Search Results (7,208)

Background/Objectives: Permanent colostomy requires significant physical and psychological adaptation. Patients often experience reduced physical activity, impaired quality of life, and fear of movement. Current exercise recommendations are inconsistent, and no consensus exists on safe return to activity. This study aims to evaluate the effect of a 12-week home-based graded exercise programme on physical activity, quality of life, kinesiophobia, exercise capacity, and self-efficacy in patients with permanent colostomies. Methods: This randomized controlled trial will recruit 51 adults with permanent colostomies, beginning six weeks post-surgery. Participants will be randomized (1:1) to an intervention or control group. The intervention group will receive a 12-week home-based exercise programme including patient education, resistance and core training, and progressive aerobic walking. The control group will receive standard medical care and an informational leaflet. Primary outcomes include physical activity (steps/day), quality of life (Stoma-QoL), kinesiophobia (Tampa Scale), exercise capacity (6-Minute Walk Test), and self-efficacy (General Self-Efficacy Questionnaire). Follow-up will be conducted at baseline, post-intervention, and six months. Data will be analyzed using intention-to-treat principles with a significance threshold of p < 0.05. Conclusions: This trial will be the first to assess the effects of a structured, home-based graded exercise programme in individuals with permanent colostomies. The findings are expected to provide evidence on the efficacy of exercise for improving physical and psychological outcomes in this population and to inform clinical guidelines for safe, individualized activity resumption. Full article

Forest ecosystems are vital for climate mitigation, yet predicting their carbon (C) sequestration remains challenging, especially in urban-proximal regions. This study investigates the C storage dynamics across five major forest types in the Chang-Zhu-Tan Green Heart, a critical ecological buffer zone in China’s Yangtze River Mid-Reach urban agglomeration. We integrated field measurements with structural equation and random forest modeling to analyze vertical C distribution and its drivers. The results revealed that over 90% of vegetation C was stored in the tree layer, with soil C highest in evergreen broad-leaved forests (41.26 Mg C/ha). Biological factors (i.e., tree volume and biomass) primarily drove vegetation C (52–73% of variance), while non-biological factors (soil properties and micronutrients) predominantly regulated soil C. We identified distinct age-related trajectories: J-shaped accumulation in broad-leaved forests versus S-shaped patterns in coniferous and mixed forests. These findings provide a mechanistic framework for forest-type-specific management strategies to enhance C sequestration in urban-agglomeration buffer zones. Full article

Reproductive performance in rabbits is highly sensitive to seasonal environmental variation and management practices, while the proportion of variance attributable to additive genetics for litter-level traits is typically low. The objective of this study was to evaluate the effects of year and season on litter size at birth (BR), litter size at weaning (WR), and weaning rate (WT), and to estimate the heritability of these traits in a commercial rabbit farm. A total of 770 kindling events recorded between 2015 and 2021 were analyzed. The mixed model for BR included the fixed effects of year and season, and the random effects of sire and residual error. The model for WR included the same structure, with BR added as a covariate. Least-squares means for fixed effects were used for pairwise comparisons using Tukey’s test. Year and season effects were significant for BR (p < 0.005), and the year effect was also significant for WR (p < 0.021). Litter size at birth ranged from 7.80 (dry season) to 9.21 (year 2020), with higher means observed during the semi-dry (8.52) and humid (8.56) seasons compared to the dry season (7.80). Litter size at weaning varied between 4.65 and 5.81 kits depending on the year. Weaning rate showed interannual variation (56.1–68.2%), but seasonal differences did not reach statistical significance (p < 0.075). Heritability estimates from the sire variance component were low: 0.01 for BR, 0.04 for WR, and 0.05 for WT. These results indicate that phenotypic variation in prolificacy in this population was predominantly driven by interannual and seasonal environmental factors, as well as perinatal management practices, while the additive genetic contribution was marginal. Full article

