Search Results (775)

As contemporary society and the global economy become increasingly dependent on satellite-based systems, the need for reliable and resilient positioning, navigation, and timing (PNT) services has never been more critical. This study investigates the impact of the geomagnetic storm that occurred in May 2024 on the performance of global navigation satellite system (GNSS) low-cost permanent stations. The research evaluates the influence of ionospheric disturbances on both positioning performance and raw GNSS observations. Two days were analyzed: 8 May 2024 (DOY 129), representing quiet ionospheric conditions, and 11 May 2024 (DOY 132), coinciding with the peak of the geomagnetic storm. Precise Point Positioning (PPP) and static relative positioning techniques were applied to data from a low-cost GNSS station (DYVA), supported by comparative analysis using a nearby geodetic-grade station (TRDS00NOR). The results showed that while RMS positioning errors remained relatively stable over 24 h, the maximum errors increased significantly during the storm, with the 3D positioning error nearly doubling on DOY 132. Short-term analysis revealed even larger disturbances, particularly in the vertical component, which reached up to 3.39 m. Relative positioning analysis confirmed the vulnerability of single-frequency (L1) solutions to ionospheric disturbances, whereas dual-frequency (L1+L2) configurations substantially mitigated errors, highlighting the effectiveness of ionosphere-free combinations during storm events. In the second phase, raw GNSS observation quality was assessed using detrended GPS L1 carrier-phase residuals and signal strength metrics. The analysis revealed increased phase instability and signal degradation on DOY 132, with visible cycle slips occurring between epochs 19 and 21. Furthermore, the average signal-to-noise ratio (SNR) decreased by approximately 13% for satellites in the northwest sky sector, and a 5% rise in total cycle slips was recorded compared with the quiet day. These indicators confirm the elevated measurement noise and signal disruption associated with geomagnetic activity. These findings provide a quantitative assessment of low-cost GNSS receiver performance under geomagnetic storm conditions. This study emphasizes their utility for densifying GNSS infrastructure, particularly in regions lacking access to geodetic-grade equipment, while also outlining the challenges posed by space weather. Full article

This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial vehicles (UAVs) to mitigate error accumulation, preserve map consistency, and operate reliably in environments without GPS. This integration facilitates sustained autonomous navigation with estimation error remaining bounded over extended trajectories. Theoretical validation is provided through a nonlinear observability analysis, highlighting the general benefits of integrating range data into the SLAM framework. The system’s performance is evaluated through both virtual experiments and real-world flight data. The real-data experiments confirm the practical relevance of the approach and its ability to improve estimation accuracy in realistic scenarios. Full article

Technological innovation has significantly transformed the field of statistics, not only in data analysis but also in data collection. Traditional methods based on direct observation have evolved into hybrid approaches that combine passively collected data (e.g., from GPS or accelerometers) with active user input through digital interfaces. This evolution has led to Smart Surveys—next-generation tools that leverage smart devices, such as smartphones and wearables, to collect data actively (via questionnaires or images) and passively (via embedded sensors). Smart Surveys offer strategic value in industrial contexts by enabling real-time data collection on worker behavior, environments, and operational conditions. However, the heterogeneity of such data poses challenges in management, integration, and quality assurance. This study proposes a modular system architecture incorporating gamification elements to enhance user participation, particularly among hard-to-reach worker segments, such as mobile or shift workers. By leveraging motivational strategies and interactive feedback mechanisms, the system seeks to foster greater engagement while addressing critical data security and privacy concerns within industrial Internet of Things (IoT) environments. Full article

