Search Results (1,615)

Background/Objectives: Raghib syndrome is a rare congenital heart disease where the left superior vena cava (LSVC) drains into the left atrium in addition to an absent coronary sinus and an atrial septal defect. This condition can lead to hemodynamic impairment and ultimately put the patient at a high risk of stroke. While computational flow modeling enables predictive understanding of the heart hemodynamic, augmented reality (AR) provides a deeper understanding of complex anatomic geometries. This study shows the case of a 53-year-old woman with Raghib syndrome in whom preoperative planning was supported by the integration of computational fluid dynamics (CFD) and AR. Methods: Three-dimensional (3D) anatomical models were segmented from CT imaging and used to perform CFD simulations of pre- and post-operative hemodynamics, quantifying the left-to-right shunt and confirming restoration of normal intracardiac flow after surgical repair. Results: The AR application developed allowed surgeons to visualize and manipulate holographic cardiac models, enabling accurate assessment of LSVC length, vessels distances and septal defect geometry using HoloLens 2 headsets. Conclusions: This work demonstrates the feasibility and adds value of the immersive approach coupled with quantitative in silico flow analysis for preoperative planning of complex congenital heart disease. Full article

Phytol, an acyclic diterpene alcohol and a key lipophilic side-chain moiety of chlorophyll, is widely distributed in nature. It exhibits potent antioxidant, anti-inflammatory, analgesic and broad-spectrum antibacterial activities. Notably, phytol can also effectively inhibit melanin production, repair the skin barrier, and exert profound anti-aging effects, making it a highly promising ingredient for daily skincare with substantial industrial application value. The skincare benefits of phytol are primarily achieved by constructing a multi-dimensional regulatory network involving defense, modulation and repair. Compared to conventional retinol-based skincare ingredients, phytol exhibits superior biocompatibility, mild irritation, and remarkable safety advantages. Nevertheless, its application is hindered by inherent limitations, including strong hydrophobicity, spontaneous aggregation tendency, and poor photothermal stability. These drawbacks severely restrict its dispersibility, storage stability and percutaneous bioavailability in aqueous cosmetic formulations. To address these deficiencies, nanodrug delivery systems (NDDS), such as liposomes, nanoemulsions, solid lipid nanoparticles and PLGA nanoparticles, have been widely employed. These nanocarriers can penetrate the skin barrier via the size effect, enabling targeted skin delivery and long-term controlled release of phytol. This review systematically summarizes the biological sources and metabolic fate of phytol, as well as its multi-mechanistic pharmacological effects on the skin. Furthermore, we outline the current application status and industrial development trends of phytol in mainstream cosmetics worldwide. This work aims to provide theoretical basis and forward-looking references for the development of high-efficiency, safe and stable phytol-derived skincare raw materials and topical formulations. Highlights: (1) Phytol, a natural acyclic diterpene alcohol, exerts multi-dimensional skincare effects including antioxidant, anti-inflammatory, whitening, anti-aging, and skin barrier repair activities via a defense–modulation–repair regulatory network. (2) Phytol may act as a mild, non-irritating functional alternative to retinoids, targeting PPAR/RXR pathways and avoiding TRPV1-mediated irritation, making it suitable for sensitive skin. (3) Poor water solubility and instability hinder phytol’s translation; nanodelivery systems effectively improve solubility, permeability, and sustained release. Full article

This article presents a critical review of the development of window systems used in civil engineering, interpreting them not as discrete construction products, but as complex technical and material systems whose actual value emerges from the interdependence of structural configuration, material composition, thermal performance, durability, and manufacturing and implementation constraints. The review discusses the evolution of windows from simple envelope elements providing daylight, ventilation, and weather protection into advanced building-envelope systems associated with energy efficiency, occupant comfort, in-service durability, and environmental responsibility. Particular attention is given to the principal families of window systems, including PVC-U, aluminium, timber, steel, façade, hybrid, and composite-based solutions. The analysis shows that improving the thermal insulation of a single component is not, in itself, a sufficient criterion for evaluating system quality. Declared performance may be constrained by thermal bridges at the installation interface, ageing of sealing systems, imperfections in joining processes, material deformation, and difficulties related to repair, disassembly, and recycling. From the perspective of sustainable construction, window systems should therefore be assessed across their whole life cycle, taking into account energy effectiveness, in-service stability, technological feasibility, renovation potential, and the possibility of closing material loops. The review also identifies the need for further research into integrated assessment methods, the long-term durability of advanced frame systems, the role of the window-to-wall interface, and verifiable strategies for circularity. Full article

