Search Results (14,549)

This study introduces a composite method that integrates a diamond-coated tubular grinding electrode with short electric arc machining (SEAM) for GH4099. Mechanical micro-grinding and arc erosion act concurrently within the inter-electrode gap, enabling an in situ “erode–dress” coupling in which the grinding action levels nascent craters and promotes debris evacuation while SEAM supplies localized thermal–electrical energy for removal. A design-of-experiment scheme probes discharge and grinding factors, and performance is evaluated by material removal behavior, electrode wear, and surface integrity. Within a robust window (12–24 V; 500–2000 r/min), the composite process sustains stable discharges without catastrophic melting at 24 V and yields dense, uniform textures. Representative surfaces show controllable areal roughness (Sa ≈ 14–27 µm across 80#–600#), reflecting a practical finishing–efficiency trade-off. Multi-scale characterization (3D topography, cross-sectional metallography, SEM) evidences suppression of recast steps, macro-protrusions, and irregular pits, with more evenly distributed, shallower grinding traces compared to those with single-mode SEAM. The comparative analyses clarify discharge stabilization and recast-layer mitigation mechanisms, establishing a feasible pathway to high-quality, high-efficiency composite SEAM of GH4099 without resorting to overly aggressive electrical conditions. Full article

As a crucial component of aero-engines, the accurate measurement of the motion parameters of the vector nozzle is of great significance for thrust vector control and flight performance improvement. This paper designs a dual-bearing rotating nozzle to simulate the motion of a real vector nozzle and a multi-view vision platform specifically for measuring the motion parameters of the vector nozzle. To address the parameter measurement problem, this paper proposes a technical solution of “3D reconstruction-point cloud segmentation-fitting measurement”. First, a deep learning-based multi-view stereo vision method is used to reconstruct the nozzle model in 3D. Then, the point transformer method is used to specifically extract the point cloud of the nozzle end face region. Finally, the RANSAC method is used to fit a spatial circle to the end face point cloud for measurement purposes. This paper verifies the effectiveness of the proposed method through multiple sets of comparative experiments and evaluates the reliability and stability of the measurement system using analysis of variance in measurement system analysis. The final analysis results show that the $GRR\%$ and NDC of the measurement system are 2.81% and 7.69%, respectively, and 1.84%, 50, 18, and 76, respectively, indicating that the measurement system has high accuracy and stability. Full article

This paper presents a theoretical study of the structural, electronic, and elastic properties of gallium-doped CuInSe₂ using the GGA exchange-correlation functional with the Hubbard correction for five Ga compositions: 0, 0.25, 0.5, 0.75, and 1. The found lattice parameters decrease with gallium composition and obey Vegard’s law. Traditional DFT calculations fail to explain the band structure of Copper Indium Gallium Selenide compounds (CIGS). The use of Hubbard corrections of d-electrons of copper, indium, gallium, and p-electrons of selenium opens the gap, showing a semiconductor’s behavior of CuInGaSe2 alloys in the range 1.04 eV to 1.88 eV, which are in good agreement with available experimental data and current theory using an expensive hybrid exchange-correlation functional. The obtained formation energies for the different gallium compositions are close to −1 eV/atom, and the phonon spectra indicate the thermodynamic stability of these alloys. The values of the elastic constant satisfy the Born elastic stability conditions, suggesting that these compounds are mechanically stable. Moreover, we compute the bulk modulus (B), shear modulus (G), Young’s modulus (E), Poisson ratio (p), Pugh’s ratio (r), and average Debye speed ($v$), and the Debye temperature (${\mathsf{\Theta }}_{\mathrm{D}}$) with the Ga composition. There is a symmetry between our results and the experimental data, as well as earlier simulation results. Full article

Callus induction is a complex procedure in plant organ, cell, and tissue culture that underpins processes such as metabolite production, regeneration, and genetic transformation. It is important to monitor callus formation alongside subjective evaluations, which require labor-intensive care. In this research, the first curated lentil (Lens culinaris) callus dataset for instance segmentation was experimentally generated using three genotypes as one data set: Firat-87, Cagil, and Tigris. Leaf explants were cultured on MS medium fortified with different concentrations of gross regulators of BA and NAA to induce callus formation. Three biologically relevant stages, the leaf stage, the green callus, and the necrosis callus, were produced. During this process, 122 high-resolution images were obtained, resulting in 1185 total annotations across them. The dataset was evaluated across four successive generations (v5/7/8/11) of YOLO deep learning models under identical conditions using $mAP$, $Dice$ coefficient, $Precision$, $Recall$, and $IoU$, together with efficiency metrics including parameter counts, FLOPs, and inference speed. The results show that anchor-based variants (YOLOv5/7) relied on predefined priors and showed limited boundary precision, whereas anchor-free designs (YOLOv8/11) used decoupled heads and direct center/boundary regression that provided clear advantages for callus structures. YOLOv8 reached the highest instance segmentation precision with mAP50@0.855, while it matched the accuracy with greater efficiency and achieved real-time inference with 166 FPS. Full article

