Bharathiar University

Maintaining optimal inventory levels is vital for supply chains to ensure timely supply availability, particularly during critical periods like sudden outbreaks. Traditional predictive models often fail to adapt to dynamic demand patterns, resulting in excessive costs and stockouts. This study addresses these challenges by proposing a novel fuzzy multi-criteria decision-making framework to select the optimal advanced predictive analytics for demand forecasting. Using t-spherical hesitant fuzzy sets, three decision experts evaluate alternatives against essential conflicting criteria, including accuracy, cost, adaptability, efficiency, complexity, and scalability. The logarithmic methodology of additive weights determines the relative significance of these criteria, capturing both quantitative and qualitative dimensions. The alternatives are ranked using an interactive multi-criteria decision-making approach, with AI analytics emerging as the most efficient method, followed by big data analytics in second place. The results demonstrate AI analytics’ ability to balance high accuracy, adaptability, and efficiency with manageable complexity and scalability, ensuring a cost-effective inventory strategy. Stability and robustness of the proposed model is validated through sensitivity and comparative analyses. By integrating advanced analytics with expert judgments, this study enhances demand forecasting models, enabling better inventory optimization, improved responsiveness during critical periods, and reduced operational costs. This innovative approach addresses uncertainties and paves the way for resilient supply chain management.

In this study, we explore the potential of structural vibration control for mitigating seismic hazards in civil engineering structures. The traditional control algorithms face challenges due to their mathematical complexity and the intricate dynamic behavior of structures. Therefore, to address these issues, the research proposes an Linear Quadrat ic Gaussian based Particle Swarm Optimized (LQG-PSO) semi-active control algorithm for managing the force exerted by Magneto-rheological (MR) dampers. The main goal is to enhance the structural response and stability of a benchmark space-framed structure encountered with various earthquake time histories. Additionally, the Linear Quadratic Gaussian (LQG) design employs additive white Gaussian noises as inputs to stabilize the control system and determine the ideal control force. Moreover, the proposed LQG-PSO semi-active control algorithm efficiently assesses the optimum value of weighting matrices and demonstrates superior convergence capabilities compared to other optimization techniques. In support of the proposed control methodology, numerical and experimental investigations are carried out. Also, we compare the proposed methodology with a Linear Quadratic Gaussian-based constrained binary-coded Genetic Algorithm (LQG-GA) and a passive Tuned Liquid Column Damper (TLCD) design-based controllers. The resultant outcomes depicts that constructed method performs well and visualizes the significant reductions in the top floor peak displacement, Fast Fourier Transform (FFT) and Root Mean Square(RMS) of Power Spectral Density (PSD) around 90−97% than the conventional one and LQG-GA based controller. Illustrations through the Monte-Carlo simulation conducted based on 10000 experiments confirm that the probability of occurring peak displacement less than the minimum displacement value obtained by the proposed LQG-PSO semi-active control algorithm is extremely high in all simulations compared to that of LQG-GA based controller. The study visualizes the efficacy of the configured LQG-PSO semi-active control algorithm in the way of reduced stable linear motions to a space-framed structure that behaves in a non-linear fashion due to severe environmental seismic loads.

A new nitroquinoline carbaldehyde based precursor 4‐chloro‐8‐nitro‐1,2‐dihydroquinoline‐3‐carbaldehyde (5a–c) and their thiosemicarbazone derivative (7a–d) were synthesized and characterized through respective spectroscopic techniques and theoretical studies. Computational studies using Density Function Theory (DFT) were employed to study the feasibility of formation and the Fukui analysis was explored to deduce bio‐active sites. In‐silico molecular docking showed a correlation with the binding sites as inferred from DFT. In‐silico exploration of druglikedness parameters such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis of all the synthesized compounds indicated good gastro‐intestinal absorption as well as non‐hepatotoxicity indicating their compatibility with oral administration. The selected compounds (7a and 7d) were screened for in‐vitro cytotoxic studies toward cervical cancer (HeLa) cell lines and the compound 7a which showed better activity (19.1 µm) was further selected for bio‐imaging studies. Fluorescence analysis of apoptosis shows the arrest of proliferation in a concentration‐dependent manner. Thereby, at the IC50 concentration calculated, compound 7a exhibits potent anti‐proliferative activity to be compatible for a chemotherapeutic agent.

