Science and technology of nanosized bimetallic oxide nanomaterials records the various properties and applications. Especially biomedical applications are viewed in particular due to its nanosized particle size. The present experimentation is reporting the microwave-assisted synthesis of nanosized bimetallic oxides like copper ferrite (CuFe2O4) and barium titanate (BaTiO3) by solid state combustion route using poly (vinyl alcohol) (PVA) as a fuel. The structural and morphological characterizations of the bimetallic oxide nanomaterials are performed out by X-ray diffraction (XRD) and scanning electron micrograph (SEM) tools respectively. These analyses report the crystalline nature of both samples. EDX spectral study is also undertaken to know the existence of different metals in the above-mentioned samples. Bonding nature of the bimetallic oxide samples were readied by Fourier transfer infrared (FT-IR) instrumentation. The study reviewed the varied vibrational modes confirms the phase formation of the samples. UV-Vis and thermal study of these bimetallic oxide samples are also studied extensively to know the thermal and absorption behavior respectively. TGA of both the samples are traced and are showing decomposition at rapid rate. In addition, the maximum absorption peaks due to π - π* transition confirms the sample formation. Antimicrobial activity of the prepared oxide samples was studied for antibacterial and antifungal behavior. Both samples showing considerable activity against various bacteria and fungi.

Lean manufacturing in the manufacturing sector corresponds to lean management in the service sector. When health services are considered, it is seen that the concepts that are important in the manufacturing sector have the same importance for health services. Because waste is the main problem of every sector. Lean management approach in the health sector enables improvements by developing solutions to many other problems of healthcare professionals, patients and hospitals, such as reducing transaction costs, ensuring patient safety, and reducing processing times. This study was carried out with the aim of establishing a lean health system by selecting the most appropriate lean management methodology in order to identify and prevent waste in health processes and to increase efficiency and service quality. In the study, the evaluation of lean methodologies that can be applied to prevent waste in health services is considered as a multi-criteria decision-making problem and a model has been proposed. In the proposed model, wastes were determined as research criteria and lean methodologies were determined as research alternatives. The solution phase of the problem was carried out in two stages with multi-criteria decision-making techniques. In the solution phase, the importance weights of 24 decision criteria were calculated with the SWARA method, while 5 alternatives were evaluated based on the decision criteria with the WASPAS method and the most suitable alternative was selected.

An isocratic HPLC technique was exploited and validated for the quick simultaneous separation and measurement of cefotaxime and paracetamol in vials dosage forms, with a total analysis time of 3 minutes. The process of separation was carried out on a Thermo Scientific® Venusil XBPC18 (L) (5µm, 4.6x250 mm) using a mobile phase of ACN: distilled water (70:30, v/v) at the ambient temperature. The flow rate used in the experiment was 1 mL/min, and the highest level of absorption was determined by high-performance liquid chromatography with photodiode array detection (HPLC-PDA) employing a PDA detector set at a wavelength of 255 nm. The established retention times for cefotaxime and paracetamol were 1.79 and 2.97 minutes, respectively, suggesting reduced analysis duration. The observed limits of detection for ceftaxime and paracetamol were 4.2×10-5 and 1.2×10-5 µg/mL, respectively, indicating a significant level of sensitivity in the approach. The approach was subsequently verified in accordance with the requirements set out by the Food and Drug Administration (FDA) for the quantification of medicines in vial dosage form.

Nowadays, strategic planning and the establishment of long-term objectives are pivotal for companies from diverse economies based on geographical location, demographic distribution, financial challenges, and distinct customer portfolios. Defining strategic goals is vital more than ever however requires acceptance, adoption, and execution by the entire workforce. In this sense, this paper presents an integrated methodology that combines the Balanced Scorecard and the Interval-Valued Hesitant Fuzzy DEMATEL approaches to enhance strategic management in a Medical Device SME located in Istanbul-Turkey. The proposed approach aims to provide a holistic perspective on organizational performance and facilitate decision-making processes for sustainable growth and competitive advantage.

In this work, ribonucleoside products have been prepared by employing stannic tetrachloride (SnCl4) and natural phosphate as catalysts. The obtained result suggests that this catalyst facilitates the reactions of ribonucleoside-like products in a stereo-controlled manner, exhibiting β-selectivity when reacting with trimethylsilyl uracil, the ribonucleosides derivatives were obtained in suitable yields. In addition, we have performed the molecular docking analysis, demonstrated that the synthesized compounds have potential for antiviral activity against the omicron variant of coronavirus, and the D3, D4, E and F products have a higher binding energy than the molnupiravir drug, indicating that these products may be a probable drugs for the omicron variant.

