Search Results (5,652)

The recent rapid developments in the field of artificial intelligence (AI) indicate that it is an extremely dynamic field that is constantly evolving and penetrating all levels of human activity. The cultural sector therefore cannot remain unaffected. In this paper, we explore how artificial intelligence can collaborate with museum and cultural heritage professionals to create optimal cultural experiences for visitors, as a starting point for student training on AI tools. Starting with a search for appropriate large language models, students tested various tools to determine the most suitable tool for cultural content management. They then connected common elements found across different cultural spaces to construct specific and emotional narratives. The students also explored ways in which artificial intelligence tools can go beyond the creation of narratives and create entire virtual worlds for use in CAVE environments. Thus, the present work studies a wide spectrum of possible uses of AI in the cultural domain, spanning from the creation of engaging narratives to the creation of engaging virtual worlds, with the goal of introducing students to the challenges and potential of generative AI. Full article

Autonomous racing serves as a challenging testbed that exposes the limitations of perception-decision-control algorithms in extreme high-speed environments, revealing safety gaps not addressed in existing autonomous driving research. However, traditional control techniques (e.g., FGM and MPC) and reinforcement learning-based approaches (including model-free and Dreamer variants) struggle to simultaneously satisfy sample efficiency, prediction reliability, and real-time control performance, making them difficult to apply in actual high-speed racing environments. To address these challenges, we propose LiDAR Dreamer, a novel world model specialized for LiDAR sensor data. LiDAR Dreamer introduces three core techniques: (1) efficient point cloud preprocessing and encoding via Cartesian Polar Bar Charts, (2) Light Structured State-Space Cells (LS3C) that reduce RSSM parameters by 14.2% while preserving key dynamic information, and (3) a Displacement Covariance Distance divergence function, which enhances both learning stability and expressiveness. Experiments in PyBullet F1TENTH simulation environments demonstrate that LiDAR Dreamer achieves competitive performance across different track complexities. On the Austria track with complex corners, it reaches 90% of DreamerV3’s performance (1.14 vs. 1.27 progress) while using 81.7% fewer parameters. On the simpler Columbia track, while model-free methods achieve higher absolute performance, LiDAR Dreamer shows improved sample efficiency compared to baseline Dreamer models, converging faster to stable performance. The Treitlstrasse environment results demonstrate comparable performance to baseline methods. Furthermore, beyond the 14.2% RSSM parameter reduction, reward loss converged more stably without spikes, improving overall training efficiency and stability. Full article

Background: As an extremely sensitive organ, particularly during in utero development, the brain has intrinsic systems to reduce the risk of cerebral damage in cases of insult, such as energy deprivation, due to a mechanism of positive balance in cerebral oxygen–energy substrate demand and supply. This mechanism is called cerebral autoregulation and is present in both the fetal and adult brain. The inaccessibility of the fetal brain to currently available measurement techniques limits its knowledge. Physiological and pathological alterations of fetal cerebral blood flow (CBF) can be assessed during the latter half of pregnancy using sonographic Doppler studies. The limited studies on this subject suggest a potential role for Doppler assessment of the fetal internal carotid artery. Objective: This article reviews the concept of CBF autoregulation and the role of fetal Doppler studies in various brain vascular territories in clinical practice. Methods: A PubMed search was performed, and 156 English articles were used as references in this bibliographic review, published between January 1996 and December 2021. Results: The study of fetal CBF involves indirect observation; the fetal brain constantly changes its characteristics towards complete maturation, which will be fully accomplished only after birth, and the maternal environment influences this process. Conclusions: Doppler study of the internal cerebral artery might be useful in clinical practice. However, technical issues for its study are not established, there are no reference curves, and studies on its clinical value have limited applicability. Full article

Soil contamination by microplastics (MPs) is a global problem, exacerbated by the growing production of plastics and low levels of recycling. Considering that agricultural lands constitute a significant part of the land surface (37.7%), the study of the influence of MPs on the carbon cycle in such ecosystems is extremely important for understanding the global carbon balance. This work aims to develop a standardized protocol for determining the effects of microplastics (MPs) on the carbon cycle in agricultural soils. Differences in existing research protocols hinder comparability and limit conclusions about real-world impacts. A preliminary proposal for standardizing the protocol for the determination of MPs influence on the CO₂ and/or CH₄ emission in agricultural seeks to improve reproducibility and transparency in future studies. The protocol incorporates a wide variety of MPs characteristic in agricultural soils and allows experiments at realistic contamination levels, reflecting both current and projected future scenarios. Key recommendations include several points. Stringent contamination control during sample collection and preparation is of paramount importance. The selection of microplastic types and concentrations specific to agricultural environments is also recommended. Furthermore, maintaining consistent experiment durations is crucial, and the utilization of gas chromatography for analysis is highly desirable. Full article

