Search Results (1,905)

Problem: Controlling nonlinearly parameterized systems with unknown disturbances remains challenging because classical adaptive approaches rely on separation-of-variables and reparameterization techniques, leading to increased parameter dimensions, conservative stability bounds, and implementation complexity. Objective: This paper develops a data-driven predictive fuzzy adaptive control (DD-PFAC) framework that eliminates the need for separation techniques while achieving superior tracking performance and formally certified stability. Novelty: The key innovation is a two-layer architecture. Layer 1 provides direct fuzzy approximation of composite nonlinear functions (system dynamics plus disturbance bound) without parameter reparameterization, reducing parameter complexity from $O\left(qn\right)$ to $O\left(nN\right)$. Layer 2 employs Hankel matrix-based predictive optimization to adaptively tune both control gains $c_i\left(k\right)$ and adaptation rates ${\gamma }_i\left(k\right)$ online using 80–150 recent input–output samples. Methodology: A Lyapunov function augmented with a prediction-error term is used to prove uniform ultimate boundedness of all closed-loop signals. A projection-based recursive least-squares algorithm updates the gain parameters online while guaranteeing $c_i\left(k\right)\ge c_{min}>0$ at all times. Results: Comparative simulations demonstrate 31.4% reduction in integral square error, 27.8% reduction in mean absolute error, and 37.4% reduction in steady-state error versus traditional adaptive fuzzy control. A four-group ablation study confirms that adaptive gain scheduling contributes 27.7% and predictive compensation contributes 6.5% to the total MAE improvement. Robustness tests validate consistent 28–32% performance advantage across sinusoidal, pulse, step, and large-disturbance scenarios. Full article

Quantitatively characterizing the dynamic evolution of fracture swarms under offset well low-frequency distributed acoustic sensing (LF-DAS) monitoring remains a significant challenge. This study proposes a physics-data dual-driven closed-loop inversion framework to address this problem. The framework consists of three core modules: (1) a fluid–solid coupled semi-analytical forward model applicable to variable-rate injection and shut-in conditions; (2) an automatic key feature identification method based on multi-scale scanning and physical polarity constraints; and (3) a dynamic inversion model for fracture swarms based on adaptive particle swarm optimization (APSO). Validation against the classical PKN model confirms that the proposed forward model accurately reproduces the fundamental fracture propagation behavior, with good agreement in fracture half-length and net pressure evolution. In synthetic inversion cases, the method successfully recovers the number of fractures, the dynamic flow rate allocation history, fracture length evolution, and the spatiotemporal strain rate response. A field application further demonstrates that three dominant fractures were generated during stimulation, reaching the vicinity of the monitoring well at 18, 27, and 46 min with corresponding spacings of approximately 21 m and 16 m. The proposed framework provides a new route for advancing LF-DAS monitoring from qualitative interpretation to quantitative dynamic inversion. Full article

The impact of environmental disturbances and sensor deployment variations on damage identification represents a critical bottleneck that constrains the practical effectiveness of structural health monitoring. Existing methods addressing these challenges often suffer from poor interpretability due to information loss during feature extraction or exhibit insufficient sensitivity in identifying early-stage minor damage. This paper proposes a damage identification method based on the Shapelet Transform and Random Forest classifier, which extracts highly interpretable local shape features from vibration response signals to achieve robust identification of structural state changes. The study utilizes measured random vibration response data from a timber truss bridge. The dataset comprises four reference states collected on different dates and five damage states simulated by additional masses ranging from +23.5 g to +193.7 g, with sensors deployed in both vertical and horizontal directions. The Shapelet Transform selects local subsequences with high information gain from the original time series as features, which are subsequently classified using the Random Forest algorithm. The experimental design systematically investigates the influence of different damage severities, sensor locations, and environmental variations on method performance. The results demonstrate that with a Shapelet extraction time of 10 min, the method achieves 100% identification accuracy across multiple operating conditions comprehensively considering environmental variations, sensor location differences, and varying damage severities. When the extraction time is reduced to 5 min, 3 min, and 1 min, the average accuracies are 93.98%, 89.51%, and 58.48%, respectively. The method effectively identifies the minimum simulated damage (+23.5 g), which represents only 0.07% of the total structural mass, while maintaining stable performance under varying sensor locations and environmental conditions. Compared to traditional methods based on global frequency-domain features or statistical characteristics, the proposed method extracts physically meaningful local Shapelet features, offering significant advantages in interpretability. In contrast to deep learning approaches, this method demonstrates greater robustness under limited sample conditions. This study confirms that the combined framework of the Shapelet Transform and Random Forest can effectively address multiple real-world challenges in structural health monitoring, delivering high accuracy, strong robustness, and excellent interpretability, thereby providing a novel approach for developing practical real-time damage identification systems. Full article

