23 research outputs found
Navigating the Paradox: Climate Change, Cutting-Edge Technologies, and Groundwater Sustainability
No full textThis article explores the paradoxical relationship between climate change, advanced technologies, and groundwater sustainability. It highlights how emerging technologies like artificial intelligence, blockchain, and the Internet of Things (IoT) offer innovative solutions for optimizing groundwater management while addressing climate change impacts. However, the chapter also warns of the environmental risks associated with these technologies, particularly their energy consumption and e-waste generation, which can further exacerbate climate challenges. The chapter examines practical applications such as desalination, precision farming, and water harvesting, evaluating their contributions to groundwater management and their environmental footprints. It argues that the net impact of these technologies depends largely on their design, implementation, and governance frameworks. The research identifies best practices to maximize benefits while minimizing negative environmental consequences. This work addresses key issues of water scarcity and the need for sustainable water supplies in a changing climate. It underscores the importance of fresh water for essential industries, including agriculture, energy production, and mineral processing, while acknowledging the profound effects of climate change and societal shifts on traditional water sources. The chapter also discusses the risks associated with technological investments in water management, such as toxic waste emissions, geopolitical tensions, and corruption. It emphasizes that emissions from these processes contribute significantly to rising atmospheric temperatures and water vapor levels, intensifying climate change. The chapter concludes by advocating for a holistic approach to water management, balancing the costs, benefits, and risks of emerging technologies. It highlights the potential of green engineering advancements and efficient water treatment methods, such as desalination and cleaner urban designs, to sustainably provide fresh groundwater for various uses. The chapter integrates data analytics from engineering and public health performance metrics to establish safe industry targets and calls for responsible governance to ensure technologies contribute positively to both groundwater sustainability and climate change mitigation
Leveraging Artificial Intelligence for Enhancing Wheat Yield Resilience Amidst Climate Change in Sub-Saharan Africa
No full textThe introduction of a deep learning-based method for non-destructive leaf area index (LAI) assessment has enhanced rapid estimation for wheat and similar crops, aiding crop growth monitoring, water, and nutrient management. Convolutional Neural Network (CNN)-based algorithms enable accurate, non-destructive quantification of seedling leaf areas and assess LAI across diverse genotypes and environments, demonstrating adaptability. Transfer learning, known for efficiency in plant phenotyping, was tested as a resource-saving approach for training the wheat LAI model. These advancements support wheat breeding, facilitate genotype selection for varied environments, accelerate genetic gains, and enhance genomic selection for LAI. By capturing diverse environments, this method can improve wheat resilience to climate change. Additionally, advances in machine learning and data science enable better prediction and distribution mapping of global wheat rust pathogens, a major agricultural challenge. Accurate risk identification allows for timely and effective control measures. Moreover, wheat lodging prediction models using CNNs can assess lodging-prone varieties, influencing selection decisions to improve yield stability. These artificial intelligence-driven techniques contribute to sustainable crop growth and yield enhancement, especially in the context of climate change and increasing global food demand
Role of Redox Reactions and AI-Driven Approaches in Enhancing Nutrient Availability for Plants
No full textEmpirical studies have shown that environmental variability in the field remains uncontrolled in certain cases, with research often conducted at a limited number of agricultural sites. Direct measurements of redox potential in soils have been reported, yet quantifying rapid changes in this variable across microsites proves inaccessible in situ. Existing measurements of redox potential also fail to account for variability in the identity of reduced or oxidized compounds. Additionally, methodological constraints and researcher bias, particularly in studies focusing on processes in reduced sediments, may impair interpretations of anabolic reactions resulting from oxidation.Case studies further indicate that the effects of redox potential on nitrification, net mineralization, or immobilization of other nutrients often remain unmeasured. As a result, increased denitrification might stimulate nitrification, reducing the effects of nitrogen immobilization due to increasing carbon storage in environments where reduction predominates.Given the absence of studies specifically exploring the balance between reduction and oxidation in relation to nutrient availability, assessing the magnitude and likelihood of methodological shortcomings based on prior field research remains challenging. Existing research serves as a foundation for understanding how this balance may significantly influence nutrient dynamics and availability at larger scales. Future studies manipulating redox potential in the field should consider factors that could disproportionately facilitate reductions before an eastward shift occurs in the balance between oxidation and reduction in response to organic matter addition. Addressing these gaps will enhance understanding of redox reactions and their potential role in stimulating denitrification and sulfide responses
