Search Results (708)

High-sensitivity detection of metal contaminants during gas-insulated equipment (GIE) manufacturing is crucial to mitigating insulation risks. In this study, detection tests of metal contaminants are performed using the conventional partial discharge measurement (CPDM), UHF, and photon counting (PC) methods on a 252 kV GIS chamber. The results indicate that the PC method exhibits high sensitivity to micrometer-sized metal dust, while the UHF sensor performs better in detecting the millimeter-sized single wire-shaped particle. The CPDM method has no sensitivity advantage in either of the above cases. For sub-millimeter-sized block contaminants, all three methods exhibit comparable sensitivity. Moreover, a comprehensive statistical index is introduced to evaluate the discharge activity of different metal defects, enabling a more robust quantitative comparison. Full article

Sprint performance improves throughout adolescence as a result of both structural and neuromuscular development. In particular, increases in body height, lower limb length, and muscle volume have been consistently linked to improvements in sprint performance. However, allometric scaling of force, speed, and power remain lower in adolescents compared to adults even after structural differences are accounted for, implicating neural factors as independent contributors to performance development. The purpose of this study was to investigate differences in neuromuscular activation patterns of the lower limb muscles during maximal sprinting between youth male athletes across two age groups (U19: 17–19 years; U16: 13–16 years). Eighteen athletes performed a 50 m maximal sprint. Spatiotemporal variables (running speed, step frequency, step length) were measured over 30–50 m using a high-speed camera (240 Hz) and timing gates. Electromyographic (EMG) signals were recorded simultaneously from ten lower limb muscles using wireless EMG sensors (2000 Hz): rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gluteus maximus (Gmax), gluteus medius (Gmed), vastus lateralis (VL), vastus medialis (VM), tibialis anterior (TA), gastrocnemius (GAS), and soleus (SOL). Root mean square (RMS) amplitude was calculated across four gait phases (contact, early-swing, mid-swing, late-swing) and normalised to maximal voluntary Isometric contraction (%MVIC). The U19 group demonstrated significantly greater running speed (U19: 9.49 ± 0.39 vs. U16: 8.67 ± 0.25 m·s⁻¹, p < 0.001), step frequency (U19: 4.49 ± 0.12 vs. U16: 4.35 ± 0.16 Hz, p = 0.004), and step length (U19: 2.12 ± 0.12 vs. U16: 1.99 ± 0.06 m, p = 0.010) than U16. The overall pattern of lower limb muscle activation across the gait cycle was broadly similar between groups; however, a significant group × phase interaction was observed for RF (p = 0.003, F = 5.257, η² = 0.247), with post hoc analysis revealing greater RF activation during early swing in U19 (p = 0.033). These findings may indicate that sprint-specific training in youth athletes is associated with not only structural but also neuromuscular differences, specifically reflecting enhanced RF recruitment during the phase-critical moment of early swing—a window in which high-threshold motor unit activation is most mechanically decisive. EMG-based assessment of hip flexor activation during maximal sprinting may provide a complementary tool, pending further validation, for talent identification and training prescription in youth track and field. Full article

Construction activities within the Oil and Gas Industry present many challenges and inherent dangers for workers. Many current solutions available in the market lack real-time insights and predictive capabilities for proactive decision-making and operational safety. This industry demands robust solutions to enhance safety, improve security, and increase productivity, and this is where Human Digital Twins (HDTs), Wearables, and Artificial Intelligence (AI) play an important role. HDTs aim to create a dynamic digital replica of workers, integrating data from wearables, IIoT sensors, information systems, and any other data source available, enabling continuous monitoring of physiological and cognitive conditions. Complementing HDTs, AI-powered wearables collect enriched data from embedded sensors, such as heart rate, blood oxygen, or fatigue, and integrate deep learning algorithms to predict potential incidents directly on the edge before they happen, thanks to their high computing capabilities. This review represents the state-of-the-art for HDTs and AI-powered wearables for the Oil and Gas industry. It explores current technological developments, their applications, the challenges and limitations of deploying them into a real-world project, and also synthesizes current research and industry practices, highlighting their potential in human health and safety. Additionally, it identifies future research directions to overcome existing barriers. Full article

