Diabetes mellitus is a serious health disease that affects people all over the world. The number of persons identified with diabetes mellitus rises each year. α -Glucosidase is a digestive enzyme used to control diabetes mellitus. The searching for new potent α-glucosidase inhibitors capable of delaying carbohydrate digestion in the human body is an important strategy towards control of diabetes mellitus. In this work, a series of quinoline-based Schiff base derivatives already identified as α-glucosidase inhibitory activity was studied by using 2D/3D-QSAR approach. The best HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA models were constructed using thirteen molecules in the training set, resulting in favorable values of Q2 (0.834 and 0.607), and high values of R2 (0.985 and 0.912), respectively. The generated HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA contour plots were precious for designing and enhancing the α-glucosidase inhibitory activity of quinoline-based Schiff base molecules. Considering these results, two novel α-glucosidase compounds were designed to possess significant activity. The newly suggested molecules showed good outcomes in the preliminary in silico ADME/Tox evaluations. Molecular docking results revealed that the new designed inhibitors have a good stability in the active pocket of the studied receptor compared to voglibose, clinically used as an α-glucosidase inhibitor. MD simulation and MM-GBSA results confirmed the molecular docking outcomes. Finally, DFT analysis was useful in determining the most electrophilic and nucleophilic centers of the two designed α-glucosidase inhibitors.

Production management operating in recent competitive marketplaces must satisfy the client desired quality and shorter order lead-time and avoid internal fabricating disruption caused by inevitable defects and stochastic equipment failures. Achieving these operational tasks without undesirable quality goods, missing due dates, and fabrication interruption help the management minimize operating expenditures. Motivated by assisting manufacturing firms in the situations mentioned this study explores a manufacturing system that features quality reassurances through reworking or removal of defectives, correction of probabilistic failures, and partial overtime and outsourcing options for reducing uptime. This study finds the function of system operating expenditures through model building, mathematical formulations, optimization approaches, and algorithm proposition, shows its convexity, and derives the optimal batch time for the studied manufacturing model. Finally, this study offers numerical illustrations to confirm our work’s applicability and disclose its capability to provide various profound crucial system information that helps the management make strategic operating decisions.

In an increasingly advanced digital era, micro, small, and medium enterprises (MSMEs) face new challenges and opportunities in enhancing their sales performance. The use of innovative technologies, such as ChatGPT and Customer Relationship Management (CRM), is key to improving operational efficiency and strengthening MSME competitiveness. This study aims to analyze the impact of integrating ChatGPT and CRM on MSME sales performance with operational efficiency as a mediating variable. The research employs a quantitative approach using SEM-PLS methodology to explore the relationships between relevant variables. The study was conducted on 100 MSMEs in Subang Regency, Indonesia, using an online questionnaire as the data collection tool. The findings indicate that the integration of ChatGPT and CRM significantly affects MSME sales performance in Subang Regency, with operational efficiency as a mediating variable. First, ChatGPT has been shown to have a significant positive impact on MSME sales performance. This technology facilitates the adoption of new technologies, enhances customer interaction, and enables better service personalization, which directly impacts increased sales volume, sales growth, and revenue. Second, effective CRM implementation also demonstrates a significant positive influence on MSME sales performance. Good customer data management, customer satisfaction, and customer loyalty contribute to increased sales volume, sales growth, and revenue. Third, operational efficiency proves to play a significant mediating role in the relationship between ChatGPT and CRM integration and MSME sales performance. Improvements in operational efficiency through reduced processing times, optimized resource use, and cost reduction support increased sales volume, sales growth, and revenue.

In an increasingly advanced digital era, micro, small, and medium enterprises (MSMEs) face new challenges and opportunities in enhancing their sales performance. The use of innovative technologies, such as ChatGPT and Customer Relationship Management (CRM), is key to improving operational efficiency and strengthening MSME competitiveness. This study aims to analyze the impact of integrating ChatGPT and CRM on MSME sales performance with operational efficiency as a mediating variable. The research employs a quantitative approach using SEM-PLS methodology to explore the relationships between relevant variables. The study was conducted on 100 MSMEs in Subang Regency, Indonesia, using an online questionnaire as the data collection tool. The findings indicate that the integration of ChatGPT and CRM significantly affects MSME sales performance in Subang Regency, with operational efficiency as a mediating variable. First, ChatGPT has been shown to have a significant positive impact on MSME sales performance. This technology facilitates the adoption of new technologies, enhances customer interaction, and enables better service personalization, which directly impacts increased sales volume, sales growth, and revenue. Second, effective CRM implementation also demonstrates a significant positive influence on MSME sales performance. Good customer data management, customer satisfaction, and customer loyalty contribute to increased sales volume, sales growth, and revenue. Third, operational efficiency proves to play a significant mediating role in the relationship between ChatGPT and CRM integration and MSME sales performance. Improvements in operational efficiency through reduced processing times, optimized resource use, and cost reduction support increased sales volume, sales growth, and revenue.

