Search Results (168)

Conventional ground reaction force (GRF) and load rate (LR) analyses may overlook temporal and waveform characteristics that reflect injury status and acuity. This study used an alternative GRF processing methodology to characterize GRF waveforms among runners with symptomatic medial tibial stress fractures (MTSS) and those recovering from tibial stress fractures (TSF; both unilateral [UL] and bilateral [BL]). This cross-sectional analysis of runners (n = 66) included four groups: symptomatic MTSS, recovering from UL or BL TSF, or uninjured case-matched controls. Participants ran at self-selected speed on an instrumented treadmill. Kinematics were collected with a 3D optical motion analysis system. Double-Gaussian models described the biphasic loading pattern of running gait (initial impact, active phases). Gaussian parameters described relative differences in the GRF waveform by injury condition. LR was calculated using the central difference numerical derivative of the raw normalized net force data. During the impact phase (0–20% of stance), controls and BL TSF produced higher GRF amplitudes than UL TSF and MTSS (p < 0.05). BL TSF and controls had greater maximal positive LR and minimum LR than UL TSF and MTSS. Peak medial GRF was 18–43% higher in the BL TSF group than in MTSS and UL TSF (p < 0.05). Correlations existed between tibial pain severity and early stance net GRF (r = 0.512; p = 0.016) and between pain severity and the duration since diagnosis for LR values during the impact phase (r values = 0.389–0.522; all p < 0.05). Collectively, these data suggest that this waveform modeling approach can differentiate injury status and pain acuity in runners. Early stance GRF and LR may offer novel insight into the management of running-related injuries. Full article

This study investigates the pyrolysis of construction, renovation, and demolition (CRD) wood waste to produce biochar, with a focus on its robustness, scalability, and characterization for energy and environmental applications. Pyrolysis conditions, including the temperature, biomass residence time (BRT), and feedstock mass, were varied to evaluate their effects on biochar properties. High-temperature biochars (B800) showed the highest fixed carbon (FC) (87%) and thermostable fraction (TSF) (96%) and the lowest volatile carbon (VC) (9%), with a high carbon content (92%), a large BET surface area (300 m²/g), and a high micropore volume (0.146 cm³/g). However, the hydrogen (0.9%) and oxygen (2.2%) content, Van-Krevelen parameters (H/C: 0.1; O/C: 0.02), and biochar yield (21%) decreased with increasing temperature. Moderate-temperature biochars (B600) have balanced physicochemical properties and yields, making them suitable for adsorption applications. Methyl orange dye removal exceeded 90% under the optimal conditions, with B600 fitting well with the Freundlich isotherm model (R² = 0.97; 1/n = 0.5) and pseudo-second-order kinetic model (R² = 1). The study highlights biochar’s suitability for varied applications, emphasizing the need for scalability in CRD wood pyrolysis. Full article

In contrast to terrestrial networks, the rapid movement of low-earth-orbit (LEO) satellites causes frequent changes in the topology of intersatellite links (ISLs), resulting in dynamic shifts in transmission paths and fluctuations in multi-hop latency. Moreover, limited onboard resources such as buffer capacity and bandwidth competition contribute to the instability of these links. As a result, providing reliable quality of service (QoS) for time-sensitive flows (TSFs) in LEO satellite networks becomes a challenging task. Traditional terrestrial time-sensitive networking methods, which depend on fixed paths and static priority scheduling, are ill-equipped to handle the dynamic nature and resource constraints typical of satellite environments. This often leads to congestion, packet loss, and excessive latency, especially for high-priority TSFs. This study addresses the primary challenges faced by time-sensitive satellite networks and introduces a management framework based on software-defined networking (SDN) tailored for LEO satellites. An advanced queue management and scheduling system, influenced by terrestrial time-sensitive networking approaches, is developed. By incorporating differentiated forwarding strategies and priority-based classification, the proposed method improves the efficiency of transmitting time-sensitive traffic at multiple levels. To assess the scheme’s performance, simulations under various workloads are conducted, and the results reveal that it significantly boosts network throughput, reduces packet loss, and maintains low latency, thus optimizing the performance of time-sensitive traffic in LEO satellite networks. Full article

