Search Results (2,015)

This article introduces the design and implementation of an Unmanned Surface Vehicle (USV), named “AquaMIB”, which introduces a novel and integrated approach for real-time and autonomous water quality monitoring in aquatic environments. The system integrates modular hardware and software, combining sensors for temperature, pH, conductivity, dissolved oxygen, and oxidation reduction potential with GPS, LiDAR, a digital compass, communication modules, and a dedicated power unit. Software components include Python on a Raspberry Pi for navigation and control, C on an Atmega 324P for sensing, C++ on an Arduino Uno for remote control, and C#/JavaScript for the web-based control center. Users assign task points, and the USV autonomously navigates, collects data, and transmits it via RESTful API. Field trials showed 96.5% navigation accuracy over 2.2 km, with 66% of task points reached within 3 m. A total of 120 measurements were processed in real time and visualized as GIS-based spatial maps. The system demonstrates a cost-effective, modular solution for aquatic monitoring. The system’s ability to generate real-time GIS maps enables immediate identification of environmental anomalies, transforming raw sensor data into an actionable decision-support tool for aquatic management. Full article

Background: Sleep is a key determinant of recovery and performance in elite athletes, yet its optimization extends beyond sleep duration alone and encompasses multiple subjective and physiological dimensions. Environmental factors, including the sleep surface, represent modifiable components of sleep that may influence perceived sleep quality. This study aimed to examine whether an individually adjustable modular sleep system improves subjective sleep quality in elite athletes and whether alterations in objective sleep metrics, circadian timing, or nocturnal autonomic physiology accompany such changes. Methods: Forty-three elite athletes participated in this pre–post-intervention study (without a control group). Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while objective sleep and physiological parameters were recorded using a wearable device (Oura Ring, 3rd generation). Outcomes were averaged across three consecutive nights at baseline (T0) and post-intervention (T1). Baseline values were derived from the final three nights of a standardized pre-intervention monitoring period (minimum 7 nights), and post-intervention values from the final three nights following a standardized intervention exposure period (minimum 14 nights). Statistical analyses included paired frequentist tests and complementary Bayesian paired-sample analyses. Results: Subjective sleep quality improved significantly following the intervention, with a mean reduction in PSQI score of 0.67 points (p < 0.001). In contrast, no meaningful changes were observed in total sleep time (p = 0.28), REM duration (p = 0.26), circadian timing (p = 0.47), or nocturnal minimum heart rate (p = 0.42), as supported by the absence of physiological changes in these parameters. Conclusions: It seems that an individually adjustable sleep system can be able to improve perceived sleep quality in elite athletes without disrupting sleep architecture, circadian regulation, or nocturnal autonomic function. In athletes whose sleep duration and physiological sleep metrics are already near optimal, such micro-environmental interventions may offer a feasible, low-risk means of enhancing recovery by targeting subjective sleep quality. This dimension dissociates from objective sleep measures. Optimizing the sleep surface may therefore represent a practical adjunct to existing recovery strategies in high-performance sport. Full article

A device-free human detection system based on a received signal strength indicator (RSSI) monitors and analyzes the change of RSSI signals to detect human movements in a wireless network. This study proposes and implements a real-time, device-free human detection system based on an RSSI distribution-based detection method with an autonomous threshold. The novelty and contribution of our solution is that the RSSI distribution concept is considered and used to calculate the optimal threshold setting for human detection, while thresholds can be automatically determined from RSSI data streams gathered from test environments. The proposed system can efficiently work without requiring an offline phase, as introduced in many existing works in the research literature. Experiments using 2.4 GHz IEEE 802.15.4 technology have been carried out in indoor environments in two laboratory rooms with different numbers of wireless links, human movement patterns, and movement speeds. Experimental results show that, in all test scenarios, the proposed method can monitor and detect human movement in a wireless network in real time. It outperforms a comparative method and achieves high accuracy (i.e., 100% detection accuracy) with a low computational complexity requirement. Full article

The Cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on time, access and operational procedures. This study applies a confined-space UAV equipped with LiDAR-based SLAM navigation to document and assess the stability of the vertical rock wall leading to “La Hoya” Hall, a structurally sensitive sector of the cave. Twelve autonomous and assisted flights were conducted, generating dense LiDAR point clouds and video sequences processed through videogrammetry to produce high-resolution 3D meshes. A Mask R-CNN deep learning model was trained on manually segmented images to explore automated crack detection under variable illumination and viewing conditions. The results reveal active fractures, overhanging blocks and sediment accumulations located on inaccessible ledges, demonstrating the capacity of UAV-SLAM workflows to overcome the limitations of traditional surveys in confined subterranean environments. All datasets were integrated into the DiGHER digital twin platform, enabling traceable storage, multitemporal comparison, and collaborative annotation. Overall, the study demonstrates the feasibility of combining UAV-based SLAM mapping, videogrammetry and deep learning segmentation as a reproducible baseline workflow to inform preventive conservation and future multitemporal monitoring in Paleolithic caves and similarly constrained cultural heritage contexts. Full article

Promoting the development of aerospace vehicles toward structural–functional integration and intelligent sensing is a key strategy for achieving lightweight, high-reliability, and autonomous operation and maintenance of next-generation aircraft. However, traditional external sensors face significant limitations because of their bulky size, installation challenges, and incompatibility with aerodynamic surfaces. These issues are particularly pronounced on complex, high-curvature substrates, where achieving conformal bonding is difficult, thus restricting their application in critical components. In this study, aerosol jet printing (AJP) was employed to directly fabricate silver nanoparticle-based temperature sensors with real-time monitoring capabilities on the surface of high-curvature stainless steel sleeves, which serve as typical engineering components. This approach enables the in situ manufacturing of high-precision conformal sensors. Through optimized structural design and thermal treatment, the sensors exhibit reliable temperature sensitivity. Microscopic characterization reveals that the printed sensors possess uniform linewidths and well-defined outlines. After gradient sintering at 250 °C, a dense and continuous conductive path is formed, ensuring strong adhesion to the substrate. Temperature-monitoring results indicate that the sensor exhibits a nearly linear resistance response (R² > 0.999) across a broad detection range of 20–200 °C. It also demonstrates high sensitivity, characterized by a temperature coefficient of resistance (TCR) of 2.15 × 10^−3/°C at 20 °C. In repeated thermal cycling tests, the sensor demonstrates excellent repeatability and stability over 100 cycles, with resistance fluctuations kept within 0.5% and negligible hysteresis observed. These findings confirm the feasibility of using AJP technology to fabricate high-performance conformal sensors on complex surfaces, offering a promising strategy for the development of intelligent structural components in next-generation aerospace engineering. Full article

This paper presents the design and optimisation of a low-power embedded sensor-node architecture for real-time environmental monitoring with on-board machine-learning inference. The proposed system integrates heterogeneous sensing elements for air quality and ambient parameters (temperature, humidity, gas concentration, and particulate matter) into a modular embedded platform based on a low-power microcontroller coupled with an energy-efficient neural inference accelerator. The design emphasises end-to-end energy optimisation through adaptive duty-cycling, hierarchical power domains, and edge-level data reduction. The embedded machine-learning layer performs lightweight event/anomaly detection via on-device multi-class classification (normal/anomalous/critical) using quantised neural models in fixed-point arithmetic. A comprehensive system-level analysis, performed via MATLAB Simulink simulations, evaluates inference accuracy, latency, and energy consumption under realistic environmental conditions. Results indicate that the proposed node achieves 94% inference accuracy, 0.87 ms latency, and an average power consumption of approximately 2.9 mWh, enabling energy-autonomous operation with hybrid solar–battery harvesting. The adaptive LoRaWAN communication strategy further reduces data transmissions by ≈88% relative to periodic reporting. The results indicate that on-device inference can reduce network traffic while maintaining reliable event detection under the evaluated operating conditions. The proposed architecture is intended to support energy-efficient environmental sensing deployments in smart-city and climate-monitoring contexts. Full article

