Search Results (1,018)

Deepfake technology, driven by advances in artificial intelligence (AI) and deep learning (DL), has become one of the foremost threats to digital trust and the authenticity of information. Despite the rapid development of deepfake detection methods, the dynamic evolution of generative models continues to outpace current mitigation efforts. This highlights the pressing need for more effective and proactive deepfake prevention strategy. This study introduces a comprehensive and multifaceted deepfake prevention framework that leverages both technical and non-technical countermeasures and involves collaboration among key stakeholders in a unified structure. The proposed framework has four modules: trusted content assurance, detection and monitoring, awareness and human-in-the-loop verification, and policy, governance, and regulation. The framework uses a combination of hybrid watermarking and embedding techniques, as well as cryptographic digital signature algorithms (DSAs) and blockchain technologies, to make sure that the media is authentic, traceable, and cannot be denied. Comparative experiments were conducted in this research using both classical and post-quantum DSAs to evaluate their efficiency, resource consumption, and gas costs in blockchain operations. The results revealed that the Falcon-512 algorithm outperformed other post-quantum algorithms while consuming fewer resources and lowering gas costs, making it a preferable option for real-time, quantum-resilient deepfake prevention. The framework also employed AI-based detection models and human oversight to enhance detection accuracy and robustness. Overall, this research offers a novel, multifaceted, and governance-aware strategy for deepfake prevention. The proposed approach significantly contributes to mitigating deepfake threats and offers a practical foundation for secure and transparent digital media ecosystems. Full article

Generative Artificial Intelligence (GAI) technologies are revolutionizing productivity and creativity across educational and engineering contexts. This study addresses a critical gap by examining the key factors influencing the successful integration of GAI tools to enhance faculty and student productivity, with a focus on higher education and its role in advancing sustainable development. Specifically, it investigates challenges, opportunities, and essential conditions for effective GAI adoption that support not only academic excellence but also the preparation of engineers capable of addressing global sustainability challenges in line with the United Nations Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production). A preliminary literature review identified significant factors requiring attention, further refined through interviews with 14 students and 13 faculty members, and expanded upon via a survey involving 54 students and 42 faculty members. Participants rated the significance of various factors on a five-point Likert scale, allowing for the calculation of the Relative Importance Index (RII). The findings reveal that while compliance with ethical standards and bias mitigation emerged as the most significant concerns, mid-level considerations such as institutional support, training, and explainability are critical for fostering GAI adoption in sustainable learning environments. Foundational elements, including robust technical infrastructure, data security, and scalability, are vital for long-term success and alignment with responsible and sustainable innovation. Notably, this study highlights a divergence in perspectives between faculty and students regarding GAI’s impact on productivity, with faculty emphasizing ethical considerations and students focusing on efficiency gains. This study offers a comprehensive set of considerations and insights for guiding GAI integration in educational and engineering settings. It emphasizes the need for multidisciplinary collaboration, continuous training, and strong governance to balance innovation, responsibility, and sustainability. The findings advance theoretical understanding and provide practical insights for academia, policymakers, and technology developers aiming to harness GAI’s full potential in fostering sustainable engineering education and development. Full article

The energy sector profoundly influences both economic growth and ecosystems, making it pivotal to human development. Substantial evidence confirms that scientific and technological innovations, particularly breakthrough achievements, are key drivers of sustainable development in this sector. Consequently, comprehending the evolutionary trajectory of such breakthroughs is crucial for policymakers and practitioners to refine their strategic approaches. To analyze the evolution of energy-related breakthrough innovations, this study leverages a dataset of 552 projects awarded China’s State Science and Technology Advancement Prize. By employing large language models and word cloud analysis, we trace the shifting research priorities of the 552 projects to delineate the pathway of scientific and technological development in the energy sector. Furthermore, we utilize social network analysis to reveal the evolving collaboration patterns underlying these innovations. Our findings indicate that, consistent with China’s current energy consumption structure, most innovations remain concentrated in fossil fuels. However, a clear trend emerges: research is focusing less on fossil fuels and more on clean energy and high efficiency equipment. Regarding collaborative innovation, the pattern in China differs from that of countries like the United States; in China, universities, rather than enterprises, occupy the central position in innovation networks. The insights from this study can assist government officials in designing effective policies to support breakthrough innovations across different types of research entities. Moreover, research entities can adjust their collaboration strategies based on our findings to enhance innovation performance. Full article