Immune checkpoint blockade has become an important component of treatment for several gynecologic malignancies, but its clinical value varies substantially by tumor lineage, molecular subtype, disease setting, and treatment backbone. This narrative review summarizes the biological rationale, clinical evidence, predictive biomarkers, combination strategies, toxicity considerations, and future directions of immunotherapy across gynecologic cancers. In cervical cancer, checkpoint blockade has evolved from later-line recurrent disease to first-line systemic therapy and, more recently, to curative-intent chemoradiotherapy combinations. In endometrial cancer, mismatch repair-deficient/microsatellite instability-high (dMMR/MSI-H) tumors show the clearest sensitivity to programmed cell death protein 1 (PD-1) blockade, whereas mismatch repair-proficient/microsatellite stable (pMMR/MSS) disease generally requires combination strategies. In ovarian cancer, broad unselected checkpoint inhibitor strategies have generally failed; ENGOT-ov65/KEYNOTE-B96 demonstrated statistically significant progression-free survival (PFS) and overall survival (OS) improvement with pembrolizumab plus weekly paclitaxel, with or without bevacizumab, in a defined platinum-resistant population, although the absolute median PFS gain in PD-L1 combined positive score (CPS) ≥1 disease was modest. Gestational trophoblastic neoplasia (GTN) offers a biologically distinctive setting because of trophoblastic immune tolerance, whereas selected rare gynecologic malignancies may be considered for immunotherapy on the basis of HPV association, tumor-agnostic biomarkers, or signals observed in clear-cell cohorts. Accordingly, the clinical value of immunotherapy should be interpreted according to tumor lineage, disease setting, validated biomarkers, treatment backbone, and the incremental contribution of each treatment component. Full article

Ionizing radiation can induce DNA damage through direct or indirect mechanisms and activate the DNA damage response network, thereby influencing cellular repair, cell cycle regulation and cell fate determination. MicroRNAs (miRNAs), as key post-transcriptional regulatory factors, play a vital role in modulating the radiation response and cell fate. This review summarizes radiation-induced changes in miRNA expression and the molecular regulatory mechanisms they mediate, and further discusses recent advances in their application in the assessment of radiation damage, the prediction of radiotherapy response, and therapeutic interventions. Although miRNAs possess significant translational value, their multi-target regulatory characteristics, tissue- and environment-dependence, as well as limitations in clinical detection and delivery systems, continue to hinder their application. In the future, the integration of multi-omics technologies, artificial intelligence analysis and precision delivery strategies is expected to further elucidate the miRNA-mediated radiation response network and advance their application in precision radiology. Full article

Predicting target intent in complex, dynamic multi-agent environments remains a formidable challenge due to incomplete perception and the highly dynamic nature of multi-target interactions. Conventional approaches—such as D-S evidence theory, expert systems, and Recurrent Neural Networks (RNNs)—are often constrained by data incompleteness and rigid behavioral assumptions, limiting their adaptability to dynamic high-value target identification and multi-target situational awareness on the ground. To address these challenges, a novel framework termed Threat Field–Gated Recurrent Unit (TF-GRU) is proposed. The TF-GRU framework integrates threat field modeling with a dynamic repair mechanism to enhance intent prediction under partial perception. Specifically, threat field modeling associates target attributes with intentions through the construction of static and dynamic threat fields, effectively capturing the temporal and semantic relationships among multiple targets. A particle filtering and dynamic time warping fusion strategy (PF-DTW) is employed to repair data gaps via short-term filtering and long-term trajectory matching, further refined by a neighborhood-angle constraint for accurate multi-target state estimation. In addition, trajectory and threat field features are processed using a Mish activation function and a threat-adaptive gating mechanism, which dynamically regulate information flow within the recurrent unit to model behavioral evolution. Experimental evaluations demonstrate that TF-GRU significantly enhances intent prediction accuracy under incomplete data conditions, thereby improving comprehensive situational awareness and supporting high-confidence decision-making in dynamic multi-target scenarios. Full article

