Search results for: principals' emotionally intelligent behaviours

Authors: Qasim HamaKhurshid HamaMurad, Normal Mat Jusoh, Uznir Ujang

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The concept of the city with an infrastructure of (information and communication) Technology embraces several definitions depending on the meanings of the word "smart" are (intelligent city, smart city, knowledge city, ubiquitous city, sustainable city, digital city). Many definitions of the city exist, but this chapter explores which one has been universally acknowledged. From literature analysis, it emerges that Smart City is the most used terminologies in literature through the digital database to indicate the smartness of a city. This paper share exploration the research from main seven website digital databases and journal about Smart City from "January 2015 to the February of 2020" to (a) Time research, to examine the causes of the Smart City phenomenon and other concept literature in the last five years (b) Review of words, to see how and where the smart city specification and relation different definition And(c) Geographical research to consider where Smart Cities' greatest concentrations are in the world and are Malaysia has interacting with the smart city, and (d) how many papers published from all Malaysia from 2015 to 2020 about smart citie. Three steps are followed to accomplish the goal. (1)The analysis covered publications Build a systematic literature review search strategy to gather a representative sub-set of papers on Smart City and other definitions utilizing (GoogleScholar, Elsevier, Scopus, ScienceDirect, IEEEXplore, WebofScience, Springer) January2015-February2020. (2)A bibliometric map was formed based on the bibliometric evaluation using the mapping technique VOSviewer to visualize differences. (3)VOSviewer application program was used to build initial clusters. The Map of Bibliometric Visualizes the analytical findings which targeted the word harmony.

Keywords: bibliometric research, smart city, ICT, VOSviewer, urban modernization

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

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The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

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Authors: Joao Ferreira Do Rosario, Nuno Gustavo, Ana Machado, Lurdes Calisto, Luisa Carvalho, Georgette Andraz

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Currently, tourism small and medium enterprises (TSMEs) face serious economic and financial problems, making recovery efforts difficult. How the pandemic will affect tourists' behavior is still to be known. Will tourists be even more cautious regarding their choices or, on the contrary, will they be more adventurers with an enormous desire to travel in search of the lost freedom? Tourists may become even more demanding when traveling, more austere, or less concerned and eager to socialize. Adjusting to this "new tourist" is an added challenge for tourism service providers. Generation Z made up of individuals born in 1995 and following years, currently tends to assume a particular role and meaning in the present and future economic and social context, considering that we are facing the youngest workforce as well as tomorrow's consumers. This generation is distinguished from others as it is the first generation to combine a high level of education and technological knowledge and to fully experience the digital world. These young people are framed by a new value system that can explain new behaviours and consumption, namely, in the context of tourism. All these considerations point to the importance of investigating this target group as it is essential to understand how these individuals perceive, understand, act, and can be involved in a new environment built around a society regulated by new priorities and challenges of a sustainable nature. This leads not only to a focus on short-term market choices but mainly to predict future choices from a longer-term perspective. Together with the social background of a person, values are considered a stable antecedent of behavior and might therefore predict not just immediate, but also future choices. Furthermore, the meaning attributed to travel has a general connotation and goes beyond a specific travel choice or experience. In other words, values and travel's meaning form a chain of influences on the present and future travel behavior. This study explores the social background and values of Generation Z travelers vs the meaning these tourists give to travel. The aim is to discover in their present behavior cues to predict travel choices so that the future of tourism can be secured. This study also provides data for predicting the tourism choices of youngsters in the more immediate future. Methodologically, a quantitative approach was adopted based on the collection of data through a survey. Since academic research on Generation Z of tourists is still scarce, it is expected to contribute to deepening scientific knowledge in this area. Furthermore, it is expected that this research will support tourism professionals in defining differentiated marketing strategies and adapted to the requirements of this target, in a new time.

Keywords: Generation Z, travel behavior, travel meaning, Generation Z Values

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Authors: P. Vindin, R. Cordier, N. J. Wilson, H. Lee

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Engagement in community-based activities such as education, employment, and social relationships can improve the quality of life for individuals with Autism Spectrum Disorder (ASD). Community mobility is vital to attaining independence for individuals with ASD. Learning to drive and gaining a driver’s license is a critical link to community mobility; however, for individuals with ASD acquiring safe driving skills can be a challenging process. Issues related to anxiety, executive function, and social communication may affect driving behaviours. Driving training and education aimed at addressing barriers faced by learner drivers with ASD can help them improve their driving performance. A multi-site randomized controlled trial (RCT) was conducted to evaluate the effectiveness of an autism-specific driving training intervention for improving the on-road driving performance of learner drivers with ASD. The intervention was delivered via a training manual and interactive website consisting of five modules covering varying driving environments starting with a focus on off-road preparations and progressing through basic to complex driving skill mastery. Seventy-two learner drivers with ASD aged 16 to 35 were randomized using a blinded group allocation procedure into either the intervention or control group. The intervention group received 10 driving lessons with the instructors trained in the use of an autism-specific driving training protocol, whereas the control group received 10 driving lessons as usual. Learner drivers completed a pre- and post-observation drive using a standardized driving route to measure driving performance using the Driving Performance Checklist (DPC). They also completed anxiety, executive function, and social responsiveness measures. The findings showed that there were significant improvements in driving performance for both the intervention (d = 1.02) and the control group (d = 1.15). However, the differences were not significant between groups (p = 0.614) or study sites (p = 0.842). None of the potential moderator variables (anxiety, cognition, social responsiveness, and driving instructor experience) influenced driving performance. This study is an important step toward improving community mobility for individuals with ASD showing that an autism-specific driving training intervention can improve the driving performance of leaner drivers with ASD. It also highlighted the complexity of conducting a multi-site design even when sites were matched according to geography and traffic conditions. Driving instructors also need more and clearer information on how to communicate with learner drivers with restricted verbal expression.

