Search Results (140)

The gear transmission system is the core component in industrial equipment, and its wear state directly affects the reliability and use life of equipment. The wear debris image contains important information on the mechanical wear state. By processing it and analyzing the characteristics and types of wear debris, the health status of mechanical equipment and components can be evaluated. However, wear debris images collected in real time are often affected by Gaussian noise. The improved K-SVD dictionary learning algorithm was used in this paper to remove Gaussian noise, using objective metrics to demonstrate the effectiveness of the improved K-SVD algorithm for wear debris images. Secondly, the improved marked watershed segmentation algorithm (B-FSL) was studied to segment the wear debris chains. After that, the boundary energy (BE) characteristics of the wear debris were extracted to warn about the severe wear state of equipment in advance, an EfficientNetB3 network based on transfer learning was constructed for the recognition and classification of the wear debris image, and the severity of the wear of the mechanical equipment was analyzed. Finally, an experiment was conducted to validate the above methods, proved that the BE characteristics of the wear debris can predict the failure of a planetary gearbox in advance, with the accuracy of the wear debris recognition and classification algorithm exceeding 98%. Full article

This study examines the seismic stability of an underground powerhouse cavern located in the Lesser Himalayan region of Nepal. Both static and seismic loading conditions are analyzed using the finite element method (FEM) and the distinct element method (DEM). Rock mass properties are derived from field investigations and laboratory testing, while empirical correlations are applied to estimate rock mass strength and deformation modulus. Pseudo-static analyses are performed using the FEM-based software Rock and Soil-2-Dimensionsl (RS²) Version 11.027, and dynamic analyses are conducted using the DEM-based software Universal Distinct Element Code (UDEC) Version 5.0 to evaluate deformation and stress redistribution around the cavern. Seismic fragility curves are developed to quantify the probability of damage under varying seismic intensities. Results indicate that a peak ground acceleration (PGA) of 0.25 g increases cavern wall deformation by approximately 15–20 mm compared to static conditions. Fragility analysis shows a probability exceeding 68% for slight damage, while the probability of collapse remains low at approximately 1.7%. Seismic loading also significantly alters stress redistribution along the cavern boundary. Overall, the combined use of numerical modeling and fragility analysis provides a probabilistic framework for assessing seismic risk in underground caverns, offering valuable insights for the design and safety evaluation of hydropower projects in seismically active Himalayan regions. Full article

Background/Objectives: Antimicrobial resistance (AMR) surveillance is a cornerstone of national AMR strategies but requires sustained, cross-sectoral financing. While the need for such financing is well recognized, its quantification remains scarce in low- and middle-income countries. This study aimed to estimate the full costs of AMR surveillance across the human health, animal health, and food sectors (2021–2030) in selected facilities in Nepal and generate evidence to inform sustainable financing. Methods: A bottom-up micro-costing approach was used to analyze data from five sites. Costs were adjusted for inflation using projected gross domestic product deflators, and probabilistic sensitivity analyses were conducted to assess uncertainty in laboratory sample volumes under four scenarios. Results: The total cost of AMR surveillance in Nepal was $6.7 million: $3.4 million for human health (50.3% out of the aggregated costs), $2.7 million for animal health (39.8%), and $0.7 million for the food sector (9.9%). Laboratories accounted for >90% of total costs, with consumables and personnel as the main cost drivers. Average cost per sample was $150 (animal), $64 (food), and $6 (human). Conclusions: This study offers the first robust, multi-sectoral 10-year cost estimates of AMR surveillance in Nepal. The findings highlight that sustaining AMR surveillance requires predictable domestic financing, particularly to cover recurrent laboratory operations as donor support declines. These results provide cost evidence to support future budgeting and policy planning toward sustainable, nationally financed AMR surveillance in Nepal. Full article

