Search Results (25,254)

Ensuring the reliability of solder joints is essential for stable operation in electric vehicle chargers, particularly for components assembled using surface-mount technology. Therefore, we developed a defect inspection system for welding joint defects using a Faster Region-based Convolutional Neural Network model to classify results as insufficient defect, shifting defect, and normal (pin-qualified) on voltage control IC pins. The model was trained on 72,000 pin samples and achieved a training accuracy of 99.93%. Evaluation of 65,700 pin samples resulted in an accuracy of 98.89%. The experimental results demonstrate that the system provides stable recognition of reflective solder joints, reliably identifies critical pin-level defects, and is suitable for deployment in practical inspection environments. Full article

Background: The long-term cardiovascular safety of high-dose intravenous mesenchymal stem cell (MSC) therapy remains insufficiently characterized in real-world clinical settings. Methods: We conducted a single-center retrospective observational study of patients who received high-dose intravenous MSC therapy. Cardiovascular events were identified through follow-up records. Observed event incidence was compared descriptively with age-adjusted population reference data. Statistical analyses were performed using two-sided Poisson methods. Results: Among treated patients, a total of four cardiovascular events were recorded during follow-up. The observed incidence did not demonstrate an excess signal compared with reference population data. No clustering of events was observed in the early post-infusion period. Sensitivity analyses yielded consistent findings. Conclusions: In this real-world cohort, high-dose intravenous MSC therapy was not associated with an apparent increase in cardiovascular event incidence. Given the observational design and limited event number, larger prospective studies are warranted to further characterize long-term cardiovascular safety. Full article

Habitat quality is critical for spatial planning strategies and ecological conservation initiative, evaluating the health of the natural environment that supports human survival. However, current approaches pay insufficient attention to revealing the evolution and spatial heterogeneity of the habitat quality simultaneously. In this study, a comprehensive and practical framework was therefore developed for mechanistic habitat quality analysis, which incorporates an adaptable evolutionary model alongside multiple spatial statistical methods. Ningxia, located in Northwest China, was selected as a case study area due to its fragile ecosystem. The proposed framework was then applied to characterize the evolutionary process and spatial heterogeneity of habitat quality in Ningxia. Key factors driving spatial heterogeneity were also found at the same time. From 2000 to 2024, habitat quality in Ningxia is characterized by good habitat and shows significant improvement, following a progressive trajectory. The proportion of poor habitat has been significantly reduced from 29.26% to 24.63%, while that of excellent habitat has been increased from 1.68% to 2.33% over the past two decades. Variation in habitat quality is more pronounced in northern and southern regions, while remaining relatively stable in the central Yellow River ecological corridor. Both natural and socioeconomic factors have an impact on the habitat change in this region, such as the Normalized Difference Vegetation Index (NDVI), Net Primary Productivity (NPP), and Gross Domestic Product (GDP). Vegetation factors play vital roles in spatial variation in habitat quality, while the influences of socioeconomic factors are relatively small. The spatial heterogeneity is driven by nonlinear synergistic effects among numerous factors. This paper developed a feasible framework to retrieve the evolution and spatial heterogeneity pattern of habitat quality, which provides a robust methodology for further habitat assessment at the ecologically fragile regions worldwide. Full article

Background: Vascular malformations are congenital structural abnormalities of the blood vessels that may present at any age. In the vulvovaginal region, these lesions are uncommon and frequently misdiagnosed because their clinical appearance overlaps with common gynecologic conditions, particularly Bartholin’s gland cyst or abscess. Inappropriate surgical intervention without prior vascular evaluation may result in hemorrhage, incomplete treatment, and recurrence. Methods: A structured narrative review of the literature was performed using PubMed/MEDLINE and EMBASE databases (January 2000–April 2024) to summarize the classification, pathophysiology, clinical presentation, imaging characteristics, differential diagnosis, and management of vulvovaginal vascular malformations. Publications addressing vascular anomalies in other anatomical locations were also included when clinically relevant. A representative clinical case confirmed by histopathologic and molecular analysis is presented to illustrate the diagnostic pitfalls. Results: Vulvovaginal vascular malformations are predominantly low-flow venous lesions but may include high-flow arteriovenous malformations. A clinical examination alone is insufficient for diagnosis. Doppler ultrasonography is the recommended initial imaging modality, followed by magnetic resonance imaging to define the lesion extent and flow characteristics. Misdiagnosis most commonly occurs when lesions are treated as Bartholin’s gland pathology without prior imaging. Low-flow lesions are generally managed with sclerotherapy or planned surgical excision, whereas high-flow lesions require embolization and multidisciplinary care. Hormonal and hemodynamic changes, including pregnancy, may precipitate enlargement or thrombosis. Conclusions: Vascular malformations should be considered in the differential diagnosis of atypical vulvar masses. Preoperative imaging is essential in order to avoid inappropriate surgical procedures. A structured diagnostic approach combining clinical assessment and imaging enables correct classification and guides treatment. The presented case demonstrates a typical diagnostic pitfall and emphasizes the importance of recognizing vascular lesions in gynecologic practice. Full article

