This study bridges the gap between Corporate Social Responsibility (CSR) principles and machining optimization, focusing on CNC turning operations for AISI 304 stainless steel. As industries increasingly prioritize sustainability and responsible production, this research aims to optimize machining parameters cutting speed, depth of cut, feed rate, and lubricant quantity to enhance operational efficiency and environmental stewardship. The optimization targets improved production metrics and key CSR factors, such as reducing energy consumption, minimizing material waste, and promoting worker safety. The integration of CSR into machining is achieved by aligning environmental impact metrics (e.g., energy consumption, tool wear, and waste reduction) with performance indicators like Material Removal Rate (MRR) and surface roughness (Ra). By optimizing these parameters using the Taguchi method, the study demonstrates how sustainable machining can simultaneously lower environmental footprints and enhance production outcomes. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to predict machining performance, with minimal deviation between predicted and experimental results, confirming the model’s accuracy. The optimal settings obtained through single-objective optimization are: MRR- cutting speed: 45 m/min, depth of cut: 0.4 mm, feed rate: 0.65 mm/rev, lubricant quantity: 0.6 ml/sec; Surface Roughness (Ra)- cutting speed: 65 m/min, depth of cut: 0.6 mm, feed rate: 0.35 mm/rev, lubricant quantity: 1 ml/sec; Cutting Force (CF)- cutting speed: 35 m/min, depth of cut: 0.4 mm, feed rate: 0.20 mm/rev, lubricant quantity: 1 ml/sec. This study presents a novel approach to integrating CSR-driven sustainability into machining processes, offering a framework where operational efficiency and social responsibility reinforce each other in modern manufacturing environments.

This paper explores the thermal and hydrodynamic characteristics of a GO–SWCNT–MWCNT/H2O ternary nanofluid when passing through a stretching sheet under steady and unsteady flow with the consideration of magnetic field and thermal radiations. Similarity variables are used to transform the governing nonlinear partial differential equations, which are solved in the finite element method (FEM). The effect on the velocity, temperature, concentration, and skin-friction coefficient, Nusselt number, and Sherwood number of nanoparticle volume fractions, magnetic parameter, Prandtl number, and Schmidt number is examined. It has been found that when nanoparticle volume fractions are increased, thermal performance is greatly increased, and that the thermal boundary layer is observably thickening. Growth of the magnetic parameter decreases velocity owing to the effects of Lorentz forces and increases temperature owing to the improvements in Joule heating. With increasing Prandtl number, the thickness of the thermal boundary layer is decreased and the rate of convective heat transfer is enhanced. The models of artificial neural network (ANN) are applied to gain better predictive power through the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms on FEM-generated data. BR model has a higher predictive accuracy as its overall regression coefficient is R = 0.92195 than the LM model with regression coefficient R = 0.61285. The results give a quantitative understanding of how ternary nanofluid systems may be optimized to serve advanced thermal management systems in industrial cooling systems and energy systems.

Food safety risk assessment is a complex multi-criteria decision-making (MCDM) issue characterized by high uncertainty in both data and expert opinions. Traditional MCDM methods struggle to effectively manage this uncertainty and subjectivity. This paper extends the multi-attributive ideal-real comparative analysis (MAIRCA) method to an uncertain decision-making environment by embedding it within a cubic Pythagorean fuzzy (CuPyF) framework, integrating a variation coefficient similarity measure (VCSM) and the rank-sum (RS) method. Cubic Pythagorean fuzzy sets (CuPyFSs) are used to represent both precise and interval-valued information, enabling better uncertainty modeling. The proposed VCSM objectively determines criteria weights, while the RS method provides subjective weights, leading to balanced comprehensive weighting. The extended CuPyF-based MAIRCA method is then applied to rank alternatives and select the optimal solution. A food safety case study validates the model, demonstrating that it delivers stable, discriminative, and interpretable results, outperforming traditional MCDM models and offering policymakers a reliable and scientific tool for food safety risk management.

