To question the dominanceof the dimensions of service quality using the embryonic HESQUAL model onstudents' satisfaction. Besides, the study will identify the mediating effect ofa department’s commitment and a moderating effect of self-efficacy on therelationship between students’ satisfaction and academic performance. The study was a one-time dimensionaland used a simple random sampling method which is suitable for quantitativestatistical analysis and homogeneous populations in nature. Analysis resultingfrom SmartPLS by using 431 responses from undergraduate and graduate students of public universities inBangladesh. The study found that students arefocused on core educational qualities and physical environmental facilities tobe satisfied. Thoroughly,students' satisfaction has a direct impact on academic performance butsurprisingly no moderating precedence of self-efficacy on the connectionbetween student satisfaction and academic performance was seen. Noticeably, theextension of the HESQUAL model of department commitment finds significant influenceson both satisfaction and academic performance. However, the modified HESQUEL will quantify thequality dimensions in higher education, and its application in this study candepict a greater understanding of the quality dimensions required to beprioritized by university authorities and policymakers.

Training and productivity have been found positively correlated in theory and practice. The pattern of the relationship, however, was not explored in pre - and post-regulation in the conceived sector. This paper aims to observe the effects of regulation and training expenses along with other covariates on employee productivity in the Microfinance sector of Bangladesh. Using annual data of MRA-licensed MFIs, we have estimated both panel and cross-sectional regression models. The regression results confirm the theoretical relationship between training expenses and employee productivity. The regulation also worked positively in enhancing employee productivity. However, in the early stage of regulation, the average productivity gain due to regulation was substantial and was showing an increasing trend but then it declined and reached a constant level of about 4% - 5% each year. Between 2008 and 2011, both regulation and training positively contributed to the gains in average productivity of the employees. After 2012, there was a positive trend of average productivity elasticity of training expenses but there was a flat effect of regulation after 2012. Regulation was found to short-run shifter in the average productivity of employees while training expenses had a positive trend effect.

In today's dynamic and uncertain environments, effective decision-making processes are essential for navigating complex challenges. This paper proposes an innovative approach utilizing Fermatean fuzzy sets to enhance decision-making within heterogeneous group dynamics. Through a systematic mathematical framework, our method integrates expert preferences to find out the comparative weight of decision attribute, leveraging both Fermatean fuzzy sets and entropy calculations. Furthermore, we introduce a novel technique to assess the significance of individual experts' opinions, accounting for specific contextual factors. By synthesizing performance ratings, criteria weights, and expert inputs, our approach offers a comprehensive decision-making model. We introduce the concept of the proximity coefficient to address existing methodological limitations, enhancing the accuracy of decision outcomes. To validate our methodology, we apply it to a practical scenario involving warehouse location selection. Additionally, analysis of sensitivity is conducted to evaluate the robustness of our method across diverse scenarios, demonstrating its efficacy in uncertain environments. This research contributes to advancing decision-making practices in complex and uncertain contexts, offering a valuable tool for addressing real-world challenges.

In food processing factories, especially when dealing with perishable items, managing inventory is crucial as it impacts sales, quality, and customer satisfaction. However, inventory management is often complicated by the costs and intricacies of the supply chain. This research aims to create a conceptual model that connects the costs and complexities of the supply chain with satisfaction, sales, and quality through optimized inventory management. The study involves a case analysis of thirteen food processing factories, using a structured questionnaire for data collection. To validate the proposed framework, PLS-SEM was employed. The framework addressed five key research questions, and the results confirmed that inventory management is essential for maintaining quality, sales, and satisfaction, and that supply chain costs and complexity influence inventory management.

Continuously increased order and variability of the inventory in the uppermost level of the supply chain node create the Bullwhip effect. In the context of closed-loop supply chains, this dynamic phenomenon is still little understood despite modern nations' increasing interest in exploring the potential for a circular economy. The problem-specific literature has produced results that are a little bit contradictory. I derive formulas in four archetypes for computing inventory order and variance amplification with different information transparency structures to better understand the Bullwhip Effect in the closed-loop structure. It’s interesting to note that the visibility of the supply chain's degree significantly influences how lead time and return rate affect the performance of that system. From this vantage point, I may review differences from earlier studies. Later on, I switched the perspective of the study from operational to economic. Here, the ideal return rate was established, and the four closed-loop supply chain (CLSC) archetypes where it might be expressed were provided. I demonstrate that the lead times, demand unpredictability, and the cost structure of all nodes affect the ideal rate of return. In this study, I also address pertinent management implications and the properties of various closed-loop systems from the perspective of Bangladesh.

The poor maintenance and usage of the equipment and machines of a vegetable oil manufacturing company adversely affect its competitive advantage. These industries are faced with numerous equipment maintenance challenges in the path to increasing their throughput as well as profitability. To address the said maintenance challenges, process data were obtained for the Overall Equipment Effectiveness (OEE) factors after their Total Productive Maintenance (TPM) implementation in the company. Minitab 21 software was used to analyze the data collected, and the results showed that the mean for quality, availability, and performance obtained were 96.929%, 63.35%, and 61.20%, respectively. This shows that the quality of products is the greatest OEE factor that vegetable oil manufacturing companies must consider meticulously to reduce the six big losses in their production processes. Response Surface Method (RSM) with Central Composite design, with the application of Design Expert 13 software, was used to model, analyze, and optimize the Overall Equipment Effectiveness (OEE) using availability, quality, and performance as the input parameters. The analysis of both the actual and coded values, which is the main contribution of the study, showed that quality has the greatest value followed by availability and performance. It was found that, to effectively reduce the six big losses, the quality, performance, and availability should be targeted as 98.3052%, 81.6022%, and 80.103%, respectively.

