This paper seeks to examine the relationship between determinants of blood transfusion sustainability (BTS) that is master production scheduling (MPS) and total quality management (TQM) of Uganda. The study was founded on four objectives. The study looked at the direct relationship between MPS and the BTS, direct relationship between MPS and TQM, direct relationship between TQM and BTS. It also assessed how TQM mediated the direct relationship between MPS and BTS. The study used a quantitative method. A survey questionnaire was administered to collect data from 367 staff of regional blood banks and government university teaching hospital blood banks; and 213 were found to be usable. The main analysis was done using structural equation modeling. The study found that MPS had a positive and significant relationship with the BTS. The study found that the relationship between MPS and TQM was positive and significant. The study also found that the relationship between TQM and BTS was positive and significant. The study concluded that the effect of MPS on BTS was not mediated by TQM. It was recommended that blood banks seeking to achieve transfusion sustainability must invest in understanding the sector in which they operate. This includes understanding the blood demand requirement, customer focus, people involvement and timely delivery. The various stakeholders in the blood supply chain i.e. blood banks, hospital blood banks, funding agents, ministry of health, must also integrate to enhance the transfusion sustainability. Blood banks performance measures essentially timely delivery was very critical for saving lives of patients in need of blood. The study has provided a new conceptual framework that investigates the TQM mediating effect on the relationship of MPS and BTS, and thus can serve as an incentive for more research to be conducted in this regard in blood banks of different developing countries. The authors also proposed identifying the effect of other TQM factors such as process management, system management, supplier relationship and top management on BTS.

The selection of an optimal supplier is a critical and open challenge in supply chain management. While experts' assessments significantly influence the supplier selection process, their subjective interactions can introduce unpredictable uncertainty. Existing methods have limitations in effectively representing and handling this uncertainty. The paper aims to address these challenges by proposing a novel approach that leverages q-rung orthopair fuzzy sets (q-ROFSs). The novelty of the proposed approach lies in its ability to accurately capture experts' preferences through the use of q-ROFSs, which offer membership and non-membership degrees, providing a broader expression space compared to conventional fuzzy sets. Additionally, it incorporates social network analysis (SNA) to effectively consider the trust relationships among experts. The proposed approach is divided into three stages. The first stage, presents a novel method to determine experts' weights by combining SNA, the Bayesian formula, and the maximum entropy principle. This approach allows for a more precise representation of varying levels of expertise and influence among experts, addressing the uncertainty arising from subjective interactions. The second stage introduces a hybrid weight determination method to determine criteria weights within the context of q-ROFSs. Entropy plays a crucial role in capturing fuzziness and uncertainty in q-ROFSs, while the projection measure simultaneously provides information about the distance and angle between alternatives. By employing both objective weights estimated using entropy and normalized projection measure and subjective weights derived using an aggregation operator and a score function, the presented approach achieves a comprehensive assessment of criteria importance. To incorporate both subjective and objective weights effectively, game theory is applied which allows us to align decision-making with both quantitative and qualitative aspects, making the method more versatile and applicable. The third stage redefines the traditional Combined Compromise Solution (CoCoSo) method using Bonferroni mean operators which captures interrelationships among arguments to be aggregated. Furthermore, in recognition of the importance of an expert risk preferences and psychological behaviors, we apply regret theory, ensuring that the chosen solutions align more effectively with their underlying preferences and aspirations. The applicability and effectiveness of the proposed approach are demonstrated through a numerical example of green supplier selection. The comparative analysis illustrates the practicality and real-world relevance while the sensitivity analysis, confirms the stability and robustness of the proposed approach.

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

Additive Manufacturing (AM) is becoming the leading innovation in many fields due to its ease in generating a 3D object by adding one layer of material over the other from a source of Computer Aided Design (CAD) model as input file. Fused Deposition Modeling (FDM) is one among the technologies available in AM, which works on material extrusion process for which the material is served in filament shape. The practice of utilizing the resources effectively by meeting the requirements of subsequent generations is internationally referred to as Sustainable Manufacturing (SM). It deals with the issues that impact the economy, society and environment. Green manufacturing approaches like reduce, reuse and recycle theories are linked with 3D Printing. In this paper research has been conducted on the studies of sustainability of the parts produced on FDM for ASTM D638 Type- IV standard tensile test specimen to optimize the process parameters for Acrylonitrile Butadiene Styrene (ABS) material by using Design of Experiments (DOE) through Taguchi technique and Analysis of Variance (ANOVA). The variables considered are print speed, orientation, layer thickness and print temperature and the responses studied are energy consumption, CO2 emission, dimensional accuracy, surface roughness and mechanical properties. The primary aim of this research is to reduce the energy consumption and CO2 emission without compromising mechanical properties, in order to achieve sustainability by finding the optimum values for the input process parameters.