Keywords: autism spectrum disorder, community mobility, driving training, transportation

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Authors: Derwin K. C. Chan, Alfred S. Y. Lee

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The integrated model of self-determination theory and the theory of planned behaviour has been successfully applied to explain individuals’ adherence to health behaviours, including behavioural adherence toward rehabilitation. This study was a randomised controlled trial that examined the effectiveness of an mHealth intervention (i.e., mobile application) developed based on this integrated model in promoting treatment adherence of patients of anterior cruciate ligament rupture during their post-surgery home-based rehabilitation period. Subjects were 67 outpatients (aged between 18 and 60) who undertook anterior cruciate ligament (ACL) reconstruction surgery for less than 2 months for this study. Participants were randomly assigned either into the treatment group (who received the smartphone application; N = 32) and control group (who receive standard treatment only; N = 35), and completed psychological measures relating to the theories (e.g., motivations, social cognitive factors, and behavioural adherence) and clinical outcome measures (e.g., subjective knee function (IKDC), laxity (KT-1000), muscle strength (Biodex)) relating to ACL recovery at baseline, 2-month, and 4-month. Generalise estimating equation showed the interaction between group and time was significant on intention was only significant for intention (Wald x² = 5.23, p = .02), that of perceived behavioural control (Wald x² = 3.19, p = .07), behavioural adherence (Wald x² = 3.08, p = .08, and subjective knee evaluation (Wald x² = 2.97, p = .09) were marginally significant. Post-hoc between-subject analysis showed that control group had significant drop of perceived behavioural control (p < .01), subjective norm (p < .01) and intention (p < .01), behavioural adherence (p < .01) from baseline to 4-month, but such pattern was not observed in the treatment group. The treatment group had a significant decrease of behavioural adherence (p < .05) in the 2-month, but such a decrease was not observed in 4-month (p > .05). Although the subjective knee evaluation in both group significantly improved at 2-month and 4-month from the baseline (p < .05), and the improvements in the control group (mean improvement at 4-month = 40.18) were slightly stronger than the treatment group (mean improvement at 4-month = 34.52). In conclusion, the findings showed that the theory driven mobile application ameliorated the decline of treatment intention of home-based rehabilitation. Patients in the treatment group also reported better muscle strength than control group at 4-month follow-up. Overall, the mobile application has shown promises on tackling the problem of orthopaedics outpatients’ non-adherence to medical treatment.

Keywords: self-determination theory, theory of planned behaviour, mobile health, orthopaedic patients

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Hao Wang, Shuguo Pan

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The Global Navigation Satellite System (GNSS) signal received by the dynamic vehicle in the harsh environment will be frequently interfered with and blocked, which generates gross error affecting the positioning accuracy of the GNSS/Inertial Navigation System (INS) integrated navigation. Therefore, this paper put forward an improved robust Cubature Kalman filter (CKF) algorithm for single-frequency GNSS/INS tightly coupled system ambiguity resolution. Firstly, the dynamic model and measurement model of a single-frequency GNSS/INS tightly coupled system was established, and the method for GNSS integer ambiguity resolution with INS aided is studied. Then, we analyzed the influence of pseudo-range observation with gross error on GNSS/INS integrated positioning accuracy. To reduce the influence of outliers, this paper improved the CKF algorithm and realized an intelligent selection of robust strategies by judging the ill-conditioned matrix. Finally, a field navigation test was performed to demonstrate the effectiveness of the proposed algorithm based on the double-differenced solution mode. The experiment has proved the improved robust algorithm can greatly weaken the influence of separate, continuous, and hybrid observation anomalies for enhancing the reliability and accuracy of GNSS/INS tightly coupled navigation solutions.

Keywords: GNSS/INS integrated navigation, ambiguity resolution, Cubature Kalman filter, Robust algorithm

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Authors: Serge Tshibangu, Turgay Celik, Zenzo Ncube

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Road crashes kill nearly over a million people every year, and leave millions more injured or permanently disabled. Various annual reports reveal that the percentage of fatal crashes due to fatigue/driver falling asleep comes directly after the percentage of fatal crashes due to intoxicated drivers. This percentage is higher than the combined percentage of fatal crashes due to illegal/Un-Safe U-turn and illegal/Un-Safe reversing. Although a relatively small percentage of police reports on road accidents highlights drowsiness and fatigue, the importance of these factors is greater than we might think, hidden by the undercounting of their events. Some scenarios show that these factors are significant in accidents with killed and injured people. Thus the need for an automatic drivers fatigue detection system in order to considerably reduce the number of accidents owing to fatigue.This research approaches the drivers fatigue detection problem in an innovative way by combining cues collected from both temporal analysis of drivers’ faces and environment. Monotony in driving environment is inter-related with visual symptoms of fatigue on drivers’ faces to achieve fatigue detection. Optical and infrared (IR) sensors are used to analyse the monotony in driving environment and to detect the visual symptoms of fatigue on human face. Internal cues from drivers faces and external cues from environment are combined together using machine learning algorithms to automatically detect fatigue.

