The Relationship of Individual, Psychological and Organizational Factors with Nurses' Hand Hygiene Adherence

Abstract: The background of low hand hygiene adherence is a problem where this is important to maintain the quality and safety of patients in hospitals. Individual factors, psychological factors and organizational factors influence adherence with five moments hand hygiene which is still low. The aim of this research was to identify factors related to nurses' adherence in implementing the five moments hand hygiene. The correlational analytical research method with a cross-sectional approach was carried out at RSUD Sumedang at November 2023 with samples of 123 nurses. The questionnaire used was based on Herzbergh's motivation theory and the Manchester Clinical Supervison Scale 26-item (MCSS-26). Univariate analysis with frequency distribution, bivariate test using Chi Square, and multivariate analysis with logistic regression. The results show that there is dominant influencing factors supervision (3.178), attitude (2.921) and knowledge (0.138). The conclusion of this research is that there is a relationship between individual factors, psychological factors and organizational factors on adherence with five moments hand hygiene . The dominant influencing factors are supervision, attitude and knowledge. Suggestions for increasing knowledge through education and training as well as regular feedback, giving rewards to increase motivation. Increased supervision through strengthening the functions of IPCLN and IPCN.


Leveraging the Jetson Nano AI Kit for Machine Learning in Quantifying Gender Bias in Image Captioning

Abstract: In an era where artificial intelligence intertwines with ethical concerns, this paper delves into the practical deployment of the Jetson Nano AI kit to assess gender bias in image captioning. As the AI landscape evolves, the Jetson Nano stands out as an effective tool for environments with limited resources, leveraging its edge computing capabilities to reshape AI research. The paper examines the foundational concepts of gender bias quantification and its real-world application using the Jetson Nano, highlighting its versatility in constrained environments. Throughout the exploration, the seamless integration of the Jetson Nano into machine learning processes is detailed, shedding light on crucial optimizations and adjustments. Challenges encountered are also analyzed, offering insights for researchers undertaking similar projects. Ultimately, the research underscores not just Jetson Nano's role in edge computing but also the imperative of confronting gender bias in AI-generated image descriptions. By melding ethical considerations with edge computing, this paper paves the way for a more balanced and effective AI future.


Utilization of Printing Plate Waste As Applied Chemistry Learning Media For Using Engraving and Knocking Techniques

Abstract:
This study aims to utilize printing plate waste as alternative to making applied chemistry learning media using engraving and knocking techniques. The method includes three stages namely preproduction, production, and postproduction. Activities carried out in preproduction were collecting data through observation, interviews, literature studies, making pattern designs and teaching materials, and preparing tools and materials. The production stage focused on manufacturing the product. The product was hammered using hammer wrapped in paper adhesive and also engraved using CNC lasercutting machine. The postproduction stage included monitoring and evaluation and improvement of research products. The result was a set of learning media consisting of five media made of plates and one stand where media was placed. This learning media can be used in applied chemistry lectures to make it easier for graphic engineering students at Politeknik Negeri Media Kreatif to understand the lessons taught in class.
 Keywords: printing plate waste, applied chemistry media, engraving, knocking techniques


Synchronization of Complex System by Designing a Controller

Abstract: Synchronization of two complex identical systems using nonlinear control is presented by this paper. For emphasis on the chaos behavior, initial conditions sensitivity and Lyapunov exponent is calculated. For the synchronization of the complex master and slave system, the nonlinear control method is proposed effectively.


Online Voting System Using Aes Algorithm with OTP Validation

Abstract: This project introduces the integrity of the electoral process and faces increasing challenges, primarily due to limited means of voter identification, which often rely solely on physical voter identification cards. As a result, vulnerability to fraudulent election practices has increased. To address these concerns, our project introduces an innovative solution that uses both Android and web applications to securely store and verify voter identities. The system incorporates a robust One-Time Password (OTP) authentication mechanism that uses the voter's unique ID for login and cross-checks with a centralized database. Admin-generated OTPs provide an additional layer of verification, thereby improving system security. This streamlined process not only speeds up voter verification but also reduces voting queue time, promising a more efficient and secure voting experience


Prediksi Beban Daya Listrik (W) Menggunakan Metode Support Vector Machine (SVM) Pada Listrik Token Rumah Tangga

Abstract: Electricity is an essential need that cannot be separated from daily human life, especially in the modern era where many devices require electrical energy. The demand for electrical energy is increasing over time, thus the electricity providers must have sufficient capacity to meet the demand. However, there is still a lot of wasted energy due to the mismatch between the demand and the supplied amount of electricity, leading to significant electricity wastage. In this regard, appropriate calculations are needed to determine the amount of electrical power required, hence the need for a prediction method to determine the required power in real-time. The Support Vector Machine method is expected to predict the electrical load accurately, enabling users to determine the required load for the next period. It is hoped that this prediction will achieve an accuracy rate above 85%.


Determinants of Institutional Ownership in the MENA Region

Abstract: This study explores the main determinants of institutional ownership in the MENA region. Using a large sample of 262 listed companies across MENA countries, this study focuses on company and country characteristics that might explain institutional behavior. We attempt to answer the following questions: First, what are the main categories of institutional investors operating in MENA countries? Secondly, what factors drive institutional ownership? Results reveal a weak presence of institutional investors in the MENA region. Unlike foreign and passive investors, domestic and active institutional investors dominate the companies’ ownership. Such findings raise serious issues about how to attract foreign institutional investors. Results also suggest that value stocks appeal to institutional investors, whatever their origin (foreign or domestic) and kind (active or passive). Institutional investors look for large, profitable, and liquid companies that pay high dividends. They prefer investing in highly indebted companies as they consider debt an efficient mechanism to mitigate agency problems. Corporate governance and information disclosure are also crucial determinants of institutional ownership. However, foreign-domestic and active-passive institutional investors have different investment preferences. Our study would contribute to a better understanding of ownership endogeneity within an emerging context. Results would help professionals, managers, and policymakers to adopt appropriate reforms to offer an appealing business climate and attract a large base of institutional investors, not only foreign and active investors.


Institutional quality, information and communication technologies and gender inclusion nexus: global comparative evidence

Abstract: Purpose Despite the longstanding male dominance in the socio-politico-economic spheres, recent decades have witnessed remarkable improvements in gender inclusion. Although the issue of gender inclusion has been widely documented, answers to the question of whether institutional arrangements and information technology shape gender inclusion remain contentious. This study, therefore, empirically examines the effects of institutional quality and ICT penetration on gender inclusion on a global scale. Design/methodology/approach To control for the endogeneity of modeled variables and cross-sectional dependence inherent with large panel datasets, the study employs the Driscoll-Kraay Fixed Effects (DKFE) and the system Generalised Method of Moments (GMM) estimators for a panel of 142 countries from 1996 to 2020. Findings The empirical findings from the DKFE and system GMM estimators reveal that strong institutions significantly enhance gender inclusion. Moreover, by disaggregating institutional quality into various governance indicators, we show that besides corruption control, which has a positive but insignificant effect on women’s empowerment, other governance indicators significantly enhance gender inclusion. Furthermore, there is evidence that various ICT measures promote gender inclusion. Practical implications The study results suggest that policymakers in developing countries should implement stringent measures to curb corruption. Moreover, policymakers in low-income countries should create avenues to facilitate women’s access to ICTs. Hence, policymakers in low-income countries should create and equip ICT training centers and render them accessible to all categories of women. Furthermore, developed countries with high-tech knowledge could help developing countries by organizing free training workshops and sensitization campaigns concerning the use of ICTs vis-à-vis women empowerment in various fields of life. Originality/value The present study fills a significant research gap by comprehensively exploring the nexuses between governance, ICT penetration, and the socio-politico-economic dimensions of gender inclusion from a global perspective. Besides the paucity of studies in this regard, the few existing studies have either been focused on region and country-specific case studies in developed or developing economies. Moreover, this study is timely, given the importance placed on gender inclusion (SDG5), quality of institutions (SDG16), and ICT penetration (SDG9) in the 2015–2030 global development agenda.


