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Purpose of Review We survey operational models of water-distribution systems. Although such modeling is important in its own right, our focus is motivated by the growing desire to examine and manage the nexus between water-distribution and electricity systems. As such, our survey discusses computational challenges in modeling water-distribution systems, co-ordination dynamics between water-distribution and electricity systems, and gaps in the literature. Recent Findings Modeling water-distribution systems is made difficult by their highly non-linear and non-convex physical properties. Co-ordinating water-distribution and electricity systems, especially with the growing supply and demand uncertainties of the latter, requires fast optimization techniques for real-time system management. Although many works suggest means of co-ordinating the two systems, practical applications are limited, due to the systems having separate and autonomous management and ownership. Nonetheless, recent works are navigating this challenge, by seeking methods to foster improved co-ordination of the two systems while respecting their autonomy. Additionally, with the backdrop of increased security threats, there is a growing need to bolster infrastructure protection, which is complicated by the intertwined nature of the two systems. Summary By providing a steady supply of potable water to satisfy residential, commercial, agricultural, and industrial demands, water-distribution systems are pivotal components of modern society and infrastructure. The extant literature presents many models and optimization strategies that are tailored for operating water-distribution systems. Yet, there remain unexplored problems, particularly related to simplifying model computation, capturing the flexibility of water-distribution systems, and capturing interdependencies between water-distribution and other systems and infrastructures. Future research that addresses these gaps will allow greater operational efficiency and resilience. 

Coal has long history in Ohio and across the Appalachian region (Crowell 1995History of Coal-Mining Industry in Ohio State of Ohio, Department of Natural Resources, Division of Geological survey). The industry has had a major impact on the communities in various ways from underground mining, surface mining, and coal- fired electricity generation (Keenan and Robert 2010An Ecopolitical System of Global Significance” in “Always A River: The Ohio River and the American Experience; Lobao et al 2016Rural Sociol.81343–86). As the U.S. moves away from coal, the mines and coal-fired power plants close, creating significant economic hardships for the communities that relied on the coal industry (Blaackeret al2012Organ. Environ.25385–401; Grubert 2012Energy Policy44174–84; Grubert 2020Science3701171–3; Haggertyet al2018Resour. Policy5769–80). Yet even after the industry has left, the residents of many towns still felt connected to coal and still consider themselves a ‘coal community’. Local history and industry messaging help re- enforce this idea, but those factors are part of a larger phenomenon around the growing and shifting image of coal (Bell and York 2010Rural Sociol.75111–43; Lewin 2019Soc. Probl.6651–68). This article examines how the image of coal has grown over time to be associated with many different values that coal community members identify with and want to attach to themselves. From hardworking coal miners, to town-defining power plant smokestacks, to hunting and fishing on reclaimed coal lands. The image of coal has come to represent a myriad of things that still represent these coal communities allowing them to interact with the image of coal long after the industry and tangible impact of coal has left. In analyzing interview data with fifty coal employees, local leaders and town residents from across four coal communities across southeast Ohio and northern West Virginia at varying stages of coal transition, this article uses concepts from postmodern social theory to illustrate the nature of how the meanings and identity of coal towns persist even after there is no longer coal. The findings advance our understanding of how coal-dependent communities continue to grapple with the societal transition away from coal energy and provide context for addressing the coal transition beyond economic factors. 

Weak sustainability (WS) requires that the inclusive wealth (IW) of a place (e.g., the world, a nation, or a sub-national region) be non-decreasing over a long time. The WS framework provides a more complete account of the sustainability of a place than do sustainability indicators or conventional economic measures, such as gross domestic product. However, while many decisions that affect sustainability are made at regional and local levels, the abstract theory of WS was developed without explicit recognition of the porosity of geographic boundaries and the interdependencies of regions. In this paper, we make three contributions: a carefully reasoned defense of IW per capita as the WS criterion, an improved understanding of the relationship between mobility, labor productivity, and regional economic growth, and an empirical application to US counties that demonstrates the feasibility of empirical regional WS assessment by summarizing Jones’ research. This analysis, extending the framework developed by Arrow and co-authors, accounts for more region-specific factors related to population, most notably the labor productivity component of health capital, and assesses IW per capita for all 50 states and 3108 counties in the US from 2010 to 2017. These improved methods revealed substantially more states and counties that were not WS relative to results using the Arrow et al. framework. The not-WS counties exhibited a distinct rural bias, as regional scientists have suspected but, nevertheless, the majority of rural counties were WS. Our work demonstrated that regional WS assessment is feasible, produces results that are consistent with prior expectations based on reasoning and empirical research, and has the potential to provide fresh insights into longstanding questions of regional development. 