This research work utilizes machine learning, and taguchi method to optimize and predict the tensile performance of short carbon fiber reinforced polyamide (CF-PA) 3D printed by fused filament fabrication (FFF) technique. The process parameters play a major role in making novel material with help of FFF technology. In this research work, tensile samples are 3D printed based on different FFF process parameters, such as raster orientations, printer speed, and layer height as per ASTM D3039. While analysis of variance (ANOVA) confirms the statistical dominance of raster orientation (48.8

A flow boiling examination of water along with a nanofluid composed of water-Al2O3 flowing through an annulus has been carried out. The examination was carried out for the mass flux, pressure, and heat flux. The volumetric concentration of Al2O3-water nanofluid was constant at 0.10 %. Experimental results were validated with Chen correlation which showed good agreement. Results demonstrated that the heat transfer coefficient of both water-Al2O3 nanofluid and water enhanced with the enhancement in pressure, heat flux, and mass flux. Water-Al2O3 nanofluid showed better performance than water and the best heat transfer coefficient was observed for water-Al2O3 nanofluid at pressure 1.5 bar, heat flux 144 kWm-2, and mass flux 1,015 kgm-2s-1. A linear heat transfer coefficient correlation was generated for water-Al2O3 nanofluid. This research created the XGBJSO machine learning model (XGBoost with Jelly Fish Search Optimizer), which showed remarkable predicted accuracy for the heat transfer coefficient of Al2O3 nanofluid in water. A high R2 score of 0.99986 for the training set and 0.98453 for the testing set, showed a significant correlation between predicted and actual values. The XGBJSO framework offers a scalable and adaptable solution for thermal system optimization. This work advances the field of heat transfer prediction and provides a foundation for future research in thermal management.

Metal oxide-metal organic framework (MO@MOF) composites have emerged as a versatile class of hybrid materials in which controlled interfacial architecture governs structure-property-performance relationships. In these systems, MOFs function not only as porous hosts but also as structural templates and precursors for generating metal oxides with tailored dispersion and defect chemistry. This review systematically examines synthetic strategies for constructing MO@MOF composites, including in situ growth, post-synthetic incorporation, and MOF-derived conversion routes, with particular emphasis on how these approaches regulate interfacial bonding, electronic coupling, and structural stability. Rather than focusing on a single application, the discussion highlights the central role of interface engineering in enhancing adsorption, photocatalysis, electrocatalysis, and energy storage performance. Key interfacial phenomena such as heterojunction formation, charge-transfer pathways, defect modulation, and nanoparticle confinement are critically analyzed to establish clear structure-interface-function correlations. Current challenges, including pore blockage, interfacial degradation, and scalability limitations, are also addressed alongside emerging design strategies for robust interface control. By consolidating recent advances in interface-mediated material design, this review provides fundamental insights and practical guidelines for developing high-performance MO@MOF composites for sustainable energy and environmental technologies.

In this study, reduced graphene oxide (RGO)-decorated Co3O4 nanorods were synthesized via a hydrothermal method and characterized for supercapacitor applications. XRD, FTIR, Raman, SEM, HRTEM, and XPS analyses confirmed the successful formation of pure-phase cubic Co3O4 and the effective integration of RGO. Among the samples, Co3O4@180 °C showed the highest crystallinity and was selected for RGO decoration. The RGO/Co3O4@180 °C nanocomposite exhibited superior electrochemical performance due to synergistic effects between the pseudocapacitive Co3O4 nanorods and the high conductivity of RGO. Cyclic voltammetry and galvanostatic charge–discharge tests revealed a specific capacitance of 710 F/g at 10 mV/s and 681 F/g at 2 A/g for the RGO/Co3O4@180 °C electrode. The enhanced performance is attributed to improved electron transport, higher surface area, and better electrolyte accessibility provided by RGO.