This study presents a novel federated learning (FL) methodology implemented directly on STM32-based microcontrollers (MCUs) for energy-efficient smart irrigation. To the best of our knowledge, this is the first work to demonstrate end-to-end FL training and aggregation on real STM32 MCU clients (STM32F722ZE), under realistic energy and memory constraints. Unlike most prior studies that rely on simulated clients or high-power edge devices, our framework deploys lightweight neural networks trained locally on MCUs and synchronized via message queuing telemetry transport (MQTT) communication. Using a smart agriculture (SA) dataset partitioned by soil type, 7 clients collaboratively trained a model over 3 federated rounds. Experimental results show that MCU clients achieved competitive accuracy (70–82%) compared to PC clients (80–85%) while consuming orders of magnitude less energy. Specifically, MCU inference required only 0.95 mJ per sample versus 60–70 mJ on PCs, and training consumed ∼70 mJ per epoch versus nearly 20 J. Latency remained modest, with MCU inference averaging 3.2 ms per sample compared to sub-millisecond execution on PCs, a negligible overhead in irrigation scenarios. The evaluation also considers the payoff between accuracy, energy consumption, and latency through the Energy Latency Accuracy Index (ELAI). This integrated perspective highlights the trade-offs inherent in deploying FL on heterogeneous devices and demonstrates the efficiency advantages of MCU-based training in energy-constrained smart irrigation settings. Full article

With the increasing deployment of unmanned systems in maritime patrol, coastal monitoring, and environmental mapping, achieving effective UAV-USV collaboration in dynamic environments remains challenging. This paper proposes an edge-intelligence-driven collaborative control framework that integrates unified data modeling, multi-objective task scheduling, lightweight fault-tolerant middleware, and multi-sensor fusion. A Weighted Kalman Filter combines UAV imaging and USV sonar data to enhance perception accuracy, while NSGA-II optimizes task allocation considering completion time, energy consumption, and sensing reliability. The framework was validated through representative maritime scenarios, including patrol and coastal sediment mapping, on a virtual simulation platform. Results show improved task efficiency, energy utilization, communication latency, and robustness compared with single-platform and centralized scheduling approaches. The proposed method provides a balanced optimization of execution efficiency, energy consumption, data accuracy, and resilience, offering a reliable solution for large-scale maritime applications. Full article

At present, the construction of China’s shared manufacturing platform is developing rapidly. However, it is still in the stage of practical exploration, facing numerous challenges, such as difficulties in resource integration, immature business models, and a weak digital foundation. This paper takes Changzhou Zhiyun Tiangong’s “Super Virtual Factory” as an example, utilizing the grounded theory to conduct a case study on this shared manufacturing platform. Using a ‘condition-action-result’ framework, this paper explores the value co-creation (VCC) mechanism in a shared manufacturing ecosystem. We analyze how digital intelligence convergence (DIC) and supply chain collaboration (SCC) facilitate the digital intelligence transformation of consumption, production capacity, and products. The study finds that consumer insight, technological drive, government support, enterprise challenges, and the Changzhou home appliance industry cluster are the internal driving forces for the shared manufacturing ecosystem to carry out industrial ecological VCC; DIC and SCC are the two key elements for digital intelligence technology empowerment. Digital intelligence technology is empowered from three aspects—technology, resources, and structure—enabling organizational members with capability and authority while achieving “decentralization” of industrial chains. Finally, digital intelligence empowerment enables the shared manufacturing ecosystem to achieve VCC of the industrial ecosystem, thereby establishing a VCC model for the digital intelligence empowerment shared manufacturing ecosystem. The results of the study not only help enrich the theory of VCC in shared manufacturing platforms but also provide practical insights for the digital intelligence transformation of traditional manufacturing enterprises. Full article

