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Limited evidence exists on the costs of scaled‐up multisectoral nutrition programmes. Such evidence is crucial to assess intervention value and affordability. Evidence is also lacking on the opportunity costs of implementers and participants engaging in community‐level interventions. We help to fill this gap by estimating the full financial and economic costs of the United States Agency for International Development‐funded Suaahara II (SII) programme, a scaled‐up multisectoral nutrition programme in Nepal (2016–2023). We applied a standardized mixed methods costing approach to estimate total and unit costs over a 3.7‐year implementation period. Financial expenditure data from national and subnational levels were combined with economic cost estimates assessed using in‐depth interviews and focus group discussions with staff, volunteers, community members, and government partners in four representative districts. The average annual total cost was US $908,948 per district, with economic costs accounting for 47% of the costs. The annual unit cost was US$ 132 per programme participant (mother in the 1000‐day period between conception and a child's second birthday) reached. Annual costs ranged from US $152 (mountains) to US$ 118 (plains) per programme participant. Personnel (63%) were the largest input cost driver, followed by supplies (11%). Community events (29%) and household counselling visits (17%) were the largest activity cost drivers. Volunteer cadres contributed significant time to the programme, with female community health volunteers spending a substantial amount of time (27 h per month) on SII activities. Multisectoral nutrition programmes can be costly, especially when taking into consideration volunteer and participant opportunity costs. This study provides much‐needed evidence of the costs of scaled‐up multisectoral nutrition programmes for future comparison against benefits. The average unit cost of Suaahara II, a scaled up multisectoral nutrition programme covering 42 of 77 districts in Nepal, was US $132 per programme participant (mother in the 1000‐day period) and US$ 76 per mother–child pair reached per year. Community events designed to improve access to and demand for nutritious foods accounted for the largest share of activity costs (29%). Opportunity costs of volunteer and participant time are high, accounting for 47% of total costs. Female Community Health Workers, already engaged in community nutrition activities, spent significant time on implementing Suaahara II activities at 27 h per month. Complex multisectoral programmes often require significant resources to ensure coordination and sufficient technical support at national and subnational levels.

This research explores the methods for understanding groundwater springs distribution and occurrence using Geographic Information System (GIS) and Machine Learning technique in data poor areas of the Central Himalayas. The objectives of this study are to analyse the distribution of natural springs, evaluate three random forest models for its predictability and establish a model for the prediction of occurrence of springs. This study evaluates the primary causal factors for occurrence of springs. The data used in this study consists of 20 parameters based on topography, geology, lithology, hydrology and land use as causal factors, whereas 621 spring location and discharge (n = 621) measured during 2014–2016 and 815 non-spring locations (generated by GIS tool) use as supporting evidence to train (80%) and test (20%) the prediction model. Results show that the Bootstrap method is comparatively reliable (92% accuracy) over Boosted tree (64% accuracy) and Decision tree (74% accuracy) methods to classify and predict the occurrence of springs in the watershed. Bootstrap Forest shows the high Prediction rate for True Positive (82% actual spring predicted as a spring) and True Negative (89% actual non-spring predicted as non-spring), and the model seems consistent in both responses. This model was then applied to an independent dataset to predict spring location estimates with 75% accuracy. Therefore, spatial statistical methods prove efficient at predicting spring occurrence in data poor regions.

Non-communicable diseases (NCDs) are on the rise in Nepal. Consumption of alcohol and tobacco products remains high. Taxes on these products are significantly below the rate recommended by the WHO. In an effort to understand the reasons behind the slow progress towards the adoption of higher health taxes to curb NCDs, we documented the perceptions of key stakeholders on health taxes, including perceived barriers and facilitators to adopting higher health taxes. We conducted 45 in-depth interviews with individuals comprising government officials; producers, wholesale distributors and sellers of alcohol and tobacco products; and consumers and representatives from civil society organisations. We conducted a thematic analysis of the resulting data. Respondents from alcohol and tobacco industries are not supportive of higher health taxes. They argued that higher taxes can increase illicit trade and worsen inequality. Strikingly, several government officials shared the industries' concerns, arguing that health taxes have limited potential to reduce consumption of alcohol and tobacco products to help curb NCDs. In terms of barriers to adoption of higher health taxes, several local government representatives opined that close ties between industries and politicians at the federal level is a major hindrance. In order to adopt higher health taxes, the government will need to counter the false narrative pushed by alcohol and tobacco industries on the negative economic effects of such taxes. Health taxes earmarked for NCDs need to reflect the amount of revenue raised, reoriented towards prevention efforts and communicated clearly to the public.