Background: Congenital Insensitivity to Pain and Anhidrosis (CIPA) is a rare autosomal recessive disorder characterized by congenital analgesia, anhidrosis, and multisystem involvement affecting the musculoskeletal, cutaneous, oral, and para-oral structures. This case report describes the oral phenotype and multidisciplinary clinical management of a child with CIPA. Case Description: A 9-year-old boy presented with poor oral hygiene, multiple severely damaged teeth, masticatory difficulty, limited mouth opening, impaired bolus control, and para-oral traumatic injuries. Medical and orthopedic history indicated recurrent painless fractures, self-inflicted injuries, cutaneous scarring, and recurrent hyperpyrexia. Oral self-injury associated with CIPA was suspected and supported by the Nociception Assessment Test and Minor’s Iodine–Starch Test. Although the clinical findings were suggestive of CIPA, the diagnosis remained presumptive due to the absence of confirmatory molecular or histopathological testing. Management: A wearable wireless continuous temperature-monitoring device was prescribed to assist in tracking hyperpyrexia associated with CIPA (RHA-CIPA). A conservative, staged, multidisciplinary treatment was planned rather than full-mouth extraction, emphasizing prevention of dental sepsis and mitigation of future self-injury. Dental procedures were performed under local anesthesia to manage discomfort related to tactile hyperesthesia. To reduce nocturnal biting and oral trauma, a hard acrylic occlusal protector was fabricated using an intraoral scanner and a 3D-printed cast. The patient was followed for 12 months. Outcomes: At the 12-month follow-up, clinical improvement was observed, with particularly notable gains in cheek elasticity and soft tissue resilience. Conclusions: This case highlights the considerable challenges involved in the interdisciplinary management of children with CIPA, including oral self-injury prevention, limited mouth opening, and the necessity of close coordination with medical specialties. These findings are descriptive observations of a single case and do not establish efficacy or generalizability of any intervention. Full article

There are observed unprecedented dynamics in transportation electrification—especially in electric vehicles (even being tested as autonomous units in some regions). The expected improvements in charging and driving distances strive toward higher power levels of passenger cars, public transportation, and trucks, thus leading to elevations of on-board voltage levels. It is expected that the kilovolt level will be crossed soon, thus implying testing at a few kV. To achieve efficient power conversion while maintaining high-power density, new classes of wide-band semiconductors are being implemented; however, fast-switching and ultra-short rise times may result in faster electrical insulation deterioration. The challenges and trends in the development of the high-voltage insulation of various EV components are analyzed. Insulation performance evaluation criteria are discussed, including partial discharges and monitoring approaches. In this context, the development of the transportation segment’s electrification is closely connected with high-voltage insulation problems. Full article

This study investigates the vibrations at the Castello Svevo-Normanno in Aci Castello (Catania), focusing on its historical and cultural significance. The research aims to analyze vibration levels and frequency distribution to achieve two objectives: protecting historical artifacts and structures through preventive vibration analysis and exploring the use of kinetic energy for powering autonomous systems. The study specifically focuses on the indoor context to understand its unique vibrational characteristics. Measurements were recorded along the X, Y, and Z axes, with detailed analysis of the Z axis using Fast Fourier Transform (FFT) and Power Spectral Density (PSD). The results revealed consistent vibration patterns across all axes, with the Z axis significantly influenced by environmental factors such as wind and sea movement. These findings provide valuable insights for designing optimized energy harvesting systems, electromechanical converters, and monitoring devices suitable for operation in this specific historical context. Full article

Autonomous drone swarms are increasingly deployed in critical domains such as infrastructure inspection, environmental monitoring, and emergency response. While their distributed operation enables scalability and resilience, it also introduces new vulnerabilities, particularly in authentication and trust establishment. Conventional cryptographic solutions, including public key infrastructures (PKI) and symmetric key protocols, impose computational and connectivity requirements unsuited to resource-constrained and external infrastructure-free swarm deployments. In this paper, we present a decentralized authentication scheme rooted in hardware security primitives (HSPs); specifically, Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs). The protocol leverages master-initiated token broadcasting, iterative HSP seed evolution, randomized response delays, and statistical trust evaluation to detect cloning, replay, and impersonation attacks without reliance on centralized authorities or pre-distributed keys. Simulation studies demonstrate that the scheme achieves lightweight operation, rapid anomaly detection, and robustness against wireless interference, making it well-suited for real-time swarm systems. Full article

