Search Results (10,272)

Aortic diseases, including thoracic and abdominal aneurysms as well as aortic dissections, represent life-threatening vascular disorders characterized by progressive wall degeneration and inflammation. Increasing evidence indicates that oxidative stress is a central driver of aortic pathology through the induction of DNA damage in vascular smooth muscle cells and endothelial cells. Oxidative DNA lesions activate the DNA damage response (DDR), a highly coordinated network of damage sensors, signaling kinases, and repair effectors that determines cell fate decisions such as DNA repair, apoptosis, or cellular senescence. In aortic tissue, persistent or dysregulated DDR signaling contributes to chronic inflammation, extracellular matrix degradation, and loss of vascular integrity. Key molecular regulators, including base excision repair enzymes OGG1 and APE1, as well as DDR mediators such as ATM, ATR, p53, PARP, and NOTCH1, integrate oxidative stress signals with pro-inflammatory and pro-degenerative pathways. Aberrant activation of these mechanisms promotes vascular smooth muscle cell VSMC phenotypic switching from contractile to synthetic phenotype, endothelial dysfunction, and senescence-associated secretory responses, thereby accelerating aortic wall weakening and aneurysm progression. This review highlights the mechanistic links between oxidative stress-induced DNA damage, DDR pathway activation, and vascular remodeling in aortopathies. A deeper understanding of these molecular interactions may uncover novel biomarkers and therapeutic targets aimed at limiting inflammation, preserving genomic stability, and preventing catastrophic aortic events. This work represents a narrative review and therefore has inherent limitations in terms of systematic literature search and selection. Full article

Wildfire monitoring in tropical regions requires robust frameworks capable of transforming heterogeneous satellite detections into consistent, event-level information suitable for decision support. This study presents the DescrEVE Fogo (Descrição de Eventos de Fogo) framework, a relational and scalable system that models daily fire events in Brazil by integrating Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections within a unified Structured Query Language (SQL)/PostGIS environment. The framework formalizes a mathematical and computational model that defines and tracks fire fronts and multi-day fire events based on explicit spatio-temporal rules and geometry-based operations. Using database-native functions, DescrEVE Fogo aggregates daily fronts into events and computes intrinsic and environmental descriptors, including duration, incremental area, Fire Radiative Power (FRP), number of fronts, rainless days, and fire risk. Applied to the 2003–2025 archive of the Brazilian National Institute for Space Research (INPE) Queimadas Program, the framework reveals that the integration of VIIRS increases the fraction of multi-front events and enhances detectability of larger and longer-lived events, while the overall regime remains dominated by small, short-lived occurrences. A simple, prototype fire-type rule distinguishes new isolated fire events, possible incipient wildfires, and wildfires, indicating that fewer than 10% of events account for more than 40% of the area proxy and nearly 60% of maximum FRP. For the 2025 operational year, daily ignition counts show strong temporal coherence with the Global Fire Emissions Database version 5 (GFEDv5), albeit with a systematic positive bias reflecting differences in sensors and event definitions. A case study of the 2020 Pantanal wildfire illustrates how front-level metrics and environmental indicators can be combined to characterize persistence, spread, and climatic coupling. Overall, the database-native design provides a transparent and reproducible basis for large-scale, near-real-time wildfire analysis in Brazil, while current limitations in sensor homogeneity, typology, and validation point to clear avenues for future refinement and operational integration. Full article

This paper addresses the stringent requirements of high-precision equipment for broadband, contactless active vibration isolation by tackling three key research gaps: the lack of an integrated design deeply coupling vertical and lateral subsystems, the absence of explicit characterization of the base-to-load vibration transmission chain in dynamic models, and the disconnect between theory and application due to spatial sensor–actuator mismatch. To bridge these gaps, a novel five-degree-of-freedom active magnetic levitation vibration isolation system is proposed. Its core contributions are threefold. First, an electromagnetic-structure co-design method based on the equal magnetic reluctance principle is introduced, enabling a globally optimized, integrated actuator layout that maximizes force density within spatial constraints. Second, a dynamic model incorporating explicit base kinematic excitation is established, clearly revealing the complete physical mechanism of vibration transmission through the suspension gap and providing an accurate foundation for model-based control. Third, a coordinate reconstruction control model is constructed, which transforms the ideal center-of-mass-based dynamics into a design model using only measurable gap signals via systematic coordinate transformations, thereby fundamentally eliminating control deviations from physical spatial mismatch. This work provides a comprehensive theoretical framework and solution for next-generation high-performance active vibration isolation platforms, encompassing integrated design, precise modeling, and engineering implementation. Full article