This research focuses on production releasing and routing allocation problems in re-entrant mixed-flow shops. Since re-entrant mixed flow shops are complex and dynamic, many studies evaluate release plans by developing discrete event simulation models and selecting the optimal solution according to the estimation results. However, a high-accurate discrete event simulation model requires a lot of computation time. In this research, we develop an effective multi-fidelity optimization method to address product release planning problems for re-entrant mixed-flow shops. The proposed method combines the advantages of rapid evaluation of analytical models and accurate evaluation of simulation models. It conducts iterative optimization using a low-fidelity mathematical estimation model to find good solutions and searches for the optimal solution via a high-fidelity simulation estimation model. Computational results of large-scale production releasing and routing allocation problems illustrate that the proposed approach is good at addressing large-scale problems in re-entrant mixed-flow shops.

A number of 3-aryl-5,6-dihydroimidazo[2,1-b][1,3]thiazoles were synthesized by cyclocondensation of imidazolidine-2-thione with phenacyl bromides, and their antimicrobial and antioxidant activity was evaluated. Bioscreening confirmed the moderate antibacterial activity against the reference strains of bacteria Staphylococcus aureus, Escherichia coli and Proteus vulgaris and excellent antifungal activity against Candida albicans. It was found that 3-(4-chlorophenyl)-5,6-dihydroimidazo[2,1-b]thiazole 4h (MIC = 15.625 μg/ml) has twice the antifungal effect compared to the control drug Furacilin. The study of the antioxidant activity of the synthesized compounds proved their ability to inhibit 60–97% of DPPH radicals. The best antiradical effect was found for 4-(5,6-dihydroimidazo[2,1-b]thiazol-3-yl)phenol 4e (I = 97%). A probable mechanism of its action was proposed involving the formation of a radical cation by the SET process. For the most active antioxidants 4e-g, reactivity and electrostatic surface potential were evaluated using the DFT method, and molecular docking was studied on the human peroxiredoxin 5 protein model.

This study aims at exploring the effect of marketing 5.0 as a whole construct on marketing performance and the moderating role of customer resources between these two variables. Moreover, the study aims at examining the effects of marketing 5.0 dimensions, i.e., predictive marketing, contextual marketing, augmented marketing, and agile marketing on marketing performance as well as the moderating role of customer resources in the effect of each dimension on marketing performance. Collecting data by a closed-end questionnaire from a sample consisting of 186 managers and sales persons in clothing shops, the results pointed out that there is a statistically significant effect of marketing 5.0 on marketing performance and there is a statistically significant moderating effect of customer resources between marketing 5.0 and marketing performance. Furthermore, the results revealed that three dimensions of marketing 5.0, i.e., predictive marketing, contextual marketing, and augmented marketing, exerted significant effects on marketing performance. As well, customer resources significantly moderated the effects of predictive marketing and augmented marketing on marketing performance. Such results contribute to marketing performance literature through highlighting the importance of both marketing 5.0 and customer resources together in enhancing marketing performance.

This paper considers the general lot sizing and scheduling problem (GLSP) in single level capacitated environments with sequence dependent item changeovers. The proposed model simultaneously determines the production sequence of multiple items with capacity-constrained dynamic demand and lot size to minimize overall costs. First, a mixed-integer programming (MIP) model for the GLSP is developed in order to solve smaller size problems. Afterwards, a matheuristic algorithm that integrates Simulated Annealing (SA) algorithm and the proposed MIP model is devised for solving larger size problems. The proposed matheuristic approach decomposes the GLSP into sub-problems. The proposed SA algorithm plays the controller role. It guides the search process by determining values for some of the decision variables and calls the MIP model to identify the optimal values for the remaining decision variables at each iteration. Extensive numerical experiments on randomly generated test instances are performed in order to evaluate the performance of the proposed matheuristic method. It is observed that the proposed matheuristic based solution method outperforms the MIP and SA, if they are used alone for solving the present GLSP.

By the reaction of substituted benzaldehydes and indol-3-carbaldehydes (2E)-2-cyano-N-[5-(R-benzyl)-1,3-thiazol-2-yl]-3-(R1-phenyl)prop-2-enamides 3-(1-R¹-1H-indol-3-yl)-2-cyano-N-(5-(R-benzyl)-1,3-thiazol-2-yl)prop-2-enamides were synthesised. The antitumor activity of prepared compounds were investigated. 3-(1-Benzyl-¹H-indol-3-yl)-2-cyano-N-(5-(2-chlorobenzyl)-1,3-thiazol-2-yl)prop-2-enamide (7b) has been identified as hit compound with values of MG-MID GI50 = 3.903 µM, TGI = 29.10 µM, LC50 = 57.54 µM.

This study examines the influence of mental accounting, financial attitudes, financial knowledge, and financial self-efficacy on the financial behavior of diligent students in using fintech. This study differs from previous research because it uses a sample of students who already have a business and often use fintech. Data collection was carried out through the distribution of questionnaires to respondents. This study found that financial attitudes and self-efficacy had a significant effect on financial behavior, while financial knowledge had no significant effect on financial self-efficacy and mental accounting. Mental accounting significantly affects financial attitudes, financial behavior, and financial self-efficacy. These findings prove the importance of mental accounting in increasing the confidence and effectiveness of students who often use fintech and have a business in making financial decisions. This research contributes to developing the theory of planned behavior in the context of financial behavior. It reveals that financial literacy does not necessarily increase financial self-efficacy and mental accounting, especially among students who often use fintech and are just starting a business.