Navigating the Paradox: Climate Change, Cutting-Edge Technologies, and Groundwater Sustainability
No full textThis article explores the paradoxical relationship between climate change, advanced technologies, and groundwater sustainability. It highlights how emerging technologies like artificial intelligence, blockchain, and the Internet of Things (IoT) offer innovative solutions for optimizing groundwater management while addressing climate change impacts. However, the chapter also warns of the environmental risks associated with these technologies, particularly their energy consumption and e-waste generation, which can further exacerbate climate challenges. The chapter examines practical applications such as desalination, precision farming, and water harvesting, evaluating their contributions to groundwater management and their environmental footprints. It argues that the net impact of these technologies depends largely on their design, implementation, and governance frameworks. The research identifies best practices to maximize benefits while minimizing negative environmental consequences. This work addresses key issues of water scarcity and the need for sustainable water supplies in a changing climate. It underscores the importance of fresh water for essential industries, including agriculture, energy production, and mineral processing, while acknowledging the profound effects of climate change and societal shifts on traditional water sources. The chapter also discusses the risks associated with technological investments in water management, such as toxic waste emissions, geopolitical tensions, and corruption. It emphasizes that emissions from these processes contribute significantly to rising atmospheric temperatures and water vapor levels, intensifying climate change. The chapter concludes by advocating for a holistic approach to water management, balancing the costs, benefits, and risks of emerging technologies. It highlights the potential of green engineering advancements and efficient water treatment methods, such as desalination and cleaner urban designs, to sustainably provide fresh groundwater for various uses. The chapter integrates data analytics from engineering and public health performance metrics to establish safe industry targets and calls for responsible governance to ensure technologies contribute positively to both groundwater sustainability and climate change mitigation
Leveraging Artificial Intelligence for Enhancing Wheat Yield Resilience Amidst Climate Change in Sub-Saharan Africa
No full textThe introduction of a deep learning-based method for non-destructive leaf area index (LAI) assessment has enhanced rapid estimation for wheat and similar crops, aiding crop growth monitoring, water, and nutrient management. Convolutional Neural Network (CNN)-based algorithms enable accurate, non-destructive quantification of seedling leaf areas and assess LAI across diverse genotypes and environments, demonstrating adaptability. Transfer learning, known for efficiency in plant phenotyping, was tested as a resource-saving approach for training the wheat LAI model. These advancements support wheat breeding, facilitate genotype selection for varied environments, accelerate genetic gains, and enhance genomic selection for LAI. By capturing diverse environments, this method can improve wheat resilience to climate change. Additionally, advances in machine learning and data science enable better prediction and distribution mapping of global wheat rust pathogens, a major agricultural challenge. Accurate risk identification allows for timely and effective control measures. Moreover, wheat lodging prediction models using CNNs can assess lodging-prone varieties, influencing selection decisions to improve yield stability. These artificial intelligence-driven techniques contribute to sustainable crop growth and yield enhancement, especially in the context of climate change and increasing global food demand
Role of Redox Reactions and AI-Driven Approaches in Enhancing Nutrient Availability for Plants
No full textEmpirical studies have shown that environmental variability in the field remains uncontrolled in certain cases, with research often conducted at a limited number of agricultural sites. Direct measurements of redox potential in soils have been reported, yet quantifying rapid changes in this variable across microsites proves inaccessible in situ. Existing measurements of redox potential also fail to account for variability in the identity of reduced or oxidized compounds. Additionally, methodological constraints and researcher bias, particularly in studies focusing on processes in reduced sediments, may impair interpretations of anabolic reactions resulting from oxidation.Case studies further indicate that the effects of redox potential on nitrification, net mineralization, or immobilization of other nutrients often remain unmeasured. As a result, increased denitrification might stimulate nitrification, reducing the effects of nitrogen immobilization due to increasing carbon storage in environments where reduction predominates.Given the absence of studies specifically exploring the balance between reduction and oxidation in relation to nutrient availability, assessing the magnitude and likelihood of methodological shortcomings based on prior field research remains challenging. Existing research serves as a foundation for understanding how this balance may significantly influence nutrient dynamics and availability at larger scales. Future studies manipulating redox potential in the field should consider factors that could disproportionately facilitate reductions before an eastward shift occurs in the balance between oxidation and reduction in response to organic matter addition. Addressing these gaps will enhance understanding of redox reactions and their potential role in stimulating denitrification and sulfide responses