The high-precision prediction of near-space atmospheric temperature holds significant importance for aerospace, national defense security, and climate change research. To address the deficiencies of extracting features in conventional convolutional neural networks, this paper designs a ConvLSTM hybrid model that combines the spatiotemporal feature extraction capability of 3D convolution with a residual attention mechanism, effectively capturing the dynamic evolution patterns of the near-space temperature field. The comparative analysis with various models, including GRU, shows that the proposed model demonstrates superior performance, achieving an RMSE of 2.433 K, a correlation coefficient R of 0.993, and an MRE of 0.76% on the test set. Seasonal error analysis reveals that the prediction stability is better in winter than in summer, with errors in the mesosphere primarily stemming from the complexity of atmospheric processes and limitations in data resolution. Compared to traditional CNNs and single time-series models, the proposed method significantly enhances prediction accuracy, providing a new technical approach for near-space environmental modeling. Full article

Dam break problem-induced floods can trigger hazardous dike overtopping, demanding predictions that are fast, accurate, and interpretable. We pursue two objectives: (i) introducing a new alpha evolution (AE) optimization scheme to improve tree-model predictive accuracy, and (ii) developing a cluster-wise modeling strategy in which regimes are defined by wave characteristics. Using a dataset generated via physical model experiments and smoothed particle hydrodynamics (SPH) numerical simulations, we first group samples via hierarchical clustering (HC) on the Froude number $\left(\mathrm{Fr}\right)$, wave nonlinearity $\left(R\right)$, and relative distance to the dike $\left(D\right)$. We then benchmark CatBoost, XGBoost, and ExtraTrees within each cluster and select the best-performing CatBoost as the baseline, after which we train standard CatBoost and its AE-optimized variant. Under random train–test splits, AE-CatBoost achieves the strongest generalization for predicting relative run-up distance $H_m$ (testing dataset $R^2=0.9803$, $\mathrm{RMSE}=0.0599$), outperforming particle swarm optimization (PSO) and grid search (GS)-tuned CatBoost. We further perform TreeSHAP analyses on AE-CatBoost for global, local, and interaction attributions. SHAP analysis yields physics-consistent explanations: D dominates, followed by H and L, with a weaker positive effect of $\mathrm{Fr}$ and minimal influence of R; $H\times D$ is the strongest interaction pair. Overall, AE optimization combined with HC-based cluster-wise modeling produces accurate, interpretable overtopping predictions and provides a practical route toward field deployment. Full article

Pediatric craniofacial fractures represent a significant diagnostic challenge due to age-dependent anatomical variability, subtle fracture lines, and the limited availability of specialists during emergency care. Computed tomography (CT) remains the gold standard for fracture assessment; however, small or non-displaced fractures may be easily overlooked. Advances in artificial intelligence offer opportunities for automated support in injury detection. In this study, we developed a 2.5D deep learning model that uses multiplanar CT reconstructions (axial, coronal, sagittal) as a three-channel input to a ResNet-18 network with transfer learning. A total of 63 high-quality CT examinations were included and split into a training set (n = 44) and a validation set (n = 19). The preprocessing pipeline included isotropic resampling, bone-window normalization, and the generation of 384 × 384 images. Model performance was evaluated using ${\mathrm{AUC}}_{ROC}$, ${\mathrm{AUC}}_{PR}$, sensitivity, specificity, precision, F1-score, and threshold optimization using Youden’s index. The proposed model achieved ${\mathrm{AUC}}_{ROC}$ = 0.822 and ${\mathrm{AUC}}_{PR}$ = 0.855. At the optimal decision threshold (t = 0.54), sensitivity reached 0.556, specificity 1.000, and F1-score 0.714. Grad-CAM visualizations correctly highlighted fracture-related structures, which was confirmed in an independent clinical assessment. The results demonstrate the potential of the proposed model as a decision-support tool for pediatric craniofacial trauma, providing a foundation for future extensions toward radiomics and hybrid deep-radiomic pipelines. Full article

