Search results for: health social network

Authors: Zeinab Aliyas, Diba Mahboubi

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Leisure walking is known as one of the most common types of physical activity that perform in purpose of recreation or health, which in turn may affect resident’s health. In the recent years, promoting leisure walking activity in neighborhood areas become as one of the important issues regarding promoting mental and physical health, however; the level of physical inactivity is rising in many societies including Iran. As it was proven that the tendency to walk out of choice is not encouraging among Iranian people. Hence; understanding the main concern of residents regarding walking activity in their neighborhoods can help in increasing the tendency to do leisure activity among residents. Built environment, social and individual factors are known as the main factors that affect decision to walk, in this regard, the study aimed to investigate the influence of personal and social factors that prevent residents to walk for recreation or exercise in their neighborhoods. Hence the fear of crime and personal barriers were examined in the current research as social and personal factors respectively. To collect the required data, 500 questionnaires by using systematic sampling were distributed from March to May 2016 in four residential neighborhoods of Bandar Abbas in Iran out which 411 questionnaire turned out to be qualified to be used in the study. The Smart-PLS was used to analyze the data. The findings of the study revealed that personal and fear of crime both have significant influence on the level of recreation and exercise walking in the neighborhood areas. The study found that fear of crime has the higher influence on exercise and recreational walking behavior in comparison to individual factors. It was revealed that social factors such as fear of crime in the neighborhoods might be more important than the personal reason for walking optionally in the surrounding environment. The finding of this study can help urban and health researcher to know the significant influence of fear of crime and individual attitudes on the level of leisure walking activity, in addition, the findings of the study suggest that urban planners and designers, as well as public health promoters, need to highly consider the contribution of neighborhoods' social environment variables as well as individual variables to promote walking behavior changes among adult population.

Keywords: exercise walking, fear of crime, neighborhood, personal barriers, recreation walking

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Authors: Afshin Moazami, Andrea Schiffauerova

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The objective of this work is to investigate the development and the role of collaboration networks in the creation of knowledge and innovations in the US and Canada, with a special focus on Quebec. In order to create scientific networks, the data on journal articles were extracted from SCOPUS, and the networks were built based on the co-authorship of the journal papers. For innovation networks, the USPTO database was used, and the networks were built on the patent co-inventorship. Various indicators characterizing the evolution of the network structure and the positions of the researchers and inventors in the networks were calculated. The comparison between the United States, Canada, and Quebec was then carried out. The preliminary results show that the nature of scientific collaboration networks differs from the one seen in innovation networks. Scientists work in bigger teams and are mostly interconnected within one giant network component, whereas the innovation network is much more clustered and fragmented, the inventors work more repetitively with the same partners, often in smaller isolated groups. In both Canada and the US, an increasing tendency towards collaboration was observed, and it was found that networks are getting bigger and more centralized with time. Moreover, a declining share of knowledge transfers per scientist was detected, suggesting an increasing specialization of science. The US collaboration networks tend to be more centralized than the Canadian ones. Quebec shares a lot of features with the Canadian network, but some differences were observed, for example, Quebec inventors rely more on the knowledge transmission through intermediaries.

Keywords: Canada, collaboration, innovation network, scientific network, Quebec, United States

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Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

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Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition

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Authors: G. Baraneedaran, Deepak Singh, Kollipara Tejesh

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The wireless sensors network has been an active research area over the y-ear passed. Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively, an energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. A spontaneous reconstruction mechanism for Local topology is designed additionally. In this experiment, FAF-EBRM is compared with LEACH and EECU, experimental results show that FAF-EBRM outperforms LEACH and EECU, which balances the energy consumption, prolongs the function lifetime and guarantees high Qos of WSN.