Landslide susceptibility mapping is crucial for landslide risk management in mountainous areas like Nepal. However, the performance of a landslide susceptibility model is often compromised by multicollinearity among landslide causative factors. While feature selection techniques are recognized as essential preprocessing steps, most studies lack systematic comparisons of how different selection methods affect traditional models under identical conditions. This study addresses this gap by evaluating Weighted Overlay (WO), Multiple Linear Regression (MLR), and Logistic Regression (LR) using Correlation Analysis, Variance Inflation Factor (VIF), and Information Gain (IG) feature selection techniques. It is found that LR with Correlation Analysis results in 69.30% accuracy and 75.48% Area Under the Receiver Operating Characteristic Curve (AUC-ROC) while maintaining balanced precision (64.47%) and recall (85.96%). The WO model yields outstanding landslide recognition (90.18% recall) with VIF analysis despite a lower precision value (56.74%). MLR with IG analysis achieves reliable performance (62.11% accuracy, 64.76% AUC-ROC) for regional assessments. The study offers practical guidelines for method selection based on assessment goals, emphasizing the trade-off between statistical optimization and physical interpretability in susceptibility mapping. Full article

Community-managed forests within agroforestry landscapes are vital for both carbon sequestration and agricultural sustainability. This study assesses the Hariyali Community Forest (HCF) in western Nepal, emphasizing its role in carbon storage within a Sal (Shorea robusta)-dominated lowland forest containing diverse native and medicinal species. Stratified field inventories combined with satellite-derived biomass and land-use/land-cover data were used to quantify carbon stocks and spatial trends. In 2022, the mean aboveground carbon density was 165 tC ha⁻¹, totaling approximately 101,640 tC (~373,017 tCO₂e), which closely matches satellite-based trends and indicates consistent carbon accumulation. Remote sensing from 2015–2022 showed a net tree cover gain of 427 ha compared to a 2000 baseline of 188 ha, evidencing effective community-led regeneration. The 615 ha Sal-dominated landscape also sustains agroforestry, small-scale horticulture, and subsistence crops, integrating livelihoods with conservation. Temporary carbon declines between 2020 and 2022, linked to localized harvesting and management shifts, highlight the need for stronger governance and local capacity. This study, among the first integrated carbon assessments in Nepal’s lowland Sal forests, demonstrates how community forestry advances REDD+ (Reducing Emissions from Deforestation and Forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks in developing countries) objectives while enhancing rural resilience. Linking field inventories with satellite-derived biomass and land-cover data situates community forestry within regional environmental change and SDG (Sustainable Development Goals) targets (13, 15, and 1) through measurable ecosystem restoration and livelihood gains. Full article

This study integrates geospatial modeling with multi-criteria decision analysis for an improved approach to landslide susceptibility mapping (LSM). This approach addresses key challenges in LSM through sophisticated multicollinearity analysis and machine learning strategies. We compared three machine learning models for weighting, and of them the Permutation-Weighted model yielded the best prediction results, with an Area Under Curve (AUC) of 95%, an accuracy of 69%, and a recall of 66%. To resolve perfect multicollinearity (r = 1) between land use land cover (LULC) and geological factors, we implemented Principal Component Analysis (PCA). The selected factors demonstrated strong predictive power, with the PCA-derived features exhibiting the best performance, having a Variation Inflation Factor (VIF) of 1.004. Slope appeared as the most influential factor (51.7% contribution), while the Topographic Wetness Index (TWI) was less dominant with only 6.6%. Multiple landslide susceptibility mapping methods yielded consistent results, with 29.8–30.1% of the study area showing moderate susceptibility and 35.2–36.9% in the high to very high susceptibility class. The model also incorporated vulnerability parameters weighted by the United Nations Office for Disaster Risk Reduction (UNDRR) indicators, including farmland, buildings, bare land, water bodies, roads, and amenities to generate hazard, vulnerability, and risk maps. The results were verified through visual comparison with high-resolution Google Earth imagery. The Permutation-Weighted model performed better than others, categorizing 12.4% at high-risk, while Random Forest (RF) categorized 7.2% at high risk. This study makes three key contributions: (1) It establishes the effectiveness of PCA/VIF for variable selection, (2) it provides a comparison of machine learning weighting techniques, and (3) it validates a workflow applicable to data-scarce regions. Full article