Factors Influencing the Growth of Non-Performing Loans. Fresh Evidence from Rwanda, Autoregressive Distributed Lag Approach

Abstract: This study empirically examined the link between the quality of the loans and their determinants in the banking sector in Rwanda. The study used quarterly time-series data collected from the National Bank of Rwanda (BNR) and National Institute of Statistics of Rwanda (NISR) from Quarter1, 2012, to Quarter 4, 2021, in Rwanda. ARDL-ECM was used to estimate the long and short-run relationships between variables of interest. Several tests, such as Bound/Co-integration and Toda-Yamamoto Causality, were tested in this study. Impulse responses were also conducted. Empirical results indicated that both exchange rate, lending interest rate and inflation rate statistically affect the non-performing loans ratio in the short and long run. Furthermore, the analysis indicated that the Loans to Deposits Ratio and Capital Adequacy Ratio have no statistically significant effect on the Non-Performing Loans Ratio. The short-run impact of the Dummy of the COVID-19 pandemic introduced in the model was prevalent and found to be negatively correlated with NPLs in the long run, from which we recommend further studies to ascertain the direction of its causal effect. The results indicated that previous deviation or shock from the long-run equilibrium will be corrected in the current period. As policy implications, the study suggests that Banks should cut interest rates on loans to make them less expensive for borrower's to meet their commitments. Furthermore, the Regulatory Authority should put more effort into stabilizing the inflation and exchange rates, which were found to exert a strong and positive impact on the Non-Performing Loans Ratio.


Perceptions of Peer Coaching Technique by the Alternative Learning System Community Implementers

Abstract: Peer coaching is a process where peers collaborate to improve their skills, knowledge, and abilities in a particular area. The peer coaching method begins with the individual defining the problem or goal that they wish to work on. The Alternative Learning System (ALS) is a program of the Philippine Department of Education that offers non-formal education to out-of-school youths, adults, and other marginalized communities. The respondents of this study were the two hundred (200) community implementers of the Second Congressional District of the Division of Misamis Oriental from the municipalities of Opol to Lugait during the School Year 2022-2023. This study is adapted and modified from the study of Gutierez et al. (2018) on Peer Coaching in a Research-Based Teachers Professional Learning Method for Lifelong Learning: A Perspective and employed a quantitative research design, with data collected from the two hundred (200) ALS community implementers in the second congressional district of Misamis Oriental. Frequency and percentage, mean and standard deviation, regression analysis, and multiple correlation analysis. Findings indicate that the teacher respondents level of perception towards the peer coaching technique is highly effective in terms of teachers self-efficacy, understanding of the teaching and learning process, decision-making, and reflective practice. The teacher respondents level of content knowledge on the learning strand in terms of scientific and critical thinking skills, life and career skills, and understanding the self and society showed significant effects towards the peer coaching technique. Furthermore, the weak but significant relationship between the designation of the teacher-respondent and the length of service as an ALS implementer implies that more experienced teachers and those in leadership roles may be more effective in adopting peer coaching strategies. Teachers should provide a comfortable working environment, give enough training and seminars, encourage ALS Implementers to attend conferences and seminars and engage in training and development programs to assist workers in enhancing their skills and competence.


SELF-READINESS TO CHANGE AMONG TEACHERS IN CAGAYAN DE ORO CITY: BASIS FOR PERSONALITY DEVELOPMENT PLAN

Abstract: This study was primarily undertaken to determine the level of assessment on self- readiness to change among public elementary school teachers in Cagayan de Oro City, School Year 2022-2023. The research problems include the profiling of the respondents the level of assessment on self- readiness to change the relationship between teachers self-readiness to change and respondents profile and personality development plan on self-readiness to change based on the findings of the study. The respondents of the study were the 280 public elementary school teachers in the schools where the study was conducted. Descriptive survey method of research was used in this study. The questionnaire used in the collection of pertinent data was adapted from Robert Kriegel & David Brandt on Change Readiness Questionnaire. The data were analyzed using frequency, mean, percentage, and standard deviation to describe the variables in the study. Moreover, Pearson Product Moment Correlation (r) was used to determine the significant relationship between teachers readiness to change and their profile. Findings revealed that the level of self-readiness to change among teachers on the following traits: resourcefulness, adaptability, optimism, confidence, adventurousness, tolerance for ambiguity, and passion is ready described as agree. There is a significant relationship between teachers self- readiness to change and their profile. Thus, self-readiness to change can help the person to sustain the desired change or goal and match personality development plan to individual need. Based on these findings, it was recommended that teachers should be more adaptive to change to keep up in an ever-changing world.


INKLUZIVNA DIMENZIJA CJELOŽIVOTNOG UČENJA

Abstract: The article analyses the relationship between lifelong learning and inclusive policies for persons with disabilities. The first part analyses the concept of inclusiveness in lifelong learning and the application of this concept in educational policies. The emphasis is on the educational obstacles faced by persons with disabilities and the opportunities that the concept of lifelong learning provides for their inclusion in various forms of education and learning. In doing so, dispositional, situational and institutional obstacles for persons with disabilities and different dimensions of inclusivity are distinguished. The second part of the article analyses the validity of this conceptual framework for describing the educational inclusion of persons with disabilities in Croatia. The difficulties in ensuring the coherence of the policy towards persons with disabilities and the importance of the time dimension in the implementation of these policies are described. Special attention is paid to the final results of inclusive policies. This refers to the lower inclusion of persons with disabilities, their earlier school leaving, and lower overall educational achievements compared to the general population. The analysis is based on data from research on the education of persons with disabilities on obstacles to the inclusion of persons with disabilities in the process of lifelong learning, which were conducted as part of the Academic Network of European Disability Experts and within the thematic network Lifelong education available to all.


Geochemical study to determine what elements are most influential in soil fertility in the southern part of Lampung region

Abstract: Abstract Lampung is one of the best provinces in Indonesia in the agricultural sector. Research on agriculture in Lampung continues to develop over time. To expand land, research on agricultural land is needed. Agricultural soil contains chemical elements that support soil fertility. The aim of this research is to find out which chemical elements have the best correlation with soil fertility. The samples used were agricultural soil samples from the South Lampung and Pesawaran areas. The method used is a geochemical method with pH measurements and X-Ray Fluorescence testing. The results obtained were that the Mg element had a very strong correlation with soil fertility so that in the future the Mg element could be used as an indicator of soil fertility levels.


WTO, Women’s Empowerment and Financial Inclusion

Abstract: Promoting financial inclusion for women is a goal supported by many multilateral organizations and policymakers globally, and the WTO is no exception. This article argues that financial inclusion is crucial to empower women economically and socially. It emphasizes historical barriers that women have faced in accessing financial services. In addition, it underlines the importance of incorporating women's financial inclusion into the World Trade Organization (WTO) and advocates for leveraging existing structures within the WTO, such as the Council for Trade in Services and the Committee on Trade in Financial Services, to advance this issue. The objective is to facilitate the exchange of experiences and best practices among Members in a structured manner, addressing the regulatory framework of financial institutions and learning from each other, rather than creating new rights or obligations or launching joint initiatives. Financial Inclusion, World Trade Organization, Women’s Empowerment, Gender, Trade in Services, Financial Services, WTO Ministerial Conference, Committee on Trade in Financial Services, Remittances, and Gender Equality


Semiautomatic assessment of immunofluorescence microscopy on blood smears in inherited platelet disorders using artificial intelligence: a proof of concept.

Abstract: Introduction Inherited platelet disorders (IPD) are a group of rare diseases caused by mutations in more than 70 genes. Despite advances in molecular diagnosis, platelet phenotyping still represents a cornerstone in the diagnostic workup of IPD. Immunofluorescence microscopy on the blood smear, combined with light microscopy, has been established as a screening tool for IPD and validated as sensitive and specific for 9 of the most frequent IPD [1]. A major drawback of the method lies in the subjective nature of morphological evaluation. Recent advances in computer vision and artificial intelligence tools offer improved performance and increasing accessibility of these technologies even for diagnostic laboratories.


Do Democracy and Government Capacity Positively Contribute to Technological Innovation?

Abstract:
Purpose: In this study, we analyze the f ollowing two points based on these existing studies. F irst, we examine the c orrelation between democracy and technological innovation, using cross-national data.We examine whether more democratized countries have an advantage over less democratic c ountries in t erms o f technological i nnovation, and whether d emocratic countries have an advantage over non-democratic countries in terms of innovation performance. we examine how government capabilities affect technological innovation by dividing them into democratic and non-democratic countries. We aim to analyze whether government capacity is important for technological innovation not only in democratic countries but also in non-democratic countries.
 Research design, data, and methodology: This paper analyzed how political systems, especially democracy and government capacity, affect technological innovation for the period 1997-2017 in 101 countries using data extracted from the World Bank’s World Development Indicators. The relationship between political systems and technological innovation was examined using the Negative Binomial Regression Model (NBRM), which is commonly used to analyze count data with the characteristic of over-dispersion.
 Results: The findings indicate that as democratization progresses, there is a corresponding increase in innovation performance. Additionally, democratic nations demonstrate higher levels of innovation compared to non-democratic countries. Furthermore, the research reveals that countries with robust government capacity, as measured by factors such as the effectiveness of public officials, provision of public services, regulation and support of the private sector, and corruption control, tend to achieve superior innovation performance.
 Implications: These f indings support t he a rgument put forth b y Acemoglu a nd R obinson, s uggesting that i n order to a ttain s ustained economic development and innovation, countries require a certain level of government capacity associated with the operation of exploitative economic institutions. The escalating power struggle between the United States and China has now evolved into a technological competition between market democracies and s tate capitalism. Consequently, there i s a f ervent debate regarding which political systems and forms of capitalism have a positive influence on innovation. This study is expected to offer valuable insights and serve as a crucial reference point for this ongoing discourse.


