Search Results (1,999)

Nepal’s National Health Insurance Program (NHIP), launched in 2016, continues to show low enrollment rates and substantial socio-economic and geographical inequalities hinder the progress towards universal health coverage (UHC). This study uses a composite indicator of intersectional disadvantages to examine how multiple equity markers (wealth quintile, education status and ethnicity) interact to shape inequalities in NHIP coverage. Data were drawn from the nationally representative 2022 Nepal Demographic and Health Survey. Key predictors are wealth status, education, ethnicity, residence, province, ecological zone and marginalization status. A composite measure of intersectional disadvantage was constructed using three socioeconomic dimensions: wealth, education, and ethnicity. Binary logistic regression, concentration indices, and concentration curves were used to assess the patterns of inequality in NHIP coverage. Results show that only 10.2% of men and 10.8% of women were enrolled in the NHIP. Enrollment varied markedly by province, with highest in Koshi (21.8% for men and 22.9% for women) and lowest in Madhesh (3.1% for men and 2.7% for women). Enrollment was disproportionately higher among wealthier, more educated, and ethnically advantaged groups. This disparity is starkest for those with an intersection of triple disadvantage (poor, illiterate, and disadvantaged ethnicity) and had substantially lower coverage (3.0% for men and 3.4% for women) compared to those facing no disadvantage (18.4% for men and 22.9% women). The concentration curve analysis confirmed that wealthier women and men had greater access to NHIP. Multivariable analysis showed that women and men with no disadvantages were more likely to be enrolled in NHIP than individuals in triple-disadvantage groups. These findings highlight persistent inequities in NHIP, which undermine its contribution to financial risk protection. Targeted interventions are urgently required, including effective implementation of existing subsidies for poor households, expansion of health facility networks in underserved provinces like Madhesh, and tailored outreach programs that address the intersection of ethnicity, wealth, and education in both genders to accelerate equitable progress towards UHC. Full article

Every year, floods disrupt the lives of hundreds of millions of people worldwide. Their impacts are further intensified by climate change, rapid urbanization, and land-use changes, making it crucial to identify areas most susceptible to flooding. While machine learning (ML) models have proven effective in identifying flood susceptibility, their validity and the integration of human risk remain underexplored in geomorphologically complex and highly flood-prone regions. This study developed an ensemble ML framework for flood susceptibility mapping in the Kosi Megafan, located in Nepal and India. We compared its performance with established ML models and a one-dimensional convolutional neural network (1D-CNN), validated results using Dartmouth Flood Observatory (DFO) and Sentinel-1 SAR (Synthetic Aperture Radar) data, and assessed the population exposed to high-risk zones. A total of 13 (8 retained) flood conditioning factors (FCFs) were derived from remote sensing datasets, and a flood inventory was created to train multiple ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), 1D-CNN, and a Stacked Ensemble model. Among these, the stacked ensemble model achieved the highest performance (AUC = 0.76, accuracy = 0.70, precision = 0.69, recall = 0.72, F1-score = 0.70). The resulting susceptibility map identified high-risk zones mainly in the southern and southwestern Megafan, showing strong spatial agreement with the Sentinel-1-derived flood inventory and the DFO flood data (1992–2022). This study highlights the effectiveness of combining SAR-derived flood evidence with ensemble ML approaches for accurate and scalable flood susceptibility mapping in data-scarce, hazard-prone basins. Ultimately, the research supports efforts to build resilience and mitigate the long-term impact of flooding in the region. Full article

Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose severe threats to lives, infrastructure, and post-disaster recovery. While machine learning (ML) and deep learning (DL) approaches to coseismic landslide susceptibility mapping have advanced considerably, spaceborne interferometric synthetic aperture radar (InSAR) products, particularly line-of-sight (LOS) displacement and coherence-based damage proxy maps (DPMs), remain underutilized in event-based frameworks. This study develops and evaluates a multi-factor coseismic landslide probability model that integrates InSAR-derived deformation metrics with geomorphic and hydrologic predictors to support rapid post-earthquake hazard assessment. Using the 25 April 2015 Mw 7.8 Gorkha earthquake as a case study, LOS displacement was derived from ALOS-2 PALSAR-2 ScanSAR interferometry, and the normalized channel steepness index (K_sn) was computed from a digital elevation model. Fourteen conditioning factors were used to train five architectures: Random Forest (RF), XGBoost, CNN, U-Net, and DeepLabV3. Spatial autocorrelation was mitigated using a leave-one-basin-out three-fold spatial cross-validation strategy, with models evaluated on a patch-based domain comprising 655,360 pixels at a positive-class prevalence of 6.35%, establishing a no-skill AUC-PR baseline of 0.0635. InSAR integration consistently improved model performance under high class imbalance, increasing AUC-PR across all models by 7.8% to 17.3%. Random Forest achieved the highest AUC-PR (0.7940, nearly 12.5 times the baseline) and CSI (0.3027), providing the best balance between landslide recall (88.09%) and non-landslide specificity (88.68%) with the lowest false alarm rate (11.32%). XGBoost attained the highest AUC-ROC (0.9501) but exhibited lower recall (83.73%) and poorer calibration (Brier = 0.1397). Among DL models, DeepLabV3 produced the best-calibrated probabilities (Brier = 0.0693) and the highest CSI (0.2307), while U-Net offered the most balanced DL performance and CNN achieved the highest recall (92.40%) at the expense of elevated false alarms. Permutation feature importance identified K_sn as the dominant predictor, highlighting the strong tectono-geomorphic control on coseismic landslide occurrence. These results demonstrate that integrating InSAR-derived products substantially enhances landslide hazard assessment and supports more reliable rapid response in the Nepal Himalaya. Full article

