A series of new 4-(1,3,4-thiadiazol-2-yl)-containing polysubstituted pyrroles 3 a-k has been synthesized by a preparative convenient method from ethyl 5-chloro-4-formyl-1H-pyrrole-3-carboxylates 1 a-e, which were selectively transformed into the corresponding polysubstituted pyrrole-4-carboxylic acids 2 а-е using sodium hypochlorite as an oxidizer. Further, they were transformed into the target compounds with a high yield using the cyclocondensation with N-mono- or N,N-disubstituted thiosemicarbazides in the boiling phosphorus trichloroxide. As seen from the screening of antimicrobial activity, the synthesized compounds exhibit the inhibiting and bactericide activity against some bacteria and fungi. The highest activity has been established for the compounds 3 a, c, e-h, j against the strain Klebsiella pneumoniae (МІС=31.25 µg/mL). The calculated HOMO energy level proves that the compound 3 с is the most reactive ligand for the interaction with a protein receptor. The molecular docking data show that the compound 3 h has the highest affinity to the ThiM Klebsiella pneumoniae kinase.

The automotive industry faces significant challenges stemming from market instability and supply chain complexities. This study examines how proactive risk management, collaboration with OEMs, and the adoption of digital tools influence supply chain performance in the Moroccan automotive sector. Survey data from 135 industry experts were analyzed using a structural equation modeling approach to test three hypotheses. The results show that proactive risk management significantly enhances service levels, while collaboration with OEMs strengthens risk management practices. Additionally, digital tools facilitate collaboration, albeit with a moderate overall effect. This research offers actionable insights for addressing uncertainties in automotive supply chains.

The exponential expansion of the Internet of Things (IoT) has fundamentally transformed the way people, machines, and gadgets communicate, resulting in unparalleled levels of interconnectedness. Nevertheless, the growth of IoT has also brought up notable security obstacles, requiring the creation of strong intrusion detection systems to safeguard IoT networks against hostile actions. This study investigates the utilization of fog computing architectures in conjunction with machine learning approaches to improve the security of the IoT. The UNSW-NB15 dataset, containing an extensive range of network traffic characteristics, is used as the basis for training and assessing the machine learning models. The study specifically applies and evaluates the performance of various models, including linear regression, Ridge classifier, SGD classifier, and ensemble learning. Furthermore, the findings indicate that these models are capable of accurately identifying intrusions, with success rates of 94%, 97%, 96.60%, and 96.50%, respectively. The Ridge Classifier demonstrates exceptional accuracy, highlighting its potential for effective implementation in IoT security frameworks. The results emphasize the efficacy of combining machine learning with fog computing to tackle the distinct security obstacles faced by IoT networks. In the future, our work will prioritize optimizing these models for real-time applications, improving their scalability, and investigating more advanced ensemble strategies to enhance detection accuracy. The project intends to enhance these areas to create a comprehensive and scalable intrusion detection system that can offer strong security solutions for the growing IoT environment. This will guarantee the integrity and dependability of linked devices and systems.

In his research, he has established an analysis of the consumption of renewable energy and its impact on CO2 emissions in Peru, 1990-2020. The research employs a quantitative approach and longitudinal non-experimental design, with a multiple linear regression model. It uses time series drawn from the World Bank on renewable energy consumption and energy consumption. A progressive increase was reflected mainly driven by industrial growth, fossil fuel consumption and changes in consumption and production patterns.

In today's rapidly changing business environment, companies need to be dynamic, adapting their internal processes to respond effectively to external changes. Information Technology has become crucial for businesses to enhance their ability to manage and adapt to change. This study examines East Java manufacturing companies that have heavily invested in sustainable IT systems to enhance their supply chain responsiveness and resilience through improved purchasing strategies and production flexibility. The study collected data from companies that had implemented IT for at least three years, with respondents being permanent employees with a minimum of two years' experience. Analysis of 108 survey responses using SmartPLS 4 revealed significant impacts of IT implementation on purchasing strategy (0.610), production flexibility (0.363), and supply chain responsiveness (0.164). Furthermore, purchasing strategy influenced production flexibility (0.367), supply chain responsiveness (0.348), and resilience (0.166). Production flexibility also affected supply chain responsiveness (0.348) and resilience (0.343), while responsiveness impacted resilience by 0.306. These findings provide a practical contribution for functional managers in companies regarding the importance of IT investment in developing effective purchasing, production, and marketing strategies to meet market demands swiftly. The research contributes to supply chain strategy and resilience theory while highlighting the significance of strong collaboration with external partners for top management.