Real-Time Sign Language Recognition and Translation Using Deep Learning Techniques

Abstract: Sign Language Recognition (SLR) recognizes hand gestures and produces the corresponding text or speech. Despite advances in deep learning, the SLR still faces challenges in terms of accuracy and visual quality. Sign Language Translation (SLT) aims to translate sign language images or videos into spoken language, which is hampered by limited language comprehension datasets. This paper presents an innovative approach for sign language recognition and conversion to text using a custom dataset containing 15 different classes, each class containing 70-75 different images. The proposed solution uses the YOLOv5 architecture, a state-of-the-art Convolutional Neural Network (CNN) to achieve robust and accurate sign language recognition. With careful training and optimization, the model achieves impressive mAP values (average accuracy) of 92% to 99% for each of the 15 classes. An extensive dataset combined with the YOLOv5 model provides effective real-time sign language interpretation, showing the potential to improve accessibility and communication for the hearing impaired. This application lays the groundwork for further advances in sign language recognition systems with implications for inclusive technology applications.


A multi-view heterogeneous knowledge learning method for personalized POI recommendation

Abstract: Existing POI recommendation methods often fail to capture the fine-grained preferences of users and face the challenge of modeling multiple relationships. Moreover, knowledge graph-based recommendation methods are limited in storing dynamic user trajectories, making them unsuitable for POI recommendation scenarios. In this paper, we propose a Multi-View Heterogeneous Knowledge learning model that utilizes techniques for heterogeneous knowledge representation learning and multi-view context modeling. Our model comprehensively models user preferences and the relationships between users and POIs by utilizing information from users’ visiting sequences and POI attributes knowledge graph. Specifically, we design a heterogeneous knowledge embedding method to learn the representation of users and POIs using POI attribute knowledge and users’ visiting sequences. Additionally, we constructed a user trajectory similarity graph and a POI attribute similarity graph to explore potential relations between users and between POIs. The former measures the similarity of user behaviors based on user visit sequences, and the latter quantifies the similarity between different POIs through a novel feature mapping method. Finally, we propose a multi-view hybrid learning method that combines unsupervised and supervised learning paradigms to model complex relationships, improving the overall recommendation performance. Extensive experiments on real-world datasets validate the effectiveness of our method.


PRECEDENT NAMES FROM DIFFERENT SOURCES IN ENGLISH LANGUAGE PRINT MEDIA

Abstract: The article, based on precedent names identified in the English-language print media, establishes their source areas (politics, cinema, literature, music, religion), the frequency and relevance of individual precedent names and their source, use in texts and, mainly, in article headings to attract the reader's attention and increase the impact on his opinion.


Semiquantum proxy blind signature based on quantum teleportation

Abstract: <p indent="0mm">In this paper, we propose a novel semiquantum proxy blind signature scheme using quantum teleportation based on <italic>χ</italic> states, where the original message owner, proxy signer, and third party are quantum participants with complete quantum capabilities, while the original signer and signature verifier are semiquantum participants with limited quantum capabilities. Our protocol not only has complete blindness, unforgeability, and nonrepudiation, but it also resists the attack behavior of an eavesdropper. Because of the limited quantum capabilities of the original signer and signature verifier, our protocol does not require them to prepare and measure the quantum superposition and quantum entangled states when used in practical applications. Hence, our protocol can reduce the burden of quantum state preparation and measurement for the original signer and signature verifier.


Development of a smart home suitability indicator and indicative self-assessment platform for the disabled facilities grants (DFGs)

Abstract: Purpose Delay in housing adaptation is a major problem, especially in assessing if homes are suitable for the occupants and in determining if the occupants are qualified for the Disabled Facilities Grant (DFG). This paper describes the development of two self-administered intelligent integrated assessment tools from the DFG Adapt-ABLE system: (1) The Home Suitability Assessment Platform, which is a preventive mechanism that allows assessment of the suitability of homes based on occupants’ mobility status and (2) an indicative assessment platform that determines if the applicants are qualified for the DFG to prevent lengthy delays. Design/methodology/approach The adopted method aligned with a development study approach: a grounded literature review, a severity measurement approach, two stakeholder engagement workshops, four brainstorming sessions and four focus group exercises. The system development relied on Entity–Relationship Diagram (ERD) technique for data structures and database systems design. It uses DFG context sensitivity with alignment with DFG guidance, interlinkages and interoperability between the assessment tools and other platforms of the integrated Adapt-ABLE system. Findings The assessment tools are client-level outcomes related to accessibility, usability and activity based on the assessment process. The home suitability platform shows the percentage of the suitability of a home with assessment results that suggest appropriate action plans based on individual mobility status. The indicative assessment combines the function of referral, allocation, assessment and test of resources into an integrated platform. This enables timely assessment, decision-making and case-escalation by Occupational Therapists based on needs criteria and the eligibility threshold. Originality/value These assessment tools are useful for understanding occupants’ perception of their physical housing environment in terms of accessibility, suitability and usability based on basic activities of daily living and their mobility status. The indicative self-assessment tool will substantially cut down the application journey. The developed tools have been recommended for use in the CSJ Disability Commission report and the UK government Guidance on DFGs for local authorities in England.


Deep survival analysis model for incident clearance time prediction

Abstract: Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers "duration dependence" of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.


Knowledge graph‐driven data processing for business intelligence

Abstract: Abstract With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre‐training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use‐cases from various domains. This article is categorized under: Algorithmic Development > Text Mining Application Areas > Business and Industry


Benchmarking YOLOv5 models for improved human detection in search and rescue missions

Abstract: Drone or unmanned aerial vehicle (UAV) technology has undergone significant changes. The technology allows a UAV to carry out a wide range of tasks with an increasing level of sophistication, since drones can cover a large area with cameras. Meanwhile, the increasing number of computer vision applications utilizing deep learning provides a unique insight into such applications. The primary target in UAV-based detection applications is humans, yet aerial recordings are not included in the massive datasets used to train object detectors, which makes it necessary to gather the model data from such platforms. You only look once (YOLO) version 4, RetinaNet, faster region-based convolutional neural network (R–CNN), and cascade R–CNN are several well-known detectors that have been studied in the past using a variety of datasets to replicate rescue scenes. Here, we used the search and rescue (SAR) dataset to train the you only look once version 5 (YOLOv5) algorithm to validate its speed, accuracy, and low false detection rate. In comparison to YOLOv4 and R–CNN, the highest mean average accuracy of 96.9% is obtained by YOLOv5. For comparison, experimental findings utilizing the SAR and the human rescue imaging database on land (HERIDAL) datasets are presented. The results show that the YOLOv5-based approach is the most successful human detection model for SAR missions.


Contextualised dictionary literacy, information literacy and information behaviour in the e-environment

Abstract: Purpose Needs for information literacy, disparities in society, bridging digital divides, richness of information sources in electronic (e-)environments and the value of dictionaries have often been propagated. To improve information sources and information literacy training, information behaviour must be understood (i.e. all information activities). This paper conceptualises new opportunities for information sources (e.g. electronic dictionaries) to all society sectors, dictionary literacy and research lenses such as lexicography to supplement information literacy and behaviour research. Design/methodology/approach A scoping review of information literacy and behaviour, lexicography and dictionary literature grounds the conceptualisation of dictionary literacy, its alignment with information literacy, information activities and information behaviour and lexicography as additional research lens. Findings Research lenses must acknowledge dictionary use in e-environments, information activities and skills, meanings of information and dictionary literacy, the value of e-dictionaries, alignment with information behaviour research that guides the development of information sources and interdisciplinary research from, e.g. lexicography – thus contextualisation. Research limitations/implications Research implications – information behaviour and information literacy research can be enriched by lexicography as research lens. Further conceptualisation could align information behaviour, information literacy and dictionary literacy. Practical implications Dictionary training, aligned with information literacy training, can be informed by this paper. Social implications The value of dictionary literacy for all sectors of societies can be improved. Originality/value Large bodies of literature on information behaviour and lexicography individually do not cover combined insights from both.


Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

Abstract: A bstract Effective String Theory (EST) represents a powerful non-perturbative approach to describe confinement in Yang-Mills theory that models the confining flux tube as a thin vibrating string. EST calculations are usually performed using the zeta-function regularization: however there are situations (for instance the study of the shape of the flux tube or of the higher order corrections beyond the Nambu-Goto EST) which involve observables that are too complex to be addressed in this way. In this paper we propose a numerical approach based on recent advances in machine learning methods to circumvent this problem. Using as a laboratory the Nambu-Goto string, we show that by using a new class of deep generative models called Continuous Normalizing Flows it is possible to obtain reliable numerical estimates of EST predictions.


More Than Meets the Eye: Misconduct and Decoupling Against Blockchain for Supply Chain Transparency

Abstract: The fashion industry has consistently ranked high in terms of its association with modern slavery. While scandals like the Rana Plaza incident have focused the world's attention on Asia, in Brazil, over 35,000 people have been rescued from conditions analogous to slavery in the past 15 years. Outsourcing and the underlying social structures that blur the implementation of outsourcing are central to this problem. Fashion supply chains are highly fragmented and labour intensive, exhibiting power asymmetry and informality. Although supply chain research has focused on how focal firms can control or improve supplier practices, this study examines focal firm misconduct and decoupling. Our research presents an intervention aimed at developing a blockchain solution to create a census of fashion working conditions in Brazil. The project included two non-governmental organisations, two fashion brands and their suppliers, the fashion retail association, a blockchain start-up and the research team (18 organisations). We contribute to both theory and practice, revealing that the blockchain's potential to ease transaction costs is outweighed by governance costs related to third-party supervision. We show that focal firms justify unfair purchasing practices via victimisation and diverting attention from themselves to consumers (‘unwilling to pay’) and suppliers (‘not behaving as expected’), unveiling how these drivers of organisational misconduct lead to supplier decoupling (means–end) and focal firm decoupling (policy–practice). Such underlying social structures sustain inaction or, at best, advance focal firms’ visibility of their supply chains while offering no true transparency to the broader society.


Evaluating completion rates of COVID-19 contact tracing surveys in New York City

Abstract: Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs.To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021.We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used.The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%.These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.


Real-time anomaly detection of short-term traffic disruptions in urban areas through adaptive isolation forest

Abstract: The escalating congestion impacts of short-term traffic disruptions, such as double parking or short-duration work zones, are gaining increased attention. This study introduces an enhanced isolation forest-based unsupervised anomaly detection algorithm to detect short-term traffic disruptions in urban areas. To enable the detection in real-time, a sliding window approach is introduced to allow the streaming data to be read and processed according to the window size. An adaptive threshold that automatically adjusts itself to overcome the limitations of the miss rate on local anomalies is shown to further enhance the model predictions. The proposed algorithm is empirically validated on four study sites in Manhattan, New York City. The experimental results demonstrate that the proposed unsupervised algorithm can effectively detect different types of traffic anomalies including accidents, work zones/road closures, traffic jams, double parking events and police activities. On all sites, the average detection rate is 81.1% for traffic jams and 89.6% for police activities, respectively. For three out of the four sites, the detection rate ranges from 71.4% to 100% for accidents, work zones and double parking. An optimizer using high-pass filter is also presented to further improve off-line detection. The primary advantages of this proposed computationally-efficient method are that its only required data input is travel time information and that it does not need labeled data for training, which make it highly deployable for real-time operational applications and can be easily adopted by other cities.


A control scheme for interval type‐2 T‐S fuzzy systems at constrained bit rates: A coding and decoding scheme

Abstract: Summary This study focuses on a class of interval type‐2 T‐S fuzzy systems under bit rate constraint(BRC), which can adapt to complex systems more flexibly than the traditional interval type‐1 T‐S fuzzy model. In order to guarantee the security of data transmission, and considering that the channel is affected by the bit rate during transmission, this study introduces a codec scheme in the interval type‐2 T‐S fuzzy system and applies it to the channel from the sensor to the controller. With the support of the codec scheme, this study explores the sufficient conditions to satisfy the data recoverability, and based on the satisfaction of these conditions, the system is proposed to satisfy the sufficient conditions for input‐to‐state stability (ISS) and the controller gain. Through the in‐depth analysis in this paper, this study proposes a control scheme that can fit complex systems more accurately and also confirms the importance of considering bit rate constraints in communication systems. Finally, the paper is validated by simulation with two specific examples, which fully demonstrate the superiority and effectiveness of the proposed control scheme.


ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation

Abstract: Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT images is a tedious task, and due to the diversity of these images and varying technical skills of professionals, segmentation results can be inconsistent. To address this problem, a novel ASD-Net network is proposed in this paper for kidney and kidney tumor segmentation tasks. First, the proposed network employs newly designed Adaptive Spatial-channel Convolution Optimization (ASCO) blocks to capture anisotropic information in the images. Then, other newly designed blocks, i.e., Dense Dilated Enhancement Convolution (DDEC) blocks, are utilized to enhance feature propagation and reuse it across the network, thereby improving its segmentation accuracy. To allow the network to segment complex and small kidney tumors more effectively, the Atrous Spatial Pyramid Pooling (ASPP) module is incorporated in its middle layer. With its generalized pyramid feature, this module enables the network to better capture and understand context information at various scales within the images. In addition to this, the concurrent spatial and channel squeeze & excitation (scSE) attention mechanism is adopted to better comprehend and manage context information in the images. Additional encoding layers are also added to the base (U-Net) and connected to the original encoding layer through skip connections. The resultant enhanced U-Net structure allows for better extraction and merging of high-level and low-level features, further boosting the network's ability to restore segmentation details. In addition, the combined Binary Cross Entropy (BCE)-Dice loss is utilized as the network's loss function. Experiments, conducted on the KiTS19 dataset, demonstrate that the proposed ASD-Net network outperforms the existing segmentation networks according to all evaluation metrics used, except for recall in the case of kidney tumor segmentation, where it takes the second place after Attention-UNet.


NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour

Abstract: Abstract Background Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status. Results & discussion NiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting. Conclusion The NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual’s daily health-related behavior. NiMBaLWear’s focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health .


Artificial intelligence in voice assistants: User benefits explored

Abstract: Abstract This study demonstrates that consumers' perceptions of voice assistant device (VAD) elements promote psychological ownership and interface flow experience, resulting in satisfaction with, and loyalty toward VADs. To that end, this study collected VAD users in the United States and conducted structural equation modeling to empirically examine the relationships among the variables. The empirical findings indicated that perceived artificiality and intelligence had significant influences on psychological ownership and flow experience. In addition, the empirical results demonstrated that psychological ownership resulted in flow experience with VADs, significantly affecting customer satisfaction and loyalty toward VADs. Finally, customer satisfaction had a significant impact on loyalty. Based on the empirical findings, this study provided meaningful implications for marketing scholars and practitioners.


Into the ether or the state? Legibility theory and the cryptocurrency markets

Abstract: Abstract There is substantial cross-national variation in the level of regulatory clarity surrounding cryptocurrencies. What explains these differences? And, more broadly, what drives the divergent historical development of market regulation in different jurisdictions? To answer these questions, we present a new conceptual framework centered on the concept of market legibility. This term, inspired by the sociological literature, refers to the extent to which markets are made legible to the state through standardization. We contend that state supply of, and market demand for, legibility drives the primary political-economic dynamics of market regulation. Specifically, these factors combine to produce ideal type states of legibility that correspond to both distinct stages of market development and the relative level of regulatory clarity in any one jurisdiction. This framework is utilized to conduct a comparative historical analysis of cryptocurrency regulation in the EU, US, UK, and Japan. By performing these tasks, this article corrects the common assumption that states are constantly striving to impose their authority on unwilling markets. It demonstrates instead that state and private actor preferences to make markets legible vary, conditioning, in turn, the political economy of regulatory governance.


