In developing countries, urban mobility is a significant challenge due to convergence of population growth and the economic attraction of urban centers. This convergence of factors has resulted in an increase in the demand for transport services, affecting existing infrastructure and requiring the development of sustainable mobility solutions. In order to tackle this challenge, it is necessary to create optimal services that promote sustainable urban mobility. The main objective of this research is to develop and validate a comprehensive methodology framework for assessing and selecting the most sustainable and environmentally responsible urban mobility services for decision makers in developing countries. By integrating fuzzy multi-criteria decision-making techniques, the study aims to address the inherent complexity and uncertainty of urban mobility planning and provide a robust tool for optimizing transportation solutions for rapid urbanization. The proposed methodology combines three-dimensional fuzzy methods of type-1, including AHP, TOPSIS and PROMETHEE, using the Borda method to adapt subjectivity, uncertainty, and incomplete judgments. The results show the advantages of using integrated methods in the sustainable selection of urban mobility systems. A sensitivity analysis is also performed to validate the robustness of the model and to provide insights into the reliability and stability of the evaluation model. This study contributes to inform decision-making, improves policies and urban mobility infrastructure, promotes sustainable decisions, and meets the specific needs of developing countries.

The current study provides a comprehensive analysis of MHD hybrid nanofluids and stagnation point flow toward a porous stretched cylinder in the presence of thermal radiation. Here, alumina (Al₂O₃) and copper (Cu) are considered the hybrid nanoparticles, while water (H₂O) is the base fluid. To begin, the required similarity transformations are applied to transform the nonlinear coupled PDEs into nonlinear coupled ODEs. The obtained highly nonlinear sets of ODEs are then solved analytically by using the HAM procedure. The calculations of the thermal radiation term in the energy equation are done based on the Roseland approximation. The result of various embedded variables on temperature and velocity profiles is drawn and explained briefly. Aside from that, the numerical solution of well-known physical quantities, like skin friction and the Nusselt number, is computed by means of tables for the modification of the relevant parameter. The analysis shows that the magnetic field has opposite behavior on θ(η) and f'(η) profiles. It is seen that more magnetic factors M decline f'(η) and upsurge θ(η). Moreover, the behavior of skin friction and the Nusselt number are the same for the magnetic parameter M. Meanwhile, a higher Reynolds number R_e declines temperature profile and skin friction while upsurging the local Nusselt number. There are many applications of this study that are not limited to engineering and manufacturing, such as polymer industry, crystal growth, tumor therapy, plasma, fusing metal in electric heaters, nuclear reactors, asthma treatment, gastric medication, cooling of atomic systems, electrolytic biomedicine, helical coil heat exchangers, axial fan design, polymer industry, plane counter jets, and solar collectors.

This study aims to investigate the enhancement in electrical efficiency of a polycrystalline photovoltaic (PV) module. The performance of a PV module primarily depends upon environmental factors like temperature, irradiance, etc. Mainly, the PV module performance depends upon the panel temperature. The performance of the PV module has an inverse relationship with temperature. The open circuit voltage of a module decreases with the increase in temperature, which consequently leads to the reduction in maximum power, efficiency, and fill factor. This study investigates the increase in the efficiency of the PV module by lowering the panel temperature with the help of water channel cooling and water-channel accompanied with forced convection. The two arrangements, namely, multi-inlet outlet and serpentine, are used to decrease the temperature of the polycrystalline PV module. Copper tubes in the form of the above arrangements are employed at the back surface of the panel. The results demonstrate that the combined technique is more efficient than the simple water-channel cooling technique owing to multi-heat dissipation and effective heat transfer, and it is concluded that the multi-inlet outlet cooling technique is more efficient than the serpentine cooling technique, which is attributed to uniform cooling over the surface and lesser pressure losses.

Nanoscale zero-valent iron (nZVI) is thought to be the most effective remediation material for contaminated soil, especially when it comes to heavy metal pollutants. In the current high-industrial and technologically advanced period, water pollution has emerged as one of the most significant causes for concern. In this instance, silica was coated with zero-valent iron nanoparticles at 650 and 800 ℃. Ferric iron with various counter-ions, nitrate (FN) and chloride (FC), and sodium borohydride as a reducing agent were used to create nanoscale zero-valent iron in an ethanol medium with nitrogen ambient conditions. X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) techniques were employed to describe the structures of the generated zero-valent iron nanoparticles. Further, we investigated the electrical properties and adsorption characteristics of dyes such as alizarin red in an aqueous medium. As a result, zero-valent nano iron (nZVI), a core-shell environmental functional material, has found extensive application in environmental cleanup. The knowledge in this work will be useful for nZVI-related future research and real-world applications.

Employee retention promotes positivity in an organization and improves employers’ brand value. As the human resource department operates with the objective of improving employees’ contribution towards the organization, meaningful work is an important topic in the core areas of human resource development (HRD), such as employee involvement, motivation, and personal development. Not only salary, benefits, working environment, and status but also the factors that determine whether you enjoy going to work every day are whether you believe that your work makes a meaningful contribution. In HRD, meaningful work comes to the forefront through a connection with a high level of commitment. Thus, this study aims to establish the relationship between meaningful and purposeful jobs affecting employee retention and the mediating factors of person organization fit (POF) and person job fit (PJF). A cross-sectional study involving a survey methodology was used to collect data from 150 white-collar employees working in the IT, banking, textile, and multinational companies in Bangladesh. The results indicate that job meaningfulness has a positive relationship with employee retention (p-value = 0.031) and both the mediating factors of PJF (p-value = 0.040) and POF (p-value = 0.028). The results also indicate that while POF positively influences employee retention (p-value = 0.019), PJF has no significant influence on employee retention (p-value = 0.164). Thus, promoting employee job meaningfulness and purpose in the workplace may represent an opportunity for organizations to improve employee engagement and retention.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.