Background/Objectives: Since ¹⁷⁷Lu-DOTATATE was approved for patients with somatostatin receptor (SSTR)-positive gastroenteropancreatic neuroendocrine tumors (NETs), tumor flare reactions including increased pain and small bowel obstruction (SBO) have been reported. Retrospective reviews report some success in using corticosteroids for treatment and prophylaxis of tumor flare reactions from ¹⁷⁷Lu-DOTATATE. Given that corticosteroids are used in practice to help prevent tumor flare reactions based on limited evidence, we aimed to assess if this practice was efficacious in our patient population. Methods: In this retrospective and single-institution study, we identified adult patients with NETs who were treated with ¹⁷⁷Lu-DOTATATE between 1 October 2019 and 31 December 2024; these patients received corticosteroids as prophylaxis for flare reactions due to high burden of disease, significant peritoneal or mesenteric disease, or disease involvement of critical structures as determined by the treating provider. Variables including demographics, diagnosis, treatment history, steroid dosing, and outcomes were collected within a RedCAP database. Results: Forty-six patients were identified as having received corticosteroid prophylaxis to prevent a tumor flare reaction due to ¹⁷⁷Lu-DOTATATE. Patients had a median age of 66, and 50% were female. The primary disease site was the small intestine (72%) followed by the pancreas (9%). The majority of patients had World Health Organization (WHO) grade 1 (41%) or WHO grade 2 (35%) diseases. Most patients (83%) received corticosteroids prior to the initiation of ¹⁷⁷Lu-DOTATATE, while 17% of patients received corticosteroids due to having a previous tumor flare after ¹⁷⁷Lu-DOTATATE administration. Despite corticosteroid prophylaxis, 28% of patients still experienced a tumor flare event, with three patients experiencing multiple tumor flare events. Small bowel obstructions occurred in 7% of patients and increased abdominal pain in 22% of patients. Adverse events (AEs) due to corticosteroids occurred in 28% of patients. Conclusions: Short-course corticosteroid prophylaxis to prevent tumor flare reactions in high-risk patients with neuroendocrine tumors treated with ¹⁷⁷Lu-DOTATATE did not appear to decrease the incidence of tumor flare reactions compared to previously reported numbers. Randomized, placebo-controlled trials looking at the use of corticosteroids to prevent tumor flare reactions in patients treated with ¹⁷⁷Lu-DOTATATE are needed to fully elucidate the safety and efficacy of corticosteroids used in this setting and to determine the impact on treatment outcomes. Full article

Objectives: This systematic review aimed to evaluate the association between oral health—particularly dental caries and dysbiosis of the oral microbiome—and respiratory diseases across different age groups and clinical settings, with emphasis on microbial overlap, clinical outcomes, and preventive strategies. Methods: A systematic search was conducted in PubMed, Scopus, Embase, Web of Science, and the Cochrane Library up to June 2025. Eligible studies included randomized controlled trials, cohort, case–control, and cross-sectional investigations examining the relationship between oral diseases or microbiome alterations and respiratory outcomes. Data on study design, population, oral health parameters, microbial taxa, and respiratory endpoints were extracted. Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT, 2018). Results: Twenty studies met the inclusion criteria, encompassing pediatric, adult, and elderly populations. Poor oral health, reflected by higher caries indices and periodontal inflammation, was consistently associated with increased risk of lower respiratory tract infections (LRTI), aspiration events, ventilator-associated pneumonia (VAP), and impaired pulmonary function. Oral microbiome analyses revealed enrichment of Veillonella, Prevotella, Klebsiella, and Pseudomonas species in both oral and airway samples, supporting the oral cavity as a reservoir for respiratory pathogens. Interventional evidence from intensive care and nursing home settings demonstrated that structured oral care—particularly daily toothbrushing and chlorhexidine-based plaque control—significantly reduced pneumonia incidence. Conclusions: This review confirms a clinically relevant and biologically plausible link between oral dysbiosis, dental caries, and respiratory disease. Oral biofilms contribute to infection risk through microaspiration and microbial seeding of the lower airways. Integrating oral screening, hygiene maintenance, and treatment of active oral disease into respiratory care pathways may reduce respiratory morbidity and mortality, particularly among high-risk populations such as ICU patients, older adults, and individuals with chronic lung disease. Full article

