Search Results (8,582)

Ambiguity Resolution (AR) is regarded as an effective technique for enhancing positioning accuracy and reducing convergence time in Precise Point Positioning (PPP). However, the Wide-Lane Fractional Cycle Bias (WL FCB) and Narrow-Lane Fractional Cycle Bias (NL FCB) needed for AR are generated from network solutions based on numerous globally distributed stations, leading to considerable computational load and processing time. A prediction model for FCB is proposed using the Genetic Algorithm Optimized Backpropagation Neural Network (GA-BPNN), and high-precision predictions of WL and NL FCB for Day of Year (DOY) 321 in 2023 are successfully achieved. Comparisons with iGMAS products show that predicted WL FCB deviations are within 0.01 cycles, and predicted NL FCB over 12 h deviates within 0.1 cycles (excluding satellite C20). The performance of three PPP schemes, Float, Fixed (based on FCB from iGMAS), and BP-Fixed (based on FCB predicted by GA-BPNN), is compared through experiments. For GPS + BDS-3, the accuracies of the BP-Fixed scheme are 0.0034 m, 0.0039 m, and 0.0100 m in the east, north, and up directions, respectively. The ambiguity fixed rates reach 98.62% for BP-Fixed. These outcomes confirm that the positioning performance using the predicted FCB of GA-BPNN is highly consistent with that using FCB products. Full article

This study investigated the chemical and autogenous shrinkage behaviour of limestone calcined clay cement (LC³) pastes incorporating calcined clays sourced from Australia, France, and India. Hydration development and microstructural evolution were examined using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and pore-size distribution analysis. Results showed that LC³ mixes hydration accelerates during early phases, with the main silicate hydration peak appearing more prominently than that in the GP and FA reference pastes, indicating increased nucleation and growth of hydration products due to the limestone filler effect. LC³ pastes exhibited higher autogenous shrinkage overtime, strongly influenced by calcined clay reactivity and particle fineness. A clear correlation was observed between pore refinement and autogenous deformation during the early phases (7 days): pastes with a greater volume of fine pores showed higher early-age autogenous shrinkage during the first 7 days of hydration. In contrast, the chemical shrinkage of LC³ mixes was comparable to that of the GP and FA systems at early ages (≤7 days) but became lower after 28 days, attributed to both the matrix densification and additional nucleation sites provided by the limestone. Overall, LC³ reduces long-term chemical shrinkage and densifies the microstructure; however, the refined pore structure and increased internal water demand lead to higher autogenous shrinkage. These findings demonstrate a direct link between hydration-driven microstructural evolution (phase formation and pore refinement) and the resulting shrinkage behaviour. Full article

The Internet of Things (IoT) and Edge Computing (EC) play an essential role in today’s communication systems, supporting diverse applications in industry, healthcare, and environmental monitoring; however, these technologies face a major challenge in accurately determining the geographic origin of sensed data, as such data are meaningful only when their source location is known. The use of Global Positioning System (GPS) is often impractical or inefficient in many environments due to limited satellite coverage, high energy consumption, and environmental interference. This paper recruits the Distance Vector-Hop (DV-Hop), Jellyfish Search (JS), and Artificial Rabbits Optimization (ARO) algorithms and presents an innovative GPS-free positioning framework for three-dimensional (3D) EC environments. In the proposed framework, the basic DV-Hop and multi-angulation algorithms are generalized for three-dimensional environments. Next, both algorithms are structurally modified and integrated in a complementary manner to balance exploration and exploitation. Furthermore, a Lévy flight-based perturbation phase and a local search mechanism are incorporated to enhance convergence speed and solution precision. To evaluate performance, sixteen 3D IoT environments with different configurations were simulated, and the results were compared with nine state-of-the-art localization algorithms using MSE, NLE, ALE, and LEV metrics. The quantitative relative improvement ratio test demonstrates that the proposed method is, on average, 39% more accurate than its competitors. Full article

