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Environmental degradation has become a critical structural challenge for sustainable development, particularly in regions where economic growth remains closely linked to natural resource exploitation. In Latin America, and specifically within ALADI countries, limited empirical evidence exists on the dynamic interactions among economic growth, trade integration, energy efficiency, and digital transformation in shaping environmental pressures. This study addresses this gap by employing a dynamic panel data approach based on System GMM for the period 2000–2021. The results reveal that economic growth and trade openness have a positive, statistically significant effect on the ecological footprint, confirming the persistence of scale effects and the absence of structural decoupling between economic expansion and environmental degradation. In contrast, energy intensity and ICT service exports, although positively associated with environmental pressure, did not show statistically significant effects, suggesting that their role in driving sustainability transitions remains limited under current structural conditions. These findings highlight that structural economic factors predominantly drive environmental dynamics in the ALADI region, while the estimated effects associated with technological and efficiency-related variables remain comparatively weak and statistically inconclusive under current structural conditions. From a policy perspective, the study underscores the need for deeper structural transformations, including cleaner energy transitions, stronger environmental regulation in trade, and a more effective integration of digitalization into sustainability strategies. The study contributes to the literature by providing robust dynamic evidence on socio-environmental interactions in developing economies and advancing the understanding of sustainability transitions in Latin America. Full article

Producing hydrogen from ethanol steam reforming (ESR) is a carbon-neutral and environment-friendly method, which has been expected to reduce the excessive emission of environmental pollution and over-exploitation of fossil resources. Currently, great advances have been made on heterogeneous catalysts, but an in-depth and more comprehensive understanding to further promote this reaction process is still required. Herein, the thermodynamic and kinetic analyses of ESR are firstly highlighted. Then, various reaction pathways of ESR are discussed in detail, respectively combined with experimental studies and density functional theory calculations. On this basis, the key factors affecting the catalytic performance over non-noble and noble metal catalysts are summarized, such as alloying, optimization of the preparation methods, promoter addition and support modification. In addition, the process intensification technologies, including catalytic membrane reactors, adsorption-enhanced reforming and microchannel reactors, are analyzed regarding breaking the thermodynamic limitations and improving the heat and mass transfer efficiency. Finally, the challenges and potential strategies of ESR in the research of dynamic reaction mechanisms, regulation of catalyst stability and integration of intensification technologies are summarized. Full article

Unmanned aerial vehicle (UAV) path planning is one of the core technologies for realizing precision agricultural operations. In complex farmland environments involving terrain obstacles, tall tree canopies, high-voltage power lines, and restricted no-fly zones, this problem is transformed into a typical multi-objective and multi-constraint optimization problem. Dense constraints drastically narrow the feasible solution space and impose stringent requirements on the convergence, real-time performance, and robustness of planning algorithms. To address this issue, this paper proposes a novel meta-heuristic algorithm: the Agricultural Planting Whole-Cycle Management Optimization (APWMO) algorithm. By integrating the cultivation strategy aligned with crop growth cycle dynamics, the demonstration farmland-based elite guidance mechanism, and the elite archive pruning operation, it achieves a dynamic balance between global exploration and local exploitation. Comparative experiments with 15 advanced meta-heuristic algorithms on the 30-dimensional CEC2017 benchmark test suite show that APWMO achieves the best performance in terms of convergence accuracy, convergence speed, and search stability. Furthermore, the effectiveness of the proposed algorithm is verified in four 3D farmland path planning tasks with different objective weights and complexity levels. Experimental results confirm that APWMO has excellent path planning performance in complex farmland environments and can provide efficient technical support for practical agricultural UAV tasks such as plant protection spraying, crop growth monitoring, and farmland surveying. Full article

