Search Results (345)

Electric motors play a decisive role in electric vehicles by converting electrical energy into mechanical motion across various drivetrain components. However, failures in these motors can interrupt the motor function, with approximately 40% of these failures stemming from bearing issues. Key contributors to bearing degradation include shaft voltage, bearing current, and electric discharge material spalling current, especially in motors powered by inverters or variable frequency drives. This review explores the tribological and vibrational aspects of bearing currents, analyzing their mechanisms and influence on electric motor performance. It addresses the challenges faced by electric vehicles, such as high-speed operation, elevated temperatures, electrical conductivity, and energy efficiency. This study investigates the origins of bearing currents, damage linked to shaft voltage and electric discharge material spalling current, and the effects of lubricant properties on bearing functionality. Moreover, it covers various methods for measuring shaft voltage and bearing current, as well as strategies to alleviate the adverse impacts of bearing currents. This comprehensive analysis aims to shed light on the detrimental effects of bearing currents on the performance and lifespan of electric motors in electric vehicles, emphasizing the importance of tribological considerations for reliable operation and durability. The aim of this study is to address the engineering problem of bearing failure in inverter-fed EV motors by integrating electrical, tribological, and lubrication perspectives. The novelty lies in proposing a conceptual link between lubricant breakdown and damage morphology to guide mitigation strategies. The study tasks include literature review, analysis of bearing current mechanisms and diagnostics, and identification of technological trends. The findings provide insights into lubricant properties and diagnostic approaches that can support industrial solutions. Full article

This study involved the fabrication of poly (vinyl alcohol) (PVA) nanocomposite films using the solution-casting process, which incorporated strontium-coated silver oxide (Sr-Ag₂O) nanoparticles generated by a plant-extract assisted method. Various characterization techniques, such as XRD, SEM, TEM, UV, and FTIR, showed the formation and uniform distribution of Sr-Ag₂O nanoparticles in the PVA film, which are biocompatible nanocomposite films. The presence of hydroxyl groups leads to appreciable mixing and interaction between the Sr-Ag₂O nanoparticles and the PVA polymer. Mechanical and thermal results suggest enhanced tensile strength and increased thermal stability. In addition, the sample of PVA/Sr-Ag₂O (1.94/0.06 wt. ratio) nanocomposite film showed decreased hydrophilicity, lower hemolysis, non-toxicity, and appreciable cell migration activity, with nearly 19.95% cell migration compared to the standard drug, and the presence of Sr-Ag₂O nanoparticles favored the adhesion and spreading of cells, which triggered the reduction in the gaps. These research findings suggest that PVA/Sr-Ag₂O nanocomposite films with good mechanical, antimicrobial, non-toxic, and biocompatible properties could be applied in biological wound-healing applications. Full article

In today’s industrial applications, cutting fluids have attained prime importance due to their all-round features, including increase of tool life by lubrication of the tool at the tool–workpiece interface. This study compares the effects of mango seed oil and pongamia oil on cutting force and surface morphology during the turning of AISI 304 steel. The design of experiments was applied using Taguchi’s method with an L9 array of experiments. During the experiment, it was discovered that mango seed and pongamia-based cutting fluid exhibited the lowest contact angles of 22.1° and 24.4°, respectively, at a 97:3 volumetric concentration of deionized water and eco-friendly oil. The use of mango seed oil as a cutting fluid with MQL (Minimum Quantity Lubrication) resulted in the lowest surface roughness of 0.809 µm, compared to 0.921 µm with pongamia-based cutting fluid. The cutting force was reduced by a maximum of 28.68% using mango seed-based cutting fluid, compared to pongamia-based cutting fluid. ANOVA analysis revealed that feed rate had the maximum influence on the optimization of output parameters for mango seed cutting fluid. For pongamia-based cutting fluid, feed rate had the maximum influence on cutting force, while the depth of cut had the strongest influence on surface roughness. Full article

This study presents a multi-modal investigation into the wear behavior of bio-based epoxy composites reinforced with multi-walled carbon nanotubes (MWCNTs) at 0–0.75 wt%. A Taguchi L16 orthogonal array was employed to systematically assess the influence of MWCNT content, load (20–50 N), and sliding speed (1–2.5 m/s) on wear rate (WR), coefficient of friction (COF), and surface roughness (Ra). Statistical analysis revealed that MWCNT content contributed up to 85.35% to wear reduction, with 0.5 wt% identified as the optimal reinforcement level, achieving the lowest WR (3.1 mm³/N·m) and Ra (0.7 µm). Complementary morphological characterization via SEM and AFM confirmed microstructural improvements at optimal loading and identified degradation features (ploughing, agglomeration) at 0 wt% and 0.75 wt%. Regression models (R² > 0.95) effectively captured the nonlinear wear response, while a Random Forest model trained on GLCM-derived image features (e.g., correlation, entropy) yielded WR prediction accuracy of R² ≈ 0.93. Key image-based predictors were found to correlate strongly with measured tribological metrics, validating the integration of surface texture analysis into predictive modeling. This integrated framework combining experimental design, mathematical modeling, and image-based machine learning offers a robust pathway for designing high-performance, sustainable nanocomposites with data-driven diagnostics for wear prediction. Full article