Background: Obesity, type 2 diabetes mellitus, and hypertension are increasingly prevalent components of metabolic syndrome and major contributors to cardiovascular disease and chronic kidney disease progression; however, in end-stage kidney disease an “obesity paradox” has been described, with higher body mass index (BMI) sometimes associated with improved survival on hemodialysis. Material and methods: This retrospective, single-center Eastern European cohort study aimed to characterize mortality and its causes around hemodialysis initiation in the contemporary era of cardiometabolic prevention and to test whether the obesity paradox persists at this high-risk transition. Adult patients initiating dialysis at the “Pius Brânzeu” Emergency Clinical Hospital (Timișoara, Romania) between January 2022 and December 2025 (n = 268; median age 66 years; 61% male; median eGFR 6.4 mL/min/1.73 m²) were analyzed using Kaplan–Meier methods and Cox regression, with comprehensive baseline clinical, laboratory, echocardiographic, medication, infection, and vascular access data; follow-up was obtained at 3, 6, 12, 24, and 36 months. Results: Late referral was common (61% < 3 months of nephrology follow-up), dialysis initiation was predominantly urgent (only 16% scheduled), and central venous catheters were the main access (81%), with substantial comorbidity burden (cardiovascular disease 71%, hypertension 90%) and frequent infections at initiation. BMI categories were non-obese (<25 kg/m², 30%), overweight (25–29.9 kg/m², 48%), and obese (≥30 kg/m², 22%); diabetes prevalence rose with BMI (32% to 58%). Unadjusted mortality did not differ by BMI (19.8%, 18.8%, 15.3%; log-rank p = 0.622), yet multivariable Cox models showed overweight status independently reduced mortality (HR 0.22 at 3 months, 0.29 at 1 year, 0.31 at 3 years vs. non-obese), whereas obesity was not protective. Early mortality was driven mainly by age ≥ 65 years, while diabetes and chronic obstructive pulmonary disease predicted later mortality; longer pre-dialysis follow-up time was strongly protective (HR per year 0.70 at 3 years), and higher intact parathyroid hormone showed an inverse association with 1-year mortality. Conclusions: These findings show a modified obesity paradox at dialysis initiation in which moderate excess weight, but not obesity, is associated with improved adjusted survival, underscoring the clinical importance of earlier nephrology engagement and individualized nutritional and risk-factor management during the pre-dialysis and early dialysis periods. Full article

Micro-meteorological monitoring systems have been widely deployed in power grids, providing essential data to support the prevention and mitigation of ice- and wind-related disasters. However, understanding of the associated error mechanisms and quantitative evaluations under freezing rain and snow remains limited, particularly in complex field environments. This study presents a field-based quantitative assessment of two key variables, air temperature and wind speed, based on comparative observations collected over multiple winter icing cycles. We analyze the coupled effects of low temperature, ice accretion, and solar radiation on temperature measurements through multi-configuration sensor comparison, and characterize the dynamic response of cup anemometers under icing conditions using cross-correlation lag analysis. Results show that temperature error is dominated by sensor installation configuration and solar radiation. Under weak solar radiation, unshielded sensors tend to record lower temperatures than a standard Stevenson screen, but once radiation exceeds 200 W/m², they warm rapidly and exhibit maximum positive biases of ~8–10 °C. Ice accretion further induces a cold bias of ~1 °C and a response lag of 5–18 min, while suppressing the rapid warming driven by shortwave radiation. For wind measurements, cup anemometers show clear underestimation during ice accretion, with the error increasing nonlinearly with ice thickness to ~20% before freezing-induced failure occurs. These findings provide a basis for improved sensor deployment and interpretation of field monitoring data in cold, humid, and icing-prone environments, although the quantitative results are site-dependent. Full article

