Search Results (308)

Background/Objections: One of the most common surgical procedures performed internationally is the cesarean section. It is known to be associated with intense postoperative pain and a slow recovery process. Focusing on surgical techniques, especially the type of fascial closure, is an area that has received very little attention when it comes to postoperative pain and rapid recovery. Using a mixed-methods approach, the primary objective of this study was to assess the impact of guide-suture-assisted modified fascial closure on postoperative pain and early mobilization after cesarean sections. Methods: Women undergoing elective cesarean sections with Pfannenstiel’s incision were the study participants of this prospective, single-center, randomized mixed-methods study. Participants were enrolled in the study and randomized to either classical continuous fascial closure or guide-suture-assisted modified fascial closure, which was carried out in a 1:1 ratio. Quantitative data assessed postoperative pain through the Visual Analog Scale (VAS), a Numeric Rating Scale (NRS), and the Short-Form McGill Pain Questionnaire (SF-MPQ), and functional recovery was assessed through walking distances at postoperative 6, 12, 24, and 48 h. Qualitative data were collected via semi-structured interviews and analyzed through conventional content analysis to understand the patients’ perceptions of pain and recovery experiences. Results: The first 24 h postoperative period pain levels were significantly lower for the modified fascial closure group versus the classical closure group (p < 0.05). Moreover, the modified closure group had a significantly better functional recovery, evidenced by walking greater distances at 12, 24, and 48 h postoperative. Qualitative results indicated improved comfort and stronger early mobilization confidence, in addition to less movement apprehension, consistent with the above results, among those with the modified technique. Conclusions: The modified fascial closure technique with guide suture was linked to less pain in the early postoperative period and better functional recovery after cesarean section. This technique is a good candidate for addition to standard obstetric procedures since it is cost effective, easily added, and surgical practice will improve comfort for mothers and assist with early mobilization. Full article

Radar plays a pivotal role throughout the entire Counter-Unmanned Aerial Vehicle (C-UAV) process, and there is an urgent need for radar technologies capable of effectively detecting and recognizing non-cooperative Unmanned Aerial Vehicles (UAVs). However, the commonly emphasized UAV True Positive Ratio (TPR) fails to adequately reflect radar performance in environments with high bird density. Frequent bird activity leads to numerous false UAV alarms and unreliable recognition results. To address this issue, this paper introduces the concept of UAV Recognition Confidence (URC), a comprehensive metric that quantifies the credibility of UAV recognition by jointly considering recognition performance indicators and environmental factors. Simulations and field measurements employ a bird random walk model and real-time trajectory statistics to represent the dynamic population variations of birds. Both simulation and X-band radar experimental results verify that the proposed URC framework can effectively characterize the recognition capability of radar systems by capturing the complex interactions between the UAV and surrounding avian activities. Full article

Branching processes are stochastic models describing the evolution of populations in which individuals reproduce and die independently over time. In the classical setting, an individual’s reproductive capacity is fixed throughout its lifetime. However, in real-world situations, fertility typically rises during a juvenile phase, peaks at maturity, and subsequently declines. In order to capture this feature, we introduce a branching random walk with ageing, as an extension of the classical branching random walk, by assigning each individual an age-dependent reproductive rate. Our model differs from classical age-dependent processes such as the Bellman–Harris model, where the remaining lifespan depends on age, while the rate of reproduction is fixed within that lifetime. As in the classical case, branching random walks with ageing are parametrised by $\lambda >0$, which tunes the reproductive speed and may be seen as a characteristic of the population. The thresholds of $\lambda$ separating extinction and survival are the global and local critical parameters. We characterise the value of the local critical parameter and provide a lower bound for the global critical parameter. We identify a class of ageing branching random walks for which this lower bound coincides with the global critical parameter. We study how local modifications to the reproduction and ageing rates may change the critical parameters. This is of practical interest: in species preservation, one may want to lower the critical parameters, so that $\lambda$ exceeds them, and there is a positive probability of survival. On the other hand, in epidemic control, the goal is to increase the critical parameters, since if $\lambda$ is below them, then the epidemic is eventually going to disappear. We compute the expected number of individuals alive in a branching process with ageing and show that, contrary to the behaviour of classical branching processes, it may exhibit an initial growth even when the population is ultimately destined for extinction. Full article

