Search Results (461)

Reliable and fast communication between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is essential for effective coordination in agricultural settings, particularly when human involvement is part of the system. This study systematically compares two communication architectures representing centralized and decentralized communication frameworks: (a) MAVLink (decentralized) and (b) Farm Management Information System (FMIS) (centralized). Field experiments were conducted in both empty field and orchard environments, using a rotary UAV for worker detection and a UGV responding to intent signaled through color-coded hats. Across 120 trials, the system performance was assessed in terms of communication reliability, latency, energy consumption, and responsiveness. FMIS consistently demonstrated higher message delivery success rates (97% in both environments) than MAVLink (83% in the empty field and 70% in the orchard). However, it resulted in higher UGV resource usage. Conversely, MAVLink achieved reduced UGV power draw and lower latency, but it was more affected by obstructed settings and also resulted in increased UAV battery consumption. In conclusion, MAVLink is suitable for time-sensitive operations that require rapid feedback, while FMIS is better suited for tasks that demand reliable communication in complex agricultural environments. Consequently, the selection between MAVLink and FMIS should be guided by the specific mission goals and environmental conditions. Full article

The growing importance of microgrids—linking buildings with distributed energy resources and storage—is driving the evolution of Smart Local Energy Systems (SLESs). These systems require advanced modeling and simulations to address growing complexity, decentralization, and interoperability. This review presents an analysis of commonly used environments and methods applied in the design and operation of SLESs. Particular emphasis is placed on their capabilities for multi-domain integration, predictive control, and smart automation. A novel contribution is the identification of Closed Ecological Systems (CES) and Life Support Systems (LSSs)—fully or semi-isolated environments designed to sustain human life through autonomous recycling of air, water, and other resources—as promising new application domains for SLES technologies. This review explores how concepts developed for building and energy systems, such as demand-side management, IoT-based monitoring, and edge computing, can be adapted to CES/LSS contexts, which demand isolation, autonomy, and high reliability. Challenges related to model integration, simulation scalability, and the bidirectional transfer of technologies and modeling between Earth-based and space systems are discussed. This paper concludes with a SWOT analysis and a roadmap for future research. This work lays the foundation for developing sustainable, intelligent, and autonomous energy infrastructures—both terrestrial and extraterrestrial. Full article

Wastewater treatment plants (WWTPs) are critical infrastructure that lessen the environmental impacts of human activity by stabilizing wastewaters laden with organics, chemicals, and nutrients. WWTPs face an increasing global population, greater wastewater volumes, stricter environmental regulations, and additional societal pressures to implement more sustainable and energy-efficient waste management strategies. WWTPs are energy-intensive facilities that generate significant GHG emissions and involve high operational costs. Therefore, improving the process efficiency can lead to widespread environmental and economic benefits. One promising approach is to integrate anaerobic digestion (AD) with hydrothermal carbonization (HTC) to enhance sludge treatment, optimize energy recovery, create valuable bio-based materials, and minimize sludge disposal. This study employs an LCA to evaluate the environmental impact of coupling HTC with AD compared to conventional AD treatment. HTC degrades wastewater sludge in an aqueous medium, producing carbon-dense hydrochar while reducing sludge volumes. HTC also generates an aqueous byproduct containing >30% of the original carbon as simple organics. In this system model, the aqueous byproduct is returned to AD to generate additional biogas, which then provides heat and power for the WWTP and HTC process. The results indicate that the integrated AD + HTC system significantly reduces environmental emissions and sludge volumes, increases net energy recovery, and improves wastewater sludge valorization compared to conventional AD. This research highlights the potential of AD + HTC as a key circular bioeconomy strategy, offering an innovative and efficient solution for advancing the sustainability of WWTPs. Full article

The construction of cascade reservoir systems (CRSs) is increasing globally, providing reliable energy and water resources for human social development, while also having significant impacts on the watershed water environment, particularly in terms of nitrogen and phosphorus distribution in the rivers and lakes of these areas. Watershed management authorities urgently need model tools that can comprehensively analyze the sources of nitrogen and phosphorus in CRSs and the nitrogen and phosphorus cycling in lakes and reservoirs. Therefore, this study establishes a model framework that includes a watershed nutrient load model and a hierarchical reservoir nutrient cycling model, validating and analyzing this framework in the Water Diversion Basin from the Luanhe River to Tianjin (WDBLT) in North China, which yields nitrogen and phosphorus substance flows over different time scales. The conclusions show that banning cage culture and curbing point sources improved reservoir water quality, and the internal TP flux serves as a key environmental indicator. This model framework is scientifically sound, easy to operate, and does not require high data demands, demonstrating high practical value for similar water environmental management in CRS. Full article