The development of sustainable coatings that combine reprocessability with active functionalities remains a central challenge for the composites sector. In this work, a healable, electrically conductive coating was formulated using epoxidized castor oil (ECO) as a bio-based matrix, dibutyl phosphate (DBP) as a transesterification catalyst, and short recycled carbon fibres (RCFs, 2 mm in length) as a conductive filler at loadings of 10 and 20 phr. Formulations were UV-cured via cationic photopolymerization and characterized across the full liquid-to-solid processing chain. FT-IR and photo-DSC showed that increasing RCF content progressively reduced curing rate and conversion, an effect attributed to light scattering/absorption by the fibres and restricted chain mobility, although gel content remained above 98% in all cases. DMTA showed that RCF did significantly affect the glass transition temperature but markedly increased the rubbery storage modulus and apparent crosslink density, consistent with a physical reinforcement mechanism. Stress relaxation tests confirmed the dynamic bond exchange behaviour in all formulations, with the apparent activation energy decreasing from 112 kJ/mol for the neat resin to 33–34 kJ/mol upon RCF incorporation. This significant reduction suggests that the presence of RCF facilitates the bond-exchange process, potentially through interfacial interactions between the polymer network and the fibre surface. However, the specific molecular mechanism responsible for this effect cannot be established from the present data. Electrical conductivity peaked at 10 phr RCF (3.6 × 10⁻³ S/m), enabling measurable Joule heating, while the 20 phr formulation showed reduced conductivity linked to voids and lower conversion. Thermally triggered healing at 120 °C for 6 h restored mechanical integrity, which is higher than reference values, demonstrating the coating’s capacity for repeated repair through its dynamic covalent network. Full article

Taking the repair of prefabricated hole defects in 2024 aluminum alloy thin-walled components
by friction stir spot welding (FSSW) as the research object, the evolution laws
of microstructure and mechanical properties of FSSW-repaired joints of thin-walled components
were clarified through process experiments and numerical simulations. The
collaborative effect of the temperature field and material flow field during the FSSW repair
process and their regulation laws on the microstructure and properties were revealed. The
results show that as the repair speed increases, the macroscopic surface quality of the FSSW
joint improves. When the repair speed reaches 2000 r/min, a high-quality repaired joint
with a smooth and flat surface and no porosity defects can be obtained. Meanwhile, within
the repair speed range of 800 to 2000 r/min, the grains undergo dynamic recrystallization
(DRX) due to the combined effect of heat and mechanical forces, eventually forming a
uniform equiaxed grain structure in the weld core area. ABAQUS 2023 simulation verifies
the temperature distribution during the FSSW repair process. When the repair speed is
2000 r/min, the maximum temperature obtained from the simulation is 431.1 ◦C, which
agrees with the measured value from the experiment. The simulation results further reveal
that when the repair speed increases from 1200 r/min to 2000 r/min, the material fluidity
significantly enhances, and the flow velocity on the advancing side is always higher than
that in other areas. At the rotational speed of 2000 r/min, the plastic material flows continuously
from the periphery and eventually fills the defect area completely. The fracture
mode of the FSSW-repaired joint is mainly ductile fracture. With the increase in the repair
speed, the number of dimples at the fracture surface increases significantly. When the
rotational speed reaches 2000 r/min, the joint achieves the best mechanical properties, and
the FSSW-repaired joint reaches the maximum tensile strength of 169 MPa. Full article

