This paper presents a sequential edge swap (SQES) mechanism to design a robust network for a healthcare system utilising energy and communication range of nodes. Two operations: sequential degree difference operation (SQDDO) and sequential angle sum operation (SQASO) are performed to enhance the robustness of network. With equivalent degrees of nodes from the network's centre to its periphery, these operations build a robust network structure. Disaster attacks that have a substantial impact on the network are carried out using the network information. To identify a link between the malicious and disaster attacks, the Pearson coefficient is employed. SQES creates a robust network structure as a single objective optimisation solution by changing the connections of nodes based on the positive correlation of these attacks. SQES beats the current methods, according to simulation results. When compared to hill-climbing algorithm, simulated annealing, and ROSE, respectively, the robustness of SQES is improved by roughly 26%, 19% and 12%.

The increased volume of accessible internet data creates an opportunity for researchers and practitioners to improve time series forecasting for many indicators. In our study, we assess the value of web traffic data in forecasting the number of short-term visitors travelling to Australia. We propose a feature-based model selection framework which combines random forest with feature ranking process to select the best performing model using limited and informative number of features extracted from web traffic data. The data was obtained for several tourist attraction and tourism information websites that could be visited by potential tourists to find out more about their destinations. The results of random forest models were evaluated over 3- and 12-month forecasting horizon. Features from web traffic data appears in the final model for short term forecasting. Further, the model with additional data performs better on unseen data post the COVID19 pandemic. Our study shows that web traffic data adds value to tourism forecasting and can assist tourist destination site managers and decision makers in forming timely decisions to prepare for changes in tourism demand.

1. Jean-Paul Rouve, à l’affiche de “La Vallée des fous” : “Seules les failles sont intéressantes”  Télérama.fr
2. “La Vallée des fous” : l’émouvante régate dans son jardin de Jean-Paul Rouve  Télérama.fr
3. Notre critique de La Vallée des fous , une odyssée immobile pleine de panache  Le Figaro
4. "Après le tournage du film, moi aussi, j'ai fait un voyage, mais pas dans un bateau" : le tour du monde selon Xavier Beauvois dans "La Vallée des fous"  franceinfo
5. Jean-Paul Rouve à la conquête du Vendée Globe depuis son jardin  20 Minutes

1. Ruffin souhaiterait une "colère sociale" mais craint la résignation  Bourse Direct
2. Auchan va fermer une dizaine de magasins et voici les premières villes concernées  Linternaute.com
3. François Ruffin souhaiterait une « colère sociale » mais craint la résignation  20 Minutes
4. Dans la grande distribution, des emplois amenés à changer... voire à disparaître ?  France Culture
5. Meuse. Fermeture d’Auchan : les commerçants de la galerie marchande de Bar-le-Duc s’inquiètent pour leur avenir  Est Républicain

Special counsel Jack Smith looking at stepping down before Trump takes office and is discussing how to end prosecutions  CNN

1. Bear encounter recorded in Whistler Village prompts conservation officer warning  CTV News Vancouver
2. Tourists 'shocked' to see man approach black bear in Whistler  Pique Newsmagazine

1. Trump says Elon Musk, Vivek Ramaswamy will lead the Department of Government Efficiency  The Globe and Mail
2. Why is Elon Musk becoming Donald Trump's efficiency adviser?  BBC.com
3. Elon Musk and Vivek Ramaswamy will lead new 'Department of Government Efficiency' in Trump administration  CTV News
4. George Conway: Musk, Ramaswamy to lead ‘nonexistent department’  The Hill

Aiming at the problem of aggravation of traffic congestion caused by unstable signal control of traffic signal control system, the Multi-Agent Deep Deterministic Policy Gradient-based Traffic Cyclic Signal (MADDPG-TCS) control algorithm is used to control the time and data dimensions of the signal control scheme. The results show that the maximum vehicle delay time and vehicle queue length of the proposed algorithm are 11.33 s and 27.18 m, which are lower than those of the traditional control methods. Therefore, this method can effectively reduce the delay of traffic signal control and improve the stability of signal control.