In this paper, we construct a probabilistic model of a sliding mode. This model is based on the moment a random walk with positive jumps crosses a certain critical level. It is assumed that the jump magnitude has a geometric distribution. If the initial state is negative and the critical level is zero, then after crossing this level, a random walk begins in the opposite direction until it crosses zero again. As a result, motion orthogonal to the slip line is defined as a regenerative process, in which the moments of regeneration are the moments of zero crossings from right to left. An estimate of the Qi Fan metric of the maximum deviation of this random walk over a certain time interval is constructed under the assumption that the time and magnitude of the jumps are reduced by a factor of m. This estimate is found to be of the order of $lnm/m$ as $m\to \infty$ and characterizes the deviation of a random trajectory orthogonal to the slip line. In the model of motion along a slip line, its velocity is assumed to have fixed values when the trajectory of motion orthogonal to the slip line is above or below zero. Using the central limit theorem for the integral of a regenerative process, an estimate of the non-uniformity of motion of a random trajectory along the slip line is constructed. It is found that the characteristic magnitude of this non-uniformity is of the order of $1/m$ as $m\to \infty$. This indicates that the accumulation of random errors during motion along the slip line is significantly faster than during motion orthogonal to the slip line. Full article

It is necessary to understand the complicated interplay between diseases and medicinal plants to find new curing agents that may be used in natural sources. Nevertheless, the state of interaction between diseases and plants today is not fully developed yet, and the potentially productive plant-based treatment can hardly be identified rationally. In order to elaborate on this challenge, we will offer a heterogeneous network approach to the prediction of novel disease–plant associations by using the Random Walk with Restart (RWR) algorithm. The framework combines three significant relational networks, including (i) a disease–plant association network, which has been built using curated literature and biological databases, (ii) a disease–disease similarity net, which is constructed using shared symptoms and therapeutic profiles, and (iii) a plant–plant similarity net using phytochemical and functional similarities. These elements are integrated into a homogeneous graph that is heterogeneous in nature, and thus, information flows through related nodes. The model begins by finding RWR between known disease or plant nodes and develops the network by exploring the graph further to make estimates of the probability of association between disease and plant networks that were not previously connected. Experimental tests show that the proposed model has an excellent predictive ability, ROC-AUC of 0.9987, PR-AUC equal to 0.915, and Precision = 10 of 1.0, significantly better than the results of the base models, including Random- and Degree-based models. The bootstrap analysis supported the strength of the model as the mean ROC-AUC was 0.9987 with a standard deviation of 0.00051. The suggested structure offers an effective computational methodology to systematically explore disease–plant interactions to aid in finding novel herbal drugs to treat diseases and speed up the drug discovery process by means of inference based on networks. Full article