Gas sensors based on metal oxide semiconductors (MOS) have attracted significant attention in monitoring of methane emission and leakage monitoring due to their high sensitivity, fast response time, simple structure and low cost. However, the high power consumption caused by long-term high-temperature operation of MOS sensors restricts their application in mobile and portable devices. In this study, MOF-derived Co₃O₄ dodecahedrons for low-concentration methane detection at room temperature was prepared using Zeolitic Imidazolate Framework-67 (ZIF-67) as a template and with various calcination temperatures. Among them, the Co₃O₄-350 calcined at 350 °C exhibited the optimal CH₄ sensing performance at room temperature, with a response of R_g/R_a = 1.53 to 2000 ppm CH₄. This enhanced gas sensing performance is attributed to the highest Co³⁺ proportions and the largest specific surface area in Co₃O₄-350 nanomaterials, which provided more active sites for gas adsorption and reaction. To address the challenge of slow response speed and irrecoverability during CH₄ detection at room temperature, the Co₃O₄ nanomaterials were printed onto a micro-heater plate (MHP) to form a MEMS gas sensor. By introducing a pulse heating mode to the MEMS sensor, the response and recovery time were significantly reduced to 26 s and 21 s, respectively. This enhancement improves both the efficiency and reliability of the MEMS gas sensor for early-stage detection of CH₄ leaks in various industrial applications. Full article

Falls remain a critical safety concern in surveillance settings, yet monocular RGB methods often degrade in multi-person scenes with occlusion and loss of three-dimensional cues. This study proposes a neuro-symbolic framework that restores physically interpretable depth proxies from monocular video and fuses them with skeleton-based spatio-temporal inference for robust fall detection. The pipeline estimates per-frame depth and 2D skeletons, recovers world coordinates for key joints, and derives absolute neck height and vertical descent rate for rule-based adjudication, while a neural method operates on joint trajectories; final decisions combine both streams with a logical policy and short-horizon temporal consistency. Experiments in a realistic indoor testbed with multi-person activity compare three configurations—neural, symbolic, and fused. The fused neuro-symbolic method achieved an accuracy of 0.88 and an F1 score of 0.76 on the real surveillance test set, outperforming the neural method alone (accuracy 0.81, F1 0.64) and the symbolic method alone (accuracy 0.77, F1 0.35). Gains arise from complementary error profiles: depth-derived, rule-based cues suppress spurious positives on non-fall frames, while the neural stream recovers true falls near rule boundaries. These findings indicate that integrating monocular depth proxies with interpretable rules improves reliability without additional sensors, supporting deployment in complex, multi-person surveillance environments. Full article

This paper presents a cover lens concept for camera modules based on surface acoustic waves (SAW) to mitigate the degradation of physical AI optical sensor field-of-view performance caused by surface contamination. The proposed approach utilizes a single-phase unidirectional transducer (SPUDT) that intentionally breaks left–right symmetry through a geometrically asymmetric electrode array to generate SAW, thereby removing droplet contamination. First, the acoustic streaming induced inside a single sessile droplet by the SAW was visualized, and the dynamic behavior of the droplet upon SAW actuation was observed using a high-speed camera. The internal flow developed into a recirculating vortex structure with directional deflection relative to the SAW propagation direction, indicating a symmetry-broken streaming pattern rather than a purely symmetric circulation. Upon the application of the SAW, the droplet was confirmed to move a total of 7.2 mm along the SAW propagation direction, accompanied by interfacial deformation and oscillation. Next, an analysis of transport trajectories for five sessile droplets dispensed at different y-coordinates ($y_1$–$y_5$) revealed that all droplets were transported along the x-axis regardless of their initial positions. Furthermore, the analysis of transport velocity as a function of droplet viscosity (1 $\mathrm{c}\mathrm{P}$ and 10 $\mathrm{c}\mathrm{P}$) and volume (2$\mathrm{\mu }\mathrm{L}$, 4$\mathrm{\mu }\mathrm{L}$, and 6 $\mathrm{\mu }\mathrm{L}$) demonstrated that the transport velocity gradually increased with driving voltage but decreased as viscosity increased under identical actuation conditions. Finally, the proposed cover lens was applied to an automotive front camera module to verify its effectiveness in improving object recognition performance by removing surface contamination. Based on its simple structure and driving principle, the proposed technology is deemed to be expandable as a surface contamination cleaning technology for various physical AI perception systems, including intelligent security cameras and drone camera lenses. Full article