Background/Objectives: Blood glucose prediction (BGP) for individuals with type 1 diabetes (T1D) is a clinically essential yet highly challenging task in time series forecasting (TSF) and an important problem in personalised medicine. Accurate bespoke BGP is crucial for individualised T1D management, reducing complications, and supporting patient-specific glycaemic risk mitigation. However, the pronounced volatility of glycaemic fluctuations in T1D, combined with the need for mathematical rigor and clinical relevance, hampers reliable prediction. This complexity underscores the demand to explore and enhance more advanced techniques. While adversarial learning is adept at modelling intricate data variability, its potential for BGP remains largely untapped. Methods: This work presents a novel approach for BGP by addressing a key limitation in conventional adversarial learning when applied to this task. Typically, these methods optimise prediction accuracy within a set horizon by minimising adversarial loss. This focus overlooks how predictions align with longer-term patterns, which are critical for clinical relevance in BGP, thereby yielding suboptimal results. To overcome this limitation, we introduce collaborative augmented adversarial learning, designed to improve the model’s temporal awareness. Incorporating collaborative interaction optimisation, this approach enables the model to reflect extended time dependencies beyond the immediate horizon, thereby improving both the clinical reliability of predictions and overall predictive performance. We develop and evaluate four learning systems for BGP: independent learning, adversarial learning, collaborative learning, and adversarial collaborative learning. The proposed systems were evaluated for two clinically relevant prediction horizons, namely 30 min and 60 min ahead. Results: The interdependent collaboratively augmented learning frameworks, validated using the well-established Ohio T1D datasets, demonstrate statistically significant superior performance in both clinical and mathematical evaluations. Conclusions: Beyond advancing BGP accuracy and clinical reliability, the proposed approach supports personalised medicine by improving subject-specific glucose forecasting from CGM data, with potential relevance for more individualised diabetes monitoring and decision support. The proposed approach also opens new avenues for advancements in other complex TSF domains, as outlined in our future work. Full article

Background: The Global Positioning System (GPS) and wearable monitoring technologies are increasingly applied in sport science to quantify training load; however, data from female cross-country skiers in nations with emerging competitive programs remain scarce. This case series covering the complete national team roster analyzed the complete annual training cycle of the Korean women’s national cross-country skiing team (KCF) using GPS and heart rate-based wearable sensors. Methods: All three national team members were monitored throughout the 2022–2023 season (52 weeks), structured into General Preparation Period 1 (April–July), General Preparation Period 2 (August–November), and Competition Period (December–March). Individualized five-zone intensity thresholds were established through graded exercise testing on a roller ski treadmill with ventilatory threshold and blood lactate determination, independently assessed by two exercise physiologists (PhD level). Results: The total annual training volume was 667.72 h, comprising roller/on-snow skiing (54.0%), running (23.3%), and strength training (22.7%). The endurance-only intensity distribution demonstrated a polarized pattern (Zones 1–2: 91.5%). The total annual training distance reached 4673.30 km. The mean FIS points were 108.46 ± 38.60, and the mean VO₂max was 60.17 ± 6.11 mL·kg⁻¹·min⁻¹. Conclusions: When benchmarked against world-class female (WCF) standards (800–950 h annually), the overall training volume was approximately 18–30% lower. The relative strength training allocation (22.7%) exceeded typical WCF values (10–15%). These observations should be interpreted cautiously given the small sample size and cross-study comparison design, using published literature-based benchmarks. Full article