The growing imperative to mitigate climate change and accelerate the shift toward energy sustainability has called for a critical evaluation of heat and electricity generation methods. This article presents a comparative life cycle assessment (LCA) of solar and biogas energy systems on a common basis of 1 kWh of useful energy using SimaPro, the ReCiPe 2016 methodology (both midpoint and endpoint indicators), and cumulative energy demand (CED) analysis. This study is the first to evaluate co-located solar PV, solar thermal compound parabolic concentrator (CPC) and biogas combined heat and power (CHP) systems with in situ data collected under identical climatic and operational conditions. The project costs yield levelized costs of electricity (LCOE) of INR 2.4/kWh for PV, 3.3/kWh for the solar thermal dish and 4.1/kWh for biogas. However, the collaborated findings indicate that neither solar-based systems nor biogas technology uniformly outperform the others; rather, their effectiveness hinges on contextual factors, including resource availability and local policy incentives. These insights will prove critical for policymakers, industry stakeholders, and local communities seeking to develop effective, context-sensitive strategies for sustainable energy deployment, emissions reduction, and robust resource management. Full article

This study investigated the effects of dietary Gonadotropin-releasing hormone (GnRH) and domperidone on the reproductive performance of Devario devario during a 40-day trial. Five treatment groups received varying doses of GnRH (100, 50, 25, 12.5 µg/kg body weight) in combination with domperidone (50, 25, 12.5, 6.25 mg/kg body weight), embossed in a gel-based diet alongside a control group without the exogenous hormones. Reproductive performance was examined by measuring the gonadosomatic index, fecundity, reproductive hormone levels, and histological features of the gonads, blood parameters, and antioxidant enzyme activity. The T1 group (100 µg GnRH + 50 mg domperidone) exhibited the highest GSI in both sexes. The histological analysis of testes from T1, T2 (50 µg GnRH + 25 mg domperidone), and T3 (25 µg GnRH + 12.5 mg domperidone) groups revealed an increased presence of late-stage spermatids and spermatozoa. In females, the T2 group produced the highest proportion of advanced-stage oocytes and demonstrated the greatest absolute fecundity (1300 ± 23 eggs). However, the control group showed the highest fertilization and hatching rates. Testosterone levels were significantly elevated in the T3 group, while vitellogenin levels increased in the T1 and T2 groups. Antioxidant enzyme activity varied, with the T1 group displaying higher superoxide dismutase activity in gills and liver, and the T2 group showing increased SOD activity in muscle and brain. Improvements in haematological parameters were observed across all treatments. These results suggest that an optimal dose of 50 µg GnRH + 25 mg domperidone can enhance reproductive performance in D. devario. Full article

Epilepsy is one of the most prevalent chronic medical conditions that really can affect individuals at any age. A broader study of the pathogenesis of the epileptic condition will probably serve as the cornerstone for the development of new antiepileptic remedies that aim to treat epilepsy symptomatically as well as prevent the epileptogenesis process or regulate its progression. Cellular changes in the brain include oxidative stress, neuroinflammation, inflammatory cell invasion, angiogenesis, and extracellular matrix associated changes. The extensive molecular profiling of epileptogenic tissue has revealed details on the molecular pathways that might start and sustain cellular changes. In healthy brains, epilepsy develops because of vascular disruptions, such as blood–brain barrier permeability and pathologic angiogenesis. Key inflammatory mediators are elevated during epileptic seizures, increasing the risk of recurrent seizures and resulting in secondary brain injury. Prostaglandins and cytokines are well-known inflammatory mediators in the brain and, after seizures, their production is increased. These inflammatory mediators may serve as therapeutic targets in the clinical research of novel antiepileptic medications. The functions of inflammatory mediators in epileptogenesis are covered in this review. Oxidative stress also plays a significant role in the pathogenesis of various neurological disorders, specifically epilepsy. Antioxidant therapy seems to be crucial for treating epileptic patients, as it prevents neuronal death by scavenging excess free radicals formed during the epileptic condition. The significance of antioxidants in mitochondrial dysfunction prevention and the relationship between oxidative stress and inflammation in epileptic patients are the major sections covered in this review. Full article