Early prediction of cancer is crucial for effective treatment decisions. Stomach cancer is one of the worst malignancies in the world because it does not reveal the growth in symptoms. In recent years, non-invasive diagnostic methods, particularly exhaled breath analysis, have attracted interest in detecting stomach cancer. This review discusses invasive and non-invasive diagnostic methods for stomach cancer, with a special emphasis on breath analysis and electronic nose (e-nose) technology. Various analytical methods have been used to analyze volatile organic compounds (VOCs) associated with stomach cancer. Gas chromatography–mass Spectrometry is one of the most widely used techniques. These techniques enable the detection and analysis of VOCs, offering a promising route for early stomach cancer diagnosis. The e-nose system has been introduced as a cost-effective and portable alternative for VOC detection in stomach cancer to overcome the challenges associated with conventional methods. This review discusses the advantages and disadvantages of the e-nose system. This review recommends that e-nose sensors, combined with advanced pattern recognition techniques, be utilized to enable rapid and reliable diagnosis of stomach cancer.

The challenge presented by fossil fuel energy usage and rising costs of energy storage materials has spurred the development of notably inexpensive and eco‐friendly materials for energy storage. In this situation, the activated carbon derived from coconut shells through a straightforward activation process served as the active material in electrodes for environmentally conscious supercapacitors. Advanced studies using X‐ray diffraction (XRD), ultraviolet (UV) spectroscopy, field emission scanning electron microscope (FESEM), and Fourier‐transform infrared (FTIR) spectroscopy have confirmed that the activated carbon produced through this method exhibits a graphitic phase. Electrodes fabricated from this activated carbon demonstrated a specific capacitance of 8.99 F g⁻¹ in an aqueous electrolyte, utilizing expanded graphite sheets as the current collector substrates. Notably, when these electrodes were assembled with a polyethylene separator and used in a configuration that included charge collection from primary, scatter radiation, and electrolyte, they exhibited impressive storage capabilities and energy‐power handling capacities. Specifically, they achieved a specific capacitance of 16.46 F g⁻¹, an energy density of 0.57 W h g⁻¹, and a power density of 41.13 W kg⁻¹ at a current density of 1 A g⁻¹. These high‐performance values were maintained even at 30 A g⁻¹, demonstrating the potential for broad applications in energy and power storage. After irradiation, the supercapacitor reached the specific capacitance of 72.56 F g⁻¹, an energy density of 2.52 W h g⁻¹, and a power density of 181.33 W kg⁻¹ at a current density of 1 A g⁻¹. To our knowledge, this represents a novel method for creating supercapacitors using activated carbon, which is derived from scatter radiation, to enhance charge collection.

Purpose In the contemporary education system, the healthy development of children is a pivotal objective. As a result, the social well-being of children became a significant component of classroom management. The aim of the study was to explore how primary school teachers in central Kerala comprehend social well-being while managing child learners. This was strategically carried out by examining embedded conceptions about social well-being in descriptions of student management by teachers. Design/methodology/approach The data utilised in the current paper were obtained through in-depth interviews with teachers employed in government primary schools in central Kerala, India. The refined data were then subjected to manual thematic analysis. Findings Four distinct themes were identified from the data. It included social well-being as absence of disruptions, social well-being as quality relationships, social well-being as classroom belongingness and social well-being as active classroom engagement. The study asserts that teachers' conceptions were more focused on creating a well-balanced social atmosphere in the classroom, which can improve the sense of well-being among all, rather than the social well-being of individual child learners with respect to their social context. Originality/value The knowledge generated by the present study is noteworthy since it deals with the comprehension of teachers regarding social well-being in managing child learners. Further, the study delves into areas such as conceptions regarding social well-being (how), the components of social well-being teachers focus on for management practices (what) and the reasons for selecting the particular focus domains (why).