The shift from fossil fuels to renewable energy is a key component in achieving global sustainability and dealing with climate change. Natural resources, such as sunlight, air, water, and biomass, have tremendous potential to create clean energy; however, exploiting these resources in an efficient, stable, and large-scale integration manner is difficult due to their variable and distributed nature. Artificial intelligence (AI) approaches that mimic human learning and decision-making have recently become viable approaches to solving renewable energy problems. This study mainly examines the current landscape of AI applications across solar, wind, hydro, geothermal, ocean, hydrogen, bioenergy, and hybrid energy systems. AI enhances renewable energy forecasting, improves power system frequency analysis and stability assessments, and optimizes dispatch and distribution networks. Beyond technical optimization, AI also contributes to broader sustainability goals, including energy efficiency improvements, intelligent smart grid management, and enabling mechanisms such as carbon trading and circular economy practices to reduce exposure to climate extremes. Drawing on evidence from a range of renewable energy areas, this review highlights the importance of AI in bridging the link between technological innovation and sustainable energy management. This paper discusses potential future research avenues, such as building sophisticated AI designs, energy-efficient chips, and data communication networks. Ultimately, the synergy between AI and renewable energy systems represents not only a technological advancement but also a transformative pathway toward a resilient, low-carbon future. Full article

Infrared imaging technology has been widely adopted for industrial gas leak detection due to its capability for large field-of-view, long-range, and dynamic monitoring. However, in practical applications, natural object interference within the scene, together with the blurred contours and low contrast of infrared images, severely degrades the performance of gas detection and leakage region segmentation. To address these challenges, this paper proposes a gas leak detection method that integrates gas characteristics with spatiotemporal information. Specifically, the non-solid characteristics of gas are incorporated to constrain the foreground extraction process of the Gaussian Mixture Model (GMM), thereby suppressing interfering moving objects. Furthermore, by exploiting the spatiotemporal information in infrared image sequences, a multi-scale cross-attention fusion model is designed to fuse multi-scale and global feature representations, improving the accuracy of foreground detection. Finally, density-based clustering is employed to achieve complete segmentation of gas regions with irregular shapes. Experimental results demonstrate that the proposed method effectively suppresses interference from solid objects, accurately detects gas leakage, and successfully segments the diffusion regions. Compared with existing approaches, the proposed method shows significant advantages and provides a valuable reference for research on infrared imaging-based gas leak detection. Full article

Hydraulic fracturing is a key technology for the commercial exploitation of deep shale gas reservoirs, and accurate prediction of rock-mechanical parameters is essential for optimizing these operations. Conventional approaches primarily rely on empirical formulas based on longitudinal and transverse wave velocities; however, obtaining transverse wave data is challenging, and these formulas often lack accuracy. Conventional machine learning algorithms also exhibit limited predictive performance and generalization due to the intrinsic heterogeneity of rock-mechanical data. Therefore, to address the extreme heterogeneity and complex nonlinear logging responses inherent in deep shale gas reservoirs in the Zigong (ZG) block, this study proposes a geology-tailored deep learning framework, CNN-BiGRU-AT. Unlike generic machine learning applications, this architecture is specifically designed to decode complex stratigraphic signals: the convolutional neural network (CNN) module extracts multi-scale spatial features to capture abrupt lithological transitions; the bidirectional gated recurrent units (BiGRUs) analyzes the continuous depth-sequential dependencies of overlying and underlying strata; and the attention mechanism (AT) dynamically regulates the weight allocation of critical input geophysical parameters, thereby delivering a geophysically informative and highly robust predictive performance. This paper employs the CNN-BiGRU-AT model to predict the Brittleness index (BI), using the ZG block as an example. The results demonstrate that the coefficient of determination (R²) for the brittleness index on the test dataset achieved 0.969, representing a 12% improvement over conventional models. The high accuracy of this model satisfies the precision requirements for predicting rock-mechanical parameters, thereby offering reliable theoretical support for optimizing hydraulic fracturing operations in deep shale gas reservoirs. Full article

