Search Results (17,200)

With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

The emergence of the Internet of Things (IoT) has brought about a significant technological shift, coupled with the rise of intelligent computing. IoT integrates various digital and analogue devices with the Internet, enabling advanced communication between devices and humans.The pervasive adoption of IoT has transformed urban infrastructures into interconnected smart cities. Here, we propose a framework that mathematically models and automates power consumption management for IoT devices in smart city environments ranging from residential buildings to healthcare settings. The proposed framework utilises set theoretic association-rule mining and combines unsupervised preprocessing with frequent-item set mining and iterative numerical optimisation to reduce non-critical energy consumption. Readings are first converted into binary transaction matrices; then a modified Apriori algorithm is applied to extract high-confidence usage patterns and association rules. Dimensionality reduction techniques compress these transaction profiles, while the Gauss–Seidel method computes control set points that balance energy efficiency. The resulting rule set is deployed through a web portal that provides real-time device status, remote actuation, and automated billing. These associative rules generate predictive control functions, optimise the response of the framework, and prepare the framework for future events. A web portal is introduced that enables remote control of IoT devices and facilitates power usage monitoring, as well as automated billing. Full article

Diesel engines serve as critical power sources across transportation and industrial fields, and their fault diagnosis is essential for ensuring operational safety and system reliability. However, acquiring sufficient and effective operational data remains a significant challenge due to the high complexity of the systems. As a modeling method that incorporates expert knowledge, the belief rule base (BRB) demonstrates strong potential in resolving such challenges. Nevertheless, the reliance on expert knowledge constrains its practical application, particularly in complex engineering scenarios. To overcome this limitation, this study proposes a reliability fault diagnosis method for diesel engines based on the belief rule base with data-driven initialization (DI-BRB-R), which aims to improve modeling capability under conditions of limited expert knowledge. Specifically, the approach first employs fuzzy c-means clustering with the Davies–Bouldin index (DBI-FCM) to initialize attribute reference values. Then, a Gaussian membership function with Laplace smoothing (LS-GMF) is developed to initialize the rule belief degrees. Furthermore, to guarantee the reliability of the model optimization process, a group of reliability guidelines is introduced. Finally, the effectiveness of the proposed method is validated through an example of fault diagnosis of the WD615 diesel engine. Full article

Because of their substantial weight and high centers of gravity, commercial vehicles require braking systems that ensure maximum performance and safety. Accurate braking control is vital for preserving safe vehicle dynamics by preventing lateral instability due to excessive deceleration or rear-wheel lock-up. Considering the growing demand for safety in medium-duty commercial vehicles, we introduce a rule-based dynamic braking controller for pneumatic electronic parking brake (EPB) systems. The proposed system is established using a model-based design (MBD) framework involving a V-cycle development process. The rule-based controller is designed to control the braking force based on wheel slip, thereby ensuring both adequate braking distance and lateral stability during emergency braking. Simulations and real-vehicle tests confirmed that the proposed control strategy can maintain lateral stability across varying loading and road-surface conditions. The results highlight the dynamic braking capability of the proposed pneumatic EPB system and its feasibility as an emergency braking solution. The effectiveness of the proposed controller in preventing wheel lock supports the use of MBD for developing safety-aware controllers. Full article

Understanding the mechanisms governing forest community assembly across different growth stages is essential for revealing succession dynamics and guiding forest restoration. While much attention has been given to overstory trees, the understory regeneration layer, critical for forest succession, remains less explored, particularly regarding its stage-specific survival strategies and assembly processes. This study investigates the natural regeneration of Quercus variabilis forests in northern China, focusing on the transition from early to later growth stages. Our objectives were to (1) identify the phylogenetic and functional structures of regeneration communities at early and later stages, (2) explore their responses to environmental gradients, and (3) assess the roles of deterministic and stochastic processes in shaping community assembly. We integrated phylogenetic structure, functional traits, and environmental gradients to examine natural regeneration communities. The results revealed clear stage-dependent patterns: communities exhibited random phylogenetic and functional structures in the early growth stage, suggesting a dominant role of stochastic processes during early recruitment. In contrast, communities showed phylogenetic clustering and functional overdispersion in later growth stages, indicating the increasing influence of environmental filtering and interspecific competition as individuals developed. Generalized Dissimilarity Modeling (GDM) further revealed that dispersal limitation and pH were key predictors of phylogenetic β-diversity in the later growth stage, while total phosphorus drove functional β-diversity in the later growth stage. No significant predictors were found for β-diversity in the early stage. These findings highlight the shift from stochastic to deterministic processes during forest regeneration, emphasizing the stage-dependent nature of assembly mechanisms. Our study elucidates the stage-specific assembly rules of Q. variabilis forests and offers theoretical guidance for stage-targeted interventions in forest management to promote positive succession. Full article