Marine concrete structures are continuously exposed to harsh marine environments—salt, waves, and biological fouling—that accelerate corrosion and cracking, increasing maintenance costs. Traditional Non-Destructive Testing (NDT) techniques often fail to detect early damage due to signal attenuation and noise in underwater conditions. This study critically reviews recent advances in Artificial Intelligence-integrated NDT (AI-NDT) technologies for marine concrete, focusing on their quantitative performance improvements and practical applicability. To be specific, a systematic comparison of vision-based and signal-based AI-NDT techniques was carried out across reported field cases. It was confirmed that the integration of AI improved detection accuracy by 17–25%, on average, compared with traditional methods. Vision-based AI models such as YOLOX-DG, Cycle GAN, and MSDA increased mean mAP 0.5 by 4%, while signal-based methods using CNN, LSTM, and Random Forest enhanced prediction accuracy by 15–20% in GPR, AE, and ultrasonic data. These results confirm that AI effectively compensates for environmental distortions, corrects noise, and standardizes data interpretation across variable marine conditions. Lastly, the study highlights that AI-enabled NDT not only automates data interpretation but also establishes the foundation for predictive and preventive maintenance frameworks. By linking data acquisition, digital twin-based prediction, and lifecycle monitoring, AI-NDT can transform current reactive maintenance strategies into sustainable, intelligence-driven management for marine infrastructure. Full article

Aiming at the problems of fixed trigger patterns that are prone to detection in existing invisible backdoor attacks, this paper proposes a backdoor attack method that integrates local binary pattern (LBP) with dynamic randomized least significant bit (LSB) steganography. The multi-bit coding characteristic of LBP is leveraged to enrich the representational expressiveness of trigger information within the embedding budget, combined with LSB steganography to maintain visual imperceptibility, and a pseudo-random number generator (PRNG) is introduced to randomize embedding locations to mitigate detectors that rely on fixed-position patterns. Experiments show that the proposed method demonstrates potential advantages in terms of steganography, attack success rate, and anti-detection capability on both CIFAR-10 and Tiny-ImageNet datasets. Among them, the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) reach up to 0.98 and above 36 dB in terms of covertness, respectively. In anti-detection experiments, the attack method maintains high attack success rates under D-BR defense (CIFAR-10: Test_ASR > 85%; Tiny-ImageNet: Test_ASR > 95%), while under SPECTRE defense—a spectral-based statistical method—the defender’s leakage detection rate of poisoned samples remains low (CIFAR-10: 5.96%; Tiny-ImageNet: 10.56%). This clearly validates the proposed attack’s robustness against mainstream defense mechanisms. Full article

The continuous reduction in critical dimensions and the escalating demands for higher throughput are driving motion platforms to operate under increasingly complex conditions, including multi-axis coupling, structural nonlinearities, and time-varying operational scenarios. These complexities make the trade-offs among precision, speed, and robustness increasingly challenging. Traditional Proportional–Integral–Derivative (PID) controllers, which rely on empirical tuning methods, suffer from prolonged trial-and-error cycles and limited transferability, and consequently struggle to maintain optimal performance under these complex working conditions. This paper proposes an adaptive β–Proximal Policy Optimization with Random Network Distillation (β-PPO-RND) parameter optimization within the Prescribed Performance Control (PPC) framework. The adaptive coefficient β is updated based on the temporal change in reward difference, which is clipped and smoothly mapped to a preset range using a hyperbolic tangent function. This mechanism dynamically balances intrinsic and extrinsic rewards—encouraging broader exploration in the early stage and emphasizing performance optimization in the later stage. Experimental validation on a Permanent Magnet Linear Synchronous Motor (PMLSM) platform confirms the effectiveness of the proposed approach. It eliminates the need for manual tuning and enables real-time controller parameter adjustment within the PPC framework, achieving high-precision trajectory tracking and a significant reduction in steady-state error. Experimental results show that the proposed method achieves MAE = 0.135 and RMSE = 0.154, representing approximately 70% reductions compared to the conventional PID controller. Full article

Composite modular panels are increasingly used in modern buildings, yet their layered behavior makes mechanical characterization and modeling difficult. This study presents a novel hybrid framework that integrates analytical, numerical, and AI-driven approaches for the mechanical characterization of composite panels. The system combines a layered concrete configuration with embedded steel reinforcement, and its performance was evaluated through experimental testing, analytical formulation, finite element simulations, and artificial intelligence techniques. Full-scale bending and shear tests were conducted and results in terms of displacements were compared with in silico simulations. The equivalent elastic modulus and thickness were suggested via a closed-form analytical procedure and validated numerically, showing less than 3% deviation from experiments. These equivalent parameters were used to simulate the dynamic response of a two-storey prototype building under harmonic excitation, with simulated modal periods differing by less than 10% from experimental data. To generalize the method, a parametric dataset of 218 panel configurations was generated by varying material and geometric properties. Machine learning models including Artificial Neural Network, Random Forest, Gradient Boosting, and Extra Trees were trained on this dataset, achieving R² > 0.98 for both targets. A graphical user interface was developed to integrate the trained models into an engineering tool for fast prediction of equivalent properties. The proposed methodology provides a unified and computationally efficient approach that combines physical accuracy with practical usability, enabling rapid design and optimization of composite panel structures. Full article

