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Eric Garcia [14]Eric Bayruns Garcia [5]
  1. Expression-Style Exclusion.Eric Bayruns Garcia - 2019 - Social Epistemology 33 (3):245-261.
    I describe a phenomenon that has not yet been described in the epistemology literature. I label this phenomenon expression-style exclusion. Expression-style exclusion is an example of how s...
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  2.  76
    Smart City and IoT Data Collection Leveraging Generative AI.Eric Garcia - manuscript
    The rapid urbanization of modern cities necessitates innovative approaches to data collection and integration for smarter urban management. With the Internet of Things (IoT) at the core of these advancements, the ability to efficiently gather, analyze, and utilize data becomes paramount. Generative Artificial Intelligence (AI) is revolutionizing data collection by enabling intelligent synthesis, anomaly detection, and real-time decision-making across interconnected systems. This paper explores how generative AI enhances IoT-driven data collection in smart cities, focusing on applications in transportation, energy, public (...)
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  3.  64
    AI-Driven Water Management Systems for Sustainable Urban Development.Eric Garcia - manuscript
    Water scarcity and inefficient water management are critical challenges for rapidly growing urban areas. Traditional water distribution systems often suffer from leaks, wastage, and inequitable access, exacerbating resource shortages. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban water management by enabling real-time monitoring, predictive maintenance, and efficient resource allocation. By integrating data from smart meters, pressure sensors, and weather forecasts, cities can reduce water losses, improve distribution efficiency, and ensure equitable access. Experimental results demonstrate significant (...)
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  4.  54
    AI-Driven Healthcare Optimization in Smart Cities.Eric Garcia - manuscript
    Urbanization poses significant challenges to healthcare systems, including overcrowded hospitals, inequitable access to care, and rising costs. Artificial Intelligence (AI) and the Internet of Things (IoT) offer transformative solutions for optimizing healthcare delivery in smart cities. This paper explores how AI-driven predictive analytics, combined with IoT-enabled wearable devices and telemedicine platforms, can enhance patient outcomes, streamline resource allocation, and reduce urban health disparities. By analyzing real-time health data and predicting disease outbreaks, this study demonstrates the potential of AI to revolutionize (...)
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  5.  54
    Resilient Urban Energy Systems: AI-Enabled Smart City Applications.Eric Garcia - manuscript
    The growing demand for energy in urban environments, coupled with the urgent need to reduce carbon emissions, necessitates innovative approaches to power generation, distribution, and consumption. Artificial Intelligence (AI)-driven smart grids offer a transformative solution by optimizing energy efficiency, integrating renewable resources, and ensuring grid stability. This paper explores how machine learning and IoT-enabled predictive analytics can enhance smart grid performance in urban areas. By addressing challenges such as demand forecasting, load balancing, and renewable energy intermittency, this study demonstrates the (...)
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  6.  53
    AI-Driven Water Management Systems for Sustainable Smart cities.Eric Garcia - manuscript
    The growing volume of urban waste poses significant environmental and economic challenges for cities worldwide. Traditional waste management systems often rely on inefficient collection routes, inadequate recycling processes, and excessive landfill usage. This paper explores how Artificial Intelligence (AI) and IoT technologies can revolutionize waste management in smart cities by enabling real-time monitoring, automated sorting, and optimized collection routes. By integrating data from smart bins, robotic sorting systems, and predictive analytics, cities can achieve zero-waste goals and promote circular economy practices. (...)
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  7.  49
    AI-Enhanced Public Safety Systems in Smart Cities.Eric Garcia - manuscript
    Ensuring public safety is a critical challenge for rapidly growing urban areas. Traditional policing and emergency response systems often struggle to keep pace with the complexity and scale of modern cities. Artificial Intelligence (AI) offers a transformative solution by enabling real-time crime prediction, optimizing emergency resource allocation, and enhancing situational awareness through IoT-enabled systems. This paper explores how AI-driven analytics, combined with data from surveillance cameras, social media, and environmental sensors, can improve public safety in smart cities. By addressing challenges (...)
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  8.  47
    AI-Optimized Urban Green Spaces: Enhancing Biodiversity and Sustainability in Smart Cities.Eric Garcia - manuscript
    Urban green spaces are vital for mitigating climate change, enhancing biodiversity, and improving citizen well-being. However, traditional methods of designing and managing these spaces often lack the precision and scalability needed to address modern urban challenges. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban green spaces in smart cities. By integrating satellite imagery, soil sensors, and machine learning models, cities can dynamically monitor plant health, predict ecological impacts, and design green zones that maximize biodiversity and (...)
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  9.  46
    AI-Enhanced Urban Mobility: Optimizing Public Transportation Systems in Smart Cities.Eric Garcia - manuscript
    Urban transportation systems face significant challenges due to increasing congestion, inefficient routes, and fluctuating passenger demand. Traditional public transportation networks often struggle to adapt dynamically to these challenges, leading to delays, overcrowding, and environmental inefficiencies. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban mobility by enabling real-time route optimization, demand forecasting, and passenger flow management. By integrating data from GPS trackers, fare collection systems, and environmental sensors, cities can reduce travel times, enhance commuter satisfaction, and (...)