Background: Professional football requires more attention in planning work regimens that balance players’ sports performance optimization and reduce their injury probability. Machine learning applied to sports science has focused on predicting these events and identifying their risk factors. Our study aims to (i) analyze the differences between injury incidence during training and matches and (ii) build and classify different predictive models of risk based on players’ internal and external loads across four sports seasons. Methods: This investigation involved 96 male football players (26.2 ± 4.2 years; 181.1 ± 6.1 cm; 74.5 ± 7.1 kg) representing a single professional football club across four analyzed seasons. The research was designed according to three methodological sets of assessments: (i) average season performance, (ii) two weeks’ performance before the event, and (iii) four weeks’ performance before the event. We applied machine learning classification methods to build and classify different predictive injury risk models for each dataset. The dependent variable is categorical, representing the occurrence of a time-loss muscle injury (N = 97). The independent variables include players’ information and external (GPS-derived) and internal (RPE) workload variables. Results: The Kstar classifier with the four-week window dataset achieved the best predictive performance, presenting an Area Under the Precision–Recall Curve (AUC-PR) of 83% and a balanced accuracy of 72%. Conclusions: In practical terms, this methodology provides technical staff with more reliable data to inform modifications to playing and training regimens. Future research should focus on understanding the technical staff’s qualitative vision of predictive models’ in-field applicability. Full article

Global Positioning System (GPS) spoofing poses a significant threat to the reliability of unmanned aerial vehicle (UAV) navigation systems that rely heavily on Global Navigation Satellite Systems (GNSS). To address this challenge, we propose a detection framework named PIR–PSO–XGBoost, which integrates Physics-Informed Residual (PIR) modeling with Particle Swarm Optimization (PSO) and Extreme Gradient Boosting (XGBoost). Unlike existing detection frameworks that rely on handcrafted features or deep black box models, the proposed method introduces a physically interpretable residual construction process that captures signal inconsistencies by enforcing temporal and carrier level consistency across GNSS observables. These residuals, combined with conventional navigation features, are used to train an XGBoost-based classifier, while PSO is employed to perform global hyperparameter tuning to enhance model generalization and robustness across diverse spoofing scenarios. This design improves interpretability and computational efficiency, addressing the limitations of traditional feature engineering and deep learning-based detectors. Experimental results on a real-world GPS spoofing dataset demonstrate that the proposed framework achieves a classification accuracy of 95.26% and an F1-score of 95.28%, significantly outperforming conventional learning baselines. These findings confirm that combining physics-guided feature construction with swarm optimized learning yields a robust, efficient, and deployable solution for GPS spoofing detection in UAV applications. Full article

This research pioneers the incorporation of grape pomace (GP) as a sustainable additive in unfired adobe construction materials, establishing a novel circular pathway that valorises agro-waste in zero-emission, low-energy building components. Five mix designs were developed with GP contents of 0%, 2.5%, 5%, 7.5%, and 10% by weight, using a soil matrix composed of 15% clay, 25% silt, and 60% sand with a 20% water content. Comprehensive characterization included physical properties, mechanical performance, thermal behavior, acoustic properties, and durability assessment. The incorporation of GP demonstrated dose-dependent effects on all measured properties. Bulk density decreased linearly from 1951 kg/m³ (0%GP) to 1595 kg/m³ (10%GP), representing an 18.3% reduction. Optimal mechanical performance was achieved at a 2.5–5% GP content, with compressive strength ranging from 1.51–1.64 MPa and flexural strength of 0.56–0.80 MPa, while higher GP contents resulted in significant strength reductions. Thermal conductivity improved substantially, decreasing from 0.99 to 0.25 W/Mk (66% RH) with increasing GP content, indicating enhanced insulation properties. The sound insulation performance showed a single-value sound reduction index (Rw) of 41–43 dB for all compositions, making them suitable for facade applications. Statistical analysis revealed significant correlations between GP content and material properties. The results indicate an optimal GP content of around 5%, which balances mechanical integrity, thermal performance, and durability while providing environmental benefits through the valorization of agro-waste. This research offers a sustainable approach for producing low-energy, eco-friendly building materials by incorporating grape pomace into unfired adobe, promoting waste valorization and improved thermal and acoustical insulation for green construction. Further research is needed to assess durability performance, standardize production methods, and evaluate large-scale implementation. Full article