This article presents PrevOccupAI-HAR, a new publicly available dataset designed to advance smartphone-based human activity recognition (HAR) in office environments. PrevOccupAI-HAR comprises two sub-datasets: (1) a model development dataset collected under controlled conditions, featuring 20 subjects performing nine sub-activities associated to three main activity classes (sitting, standing, and walking), and (2) a real-world dataset captured in an unconstrained office setting captured from 13 subjects carrying out their daily office work for six hours continuously. Three machine learning models—namely, k-nearest neighbors (KNN), support vector machine (SVM), and Random Forest (RF)—were trained on the model development dataset to classify the three main classes independently of sub-activity variation. The KNN, SVM, and RF models achieved accuracies of 90.94%, 92.33%, and 93.02%, respectively, on the development dataset. When deployed on the real-world dataset, the models attained mean accuracies of 69.32%, 79.43%, and 77.81%, reflecting performance degradations between 21.62% and 12.90%. Analysis of sequential predictions revealed frequent short-duration misclassifications, predominantly between sitting and standing, resulting in unstable model outputs. The findings highlight key challenges in transitioning HAR models from controlled to real-world contexts and point to future research directions involving temporal deep learning architectures or post-processing methods to enhance prediction consistency. Full article

This study presents a passive mechanical filter designed to enhance sub-Hertz Venusquake detection by shaping the seismic transfer path. The mechanism uses a tunable, high-Q pendulum mounted inside a cylindrical enclosure on a three-ring gimbal to ensure self-leveling and alignment in gravity on uneven terrain. Unlike approaches that rely on broadband digitization and require active control and a stable power supply, this housing–gimbal mechanism performs mechanical filtering for sub-Hz signal amplification and higher frequency attenuation without power. Response spectrum analysis shows that the transmissibility can be tuned to achieve peak sensitivities in the 0.5–0.8 Hz range. When tuned to 50–55 mm pendulum length and under assumed undamping, the pendulum-mounted mechanism improves detectability at best by 10–100× relative to a bare sensor for moderate magnitude ($M_s$ = 3–6) in a 12 h observation window, with signal-to-noise (SNR) ratio of 3, and amplitude spectrum density (ASD) of 10⁻⁸ m/s²/√Hz. Furthermore, we extrapolate that the predicted minimum detectable event rates follow $N_m^{min}\propto {\left(\mathrm{S}\mathrm{N}\mathrm{R}\right)}^{1.2}{\left(\mathrm{A}\mathrm{S}\mathrm{D}\right)}^{1.2}{f}_s^{0.6}$, where $f_s$ is the quake wave frequency. The damping ratio, considering both structural damping and viscous drag, is estimated to be in the order of 10⁻³ to 10⁻². A probabilistic sensitivity analysis is performed to account for the inherent uncertainty in the spectral mismatch between the narrowband sub-Hz resonance of the designed mechanical filter and the peak frequencies of seismic events; the derived probability model suggests strategies for improving the detection probability in the 0.01–1 Hz range. Full article

For next-generation biomedical and biochemical sensor nodes, the analog front-end demands a direct interface with current-output sensors, extreme miniaturization, and nanowatt power consumption to enable energy autonomy. This work directly addresses these needs by presenting a comparative analysis of four minimalist, first-order, current-mode $\Delta \Sigma$ modulator ($\Delta \Sigma$M) architectures. Optimized for ultra-low-voltage operation (supply $\le 0.5$ V), the investigated topologies—including resistive, switched-capacitor, and current-reference-based cores—exploit passive integration and charge-domain feedback, eliminating the need for power-hungry active blocks. Detailed circuit-level simulations confirm that, with ad hoc techniques, it is possible to achieve stable first-order noise shaping in the deep near-threshold region, delivering up to 10-bit resolution while consuming less than 10 nW at a 0.5 V supply voltage achieving a signal bandwidth in the sub-10 hertz range. This study validates that robust $\Delta \Sigma$ conversion is feasible under extreme area and power constraints by leveraging architectural simplicity. The clear performance–complexity trade-offs outlined make these current-mode architectures ideal candidates for monolithic integration within miniaturized, energy-autonomous sensing systems. Full article

