Search Results (336)

The Small Island Developing States (SIDSs) in the Latin American and Caribbean region remain among the most vulnerable to climate change, as increasingly frequent and severe disasters threaten infrastructure, human life, and progress toward the Sustainable Development Goals. Addressing these risks requires urgent regional and localized approaches grounded in coordinated climate risk assessment, anticipatory action, and Earth observation science-informed modeling with key support from a strong global community of practice. However, barriers remain to achieving local adaptation measures, including global action measures that conclude before local uptake of climate resilience practices are realized, reinforcing cycles of project impermanence. In this paper, we detail a Jamaica-focused case study that articulates such barriers impeding science and data-informed disaster risk reduction strategies, policies, and durable project implementation. The case study was a longitudinal co-development initiative led by a team of Jamaican and international interdisciplinary, cross-sector experts on climate-related disasters in SIDS. Using principles of co-design, discourse analysis, and systems thinking, the study underscores the need for a place-based framework that centers relevant sectors of society and often-marginalized voices as foundational to bottom-up climate resilience. The resulting Relationship and Place-Based Framework offers a model for localized climate science and technology development and ethical international collaboration for climate action that emphasizes local ownership and self-determination, as bottom-to-top feedback loops are key for managing multi-hazard dynamics and residual risks. Full article

Due to the reinforcement of energy efficiency regulations and the pursuit of sustainable development goals, the demand for high-efficiency electric motors has been steadily increasing. Rare-earth permanent magnets such as neodymium (Nd) and samarium (Sm) provide high power density, but their high cost and unstable supply chains have led to growing interest in ferrite-based motors. Ferrite magnets offer excellent cost-effectiveness; however, their relatively low remanent flux density and coercivity result in reduced motor performance. To compensate for these limitations, a spoke-type flux-concentrating structure is commonly employed to enhance the air-gap flux density. Nevertheless, in spoke-type motors, the magnets are deeply embedded within the rotor, making it difficult to achieve a sufficient magnetization rate during post-assembly magnetization. In this study, an optimized magnetizing yoke is proposed to achieve a post-assembly magnetization rate of over 99% while suppressing the irreversible demagnetization of untargeted magnets. Finite element analysis (FEA) results for a 10-pole ferrite rotor confirm that the proposed structure demonstrates excellent magnetization performance and effectively mitigates irreversible demagnetization. Full article

Recent space exploration roadmaps from China, the United States, and Russia highlight the establishment of Mars bases as a major objective. Future deep-space missions will span the inner solar system and extend beyond the asteroid belt, demanding network control systems that sustain reliable communication and efficient scheduling across vast distances. Current centralized or regionalized technologies, such as the Deep-Space Network and planetary relay constellations, are limited by long delays, sparse visibility, and heterogeneous onboard resources, and thus cannot meet these demands. To address these challenges, we propose a dual-center architecture, DualSynNet, anchored at Earth and Mars and enhanced by Lagrange-point relays and a minimal heliocentric constellation to provide scalable multi-mission coverage. On this basis, we develop a federated multi-agent reinforcement learning framework with graph attention (Fed-GAT-MADDPG), integrating centralized critics, decentralized actors, and interplanetary parameter synchronization for adaptive, resource-aware scheduling. A unified metric system: Reachability, Rapidity, and Availability, is introduced to evaluate connectivity, latency, and resource sustainability. Simulation results demonstrate that our method increases task completion to 52.4%, reduces deadline expiration, constrains rover low-state-of-charge exposure to approximately 0.8%, and maintains consistently high hardware reliability across rover and satellite nodes. End-to-end latency is reduced, with a shorter tail distribution due to fewer prolonged buffering or stagnation periods. Ablation studies confirm the essential role of graph attention, as removing it reduces completion and raises expiration. These results indicate that the integration of a dual-center architecture with federated graph reinforcement learning yields a robust, scalable, and resource-efficient framework suitable for next-generation interplanetary exploration. Full article