Antimicrobial peptides (AMPs) are a primary defense against pathogens. Here, we examined the interaction of two BP100 analogs, R²R⁵-BP100 (where Arg substitutes Lys 2 and 5) and R²R⁵-BP100-A-NH-C₁₆ (where an Ala and a C₁₆ hydrocarbon chain are added to the R²R⁵-BP100 C-terminus), with membrane models. Large unilamellar vesicles (LUVs) and giant unilamellar vesicles (GUVs) were prepared with the major lipids in Gram-positive (GP) and Gram-negative (GN) bacteria, as well as red blood cells (RBCs). Fluorescence data, dynamic light scattering (DLS), and zeta potential measurements revealed that upon achieving electroneutrality through peptide binding, vesicle aggregation occurred. Circular dichroism (CD) spectra corroborated these observations, and upon vesicle binding, the peptides acquired α-helical conformation. The peptide concentration, producing a 50% release of carboxyfluorescein (C₅₀) from LUVs, was similar for GP-LUVs. With GN and RBC-LUVs, C₅₀ decreased in the following order: BP100 > R²R⁵-BP100 > R²R⁵BP100-A-NH-C₁₆. Optical microscopy of GP-, GN-, and RBC-GUVs revealed the rupture or bursting of the two former membranes, consistent with a carpet mechanism of action. Using GUVs, we confirmed RBC aggregation by BP100 and R²R⁵-BP100. We determined the minimal inhibitory concentrations (MICs) of peptides for a GN bacterium (Escherichia coli (E. coli)) and two GP bacteria (two strains of Staphylococcus aureus (S. aureus) and one strain of Bacillus subtilis (B. subtilis)). The MICs for S. aureus were strain-dependent. These results demonstrate that Lys/Arg replacement can improve the parent peptide’s antimicrobial activity while increasing hydrophobicity renders the peptide less effective and more hemolytic. Full article

A novel adaptive intelligent control system (AICS) with learning-while-controlling capability is developed for a highly nonlinear single-input single-output plant by redesigning the conventional model reference adaptive control (MRAC) framework, originally based on first-order Lyapunov stability, and employing customized neural networks. The AICS is designed with a simple structure, consisting of two main subsystems: a meta-learning-triggered mechanism-based physics-informed neural network (MLTM-PINN) for plant identification and a self-tuning neural network controller (STNNC). This structure, featuring the triggered mechanism, facilitates a balance between high controllability and control efficiency. The MLTM-PINN incorporates the following: (I) a single self-supervised physics-informed neural network (PINN) without the need for labelled data, enabling online learning in control; (II) a meta-learning-triggered mechanism to ensure consistent control performance; (III) transfer learning combined with meta-learning for finely tailored initialization and quick adaptation to input changes. To resolve the conflict between streamlining the AICS’s structure and enhancing its controllability, the STNNC functionally integrates the nonlinear controller and adaptation laws from the MRAC system. Three STNNC design scenarios are tested with transfer learning and/or hyperparameter optimization (HPO) using a Gaussian process tailored for Bayesian optimization (GP-BO): (scenario 1) applying transfer learning in the absence of the HPO; (scenario 2) optimizing a learning rate in combination with transfer learning; and (scenario 3) optimizing both a learning rate and the number of neurons in hidden layers without applying transfer learning. Unlike scenario 1, no quick adaptation effect in the MLTM-PINN is observed in the other scenarios, as these struggle with the issue of dynamic input evolution due to the HPO-based STNNC design. Scenario 2 demonstrates the best synergy in controllability (best control response) and efficiency (minimal activation frequency of meta-learning and fewer trials for the HPO) in control. Full article

Most people utilize their time indoors, either at home or in the workplace. However, certain human interventions badly affect the indoor atmosphere, causing potential health problems for occupants. This study aims to propose an air monitoring device integrated with an air purifier that monitors the pollutants affecting the indoor environment and automatically turns on/off the air purifier based on the pollution level. In the system, MQ7, MQ2, DHT11, and GP2Y1010AU0F sensors are integrated with ESP32 to detect indoor air pollutants, e.g., carbon monoxide (CO), methane (CH₄), temperature, humidity, and PM2.5. Data were collected for 30 days by mounting a proposed device in different indoor locations, including a poorly ventilated average living room, an indoor kitchen, and a crowded office space. The emission of carbon monoxide (CO) and methane (CH₄) was at 29.4 ppm and 10.9 ppm, PM2.5 was detected as 3 µg/m³, and the temperature and humidity were at 23 °C and 28%, respectively. Utilizing the Wi-Fi ability of ESP32, the data were transferred to the ThingSpeak IoT platform for the live tracking and analysis of the indoor atmosphere. Observing the measured data, the proposed system’s accuracy was calculated by comparing the results against a known standard device, which was estimated to be 95%. To protect the designed system, a protective case was also designed. Full article