Wearable and implantable Lab-on-Chip (LoC) biosensors are revolutionizing healthcare by enabling continuous, real-time monitoring of physiological and biochemical parameters in non-clinical settings. These miniaturized platforms integrate sample handling, signal transduction, and data processing on a single chip, facilitating early disease detection, personalized treatment, and preventive care. This review comprehensively explores recent advancements in LoC biosensing technologies, emphasizing their application in skin-mounted patches, smart textiles, and implantable devices. Key innovations in biocompatible materials, nanostructured transducers, and flexible substrates have enabled seamless integration with the human body, while fabrication techniques such as soft lithography, 3D printing, and MEMS have accelerated development. The incorporation of nanomaterials significantly enhances sensitivity and specificity, supporting multiplexed and multi-modal sensing. We examine critical application domains, including glucose monitoring, cardiovascular diagnostics, and neurophysiological assessment. Design considerations related to biocompatibility, power management, data connectivity, and long-term stability are also discussed. Despite promising outcomes, challenges such as biofouling, signal drift, regulatory hurdles, and public acceptance remain. Future directions focus on autonomous systems powered by AI, hybrid wearable–implantable platforms, and wireless energy harvesting. This review highlights the transformative potential of LoC biosensors in shaping the future of smart, patient-centered healthcare through continuous, minimally invasive monitoring. Full article

Agentic AI and Large Language Models (LLMs) are transforming how language is understood and generated while reshaping decision-making, automation, and research practices. LLMs provide underlying reasoning capabilities, and Agentic AI systems use them to perform tasks through interactions with external tools, services, and Application Programming Interfaces (APIs). Based on a structured scoping review and thematic analysis, this study identifies that core challenges of LLMs, relating to security, privacy and trust, misinformation, misuse and bias, energy consumption, transparency and explainability, and value alignment, can propagate into Agentic AI. Beyond these inherited concerns, Agentic AI introduces new challenges, including context management, security, privacy and trust, goal misalignment, opaque decision-making, limited human oversight, multi-agent coordination, ethical and legal accountability, and long-term safety. We analyse the applications of Agentic AI powered by LLMs across six domains: education, healthcare, cybersecurity, autonomous vehicles, e-commerce, and customer service, to reveal their real-world impact. Furthermore, we demonstrate some LLM limitations using DeepSeek-R1 and GPT-4o. To the best of our knowledge, this is the first comprehensive study to integrate the challenges and applications of LLMs and Agentic AI within a single forward-looking research landscape that promotes interdisciplinary research and responsible advancement of this emerging field. Full article

The enteroendocrine system of the gastrointestinal (GI) tract is the largest endocrine organ in the human body, playing a central role in the regulation of hunger, satiety, digestion, and energy homeostasis. Numerous factors—including dietary components, physical activity, and the gut microbiota—affect the secretion of GI hormones. This study aims to analyze how these factors modulate enteroendocrine function and influence systemic metabolic regulation. This review synthesizes the current scientific literature on the physiology and distribution of enteroendocrine cells and mechanisms of hormone secretion in response to macronutrients, physical activity, and microbial metabolites. Special attention is given to the interactions between gut-derived signals and central nervous system pathways involved in appetite control. Different GI hormones are secreted in specific regions of the digestive tract in response to meal composition and timing. Macronutrients, particularly during absorption, stimulate hormone release, while physical activity influences hormone concentrations, decreasing ghrelin and increasing GLP-1, PYY, and leptin levels. The gut microbiota, through fermentation and metabolite production (e.g., SCFAs and bile acids), modulates enteroendocrine activity. Species such as Akkermansia muciniphila are associated with improved gut barrier integrity and enhanced GLP-1 secretion. These combined effects contribute to appetite regulation and energy balance. Diet composition, physical activity, and gut microbiota are key modulators of gastrointestinal hormone secretion. Their interplay significantly affects appetite regulation and metabolic health. A better understanding of these relationships may support the development of personalized strategies for managing obesity and related disorders. Full article

Central Asia (CA) faces growing Water–Energy–Food (WEF) Nexus challenges, due to its complex transboundary water management, legacy Soviet-era water infrastructure, and increasing climate and socio-economic pressures. This study presents the development of a System Dynamics Model (SDM) to evaluate WEF interdependencies across the Aral Sea Basin (ASB), including the Amu Darya and Syr Darya river basins and their sub-basins. Different downscaling strategies based on the area, population, or land use have been applied to process open-access databases at the national level in order to match the scope of the study. Climate and socio-economic assumptions were introduced through the integration of already defined Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The resulting SDM incorporates more than 500 variables interacting through mathematical relationships to generate comprehensive outputs to understand the WEF Nexus concerns. The SDM was successfully calibrated and validated across three key dimensions of the WEF Nexus: final water discharge to the Aral Sea (Mean Absolute Error, MAE, <5%), energy balance (MAE = 4.6%), and agricultural water demand (basin-wide MAE = 1.2%). The results underscore the human-driven variability of inflows to the Aral Sea and highlight the critical importance of transboundary coordination to enhance future resilience. Full article

Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. The study emphasizes challenges in energy use and advocates for sustainable management practices. By forecasting energy demand over the next three years using linear regression, it provides actionable insights for energy providers, enhancing resilience in urban environments impacted by climate change. The findings deepen our understanding of energy dynamics across various building types and promote a sustainable energy future. Stakeholders will receive targeted recommendations for aligning energy production with consumption trends while meeting environmental responsibilities. Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R²), ensuring robust analysis. Training times for models in the LUCIA building ranged from 2 to 19 s, with the Decision Tree model showing the shortest times, highlighting the need to balance computational efficiency with model performance. Full article

The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t-tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, $p<0.001$) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis ($k=4$, silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis ($R^2=0.68$, $p<0.001$) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use. Full article

The Beijing–Tianjin–Hebei (BTH) region is one of China’s most water-scarce yet economically vital areas, facing increasing challenges due to climate change and intensive human activities. This study develops an integrated Water–Food–Energy–Ecology (WFEE) simulation and regulation model to assess the system’s stability under coordinated development scenarios and extreme climate stress. A 500-year precipitation series was reconstructed using historical drought and flood records combined with wavelet analysis and machine learning models (Random Forest and Support Vector Regression). Results show that during the reconstructed historical megadrought (1633–1647), with average precipitation anomalies reaching −20% to −27%, leading to a regional water shortage rate of 16.9%, food self-sufficiency as low as 44.7%, and a critical reduction in ecological river discharge. Under future recommended scenario with enhanced water conservation, reclaimed water reuse, and expanded inter-basin transfers, the region could maintain a water shortage rate of 2.6%, achieve 69.3% food self-sufficiency, and support ecological water demand. However, long-term water resource degradation could still reduce food self-sufficiency to 62.9% and ecological outflows by 20%. The findings provide insights into adaptive water management, highlight the vulnerability of highly coupled systems to prolonged droughts, and support regional policy decisions on resilience-oriented water infrastructure planning. Full article

The Three Gorges Reservoir Area in China presents a critical conflict between industrial development and ecological conservation. It functions as a key hub for water management, energy production, and shipping, while also serving as a vital zone for ecological and environmental protection. Focusing on Zigui County, this study developed a 16-indicator evaluation system integrating geological, ecological, and socioeconomic factors. It utilized the Analytic Hierarchy Process (AHP), coefficient of variation (CV), and the Real-Coded Accelerating Genetic Algorithm-Projection Pursuit (RAGA-PP) model for evaluation, the latter of which optimizes the projection direction and utilizes PP to transform high-dimensional data into a low-dimensional space, thereby obtaining the values of the projection indices. The findings indicate the following: (1) The RAGA-PP model outperforms conventional AHP-CV methods in assessing Zigui County’s eco-geological environment, showing superior accuracy (higher Moran’s I) and spatial consistency. (2) Hotspot analysis confirms these results, revealing distinct spatial patterns. (3) From 2000 to 2020, “bad” quality areas decreased from 17.31% to 12.33%, while “moderate” or “better” zones expanded. (4) This improvement reflects favorable natural conditions and reduced human impacts. These trends underscore the effectiveness of China’s ecological civilization policies, which have prioritized sustainable development through targeted environmental governance, afforestation initiatives, and stringent regulations on industrial activities. Full article

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

The modern workplace grapples with a human energy crisis, characterized by chronic exhaustion, disengagement, and emotional depletion among employees. Traditional well-being initiatives often fail to address this systemic challenge, particularly in industrial contexts. This study introduces the Human Energy Management System (HEMS), a strategic framework to develop, implement, and refine strategies for optimizing workforce energy. Grounded in Industry 5.0’s human-centric, resilient, and sustainable principles, HEMS integrates enterprise risk management (ERM), design thinking, and the Plan-Do-Check-Act (PDCA) cycle. Employing a qualitative Design Science Research (DSR) methodology, the study reframes human energy depletion as an organizational risk, providing a proactive, empathetic, and iterative approach to mitigate workplace stressors. The HEMS framework is developed and evaluated through theoretical modeling, literature benchmarking, and secondary case studies, rather than empirical testing, aligning with DSR’s focus on conceptual validation. Findings suggest HEMS offers a robust tool to operationalize human energy reinforcement strategies in industrial settings. Consistent with the European Union’s vision for human-centric industrial transformation, HEMS enables organizations to foster a resilient, engaged, and thriving workforce in both stable and challenging times. Full article