Keywords: intelligent transportation systems, bayesian networks, yawning computing, machine learning algorithms

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Authors: Jerry Fiave, Dacosta Aboagye, Stephen Ayisi-Addo, Mabel Kissiwah Asafo, Felix Osei-Sarpong, Ebenezer Kye-Mensah, Renee Opare-Otoo

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Background: Acceptability and preference of social and behaviour change (SBC) materials by target audiences is an important determinant of effective health communication outcomes. In Ghana, however, pre-test and post-test studies on acceptability and preference of specific SBC materials for specific audiences are rare. The aim of this study was therefore to assess the acceptability and preference of printed posters on COVID-19 related stigma as suitable SBC materials for health workers to influence behaviours that promote uptake of HIV-focused services. Methods: A total of 218 health workers who provide HIV-focused services were purposively sampled in 16 polyclinics where the posters were distributed in the Greater Accra region of Ghana. Data was collected in March 2021 using an adapted self-administered questionnaire in Google forms deployed via WhatsApp to participants. The data were imported into SPSS version 27 where chi-square test and regression analyses were performed to establish association as well as strength of association between variables respectively. Results: A total of 142 participants (physicians, nurses, midwives, lab scientists, health promoters, diseases control officers) made up of 85(60%) females and 57(40%) males responded to the questionnaire, giving a response rate of 65.14%. Only 88 (61.97%) of the respondents were exposed to the posters. The majority of those exposed said the posters were informative [82(93.18%)], relevant [85(96.59%)] and attractive [83(94.32%)]. They [82(93.20%)] also rated the material as acceptable with no statistically significant association between category of health worker and acceptability of the posters (X =1.631, df=5, p=0.898). However, participants’ most preferred forms of material on COVID-19 related stigma were social media [38(26.76%)], television [33(23.24%)], SMS [19(13.38%)], and radio [18(12.70%)]. Clinical health workers were 4.88 times more likely to prefer online or electronic versions of SBC materials than nonclinical health workers [AOR= 4.88 (95% CI= 0.31-0.98), p=0.034]. Conclusions: Printed posters on COVID-19 related stigma are acceptable SBC materials in communicating behaviour change messages that target health workers in promoting uptake of HIV-focused services. Posters are however, not among the most preferred materials for health workers. It is therefore recommended that material assessment studies are conducted to inform the development of acceptable and preferred materials for target audiences.

Keywords: acceptability, AIDS, HIV, posters, preference, SBC, stigma, social and behaviour change communication

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Authors: Carlos M. Goncalves, Luisa Duarte, Teresa Cartaxo

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The aim of this work was to understand the reasons behind the underutilization of electroconvulsive therapy (ECT) in the younger population and raise possible solutions. We conducted a non-systematic review of literature throughout a search on PubMed, using the terms ‘children’, ‘adolescents’ and ‘electroconvulsive’, ‘therapy’. Candidate articles written in languages other than English were excluded. Articles were selected according to title and/or abstract’s content relevance, resulting in a total of 5 articles. ECT is a recognized effective treatment in adults for several psychiatric conditions. As in adults, ECT in children and adolescents is proven most beneficial in the treatment of severe mood disorders, catatonia, and, to a lesser extent, schizophrenia. ECT in adults has also been used to treat autism’s self-injurious behaviours, Tourette’s syndrome and resistant first-episode schizophrenia disorder. Despite growing evidence on its safety and effectiveness in children and adolescents, like those found in adults, ECT remains a controversial and underused treatment in patients this age, even when it is clearly indicated. There are various possible reasons to this; limited awareness among professionals (lack of knowledge and experience among child psychiatrists), stigmatic public opinion (despite positive feedback from patients and families, there is an unfavourable and inaccurate representation in the media, contributing to a negative public opinion), legal restrictions and ethical controversies (restrictive regulations such as a minimum age for administration), lack of randomized trials (the currently available studies are retrospective, with small size samples, and most of the publications are either case reports or case series). This shows the need to raise awareness and knowledge, not only for mental health professionals, but also to the general population, through the media, regarding indications, methods and safety of ECT in order to provide reliable information to the patient and families. Large-scale longitudinal studies are also useful to further demonstrate the efficacy and safety of ECT and can aid in the formulation of algorithms and guidelines as without these changes, the availability of ECT to the younger population will remain restricted by regulations and social stigma. In conclusion, these results highlight that lack of adequate knowledge and accurate information are the most important factors behind the underutilization of ECT in younger population. Mental healthcare professionals occupy a cornerstone position; if data is given by a well-informed healthcare professional instead of the media, general population (including patients and their families) will probably regard the procedure in a more favourable way. So, the starting point should be to improve health care professional’s knowledge and experience on this choice of treatment.

Keywords: adolescents, children, electroconvulsive, therapy

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Authors: Stephan Boehm, Julia Engel, Judith Eisser