Glacier inventories are critical for monitoring glacier response to climate change, providing constraints for glacier modeling studies and for assessing the impacts of glacier retreat on ecosystems and human societies. In the Bhutanese Himalaya, an up-to-date glacier inventory and a systematic analysis of decadal-scale glacier changes is lacking. Here, we present three glacier inventories (1976, 1998, and 2024) for this region. Manual mapping of glacier outlines from multi-source satellite imagery and the Copernicus digital elevation model (DEM) are used to derive a glacier inventory with associated topographic attributes. We found that 1871 glaciers existed in this region in 1976, covering an area of 2297.07 ± 117.15 km². By 1998 this number had reduced to 1803 glaciers, covering 2106.99 ± 90.60 km². In 2024, only 1697 glaciers remained, covering 1584.18 ± 36.37 km². A total of 89 (1976–1998) and 435 (1998–2024) glaciers became extinct in the Bhutanese Himalaya during these two time periods, and glacier area decrease accelerated from ~0.38% yr⁻¹ to ~0.95% yr⁻¹. Lake-terminating glaciers retreated almost three times faster (~32.2 m yr⁻¹) than land-terminating (~10.4 m yr⁻¹) glaciers during the observation period. Debris-covered glacier area increased from 112.79 ± 11.50 km² in 1976 to 128.89 ± 10.50 km² in 2024. Glaciers on the South Bhutanese Himalaya (draining into Bhutan) experienced faster glacier retreat than the glaciers of the North Bhutanese Himalaya (draining into the Tibetan Autonomous Region). ERA5-Land reanalysis data show that summer decadal average temperature in this region increased by 0.003 °C yr⁻¹ between 1976 and 1998 and 0.020 °C yr⁻¹ between 1998 and 2024, with the increase in warming rate coinciding with accelerated glacier retreat after 1998. Our updated glacier inventories will be useful for assessments of global sea level change, mountain hazards, and water resources. Full article

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the system’s autonomous control structure using an explainable artificial intelligence (XAI)-based method. The intelligent drying system, operating for approximately 17.5 h under two temperatures (50 °C and 65 °C) and two air speeds (0.63 m/s and 1.03 m/s), continuously adjusted the temperature and air speed using a PLC-based control mechanism; it ensured stable control throughout the process by monitoring parameters such as product weight, moisture, inlet–outlet temperatures, and air speed in real time. Experimental results showed that drying performance varied significantly with operating conditions, with product mass decreasing from 450 g to 103 g. The innovative aspect of the study is that it obtained quantitative, interpretable rules without discretization by applying the oscillatory chaotic sunflower optimization algorithm (OCSFO) to multidimensional control and process data for the first time. Thanks to its chaotic search mechanism, OCSFO accurately analyzed complex drying dynamics and created rules that achieved over 90% success for high, medium, and low performance classes. The obtained explainable rules clearly demonstrate that drying temperature and air velocity are the dominant determining parameters for drying efficiency, while energy consumption and cabin temperature distribution play a supporting role in distinguishing between efficiency classes. These rules clearly demonstrate how changes in controlled temperature and air velocity, combined with product weight and heat transfer, affect drying performance. Thus, the study offers a robust framework that identifies critical factors affecting drying performance through a transparent artificial intelligence approach that leverages both the autonomous control system and XAI-based rule mining. Full article