The COVID-19 pandemic constitutes one of the most significant exogenous shocks to global financial markets in recent history, raising questions about the robustness of market efficiency under extreme uncertainty. This study examines whether the pandemic affected the weak-form efficiency of the Bitcoin market or merely heightened volatility without introducing return predictability. Using daily Bitcoin log returns from January 2013 to February 2026, the analysis first evaluates weak-form market efficiency through the Variance Ratio (VR) test. The VR statistics remain close to unity across multiple holding horizons, and the null hypothesis of a random walk cannot be rejected, indicating that daily Bitcoin returns are consistent with weak-form efficiency. Building on this baseline, an Interrupted Time Series (ITS) framework is employed to assess whether the onset of the COVID-19 pandemic in March 2020 led to structural changes in Bitcoin return dynamics. The ITS results reveal no statistically significant changes in level or slope following the outbreak. To further account for autoregressive and moving-average dynamics while explicitly modelling the intervention, an ARIMAX (0, 0, 7) model with COVID-19 intervention variables is estimated. Both the pandemic dummy and its interaction term are statistically insignificant, indicating no material change in the return-generating process after controlling for serial dependence. The moving-average structure indicates that shocks dissipate over approximately one trading week, consistent with weekly trading cycles and liquidity patterns in cryptocurrency markets rather than persistent return predictability. Diagnostic checks, including the Ljung–Box and Shapiro–Wilk tests, confirm the absence of residual autocorrelation and support the model’s white-noise properties. Although volatility increased during the pandemic period, daily Bitcoin returns continued to align with weak-form market efficiency. The evidence, therefore, suggests that COVID-19 served as a stressor without generating persistent inefficiencies. These findings reinforce the distinction between volatility and predictability, demonstrating that heightened uncertainty does not necessarily undermine informational efficiency. Full article

Hepatic cirrhosis is a progressive chronic liver disease driven by sustained inflammation, cell death, and tissue remodeling, and effective disease-modifying options remain limited. Here, we applied a multiscale interactome framework to prioritize candidate herbs and active compounds for hepatic cirrhosis. Herb–compound associations were collected from the OASIS database and mapped to experimentally supported compound–target interactions (DrugBank/TTD/STITCH), while cirrhosis-related proteins were curated from DisGeNET. Using a biased random-walk algorithm, we generated disease and herb/compound diffusion profiles on the multiscale network and ranked candidates by profile similarity and target overlap. Among the top-ranked herbs, Magnoliae Cortex, Notoginseng Radix et Rhizoma, Polygoni Cuspidati Rhizoma et Radix, and Capsici Fructus were supported by prior literature, whereas several high-ranking herbs lacked curated evidence and were highlighted as underexplored candidates, including Saposhnikoviae Radix and Fritillariae Cirrhosae Bulbus. Enrichment analyses indicated convergence on inflammatory and innate-immune pathways (TNF, Toll-like receptor, NF-κB) and apoptosis-related processes, with additional signals involving HIF-1 and PI3K–Akt pathways. Disease-focused subnetworks suggested mechanistic hypotheses for evidence-lacking compounds, including bergapten, oleic acid, and octadecanoic acid. Overall, we systematically prioritize herbal candidates and provides a mechanistic basis for follow-up validation in hepatic cirrhosis. Full article

This work presents a high-performance piezoelectric MEMS yaw gyroscope fabricated on a single-crystal silicon platform, which achieves a quality factor of 75 k—the highest reported to date among silicon-based piezoelectric gyroscopes. The device employs a wide annular resonator that operates at 132 kHz in the in-plane wineglass mode. To maximize transduction efficiency, we develop an analytical model that relates output charge to the area-integrated in-plane stress under modal deformation, and we use this model to guide parametric optimization of the annular width. The resulting geometry simultaneously enhances the mechanical quality factor and the piezoelectric coupling. A back-etching fabrication process is used to eliminate front-side release holes, thereby preserving structural continuity and suppressing thermoelastic damping. In open-loop rate mode operation with a native frequency split of 28 Hz, the gyroscope demonstrates an angle random walk of 0.34°/√h and a bias instability of 8.19°/h. These performance metrics are comparable to those of state-of-the-art lead zirconate titanate (PZT)-based annular gyroscopes, while the use of lead-free aluminum nitride as the transduction material ensures compliance with RoHS environmental regulations. Full article