The banking sector has experienced a substantial technological shift that has opened up new and better opportunities for its customers. Based on their technological expenditures, the study assessed the two biggest public Indian banks and the two biggest private Indian banks. The most crucial statistical techniques used to demonstrate the aims are realistic are bivariate correlations and ordinary least squares. This work aims to establish a connection between research and a technology index that serves as a proxy for operational efficiency. The results show that for both public and private banks, the technology index positively influences operational efficiency metrics like IT costs, marketing costs, and compensation costs. This suggests that when the technology index increases, so do IT, marketing, and compensation costs, even though it has been shown that the technology index favourably improves operational efficiency measures like depreciation and printing. This means that the cost to banks is high despite greater investment in technology for these activities.

This article explores dual affiliated graduate students that conduct research involving both universities and firms, which we conceptualise as a form of academic engagement, e.g., knowledge networks. We explore what they do during their studies, and their perceptions about their contributions to the firm's capacities for technology and innovation. So far, university-industry interactions in engineering are less researched than other fields, and this qualitative study focuses upon one department of Electrical Engineering in Sweden. First, we define and describe how the partner firms and universities organise this research collaboration as a form of academic engagement. Secondly, we propose a conceptual framework specifying how graduate students act as boundary-spanners between universities and firms. This framework is used for the empirical analysis, when exploring their perceptions of impact. Our results reveal that they primarily engage in problem-solving activities in technology, which augment particularly the early stages of absorptive capacities in firms.

Aim/Purpose: Improving public schools is a focus of federal legislation in the United States with much of the burden placed on principals. However, preparing principals for this task has proven elusive despite many changes in programming by institutions of higher learning. Emerging technologies that rely on augmented and virtual realities are posited to be powerful pedagogical tools for closing this gap. Background: This study investigated the effects of immersive simulation technologies on principals’ self-efficacy after treatment and the perceived significance of the design of the immersive simulation experience as an effective tool for adult learners. Methodology: The investigator employed a multiple-methods study that relied on a purposive sample of graduate students enrolled in educational leadership programs at two small universities in the southeastern United States. Participants completed a two-hour module of immersive simulation designed to facilitate transfer of knowledge to skills thereby increasing their self-efficacy. Contribution: This paper contributes to a small body of literature that examines the use of immersive simulation to prepare aspiring principals. Findings: The findings indicate moderate effect sizes in changes in self-efficacy, positive attitudes toward immersive simulation as a pedagogical tool, and significance in the design of immersive simulation modules. This suggests that immersive simulation, when properly designed, aids principals in taking action to improve schools. Recommendations for Practitioners: Educational leadership programs might consider the use of immersive simulations to enhance principals’ ability to meet the complex demands of leading in the 21st century. Impact on Society: Principals may be more adept at improving schools if preparation programs provided consistent opportunities to engage in immersive simulations. Future Research: Future research should be conducted with larger sample sizes and longitudinally to determine the effectiveness of this treatment.

The present study attempts to find out the determinants of FinTech adoption for financial inclusion by a microfinance institution in India. The factors such as efficiency, consistency, convenience, reliability are taken as predictors of organisational attitude. Similarly, organisational attitude, ease of use, and perceived benefits are considered as antecedents of organisational adoption intention of FinTech in microfinance institutions of India. The purposive sampling technique was used to get a filled survey instrument by target samples. The results indicate that convenience and consistency in the use of FinTech applications build a favourable attitude to adopt it. Furthermore, perceived benefits are the most important antecedents of the adoption intention of FinTech in the microfinance institution in India. Additionally, the reliability of the application has a positive but insignificant impact on organisational attitude to adopt FinTech. The implications of the present study are discussed.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

The transition from printed to electronic sources of information has resulted in a profound change to the way procurement officers seek information. Furthermore, in the past decade there have been additional technological revolutions that are expected to further affect the procurement process. In this paper, we conduct a survey among forty nine university procurement officers in Israel to examine to what extent procurement officers have adapted to smartphones and tablets by testing how frequently officers use notebooks, smartphones, and tablets for work-related and leisure purposes. We find that while officers prefer electronic sources of information over printed sources of information, officers have not yet adapted to the later technological advances (i.e., smartphones and tablets). Notebooks are more frequently used than either smartphones or tablets for work-related and leisure purposes. One explanation behind this result is that officers are not skilled in using smartphone and tablets applications. This implies that training officers in the use of these devices may improve their performance.