Physiological monitoring has become an inherently interdisciplinary field, merging advances in engineering, chemistry, biology, medicine, and data analytics to create sensors that continuously track the vital signals of the body. These developments are enabling more personalized and preventive healthcare, as wearable (bio)sensors and intelligent algorithms can detect subtle physiological changes in real-time. In the Special Issue ‘Advances in (Bio)Sensors for Physiological Monitoring’, researchers from diverse domains contributed 18 papers showcasing cutting-edge sensor technologies and applications for health and performance monitoring. In this review, we summarize these contributions by grouping them into logical themes based on their focus: (1) cardiovascular and autonomic monitoring, (2) glucose and metabolic monitoring, (3) wearable sensors for movement and musculoskeletal health, (4) neurophysiological monitoring and brain–computer interfaces, and (5) innovations in sensor technology and methods. This thematic organization highlights the breadth of the research, spanning from fundamental sensor hardware to data-driven analytics, and underscores how modern (bio)sensors are breaking traditional boundaries in healthcare. Full article

This article presents the Intelligent Monitoring System (IMS), an AI-assisted, low-latency surveillance platform designed for defense environments. The study addresses the need for real-time autonomous situational awareness by integrating high-speed video transmission with advanced computer vision analytics in constrained network settings. The IMS employs a hybrid transmission architecture based on RTSP for ingestion and WHEP/WebRTC for distribution, orchestrated via MediaMTX, with the objective of achieving end-to-end latencies below one second. The methodology includes a comparative evaluation of video streaming protocols (JPEG-over-WebSocket, HLS, WebRTC, etc.) and AI frameworks, alongside the modular architectural design and prolonged experimental validation. The detection module integrates YOLOv11 models fine-tuned on the VisDrone dataset to optimize performance for small objects, aerial views, and dense scenes. Experimental results, obtained through over 300 h of operational tests using IP cameras and aerial platforms, confirmed the stability and performance of the chosen architecture, maintaining latencies close to 500 ms. The YOLOv11 family was adopted as the primary detection framework, providing an effective trade-off between accuracy and inference performance in real-time scenarios. The YOLOv11n model was trained and validated on a Tesla T4 GPU, and YOLOv11m will be validated on the target platform in subsequent experiments. The findings demonstrate the technical viability and operational relevance of the IMS as a core component for autonomous surveillance systems in defense, satisfying strict requirements for speed, stability, and robust detection of vehicles and pedestrians. Full article

Background/Objectives: Sacubitril/Valsartan is a cornerstone therapy to improve outcomes in patients with heart failure with reduced ejection fraction (HFrEF). This study aimed to investigate the effect of Sacubitril/Valsartan on cardiac autonomic balance using physiological sensor data obtained from implantable cardioverter-defibrillators (ICDs) or cardiac resynchronization therapy defibrillators (CRT-Ds). Methods: This observational study involved 54 ICD and CRT-D patients who initiated Sacubitril/Valsartan therapy to treat HFrEF. The evaluated key parameters included heart rate variability (HRV), 24 h mean heart rate (24 h-HR), and nocturnal heart rate (nHR). Device electrical parameters and ventricular arrhythmias were also assessed. The data were collected by remote monitoring and averaged over a 7-day window at baseline (before treatment) and at 3 and 12 months after treatment initiation. Results: Sacubitril/Valsartan significantly improved HRV at 3 months (from 78.6 ms [interquartile range: 54.2–104.6] to 80.8 ms [60.8–108.0]; p = 0.041), reduced 24 h-HR (from 73.2 bpm [67.3–77.7] to 69.9 bpm [64.2–75.7]; p = 0.016), and reduced nHR (from 63.0 bpm [58.1–70.0] to 60.4 bpm [56.0–68.6]; p = 0.028). No significant changes in HRV, 24 h-HR, and nHR were observed between 3- and 12-month follow-up. The device electrical parameters were not influenced by the treatment. While the overall ventricular arrhythmia burden did not change post-treatment, patients with pre-treatment arrhythmias experienced a significant reduction in episodes from 2.97 (pre-treatment) to 0.82 (post-treatment) events per 100 patient years (p = 0.008). Conclusions: Sacubitril/Valsartan therapy in HFrEF patients was associated with statistically significant changes in cardiac autonomic indices, including a small increase in HRV and a slight reduction in heart rate, mainly during the first three months of treatment. Full article