Essays on Decentralization and Political Institutions

This dissertation comprises two essays on decentralization and political institutions. The first chapter of the dissertation investigates how national levels of corruption are influenced by the interaction of two factors in political decentralization: the presence of local elections and the organizational structure of national parties. Previous studies have focused primarily on the role of fiscal decentralization on corruption and have mostly ignored the institutions of political decentralization. Using new data in a series of expansive models across multiple countries and years, we find that corruption will be lower when local governments are more accountable to and more transparent towards their constituents. This beneficial arrangement is most likely when local elections are combined with non-integrated political parties, where party institutions themselves are decentralized from national control. Such an institutional arrangement maximizes local accountability by putting the decision to nominate and elect local leaders in the hands of those best in a position to evaluate their honesty – local electors. The second chapter analyzes how political institutions, and in particular party institutionalization, can mediate the impact of fiscal decentralization on climate change. Decentralization has remained an important shift in governance structure throughout the world in the past few decades. The economics literature, thus far, has not provided conclusive evidence regarding the impact of fiscal decentralization on combatting climate change. Decentralized decision making may be seen as antagonistic to the large externalities that typically characterize climate change policies. However, the local under-provision of public goods with externalities may be mediated by the presence of “institutionalized political parties.” These latter have a stable party organizational structure and strong linkage to voters, providing the incentives and capacity to shape the incentives of local elected officials. Using a large panel data set for 75 countries from 1971 to 2018, we find that the presence of strong party institutionalization significantly improves the functional role of fiscal decentralization in combating climate change, when the latter is measured by the reduction of CO2 emissions and the promotion of renewable energy consumption

Farming in the mountains of Nepal: crops, soil fertility, livelihoods and farm-forest linkages

In national plans, policies, and earlier development programs, livelihoods of mountain people in the Nepal Himalayas were overlooked, rendering them more susceptible to climatic risk and disaster. The management of marginal mountain agricultural land is crucial for food security, improved living conditions, and environmental protection. For enhancing livelihoods and ecological benefits, mountain agriculture is vital, however, a consolidated review on mountain farming is limited in Nepal. We used "mountain" AND "Nepal" AND "farming" OR "agriculture" in the literature's title published between 1978 and 2021 on Google Scholar and did an in-depth review of papers on the four thematic areas: mountain crops, soil fertility, livelihoods, and farm-forestry linkages. We observed a variety of nutrient-rich mountain crops with a market potential as niche products, low and deteriorating soil fertility of agricultural lands, a weakening of the farm-forest links, and an increase in the diversity of mountain livelihood choices. Small landholdings, labor outmigration mainly men, feminization of mountain farming, and food insecurity are the greatest challenges to the growth of agriculture in mountainous regions. There are, however, ample opportunities to make mountain regions green through agroforestry and community forests, to improve livelihoods by introducing niche value chains for products, to explore payment for ecosystem services through downstream-upstream linkages, and to recognize their traditional knowledge and practices through citizen science research and development

Variability and path coefficient analysis for yield attributing traits of mungbean (Vigna radiata L.)

Seven mungbean genotypes were studied to estimate the genetic variability and path coefficient analysis for yield attributing traits at Agronomy farm of Institute of Agriculture and Animal Science (IAAS), Paklihawa Campus, Rupandehi, Nepal during summer season of 2017. The experiment was conducted with four replications in a randomized complete block design. Pant-5 and Maya were found high yielding genotypes. High genotypic coefficient of variation was exhibited by secondary branches and seed yield per plant. The low genotypic coefficient of variation was given by pod length, number of grains per pod and days to 50% flowering. High heritability was shown by test weight, secondary branches and seed yield per plant. Yield was correlated positively with days to flowering, pod length, primary branches per plant, test weight, biological, seed yield per plant and number of pods per plant. Biological yield, pod length, days to 50% flowering and no. of grains per pod contributed maximum positive and direct effect on yield indicating these three traits should be given emphasis while selecting high yielding mungbean cultivar for irrigated condition

Forecasting TSPLOST Revenues in Georgia

PI# 0018101Due to the long-term planning involved in the design and construction of transportation infrastructure projects, transportation planners need reliable long-run forecasts and stable sources of revenue over the project timeline. Funding streams typically vary from period to period due to inherent volatility in the funding source or unforeseen events, or both. Additional understanding of the economy and improved forecasting techniques can reduce some of the uncertainty of volatile revenue sources. While improved forecasting techniques are not capable of anticipating unexpected events, such as a hurricane or wildfire, budget strategies do exist that can mitigate volatility in revenues arising from unforeseen events. These strategies include the use of rainy day funds and revenue diversification, among others. This research focuses on three areas of improvement: (1) documenting the economic and demographic factors that influence sales tax collections; (2) reviewing the available forecasting models and determining which types best suit the [Transportation Special Purpose Local Option Sales Tax] TSPLOST regions of Central Savannah, River Valley, Heart of Georgia, and Southern Georgia; and (3) analyzing best practices in budgeting for transportation sales taxes using a case study approach