This study proposes a new scenario planning approach which consists of two main stages: evaluating scenarios under multiple criteria and selecting a manageable number of representative scenarios covering a wide range of future developments. In the evaluation stage, the interaction between criteria has been considered, which offers a significant contribution both to the scenario literature and practice. In the selection stage, a mathematical programming model has been developed to ensure the selection of distinct scenarios with high evaluation values. The approach is applied to an urban mobility system in a metropolitan area. The selected scenarios provide valuable insights into the future of urban mobility, serving as a basis for identifying strategies. The proposed approach does not offer a solution only for scenario planning problems, it can be effectively applied to similar problems in different areas.

In the new stage of promoting high-quality economic development, the effect of the transformation of development momentum and the ability of sustainable development has become the key factors for the competitiveness of provinces in China. Especially in the context of the impact of COVID-19 and the obstacles of world trade protectionism, the sustainability of development performance is increasingly important. In the past, when evaluating the development performance of various provinces in China, a single index weight was usually used. In view of evaluation criteria, the lack of consideration of regional differentiation factors would result in the evaluation results deviating from reality. This paper introduces the entropy weight method to determine the weight of regional indicators of differentiated development. Based on the space-time probability function, a grey clustering evaluation model of regional development performance is constructed to conduct a comprehensive grey evaluation of the development performance of various provinces in China from 2009 to 2019. It is found that the new evaluation model can correct the deficiencies of similar probability functions and single index weight and obtain more accurate evaluation results. It’s found that the development performance evaluation results of each province are always in a dynamic adjustment process, which needs to be verified with the help of subsequent expansion analysis.

This paper proposes a matheuristic approach for the location-routing of industrial platforms of the pineapple supply chain problem. We have proposed a three-phase methodology to solve the considered problem. The first phase consists of obtaining the potential supply in terms of suitability and productivity, the potential location of platforms, and the times of the value chain echelons. In the second phase, a mathematical optimization model for the location problem of platforms considering the coverage in terms of timing is proposed. Finally, the final phase proposes a cluster-routing and a granular reactive tabu search approach for the routing phase. The proposed methodology uses official information on production times, speed, and capacity and georeferenced aptitude, spatial, economic, and land yield information for the first time. The proposed approach has been validated through scenarios, particularly pineapple exports for the Colombian country. The obtained results show the efficiency of the proposed approach.

In the contemporaneous sustainable manufacturing scenario and fourth industrial revolutions, requirements of most cutting-edge CNC machine tools are indispensable for finished products with high accuracy, precision and green complaints in particular. Such requirements have impelled the advanced manufacturing industries to evaluate and choose the proper CNC machine tools for best customized performances. In the face of proper and effective green evaluation, this paper incorporates a heterogeneous expert group based decision framework considering multiple significant technical and green criteria by assessing relative importance of diverse conflicting criteria having substantial contribution in performance analysis of CNC machine tools. As a demonstration of the suggested mathematical model, three real life decision making problems related to 3 axes-CNC machine tools based on the collected quantitative and linguistic data from catalogues, manufacturer’s portals, questionnaires, customer reviews etc. are established. The calculated findings are close to those obtained by previous researchers as well as are verified by well-established techniques. Besides, sensitivity and statistical analysis are performed to examine the robustness and stability of the ranking orders of the alternatives as well as to investigate the efficacy and consistency of the proposed method. Hence thus proposed formulated MCDM approach proves to be a highly effective and reliable decision making tool for choosing the most suitable CNC machine tools.

Human migration is based on pull factors that individuals evaluate when it comes to moving to a different territory. Likewise, employee attrition is a phenomenon that represents the tendency to a reduction in employees within an organization. This research paper aims to develop and evaluate machine learning algorithms, namely Decision Tree, Random Forest, and Binary Logistic Regression, to predict employee attrition using the IBM dataset available on Kaggle. The objective is to provide organizations with a proactive approach to employee retention and human resource management by creating accurate predictive models. Employee attrition has significant implications for an organization's reputation, profitability, and overall structure. By accurately predicting employee attrition, organizations can identify the factors contributing to it and implement data-driven human resources management practices. This study contributes to improving decision-making processes, including hiring and firing decisions, and ultimately enhances an organization's capital. The IBM dataset used in this study consists of anonymized employee records and their employment outcomes. It provides a comprehensive HR data representation for analysis and prediction. Three machine learning algorithms, Decision Tree, Random Forest, and Binary Logistic Regression, were utilized in this research. These algorithms were selected for their potential to improve accuracy in predicting employee attrition. The Logistic Regression model yielded the highest accuracy of 87.44% among the tested algorithms. By leveraging this study's findings, organizations can develop predictive models to identify factors contributing to employee attrition. These insights can inform strategic decisions and optimize human resource management practices.