Today, unmanned aerial vehicles (UAVs) are heavily dependent on Global Navigation Satellite Systems (GNSSs) for positioning and navigation. However, GNSS signals are vulnerable to jamming and spoofing attacks. This poses serious security risks, especially for military operations and critical civilian missions. In order to solve this problem, an image-based geolocation system has been developed that eliminates GNSS dependency. The proposed system estimates the geographical location of the UAV by matching the aerial images taken by the UAV with previously georeferenced high-resolution satellite images. For this purpose, common visual features were determined between satellite and UAV images and matching operations were carried out using methods based on the homography matrix. Thanks to image processing, a significant relationship has been established between the area where the UAV is located and the geographical coordinates, and reliable positioning is ensured even in cases where GNSS signals cannot be used. Within the scope of the study, traditional methods such as SIFT, AKAZE, and Multiple Template Matching were compared with learning-based methods including SuperPoint, SuperGlue, and LoFTR. The results showed that deep learning-based approaches can make successful matches, especially at high altitudes. Full article

This work presents the structural analysis and validation of a sub-250 g FPV drone chassis, emphasizing both theoretical rigor and practical applicability. The novelty of this contribution lies in four complementary aspects. First, the structural philosophy introduces a screwless frame with interchangeable arms, joined through interlocking mechanisms inspired by traditional Japanese joinery. This approach mitigates stress concentrations, reduces weight by eliminating fasteners, and enables rapid arm replacement in the field. Second, validation relies on nonlinear static and transient FEM simulations, explicitly including crash scenarios at 5 m/s, systematically cross-checked with bench tests and instrumented flight trials. Third, unlike most structural studies, the framework integrates firmware (Betaflight), GPS, telemetry, and real flight performance, linking structural reliability with operational robustness. Finally, a practical materials pathway was implemented through a dual-track strategy: PETG for rapid, low-cost prototyping, and carbon fiber composites as the benchmark for production-level performance. Nonlinear transient FEM analyses were carried out using Inventor Nastran under multiple load cases, including maximum motor acceleration, pitch maneuvers, and lateral impact at 40 km/h, and were validated against simplified analytical models. Experimental validation included bench and in-flight trials with integrated telemetry and autonomous features such as Return-to-Home, demonstrating functional robustness. The results show that the prototype flies correctly and that the chassis withstands the loads experienced during flight, including accelerations up to 4.2 G (41.19 m/${\mathrm{s}}^2$), abrupt changes in direction, and high-speed maneuvers reaching approximately 116 km/h. Quantitatively, safety factors of approximately 5.3 under maximum thrust and 1.35 during impact confirm sufficient structural integrity for operational conditions. In comparison with prior works reviewed in this study, the key contribution of this work lies in unifying advanced, crash-resilient FEM simulations with firmware-linked flight validation and a scalable material strategy, establishing a distinctive and comprehensive workflow for the development of sub-250 g UAVs. Full article

Adductor-related groin pain (AGP) is a prevalent and frequently recurrent chronic injury among soccer players. This study investigated the impact of AGP history on kicking kinematics, kinetics, and patient-reported outcomes in regional-league soccer players using one-dimensional statistical parametric mapping (1D-SPM). Twenty male athletes were allocated to a group with prior AGP (GP group: n = 8) or without AGP (non-GP group, n = 12), and evaluated during maximal instep and inside-foot kicks using three-dimensional motion analysis and the Copenhagen Hip and Groin Outcome Score (HAGOS). The GP group reported significantly lower HAGOS for pain and quality of life. The 1D-SPM analysis revealed that the GP group employed a compensatory kinetic chain strategy, characterized by impaired trunk–pelvis rotation, increased reliance on the stance leg (SL) for stability, and altered kicking leg (KL) mechanics with reduced hip flexion power. These findings reveal that the underlying deficit in AGP is not isolated muscle weakness but a ‘lack of adaptability in motor control’, resulting in inefficient load distribution and contributing to the high recurrence rates in the adductors and SL. Rehabilitation should adopt a kinetic chain-oriented approach that also addresses stance limb function to mitigate recurrence and optimize performance. Full article

