Search Results (899)

In the field of wind turbine spindle bearing fault diagnosis, real-time monitoring of its operation is challenging. The state monitoring visualization is limited, fault data and sample labels are scarce, and fault data distribution varies under different operational conditions, leading to low diagnosis accuracy and slow diagnosis speed. To address these challenges, a wind turbine spindle bearing fault diagnosis method based on Unity 3D and transfer learning is proposed. Based on the characteristics of the wind turbine spindle bearing structure and operation, a digital twin model is established. The twin data transmit the necessary information to each module in various file formats. Additionally, the signal processing method (RB), combined with a random convolution layer and blind deconvolution, is employed to enhance the diversity of fault features. The processed signal is then fed into an improved residual network model with an efficient channel attention mechanism. Finally, the model incorporates related alignment and joint maximum mean difference for fault diagnosis. This model not only improves the extraction of key features but also adapts to edge and condition distributions through domain adaptation, enabling cross-domain identification. The digital twin system is implemented in Unity 3D, incorporating functions such as user login, wind turbine spindle bearing state monitoring, fault diagnosis, and fault warning, demonstrating practical applicability. Experimental results validate the effectiveness and superiority of the proposed method in fault diagnosis across various transfer learning tasks. Full article

The existing methods for transformer core-loosening fault diagnosis primarily focus on fundamental frequency analysis, neglecting higher-frequency components, which limits early detection accuracy. This study proposes a comprehensive approach integrating full-band vibration analysis, including high-order harmonics, to enhance diagnostic precision. A theoretical model coupling magnetostriction and thermodynamics was developed, combined with empirical mode decomposition (EMD) and Pearson’s correlation coefficient for fault characterization. A 10 kV transformer core vibration test platform was constructed, capturing signals under normal, partially loose, and completely loose states. The simulation results aligned with the experimental data, showing vibration accelerations of 0.01 m/s² (Phase A) and 0.023 m/s² (Phase B). A multi-physics coupling model incorporating Young’s modulus variations simulated core loosening, revealing increased high-frequency components (up to 1000 Hz) and vibration amplitudes (0.2757 m/s² for complete loosening). The joint EMD–Pearson method quantified fault severity, yielding correlation values of 0.0007 (normal), 0.0044 (partial loosening), and 0.0116 (complete loosening), demonstrating a clear positive correlation with fault progression. Experimental validation confirmed the model’s reliability, with the simulations matching the test results. This approach addresses the limitations of traditional methods by incorporating high-frequency analysis and multi-physics modeling, significantly improving early fault detection accuracy and providing a quantifiable diagnostic framework for transformer core health monitoring. Full article

Hip dysplasia (HD) is a prevalent disease in medium- to large-breed dogs, characterized by joint laxity and degenerative joint changes. The early diagnosis of HD poses significant challenges, as radiographic imaging often identifies the disease only in advanced stages. Conversely, ultrasonography, a non-invasive and cost-effective imaging modality, offers the potential for earlier detection by evaluating the surrounding soft tissues and synovial changes. This study aimed to assess the relationship between the ventral hip ultrasonographic findings, and hip joint laxity evaluated through stress radiographs on 22 young Estrela Mountain dogs (n = 44 hips) aged 4 to 8 months. Key ultrasound measurements included synovial fluid in the cranial femoral neck recess (CFNR) and capsular-synovial fold thickness (CFT). Radiographic laxity was estimated by measuring the distraction index (DI). The median (quartile 25–75%) of the CFNR area, CFT, and DI were 44.00 (27.00–52.25) mm², 3.10 (2.68–3.55) mm, and 0.38 (0.34–0.40), respectively. The Spearman correlation coefficient was statistically significant between all of these variables (p < 0.05). The ventral ultrasonographic approach to the hip joint revealed potential, considering the early diagnosis of HD in dogs, by showing relationships between changes in periarticular soft tissues and joint laxity. Further studies are needed to associate ultrasonographic findings with radiographic signs of HD and related clinical signs in dogs. Full article

