Search Results (56,031)

To address the issues of slow response to preceding vehicles and poor string stability in distributed platoon control of connected and autonomous vehicles (CAVs) under mixed traffic flow, this paper proposes a sliding mode control method based on LSTM trajectory prediction, denoted as LSTM-SMC, within a multi-agent framework. The LSTM model is trained using the HighD naturalistic driving dataset to achieve high-precision prediction of the leader vehicle’s trajectory over a horizon of 3 s, with root mean square errors (RMSE) of 8.52 m in the X-direction and 0.896 m in the Y-direction. The predicted trajectory information is converted into a preview error and embedded directly into the design of the sliding surface, enabling each following vehicle to anticipate disturbances before they propagate. A diminishing preview gain strategy (${\gamma }_1=0.4$, ${\gamma }_2=0.2$, ${\gamma }_3=0.1$) is employed to suppress error propagation along the platoon, while a saturation function is introduced to eliminate chattering and ensure smooth control inputs. Three simulation scenarios—prescribed leading, HDV (human-driven vehicle) leading, and curved road scenario—are constructed to validate the proposed method against traditional constant time headway (CTH) control, pure sliding mode control (SMC), and LSTM-MPC. Results demonstrate that under extreme conditions, the proposed method reduces the speed RMSE of the 3rd following vehicle by 18.3% compared to CTH and by 39.7% compared to SMC. Under HDV leading conditions, all string stability amplification factors are less than 1, and the position RMSE of the 3rd vehicle is only 5.03 m in the curved road scenario. Compared with LSTM-MPC, the proposed LSTM-SMC achieves comparable tracking accuracy while reducing computational cost by 1.43–3.51×. The proposed method achieves a native integration of prediction and robust control, significantly improving tracking accuracy, string stability, and computational efficiency across diverse operating conditions in mixed traffic flow. Full article

Electric Vehicle drive systems must provide fast torque response, low or minimal torque ripple, robustness to both parameter variations and external disturbances. Permanent Magnet Synchronous Motors (PMSMs) are commonly found in electric vehicle propulsion applications due to their high power density, high efficiency, and excellent dynamic performance. However, performance degradation in torque control of PMSMs under time-varying conditions arises from the nonlinear characteristics of motors and their high sensitivity to changes in system parameters. This paper presents a torque-control method with high dynamic bandwidth that combines three techniques: (1) Nonlinear Sliding Mode Torque Control; (2) Predictive Current Control; and (3) Disturbance Estimation. The sliding mode controller provides improved robustness against uncertainties about the system. In addition, the predictive current control provides improved accuracy in current tracking and significantly reduces the time required to achieve a steady state. A disturbance observer is used to compensate for load disturbances and model errors in the motor model. The integrated control architecture is simulated and modeled in MATLAB/Simulink for a typical EV driving environment. The simulation framework produced faster and more accurate torque tracking than conventional PI-type vector controllers, as well as reduced torque ripple and improved disturbance rejection under similar operating conditions. The results demonstrate that the proposed method is a viable candidate for high-performance EV propulsion systems while acknowledging practical limitations such as chattering, tuning complexity, sampling time sensitivity, and the need for further experimental validation. Full article

Acromegaly is a rare chronic endocrine disorder characterized by delayed diagnosis, multisystem comorbidity, and heterogeneous therapeutic response. We aimed to describe the clinical characteristics, tumor profile, treatment patterns, biochemical control, pituitary insufficiencies, and comorbidity burden in an endocrinology tertiary center in northeastern Romania. This observational retrospective study included 87 adult patients admitted for general inpatient evaluation between December 2023 and November 2024, with retrospective data collected from diagnosis and follow-up assessed through the last available hospital visit at St. Spiridon Clinical Emergency Hospital. Clinical, hormonal, imaging, and therapeutic data were analyzed using descriptive statistics and inferential statistical tests. Most patients were diagnosed in middle adulthood, with a female predominance. Macroadenomas and extrasellar extension were common, consistent with advanced tumor stage at presentation. Treatment was predominantly multimodal, with surgery as the main therapeutic intervention and somatostatin receptor ligands as the main medical treatment backbone. Biochemical improvement was observed over time, although complete remission was achieved only in a subset of patients. These findings describe the clinical and therapeutic complexity of acromegaly in a single tertiary-center inpatient cohort and support the need for individualized long-term monitoring. Full article