This article presents PrevOccupAI-HAR, a new publicly available dataset designed to advance smartphone-based human activity recognition (HAR) in office environments. PrevOccupAI-HAR comprises two sub-datasets: (1) a model development dataset collected under controlled conditions, featuring 20 subjects performing nine sub-activities associated to three main activity classes (sitting, standing, and walking), and (2) a real-world dataset captured in an unconstrained office setting captured from 13 subjects carrying out their daily office work for six hours continuously. Three machine learning models—namely, k-nearest neighbors (KNN), support vector machine (SVM), and Random Forest (RF)—were trained on the model development dataset to classify the three main classes independently of sub-activity variation. The KNN, SVM, and RF models achieved accuracies of 90.94%, 92.33%, and 93.02%, respectively, on the development dataset. When deployed on the real-world dataset, the models attained mean accuracies of 69.32%, 79.43%, and 77.81%, reflecting performance degradations between 21.62% and 12.90%. Analysis of sequential predictions revealed frequent short-duration misclassifications, predominantly between sitting and standing, resulting in unstable model outputs. The findings highlight key challenges in transitioning HAR models from controlled to real-world contexts and point to future research directions involving temporal deep learning architectures or post-processing methods to enhance prediction consistency. Full article

Some open problems regarding fractional powers of the negative generator of a discrete-time random walk and a Markov process are discussed. The suggested approach combines analytic and probabilistic ideas and may be useful for developing fractional operators with multidimensional and/or abstract discrete arguments. Full article

The output of MEMS gyroscopes is highly vulnerable to ambient temperature variations, which induce temperature drift errors and degrade navigation precision. Consequently, temperature compensation for MEMS gyroscope outputs is of critical importance. To address this issue, this study proposes a novel temperature compensation model for the dual-mass vibration MEMS gyroscope (DMVMG), which integrates the TTAO-VMD, 1D-CNN-Bi-GRU-Attention, and SHAKF algorithms. The implementation process of the proposed model is as follows: firstly, the structural configuration and fundamental operating principle of the DMVMG are elaborated. Secondly, the temperature error compensation model is constructed based on the fusion of the TTAO-VMD, 1D-CNN-Bi-GRU-Attention, and SHAKF algorithms. Thirdly, the raw output signal of the DMVMG is preprocessed using the TTAO-VMD algorithm, which decomposes the signal into four distinct components, namely high-frequency noise, white noise, mixed noise, and temperature-induced noise. Subsequently, the high-frequency and white noise components are eliminated, while the mixed noise component is filtered via the SHAKF algorithm. On this basis, the 1D-CNN-Bi-GRU-Attention algorithm is adopted to establish the temperature error compensation model, with the temperature, temperature change rate, time, and temperature-induced noise as input variables. Finally, the optimized signal components are reconstructed to yield the temperature-compensated output of the DMVMG. The experimental results based on the Allan variance method demonstrate that the angle random walk (N) is reduced from 18.56 $°/\sqrt{h}$ to 0.17 $°/\sqrt{h}$, and the bias instability (B) is decreased from 32.76 °/h to 0.82 °/h, verifying the effectiveness of the proposed method. Full article

The diffusion and trapping of particles in complex porous media are fundamental processes in materials science and bioengineering. This study systematically investigates the influence of pore radius on particle capture kinetics within a three-dimensional cylindrical pore containing randomly distributed absorbing traps. Numerical simulations were performed for a wide range of pore radii (from 3a to 81a, a is a minimal length of the problem, arbitrary unit) and trap concentrations M (from 100 to 5090, these numbers are determined by the pore geometry) using a random walk algorithm. The particle lifetime (τ), characterizing the capture rate, was calculated and analyzed. Results reveal three distinct capture regimes dependent on trap concentration: a diffusion-limited regime at low concentration M (<1000), a transition regime at medium M (1000 < M < 2000), and a trap-density-dominated saturation regime at high M (>2000). For each regime, optimal approximating functions for τ(M) were identified. Furthermore, empirical relationships between the approximating coefficients and the pore radius were derived, which enable the prediction of particle lifetimes. The findings demonstrate that while the pore radius significantly impacts capture kinetics at low trap densities, its influence diminishes as trap concentration increases, converging towards a universal behavior dominated by trap density. Full article

