In current paper, a novel, green, efficient and recyclable magnetic nanocatalyst (α-Fe₂O₃@MoS₂@Ni) was prepared and characterized by spectroscopic and microscopic techniques (FT-IR, XRD, SEM, EDX, TEM and VSM). α-Fe₂O₃@MoS₂@Ni magnetic nanoparticles (MNPs) were utilized for preparation of 1,2,4-triazolidine-3-thiones and spiro-triazole hybrid series. In this method, a series of triazole derivatives prepared from the reaction between thiosemicarbazide or semicarbazide with diverse isatine derivatives or various arylaldehydes and ketones at room temperature in water as solvent with great yields and short reaction times. α-Fe₂O₃@MoS₂@Ni MNPs was simply removed from the reaction mixture and was reused for five times without any notable changes in its catalytic activity.

Triazolo[3,4-b]thiadiazoles are a class of heterocyclic compounds, which have attracted great interest in medicinal chemistry owing to their wide range of pharmacological activities. A number of triazoles fused to thiadiazoles are incorporated into a wide variety of therapeutically important compounds possessing a broad spectrum of biological activities. Considering such a significant pharmacological potential, as well as wide synthetic possibilities triazolo-thiadiazoles have received considerable attention from scientific community and are extensively used for construction of prospective drug-likes molecules. In this review, we summarized the literature data about the main synthetic approaches for obtaining condensed heterocyclic compounds based on triazolo[3,4-b][1,3,4]thiadiazole scaffold as promising objects for modern bioorganic and medicinal chemistry.

Herein, modern green synthesis approaches are studied to assemble N-heterylarenes scaffolds via a non-catalyzed intramolecular 5/6-exo-dig cyclocondensation reaction based on the in-situ generation of arenehydrazonopentan-/hexan-2-one. Accordingly, the formation of two N-heteryl moieties, pyrazol-1-yl and pyridazin-1(4H)-yl, was initially studied. Therefore, appropriate fluorophenyl was first converted into their respective phenylhydrazine by SNAr reaction and then reacted with different trigonal carbonyl bielectrophiles (-CO-, -CO₂R, and -CO₂H) in ethanol in the presence of US, MW, IR, and an IR· US irradiation mixture. Cyclic nitrogenous cores were best obtained when subjected to microwave irradiation with ketone and arylhydrazine as starting reagents, allowing them to get excellent yields quickly. Arylhydrazine reactants featuring Π-donor groups underwent the best 5/6-exo-dig type annulment reaction. Presumably, the observed improvements in EDG-dependent reaction efficiency reflect changes in the nucleophilicity of arylhydrazines intermediates as α-nucleophilic reactants.

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 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.