This study examined how variability in phonetic training input (high vs. low) influences the perception and acquisition of Spanish stop consonants by English-speaking beginners. A total of 128 participants completed 20 online identification sessions targeting /p, t, k, b, d, g/. In the high-variability condition (HVPT), learners heard tokens from six speakers, and in the low-variability condition (LVPT), all input came from a single speaker. Training followed an interleaved-talker design with immediate feedback, and perceptual learning was evaluated using a Bayesian hierarchical logistic regression analysis. Results showed improvement across sessions for both groups, with identification accuracy reaching ceiling by the end of the training sessions. Differences between HVPT and LVPT were small: LVPT showed steeper categorization trajectories in some cases due to slightly lower baselines, but neither condition yielded a measurable advantage. The pattern observed suggests that for boundary-shift contrasts such as Spanish stops, perceptual improvements are driven primarily by input quantity rather than variability. This interpretation aligns with input-based models of L2 speech learning (SLM-r, L2LP) and underscores the role of repeated exposure in restructuring phonological categories. Full article

Background: Optical coherence tomography angiography (OCT-A) provides quantitative assessment of retinal and peripapillary microvasculature and has emerged as a promising tool for glaucoma diagnostics. However, its sensitivity for detecting early glaucomatous progression over short intervals remains uncertain. This study evaluated cross-sectional and short-term longitudinal OCT-A vessel density (VD) metrics in primary open-angle glaucoma (POAG) and explored their relationships with structural (RNFL) and functional (MD) measures. Methods: Sixty eyes (30 POAG, 30 controls) underwent baseline and 6-month examinations including intraocular pressure (IOP), standard automated perimetry (SAP), structural OCT, and OCT-A (RTVue XR Avanti; AngioVue). Parameters analyzed included peripapillary VD (PP-VD), parafoveal VD (PF-VD), foveal avascular zone (FAZ) metrics, FD-300, and RNFL thickness. Between-group comparisons used t-tests or Mann–Whitney U tests. Effect sizes (Cohen’s d), 95% confidence intervals (CI), and ANCOVA models (adjusted for baseline, age, and sex) were included. Longitudinal change was defined as Δ = 6 months − baseline. Pearson correlations evaluated structure–vascular associations. Results: At baseline, POAG eyes showed significantly lower PP-VD, PF-VD, thinner RNFL, and worse MD (all p < 0.001). Strong correlations were observed between RNFL and PP-VD (r ≈ 0.7). Over 6 months, glaucoma eyes showed small but statistically significant reductions in RNFL (Δ = −1.04 µm), MD (Δ = −0.10 dB), and PP-VD (Δ = −0.57%), whereas controls remained stable. However, the absolute OCT-A changes were small and largely within the known range of test–retest variability. ANCOVA demonstrated a significant adjusted group effect only for PP-VD (B = −1.22%, 95% CI −1.53 to −0.90; p < 0.001). Conclusions: OCT-A demonstrated clear cross-sectional differences between POAG and controls and strong structure–vascular associations. However, with only two measurements over a 6-month interval, the study cannot distinguish true glaucomatous progression from physiological or device-related variability. Short-term changes should therefore be interpreted cautiously. PP-VD remains the most robust and consistent OCT-A parameter, but larger, longer, and prospectively powered studies are required to validate OCT-A as a reliable biomarker for progression. Full article

The purpose of this cohort study was to evaluate participants’ general self-management and experiences of autonomy while attending diabetes camp using quantitative and qualitative data collection. Through a partnership, an outdoor diabetes camp was designed to assist youth with type 1 diabetes (T1D) in their management. The REACH Teen program conducted a week-long summer camp for youth with T1D. The study was designed through Outcome-Focused Programming grounded in Self-Determination Theory (SDT) to meet campers’ needs of autonomy, competence, and relatedness. Campers participated in outdoor activities and diabetes education designed to increase healthy behaviors. Twenty-three campers completed a 24-item pre- and post-camp questionnaire measuring participants’ perceived levels of satisfaction or frustration of their three basic psychological needs. At the conclusion of camp, 21 youth participated in 35-min focus group interviews. Through a paired-sample t-test, all three measures were trending in a positive direction, with relatedness (R) being the closest to significance. Cloud-based biometric data was used to compute the percentage of TIR for the week, during camp hours. The results from the focus group interviews revealed three themes: lack of outside understanding, varying levels of autonomy, and experiences at REACH. Not reporting TIR data outside of camp was a limitation of this study. Diabetes medical specialty camps grounded in SDT can provide an opportunity for campers to internalize healthy behaviors needed to manage their diabetes. Full article