This study aims to examine the fundamental determinants of economic growth in G20 countries in the context of institutional structure, innovation, and education. The significance of the research lies in revealing that sustainable economic growth is shaped not only by traditional macroeconomic factors but also by the effectiveness of institutions, innovation capacity, and human capital investments. The existing literature contains limited studies that comprehensively address the interactions between these three variables and economic growth, specifically in G20 countries. The study applies panel data analysis to G20 countries for the period 2005–2024 and performs panel Granger causality analysis using fixed and random effects models after horizontal section dependence, unit root, and cointegration tests. Empirical findings show that institutions, innovation, and education variables have significant and positive effects on economic growth. Granger causality test results reveal that these variables unidirectionally drive growth, while growth has no feedback effect on these factors. The findings indicate that strengthening institutional reforms, encouraging R&D and innovation investments, and increasing human capital capacity are critical for sustainable and high-quality economic growth for policymakers. Full article

To address the complexity of tensile mechanical behavior in fissured rock masses, this study conducted Brazilian splitting tests and numerical simulations on yellow sandstone containing randomly distributed fissures. Based on secondary development of the ABAQUS platform, a numerical model considering the spatial distribution of mineral components was established. A random fissure network was generated using the Weibull distribution, and crack propagation was characterized by employing cohesive elements. The influence mechanisms of the fissure inclination angle (θ = 0°~90°) and fissure ratio (R = 3~15%) on Brazilian tensile strength, failure mode, and crack propagation were systematically analyzed. The research demonstrates the following: (1) Brazilian tensile strength exhibits an overall decreasing trend with an increasing fissure ratio, while the effect of the fissure inclination angle is non-monotonic: at a low fissure ratio (R = 3%), Brazilian tensile strength shows a “decrease–increase–decrease” characteristic; at a medium to high fissure ratio (R ≥ 9%), Brazilian tensile strength continuously increases with an increasing fissure inclination angle. (2) The fissure ratio dominates the deviation of the failure path (deviation intensifies when θ ≤ 67.5° and is minimal at θ = 90°). At the mesoscale, the proportion of tensile cracks increases with an increasing R, while the contribution of shear cracks significantly enhances with an increasing θ (sharply increasing after θ > 45°). (3) Crack propagation is controlled by the spatial interaction of initial cracks. Under the combined action of a high inclination angle (θ = 90°) and high fissure ratio (R = 15%), a tensile–shear composite failure pattern forms, characterized by dual-source crack initiation and central coalescence. This study provides a mesoscale mechanical basis for the stability assessment of engineering structures in fissured rock masses. Full article

Earthquake-induced soil liquefaction represents a critical geotechnical challenge due to its nonlinear soil–seismic interactions and its impact on structural safety. Traditional empirical methods often rely on simplified assumptions, limiting their predictive capability. This study develops and compares six machine learning (ML) classifiers—namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Random Forest (RF), Decision Tree (DT), and Naïve Bayes (NB)—to evaluate liquefaction susceptibility using an original dataset of 461 soil layers obtained from borehole penetration tests in the Edremit region (Balıkesir, NW Turkey). The models were trained and validated using normalized geotechnical and seismic parameters, and their performance was assessed based on accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Results demonstrate that SVM, ANN, and kNN consistently outperformed other models, achieving test accuracies above 93%, F1 scores exceeding 98%, and AUC values between 0.933 and 0.953. In contrast, DT and NB exhibited limited generalization (test accuracy of 84–88% and AUC of 0.78–0.82), while RF showed partial overfitting. In contrast, DT and NB exhibited weaker generalization, with test accuracies of 84% and 88% and AUC values of 0.78 and 0.82, respectively, while RF indicated partial overfitting. The findings confirm the superior capability of advanced ML models, particularly SVM, ANN, and kNN, in capturing complex nonlinear patterns in soil liquefaction. This study provides a robust framework and original dataset that enhance predictive reliability for seismic hazard assessment in earthquake-prone regions. Full article