Landslides are one of the most destructive geological disasters affecting Japan every year, resulting in huge losses in life and property. Numerous susceptibility studies have been conducted to minimize the risk of landslides; however, most of these studies do not differentiate landslide types. This study examines the differences in landslide depth, volume and the risk imposed between shallow and deep-seated landslide types. Shallow and deep-seated landslide prediction is useful in utilizing emergency resources by prioritizing target areas while responding to sediment related disasters. This study utilizes a 2-m DEM derived from airborne Light detection and ranging (Lidar), geological information and support vector machines (SVMs) to study the 1225 landslides triggered by the M 6.8 Chuetsu earthquake in Japan and the successive aftershocks. Ten factors, including elevation, slope, aspect, curvature, lithology, distance from the nearest geologic boundary, density of geo- logic boundaries, distance from drainage network, the compound topographic index (CTI) and the stream power index (SPI) derived from the DEM and a geological map were analyzed. Iterated over 10 random instances the average training and testing accuracy of landslide type prediction was found to be 89.2 and 77.8%, respectively. We also found that the overall accuracy of SVMs does not rapidly decrease with a decrease in training samples. The trained model was then used to prepare a map showing probable future landslides differentiated into shallow and deep-seated landslides.

Extraction of knickpoints or knickzones from a Digital Elevation Model (DEM) has gained immense significance owing to the increasing implications of knickzones on landform development. However, existing methods for knickzone extraction tend to be subjective or require time-intensive data processing. This paper describes the proposed Knickzone Extraction Tool (KET), a new raster-based Python script deployed in the form of an ArcGIS toolset that automates the process of knickzone extraction and is both fast and more user-friendly. The KET is based on multi-scale analysis of slope gradients along a river course, where any locally steep segment (knickzone) can be extracted as an anomalously high local gradient. We also conducted a comparative analysis of the KET and other contemporary knickzone identification techniques. The relationship between knickzone distribution and its morphometric characteristics are also examined through a case study of a mountainous watershed in Japan.

Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes. The proposed method will be useful for preparing hazard maps that express the relative probability of a collapse in similar regions.

Environmental conditions in western Nepal are experiencing a possible threat to economic losses and sustainability, especially due to decreased productivity and increased health risks. This research investigates the maximum willingness to pay (WTP) of the local community for environmental quality improvement programs by using the contingent valuation technique. It also explores socio-economic and behavioral determinants that influence the maximum WTP for environmental quality improvement. A cross-sectional analytical design is employed using primary data obtained through in-depth face-to-face interviews with people in the community, interviews with key informants, focus group discussions and direct observations. Of the total of 420 households sampled, 72% were willing to pay for the environmental improvement program. The average WTP of households per annum for environmental protection at the community level is given as Nepalese rupees (NPR) 1909 (confidence interval—CI: 1796–2022). Environmental factors (prolonged drought, sporadic rains and drying sprout), socio-economic factors (family size, occupation, regular saving habits in microfinance, distance to the nearest health facility, health insurance enrollment, owning a home and owning arable land) and behavioral factors (cleanliness of the toilet) are the major factors influencing the household’s WTP decision. The findings of this study provide an important guideline and basis for the implementation of cost sharing in environmental quality improvement programs among the community, governments and other stakeholders in this sector.

China’s rise as a global power corresponded with a diminution of Taiwanese diplomacy, which has left Central America as the last region to host a continuous bloc of countries that recognize the ROC. In this article, we argue that China’s success in gaining diplomatic recognition from Taiwan’s former allies has largely resulted from China's economic policy, specifically its promises of large-scale infrastructure projects and the integration of Central American economies with Chinese markets. However, there are limits to how far China has advanced in gaining full recognition from the region. The competing political and economic interests of China, Taiwan, the United States, and the Central American countries themselves, continue to influence patterns of diplomatic switching. More specifically, we argue that the threat of punitive measures from the United States combined with a turn in Taiwanese diplomacy toward assistance efforts to combat Covid-19 may deter future switching in the short to medium-term. Our analysis offers case studies of four Central American countries (Costa Rica, Panama, El Salvador and Nicaragua) to illustrate the multi-year processes by which China’s economic strategy leads to diplomatic switching and examine the paths ahead for the remaining holdouts facing the prospect of economic and political penalties by the United States.