Insects represent powerful experimental systems for investigating host–microorganism interactions, providing valuable insights into bacterial pathogenicity, immune regulation, symbiosis, and antimicrobial discovery. This review examines the complex relationships between insects and bacteria, focusing on the mechanisms that control infection, immune activation, and microbial adaptation. Particular attention is given to the routes of pathogen entry and to the conserved innate immune pathways that coordinate host defenses, including the Toll, Imd, Duox, and Jak/Stat signaling cascades. The review illustrates how bacterial pathogens exploit toxins, immune evasion strategies, and metabolic adaptation to overcome host defenses, while insects rely on tightly regulated cellular and humoral responses, antimicrobial peptides, melanization, and microbiota-mediated homeostasis. Interactions between pathogenic and commensal bacteria in the insect gut are discussed in the context of immune tolerance, dysbiosis, and ecological adaptation. The dual role of bacterial virulence factors in both pathogenesis and symbiosis is highlighted through examples involving entomopathogenic bacteria such as Photorhabdus spp., Xenorhabdus spp., and Bacillus thuringiensis. In addition, the review summarizes the use of insect models, including Drosophila melanogaster, Galleria mellonella, Bombyx mori, and Apis mellifera, in experimental infections aimed at studying virulence mechanisms, host immune responses, and antimicrobial efficacy. Finally, multi-omic approaches, including transcriptomics, metabolomics, epigenomics, and single-cell technologies are discussed as transformative tools for dissecting host–microbe interactions at molecular and systems levels. Overall, insect–bacteria interactions emerge as dynamic and evolutionarily shaped systems in which immunity, metabolism, microbiota composition, and environmental factors are closely interconnected, offering important perspectives for both basic research and the development of sustainable biocontrol and antimicrobial strategies. Full article

Near-field communication is regarded as a key enabling technology for future 6G wireless systems. However, when operating over wide bandwidths, the beam split effect arising from frequency-independent analog phase shifters leads to significant beamforming gain degradation. Different from existing works that address this issue through true-time-delay hardware, this paper exploits the emerging movable antenna technology for beam split alleviation. Specifically, we consider a movable antenna-enabled near-field wideband uplink system with an analog beamforming architecture. Under this setup, we jointly optimize the analog phase shifts and antenna positions to maximize the minimum beamforming gain across all subcarriers. The formulated problem is highly non-convex due to the constant-modulus constraint on the analog combiner and the nonlinear dependence of the near-field channel on antenna positions, which makes conventional optimization methods difficult to apply. To this end, we develop a deep reinforcement learning framework based on the soft actor–critic algorithm that operates in a continuous action space and effectively handles the non-smooth max-min objective. Simulation results show that the proposed approach alleviates the beam split effect and achieves a higher minimum beamforming gain than conventional schemes. Full article

Intel Trust Domain Extensions (TDX) is a Confidential Virtual Machine (CVM) technology that provides hardware-enforced isolation through Trusted Execution Environments (TEEs). While TDX effectively mitigates interrupt-based stepping attacks, it remains vulnerable to fine-grained timing-based controlled-channel attacks such as T-Time, which exploit precise dwell-time measurements between consecutive page faults to infer secret-dependent control flows even within a single memory page. Existing page-level confinement defenses are insufficient against such timing attacks. In this paper, we propose RandDelay, a lightweight defense mechanism that raises the measurement budget required for a successful T-Time attack by injecting a cryptographically random latency into the SEAMCALL handler of the TDX module. We argue that SEAMCALL is the most practical and effective injection point among mandatory boundary handlers: it lies strictly between the attacker’s timestamp $T_s$ and the victim’s secret-dependent code execution, ensuring that every dwell-time measurement is corrupted by an independent random variable. We further integrate an anomaly-based page-fault rate limiter (RandDelay+) to prevent statistical averaging attacks. Security analysis shows that RandDelay raises the minimum number of measurements required for a successful attack beyond the budget enforced by RandDelay+, rendering the attack impractical under the analytical model and assumed parameter settings. We discuss implementation considerations within the TDX module firmware, expected performance overhead, and generalization to other TEE platforms. This paper contributes a design rationale, a quantitative tuning rule, and an analytical security–overhead model that together provide a deployable baseline for empirical follow-up. The proposed defense has not been experimentally validated on a deployed TDX system; a prototype, simulation, or trace-driven study is identified as essential future work. Full article