The adoption of supply chain management (SCM) is pivotal in boosting organizational competitiveness and performance. This study examines the relationships among SCM, firm value, company performance, and competitive advantage within the context of micro, small, and medium enterprises (MSMEs) in Surabaya, East Java, Indonesia. Employing a quantitative approach, we analyzed data collected from a sample of 100 MSMEs using SmartPLS to test various hypotheses. The findings indicate that effective SCM significantly influences a firm’s value, performance, and competitive advantage. This study supports the mediating role of firm value by showcasing its robust influence on the relationship between SCM and competitive advantage. However, the mediating role of company performance on competitive advantage appears weaker. The discussion integrates relevant literature, highlighting the pivotal role of SCM strategies in enhancing productivity, fulfilling customer desires, and increasing competitiveness. This study also underscores the critical link between financial performance, value creation, and competitive advantage. Overall, this study contributes to the ongoing development of practical SCM knowledge by providing valuable insights for MSMEs seeking to navigate the challenges of an evolving business landscape.

In the present investigation, a library of 1,8-dioxo-octahydroxanthene derivatives have been synthesised by the cyclo-condensation reaction of aryl aldehyde with two moles of dimedone under solvent free condition in the presence of Novel Pumice supported perchloric acid (Pumice@HClO4) as a less expensive, recyclable, thermodynamically stable and non-toxic catalyst. The significant advantages of this novel protocol are experimental simplicity, easy work-up procedure, good to excellent yields of product and more sustainable as the efficiency of the readily available catalytic material.

This paper investigates the impact of dual phase latency caused by the reflection of plane waves that propagate in a swelling porous thermoelastic medium with an impedance boundary. Two transversal waves (SVS, SVF), a thermal wave (T), and two longitudinal waves (Ps and Pf) propagate with distinct velocities. Reflection coefficients are determined by the incidence of these waves, and energy ratios for reflected waves are calculated and illustrated using these amplitude ratios. In this particular instance, the current model was downsized to an LS model. It has been noted that the energy ratios acquired are significantly influenced by dual phase lag. The results that have been obtained may be beneficial in a variety of engineering problems that are related to structure.

New hybrids containing acridine and α-amino phosphonate scaffolds 6a-h were synthesized by three one-pot reactions via Kabachnik–Fields reaction of 9-aminoacridine derivatives 3a-d, aldehydes 4a,b and triphenyl phosphite 5 in equimolar ratio under stirring at room temperature. The installation of the afforded hybrids was affirmed by different spectroscopic methods as IR, 1HNMR, 13CNMR, 31P NMR, MS and elemental analysis. The antibacterial activities of hybrids 6a-h were screened against both Gram-positive and Gram-negative bacteria of four pathogenic strains, comparable to ampicillin and chloramphenicol drugs. Most hybrids showed higher activity compared to the reference drugs. Notably, the antibacterial performance of 6c was more substantial than other analogues with best minimum inhibitory concentration (MIC) and highest zone of inhibition (ZOI) of 31, 34, 35, 38 mm, against Serratia marcescens, Streptococcus mutans, MRSA and Klebsiella pneumoniae clinical isolates respectively. In addition, Transmission Electron Microscopes (TEM) was carried out to study the mode of action of 6c with MRSA, the results confirmed that the titled hybrid caused the disruption of bacterial cell wall. Moreover, the time-dependent kill study revealed that the investigated compounds were time and dose-effective bactericidal agents and could be used as potential leads for further development and optimization of novel antibacterial agents.

This document presents a simulation-based method for the polyhedra packing problem (PPP). This problem refers to packing a set of irregular polyhedra (convex and concave) into a cuboid with the objective of minimizing the cuboid’s volume, considering non-overlapping and containment constraints. The PPP has applications in additive manufacturing and packing situations where volume is at a premium. The proposed approach uses Unity® as the simulation environment and considers nine intensification and two diversification movements. The intensification movements induce the items within the cuboid to form packing patterns allowing the cuboid to decrease its size with the help of gravity-like accelerations. On the other hand, the diversification movements are classic transition operators such as removal and filling of pieces and enlargement of the container, which allow searching on different solution neighborhoods. All simulated movements were hybridized with a probabilistic tabu search. The proposed methodology (with and without the hybridization) was compared by benchmarking with all previous works solving the PPP with irregular items. Results show that satisfactory solutions were reached in a short time; even a few published results were improved.

The objective of this research is to investigate the impact of Artificial Intelligence (AI) on improving the quality of financial reporting in the Islamic banking industry. The study is conducted within the theoretical framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). The study utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the data collected from a sample of 364 professionals working in the field of Islamic banking. The results of our study suggest that Performance Expectancy, Effort Expectancy, and Social Influence are important factors in predicting individuals' Behavioural Intention to use Artificial Intelligence (AI). Additionally, the presence of Facilitating Conditions further enhances the impact of these factors on individuals' actual Use Behaviour. Significantly, it was shown that Use Behaviour played a significant role in determining the perceived quality of financial reporting. Nevertheless, the study did not find empirical evidence to demonstrate the direct influence of Behavioural Intention on Financial Reporting Quality. This implies that the actual implementation of Artificial Intelligence is required to fully realize its advantages. The use of artificial intelligence (AI) into governance frameworks presents a potentially advantageous pathway for Islamic banks to uphold Shariah principles, while concurrently bolstering accountability and fostering ethical banking practices.