The most common course delivery model is based on teacher (knowledge provider) - student (knowledge receiver) relationship. The most visible symptom of this situation is over-reliance on textbook’s tutorials. This traditional model of delivery reduces teacher flexibility, causes lack of interest among students, and often makes classes boring. Especially this is visible when teaching Computer Literacy courses. Instead, authors of this paper suggest a new active model which is based on MS Office simulation. The proposed model was discussed within the framework of three activities: guided software simulation, instructor-led activities, and self-directed learning activities. The model proposed in the paper of active teaching based on software simulation was proven as more effective than traditional.

Aim/Purpose: The aim of this study was to examine the sense of challenge and threat, negative feelings, self-efficacy, and motivation among students in a virtual and a blended course on multicultural campuses and to see how to afford every student an equal opportunity to succeed in academic studies. Background: Most academic campuses in Israel are multicultural, with a diverse student body. The campuses strive to provide students from all sectors, regardless of nationality, religion, etc., the possibility of enjoying academic studies and completing them successfully. Methodology: This is a mixed-method study with a sample of 484 students belonging to three sectors: general Jewish, ultra-orthodox Jewish, and Arab. Contribution: This study’s findings might help faculty on multicultural campuses to advance all students and enable them equal opportunity to succeed in academic studies. Findings: Significant sectorial differences were found for the sense of challenge and threat, negative feelings, and motivation. We found that the sense of challenge and level of motivation among Arab students was higher than among the ultra-orthodox Jewish students, which, in turn, was higher than among the general Jewish student population. On the other hand, we found that the perception of threat and negative feelings among Arab students were higher than for the other two sectors for both the virtual and the blended course. Recommendations for Practitioners: Significant feedback might lessen the sense of threat and the negative feelings and be a meaningful factor for the students to persevere in the course. Intellectual, emotional, and differential feedback is recommended. Not relating to students’ difficulties might lead to a sense of alienation, a lack of belonging, or inability to cope with the tasks at hand and dropout from the course, or even from studies altogether. A good interaction between lecturer and student can change any sense of incompetence or helplessness to one of self-efficacy and the ability to interact with one’s surroundings. Recommendations for Researchers: Lecturers can reduce the sense of threat and negative feelings and increase a student’s motivation by making their presence felt on the course website, using the forums to manage discussions with students, and enabling and encouraging discussion among the students. Impact on Society: The integration of virtual learning environments into the learning process might lead to the fulfilment of an educational vision in which autonomous learners realize their personal potential. Hence they must be given tasks requiring the application of high learning skills without compromise, but rather with differential treatment of students in order to reduce negative feelings and the sense of threat, and to reduce the transactional distance. Future Research: Further studies should examine the causes of negative feelings among students participating in virtual and blended courses on multicultural campuses and how these feelings can be handled.

Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

Aim/Purpose. Higher education institutions face difficulties and challenges when it comes to distance learning. The purpose of this paper is to examine self-efficacy indicators and student satisfaction during online English classes. Background. E-learning has been very relevant since the Covid-19 era and is still relevant today. It is possible for students to study regardless of their location or time. By measuring students’ self-efficacy, instructors can gain valuable insights into their students’ ability to create social interaction, cope with technology, and acquire knowledge and tools to manage the learning process. Methodology. This study uses mixed methods along with two measurements. Before and after the course, quantitative and qualitative data were collected. Higher education students in Israel participated. A total of 964 students enrolled in English as a foreign language courses at the pre-basic, basic, and advanced levels. Contribution. Analyzing self-efficacy from several angles provides insight into students. What influences students’ confidence and belief in their ability to succeed in online courses. Moreover, how students perceive their own learning and how they cope with challenges. Findings. Compared to the measurement before the course, self-efficacy decreased on average. Most significant decreases occurred in ‘creating social interactions’ and ‘acquirement of knowledge and tools’ to manage the learning process. A slight decrease was observed in the ability to cope with technology. Additionally, self-efficacy and satisfaction with the course were positively correlated. Recommendations for Practitioners. An overview is provided of the most effective tools and techniques for teaching languages in digital format in this paper. This will allow instructors to design and deliver courses in a more effective way. Thus, they will be able to make better informed decisions, resulting in better outcomes for students. Recommendations for Researchers. Distance Learning courses should resemble the common digital environments in everyday life, rather than imitating face-to-face courses mainly in the field of social interaction. Impact on Society. Digital tools should be encouraged that facilitate effective learning processes instead of sticking to traditional methods that characterize face-to-face courses. Using common interfaces in daily use among the general population will enable the implementation of these recommendations. Future Research. Future studies could be helpful if they compared the English courses developed in the CEFR model with those taught face-to-face as well as those taught online. In addition, motivation and self-monitoring should be examined in both synchronous and asynchronous courses as well.

Aim/Purpose: The purpose of this study is to investigate the mechanism of the front/back-office structure affecting new service development (NSD) performance and examine the role of knowledge transfer in the relationship between front/back-office structure and NSD. Background: The separation of front and back-office has become the prevailing trend of the organizational transformation of modern service enterprises in the digital era. Yet, the influence of front and back-office separation dealing with new service development has not been widely researched. Methodology: Building on the internal social capital perspective, a multivariate regression analysis was conducted to investigate the impact of front/back-office structure on the NSD performance through knowledge transfer as an intermediate variable. The data was collected through a survey questionnaire from 198 project-level officers in the commercial banking industry of China. Contribution: This study advances the understanding of front/back-office structure’s influence mechanism on new service development activity. It reveals that knowledge transfer plays a critical role in bridging the impact of front and back-office separation to NSD performance under the trend of digitalization of service organizations. Findings: This study verified the positive effects of front/back-office social capital on NSD performance. Moreover, knowledge transfer predicted the variation in NSD performance and fully mediated the effect of front/back-office social capital on NSD performance. Recommendations for Practitioners: Service organizations should optimize knowledge transfer by promoting the social capital between front and back-office to overcome the negative effect organizational separation brings to NSD. Service and other organizations could explore developing an internal social network management platform, by which the internal social network could be visualized and dynamically managed. Recommendation for Researchers: The introduction of information and communications technology not only divides the organization into front and back-office, but also reduces the face-to-face customer contact. The impacts of new forms of customer contact to new service development and knowledge transfer between customer and service organizations call for further research. Along with the digital servitization, some manufacturing organizations also separate front and back-offices. The current model can be applied and assessed further in manufacturing and other service sectors. Impact on Society: The conclusion of this study guides us to pay attention to the construction of social capital inside organizations with front/back-office structure and implicates introducing and developing sociotechnical theory in front/back-office issue undergoing technological revolution. Future Research: As this study is based on the retail banking industry, similar studies are called upon in other service sectors to identify differences and draw more general conclusions. In addition, as the front and back-offices are being replaced increasingly by information technology such as artificial intelligence (AI), it is necessary to advance the research on front/back-office research with a new theoretical perspective, such as sociotechnical theory.

