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In conflict-prone situations, access to markets is necessary to restore economic growth and generate the preconditions for peace and reconstruction. Hence, the rehabilitation of damaged transport infrastructure has emerged as an overarching investment priority among donors and governments. This paper brings together two distinct strands of literature on the effects of conflict on welfare and on the economic impact of transport infrastructure. The theoretical model explores how transport infrastructure affects conflict incidence and welfare when selection into rebel groups is endogenous. The implications of the model are tested with data from the Democratic Republic of Congo. The analysis addresses the problems of the endogeneity of transport costs and conflict using a novel set of instrumental variables. For transport costs, a new instrument is developed, the "natural-historical path," which measures the most efficient travel route to a market, taking into account topography, land cover, and historical caravan routes. Recognizing the imprecision in measuring the geographic impacts of conflict, the analysis develops a spatial kernel density function to proxy for the incidence of conflict. To account for its endogeneity, it is instrumented with ethnic fractionalization and distance to the eastern border. A variety of indicators of well-being are used: a wealth index, a poverty index, and local gross domestic product. The results suggest that, in most situations, reducing transport costs has the expected beneficial impacts on all the measures of welfare. However, when there is intense conflict, improvements in infrastructure may not have the anticipated benefits. The results suggest the need for more nuanced strategies that take into account varying circumstances and consider actions that jointly target governance with construction activities.

Armed Conflict --- Conflict --- Conflict and Development --- Drc --- Economic Theory & Research --- Ethnic Fractionalization --- Kernel Density --- Macroeconomics and Economic Growth --- Natural Historical Path --- Nightlight Data --- Post Conflict Reconstruction --- Poverty Reduction --- Rural Poverty Reduction --- Transport --- Transport Economics Policy & Planning

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Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries.

Technology: general issues --- History of engineering & technology --- fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D–S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion --- n/a --- D-S evidence theory

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Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries.

fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D–S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion --- n/a --- D-S evidence theory

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The landscape of healthcare is dynamic, gradually becoming more complicated with factors beyond simple supply and demand. Similar to the diversity of social, political and economic contexts, the practical utilization of healthcare resources also varies around the world. However, the spatial components of these contexts, along with aspects of supply and demand, can reveal a common theme among these factors. This book presents advancements in GIS applications that reveal the complexity of and solutions for a dynamic healthcare landscape.

Humanities --- Social interaction --- GIS --- urban health --- health clusters --- kernel density --- hotspot analysis --- healthcare planning --- health geomatics --- public health --- emergency medical facilities --- traffic jam --- megacity --- network-based location-allocation model --- Beijing --- healthcare critical infrastructure --- geovisualization --- geographic information system --- colored petri net --- COVID-19 --- social media data --- sina weibo --- spatiotemporal characteristics --- automated external defibrillator --- public access defibrillation --- out-of-hospital cardiac arrest --- resuscitation --- risk mapping --- geographical accessibility --- local scale --- municipality --- healthcare services --- spatial planning --- decentralization --- usability assessment --- web GIS --- cancer --- service area --- geospatial health --- spatial disparities --- accessibility --- subway expansion --- public transport network --- cross-border cooperation --- geographic information systems --- Iberian borderland --- strategic planning --- sustainable planning --- disaster preparedness --- smart cities --- sustainable cities --- food desert --- regression analysis --- GIS --- urban health --- health clusters --- kernel density --- hotspot analysis --- healthcare planning --- health geomatics --- public health --- emergency medical facilities --- traffic jam --- megacity --- network-based location-allocation model --- Beijing --- healthcare critical infrastructure --- geovisualization --- geographic information system --- colored petri net --- COVID-19 --- social media data --- sina weibo --- spatiotemporal characteristics --- automated external defibrillator --- public access defibrillation --- out-of-hospital cardiac arrest --- resuscitation --- risk mapping --- geographical accessibility --- local scale --- municipality --- healthcare services --- spatial planning --- decentralization --- usability assessment --- web GIS --- cancer --- service area --- geospatial health --- spatial disparities --- accessibility --- subway expansion --- public transport network --- cross-border cooperation --- geographic information systems --- Iberian borderland --- strategic planning --- sustainable planning --- disaster preparedness --- smart cities --- sustainable cities --- food desert --- regression analysis

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Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries.

Technology: general issues --- History of engineering & technology --- fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D-S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion --- fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D-S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion