California mandated that 100% of vehicles sold must be electric by 2035. As electric vehicles (EVs) reach a higher penetration of the car sector, cities will need to provide publicly accessible charging stations to me...California mandated that 100% of vehicles sold must be electric by 2035. As electric vehicles (EVs) reach a higher penetration of the car sector, cities will need to provide publicly accessible charging stations to meet the charging demand of people who do not have access to a private charging spot like a personal garage. We have chosen to limit our scope to San Diego County due to its non-trivial size, well-defined shape, and dependence on personal vehicles;this project models 100% of current vehicles as electric, roughly 2.5 million. By planning for the future, our model becomes more useful as well as more equitable. We anticipate that our model will find locations that can service multiple population centers, while also maximizing distance to other stations. Sensitivity analysis and testing of our algorithms are conducted for Coronado Island, an island with 24,697 residents. Our formulation is then scaled to set the parameters for the whole county.展开更多
As flood extreme occurrences are projected to increase in intense and frequency due to climate change, the assessment of vulnerability and the identification of the most vulnerable areas, populations, assets and syste...As flood extreme occurrences are projected to increase in intense and frequency due to climate change, the assessment of vulnerability and the identification of the most vulnerable areas, populations, assets and systems are an urgent need. Vulnerability has been widely discussed and several flood projection tools have been developed using complex hydrological models. However, despite the significant contribution of flood projection maps to predicting the impact of potential floods, they are difficult and impractical to use by stakeholders and policy makers, while they have proven to be inefficient and out of date in several cases. This research aims to cover the gaps in coastal and riverine flood management, developing a method that models flood patterns, using geospatial data of past large flood disasters. The outcomes of this research produce a five scale vulnerability assessment method, which could be widely implemented in all sectors, including transport, critical infrastructure, public health, tourism, constructions etc. Moreover, they could facilitate decision making and provide a wide range of implementation by all stakeholders, insurance agents, land-use planners, risk experts and of course individual. According to this research, the majority of the elements exposed to flood hazards, lay at specific combinations between 1) elevation (Ei) and 2) distance from water-masses (Di), expressed as (Ei, Di), including: 1) in general landscapes: ([0 m, 1 m), [0 km, 6 km), [0 m - 3 m), [0 km, 3 km)) and ([0 m - 6 m), [0 km, 1 km)), 2) in low laying regions: ([0 m, 1 m), [0 km, 40 km), [0 m - 3 m), [0 km, 30 km)) and ([0 m - 6 m), [0 km, 15 km)) and 2) in riverine regions: ([0 m, 4 m), [0 km, 3 km)). All elements laying on these elevations and distances from water masses are considered extremely and highly vulnerable to flood extremes.展开更多
Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grow...Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.展开更多
文摘California mandated that 100% of vehicles sold must be electric by 2035. As electric vehicles (EVs) reach a higher penetration of the car sector, cities will need to provide publicly accessible charging stations to meet the charging demand of people who do not have access to a private charging spot like a personal garage. We have chosen to limit our scope to San Diego County due to its non-trivial size, well-defined shape, and dependence on personal vehicles;this project models 100% of current vehicles as electric, roughly 2.5 million. By planning for the future, our model becomes more useful as well as more equitable. We anticipate that our model will find locations that can service multiple population centers, while also maximizing distance to other stations. Sensitivity analysis and testing of our algorithms are conducted for Coronado Island, an island with 24,697 residents. Our formulation is then scaled to set the parameters for the whole county.
文摘As flood extreme occurrences are projected to increase in intense and frequency due to climate change, the assessment of vulnerability and the identification of the most vulnerable areas, populations, assets and systems are an urgent need. Vulnerability has been widely discussed and several flood projection tools have been developed using complex hydrological models. However, despite the significant contribution of flood projection maps to predicting the impact of potential floods, they are difficult and impractical to use by stakeholders and policy makers, while they have proven to be inefficient and out of date in several cases. This research aims to cover the gaps in coastal and riverine flood management, developing a method that models flood patterns, using geospatial data of past large flood disasters. The outcomes of this research produce a five scale vulnerability assessment method, which could be widely implemented in all sectors, including transport, critical infrastructure, public health, tourism, constructions etc. Moreover, they could facilitate decision making and provide a wide range of implementation by all stakeholders, insurance agents, land-use planners, risk experts and of course individual. According to this research, the majority of the elements exposed to flood hazards, lay at specific combinations between 1) elevation (Ei) and 2) distance from water-masses (Di), expressed as (Ei, Di), including: 1) in general landscapes: ([0 m, 1 m), [0 km, 6 km), [0 m - 3 m), [0 km, 3 km)) and ([0 m - 6 m), [0 km, 1 km)), 2) in low laying regions: ([0 m, 1 m), [0 km, 40 km), [0 m - 3 m), [0 km, 30 km)) and ([0 m - 6 m), [0 km, 15 km)) and 2) in riverine regions: ([0 m, 4 m), [0 km, 3 km)). All elements laying on these elevations and distances from water masses are considered extremely and highly vulnerable to flood extremes.
文摘Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.
