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The development of fractional-order control has been derived from the mathematical generalization of classical calculus and has become an important tool in the modeling and control of dynamical systems with memory and hereditary effects. In spite of the rapid development of this area of control theory and applications, the overall scientific development, structure, and engineering relevance of fractional-order control remain insufficiently understood. In this paper, we address this problem by combining large-scale bibliometric analysis with representative controller performance studies. A total of 6482 publications indexed in the Web of Science database during the period 2010–2026 are analyzed. The bibliometric results indicate that fractional-order control is an increasingly connected global research field with strong roots in fractional calculus, advanced control theory, and growing interdisciplinary links with applied mathematics, automation, and computer science. To further illustrate controller level behavior, representative simulations are performed on a fractional-order time-delay process and an uncertain nonlinear system. For the fractional-order time-delay process, a well-tuned PID controller is compared with a realizable FOPID controller implemented through Oustaloup recursive approximation. The results show that the FOPID controller improves several performance measures, including overshoot, settling time, control energy, total variation, and sensitivity peak, while the comparison is interpreted as a performance trade-off rather than universal superiority. For the uncertain nonlinear system, fractional-order sliding mode control produces smoother control action and substantially reduces chattering. By combining bibliometric mapping with representative performance evaluation, this paper provides a comprehensive overview of fractional-order control as a globally active and practically relevant discipline in control engineering. Full article

In this paper, we investigate refined level sets associated with branching random walks on the boundary of a supercritical Galton–Watson tree. More precisely, for a prescribed deterministic sequence $\mathbf{s}:=(r_n,{\tilde{r}}_n)$, we introduce refined deviation sets, denoted by $E_{\mathbf{s}}(\alpha ,\beta )$, consisting of boundary points for which the deviations $S_n\mathsf{X}\left(t\right)-\alpha S_n\tilde{\mathsf{X}}\left(t\right)$ and $S_n\mathsf{Y}\left(t\right)-\beta S_n\tilde{\mathsf{Y}}\left(t\right)$ are asymptotically equivalent to $r_n$ and ${\tilde{r}}_n$, respectively, as $n\to \infty$. This setting extends the classical law-of-large-numbers level sets by allowing for controlled deviations around the typical linear behavior of the branching random walks. By constructing suitable inhomogeneous Mandelbrot measures, we establish sufficient conditions under which the sets $E_{\mathbf{s}}(\alpha ,\beta )$ have maximal Hausdorff and packing dimensions. Full article

The sealing reliability of hydrogen-cooled generator systems in nuclear power plants is directly related to unit safety and outage critical-path optimization. Conventional airtightness pressure-holding tests usually use the 24 h leakage result as the acceptance criterion, but this occupies a long maintenance window. Early pressure and temperature signals are affected by thermal equilibration, environmental disturbances, and gas–oil coupling, making direct early assessment difficult. Based on historical pressure-holding test data from multiple nuclear power plants, this study develops a short-duration auxiliary assessment method. Test records from different plants are converted into a unified equivalent leakage rate, and a standardized dataset is established. A multi-branch framework is then developed, including leakage-trend prediction, local fluctuation identification, and feature-space validation. A conservative review strategy is introduced to support safety-oriented field decision making. The validation results show that the first 12 h monitoring data can support assessment of the 24 h leakage state. No false negatives were observed within the limited validation set. Samples with inconsistent outputs, near-threshold predictions, or abnormal feature-space locations are recommended for extended pressure holding and further review. Full article

Colloidal gold dietary supplements intended for oral consumption are increasingly marketed as nano-enabled products, yet their physicochemical characteristics and biological effects remain insufficiently documented. In this study, commercially available colloidal gold supplements produced and marketed in Romania (30, 55, and 110 mg/L) were investigated to determine their classification as nanomaterials and to assess their preliminary biological effects in the context of oral exposure. Transmission electron microscopy revealed a narrow particle size distribution (4–11 nm), while SAED and EDX confirmed the presence of metallic gold nanoparticles. UV-VIS spectroscopy showed the characteristic surface plasmon resonance, supported by comparison with citrate-stabilized reference AuNPs (5–20 nm). DLS and zeta potential measurements indicated stable electrostatically stabilized colloids. According to the current EU definition, the number-based size distribution supports classification as nanomaterials. Manufacturer-recommended daily intakes were compared with doses reported in the literature using HED conversion to contextualize oral exposure. In vitro assays showed no pronounced acute cytotoxic or antitumoral effects on HCT-8 cells and no inhibitory effects on selected LAB. However, increased cytotoxicity was observed in HEK293 cells exposed to the dietary supplement formulation compared with the corresponding standard AuNP formulation. These results underscore the importance of considering cell-specific responses when evaluating the safety of nano-enabled dietary supplements and support the need for long-term toxicological studies. Full article