Low-cost missions are ideal for applications that require spacecraft formation flying. The use of GNSS signals provides an economical solution to determine the orbital status of the formation. This paper facilitates the development of such missions by simulating spacecraft orbital formation conditions through the use of software-defined radio to generate the GNSS signals being received by each spacecraft. The simulation environment integrates low-cost commercial GNSSs, one for each member of the formation, to capture the signals generated. The analysis of the recorded raw signals shows that the instrumental error of the receivers is predominant because they have not been designed to work in orbital conditions. In addition to noise, the bias errors introduced must be taken into account by the mathematical trilateration methods, which can be very sensitive to these errors. This paper shows how sensitivity can be quantified using the condition number for matrix inversion. A condition number analysis determines that the optimal solution for trilaterating the orbital position of a spacecraft should use as few GNSS satellites as possible. The paper also introduces how to use the condition number to evaluate different methods for determining the state of the spacecraft formation: the independent trilateration method, the difference method, and the double difference method. The comparison of the methods shows that the difference and double difference methods are more sensitive to instrumental errors, because they are worse conditioned, but can be improved by reducing their order. Despite the limitations shown, at best, errors in the relative positions of the spacecrafts of the order of metres are obtained, demonstrating the feasibility of this type of mission and the usefulness of the condition number analysis method presented. Full article

Medical waste management is a growing concern in Kazakhstan. Despite the presence of a regulatory framework, the current medical waste disposal system suffers from fragmentation, lack of transparency, and inefficient communication between stakeholders. These limitations result in illegal dumping, environmental pollution, and increased health risks. This paper presents the development and validation of an integrated Internet of Things (IoT)-based system designed to optimize and automate the monitoring, collection, and disposal of medical waste. The proposed architecture includes Global Positioning System (GPS) tracking, real-time sensor monitoring, cloud data analytics, and predictive routing algorithms, enabling efficient logistics and regulatory compliance. Utilizing a microcontroller and sensors, the system continuously transmits data to a centralized server for monitoring. Experimental deployments across urban and suburban routes in the Zhambyl region demonstrate that the system achieves a Circular Error Probable (CEP₅₀) of 11 m and a 95% positioning accuracy within 23 m, which aligns acceptably with the requirements for city-level route optimization. Statistical analysis confirms that the observed positioning accuracy is consistent with an urban propagation model and adequate for municipal dispatching, though it remains below automotive-grade precision. The system is further supported by a robust power supply solution, allowing up to 49 h of autonomous operation. Full article

Football requires repeated sprint ability for game-changing moments; however, the demand on the skeletal muscles is unknown. The aim of the current study was to determine the muscle oxygen response during duplicate sprints in professional footballers. Eight male professional footballers (age: 29 ± 5 y; height: 181 ± 8 cm; weight: 78 ± 8 kg) were recruited. Participants wore their normal GPS unit and completed their normal match warm-up before near-infrared monitors were attached to the rectus femoris and bicep femoris muscles. Participants then completed two 30 m sprints with 10 s of recovery, while GPS data and muscle oxygenation were recorded. Max speed was unaltered across the two sprints (s1: 8.4 ± 0.3 m.s⁻¹; s2: 8.4 ± 0.4 m.s⁻¹), but max acceleration (s1: 5.0 ± 1.5 m.s⁻²; s2: 3.7 ± 1.2 m.s⁻²) and time to max acceleration (s1: 1.0 ± 0.3 s; s2: 1.8 ± 0.8 s) were significantly different in sprint 2 compared with sprint 1. Change in muscle oxygenation was greater in the bicep femoris muscle than in the rectus femoris muscle in sprint 1 (right BF: 37.0 ± 14.7%; right RF: 23.4 ± 14.8%). Time to fast delay was longer in sprint 2 than in sprint 1 in the bicep femoris muscle (right BFs1: 1.6 ± 1.2 s; right BFs2: 5.2 ± 2.3 s), reflecting different recovery kinetics in the two muscles. During duplicate sprints there is a difference in oxygen response between the two muscles, and the overall recovery of the bicep femoris is much slower. This suggests poorer conditioning of the bicep femoris muscle, which may impact injury risk in professional football players. Full article