Women’s cellphone access and ownership in rural Uganda: implications for self-care interventions

Abstract: Abstract Background The World Health Organization (WHO) call for cervical cancer elimination includes increasing global cervical screening coverage. HPV-based self-collection (HPV-SC) is a promising screening model for low- and middle-income countries (LMICs), and while digital technology, such as cellphones, can be used to streamline HPV-SC, there is limited data on digital technology penetration in LMICs. Determining women’s cellphone access is critical to understanding the feasibility of using cellphones to support HPV-SC. Methods This study is a secondary analysis of a larger clinical trial. Participants of a cluster-randomized trial comparing HPV-SC models in Uganda completed a survey, including questions about demographics, cellphone access/ownership, prior cervical cancer screening (CCS), and willingness to receive CCS information by text. A logistic regression model was used to determine adjusted rates of cellphone ownership using survey variables as factors. Results Of 2019 participants, 76.1% owned a cellphone. In non-cellphone owners ( n = 483), 82.4% had daily cellphone access and 7.3% had no access. Compared to non-cellphone owners, cellphone owners were significantly older, more educated, closer to major health centers, more likely to have prior CCS, and more willing to receive a CCS text. In the logistic regression model, the aforementioned variables were all significantly associated with the odds of owning a cellphone. Conclusions As health care systems consider adopting HPV-SC, it is imperative to understand digital technology penetration. The majority of participants were cellphone owners and were willing to receive CCS information by text; however, significant socioeconomic and demographic differences remain between cellphone owners and non-owners. Further investigation is needed to understand whether HPV-SC using cellphones is feasible in similar settings. Trial registration ISRCTN, 12767014 . ClinicalTrials.gov, NCT04000503 .


The impact of personality traits and cultural values on coordination effectiveness: A study of software development teams effectiveness

Abstract: Abstract Software development projects depend on collaborative teams. In the past 50 years of research, various studies have explored the effect of software engineer's personality traits and cultural values on team performance. These studies have led to better understand these relationships; however, how the personality traits and cultural values influence the team effectiveness is still far away in the literature. This research aims to investigate the relationships between social and psychological complexities (including personality traits and cultural values), team coordination, team motivation, and team success (which comprises team effectiveness and team climate) to explore the impact of social and psychological issues on software team success. An online survey targeting software development professionals and unstructured interviews were followed for data collection. We received 112 responses from software developers working in different countries. Findings indicate that personality traits and cultural values, that is, consciousness, openness, harmony, and autonomy, have positive relationship with team coordination effectiveness, while other factors such as neuroticism, embeddedness, hierarchy, and mastery were found to be related negatively with it. These negative relationships can be mitigated by motivating team members appropriately. Based on our research findings, we conclude that the negative impact caused by different personality and cultural traits could be reduced by improving team coordination effectiveness using effective motivation.


A performance analysis of Basin hopping compared to established metaheuristics for global optimization

Abstract: During the last decades many metaheuristics for global numerical optimization have been proposed. Among them, Basin Hopping is very simple and straightforward to implement, although rarely used outside its original Physical Chemistry community. In this work, our aim is to compare Basin Hopping, and two population variants of it, with readily available implementations of the well known metaheuristics Differential Evolution, Particle Swarm Optimization, and Covariance Matrix Adaptation Evolution Strategy. We perform numerical experiments using the IOH profiler environment with the BBOB test function set and two difficult real-world problems. The experiments were carried out in two different but complementary ways: by measuring the performance under a fixed budget of function evaluations and by considering a fixed target value. The general conclusion is that Basin Hopping and its newly introduced population variant are almost as good as Covariance Matrix Adaptation on the synthetic benchmark functions and better than it on the two hard cluster energy minimization problems. Thus, the proposed analyses show that Basin Hopping can be considered a good candidate for global numerical optimization problems along with the more established metaheuristics, especially if one wants to obtain quick and reliable results on an unknown problem.


The relationship between nursing professionalism and nursing intention for patients with emerging infectious diseases of nursing students who had experienced COVID-19 pandemic: the mediating effect of e-Health literacy

Abstract: Purpose: This study aimed to identify the relationship between nursing professionalism and nursing intention for patients with emerging infectious diseases of nursing students who had experienced coronavirus disease 2019 (COVID-19), with a focus on the mediating effect of e-Health literacy.Methods: The study surveyed 177 nursing students who had experienced COVID-19. The data were collected using self-reported questionnaires. The collected data were analyzed using IBM SPSS statistics 25.0, and the mediating effect was analyzed through the SPSS Process macro model 4.Results: Nursing professionalism (β=.26, p=.002) and e-Health literacy (β=.18, p=.021) were found to be significant predictors of nursing intention for patients with emerging infectious diseases. In addition, e-Health literacy partially mediated the relationship between nursing professionalism and nursing intention for patients with emerging infectious diseases.Conclusion: e-Health literacy was a mediating factor in the relationship between the nursing professionalism and nursing intention of nursing students for patients with emerging infectious diseases. In order to improve nursing intention of nursing students for patients with emerging infectious diseases, it is important to develop an education program that can enhance their e-Health literacy as well as nursing professionalism.


A Case Study on the Educational Effectiveness of Online English Content Using the Storytelling Learning Method: Focusing on Students’ Perceptions and Opinions

Abstract: This is a case study designed to determine the educational effect of online English content using the Storytelling Learning Method (SLM). The study focused on the course Learning English through Film offered by a cyber university in Seoul. A survey was conducted among 1,024 students from 86 universities who participated in the course. The course was based on the film <i>A Dog’s Way Home</i> (Smith, 2019) and followed the story of the film for 15 weeks over a semester. At the end of the semester, a survey was carried out to assess the following: 1) satisfaction with the online English learning content and improvement of English skills, 2) the effectiveness of SLM, and 3) students’ perceptions of their English skill improvement. The survey results revealed that 76% of the students found the SLM to be effective, and 92% expressed their interest and active participation in the course. In addition, 80% of the students reported that the online classes were more helpful in learning English, with listening and understanding context identified as the most improved language skills. These findings support the effectiveness of strategically structured course content utilizing SLM in terms of enhancing students’ motivation to learn and their academic achievements.


Implementasi Smart Home pada Platform Apple Homekit dan Google Home dengan Raspberry Pi 4B

Abstract: This research examines the significant impact of technological advances, especially in the Internet of Things (IoT) paradigm, on various aspects of human life strongly manifested during the In-dustrial Revolution Era 4.0. The main focus of this research is on the application of advanced and innovative IoT concepts in the context of smart homes, integrating popular platforms such as Apple HomeKit and Google Home. The temperature sensor (DHT11) and light sensor (LDR) play a key role as important input elements, enabling the optimization of smart home automation functions. Raspberry Pi 4B was chosen as the main platform, the "brain" of the system, providing reliable computing capabilities. Using four-channel relays, this research specifically aims to increase ef-ficiency and integration in controlling devices in the smart home ecosystem. By emphasizing the concept of optimization, this research proposes a smart solution that is expected to not only provide an integrated and efficient experience for smart home users but also unlock the potential for further development of IoT technology. As a result, the implementation of the solutions proposed in this research is expected to create a smart home that is not only technologically smart but also responsive to the needs and preferences of its occupants, paving the way for further in-novation in the development and application of the Internet of Things in various contexts of human life.


Efficiency analysis of fixed and axis tracking options of photovoltaic systems to be installed in a marina

Abstract: Electricity consumption is an important cost in businesses operating in the service sector such as marinas. Although the use of solar power for electricity has become widespread in various fields in Türkiye, marinas have yet to fully utilize this technology. The aim of this study is to examine the feasibility of supplying the electricity needs of a marina with a photovoltaic (PV) system. For this purpose, a marina in Muğla (Bodrum) was selected, and monthly/yearly electricity needs were determined. In this study, the PV installed capacity needed to meet the marina's electricity demand was selected. Simulations were performed for three different options using the Photovoltaic Geographical Information System (PVGIS). According to the calculations, a 2012 kW single-axis tracking PV system generated 4,469,618 kWh/year of electricity, which is sufficient to meet the annual electricity demand. It has been determined that the installation cost payback period is 3.91 years for the single-axis tracking PV system. In addition, it was observed that both the dual-axis tracking and single-axis tracking PV systems were more efficient than the fixed options.