Some of the most intense thunderstorms and extreme weather events on Earth occur in the Hindu Kush Himalaya (HKH) region of Southern Asia. The need to provide end users, stakeholders, and decision makers with accurate forecasts and alerts of extreme weather is critical. To that end, a cutting edge weather modeling framework coined the High Impact Weather Assessment Toolkit (HIWAT) was created through the National Aeronautics and Space Administration (NASA) SERVIR Applied Sciences Team (AST) effort, which consists of a suite of varied numerical weather prediction (NWP) model runs to provide probabilities of straight-line damaging winds, hail, frequent lightning, and intense rainfall as part of a daily 54 h forecast tool. The HIWAT system was first deployed in 2018, and the recently released model archive hosted by the Global Hydrometeorology Resource Center (GHRC) Distributed Active Archive Center (DAAC) provides daily model outputs for the years of 2018–2022. With a nested modeling domain covering Nepal, Bangladesh, Bhutan, and Northeast India, the HIWAT archive spans the critical pre-monsoon and monsoon months of March–October when severe weather and flooding are most frequent. As part of NASA’s Transformation To Open Science (TOPS), this data archive is freely available to practitioners and researchers. Full article

Large language models (LLMs) are trained on huge datasets, which allow them to answer questions from various domains. However, their expertise is confined to the data that they were trained on. In order to specialize LLMs in niche domains like healthcare, various training methods can be employed. Two of these commonly known approaches are retrieval-augmented Generation and model fine-tuning. Five models—Llama-3.1-8B, Gemma-2-9B, Mistral-7B-Instruct, Qwen2.5-7B, and Phi-3.5-Mini-Instruct—were fine-tuned on healthcare data. These models were trained using three distinct approaches: retrieval-augmented generation (RAG) alone, fine-tuning (FT) alone, and a combination of both (FT+RAG) on the MedQuAD dataset, which covers a wide range of medical topics including disease symptoms, treatments, medications, and more. Our findings revealed that RAG and FT+RAG consistently outperformed FT alone across most models, particularly LLAMA and PHI. LLAMA and PHI excelled across multiple metrics, with LLAMA showing superior overall performance and PHI demonstrating strong RAG/FT+RAG capabilities. QWEN lagged behind in most metrics, while GEMMA and MISTRAL showed mixed results. Full article

As the population increases, the growing demand for residential housing escalates construction activities, significantly impacting global warming by contributing 42% of primary energy use and 39% of global greenhouse gas (GHG) emissions. This study addresses a gap in research on lifecycle assessment (LCA) for Indian residential buildings by evaluating the full cradle-to-grave carbon footprint of a typical single-family house in Northern India. A BIM-based LCA framework was applied to a 110 m² single-family dwelling over a 60-year life span. Operational use performance and climate analysis was evaluated via cove tool. The total carbon footprint over a 60-year lifespan was approximately 5884 kg CO₂e, with operational energy use accounting for about 87% and embodied carbon approximately 11%. Additional impacts came from maintenance and replacements. Energy usage was calculated as 71.76 kWh/m²/year and water usage as 232.2 m³/year. Energy consumption was the biggest driver of emissions, but substantial impacts also stemmed from material production. Cement-based components and steel were the largest embodied carbon contributors. Under the business-as-usual (BAU) scenario, the operational emissions reach approximately 668,000 kg CO₂e with HVAC and 482,000 kg CO₂e without HVAC. The findings highlight the necessity of integrating embodied carbon considerations alongside operational energy efficiency in India’s building codes, emphasizing reductions in energy consumption and the adoption of low-carbon materials to mitigate the environmental impact of residential buildings. Future work should focus on the dynamic modeling of electricity decarbonization, improved regional datasets, and scenario-based LCA to better support India’s transition to net-zero emissions by 2070. Full article

This paper investigates the use of Al₂O₃ nano-powder-stirred micro-EDM process for generating micro-channels. This study focuses on the effect of critical machining process parameters, such as capacitance levels and nano-powder concentration, on the micro-channel fabrication performance in terms of TWR, MRR, depth, and width. A two-stage nested ANOVA is employed to understand the effect of powder concentration within different capacitance levels. The results show that the powder concentration significantly influences the system’s performance in conjunction with the capacitance. At low (100 pF) and high (1000 pF) capacitance, the addition of Al₂O₃ nano-powder increases the MRR, depth, and width but decreases TWR up to a concentration of 1.0 g/L. A desirability function analysis (DFA) tool identified the best overall performance from 14 experiments, revealing that 100 pF and 1 g/L yield the optimal outcomes. Full article

Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. Adapting anomaly detection models to new camera viewpoints or environmental variations within the same scenario remains a significant challenge. Extending these models to entirely different surveillance environments or scenarios often requires extensive retraining, which can be both resource-intensive and time-consuming. To overcome these limitations, model frameworks, i.e., the video anomaly detector model, have been proposed, leveraging the meta-learning framework for faster adaptation using swin transformer for feature extraction to new concepts. In response, the dataset named MSAD (multi-scenario anomaly detection) having 14 different scenarios from multiple camera views, is the high resolution anomaly detection dataset that includes diverse motion patterns and challenging variations such as varying lighting and weather conditions, offering a robust foundation for training advanced anomaly detection models. Experiments validate the effectiveness of the proposed framework, which integrates model-agnostic meta-learning (MAML) with a ten-shot, one-query adaptation strategy. Leveraging the swin transformer as a spatial feature extractor, the model captures rich hierarchical representations from surveillance videos. This combination enables rapid generalization to novel viewpoints within the same scenario and maintains competitive performance when deployed in entirely new environments. These results highlight the strength of MAML in few-shot learning settings and demonstrate its potential for scalable anomaly detection across diverse surveillance scenarios. Full article

The $\mathsf{\mu }$PPET [mu($\mathsf{\mu }$)on Probe with J-PET] project aims to investigate the “Muon Puzzle” seen in cosmic ray air showers. This puzzle arises from the observation of a significantly larger number of muons on Earth’s surface than that predicted by the current theoretical models. The investigated hypothesis is based on recently observed asymmetries in the parameters for the strong interaction cross-section and trajectory of an outgoing particle due to projectile–target polarization. The measurements require detailed information about muons at the ground level, including their track and charge distributions. To achieve this, the two PET scanners developed at the Jagiellonian University in Krakow (Poland), the J-PET detectors, will be employed, taking advantage of their well-known resolution and convenient location for detecting muons that reach long depths in the atmosphere. One station will be used as a muon tracker, while the second will reconstruct the core of the air shower. In parallel, the existing hadronic interaction models will be modified and fine-tuned based on the experimental results. In this work, we present the conceptualization and preliminary designs of $\mathsf{\mu }$PPET. Full article

Polyurethane (PU) is widely used due to its attractive properties, but the shift to a low-carbon economy necessitates alternative, renewable feedstocks for its production. This review examines the synthesis, properties, and sustainability of bio-based PU materials, focusing on renewable resources such as lignin, vegetable oils, and polysaccharides. It discusses recent advances in bio-based polyols, their incorporation into PU formulations, and the use of bio-fillers like chitin and nanocellulose to improve mechanical, thermal, and biocompatibility properties. Despite promising material performance, challenges related to large-scale production, economic feasibility, and recycling technologies are highlighted. The paper also reviews life cycle assessment (LCA) studies, revealing the complex and context-dependent environmental benefits of bio-based PU materials. These studies indicate that while bio-based PU materials generally reduce greenhouse gas emissions and non-renewable energy use, their environmental performance varies depending on feedstock and formulation. The paper identifies key areas for future research, including improving biorefinery processes, optimizing crosslinker performance, and advancing recycling methods to unlock the full environmental and economic potential of bio-based PU in commercial applications. Full article

The swift progression of nanotechnology has transformed the food and dairy industries through the facilitation of functional foods, nutraceuticals, and antimicrobial systems. This review examines the environmentally friendly synthesis of nanoparticles (NPs) through the utilization of microorganisms, offering a sustainable and biocompatible alternative to traditional physical and chemical approaches. This study primarily aims to investigate the contemporary trends, mechanisms, and microbial species associated with NP biosynthesis, as well as to evaluate NPs’ techno-functional applications in food and dairy processing. The specific objectives encompass analysis of the synthesis pathways—both intracellular and extracellular—utilized by bacteria, fungi, yeasts, and algae. Additionally, an evaluation of the physicochemical properties and biological activities (including antibacterial, antioxidant, and antifungal effects) of synthesized NPs will be conducted, alongside the identification of their potential applications in food preservation, packaging, and fortification. The review emphasizes notable advancements in laboratory-scale applications, especially concerning yogurt fortification, biofilm suppression, and antimicrobial food coatings. Nonetheless, commercial application is constrained by issues related to scalability, purification, stability, regulatory adherence, and toxicity evaluation. Future investigations ought to focus on enhancing bioreactor systems, leveraging microbial consortia, utilizing food and agricultural waste as substrates, and implementing omics technologies to elucidate biosynthetic mechanisms. Furthermore, the standardization of synthesis protocols and the improvement of regulatory frameworks will be crucial in closing the divide between experimental achievements and NPs’ application in industry. In a nutshell, the microbial-mediated green synthesis of NPs offers a promising pathway for the advancement of safe, sustainable, and functional innovations within the food and dairy sectors. Full article