Increasing demand for land and resources in Himalayan catchments is altering hydrological processes and threatening freshwater ecosystems. Sediment mobilization and nutrient fluxes, especially during monsoon rainfall events, are intensifying the degradation of water bodies. This study investigates land cover change and its effects on nutrient dynamics in the Phewa Lake catchment, Nepal. Landsat imagery from 1990 to 2021, processed through Google Earth Engine, was used to map land changes. Nutrient loading for the two time periods was estimated with the InVEST model. Surface soils were sampled across the catchment to analyze nitrogen and phosphorus distribution, while their particle-bound transport to the lake was assessed through riverbed sediments and the suspended sediments collected during monsoon rainfalls. Pre-monsoon water quality was examined to evaluate eutrophication levels across different lake zones. Results reveal forest recovery in the upper catchment, but agricultural land in the lower catchment is being rapidly converted to urban areas. While forest recovery has enhanced sediment retention, nutrient inputs to the lake, particularly nitrogen and phosphorus, have increased. Fertilizer leaching and untreated sewage emerge as key sources in rural and urban areas, respectively. Seasonal constraints of the dataset may underestimate the overall extent of water quality deterioration, as indicated by high nutrient loads in monsoon suspended sediments. Overall, this study highlights the dual effect of land cover change: forest regrowth coincides with rising nutrient discharge. Without timely interventions, growing urban populations in the region may face worsening water quality challenges. Full article

Urbanization, characterized by population growth and socioeconomic development, is a major driving factor of land use land cover (LULC) change. A spatio-temporal understanding of land cover change is crucial, as it provides essential insights into the pattern of urban development. This study conducted a longitudinal analysis of LULC change in order to evaluate the tradeoffs of urban growth and sustainability challenges in the Himalayan region. Landsat time-series satellite imagery from 1988 to 2024 were analyzed for two major cities in Nepal—Kathmandu metropolitan city (KMC) and Pokhara metropolitan city (PMC). The LULC classification was conducted using a machine learning support vector machine (SVM) approach. For this study period, our analysis showed that KMC and PMC witnessed urban growth of over 400% and 250%, respectively. In the next step, LULC change and urban expansion patterns were predicted based on the urban development indicator using the Cellular Automata Markov chain (CA-Markov) model for the years 2040 and 2056. Based on the CA-Markov chain analysis, the projected expansion areas of the urban area for the two future years are 282.39 km² and 337.37 km² for Kathmandu, and 93.17 km² and 114.15 km² for PMC, respectively. The model was verified using several Kappa variables (K-location, K-standard, and K-no). Based on the LULC trends, the majority of urban expansion in both the study areas has occurred at the expense of prime farmlands, which raises grave concern over the sustainability of the food supply to feed an ever-increasing urban population. This haphazard urban sprawl poses a significant challenge for future planning and highlights the urgent need for effective strategies to ensure sustainable urban growth, especially in restoring local food supply to alleviate over-reliance on long-distance transport of agro-produce in high-altitude mountain regions. The alternative planning of sustainable urban growth could involve adequate consideration for urban farming and community gardening as an integral part of the urban fabric, both at the household and city infrastructure levels. Full article