Osteosarcoma is a primary malignant bone tumor characterized by the production of an osteoid matrix. Although histology remains the definitive diagnostic standard, imaging plays a crucial role in diagnosis, therapeutic planning, and follow-up. Conventional radiography serves as the initial checkpoint for detecting this pathology, which often presents diagnostic challenges due to vague and nonspecific symptoms, especially in its early stages. Today, the integration of different imaging techniques enables an increasingly personalized diagnosis and management, with each contributing unique and complementary information. Conventional radiography typically initiates the imaging assessment, and the Bone Reporting and Data System (Bone-RADS) of the Society of Skeletal Radiology (SSR) is a valuable tool for stratifying the risk of suspicious bone lesions. CT is the preferred modality for evaluating the bone matrix, while bone scans and PET/CT are effective for detecting distant metastases. MRI reveals the extent of the lesion in adjacent soft tissues, the medullary canal, and joints, as well as its relationship to neurovascular structures and the presence of skip lesions. Advanced techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), and perfusion MRI help characterize the tumor environment and assess treatment response. Osteosarcoma comprises a range of subtypes with differing clinical and imaging characteristics, some of which are particularly distinctive, such as in the case of telangiectatic osteosarcoma. Knowledge of these variants can guide radiologists in the differential diagnosis, which includes both central and surface forms, ranging from highly aggressive to more indolent types. In this review, we present a wide range of representative cases from our hospital case series to illustrate both typical and atypical imaging presentations. Finally, we discuss recent advancements and challenges in applying artificial intelligence approaches to the imaging of osteosarcoma. Full article

Osteoarthritis (OA) and rheumatoid arthritis (RA) are joint diseases that share similar clinical features but have different etiologies, making a differential diagnosis particularly challenging. Background/Objectives: Utilizing advanced machine learning (ML) techniques on metabolomic data, this study aimed to identify key metabolites in synovial fluid (SF) that could aid in distinguishing between OA and RA. Methods: Metabolite data from the MetaboLights database (MTBLS564), analyzed using nuclear magnetic resonance (NMR), were processed using normalization, a principal component analysis (PCA), and a partial least squares discriminant analysis (PLS-DA) to reveal prominent clustering. Results: Decision forests and random forest classifiers, optimized using genetic algorithms (GAs), highlighted a selection of a few metabolites—primarily glutamine, pyruvate, and proline—with significant discriminative power. A Shapley additive explanations (SHAP) analysis confirmed these metabolites to be pivotal predictors, offering a streamlined approach for clinical diagnostics. Conclusions: Our findings suggest that a minimal set of key metabolites can effectively be relied upon to distinguish between OA and RA, supported by an optimized ML model achieving high accuracy. This workflow could streamline diagnostic efficiency and enhance clinical decision-making in rheumatology. Full article

Background/Objectives: Due to rigidity, musculoskeletal pain is more common in people with Parkinson’s disease (PD) compared with age-matched older adults, and the shoulder is one of the body parts that is most involved. In the literature, there is no clear standard for the treatment of shoulder pain in people with PD. This clinical trial study aimed to evaluate the effectiveness of physiotherapy treatment for people with PD with painful shoulders. Methods: The main goals were improvements in pain intensity, balance, quality of life (QoL), and activities of daily living (ADL), evaluated with the Parkinson’s Disease Questionnaire 39 (PDQ-39), Berg Balance Scale (BBS), Community Integration Questionnaire (CIQ-R), 12-Item Short-Form Survey (SF-12), Disabilities of the Arm, Shoulder, and Hand (DASH) scale, and Numeric Pain Rating Scale (NPRS). Also, the evaluation comprised range of motion (ROM) evaluation with a goniometer and the Medical Research Council (MRC) scale. The inclusion criteria of this study were a diagnosis of PD associated with shoulder pain, and a stage of disease of 1–2 on the Hoehn and Yahr scale. Results: The sample comprised 16 participants; the mean age of the participants was 72. Through feedback collected from the individuals participating in this study, it emerged that the rehabilitation approach specifically designed for individuals suffering from shoulder pain associated with Parkinson’s disease produced remarkable results. Conclusions: These results were confirmed by a series of statistically significant data, which showed significant improvements in several areas: joint mobility, muscle strength, motor coordination, the ability to perform daily activities, emotional state, pain reduction, QoL improvement, and balance in both dynamic and static conditions. Full article