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During the last decade communication in the Internet transformed from a broadcast to a conversational model by supporting more interactive features, enabling user generated content and introducing social media networks. Another important trend with a significant impact on electronic commerce is a massive usage shift from desktop to mobile devices. However, a presentation of product- or service-related information accumulated on websites, micro pages or portals often remains the pivot and focal point of a customer journey. A more recent change of user behavior –especially in younger user groups and in Asia– is going along with the increasing adoption of messaging applications supporting almost real-time but asynchronous communication on mobile devices. Mobile apps of this type cannot only provide an alternative for traditional one-to-one communication on mobile devices like voice calls or short messaging service. Moreover, they can be used in mobile commerce as a new marketing and sales channel, e.g., for product promotions and direct marketing activities. This requires a new way of customer interaction compared to traditional mobile commerce activities and functionalities provided based on mobile web-sites. One option better aligned to the customer interaction in mes-saging apps are so-called chatbots. Chatbots are conversational programs or dialog systems simulating a text or voice based human interaction. They can be introduced in mobile messaging and social media apps by using rule- or artificial intelligence-based imple-mentations. In this context, a comparative analysis is conducted to examine the impact of using traditional websites or chatbots for promoting a product in an impulse purchase situation. The aim of this study is to measure the impact on the customers’ user experi-ence and emotions. The study is based on a random sample of about 60 smartphone users in the group of 20 to 30-year-olds. Participants are randomly assigned into two groups and participate in a traditional website or innovative chatbot based mobile com-merce scenario. The chatbot-based scenario is implemented by using a Wizard-of-Oz experimental approach for reasons of sim-plicity and to allow for more flexibility when simulating simple rule-based and more advanced artificial intelligence-based chatbot setups. A specific set of metrics is defined to measure and com-pare the user experience in both scenarios. It can be assumed, that users get more emotionally involved when interacting with a system simulating human communication behavior instead of browsing a mobile commerce website. For this reason, innovative face-tracking and analysis technology is used to derive feedback on the emotional status of the study participants while interacting with the website or the chatbot. This study is a work in progress. The results will provide first insights on the effects of chatbot usage on user experiences and emotions in mobile commerce environments. Based on the study findings basic requirements for a user-centered design and implementation of chatbot solutions for mobile com-merce can be derived. Moreover, first indications on situations where chatbots might be favorable in comparison to the usage of traditional website based mobile commerce can be identified.

Keywords: chatbots, emotions, mobile commerce, user experience, Wizard-of-Oz prototyping

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Authors: Suchitra Chnadrashekhar

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Earlier looked as a support function in an organization information technology has now become a critical utility to manage their daily operations. Organizations are processing huge amount of data which was unimaginable few decades before. This has opened the opportunity for IT sector to help industries across domains to handle the data in the most intelligent manner. Presence of IT has been a leverage for the Oil & Gas industry to store, manage and process the data in most efficient way possible thus deriving the economic value in their day-to-day operations. Proper synchronization between Operational data system and Information Technology system is the need of the hour. Predictive analytics supports oil and gas companies by addressing the challenge of critical equipment performance, life cycle, integrity, security, and increase their utilization. Predictive analytics go beyond early warning by providing insights into the roots of problems. To reach their full potential, oil and gas companies need to take a holistic or systems approach towards asset optimization and thus have the functional information at all levels of the organization in order to make the right decisions. This paper discusses how the use of predictive analysis in oil and gas industry is redefining the dynamics of this sector. Also, the paper will be supported by real time data and evaluation of the data for a given oil production asset on an application tool, SAS. The reason for using SAS as an application for our analysis is that SAS provides an analytics-based framework to improve uptimes, performance and availability of crucial assets while reducing the amount of unscheduled maintenance, thus minimizing maintenance-related costs and operation disruptions. With state-of-the-art analytics and reporting, we can predict maintenance problems before they happen and determine root causes in order to update processes for future prevention.

Keywords: hydrocarbon, information technology, SAS, predictive analytics

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: Derejaw Lake Melie, Alemu Kumlachew Tegegne

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The increasing prevalence of social media in Ethiopia has exacerbated societal challenges by fostering the proliferation of negative emotional posts and comments. Illicit use of social media has further exacerbated divisions among the population. Addressing these issues through manual identification and aggregation of emotions from millions of users for swift decision-making poses significant challenges, particularly given the rapid growth of Amharic language usage on social platforms. Consequently, there is a critical need to develop an intelligent system capable of automatically detecting and categorizing negative emotional content into social, religious, and political categories while also filtering out toxic online content. This paper aims to leverage sentiment analysis techniques to achieve automatic detection and filtering of negative emotional content from Amharic social media texts, employing a comparative study of deep learning algorithms. The study utilized a dataset comprising 29,962 comments collected from social media platforms using comment exporter software. Data pre-processing techniques were applied to enhance data quality, followed by the implementation of deep learning methods for training, testing, and evaluation. The results showed that CNN, GRU, LSTM, and Bi-LSTM classification models achieved accuracies of 83%, 50%, 84%, and 86%, respectively. Among these models, Bi-LSTM demonstrated the highest accuracy of 86% in the experiment.

Keywords: negative emotion, emotion detection, social media filtering sentiment analysis, deep learning.

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Authors: Fatih Topaloğlu

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Although philosophy is inherently a theoretical and intellectual activity, it should not be denied that environmental conditions influence the formation and shaping of philosophical thought. In this context, it should be noted that the Philosophy of Religion has been influential in the debates in the West, especially since the beginning of the 20th century, and that this influence has dimensions that cannot be limited to academic or intellectual fields. The issues and problems that fall within the field of interest of Philosophy of Religion are followed with interest by a significant proportion of society through popular publications. Philosophy of Religion has its share in many social, economic, cultural, scientific, political and ethical developments. Philosophy of Religion, in the most general sense, can be defined as a philosophical approach to religion or a philosophical way of thinking and discussing religion. Philosophy of Religion tries to explain the epistemological foundations of concepts such as belief and faith that shape religious life by revealing their meaning for the individual. Thus, Philosophy of Religion tries to evaluate the effect of beliefs on the individual's values, judgments and behaviours with a comprehensive and critical eye. The Philosophy of Religion, which tries to create new solutions and perspectives by applying the methods of philosophy to religious problems, tries to solve these problems not by referring to the holy book or religious teachings but by logical proofs obtained through the possibilities of reason and evidence filtered through the filter of criticism. Although there is no standard method for doing Philosophy of Religion, it can be said that an approach that can be expressed as thinking about religion in a rational, objective, and consistent way is generally accepted. The evaluations made within the scope of Philosophy of Religion have two stages. The first is the definition stage, and the second is the evaluation stage. In the first stage, the data of different scientific disciplines, especially other religious sciences, are utilized to define the issues objectively. In the second stage, philosophical evaluations are made based on this foundation. During these evaluations, the issue of how the relationship between religion and philosophy should be established is extremely sensitive. The main thesis of this paper is that the Philosophy of Religion, as a branch of philosophy, has been affected by the conditions caused by the historical experience through which it has passed and has differentiated its subjects and the methods it uses to realize its philosophical acts over time under the influence of these conditions. This study will attempt to evaluate the validity of this study based on the "Introduction to Philosophy of Religion" literature, which we assume reflects this differentiation. As a result of this examination will aim to reach some factual conclusions about the nature of both philosophical and religious thought, to determine the phases that the Philosophy of Religion as a discipline has gone through since the day it emerged, and to investigate the possibilities of a holistic view of the field.