This study investigates the impact of domestic sewage wastewater irrigation on the cultivation of cassava (Manihot esculenta Crantz) from selected agrofields of Palakkad, Kerala with a comparison of well-water-irrigated M. esculenta from the same area. For this, the physicochemical parameters of respective water and soil samples were analysed. Further, to understand the post-harvest crop quality which includes the peeled tuber, peel, leaf and stem proximate composition, and mineral contents including heavy metal composition and in vitro antioxidant potential. The results of the physicochemical parameters of both domestic sewage wastewater and well-water irrigated samples (both water and soil) were observed within the permissible limits according to the WHO/FAO suggested pattern. Interestingly, heavy metals such as Zn, Cd, Cr, Cu, and Ni were within the admissible limit in respective samples from both fields. In general, when compared with well-water irrigated cassava crop samples, the antioxidant potential was significantly higher (p < 0.05) in sewage wastewater irrigated cassava samples. The present study demonstrated that domestic sewage water was found to have promising physicochemical properties that allow for safe use for irrigation as liquid organic fertilizers and the cassava crop residues from such agrofields could also be exploited as potential animal feed. Graphical Abstract

The study focuses on how to optimally handle production, maintenance, and inventory systems in segmented markets with deteriorating products. We determine the optimal circumstances to optimize productivity and maintenance by applying Pontryagin’s approach. Optimizing production rates and maintenance rates across segments is the first thing we solve when the market demand spans multiple destinations. Assuming the company can independently allocate inventory to each segment, we also solve a demand and inventory problem with multiple destinations and single-source production. Numerical solutions are provided using the Runge-Kutta method. These results provide useful information for improving segmented inventory strategies, especially in markets with varying demand and deteriorating goods.

This study aimed to assess the medicinal properties of Breynia retusa, a plant rich in phytocompounds predominantly used as an ethnomedicinal agent in Western Ghats, which appeared to be promising for therapeutic use, especially in the treatment of ovarian cancer. Herein, its cytotoxic potential on ovarian cancer cell lines SKOV-3, neurotoxicity, antioxidant activity, and molecular docking was determined to aid in explaining the mechanisms of interactions with proteins related to ovarian cancer. B. retusa methanolic extract demonstrated exuberant antioxidant activity, with 81.91% scavenging ability of DPPH radicals and efficient reduction of phosphomolybdenum (22.98 mg ascorbic acid equivalents antioxidant capacity/g extract). The extract proved to be an important anti-inflammatory agent through membrane stabilization inhibition of 83%. The cytotoxicity study against the SKOV-3 cell line indicated an IC50 value of 34.01 µg/mL and a very negligible neurotoxicity in SH-SY5Y cell lines. The GC–MS and HPLC profiling indicated many anticancer compounds in the extract such as secalciferol, methyl gallate, ricinoleic acid, gallic acid, and naringenin. The docking study showed significant interactions of secalciferol molecules with the key ovarian cancer proteins, which include IGF1 (−6.758 kcal/mol) and c-ERBB2 (−4.281 kcal/mol). Fatty acid derivatives and methyl gallate showed efficient dock scores (< −5.0 kcal/mol) with antioxidant (catalase and superoxide dismutase) enzymes and inflammatory cytokines (IL-6 and COX-1), respectively, as evidences of antioxidant and anti-inflammatory potentials. The bio-accessibility of phenolics and their antioxidant activity ranged above 90%, indicating the promising bioavailability of phytochemicals expected in vivo. Hence the current study emphasizes the anticancer potential of B. retusa phytocompounds that appeared to interact very strongly with ovarian cancer targets and confirms the dose-dependent cytotoxic and antioxidant activities of B. retusa methanolic extract.