GPSs typically have an accuracy ranging from a few meters to several tens of meters. However, when corrected using various methods, they can achieve an accuracy of several tens of centimeters. In autonomous driving, a positioning accuracy of less than 50 cm is required for lane-level positioning, route generation, and navigation. However, in environments where GPS signals are blocked, such as tunnels and underground roads, absolute positioning is impossible. Instead, relative positioning methods integrating IMU, IVN, and cameras are used. These methods are influenced by numerous variables, however, such as vehicle speed and road conditions, resulting in lower accuracy. In this study, we conducted experiments on current vehicle navigation technologies using an autonomous driving simulation vehicle in the Suri–Suam Tunnel of the Seoul Metropolitan Area 1st Ring Expressway. To recognize objects (lane markings/2D/3D) for position correction inside the tunnel, data on tunnel and underground road infrastructure in Seoul and Gyeonggi Province was collected, processed, refined, and trained. Additionally, a Loosely Coupled-based Kalman Filter was designed and applied for the fusion of GPSs, IMUs, and IVNs. As a result, an error of 113.62 cm was observed in certain sections. This suggests that while the technology is applicable for general vehicle lane-level navigation in ultra-long tunnels spanning several kilometers for public service, it falls short of meeting the precision required for autonomous driving systems, which demand lane-level accuracy. Therefore, it was concluded that infrastructure-based absolute positioning technology is necessary to enable precise navigation inside tunnels. Full article

Global Navigation Satellite System (GNSS) receivers may produce measurement outliers in real-world applications owing to various circumstances, including poor signal quality, multipath effects, data loss, satellite signal loss, or electromagnetic interference. This can lead to a noise distribution that is non-Gaussian heavy-tailed, affecting the effectiveness of satellite navigation filters. This paper presents a robust Extended Kalman Filter (EKF) based on the Maximum Correntropy Criterion with a Student’s t kernel (STMCCEKF) for GPS navigation under non-Gaussian noise. Unlike traditional EKF and Gaussian-kernel MCCEKF, the proposed method enhances robustness by leveraging the heavy-tailed Student’s t kernel, which effectively suppresses outliers and dynamic observation noise. A fixed-point iterative algorithm is used for state update, and a new posterior error covariance expression is derived. The simulation results demonstrate that STMCCEKF outperforms conventional filters in positioning accuracy and robustness, particularly in environments with impulsive noise and multipath interference. The Student’s t-distribution kernel efficiently mitigates heavy-tailed non-Gaussian noise, while it adaptively adjusts process and measurement noise covariances, leading to improved estimation performance. A detailed explanation of several key concepts along with practical examples are discussed to aid in understanding and applying the Global Positioning System (GPS) navigation filter. By integrating cutting-edge reinforcement learning with robust statistical approaches, this work advances adaptive signal processing and estimation, offering a significant contribution to the field. Full article

This study presents a reanalysis of existing data to clarify how the visual system processes simple dynamic Glass patterns (GPs), with a particular focus on translational configurations. By combining datasets from previous studies, we apply a mixed-effects modeling approach—which offers advantages over the statistical methods used in previous studies—to investigate the contributions of pattern update rate and number of unique frames to perceptual sensitivity. Our findings indicate that the number of unique frames is the most robust predictor of discrimination thresholds, supporting the idea that the visual system integrates global form information across multiple frames—a process consistent with spatiotemporal summation. In contrast, the pattern update rate showed a weaker, though statistically significant, effect. This suggests that faster updates help preserve temporal consistency between frames, facilitating global form extraction. These results align with previous observations on complex dynamic GPs, where discrimination thresholds decrease with more unique frames, suggesting that the summation of form signals across time plays a key role in form–motion perception. By adopting a mixed-effects modeling approach, our reanalysis provides new insights into the mechanisms underlying global form perception in dynamic GPs. Full article