Urban floods have become a major systemic risk to sustainable urban development under climate change and increasingly frequent extreme hydro-meteorological events. Yet evidence on the long-term evolution of urban flood resilience (UFR) and its structural constraints at the provincial scale remains limited. This study develops a PSR-based framework to assess UFR and diagnose its dominant obstacles using data for 21 prefecture-level cities in Sichuan Province from 2011 to 2023, including meteorological, geomorphological, socioeconomic, infrastructure, environmental, and public service indicators. A combined AHP–EWM is used to integrate subjective and objective information, TOPSIS is applied to derive a composite UFR index and subsystem scores, and an obstacle degree model is employed to identify key constraints and their temporal evolution. Results show that: (1) UFR in Sichuan Province fluctuated but increased overall during 2011–2023, reaching its highest level in 2023; (2) resilience improvement was driven mainly by the response subsystem, while the pressure subsystem showed the greatest interannual variability; and (3) the annual top five obstacles were highly persistent and insufficient response capacity was the dominant long-term constraint on resilience enhancement. These findings underscore that improving the adequacy, institutional robustness, and operational stability of response systems is central to enhancing UFR. This study provides empirical support for the assessment of provincial-scale resilience and policy-oriented flood risk governance. Full article

As in other cultures, university students in Nepal struggle with significant academic pressure, which often leads to academic anxiety and depression. The current study aims to expand awareness of the presence, prevalence, and impact of student academic anxiety and depression among Nepalese university students as well as to test an emerging approach to universal screening to identify learners’ need profiles to promote targeted intervention supports. Participants included 547 Nepalese college students who completed the Academic Anxiety Scale (AAS) and the University Student Depression Inventory (USDI). Confirmatory factor analysis (CFA) was conducted to evaluate the validity of the Nepalese versions. Finally, comparative analyses using an archival dataset of students from the United States explored consistencies across cultural contexts. Nepalese translations of both scales demonstrated high reliability and validity and identified similarities in patterns of expressed academic anxiety and depression across cultures. Furthermore, four profiles of need were generated based on levels of anxiety, depression, and academic motivation. The results supported clear recommendations for tiered interventions in specific domains of emotion regulation. This initial large-scale study of academic anxiety and depression in a Nepalese university population provided confirmation that the models of anxiety and depression as well as incidence levels were consistent with existing research from other contexts. Moreover, the results provided strong confirmation that universal screening with simplified self-report measures can identify clear patterns of need among students, which can be aligned with targeted tiered interventions to support student thriving. Full article

Rain-induced shallow landslides persistently disrupt Nepal’s mountain roads, frequently leading to fatalities, transport disruptions, and economic losses. This study develops physically validated, site-specific rainfall thresholds for the landslide-prone Kanti National Roadway (H37) by integrating empirical intensity–duration (I-D) analysis, antecedent rainfall metrics, and satellite-derived soil moisture data. Using 35 years of rainfall records (1990–2024) and 59 field-verified landslides (2017–2024), we derived a localized I-D threshold: I = 19.37 × D^−0.6215 (I: rainfall intensity in mm/h; D: duration in hours), effective for durations of 48–308 h, encompassing short intense storms and prolonged moderate rainfall. The Cumulative Antecedent Rainfall (CAR) method associated most failures with 3-day totals, while the Antecedent Precipitation Index (API) showed superior performance, with a 10-day threshold of 77 mm capturing all events. For physical validation, NASA’s SMAP Level-4 root-zone (0–100 cm) soil moisture data revealed a 1-day lag in response to rainfall; after adjustment, trends matched API saturation predictions and identified an inverse rainfall–moisture pattern before the 11 August 2019 landslide, indicating a potential instability precursor. This integration enhances predictive accuracy, bolsters mechanistic understanding of landslide hazards, and offers a scalable, cost-effective early-warning framework for data-scarce mountain regions, aiding climate-resilient infrastructure in regions with intensifying rainfall extremes. Full article