An examination of two areas of promotion and assortment planning in an environment is attempted in this paper. Sales promotion is a marketing strategy used by retailers to increase sales and profits by retaining customers and preventing them from switching to their competitors. Various products are available on the market that can substitute each other, so the best product assortment must be determined as well. In order to model the above subject, a nonlinear integer programming problem is proposed. Model solution involves rephrasing the problem as mixed integer linear programming. Small- and medium-sized problems can therefore be solved using MIP solver software. Firefly algorithms are designed to solve large-scale problems. According to the numerical results, determining the best product assortment for stores must also be done simultaneously with finding the optimal promotion. As a matter of fact, the promotion of the products significantly affects the assortment scenarios for the stores. Consequently, the selection of the promotional discount may result in large profit losses if the assortment planning is not taken into consideration. In order to assess the importance and sensitivity of the model parameters, a sensitivity analysis is conducted. The sensitivity analysis demonstrates that the model is able to respond to changes in market demand and competition, and provides an effective tool for chain stores to optimize their promotion and assortment strategies. To further validate the effectiveness of the model, a case study is conducted in Tehran, Iran. The results of the case study demonstrate the ability of the model to effectively optimize promotion and assortment strategies in real-world settings. Overall, the proposed model provides a valuable tool for chain stores to optimize their promotion and assortment strategies, and improve their market competitiveness.

The aim of this study is to assess and rank Anadolu Sigorta Company's sustainability performance between 2018 and 2022 using the hybrid SWARA-MEREC-COBRA model. The sustainability performance evaluation criteria's importance weights were determined using both subjective and objective methods. The SWARA algorithm was used to determine the weights subjectively based on expert opinion, while the MEREC algorithm was used to determine them objectively. The final importance weights were obtained by combining the results of both methods. When evaluating the sustainability performance of Anadolu Sigorta Company, the most important criterion was total paper consumption, while the criterion with the least impact was the number of female employees. The COBRA ranking algorithm was used to rank the alternatives, and it was determined that the company's best sustainability performance was in 2022, while the worst was in 2018. Different sensitivity analyses were used to test the consistency of the proposed model.

This study aimed to build a model and set of criteria to evaluate and select business accounting software providers for Truong Son Technology Investment and Development Joint Stock Company. The study helped choose the most suitable supplier that meets the criteria that the company desired. The research proposed the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) model and was also applied in the case of selecting accounting software suppliers for Truong Son Technology Investment and Development Joint Stock Company. There were 5 business accounting software packages considered: Misa business accounting software, Bravo accounting software, FAST Accounting software, Effect accounting software, and Gamma accounting software. The ranking results of accounting software providers at Truong Son Technology Investment and Development Joint Stock Company were as follows: A1 (Misa Business Accounting Software) was the best supplier, second-ranking was A3 (FAST Accounting software), followed by A2 (Bravo Accounting Software) in third place. The findings of this study could be used as a valuable reference for businesses to choose the best supplier.

The purpose of this research is to explore the relationships between information literacy, digital literacy, media literacy, Attitude toward use, and employability to examine the role of information and digital literacy in influencing employees' intentions to use technology in the workplace. The research sample for Indonesia was 250 respondents and for Malaysia there were 298 respondents. The data collection method uses Google Forms, distributed to respondents through purposive random sampling technique. The research results indicate that in Indonesia, Computer Literacy (CL) on Perceived Usefulness (PU), PU on Attitude towards Use (ATT), and ATT on Employability (EMP) have a big influence. On the other hand, Information Literacy (IL) and CL have a small influence on Employability. Likewise, Perceived Ease of Use (PEOU) has little influence on ATT. Malaysia, which has a big influence is Digital Literacy (DL) on Employability (EMP) and Perceived Ease of Use (PEOU). However, DL has a small influence on PU, as does IL on EMP, PEOU, and PU. Likewise, the influence of PEOU on PU is small.

Poverty is a complex and multidimensional social problem faced by almost all countries including Indonesia. Poverty alleviation efforts require accurate and up-to-date data to ensure that social welfare programs can effectively run and target appropriate beneficiaries. Bekasi Regency, as one of the regions that faces significant challenges in managing poverty, needs the right strategy in updating and utilizing poverty data to support social welfare programs. This study analyzes a policy framework for updating and utilizing the poverty data. The data were collected from focus group discussions with experts and stakeholders who were competent in updating and utilizing the poverty data. MULTIPOL method was used to analyze the data. The results of the study show that the best strategy for updating and utilizing poverty data in the scenario is to integrate scenarios with priority policy with digital budgeting policy, as well as priority actions providing incentives. This study makes an important contribution to regional development planning, especially poverty alleviation strategies based on accurate and integrative data.