Background/Objectives: The mechanisms underlying the positive association between reading and rhythmic skills remain unclear. Our goal was to systematically test between two major explanations: the Temporal Sampling Framework (TSF), which highlights the relation between rhythm and speech encoding, and a competing explanation based on rhythm’s role in enhancing prediction within visual and auditory sequences. Methods: We compared beat versus duration perception for their associations with encoding and sequence learning (prediction-related) tasks, using both visual and auditory sequences. We also compared these associations for Portuguese vs. Greek participants, since Portuguese stress-timed rhythm is more compatible with music-like beats lasting around 500 ms, in contrast to the syllable-timed rhythm of Greek. If rhythm acts via speech encoding, its effects should be more salient in Portuguese. Results: Consistent with the TSF’s predictions, we found a significant association between beat perception and auditory encoding in Portuguese but not in Greek participants. Correlations between time perception and sequence learning in both modalities were either null or insufficiently supported in both groups. Conclusions: Altogether, the evidence supported the TSF-related predictions in detriment of the Rhythm-as-Predictor (RaP) hypothesis. Full article

In the domain of financial markets, deep learning techniques have emerged as a significant tool for the development of investment strategies. The present study investigates the potential of time series forecasting (TSF) in financial application scenarios, aiming to predict future spreads and inform investment decisions more effectively. However, the inherent nonlinearity and high volatility of financial time series pose significant challenges for accurate forecasting. To address these issues, this paper proposes the IGWO-MALSTM model, a hybrid framework that integrates Improved Grey Wolf Optimization (IGWO) for hyperparameter tuning and a multi-head attention (MA) mechanism to enhance long-term sequence modeling within the long short-term memory (LSTM) architecture. The IGWO algorithm improves population diversity during initialization using the Mersenne Twister, thereby enhancing the convergence speed and search capability of the optimizer. Simultaneously, the MA mechanism mitigates gradient vanishing and explosion problems, enabling the model to better capture long-range dependencies in financial sequences. Experimental results on real futures market data demonstrate that the proposed model reduces Mean Square Error (MSE) by up to 61.45% and Mean Absolute Error (MAE) by 44.53%, and increases the R² score by 0.83% compared to existing benchmark models. These findings confirm that IGWO-MALSTM offers improved predictive accuracy and stability for financial time series forecasting tasks. Full article

The Central Depression of the Junggar Basin relies heavily on Permian lacustrine mudstone for deep-seated hydrocarbon sealing. This research investigated how the fluorescence parameters of caprock samples responded to the leakage of palaeo-oil zones based on measurements from SEM, Rock-Eval, and X-ray diffraction analysis. First, two sets of control experiments were conducted to establish the proper grain-size range of 100–140 mesh for testing caprock samples in the research area using quantitative fluorescence technology. Subsequently, based on the examination of the rock pyrolysis parameters and the fluorescence parameters against TOC values, the conjecture was formed that the quantitative fluorescence technology test results were mostly unaffected by the primary hydrocarbons. Lastly, four fluorescence parameters were used to assess seal integrity: quantitative grain fluorescence intensity of the extract (QGF E intensity, the meaning of QGF is the same in this study), QGF spectral peaks (QGF λmax), the ratio of QGF intensity to fluorescence intensity at 300 nm on the QGF spectrum (QGF index), and total scanning fluorescence spectral ratio R₁ (TSF R₁). The Permian caprock can effectively seal hydrocarbons as evidenced by the decrease of QGF E intensity and QGF index values with depth. When hydraulic fracturing causes caprock failure, it can lead to complete leakage of hydrocarbons from the palaeo-oil zones. As the depth becomes shallower, the QGF E intensity value increases, the QGF index value decreases. Due to the differences in the migration pathways of hydrocarbons in the caprock, those leaked from the Permian palaeo-oil zone into the well PD1 caprock are mainly condensate and light–normal crude oil, while the hydrocarbons from the Carboniferous palaeo-oil zone into the well MS1 caprock consist predominantly of light–normal crude oil and medium–heavy crude oil. Full article