Temporal Knowledge Graph Inference (TKGI) is a cornerstone for intelligent decision-making in dynamic scenarios, but existing models face critical bottlenecks, including inadequate complex-context modeling, a lack of entity importance quantification, insufficient novel-event reasoning accuracy, and weak domain adaptability. To address these issues, this study proposes a semantics-enhanced model (LLM-DSaR) integrating Large Language Models (LLMs), temporal attention networks, and optimized contrastive learning. Specifically, a two-stage LLM semantic enhancement (LLM1 + LLM2) framework first generates structured semantic analysis reports via adaptive prompt engineering, and then extracts domain-specific semantic embeddings from the last-layer hidden states through pooling and linear projection, which are further fused with TransE-based structural embeddings; meanwhile, LLM2 mitigates data sparsity in novel-event reasoning; a dynamic weight fusion (DWF) framework adaptively assigns feature weights to achieve deep feature synergy; an LLM-enhanced contrastive-learning module strengthens event clustering and discrimination. Experiments on five public datasets and a self-constructed Robotics Temporal Knowledge Graph (RTKG) show LLM-DSaR outperforms 16 baselines: on RTKG, its MRR is 10.35 percentage points higher than GCR, and Hits@10 reaches 88.87%. Ablation experiments validate core modules’ effectiveness, confirming LLM-DSaR adapts to professional scenarios like robot maintenance prediction, providing a novel technical paradigm for complex-domain TKG reasoning. Full article

Biodiversity monitoring in tropical ecosystems is constrained by limited infrastructure, insufficient localized datasets, and reliance on cloud-based tools. We introduce BEMAX, a lightweight convolutional neural network for offline classification of endangered tree species in the Bohol Biodiversity Complex, Philippines. A curated leaf-image dataset from five species and an unknown class was collected using a Raspberry Pi camera. The MobileNetV2-based model achieved a 93.89% validation accuracy and an 88.33% field accuracy. Deployed on a Raspberry Pi 4 with touchscreen and camera integration, BEMAX demonstrates embedded AI as a replicable framework for conservation in data-scarce environments. Full article

Timber frame diaphragms play a central role in the lateral stability of modern timber buildings, yet current design codes insufficiently capture their nonlinear behaviour and governing failure mechanisms. This study evaluates two finite element modelling strategies to improve the prediction of diaphragm response. The first strategy, implemented in MATLAB^®, explicitly models the nonlinear behaviour of sheathing-to-framing (STF) connections using an oriented orthogonal multilinear damage law. Validation against experimental tests on partially anchored and fully anchored diaphragms as well as in-plane bending specimens demonstrated accurate predictions of stiffness and force–displacement behaviour in both the linear-elastic and elastoplastic ranges. Deviations in peak load predictions for the detailed model reached up to approximately 25%, while stiffness predictions remained within approximately 10% of the experimental values. The second approach, implemented in commercial structural engineering software, represents STF connections by uncoupled elastoplastic spring elements. Although post-peak softening cannot be captured, peak capacities were predicted within approximately 3–5% for several configurations, with reliable stiffness estimates in most cases. A quantitative comparison using the normalised root mean square error between experimental and numerical force-displacement curves yielded values between approximately 5% and 14%, indicating good agreement between the numerical predictions and the experimental behaviour. Overall, the detailed model enables high-fidelity nonlinear analysis and insight into failure mechanisms, whereas the simplified spring approach offers a practical and computationally efficient modelling strategy suitable for routine engineering design. Full article