The abuse of γ-hydroxybutyric acid (GHB) and its precursors, γ-butyrolactone (GBL) and 1,4-butanediol (1,4-BD), has increased in recent years, with these substances frequently being illicitly added to beverages. GHB is colorless and odorless and exhibits anesthetic and hypnotic psychoactive effects, which are often exploited in drug-facilitated sexual assault, posing a significant public safety concern. Chromatography–tandem mass spectrometry is a conventional analytical approach for narcotic drug determination due to its high sensitivity and accuracy; however, its large instrumentation footprint and high operational cost limit its suitability for on-site rapid screening. In response to the growing demand for field-deployable analytical tools, rapid detection technologies for GHB have progressively evolved. This review summarizes and compares the advantages and limitations of current rapid detection methods for GHB and discusses their potential future developmental trends, with the aim of providing a reference for researchers and relevant authorities. Full article

This study investigates the design, development, and practical exploitation of an educational ontology for classical guitar instruction, within a semantically driven and application-oriented framework. The proposed approach aims to bridge the gap between formal knowledge representation and its functional use in real educational contexts. The ontology is developed using OWL in the Protégé environment and systematically models core pedagogical elements, including learning objectives, technical skills, instructional practices, and assessment processes, in alignment with the official curriculum. The semantic model is stored and managed as an RDF graph within a GraphDB repository, where it supports consistency checking and semantic querying through SPARQL. For application development, the ontological model is subsequently translated into a structured tabular schema suitable for the AppSheet low-code environment. Thus, GraphDB functions as a semantic validation and knowledge management layer, whereas the educational application operates on an application-oriented representation derived from the ontology rather than on a live RDF backend. The proposed three-tier architecture (Ontology–GraphDB–Application) demonstrates how Semantic Web technologies can support the transformation of abstract knowledge models into functional educational systems. The results highlight the capacity of ontology-driven approaches to enhance the organization, reusability, and pedagogical coherence of instructional knowledge, while enabling scalable and accessible application development through low-code technologies. The study contributes to the field of educational technology by providing a practical framework for integrating semantic knowledge representation into music education and laying a semantic foundation for future extensions toward adaptive and intelligent learning environments. Full article

Under the paradigm of the Internet of Things (IoT), the processing of large-scale data not only imposes higher demands on data-sharing efficiency but also increases the risk of user privacy leakage. To address these challenges, this paper proposes a blockchain-assisted traceable and revocable broadcast encryption scheme for preventing malicious encryptors (BATR). To resist trapdoor attacks by malicious encryptors, the scheme utilizes the uniform distribution property of hash function outputs to generate the random numbers required for the encryption algorithm. To block malicious users from leaking private keys, which attackers could exploit to construct piracy decoders with decryption capabilities, the scheme enhances the traditional broadcast encryption system by incorporating public tracing and revocation mechanisms. The scheme employs personalized transmission technology, allowing data owners to share public data with a set of authorized users while also sharing personalized data with specific authorized users. Additionally, users communicate using pseudonyms to ensure that their real identities are not accessible to third parties, thereby meeting privacy protection requirements. With the assistance of blockchain, trusted authorities and users can invoke smart contract interfaces to trigger blockchain peer nodes to execute smart contracts, thereby acquiring or updating identity authentication information stored on the blockchain to achieve secure authentication. This paper provides an analysis of the correctness and security of BATR, demonstrating that BATR satisfies chosen-ciphertext security under the Random Oracle Model. We also present performance evaluations and describe the experimental setup used to obtain operation-time baselines. Finally, this paper conducts a performance analysis of the BATR scheme, which exhibits high computational efficiency and compact communication bandwidth, resulting in significant performance improvements. Full article