Objectives: Tibial shaft fractures (TSFs) represent the most common diaphyseal fractures in adults. The gold-standard treatment is intramedullary nailing. Recently, the suprapatellar technique has been increasingly adopted due to its ability to reduce complications associated with the infrapatellar approach. Currently, no clinical score for leg fractures comprehensively assesses the entire lower limb. Therefore, we reviewed the main lower-limb scores available in the literature and developed a new clinical evaluation tool for tibial shaft fractures. The aim of our study was to report our experience with both techniques, to compare the outcomes of our prospective study with the international literature, and to propose a new, easy-to-apply, and reproducible clinical score that evaluates the specific functions of the entire lower limb. Methods: We conducted a prospective analysis of 920 tibial shaft fractures treated with intramedullary nailing via either a suprapatellar or infrapatellar approach. Patients were divided into two groups: Group A, including 420 patients treated with the infrapatellar approach; Group B, including 500 patients treated with the suprapatellar approach. Follow-up included clinical and radiographic assessments at 1, 3, and 6 months, and annually thereafter. We evaluated differences in patient positioning, operation time, radiation exposure, healing rate, incidence of pseudarthrosis and infection, return to ambulation, residual knee pain and fracture site, persistent lameness, and deformities. For the clinical assessment, we devised a new score—the Catania Hospital Score (CHS)—by integrating the most relevant clinical items from existing lower-limb evaluation tools. The CHS includes anterior knee pain (20 points), lameness (5 points), swelling (10 points), stair-climbing ability (10 points), tibial pain (15 points), the ability to perform daily activities (20 points), and evaluation of deformities (varus/valgus, shortening, rotation, and recurvatum/procurvatum (40 points)), for a total of 120 points. Results: Statistically significant differences were observed in Group B regarding a shorter surgical time, a reduced patient positioning time, and decreased radiation exposure. The CHSs were significantly better for Group B at the 3- and 6-month follow-ups. No statistically significant differences were found in infection or pseudarthrosis rates between the two groups. Notably, no cases of chronic knee pain were reported in patients treated with the suprapatellar approach. Conclusions: Both surgical approaches are valid and effective. However, our findings indicate that the suprapatellar approach reduces the complications of the infrapatellar technique, improves postoperative outcomes, and does not result in chronic knee pain. The CHS provides a comprehensive, practical, and reproducible tool to assess functional recovery in patients treated with intramedullary tibial nailing. Full article

Despite advancements in divergence and distance measures across fuzzy set extensions, the development of such measures for Globular T-Spherical Fuzzy Sets (G-TSFSs) remains significantly unexplored. Existing approaches often fall short in capturing the rich semantics and high-dimensional uncertainty that G-TSFSs represent, limiting their utility in complex decision environments. This study is motivated by the need to fill this critical gap and advance decision science through more expressive and structurally aligned tools. This paper introduces a suite of novel divergence measures (Div-Ms) specifically formulated for G-TSFSs, a powerful tool for capturing uncertainty in multi-criteria group decision-making (MCGDM) under complex conditions. These Div-Ms serve as the foundation for developing new distance measures (Dis-Ms) and similarity measures (SMs), where both Dis-Ms and SMs are symmetry-based and their essential mathematical properties and supporting theorems are rigorously established. Leveraging these constructs, we propose a robust G-TSF-TOPSIS framework and apply it to a real-world problem, selecting optimal solar energy systems (SESs) for a university context. The model integrates expert evaluations, assuming equal importance due to their pivotal and complementary roles. A sensitivity analysis over the tunable parameter (ranging from 4.0 to 5.0 with an increment of 0.2) confirms the robustness and stability of the decision outcomes, with no changes observed in the final rankings. Comparative analysis with existing models shows superiority and soundness of the proposed methods. These results underscore the practical significance and theoretical soundness of the proposed approach. The study concludes by acknowledging its limitations and suggesting directions for future research, particularly in exploring adaptive expert weighting strategies for broader applicability. Full article

Background: This study examines the application of Artificial Intelligence (AI) and Big Data Analytics (BDA) in enhancing humanitarian supply chain resilience, focusing on Ghana and South Africa. Despite their potential, AI-BDA applications are underexplored in disaster response, particularly in developing economies. Methods: An explanatory research design using a quantitative approach was employed, analyzing data from 200 supply chain professionals in both nations. Structured questionnaires assessed the implementation of four key AI-BDA techniques: Time-Series Forecasting (TSF), Early Warning Systems (EWS), Logistics Optimization (LO), and Real-time Monitoring (RTM). Exploratory factor analysis and regression analysis were conducted to evaluate the relationship between these techniques and supply chain resilience, controlling for organizational size and technological readiness. Results: The findings indicate that AI-BDA techniques significantly improve humanitarian supply chain resilience, with TSF and LO demonstrating the highest predictive power. Additionally, technological readiness facilitates the adoption of these techniques. Conclusions: While AI-BDA offers substantial benefits, opportunities for greater adoption remain, particularly in real-time monitoring and predictive analytics. Humanitarian organizations should invest in capacity-building initiatives, enhance data quality, and foster multi-stakeholder partnerships to maximize the impact of AI-BDA. Full article