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  10.  41
    AI-Driven Smart Parking Systems: Optimizing Urban Parking Efficiency and Reducing Congestion.Eric Garcia - manuscript
    Urban parking systems are a significant contributor to traffic congestion and driver frustration, with studies showing that up to 30% of urban traffic is caused by drivers searching for parking. Traditional parking systems often lack real-time data and adaptability, leading to inefficiencies such as overfilled lots and underutilized spaces. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban parking by enabling real-time parking space detection, demand forecasting, and dynamic pricing. By integrating data from IoT sensors, traffic (...)
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  11.  39
    AI-Driven Smart Wastewater Management: Enhancing Urban Water Sustainability and Resource Recovery.Eric Garcia - manuscript
    Urban wastewater management is a critical component of sustainable water cycles, but traditional systems often struggle with inefficiencies such as high operational costs, resource wastage, and environmental pollution. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban wastewater management by enabling real-time monitoring, predictive maintenance, and resource recovery. By integrating data from IoT sensors, water quality monitors, and treatment plants, cities can improve water quality, reduce operational costs, and recover valuable resources such as energy and nutrients. (...)
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  12.  37
    AI-Driven Air Quality Monitoring and Management in Smart Cities.Eric Garcia - manuscript
    Air pollution is a critical challenge for urban areas, contributing to public health crises and environmental degradation. Traditional air quality monitoring systems often lack the granularity and adaptability needed to address dynamic pollution sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance air quality management in smart cities by enabling real-time monitoring, pollution source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, satellite imagery, and traffic systems, cities can reduce pollution levels, (...)
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  13.  36
    AI-Driven Smart Lighting Systems for Energy-Efficient and Adaptive Urban Environments.Eric Garcia - manuscript
    Urban lighting systems are essential for safety, security, and quality of life, but they often consume significant energy and lack adaptability to changing conditions. Traditional lighting systems rely on fixed schedules and manual adjustments, leading to inefficiencies such as over-illumination and energy waste. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban lighting by enabling real-time adjustments, energy savings, and adaptive illumination based on environmental conditions and human activity. By integrating data from motion sensors, weather forecasts, (...)
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  14.  31
    AI-Driven Energy Efficiency in Smart Buildings: Optimizing Consumption and Reducing Carbon Footprints.Eric Garcia - manuscript
    Buildings account for a significant portion of global energy consumption and carbon emissions, making energy efficiency a critical focus for urban sustainability. Traditional building management systems often lack the adaptability and precision needed to optimize energy usage dynamically. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance energy efficiency in smart buildings by enabling real-time monitoring, predictive maintenance, and adaptive control systems. By integrating data from smart meters, occupancy sensors, and environmental monitors, cities can reduce energy waste, (...)
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  15.  30
    AI-Driven Noise Pollution Monitoring and Mitigation in Smart Cities.Eric Garcia - manuscript
    Noise pollution is a growing concern in urban areas, contributing to public health issues such as stress, sleep disturbances, and hearing loss. Traditional noise monitoring systems often lack the granularity and adaptability needed to address dynamic noise sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance noise pollution management in smart cities by enabling real-time monitoring, source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, traffic systems, and urban infrastructure, cities can (...)
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  16. On Anticipatory-Epistemic Injustice and the Distinctness of Epistemic-Injustice Phenomena.Eric Bayruns Garcia - 2021 - Social Epistemology Review and Reply Collective 7 (10):48-57.
    I present distinctness conditions that an epistemic-injustice phenomenon should meet to count as distinct from other such phenomena and I use these conditions to evaluate anticipatory-epistemic injustice’s distinctness in relation to testimonial smothering. Even though I argue that the phenomenon that Lee helpfully describes may not be distinct from testimonial smothering, I argue that the notion of distinctness itself should not be the primary or most important criterion that epistemic-injustice theorists use to determine whether such phenomena should feature in the (...)
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  17. Racial Injustice and information flow.Eric Bayruns García - 2021 - Feminist Philosophy Quarterly 7 (4):1-18.
    I submit that the critical epistemology of race and standpoint literature has not explicitly focused on the properties of information about, say, racial or gender injustice in a way similar to how epistemologists have focused on propositions and information when they describe propositional justification. I describe information in the racial-injustice-information domain in a way similar to how epistemologists describe propositional justification. To this end, I argue (C1) that if subjects in racially unjust societies tend to violate norms that promote a (...)
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  18. Testimonial Smothering’s Non-Epistemic Motives: A Reply to Goetze and Lee.Eric Bayruns García - 2022 - Social Epistemology Review and Reply Collective 1 (11):18-20.
    I argue that according to Kristie Dotson, non-epistemic motives such as social, ethical and material harm can motivate a speaker to smother her testimony. I present this exegesis of Dotson's view of testimonial smothering in response to J. L. Lee's and Trystan Goetze's reply to my commentary of Lee's view that anticipatory epistemic injustice is distinct from testimonial smothering.
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  19. Judging Students and Racial Injustice.Eric Bayruns Garcia - 2021 - APA Newsletter on Hispanic/Latino Issues in Philosophy 1 (21):15-20.
    I will argue that just and accurate assessment must involve taking into account how racial injustice affects students’ performance in their work. To this end, I will motivate what I call the RACIAL-INJUSTICE-ASSESSMENT THESIS. According to this thesis, instructors must account for how racial injustice affects a student’s work for an instructor’s judgment of her work to count as just. To motivate the RACIAL-INJUSTICE ASSESSMENT THESIS, I will defend the ACCURACY THESIS and the JUSTICE THESIS. According to the ACCURACY THESIS, (...)
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