Nanostructures of CoAl2O4 and RGO/CoAl2O4 were synthesized via a hydrothermal method and evaluated using various characterization techniques, including XRD, FTIR, FE-SEM, EDAX, HRTEM, XPS, and CV. XRD analysis revealed that the samples exhibit a cubic crystal structure with an Fd3m space group. The average crystallite sizes of CoAl2O4 nanoparticles synthesized at 6 h and 24 h were determined to be 25 nm and 36 nm, respectively, while the RGO/CoAl2O4 composite exhibited a crystallite size of 51 nm. FTIR spectra indicated a gradual shift and intensification of absorption bands upon incorporating RGO into CoAl2O4. Morphological analysis using FESEM and HRTEM confirmed the formation of porous cubic-shaped nanostructures integrated with RGO nanosheets. Elemental composition was identified through EDAX, while XPS provided a detailed analysis of the chemical states. The CoAl2O4 sample exhibited a maximum specific capacitance of 245 Fg− 1, whereas the RGO/CoAl2O4 nanocomposites achieved a significantly higher value of 410 Fg− 1 at a scan rate of 5 mV s− 1. The antibacterial and antifungal activities of the samples illustrated that the RGO/CoAl2O4 composite demonstrated higher inhibition zones compared to CoAl2O4, indicating enhanced biological efficacy. Overall, RGO/CoAl2O4 composites exhibited improved electrochemical performance for supercapacitor applications and greater biological activity against bacterial and fungal pathogens than their CoAl2O4 counterparts.

Abstract Transmissible hospital-acquired infections (HAIs) arise from complex, time-varying interactions among patients, healthcare workers, and clinical environments. Although data-driven approaches like graph neural networks (GNNs) effectively model these contacts, they often function as black boxes that over-look established epidemiological principles, limiting interpretability and clinical trust. Inspired by physics-informed neural networks, we propose a epidemiology-informed GNN (EIGNN) framework for patient-level state transitions prediction in dynamic hospital settings, integrating mechanistic epidemiological models into GNNs in a principled manner. Patient-level risk factors learned from dynamic contact networks are jointly leveraged to infer latent epidemiological states, predict state transitions across multiple horizons, and estimate key epidemiological parameters, including transmission and recovery rates. We evaluate the approach on a real-world hospital-onset COVID-19 cohort and two public datasets simulating viral and bacterial HAIs. Across multiple architectures and horizons, EIGNNs achieves AUC-ROC up to 98.46% while providing interpretable, mechanistically consistent insights, offering a transparent tool for infection prevention and control.

Despite the advancements achieved in chemotherapy, cancer continues to remain a formidable and lethal global threat, ranking as the second leading cause of death worldwide. The development of chemoresistance poses a significant hurdle in cancer treatment. Nonetheless, a therapeutic strategy known as chemosensitization has emerged to counteract cancer cell resistance, wherein the efficacy of one drug is augmented by another. Accumulating evidence suggests that natural products have attracted considerable attention in the cancer therapeutic realm due to their ability to combat multidrug resistance with minimal side effects. Ginsenosides, triterpene saponins extracted from Panax ginseng, have demonstrated significant anticancer activity while exhibiting relatively low toxicity and reduced adverse effects. Co-administration of ginsenosides with chemotherapeutic drugs has been shown to trigger apoptosis, as evidenced by an increased Bcl-2-associated X protein (Bax)/B-cell lymphoma 2 (Bcl-2) ratio, inhibit angiogenesis through suppression of vascular endothelial growth factor (VEGF); and hinder replicative immortality by downregulating stemness-associated markers such as octamer-binding transcription factor 4 (Oct4), Nanog, and sex determining region Y-box 2 (SOX2) in various cancers. Additionally, ginsenosides modulate key chemoresistance pathways, including nuclear factor-kappa B (NF-κB), signal transducers and activators of transcription (STAT), and phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt), as well as their downstream targets, thereby rendering cancer cells more susceptible to chemotherapy. Notably, ginsenosides have been shown to modulate the tumor microenvironment and mitigate the side effects associated with chemotherapeutic drugs. This review aims to consolidate findings from preclinical and clinical studies to elucidate the role of ginsenosides as effective chemosensitizing agents.

Rice straw represents a plentiful agricultural by-product that remains largely underexploited, particularly for composite reinforcement, due to poor fiber-matrix interactions and its high amorphous fraction. In this study, environmentally benign surface modification strategies were explored to ensure better mutual performance of rice-straw grains with an epoxy pattern. Four activation approaches were evaluated: ultrasonic treatment (P1), ultrasonic assisted with sodium carbonate (P2), plasma exposure (P3), and a combined Na2CO3-plasma sequence (P4). Fibers were processed using 5% w/v Na2CO3 solution and low-pressure plasma at 13.56 MHz, followed by fabrication of epoxy composites. The materials were examined through several analytical methods, including flexural evaluation (ASTM D790), FTIR spectroscopy, SEM-EDX imaging, XRD diffraction, TGA/dTG thermal analysis, and BET surface analysis. An overall enhancement in mechanical characteristics was detected as the degree of treatment increased. Sample P1 had a flexural durability of approximately 109.1 MPa, while sample P4 showed elasticity at 162.0 MPA and the range of modalities in terms of their articulation was expanded by 5.625 GPa (passive modulus) from 3.709 GPa when tested against other methods. SEM micrographs revealed remarkable surface alterations, such as a 131% rise in micro-texture roughness (from 0.344 to 0.796), resin deposition reaching 90.8%, a 72% decline in pore or void fraction (from 3.319% to 0.917%), and an 84% reduction in silica or ash residues. XRD profiles showed more pronounced cellulose-I reflections at 15.7°, 22.6°, and 34.6°, alongside the suppression of the amorphous halo (18-20°), signifying increased crystallinity, particularly in P4 fibers. TGA results demonstrated reduced char residue and higher, sharper Tmax peaks, confirming improved thermal stability. Among the treatments, the Na2CO3-assisted plasma approach (P4) provided the most substantial enhancement, offering a scalable and sustainable method to upgrade rice-straw fibers for structural composite applications.

Additive manufacturing offers a cutting-edge technique for producing customized implants tailored to individual patients. In this study, novel three-dimensional (3D) scaffold was fabricated from oxidized agarose reinforced with polyethylene glycol by direct ink writing technique. Agarose was chemically modified to agarose dialdehyde using sodium meta periodate to enhance the functionality with other polymers. The ink blend was tuned at different ratios for better printability, layer fidelity, and viscosity. The silver-doped mesoporous bioactive glass nanoparticles were incorporated into the ink blend to provide biological properties. The fabricated scaffold underwent in-vitro conditions aimed at evaluating potency for wound healing application. The engineered 3D scaffold crosslinked in a calcium chloride bath, lately confirmed via fourier transform infrared spectroscopy. The interconnected porous structure delivered through crosslinking provides the essential swelling similar to 250 %, which is promising for cell (Human Dermal Fibroblasts) proliferation and adhesion. The developed 3D matrix degrades similar to 65 % in phosphate buffer saline , facilitating the controlled release of silver ions similar to 1.05 mg/mL, confirmed via inductively coupled plasma optical emission spectroscopy. The released silver ions are potent for bactericidal efficacy against Staphylococcus aureus and Escherichia coli examined through turbidity tests. Herein, the study offers a promising biomimetic 3D network to provide vital hydrophilicity, biological efficacy, and controlled ions release, which makes a PEG/OAG/Ag-MBGNs scaffold a suitable candidate potentially for wound healing applications.

BACKGROUND:As coronavirus disease 2019 (COVID-19) is integrated into existing infectious disease control programs, it is important to understand the comparative clinical impact of COVID-19 and other respiratory diseases. METHODS:We conducted a retrospective cohort study of patients with symptomatic healthcare-associated COVID-19 or influenza reported to the nationwide, hospital-based surveillance system in Switzerland. Included patients were adults (aged ≥18 years) hospitalized for ≥3 days in tertiary care and large regional hospitals. Patients had COVID-19 symptoms and a real-time polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection ≥3 days after hospital admission between 1 February 2022 and 30 April 2023, or influenza symptoms and a real-time polymerase chain reaction-confirmed influenza A or B infection ≥3 days after hospital admission between 1 November 2018 and 30 April 2023. Primary and secondary outcomes were 30-day in-hospital mortality and admission to intensive care unit, respectively. Cox regression (Fine-Gray model) was used to account for time dependency and competing events, with inverse probability weighting to adjust for confounding. RESULTS:We included 2901 patients with symptomatic, healthcare-associated COVID-19 (Omicron) and 868 patients with symptomatic, healthcare-associated influenza from 9 hospitals. We found a similar case fatality ratio between healthcare-associated COVID-19 (Omicron) (6.2%) and healthcare-associated influenza (6.1%) patients; after adjustment, patients had a comparable subdistribution hazard ratio for 30-day in-hospital mortality (0.91; 95% confidence interval, .67-1.24). A similar proportion of patients were admitted to the intensive care unit (2.4% COVID-19; 2.6% influenza). CONCLUSIONS:COVID-19 and influenza continue to cause severe disease among hospitalized patients. Our results suggest that in-hospital mortality risk of healthcare-associated COVID-19 (Omicron) and healthcare-associated influenza are comparable.