This study examines financial integration and contagion across South Asia’s emerging and frontier markets during the 2001–2013 period, encompassing both the global financial and Eurozone crises. Employing a multi-factor asset pricing model within an EGARCH framework, we disentangle systematic global exposures from idiosyncratic shocks originating in the U.S. and Eurozone. By formally testing for structural changes in both mean returns and conditional variance, we uncover a striking “integration-contagion paradox.” While frontier markets (Bangladesh, Nepal) appear segmented from global pricing signals in tranquil times, they remain acutely susceptible to second-moment volatility contagion during stress periods. In contrast, India exhibits strong systematic return integration yet remains relatively insulated from volatility cascades. These results challenge the conventional view that financial segmentation offers a robust shield against systemic risk, revealing that a lack of global integration does not immunize markets against the transmission of global uncertainty. Full article

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 explores the lived experiences of pregnant women in rural Nepal navigating maternal healthcare amidst intersecting structural barriers. Using the Social Determinants of Health framework and intersectionality, we examine how geographic isolation, inadequate infrastructure, and economic hardship compound risks to timely and safe maternal care. Twenty in-depth interviews were conducted at a district hospital in the eastern region of Koshi Province, Nepal. Four major themes were identified through inductive thematic analysis. These are: geographic vulnerability and transport challenges; gaps in rural maternal health provision; accommodation and institutional support deficits; and economic vulnerability and hidden costs of care. Findings reveal that poor road conditions, unreliable transport, and limited diagnostic services force women to undertake long, costly journeys, often requiring temporary relocation without institutional accommodation support. Despite policies such as the Safe Motherhood Programme, implementation gaps persist, leaving women to bear significant financial and emotional burdens. These experiences underscore systemic inequities in resource distribution and highlight the compounded disadvantage faced by women from rural and marginalised communities. To ensure equitable maternal healthcare, this study advocates for the decentralisation of health services and the implementation of inclusive financial protection policies tailored to the needs of women from rural and marginalised communities. To promote equitable maternal healthcare, we recommend strengthening rural health infrastructure, implementing maternity waiting homes, and expanding financial protection schemes tailored to vulnerable populations. This research offers critical insights for policymakers to address maternal health inequalities and advance Nepal’s progress toward Universal Health Coverage and Sustainable Development Goal 3 (Ensure healthy lives and promote well-being for all at all ages). Full article

Regional trade liberalization via preferential agreements increasingly shapes economic outcomes in small open economies embedded in overlapping regional frameworks. This study evaluates the short-run economy-wide effects of tariff and non-tariff measure (NTM) reforms under the South Asian Free Trade Area (SAFTA) and the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) using a Computable General Equilibrium (CGE) model calibrated to the GTAP 10 database. Gravity-based estimates of ad valorem equivalents (AVEs) of NTMs are integrated into the CGE framework, enabling explicit modeling of regulatory barriers alongside tariff reductions. Policy simulations examine scenarios involving a 90 percent tariff cut and a 50 percent NTM reduction, applied individually and jointly, under a short-run closure with fixed factor endowments and a trade balance for Nepal. Results indicate that combined liberalization yields positive macroeconomic adjustments, with real GDP rising by about one percent and exports increasing by over 14 percent, driven primarily by the manufacturing sector, particularly textiles, while agricultural responses vary by exposure to NTMs. These findings provide policy-relevant evidence on the relative effectiveness of tariff and regulatory reforms, informing strategies for deeper regional integration and enhanced competitiveness in small, structurally constrained economies. Full article

Highly pathogenic avian influenza (HPAI) is a transboundary and zoonotic viral disease affecting poultry and wild birds in many countries worldwide. Globally, HPAI outbreaks have led to the death or culling of hundreds of millions of birds over the past two decades and have caused nearly 1000 confirmed human H5N1 infections, with a case fatality rate of approximately 50%. Asia and Europe remain among the most affected regions, with recurrent outbreaks linked to intensive poultry production, live bird markets, and migratory bird pathways. In Nepal, HPAI has been reported since 2009, with more than 320 outbreaks recorded and over 2.7 million birds lost, alongside one confirmed human fatality. Control measures rely largely on stamping out, movement restrictions, and surveillance; however, gaps in farm-level biosecurity, informal cross-border poultry trade, and limited vaccination use continue to sustain vulnerability. Strengthened multisectoral coordination under a One Health framework, integrating veterinary and public health surveillance, molecular monitoring, community awareness, and risk-based biosecurity enforcement, is essential to reduce the impact of HPAI and mitigate future zoonotic and pandemic risks. Full article