Fast, spark-free detection of hydrogen leaks is indispensable for large-scale hydrogen deployment, yet electronic sensors remain power-intensive and prone to cross-talk. Optical schemes based on surface plasmons enable remote read-out, but single-metal devices offer either weak H₂ affinity or poor plasmonic quality. Here we employ full-wave finite-difference time-domain (FDTD) simulations to map the hydrogen response of nanohole arrays (NAs) that can be mass-produced by colloidal lithography. Square lattices of 200 nm holes etched into 100 nm films of Pd, Mg, Ti, V, or Zr expose an intrinsic trade-off: Pd maintains sharp extraordinary optical transmission modes but shifts by only 28 nm upon hydriding, whereas Mg undergoes a large dielectric transition that extinguishes its resonance. Vertical pairing of a hydride-forming layer with a noble metal plasmonic cap overcomes this limitation. A Mg/Pd bilayer preserves all modes and red-shifts by 94 nm, while the predicted optimum Ag (60 nm)/Mg (40 nm) stack delivers a 163 nm shift with an 83 nm linewidth, yielding a figure of merit of 1.96—surpassing the best plasmonic hydrogen sensors reported to date. Continuous-film geometry suppresses mechanical degradation, and the design rules—noble-metal plasmon generator, buried hydride layer, and thickness tuning—are general. This study charts a scalable route to remote, sub-ppm, optical hydrogen sensors compatible with a carbon-neutral energy infrastructure. Full article

To study the impact of the psychological and behavioral characteristics of people, fire environment, and evacuation routes on fire evacuation efficiency, this study focuses on a university library as the research subject. A fuzzy logic algorithm is employed to analyze how psychological and behavioral traits influence initial evacuation speed during a fire. Also, fire data simulated using PyroSim software is integrated, with gas temperature, CO concentration, and visibility quantified through empirical formulas to adjust the reduction factor of evacuation speed, examining the effects of fire-generated products on evacuation performance. By incorporating fire environment factors into the heuristic function and refining pheromone update rules through iterative strategies, the ant colony algorithm is enhanced to achieve path planning. Results show that the psychological–environmental-route correction method improves evacuation efficiency by 16.2% compared to traditional methods without correction. This demonstrates that the proposed correction method can improve the efficiency of building fire evacuation and provides theoretical support and technical solutions for future library fire safety management. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Automotive suspension systems are key to improving ride comfort and handling stability. Over the past decades, active and semi-active suspensions have become a focal point in automotive engineering and have been widely adopted in the industry. Skyhook-based control and its related methodologies, as a mature and viable solution, have been extensively implemented in vehicles. Despite the large number of research papers available on this topic, there remains a lack of comprehensive and up-to-date surveys in the literature that compare various Skyhook-based suspension control systems and their effectiveness. To bridge this gap, this paper systematically reviews the research progress in active and semi-active suspension controllers based on Skyhook principles over recent decades. Representative methods within major control rules are reported, and their characteristics, along with critical performance metrics, are critically analyzed. This paper also explores the development trends of Skyhook-based control. Full article