Career decisions are among the most consequential choices individuals make, profoundly shaping their long-term stability and overall life satisfaction. The literature suggests that behavioral biases, specifically overconfidence, herd mentality, social comparison, status quo bias, and optimism bias, can exert considerable influence on these decisions, thereby shaping students’ future career trajectories. This study adopts a behavioral perspective to examine how these biases influence career choices within a collectivist social context. A survey of 360 undergraduate and graduate business students was conducted. The collected data were analyzed using an integrated approach that combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN), enabling the use of both linear and non-linear methods to analyze the relationship between cognitive biases and career choices. Our findings reveal that while all five biases have a measurable impact, status quo bias and social comparison are the dominant factors influencing students’ career decisions. These results underscore the need for interventions that foster self-awareness, independent decision-making, and critical thinking. Such insights can guide educators, career counselors, and policymakers in designing programs to mitigate the negative effects of cognitive biases on career decision-making, ultimately enhancing career satisfaction and workforce efficiency. Full article

Albumin is the most abundant protein synthesized exclusively by the hepatocytes in the liver. Once secreted into plasma, it helps in the maintenance of osmotic pressure, as well as the exertion of defensive roles such as anti-oxidative and anti-inflammatory functions. Dysregulation in the synthesis and clearance of albumin is observed in various hepatic and extra-hepatic diseases. Abnormal levels of albumin could be either a cause or an effect of various pathological ailments, including hepatic, cardiac, renal, neurological, etc. Owing to its long half-life and multiple binding sites in its heart-shaped structure, it interacts with various internal agents, such as hormones, or external substances like drugs, which is why transportation can be one of its many functions. Additionally, albumin’s drug interactions, as well as displacement of albumin–drug binding, could have serious clinical consequences, and careful considerations should be made in determining an appropriate drug regimen to achieve a desired therapeutic outcome with minimal side effects. Moreover, albumin also undergoes several post-translational modifications that can influence its physiological roles, including drug binding and antioxidant functions. Furthermore, it has a complicated role in physiology, where it can help in maintaining plasma oncotic pressure and prevent endothelial cell apoptosis but can have adverse effects on the lungs and kidneys. These adverse effects are mainly attributed to ER stress and inflammasome activation. This narrative review provides an overview of the general biology of albumin and its effects in physiology, with a focus on its beneficial and adverse effects and the underlying molecular mechanisms. Full article

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article

Butterflies have nectar-feeding preferences based on various floral characteristics, including flower shape, size, color, fragrance, and nectar composition, which in turn affect their survival, reproduction, and roles in pollination. The National Botanical Garden (NBG) in Lalitpur, Nepal, holds a variety of flowering plants and butterfly populations, providing a suitable study site to test the hypotheses on floral preferences of butterflies. This study assessed the floral preferences of the butterfly community in the NBG based on flower color, the origin of flowering plants (native and alien), and the type of plants (herbs and shrubs). It also tested the association between butterfly proboscis lengths and corolla tube lengths of flowers. Data were collected from 10 blocks (each 5 × 5 m²) through direct observation during the spring and autumn seasons, from March to October 2022. A total of 24 species of butterflies were recorded during the study period, with the chocolate pansy (Junonia iphita) being the most abundant. The relative abundance of pink flowers was higher in the NBG, but the butterflies’ visitation frequency was significantly higher on yellow flowers (p < 0.05) than on other colors. The visitation frequencies of butterflies significantly varied with the flowers’ origin and types. Butterflies visited flowers of alien origin more frequently than native ones (p < 0.05) and those of herbs over shrubs (p < 0.05). Flowers from alien plants, such as Calluna vulgaris and Viola tricolor, were among the most frequently visited. The proboscis length of butterflies showed a significantly strong positive correlation with the corolla tube length of flowers (τ = 0.74, p < 0.001). These results can inform conservation practices and garden management strategies aimed at supporting butterfly diversity through the intentional selection of floral resources. Full article