Winter precipitation plays an important role in the Himalayan region. However, its reliable assessment is difficult due to sparse ground precipitation measurements, limited ability to capture heterogeneity, and snowfall undercatch. Recent advances in satellite-based winter precipitation products (SPPs) enable comprehensive, consistent spatial data in this region; however, despite rapid improvements and the increased availability of SPPs, their accuracy is still uncertain. This calls for rigorous evaluation across several regions. This study presents a new SPP evaluation method that extends existing frameworks by adding two additional indicators—spatial correlation and the water balance consistency ratio (WBCR) to create a unified multi-criteria matrix for selecting spatially and hydrologically consistent products from among 11 latest and earlier SPPs from the global satellite mapping of precipitation (GSMaP) and The integrated multi-satellite retrievals for the global precipitation measurement Mission (IMERG) in the Kabul, Dudhkoshi, and Chamkharchu River basins. The results show that the latest non-calibrated product performed significantly better than earlier releases, demonstrating improved ability to capture precipitation events, spatial heterogeneity, and WBCR across all three basins. However, the performance of those SPPs varies substantially across regions. GSMaP gauge-calibrated product performance was more consistent across conventional multi-criteria assessment and WBCR, but their inability to capture spatial heterogeneity limits their applicability for sub-catchment water resource management. On the other hand, IMERG Final V07 (gauge-calibrated) performed exceptionally well across all regions, although its 3.5 month latency limits near-real-time applications. Therefore, GSMaP NRT V08 is suitable for real-time applications, given its short ~4 h latency and relatively good performance across all three basins. Future studies using the selected products will provide reliable information for policymakers and will support water hazard risk reduction. Full article

Wheat blast, caused by Magnaporthe oryzae pathotype Triticum (MoT), is an emerging fungal disease that poses a serious threat to global wheat production. In Zambia, where wheat is increasingly becoming a vital component for food and nutritional security, the emergence and spread of wheat blast is a growing concern under the influence of climate and agricultural practices changes. This review assesses the risk of wheat blast expansion in Zambia by examining regional climatic trends, future climate projections, crop suitability, and the ecological requirements of MoT. Potential disease hotspots are identified, and integrated management strategies, including chemical, cultural, and biotechnological approaches are evaluated. The review highlights the urgent need for coordinated disease surveillance, the development and deployment of resistant cultivars, and climate-resilient farming practices. By consolidating current knowledge and outlining sustainable management strategies, this paper aims to support effective disease mitigation and safeguard wheat production in Zambia in the face of climate change. Full article

Greenhouse gas emissions from sanitation systems remain underquantified, particularly when considering the entire service chain. Previous studies have largely focused on emissions from containment, with limited attention to later stages such as collection, transport, treatment and disposal. To address this gap, this research comprehensively estimates greenhouse gas (GHG) emissions from sanitation systems in Lahan municipality, Nepal. We used an extended version of the IPCC-based Tier-1 approach. Data collection included a household survey and key informant interviews. In scenario A, the baseline total annual emissions are 8.7 Gg CO₂e, mostly from the digestion of faecal sludge in the containment (7.3 Gg CO₂e). In scenario B, when a projected faecal sludge treatment plant (FSTP) is built and in operation, annual emissions reach 10.0 Gg CO₂e, driven by methane emitted by the anaerobic digester in the plant. Scenario C considers climate mitigation strategies: increasing the share of households emptying their containments, increased emptying frequency and adding of methane capture in the FSTP. This can reduce annual emissions to 7.9 Gg CO₂e per year, which is 21% less than in scenario B. Our results suggest that methane capture in the FSTP is the most critical mitigation strategy. Full article

The threat of antimicrobial resistance in aquatic environments, particularly riverine systems, is escalating, in part due to effluents discharged from healthcare facilities. This issue has been recognized not only in Japan but also in other Asian countries such as Indonesia and Nepal. Nevertheless, existing research remains limited, prompting an investigation into the prevalence of antimicrobial-resistant bacteria in the upstream and downstream sites of environmental rivers. In 2024, six samples were collected from three rivers in Hyogo Prefecture, Japan; five samples from five river sites in Indonesia; and three samples from downstream sites of rivers in Kathmandu, Nepal. These samples were subjected to selective culture–based Next Generation Sequencing and resistome analyses, based exclusively on the selective culture of bacteria propagated on CHROMagar ESBL plates. In Japan and Indonesia, Pseudomonas, Stenotrophomonas and Acinetobacter were frequently detected, whereas Klebsiella was overwhelmingly predominant in Nepal. Significant differences in the similarity of bacterial community composition among sampling sites across the three countries were observed (p < 0.001). Notably, Nepal exhibited the highest abundance level of antimicrobial resistance genes among the three countries, largely consisting of β-lactam resistance genes. In conclusion, these analyses elucidated substantial differences in bacterial community composition and degrees of environmental contamination. Full article