Search Results (837)

Background: Fibromyalgia syndrome (FMS) and post-traumatic stress disorder (PTSD) may co-occur and are associated with increased symptom burden, functional impairment, and reduced quality of life. Accumulating evidence suggests shared neurobiological mechanisms. Trauma-focused interventions targeting maladaptive memory processes may therefore represent a relevant therapeutic approach in this population. Objective: To evaluate the feasibility, tolerability, and preliminary clinical associations of a brief reconsolidation-based therapy in women with comorbid FMS and PTSD. Methods: This multicenter pilot study included adult women diagnosed with FMS and PTSD who underwent six sessions of reconsolidation therapy combining traumatic memory reactivation with propranolol administration. Clinical outcomes were assessed at baseline and at 3-month follow-up using the Fibromyalgia Impact Questionnaire (FIQ), the Impact of Event Scale–Revised (IES-R), the Montgomery–Åsberg Depression Rating Scale (MADRS), the Beck Depression Inventory (BDI), the Rosenberg Self-Esteem Scale (RSES), and the SF-36. Changes over time were analyzed using paired statistical tests and linear mixed-effects models. Results: Fourteen participants completed the intervention and follow-up assessments. The intervention was feasible and well tolerated. Changes over time were observed in fibromyalgia-related quality of life (FIQ scores), PTSD symptom severity (IES-R), and depressive symptoms (MADRS, BDI), as well as in selected SF-36 domains, including vitality, social functioning, and mental health. A progressive decrease in IES-R scores was observed across treatment sessions. Conclusions: This pilot study suggests that reconsolidation-based therapy is feasible in women with comorbid FMS and PTSD and was associated with changes in PTSD symptoms and fibromyalgia-related functional impact. Given the exploratory design and absence of a control group, these findings should be interpreted cautiously and warrant confirmation in larger, controlled trials. Full article

Accurate and reliable wind speed measurement is essential for applications such as wind power generation and meteorological monitoring. Data fusion from multiple anemometers mounted on wind measurement towers is a key approach to obtaining high-precision wind speed information. In this study, a hierarchical data fusion strategy is proposed to enhance both the quality and efficiency of multi-sensor fusion on wind measurement towers. At the local fusion stage, multi-sensor wind speed data are denoised and fused using an unscented Kalman filter enhanced with fuzzy logic and a robustness factor (FLR-UKF). At the global decision fusion stage, decision-level fusion is achieved through an extreme learning machine (ELM) neural network optimized by a Q-learning-improved Aquila optimizer (QLIAO-ELM). By incorporating a spiral surrounding attack mechanism and a Q-learning-based adaptive strategy, QLIAO-ELM significantly enhances global search capability and convergence speed, enabling the ELM network to obtain superior parameters within limited computational time. Consequently, the accuracy and efficiency of decision fusion are improved. Experimental results show that, during the local fusion phase, the RMSE of FLR-UKF is reduced by 26.46% to 28.6% compared to the traditional UKF; during the global fusion phase, the RMSE of QLIAO-ELM is reduced by 27.1% and 14.0% compared to ELM and ISSA-ELM, respectively. Full article

As generative speech synthesis produces near-human synthetic voices and reliance on online media grows, robust audio-deepfake detection is essential to fight misuse and misinformation. In this study, we introduce the Arabic Fake Audio Dataset for Modern Standard Arabic (AFAD-MSA), a curated corpus of authentic and synthetic Arabic speech designed to advance research on Arabic deepfake and spoofed-speech detection. The synthetic subset is generated with four state-of-the-art proprietary text-to-speech and voice-conversion models. Rich metadata—covering speaker attributes and generation information—is provided to support reproducibility and benchmarking. To establish reference performance, we trained three AASIST models and compared their performance to two baseline transformer detectors (Wav2Vec 2.0 and Whisper). On the AFAD-MSA test split, AASIST-2 achieved perfect accuracy, surpassing the baseline models. However, its performance declined under cross-dataset evaluation. These results underscore the importance of data construction. Detectors generalize best when exposed to diverse attack types. In addition, continual or contrastive training that interleaves bona fide speech with large, heterogeneous spoofed corpora will further improve detectors’ robustness. Full article