Over the past decade, much effort has been made in identifying and characterizing systems that can form a building block of quantum networks, among which semiconductor quantum dots (QD) and spontaneous parametric down-conversion (SPDC) source are two of the most promising candidates. The work presented in this thesis will be centered on investigating and engineering the mentioned systems for generating customizable single photons. A type-II SPDC source can generate a highly flexible pair of entangled photons that can be used to interface disparate quantum systems. In this thesis, we have successfully implemented a cavity-SPDC source that emits polarization correlated photons at 942 nm with a lifetime of 950-1050ps that mode matches closely with InAs/GaAs QD photons. The source emits 80 photon pairs per second per mW pump power within the 150MHz bandwidth. Though the detection of idler photons, the source is capable of emitting heralded photons with g2?0.5 for up to 40 mW pump power. For a low pump power of ~5 mW, the heralded g2 is 0.06, indicating that the system is an excellent heralded single photon source. By directly exciting a single QD with cavity-SPDC photons, we have demonstrated a heralded-absorption of SPDC photons by QD, resulting in the coupling of the two systems. Due to the large pump bandwidth, the emitted source is highly multimode in nature, requiring us to post-filter the downconverted field, resulting in a lower photon pair emission rate. We propose placing an intra-cavity etalon to suppress the multi-mode emissions and increase the photon count rate. Understanding and experimentally implementing two-photon interference (HOM) measurements will be crucial for building a scalable quantum network. A detailed theoretical description of HOM measurements is given and is experimentally demonstrated using photons emitted by QD. Through HOM measurements we demonstrated that the QD sample in the study is capable of emitting indistinguishable photons, with the visibility exceeding 95%. As an alternative approach to modifying the spectral mode of single photons, we performed phase modulation of photons emitted by a QD to generate additional sidebands that are separated by several GHz. By performing HOM measurements, we have shown that the central component and the sidebands are in the superposition states and the spectrally modified photons have a well-preserved indistinguishability. Such spectrally engineered photons can be used for phase-encoded cryptography applications. These experimental results should lay the foundations towards building a scalable hybrid quantum network.

The World Health Organization recommends two passive immunisation strategies to prevent respiratory syncytial virus (RSV) disease in young infants. Both are being introduced in high-income settings, but their affordability and cost-effectiveness have not been evaluated in many low- and middle-income countries. Preliminary estimates of cost-effectiveness are needed to guide immunisation policy and planning in Nepal. We estimated the potential health impact and cost-effectiveness of introducing a maternal vaccine (RSVpreF) or long-acting infant monoclonal antibody (mAb) (nirsevimab) over the period 2025-34 in Nepal. We compared both interventions to the status quo (no intervention) and to each other. Model inputs included health care cost estimates from a recent prospective cost-of-illness study in Kathmandu, as well as the latest efficacy data from clinical trials. The primary outcome measure was the incremental cost (2023 USD) per disability-adjusted life year (DALY) averted from a governmental health perspective. We conducted a range of deterministic analyses, including scenarios that incorporated a societal perspective and a seasonal approach. Additionally, we performed probabilistic uncertainty analyses to assess decision uncertainty and estimated the likelihood of cost-effectiveness for each intervention across a range of willingness-to-pay thresholds. Introducing a maternal vaccine (USD 5/dose, 81% coverage, 69% efficacy, 6 months protection) or long-acting infant mAb (USD 5/dose, 97% coverage, 77% efficacy, 5 months protection) could prevent >2300 deaths and >50 000 hospital admissions over ten years. The discounted immunisation programme costs were estimated to be USD 30 and USD 35 million, respectively. Compared to the status quo, the maternal vaccine and the long-acting infant mAb were estimated to cost USD 387 and USD 486 per DALY averted, respectively, which is around 0.3 times and 0.4 times the national gross domestic product (GDP) per capita. There was a 95% probability that the maternal vaccine would be cost-effective at USD 5 per dose, assuming a willingness-to-pay threshold of 0.5 times the national GDP per capita. With our base case assumptions, the maternal vaccine dominated the mAb (i.e. generated more health benefits at a lower cost). However, the results (and the rank order of interventions) were sensitive to the dose price, efficacy, duration of protection, and RSV disease burden estimates. Cost-effectiveness of the mAb improves with timely administration or when a seasonal approach is implemented. New passive immunisation strategies have the potential to prevent a substantial number of RSV-related hospitalisations and deaths in Nepal. Cost-effectiveness and product choice will heavily depend on the price negotiated for each product.