This review examines the current status and future potential of plasma-induced acidification (PIA) as a sustainable method for managing nitrogen-rich organic waste streams such as livestock slurry and digestate. Conventional acidification using sulfuric or nitric acid reduces ammonia (NH₃) emissions but raises concerns related to safety, cost, and environmental impacts. Plasma-assisted systems offer an alternative by generating reactive nitrogen and oxygen species (RNS/ROS) in situ, lowering pH and stabilizing ammonia (NH₃), as ammonium (NH₄⁺), thereby enhancing fertiliser value and reducing emissions of NH₃, methane (CH₄), and odours. Key technologies such as dielectric barrier discharge (DBD), corona discharge, and gliding arc reactors show promise in laboratory-scale studies, but barriers like energy consumption, scalability, and N₂O trade-offs limit commercial adoption. The paper reviews the mechanisms behind PIA, compares it to conventional approaches, and assesses its agronomic and environmental benefits. Valorisation opportunities, including the recovery of nitrate-rich fractions and integration with biogas systems, align plasma treatment with circular economy goals. However, challenges remain, including reactor design, energy efficiency, and lack of recognition as a Best Available Technique (BAT). A roadmap is proposed for transitioning from lab to farm-scale application, involving cross-sector collaboration, lifecycle assessments, and policy support to accelerate adoption and realise environmental and economic gains. Full article

Climate change affects both the quantity and quality of water resources, amplifying the water crisis, slowing progress toward achieving the Sustainable Development Goals (SDGs), and contributing to the needs of future generations. To address these challenges, this study presents an interdisciplinary synthesis of the literature on the subject, highlighting the impact of climate change on water resources (surface water and groundwater). The escalating global demand for water, driven by factors such as population growth, urbanization, and industrial development, is placing significant pressure on water resources. This situation needs sustainable management solutions to mitigate the environmental impacts associated with increased water consumption and climate change. The methodology included bibliometric analysis using VOSviewer version 1.6.19, a software tool for constructing and visualizing bibliometric networks, and systematic analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. 155 records were used in this review from a total number of 1344 documents searched in Science Direct, Scopus and Google Scholar databases. The results indicate that research on the consequences of climate change on water quality remains in its infancy. This study highlights the effects of climate change on water quality indicators, including physicochemical, microbiological, and micropollutants, as well as the implications for human health and water supply infrastructure. Climatic factors, such as rising temperatures and changing precipitation patterns, are particularly important because they control processes fundamental to sustaining life on the planet. The main conclusions are that climate change accelerates the degradation of drinking water quality and amplifies public health risks. These findings highlight the need for rigorous assessments and the development of integrated adaptation strategies involving collaboration among water operators, decision-makers, the scientific community, and climate change specialists. Full article

The state of charge (SOC) serves as a critical indicator for evaluating the remaining driving range of electric vehicles (EVs), and its prediction is of significance for alleviating range anxiety and promoting the development of the EVs industry. This study addresses two key challenges in current SOC prediction technologies: (1) the scarcity of multi-step prediction research based on real driving conditions and (2) the poor performance in multi-scale temporal feature extraction. We innovatively propose the Wavelet-Guided Temporal Feature Enhanced Informer (WG-TFE-Informer) prediction model with two core innovations: a wavelet-guided convolutional embedding layer that significantly enhances anti-interference capability through joint time-frequency analysis and a temporal edge enhancement (TEE) module that achieves the collaborative modeling of local microscopic features and macroscopic temporal evolution patterns based on sparse attention mechanisms. Building upon this model, we establish a multidimensional SOC energy consumption prediction system incorporating battery characteristics, driving behavior, and environmental terrain factors. Experimental validation with real-world operating data demonstrates outstanding performance: 1-min SOC prediction accuracy achieves a mean relative error (MRE) of 0.21% and 20-min SOC prediction exhibits merely 0.62% error fluctuation. Ablation experiments confirm model effectiveness with a 72.1% performance improvement over baseline (MRE of 3.06%) at 20-min SOC prediction, achieving a final MRE of 0.89%. Full article