AI-Powered Satellite Imagery Processing for Global Air Traffic Surveillance
No full textThe increasing complexity of global air traffic management requires innovative surveillance solutions beyond traditional radar. This chapter explores the integration of artificial intelligence (AI) and machine learning (ML) in satellite imagery processing for enhanced air traffic surveillance. The proposed AI framework utilizes satellite remote sensing, computer vision algorithms, and geo-stamped aircraft data to improve real-time detection and classification. It addresses limitations in conventional systems, particularly in areas lacking radar coverage. The study outlines a three-phase approach: extracting radar coverage from satellite imagery, labeling data with geo-stamped aircraft locations, and applying deep learning models for classification. YOLO and Faster R-CNN models distinguish aircraft from other objects with high accuracy. Experimental trials demonstrate AI-enhanced satellite monitoring\u27s feasibility, achieving improved detection in high-traffic zones. The system enhances situational awareness, optimizes flight planning, reduces airspace congestion, and strengthens security. It also aids disaster response by enabling rapid search-and-rescue missions. Challenges like adverse weather and nighttime monitoring remain, requiring infrared sensors and radar-based techniques. By combining big data analytics, cloud computing, and satellite monitoring, the study offers a scalable, cost-effective solution for future air traffic management. Future research will refine models and expand predictive analytics for autonomous surveillance, revolutionizing aviation safety and operational intelligence
Conducta antisocial que influye en el rendimiento académico de los estudiantes en un entorno educativo en Kenia
No full textAntisocial behavior has increased and affects learning processes, especially among students who engage in deviant behavior. This study investigated the causes and consequences of antisocial behavior among secondary school students in Nyamira, Kenya, with the aim of proposing educational intervention strategies. A qualitative approach supported by descriptive quantitative data was used. The sample consisted of 67 students randomly selected from six schools. Interviews, open-ended questionnaires, psychological tests, and a phenomenological approach were used to analyze participants\u27 experiences. Although the design was qualitative, percentages were incorporated to describe trends within the group. The findings revealed that alcohol consumption (66% in men, 34% in women) and smoking (32%, men only) were the most common behaviors. Eighty-one percent rejected drug use, and 39% had attempted suicide. Furthermore, 93% considered that the media influences antisocial behavior. It is concluded that these behaviors are associated with dysfunctional family environments, social pressure, and lack of guidance. It is recommended to implement both general and individual preventive strategies, including psychological support, differentiated pedagogical interventions, and collaborative efforts between the school, family, and community.El comportamiento antisocial ha aumentado y afecta los procesos de aprendizaje, especialmente entre estudiantes que adoptan conductas desviadas. Este estudio investigó las causas y consecuencias del comportamiento antisocial en estudiantes de secundaria de Nyamira, Kenia, con el objetivo de proponer estrategias de intervención educativa. Se empleó un enfoque cualitativo apoyado en datos cuantitativos descriptivos. La muestra estuvo compuesta por 67 estudiantes seleccionados aleatoriamente de seis escuelas. Se utilizaron entrevistas, cuestionarios abiertos, pruebas psicológicas y un enfoque fenomenológico para analizar las experiencias de los participantes. Aunque el diseño fue cualitativo, se incorporaron porcentajes para describir tendencias dentro del grupo. Los hallazgos revelaron que el consumo de alcohol (66% en hombres, 34% en mujeres) y el tabaquismo (32%, solo hombres) fueron las conductas más comunes. Un 81% rechazó el uso de drogas, y el 39% había intentado suicidarse. Además, el 93% consideró que los medios influyen en la conducta antisocial. Se concluye que estas conductas están asociadas a contextos familiares disfuncionales, presión social y falta de orientación. Se recomienda aplicar estrategias preventivas tanto generales como individuales, incluyendo apoyo psicológico, acciones pedagógicas diferenciadas y trabajo conjunto entre escuela, familia y comunidad
Machine Learning-Based and AI Powered Satellite Imagery Processing for Global Air Traffic Surveillance Systems
No full textThe unprecedented growth of global air traffic has put immense pressure on the air traffic management systems. In light of that, global air traffic situational awareness and surveillance are indispensable, especially for satellite-based aircraft tracking systems. There has been some crucial development in the field; however, every major player in this arena relies on a single proprietary, non-transparent data feed. This is where this chapter differentiates itself. AIS data has been gaining traction recently for the same purpose and has matured considerably over the past decade; however, satellite-based communication service providers have failed to instrument significant portions of the world’s oceans. This study proposes a multimodal artificial intelligence-powered algorithm to boost the estimates of global air traffic situational awareness using the Global Air Traffic Visualization dataset. Two multimodal artificial intelligence agents categorically detect air traffic streaks in a huge collection of satellite images and notify the geospatial temporal statistical agent whenever both modalities are in concordance. A user can fine-tune the multimodal threshold hyperparameter based on the installed detection rate of datasets to get the best satellite-derived air traffic estimates.
