a possible change in the proposed rules. In this paper, the architecture of the software system is based on five viewpoints offered by the ArchiMate modeling, which was carried out with the modeling tool Coloso. [ABSTRACT FROM PUBLISHER]

Robotics applications to healthcare have become more prominent in the last few years. Some of the many robotics applications in healthcare include surgical robots, pharmacy robots, robotic telemedicine, robotics-assisted infectious disease management, robotics rehabilitation medicine and many more. The authors propose a grey-level correlation algorithm using neural networks to efficiently solve medical resource management problems. [Extracted from the article]

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This article provides a deep analysis of the water quality at the upper basin of the Bogota River (Colombia) between 2008 and 2017. The Water Quality Index has been the indicator employed to determine the ecological status of the river. This index was chosen in order to normalize the analysis, given that it is commonly used by the Institute of Hydrology, Meteorology and Environmental Studies, a government agency of the Ministry of Environment and Sustainable Development of Colombia, to determine the state of surface effluents. The results obtained were organized in a double-entry matrix in order to relate the variables of the sample period and the sampling station. The research revealed an insufficient quality of water, demonstrating that the high stretch of the Bogota River basin has, in general, regular or acceptable water quality, while only five stations showed an acceptable status. Surprisingly, the stations located close to the wastewater treatment plants of the municipalities of Choconta, Suesca, Gachancipa, and Tocancipa, as well as Rio Negro, have a poor water quality, discharging a high load of contaminants into the river. Although great efforts have been made by Colombian authorities to restore the critical state of the majority of their aquatic ecosystems, recent implementation of policies and instruments have not shown significant achievements yet. For this reason, this study aims to present a powerful decision-tool for the monitoring and evaluation of correction measures implemented on this river basin. The data used in this research were provided by the Regional Autonomous Corporation of Cundinamarca. [ABSTRACT FROM AUTHOR]