In the context of rapid development within the logistics sector and widespread advocacy for sustainable development, this paper proposes enhancements to the task scheduling and path planning components of four-way shuttle systems. The focus lies on refining and innovating modeling approaches and algorithms to address issues in complex environments such as uneven task distribution, poor adaptability to dynamic conditions, and high rates of idle vehicle operation. These improvements aim to enhance system performance, reduce energy consumption, and achieve sustainable development. Therefore, this paper presents an energy-saving and efficiency-enhancing optimization study for a four-way shuttle-based high-density automated warehouse system, utilizing deep reinforcement learning. In terms of task scheduling, a collaborative scheduling algorithm based on an Improved Genetic Algorithm (IGA) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) has been designed. In terms of path planning, this paper provides the A*-DQN method, which integrates the A* algorithm(A*) with Deep Q-Networks (DQN). Through combining multiple layout scenarios and adjusting various parameters, simulation experiments verified that the system error is within 5% or less. Compared to existing methods, the total task duration, path planning length, and energy consumption per order decreased by approximately 12.84%, 9.05%, and 16.68%, respectively. The four-way shuttle vehicle can complete order tasks with virtually no conflicts. The conclusions of this paper have been validated through simulation experiments. Full article

Commercial complexes, as major sources of urban energy consumption and carbon emissions, face urgent demands for efficiency improvement under the “dual-carbon” strategy. This paper develops a Building Information Modeling (BIM)-enabled life-cycle energy management framework to address fragmented monitoring, weak coordination, and data silos inherent in traditional approaches. Methodologically, a structured literature review was conducted to identify inefficiencies and draw lessons from global practices. An enhanced Delphi method was then applied to refine 12 key evaluation indicators spanning six dimensions—policy, economic, social, technological, environmental, and compliance—which were subsequently integrated into a BIM platform. This integration enables real-time energy monitoring, multi-system diagnostics, and cross-phase collaboration across the design, construction, and operation stages. An empirical case study of the Zhongjian Plaza project in Shanghai demonstrates that the proposed framework not only enhances energy efficiency and reduces life-cycle costs, but also improves user comfort while aligning with both domestic green building standards and international sustainability targets. Overall, the study provides a replicable methodology and practical reference for the smart and low-carbon operation of large-scale commercial complexes, thereby offering strategic insights for advancing sustainable urban development. Full article

Anthropogenic climate change drives ocean acidification, which alters marine iodine cycling and increases bioaccumulation in marine ecosystems. This environmental shift may alter marine iodine cycling and, under certain conditions, lead to increased dietary and atmospheric iodine exposure, particularly in coastal populations, with potential risks for thyroid dysfunction and downstream cardiovascular complications. Experimental data suggest that acidification may enhance iodine uptake in marine organisms such as kelp and seafood, with possible implications for consumption by humans. Because chronic iodine excess has already been associated with thyroid disease and its related cardiovascular disorders, these connections are worthy of further examination. In this narrative review we provide a synthesis of the possible mechanistic pathways by which ocean acidification, iodine bioavailability, thyroid function, and cardiovascular health may be connected. We also highlight the need for ongoing investigation, environmental monitoring, and interdisciplinary collaboration to further explain and address these tentative associations. Full article