To address the current issues in soil organic carbon (SOC) content prediction where data preprocessing relies on expert experience to formulate fixed rules, resulting in a lack of uniform standards and insufficient consideration of regional soil heterogeneity; while hyperparameter tuning faces problems of high computational costs and excessively long runtimes, this study proposes an intelligent modeling workflow driven by Large Language Models (LLM). This workflow focuses on optimizing two key aspects of SOC Random Forest modeling: data preprocessing and hyperparameter tuning. Results: The LLM-defined rules achieved sample retention rates of 55.33% and 61.90% in the two regions, respectively, showing more significant differences compared to traditional hard-coded rules (56.2% and 59.3%), and the mean soil organic carbon content deviations (30.27% and 20.05%) were both lower than those of traditional hard-coding. At the same time, the mean soil organic carbon content values in both regions closely matched the effectiveness of other methods, indicating that the large language model has effectively captured regional soil differences. With only a single evaluation of hyperparameter optimization, the adaptive model achieved test set R² values of 0.394 and 0.694 in the black soil region and the aeolian sandy soil region, respectively, with root mean square error values of 8.76 g/kg and 6.07 g/kg—its performance is comparable to that of Grid Search and Random Search, while computational efficiency improved by over 95%. Performance comparisons with eXtreme Gradient Boosting (XGBoost) and Partial Least Squares Regression (PLSR) show that the LLM-optimized Random Forest achieved R² = 0.394 and RMSE = 8.76 g/kg in the black soil region, and R² = 0.694 and RMSE = 6.07 g/kg in the windblown sandy soil region, demonstrating practical application value. Full article

Background: Addictive disorders are highly heterogeneous and frequently comorbid, limiting the clinical utility of categorical diagnoses. Transdiagnostic pharmacology seeks to address these limitations by targeting symptom dimensions and shared neurobiological processes across addictions. Methods: We conducted a theory-driven narrative review of studies indexed in MEDLINE, PubMed, LILACS, and Web of Science (October–November 2025), integrating clinical, mechanistic, and dimensional evidence. Findings were organized using the Dysregulation Phenomena of the Three Main Modes of the Predostatic Mind and the Advanced Cognitive Emotional Regulation Therapy (DREXI3/ACERT) framework, which conceptualizes addiction as dysregulation across three interacting systems—Alarm, Seeking, and Balance—and six transdiagnostic symptom dimensions, with a proposed expansion into twenty clinically observable domains (TDPM-20). Results: Pharmacological interventions consistently target neurobiological systems related to stress, reward, impulsivity, and compulsivity. Across studies, the most clinically relevant outcomes remain abstinence, reduction in substance use, and treatment retention. While these outcomes are essential, expanding outcome frameworks to incorporate dimensional and mechanistically informed measures may enhance the identification of clinically meaningful subgroups. Across studies, multiple pharmacological classes show transdiagnostic potential, but their clinical application remains variably aligned with dimensional clinical profiles. Conclusions: A dimensionally oriented approach grounded in neurobiological principles may improve alignment between clinical processes and therapeutic strategies. The DREXI3/ACERT model provides a structured framework for individualized treatment planning and research integration. This approach should be understood as complementary to, rather than a replacement for, established evidence-based treatments for specific substance use disorders, particularly in contexts where therapeutic options remain limited or insufficient. Advancing transdiagnostic pharmacology will require broader dimensional stratification, expanded outcome frameworks capable of capturing patient heterogeneity, and integrative trial designs to strengthen precision psychiatry in addictive disorders. Full article

Objectives: Occupational stress, resilience and 5-hydroxytryptamine receptor 2A gene (HTR2A) polymorphisms potentially influence sleep quality. However, evidence of their effects and relationships remains ambivalent and insufficient. Therefore, this study investigated the association of occupational stress, resilience, HTR2A polymorphisms, and their interactions with sleep quality. Methods: Using a cross-sectional design, 809 Chinese Han subjects (47% female and 53% male; age: 33.1 ± 6.3 years) were genotyped for HTR2A rs6313 polymorphism. Occupational stress, resilience and sleep quality were measured using Work Stress Scale, Connor-Davidson Resilience Scale, and Pittsburgh Sleep Quality Index, respectively. Results: Higher occupational stress was significantly correlated with poorer sleep quality (odds ratio (OR) = 2.020, 95% confidence interval (CI): [1.736, 2.394], p = 0.00031), while higher resilience was significantly correlated with better sleep quality (OR = 0.610, 95% CI: [0.522, 0.697], p = 0.00047). Occupational stress played a mediating role in the association between resilience and sleep quality (indirect: β₁β₂ = −0.067, 95% CI: [−0.101, −0.041], p = 0.00045; direct: β₃ = −0.119, 95% CI: [−0.205, −0.032], p = 0.008). The rs6313 polymorphism moderated the association between resilience and sleep quality (β₆ = 0.786, 95% CI: [0.092, 1.422], p = 0.027), but not the indirect effect. Conclusions: Resilience is associated with better sleep quality both directly and by attenuating the negative correlation between occupational stress and sleep quality, and the rs6313 polymorphism is associated with modifying the relationship between resilience and sleep quality (but not occupational stress and sleep quality), which suggests potential distinct biological association patterns for resilience and stress. Subjects with TT and TC/CC genotypes had different sleep quality response to resilience, implying potential molecular mechanisms of resilience. Our findings provide implications for the prevention and intervention of stress-related sleep problems in occupational populations by targeting modifiable factors including occupational stress and individual resilience. Full article