HABERTAN: Game Petualangan 3D Dengan Tema Pemilahan Sampah Sebagai Upaya Pendekatan Inovatif Untuk Pengenalan Lingkungan

Abstract: The increasing awareness of the importance of maintaining forest cleanliness as a primary ecosystem is becoming more urgent, especially in remote areas with limited deep socialization. This research aims to develop an innovative game titled "HABERTAN" focusing on waste sorting in forest areas to raise public awareness of the waste present in the forest. By utilizing a 3D model for visual assets with stages of 2D sketch, 3D modeling, UV Mapping, and Texture Baking. The development of this game is conducted using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) method. The ADDIE method is used to ensure a systematic game development process, starting from needs analysis to performance evaluation. The goal of this game is to provide players with an interactive experience that not only entertains but also provides a deep understanding of the impact of waste on forest sustainability. The game "HABERTAN (Let's Clean the Forest)" is designed to provide players with a fun and educational learning experience. The results of the development of this game show that "HABERTAN" provides a unique experience for players, with the ability to learn while playing. It is hoped that this game can be an effective tool in raising public awareness of the importance of maintaining forest cleanliness, especially in the context of waste sorting. Through this innovative approach, it is hoped to encourage active participation from the community in preserving forests as extremely valuable natural resources.


Implementasi Chatbot Kesehatan Kucing Melalui Dialogflow dan Telegram untuk Pemberian Informasi Penyakit dan Perawatan

Abstract: Cats are an integral part of human life, but their health needs are often complicated for their owners. This research proposes and implements a Chatbot system using Natural Language Processing (NLP) through the Dialogflow framework to provide advice on cat care and treatment. This method is intended to help cat owners understand basic care needs and identify symptoms of illness their cat may be exhibiting. Using Dialogflow as the main framework enables intuitive and responsive interactions between chat owners and Chatbots. This system has gone through comprehensive stages of analysis, development and testing to ensure reliability and accuracy in providing cat health care information. This test also includes several real-life cases to validate the Chatbot's ability to provide appropriate treatment advice and solutions for cat health problems. The results of this research show that the application of Chatbots via Dialogflow has great potential in helping cat owners in caring for and dealing with diseases in cats. With the ability to understand natural language and provide accurate information, this Chatbot can be a useful tool to increase cat owners' understanding of proper care and early treatment of possible illnesses in their pets.


Automated Systems and Artificial Intelligence

Abstract: There are two human rights properties overshadowing academic discourse and policy documents in relation to automated systems and artificial intelligence which are operating in the digital realm. These properties are about the trust in artificial intelligence and automated systems, and secondly, of robustness. The latter appears in two aspects related to law governing the AI solutions and the AI itself. The degree of our trust in physical judges is related to our expectation that they are capable of distrusting the law. And our trust in automated systems and artificial intelligence is weaker because we assume that they completely trust the law. It could be said that we cannot find a better positivist than artificial intelligence. The moment artificial intelligence can independently exercise disavowal and intolerability formulas, that is, articulate a reasoned understanding that a specific legal norm or rule is profoundly unfair, then the non-coherence disappears. However, then we would no longer be dealing with artificial intelligence; perhaps we could term such a decision-maker an artificial brain.


The E-state and Fundamental Rights

Abstract: There are two fundamentally related perceptions, which are of a religious nature, about the e-state from the perspective of fundamental rights. The first hails e-state positivity by suggesting that public power draws closer to the people because this framework is providing more efficient, transparent and neutral procedures and outcomes. Here fundamental rights would therefore appear to be more safeguarded since e-solutions minimise arbitrariness. The opposite perception suggests there is obscurity and distrust despite the apparent efficiency and neutrality. Four caveats appear in relation to the image of e-state through the non-coherence theory. These are: the dominance of human rights rhetoric, questionability of the success of the e-state, the expectation of horizontality and the potential road to a police state.


Focus Realization in Heritage Spanish

Abstract: We shed light on the question of how narrow information (F) and contrastive focus (CF) are intonationally and syntactically realized by heritage speakers (HSs) of Peninsular Spanish (PS) who have German as their second L1, and compare their data to those of monolingual speakers (MSs) of PS. Results from a production experiment show clear differences between the groups with respect to preferred syntactic strategies and, consequently, the intonational realization of focal pitch accents. The preferred strategy of HSs is stress shift, followed by p-movement and simple clefts, for both focus types. Conversely, MSs mostly use different strategies for each focus type; that is, pseudo-clefts and p-movement for F, and simple clefts and focus fronting for CF. Interestingly, stress shift is not a relevant option. The attested differences support the view that the interface between discourse on one hand, and syntax and phonology on the other, is challenging for bilingual speakers (Sorace, 2011).


Exploring the Security Vulnerability in Frequency-Hiding Order-Preserving Encryption

Abstract: Frequency-hiding order-preserving encryption (FH-OPE) has emerged as an important tool in data security, particularly in cloud computing, because of its unique ability to preserve the order of plaintexts in their corresponding ciphertexts and enable efficient range queries on encrypted data. Despite its strong security model, indistinguishability under frequency analyzing ordered chosen plaintext attack (IND-FA-OCPA), our research identifies a vulnerability in its design, particularly the impact of range queries. In our research, we quantify the frequency of data exposure resulting from these range queries and present potential inference attacks on the FH-OPE scheme. Our findings are substantiated through experiments on real-world datasets, with the goal of measuring the frequency of data exposure resulting from range queries on FH-OPE encrypted databases. These results quantify the level of risk in practical applications of FH-OPE and reveal the potential for additional inference attacks and the urgency of addressing these threats. Consequently, our research highlights the need for a more comprehensive security model that considers the potential risks associated with range queries and underscores the importance of developing new range-query methods that prevent exposing these vulnerabilities.


Prediksi Harga Minyak Dunia Dengan Fuzzy Time Series

Abstract: The government uses economic growth as a metric to measure prosperity and the effectiveness of economic development. However, oil price fluctuations can increase inflation through increasing fuel prices and have an impact on the world economy, including Indonesia. This research will apply a fuzzy time series using the Cheng method to predict world oil prices. The results of this research show that the MAPE value for forecasting world oil prices using the Fuzzy Time Series Cheng method is 2%, which means the forecasting model has very good value.


How are texts analyzed in blockchain research? A systematic literature review

Abstract: Abstract This paper provides a systematic literature review of text analysis methodologies used in blockchain-related research to comprehend and synthesize existing studies across disciplines and define future research directions. We summarize the research scope, text data, and methodologies of 124 papers and identify the two most common combinations of these dimensions: (1) papers that focus on specific cryptocurrencies tend to apply sentiment analysis to instant user-generated content or news articles to discover the correlations between public opinion and market behavior, and (2) studies that examine the broad concept of blockchain with text data from documents published by companies tend to apply topic modeling techniques to explore classifications and trends in blockchain development. We discover five major research topics in the academic literature: relationship discovery, cryptocurrency performance prediction, classification and trend, crime and regulation, and perception of blockchain. Based on these findings, we highlight three potential research directions for researchers to select topics and implement suitable methodologies for text analysis.


Exploring Bitcoin dynamics against the backdrop of COVID-19: an investigation of major global events

Abstract: Abstract COVID-19 has significantly influenced global financial markets, including Bitcoin. Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes, which were mostly due to the pandemic. This research not only analyzes the contagion effects of COVID-19 but also considers aftermath events beyond the first pandemic wave to examine spillovers of Bitcoin. The study employs Diebold and Yilmaz’s method to explore the static and dynamic spillovers of the selected variables and identifies several major global events, including crypto-specific affairs, macroeconomic policies, and geopolitical conflicts, to explain the new market dynamics of Bitcoin using network analysis. The findings identify a few high-contagion periods related to Bitcoin. The paper also found that Bitcoin is more likely to produce extreme returns and is more connected to other markets. Contagion effects “from” and “to” other markets are asymmetrical in terms of arrival time and market response. Bitcoin is more likely to be affected by other markets in extreme situations and receives spillovers from them sooner than it transmits spillovers to others. In the context of various global events, impacts arising from developed countries are stronger. China still has some impact on cryptocurrency markets, but they are waning. Bitcoin is thus not a safe haven from the shocks of global events, but can sometimes work as a hedge or diversifier. The results offer alternative explanations for Bitcoin’s different market dynamics and enrich our understanding of Bitcoin’s safe haven, hedge, and diversifier properties within a diversified portfolio.