Interlocking Compressed Earth Blocks (ICEBs) offer a sustainable alternative to conventional fired-clay bricks but remain hindered by inconsistent geometric designs and limited standardization. This study develops a stakeholder-weighted Analytic Hierarchy Process (AHP) framework to evaluate and select the most suitable ICEB geometry for sustainable and seismic-ready construction in developing regions. Five evaluation criteria—size, weight, interlocking effectiveness, reinforcement/grout provision, and handling ergonomics—were prioritized based on expert input from masons, engineers, architects, and researchers. The synthesized results ranked the HiLo-Tec-type geometry highest, followed by Thai-Rhino, Auram, and Hydraform designs. Unit weight (0.289) and reinforcement capacity (0.261) emerged as dominant decision factors. Sensitivity analysis confirmed the robustness of rankings under varying weight perturbations. The AHP framework identifies the top-ranked geometry, whose structural performance was examined experimentally through a full-scale cyclic test on a grouted double-wythe ICEB wall, revealing enhanced ductility and residual strength compared with traditional brick masonry. The proposed framework demonstrates that selected ICEB geometry can balance ergonomic and structural performance while meeting seismic resilience demands. Beyond geometry selection, the model provides a replicable decision-support tool adaptable for regional material innovations in sustainable construction. Full article

The opening degree of longitudinal joints in the segmental lining of cross-passages in soft soil strata directly affects structural safety during tunnel construction. This study utilizes field monitoring data from the F-capping segment of Ring 30 in the Guangzhou–Nanzhou Intercity Railway Connecting Tunnel. Employing multivariate linear regression analysis, it investigates the variation patterns in longitudinal joint opening in connecting tunnel segments under changes in earth pressure, water pressure, axial force, and reinforcement stress. The fitted results for joint opening are compared with field monitoring data, demonstrating good agreement. The results indicate that axial force and reinforcement stress exert minimal influence on longitudinal joint opening in soft soil sections. Conversely, hydrostatic pressure and earth pressure exhibit moderate linear correlations with joint opening: opening increases with rising hydrostatic pressure and decreases with increasing earth pressure. These findings, based on short-term monitoring data from a single ring during construction, provide preliminary theoretical and empirical support for understanding joint behavior in site-specific soft soil conditions. Further validation is required for generalized early warning systems. Full article

In this paper, we investigate the Adaptive Service Migration (ASM) problem in dynamic satellite edge computing networks, focusing on Low Earth Orbit satellites with time-varying inter-satellite links. We formulate the ASM problem as a constrained optimization problem, aiming to minimize overall service cost, which includes both interruption cost and processing cost. To address this problem, we propose ASM-DRL, a deep reinforcement learning approach based on the soft Actor-Critic framework. ASM-DRL introduces an adaptive entropy adjustment mechanism to dynamically balance exploration and exploitation, and adopts a dual-Critic architecture with soft target updates to enhance training stability and reduce Q-value overestimation. Extensive simulations show that ASM-DRL significantly outperforms baseline approaches in reducing service cost. Full article

As the teachings continued to expand within Daoism, Thunder Rituals (Leifa 雷法) inevitably faced the crucial question of how to integrate the newly emphasized thunder element within the traditional Five-Phase system upon its emergence. To address this, Wang Wenqing (王文卿), the founding master of Shenxiao(神霄 Daoism school) Daoism who held a pivotal position in the realm of Thunder Rituals, creatively proposed the theory of “Five Zi Returning to Geng” (五子歸庚). On the one hand, drawing upon the Najia (納音 Stem–Branch Correspondence) theory from the Zhou Yi Can Tong Qi, this theory posits that thunder corresponds to the number five, occupies the central position, and belongs to the earth element, thereby reinforcing the core thesis of Leifa’s internal alchemy that thunder is generated through the interaction of water and fire. On the other hand, by ingeniously adapting the Nayin method of the Sixty JiaZi (六十甲子), it offers a creative interpretation of the abstract relationship between thunder and the Five Phases, asserting that all phases ultimately converge toward the central Geng/thunder. Together, these two aspects demonstrate that thunder in fact occupies a central position alongside earth within the Five-Phase system. This theory not only provides a sophisticated resolution to the question of thunder’s relationship with the Five Phases but also furnishes solid theoretical support for the elevated status of Thunder Rituals. Full article