Aim/Purpose: The understanding of disease spread dynamics in the context of air travel is crucial for effective disease detection and epidemic intelligence. The Susceptible-Exposed-Infectious-Recovered-Hospitalized-Critical-Deaths (SEIR-HCD) model proposed in this research work is identified as a valuable tool for capturing the complex dynamics of disease transmission, healthcare demands, and mortality rates during epidemics. Background: The spread of viral diseases is a major problem for public health services all over the world. Understanding how diseases spread is important in order to take the right steps to stop them. In epidemiology, the SIS, SIR, and SEIR models have been used to mimic and study how diseases spread in groups of people. Methodology: This research focuses on the integration of air traffic network data into the SEIR-HCD model to enhance the understanding of disease spread in air travel settings. By incorporating air traffic data, the model considers the role of travel patterns and connectivity in disease dissemination, enabling the identification of high-risk routes, airports, and regions. Contribution: This research contributes to the field of epidemiology by enhancing our understanding of disease spread dynamics through the application of the SIS, SIR, and SEIR-HCD models. The findings provide insights into the factors influencing disease transmission, allowing for the development of effective strategies for disease control and prevention. Findings: The interplay between local outbreaks and global disease dissemination through air travel is empirically explored. The model can be further used for the evaluation of the effectiveness of surveillance and early detection measures at airports and transportation hubs. The proposed research contributes to proactive and evidence-based strategies for disease prevention and control, offering insights into the impact of air travel on disease transmission and supporting public health interventions in air traffic networks. Recommendations for Practitioners: Government intervention can be studied during difficult times which plays as a moderating variable that can enhance or hinder the efficacy of epidemic intelligence efforts within air traffic networks. Expert collaboration from various fields, including epidemiology, aviation, data science, and public health with an interdisciplinary approach can provide a more comprehensive understanding of the disease spread dynamics in air traffic networks. Recommendation for Researchers: Researchers can collaborate with international health organizations and authorities to share their research findings and contribute to a global understanding of disease spread in air traffic networks. Impact on Society: This research has significant implications for society. By providing a deeper understanding of disease spread dynamics, it enables policymakers, public health officials, and practitioners to make informed decisions to mitigate disease outbreaks. The recommendations derived from this research can aid in the development of effective strategies to control and prevent the spread of infectious diseases, ultimately leading to improved public health outcomes and reduced societal disruptions. Future Research: Practitioners of the research can contribute more effectively to disease outbreaks within the context of air traffic networks, ultimately helping to protect public health and global travel. By considering air traffic patterns, the SEIR-HCD model contributes to more accurate modeling and prediction of disease outbreaks, aiding in the development of proactive and evidence-based strategies to manage and mitigate the impact of infectious diseases in the context of air travel.