In sub-Saharan Africa, land degradation and climate change continue to undermine agricultural productivity by reducing soil productivity and water availability. This review identifies soil and water conservation technologies successfully applied in climatically analogous regions of sub-Saharan Africa with the aim of informing effective technology transfer to Senegal, particularly Sédhiou and Tambacounda. Using K-means clustering on WorldClim bioclimatic variables, 35 comparable countries were identified, of which 17 met inclusion criteria based on data availability and ≥60% climatic similarity. Eighty-five technologies were documented and assessed for their compatibility across rainfall patterns, land gradients, and uses, with 12 emerging as consistently effective. Quantitative evidence shows that zai/tassa pits, stone bunds, and half-moons increase crop yields by 50–200%, while stone bunds and mulching reduce runoff by up to 80% and improve soil moisture retention. Terracing and tied-ridging were also linked to higher water-use efficiency, with tied-ridging increasing soil moisture by 13%. Burkina Faso, Kenya, and Malawi lead in adoption and diversity, whereas Senegal lags due to institutional gaps, limited funding, and weak extension systems. These technologies offer a readily available, evidence-based toolkit for building agricultural resilience in Senegal. However, their successful adoption requires stronger policy integration, stakeholder empowerment, cross-border learning, and private-sector engagement. Full article

Individuals with developmental disabilities often experience physical and mental chronic conditions from early childhood, which can negatively affect their education, employment, and social participation without appropriate interventions. These impairments frequently limit the acquisition of essential daily living skills, including personal hygiene skills. This study examined a multicomponent intervention package—comprising video prompting, step-by-step instruction based on task analysis, systematic error correction, and reinforcement—to support the acquisition of handwashing skills for two adolescents with developmental disabilities (Autism Spectrum Disorder and Intellectual Disability) and toothbrushing skills for one adolescent. A nonconcurrent multiple-baseline design across participants and skills was employed, including baseline, intervention, maintenance, and generalization phases. The intervention was conducted over eight weeks. The results indicated low and stable baseline performance for both participants, followed by a systematic increase in performance after the introduction of the intervention, reaching accuracy levels between 80% and 91%. Participants demonstrated meaningful improvements in hygiene skill performance following intervention. These gains were maintained over time and generalized to new settings, although a few task steps continued to require prompting. Furthermore, teachers and parents rated the intervention as highly feasible, practical, and useful for supporting hygiene skills, while students reported enjoyment, perceived improvement, and willingness to participate again. Overall, the findings suggest that structured, evidence-based instructional approaches may support increased functional participation in essential daily living skills, particularly when complete independence may not be attainable for all individuals. Full article

To enhance the sustainable power regulation capability of hydroelectric unit regulation systems (HURS) under modern power system requirements, this paper proposes an optimal linear quadratic regulator (LQR)-based power control strategy optimized using an improved Salp Swarm Algorithm (ISSA). First, comprehensive mathematical models of the hydraulic, mechanical, and electrical subsystems of HURS are established, enabling a unified state-space representation suitable for LQR controller design. Then, the weighting matrices of the LQR controller are optimally tuned via ISSA using a hybrid objective function that jointly considers dynamic response performance and control effort, thereby contributing to improved energy efficiency and long-term operational sustainability. A large-scale hydropower unit operating under weakly stable conditions is selected as a case study. Simulation results demonstrate that, compared with conventional LQR tuning approaches, the proposed ISSA-LQR controller achieves faster power response, reduced overshoot, and enhanced robustness against operating condition variations. These improvements effectively reduce unnecessary control actions and mechanical stress, supporting the reliable and sustainable operation of hydroelectric units. Overall, the proposed method provides a practical and effective solution for improving power regulation performance in hydropower plants, thereby enhancing their capability to support renewable energy integration and contribute to the sustainable development of modern power systems. Full article