Electricity systems in many parts of the world are becoming more dependent upon natural gas as an electricity-generation fuel. As such, electricity and natural-gas markets are becoming more interconnected. Contemporaneously, some electricity and natural-gas markets are integrating vertically, through the merger of electricity and natural-gas suppliers. The market-efficiency impacts of such vertical integration are unclear. On one hand, vertical integration could exacerbate market power, whereas on another it could mitigate double marginalization. To study this question, this paper develops a Nash–Cournot model of the two interconnected markets. The model is converted into a linear complementarity problem, which allows deriving Nash equilibria readily. Some theoretical results are derived for the case of a merger involving symmetric firms. In addition, the model is applied to a stylized example with a range of parameter values. We find that integration is social-welfare enhancing—which implies that mitigating double marginalization outweighs the exercise of market power. In most cases, the effects of merger can give rise to a prisoner’s-dilemma-type outcome. Merger is beneficial to the merging firms. However, profits of non-merging firms and total supplier profits decrease following a merger. Overall, our results suggest that vertical integration in energy markets may be socially beneficial. JEL Classification:C61, C72, D43, L1, L94, L95, Q4 

Plant counting is a critical aspect of crop management, providing farmers with valuable insights into seed germination success and within-field variation in crop population density, both of which are key indicators of crop yield and quality. Recent advancements in Unmanned Aerial System (UAS) technology, coupled with deep learning techniques, have facilitated the development of automated plant counting methods. Various computer vision models based on UAS images are available for detecting and classifying crop plants. However, their accuracy relies largely on the availability of substantial manually labeled training datasets. The objective of this study was to develop a robust corn counting model by developing and integrating an automatic image annotation framework. This study used high-spatial-resolution images collected with a DJI Mavic Pro 2 at the V2–V4 growth stage of corn plants from a field in Wooster, Ohio. The automated image annotation process involved extracting corn rows and applying image enhancement techniques to automatically annotate images as either corn or non-corn, resulting in 80% accuracy in identifying corn plants. The accuracy of corn stand identification was further improved by training four deep learning (DL) models, including InceptionV3, VGG16, VGG19, and Vision Transformer (ViT), with annotated images across various datasets. Notably, VGG16 outperformed the other three models, achieving an F1 score of 0.955. When the corn counts were compared to ground truth data across five test regions, VGG achieved an R2 of 0.94 and an RMSE of 9.95. The integration of an automated image annotation process into the training of the DL models provided notable benefits in terms of model scaling and consistency. The developed framework can efficiently manage large-scale data generation, streamlining the process for the rapid development and deployment of corn counting DL models. 

One of the most important and widespread corn/maize virus diseases is maize dwarf mosaic (MDM), which can be induced by sugarcane mosaic virus (SCMV). This study explores a machine learning analysis of five-band multispectral imagery collected via an unmanned aerial system (UAS) during the 2021 and 2022 seasons for SCMV disease detection in corn fields. The three primary objectives are to (i) determine the spectral bands and vegetation indices that are most important or correlated with SCMV infection in corn, (ii) compare spectral signatures of mock-inoculated and SCMV-inoculated plants, and (iii) compare the performance of four machine learning algorithms, including ridge regression, support vector machine (SVM), random forest, and XGBoost, in predicting SCMV during early and late stages in corn. On average, SCMV-inoculated plants had higher reflectance values for blue, green, red, and red-edge bands and lower reflectance for near-infrared as compared to mock-inoculated samples. Across both years, the XGBoost regression model performed best for predicting disease incidence percentage (R2 = 0.29, RMSE = 29.26), and SVM classification performed best for the binary prediction of SCMV-inoculated vs. mock-inoculated samples (72.9% accuracy). Generally, model performances appeared to increase as the season progressed into August and September. According to Shapley additive explanations (SHAP analysis) of the top performing models, the simplified canopy chlorophyll content index (SCCCI) and saturation index (SI) were the vegetation indices that consistently had the strongest impacts on model behavior for SCMV disease regression and classification prediction. The findings of this study demonstrate the potential for the development of UAS image-based tools for farmers, aiming to facilitate the precise identification and mapping of SCMV infection in corn.