Keywords: content analysis, culture, history, philosophy of religion, method

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Authors: Chen Chou, Feng-Tyan Lin

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Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization

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Authors: Mrinalini Ranjan, Sudheesh Chethil

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Epilepsy is a common neurological disorder characterised by the recurrence of seizures. Electroencephalogram (EEG) signals are complex biomedical signals which exhibit nonlinear and nonstationary behavior. We use two methods 1) Detrended Fluctuation Analysis (DFA) and 2) Recurrence Plots (RP) to capture this complex behavior of EEG signals. DFA considers fluctuation from local linear trends. Scale invariance of these signals is well captured in the multifractal characterisation using detrended fluctuation analysis (DFA). Analysis of long-range correlations is vital for understanding the dynamics of EEG signals. Correlation properties in the EEG signal are quantified by the calculation of a scaling exponent. We report the existence of two scaling behaviours in the epileptic EEG signals which quantify short and long-range correlations. To illustrate this, we perform DFA on extant ictal (seizure) and interictal (seizure free) datasets of different patients in different channels. We compute the short term and long scaling exponents and report a decrease in short range scaling exponent during seizure as compared to pre-seizure and a subsequent increase during post-seizure period, while the long-term scaling exponent shows an increase during seizure activity. Our calculation of long-term scaling exponent yields a value between 0.5 and 1, thus pointing to power law behaviour of long-range temporal correlations (LRTC). We perform this analysis for multiple channels and report similar behaviour. We find an increase in the long-term scaling exponent during seizure in all channels, which we attribute to an increase in persistent LRTC during seizure. The magnitude of the scaling exponent and its distribution in different channels can help in better identification of areas in brain most affected during seizure activity. The nature of epileptic seizures varies from patient-to-patient. To illustrate this, we report an increase in long-term scaling exponent for some patients which is also complemented by the recurrence plots (RP). RP is a graph that shows the time index of recurrence of a dynamical state. We perform Recurrence Quantitative analysis (RQA) and calculate RQA parameters like diagonal length, entropy, recurrence, determinism, etc. for ictal and interictal datasets. We find that the RQA parameters increase during seizure activity, indicating a transition. We observe that RQA parameters are higher during seizure period as compared to post seizure values, whereas for some patients post seizure values exceeded those during seizure. We attribute this to varying nature of seizure in different patients indicating a different route or mechanism during the transition. Our results can help in better understanding of the characterisation of epileptic EEG signals from a nonlinear analysis.

Keywords: detrended fluctuation, epilepsy, long range correlations, recurrence plots

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Authors: Sophie Oakes-Rogers, Di Bailey, Karen Slade

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Female offenders are a uniquely vulnerable group, who are at high risk of suicide. Whilst the prevention of self-harm and suicide remains a key global priority, we need to better understand the relationship between these challenging behaviours that constitute a pressing problem, particularly in environments designed to prioritise safety and security. Method choice is unlikely to be random, and is instead influenced by a range of cultural, social, psychological and environmental factors, which change over time and between countries. A key aspect of self-harm and suicide in women receiving forensic care is the lack of free access to methods. At a time where self-harm and suicide rates continue to rise internationally, understanding the role of these influencing factors and the impact of current suicide prevention strategies on the use of near-lethal methods is crucial. This poster presentation will present findings from 25 interviews and 3 focus groups, which enlisted a Participatory Action Research approach to explore the differences between self-harming and suicidal behavior. A key element of this research was using the lived experiences of women receiving forensic care from one forensic pathway in the UK, and the staffs who care for them, to discuss the role of near-lethal self-harm (NLSH). The findings and suggestions from the lived accounts of the women and staff will inform a draft assessment tool, which better assesses the risk of suicide based on the lethality of methods. This tool will be the first of its kind, which specifically captures the needs of women receiving forensic services. Preliminary findings indicate women engage in NLSH for two key reasons and is determined by their history of self-harm. Women who have a history of superficial non-life threatening self-harm appear to engage in NLSH in response to a significant life event such as family bereavement or sentencing. For these women, suicide appears to be a realistic option to overcome their distress. This, however, differs from women who appear to have a lifetime history of NLSH, who engage in such behavior in a bid to overcome the grief and shame associated with historical abuse. NLSH in these women reflects a lifetime of suicidality and indicates they pose the greatest risk of completed suicide. Findings also indicate differences in method selection between forensic provisions. Restriction of means appears to play a role in method selection, and findings suggest it causes method substitution. Implications will be discussed relating to the screening of female forensic patients and improvements to the current suicide prevention strategies.