Scaling Agile in industrial software projects is a process control problem that must balance governance, scalability, and adaptability while keeping decisions auditable. We present a hybrid fuzzy multi-criteria decision-making (MCDM) framework that combines Fuzzy Analytic Hierarchy Process (FAHP) for uncertainty-aware weighting with a tunable VIKOR–PROMETHEE ranking stage. Weighting and ranking are kept distinct to support traceability and parameter sensitivity. A three-layer hierarchy organizes twenty-two criteria across organizational, project, group, and framework levels. In a single-enterprise validation with two independent expert panels (n = 10 practitioners), the tuned hybrid achieved lower rank error than single-method baselines (mean absolute error, $\mathrm{MAE}$ = 1.03; Spearman $\rho$ = 0.53) using pre-specified thresholds and a transparent $\alpha +\beta$ = 1 control. The procedure is practical for process governance: elicit priorities, derive fuzzy weights, apply the hybrid ranking, and verify stability with sensitivity analysis. The framework operationalizes modeling, optimization, control, and monitoring of scaling decisions, making trade-offs explicit and reproducible in industrial settings. Full article

Background/Objectives: This systematic review aimed to quantify the extent of wear of opposing posterior natural enamel in patients with single-unit ceramic crowns. Methods: A comprehensive search was conducted in PubMed/MEDLINE, Embase, Web of Science, Cochrane Library, and ProQuest through September 2025. A meta-analysis was performed using the inverse variance method. Results: Nine clinical studies (5 randomized controlled trials and 4 prospective studies) involving 203 patients (2015–2025) were included. All studies evaluated monolithic zirconia; two also assessed monolithic lithium disilicate, and three included metal-ceramic restorations with feldspathic veneering. Follow-up ranged from 6 to 24 months. Meta-analysis revealed significant enamel wear from zirconia (p < 0.05; MD: −1.32; 95% CI: −2.06 to −0.57; I² = 94%) and lithium disilicate (p < 0.05; MD: −0.45; 95% CI: −0.71 to −0.19; I² = 2%). Feldspathic ceramics did not show significant enamel wear (p = 0.06; MD: −2.77; 95% CI: −5.66 to 0.13; I² = 96%). Conclusions: Ceramic materials generally cause greater wear on opposing posterior natural enamel than enamel-to-enamel contact. Monolithic zirconia and lithium disilicate crowns produced higher antagonist wear, whereas metal-ceramic restorations with feldspathic veneering appeared more conservative for preserving posterior enamel. Full article

This case report describes mandibular distraction osteogenesis (DO) in a six-year-old patient with first and second branchial arch syndrome and obstructive sleep apnea, in whom 3D surgical planning was combined with ultrasonography (US) for postoperative monitoring. The aim was to illustrate how patient-specific 3D modeling and a structured ultrasonography protocol can support safe mandibular advancement while limiting radiation exposure in a pediatric patient with complex craniofacial deformity. Preoperatively, a 3D-printed model of the mandible, generated from a cone beam computed tomography (CBCT) scan, was used to guide precise osteotomy planning and vector orientation. The surgical procedure was conducted using a Risdon approach and piezoelectric tools to ensure minimal trauma. Postoperative monitoring incorporated serial panoramic radiography and US at predefined time points to assess gap size, callus formation, and vascularity during distraction and consolidation. US identified early callus formation, progressive cortical bridging, and preserved callus vascularity, and, together with radiographic findings, guided the timing of distraction termination and distractor removal at 16 weeks. This case adds to the limited literature on pediatric mandibular DO by demonstrating the feasibility of integrating patient-specific 3D virtual planning with US-based follow-up to improve the safety, precision, and radiation-conscious management of DO in pediatric patients with complex craniofacial deformities. Full article

Artificial Intelligence (AI)-enabled diagnosis has emerged as a promising avenue for revolutionizing medical image analysis, such as X-ray analysis, across a wide range of healthcare disciplines, including dentistry, consequently offering swift, efficient, and accurate solutions for identifying various dental conditions. In this study, we investigated the application of the YOLOv9 model, a cutting-edge object detection algorithm, to automate the diagnosis of dental diseases from X-ray images. The proposed methodology encompasses a comprehensive analysis of dental datasets, as well as preprocessing and model training. Through rigorous experimentation, remarkable accuracy, precision, recall, mAP@50, and an F1-score of 84.89%, 89.2%, 86.9%, 89.2%, and 88%, respectively, are achieved. With significant improvements over the baseline model of 17.9%, 15.8%, 18.5%, and 16.81% in precision, recall, mAP@50, and F1-score, respectively, with 7.9 ms inference time. This demonstrates the effectiveness of the proposed approach in accurately identifying dental conditions. Additionally, we discuss the challenges in automated diagnosis of dental diseases and outline future research directions to address knowledge gaps in this domain. This study contributes to the growing body of literature on AI in dentistry, providing valuable insights for researchers and practitioners. Full article