Background/Objectives: Staphylococcus aureus is a major pathogen of concern in raw milk due to its potential to cause foodborne illness and its increasing antimicrobial resistance (AMR). In Romania, data on the occurrence and resistance patterns of S. aureus in raw sheep milk from traditional farming systems remain limited. This study investigated the presence and antimicrobial resistance of S. aureus in 106 raw sheep milk samples collected from traditional farms in the Banat region of western Romania. Methods: Coagulase-positive staphylococci (CPS) were enumerated using ISO 6888-1:2021 protocols. Isolates were identified at the species level using the Vitek 2 system and molecularly confirmed via PCR targeting the 16S rDNA and nuc genes. Methicillin resistance was assessed by detecting the mecA gene. Antimicrobial susceptibility was determined using the Vitek 2 AST-GP79 card. Results: CPS were detected in 69 samples, with S. aureus confirmed in 34.9%. The mecA gene was identified in 13.5% of S. aureus isolates, indicating the presence of methicillin-resistant S. aureus (MRSA). Resistance to at least two antimicrobials was observed in 97.3% of isolates, and 33 strains (89.2%) met the criteria for multidrug resistance (MDR). The most frequent MDR phenotype involved resistance to lincomycin, macrolides, β-lactams, tetracyclines, and aminoglycosides. Conclusions: The high prevalence of S. aureus, including MRSA and MDR strains, in raw sheep milk from traditional farms represents a potential public health risk, particularly in regions where unpasteurized dairy consumption persists. These findings underscore the need for enhanced hygiene practices, prudent antimicrobial use, and AMR monitoring in small-scale dairy systems. Full article

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control precision. Initially, a binocular vision system is employed for target detection, enabling the identification of lane curvature initiation points and speed bumps, with real-time distance measurements. Subsequently, the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data facilitates the extraction of road elevation profiles ahead of the vehicle. A BP-PID control strategy is implemented to formulate mode-switching rules for the active suspension under three distinct road conditions: flat road, curved road, and obstacle road. Additionally, an ant colony optimization algorithm is utilized to fine-tune four suspension parameters. Utilizing the hardware-in-the-loop (HIL) simulation platform, the observed reductions in vertical, pitch, and roll accelerations were 5.37%, 9.63%, and 11.58%, respectively, thereby substantiating the efficacy and robustness of this approach. Full article

Rowing is a cyclic sport that consists of repetitive biomechanical actions, with performance being influenced by the balance between propulsive and resistive forces. The current study aimed to assess the relationships between intracycle velocity variation (IVV) and key biomechanical and performance variables in male and female single scullers. Twenty-three experienced rowers (10 females) completed a 2000 m rowing competition, during which boat position and velocity were measured using a 15 Hz GPS, while cycle rate was derived from the integrated triaxial accelerometer sampling at 100 Hz. From these data, it was possible to calculate distance per cycle, IVV, the coefficient of velocity variation (CVV), and technical index values. Males presented higher mean, maximum and minimum velocity, distance per cycle, CVV, and technical index values than females (15.40 ± 0.81 vs. 13.36 ± 0.88 km/h, d = 0.84; 21.39 ± 1.68 vs. 18.77 ± 1.52 km/h, d = 1.61; 11.15 ± 1.81 vs. 9.03 ± 0.85 km/h, d = 1.45; 7.68 ± 0.32 vs. 6.89 ± 0.97 m, d = 0.69; 14.13 ± 2.02 vs. 11.64 ± 1.93%, d = 2.06; and 34.25 ± 4.82 vs. 26.30 ± 4.23 (m²/s·cycle), d = 4.56, respectively). An association between mean velocity and intracycle IVV, CVV, and cycle rate (r = 0.68, 0.74 and 0.65, respectively) was observed in males but not in female single scullers (which may be attributed to anthropometric specificities). In female single scullers, mean velocity was related with distance per cycle and was associated with technical index in both males and females (r = 0.76 and 0.66, respectively). Despite these differences, male and female single scullers adopted similar pacing strategies and CVV remained constant throughout the 2000 m race (indicating that this variable might not be affected by fatigue). Differences were also observed in the velocity–time profile, with men reaching peak velocity first and having a faster propulsive phase. Data provided new information on how IVV and CVV relate to commonly used biomechanical variables in rowing. Technical index (r = 0.87): distance per cycle was associated with technical index in both males and females (r = 0.76 and 0.66, respectively). Future studies should include other boat classes and other performance variables such as the power output and arc length. Full article