Keywords: energy balance, forward-aware factor (FAF), forward energy density, link weight, network performance

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Authors: Jungyeol Hong, Dongjoo Park

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The urban transit system is a critical part of a solution to the economic, energy, and environmental challenges. Furthermore, it ultimately contributes the improvement of people’s quality of lives. For taking these kinds of advantages, the city of Seoul has tried to construct an integrated transit system including both subway and buses. The effort led to the fact that approximately 6.9 million citizens use the integrated transit system every day for their trips. Diagnosing the current transit network is a significant task to provide more convenient and pleasant transit environment. Therefore, the critical objective of this study is to establish a methodological framework for the analysis of an integrated bus-subway network and to examine the relationship between subway ridership and parameters such as network topology measures, bus demand, and a variety of commercial business facilities. Regarding a statistical approach to estimate subway ridership at a station level, many previous studies relied on Ordinary Least Square regression, but there was lack of studies considering the endogeneity issues which might show in the subway ridership prediction model. This study focused on both discovering the impacts of integrated transit network topology measures and endogenous effect of bus demand on subway ridership. It could ultimately contribute to developing more accurate subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers Seoul city in South Korea, and it includes 243 subway stations and 10,120 bus stops with the temporal scope set during twenty-four hours with one-hour interval time panels each. The subway and bus ridership information in detail was collected from the Seoul Smart Card data in 2015 and 2016. First, integrated subway-bus network topology measures which have characteristics regarding connectivity, centrality, transitivity, and reciprocity were estimated based on the complex network theory. The results of integrated transit network topology analysis were compared to subway-only network topology. Also, the non-recursive approach which is Three-Stage Least Square was applied to develop the daily subway ridership model as capturing the endogeneity between bus and subway demands. Independent variables included roadway geometry, commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. Consequently, it was found that network topology measures were significant size effect. Especially, centrality measures showed that the elasticity was a change of 4.88% for closeness centrality, 24.48% for betweenness centrality while the elasticity of bus ridership was 8.85%. Moreover, it was proved that bus demand and subway ridership were endogenous in a non-recursive manner as showing that predicted bus ridership and predicted subway ridership is statistically significant in OLS regression models. Therefore, it shows that three-stage least square model appears to be a plausible model for efficient subway ridership estimation. It is expected that the proposed approach provides a reliable guideline that can be used as part of the spectrum of tools for evaluating a city-wide integrated transit network.

Keywords: integrated transit system, network topology measures, three-stage least squared, endogeneity, subway ridership

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Authors: A. Kardi, R. Zagrouba, M. Alqahtani

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The Internet of Everything (IoE) presents today a very attractive and motivating field of research. It is basically based on Wireless Sensor Networks (WSNs) in which the routing task is the major analysis topic. In fact, it directly affects the effectiveness and the lifetime of the network. This paper, developed from recent works and based on extensive researches, proposes a taxonomy of routing protocols in WSNs. Our main contribution is that we propose a classification model based on nine classes namely application type, delivery mode, initiator of communication, network architecture, path establishment (route discovery), network topology (structure), protocol operation, next hop selection and latency-awareness and energy-efficient routing protocols. In order to provide a total classification pattern to serve as reference for network designers, each class is subdivided into possible subclasses, presented, and discussed using different parameters such as purposes and characteristics.

Keywords: routing, sensor, survey, wireless sensor networks, WSNs

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Authors: Andre Slonopas, Zona Kostic, Warren Thompson

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Obfuscation is one of the most useful tools to prevent network compromise. Previous research focused on the obfuscation of the network communications between external-facing edge devices. This work proposes the use of two edge devices, external and internal facing, which communicate via private IPv4 addresses in a software-defined pseudo-random IP hopping. This methodology does not require additional IP addresses and/or resources to implement. Statistical analyses demonstrate that the hopping surface must be at least 1e3 IP addresses in size with a broad standard deviation to minimize the possibility of coincidence of monitored and communication IPs. The probability of breaking the hopping algorithm requires a collection of at least 1e6 samples, which for large hopping surfaces will take years to collect. The probability of dropped packets is controlled via memory buffers and the frequency of hops and can be reduced to levels acceptable for video streaming. This methodology provides an impenetrable layer of security ideal for information and supervisory control and data acquisition systems.