Keywords: forensic mental health, method substitution, restriction of means, suicide

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Authors: Zainab Atia, Hongwei He, Panagiotis Sarantopoulos

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Over the past few years, firms have witnessed a massive increase in sophisticated algorithmic deployment, which has become quite pervasive in today’s modern society. With the wide availability of data for retailers, the ability to track consumers using algorithmic pricing has become an integral option in online platforms. As more companies are transforming their businesses and relying more on massive technological advancement, pricing algorithmic systems have brought attention and given rise to its wide adoption, with many accompanying benefits and challenges to be found within its usage. With the overall aim of increasing profits by organizations, algorithmic pricing is becoming a sound option by enabling suppliers to cut costs, allowing better services, improving efficiency and product availability, and enhancing overall consumer experiences. The adoption of algorithms in retail has been pioneered and widely used in literature across varied fields, including marketing, computer science, engineering, economics, and public policy. However, what is more, alarming today is the comprehensive understanding and focus of this technology and its associated ethical influence on consumers’ perceptions and behaviours. Indeed, due to algorithmic ethical concerns, consumers are found to be reluctant in some instances to share their personal data with retailers, which reduces their retention and leads to negative consumer outcomes in some instances. This, in its turn, raises the question of whether firms can still manifest the acceptance of such technologies by consumers while minimizing the ethical transgressions accompanied by their deployment. As recent modest research within the area of marketing and consumer behavior, the current research advances the literature on algorithmic pricing, pricing ethics, consumers’ perceptions, and price fairness literature. With its empirical focus, this paper aims to contribute to the literature by applying the distinction of the two common types of algorithmic pricing, dynamic and personalized, while measuring their relative effect on consumers’ behavioural outcomes. From a managerial perspective, this research offers significant implications that pertain to providing a better human-machine interactive environment (whether online or offline) to improve both businesses’ overall performance and consumers’ wellbeing. Therefore, by allowing more transparent pricing systems, businesses can harness their generated ethical strategies, which fosters consumers’ loyalty and extend their post-purchase behaviour. Thus, by defining the correct balance of pricing and right measures, whether using dynamic or personalized (or both), managers can hence approach consumers more ethically while taking their expectations and responses at a critical stance.

Keywords: algorithmic pricing, dynamic pricing, personalized pricing, price ethicality

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Authors: Morteza Ahmadpur, Ilgin Gokasar, Saman Ghaffarian

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That Transportation system plays an important role in management of societies is an undeniable fact and it is one of the most challenging issues in human beings routine life. But by increasing the population in urban areas, the demand for transportation modes also increase. Accordingly, it is obvious that more flexible and dynamic transportation system is required to satisfy peoples’ requirements. Nowadays, there is significant increase in number of environmental issues all over the world which is because of human activities. New technological achievements bring new horizons for humans and so they changed the life style of humans in every aspect of their life and transportation is not an exception. By using new technology, societies can modernize their transportation system and increase the feasibility of their system. Real–time Taxi sharing systems is one of the novel and most modern systems all over the world. For establishing this kind of system in an urban area it is required to use the most advanced technologies in a transportation system. GPS navigation devices, computers and social networks are just some parts of this kind of system. Like carpooling, real-time taxi sharing is one of the best ways to better utilize the empty seats in most cars and taxis, thus decreasing energy consumption and transport costs. It can serve areas not covered by a public transit system and act as a transit feeder service. Taxi sharing is also capable of serving one-time trips, not only recurrent commute trips or scheduled trips. In this study, we describe the requirements and parameters that we need to establish a useful real-time ride sharing system for an urban area. The parameters and requirements of this study can be used in any urban area.

Keywords: transportation, intelligent transportation systems, ride-sharing, taxi sharing

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Authors: Caroline Dickson

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While adolescence can be a challenging time, it may also be a time of opportunity. Whereas adolescents with a physical disability negotiate the adolescent developmental stage with similar issues to able-bodied adolescents, they additionally may encounter developmental problems which may impede their adulthood. In part due to the restricted opportunities disabled adolescents experience, they may experience difficulty with mastering this developmental stage. As is well documented, health and wellbeing are positively associated with participating in physical activity. However, the little research available suggested that Pacific adolescents generally are participating in less physical activity than adolescents of other ethnic groups. Objective/Study: The main aim of the study (from a larger mixed method study), was to explore physical activity participation in Pacific adolescent girls with a physical disability in relation to their physiological and psychological wellbeing. The qualitative descriptive study comprised of seven interviews with Pacific adolescent girls and their mothers in a family setting and also included the providers of services to Pacific girls with a physical disability. Including the providers of disability services allowed the researchers to identity a further understanding into challenges of participation for the Pacific adolescent girls and their families while the girls were attempting to participate in physical activity. The purpose of the talanoa (face-to-face interviews that were deemed informal) was to identify partaking and factors influencing participation in physical activity, whilst listening to the voices of the participants. The stories revealed the multitude of factors that influenced physical activity for the Pacific girls with a physical disability. Results: Findings from the qualitative descriptive study found that through physical activity, the Pacific adolescent girls with a physical disability experienced benefits from participation. The findings suggested that these girls wanted to participate in physical activity and clearly indicated the physical activities they preferred. Amongst the physiological and psychological benefits of the Pacific adolescents engaging in physical activity, the adolescents were able to develop positive social relationships, experience autonomy, and generally, their self-worth improved while building confidence. Nevertheless, the adolescents experienced a multitude of factors impeding their engagement in physical activity including cultural stigmas. Their participation was influenced by the interplay of a range of gender, cultural, age-related (adolescence) and socio-economic factors alongside policy and structurally related constraints. Conclusion: Physical activity has the potential to improve the general physiological and psychological health of all adolescents. It should be prioritised particularly in vulnerable populations where they may have limited access. As the Pacific adolescents with a physical activity are dependent on their families for physical activity participation, it is imperative the family be included and consulted. To increase participation, and reduce sedentary behaviours, factors influencing both participation and non-participation need to be considered.