5G: The Fututre of Improved Road Safety and Autonomous Vehicles

Digital Revolution caused the telecommunication network to reshape the current telecom technology and provide good foundation for the development of connected vehicles. Cur-rently, the Release 16 of 5G shows that around the world, Vehicle-to-everything (V2X) test-ing is being conducted on a large scale. In comparison with 4G, the 5G network provides multitude of potential and opportunities for V2X applications and autonomous vehicles. The purpose of this thesis is to study the concept of evolution of 5G, their historical back-ground, and their architecture. The 5G technology can be used in vehicular communication devices to minimize the road casualties. The goal of this thesis is also to clarify the benefit of 5G technology in the driverless driving sector. The thesis introduces V2X capabilities with 5G to prevent unnecessary road accidents. The description of 5G architecture and features are carried out with two types of V2X commu-nication technologies, Short-range communication (DSRC) and Cellular-V2X(C-V2X) are explained. Description of the major applications of V2X along with advance use cases and the security of 5G in detail are given at the en

One health: The interface between veterinary and human health

One Health is an emerging global key concept integrating human and animal health through international research and policy. The complex relationships between the human and animal have resulted in a human-animal-environment interface since prehistorical times. The people, animals, plants, and the environment are so intrinsically linked that prevention of risks and the mitigation of effects of crises that originate at the interface between humans, animals, and their environments can only improve health and wellbeing. The “One Health” approach has been successfully implemented in numerous projects around the world. The containment of pandemic threats such as avian influenza and severe acute respiratory syndrome within months of outbreak are few examples of successful applications of the One Health paradigm. The paper begins with a brief overview of the human-animal interface and continues with the socio-economic and public health impact caused by various zoonotic diseases such as Middle East respiratory syndrome, Influenza, and Ebola virus. This is followed by the role of “One Health” to deal the global problem by the global solution. It emphasizes the interdisciplinary collaboration, training for health professionals and institutional support to minimize global health threats due to infectious diseases. The broad definition of the concept is supposed to lead multiple interpretations that impede the effective implementation of One Health approach within veterinary profession, within the medical profession, by wildlife specialists and by environmentalists, while on the other side, it gives a value of interdisciplinary collaboration for reducing threats in human-animal-environment interface

Phage-Based Artificial Niche: The Recent Progress and Future Opportunities in Stem Cell Therapy

Self-renewal and differentiation of stem cells can be the best option for treating intractable diseases in regenerative medicine, and they occur when these cells reside in a special microenvironment, called the “stem cell niche.” Thus, the niche is crucial for the effective performance of the stem cells in both in vivo and in vitro since the niche provides its functional cues by interacting with stem cells chemically, physically, or topologically. This review provides a perspective on the different types of artificial niches including engineered phage and how they could be used to recapitulate or manipulate stem cell niches. Phage-based artificial niche engineering as a promising therapeutic strategy for repair and regeneration of tissues is also discussed

RETRACTED: Review of brucellosis in Nepal

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. The article is a duplicate of a paper that has already been published in Epidemiol Health (Vol. 38 (2016), https://doi.org/10.4178/epih.e2016042). One of the conditions of submission of a paper for publication is that authors declare explicitly that the paper is not under consideration for publication elsewhere. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process

Clinical spectrum and management of dystonia in patients with Japanese encephalitis: A systematic review

Abstract Background Japanese encephalitis (JE) is a potentially fatal viral infection with a wide range of manifestations and can also present with a variety of movement disorders (MD) including dystonia. Dystonic features in JE are uncommon. Here, we have tried to summarize the clinical features and management of dystonia among JE patients with a comprehensive literature search. Methods Various databases, including PubMed, Embase, and Google Scholar, were searched against the predefined criteria using suitable keywords combination and boolean operations. Relevant information from observational and case studies was extracted according to the author, dystonic features, radiological changes in the brain scans, treatment options, and outcome wherever provided. Result We identified 19 studies with a total of 1547 JE patients, the diagnosis of which was confirmed by IgM detection in serum and/or cerebrospinal fluid in the majority of the patients (88.62%). 234 (15.13%) of JE patients had dystonia with several types of focal dystonia being present in 131 (55.98%) either alone or in combination. Neuroimaging showed predominant involvement of thalami, basal ganglia, and brainstem. Oral medications including anticholinergics, GABA agonists, and benzodiazepines followed by botulinum toxin were the most common treatment modalities. Conclusion Dystonia can be a disabling consequence of JE, and various available medical therapies can significantly improve the quality of life. Owing to insufficient studies on the assessment of dystonia associated with JE, longitudinal studies with a larger number of patients are warranted to further clarify the clinical course, treatment, and outcome of dystonia

A novel solution of an elastic net regularisation for dementia knowledge discovery using deep learning

Accurate classification of Magnetic Resonance Images (MRI) is essential to accurately predict Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) conversion. Meanwhile, deep learning has been successfully implemented to classify and predict dementia disease. However, the accuracy of MRI image classification is low. This paper aims to increase the accuracy and reduce the processing time of classification through Deep Learning Architecture by using Elastic Net Regularisation in Feature Selection. The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularisation. Initially, the MRI images are fed into CNN for features extraction through convolutional layers alternate with pooling layers, and then through a fully connected layer. After that, the features extracted are subjected to Principle Component Analysis (PCA) and Elastic Net Regularisation for feature selection. Finally, the selected features are used as an input to Extreme Machine Learning (EML) for the classification of MRI images. The result shows that the accuracy of the proposed solution is better than the current system. In addition to that, the proposed method has improved the classification accuracy by 5% on average and reduced the processing time by 30 ~ 40 seconds on average. The proposed system is focused on improving the accuracy and processing time of MCI converters/non-converters classification. It consists of features extraction, feature selection, and classification using CNN, FreeSurfer, PCA, Elastic Net, and Extreme Machine Learning. Finally, this study enhances the accuracy and the processing time by using Elastic Net Regularisation, which provides important selected features for classification