Modern oil refining is faced with the need to maximize raw material processing in the face of fierce competition and environmental requirements. Therefore, the fluid catalytic cracking (FCC) process, key to the production of high-octane gasoline, requires special attention to automation efficiency. Maintaining optimal reactor temperature is a complex scientific and technical challenge, the solution to which directly impacts the yield of target products and the service life of the catalyst. Existing automatic control systems often fail to cope with process transients, nonlinearities, and time delays, making the search for new control approaches highly relevant. The scientific significance of this study lies in the system analysis and quantitative comparison of the effectiveness of classical control laws (P, PI, PID) applied to a plant with a delay. For the first time, a rigorous comparative analysis of tuning methods—analytical (based on phase margin specifications) and automated (using the PID Tuner tool in MATLAB Simulink R2024b)—is performed for a plant characterized as a second-order system with time delay, formed by the series connection of two first-order lag elements with transport delay. The results contribute to automatic control theory by clearly demonstrating the limitations of the proportional controller and the insufficient speed of the integral controller, as well as confirming the hypothesis that a PID law is necessary to achieve a balance between accuracy and response speed under inertia conditions. The practical significance of the work is confirmed by the development of an optimized automatic temperature control system. Using the PID Tuner tool, we achieved critical industrial performance indicators: zero static error, minimal control time (44 s), and acceptable overshoot (9.6%). The system’s robustness (maintaining stability with changes in plant parameters by 30–40%) and its invariance to the main disturbance (catalyst temperature fluctuations), confirmed during simulation, guarantee the viability of the proposed solution under real-world production conditions. Implementation of such a controller will minimize deviations from the process conditions, leading to increased yield of light petroleum products and an extended service life of the expensive catalyst, providing direct economic benefits. Full article

Purpose: This study aimed to comprehensively map the structural impacts of ROP on all ocular structures, including and extending beyond the inner retina and the associated long-term sequelae that manifest into adulthood. Methods: This scoping review identified studies on animal oxygen-induced retinopathy and clinical retinopathy of prematurity using a multi-database search. Study selection and data extraction were performed independently by multiple reviewers using Covidence software. Results: ROP results in lasting ocular complications. Posterior segment findings include choroidal insufficiency, photoreceptor dysfunction, and retinal detachment. Anterior segment complications involve a higher incidence of angle-closure glaucoma, strabismus, and significant myopia. Conclusions: This scoping review was conducted and reported in accordance with the PRISMA-ScR guidelines, though it is limited by the exclusion of non-English studies. Lifelong ophthalmic monitoring is essential for ROP patients due to persistent anterior and posterior segment complications. This study also identifies key future research priorities, including elucidating mechanisms of foveal development and conducting longitudinal studies. Furthermore, as neonatal intensive care expands in low and middle-income regions, international collaboration is vital to guide screening and treatment and prevent a debilitating surge of ROP. Full article

Collaborative decarbonization between ports and shipping companies is critical to the low-carbon transition of maritime supply chains. Driven by the new energy transition, vertical technology spillovers have become a key force shaping vertical collaborative emission reduction. However, the mechanisms through which spillovers affect strategic interactions remain unclear, the theoretical basis for emission reduction strategies is insufficient, and practical issues such as benefit sharing and coordination mechanisms are underexplored. To fill these gaps, this study makes three contributions. Theoretically, we incorporate vertical technology spillovers and joint benefit–cost sharing into the port–shipping collaborative emission reduction framework, enriching supply-chain-level spillover theory. Methodologically, we combine an evolutionary game model with a scale-free network to simulate strategy diffusion and conduct scenario comparisons, linking theoretical modeling with industrial practice. Empirically, we confirm that ports act as leaders in collaborative decarbonization, and port-centered resource allocation drives the systemic low-carbon transition of the maritime sector. The findings show that the share of agents adopting active emission reduction strategies first rises and then falls with vertical technology spillover intensity, peaking at a moderate level. The impacts of core factors vary significantly across spillover scenarios. Port-centered resource allocation and benefit distribution are crucial to improving overall participation willingness. Ports are not merely participants but irreplaceable coordinators in the maritime supply chain. These results provide targeted policy and practical guidance for ports and shipping companies to promote global green and low-carbon maritime development. Full article