The increasing exposure to nanoparticles raises a concern over their toxicity. Incidentally, reactive oxygen species (ROS) are produced as a result of the nanoparticle’s physicochemical characteristics and interactions with intracellular elements, primarily enzymes, leading to oxidative stress. In this context, the extent of oxidative stress resulting from the toxicity of titanium dioxide nanoparticles (TiO2-NPs) on the cardiovascular system has not yet been thoroughly investigated. Initially, the gel/label-free proteomics (nLC-HRMS/MS) method was used to examine human serum protein interaction and corona composition. Furthermore, different oxidative stress assays (superoxide, total ROS, mitochondrial ROS, and lipid peroxidation) and cell stress assays (apoptosis, ER stress, mitochondrial dysfunction, autophagy, and hypertrophy) were performed in conjunction with endothelial (rat aortic cells) and cardiomyoblast (H9c2) cell cultures. In addition, expression studies (RT-qPCR and immunofluorescence), kinase signalling, and siRNA-mediated gene knockout (NOX2 and XO) studies were conducted. Alongside, in ovo effects on the heart’s antioxidant enzymes (SOD and CAT) and metabolomic pathways (1H NMR) confirmed the involvement of oxidative stress in cardiotoxicity. The present results demonstrate a dose-dependent increase in cytotoxicity via the activation of caspase 3 and 9. The dose-dependent increase and its synergistic relationship with cardiovascular stress signalling (ET-1 and Ang-II) highlight the significant role of oxidative stress in nanoparticle toxicity. In summary, this study expands our understanding of the precise health risks associated with human exposure by establishing a connection between the role of the redox system and molecular stress pathways in TiO2-NPs-induced cardiotoxicity.

The significant challenges of rapid urbanization and population growth have led to waste generation. Currently, selecting the most appropriate solid waste disposal technique is crucial for ensuring environmental sustainability and public health protection. This study presents an extended interval-valued q-Rung Orthopair Picture Bipolar Fuzzy Set (IVq-ROPBFS) based Multi-Criteria Decision-Making (MCDM) approach for municipal solid waste disposal method selection. After a comprehensive literature review of this application, nine factors were considered: initial cost, operational cost, Transportation, Ecological risks, carbon emissions, air pollution, Feasibility, Reliability, Capacity, Efficiency, Waste recovery, and energy recovery. This study introduces IVq-ROPBFS to formulate the decision matrix, properties of IVq-ROPBFS, Extended Integrated Determination of Objective CRIteria Weights (IDOCRIW) combining CRiteria Importance Through Intercriteria Correlation (CRITIC) and Criterion Impact Loss (CILOS) to calculate the weights of the given factors. Moreover, the ranking was analyzed using the EXtended Preference Ranking Organization Method for Enrichment of Evaluations- II (EXPROM-II). The proposed model alternative ranking results were as follows: incineration (0.1227) > plasma gasification (0.1013) > bioremediation (0.0825) > composting ($-$ 0.1179) > landfill ($-$ 0.1883). The results of the proposed method were validated through fifteen cases for comparative analysis, Spearman correlation, and sensitivity analysis to demonstrate reliability and efficiency. The results show that the implementation of incineration will provide more energy recovery, volume reduction, pathogen destruction, land space conservation, and regulatory compliance, which are most appropriate for complex solid waste management landscapes.

In this work, we synthesized a novel surface-activated, bio-degradable Zein/Chitosan (CZ) composite material by subjecting it to plasma-based surface modification. The contact angle analysis showed that oxygen plasma is more efficient in modifying the composite material. FTIR analysis confirmed the incorporation of Osingle bondH functional groups in plasma treated samples with a broad peak around 3260 cm−1. Surface morphological analysis shows a rougher texture with visible voids on the plasma-treated films. Roughness values of the plasma treated CZ1, CZ2 and CZ3 were 73.50, 73.34, and 79.97 nm. OES spectrum is recorded, the species present are of pure oxygen and the electron temperature was calculated as 1.82 eV from the line-intensity ratio method. In the removal studies of Cu (II) ions, plasma-treated CZ1 showed an adsorption capacity of 195.14 mg/g, a higher value than the other samples. Pseudo-second-order kinetics provided a good fit for the experimental data. Langmuir and Freundlich adsorption isotherms provide a good fit suggesting that the adsorption is not restricted to monolayer. Thermodynamic studies show that the adsorption is spontaneous and exothermic in low temperatures. The produced surface-modified CZ promises an effective removal of heavy metals which would encourage the use of natural and sustainable materials for wastewater treatment.