Post-Traumatic Stress Disorder (PTSD) poses complex clinical challenges due to its emotional volatility, contextual sensitivity, and need for personalized care. Conventional AI systems often fall short in therapeutic contexts due to lack of explainability, ethical safeguards, and narrative understanding. We propose a hybrid neuro-symbolic architecture that combines Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), symbolic controllers, and ensemble classifiers to support clinicians in PTSD follow-up. The proposal integrates real-time anonymization, session memory through patient-specific RAG, and a Human-in-the-Loop (HITL) interface. It ensures clinical safety via symbolic logic rules derived from trauma-informed protocols. The proposed architecture enables safe, personalized AI-driven responses by combining statistical language modeling with explicit therapeutic constraints. Through modular integration, it supports affective signal adaptation, longitudinal memory, and ethical traceability. A comparative evaluation against state-of-the-art approaches highlights improvements in contextual alignment, privacy protection, and clinician supervision. Full article

The short-time behavior of the survival probability of a system governed by a time-dependent non-Hermitian Hamiltonian is derived using to the second-order perturbative approach. The resulting expression allows for the analysis of some situations which could be of interest in the field of quantum technology. For example, it becomes possible to predict a quantum Zeno effect even in the presence of decay processes. Full article

Maneuvering extended object tracking has garnered significant attention owing to the continuous advancements in the resolution capabilities of modern high-precision radar sensors. The efficacy of tracking algorithms for such objects is heavily contingent upon the design of the model set. However, existing methodologies for model set design often yield suboptimal performance when confronted with highly maneuvering extended objects. The expected model augmentation (EMA) algorithm offers a data-driven mechanism for updating the model set in real time. Despite its advantages, the EMA algorithm is constrained by the fixed parameters of its basic models and static transition probabilities between models, thereby limiting its adaptability to extended objects exhibiting complex and dynamic maneuvering behaviors. To address these limitations, this paper proposes a modified variable structure multiple model (VSMM) framework for maneuvering extended object tracking, referred to as the adaptive grid expected model augmentation based on the golden section (GSAG-EMA) algorithm. The approach adaptively adjusts both the model structure and parameters in a grid-based format to accommodate the varying maneuvering patterns. It incorporates both local and global weighting schemes, with two models within the grid based on the golden section. Furthermore, the transition probability matrix is dynamically updated following specific rules, and the execution strategy for each module is determined according to the filtering results. Simulation results under both weak and strong maneuvering scenarios demonstrate that the proposed GSAG-EMA algorithm consistently outperforms the IMM-based, EMA, and AG-BMA algorithms in terms of root mean square error (RMSE) and Hausdorff distance, thereby substantiating its superior tracking performance. Full article

Palmprint recognition is becoming more and more common in the fields of security authentication, mobile payment, and crime detection. Aiming at the problem of small sample size and low recognition rate of palmprint, a small-sample palmprint recognition method based on image expansion and Dynamic Model-Agnostic Meta-Learning (DMAML) is proposed. In terms of data augmentation, a multi-connected conditional generative network is designed for generating palmprints; the network is trained using a gradient-penalized hybrid loss function and a dual time-scale update rule to help the model converge stably, and the trained network is used to generate an expanded dataset of palmprints. On this basis, the palmprint feature extraction network is designed considering the frequency domain and residual inspiration to extract the palmprint feature information. The DMAML training method of the network is investigated, which establishes a multistep loss list for query ensemble loss in the inner loop. It dynamically adjusts the learning rate of the outer loop by using a combination of gradient preheating and a cosine annealing strategy in the outer loop. The experimental results show that the palmprint dataset expansion method in this paper can effectively improve the training efficiency of the palmprint recognition model, evaluated on the Tongji dataset in an N-way K-shot setting, our proposed method achieves an accuracy of 94.62% ± 0.06% in the 5-way 1-shot task and 87.52% ± 0.29% in the 10-way 1-shot task, significantly outperforming ProtoNets (90.57% ± 0.65% and 81.15% ± 0.50%, respectively). Under the 5-way 1-shot condition, there was a 4.05% improvement, and under the 10-way 1-shot condition, there was a 6.37% improvement, demonstrating the effectiveness of our method. Full article

