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The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

Using the theoretical framework of social comparison theory (SCT), this study investigates the effects of employee envy on service sabotage behaviors in the hospitality industry. It further examines the complex dynamics of self-performance and job dissatisfaction in this context. Notably, this paper seeks to determine the potential moderating role of perceived employability in the interactions between service sabotage, employee envy, job dissatisfaction and self-performance.

Our research structure was divided into four distinct models. The findings of Model 1 highlight the significant impact of employee envy on service sabotage. The analysis in Model 2a shows that job dissatisfaction acts as a partial mediator in the employee envy and service sabotage linkage. On the other hand, Model 2b reveals self-performance as yet another partial mediator between envy-service sabotage relationships. In turn, Model 3 demonstrates that job dissatisfaction and self-performance play a serial mediation role in the envy-service sabotage relationship. In addition, our research shows that perceived employability effectively moderates the three proposed paths within these relationships.

Our research structure was divided into four distinct models. The findings of Model 1 highlight the significant impact of employee envy on service sabotage. The analysis in Model 2a shows that job dissatisfaction acts as a partial mediator in the employee envy and service sabotage linkage. On the other hand, Model 2b reveals self-performance as yet another partial mediator between envy-service sabotage relationships. In turn, Model 3 demonstrates that job dissatisfaction and self-performance play a serial mediation role in the envy-service sabotage relationship. In addition, our research shows that perceived employability effectively moderates the three proposed paths within these relationships.

Hotel managers must keep a close eye on their front-of-house staff to avoid any unintentional or direct interactions with customers. Equally important is the consistent and impartial treatment of all employees, which is an important consideration for managers to consider because it can help mitigate employee envy and job dissatisfaction.

This study seeks to enhance understanding of SCT by emphasizing perceived employability as a boundary influencing the relationships between these factors and desired outcomes in the hotel industry, such as job dissatisfaction, self-performance and service sabotage. This paper is an initial attempt to investigate the underlying mechanisms in the relationship between envy and service sabotage.

The purpose of this paper is to investigate the performance of Passive Direct Methanol Fuel Cell (PDMFC) experimentally using various Membrane Electrode Assembly (MEA) shapes such as square, rectangle, rhombus, and circle with equal areas and equal perimeters. The variation in MEA shape/size is achieved by altering gasket openings in the dynamic regions.

In the equal areas of MEA shapes, gasket opening areas of 1963.5 (+/−0.2) mm2 are used. Whereas in the equal perimeters of shapes, gasket opening perimeters of 157.1 (+/−0.2) mm are used. In this experimentation, Nickel-201 current collectors with 45.3% of circular openings are used on both the anode and cathode sides. The experiment is carried out at a 5 molar methanol concentration to find out the highest power density of the cell.

In the equal areas, among the shapes that are chosen for investigation, the square shape opening consisting of a perimeter of 177.2 mm has developed a maximum power density of 6.344 mWcm⁻² and a maximum current density of 65.2 mAcm⁻². Similarly, in equal perimeters, the rhombus shape opening with an area of 1400 mm2 has developed a maximum power density of 7.714 mWcm⁻² and a maximum current density of 85.3 mAcm⁻².

The novelty of this research work is instead of fabricating various shapes and sizes of highly expensive MEAs, the desired shapes and sizes of the MEA are achieved by altering gasket openings over dynamic regions to find out the highest power density of the cell.

Building on the theory of brand psychological ownership, this paper aims to explore the mediating role of brand psychological ownership in the relationship between brand personality (innocence/coolness) and consumers’ preferences, as well as identify the boundary conditions of this relationship.

To test the hypotheses, a series of four experiments were conducted in Wuhan, a city in southern China, using questionnaires administered at two universities and two supermarkets. Hypotheses were tested using PLS-SEM in SmartPLS 4.

The results indicate that brand personality, specifically the dimensions of innocence and coolness, has a significant impact on consumers’ brand preferences. Brands with a cool personality are preferred over those with an innocent personality. Moreover, the relationship between brand personality and consumers’ brand preferences is moderated by power motivation and identity centrality.

This study contributes to the literature by differentiating between brand personality of innocence and coolness as two separate constructs and proposing brand psychological ownership as a mechanism through which brand personality affects brand preferences. The study’s samples were drawn from universities and supermarkets in southern China, providing evidence for the significant moderating effects of power motivation and identity centrality on consumers’ brand preferences.

This study aims to assess the nutritional status and physical fitness status of school children among major ethnic and Dalit populations, in Rajshahi, Bangladesh.

Data were collected through structured questionnaires administered to mothers or caregivers of the children, followed by physical fitness assessments. Anthropometric indicators were used to evaluate nutritional status, including body mass index-for-age, height-for-age, and weight-for-height, based on Centre for Disease Control guidelines. Physical fitness was assessed through tests for flexibility, abdominal, muscular, speed, and lower limb strength. Statistical analysis was performed using SPSS.