In the digital era, protecting the integrity and ownership of digital content is increasingly crucial, particularly against unauthorized copying and tampering. Traditional watermarking techniques often struggle to remain robust under various image manipulations, leading to a need for more resilient methods. To address this challenge, we propose a novel watermarking technique that integrates the Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Schur matrix factorization to embed a QR code as a watermark into digital images. Our method was rigorously tested across a range of common image attacks, including histogram equalization, salt-and-pepper noise, ripple distortions, smoothing, and extensive cropping. The results demonstrate that our approach significantly outperforms existing methods, achieving high normalized correlation (NC) values such as 0.9949 for histogram equalization, 0.9846 for salt-and-pepper noise (2%), 0.96063 for ripple distortion, 0.9670 for smoothing, and up to 0.9995 under 50% cropping. The watermark consistently maintained its integrity and scannability under all tested conditions, making our method a reliable solution for enhancing digital copyright protection. Full article

Metronidazole (MTZ), a widely used antiamoebic and antibacterial drug, has been linked to male reproductive damage. The aim of this study was to investigate Olea europaea L. and Equisetum arvense L. ethanol extracts for the protection against testicular toxicity and male infertility caused by MTZ, and to characterize the underlying mechanisms. Forty-two male rats were divided into six groups. The animals in group 1 served as the controls and received a daily oral dose (1 mL) of the vehicle. The animals in group 2 received metronidazole at doses of 400 mg/kg. Group 3 was treated with E. arvense extract at doses of 100 mg/kg. Group 4 was treated with O. europaea leaf extract at doses of 400 mg/kg. Group 5 was treated with metronidazole and E. arvense extract at doses of 400 and 100 mg/kg, respectively. Group 6 was treated with metronidazole with O. europaea leaf extract at doses of 400 and 400 mg/kg, respectively. The rats were given a daily oral dose of different treatments for 60 days, after which the animals were euthanized to study the histopathological and molecular changes in the testis and the sperm count in the epididymis. The testosterone levels, MDA levels, and GSH contents were also assessed in the rats in all groups. The findings revealed that the MTZ treatment caused a substantial increase in MDA levels and upregulated the NBN gene expression relative to the control. Moreover, the MTZ treatment produced significant reductions in the sperm count and viability, testosterone levels, and GSH content, and downregulated the INSL-3, STAR, HSD-3β, and CYP11A1 gene expression compared to the control. The adverse effects in testicular tissue were significantly reduced in rats given the O. europaea leaves and E. arvense treatment. The findings may show that MTZ can enhance testicular toxicity and infertility, but both plant extracts can prevent these harmful consequences. Full article

Background/Objectives: Barrett’s esophagus (BE) is a precursor to esophageal adenocarcinoma (EAC), and neoadjuvant chemoradiation therapy (NCRT) is commonly used in the treatment of EAC. However, the impact of NCRT on non-tumorous BE and dysplasia is poorly understood. Our study aims to evaluate the effects of NCRT on BE segment length and dysplasia in patients undergoing esophagectomy for EAC. Methods: This multicenter, retrospective cohort study includes EAC patients who underwent esophagectomy with or without NCRT between 2014 and 2020. Patients with histologically confirmed BE and dysplasia (low- or high-grade) were analyzed. Preoperative and postoperative pathology were compared to assess BE regression, dysplastic changes, and segment length. Statistical analyses included chi-square and t-tests, with p < 0.05 considered significant. Results: Of 101 patients who were diagnosed with EAC, 28 patients were found to have BE, with 18 receiving NCRT in addition to surgery and 10 undergoing surgery alone. The NCRT group showed significantly higher BE regression than the control group (77.8% versus 10%, p < 0.001). Regression of dysplasia occurred in 66.7% of the NCRT group versus 20% of the control group (p = 0.079) and residual dysplasia was lower in the NCRT group (33.3%) compared to the control group (80%) (p = 0.018). Conclusions: NCRT significantly reduces BE and dysplasia, suggesting it may improve surgical outcomes by minimizing residual disease. These findings support the potential of NCRT to enhance surgical precision in EAC treatment, though further research is needed to explore underlying mechanisms and refine treatment strategies. Full article