Keywords: moving target defense, cybersecurity, network security, hopping randomization, software defined network, network security theory

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Authors: Muntadher Sallal, Gareth Owenson, Mo Adda, Safa Shubbar

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Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a delay overhead in information propagation, which makes the system vulnerable to double-spend attacks. Aiming at alleviating the propagation delay problem, this paper introduces proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol, that are based on how clusters are formulated and how nodes can define their membership, is to improve the information propagation delay in the Bitcoin network. In MNBC protocol, physical internet connectivity increases, as well as the number of hops between nodes, decreases through assigning nodes to be responsible for maintaining clusters based on physical internet proximity. We show, through simulations, that the proposed protocol defines better clustering structures that optimize the performance of the transaction propagation over the Bitcoin protocol. The evaluation of partition attacks in the MNBC protocol, as well as the Bitcoin network, was done in this paper. Evaluation results prove that even though the Bitcoin network is more resistant against the partitioning attack than the MNBC protocol, more resources are needed to be spent to split the network in the MNBC protocol, especially with a higher number of nodes.

Keywords: Bitcoin network, propagation delay, clustering, scalability

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Deepak Singh

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AI technology is rapidly being integrated into the healthcare system, bringing many ethical and social implications. This literature review examines the various aspects of this phenomenon, focusing on the ethical considerations of using AI in healthcare, such as how it might affect patient autonomy, privacy, and doctor-patient relationships. Furthermore, the review considers the potential social implications of AI in Healthcare, such as the potential for automation to reduce the availability of healthcare jobs and the potential to widen existing health inequalities. The literature suggests potential benefits and drawbacks to using AI in healthcare, and it is essential to consider the ethical and social implications before implementation. It is concluded that more research is needed to understand the full implications of using AI in healthcare and that ethical regulations must be in place to ensure patient safety and the technology's responsible use.

Keywords: AI, healthcare, telemedicine, telehealth, ethics, security, privacy, patient, rights, safety

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Authors: Helena Baffoe

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Context: In the United States, there have been significant advancements in programs aimed at improving the lives of individuals with mental health disorders and substance abuse problems. However, public attitudes and beliefs regarding these issues have not improved correspondingly. This study aims to explore the perceptions and beliefs surrounding mental health disorders and substance abuse in the context of data analytics in the field of social work. Research Aim: The aim of this research is to provide empirical evidence on the beliefs and perceptions regarding mental health disorders and substance abuse. Specifically, the study seeks to answer the question of whether being diagnosed with a mental disorder implies a diagnosis of substance abuse. Additionally, the research aims to analyze the specific roles that social workers can play in addressing individuals with mental disorders. Methodology: This research adopts a data-driven methodology, acquiring comprehensive data from the Substance Abuse and Mental Health Services Administration (SAMHSA). A noteworthy causal connection between mental disorders and substance abuse exists, a relationship that current literature tends to overlook critically. To address this gap, we applied logistic regression with an Instrumental Variable approach, effectively mitigating potential endogeneity issues in the analysis in order to ensure robust and unbiased results. This methodology allows for a rigorous examination of the relationship between mental disorders and substance abuse. Empirical Findings: The analysis of the data reveals that depressive, anxiety, and trauma/stressor mental disorders are the most common in the United States. However, the study does not find statistically significant evidence to support the notion that being diagnosed with these mental disorders necessarily implies a diagnosis of substance abuse. This suggests that there is a misconception among the public regarding the relationship between mental health disorders and substance abuse. Theoretical Importance: The research contributes to the existing body of literature by providing empirical evidence to challenge prevailing beliefs and perceptions regarding mental health disorders and substance abuse. By using a novel methodological approach and analyzing new US data, the study sheds light on the cultural and social factors that influence these attitudes.

Keywords: mental health disorder, substance abuse, empirical evidence, logistic regression with IV

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Authors: Uvais Qidwai, Amor Moursi, Mohamed Tahar, Malek Hamad, Hamad Alansi

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This paper focuses on the development and implementation of an advanced plant health monitoring system with an AI backbone and IoT sensory network. Our approach involves addressing the critical environmental factors essential for preserving a plant’s well-being, including air temperature, soil moisture, soil temperature, soil conductivity, pH, water levels, and humidity, as well as the presence of essential nutrients like nitrogen, phosphorus, and potassium. Central to our methodology is the utilization of computer vision technology, particularly a night vision camera. The captured data is then compared against a reference database containing different health statuses. This comparative analysis is implemented using an AI deep learning model, which enables us to generate accurate assessments of plant health status. By combining the AI-based decision-making approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.

Keywords: deep learning image model, IoT sensing, cloud-based analysis, remote monitoring app, computer vision, fuzzy control

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Authors: Xingyu Gao, Qiang Wu, Yuanyuan Liu, Yue Yang

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In the era of the knowledge economy, the relationship between scientific knowledge and patents has garnered significant attention. Understanding the intricate interplay between the foundations of science and technological innovation has emerged as a pivotal challenge for both researchers and policymakers. This study establishes a coupled network of artificial intelligence patents based on co-cited scientific papers. Leveraging centrality metrics from network analysis offers a fresh perspective on understanding the influence of information flow and knowledge sharing within the network on patent impact. The study initially obtained patent numbers for 446,890 granted US AI patents from the United States Patent and Trademark Office’s artificial intelligence patent database for the years 2002-2020. Subsequently, specific information regarding these patents was acquired using the Lens patent retrieval platform. Additionally, a search and deduplication process was performed on scientific non-patent references (SNPRs) using the Web of Science database, resulting in the selection of 184,603 patents that cited 37,467 unique SNPRs. Finally, this study constructs a coupled network comprising 59,379 artificial intelligence patents by utilizing scientific papers co-cited in patent backward citations. In this network, nodes represent patents, and if patents reference the same scientific papers, connections are established between them, serving as edges within the network. Nodes and edges collectively constitute the patent coupling network. Structural characteristics such as node degree centrality, betweenness centrality, and closeness centrality are employed to assess the scientific connections between patents, while citation count is utilized as a quantitative metric for patent influence. Finally, a negative binomial model is employed to test the nonlinear relationship between these network structural features and patent influence. The research findings indicate that network structural features such as node degree centrality, betweenness centrality, and closeness centrality exhibit inverted U-shaped relationships with patent influence. Specifically, as these centrality metrics increase, patent influence initially shows an upward trend, but once these features reach a certain threshold, patent influence starts to decline. This discovery suggests that moderate network centrality is beneficial for enhancing patent influence, while excessively high centrality may have a detrimental effect on patent influence. This finding offers crucial insights for policymakers, emphasizing the importance of encouraging moderate knowledge flow and sharing to promote innovation when formulating technology policies. It suggests that in certain situations, data sharing and integration can contribute to innovation. Consequently, policymakers can take measures to promote data-sharing policies, such as open data initiatives, to facilitate the flow of knowledge and the generation of innovation. Additionally, governments and relevant agencies can achieve broader knowledge dissemination by supporting collaborative research projects, adjusting intellectual property policies to enhance flexibility, or nurturing technology entrepreneurship ecosystems.

Keywords: centrality, patent coupling network, patent influence, social network analysis

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Authors: M. Kebisek, L. Spendla, M. Kopcek, T. Skulavik

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In our paper we describe the security capabilities of data collection. Data are collected with probes located in the near and distant surroundings of the company. Considering the numerous obstacles e.g. forests, hills, urban areas, the data collection is realized in several ways. The collection of data uses connection via wireless communication, LAN network, GSM network and in certain areas data are collected by using vehicles. In order to ensure the connection to the server most of the probes have ability to communicate in several ways. Collected data are archived and subsequently used in supervisory applications. To ensure the collection of the required data, it is necessary to propose algorithms that will allow the probes to select suitable communication channel.