The high rate of bike commuting around the globe has greatly transformed the mode of transportation in cities, but the high speeds of motorized cycling have contributed to a high rate of serious road trauma. Although conventional helmets offer necessary passive structural protection, they do not consider the most important aspect of the emergency response, which is the Golden Hour the time frame during which medical intervention can have the most significant impact. This paper is a development and validation of an autonomous, low-cost smart helmet architecture that is programmed to operate in real-time to detect accidents and autonomously inform the operator of accidents. The system is built up of an ESP32 microcontroller with a multi-modal sensor package, which comprises an inertial measurement unit (IMU), force-impact sensors, and MQ-3 alcohol sensors to conduct proactive safety screening. To overcome the single threshold limitation of unreliable systems, a time-windowed sensor-fusion algorithm was applied in order to distinguish between normal riding dynamics and bona fide collisions. This reasoning involves concurrent cues of high-G inertial rotations and physical impacting features over a time window of 500 ms to reduce spurious activations. The architecture of the system is completely self-sufficient and employs an in-built GPS-GSM module to send the geographical location through SMS without the need to have a smartphone connection. The prototype was also put through 150 experimental tests, with some conducted in laboratories, and real-world running tests in diverse terrains. The findings reveal an accuracy in detection of 93.7, a false positive rate (FPR) of 2.6 and a mean emergency alert latency of 2.8 s. In addition, it was found that structural integrity was confirmed at ECE 22.05 impact conditions using Finite Element Analysis (FEA), with a safety factor of 1.38. These quantitative results mean that the proposed system is an effective way to address a cultural shift between passive structural protection and active rescue intervention as a statistical and computationally efficient safety measure of modern micro-mobility. Full article

Footrot is one of the top five globally important diseases of sheep and causes lameness, leading to poor welfare and productivity. Transmission of Dichelobacter nodosus, the causative agent, occurs via surfaces such as pasture or bedding and persistence occurs from diseased sheep shedding bacteria into the environment; D. nodosus cannot replicate off host. High resolution proximity sensors were deployed on a flock of Poll Dorset sheep for 10–17 days at several points of the production cycle (teasing, tupping, pregnancy, and lactation (<6-week-old lambs)) between July 2018 and May 2021. Association indices between pairs of sheep were calculated, and outbreaks of footrot were simulated using a network-based susceptible-exposed-infected-recovered model. Two management approaches were modelled (1) where sheep were treated either not promptly, or effectively, resulting in long recovery times (28–100 days) and (2) where sheep were treated and recovered within 15 days, assuming ‘active management’ of footrot by the farmer using ‘best practice’ of prompt recognition of lame sheep and parenteral and topical antibiotics. Under ‘active management’ conditions (scenario 2), outbreak sizes were smaller at all points of the production cycle. This adds to existing evidence that prompt, effective treatment of sheep at all stages of the production cycle is key to reducing the prevalence of footrot in the flock, including at breeding when sheep are more likely to be in close contact. Full article

Giving consideration to cooking activity is important for sustainable housing. In contexts of limited ventilation, imposed by energy saving concerns, cooking causes deterioration of indoor air quality (IAQ) and occupants’ discomfort. This study presents a cooking event detection system that may support IAQ control to minimize the impact of cooking. The system consists of a multi-sensor device and a deep-learning neural network (DNN). The device monitors temperature (T), relative humidity (RH), suspended particulate matter (PM), CO₂, the responses of sensors to volatile organic compounds (VOCs), and other gases (NO₂, CO, CH₂O) in the kitchen zone. The collected data are processed by the DNN. The detection system generates a response every 7 s, indicating either ’COOKING’ or ’NO COOKING’. Feature vector selection was based on classification performance and cost considerations. Cooking event misdetections generate unjustified IAQ control costs: economic ones (UEC), when the system detects a non-existent event, and environmental ones (UEN), when the system fails to detect an actual event. In this study, several well-performing detection systems were developed, with miss rates ranging from 5.1% to 20.5% and false detection rates ranging from 7.7% to 11.7%. The results show that gas sensor responses—particularly to VOCs—had greater utility for cooking event detection compared with T, RH, CO₂, and PM. The cost analysis demonstrated that IAQ control supported by the developed cooking event detection systems could generate higher total unjustified environmental costs when the unit cost ratio UEN/UEC exceeded 1.25, or higher total unjustified economic costs when the unit cost ratio UEN/UEC was below 1.43. We believe this work will contribute to the development of novel automatic IAQ control systems supported by event detection. Full article