Gestational diabetes mellitus (GD)-induced gut dysbiosis in pregnant mothers may increase the risk of cognitive impairment and neurological disorders in both the mother and offspring as they age. Restoring gut balance could improve cognitive outcomes for both. Despite advancements in GD treatment, side effects have increased, and long-term neurocognitive impacts on offspring born to GD mothers remain underexplored. This study uses a GD mouse model, inducing pancreatic dysfunction in 3-month-old pregnant C57BL/6J mice with Streptozotocin. The efficacy and mechanism of the prebiotic phytocompound green leaf extract (Allmania nodiflora) were assessed, with metformin as the standard. GD dams exhibited weight and glucose reduction, pancreatic IL-6 elevation, GLUT3 reduction, astroglia changes in the cerebral cortex, gut barrier impairment, cognitive impairment, and heightened anxiety compared to controls. Bacterial 16s rRNA sequencing revealed dysbiosis, with reduced Erysipelotrichales in GD dams compared to controls. Metformin lowered blood glucose levels but failed to rescue functional and behavioral phenotypes in both GD dams and offspring. Phytocompound treatment improved blood glucose, reduced pancreatic inflammation, improved gut barrier integrity, reversed dysbiosis, and enhanced brain health. It rescued behavioral deficits and improved cognitive outcomes in offspring, suggesting the prebiotic phytocompound may be a more effective therapeutic agent for GD in humans.

The study examines the relationship between inclusive finance and the demographic status of rural and tribal women in eight rural districts of Tamil Nadu. It highlights the challenges of financial exclusion caused by factors like lack of infrastructure and accessibility. Through a survey of 384 respondents, the study analyses demographic factors such as age, caste, income, education, marital status, occupation, and bank affiliation. The findings emphasize the need for tailored financial literacy programs and improved access to banking services to promote socio-economic empowerment among rural and tribal women, ensuring equitable financial inclusion across diverse groups.

The vacuole is a key cellular organelle in sugarcane, playing crucial roles in metabolism, sugar storage, and detoxification. In this study, we established a reliable protocol for isolating vacuoles from mature sugarcane stems through enzymatic digestion, employing 2.5% Cellulase R-10 and 0.4% Macerozyme R-10. Integrity and morphology were validated using neutral red and MDY-64 dyes, ensuring reliable vacuole isolation for downstream metabolomic analysis. Untargeted metabolomics of stem and vacuole using Gas chromatography–mass spectrometry (GC–MS) identified 105 metabolites, with 85 in high abundance, including 34 unique to the stem, 24 to the vacuole, and 27 shared between both compartments. Metabolites were categorized into nine groups, with hydrocarbons (alkanes), benzenoids, and lipid-like molecules being the most prominent. Differential metabolite analysis revealed significant abundance of stem and vacuole metabolites, suggesting their involvement in sucrose metabolism and vacuolar functions. Molecular docking confirmed strong interactions (docking scores < -5) between high-abundance metabolites with proteins involved in sucrose accumulation and vacuolar processes. These findings deepen our understanding of metabolite compartmentalization and their specialized functions in sugarcane, emphasizing the vacuole's role in sugar storage, membrane stability, and detoxification mechanisms.

Purpose This review aimed to evaluate the diagnostic performance of Neck Imaging Reporting and Data System (NIRADS) categories using 2-deoxy 2-(Fluorine-18) fluoro D-glucose positron emission tomography integrated with computed tomography (F-18 FDG PET/CT) to diagnose recurrence or treatment failure in the post-treatment assessment of head and neck squamous cell carcinoma (HNSCC). Methods A systematic search of the indexed medical literature was conducted till 7 November 2024 using PubMed, Scopus, Cochrane Library and Google Scholar to include studies reporting post-treatment recurrence rates on F-18-FDG PET/CT as per the NIRADS criteria. Studies were qualitatively assessed for risk of bias using the QUADAS-2 tool. We categorized patients with ‘NI-RADS≤2’ as low risk of recurrence and ’NI-RADS ≥3’ as high risk of recurrence. Diagnostic performance of NI-RADS was evaluated through weighted-pooling of standard metrics of diagnostic accuracy, diagnostic Odd’s ratio (DOR) and summary receiver operator characteristic (SROC) curve analysis. Results Out of 1632 records identified, 8 studies with 7 datasets were included, with over 1200 patients and over 1300 F-18 FDG PET/CT scans. All studies were retrospective, with presence of a risk for bias, publication bias and data heterogeneity. The time between treatment and F-18 FDG PET/CT assessment was 8–17 weeks. For combined sites (primary, nodal or distant sites), the ability of ’NIRADS ≥3’ over ‘NIRADS ≤2’ to detect a recurrence was acceptable with a pooled sensitivity, specificity, positive and negative likelihood ratios of 0.68 (95% Confidence Interval, CI 0.63–0.73), 0.90 (95% CI, 0.88–0.92), 6.01 (95% CI, 2.9– 12.6) and 0.47 (95% CI, 0.31– 0.71) respectively, with a DOR of 14.13 (95% CI, 9.78–20.42) and an area under the curve, AUC of 0.859 (Standard Error, SE -0.018) on SROC analysis Conclusion In F-18 FDG PET/CT done on post-treatment HNSCC patients, the diagnostic performance of NIRADS ≥3 categories over NIRADS ≤ 2 categories in detecting treatment failure or recurrence at combined primary, nodal or distant sites was acceptable, with a low certainty of evidence. The NIRADS categories, especially NIRADS ≥3 and NIRADS ≤2 categories should be routinely reported in post-treatment F-18 FDG PET/CT scans in HNSCC patients. (No funding received; PROSPERO No. CRD42022376017)