This study aimed to develop an industrially scalable coating route for enhancing the oxidation resistance of carbon fiber fabrics, a critical requirement for next-generation aerospace and high-temperature composite structures. To achieve this goal, synthesis of hexagonal boron nitride (h-BN) layers was achieved via a single wet step in which the fabric was impregnated with an ammonia–borane/THF solution and subsequently nitrided for 2 h at 1000–1500 °C in flowing nitrogen. Thermogravimetric analysis coupled with X-ray diffraction revealed that amorphous BN formed below ≈1200 °C and crystallized completely into (002)-textured h-BN (with lattice parameters a ≈ 2.50 Å and c ≈ 6.7 Å) once the dwell temperature reached ≥1300 °C. Complementary XPS, FTIR and Raman spectroscopy confirmed a near-stoichiometric B:N ≈ 1:1 composition and the elimination of O–H/N–H residues as crystallinity improved. Low-magnification SEM (100×) confirmed the uniform and large-area coverage of the BN layer on the carbon fiber tows, while high-magnification SEM revealed a progressive densification of the coating from discrete nanospheres to a continuous nanosheet barrier on the fibers. Oxidation tests in flowing air shifted the onset of mass loss from 685 °C for uncoated fibers to 828 °C for the coating produced at 1400 °C; concurrently, the peak oxidation rate moved ≈200 °C higher and declined by ~40%. Treatment at 1500 °C conferred no additional benefit, indicating that 1400 °C provides the optimal balance between full crystallinity and limited grain coarsening. The resulting dense h-BN film, aided by an in situ self-healing B₂O₃ glaze above ~800 °C, delayed carbon fiber oxidation by ≈140 °C. Overall, the process offers a cost-effective, large-area alternative to vapor-phase deposition techniques, positioning BN-coated carbon fiber fabrics for robust service in extreme oxidative environments. Full article

Soil contamination by heavy metals from industrial and mining activities poses a significant global threat to both environmental and human health, particularly in brownfields—abandoned or underutilized industrial areas that frequently accumulate pollutants. Climate change exacerbates this issue by intensifying extreme events such as floods, which can enhance contaminant mobility and compromise the reliability of conventional remediation methods. This study evaluated the in situ application of a sustainable soil washing technique based on hydrocycloning at a contaminated site in Canoas (Porto Alegre, Brazil), a flood-prone area heavily impacted by the 2024 climate disaster. The method physically separates heavy metals by concentrating them into a fine, high-contamination fraction for controlled disposal. Approximately 3019 m³ of soil was treated, recovering 93.4% of the material (coarse and fine sand) for potential reuse and isolating only 6.6% (200 m³) as hazardous waste. Chemical analyses confirmed that the recovered fractions complied with regulatory limits for heavy metals, while contaminants were effectively retained in the sludge and safely disposed of through landfills. During the April–May 2024 flood events, although the site was inundated, no significant erosion of the backfilled material was registered. The results support hydrocycloning-based soil washing as a robust and climate-resilient approach to adaptive remediation in contaminated environments. Full article

Fiber-optic sensing (FOS) technology has emerged as a cutting-edge research focus in the sensor field due to its miniaturized structure, high sensitivity, and remarkable electromagnetic interference immunity. Compared with conventional sensing technologies, FOS demonstrates superior capabilities in distributed detection and multi-parameter multiplexing, thereby accelerating its applications across biomedical, industrial, and aerospace fields. This paper conducts a systematic analysis of the sensing mechanisms in fiber-optic pressure sensors, with a particular focus on the performance optimization effects of fiber structures and materials, while elucidating their application characteristics in different sensing scenarios. This review further examines current manufacturing technologies for fiber-optic pressure sensors, covering key processes including fiber processing and packaging. Regarding practical applications, the multifunctional characteristics of fiber-optic pressure sensors are thoroughly investigated in various fields, including biomedical monitoring, industrial and energy monitoring, and wearable devices, as well as aerospace monitoring. Furthermore, current challenges are discussed regarding performance degradation in extreme environments and multi-parameter cross-sensitivity issues, while future research directions are proposed, encompassing the integration and exploration of novel structures and materials. By synthesizing recent advancements and development trends, this review serves as a critical reference bridging the gap between research and practical applications, accelerating the advancement of fiber-optic pressure sensors. Full article