A novel distributed-order Cattaneo–Christov model is proposed to effectively characterize non-classical heat conduction processes with memory effect and time–space relaxation behaviors originating from distributed-order fractional derivatives. A fractional physics-informed neural networks (fPINN) algorithm is employed to address both the forward and inverse problems of the distributed-order heat conduction model. For the forward problem, we propose an SfPINN algorithm that incorporates a squared loss term and employs an adaptive updating strategy for the loss-term weights. First, the boundary conditions are embedded into the network output such that they are automatically satisfied. In addition, we design a two-stage training strategy to enhance computational efficiency: in the first stage, the squared loss term associated with the initial condition is incorporated into the loss function; in the second stage, the squared residual term of the governing equation is introduced into the loss function. Numerical results show that the proposed algorithm outperforms the standard fPINN method in both solution accuracy and training iteration speed. For the inverse problem, the numerical results demonstrate that as the iteration number increases, the estimated parameter values progressively converge to their true values and finally stabilize. Full article

Bioanalytical methods to quantitate conjugated payloads are essential for assessing antibody-drug conjugate (ADC) stability and pharmacokinetics (PK). Dual-payload ADCs present analytical challenges; different linker chemistries can require complex digestion conditions to perform the cleavage. Developing separate methods for each linker combination can be time and resource demanding. Rat tritosomes—purified lysosomal fractions from Triton-treated rat liver—provide a comprehensive enzymatic mixture that mimics the lysosomal environment. The presented bioanalytical method combines immunoaffinity purification with tritosome-mediated digestion for simultaneous quantitation of dual-conjugated payloads. The method was applied to a model dual-payload ADC containing two different cytotoxic payloads, conjugated using different enzymatically cleavable linkers, with an unrelated DAR (drug-to-antibody ratio). Method validation in mouse plasma demonstrated excellent accuracy (bias ± 20%, LLOQ and ULOQ ± 25%) and precision (coefficient of variation CV% ≤ 20%, LLOQ and ULOQ ± 25%) across all concentration levels (lower to upper limit of quantitation, LLOQ to ULOQ) for both payloads, with 100% of quality control samples (QCs) meeting acceptance criteria for hybrid LC-MS/MS quantitation methods. This tritosome-based approach provides a unified, efficient platform for multi-payload ADC bioanalysis, eliminates linker-specific method optimization, and enables robust support for preclinical studies. The method has been tested for accuracy and precision on 4 different model ADCs and employed to quantify the conjugated payloads in in vivo samples from a homozygous hFcRn transgenic mouse model (Tg32) PK study, resulting in reliable data in accordance with total antibody measurements. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) and male hypogonadism (HG) are prevalent disorders that frequently coexist, suggesting a bidirectional “liver–testis axis” as a potential pathophysiological link. This review explores the mechanistic basis and clinical implications of this axis. Molecularly, metabolically stressed hepatocytes release an altered hepatokine signature—marked by reduced sex hormone-binding globulin (SHBG) and elevated fibroblast growth factor 21 (FGF21)—along with pro-inflammatory cytokines (e.g., interleukin-1 beta (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α)), which enter the systemic circulation. These factors may contribute to the impairment of Leydig cell steroidogenesis, the perturbation of blood–testis barrier integrity, and the disruption of spermatogenesis. Conversely, testicular dysfunction and subsequent testosterone deficiency promote visceral adiposity, worsen insulin resistance and amplify chronic inflammation, thereby accelerating hepatic steatosis and fibrosis. Clinically, these molecular interactions manifest as mutually worsening of MASLD and HG. Thus, the liver–testis axis establishes a framework that reveals the bidirectional crosstalk between hepatic metabolism and gonadal function, providing novel pathophysiological insights into these interconnected conditions. 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