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that affects the joints, causing swelling, pain, stiffness, and functional decline. This study aims to clarify the prevalence and risk factors of RA based on sex and age among Korean adults, providing essential data for targeted prevention and management strategies. We analyzed data from the Korea National Health and Nutrition Examination Survey 2016–2021, comprising 25,166 participants aged 20 and older. Sociodemographics, health status, and behavior factors were evaluated, with RA defined based on self-reported diagnosis. A complex sampling design was utilized to ensure representative results and multiple logistic regression was employed to determine the risk factors linked to RA. The overall prevalence of RA among Korean adults was 1.1%, showing a significant sex-based disparity: 0.6% and 1.7% in men and women, respectively. RA prevalence increased with age, peaking at 3.5% in individuals over 70. This study identified education level, subjective health status, and age as key predictors of RA. Among men, significant predictors of RA included education level and subjective health status, with a higher risk observed in men with only elementary education and poor perceived health. For women, age and subjective health status were the main risk factors, with RA risk increasing markedly in older age groups, particularly in those aged 70 and above. This study highlights the distinct prevalence and risk factors for RA among Korean adults by sex and age. Key predictors—education level, subjective health status, and age—suggest that tailored health interventions addressing these factors are crucial to reducing the RA burden and enhancing health outcomes among affected populations. Full article

Swimming is a widely practiced sport with significant physical demands, placing athletes at a considerable risk of injury, particularly in the shoulder, due to repetitive high-intensity movements. The aim of this systematic review was to analyze the patterns and frequency of injuries associated with swimming. Methods: A systematic search of the Web of Science, PubMed, Scopus, and SportDiscus databases identified nine relevant studies. Results: The findings revealed a higher incidence of injuries in female swimmers compared to males, with the shoulder being the most frequently affected joint. This disparity is partially attributed to the Female Athlete Triad, a syndrome characterized by low energy availability, menstrual dysfunction, and poor bone health, which increases injury susceptibility. Other commonly affected regions include the knee, often associated with the biomechanical demands of breaststroke, and the lumbar spine, which is impacted by degenerative changes resulting from high training volumes. However, variability in the injury measurement protocols across studies limits their comparability and highlights the need for standardized methods. Conclusions: Based on these findings, professionals in the field can identify injury patterns to enhance diagnosis and treatment, design personalized prevention programs, implement early interventions, and innovate equipment and training methods to improve swimmer safety and performance. Full article

The fault diagnosis of industrial equipment is crucial for ensuring production safety and improving the efficiency of equipment operation. With the advancement of sensor technologies, the number of data generated in industrial environments has increased dramatically. Deep learning techniques, with their powerful feature extraction and classification capabilities, have become a research hotspot in the field of fault diagnosis. However, deep learning models are vulnerable to adversarial attacks, which can lead to a decrease in diagnostic accuracy and compromise system safety. This paper proposes a Joint Projection Gradient Descent (SCS-JPGD) method based on single-channel signal features. The proposed method first introduces a gradient-based attack approach for signal samples, which can add tiny perturbations to the input samples, causing misclassification in black-box models. Secondly, a joint training strategy is proposed for gradient attacks on signal samples, aiming to enhance the model’s adaptability to small perturbations in a limited range. Experiments were conducted on the CWRU dataset under four different operating conditions. The results show that, under a deep learning model with diagnostic accuracy exceeding 90%, the joint training method allows the model to maintain an average accuracy of 84.6% even after the addition of adversarial samples, which are barely distinguishable by the human eye. The proposed SCS-JPGD method provides a safer and more accurate approach for fault diagnosis in deep learning research. Full article

Even though children’s malignant bone tumours are rare, it is crucial to understand how to identify and stage them accurately to develop an appropriate treatment plan. Ewing’s sarcoma and osteosarcoma are the two main paediatric bone malignancies and require multidisciplinary treatment involving radiologists, orthopaedists, oncologists, pathologists, and paediatricians. These neoplasms may be associated with genetic syndromes but typically occur in patients with no known germline abnormalities. With a frequency of 4.4 per million, osteosarcoma is the most common malignant bone tumour in children. Ewing’s sarcoma has an incidence of 2.5 to 3 per million, making it the second most prevalent. Clinically, these neoplasms present with pain and inflammation in the bones and joints, nocturnal pain unresponsive to drug therapy, systemic symptoms such as fever or weight loss, and persistent symptoms—all of which should prompt clinicians to initiate further diagnostic investigations. The gold standard for diagnosis includes X-ray examination and MRI, which provide an accurate assessment of tumour extension into the medullary canal and surrounding soft tissues. Fluorine-18-labelled FDG-PET scans or fluoro-deoxyglucose positron emission tomography are valuable for evaluating tumour aggressiveness and excluding metastases. A biopsy is mandatory once all other diagnostic tests have been completed. Accurate diagnosis and timely referral to an experienced clinic are essential for ensuring prompt access to treatment and improving patient outcomes. Full article