Magnetic, angular rate, and gravity (MARG) sensor-based inference is the de facto standard for mobile robot pose estimation, yet its sensor limitations necessitate fusion with absolute references. In environments where such references are unavailable, the system must rely solely on the uncertain MARG-based inference, posing significant challenges due to the resulting estimation uncertainties. This paper addresses the challenge of enhancing the accuracy of position/velocity estimations based on the fusion of MARG sensor data with shallow neural network (NN) models. The proposed methodology develops and trains a feasible cascade-forward NN to reliably estimate the true acceleration of dynamical systems. Three types of NNs are developed for acceleration estimation. The effectiveness of each topology is comprehensively evaluated in terms of input combinations of MARG measurements and signal features, number of hidden layers, and number of neurons. The proposed approach also incorporates extended Kalman and gradient descent orientation filters during the training process to further improve estimation effectiveness. Experimental validation is conducted through a case study on position/velocity estimation for a low-cost flying quadcopter. This process utilizes a comprehensive database of random dynamic flight maneuvers captured and processed in an experimental test environment with six degrees of freedom (6DOF), where both raw MARG measurements and ground truth data (three positions and three orientations) of system states are recorded. The proposed approach significantly enhances the accuracy in calculating the rotation matrix-based acceleration vector. The Pearson correlation coefficient reaches 0.88 compared to the reference acceleration, surpassing 0.73 for the baseline method. This enhancement ensures reliable position/velocity estimations even during typical quadcopter maneuvers within 10-s timeframes (flying 50 m), with a position error margin ranging between 2 to 4 m when evaluated across a diverse set of representative quadcopter maneuvers. The findings validate the engineering feasibility and effectiveness of the proposed approach for pose estimation in GPS-denied or landmark-deficient environments, while its application in unknown environments constitutes the main future research direction. Full article

This study aimed to identify distinct external load profiles of 23 semi-professional football players (22.52 ± 1.74 years) during four official matches (40 cases in total; 10 per match). Using GPS-based inertial technology WIMU PRO (Hudl, Lincoln, NE, USA), data were collected to analyze players’ physical performance. A principal component analysis (PCA) identified three performance profiles—“Total Player,” “Explosive Player,” and “Dynamic Player”—that together explained 70.08% of the variance. These profiles revealed that players may share similar physical characteristics despite occupying different on-field positions. Training players based on their physical performance profiles, rather than solely on their tactical roles, may enhance both individual development and overall team performance. This approach offers a novel framework for individualized conditioning in team sports. Full article

Glycolytic potential (GP) is an important index for evaluating meat quality in the pig industry, since high muscle glycogen content generally leads to rapid postmortem glycolysis, which contributes to low meat quality. The natural differences in meat quality between Chinese local pigs (good meat quality) and Western pigs (standard meat quality) make them the ideal models for glycolysis research. Here, we investigated the mechanisms of glycolysis through comparing transcriptome and metabolome data of biceps femoris (BF) muscle between Jinhua (JH) and Landrace × Yorkshire (LY) pigs at different ages. In this research, JH pigs exhibited lower intramuscular glycogen content than LY pigs throughout the growth period (p < 0.05). Increased phosphorylated glycogen synthase (p-GS) expression indicated reduced glycogenesis capacity in JH pigs. Pathway enrichment revealed that the differentially expressed genes (DEGs) were highly enriched in glycolysis, glycogenesis, and TCA cycle pathways, but these metabolic pathways were suppressed in JH pigs. Metabolomic analysis identified increased lipids and amino acids, but carbohydrate metabolites were decreased in JH pigs. Through integrating transcriptome and metabolome data, VASH1 was identified as a biomarker of muscle glycolysis. Mechanistically, VASH1 knockdown promoted glucose metabolism through enhancing glycolysis and glycogenesis via the AMPK signaling pathway. Our findings provided novel insights into the genetic basis of meat quality and identify VASH1 as a potential target for genetic selection to improve muscle glycolytic level and pork quality. Full article