Radio astronomy requires precise target localization and tracking to ensure accurate observations. Conventional regulation methodologies, encompassing PID controllers, frequently encounter difficulties due to orientation inaccuracies precipitated by mechanical limitations, environmental fluctuations, and electromagnetic interferences. To tackle these obstacles, this investigation presents a reinforcement learning (RL)-oriented framework for high-accuracy monitoring in radio telescopes. The suggested system amalgamates a localization control module, a receiver, and an RL tracking agent that functions in scanning and tracking stages. The agent optimizes its policy by maximizing the signal-to-noise ratio (SNR), a critical factor in astronomical measurements. The framework employs a reconditioned 12-m radio telescope at King Mongkut’s Institute of Technology Ladkrabang (KMITL), originally constructed as a satellite earth station antenna for telecommunications and was subsequently refurbished and adapted for radio astronomy research. It incorporates dual-axis servo regulation and high-definition encoders. Real-time SNR data and streaming are supported by a HamGeek ZedBoard with an AD9361 software-defined radio (SDR). The RL agent leverages the Proximal Policy Optimization (PPO) algorithm with a self-attention actor–critic model, while hyperparameters are tuned via Optuna. Experimental results indicate strong performance, successfully maintaining stable tracking of randomly moving, non-patterned targets for over 4 continuous hours without any external tracking assistance, while achieving an SNR improvement of up to 23.5% compared with programmed TLE-based tracking during live satellite experiments with Thaicom-4. The simplicity of the framework, combined with its adaptability and ability to learn directly from environmental feedback, highlights its suitability for next-generation astronomical techniques in radio telescope surveys, radio line observations, and time-domain astronomy. These findings underscore RL’s potential to enhance telescope tracking accuracy and scalability while reducing control system complexity for dynamic astronomical applications. Full article

As an important supplement to ground computing, edge computing can effectively alleviate the computational burden on ground systems. In the context of integrating edge computing with low-Earth-orbit satellite networks, this paper proposes an edge computing task offloading algorithm based on distributed multi-agent deep reinforcement learning (DMADRL) to address the challenges of task offloading, including low transmission rates, low task completion rates, and high latency. Firstly, a Ground–UAV–LEO (GUL) three-layer architecture is constructed to improve offloading transmission rate. Secondly, the task offloading problem is decomposed into two sub-problems: offloading decisions and resource allocation. The former is addressed using a distributed multi-agent deep Q-network, where the problem is formulated as a Markov decision process. The Q-value estimation is iteratively optimized through the online and target networks, enabling the agent to make autonomous decisions based on ground and satellite load conditions, utilize the experience replay buffer to store samples, and achieve global optimization via global reward feedback. The latter employs the gradient descent method to dynamically update the allocation strategy based on the accumulated task data volume and the remaining resources, while adjusting the allocation through iterative convergence error feedback. Simulation results demonstrate that the proposed algorithm increases the average transmission rate by 21.7%, enhances the average task completion rate by at least 22.63% compared with benchmark algorithms, and reduces the average task processing latency by at least 11.32%, thereby significantly improving overall system performance. Full article

Subterranean ecosystems represent some of the most unique and fragile habitats on Earth, yet they remain poorly understood and highly vulnerable to human-induced disturbances. Despite their ecological significance, these systems are rarely integrated into conservation planning, and surface-level protected areas alone are insufficient to safeguard their biodiversity. In southeastern Brazil, a karst landscape spanning approximately 1200 km², recognized as the region with the highest cave density in South America (approximately 2600 caves), is under increasing pressure from urban expansion, agriculture, and mining, all of which threaten the ecological integrity of subterranean habitats. This study sought to identify caves of high conservation priority by integrating species richness of non-troglobitic invertebrates, occurrence of troglobitic species, presence of endemic troglobitic taxa, and the degree of anthropogenic impacts, using spatial algebra and polygon-based mapping approaches. Agriculture and exotic forestry plantations (54%) and mining operations (15%) were identified as the most prevalent disturbances. A total of 32 troglobitic species were recorded, occurring in 63% of the 105 surveyed caves. Notably, seven caves alone harbor 25% of the region’s known cave invertebrate diversity and encompass 50% of its cave-restricted species. The findings highlight the global significance of this spot of subterranean biodiversity and reinforce the urgent need for targeted conservation measures. Without immediate action to mitigate unsustainable land use and resource exploitation, the persistence of these highly specialized communities is at imminent risk. Full article