With the growing heterogeneity of public transport systems, accurate representation of passenger service processes at bus stops shared by multiple operators has become increasingly important. This study develops and validates a microscopic model of passenger boarding and alighting at bus stops characterized by unstructured service patterns, diverse vehicle fleets, and irregular stopping positions. The approach focuses on individual passenger movements, enabling modeling of walking times from different waiting positions and assessing how passenger distribution and bus stopping positions affect total dwell time. Variables describing the boarding and alighting process, including waiting position, vehicle stopping position, individual boarding and alighting times, and passenger walking speed, were modeled as random variables following theoretical distributions (beta, logistic, log-normal, and normal). Bayesian estimation and bootstrap methods were applied to assess parameter stability and model fit. Field studies were conducted in two Polish cities (Kraków and Kielce) at 18 high-interference bus stop locations. Results indicate that the proposed probabilistic modeling approach enhances the accuracy of passenger flow representation and supports analysis of the effects of passenger dispersion and bus stopping position on service efficiency. The developed model can be used in microsimulation of bus stop operations, transport infrastructure design, and decision-making by transport management authorities. Full article

The indoor low-voltage power line network is characterized by highly irregular interferences, where background noise coexists with bursty impulsive noise originating from household appliances and switching events. Traditional noise models, which are considered monofractal models, often fail to reproduce the clustering, intermittency, and long-range dependence seen in measurement data. In this paper, a Multifractal Random Walk (MRW) framework tailored for Power Line Communication (PLC) noise modelling is developed. MRW is a continuous time limit process based on discrete-time random walks with stochastic log-normal variance. As such, the formulated MRW framework introduces a stochastic volatility component that modulates Gaussian increments, thus generating heavy-tailed statistics and multifractal scaling laws which are consistent with the measured PLC noise data. Empirical validation is carried out through structure function analysis and covariance of log-amplitudes, both of which reveal scaling characteristics that align well with MRW-simulated predictions. This proposed model captures both the bursty nature and correlation structure of impulsive PLC noise more effectively as compared to the conventional monofractal approaches, thereby providing a mathematically grounded framework for accurate noise generation and the robust system-level performance evaluation of PLC networks. Full article

Background: There is a critical need for feasible, non-equipment based, safe, and cost-effective exercise interventions to promote muscle strength, dynamic postural balance, and independent mobility in adolescents with cerebral palsy (CP) or spina bifida (SB). Objectives: This study aimed to examine the feasibility and preliminary response of a novel exercise program: Functionally Loaded High-Intensity Circuit Training (FUNHIT) and conventional High-Intensity Circuit Training (HIT) in adolescents with CP/SB. Methods: Enrolled participants were allocated to FUNHIT or HIT or Controls in our randomized control trial. The interventions were delivered 2×/week × 4 weeks. Feasibility was assessed through process, operational, and scientific metrics. Outcome measures included maximum walking speed, Four Square Step Test (FSST), Timed Up and Go (TUG) and its dual-task variants, Lateral Step-Up Test (LSUT), Fear of Falling (FoF) and physical activity (PA) questionnaires. Results: We tested 5 participants (1 CP, 4 SB) in our study. Recruitment and retention rates were acceptable (63% enrollment, 100% retention and adherence). FUNHIT (n = 2) participants showed improvements in maximum walking speed (8–12%), FSST (15–29%), LSUT (22–33%), and TUG (4%). The HIT participant (n = 1) demonstrated improved TUG dual-task performance (40%) and FSST (30%) only. Control participants (n = 2) had varied changes (from 0–24%) in mobility, strength, balance. No adverse events were reported. Participants successfully followed (100%) the prescribed exercise dosage over the four-week period. Conclusions: FUNHIT and HIT are feasible and safe interventions for adolescents with ambulatory CP and SB who retain motor function, showing promising preliminary improvements in muscle strength, dynamic balance, and independent mobility. Our findings need to be validated in larger samples. Full article