Keywords: Pacific adolescent girls, physical activity, physical disability, qualitative descriptive study

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Authors: Faith Eweluegim Enahoro-Ofagbe

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Worldwide, every nation is experiencing the effects of global warming. In developing countries, due to the heavy reliance on agriculture for socioeconomic growth and security, among other things, these countries are more affected by climate change, particularly with the availability of water. Floods, droughts, rising temperatures, saltwater intrusion, groundwater depletion, and other severe environmental alterations are all brought on by climatic change. Life depends on water, a vital resource; these ecological changes affect all water use, including agriculture and household water use. Therefore adequate and adaptive water usage strategies for sustainability are essential in developing countries. Therefore, this paper investigates Nigeria's challenges due to climate change and adaptive techniques that have evolved in response to such issues to ensure water management and sustainability for irrigation and provide quality water to residents. Questionnaires were distributed to respondents in the study area, central Nigeria, for quantitative evaluation of sustainable water resource management techniques. Physicochemical analysis was done, collecting soil and water samples from several locations under investigation. Findings show that farmers use different methods, ranging from intelligent technologies to traditional strategies for water resource management. Also, farmers need to learn better water resource management techniques for sustainability. Since more residents obtain their water from privately held sources, the government should enforce legislation to ensure that private borehole construction businesses treat water sources of poor quality before the general public uses them.

Keywords: developing countries, irrigation, strategies, sustainability, water resource management, water usage

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Authors: Tugba Altin

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In a time characterized by escalating climate change impacts, communities globally face extreme events with deep-reaching tangible and intangible consequences. At the intersection of these phenomena lies the profound impact on the cultural and emotional connections that individuals forge with their environments. This study casts a spotlight on the Lytton Creek Fire of 2021, showcasing it as an exemplar of both the visible destruction brought by such events and the more covert yet deeply impactful disturbances to place attachment (PA). Defined as the emotional and cognitive bond individuals form with their surroundings, PA is critical in comprehending how such catastrophic events reshape cultural identity and the bond with the land. Against the stark backdrop of the Lytton Creek Fire's devastation, the research seeks to unpack the multilayered dynamics of PA amidst the tangible wreckage and the intangible repercussions such as emotional distress and disrupted cultural landscapes. Delving deeper, it examines how affected populations renegotiate their affiliations with these drastically altered environments, grappling with both the tangible loss of their homes and the intangible challenges to solace, identity, and community cohesion. This exploration is instrumental in the broader climate change narrative, as it offers crucial insights into how these personal-place relationships can influence and shape climate adaptation and recovery strategies. Departing from traditional data collection methodologies, this study adopts an interpretive phenomenological approach enriched by hermeneutic insights and places the experiences of the Lytton community and its co-researchers at its core. Instead of conventional interviews, innovative methods like walking audio sessions and photo elicitation are employed. These techniques allow participants to immerse themselves back into the environment, reviving and voicing their memories and emotions in real-time. Walking audio captures reflections on spatial narratives after the trauma, whereas photo voices encapsulate the intangible emotions, presenting a visual representation of place-based experiences. Key findings emphasize the indispensability of addressing both the tangible and intangible traumas in community recovery efforts post-disaster. The profound changes to the cultural landscape and the subsequent shifts in PA underscore the need for holistic, culturally attuned, and emotionally insightful adaptation strategies. These strategies, rooted in the lived experiences and testimonies of the affected individuals, promise more resonant and effective recovery efforts. The research further contributes to climate change discourse, highlighting the intertwined pathways of tangible reconstruction and the essentiality of emotional and cultural rejuvenation. Furthermore, the use of participatory methodologies in this inquiry challenges traditional research paradigms, pointing to potential evolutionary shifts in qualitative research norms. Ultimately, this study underscores the need for a more integrative approach in addressing the aftermath of environmental disasters, ensuring that both physical and emotional rebuilding are given equal emphasis.

Keywords: place attachment, community recovery, disaster reponse, sensory responses, intangible traumas, visual methodologies

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Authors: Nitish Suvarna, Anjali Awasthi

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In an era marked by rapid technological advancements, the integration of Autonomous Vehicles into supply chain networks represents a transformative shift, promising to redefine the paradigms of logistics and transportation. This thesis delves into a comprehensive assessment of the impact of autonomous vehicles on supply chain performance, with a particular focus on network design, operational efficiency, and environmental sustainability. Employing the advanced simulation capabilities of anyLogistix (ALX), the study constructs a digital twin of a conventional supply chain network, encompassing suppliers, production facilities, distribution centers, and customer endpoints. The research methodically integrates Autonomous Vehicles into this intricate network, aiming to unravel the multifaceted effects on transportation logistics including transit times, cost-efficiency, and sustainability. Through simulations and scenarios analysis, the study scrutinizes the operational resilience and adaptability of supply chains in the face of dynamic market conditions and disruptive technologies like Autonomous Vehicles. Furthermore, the thesis undertakes carbon footprint analysis, quantifying the environmental benefits and challenges associated with the adoption of Autonomous Vehicles in supply chain operations. The insights from this research are anticipated to offer a strategic framework for industry stakeholders, guiding the adoption of Autonomous Vehicles to foster a more efficient, responsive, and sustainable supply chain ecosystem. The findings aim to serve as a cornerstone for future research and practical implementations in the realm of intelligent transportation and supply chain management.

