Underground station passenger flow is large, the number of parcels carried by passengers is large and varied, and the parcels carried have an impact on the fire hazard and evacuation of the station. In order to determine the weights of the passenger luggage risk and environmental factor index system in the fire risk evaluation of underground stations in a more realistic way, an optimized and improved hierarchical analysis method for determining the judgement matrix is proposed, which improves the traditional nine-scaled method and adopts the three-scaled method for the four major categories of luggage, namely, handbags, rucksacks, portable power tools and trolley cases. The advantage of this method is that there is no need for consistency judgement in determining packages with a wide range of types and uncertain contents, thus simplifying the calculation. Meanwhile, the reasonableness and reliability of the method is verified by combining it with an actual metro station fire risk assessment system.

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.

Working Capital Management (hereafter WCM) is the strategic tool that helps a company navigate through challenging economic growth, and influence its competitive performance. Thus, this study examines the impact of WCM on the competitiveness of firms operating in the non-financial sectors in Pakistan. We use the Generalized Method of Moments (GMM) technique to ensure the robustness of our results. The study findings reveal that both a large net trade cycle and surplus working capital have a substantial negative impact on firms’ competitiveness within their respective industries. These results suggest that companies should streamline their investments in working capital accounts and concentrate more resources on long-term projects that maximize value to improve their competitiveness compared to other companies. Therefore, firms that are effectively managing their short-term financial affairs are experiencing much better performance in all aspects of firm performance. The research findings highlight the urgent need for governmental initiatives designed to improve WCM practices in these industries. It is imperative for the management of  companies with excess net working capital to maximize their working capital efficiency, aligning it with industry standards to enhance competitiveness. Moreover, policymakers should prioritize easing access to financial alternatives that allow enterprises to maintain an efficient working capital structure without relying on excessive measures. Furthermore, policymakers should be cautious when determining minimum cash balance requirements in a cash-strapped economy where external financing is relatively more expensive than in other regional economies.

The employees in academic sector had to face an abrupt change due to Covid-19 pandemic and transformation of education into online and remote learning. This has led to virtual work intensity as an aftermath that negatively influences employees’ job satisfaction. In addition, due to remote working conditions, the lines between work and life had been dimmed and thus, the current situation is important to be addressed for wellbeing of academic staff. This research specifically aims to examine impact of virtual work intensity on job satisfaction among university staff. Furthermore, mediating effect of organizational support and work-life balance on the aforementioned relationship are analyzed to better understand the underlying effects. Through PLS-SEM and using a questionnaire survey, a total of 183 data were collected from teachers and administrative staff of two universities. The results show that virtual work intensity can hinder job satisfaction, while organizational support and work-life balance can improve job satisfaction of academic employees. This is due to the fact that support, and balance act against work intensity that diminishes wellbeing of individuals. This implies the vital role of organizations (e.g., human resource department) in providing support for their staff, and creating an environment, where academic staff can have a better work-life balance, leading to higher rates of job satisfaction as an important factor for psychological wellbeing.

Sustainability is a top priority for municipal administrations, particularly in large urban centers where citizens rely on transportation for work, study, and daily errands. Public transportation faces a significant challenge beyond availability, performance, safety, and comfort: balancing the cost for the city with fare attractiveness for passengers. Meanwhile, bicycles, supported by public incentives due to their clean and healthy appeal, compete with public transit. In Curitiba, the integrated transport system has been consistently losing passengers, exacerbated by the pandemic and the rise in private vehicle usage. To address this, the city is expanding bicycle infrastructure and electric bike rental services, impacting public transit revenue, and prompting the need for financial compensation to maintain affordable fares for those reliant on public transport. Therefore, this study’s objective is to analyze the bicycle’s impact on public transportation, considering the impact of public policies on economic and social efficiency, not just ecological and environmental factors. Data from six main bus lines were collected and analyzed in two separate linear regression models to verify the effects of new bicycles in circulation, bus tariffs, and weather conditions on public transportation demand. Research results revealed a significant impact of bus tariffs and fuel prices on the number of new bicycles that are diverting passengers from public transportation. The discussion may offer a different perspective on public transport policies and improve city infrastructure investments to strategically change the urban form to address social and economic issues.

This study investigated the utilization of Artificial Intelligence (AI) in the Recruitment and Selection Process and its effect on the Efficiency of Human Resource Management (HRM) and on the Effectiveness of Organizational Development (OD) in Jordanian commercial banks. The research aimed to provide solutions to reduce the cost, time, and effort spent in the process of HRM and to increase OD Effectiveness. The research model was developed based on comprehensive review of existing literature on the subject. The population of this study comprised HR Managers and Employees across all commercial banks in Jordan, and a census method was employed to gather 177 responses. Data analysis was conducted using Amos and SPSS software packages. The findings show a statistically significant positive impact of AI adoption in the Recruitment and Selection Process on HR Efficiency, which in turn positively impacted OD Effectiveness. Additionally, the study indicated that the ease-of-use of AI technologies played a positive moderating role in the relationship between the Recruitment and Selection Process through AI and HR Efficiency. This study concludes that implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness.