Search Results (7,406)

This study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations. The number of P waves is often inadequate for phase association and event location, which underscores the importance of identifying S waves. Given that individual RTS cases may consist of only hundreds of events, it is crucial for algorithms to be trained on small datasets or to detect effectively using external, global training data. To evaluate this, we compared the efficiency of a deep learning global detection model, transfer learning to the RTS database, a specialized neural network designed for RTS, and manual detection of seismic signals. Transfer learning efficiency was database dependent. Additional interpretation and parametrization of detection results are assumed. Therefore, the emphasis is on phase detection, rather than phase picking accuracy, and detection sensitivity is more important than its specificity. Phase association plays a vital role in detecting seismic signals, facilitating the elimination of most false picks. As a result, the comparisons of detections were based on parameters related to the location of seismic events. The findings indicate that neither the automatic signal detection methods nor the manual methods alone are sufficient. However, their combination significantly enhances detectability. The final catalogs cover up to 30% more events compared to the previous manual. It fulfills the main aim of applying a neural network detector, which is to increase the number of seismic events in the catalog. It may also be further utilized in the research of the triggering process, such as identifying fluid paths and determining fault geometry. Full article

The electric propulsion system is a critical determinant of unmanned aerial vehicles’ (UAVs’) operational capabilities, particularly endurance performance. This paper proposes a high-precision modeling framework for UAV electric propulsion systems to improve endurance estimation. By integrating dimensional analysis based on the Buckingham $\pi$ theorem with data-driven parameter fitting, the method accurately predicts propeller thrust, power, and motor current under varying inflow conditions using limited experimental data. The proposed models and complete implementation are publicly available, facilitating reproducibility and further research. The key novelty of this work lies in the tight integration of dimensional analysis (via Buckingham’s $\pi$ theorem) with a data-driven torque-based motor current model, enabling accurate cross-configuration predictions for both propeller aerodynamics and motor electrical characteristics using limited experimental data. The model is rigorously validated against the UIUC propeller database, a custom-built inflow test rig, and actual flight tests. The results demonstrate that the proposed approach achieves superior prediction accuracy across multiple propeller-motor configurations while significantly reducing computational costs. This work provides a reliable foundation for improving UAV endurance estimation and propulsion system design. Full article

Background: Auricular defects resulting from congenital anomalies, trauma, or oncologic resection pose significant functional and psychosocial challenges. When autologous reconstruction is not feasible or not desired, implant-retained auricular prostheses represent a reliable alternative with high patient satisfaction. This study aimed to systematically evaluate the clinical performance of craniofacial implants used for auricular prosthetic rehabilitation, focusing on implant survival, prosthetic outcomes, workflow typologies, and complications. A secondary objective was to illustrate the long-term validity of a minimally invasive navigation technique through a clinical case with 6-year follow-up. Methods: A systematic review was conducted according to PRISMA guidelines. Clinical studies published between 2005 and 2025 reporting outcomes of implant-retained auricular prostheses were searched in PubMed and Scopus databases. Data were extracted on implant type, survival rates, prosthetic performance, workflow, and complications. Risk of bias was assessed using appropriate tools based on each study design. Results: A total of thirty-two studies were included, comprising fifteen case reports, fifteen case series, one cohort study, and one prospective observational study. Implant survival was consistently high across all workflow categories, with failures predominantly associated with irradiated or anatomically compromised bone. Prosthetic outcomes were favorable, showing excellent esthetics, stable retention, and high patient satisfaction irrespective of manufacturing method, although digital and navigation-assisted workflows improved reproducibility, symmetry, and planning precision. Complication rates were low and generally limited to mild peri-abutment inflammation manageable with conservative care. The clinical case confirmed these findings, showing stable osseointegration, healthy soft tissues, and uncompromised prosthetic function at 6-year follow-up. Conclusions: Implant-retained auricular prostheses show predictable long-term success, independent of whether traditional, hybrid, or fully digital workflows are employed. Digital technologies enhance surgical accuracy, minimize morbidity, and streamline prosthetic fabrication, although high-quality comparative studies remain limited. Full article

Chronic back pain (CBP) associated with intervertebral disc degeneration (IVDD) is a leading cause of medical consultations, decreased quality of life, and temporary and permanent disability. The mechanisms of CBP development and persistence in patients with IVDD have been studied for many years, but this issue remains far from resolved. The search for predictive biomarkers that could help identify patients with IVDD at high risk for CBP continues. In recent decades, research has shown increasing interest in identifying epigenetic biomarkers for this disorder. to summarize the results of preclinical and clinical studies on the role of microRNAs (miRs) as epigenetic biomarkers of the development and progression of CBP in patients with IVDD. English-language articles; original experimental (preclinical) studies; original clinical study; assessment of changes in systemic (in blood) and/or local (in intervertebral disk (IVD)) levels of miR expression in IVDD, either independently or in comparison with healthy controls; and studies that were completed and the results of which were published. PubMed, Springer, Google Scholar, Scopus, Oxford Press, Cochrane, and e-Library databases. Charting for this scoping review involved developing a data extraction form to summarize extracts and organize data from included studies. This was an iterative process where the charting tables and figures may be refined as the review progresses. 126 studies were analyzed in detail, focusing on their study designs and comparing changes in miR expression in animal models of IVDD and in patients with IVDD compared to healthy controls. During the preparation of this scoping review and upon subsequent detailed review of the original publications, it turned out that the results of one study were not justified by the authors due to identified technological problems (the article was withdrawn by the editorial board of the journal). Therefore, we excluded the results of this study from the subsequent analysis. As a result, this section summarizes the results of 60 preclinical and 65 clinical studies. Some miRs (e.g., miR-21 and miR-132) are associated with the regulation of inflammatory pathways that contribute to increased degradation of IVD extracellular matrix and enhanced nociceptive signaling through various mechanisms, contributing to the progression of CBP. Other miRs (e.g., miR-145 and miR-223) exert protective effects, enhance regenerative potential, and alleviate CBP. Despite the promising results of these studies, there are limitations in the use of miRs as perspective epigenetic biomarkers of CBP in patients with IVDD because the pattern of potentially predictive and protective miRs in relation to the mechanisms of CBP formation and progression in IVDD has not yet been sufficiently studied. The results of some preclinical and clinical studies are contradictory. Further research is needed to clarify the role of miR signatures in animal models and clinical trials on IVDD-specific CBP. Full article

Workplace weight stigma is a form of discrimination affecting equality, health, and careers, yet occupational research remains fragmented. This gender-sensitive systematic review synthesizes evidence on workplace weight stigma among adult workers and job applicants since 2000. Following PRISMA procedures, we searched psychological, medical, sociological, and economic databases, identifying 25 included studies examining work outcomes. The corpus includes experimental vignette and correspondence studies, surveys, and qualitative designs, predominantly from high-income Western countries. Higher body weight is consistently associated with disadvantages across the employment life cycle: reduced callbacks and hiring, lower wages and wage growth, fewer promotions, and negative performance evaluations. Penalties are systematically stronger for women; intersectional analyses remain rare. Weight-based teasing, unfair treatment, and stereotype threat are linked to poorer self-rated health, psychological distress, burnout, reduced work ability, lower job satisfaction and commitment, and stronger turnover intentions. Organizational-level evidence is indirect but suggests detrimental effects on engagement and citizenship behaviors. Findings support conceptualizing workplace weight stigma as both a psychosocial hazard and a structural driver of labor-market inequality, underscoring the need for size-inclusive HR practices, leadership, and occupational risk-prevention policies. Full article

Background/Objectives: Earlier initiation of palliative care improves clinical outcomes, including better symptom relief, enhanced quality of life, and decreased use of healthcare resources in advanced disease. This systematic review aimed to identify and critically appraise existing tools, both conventionally developed and based on artificial intelligence, designed to identify patients eligible for early palliative care interventions. Methods: Six electronic databases were examined for primary research studies published between 2000 and 2025. Studies that described or evaluated screening instruments developed to support the early identification of adult patients with palliative care needs underwent dual reviewer screening and data extraction. Results: A total of 35 studies were included. Of these, 13 reported the development of screening tools and 22 focused on the external validation of these instruments. Nine tools were developed using traditional methods, and four instruments were created using artificial intelligence techniques. Significant heterogeneity was observed in tool design and target populations. Most screening tools used death prediction as a proxy, with limited integration of psychological and spiritual dimensions. External validation studies primarily focused on predicting mortality. Overall, all the tools showed moderate predictive ability. Conclusions: The ability of current screening tools to identify patients with advanced diseases who are likely to have palliative care needs remains limited. Further research is needed to develop standardized screening processes that address not only mortality prediction but also disease trajectory and functional decline. Full article

Background and purpose: The role of adjuvant radiation therapy (RT) in early-stage HER2-positive breast cancer treated with breast-conserving surgery (BCS) and systemic therapy remains uncertain in the era of HER2-targeted regimens. This study evaluates the survival impact of RT in patients aligned with the HERO RT de-escalation trial (NRG-BR008). Materials and methods: We queried the National Cancer Database for patients with early-stage HER2-positive invasive breast carcinoma treated with BCS and systemic therapy, stratified into HERO trial-aligned cohorts: Arm 1 (adjuvant systemic therapy) vs. Arm 2 (neoadjuvant systemic therapy, pathologic complete response). Within each cohort, patients receiving adjuvant RT were compared with those omitting RT. In the primary analysis, patients were propensity score matched (PSM) on demographics, diagnosis years, tumor characteristics, and trial stratification variables. Inverse probability of treatment weighting (IPTW) was additionally performed as a sensitivity analysis. Overall survival was evaluated using Kaplan–Meier, Cox regression, and restricted mean survival time (RMST). Results: In Arm 1 (818 patients, 94 deaths), 5-year OS was 96.9% with RT vs. 88.0% without RT, and 10-year OS was 94.3% vs. 68.5% (log-rank p < 0.001). RT omission was associated with higher mortality in the PSM Cox model (HR, 4.78; 95% CI, 2.84–8.02; p < 0.001), with an RMST advantage favoring RT of +2.86 months at 5 years and +12.55 months at 10 years (p < 0.001). In Arm 2 (176 patients, 10 deaths), 5-year OS was 97.6% with RT vs. 91.1% without RT, and OS at 107 months was 94.8% vs. 91.1% (log-rank p = 0.13). RT omission was not statistically significant in the PSM Cox model (HR, 3.40; 95% CI, 0.82–14.05; p = 0.09), though RMST favored RT (+1.83 months at 5 years, p = 0.004; +3.91 months at 107 months, p = 0.03). IPTW analyses were directionally consistent in Arm 1 (HR, 3.26; 95% CI, 2.52–4.21; p < 0.001) and inconclusive in Arm 2 (HR, 1.78; 95% CI, 0.80–3.95; p = 0.16). Conclusions: In this HERO-aligned national cohort, RT omission was associated with inferior OS in patients treated with adjuvant systemic therapy after BCS. Findings in the neoadjuvant pCR cohort were imprecise and hypothesis-generating. Given the retrospective registry design, lack of recurrence-specific endpoints, and potential residual confounding, results should not be interpreted as causal but support continued RT use outside prospective de-escalation trials. Full article

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analyzing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and analyzing the surges of these pollutants well before the recycling process is needed to make intelligent decisions for water cleaning. The dynamic changes in pollutants need constant monitoring and effective planning with appropriate treatment strategies. We propose an edge-computing-based smart framework that captures data from sensors, including ultraviolet, electrochemical, and microfluidic, along with other significant sensor streams. The edge devices send the data from the cluster of sensors to a centralized server that segments anomalies, analyzes the data and suggests the treatment plan that is required, which includes aeration, dosing adjustments, and other treatment plans. A logic layer is designed at the server level to process the real-time data from the sensor clusters and identify the discharge of nutrients, metals, and emerging contaminants in the water that affect the quality. The platform can make decisions on water treatments using its monitoring, prediction, diagnosis, and mitigation measures in a feedback loop. A rule-based Large Language Model (LLM) agent is attached to the server to evaluate data and trigger required actions. A streamlined data pipeline is used to harmonize sensor intervals, flag calibration drift, and store curated features in a local time-series database to run ad hoc analyses even during critical conditions. A user dashboard has also been designed as part of the system to show the recommendations and actions taken. The proposed system acts as an AI-enabled system that makes smart decisions on water treatment, providing an effective cleaning process to improve sustainability. Full article

Background: Stroke and subclinical cerebral ischemia remain important neurological complications of transcatheter aortic valve replacement (TAVR). The Sentinel cerebral embolic protection (CEP) device is designed to capture embolic debris during TAVR, but its impact on clinical and imaging outcomes remains incompletely characterized. Methods: PubMed, Embase, and Cochrane databases were systematically searched for randomized controlled trials (RCTs) comparing Sentinel CEP versus no protection when TAVR was performed. Outcomes of interest included all stroke, disabling stroke, infarct volume by diffusion-weighted MRI in protected and unprotected areas, all-cause mortality, acute kidney injury, and major vascular complications. Risk ratios (RRs) and median differences with 95% confidence intervals (CIs) were calculated using random-effects models and trial sequential analysis (TSA) assessed evidence robustness. Results: Four RCTs including 10,986 patients were analyzed. Sentinel CEP did not significantly reduce clinical stroke (RR 0.88, 95% CI 0.69–1.12) or disabling stroke (RR 0.68, 95% CI 0.41–1.14). Pooled DW-MRI data showed a significant reduction in new ischemic lesion volume within Sentinel CEP-protected territories (difference in medians –75.7 mm³; 95% CI –130.4 to –21.0). Subgroup analyses in elderly, female, and high-surgical-risk patients revealed no benefit with Sentinel CEP. Additionally, TSA indicated that current data are underpowered for definitive conclusions. Conclusions: The Sentinel CEP device during TAVR did not significantly reduce clinical stroke but was associated with lower MRI-detected ischemic lesion volumes compared with no protection. Further adequately powered RCTs integrating clinical and imaging endpoints are needed to define its role in neuroprotection during TAVR. Full article