The Implementation of Technique Used by Teacher to Develop Students’ Speaking Skill: A Case Study at Muhammadiyah University of Surakarta in 2023/2024

Abstract: Techniques are a way to assist students to explore and increase their ability. This study aimed to describe (1) the classroom techniques used by teacher and their purposes, (2) the teacher’s role in implementing the techniques, and (3) the elements of speaking improved in implementing the techniques. This research used a case study qualitative method. The subjects are English teachers instructing third-semester speaking classes in the English Education Department at Muhammadiyah University of Surakarta in 2023/2024. The researcher used observation, interview, and documentation to collect the data. The result of the research showed that teachers used some techniques in teaching speaking, such as conversation drill, debate, role play, and presentation. Debate and conversation drill is mostly used by the English teachers in teaching and learning speaking. Teachers’ role in implementing technique in teaching speaking are as controller and tutor. Elements that need to be improved in implementing techniques in teaching speaking, namely fluency and accuracy, then grammar, vocabulary, and pronunciation. The implementation of conversation drill and debate technique as a teaching technique developed the students’ speaking performance in terms of accuracy dealing with acceptable pronunciation, correct grammar, and appropriate word choice as well as making possible chance to students to work cooperatively, brainstorm ideas, develop vocabulary, and read to support an opinion. The researcher suggests that teachers use various techniques as it encourages students in speaking


MFLion‐DMN: Mayfly Lion‐optimized deep maxout network for prediction of software development effort

Abstract: Abstract The precise evaluation of predicting software development effort is a serious process in software engineering. The underestimates require more time, which seems to be the negotiation of complete efficient design and complete software testing. Thus, this paper devises a novel model for inspecting the effort taken for the design of software. The Mayfly Lion Optimization (MFLion) algorithm is devised, which ensembles the Mayfly Algorithm (MA) with the Lion Optimization Algorithm (LOA) for precise estimation. The deep maxout network (DMN) is adapted wherein the different weights are produced out of which the most suitable is infused considering the MFLion during model training. The optimal features are selected using recursive feature elimination (RFE) wherein the best features are selected and the remaining irrelevant features are eliminated from the list. The proposed MFLion obtained better performance with the smallest mean magnitude of relative error (MMRE) of 5.909 and the smallest root mean square error (RMSE) of 75.505, respectively. Each technique is produced using a separate database generated using the Promise software engineering repository. The outcomes produced from the assessment of the accuracies of models suggested that the MFLion‐DMN is a substitute to forecast software effort, which is extensively devised in engineering platforms.


Advanced systems and technologies for the enhancement of user experience in cultural spaces: an overview

Abstract: Abstract Human–computer interaction, Artificial Intelligence, and the multilingualism of digital culture open up unexpected scenarios in the contemporary design of cultural spaces with the creation of artifacts in which the analogic and digital dimensions come together to enhance the experience. Sensors and devices track user movement in the real world and translate the inputs into commands through hand gestures, speech recognition, head movements, tangible interfaces, or a combination of these elements. Through theorical models, concepts and tools, the paper reports the evolution of “User Experience” applied to personalized enjoyment and use of cultural places. The functional-performance survey of tools and technologies for perception, narrative and augmented interaction revealed models that highlight the diversity and richness of tangible and intangible cultural heritage through new forms of interaction and knowledge transfer. In this direction, new technological tools make it possible to detect, track and evaluate the personalized user experience by processing or producing large amounts of data. Therefore, on one hand the paper explores the limitations dictated by data management and user privacy in using such systems and, on the other, it prefigures new scenarios for amplifying and personalizing the user experience.


Quantifying coherence via the generalized Wigner–Yanase–Dyson skew information

Abstract: Quantum coherence is an important feature that distinguishes quantum mechanics from classical physics. In this paper, we study the coherence of a quantum state with respect to a quantum channel, and introduce a coherence measure in terms of the generalized Wigner–Yanase–Dyson skew information. The basic properties and operational significance of the measure are expounded in detail, which shows that it is indeed a veritable quantity and satisfies many desirable conditions intuitively required for the coherence measures. We illustrate the significance of characterizing channels from the coherence perspective through some examples. Finally, we consider the average coherence of a state based on the generalized Wigner–Yanase–Dyson skew information with respect to any complete mutually unbiased bases in prime power dimensional systems, as well as with respect to all orthonormal bases, and show that these two averages are equivalent since they both can be seen as the coherence of the quantum state with respect to the depolarizing quantum channel.


<scp>WD‐XRF</scp> technique for speciation of iron and quantification of its oxidation states in geological samples using L‐series of iron X‐ray spectrum

Abstract: Abstract Wavelength dispersive X‐ray fluorescence (WD‐XRF) spectrometry is a fast and widely used technique for elemental analysis of geological samples from Na to U. However, it has seldom been applied for quantitative determination of oxidation states of elements in complex matrices. In this study, we present a method to determine the oxidation states of iron in geological samples by WD‐XRF. We used the FeLα 1,2 fluorescence line as a tool for iron speciation and Fe(II)/Fe total ratio determination. We found that the normalized intensity (peak height) of FeLα 1,2 fluorescence line was linearly correlated with the spin multiplicity (2S + 1) of ferrous and ferric content in the samples. To account for the matrix effects of chemical environment (matrix and chemical bonding) on FeLα 1,2 fluorescence line, we introduced a chemical index factor (μ) that enabled accurate determination of Fe(II)/Fe total ratio in different types of geological samples. The method was validated using international certified reference materials and their mixtures and obtained promising results, with only 6 out of 55 determinations showing relative error more than 5% of the certified values.


The mediating effect of entrepreneurship on financial support and economic growth in African emerging economies

Abstract: Purpose The aim of the research is to examine in depth the relationship between financial support, entrepreneurship and economic growth in emerging African economies. Design/methodology/approach The study adopts the system-generalized methods of moments (sys-GMM) technique for data analysis and hypothesis testing on a sample of 34 African emerging economies (340 observations) from 2010 to 2019. Findings The results show that there is significant positive correlation between financial support, entrepreneurship, and economic growth. Moreover, entrepreneurship served as a partial mediator between financial support and economic growth in African emerging economies. Practical implications This research suggests that African governments should focus on entrepreneurial systems, which are essentially networks driven by the pursuit of individual opportunities and the promotion of new business creation; and introduce other forms of financial assistance, such as loans, loan guarantees, interest subsidies, technical assistance, insurance, etc. Originality/value The main novelty of the paper is that the authors empirically investigate the mediating role of entrepreneurship in the association between financial support and economic growth in 34 African emerging economies.


Partial Reasons

Abstract: Abstract Partial reasons are considerations in favor of something that, taken individually, are not sufficient to establish an obligation. I consider the extent to which partial reasons are reasons, and why they cannot be reduced to or identified with pro tanto reasons. I lay out two approaches to the content of reasons, the flat theory and the structured theory. I argue that parts of reasons are not partial reasons, by showing that natural ways to represent parts of reasons in the flat theory and the structured theory lead to overgeneration problems with regard to partial reasons. I then formulate two notions of partial reasons: one based on a notion of partial support, which is in turn captured by the notions of full support and partial content, and one based on the notion of in exact verification. I show under which conditions the two notions of partial reasons (based on partial content, and based on inexact verification) coincide.


Hate speech detection: A comprehensive review of recent works

Abstract: Abstract There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unfurl at a fast pace on digital platforms leading to various issues such as prejudice, violence and even genocide. Considering the acceptance of Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques in varied application domains, it would be intriguing to consider these techniques for automated hate speech detection. In literature, there have been efforts to recognize and categorize hate speech using varied Machine Learning (ML) and Deep Learning (DL) techniques. Hence, considering the need and provocations for hate speech detection we aim to present a comprehensive review that discusses fundamental taxonomy as well as recent advances in the field of online hate speech identification. There is a significant amount of literature related to the initial phases of hate speech detection. The background section provides a detailed explanation of the previous research. The subsequent section that follows is dedicated to examining the recent literature published from the year 2020 onwards. The paper presents some of the hate speech datasets considered for hate speech detection. Furthermore, the paper discusses different data modalities, namely, textual hate speech detection, multi‐modal hate speech detection and multilingual hate speech detection. Apart from systematic review on hate speech detection, the paper also implement several multi‐label models to compare the performance of hate speech detection by employing classic ML technique namely, Logistic Regression and DL technique namely, Long Short‐Term Memory (LSTM) and a multiclass multi‐label architecture. In the implemented architecture, we have derived two new elements to quantify the hatefulness and intensity of hatred to improve the results for hate speech detection using Indonesian tweet dataset. Empirical Analysis of the model reveals that the implemented approach outperforms and is able to achieve improved results for the underlying dataset.


Weighted structure tensor total variation for image denoising

Abstract: For image denoising problems, the structure tensor total variation (STV)-based models show good performances when compared with other competing regularization approaches. However, the STV regularizer does not couple the local information of the image and may not maintain the image details. Therefore, we employ the anisotropic weighted matrix introduced in the anisotropic total variation (ATV) model to improve the STV model. By applying the weighted matrix to the discrete gradient of the patch-based Jacobian operator in STV, our proposed weighted STV (WSTV) model can effectively capture local information from images and maintain their details during the denoising process. The optimization problem in the model is solved by a fast first-order gradient projection algorithm with a complexity result of O(1/i2). For images with different Gaussian noise levels, the experimental results demonstrate that the WSTV model can effectively improve the quality of restored images compared to other TV and STV-based models.