Methyl α-D-mannopyranoside (MAM) is a naturally occurring carbohydrate derivative that has gained attention in drug discovery due to its potential therapeutic applications, particularly as an antifungal agent. In this study, we employed a computational approach to investigate the interactions between MAM and two Candida albicans antifungal proteins, 1IYL and 1AI9, through molecular docking simulations. Furthermore, we performed a PASS (Prediction of Activity Spectra for Substances) analysis to predict MAM potential biological activities, explored the pharmacokinetic properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles, and optimized the MAM using the density functional theory (DFT) method. The molecular docking results revealed favorable binding interactions between MAM and the active sites of the 1IYL and 1AI9 proteins, suggesting potential antifungal activity. Additionally, the ADMET profiles indicated low toxicity and suitable drug-like properties, such as moderate metabolic stability and minimal risk of adverse effects. Furthermore, DFT optimization was performed to investigate the molecular geometry and electronic properties of MAM. The optimization results provided valuable information on the stability and reactivity of MAM, enabling a better understanding of its chemical behavior and potential modifications for enhanced activity. Finally, PASS prediction was employed to evaluate MAM's potential biological activities beyond its antifungal properties. The analysis revealed several potential activities, including antibacterial, antiviral, and immunomodulatory effects, expanding the scope for future research and therapeutic applications. In conclusion, this computational study sheds light on the molecular interactions, pharmacokinetic properties, ADMET profiles, DFT optimization, and PASSES predictions of MAM. These findings highlight the potential of MAM as a promising antifungal agent with favorable pharmacological properties.

The main impediment to practical application is the toxicity of lead ions in halide perovskite absorbing materials. Computing tools based on density functional theory (DFT) were used to predict the intrinsic properties of potential for double perovskites to be effective and suitable for optoelectronic applications, replacing the conventional lead halide perovskites with environmentally friendly elements. The Generalized Gradient Approximation (GGA) with Perdew-Burke-Ernzerhof (PBE) functional was used to screen homovalent alternatives for B and X-site ions in vacancy-ordered double perovskite Cs₂BX₆ (B=Pt, Ni, X= Cl, Br) for solar cell applications. Using the GGA with PBE functional, the band gap was calculated to be 1.411 eV, 0.482 eV, and 0.378 eV for the Cs₂PtBr₆, Cs₂NiCl₆, and Cs₂NiBr₆, respectively. The experimental band gap value of mother crystal's (Cs₂PtBr₆) was at 1.42 eV. Next, the DOS, PDOS and optical properties were computed using GGA with PBE functional. Then, the local density approximation (LDA) with Ceperley and Alder with Perdew and Zunger (CA-PZ) was executed to compare the GGA with PBE for electronic band structure. In addition, the OTFG ultra soft, OTFG norm conserving, ultra soft and norm conserving methods of pseudopotential were used for both GGA with PBE and LDA with CA-PZ to make and ensure the right or accurate DFT functional for those crystals. At last, the optical properties and their toxicity have been evaluated for their rational design of potential double perovskite materials with improved optoelectronic properties.

An inventory control system having multiple items in stock is developed in this paper to optimize total cost of inventory and space requirement. Inventory modeling for both the raw material storage and work in process (WIP) is designed considering independent demand rate of items and no volume discount. To make the model environmentally aware, the equivalent carbon emission cost is also incorporated as a cost function in the formulation. The purpose of this study is to minimize the cost of inventories and minimize the storage space needed. The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO). A further meta-heuristic algorithm called multi-objective bat algorithm (MOBA) is used to determine the efficacy of the result obtained from MOPSO. Taguchi method is followed to tune necessary response variables and compare both algorithm's output. At the end, several test problems are generated to evaluate the performances of both algorithms in terms of six performance metrics and analyze them statistically and graphically.

This study examines the relationship of two important constructs (the belief and attitude constructs) with managers’ proclivity for using Social Dialogue – a tool for managing people at work (patronised by ILO) for maintaining peace and harmony in Bangladeshi textile industries. Managers’ personal belief and attitude within an organisation might affect the managerial responses towards social dialogue. The obtained data from the questionnaires are analysed through the 1st generation statistical packaged software (SPSS) and hypotheses are tested using Smart PLS software package well known as 2nd generation data analysis software. Analyses results strongly supported the relationship of the belief and attitude construct with managers’ proclivity for using social dialogue. The research surveyed three hundred fifty-one managers at 49 textile industries. The findings of this study suggest that the belief and attitude construct have a significant positive relationship with managers’ penchant for supporting social dialogue. The result of the paper provides managers with a credible argument to continually question their employee perceptions and objectively analyse whether their beliefs and attitudes impact their intention to use social dialogue. The selected sample should have been from strategic, tactical, and operational level of management that remains as a research limitation.