This work aims to study and improve the long-term stability of conductimetric biosensors for urea detection in clinical and environmental samples, which are fundamentally limited by complex thermal and temporal drifts due to temperature-sensitive enzyme kinetics, variations in ionic mobility, and the progressive degradation of the sensing layer. The biosensor targets the urea concentration range 0.01–30 mM, validated against experimental data and covering the clinically relevant range for blood urea detection (2.5–7.5 mM), urine (20–40 mM), and environmental monitoring applications. Conventional calibration techniques, such as the conventional calibration method (based on reference measurements), and purely deterministic correction methods, such as deterministic methods (based on known fixed equations), often prove insufficient because they struggle to capture the non-stationary and inherently stochastic nature of these drifts. In this work, we propose an original hybrid probabilistic framework that synergistically combines machine learning and Bayesian inference for robust adaptive drift compensation. A Random Forest model is first implemented to model the deterministic nonlinear relationships between environmental parameters (temperature, pH, CO₂ concentration) and the sensor response. The residual temporal drift is then explicitly modeled as a non-stationary latent stochastic process using Bayesian inference based on a Gaussian process. This approach allows continuous online model updating, real-time uncertainty quantification, and automatic detection of anomalies. The models were trained and validated on a large dataset obtained from multiphysics simulations carried out in COMSOL Multiphysics 5.6. These simulations incorporated enzymatic reactions, thermal effects, and chemical dynamics taking place inside the sensor. Experimental results show that the hybrid approach substantially enhances sensor performance, lowering the root mean square error (RMSE) to below 0.8 μS/cm (corresponding to less than 0.5% of the full-scale response) over a wide temperature range (15–45 °C) and across extended operating periods. This represents a clear improvement over conventional compensation method. By merging the predictive power of ensemble learning with a probabilistic Bayesian model of dynamic drift, this study introduces a fresh perspective on the design of intelligent, self-adaptive, and drift-resistant conductimetric biosensors. The proposed framework holds strong potential for reliable, long-term autonomous operation in urea reliable, long-term autonomous operation in urea monitoring across biomedical diagnostics (kidney/liver function assessment) and environmental surveillance (water eutrophication prevention). Full article

Elite swimmers are exposed to sustained high training loads, early-morning sessions, and restricted recovery opportunities, all of which may increase psychological strain and compromise sleep. This study examined the effects of an 8-week Mindfulness–Acceptance–Insight–Commitment (MAIC) program, embedded within routine high-load training, on athlete-specific psychological stress, subjective sleep quality, and mindfulness in elite swimmers. A randomized controlled mixed-methods design was used. Thirty elite swimmers from a provincial high-performance program in China were randomly assigned to an MAIC group or a usual-practice control group (n = 15 per group). Quantitative outcomes were assessed at baseline, post-intervention, and three-month follow-up using the Athlete Psychological Strain Questionnaire, salivary cortisol, the Pittsburgh Sleep Quality Index, and the Athlete Mindfulness Questionnaire. Semi-structured interviews were conducted with all athletes in the MAIC group after the intervention. Mixed-design ANOVAs revealed significant Group × Time interactions for athlete-specific psychological stress, salivary cortisol, sleep quality, and mindfulness. Compared with the control group, the MAIC group showed lower psychological strain and cortisol, better subjective sleep quality, and higher mindfulness at post-intervention. At follow-up, improvements in psychological stress and mindfulness remained evident relative to baseline, whereas lower salivary cortisol and more favorable self-reported sleep quality remained evident relative to the control group. Qualitative findings further showed that MAIC was experienced as feasible, low-burden, and readily integrated into the training context. Athletes described attentional resets, acceptance-based responses to discomfort, and brief post-session or pre-sleep practices as helpful for regulating cognitive reactivity and arousal. Overall, MAIC appears to be a culturally grounded and practically viable adjunct strategy for supporting psychological regulation and self-reported sleep quality in elite swimmers during demanding training periods. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article