This study examined the mechanical, acoustic, and microstructural performance of epoxy composites reinforced with Snake Grass Fiber compression molding with a constant SGF content of 30 wt% and varying hybrid filler contents (5 to 15 wt%). Mechanical properties including tensile, flexural, compressive, impact strength, hardness, and water absorption were evaluated alongside sound absorption behavior. The incorporation of hybrid fillers significantly improved mechanical strength, surface hardness, and dimensional stability while reducing moisture uptake compared to SGF-only composites. The optimized hybrid composition exhibited superior properties, achieving tensile, flexural, compressive, and impact strengths of 58 MPa, 87 MPa, 70 MPa, and 8.98 J, respectively, with a hardness of 84 Shore D and reduced water absorption of 23%. Acoustic analysis revealed enhanced sound absorption, with a maximum absorption coefficient of 0.24 at an optimal filler-to-fiber ratio, attributed to synergy between fibrous reinforcement and porous fillers. SEM analysis confirmed uniform filler dispersion, improved interfacial bonding, and reduced voids, supporting the observed mechanical and acoustic enhancements. SGFbased hybrid agro-waste composites offer improved structural and soundabsorbing performance, making them suitable for sustainable automotive, construction, and acoustic insulation applications.

In the past five years, the COVID-19 epidemic has created unparalleled difficulties throughout the world. The COVID-19 virus has not entirely disappeared all over the world, and it is still spreading with new transformations and new variants throughout the globe. Hence, the requirement of exact and appropriate risk prediction can emphasize the care of patients and effectively allocate the resources for improving their lifespan. The prediction of risk factors for COVID-19 is highly needed for minimizing these hazards. As a result, this work develops the Fuzzy-Deep Kronecker Network + Deep Recurrent Neural Network (Fuzzy-DKRNN)-based Covid-19 risk prediction model. The data normalization is the primary stage of this process. The log scaling is utilized to provide the normalized data effectually. For selecting the significant features, the hybrid similarity measure is employed. Moreover, the Fisher score with Pearson’s Correlation Coefficient (PCC) is considered as the hybrid similarity measure. The proposed Fuzzy-DKRNN model effectively performs the COVID-19 risk prediction. In addition, the accuracy, precision, recall, and F1-Score are utilized to validate the Fuzzy-DKRNN with the ideal values like 0.908, 0.900, 0.918, and 0.890 are attained.

Industrial Wireless Sensor Networks (IWSNs) play a vital role in today’s Industrial Internet of Things (IoT) systems. The existing Enhanced Energy Optimization Model has made progress in improving energy use; however, it still faces issues with adapting to changing network traffic, slow clustering convergence, and limited optimization capabilities. To address these problems, this paper presents a hybrid intelligent framework that improves energy savings, clustering convergence, and adaptive network optimization in IWSNs. The new model combines Enhanced Aphid–Ant Mutualism Optimization (EAAM) for efficient clustering, an Improved Artificial Bee Colony (I-ABC) algorithm for ongoing energy parameter adjustments, and a Graph Neural Network (GNN)-based predictor for adapting to changing topologies and traffic conditions. EAAM provides fair cluster-head selection and minimizes control overhead, while I-ABC adjusts transmission power and duty cycles to reduce energy loss. The GNN module captures spatial correlations between sensor nodes to provide proactive energy prediction and adaptive reconfiguration, with lower training overhead than deep reinforcement learning. Experimental results on benchmark IWSN datasets verify that the proposed EAAM–I-ABC–GNN framework exhibits 18%–25% better overall energy efficiency, a 22% increase in network lifetime, and 15% improved clustering stability compared to the baseline EEOM and traditional hybrid methods. These findings validate that the proposed hybrid model substantially improves scalability, flexibility, and sustainability, providing an efficient and intelligent solution for future Industrial IoT networks.