Football performance results from the dynamic interaction between physical, tactical, technical, and psychological dimensions—each of which also influences player well-being, recovery, and readiness. However, integrated monitoring approaches remain scarce, particularly in youth and sub-elite contexts. This systematic review screened 341 records from PubMed, Scopus, and Web of Science, with 46 studies meeting the inclusion criteria (n = 1763 players; age range: 13.2–28.7 years). Physical external load was reported in 44 studies using GPS-derived metrics such as total distance and high-speed running, while internal load was examined in 36 studies through session-RPE (rate of perceived exertion × duration), heart rate zones, training impulse (TRIMP), and Player Load (PL). A total of 22 studies included well-being indicators capturing fatigue, sleep quality, stress levels, and muscle soreness, through tools such as the Hooper Index (HI), the Total Quality Recovery (TQR) scale, and various Likert-type or composite wellness scores. Tactical behaviours (n = 15) were derived from positional tracking systems, while technical performance (n = 7) was assessed using metrics like pass accuracy and expected goals, typically obtained from Wyscout^® or TRACAB^® (a multi-camera optical tracking system). Only five studies employed multivariate models to examine interactions between performance domains or to predict well-being outcomes. Most remained observational, relying on descriptive analyses and examining each domain in isolation. These findings reveal a fragmented approach to player monitoring and a lack of conceptual integration between physical, psychological, tactical, and technical indicators. Future research should prioritise multidimensional, standardised monitoring frameworks that combine contextual, psychophysiological, and performance data to improve applied decision-making and support player health, particularly in sub-elite and youth populations. Full article

In this paper, we calibrate the luminosity relation of gamma−ray bursts (GRBs) by employing artificial neural networks (ANNs) to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset is used to calibrate the Amati relation ($E_{\mathrm{p}}$-$E_{\mathrm{iso}}$) at low redshift with the ANN framework, facilitating the construction of the Hubble diagram at higher redshifts. Cosmological models are constrained with GRBs at high redshift and the latest observational Hubble data (OHD) via the Markov chain Monte Carlo numerical approach. For the Chevallier−Polarski−Linder (CPL) model within a flat universe, we obtain ${\Omega }_{\mathrm{m}}={0.321}_{-0.069}^{+0.078}$, $h={0.654}_{-0.071}^{+0.053}$, $w_0={-1.02}_{-0.50}^{+0.67}$, and $w_a={-0.98}_{-0.58}^{+0.58}$ at the 1 −$\sigma$ confidence level, which indicates a preference for dark energy with potential redshift evolution ($w_a\ne 0$). These findings using ANNs align closely with those derived from GRBs calibrated using Gaussian processes (GPs). Full article

This study analyses the physical performance variables involved in different training tasks aimed at replicating the worst-case scenarios (WCSs) observed during official matches in professional football, with a focus on playing positions and occurrences within a 1 min period. Data were collected from 188 training sessions and 42 matches of a Spanish Second Division team during the 2021/2022 season. All data were reported on a per-player basis. GPS tracking devices were used to record physical variables such as total distance, high-speed running (HSR), sprints, accelerations, decelerations, and high metabolic load distance (HMLD). Players were grouped according to their match positions: central defenders, wide players, midfielders and forwards. The results showed that none of the training tasks fully replicated the physical demands of match play. However, task TYPEs 11 (Large-Sided Games) and 9 (small-sided games with orientation and transition) were the closest to match demands, particularly in terms of accelerations and decelerations. Although differences were observed across all variables, the most pronounced discrepancies were observed in sprint and HSR variables, where training tasksfailed to reach 60% of match demands. These findings highlight the need to design more specific drills that simulate the intensity of WCS, allowing for more accurate weekly training load planning. This study offers valuable contributions for optimising performance and reducing injury risk in professional footballers during the competitive period. Full article