Early detection of infectious disease outbreaks is critical in densely populated, resource-limited settings. This study aimed to describe the community-based surveillance (CBS) system and its application of the Data–Information–Knowledge–Wisdom (DIKW) framework in Bangladesh. CBS was implemented in 12 urban wards across Dhaka South, Rajshahi, and Sylhet, where trained community volunteers conducted routine household visits to identify five priority syndromes. Data were collected through a mobile application integrated with an automated pipeline for cleaning, geocoding, cluster detection, and alert generation. Between January and June 2025, 38,489 households were visited, enrolling 128,626 individuals. The system generated 10,191 alerts and 577 clusters, predominantly for suspected dengue (58.7%), followed by acute watery diarrhea (24.1%) and influenza-like illness (10.7%). Rajshahi contributed the majority of alerts and clusters. Spatiotemporal analysis identified ward-level outbreak signals, including localized dengue peaks across all three cities. Over 98% of records were synchronized within 24 h, and more than 99% of data entry errors were automatically corrected, ensuring timely and high-quality analytics. These findings demonstrate that digital CBS can effectively transform community-level data into actionable public health intelligence, supporting early outbreak detection and response. This translation enabled timely public health actions, including targeted outbreak investigations and localized vector control measures in identified hotspots. Integration with national surveillance platforms may further strengthen health system responsiveness and epidemic preparedness. Full article

Stick–slip vibration, reversal, axial impact, and dynamic instability are major challenges in deep drilling operations and are closely associated with nonlinear bit–rock interaction. To investigate these phenomena, this study develops a nonlinear axial–torsional coupled dynamic model of a drill-string system by integrating rock surface morphology evolution with a Stribeck dry friction model. The drill string is discretized into a distributed lumped-parameter model with coupled axial and torsional degrees of freedom. A surface morphology matrix is introduced to simulate the rock-cutting process, while the Stribeck friction model is employed to characterise the nonlinear frictional behaviour at the bit–rock interface. Time-domain simulations, bifurcation analysis, and frequency spectrum analysis are performed to investigate the dynamic responses of the system. The results indicate that rock surface morphology evolution significantly influences the contact conditions and frictional behaviour at the bit–rock interface, and together with dry friction induces transitions among steady-state, multi-periodic, and chaotic motions. Stick–slip vibration is accompanied by axial impact, bit bounce, and a reduction in the dominant torsional vibration frequency. In addition, variations in both driving and frictional parameters can trigger dynamic instability and state transitions. The proposed model provides an effective framework for analysing nonlinear drilling dynamics and offers theoretical guidance for drill-string vibration suppression, drilling parameter optimisation, and efficient drilling in complex formations. Full article

The soybean aphid (Aphis glycines) and soybean cyst nematode (Heterodera glycines) are major aboveground and belowground pests of soybean (Glycine max) in the U.S. Midwest, but the molecular basis of their combined effects on soybean defense remains poorly understood. This study examines how soybean genotypes influence demographic and root-transcriptomic responses to single and combined pest infestation. Soybean cyst nematode reproduction increased under combined infestation in the susceptible cultivar but remained unchanged in the resistant cultivar, whereas soybean aphid populations declined when plants were also infested with nematodes. Root RNA-seq revealed strong time-dependent transcriptional responses, with substantially more differentially expressed genes at 30 days post-infestation than at 5 days post-infestation. Co-expression and enrichment analyses showed that early responses were associated with defense signaling, plant–pathogen interaction, and cutin, suberin, and wax biosynthesis, whereas later responses involved redox processes, isoflavonoid biosynthesis, phenylpropanoid metabolism, and one-carbon metabolism. Several differentially expressed soybean genes co-localized with known soybean cyst nematode resistance quantitative trait loci, including genes near the rhg1 region. Together, these results suggest that soybean genotypes strongly influence soybean aphid–soybean cyst nematode interactions and identify candidate genes and pathways that may contribute to durable resistance against interacting aboveground and belowground pests. Full article