Keywords: communication, computer network, data collection, probe

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Authors: Longtao He, Han Wu

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Summary: As the need for elder care increases in China due to a growing aging population and, in particular, rising cancer rates, it becomes increasingly important to also support family caregivers, who are often the main source of care. We used a qualitative meta-synthesis to systematically evaluate and integrate the caregiving experiences of family caregivers of elderly cancer patients as revealed by articles published in Chinese journals. Findings: Nine studies are included in the final analysis. The caregiver experiences they describe are synthesized into three primary themes: care needs, care burden, and care gains, with numerous secondary themes. Besides the findings that seem to align with other findings across cultures, we have highlighted three main discoveries from the synthesis that may be quite specific to the Chinese context: 1. more sub-themes related to specific caregiving skills caregivers of cancer patients; 2. a call for health professionals to improve their communication skills with family caregivers; 3. the important role of filial piety. Applications: Our findings can be used to help health social workers and relevant policymakers in China support family caregivers by identifying the education and training required for caregivers, ways to make the most of potential care gains, and ways to ease care burdens.

Keywords: cancer, Chinese family caregivers, caregiving skills, care burden, care gains, health social work

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Authors: William Marquis, Fang Dong

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There has been a growing concern in recent years over the economic and social effects of adult video gaming. It has been estimated that the number of people who played video games during the COVID-19 pandemic is close to three billion, and there is evidence that this form of entertainment is here to stay. Many people are concerned that this growing use of time could crowd out time that could be spent on alternative forms of entertainment with family, friends, sports, and other social activities that build community. For example, recent studies of children suggest that playing videogames crowds out time that could be spent on homework, watching TV, or in other social activities. Similar studies of adults have shown that video gaming is negatively associated with earnings, time spent at work, and socializing with others. The primary objective of this paper is to examine how time adults spend on video gaming could displace time they could spend working and on activities that enhance their health and well-being. We use data from the American Time Use Survey (ATUS), maintained by the Bureau of Labor Statistics, to analyze the effects of time-use decisions on three measures of well-being. We pool the ATUS Well-being Module for multiple years, 2010, 2012, 2013, and 2021, along with the ATUS Activity and Who files for these years. This pooled data set provides three broad measures of well-being, e.g., health, life satisfaction, and emotional well-being. Seven variants of each are used as a dependent variable in different multivariate regressions. We add to the existing literature in the following ways. First, we investigate whether the time adults spend in video gaming crowds out time spent working or in social activities that promote health and life satisfaction. Second, we investigate the relationship between adult gaming and their emotional well-being, also known as negative or positive affect, a factor that is related to depression, health, and labor market productivity. The results of this study suggest that the time adult gamers spend on video gaming has no effect on their supply of labor, a negligible effect on their time spent socializing and studying, and mixed effects on their emotional well-being, such as increasing feelings of pain and reducing feelings of happiness and stress.

Keywords: online gaming, health, social capital, emotional wellbeing

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Authors: Ghada Moussa, Mamoud Owais

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Transit Route Network Design Problem (TrNDP) is the most important component in Transit planning, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a novel solution methodology for the TrNDP, which goes beyond pervious traditional sophisticated approaches. The novelty of the solution methodology, adopted in this paper, stands on the deterministic operators which are tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. The solution methodology has been tested through Mandl’s benchmark network problem. The test results showed that the methodology developed in this research is able to improve the given network solution in terms of number of constructed routes, direct transit service coverage, transfer directness and solution reliability. Although the set of routes resulted from the methodology would stand alone as a final efficient solution for TrNDP, it could be used as an initial solution for meta-heuristic procedures to approach global optimal. Based on the presented methodology, a more robust network optimization tool would be produced for public transportation planning purposes.