Autonomous robotic systems designed for extraterrestrial in situ resource utilization (ISRU) will play a central role in enabling a sustained human presence on the Moon and Mars. These robots are expected to perform tasks such as regolith excavation, water extraction, oxygen production, and propellant generation under extremely harsh environmental conditions, including large temperature variations, abrasive dust, high radiation levels, and significant communication delays with Earth. Consequently, their onboard electrical systems must operate with high reliability, autonomy, and fault tolerance. A critical enabling technology for these systems is the isolated power conversion architecture, which distributes energy from primary power sources to multiple robotic subsystems, including mobility actuators, drilling systems, sensors, computing units, and thermal management modules. Future lunar and Martian missions are expected to rely on a combination of alternative energy sources, including solar photovoltaic arrays with energy storage, fuel cells, radioisotope power systems, and nuclear surface power reactors, which can provide continuous and high-density energy independent of sunlight availability. These diverse power sources require flexible and highly efficient isolated DC–DC power conversion architectures capable of managing wide input voltage ranges while ensuring electrical isolation, safety, and system stability across distributed robotic platforms. This literature review surveys recent developments in autonomous isolated power conversion architectures suitable for lunar and Martian resource extraction robots. The review examines advanced converter topologies such as resonant converters, phase-shifted full-bridge converters, dual-active bridge converters, and modular multiport power converters designed for high efficiency, high power density, and scalable power distribution. Emphasis is placed on converter architectures capable of interfacing with nuclear-powered systems and other high-energy-density sources while supporting distributed loads in robotic mining and processing systems. In addition, the paper reviews emerging autonomous control strategies, including adaptive digital control, intelligent power management, fault detection and self-recovery mechanisms, and distributed power architectures capable of maintaining stable operation under dynamic load conditions. The role of wide-bandgap semiconductor technologies, including silicon carbide (SiC) and gallium nitride (GaN), is also examined, highlighting their potential to enable higher switching frequencies, improved efficiency, reduced system mass, and enhanced thermal performance in vacuum environments. Finally, system-level considerations for integrating isolated power conversion within robotic ISRU platforms are discussed, including redundancy strategies, power bus architectures, electromagnetic compatibility, thermal management, and long-duration reliability requirements. By consolidating advances across power electronics, autonomous control, and space power systems, this review identifies key research gaps and outlines design directions for next-generation autonomous power conversion systems capable of supporting scalable lunar and Martian resource extraction infrastructures powered by both renewable and nuclear energy sources. Full article

Perovskite materials have emerged as promising candidates for gas sensing owing to their tunable structures, adjustable compositions, rich defect chemistry, and efficient charge transport properties. These characteristics enable the effective regulation of active sites, oxygen vacancies, heterointerfaces, and band alignment, thereby enhancing gas adsorption and sensing performance. This review summarizes recent advances in perovskite-based gas sensors, focusing on synthesis and fabrication strategies, structural engineering, sensing mechanisms, theoretical simulations, and intelligent sensing applications. The effects of doping, defect engineering, morphology control, and heterojunction construction on sensitivity, selectivity, response and recovery behavior, humidity tolerance, and stability are discussed. In addition, the roles of first-principles calculations and artificial intelligence in elucidating sensing mechanisms, identifying gases, predicting concentrations, and suppressing interference are highlighted. Finally, the remaining challenges and future perspectives are discussed to guide the development of stable, low-power, selective, and intelligent perovskite-based sensing systems. Full article