Target tracking in integrated sensing and communication (ISAC) systems faces critical challenges due to complex interference patterns and dynamic resource allocation between radar sensing and wireless communication functions. Classical tracking algorithms struggle with the non-Gaussian noise characteristics inherent in ISAC environments. This paper addresses these limitations through a novel hybrid ISAC-LSTM architecture that enhances Extended Kalman Filter performance using intelligent machine learning corrections. The approach processes comprehensive feature vectors including baseline EKF states, ISAC-specific interference indicators, and innovation-based statistical occlusion detection. ISAC systems exhibit fundamental symmetry through dual sensing–communication operations sharing identical spectral and hardware resources, requiring balanced resource allocation, where ${\alpha }_{\mathrm{sensing}}+{\alpha }_{\mathrm{comm}}=1$. The proposed hybrid architecture preserves this functional symmetry while achieving balanced performance across symmetric dual evaluation scenarios (normal and extreme conditions). Comprehensive evaluation across three realistic deployment scenarios demonstrates substantial performance improvements, achieving 21–24% RMSE reductions over classical methods (3.5–3.6 m vs. 4.6 m) with statistical significance confirmed through paired t-tests and cross-validation. The hybrid system incorporates fail-safe mechanisms ensuring reliable operation when machine learning components encounter errors, addressing critical deployment concerns for practical ISAC applications. Full article

Due to the complexity of the marine environment and the uncertainty of ship movements, altitude control of UAV is particularly important when approaching and landing on the deck of a ship. This paper focuses on unmanned helicopters as its research subject. Conventional altitude control systems may have difficulty in ensuring fast and stable landings under certain extreme conditions. Therefore, designing a new UAV altitude control method that can adapt to complex sea conditions has become a current problem to be solved. Designing a reinforcement learning based rotational speed compensator for UAV as a redundant controller to optimise UAV altitude control performance for the above problem. The compensator is capable of adjusting the UAV’s rotational speed in real time to compensate for altitude deviations due to external environmental disturbances and the UAV’s own dynamic characteristics. By introducing reinforcement learning algorithms, especially the DDPG algorithm, this compensator is able to learn the optimal RPM adjustment strategy in a continuous trial-and-error process, which improves the UAV’s rapidity and stability during the landing process. Full article

Understanding the variability of turbulence intensity (TI) under different wind regimes is essential for the design and safety of offshore wind turbines. The IEC Normal Turbulence Model (NTM), though widely adopted in industry, does not incorporate directional dependence or account for extreme wind events such as typhoons, which can lead to substantial underestimation of turbulence in complex offshore environments. In this study, field measurements from two coastal sites in China, Huilai and Pingtan, were analyzed. At Pingtan, two months of observations captured both normal and typhoon-affected winds, providing a unique dataset for assessing turbulence under typhoon-affected conditions. The results show that wind speeds during the typhoon-affected period were approximately 14% higher than those during normal periods. At Huilai, TI was evaluated under northeasterly and southeasterly sea breezes, revealing that the IEC NTM underestimated TI by 15–42%, with more pronounced discrepancies under northeasterly winds. Based on these findings, revised NTM parameters and correction factors are proposed for different wind conditions, enhancing the applicability of the model to offshore wind turbine design. This work underscores the importance of incorporating directional and event-specific modifications into IEC turbulence standards to ensure reliable structural assessment across diverse wind regimes. Full article

Post-disaster underground (UG) mine environments are characterized by complex and rapidly changing conditions, adding extra attenuation to propagating electromagnetic (EM) waves. One such complex condition is the extreme generation of dust and sudden rise in humidity contributing to extra attenuation effects to propagating waves, especially under varying airborne humidity and dust levels. The existing wave propagation prediction models, especially those that factor in the effect of dust particles, are deterministic in nature, limiting their ability to account for uncertainties, especially during emergency conditions. In this work, the vector parabolic equation (VPE) model is modified to include dust attenuation effects. Using the complex permittivity of dust as a random variable, the Karhunen–Loève (KL) expansion is used to generate random samples of permittivity along the drifts for which each realization is solved using deterministic VPE method. The model is validated using a modified Friis method and experimentally obtained data from literature. The findings show that accounting for dust and humidity effects stochastically captures the extra losses that would have otherwise been lost using deterministic methods. The proposed framework offers key insights for designing resilient underground wireless systems, strengthening miner tracking, and improving safety during emergencies. Full article