Federated, semi-supervised novelty detection is well suited for intrusion detection on resource-constrained Internet of Things (IoT) nodes: each device learns a model of benign traffic, shares only summary statistics, and does not transmit raw traffic samples. A previously published cross-layer federated detector, Baseline K-Means, showed that periodically merging worker statistics through a coordinator raises the detection rate, but it also exhibited a systematic side effect: after every merge, the precision decays, and the false-positive rate (FPR) climbs because the coordinator recomputes its threshold from streaming distances filtered by the closest observed anomaly, so tightens after every merge, flagging progressively more benign traffic; the threshold was also hand-tuned. We present AF-BKM, an Adaptive Federated Baseline K-Means that repairs the federated mechanism with two label-free, statistics-only enhancements, denoted as E1 and E2: (i) an adaptive decision threshold read from the benign Mahalanobis-distance distribution, requiring no manual percentile search and no attack labels (E1), and (ii) a robust, benignly anchored aggregation that blends worker means under quality weighting and outlier-worker filtering and recalibrates the threshold on a trusted benign anchor to a stable, anchor-referenced false-positive level, which a target-FPR rule can make operator-selectable instead of tightening it toward the nearest anomaly (E2). With MinMax scaling fit only on benign baseline data and non-IID federated streams on NSL-KDD, UNSW-NB15 and the N-BaIoT corpus of real traffic from commercial IoT devices, AF-BKM removes the merge-induced precision decay (the first-to-last-epoch precision change improves from $-0.134$ to $-0.002$ on NSL-KDD, from $-0.121$ to $-0.014$ on UNSW-NB15, and from $-0.170$ to $-0.009$ on N-BaIoT) and reduces the mean FPR by 30–$64\%$, depending on the dataset; all central improvements are significant across 10 seeds (Wilcoxon $p=0.002$, large effect sizes). AF-BKM preserves recall on NSL-KDD and N-BaIoT and, on the harder UNSW-NB15, exposes an explicit precision–recall trade-off through a benign target-FPR knob. In fp32, the deployed model serializes to $5.5$–52 KB, a packet is classified in 11–27 µs on a desktop CPU, and each merge round uploads a $d+3$-value summary (160–472 B) $94.6$–$98.3\%$ smaller than the same summary extended with the covariance upper triangle. A robustness study covering selected faulty-worker updates, contamination of the commissioning anchor, and detector-level white-box evasion reports the measured degradation patterns: fabricated threshold candidates have no direct path to the threshold, although a fabricated mean still reaches it indirectly through the blended centroid, and the anchor-referenced false-positive level remains stable under percent-level anchor contamination, while recall sensitivity is dataset-dependent and the evasion budget tracks the benign–attack margin of each dataset. We frame the contribution with a focused taxonomy that identifies merge-induced precision decay under non-IID workers as an open gap. Code is released for reproducibility. Full article

To investigate the influence of oblique SV-wave incidence on the dynamic response and seismic resilience of subway stations, the viscoelastic artificial boundary combined with the equivalent nodal force method was used to implement the oblique SV-wave input, and a 3D soil-structure interaction finite element model was developed. The results indicate that when subjected to oblique SV-wave incidence at small angles, the subway station structure is dominated by shear deformation. Then, the rocking response gradually rises as the angle increases. At the near-critical-angle incidence, both the rocking angle and inter-story drift ratio increase sharply, resulting in exacerbated structural damage and a degradation of lateral stiffness. Time-history analyses were conducted using 11 ground-motion records, and seismic resilience was assessed through Monte Carlo simulation. Compared with vertical incidence, the results show that oblique incidence at the near-critical angle significantly degrades the structure’s seismic resilience. Its resilience rating drops significantly. Repair costs and repair time are much higher than the values at vertical incidence. The structure’s functional recoverability was substantially reduced. The rating for the casualty index decreased sharply, exposing serious safety hazards. These results indicate that resilience assessments derived from vertical incidence cause the seismic resilience of subway stations to be overestimated. Full article