Controlled atmosphere (CA) is widely employed to preserve perishable foods, yet its potential effects on the quality of thermally processed bone broth remain poorly understood. This work systematically investigated the influences of ventilation time (0, 1, and 3 s), ventilation frequency (30, 60, 90, and 110 cycles), and cooking duration (25, 30, 38, and 45 min) on the overall quality of pork bone broth. A single-factor experimental design was adopted with three replications per treatment. Results showed that CA treatment effectively improved the sensory properties of pork bone broth, including color, aroma, and taste. Different CA processing parameters differentially affected the accumulation of diglycerides, proteins, peptides, amino acids and lipid oxidation-related flavor compounds, as well as antioxidant activities and emulsion stability. Specifically, prolonged ventilation promoted the accumulation of diglycerides and medium-sized peptides (1–7 kDa) but concurrently reduced solids, fat content, and ABTS radical scavenging activity, suggesting a trade-off between flavor precursor generation and oxidative stability. Furthermore, most quality indicators initially increased with rising ventilation frequency but subsequently declined at excessive levels, with optimal values attained at moderate frequencies. Notably, CA conditions that enhanced the formation of desirable flavor compounds also increased the accumulation of lipid oxidation byproducts, highlighting a critical balance required for achieving optimal product quality. Ultimately, it was found that a ventilation time of 1 s, a ventilation frequency of 60 cycles per minute, and a cooking duration of 30 min maximized the benefits of controlled atmosphere (CA) processing, thereby achieving optimal sensory properties, flavor profiles and nutritional composition in pork bone broth. This study provides fundamental data to support the development and quality regulation of thermally processed meat broths. Full article

Background: Geographic and demographic disparities strongly influence access to community-based healthcare, especially in rural and mountainous areas. In Italy, Ministerial Decree 77/2022 promotes a territorial reorganization based on networked care models, but practical tools for translating policy standards into operational catchment areas remain limited. Methods: We developed a transparent, data-driven allocation framework combining travel-time accessibility and population-based capacity constraints. A case study was conducted in the Province of L’Aquila, within Local Health Authority ASL 1 Avezzano–Sulmona–L’Aquila, a low-density mountainous area including 65 municipalities. Using official ISTAT data, including the 2021 national origin–destination road travel-time matrix, municipalities were allocated to 3 hub nodes and 8 spoke nodes. Population caps of 50,000 residents per hub and 40,000 per spoke were applied. Scenario analyses were performed under 20, 30, and 40 min travel-time thresholds. Results: Under the 30 min scenario, all municipalities were allocated, but the L’Aquila hub exceeded the capacity cap. A cap-compliant 30 min allocation eliminated this violation at the cost of longer upper-tail travel times. Under the 20 min scenario, only 54 municipalities were allocated, leaving 11 mountainous municipalities outside the threshold. Under the 40 min scenario, all municipalities were allocated without capacity violations. Conclusions: The proposed framework provides a reproducible approach for territorial healthcare planning and makes explicit the trade-off between accessibility and capacity compliance in hub-and-spoke network design, particularly in geographically complex mountain settings. Full article

Background: Childhood and adolescent overweight and obesity are major global public health concerns. High-intensity interval training (HIIT) has been increasingly investigated as a time-efficient intervention; however, evidence regarding its effects on multiple health-related outcomes and the influence of intervention characteristics remains inconsistent. Objective: The objective of this study was to evaluate the effects of HIIT on body composition, metabolic health, and cardiorespiratory fitness in children and adolescents with overweight or obesity. Methods: Systematic literature searches were conducted in PubMed, Web of Science, EBSCO, CNKI, Wanfang Data, and VIP databases. Randomized controlled trials were included. Risk of bias was assessed using the Cochrane Risk of Bias tool. Meta-analyses were conducted using random-effects models, and standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated. Heterogeneity, sensitivity analyses, publication bias, and certainty of evidence (GRADE) were also evaluated. Results: A total of 21 randomized controlled trials involving 652 participants (325 in the intervention groups and 327 in the control groups) were included. Compared with control conditions, HIIT significantly improved multiple outcomes related to body composition, metabolic health, and cardiorespiratory fitness, including BMI (SMD = −1.05), body fat percentage (SMD = −0.69), total cholesterol (SMD = −0.42), HOMA-IR (SMD = −1.00), and VO₂peak (SMD = 0.91), while no significant effect was observed on lean body mass. Subgroup analyses suggested that HIIT protocols with a load duration of less than 1 min were associated with greater improvements in several outcomes, particularly body fat percentage, total cholesterol, HOMA-IR, and VO₂peak. Conclusions: HIIT may improve body composition, metabolic health, and cardiorespiratory fitness in children and adolescents with overweight or obesity. However, the certainty of evidence varied across outcomes and was limited for some findings by heterogeneity, small sample sizes, and potential risk of bias. Further high-quality, large-scale randomized controlled trials with standardized HIIT protocols are needed to confirm these findings and clarify the influence of different training characteristics (e.g., exercise mode and interval structure). Full article