Aim: Brain training games offer a promising avenue for promoting cognitive engagement and healthy aging among older adults. However, little is known about how design features align with the specific needs of this demographic to promote sustained usage and thereby cognitive intervention. The aim of this study was to characterize how all aspects of the game design and player experience might influence adherence mechanisms, and assess the feasibility and acceptability of a wearable brain-activity measuring device. Methods: We use an exploratory mixed-methods approach with n = 6 community-dwelling older adults (mean age 68 ± 3.94) within a smart-home-style Living-Lab. Participants played two commercially available brain-training games. One of the games uses a wearable brain-activity measuring device. We collected System Usability Scale (SUS) and User Experience Questionnaire (UEQ) scores and conducted focus-group interviews and structured observations. We performed a qualitative theory-informed analysis through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. Results: Participants reported high motivation to play brain-training games for dementia prevention. They preferred large, high-contrast text, intuitive navigation, touch-based controls, and a relaxed pacing. The wearable device was acceptable and comfortable for home use. There were requests for a clearer meaning of brain activity scores and the integration of personalized brain data with other health apps and broader health metrics. Quantitative scales (SUS and UEQ) showed similar ratings for both games, with both meeting the threshold for acceptability. Conclusions: In this formative study, concrete design features that plausibly increase engagement, persistence and adherence were identified, alongside evidence for the feasibility of integrating a wearable brain-sensor. Our findings motivate a follow-on trial testing whether an adherence-optimized design increases the training dose and downstream cognitive outcomes. Full article

Falls represent a significant global public health issue, particularly among adults over the age of 60. This comprehensive review aims to provide an in-depth examination of current fall detection and prevention technologies. The study categorizes fall detection methods into pre-fall prediction and post-fall detection, using both wearable and unobtrusive sensors. Wearable technologies, such as accelerometers, gyroscopes, and electromyography (EMG) sensors, are explored for their efficacy in real-time fall prediction and detection. Unobtrusive methods, including camera-based systems, LiDAR, radar, ultrasonic sensors, and depth sensors, are evaluated for their ability to monitor falls without intruding on users’ daily activities. The integration of these technologies into healthcare settings is also discussed, with an emphasis on the importance of immediate response to fall events. By analyzing the operational principles, technological advancements, and practical applications of these systems, promising directions for future research and innovation in fall detection and prevention are identified. The findings highlight the need for multifaceted approaches combining various sensor technologies to enhance fall detection accuracy and response times, ultimately improving patient safety and quality of life. Full article

The development of multifunctional smart packaging materials capable of simultaneously monitoring temperature and suppressing microbial contamination is critical for next-generation food and pharmaceutical safety systems. In this study, we report the design and characterization of a polymeric film integrating a spin crossover (SCO)-based thermochromic sensor with zinc oxide (ZnO) nanoparticles serving as an antimicrobial agent. Beyond the individual functionalities, we demonstrate a synergistic effect between SCO and ZnO components. Notably, the SCO transition of the pristine SCO complex is broadened, and the hysteresis width of the transition is decreased (i.e., from 6 K to 1.5 K, 2 K, and 1.5 K for ZnO loading of 0.5%, 1%, and 2%, respectively), in the polysulfone–SCO–ZnO composites. Migration studies reveal that the co-existence of SCO and ZnO does not disrupt the low release profile of active agents, which remains low across ZnO loadings. The polymeric film exhibited dose-dependent antiproliferative activity against MCF-7 breast cancer cells, with a significant reduction in cell viability observed only at the highest tested concentration, indicating cytotoxic potential. This multifunctional platform represents a promising advancement in smart packaging design, enabling real-time thermal indication combined with the integration of ZnO as a literature-established antimicrobial component, within a non-toxic, and visually transparent system. Collectively, the material’s properties offer promising scalability for both food and pharmaceutical packaging applications where visual clarity, antimicrobial integrity, and temperature monitoring are imperative. Full article