Background and Objectives: This retrospective cohort study aimed to evaluate the association between sagittal tibial tunnel angle and radiological and clinical outcomes following transtibial pull-out repair of medial meniscus posterior root tears. Materials and Methods: A total of 81 patients who underwent arthroscopic transtibial pull-out repair between January 2022 and October 2025 and had a minimum follow-up of 6 months were retrospectively evaluated. Demographic, clinical, radiological, and functional data were analyzed. Meniscal extrusion, hip–knee–ankle angle, mechanical axis deviation, sagittal and coronal tunnel angles, International Knee Documentation Committee (IKDC) score, and Numeric Pain Rating Scale (NPRS) score were assessed preoperatively and at 6 months postoperatively. Receiver operating characteristic (ROC) analysis was performed to explore the ability of sagittal tibial tunnel angle to discriminate achievement of the patient acceptable symptom state (PASS) based on the postoperative IKDC score. The ROC-derived 51.5° value was considered an exploratory, cohort-specific threshold. For secondary between-group comparisons, patients were classified into two groups according to sagittal tibial tunnel angle: ≤51.5° and >51.5°. Results: The cohort included 56 female patients, and the mean age was 50.0 ± 9.97 years. Receiver operating characteristic analysis identified 51.5° as the exploratory cohort-specific threshold, with an area under the curve of 0.754, sensitivity of 77.8%, and specificity of 68.9%. Patients with a sagittal tunnel angle >51.5° had lower postoperative meniscal extrusion than those with an angle ≤51.5° (3.0 mm versus 4.0 mm, p < 0.001), greater reduction in meniscal extrusion (1.10 mm versus 0.3 mm, p < 0.001), higher postoperative IKDC (71.62 ± 10.69 versus 53.0 ± 15.44, p < 0.001), and lower postoperative NPRS scores (1 versus 4, p < 0.001). Achievement of the patient acceptable symptom state was also more frequent in the >51.5° group (66.7% vs. 17.9%, p < 0.001). Conclusions: A greater sagittal tibial tunnel angle measured on 6-month postoperative MRI was associated with lower concurrent meniscal extrusion, higher functional scores, and lower pain scores following transtibial pull-out repair. However, the retrospective design and short follow-up period limit causal and long-term interpretation. The cohort-derived 51.5° threshold should be considered exploratory and requires external validation before clinical application. Full article

Manufacturer datasheets state battery cycle life under conditions that rarely match how cells are used, while public cycling datasets measure degradation under conditions datasheets do not cover. We present a knowledge-graph (KG) system that represents claims, measurements, and independent tests of commercial lithium-ion cells with full provenance, detects claim-versus-measured and claim-versus-claim discrepancies conditioned on the comparability of test conditions, and supports cycle-life prediction with coverage-gated abstention. On the 124-cell Severson dataset under leave-one-policy-group-out cross-validation, graph-derived neighbor features do not significantly improve point prediction over a strong early-cycle baseline (RMSE 135 vs. 141 cycles), but graph coverage provides a statistically significant abstention signal (Spearman $\rho =-0.25$ with prediction error, $p=0.006$) that reduces retained RMSE by roughly 40% at 60% retention, where random abstention does not. Deployed zero-shot on a second cycling study of the same commercial cell, the gate abstained on all 77 cells; the counterfactual confirms every refusal (approximately 83% error had it answered), an error an ungated baseline commits silently. On a third study with commensurable features, the gate’s first partial acceptance (17 of 45 cells) is itself diagnostic: coverage acts partly as a lifetime proxy out of distribution, and five labeled cells halve retained error while leaving that proxy in place—adaptation repairs the predictor, not the selection criterion. A 70B open-weight LLM extracts datasheet claims at $F_1=0.70$ with non-deterministic output even at temperature 0; a deterministic validator with three-run consensus raises this to $F_1=0.78$ with zero unsourced values; on a held-out datasheet, precision and the zero-unsourced-value property transfer while recall falls to 0.34, localizing the extractor’s boundary at table-structured content; row-level table grounding, implemented in response, raises held-out recall to 0.63 with zero hallucinations at a measured precision cost. Reconciling claims across document variants shows that roughly one in three cross-document specification comparisons (14 of 43, three commercial cells) yields a conflict or condition mismatch, twelve involving third-party documents and two internal to a single manufacturer’s own documents. A hand-labeled, condition-annotated gold standard of 103 claims (62 development, 41 held-out; inter-annotator $\kappa =0.74$ on property naming) and a staged, human-gated literature-monitoring pipeline are released with the code. Full article