To design novel heart valve bioprostheses, it is extremely important to predict leaflet failure and fatigue for 10–20 years, as the aortic valve opens and closes approximately 40 million times per year. Most studies devoted to aortic valve leaflets mechanical tests employ uniaxial or biaxial tests, which do not fully and explicitly describe the time-dependent biomechanical behavior of this tissue. The aim of this study was to evaluate the viscoelastic response of porcine pericardium using biaxial tensile tests. Biaxial creep tests were performed on a biaxial test machine to evaluate the circumferential and axial behavior of the porcine pericardium under creep testing, and biaxial stress relaxation was used to complement creep. The results showed that the creep behavior was the same in both directions after 1 s, 60 s, 300 s, 900 s, and 1800 s. After 30 min of creep, deformation in the circumferential and radial directions was 3303 $\times {10}^{-6}$ and 5192.9 $\times {10}^{-6}$, respectively. Stress relaxation tests showed the same behavior as creep. At stress relaxation test after 30 min, the pericardium deformation in the circumferential and radial directions was 15.28 kPa and 9.6 kPa, respectively. The Prony series with Levenberg–Marquardt as the optimizer was used to obtain material parameters to use for finite element analysis. The data obtained during such tests can be employed in numerical FSI simulations of novel aortic valve bioprosthesis long-term performance in a patient’s body. Full article

Background and Objectives: Laparoscopic procedures have become a routine approach in colorectal surgery. We aimed to evaluate intraoperative, postoperative and pathological outcomes of laparoscopic right hemicolectomy in comparison with open right hemicolectomy. Materials and Methods: We reviewed our database for colorectal surgery and collected data regarding right hemicolectomies performed over a period of 10 years regarding patient characteristics, operative outcomes and postoperative outcomes. We compared laparoscopic with open right hemicolectomies. All the anastomoses in the laparoscopic group were performed intracorporeally. Results: We included 384 cases, 74 (19.3%) laparoscopic and 310 (80.7%) open right hemicolectomies. Baseline characteristics were comparable between the two groups. Conversion rate was low (2.7%). A drain was placed more often in the open colectomies (p < 0.001). Laparoscopic colectomies lasted longer by 25 min on average in the entire cohort (p = 0.002) and by 30 min in cancer-only cases without concomitant procedures (p < 0.001). Laparoscopic procedures yielded more lymph nodes (p = 0.007), as well as longer distal resection margins (p < 0.001) and total specimen (p < 0.001). There was no difference between the two approaches concerning intraoperative complications (p = 0.36) or need for transfusion (p = 0.708). There was also no difference regarding overall (p = 0.361) or major complications (p = 1), as well as anastomotic leak (p = 0.475), surgical site infections (p = 0.275) or readmission rates (p = 1). Hospitalisation duration was shorter by 3 days after laparoscopic surgery in the entire cohort (p < 0.001), as well as when cancer-only cases without concomitant procedures were considered (p < 0.001). Conclusions: Laparoscopic right hemicolectomy with intracorporeal anastomosis provides perioperative safety and pathology outcomes comparable to open surgery, while significantly reducing hospital stay. Full article

Utility-scale photovoltaic (PV) plants rely on supervisory control and data acquisition (SCADA) streams for performance verification, yet high-frequency measurements are routinely affected by sensor dropouts, intermittency, and operating-state transitions that bias regression-based capacity estimates. This study evaluates a reproducible SCADA processing workflow for capacity-style reporting and a complementary soiling–clean temperature prediction model using data from a documented October 2022 test window (5 s SCADA aggregated to 1 min). The following three filtering approaches are compared: (i) naïve thresholds (Baseline A), (ii) deterministic stability screening using ramp-rate and rolling-variability constraints (Baseline B), and (iii) an optional residual-based outlier trimming step (Method C). Capacity is estimated via a multivariate regression evaluated on a fixed-size reporting-condition subset (RC197) with day-coverage constraints. All methods achieved high fit quality on RC197 ($R^2\approx 0.99$), with Baseline B improving error and uncertainty over Baseline A (RMSE 2.05 vs. 2.18 MW; $U_{95}$ 0.97% vs. 1.03%) while preserving day coverage; Method C yielded the lowest in-sample RMSE (1.89 MW) but reduced day coverage. For temperature prediction, a baseline-plus-residual learning formulation substantially improved leave-one-day-out performance, reducing MAE/RMSE from 2.99/3.76 °C to 1.43/1.80 °C and increasing $R^2$ from 0.60 to 0.91. The results highlight trade-offs between fit tightness and representativeness in capacity-style filtering and demonstrate residual learning is an effective approach for SCADA-based thermal characterization. Full article