The Sultanate of Oman boasts remarkable biodiversity, exemplified by such species as the Arabian leopard (Panthera pardus nimr) and the Arabian oryx (Oryx leucoryx), national symbols that highlight the extensive conservation efforts required to protect the country’s natural heritage. During decades, Omani authorities have taken significant measures to safeguard wildlife and preserve the natural environment. A sanctuary dedicated to the reintroduction of the Arabian Oryx, after extinction in nature in 1972, was established in 1980 in the Al Wusta Governorate under the patronage of the Royal Diwan and currently administrated by the recently established Environment Authority. During the almost 40 years since the reintroduction and the creation of the sanctuary, the oryx population has grown slowly but constantly. In 2021, the sanctuary hosted 738 oryx, allowing the start of the reintroduction of the species into the natural environment. Small groups of animals were released into the wild in selected areas. No animal health adverse events were recorded, and mortality was generally due to injuries received as a consequence of fighting, in particular during mating season. Standard veterinary care, including control of internal and external parasites, was regularly provided. In some occasions, immunization against certain diseases, such as clostridial infections, pasteurellosis, or mycoplasmosis, was also applied. In 2023, an FMD outbreak in cattle reported in Dhofar, about 500 km from the Al Wusta sanctuary, motivated specific prophylactic actions to prevent the risk of diffusion to oryx. From December 2023 to January 2024, an immunization program was undertaken using an FMD vaccine against serotypes A, O, and SAT 1, mostly in male oryx, while pregnant oryx were avoided for abortion risk due to handling. The following year, in January 2025, a severe outbreak occurred in oryx herds held in the sanctuary. The rapid onset and the spread of clinical symptoms among animals (100% morbidity in the second day after the first appearance of signs in some individuals) were suggestive of a highly contagious disease. The animals suffered from severe depression and inappetence, rapidly followed by abundant salivation, erosions of the oral mucosa and tongue, and diarrhea, with a short course characterized by prostration and death of the animal in the most severe cases. Therapeutical attempts (administration of antibiotics and rehydration) were mostly ineffective. Laboratory investigations (ELISA and PCR) ruled out contagious bovine pleuropneumonia (CBPP), Johne’s disease and Peste des petits ruminants (PPR). Both serology and antigen detection showed positiveness to foot-and-mouth disease (FMD). Out of a total population of 669 present in the sanctuary at the beginning of the outbreak, 226 (33.78%) oryx died. Despite the vaccinal status, the 38.49% of dead animals resulted being vaccinated against FMD. Taking into account the incalculable value of the species, the outbreak represented a very dangerous event that risked wiping out the decades of conservation efforts. Therefore, all the available means, such as accrued biosecurity and adequate prophylaxis, should be implemented to prevent the recurrence of such health risks. The delicate equilibrium of wild fauna in Oman requires study and support for an effective protection, in line with the national plan “Vision 2040”, targeting the inclusion of the Sultanate within the 20 best virtuous countries for wildlife protection. Full article

Manufacturing industries are undergoing a significant transformation toward Smart Manufacturing (SM) to meet the ever-evolving demands for customized products. A major obstacle in this transition is the integration of Computer-Aided Process Planning (CAPP) with Scheduling. This integration poses challenges because of conflicting objectives that must be balanced, resulting in the Integrated Process Planning and Scheduling problem. In response to these challenges, this research introduces a novel hybridized machine learning optimization approach designed to assign and sequence setups in Dynamic Flexible Job Shop environments via dispatching rule mining, accounting for real-time disruptions such as machine breakdowns. This approach connects CAPP and scheduling by considering setups as dispatching units, ultimately reducing makespan and improving manufacturing flexibility. The problem is modeled as a Dynamic Flexible Job Shop problem. It is tackled through a comprehensive methodology that combines mathematical programming, heuristic techniques, and the creation of a robust dataset capturing priority relationships among setups. Empirical results demonstrate that the proposed model achieves a 42.6% reduction in makespan, improves schedule robustness by 35%, and reduces schedule variability by 27% compared to classical dispatching rules. Additionally, the model achieves an average prediction accuracy of 92% on unseen instances, generating rescheduling decisions within seconds, which confirms its suitability for real-time Smart Manufacturing applications. Full article