Reliable traffic speed forecasting depends on trustworthy historical road-sensor observations, yet deployed sensors may exhibit missing values, outages, noise, calibration drift, and stuck readings. Existing forecasting models are commonly evaluated on cleaned inputs, whereas conventional imputation optimizes historical reconstruction rather than downstream prediction. This study presents the Sensor-Reliability-Aware Framework with Identity-Preserved Design (SRAF-ID), a prediction-oriented speed-channel repair front-end trained end to end using only future forecasting loss. The final model requires no controlled fault-location labels during training or inference. SRAF-ID constructs same-sensor temporal and mask-aware graph-neighborhood candidates, combines them through learned two-way softmax fusion, and preserves node-identity and temporal-context features. On raw-time-disjoint 70%/10%/20% splits of the Metropolitan Los Angeles (METR-LA) and California Performance Measurement System Bay Area (PEMS-BAY) datasets, ten-seed matched stress tests cover six window-level controlled perturbations. SRAF-ID reduces faulty-average mean absolute error from 5.12 to 4.82 on METR-LA and from 1.99 to 1.94 on PEMS-BAY, corresponding to relative reductions of 5.7% and 2.4%, respectively. It achieves a lower mean MAE in all 12 dataset-fault comparisons and a lower faulty-average MAE in all ten seeds on both datasets; the clean-input MAE also decreases. Checkpoint-only tests retain positive all-sensor and affected-sensor mean gains in all eight localized dataset-condition pairs, whereas unseen 0.75-standard-deviation global drift produces small adverse means with paired intervals crossing zero. The evidence therefore supports fault-label-free robustness under the defined stress protocols while leaving field-recorded event continuity and fault frequency for external validation. Full article

This study examines the feasibility of predicting net repair costs (Estimated Cost, PLN) for multi-family residential buildings from data extracted from technical inspection reports. Rather than merely comparing algorithmic performance, the analysis was designed as a diagnostic sequence aimed at identifying the sources of prediction error. Four machine learning algorithms (Extra Trees, Random Forest, XGBoost, and GBM) were first applied to direct regression of repair cost. We then tested whether the difficulty of estimating exact cost values stems from the high variability of the target variable and whether this limitation can be mitigated by a two-stage approach: assigning observations to one of three cost-risk bands (Low, Moderate, High) and subsequently estimating cost within the assigned band. The empirical cost distribution was strongly right-skewed (median: 4500 PLN; mean: 63,402 PLN; maximum: 3,680,524 PLN). The best direct regression model achieved an R² of 0.452, while the best fully deployable two-stage model, combining an XGBoost classifier with a Random Forest regressor, achieved an R² of 0.392. When it was assumed that the actual cost-risk bands were known, an R² value of 0.839 was obtained, indicating that the main source of error is not regression within the bands, but rather the initial stage of assigning the bands. These results demonstrate that reporting a single global R² for highly skewed, weakly identifiable cost data can be misleading, and that decomposing predictive performance into band-assignment and within-band regression components provides a more informative evaluation. This article also points to concrete directions for improving the underlying database, particularly through the inclusion of variables describing repair quantity, unit of measure, and detailed repair scope. Full article

Software code testing remains a critically important but labour-intensive process in software quality assurance. Existing research evaluates large language model (LLM)-based unit test generation using various quality metrics, such as correctness, coverage, mutation score, and test code smells. However, these single metrics do not reflect the trustworthiness of the unit test generation process. Therefore, this research formalises the trustworthiness of LLM-based unit test generation as a multidimensional index comprising reliability, hallucination resistance, maintainability, functional completeness, and human-reference alignment. In this research, we investigate the effect of prompt engineering strategies on the trustworthiness of LLM-generated unit tests and compare them with human-written tests for the same focal methods. Each dimension is fed by a distinct artefact-level measurement and grounded in dependability theory and ISO/IEC 25010:2023. A centralised multi-agent system generates, builds, repairs, and measures the tests, so that all inputs are collected automatically. The index is evaluated on real-world C# focal methods across 18 model × prompt configurations and a paired human-written baseline. The human baseline achieves the highest T-UTG value (0.904), and the best configuration, Combined × Gemini, achieves 0.788. Entropy weighting identifies maintainability and hallucination resistance as the most discriminating dimensions, and a rank-acceptability analysis over the whole weight simplex confirms that this ordering does not depend on the chosen weighting scheme. Full article