Aim/Purpose: Accurate detection of vacant parking spaces is crucial for autonomous parking. Deep learning, particularly Graph Neural Networks (GNNs), holds promise for addressing the challenges of diverse parking lot appearances and complex visual environments. Our GNN-based approach leverages the spatial layout of detected marking points in around-view images to learn robust feature representations that are resilient to occlusions and lighting variations. We demonstrate significant accuracy improvements on benchmark datasets compared to existing methods, showcasing the effectiveness of our GNN-based solution. Further research is needed to explore the scalability and generalizability of this approach in real-world scenarios and to consider the potential ethical implications of autonomous parking technologies. Background: GNNs offer a number of advantages over traditional parking spot detection methods. Unlike methods that treat objects as discrete entities, GNNs may leverage the inherent connections among parking markers (lines, dots) inside an image. This ability to exploit spatial connections leads to more accurate parking space detection, even in challenging scenarios with shifting illumination. Real-time applications are another area where GNNs exhibit promise, which is critical for autonomous vehicles. Their ability to intuitively understand linkages across marking sites may further simplify the process compared to traditional deep-learning approaches that need complex feature development. Furthermore, the proposed GNN model streamlines parking space recognition by potentially combining slot inference and marking point recognition in a single step. All things considered, GNNs present a viable method for obtaining stronger and more precise parking slot recognition, opening the door for autonomous car self-parking technology developments. Methodology: The proposed research introduces a novel, end-to-end trainable method for parking slot detection using bird’s-eye images and GNNs. The approach involves a two-stage process. First, a marking-point detector network is employed to identify potential parking markers, extracting features such as confidence scores and positions. After refining these detections, a marking-point encoder network extracts and embeds location and appearance information. The enhanced data is then loaded into a fully linked network, with each node representing a marker. An attentional GNN is then utilized to leverage the spatial relationships between neighbors, allowing for selective information aggregation and capturing intricate interactions. Finally, a dedicated entrance line discriminator network, trained on GNN outputs, classifies pairs of markers as potential entry lines based on learned node attributes. This multi-stage approach, evaluated on benchmark datasets, aims to achieve robust and accurate parking slot detection even in diverse and challenging environments. Contribution: The present study makes a significant contribution to the parking slot detection domain by introducing an attentional GNN-based approach that capitalizes on the spatial relationships between marking points for enhanced robustness. Additionally, the paper offers a fully trainable end-to-end model that eliminates the need for manual post-processing, thereby streamlining the process. Furthermore, the study reduces training costs by dispensing with the need for detailed annotations of marking point properties, thereby making it more accessible and cost-effective. Findings: The goal of this research is to present a unique approach to parking space recognition using GNNs and bird’s-eye photos. The study’s findings demonstrated significant improvements over earlier algorithms, with accuracy on par with the state-of-the-art DMPR-PS method. Moreover, the suggested method provides a fully trainable solution with less reliance on manually specified rules and more economical training needs. One crucial component of this approach is the GNN’s performance. By making use of the spatial correlations between marking locations, the GNN delivers greater accuracy and recall than a completely linked baseline. The GNN successfully learns discriminative features by separating paired marking points (creating parking spots) from unpaired ones, according to further analysis using cosine similarity. There are restrictions, though, especially where there are unclear markings. Successful parking slot identification in various circumstances proves the recommended method’s usefulness, with occasional failures in poor visibility conditions. Future work addresses these limitations and explores adapting the model to different image formats (e.g., side-view) and scenarios without relying on prior entry line information. An ablation study is conducted to investigate the impact of different backbone architectures on image feature extraction. The results reveal that VGG16 is optimal for balancing accuracy and real-time processing requirements. Recommendations for Practitioners: Developers of parking systems are encouraged to incorporate GNN-based techniques into their autonomous parking systems, as these methods exhibit enhanced accuracy and robustness when handling a wide range of parking scenarios. Furthermore, attention mechanisms within deep learning models can provide significant advantages for tasks that involve spatial relationships and contextual information in other vision-based applications. Recommendation for Researchers: Further research is necessary to assess the effectiveness of GNN-based methods in real-world situations. To obtain accurate results, it is important to employ large-scale datasets that include diverse lighting conditions, parking layouts, and vehicle types. Incorporating semantic information such as parking signs and lane markings into GNN models can enhance their ability to interpret and understand context. Moreover, it is crucial to address ethical concerns, including privacy, potential biases, and responsible deployment, in the development of autonomous parking technologies. Impact on Society: Optimized utilization of parking spaces can help cities manage parking resources efficiently, thereby reducing traffic congestion and fuel consumption. Automating parking processes can also enhance accessibility and provide safer and more convenient parking experiences, especially for individuals with disabilities. The development of dependable parking capabilities for autonomous vehicles can also contribute to smoother traffic flow, potentially reducing accidents and positively impacting society. Future Research: Developing and optimizing graph neural network-based models for real-time deployment in autonomous vehicles with limited resources is a critical objective. Investigating the integration of GNNs with other deep learning techniques for multi-modal parking slot detection, radar, and other sensors is essential for enhancing the understanding of the environment. Lastly, it is crucial to develop explainable AI methods to elucidate the decision-making processes of GNN models in parking slot detection, ensuring fairness, transparency, and responsible utilization of this technology.