Accurately identifying drug off-targets is essential for reducing toxicity and improving the success rate of pharmaceutical discovery pipelines. However, current deep learning approaches often struggle to fuse chemical structure, protein biology, and multi-target context. Here, we introduce HDPC-LGT (Hybrid Dual-Prompt Cross-Attention Ligand–Protein Graph Transformer), a framework designed to predict ligand binding across sixteen human translation-related proteins clinically associated with antibiotic toxicity. HDPC-LGT combines graph-based chemical reasoning with protein language model embeddings and structural priors to capture biologically meaningful ligand–protein interactions. The model was trained on 216,482 experimentally validated ligand–protein pairs from the Chemical Database of Bioactive Molecules (ChEMBL) and the Protein–Ligand Binding Database (BindingDB) and evaluated using scaffold-level, protein-level, and combined holdout strategies. HDPC-LGT achieves a macro receiver operating characteristic–area under the curve (macro ROC–AUC) of 0.996 and a micro F1-score (micro F1) of 0.989, outperforming Deep Drug–Target Affinity Model (DeepDTA), Graph-based Drug–Target Affinity Model (GraphDTA), Molecule–Protein Interaction Transformer (MolTrans), Cross-Attention Transformer for Drug–Target Interaction (CAT–DTI), and Heterogeneous Graph Transformer for Drug–Target Affinity (HGT–DTA) by 3–7%. External validation using the Papyrus universal bioactivity resource (Papyrus), the Protein Data Bank binding subset (PDBbind), and the benchmark Yamanishi dataset confirms strong generalisation to unseen chemotypes and proteins. HDPC-LGT also provides biologically interpretable outputs: cross-attention maps, Integrated Gradients (IG), and Gradient-weighted Class Activation Mapping (Grad-CAM) highlight catalytic residues in aminoacyl-tRNA synthetases (aaRSs), ribosomal tunnel regions, and pharmacophoric interaction patterns, aligning with known biochemical mechanisms. By integrating multimodal biochemical information with deep learning, HDPC-LGT offers a practical tool for off-target toxicity prediction, structure-based lead optimisation, and polypharmacology research, with potential applications in antibiotic development, safety profiling, and rational compound redesign. Full article

Background/Objectives: Isolated cleft lips and/or palates often require orthognathic treatment. Traditional planning based on 2D images and plaster models limits precision; therefore, virtual surgical planning (VSP) and Computer-Aided Design and Computer-Aided Manufacturing (CAD/CAM) technologies are increasingly being used. The aim of this scoping review was to analyze the techniques, outcomes, and gaps in research on VSP in orthognathics for patients with isolated (non-syndromic) clefts. Methods: Searches were conducted in July 2025 in seven databases (including PubMed, Scopus, and Cochrane) without language restrictions, in accordance with the PRISMA guidelines for scoping reviews. Of the 2836 records, 36 publications were eligible after deduplication and full-text screening, and their Level of Evidence (LoE) was assessed using the Oxford CEBM scale. A risk of bias assessment was also conducted according to JBI tools. Results: The identified studies primarily comprised LoE III and IV; there were no systematic reviews or randomized controlled trials (LoE I). Descriptions of bimaxillary procedures and LeFort I osteotomies dominated. The most commonly used software was ProPlan CMF, Dolphin 3D, and Rhinoceros, although other tools have emerged in recent years. The available studies suggest that VSP increases translational and rotational accuracy and facilitates individualized treatment, and bimaxillary procedures bring better functional and aesthetic outcomes in patients with severe maxillary hypoplasia. Conclusions: Despite the growing interest in VSP in orthognathics, the scientific evidence is limited and mostly of lower quality. Well-designed prospective studies are needed to assess the long-term stability, quality of life, and cost-effectiveness of modern technologies. Full article