The rapid development of new energy has caused a sharp increase in the stochasticity on the source side of the new power system (NPS), and extreme weather along with climate variability have also led to increased stochasticity in power demand on the load side; thus, how to achieve source-load matching and enable the load to track the source under the new situation is the key to the efficient operation of the power system. Aiming at the problem that existing load regulation potential evaluation mainly focuses on physical capacity, making it difficult to reflect users’ subjective willingness to participate as well as the dynamic changes in regulation capability under different operating scenarios, this paper proposes a two-stage dynamic profiling classification method for multi-type power user loads considering regulation willingness. First, an evaluation index system is constructed from three dimensions, physical reliability, execution reliability, and behavioral willingness, to achieve the unified characterization of the regulation capabilities of heterogeneous resources such as industrial loads and electric vehicle (EV) aggregators. Second, the DBSCAN algorithm is adopted to identify typical annual operating scenarios. Finally, the Dynamic Time Warping (DTW) distance is introduced to improve the K-Means++ algorithm, achieving the profiling classification of user regulation potential. This paper takes a certain NPS demonstration park as an example for verification, and the results show that the annual operating scenarios can be divided into 4 types of typical days; the proposed DTW-K-Means++ method has better classification performance compared with traditional Euclidean distance clustering, can effectively identify the differences and dynamic migration characteristics of user regulation potential under different operating scenarios, and stably classifies users into three types of profiles: deep regulation type, agile response type, and rigid constraint type. The research results aim to provide reliable data support for the refined dispatch of the power grid by effectively quantifying the dynamic migration patterns of heterogeneous resources under variable scenarios. Full article

Objectives: Testicular torsion is a time-critical pediatric urological emergency in which delayed presentation may increase the likelihood of orchiectomy. However, preoperative estimation of the immediate intraoperative outcome remains challenging. This study aimed to develop and internally validate machine-learning models integrating ischemic timing variables and complete blood count-derived inflammatory indices to predict orchiectomy versus testicular preservation at index surgical exploration in pediatric and adolescent testicular torsion. Methods: We retrospectively analyzed 165 children and adolescents who underwent surgical exploration for confirmed testicular torsion. The primary endpoint was orchiectomy at index exploration versus testicular preservation through detorsion and/or orchiopexy. Clinical timing variables and complete blood count-derived indices, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, white blood cell-to-monocyte ratio, monocyte-to-mean platelet volume ratio, hemoglobin-to-monocyte ratio, systemic inflammatory response index, and aggregate index of systemic inflammation, were evaluated. Five supervised machine-learning algorithms—logistic regression, random forest, XGBoost, LightGBM, and support vector machine—were assessed using nested stratified cross-validation with an outer 5-fold loop and inner 3-fold hyperparameter selection. Model performance was estimated from out-of-fold predictions. Results: Orchiectomy was performed in 37 patients (22.4%), whereas testicular preservation through detorsion and/or orchiopexy was performed in 128 patients (77.6%). Symptom duration was significantly longer in the orchiectomy group than in the preservation group (48.00 [30.00–72.00] vs. 6.00 [2.00–24.00] h, p < 0.001). Monocyte count was also higher in the orchiectomy group (0.92 [0.68–1.23] vs. 0.65 [0.50–0.93] × 10⁹/L, p < 0.001). Among the combined models, XGBoost achieved the highest numerical discrimination, with a ROC-AUC of 0.926 ± 0.066 and a bootstrap 95% confidence interval of 0.856–0.966. Feature-block and interpretability analyses identified symptom duration as the dominant predictor, followed by emergency department waiting time and selected monocyte-centered inflammatory indices. Conclusions: Immediate intraoperative orchiectomy in pediatric and adolescent testicular torsion was most strongly associated with prolonged symptom duration, while selected complete blood count-derived inflammatory indices provided complementary risk information. The combined XGBoost model showed strong internal discrimination and clinically interpretable predictor patterns. However, the model was internally validated only and requires external validation before clinical implementation. Full article