Background/Objectives: Anxiety disorders are among the most common mental disorders and are often associated with significant discomfort and impaired functioning. One of the more frequent forms is social anxiety disorder, which is characterized by excessive fear of social evaluation and the avoidance of social situations. The aim of this study was to assess the potential of role-playing games (RPGs) as an alternative form of support for people with social anxiety disorder. Methods: Thirty participants aged 18–28 with a non-generalized form of social anxiety were qualified for the study and assigned to two conditions differing in session frequency (once a week vs. once every two weeks). Participants were assigned to groups based on the order of registration for the study. As the recruitment was open to the public and participants registered voluntarily, the assignment process was not strictly random and may have been influenced by self-selection factors. The intervention lasted 3 months and included elements of social exposure and social skills training within a structured RPG scenario. The study lasted from March to November 2024 at the Faculty of Social Sciences of the University of Szczecin. Standardized tools (LSAS, ISSB) were used to measure social anxiety and received social support before and after the intervention. Statistical analyses were performed using the JASP statistical program version 0.17.2. Results: The results indicate a statistically significant reduction in anxiety and avoidance in all groups, with a greater effect observed in the once-a-week group (Cohens’s d = 0.94). At the same time, an increase in perceived social support was noted, especially in the biweekly condition. The greatest changes were observed in the total support score, while specific components (emotional, informational, instrumental) showed differentiated dynamics depending on frequency. Conclusions: The findings suggest that RPG-based interventions may serve as a preliminarily effective and engaging form of support for individuals with social anxiety, contributing to symptom reduction and improved functioning in social contexts. Full article

This paper presents a comparative empirical analysis of three modeling approaches applied to the forecasting of Morocco’s Real Effective Exchange Rate (REER): the ARIMA model, the Random Forest algorithm, and the Dynamic Factor Model (DFM). Utilizing a comprehensive macroeconomic quarterly dataset spanning from 1999Q4 to 2021Q3, the study assesses the out-of-sample predictive performance of these models over a structurally dynamic period, including the transition to a more flexible exchange rate regime in 2018 and the global shock induced by the COVID-19 pandemic. The findings reveal that the Random Forest model significantly outperforms both ARIMA and DFM in terms of accuracy and adaptability to structural breaks. Variable importance analysis highlights the dominant role of real economic fundamentals, particularly industrial value added, inflation, and exports in explaining REER movements. In contrast, the ARIMA model underreacts to exogenous shocks due to its univariate structure, while the DFM suffers from a loss of predictive power likely caused by excessive dimensionality reduction. Full article

Epilepsy is a brain disorder that affects individuals; hence, preemptive diagnosis is required. Accurate classification of seizures is critical to optimize the treatment of epilepsy. Patients with epilepsy are unable to lead normal lives due to the unpredictable nature of seizures. Thus, developing new methods to help these patients can significantly improve their quality of life and result in huge financial savings for the healthcare industry. This paper presents a hybrid method integrating dynamic mode decomposition (DMD) and wavelet scattering transform (WST) for EEG-based seizure analysis. DMD allows for the breakdown of EEG signals into modes that catch the dynamical structures present in the EEG. Then, WST is applied as it is invariant to time-warping and computes robust hierarchical features at different timescales. DMD-WST combination provides an in-depth multi-scale analysis of the temporal structures present within the EEG data. This process improves the representation quality for feature extraction, which can convey dynamic modes and multi-scale frequency information for improved classification performance. The proposed hybrid approach is validated with three datasets, namely the CHB-MIT PhysioNet dataset, the Bern Barcelona dataset, and the Khas dataset, which can accurately distinguish the seizure and non-seizure states. The proposed method performed classification using different machine learning and deep learning methods, including support vector machine, random forest, k-nearest neighbours, booster algorithm, and bagging. These models were compared in terms of accuracy, precision, sensitivity, Cohen’s kappa, and Matthew’s correlation coefficient. The DMD-WST approach achieved a maximum accuracy of 99% and F1 score of 0.99 on the CHB-MIT dataset, and obtained 100% accuracy and F1 score of 1.00 on both the Bern Barcelona and Khas datasets, outperforming existing methods Full article