Present study investigates the possibilities of the effect of fillers (coconut and eggshell) on the fatigue, creep and tribological (wear and friction) performance of fibers (jute and flax) reinforced epoxy composites. The fatigue test results revealed that the Flax/Epoxy/Coconut Shell Powder composite exhibited the highest fatigue life and achieved maximum fatigue cycles at different ranges of the ultimate tensile strength (UTS), respectively, while the creep analysis demonstrated superior dimensional stability for the same composite. Frictional analysis revealed that all composite specimens exhibited maximum friction force values at an applied load of 50 N and a sliding speed of 5 m/s. The coefficient of friction was between 0.29 and 0.85 at a 3 m/s sliding speed and a 30N load for all the developed samples. Wear test results showed that a minimum SWR of 3.82 mm(3)/N-mm was achieved by JEE composite while maximum SWR was 15.08 mm(3)/N-mm by pure epoxy sample at 3 m/s sliding speed and 10 N applied load. JEC composite achieved the highest interfacial temperature among all prepared specimens at 15 degrees C, 36 degrees C, and 65 degrees C for 10, 30 and 50 N applied load at 5 m/s sliding speed. The Scanning electron microscopy appears in the presence of wear out surfaces after tribological test and helps to identify the failure mechanism after tribological performance. The morphological analysis of the FEE and FEC composites uncovered lengthened fracture patterns and broad matrix surface breakage. Additionally, fiber debonding and sliding during tribological analysis led to significant matrix damage.

Abstract The use of textile fibre-reinforced composite materials for many alternative applications has significantly increased in the twenty-first century due to their lightweight nature and high strength-to-weight ratio. In this study, false banana fibres were used as reinforcement and unsaturated polyester resin as the matrix. The optimal ratio of fibre to matrix was established through an analysis of physico-mechanical parameters, including tensile, compressive, and flexural strengths, water absorption, and void fraction, utilising Design Expert software. Additionally, deformation, Von Mises stress, Von Mises strain, and velocity were analyzed using ANSYS simulation software. The composite exhibited water absorption of 1.5% over 24 to 48 h, a void fraction of 1.02%, a tensile strength of 33.15 MPa, a compressive strength of 29.69 MPa, and a bending or flexural strength of 28.85 MPa. Furthermore, the ANSYS results showed a maximum deformation of 0.60887 mm, a maximum equivalent elastic strain of 0.0018815, a minimum value of 1.0375 × 10–10, a maximum equivalent stress of 22.27 MPa, a minimum of 1.3877 × 10–5 MPa, and a velocity streamline of 14.97 m/s at 21 rad/s. The simulated stresses were well below the material’s measured strength limits, indicating a safe design under the analysed conditions. The weight of the developed composite blade was 31% lower than that of a conventional aluminum blade.

The utilization of mineral water bottle waste as raw material in the 3D printing process using PET (Polyethylene Terephthalate) filament has the potential to reduce the environmental impact of plastic waste and support the development of sustainable manufacturing technology. This study aims to determine the effect of nozzle temperature in 3D printing using PET filament made from mineral water bottle waste on the physical and mechanical properties of the products produced. This study was conducted by varying the nozzle temperature of 3D printing (250, 255, and 260). To evaluate the properties of the printed objects, tensile, hardness, compressive, and dimensional compliance tests were conducted. The results showed that the nozzle temperature significantly affected the physical and mechanical properties of the 3D printed products. Different nozzle temperatures cause variations in the structure between layers, with higher temperatures tending to produce products with better mechanical properties. Increasing nozzle temperature also has a positive impact on product dimensional accuracy, density, tensile strength, hardness, and compressive strength. However, the best temperature that produced the best balance between physical and mechanical properties was 255 °C. In addition, this research contributes to the development of sustainable manufacturing technology by utilizing plastic water bottle as an alternative raw material in the production process.

Methicillin-susceptible Staphylococcus aureus (MSSA) clonal complex 398 (CC398) bloodstream infections (BSI) have been increasingly reported and were previously suggested to be associated with increased mortality. We conducted a retrospective cohort study of hospitalised patients with MSSA BSI at Geneva University Hospitals between 2021 and 2023. Among 294 MSSA BSI, 57 (19