Background/Objectives: Previous studies have measured a limited number of biomechanical parameters during medical rehabilitation of an anterior cruciate ligament (ACL) rupture. This study aimed to quantitatively assess changes in gait biomechanics, knee function, and lower-extremity muscle activity during after ACL reconstruction. Methods: The study included 32 patients after arthroscopic ACL reconstruction. The patients were divided into three groups based on postoperative time points: 0.5 year (12 men), 1 year (7), and over 1 year (9). Gait analysis at both self-selected and fast speeds was performed using an inertial system. Statistical analysis was performed using rank models and full-factorial orthogonal designs. Results: After 0.5 year, the timing of the gait cycle at self-selected speed was within the control group’s range and showed no significant asymmetry. With increasing speed, a decrease in knee joint range of motion was observed in the 0.5 year and 1-year groups, without achieving a full physiological increase in range of motion at long-term follow-up. Multivariate analysis revealed the greatest biomechanical imbalance during fast walking at one year and a phase-dependent effect of time after surgery, speed, and limb status on kinematics and EMG, particularly in the quadriceps. Conclusions: Basic temporal gait parameters during self-selected walking were within the control range by 0.5 year, but load-dependent knee kinematic and EMG abnormalities persisted. The knee joint’s response to increased loads remained impaired for at least one year. The persistence of phase-specific compensatory changes in kinematics and muscle activity at later stages can be assessed using exercise testing. Full article

Ryanodine receptor-like (RyR-like) is a key endoplasmic reticulum (ER) Ca²⁺ release channel governing intracellular Ca²⁺ homeostasis and cellular stress responses in invertebrates. However, its function in bivalves under high-temperature stress remains unclear. In the present study, one RyR-like was identified from Yesso scallop Patinopecten yessoensis (PyRyR-like). Its function in regulating intracellular Ca²⁺, IRE1α-mediated unfolded protein response (UPR) and apoptosis in the mantle after high-temperature (25 °C) treatment was investigated using molecular cloning, qRT-PCR, Western blot, pull-down assay, cellular calcium imaging, TUNEL and histology assays; High temperature treatment significantly increased intracellular Ca²⁺ content at 1 and 6 h (p < 0.05), but decreased it at 3, 12 and 24 h (p < 0.05); meanwhile, the cAMP level, PyPKA activity, mRNA expression level of PyRyR-like, and protein expression levels of PyFKBP12 and PyGRP78 were significantly increased at different times. However, high temperature did not affect the expression level of PyNVL and PyXBP1(S). The SPRY and RYR domains of PyRyR-like separately interacted with PyFKBP12 and PyPKA. Moreover, RyR antagonist Dantrolene reversed high-temperature-induced alterations in Ca concentration, PKA activity, and core UPR- and apoptosis-related molecules, and suppressed Caspase-3 activity. These findings suggest that PyRyR-like plays an important role in the high-temperature response of scallops by regulating intracellular Ca²⁺ homeostasis and mediating UPR activation and apoptosis, providing new insight into the molecular mechanism underlying scallop adaptation to high temperature. Full article

Interfacial yarn pull-out plays a critical role in load transfer and energy dissipation in soft ballistic materials; however, its multistage and friction-dominated nature makes comprehensive experimental characterization time-consuming and experimentally demanding. In this study, a stage-wise physics-guided machine learning (PG-HML) framework is proposed to predict the pull-out behavior of nano hexagonal boron carbide (nh-B₄C)-functionalized para-aramid fabrics using an experimentally constrained dataset. The pull-out response was decomposed into three physically meaningful deformation stages, namely crimp extension, initial interlacement rupture, and stick–slip sliding. Physics-based continuity and admissibility constraints were incorporated into the learning framework to preserve mechanical consistency across stage transitions and improve prediction robustness under limited data conditions. Comparative analyses demonstrated that the proposed PG-HML framework achieved superior predictive capability, particularly in capturing rupture transitions and post-peak stick–slip evolution, with R² values exceeding 0.98 during the crimp extension and rupture stages. Increasing nh-B₄C content enhanced interfacial friction, rupture resistance, and pull-out energy dissipation, while displacement responses gradually approached saturation under force-dominated extraction conditions. Therefore, interfacial pull-out behavior in nh-B₄C/aramid materials can be predicted with high fidelity using limited experimental input, providing a surrogate modeling strategy that enables virtual material screening and significantly reduces the experimental effort required for preliminary design and optimization of advanced soft ballistic materials. Full article