Reliable and rapid forecasting of wind turbine output power is vital for operators, particularly day-ahead and intraday market scheduling and reserve allocation. However, the inherent unpredictability, intermittency, and volatility of wind turbine output make forecasting processes difficult. To address this challenge, this study proposes a Sinusoidal Representation Network (SIREN)-based forecasting model for high-accuracy, rapid direct multi-horizon forecasting of wind turbine output power. SIREN is selected due to the periodic and symmetrical mathematical structure of its sinusoidal activation function, which allows the model to represent both low-frequency trends and high-frequency sudden changes in wind energy data. To improve data quality, compensate for asymmetric fluctuations in wind data, and provide more suitable inputs for SIREN training. Several preprocessing steps are utilized before feeding the data into the model. The proposed preprocessing step includes a moving median filter, robust scaling based on median and interquartile range, Winsorizing clipping, and a Hampel filter to reduce the effects of instantaneous noise, outliers, and local peaks without disrupting temporal continuity. Subsequently, a Savitzky–Golay smoothing is applied to attenuate high-frequency measurement noise while preserving curvature, local peaks, and physically meaningful short-term dynamics in the data. The sliding-window approach is used to formulate the multi-horizon forecasting problem directly, and a direct h-step-ahead forecasting architecture is designed, preserving structural symmetry in the time series. The SIREN is trained and tested using MATLAB with the help of two different datasets: Dataset-1 has a 10 min resolution for 1 year, and Dataset-2 has a 1 h resolution for 15 years. The forecast horizon parameter h is considered separately for each step, and the proposed SIREN is independently trained, validated, and tested for each target horizon while maintaining chronological order. The results demonstrate that the proposed model is able to yield high forecast performance for a wide spectrum of horizons ranging from 10 min to 15 days. The accuracy of the proposed model for Dataset-1 is R² of 99.6%, MSE of 0.085%, MAE of 1.7%, and MAPE of 12%, while for Dataset-2, the accuracy is R² of 98.8%, MSE of 0.3%, MAE of 3.6%, and MAPE of 23%. Ablation and sensitivity analyses are conducted to evaluate the impact of the basic components used in the proposed model on forecasting performance. In addition, combative experiments are performed using traditional time series, ML, and DL forecasting techniques to better assess the contribution of the model. The obtained results show that the SIREN-based direct forecasting approach provides strong learning capability, as well as high forecasting accuracy, for both high-resolution and low-resolution wind power data. Overall, its ability to capture the symmetric and periodic characteristics inherent in wind turbine power data makes it a promising alternative for multi-horizon wind power forecasting applications. 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

Background/Objectives: People with diabetes are more susceptible to viral respiratory infections and worse clinical outcomes related to COVID-19 and influenza. Vaccination is considered an important prevention strategy. This study aimed to analyze the vaccination status against COVID-19 and influenza among people with diabetes mellitus and associated factors. Methods: An analytical cross-sectional study was conducted between May 2024 and May 2025 in 42 Primary Health Care Units in a municipality in Minas Gerais, Brazil. A total of 316 individuals with type 1 or type 2 diabetes mellitus participated in the study. Data were collected using a structured instrument containing socioeconomic, cultural, behavioral, and clinical variables, in addition to verification of vaccination records through physical vaccination cards and information systems. Descriptive analyses and logistic regression models were performed to estimate crude and adjusted odds ratios, with respective 95% confidence intervals. Analyses were performed using Statistical Package for the Social Sciences and Stata. Results: Adherence to COVID-19 vaccination was 21.5%, whereas influenza vaccination adherence reached 85.4%. In the multivariable analysis of COVID-19 vaccination status, previous influenza vaccination (OR = 7.74; 95% CI: 1.81–33.2) and alcohol consumption (OR = 2.11; 95% CI: 1.13–3.89) were positively associated with vaccination. Conversely, access to social media or other communication channels (OR = 0.47; 95% CI: 0.24–0.92) and insulin use (OR = 0.42; 95% CI: 0.21–0.84) were associated with lower odds of COVID-19 vaccination. Regarding influenza vaccination, positive associations were identified for religious affiliation (OR = 6.46; 95% CI: 1.79–23.30), previous COVID-19 vaccination (OR = 10.2; 95% CI: 2.22–47.06), and longer duration of diabetes diagnosis (OR = 3.47; 95% CI: 1.32–9.20). In contrast, alcohol consumption (OR = 0.42; 95% CI: 0.21–0.86), insulin use (OR = 0.35; 95% CI: 0.16–0.76), and absence of medical follow-up (OR = 0.34; 95% CI: 0.13–0.85) were associated with lower odds of influenza vaccination. Conclusions: The findings revealed a heterogeneous vaccination pattern among individuals with diabetes mellitus, in which higher influenza vaccination coverage contrasted with low adherence to COVID-19 vaccination, reflecting not only differences in the historical consolidation of immunization strategies but also contemporary dynamics related to risk perception, trust, and information circulation. The strong association with previous vaccination history suggests that vaccine adherence is part of a continuum of preventive behaviors mediated by the relationship with healthcare services and by the internalization of healthcare practices over time. Full article