Being capable of high-resolution imaging and non-contact measurement, Ground Penetrating Radar (GPR) is a promising technology for the detection of unexploded ordnance (UXO). However, UXO detection is severely hindered by clutter, particularly in environments with significant surface roughness where conventional suppression methods prove ineffective. To address this, we propose a polarimetric GPR clutter suppression method based on an improved non-convex Tensor Robust Principal Component Analysis (TRPCA) framework. Specifically, a polarization-aware tensor construction scheme is designed by stacking the HH and VV channel data. This approach exploits the strong inter-channel correlation of clutter to enhance its low-rank property, while highlighting the distinct sparse signatures of targets derived from their polarimetric responses. To further optimize tensor decomposition, we introduce a non-convex Tensor Adjustable Logarithmic Norm (TALN) to overcome the estimation bias inherent in the conventional Tensor Nuclear Norm (TNN). Serving as a tighter surrogate for tensor rank, the proposed TALN regularizer improves the approximation accuracy of the low-rank component, thereby ensuring a clearer separation between clutter and targets. The resulting non-convex optimization problem is efficiently solved using Alternating Direction Method of Multipliers (ADMM). Numerical simulations and laboratory experiments demonstrate that the proposed method suppresses strong clutter stemming from rough-surface reflections more effectively than existing methods, achieving a Signal-to-Clutter Ratio (SCR) improvement of over 20 dB. Full article

Background: Child Sexual Exploitation and Abuse is a longstanding global issue, increasingly amplified by digital technologies, mobile devices, and internet access. This shift has intensified Child Online Sexual Exploitation and Abuse (COSEA). WeProtect 2020, a Global Alliance Intelligence brief report, indicated a 200% rise in online abuse forums. Existing studies focus on child protection, grooming, and survey-based analyses and draw inferences regarding grooming tactics and thematic analysis. Social issues such as underreporting, limited threat intelligence sharing, and low cyber awareness persist, leading to vulnerabilities and various exploitations. Further, a lack of social engagement and support persists, posing serious challenges for victims and law enforcement. Multiple studies have used the term Online Child Sexual Exploitation and Abuse (OCSEA) that focus on a technology-centric approach. However, the paper considers Child Online Sexual Exploitation and Abuse (COSEA) child-centric approach as we explore challenges of a child accessing the internet and engaging in online activities. Methods: This study analyses COSEA using the MITRE tactics, techniques, and procedures (TTP) framework to examine perpetrator behavior, motives, and potential attribution, considering the evolving threat landscape. Results: TTP-based analysis enables the identification of adversary intent, methods, and opportunities. The study contributions are threefold: (1) we explore COSEA and its manifestations; (2) we apply the MITRE TTP framework with subjective expert judgment to analyze perpetrator behavior and the victim; for instance, what leads victims to become complicit in wrong acts; and (3) propose mitigation strategies and stakeholder roles. Conclusion: By integrating technical, social, and behavioral perspectives, it highlights the roles of economic, societal, and deterrence factors and recommends policy, education, and collaborative threat-intelligence sharing to enhance child online safety. Full article

Unmanned Aerial Vehicles (UAVs) have become a key technology in precision agriculture, enabling efficient monitoring, inspection, and targeted interventions. However, effective UAV path planning in such environments requires the generation of safe, energy-efficient, and smooth trajectories in complex three-dimensional spaces. This paper proposes an Adaptive Q-Learning Guided Gorilla Troops Optimizer (AQGTO) for 3D UAV path planning. The proposed method integrates a state-aware Q-learning mechanism into the Gorilla Troops Optimizer (GTO), enabling the optimizer to adaptively select exploration, exploitation, and diversification strategies according to the current optimization state. A multi-objective cost function is formulated to simultaneously minimize path length, an energy-related surrogate cost, obstacle proximity, path smoothness, and altitude variation. In addition, a feasibility repair mechanism is introduced to ensure collision-free trajectories in environments with cylindrical obstacles. The proposed approach is evaluated in three representative agricultural scenarios: row-crop fields, orchard environments, and hilly terrains. Experimental results show that AQGTO achieves competitive and improved performance compared with classical A*, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the original GTO in terms of trajectory cost, path efficiency, and stability. Furthermore, an ablation study confirms that the integration of Q-learning significantly enhances optimization performance. These results suggest that AQGTO provides an effective and robust solution for UAV path planning in complex agricultural environments. Full article