Catalyst deactivation shifts the optimal operating region of exothermic fixed-bed reactors, yet most reactor digital twins focus on monitoring rather than catalyst-state-aware operating decisions. This work presents a simulation-based self-optimizing digital-twin prototype integrating a physics-based reactor model, a moving-window constrained activity estimator, and a target-optimization layer for o-xylene oxidation to phthalic anhydride in a vanadia–titania heat-exchanged fixed-bed reactor. Sparse axial temperature and conversion measurements are reconciled to estimate an axial catalyst activity profile; gas and coolant inlet temperatures are then updated subject to a hot-spot safety constraint. The estimator achieved an activity-profile root mean square error (RMSE) of 0.075, an outlet-conversion RMSE of 0.99 percentage points, and an outlet-temperature RMSE of 1.85 K. Under the baseline noisy-measurement scenario, estimated activity optimization raised the mean phthalic anhydride yield from 46.3% under fixed targets to 61.9%, within 0.14 percentage points of the true-activity optimum, while maintaining the maximum reactor temperature below 730 K. In this matched-model simulation study, this corresponds to recovering approximately 99.1% of the yield improvement available with perfect catalyst-state knowledge. The policy remained superior to fixed-target operation across all tested noise levels, sensor configurations, and kinetic pre-exponential perturbations. All results are obtained from synthetic-measurement simulations rather than experimental or plant data, and plant validation is still required to quantify structural model error. The findings demonstrate the value of linking catalyst-state estimation to operating-target adaptation in a reproducible catalytic-reactor digital-twin workflow. Full article

Wide-scale, spatio-temporal quantification of soil CO₂ efflux is essential for understanding terrestrial carbon dynamics, predicting climate change, and evaluating the carbon balance in managed and natural ecosystems. Rising global temperatures, changing land use patterns, and other activities aimed at boosting crop productivity have resulted in an increase in microbial activity, increasing the impact of soil on gas exchange. Therefore, it is important to measure ${\mathrm{CO}}_2$ gas exchange in situ, over wide areas and extended periods without manual intervention. However, current approaches such as remote sensing lacks sufficient spatial and depth resolution, while other direct measurements such as eddy covariance demand expensive infrastructure, limiting wide-scale deployment. In this work, we propose a low-cost, battery-operated ${\mathrm{CO}}_2$ sensing system that provides long-term and scalable monitoring of soil respiration and carbon flux, with the promise for high-resolution measurements. Our innovative design features a PVC-based gas chamber that periodically opens and closes to allow for gas exchange, and a sensor module with low-cost temperature, moisture, pressure, and ${\mathrm{CO}}_2$ sensors, with a low-power wireless LoRa network for real-time monitoring. Our system was rigorously validated through multiple outdoor deployments, over long periods to demonstrate its practicality. We observe that temperature, air pressure, and humidity trends show responsiveness to the environment. We also observe that ${\mathrm{CO}}_2$ emission flux rate vary significantly across different modules, underscoring the need for fine-grained spatial and temporal resolution in monitoring. Full article

Metal–organic framework (MOF)-derived materials have recently emerged as promising sensing materials because of their tunable composition, porous architecture, high surface area, and defect-rich structures. Therefore, MOF-derived materials have significantly attracted the scientific community to design and fabricate triethylamine (TEA) gas sensors. TEA is a toxic, volatile, and malodorous amine that is widely released from industrial processes, food spoilage, and environmental sources. The selective and sensitive detection of TEA is of great importance for health, safety, and environmental monitoring. Previous years have witnessed rapid growth in the development of MOF-derived materials based on TEA gas sensors. This review critically evaluates recent progress in the fabrication of MOF-derived metal oxides, mixed-metal oxides, doped systems, noble-metal-functionalized materials, carbon-containing composites, MXene-integrated architectures, and heterojunction-based TEA gas sensors. The response, selectivity, stability, and sensing mechanisms for TEA gas sensors are discussed. Furthermore, challenges and perspectives are discussed. We believe that this review may be beneficial for those actively working in the fabrication of MOF-based TEA gas sensors. Full article

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive degeneration of upper and lower motor neurons. While monogenic causes account for a minority of cases, in most cases, ALS is sporadic and likely arises from multilayer interactions of genetic architecture, aging-associated loss of genome regulation, and inflammatory stress. Long interspersed nuclear element-1 (LINE-1) retrotransposons are endogenous mobile elements that are tightly controlled through various cellular mechanisms under normal conditions. When abnormally active, they are involved in gene inactivation, expression regulation, and genomic instability, leading to cellular processes such as innate immunity and cell death. Here, we present mechanistic links between LINE-1 and ALS. These include evidence that the burden of retrotransposition-competent LINE-1s (RC-L1s) is increased in ALS genomes, positioning RC-L1 load as a candidate contributor to missing heritability in sporadic disease. We also integrate emerging data showing that LINE-1 RNA can be intrinsically toxic independently of new insertions, as it promotes chromatin opening and transcriptional epigenetic noise, particularly when nuclear RNA surveillance pathways fail in TDP-43 pathology. Finally, we review how LINE-1-derived DNA/RNA intermediates can engage innate immune sensors, highlighting the cGAS–STING axis as a plausible route from LINE-1 de-repression to neuroinflammation. Together, these concepts support a model in which genetic RC-L1 load and age-/pathology-driven LINE-1 de-repression converge on nuclear dysfunction and inflammatory amplification, suggesting concrete molecular nodes for therapeutic intervention. Full article