Continuous monitoring is key to the safety of such critical infrastructure as Tailings storage facilities. Due to the high risk of liquification of the dams, it is crucial to move the water as far as possible from the dam crest. In order to control the distance from the water to the dam, regular manual inspections need to be carried out. In this article, we propose a method for automatic detection of the water-beach line based on photographs from an unmanned aerial vehicle (UAV). An algorithm based on MobileNet v2 convolutional neural network architecture was developed for the classification of images collected by the UAV. Based on the results of this classification, the border between the water and the beach is defined. Several approaches to the model training were tested. Accuracy for the validation set reaches up to 97% for particular image fragments. Full article

Financial Time Series Forecasting (TSF) remains a critical challenge in Artificial Intelligence (AI) due to the inherent complexity of financial data, characterized by strong non-linearity, dynamic non-stationarity, and multi-factor coupling. To address the performance limitations of Spiking Neural Networks (SNNs) caused by hyperparameter sensitivity, this study proposes an SNN model optimized by an Improved Cuckoo Search (ICS) algorithm (termed ICS-SNN). The ICS algorithm enhances global search capability through piecewise-mapping-based population initialization and introduces a dynamic discovery probability mechanism that adaptively increases with iteration rounds, thereby balancing exploration and exploitation. Applied to futures market price difference prediction, experimental results demonstrate that ICS-SNN achieves reductions of 13.82% in MAE, 21.27% in MSE, and 15.21% in MAPE, while improving the coefficient of determination (R²) from 0.9790 to 0.9822, compared to the baseline SNN. Furthermore, ICS-SNN significantly outperforms mainstream models such as Long Short-Term Memory (LSTM) and Backpropagation (BP) networks, reducing prediction errors by 10.8% (MAE) and 34.9% (MSE), respectively, without compromising computational efficiency. This work highlights that ICS-SNN provides a biologically plausible and computationally efficient framework for complex financial TSF, bridging the gap between neuromorphic principles and real-world financial analytics. The proposed method not only reduces manual intervention in hyperparameter tuning but also offers a scalable solution for high-frequency trading and multi-modal data fusion in future research. Full article

In this paper, an improved online torque compensation strategy considering phase torque-generation capability is proposed to enhance the conventional torque-sharing function (TSF), thus reducing torque ripple for switched reluctance machines (SRMs). The improvements are mainly attributed to two aspects: First, the phase turn-on angle and TSF starting angle are separated. Thus, the phase turn-on angle can be advanced independently to enhance the torque-generation capability of the incoming phase. Second, to generate the desired torque with minimum current, the torque per ampere (TPA) characteristic is considered for commutation region separation. This can ensure that in each separated region, the phase with a stronger torque-tracking ability is utilized for torque error compensation. Accordingly, efficiency is not sacrificed. In addition to improving the TSF, a direct instantaneous torque control (DITC) method combined with a PWM regulator is proposed to reduce large torque increments due to the limited control frequency. As a result, the torque ripple can be further suppressed. Finally, an experimental setup is established, and tests are conducted under different working conditions. The results demonstrate the effectiveness of the proposed method. Full article

Many countries employ mining and ore processing techniques to concentrate and extract precious natural resources. However, the slow leaching of numerous dissolved elements and compounds from large quantities of waste rock and mine tailings can significantly threaten groundwater quality in the affected region. When exposed to oxygen and water, sulfide minerals in mine tailing oxidize, potentially forming acid mine drainage (AMD). Various reclamation techniques can inhibit AMD generation, including monolayer cover combined with an elevated water table (EWT), hydraulic barrier, and cover with capillary barrier effect (CCBE). Selecting the most suitable technique requires consideration of site-specific hydrogeological conditions (e.g., water table depth) and available cover materials. Numerical modeling tools such as PHT3D and MT3D can help identify optimal reclamation methods during preliminary planning stages. The 119-hectare Quémont 2 mine site near Rouyn-Noranda city will undergo reclamation following the closure of its tailings storage facilities (TSF). A three-dimensional numerical groundwater and solute-transport model were constructed and calibrated to simulate the site’s hydrogeological behavior post-closure, enabling selection of the most effective AMD control technique. Subsequently, a three-dimensional multicomponent reactive transport model incorporating various cover designs was developed, with simulations considering climate change impacts. The PHT3D model code, which integrates the PHREEQC geochemical model with the MT3D three-dimensional transport simulator, was employed to evaluate cover performance on the Quémont 2 TSF. Four reclamation configurations were tested: Cell #1 (80 cm single-layer clay cover), Cell #2 (60 cm single-layer clay-sand cover), Cell #3 (60 cm single-layer clay-silt cover), and Cell #4 (120 cm multilayer clay-sand-clay sequence). Simulations were conducted under various climate change scenarios (Representative Concentration Pathways—RCPs 2.6, 4.5, and 8.5). This paper describes the numerical model, cover materials, and modeling results both with and without covers. Results indicate that Cells #1 and #4, completely reduced sulfate in groundwater, suggesting these configurations would provide the most effective reclamation solutions for the Quémont 2 mine site. Full article