Solar Radiation Forecast Utilizing an Artificial Neural Network Model with Three Distinct Learning Algorithms

Abstract: Solar energy, which has always been at the forefront, has discovered numerous uses in a variety of fields. One of the key targets of scientists and producers in the twenty-first decade is sustainable solar energy collecting. The maximization of solar energy is totally dependent on the radiation absorbed by the photovoltaic panels. Radiation is observed using numerous equipment and calculated using diverse methods. If the device is to be totally reliant on solar energy, it must be calculated far ahead. It is difficult to work because solar radiation is affected by various factors, including region as well as seasonality. In forecast scenarios, Artificial Neural Networks (ANN) is a popular approach among scientists. Therefore, this research provides a technique for estimating solar radiation that makes use of back-propagation algorithms. The data of 17 stations in Tamil Nadu, India, were acquired for analysis and split into three clusters: training, validation, and testing. This research is focused on nine input variables and one outcome variable. Solar radiation is estimated via feed-forward back-propagation in this case. The presented approach is ascertained for training techniques such as Levenberg Marquardt (LM), Bayesian-Regularization (BR), as well as Scaled Conjugate Gradient (SCG). In all three scenarios, the resulting statistical error values and regression values prove the adequacy of the presented approach. The Root Mean Square Error (RMSE) for the BR approach is found to be the lowest, with a value of 0.0013. Also, from all statistical error and regression values, the training approach BR produces the best value than LM and SCG training approaches.


Event Data Recorder Trigger Probability in the Crash Investigation Sampling System Database

Abstract: The objective of this study was to quantify the field performance of passenger vehicle event data recorders (EDRs) in recording data into non-volatile memory at the 8 km/h delta-v (Δv) trigger threshold specified by Title 49, Part 563 of the Code of Federal Regulations (Part 563). Part 563 applies to passenger vehicles manufactured on or after September 1, 2012. The trigger threshold is distinct from the threshold required to deploy an airbag. Events meeting the trigger threshold will cause data to be preserved on the EDR even if airbags are not deployed. This is the first study to quantify EDR trigger threshold performance. This data is valuable in the evaluation of sub-airbag deployment crashes. The study was accomplished via analysis of EDR and reconstructed Δv data from 3,960 cases in the Crash Investigation Sampling System (CISS) database maintained by the National Highway Traffic Safety Administration (NHTSA). The binary presence or non-presence of an event on the EDRs of vehicles exposed to a collision was compared to the CISS reconstructed Δv for each vehicle. Logistic regression models were developed to predict the probability of an event present on the EDR at the trigger threshold. We found that vehicles manufactured by Toyota had lower Δv thresholds compared to other manufacturers. For Toyota manufactured vehicles, the probability of an EDR event at 8 km/h ranged from 87% to 99%. EDR event probabilities for non-Toyota vehicles in vehicle-to-vehicle collisions ranged from 58% to 93% for Part 563 compliant vehicles and 33% to 83% for pre-Part 563 vehicles. The persistence of EDR events in memory was analyzed using the number of ignition cycles present between events and imaging of the EDR, finding average duration of 3,595 ignition cycles for pre-existing EDR events unrelated to the CISS case.


Computational Fluid Dynamics Analysis of a Coaxial Unmanned Aerial Vehicle

Abstract: The focus of this project is on the preliminary design of an unmanned aerial vehicle (UAV) utilizing a coaxial rotor setup, taking into account its flight dynamics. Additionally, a comprehensive aerodynamic analysis is conducted using computational fluid dynamics (CFD). The idealogy for our design came from the Rukma Vimana, a flying craft mentioned in the ancient Hindu Scriptures. The design is optimized with the coaxial copter setup, a different approach that has yet to be widely explored in the UAV aspect. Coaxial rotors are installed in pairs, with one rotor mounted above the other on concentric shafts. Both rotors have the same axis of rotation, but they rotate in opposite directions (contra-rotate). CFD simulations are conducted to see how the fluid medium flows over the unconventional design of the UAV. Three different in terms of three designs heights are considered, ie, a 30 cm height model, a 35 cm height model, and a 40 cm height model. The inclination of the analysis done is parallel to gravity; therefore, we have tested for vertical flow since it is a vertical take-off and landing (VTOL) configuration. Variations in the nose cones are also carried, such as the blunt nose cone and the round nose cone. Overall, with three different heights and two variations in the nose cone, we had a total of six simulations to analyze in CFD from which 30cm performed better out of which we chose the blunt nose cone with a height of 30cm is assumed to be the best configuration as it was feasible to fabricate.


Social VR design features and experiential outcomes: narrative review and relationship map for dyadic agent conversations

Abstract: Abstract The application of virtual reality to the study of conversation and social interaction is a relatively new field of study. While the affordances of VR in the domain compared to traditional methods are promising, the current state of the field is plagued by a lack of methodological standards and shared understanding of how design features of the immersive experience impact participants. In order to address this, this paper develops a relationship map between design features and experiential outcomes, along with expectations for how those features interact with each other. Based on the results of a narrative review drawing from diverse fields, this relationship map focuses on dyadic conversations with agents. The experiential outcomes chosen include presence & engagement, psychological discomfort, and simulator sickness. The relevant design features contained in the framework include scenario agency, visual fidelity, agent automation, environmental context, and audio features. We conclude by discussing the findings of the review and framework, such as the multimodal nature of social VR being highlighted, and the importance of environmental context, and lastly provide recommendations for future research in social VR.


Blockchain for data protection and cyber fraud reduction: systematic literature review and technology adoption dynamics among gen Y and Z

Abstract: Purpose The purpose of this study is to investigate the role of blockchain technology in reducing online fraud among Gen Y and Gen Z. Design/methodology/approach This study uses a two-phase approach. Firstly, a systematic literature review is conducted to examine various aspects of blockchain technology and cyber security. Secondly, the Technology Adoption Model is used to study the adoption dynamics of blockchain-based platforms for online data exchange among 415 Gen Y and Z users. Findings The results indicate that efficiency and security, perceived ease of use, perceived usefulness, and trust have a positive effect on behavioral intention and attitude towards use. This is the first study to examine the role of blockchain technology to reduce fraud among Gen Y and Gen Z. Originality/value This study provides new insights into the adoption of blockchain-based platforms for online data exchange among Gen Y and Gen Z. The study highlights the critical areas of concern for the adoption of blockchain technology and provides managerial implications for fintech firms, banks, and e-commerce platforms. It is unique in its focus on the potential for blockchain technology to reduce fraud among Gen Y and Gen Z, which has not been previously explored in the literature.


The lonely traveller’s mind: exploring the influence of loneliness on revisit intention

Abstract: Despite the fact that feelings of loneliness offer invaluable opportunities for marketing and management, studies on its impacts and consequences in the travel context are still in their infancy. Drawing on compensatory consumption behaviour theory, and using a mixed-methods approach, this research explored the influence of loneliness (i.e. trait loneliness and state loneliness) on tourists' revisit intention. In light of the findings derived from interviews and scenario-based experiments, the research validates the positive effects of each loneliness variable on revisit intention. Moreover, the mediating effects of nostalgia on the relationships between trait loneliness and revisit intention, and between state loneliness and revisit intention, have been revealed. This study enriches the literature on tourism and consumer behaviour and offers several tailor-made suggestions for relevant practitioners.


Joint first and second order total variation decomposition for remote sensing images destriping

Abstract: Stripe noise remains a significant source of errors and image quality degradation in remote sensing systems. A prominent approach for tackling this problem is the first-order Total Variation (TV) regularization, which has a proven efficiency in dealing with stripe noise. Unfortunately, denoised images, in this case, may suffer from texture loss and staircase artefacts around smooth areas. In this paper, a novel image decomposition scheme is proposed to tackle these drawbacks. This decomposition is based on the use of directional first- and second-order TV regularizers that are employed to separate the clear image from the stripe component while considering the directionality and smoothness of the latter. The proposed model is solved using a Chambolle-based algorithm and its performance is compared to traditional destriping methods using different noise structures and intensities. The results have shown comparable performance to the existing state-of-the-art methods with some improvements in structure preservation and noise cancellation.