Evaluación del Papel de la Integración de la Inteligencia Artificial en el Desarrollo del Currículo Basado en Competencias y Estrategias de Implementación Efectiva en las Escuelas Secundarias de Nivel Junior en el Condado de Kilifi
No full textIntroducción: Este estudio examina la integración de la Inteligencia Artificial (IA) en el desarrollo del Currículo Basado en Competencias (CBC) y su implementación efectiva en las Escuelas Secundarias de Nivel Junior (JSSs) en el condado de Kilifi, Kenia. A medida que la IA sigue transformando la educación a nivel mundial, la reciente adopción del CBC en Kenia presenta una oportunidad para mejorar el aprendizaje mediante tecnologías impulsadas por IA. Metodología: Utilizando un enfoque de métodos mixtos, el estudio explora las prácticas actuales del currículo, las percepciones de educadores y formuladores de políticas sobre la integración de la IA, así como los desafíos que enfrentan las instituciones clave en la implementación de un currículo mejorado con IA. Resultados: Los hallazgos indican que la IA tiene el potencial de mejorar el desarrollo curricular mediante el aprendizaje personalizado, el análisis predictivo y la retroalimentación en tiempo real. Sin embargo, varios desafíos dificultan su adopción, entre ellos la infraestructura digital deficiente, la falta de formación docente y la resistencia al cambio. Además, las brechas en la gobernanza y las políticas limitan aún más la integración exitosa de la IA en la educación. Este estudio propone estrategias para mejorar el currículo a través de la IA, incluyendo iniciativas de capacitación para educadores, recomendaciones de políticas para una adopción estructurada de la IA y marcos para su implementación colaborativa. Conclusiones: La investigación contribuye a la toma de decisiones basada en evidencia en el desarrollo curricular y ofrece información clave para los formuladores de políticas educativas, el Instituto de Desarrollo Curricular de Kenia (KICD) y otros actores interesados en modernizar la educación a través de la IA. Al abordar las barreras de implementación, la IA puede desempeñar un papel transformador en la promoción del aprendizaje basado en competencias, preparando a los estudiantes con las habilidades necesarias para la economía digital en evolución.Introduction: This study examines the integration of Artificial Intelligence (AI) in Competence-Based Curriculum (CBC) development and its effective implementation in Junior Secondary Schools (JSSs) in Kilifi County, Kenya. As AI continues to reshape education globally, Kenya’s recent adoption of CBC presents an opportunity to enhance learning through AI-driven technologies. Methodology: Using a mixed-methods approach, the study explores current curriculum practices, the perceptions of educators and policymakers on AI integration, and the challenges faced by key institutions in implementing AI-enhanced curricula. Results: Findings indicate that AI has the potential to improve curriculum development by enabling personalized learning, predictive analytics, and real-time feedback. However, several challenges hinder AI adoption, including weak digital infrastructure, limited teacher training, and resistance to change. Additionally, governance and policy gaps further constrain the successful integration of AI in education. This study proposes strategies for AI-driven curriculum enhancement, including capacity-building initiatives for educators, policy recommendations for structured AI adoption, and frameworks for collaborative AI implementation. Conclusions: The research contributes to evidence-based decision-making in curriculum development and offers insights for educational policymakers, the Kenya Institute of Curriculum Development (KICD), and other stakeholders seeking to modernize education through AI. By addressing implementation barriers, AI can play a transformative role in fostering competency-based learning, equipping students with the skills necessary for the evolving digital economy