Approximately 45% of boys and 55% of girls had normal nutritional status. However, the study also identified varying percentages of children with stunted, underweight, wasted, and mixed nutritional issues, highlighting the prevalence of malnutrition among this population. Boys consistently outperformed girls in terms of physical fitness across all age groups, with 84% to 90% of boys meeting established fitness criteria. Girls face a 1.8-fold and 2.2-fold increased risk of wasted and underweight conditions compared to boys. Lower-income group children exhibit a 2.6-fold and 1.6-fold increased risk of wasted conditions and underweight conditions. Children consuming meals twice a day have a 2.1-fold and 2.5-fold elevated risk of wasted and underweight conditions compared to others.

This study highlights the need for interventions and strategies to improve the nutritional and health status of ethnic children in Rajshahi, Bangladesh, considering their diverse dietary habits and lifestyles. By addressing these issues, the authors can empower these communities to actively contribute to the social and economic development of the region.

Society is critically dependent on an adequate supply of hospital doctors to ensure optimal health care. Voluntary turnover amongst hospital doctors is, however, an increasing problem for hospitals. The aim of this study was to systematically review the extant academic literature to obtain a comprehensive understanding of the current knowledge base on hospital doctor turnover and retention. In addition to this, we synthesise the most common methodological approaches used before then offering an agenda to guide future research.

Adopting the PRISMA methodology, we conducted a systematic literature search of four databases, namely CINAHL, MEDLINE, PsycINFO and Web of Science.

We identified 51 papers that empirically examined hospital doctor turnover and retention. Most of these papers were quantitative, cross-sectional studies focussed on meso-level predictors of doctor turnover.

Selection criteria concentrated on doctors who worked in hospitals, which limited knowledge of one area of the healthcare environment. The review could disregard relevant articles, such as those that discuss the turnover and retention of doctors in other specialities, including general practitioners. Additionally, being limited to peer-reviewed published journals eliminates grey literature such as dissertations, reports and case studies, which may bring impactful results.

Globally, hospital doctor turnover is a prevalent issue that is influenced by a variety of factors. However, a lack of focus on doctors who remain in their job hinders a comprehensive understanding of the issue. Conducting “stay interviews” with doctors could provide valuable insight into what motivates them to remain and what could be done to enhance their work conditions. In addition, hospital management and recruiters should consider aspects of job embeddedness that occur outside of the workplace, such as facilitating connections outside of work. By resolving these concerns, hospitals can retain physicians more effectively and enhance their overall retention efforts.

Focussing on the reasons why employees remain with an organisation can have significant social repercussions. When organisations invest in gaining an understanding of what motivates their employees to stay in the job, they are better able to establish a positive work environment that likely to promote employee well-being and job satisfaction. This can result in enhanced job performance, increased productivity and higher employee retention rates, all of which are advantageous to the organisation and its employees.

The review concludes that there has been little consideration of the retention, as opposed to the turnover, of hospital doctors. We argue that more expansive methodological approaches would be useful, with more qualitative approaches likely to be particularly useful. We also call on future researchers to consider focussing further on why doctors remain in posts when so many are leaving.

The purpose of this paper is to analyze the evolution of the rules of origin (RoO) of Regional Comprehensive Economic Partnership (RCEP).

This analysis is done by comparing them with those of existing FTAs/EPAs of ASEAN with other RCEP member countries, and also examining the impact of recent mega-FTAs/EPAs, such as TPP11 and Japan-EU EPA, in which some of the member countries participated.

RCEP holds great significance in that it connects Japan and China and Japan and South Korea, which previously have not had any EPAs/FTAs, transforms this massive economic sphere from one with minutely divided and differing RoO under ASEAN plus FTAs to one that is seamlessly connected with those of having evolved into the unified RoO under RCEP, and realizes ideal production networks in Asia.

This paper makes it clear that RCEP, while based on ASEAN plus FTAs, reflects progressive provisions of recent mega-FTAs/EPAs, and adopts simpler and more systematic rules. These provisions limit the distortive effect on trade, realize ideal production networks in Asia, and are appropriate as uniform RoO connecting networks across this massive economic sphere. It also points out that there are provisions that have not been introduced and those that are considered to have been incomplete as a result of negotiations, and the possibility of evolving into more ideal RoO by utilizing the system for revisions established under the agreement.

The purpose of this study is twofold: first, to examine the direct relationship of organizational justice (OJ), psychological empowerment (PE), Leader Member Exchange (LMX), organizational citizenship behavior (OCB), LMX on PE and OCB and PE on OCB; and second, to investigate the mediating role of PE between OJ and OCB and between LMX and OCB in the service industry in a non-Western context.

A quantitative questionnaire was used to test the proposed hypotheses of the study. From employees working in service providing organizations in the UAE, 364 usable responses had been collected and data was analyzed using structural equation modeling (SEM).

OJ significantly influences PE and LMX, while its influence on OCB is insignificant. Also, LMX significantly affects PE and OCB, PE significantly impacts OCB, whereas PE and LMX significantly mediate the relationship between OJ and OCB.

Organizations should promote fairness, psychological empowerment and OCB among employees. Additionally, leaders should develop positive and productive relationships with their employees.

To the best of the authors’ knowledge, this study is one of a limited number of studies designed to analyze the hypothesized relationships within a non-Western context, specifically in the UAE.

This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.