Objectives: This overview of systematic reviews was carried out following the PRIOR guidelines. It aimed to collect and compare the results of systematic reviews on the etiology, diagnosis, and treatment standards of septic arthritis of the temporomandibular joint. Methods: ACM, BASE, Google Scholar, PubMed, and Scopus were searched on 5 January 2025, for systematic reviews on SATMJ etiology and treatment. Records underwent selection, AMSTAR 2 evaluation, data extraction, and qualitative synthesis. Results: Three systematic reviews were included, covering 38 reports (93 cases), 37 reports (91 cases), and 25 reports (40 cases), respectively. There are seven source reports common to all three reviews. The reviews co-indicate possible odontogenic etiology, differ in opinions about the impact of chronic diseases, and agree on the superiority of pharmacotherapy, though without consensus on specific antibiotics. Severe complications of SATMJ, including potentially lethal ones, were reported. Conclusions: SATMJ is a serious condition requiring urgent and precise medical intervention, yet no clear management guidelines exist. The low overlap and inconsistency of the previous systematic reviews provide a foundation for a more comprehensive synthesis. Full article

Background/Objectives: An Extensor Pollicis Longus (EPL) subcutaneous rupture is a substantial complication in post-traumatic or degenerative wrist and tendinous lesions. The diagnosis is essentially dictated by a clinical evaluation; in fact, it is characterized by the inability to extend the thumb interphalangeal joint and to retropose the thumb while the hand is resting on a surface. The tendinous transposition using the Extensor Indicis Proprius (EIP) as a donor tendon is a well-known surgical technique performed to restore functional activity to the thumb, and it is preferred for the closer cerebellar network with the thumb itself. However, there is a dearth of clinical results and scientific evidence in the literature. The aim of this study is to evaluate the return-to-work eligibility after an inveterate EPL subcutaneous rupture repaired with a transposition of the EIP. Methods: Patients who reported a subcutaneous rupture of the EPL due to rheumatic diseases or who had undergone previous hand or wrist surgery were excluded; however, all patients tested positive for traumatic wrist hypertension. The surgical technique involves three small incisions to achieve tenorrhaphy of the EIP at the distal head of the EPL. Dynamic tests are carried out intraoperatively to verify the tightness and sufficient rigidity of the suture. The objective evaluation involves the range of motion, pinch strength, and power extension of the thumb and the index finger. Patient-reported outcome measures for pain and patient satisfaction include the Numeric Pain Rating Scale and the Disabilities of the Arm, Shoulder, and Hand questionnaire. Results: A total of 12 patients were eligible (7 W, 5 M, mean age 56.3 years) and were followed for at least one year. There were no surgery-related complications. After the cast was removed 3 weeks after surgery, patients could extend their thumbs, put them back, and use their index fingers. An immediate improvement in objective and subjective assessments was reported. At 10 weeks, nine patients (75%) returned to full work with no pain and without the aid of rehabilitation; two patients (17%) returned to full work with no symptoms 2 weeks later; and only one patient (8%) with a neurological disease required physical therapy, achieving pain control and restoration of full mobility approximately six months after surgery. Conclusions: This surgical technique seems to address satisfactory results in terms of skill recovery and rapid return to work. A tailored rehabilitation program should be implemented for selected patients with neurological conditions that may prolong the adaptation process of the transposed tendon, the coordination, and the independent function of the thumb and index finger. Full article

Background: Temporomandibular disorder (TMD) is a collective term covering acute or chronic musculoskeletal and neuromuscular pain associated with the masticatory muscles, temporomandibular joint, and the associated structures. The surface electromyography (sEMG) test represents clinical potential as an additional diagnostic tool. The purpose of this study was to evaluate differences in masseter and temporalis muscle activity (in sEMG) in subjects with TMD compared to a control group. Methods: The TMD group included 45 people qualified according to the diagnostic criteria for temporomandibular disorder (DC/TMD). In the control group, there were 30 subjects. All participants underwent detailed examinations including sEMG diagnosis of the masseter and temporalis muscles. The sEMG was measured both at rest and during maximal voluntary contraction (MVC). Amplitude root mean square (A_RMS) values for the analysis were determined. Results: No differences were shown in the value of the results between TMD and control group in RS A_RMS transformed data values (p = 0.086) and in the MVC A_RMS transformed data values (p = 0.536). The presence of TMD has no significant effect on the symmetry index value for the temporalis (p = 0.42) and masseter muscles (p = 0.379). Conclusions: Analysis of both raw and normalized sEMG signals is insufficient to differentiate TMD patients from healthy individuals. Full article