Hydrogen sensing is of great significance for environmental monitoring and safety due to the low explosion limit and high flammability of hydrogen gas. In this work, layered and bulk Ni-MOF precursors are designed and pyrolyzed to obtain Ni-Layer-Pyrolysis and Ni-Bulk-Pyrolysis materials. Structural characterizations reveal that Ni-Layer-Pyrolysis inherits a layered morphology with a core–shell structure, higher graphitization degree, and more uniform active sites compared with its bulk counterpart. Electrochemical studies demonstrate that Ni-Layer-Pyrolysis exhibits lower charge-transfer resistance and higher carrier density, which facilitate efficient electron transport. Gas-sensing tests show that the Ni-Layer-Pyrolysis sensor achieves a low detection limit of 100 ppm, a sensitivity of 6.24 at 8000 ppm H₂. Moreover, it displays excellent selectivity against common interfering gases and outstanding long-term stability over 40 days. These results indicate that the layered structure and core–shell architecture play a decisive role in enhancing sensitivity, selectivity, and durability. This study provides new insights into the design of MOF-derived nanostructures for high-performance hydrogen sensors with practical application potential. Full article

In our day-to-day lives, we are regularly exposed to a wide spectrum of dangerous gases. Their origins vary, ranging from industrial activities to objects found within our very homes. Naturally, there is an interest in developing cost-efficient and durable devices that can successfully track these gases within our environment. One such candidate is represented by chemoresistive gas sensors based on metal oxides. This is due to their simple architecture and the possibility of scaling down their size, making them valid contenders for future advancements in portable gas sensors. This review focuses on chemoresistive gas sensors that have been obtained through different Physical Vapor Deposition (PVD) methods, which are easily scalable for potential technological transfer towards commercialization or are already exploited at the industrial level, and how varying different deposition parameters impacts the structure of the active material, thus modifying the gas sensing properties of the device. In this review, we report results obtained for different metal oxides: WO₃, ZnO, CeO₂, TiO₂, NiO, and SnO₂. The main findings of these studies revealed that the sensor’s response was highly impacted by oxygen deficiencies within the deposited material, the specific surface area, and the thickness of the film. Moreover, this study also delves into different strategies of functionalization that result in improved gas sensing properties. Thus, we herein report how tailoring functional properties modifies the gas sensing performance of different metal oxides. Full article