Self-efficacy is essential to learning but what happens when learning is done as a result of a collective process? What is the role of individual self-efficacy in collective problem solving? This research examines the manifestation of self-efficacy in prediction markets that are configured as collective problem-solving platforms and whether self-efficacy of traders affects the collective outcome. Prediction markets are collective-intelligence platforms that use a financial markets mechanism to combine knowledge and opinions of a group of people. Traders express their opinions or knowledge by buying and selling “stocks” related to questions or events. The collective outcome is derived from the final price of the stocks. Self-efficacy, one’s belief in his or her ability to act in a manner that leads to success, is known to affect personal performance in many domains. To date, its manifestation in computer-mediated collaborative environments and its effect on the collective outcome has not been studied. In a controlled experiment, 632 participants in 47 markets traded a solution to a complex problem, a naïve framing of the knapsack problem. Contrary to earlier research, we find that technical and functional self-efficacy perceptions are indistinguishable, probably due to a focus on outcomes rather than on resources. Further, results demonstrate that prediction markets are an effective collective problem-solving platform that correctly aggregates individual knowledge and is resilient to traders’ self-efficacy.

The purpose of this research is the exploration of the opinions and level of self-efficacy in the usage of Information Communication Technologies (ICTs) of teachers in Greek pre-schools in the learning process and administration of nurseries. By using the term “usage and utilisation of ICTs in the learning process” we mean the utilisation of the capabilities that new technologies offer in an educationally appropriate way so that the learning process yields positive results. By using the term “self-efficacy” we describe the strength of one’s belief in one’s own ability to use the capabilities he or she possess. In this way, the beliefs of the person in his or her ability to use a personal computer constitute the self-efficacy in computer usage. The research sample consists of 128 pre-school teachers that work in the prefecture of Kavala. Kavala’s prefecture is a representative example of an Education Authority since it consists of urban, suburban, and rural areas. The approach that is deemed to be the most appropriate for the exploration of such research questions is content analysis methodology and correlation analysis. The main findings of the study have shown statistically significant differences regarding the opinions and stances of the pre-school teachers for the introduction of the ICTs in the administration and the usage and utilisation of ICTs in the administration and preparation of teaching. Lastly, there were statistically significant differences between the opinions and stances of the pre-school teachers for the usage and utilisation of ICTs in the learning process. Instead, there were no statistically significant differences regarding the level of self-efficacy of the pre-school teachers in the usage and utilisation of the ICTs in the learning process. The research results could be used in the educational field as well as by Greek Ministry of Education, Research and Religious Affairs in order to take any corrective action, after the effort of Greek Ministry of Education, Research and Religious Affairs, to integrate ICT in the learning process with training courses since 2006.

Aim/Purpose: This study aims to explore levels of Technological Access (ownership, access to, and usage of computer devices as well as access to Internet services) and levels of Technological Efficacy (technology related skills) as they pertain to underserved (UNS) and underrepresented (UNR) students. Background: There exists a positive correlation between technology related access, technology related competence, and academic outcomes. An increasing emphasis on expanding online education at the author’s institution, consistent with nationwide trends, means that it is unlikely that just an increase in online offerings alone will result in an improvement in the educational attainment of students, especially if such students lack access to technology and the technology related skills needed to take advantage of online learning. Most studies on levels of Technological Access and Technological Efficacy have dealt with either K-12 or minority populations with limited research on UNS and UNR populations who form the majority of students at the author’s institution. Methodology: This study used a cross-sectional survey research design to investigate the research questions. A web survey was sent to all students at the university except first semester new and first semester transfer students from various disciplines (n = 535). Descriptive and inferential statistics were used to analyze the survey data. Contribution: This research provides insight on a population (UNS and UNR) that is expanding in higher education. However, there is limited information related to levels of Technological Access and Technological Efficacy for this group. This paper is timely and relevant as adequate access to technology and technological competence is critical for success in the expanding field of online learning, and the research findings can be used to guide and inform subsequent actions vital to bridging any educational equity gap that might exist. Findings: A critical subset of the sample who were first generation, low income, and non-White (FGLINW) had significantly lower levels of Technological Access. In addition, nearly half of the survey sample used smartphones to access online courses. Technological Efficacy scores were significantly lower for students who dropped out of or never enrolled in an online course. Transfer students had significantly higher Technological Efficacy scores while independent students (determined by tax status for federal financial aid purposes) reflected higher Technological Efficacy, but at a marginally lower level of significance. Recommendations for Practitioners: Higher education administrators and educators should take into consideration the gaps in technology related access and skills to devise institutional interventions as well as formulate pedagogical approaches that account for such gaps in educational equity. This will help ensure pathways to sustained student success given the rapidly growing landscape of online education. Recommendation for Researchers: Similar studies need to be conducted in other institutions serving UNS and UNR students in order to bolster findings and increase awareness. Impact on Society: The digital divide with respect to Technological Access and Technological Efficacy that impacts UNS and UNR student populations must be addressed to better prepare such groups for both academic and subsequent professional success. Addressing such gaps will not only help disadvantaged students maximize their educational opportunities but will also prepare them to navigate the challenges of an increasingly technology driven society. Future Research: Given that it is more challenging to write papers and complete projects using a smartphone, is there a homework gap for UNS and UNR students that may impact their academic success? What is the impact of differing levels of Technological Efficacy on specific academic outcomes of UNS and UNR students?