Keywords: integer programming, transit route design, transportation, urban planning

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Authors: Jatuporn Juyjingam, Pitak Siriwong

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This paper presents a study of factors effecting the success and failure of social enterprise in Thailand identifying communication as one of the criteria for measuring the social impact of social enterprise. The study focused on the communication driver of the SCALERS model. The research examines how communication is viewed in Thailand social enterprise. The research aims to determine how selected social enterprise uses communication in their operations. More specifically, the study aims to 1) describe the profile of social enterprise in Thailand, 2) identify the different roles of communication in the operation of social enterprise in Thailand, 3) determine Thailand social enterprise concept of communication. The study made use of the case study and cross case study research designs. For the profiling of the social enterprises, the case study was used. The researchers made use of the cross-case research design in identifying trends across the ten social enterprises and in determining the social entrepreneurs’ concept of communication. Key informant interviews were conducted with the heads or representatives of selected social enterprises, a three-part interview schedule was used to facilitate data gathering. The three parts included are 1) Profile of social enterprise in Thailand 2) How social enterprises apply communication in their operations 3) What is the key success in using communication among social enterprise in Thailand. This study is an exploratory research.

Keywords: communication, social entrepreneurship, social enterprise, sustainability development

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Authors: Kebron Abich Asnake

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The outbreak of COVID-19 has had far-reaching socio-economic consequences globally, and Ethiopia is no exception. This abstract provides a summary of a research study on the socio-economic impact of COVID-19 in Ethiopia. The study analyzes the health impact, economic repercussions, social consequences, government response measures, and opportunities for post-crisis recovery. In terms of health impact, the research explores the spread and transmission of the virus, the capacity and response of the healthcare system, and the mortality rate, with a focus on vulnerable populations. The economic impact analysis entails investigating the contraction of the GDP, employment and income loss, disruption in key sectors such as agriculture, tourism, and manufacturing, and the specific implications for small and medium-sized enterprises (SMEs), foreign direct investment, and remittances. The social impact section looks at the disruptions in education and the digital divide, food security and nutrition challenges, increased poverty and inequality, gender-based violence, and mental health issues. The research also examines the measures taken by the Ethiopian government, including health and safety regulations, economic stimulus packages, social protection programs, and support for vulnerable populations. Furthermore, the study outlines long-term recovery prospects, social cohesion, and community resilience challenges. It highlights the need to strengthen the healthcare system and finds a balance between health and economic priorities. The research concludes by presenting recommendations for policy-makers and stakeholders, emphasizing opportunities for post-crisis recovery such as diversification of the economy, enhanced healthcare infrastructure, investment in digital infrastructure and technology, and support for domestic tourism and local industries. This research provides valuable insights into the socio-economic impact of COVID-19 in Ethiopia, offering a comprehensive analysis of the challenges faced and potential pathways towards recovery.

Keywords: impact, covid, ethiopia, health

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: Sananda Mukherjee, Nandini Lakshmikantha

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Films are considered as the most influential form of mass media. To attract audience films are made on various themes and issues which are assumed to have an impact on the behavioural pattern of the society. Among the various issues that have been bothering Indian society, health is primary. Thus it is important and interesting to study how health is being projected in Bollywood which is largely considered by the world as Indian cinema. This study tries to focus its attention on some select popular movies made in the recent decade and will try to analyse its content and significance of the same with the contemporary Indian society. It is evident that some of the movies made projecting health issues have earned good box office revenues, but have they been successful in making the public understand the significance of health issues they have been trying to project, is an interesting area to understand.