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

Atractylodis Rhizoma is an important traditional Chinese medicinal material derived from two botanical origins, Maocangzhu (MCZ) and Beicangzhu (BCZ), which are difficult to distinguish by conventional morphological identification because of their similar appearance. However, differences in botanical origin may lead to variations in odor, taste, volatile constituents, and non-volatile metabolites, thereby affecting quality evaluation and clinical application. This study aimed to systematically characterize the sensory and chemical differences between MCZ and BCZ and to identify potential markers for their discrimination. A multi-dimensional analytical strategy combining electronic nose, electronic tongue, headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), and ultra-high-performance liquid chromatography–Orbitrap high-resolution mass spectrometry (UPLC-Orbitrap MS) was established. Electronic nose and electronic tongue were used to digitize odor and taste characteristics, HS-GC-IMS was employed to profile volatile organic compounds, and UPLC-Orbitrap MS was applied to characterize non-volatile metabolites. Principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), variable importance in projection (VIP) screening, permutation tests, and correlation analysis were further used to evaluate discrimination performance and screen characteristic markers. The electronic nose results showed that MCZ and BCZ exhibited distinct odor profiles, with W5S, W1W, and W1S identified as the main differential sensors, suggesting that nitrogen oxides, terpenoids, inorganic sulfides, and short-chain alkanes contributed to the odor differences between the two origins. Electronic tongue analysis further demonstrated clear taste discrimination, with sourness and richness identified as the key taste indicators. HS-GC-IMS detected 108 volatile organic compounds, and 24 volatile markers with VIP > 1.2 were screened as important contributors to the differentiation of MCZ and BCZ. Among them, propionic acid and 5-methyl-2-furancarboxaldehyde were mainly distributed in MCZ, whereas (E)-caryophyllene was present only or at higher levels in BCZ, indicating its potential as a characteristic volatile marker of BCZ. UPLC-Orbitrap MS detected 78 non-volatile constituents, and OPLS-DA screened 17 key non-volatile differential metabolites with VIP > 1.2. These results indicated that MCZ and BCZ could be clearly separated not only by sensory signals but also by volatile and non-volatile chemical profiles. This study revealed that the differences between MCZ and BCZ are mainly reflected in odor-active volatile compounds, key taste indicators, and non-volatile differential metabolites. The integration of electronic nose, electronic tongue, HS-GC-IMS, and UPLC-Orbitrap MS provides a comprehensive and reliable strategy for distinguishing the two botanical origins of Atractylodis Rhizoma. These findings provide valuable insights into the material basis underlying the sensory and chemical differences between MCZ and BCZ and offer scientific support for accurate authentication, quality evaluation, and rational clinical application of Atractylodis Rhizoma. Full article

Nitric oxide (NO) detection at low concentrations was of significant practical importance for the screening and monitoring of certain respiratory diseases, driving the demand for gas sensors with enhanced performance and reduced power consumption. This study presented a photoinduced NO gas sensor based on layer-like Ni-doped WO₃ nanomaterials operating at room temperature (RT). The synergistic effect of Ni doping and photoactivation enabled remarkable gas sensitivity across a low concentration range (10~100 ppb), achieving rapid response/recovery times (28 s/50 s) at 50 ppb under RT. The limit of detection (LOD) for NO molecule could reach below 8.29 ppb. A good linear correlation between the response value and NO concentration was demonstrated under a wide relative humidity range (20~90%). Ni doping induced oxygen vacancies while simultaneously facilitating photoinduced electron transfer for surface oxygen activation. The optimized sensor maintained good response stability after three months of ambient storage, demonstrating excellent operational durability. In situ experimental results further elucidated that the doped Ni site enhanced electron transfer from the surface to adsorbed oxygen molecules, generating superoxide radicals. This work provided fundamental insights into surface engineering strategies for developing optically modulated gas sensors and proposed a viable pathway for constructing energy-efficient exhalation monitoring systems. Full article

The global sugar industry is facing increasing challenges due to climate variability, sustainability requirements, and the need for improved operational efficiency. These pressures are driving the search for advanced technological solutions to enhance productivity and resource management. Artificial intelligence (AI) has already demonstrated significant potential across various agricultural sectors; however, a comprehensive evaluation of AI applications across the entire sugar industry value chain from crop cultivation to industrial processing and supply chain management remains limited. This review provides a detailed assessment of the current state of AI and internet of things (IoT) implementation in the sugar beet industry. It examines key applications, including precision agriculture for sugarcane and sugar beet cultivation, intelligent monitoring systems for early disease detection, and AI-driven decision support tools for resource optimization. In addition, the study explores the role of AI in sugar manufacturing processes, where machine learning and data-driven models are used to optimize milling operations, improve product quality control, and enable predictive maintenance of industrial equipment. AI technologies are also shown to enhance supply chain efficiency through improved demand forecasting, logistics optimization, and real-time data analytics. Monitoring volatile organic compounds (VOCs) is becoming increasingly important in sugar beet and sugarcane storage. Microbial activity during storage and fermentation can release VOCs such as ethanol, which act as early indicators of crop degradation and spoilage. Detecting these gases using modern gas sensors enables continuous monitoring of storage conditions and crop health. When sensor data is integrated with AI and IoT systems, it can be analyzed in real time to identify early signs of microbial activity, improve storage management, and optimize processing decisions. Such intelligent monitoring systems have the potential to reduce losses and enhance overall efficiency in the sugar production chain. Full article