Keywords: autonomous vehicle, agri-food supply chain, ALX simulation, anyLogistix

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Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

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Authors: Fahad Ibrahim, Raphaël Akamavi

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Bangladesh has encountered several industrial disasters (e.g. fire and building collapse tragedies) leading to the loss of valuable human lives. Irrespective of various institutions’ making effort to improve the safety situation, industry compliance and safety behaviour have not yet been improved. Hence, one question remains, to what extent does the institutional elements efficient enough to make any difference in improving safety behaviours? Thus, this study explores the relationship between institutional perspective and safety performance. Structural equation modelling results, using survey data from 256 RMG workers’ of 128 garments manufacturing factories in Bangladesh, show that institutional facets strongly influence management safety commitment to induce workers participation in safety activities and reduce workplace accident rates. The study also found that by upholding industrial standards and inspecting the safety situations, institutions facets significantly and directly affect workers involvement in safety participations and rate of workplace accidents. Additionally, workers involvement to safety practices significantly predicts the safety environment of the workplace. Subsequently, our findings demonstrate that institutional culture, norms, and regulations enact play an important role in altering management commitment to set-up a safer workplace environment. As a result, when workers’ perceive their management having high level of commitment to safety, they are inspired to be involved more in the safety practices, which significantly alter the workplace safety situation and lessen injury experiences. Due to the fact that institutions have strong influence on management commitment, legislative members should endorse, regulate, and strictly monitor workplace safety laws to be exercised by the factory owners. Further, management should take initiatives for adopting OHS features and conceive strategic directions (i.e., set up safety committees, risk assessments, innovative training) for promoting a positive safety climate to provide a safe workplace environment. Arguably, an inclusive public-private partnership is recommended for ensuring better and safer workplace for RMG workers. However, as our data were under a cross-sectional design; the respondents’ perceptions might get changed over a period of time and hence, a longitudinal study is recommended. Finally, further research is needed to determine the impact of improvement mechanisms on workplace safety performance, such as how workplace design, safety training programs, and institutional enforcement policies protect the well-being of workers.

Keywords: institutional perspective, management commitment, safety participation, work injury, safety performance, occupational health and safety

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Panagiotis Gkekas, Christos Sougles, Dionysios Kehagias, Dimitrios Tzovaras

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Cooperative intelligent transport systems (C-ITS) can greatly improve safety and efficiency in road transport by enabling communication, not only between vehicles themselves but also between vehicles and infrastructure. For that reason, traffic surveillance systems on the road are of great importance. This paper focuses on the development of an on-road unit comprising several magnetic sensors for real-time vehicle detection, movement direction, and speed measurement calculations. Magnetic sensors can feel and measure changes in the earth’s magnetic field. Vehicles are composed of many parts with ferromagnetic properties. Depending on sensors’ sensitivity, changes in the earth’s magnetic field caused by passing vehicles can be detected and analyzed in order to extract information on the properties of moving vehicles. In this paper, we present a prototype algorithm for real-time, high-accuracy, vehicle detection, and speed measurement, which can be implemented as a portable, low-cost, and non-invasive to existing infrastructure solution with the potential to replace existing high-cost implementations. The paper describes the algorithm and presents results from its preliminary lab testing in a close to real condition environment. Acknowledgments: Work presented in this paper was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation (call RESEARCH–CREATE–INNOVATE) under contract no. Τ1EDK-03081 (project ODOS2020).

Keywords: magnetic sensors, vehicle detection, speed measurement, traffic surveillance system

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Authors: Shirin Alabdulqader

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This research aims to investigate the concept of code-switching (CS) between English, Arabic, and the CS practices of Saudi online users via a Translanguaging (TL) lens for more inclusive view towards the nature of the data from the study. It employs Digitally Mediated Communication (DMC), specifically the WhatsApp and Twitter platforms, in order to understand how the users employ online resources to communicate with others on a daily basis. This project looks beyond language and considers the multimodal affordances (visual and audio means) that interlocutors utilise in their online communicative practices to shape their online social existence. This exploratory study is based on a data-driven interpretivist epistemology as it aims to understand how meaning (reality) is created by individuals within different contexts. This project used a mixed-method approach, combining a qualitative and a quantitative approach. In the former, data were collected from online chats and interview responses, while in the latter a questionnaire was employed to understand the frequency and relations between the participants’ linguistic and non-linguistic practices and their social behaviours. The participants were eight bilingual Saudi nationals (both men and women, aged between 20 and 50 years old) who interacted with others online. These participants provided their online interactions, participated in an interview and responded to a questionnaire. The study data were gathered from 194 WhatsApp chats and 122 Tweets. These data were analysed and interpreted according to three levels: conversational turn taking and CS; the linguistic description of the data; and CS and persona. This project contributes to the emerging field of analysing online Arabic data systematically, and the field of multimodality and bilingual sociolinguistics. The findings are reported for each of the three levels. For conversational turn taking, the CS analysis revealed that it was used to accomplish negotiation and develop meaning in the conversation. With regard to the linguistic practices of the CS data, the majority of the code-switched words were content morphemes. The third level of data interpretation is CS and its relationship with identity; two types of identity were indexed; absolute identity and contextual identity. This study contributes to the DMC literature and bridges some of the existing gaps. The findings of this study are that CS by its nature, and most of the findings, if not all, support the notion of TL that multiliteracy is one’s ability to decode multimodal communication, and that this multimodality contributes to the meaning. Either this is applicable to the online affordances used by monolinguals or multilinguals and perceived not only by specific generations but also by any online multiliterates, the study provides the linguistic features of CS utilised by Saudi bilinguals and it determines the relationship between these features and the contexts in which they appear.

Keywords: social media, code-switching, translanguaging, online interaction, saudi bilinguals

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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