Background: Pectus excavatum (PE) is the most common congenital chest wall deformity, affecting approximately 1 in 400 live births. Although familial clustering supports a genetic contribution, the molecular basis of PE remains poorly defined. This systematic review synthesizes existing evidence on genetic variants associated with PE to guide future genome-wide association studies (GWAS) and Mendelian randomization (MR) analyses. Methods: A comprehensive systematic search was conducted across all electronic databases, including Google Scholar, PubMed/MEDLINE, Web of Science, and arXiv, from inception to November 2025. Nine studies met the inclusion criteria. The search strategy utilized the terms “pectus excavatum”, “genetic variants”, “SNPs”, and “GWAS”, combined with Boolean operators. Eligible studies reported genetic associations, family-based analyses, or mechanistic investigations. The Newcastle–Ottawa Scale was used to assess study quality. Results: No population-level GWAS of isolated PE was identified. Fourteen genetic loci were reported across diverse study designs, including family-based exome sequencing (REST, SMAD4, COL5A1, COL5A2), case reports (COL1A1, COL27A1, NF1, BICD2, PTPN11), candidate gene analyses (ACAN), mouse models (GPR126, GAL3ST4), and linkage analysis implicating chromosome 18q. These genes converge on four key biological pathways: extracellular matrix and collagen metabolism, TGF-β/BMP signaling, cartilage development, and transcriptional regulation. Importantly, none of the included studies reported SNP-level effect sizes, allele frequencies, or odds ratios, precluding construction of valid MR instruments. Conclusions: Current genetic evidence for PE is largely derived from rare variants and family-based studies, with no population-level GWAS available. This critical gap limits causal inference, underscoring the urgent need for large-scale international GWAS to identify common variants and clarify the genetic architecture of PE. Full article

In the design of marine structures, structural design remains a critical activity, heavily guided by classification society rules that define dimensions of the structural elements. With the development of increasingly complex structures and the increasing use of composite materials in ship structural design, accurate knowledge of material properties, particularly those of sandwich panel cores, is essential for direct calculations. This article presents experimental data and numerical analysis results for the PVC core of sandwich panels, the selection of appropriate material models from the LS-DYNA standard material database, and the development of a user-defined material model to accurately capture the physical behaviour of foam cores in sandwich constructions. The comprehensive dataset obtained from polymer foam tests is made publicly available to support future structural calculations. Full article

Background: Upper limb loss has a profound impact on individuals’ daily activities, self-image, and social interactions. Despite continuous technological advances in upper-limb prosthetics, high rates of device abandonment persist, highlighting the need to better understand users’ functional and psychosocial needs. Methods: To gain a deeper understanding of the perspectives of upper limb amputees and the synthesis of their needs across ergonomic, functional, and psychological dimensions, this study was conducted. A systematic review was conducted following PRISMA guidelines to synthesize user-reported evidence on upper-limb prosthesis use. Articles indexed in the Web of Science database between 2016 and December 2023 were screened using predefined search terms related to upper-limb amputation, prostheses, social impact, and user needs. Studies were included if they reported direct perspectives of upper-limb prosthesis users regarding usability, functionality, and lived experience. Results: Out of 239 papers identified, 31 were included and analyzed. The findings reveal that functional performance, comfort, weight, intuitive control, and reliability are strongly interconnected with psychosocial factors such as confidence, embodiment, social participation, and acceptance. Technological advances have not consistently translated into improved alignment between prosthetic solutions and user needs, which is reflected in continued dissatisfaction and abandonment. Conclusions: This review provides a structured synthesis of user-reported needs across functional, ergonomic, and psychosocial dimensions, translating these insights into design-relevant guidelines. Emphasizing a user-centered and interdisciplinary perspective, the findings aim to support the development of upper-limb prosthetic devices that are more usable, acceptable, and aligned with users’ expectations, ultimately bridging the gap between user expectations and technological capabilities and promoting long-term adoption and quality of life. Full article

The cosmetic industry is undergoing a deep transformation driven by rapid innovation, evolving consumer expectations, and increasing demands for sustainability. Formulators are required to design products that combine functional efficacy, stability, and appealing sensory properties while adopting environmentally responsible strategies. Traditional empirical and sensory-based approaches, though valuable, are often limited by high costs, time, subjectivity and lack of reproducibility. In this context, instrumental techniques provide an objective and predictive means to optimize product performance. Rheology, texture analysis, and tribology offer complementary insights into the structure, mechanical behavior, and interfacial phenomena of cosmetic formulations, all of which are closely linked to application behavior and sensory perception. Their integration enables a quantitative correlation between formulation composition, process conditions, and tactile performance. This review critically examines recent advances in the integrated use of rheology, texture analysis and tribology in cosmetic science, highlighting their role in sensory prediction, stability assessment, scale-up and eco-design. Together, these instrumental approaches support a more data-driven and innovation-oriented formulation paradigm, enabling database development and predictive modeling. Future research should prioritize database expansion, in vivo validation and machine learning integration to further improve sensory prediction and accelerate the design of advanced cosmetic formulations. Full article