Background/Objectives: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder affecting motor neurons in the brain and spinal cord, leading to muscle weakness, atrophy, and paralysis. Treatment focuses on symptom management, using medication, physiotherapy, and nutritional support. In this context, endoscopic gastrostomy (PEG) can provide adequate feeding, hopefully improving nutrition and preventing complications. Methods: We studied ALS patients undergoing PEG over three months post-procedure, using anthropometry ((BMI)—body mass index; (MUAC)—mid-upper arm circumference; (TSF)—tricipital skinfold; (MAMC)—mid-arm muscle circumference) and laboratory data (Albumin; Transferrin; total cholesterol and hemoglobin), evaluating survival, complications, and nutritional/clinical status. Statistical analysis included Kaplan–Meier survival estimation and Cox regression to assess nutritional markers associated with survival. Results: 150 ALS patients underwent gastrostomy, mostly older adults (mean age: 66.1 years; median: 67). Mean survival was 527 [95% CI: 432–622] days, median 318 [95% CI: 236–400]. ALS bulbar subtype, MUAC and MAMC positively impacted PEG-feeding survival time (p < 0.05, Wald test). During the first three months of PEG feeding, each unit increase (cm) in MUAC and MAMC lowered death risk by 10% and 11%, respectively, highlighting the importance of nutrition care for survival. The bulbar subtype showed higher PEG feeding survival, with a 55.3% lower death hazard than the spinal subtype. There were no major PEG complications. Conclusions: ALS patients present a high risk of malnutrition. Patients that improved MAMC and MUAC in the first three PEG-fed months presented longer survival. Early PEG nutrition, even when some oral feeding is still possible, may reinforce the preventative role of enteral feeding in maintaining nutrition and potentially improving survival. Full article

The inappropriate thermal conditions resulting from increasingly severe climate issues have led to numerous complications for urban residents, decreased urban settlement comfort, and increased average and peak energy demands in built environments. Existing studies have demonstrated the significant influence of urban morphology (UM) on the urban thermal environment (UTE); however, at the meso-scale and macro-scale, UTE is often simplified to land surface temperature (LST) and building surface temperatures. To investigate the impact of UM on UTE, we developed an evaluation framework consisting of thermal sensing feedback (TSF) and LST. We employed the seven-level TSF scale to evaluate TSF data obtained from the Internet, emphasizing individualized thermal perceptions of urban spaces and reorienting UTE research towards a human-centric perspective. Using a regression model, we examined the relationships between two-dimensional and three-dimensional UM variables and UTE at the meso-scale in the central urban area of Shanghai, China, during August and December 2024. The results indicated the following: (1) The normalized difference vegetation index (NDVI), building density (BD), floor area ratio (FAR), impervious surface index (ISI), building height (BH), average building volume (ABV), sky view fraction (SVF), and building shape (BS_sh) effectively explained TSF. However, area weighted mean shape index (SHAPE_AM), aggregation index (AI), edge density (ED), elevation, building spacing (BS_sp), and spatial congestion degree (SCD) showed no significant correlation with TSF. (2) Significant variables, including NDVI, FAR, ISI, UM, BD, and BH, exhibited opposite effects on cold perception in winter compared to heat perception in summer, indicating a consistent influence on thermal perception across seasons. (3) In summer, the significant variables SVF, BS_sh, and ISI showed opposite effects on TSF and LST, while in winter, FAR demonstrated contrasting impacts on TSF and LST. The results of this study advance understanding of the mechanisms through which UM influences UTE, providing valuable insights for the development of sustainable, thermally comfortable urban environments. Full article