Background/Objectives: Appropriate nutritional management constitutes one of the key elements of conservative treatment and renal replacement therapy in patients with chronic kidney disease (CKD). The level of patients’ nutritional knowledge may significantly influence adherence to dietary recommendations, the rate of disease progression, and the frequency of complications. The aim of this study was to assess the level of nutritional knowledge, dietary habits, adherence to dietary recommendations, and nutritional status of patients with CKD according to disease stage. Methods: This cross-sectional study was conducted among 98 adult patients diagnosed with CKD. A questionnaire assessing nutritional knowledge and dietary behaviors was administered. An overall nutritional knowledge score was calculated based on eight questionnaire items assessing nutritional knowledge. Nutritional status was evaluated using the Subjective Global Assessment (SGA) and the Simplified Nutritional Appetite Questionnaire (SNAQ). Anthropometric, clinical, and biochemical data were collected. Statistical analysis was performed using tests appropriate to the data distribution. Results: The level of nutritional knowledge varied and was dependent on CKD stage. Patients in more advanced stages of the disease demonstrated significantly higher awareness of dietary recommendations compared with those in earlier stages. The median nutritional knowledge score was 6 points, with 46.9% of participants demonstrating insufficient knowledge (<6 points) and 53.1% achieving adequate knowledge (≥6 points). The greatest knowledge deficits concerned the control of phosphorus, potassium, sodium, and fluid intake. Discrepancies were also observed between declared knowledge and actual dietary behaviors. Good nutritional status (SGA A) was identified in 73 patients, risk of malnutrition or moderate malnutrition (SGA B) in 22 individuals, and severe malnutrition (SGA C) in 3 patients. SNAQ indicated good appetite in the study population, with an average consumption of three meals per day, and identified a risk of weight loss in 6% of patients. Overweight and obesity were present in more than half of the study population, while underweight was observed in 4%. Conclusions: Nutritional knowledge among patients with CKD remains insufficient, particularly in the early stages of the disease. The findings highlight the necessity of early and systematic implementation of individualized nutritional education as an integral component of slowing disease progression. Full article

Background: Parkinson’s disease (PD) is characterized by progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta, accompanied by neuromelanin loss. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) enables in vivo visualization of these changes; however, its diagnostic and clinical utility remains incompletely defined. This study evaluated the feasibility, reliability, and biological sensitivity of semi-automated NM-MRI–based substantia nigra volumetry in PD. Methods: In this prospective case–control study, 50 participants (25 PD patients and 25 healthy controls) underwent 3T NM-sensitive MRI using a high-resolution T1-weighted spin-echo sequence. Semi-automated segmentation of hyperintense substantia nigra regions was performed using Mango v3.5.1, with intracranial volume normalization derived from FreeSurfer v7.3. Four participants were excluded due to motion artifacts, yielding a final cohort of 46 subjects. Clinical assessment included the Unified Parkinson’s Disease Rating Scale (UPDRS) Part III and Hoehn and Yahr (H&Y) staging. Group comparisons, receiver operating characteristic (ROC) analysis, and reliability testing using intraclass correlation coefficients (ICC) were performed. Results: Corrected substantia nigra volume was significantly reduced in PD patients compared with controls (18% reduction; p = 0.039, Mann–Whitney U test). Semi-automated measurements demonstrated excellent agreement with manual segmentation (ICC = 0.945). ROC analysis showed moderate discriminative performance for corrected volume (AUC = 0.700; sensitivity 68.4%, specificity 74.1%). No significant correlation was observed between corrected substantia nigra volume and UPDRS-III motor scores, while a trend toward lower SNc volume was observed with advancing H&Y stage. Conclusions: Semi-automated NM-MRI volumetry detects biologically meaningful substantia nigra volume loss in early-stage Parkinson’s disease with high measurement reliability. However, diagnostic performance was moderate and insufficient for standalone clinical diagnosis or motor severity prediction. These findings support the role of NM-MRI as a complementary imaging marker within multimodal diagnostic and research frameworks rather than as an independent diagnostic tool. Full article