The harsh conditions of the space environment necessitate advanced materials capable of withstanding extreme temperature fluctuations and ultraviolet (UV) radiation. While epoxy-based composites are widely utilized in aerospace due to their favorable strength-to-weight ratio, they are prone to degradation, especially under prolonged high-energy UV-C exposure. This study investigated the mechanical and chemical stability of epoxy composites reinforced with nanosilica at 0, 2, 5, and 10 wt% before and after UV-C irradiation. Dynamic mechanical analysis (DMA) revealed that increased nanosilica content enhanced the storage modulus below the glass transition temperature (Tg) but reduced both Tg and the damping factor. Following UV-C exposure, all samples showed a decrease in storage modulus and Tg; however, composites with higher nanosilica content maintained better property retention. Frequency sweeps corroborated these findings, indicating improved instantaneous modulus but accelerated relaxation with increased nanosilica. Fourier-transform infrared (FTIR) spectroscopy of UV-C-exposed samples demonstrated significant oxidation and carboxylic group formation in neat epoxy, contrasting with minimal spectral changes in nanosilica-modified composites, signifying improved chemical resistance. Overall, nanosilica incorporation substantially enhances the thermomechanical and oxidative stability of epoxy composites under simulated space conditions, highlighting their potential for more durable performance in low Earth orbit applications. Full article

This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine learning (ML) for antenna parameter prediction with reinforcement learning (RL) for adaptive beam steering. The ML module predicts optimal geometric and material parameters for conformal antenna arrays based on mission-specific performance requirements such as frequency, gain, coverage angle, and satellite constraints with an accuracy of 99%. These predictions are then passed to a Deep Q-Network (DQN)-based offline RL model, which learns beamforming strategies to maximize gain toward dynamic ground terminals, without requiring real-time interaction. To enable this, a synthetic dataset grounded in statistical principles and a static dataset is generated using CST Studio Suite and COMSOL Multiphysics simulations, capturing the electromagnetic behavior of various conformal geometries. The results from both the machine learning and reinforcement learning models show that the predicted antenna designs and beam steering angles closely align with simulation benchmarks. Our approach demonstrates the potential of combining data-driven ensemble models with offline reinforcement learning for scalable, efficient, and autonomous antenna synthesis in resource-constrained space environments. Full article

This study experimentally investigates the mechanical and dynamic hygrothermal behavior of compressed earth block (CEB) walls subjected to simulated climatic cycles representative of a tropical humid environment. Four formulations were tested: raw soil (D0), soil with kenaf fibers (DF), soil with fibers and cement (DFC), and soil with fibers, cement, and slag (DFCL). Performance was assessed in an instrumented bi-climatic cell, enabling the determination of thermal and hygroscopic attenuation factors and time lags, complemented by standardized uniaxial compression and three-point bending tests. DFCL achieved a compressive strength of about 10 MPa, nearly twice that of DF (~6 MPa), exceeding the threshold required for buildings up to R + 1. Regarding hygrothermal behavior, DFCL exhibited the highest thermal attenuation factor (2.24) and a hygroscopic attenuation factor of 2.05, with corresponding time lags of ~0.9 h (thermal) and ~1.1 h (hygroscopic). These results highlight superior thermal inertia and moisture regulation, well suited to the constraints of tropical humid climates. Overall, the findings confirm the potential of kenaf fiber-reinforced cement–slag stabilized CEBs as a sustainable construction solution, particularly for load-bearing walls in hot and humid regions. In addition to technical performance, DFCL also offers environmental and economic advantages, as the use of local fibers and slag reduces Portland cement consumption and costs, reinforcing its sustainability potential in tropical contexts. Full article

The integration of remote sensing (RS) and artificial intelligence (AI) has revolutionized Earth observation, enabling automated, efficient, and precise analysis of vast and complex datasets. RS techniques, leveraging satellite imagery, aerial photography, and ground-based sensors, provide critical insights into environmental monitoring, disaster response, agriculture, and urban planning. The rapid developments in AI, specifically machine learning (ML) and deep learning (DL), have significantly enhanced the processing and interpretation of RS data. AI-powered models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning (RL) algorithms, have demonstrated remarkable capabilities in feature extraction, classification, anomaly detection, and predictive modeling. This paper provides a comprehensive survey of the latest developments at the intersection of RS and AI, highlighting key methodologies, applications, and emerging challenges. While AI-driven RS offers unprecedented opportunities for automation and decision-making, issues related to model generalization, explainability, data heterogeneity, and ethical considerations remain significant hurdles. The review concludes by discussing future research directions, emphasizing the need for improved model interpretability, multimodal learning, and real-time AI deployment for global-scale applications. Full article