Background/Objectives: This study examined the concurrent validity and test–retest reliability of the novel virtual reality-based Stroop test (VRST), developed based on the principles of embodied cognition. The VRST simulates a clothing-sorting task to assess inhibitory control using cognitive and behavioral (kinematic) measures. Methods: A total of 224 cognitively healthy older adults (mean age = 71.51 years) completed the VRST and a traditional Stroop test in randomized order. The VRST implemented a fixed-difficulty design consisting of 30 incongruent trials, where participants were required to sort virtual objects by their semantic category while ignoring conflicting color cues. The task duration ranged from approximately 1 to 3 min. The VRST assessed task completion time, error count, 3D movement distance, and hesitation latency. Test–retest reliability was examined after two weeks. Concurrent validity was analyzed via Pearson correlation coefficients with traditional Stroop metrics. Test–retest reliability was assessed using intraclass correlation coefficients (ICCs). Results: VRST performance metrics showed significant correlations with traditional Stroop completion time: task completion time (r = 0.821; p < 0.001), movement distance (r = 0.801; p < 0.001), and hesitation latency (r = 0.784; p < 0.001), indicating good concurrent validity. No significant correlations were observed for error counts. Test–retest analysis showed high reliability for completion time (ICC > 0.9; p < 0.001), movement distance (ICC > 0.9; p < 0.001), and hesitation latency (ICC > 0.9; p < 0.001), but not for error count. These findings suggest that the VRST provides reliable and ecologically grounded behavioral indicators of inhibitory control. Conclusions: This preliminary study supports the VRST as a valid and reliable measure of inhibitory control in healthy older adults. By combining kinematic data with realistic task contexts, the VRST extends executive function assessment beyond traditional methods. Although limited to non-clinical populations, the findings suggest its utility for detecting subtle variations in executive functioning during healthy aging, warranting further investigation across broader cognitive profiles. Full article

Chryseobacterium indologenes MA9 is a causative agent of root rot disease in Panax notoginseng (P. notoginseng), with its high incidence being a major manifestation of continuous cropping barriers, severely hindering the sustainable development of the P. notoginseng industry. In this study, a novel lytic bacteriophage, MA9V-3, was isolated from wastewater, targeting C. indologenes MA9. The phage produced clear plaques, ranging from 1 to 3 mm in diameter, with a surrounding halo. Phage MA9V-3 achieved an adsorption rate of up to 80% after 30 min of contact with C. indologenes MA9, a latent period of approximately 40 min, and an average burst-size if 160 PFU/cell. Transmission electron microscopy revealed that phage MA9V-3 possesses an icosahedral head and a contractile tail, exhibiting a typical myovirus-like morphology. According to the latest ICTV taxonomy, MA9V-3 belongs to the class Caudoviricetes, and the phage’s biocontrol efficacy and inhibitory capacity were evaluated at different multiplicity of infection (MOI s). The results showed that the highest titer recorded at 1.6 × 10¹⁰ PFU/mL. Whole-genome sequencing revealed that MA9V-3 is a double-stranded circular DNA virus, with a genome length of 103,203 bp, GC content of 34.29%, and 150 open reading frames (ORFs), one of which is related to tRNA. Only 13 of these ORFs encode known functional sequences, likely due to the limited available gene data for such phages in the database, with additional details on hypothetical proteins yet to be uncovered. Comparative database analysis confirmed that the phage genome contains no antibiotic resistance or toxin-related genes. Phage therapy experiments were performed using MA9V-3 and two other phages screened in our laboratory. The experimental results showed that phage MA9V-3 may be a potential candidate for effectively controlling the infection of Panax notoginseng by C. indologenes MA9, and offering valuable insights into the potential application of phage therapy for managing bacterial plant diseases. Full article