Plant diseases and nutrient deficiencies pose significant challenges to food production, making it crucial to identify them accurately and quickly, as their symptoms can often be similar. Prompt and precise detection is essential to implement effective measures that prevent crop losses. While computer vision techniques have demonstrated effectiveness in classification, their high computational demands have limited their adoption by farmers in the field. In this study, a Corn leaf Nutrition Deficiency and Disease network (CNDD-net) is designed based on the ResNet framework, incorporating a depth-wise separable convolution and a convolutional block attention module for a lightweight, high-performance model. The models underwent training, validation, and testing using a corn leaf nutrition deficiencies and diseases data set with seven classes implementing five-fold cross-validation. The performance of the models is assessed using average accuracy, GFLOPs, number of parameters, and model size. Following experiments involving the manipulation of the position of the attention module, the number of feature maps, and the depth of the network, the model was finalised. The CNDD-net design has a model size of 0.24 MB with 48,041 parameters and a GFLOPs of 0.18, providing an average accuracy of 96.71%. Compared to conventional models, this research demonstrates optimal performance and computational complexity, offering an efficient, lightweight solution to identify nutritional deficiencies and diseases of corn leaf, thus supporting sustainable agriculture. Full article

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. The BMA framework synthesizes four precipitation products—Climate Hazards Group Infrared Precipitation with Station (CHIRPS), the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 to 2020. We evaluated the merged dataset’s performance against its constituent datasets and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) at daily, monthly, and seasonal scales. Evaluation metrics included the correlation coefficient (CC), root mean square error (RMSE), and Kling–Gupta efficiency (KGE). The Variable Infiltration Capacity (VIC) hydrological model was further applied to assess how these datasets affect runoff simulations. The results indicate that the BMA-merged dataset substantially improves precipitation estimation accuracy when compared with individual inputs. The merged product achieved optimal daily performance (CC = 0.72, KGE = 0.70) and showed superior seasonal skill, notably reducing biases in autumn and winter. In hydrological applications, the BMA-driven VIC model effectively replicated observed runoff patterns, demonstrating its efficacy for regional long-term predictions. This study highlights BMA’s potential for optimizing hydrological model inputs, providing critical insights for sustainable water management and risk reduction in complex basins. Full article

Plant-based preparation of nanomaterials has become a recent global research focus due to its cost-effectiveness, sustainability, and environmentally friendly approach. This study aims to synthesize silver nanoparticles (HAgNPs) using red lentil husk aqueous extract (LHE) and to assess its antibacterial activity. Synthesized HAgNPs were analyzed by ultraviolet-visible spectroscopy (UV-vis.), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction spectroscopy (XRD), field emission scanning electron microscopy (FESEM), and energy dispersive X-ray (EDX) analysis. The antibacterial efficacy of synthesized HAgNPs was investigated against Staphylococcus aureus (ATCC No: 25923), Enterococcus faecalis (ATCC No: 29212), Klebsiella pneumoniae (ATCC No: 700603), and Shigella sonnei (ATCC No: 25931) at varying concentrations via the agar well diffusion scheme. The UV-vis absorption maximum observed around 420 nm directed the creation of HAgNPs. The shifting of peak positions in the FTIR spectrum of the synthesized HAgNPs compared to the FTIR spectrum peak positions of LHE indicated the involvement of biomolecules present in LHE in reducing silver ions to metallic silver. XRD examination of the prepared sample suggested face-centred cubic crystals of the HAgNPs. The average particle dimension of prepared HAgNPs was 8.9 nm by the Debye–Scherer equation. An FESEM examination of the synthesized HAgNPs revealed spherical morphology, while the EDX study showed that silver (Ag⁰) was the predominant component, comprising 62.3% by weight. The synthesized HAgNPs displayed a higher DPPH radical scavenging capacity (IC₅₀ = 38.87 ± 3.52 µg/mL) than that of LHE (IC₅₀ = 65.27 ± 1.17 µg/mL). The prepared HAgNPs exhibited significant antibacterial efficacy against some human pathogen bacteria at lower concentrations. Hence, the present study highlights an environmentally benevolent and economical synthetic approach to the preparation of HAgNPs and its potential utility for the production of biomedical products. Full article