Keywords: box office, health issues, Indian cinema, social awareness

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: Ali Asmari, Alex Symington, Htaik Than, Austin Caradonna, John Senft

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This paper presents the collaborative effort between ULC Technologies and Con Edison in developing a groundbreaking robotic cable splicing machine. The focus is on the machine's design, which integrates advanced robotics and automation to enhance safety and efficiency in power network maintenance. The paper details the operational steps of the machine, including cable grounding, cutting, and removal of different insulation layers, and discusses its novel technological approach. The significant benefits over traditional methods, such as improved worker safety and reduced outage times, are highlighted based on the field data collected during the validation phase of the project. The paper also explores the future potential and scalability of this technology, emphasizing its role in transforming the landscape of power network maintenance.

Keywords: cable splicing machine, power network maintenance, electric distribution, electric transmission, medium voltage cable

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Authors: Raquel Camprubí

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Cultural tourism has been postulated as relevant motivation for tourist over the world during the last decades. In this context, museums are the main attraction for cultural tourists who are seeking to connect with the history and culture of the visited place. From the point of view of an urban destination, museums and other cultural resources are essential to have a strong tourist supply at the destination, in order to be capable of catching attention and interest of cultural tourists. In particular, museums’ challenge is to be prepared to offer the best experience to their visitors without to forget their mission-based mainly on protection of its collection and other social goals. Thus, museums individually want to be competitive and have good positioning to achieve their strategic goals. The life cycle of the destination and the level of maturity of its tourism product influence the need of tourism agents to cooperate and collaborate among them, in order to rejuvenate their product and become more competitive as a destination. Additionally, prior studies have considered an approach of different models of a public and private partnership, and collaborative and cooperative relations developed among the agents of a tourism destination. However, there are no studies that pay special attention to museums and the strategic alliances developed to obtain mutual benefits. Considering this background, the purpose of this study is to analyze in what extent museums of a given urban destination have established strategic links and relations among them, in order to improve their competitive position at both individual and destination level. In order to achieve the aim of this study, the city of Girona (Spain) and the museums located in this city are taken as a case study. Data collection was conducted using in-depth interviews, in order to collect all the qualitative data related to nature, strengthen and purpose of the relational ties established among the museums of the city or other relevant tourism agents of the city. To conduct data analysis, a Social Network Analysis (SNA) approach was taken using UCINET software. Position of the agents in the network and structure of the network was analyzed, and qualitative data from interviews were used to interpret SNA results. Finding reveals the existence of strong ties among some of the museums of the city, particularly to create and promote joint products. Nevertheless, there were detected outsiders who have an individual strategy, without collaboration and cooperation with other museums or agents of the city. Results also show that some relational ties have an institutional origin, while others are the result of a long process of cooperation with common projects. Conclusions put in evidence that collaboration and cooperation of museums had been positive to increase the attractiveness of the museum and the city as a cultural destination. Future research and managerial implications are also mentioned.

Keywords: cultural tourism, competitiveness, museums, Social Network analysis

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Authors: Safiyya A. Abba, Shehu U. R. Aliyu

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This paper deals with Islamic social security: a discourse explores the meaning and nature of Islamic social security system. The paper reviews the social security framework and operations during the early period. The paper further identifies the instruments of Islamic social security discusses its principles and objectives. The paper discovers that Islamic social security is a personification of a comprehensive welfare approach in view of its varied instruments that are deeply rooted in the Islamic law, unique principles and realistic and achievable objectives. Furthermore, the Islamic social security system has far reaching socioeconomic implications; social justice, cohesion, equity, a catalyst for poverty eradication, income redistribution, economic growth and development.