As AI systems become more integrated into society, the relationship between humans and AI is shifting from simple automation to co-creative collaboration. This evolution is particularly important in education, where human intuition and imagination can combine with AI’s computational power to enable innovative forms of learning and teaching. This study is grounded in the #ppAI6 model, a framework that describes six levels of creative engagement with AI in educational contexts, ranging from passive consumption to active, participatory co-creation of knowledge. The model highlights progression from initial interactions with AI tools to transformative educational experiences that involve deep collaboration between humans and AI. In this study, we explore how educators and learners can engage in deeper, more transformative interactions with AI technologies. The #ppAI6 model categorizes these levels of engagement as follows: level 1 involves passive consumption of AI-generated content, while level 6 represents expansive, participatory co-creation of knowledge. This model provides a lens through which we investigate how educational tools and practices can move beyond basic interactions to foster higher-order creativity. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting the levels of creative engagement with AI tools in education. This review synthesizes existing literature on various levels of engagement, such as interactive consumption through Intelligent Tutoring Systems (ITS), and shifts focus to the exploration and design of higher-order forms of creative engagement. The findings highlight varied levels of engagement across both learners and educators. For learners, a total of four studies were found at level 2 (interactive consumption). Two studies were found that looked at level 3 (individual content creation). Four studies focused on collaborative content creation at level 4. No studies were observed at level 5, and only one study was found at level 6. These findings show a lack of development in AI tools for more creative involvement. For teachers, AI tools mainly support levels two and three, facilitating personalized content creation and performance analysis with limited examples of higher-level creative engagement and indicating areas for improvement in supportive collaborative teaching practices. The review found that two studies focused on level 2 (interactive consumption) for teachers. In addition, four studies were identified at level 3 (individual content creation). Only one study was found at level 5 (participatory co-creation), and no studies were found at level 6. In practical terms, the review suggests that educators need professional development focused on building AI literacy, enabling them to recognize and leverage the different levels of creative engagement that AI tools offer. Full article

In complex 3D maneuvering tasks (e.g., post-disaster rescue, urban operations, and infrastructure inspection), the trajectories that quadrotors need to track are often complex—containing both gentle flight phases and highly maneuverable trajectory segments. Under such trajectory tracking tasks with the composite characteristics of “gentle-high maneuvering”, quadrotors face challenges of limited onboard computing resources and short endurance, requiring a balance between trajectory tracking accuracy, computational efficiency, and energy consumption. To address this problem, this paper proposes a lightweight trajectory tracking control method based on hierarchical decision-making and dual-threshold adaptive switching. Inspired by the biological “prediction–reflection” mechanism, this method designs a dual-threshold collaborative early warning switching architecture of “prediction layer–confirmation layer”: The prediction layer dynamically assesses potential risks based on trajectory curvature and jerk, while the confirmation layer confirms in real time the stability risks through an attitude-angular velocity composite index. Only when both exceed the thresholds, it switches from low-energy-consuming Euler angle control to high-precision geometric control. Simulation experiments show that in four typical trajectories (straight-line rapid turn, high-speed S-shaped, anti-interference composite, and narrow space figure-eight), compared with pure geometric control, this method reduces position error by 19.5%, decreases energy consumption by 45.9%, and shortens CPU time by 28%. This study not only optimizes device performance by improving trajectory tracking accuracy while reducing onboard computational load, but also reduces energy consumption to extend UAV endurance, and simultaneously enhances anti-disturbance capability, thereby improving its operational capability to respond to emergencies in complex environments. Overall, this study provides a feasible solution for the efficient and safe flight of resource-constrained onboard platforms in multi-scenario complex environments in the future and has broad application and expansion potential. Full article

As global energy challenges intensify, reducing energy consumption in buildings is becoming a crucial economic and environmental priority. Despite extensive research on energy efficiency, a comprehensive synthesis that addresses emerging trends, eco-friendly insulation materials, and artificial intelligence (AI)-based methods remains limited. This study aims to bridge this gap through a bibliometric analysis of 2477 articles from the Scopus database, using the tools VOSviewer and Biblioshiny to explore several key questions: What are the dominant research trends? Who are the most influential contributors? And how are AI and sustainable insulation technologies evolving and converging to optimize energy performance? The analysis highlights major research themes, global collaboration networks, and two key strategies: eco-insulation materials, which help reduce environmental and technical costs, and AI-based solutions, which enable accurate energy predictions, real-time optimization, and material selection tailored to diverse climates and architectural contexts. Despite these advances, significant gaps remain in the development and characterization of eco-insulating materials. Future research should focus on integrating AI with sustainable insulation to enhance energy efficiency and minimize environmental impact, thereby paving the way for innovative, energy-resilient building solutions. Full article