Fascia is increasingly recognized as a dynamic functional system. It can actively sense, transmit, and regulate mechanical, sensory, and metabolic signals. Why does fascia play such a critical role in chronic pain and movement disorders? Researchers are now rethinking the pathophysiological mechanisms underlying this role. Previous systematic reviews have typically focused primarily on specific mechanisms or interventions. In contrast, this study takes a holistic view of fascial function. It integrates multiple physiological functions of the fascia: mechanical integration, sensory modulation, cellular and matrix remodeling, as well as metabolic and immune regulation. From the perspective of functional imbalance, we further explore the pathological mechanisms associated with the fascia. Building on this, we then focus on how to assess fascial function from multiple dimensions and on specific targeted interventions. For assessment, we have systematically compiled a set of multi-stage quantitative techniques. These include clinical palpation, ultrasound, and elastography, tissue mechanics testing, microdialysis, omics approaches, electrophysiological testing, and digital modeling. For interventions, we have listed a range of modulating approaches, such as manual therapy, exercise rehabilitation, dry needling and acupuncture, fascial injections, targeted drugs, and biotechnological materials derived from tissue engineering. This review summarizes a clinical decision-making framework guided by the assessment of fascial functional status. It emphasizes a systematic approach and links quantitative diagnosis with precise interventions. Additionally, it provides a literature synthesis for understanding fascial mechanisms and related disorders and offers a reference foundation for the field’s transition from empirical treatment to measurable, reproducible, and individualized practice. Full article

In recent decades, humanoids have become more popular in various applications. However, their applications in human life are more than those in industry. In this paper, a humanoid is used to capture the sets of point clouds of an object for three-dimensional reconstruction. The structured light camera is widely used across diverse 3D scanning applications due to its high resolution, rapid acquisition capability, and adaptability to various material surfaces. Therefore, the humanoid developed by our team holds a structured light camera which captures the point clouds of an object put on a platform for the reconstruction of its 3D digital model. The platform is rotated so that the structured light camera can capture the image of all view angles on the object. Meanwhile, the structured light camera captures point clouds, and the camera of the humanoid recognizes the QR code on the platform so that the sets of point clouds can be distinguished by view angles on the object. Then, the automated registration process of the point cloud sets for a 3D model based on the point-to-plane iterative closest point (ICP) algorithm is proposed. The process incorporates preprocessing techniques, such as downsampling and normal vector estimated from plane, and utilizes the ICP algorithm for registration, ultimately achieving markerless and precision automatic merging of multi-view point cloud data. Experimental results demonstrate that the proposed method with the humanoid can effectively improve the completeness and accuracy of 3D reconstruction models, significantly reduce manual intervention, and enhance the system’s versatility and practical feasibility. Key parameters adjusted for more efficient computation of the ICP algorithm are revealed. In addition, the experimental results of the proposed ICP compared with G-ICP are also included. Full article