Keywords: Islamic social security, basic needs, zakat, socioeconomic justice, equity

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Authors: Zoltan Abram

Abstract:

Background: In the last time it was hardly increased the prevalence of psychical diseases and disorders which reduce work capacity, life quality and life expectancy. Objectives: The aim of our research is to study the psychical health state of the Romanian adults living in the middle part of the country and the role of some economical, psychological and social factors, especially in relationship with depression. Methods: The study is based on a complex anonymous questionnaire, including Beck depressive scale, which was completed by a representative sample among adult population. The applied method was a combination between stratification and more-steps sampling. Results: After our results depression is the most common psychical illness with 9,1% diagnosis, but the tendency to depression, the existence of depressive symptoms is much higher than the treated illness. The percentage of suicide attempt among the studied population was 2,9%. It is analysed how gender, age, professional and social status, living and working conditions and different social factors are influencing the health state. According to Beck score, it was established a significant difference in the favour of female, elderly people, lower educational level, urban population. Conclusions: In our study it is underlined the importance of health promotion and education. It is concluded that improving living standards, modifying in a proper way the lifestyle of the population, we can positively influence the physical and mental health state of the Romanian adult population.

Keywords: Beck scale, depression, psychological aspects, suicide attempt

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Authors: Ihab Elaff

Abstract:

Simulating the heart’s electrical stimulation is essential in modeling and evaluating the electrophysiology behavior of the heart. For achieving that, there are two structures in concern: the ventricles’ Myocardium, and the ventricles’ Conduction Network. Ventricles’ Myocardium has been modeled as anisotropic material from Diffusion Tensor Imaging (DTI) scan, and the Conduction Network has been extracted from DTI as a case-based structure based on the biological properties of the heart tissues and the working methodology of the Magnetic Resonance Imaging (MRI) scanner. Results of the produced activation were much similar to real measurements of the reference model that was presented in the literature.

Keywords: diffusion tensor, DTI, heart, conduction network, excitation propagation

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Authors: Mohsin Reza, Thirunaukarasu Subramaniam, M. Rezaul Islam

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In Asia, economic and social well-being issues are rarely addressed. The major characteristics of the migrant workers in Asian countries are seriously exploited, marginalized, and infrequently looked from human rights perspective. This paper explored the opportunities and shortages of economic and social well-being for the migrant workers in Asia. A Qualitative Interpretative Meta-Synthesis (QIMS) was conducted to analyze the contextual socio-economic factors that characterized migrant workers’ economic and social well-being. It is perceived that in most of the recruiting countries, there are lacks of government commitments to the international protocols, conventions and laws that they ratified towards safeguarding migrant workers’ economic and social well-being. Results showed that the migrant workers had lack of job security, poor salary, long working hours, low access to the public services, poor health, poor living and working conditions, lack of legal rights, physical and mental threats. The finding would be important guideline to the governments, policy makers, legal rights practitioners, and human rights organizations.

Keywords: Asia, economic well-being, social well-being, migrant workers, human rights

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Authors: Ioannis Binas, Marios Moschakis

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Electrical faults in transmission and distribution networks can have great impact on the electrical equipment used. Fault effects depend on the characteristics of the fault as well as the network itself. It is important to anticipate the network’s behavior during faults when planning a new equipment installation, as well as troubleshooting. Moreover, working backwards, we could be able to estimate the characteristics of the fault when checking the perceived effects. Different transformer winding connections dominantly used in the Greek power transfer and distribution networks and the effects of 1-phase to neutral, phase-to-phase, 2-phases to neutral and 3-phase faults on different locations of the network were simulated in order to present voltage sag characteristics. The study was performed on a generic network with three steps down transformers on two voltage level buses (one 150 kV/20 kV transformer and two 20 kV/0.4 kV). We found that during faults, there are significant changes both on voltage magnitudes and on phase angles. The simulations and short-circuit analysis were performed using the PSCAD simulation package. This paper presents voltage characteristics calculated for the simulated network, with different approaches on the transformer winding connections during symmetrical and asymmetrical faults on various locations.

Keywords: Phase angle shift, power quality, transformer winding connections, voltage sag propagation

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Authors: Sami Baraketi, Jean Marie Garcia, Olivier Brun

Abstract:

Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods.

Keywords: WDM, lightpath, RWA, wavelength fragmentation, optimization, linear programming, heuristic

Procedia PDF Downloads 526