Aim/Purpose: To develop and introduce a questionnaire that investigates the informing needs, information-seeking behavior, and supplier selection of procurement officers in Israel. The questionnaire’s internal consistency reliability is given. Additionally, we describe the demographic description of the procurement officers in Israel. Background: Procurement science is an important field that affects firms’ profits in the private sector and is significant to growth, innovation, sustainability, and welfare in the public sector. There is little research about the informing needs of procurement officers in general and particularly in Israel. Methodology: A quantitative questionnaire that is sent to all the procurement officers in Israel’s procuring association. Contribution: The questionnaire that is developed in this paper may be used by other researchers and practitioners to evaluate the information needs of procurement officers. Findings: The typical procurement officer is male, with a bachelor degree and is digitally proficient. Recommendations for Practitioners: The procuring side can use the questionnaire to develop better tools for obtaining information efficiently. The supplying side can use this knowledge to improve its exposure to potential customers and address its customer’s needs better. Recommendation for Researchers: The questionnaire can address theoretical questions such as how digital literacy affects the procuring process and provide empirical findings about active research areas such as supplier selection and information-seeking behavior. Future Research: Future research will examine the relationship between the various variables and demographic features to understand why specific information needs and information-seeking behaviors arise.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Data envelopment analysis (DEA) is widely used in various fields and for various models. Inverted data envelopment analysis (inverted DEA) is an extended model of DEA. Regression analysis (RA) is a statistical process for estimating the relationships among variables based on the model of averaged image. There are no essential relations among DEA and RA and inverted DEA. We creatively combine DEA, RA and inverted DEA to propose a new model: DEA-RA-Inverted DEA model. The model realises the efficiency estimation and evaluation through a discussion of the residual variables and the residual ratio coefficients. In addition, we will demonstrate the effectiveness of the model by applying it to efficiency estimation and evaluation of 16 Chinese logistics enterprises.

This study looks at what happens to the collection of relationships (network) of service professionals during a role transition (promotion to a management role). Our setting is three professional service firms where we examine changes in relations of recently promoted service professionals (auditors, consultants, and lawyers). We take a comprehensive look at the drivers of two forms of network changes - tie loss and tie gain. Looking backward we examine the characteristics of the contact, the relationship, and social structure and identify which forces are at play in losing ties, revealing an overarching tendency for both cohesion and efficiency forces to play a role. Looking forward, we identify the effect of previous network structures that act as a "shadow of the past" and impact the quality of newly gained relations during the role transitions. Findings demonstrate that role transitions are not only influenced by a few key contacts but that the entire (extant) network of professional relationships shapes the way people reconfigure their workplace relations during a role transition.