The utilization of herbal-based remedies for disease prevention and treatment has proved its resilience, making a substantial contribution to the progress of traditional medicine. This therapeutic approach has demonstrated the efficacy of herbal drugs and has been an enduring practice. As a routine practice, the identification of plant species relies on morphological characteristics. However, there exist scenarios where the conventional strategies are inadequate due to the insufficient amount of material available for the examination. Molecular approaches for species distinction are promising, recognizing DNA or metabolite differences among taxa as biomarkers. This review focuses on the genus Canscora, known for its wide range of pharmacological uses, including antibacterial properties, anti-inflammatory, anticancer, and antioxidant. We explore the taxonomic challenges of this genus, which is made up of synonyms and morphologically overlapping features. We highlight the utility of DNA barcodes for accurate species identification and focus on investigating the taxon-specific, high-throughput DNA barcoding methods that emphasize the efficiency in validating molecules. Our findings highlight the potential of DNA barcoding to overcome the limitations of traditional techniques. This guarantees the accurate identification of Canscora species for medical purposes. The creation of a comprehensive DNA barcode library is recommended to promote phytochemical research, increase drug awareness, and protect biological diversity.

Image processing constitutes one of the most powerful and widely used computer science-related technologies, especially in the field of medical sciences. It is frequently employed to recognize and find early signs of many different cancers, including skin cancer. Then, the segmentation of skin cancer images remains a major concern for dermatologists due to inadequate lighting and other conditions associated with image acquisition. Therefore, this study addresses a new technique of diagnosing skin cancer via multilevel thresholding with enhanced fuzzy optimization. Firstly, the technique has taken two types of skin cancer images: benign and malignant skin cancer images. Precisely, in order to determine the optimal thresholds, fuzzy optimization is utilized on skin cancer images. Further, the cancerous images are thresholded using the obtained thresholds. Furthermore, using evaluation metrics, the performance of the proposed strategy and existing well-established techniques are compared with each other in the standard analysis. Finally, the experimental findings and comparisons reveal that the addressed technique improves consistency and segmentation quality when compared to current advanced techniques.

Natural corn biomass‐based electrolytes blended with polyvinyl alcohol (PVA) have been prepared as the host matrix with various concentrations of lithium chloride as the ionic dopant. The optimized composition of the cornsilk extract (CSE)/PVA blend–0.9 g CSE + 1 g PVA has been prepared by the solution casting technique. The amorphous nature of the prepared bio‐electrolyte 0.9 g CSE + 1 g PVA + 0.5wt% LiCl (CSLC 0.5) was confirmed by the X‐ray diffraction technique (XRD). This was also supported by the differential scanning calorimetry (DSC) by the lowest glass transition temperature of 46.32 °C for the optimized CSLC 0.5 membrane. Fourier transform infrared (FTIR) spectroscopy explains the complex formation of the CSE/PVA blend with the ionic dopant lithium chloride. AC impedance analysis evidenced the maximum ionic conductivity of 2.5 × 10⁻³ S cm⁻¹ for the membrane CSLC 0.5. The open circuit voltage was observed as 1.93 V, and its discharge performance has been analyzed.