Accurate recognition of lower limb activities is essential for wearable rehabilitation systems and assistive robotics like exoskeletons and prosthetics. This study introduces SDALLE, a custom hardware data acquisition system that integrates surface electromyography sensors (EMGs) and inertial measurement sensors (IMUs) into a wireless, portable platform for locomotor monitoring. Using this system, data were collected from nine healthy subjects performing four fundamental locomotor activities: walking, jogging, stair ascent, and stair descent. The recorded signals underwent an offline structured preprocessing pipeline consisting of time-series augmentation (jittering and scaling) to increase data diversity, followed by wavelet-based denoising to suppress high-frequency noise and enhance signal quality. A temporal one-dimensional convolutional neural network (1D-TCNN) with three convolutional blocks and fully connected layers was trained on the prepared dataset to classify the four activities. Classification using IMU sensors achieved the highest performance, with accuracies ranging from 0.81 to 0.95. The gyroscope X-axis of the left Rectus Femoris achieved the best performance (0.95), while accelerometer signals also performed strongly, reaching 0.93 for the Vastus Medialis in the Y direction. In contrast, electromyography channels showed lower discriminative capability. These results demonstrate that the combination of SDALLE hardware, appropriate data preprocessing, and a temporal CNN provides an effective offline sensing and activity classification pipeline for lower limb activity recognition and offers an open-source dataset that supports further research in human activity recognition, rehabilitation, and assistive robotics. Full article

Background: The clinical research laboratory plays a pivotal role in the execution of clinical studies. The accurate and consistent registration of patients is dependent on the competent use of laboratory equipment and manual techniques by technicians, ensuring the reliability of the data collected. To support these activities, the Groupe de Recherche Action en Santé (GRAS) has been registered with the College of American Pathologists (CAP) and the Clinical Laboratories Services (CLS) in Johannesburg, South Africa, for external proficiency testing (EPT) of its laboratory, as part of our commitment to quality assurance. The following report details the performance achievements over the past five years. Methods: Proficiency testing (PT) samples are dispatched to GRAS Lab three times a year (quarterly) and the results are generally returned within two to three weeks. In the field of parasitology, challenge specimens were prepared as follows: thick and thin blood films were stained with Giemsa and mounted with strips to protect them for multiple uses. Photographs, also known as whole slide images (WSIs), were also taken. For the biochemistry and haematology tests, a set of five samples were received for processing. All evaluations were carried out in accordance with the GRAS laboratory’s internal procedures. Results: The CAP laboratory’s performance in terms of the diagnosis of malaria and other blood parasites from 2020 to 2024 was 97.3% accurate (ranging from 93.33% to 100%), with 93.33%, 100%, 100%, 93.33% and 100% achieved in 2020, 2021, 2022, 2023 and 2024, respectively. The number of microscopists evaluated annually has been subject to variation according to operational staff at the time of evaluation. A total of 31 microscopists were enrolled in the CLS PT scheme, of which 73.9% were classified as ‘experts’ and 19.2% as ‘reference’ microscopists. In the field of haematology, the PT demonstrated 100% accuracy over the four-year study period. This outcome is indicative of the high-performance levels exhibited by the automated systems under scrutiny and the comparable nature of the data produced by these systems. The same trend was observed in the biochemistry PT results, with an overall score of 92.12%, ranging from 78% to 100%. Conclusions: Proficiency testing has been shown to be an effective tool for quality assurance in laboratories, helping to ensure the accuracy of malaria and other blood parasite diagnoses made by microscopists, as well as the results generated by automated systems. It has been instrumental in assisting laboratories in identifying issues related to test design and performance. Full article