Small and Medium Enterprises (SMEs) play a crucial role in the Indian economy. To remain competitive in the global market, application of Lean Manufacturing Techniques (LMT) helps SMEs to improve their processes in alignment with customer needs. The purpose of this study is to reduce overall lead time by identification and eliminating non-value added activities (NVA) from the manufacturing system. In this paper, the current manufacturing processes have been thoroughly studied using the principle of work study. Several lean approaches such as- TAKT time computation, Value stream Mapping (VSM), layout optimization, Kanban, Andon etc. have been used to make improvement in line efficiency and hence productivity. The study shows that use of line balancing technique, matches the cycle time with calculated TAKT time. And the efficiency of the line has increased approx 10% and hence balance delay got reduced. Again VSM helps to reduce the lead time of foundry shops by 51 minutes. It is one of the few studies that deal with productivity improvement in Indian SME.

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.

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.

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.

The present paper proposed the tri-potassium phosphate catalyzed synthesis of some novel series of Schiff bases (3a to 3l) derived from 2-aminopyridine using microwave technique. All synthesized Schiff bases were confirmed by different spectroscopic methods. Further all synthesized imines have been evaluated to screen for antimicrobial, antioxidant and anti-inflammatory activity. The compound 3c displays potent anti-inflammatory activity with 42.307%±1.322 inhibition of inflammation. The antioxidant activity data revealed that all synthesized Schiff bases exhibit potency to scavenge DPPH and nitric oxide radicals. Antimicrobial properties of these compounds showed moderate to excellent growth of inhibitory activity against various tested pathogens. Our observations and examination for pharmacological study towards the presence of active pharmacological functional groups present in the main structural nucleus of synthesized imines. Therefore, present study may lead to the development of a new class of anti-inflammatory and antioxidant drugs with structural modification.

A series of new pyrrole-coupled carboxamide derivatives had been synthesized and amply characterized by various techniques, viz FTIR, 1H-NMR, 13C-NMR, and LCMS. All these compounds had undergone evaluation for their PASS, BBB, pharmacophore model, ADME, and bioactive score. The antibacterial properties of all the derivatives were examined and compound 5i demonstrated strong antibacterial activity against MRSA. A membrane damage investigation supported by SEM pictures, cellular content leakage, potassium efflux, and bacterial respiration suppression showed that compound 5i had anti-MRSA properties. A good binding score of -9.11 was found for compound 5i against the MRSA protein 6FTB in an in silico molecular docking analysis, compared to a score of -10.25 for streptomycin. Based on these findings, compound 5i can be further studied in order to develop it as a drug to treat MRSA infections.

The pandemic COVID-19 has been spreading around the globe from December 2019 onwards and is considered the most infectious disease of this century. To date, there is no effective drug against SARS-CoV-2 discovered by pharmaceutical scientists, and the research is going rigorously all over the world. In this work, we examined the interaction of the already existing antivirals (Lopinavir, Atazanavir, and Remdesivir) with the structural proteins of SARS-CoV-2 using computational methods. Pharmacophore modeling of these drugs was conducted using molecular databases to determine the lead compounds from molecular databases. Pharmagist Webserver and Zinc Molecular Database were used to find out the pharmacophore and lead compounds, respectively. The drug-likeness properties of the compounds were evaluated by the SwissADME webserver. In silico studies showed that the binding affinities of the drugs followed the order Remdesivir > Atazanavir > Lopinavir. Docking and pharmacological studies revealed the potency and drug-likeness of the synthetic molecules.

The study aimed to explore the anticancer efficacy of indazole pharmacophore by analyzing a series of 109 derivatives of indazole as Tyrosine Threonine Kinase (TTK) inhibitors through quantitative activity relationship analysis using 2D and 3D QSAR techniques. The best 2D-QSAR model was generated by the MLR method, showing a high correlation coefficient (r²) of 0.9512, and good internal (q²), and external (pred_r²) cross-validation regression coefficients of 0.8998, and 0.8661, respectively. The residual values were modest, indicating good agreement between the observed and predicted pIC50 values, which suggested that the chosen model was predictably accurate. The 3D QSAR model, built using the SWF kNN approach, displayed a high internal cross-validation regression coefficient (q²) of 0.9132. Essential structural features/considerations in developing indazole as prospective anticancer medicines have been suggested. The study provides a reliable and predictive model for the prediction of anticancer activity of indazole derivatives. The identified essential structural features/considerations may be useful for the development of prospective anticancer medicines.

In this research, the photocatalytic effect was studied for sol-gel synthesized Mn/Mg co-doped metal oxide (TiO2) nanoparticles (NPs), which are encapsulated with anionic Gemini surfactant. The physicochemical properties of Mn/Mg co-doped TiO2 were investigated using a variety of characterization techniques, FT-IR, UV Vis-DRS, XRD, SEM, EDX, TEM, BET and PL. Characterization results reveal that Mn/Mg dopants stabilize TiO2 in the form of anatase phase and reduce the bandgap energy. Presence of strong chemical bonding and functional groups at the interface of dopant and TiO2 NPs was confirmed with FT-IR. The photocatalytic activity of these catalysts was assessed by the degradation of Orange II (AO7) using visible light irradiation. Of the variable nanomaterials MMT5-GS2 showed remarkable results. The photoluminescence studies revealed that OH radicals are the reactive species and responsible for oxidative photocatalytic degradation of Orange II.