Within the Saudi Arabian banking sector, the quality of work life emerges as a crucial determinant shaping employee performance. This research delves into the nuanced impacts of diverse job quality facets on employee efficacy within this domain. Employing a stratified random sampling methodology, 500 institutions were selected, yielding a 49.6% response rate, or 248 completed surveys, with the active engagement of senior management. Utilizing a quantitative paradigm, the study harnessed descriptive statistics and structural equation modeling (SEM) to elucidate the interplay between job quality dimensions and performance outcomes. The analysis revealed that elements like compensation structures, work-life equilibrium, and growth opportunities substantially influenced employee productivity. In contrast, most job quality facets garnered positive evaluations, and aspects related to wage and compensation exhibited room for enhancement. The research accentuates the imperative of elevating job quality benchmarks within the banking sector to augment employee contentment and performance metrics. This study’s insights advocate for stakeholders and policymakers to champion job quality as a pivotal driver for optimizing organizational effectiveness.

Payment for forest ecosystem services (PFES) policy is a prevalent strategy designed to establish a marketplace where users compensate providers for forest ecosystem services. This research endeavours to scrutinise the impact of PFES on households’ perceptions of forest values and their behaviour towards forest conservation, in conjunction with their socio-economic circumstances and their communal involvement in forest management. By incorporating the social-ecological system framework and the theory of human behaviours in environmental conservation, this study employs a structural equations model to analyse the factors influencing individuals’ perceptions and behaviours towards forest conservation. The findings indicate that the payment of PFES significantly increases forest protection behaviour at the household level and has achieved partial success in activating community mechanisms to guide human behaviour towards forest conservation. Furthermore, it has effectively leveraged the role of state-led social organisations to alter local individuals’ perceptions and behaviours towards forest protection.

Recently, carbon nanocomposites have garnered a lot of curiosity because of their distinctive characteristics and extensive variety of possible possibilities. Among all of these applications, the development of sensors with electrochemical properties based on carbon nanocomposites for use in biomedicine has shown as an area with potential. These sensors are suitable for an assortment of biomedical applications, such as prescribing medications, disease diagnostics, and biomarker detection. They have many benefits, including outstanding sensitivity, selectivity, and low limitations on detection. This comprehensive review aims to provide an in-depth analysis of the recent advancements in carbon nanocomposites-based electrochemical sensors for biomedical applications. The different types of carbon nanomaterials used in sensor fabrication, their synthesis methods, and the functionalization techniques employed to enhance their sensing properties have been discussed. Furthermore, we enumerate the numerous biological and biomedical uses of electrochemical sensors based on carbon nanocomposites, among them their employment in illness diagnosis, physiological parameter monitoring, and biomolecule detection. The challenges and prospects of these sensors in biomedical applications are also discussed. Overall, this review highlights the tremendous potential of carbon nanomaterial-based electrochemical sensors in revolutionizing biomedical research and clinical diagnostics.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

Cities are no longer viewed as creatures with a linear-climax-established cycle but as ecosystems with dynamic and complicated processes, with people as the primary component. Thus, we must understand urban ecology’s structure and function to create urban planning and appreciate the mechanisms, dynamics, and evolution that connect human and ecological processes. The ecological city (ecocity) is one of the city conceptions that has evolved with the perspective of urban ecology history. The concept of ecocity development within urban ecology systems pertains to recognizing cities as complex ecosystems primarily influenced by human activities. In this context, individuals actively engage in dynamic problem-solving approaches to address environmental challenges to ensure a sustainable and satisfactory quality of life for future generations. Therefore, it is necessary to study how ecocity has developed since it was initiated today and how it relates to the urban ecology perspective. This study aims to investigate the progression of scholarly publications on ecocity research from 1980 to 2023. Additionally, it intends to ascertain the trajectory of ecological city research trends, establish connections between scientific concepts, and construct an ecological city science network using keyword co-occurrence analysis from the urban ecology perspective. The present study used a descriptive bibliometric analysis and literature review methodology. The data was obtained by utilizing the Lens.org database, was conducted using the VOS (Visualization of Similarities) viewer software for data analysis. The urban ecology research area ecology of cities can be studied further from density visualization of ecosystem services and life cycle assessment. Finally, the challenges and future agenda of ecocity research include addressing humans by modeling functions or processes that connect humans with ecosystems (ecology of cities), urban design, ecological imperatives, integration research, and improving the contribution to environmental goals, spatial distribution, agriculture, natural resources, policy, economic development, and public health.