Traditional fault diagnosis methods for rolling bearings rely on nemerous labeled samples, which are difficult to obtain in engineering applications. Moreover, when unseen fault categories appear in the test set, these models fail to achieve accurate diagnoses, as the fault categories are not represented in the training data. To address these challenges, a zero-shot fault diagnosis model for rolling bearings is proposed, which realizes knowledge transfer from seen to unseen categories by constructing attribute information, thereby reducing the dependence on labeled samples. First, an attribute method Discrete Label Embedding Method (DLEM) based on word embedding and envelope analysis is designed to generate fault attributes. Fault features are extracted using the Swin Transformer model. Then, the attributes and features are input into the constructed model Distribution Consistency and Multi-modal Cross Alignment Variational Autoencoder (DCMCA-VAE), which is built on Convolutional Residual SE-Attention Variational Autoencoder (CRS-VAE). CRS-VAE replaces fully connected layers with convolutional layers and incorporates residual connections with the Squeeze-and-Excitation Joint Attention Mechanism (SE-JAM) for improved feature extraction. The DCMCA-VAE also incorporates a reconstruction alignment module with the proposed distribution consistency loss $L_{WT}$ and multi-modal cross alignment loss function $L_{MCA}$. The reconstruction alignment module is used to generate high-quality features with distinguishing information between different categories for classification. In the face of multiple noisy datasets, this model can effectively distinguish unseen categories and has stronger robustness than other models. The model can achieve 100% classification accuracy on the SQ dataset, and more than 85% on the CWRU dataset when unseen and seen categories appear simultaneously with noise interference. Full article

Human immunodeficiency virus (HIV) and hepatitis B virus (HBV) continue to be global public health issues. Globally, about 39.9 million persons live with HIV in 2023, according to the Joint United Nations Programme on HIV/AIDS (UNAIDS) 2024 Fact Sheet. Consequently, the World Health Organisation (WHO) reported that about 1.5 million new cases of HBV occur, with approximately 820 thousand mortalities yearly. Conversely, the lower percentage of HBV (30%) cases that receive a diagnosis is a setback in achieving the WHO 2030 target for zero HBV globally. This has necessitated a public health concern to repurpose antiretroviral (ARV) drugs for the treatment of HBV diseases. This review provides an introductory background, including the pros and cons of repurposing antiretrovirals (ARVs) for HBV treatment. We examine the similarities in replication mechanisms between HIV and HBV. We further investigate some clinical studies and trials of co-infected and mono-infected patients with HIV–HBV. The topical keywords including repurposing ARV drugs, repurposing antiretroviral therapy, Hepatitis B drugs, HBV therapy, title, and abstracts are searched in PubMed, Web of Science, and Google Scholar. The advanced search includes the search period 2014–2024, full text, clinical trials, randomized control trials, and review. The search results filtered from 361 to 51 relevant articles. The investigations revealed that HIV and HBV replicate via a common route known as ‘reverse transcription’. Clinical trial results indicate that an early initiation of ARVs, particularly with tenofovir disoproxil fumarate (TDF) as part of a regimen, significantly reduced the HBV viral load in co-infected patients. In mono-infected HBV, timely and correct precise medication is essential for HBV viral load reduction. Therefore, genetic profiling is pivotal for successful ARV drug repurposing in HBV treatment. Pharmacogenetics enables the prediction of the right dosages, specific individual responses, and reactions. This study uniquely explores the intersection of pharmacogenetics and drug repurposing for optimized HBV therapy. Additional in vivo, clinical trials, and in silico research are important for validation of the potency, optimum dosage, and safety of repurposed antiretrovirals in HBV therapy. Furthermore, a prioritization of research collaborations comprising of regulators and funders to foster clinically adopting and incorporating repurposed ARVs for HBV therapy is recommended. Full article