The development of effective materials for uranium extraction from seawater is vital for advancing sustainable energy solutions. However, the efficient recovery of uranium from seawater presents significant challenges due to its extremely low concentration, the presence of competing ions, and the complex marine environment. To address these issues, various materials such as inorganic and organic sorbents, chelating resins, nanostructured sorbents, and composite materials have been explored. More recently, the functionalization of carbon-based materials for enhanced adsorption properties has attracted much interest because of their high specific surface area, excellent chemical and thermal stability, and tunable porosity. These materials include activated carbon, graphene oxide, biochar, carbon cloths, carbon nanotubes, and carbon aerogels. The enhancement of carbonaceous materials is typically achieved through surface functionalization with chelating groups and the synthesis of composite materials that integrate other high-performance sorbents. This review aims to summarize the work of these functionalized carbon materials, focusing on their adsorption capacity, selectivity, and durability for uranium adsorption. This is followed by a discussion on the binding mechanisms of uranium with major chelating functional groups grafted on carbonaceous sorbents. Finally, an outlook for future research is suggested. We hope that this review will be helpful to researchers engaged in related studies. Full article

The deterioration of the global climate and accelerated urbanization have led to intense pressure on surface space resources. As a strategic development field, deep underground space has become a crucial direction for alleviating human habitation pressure. However, current research on deep underground space mostly focuses on fields such as geology and medicine, while the design of habitable environments lacks interdisciplinary integration and systematic approaches. Taking deep underground space as the research object, this study first clarifies the interdisciplinary research context through bibliometric analysis. Then, combined with geological data (ground temperature, groundwater, and ground stress, etc.) from major cities in China, it defines the characteristics of the in situ environment and the characteristics of the development and utilization of deep underground space. By comparing the habitable design experiences of extreme environments, such as space stations, Moon habitats, and desert survival modules, the study extracts five categories of design elements: natural conditions, construction status, social economy, users, and existing resources. Ultimately, it establishes a demand-oriented, five-dimensional habitable design methodology covering in situ environment adaptation, living support, medical and health services, resilience and flexibility, and existing space renovation. This research clarifies the differentiated design strategies for hundred-meter-level and kilometer-level deep underground spaces, providing theoretical support for the scientific development of deep underground space and serving as a reference for habitable design in other extreme environments. Full article

With the continuous growth in the demand for safety assurance in major projects and monitoring in extreme environments, sensor technology is facing challenges in harsh working conditions such as high temperatures, high pressures, and complex liquid media. This article focuses on typical complex environments such as underground and marine environments, systematically reviewing the basic principles, performance characteristics and the latest application progress of mechanical, optical and acoustic sensors in complex environments, and deeply analyzing their applicable boundaries and technical bottlenecks. The transmission mechanism of sensor data and the system architecture of the engineering monitoring and early warning platform were further explored, and their key roles in real-time perception and intelligent decision-making were evaluated. Finally, the core challenges and development opportunities currently faced by complex environmental sensing systems are summarized, and the future development directions, such as multi-parameter fusion, autonomous perception and edge intelligence, are prospected. This paper aims to provide a systematic theoretical basis and engineering practice reference for the design of sensors and the construction of monitoring systems in extreme environments. Full article

Drought limits plant growth, development and productivity, leading to more than 50% crop loss globally. Drought-induced oxidative stress disturbs the plant’s metabolism; however, plants activate signaling pathways to respond and adapt to drought stress. Although drought response mechanisms are well reported in several crops, these mechanisms are poorly understood in Amaranthus. As a highly nutritious crop, rich in antioxidants with the ability to survive in extreme agro-climatic environments, Amaranthus has the potential to serve as a climate-smart future crop. This review provides evidence of some drought response traits in grain and vegetable Amaranthus species. Grain amaranths are the most tolerant species, mainly through improved osmoregulation, antioxidant capacity, and gene expression. While biomass partitioning, efficient water use, and membrane stability have been reported in both grain and vegetable amaranth, the molecular response of vegetable amaranth remains limited. Thus, future research must focus on integrated biochemical, molecular, and multi-omics applications to screen and identify resilient Amaranthus genotypes under drought for sustainable agriculture. Full article