Myocardial infarction (MI) and heart failure (HF) are associated with low bone mineral density (BMD). We aimed to investigate whether MI and HF directly cause bone loss using three different experimental models of cardiac injury. Firstly, terminal myocardial infarction was induced in adult wild-type mice by coronary ligation, followed by peripheral quantitative computed tomography (pQCT) and histomorphometric and biochemical analyses at 4 and 9 weeks post-infarction. Secondly, myocardial ischemia–reperfusion injury (I/R) was performed in 4- and 9-month-old rats, followed by bone phenotyping 4 weeks after injury. Finally, transverse aortic constriction (TAC) was performed in adult wild-type mice, double Fgf23/VDR (fibroblast growth factor-23/vitamin D receptor) mutants, and VDR-deficient mice to investigate bone changes in an HF model caused by afterload-induced cardiac hypertrophy, 4 and 6 weeks after TAC. We found unchanged BMD after MI, in both the terminal ischemia model in mice and in the myocardial I/R injury model in young and aged rats. On the other hand, TAC significantly reduced especially cortical BMD in femora. Global knockout of Fgf23 in Fgf23/VDR compound mutants did not rescue the TAC-induced skeletal phenotype. Collectively, our data demonstrate that TAC-induced HF, but not MI, is causing bone loss in mice in an FGF23-independent manner. Full article

Taste receptor type 1 member 3 (TAS1R3) is a class C G protein-coupled receptor (GPCR) traditionally associated with taste perception. While its role in insulin secretion is established, its contribution to skeletal muscle glucose uptake, a process responsible for 70–80% of postprandial glucose disposal, remains unclear. TAS1R3 expression was assessed in skeletal muscle biopsies from non-diabetic and type 2 diabetes (T2D) donors using qPCR and immunoblotting. Functional studies in human LHCN-M2 myotubes involved TAS1R3 inhibition with lactisole or siRNA-mediated knockdown, followed by the measurement of insulin-stimulated glucose uptake using radiolabeled glucose assays. Rac1 activation and phospho-cofilin were analyzed by G-LISA and Western blotting, and Gαq/11 involvement was tested using YM-254890. TAS1R3 mRNA and protein levels were significantly reduced in T2D skeletal muscle. Pharmacological inhibition or the knockdown of TAS1R3 impaired insulin-stimulated glucose uptake in myotubes. TAS1R3 regulates skeletal muscle glucose uptake through a non-canonical insulin signaling pathway involving Rac1 and phospho-cofilin, independent of IRS1-AKT and Gαq/11 signaling. These findings identify TAS1R3 as a key determinant of Rac1-mediated glucose uptake and a potential therapeutic target for improving insulin sensitivity in T2D. Full article

Background/Objectives: Optic disc drusen (ODD) are an under-recognized cause of optic neuropathy, and the impact of structure–function damage on quality of life (QoL) is poorly defined. We compared systemic risk factors, ocular structure–function, and QoL in adults with ODD versus matched controls and identified determinants of impaired vision-related QoL. Methods: In a tertiary clinic, 60 adults with ultrasonography- or OCT-confirmed ODD were age- and sex-matched 1:1 to 60 controls without ODD. Retrospective clinical and imaging data (BCVA, RNFL thickness, standard automated perimetry) were combined with cross-sectional NEI VFQ-25 and EQ-5D-5L scores. Results: ODD patients more often had hypertension (51.7% vs. 31.7%, p = 0.026) and migraine (38.3% vs. 21.7%, p = 0.046). They showed worse BCVA (0.2 vs. 0.1 logMAR, p < 0.001), thinner RNFL (95.3 vs. 103.8 µm, p < 0.001), more depressed mean deviation (−4.7 vs. −1.3 dB, p < 0.001), and more frequent reproducible visual field defects (68.3% vs. 11.7%, p < 0.001). Vision-specific QoL was reduced (VFQ-25 composite 77.3 ± 11.4 vs. 89.7 ± 8.6, p < 0.001) and generic health status lower (EQ-5D utility 0.8 ± 0.1 vs. 0.9 ± 0.1, p < 0.001). In ODD, worse BCVA, more negative mean deviation and lower EQ-5D were independently associated with poorer VFQ-25 (model R² = 0.57), while older age, thinner RNFL and migraine predicted visual field defects. Conclusions: ODD are associated with substantial visual field loss and clinically meaningful decrements in vision-related and generic QoL. Full article

Background: Although anti-CD38 monoclonal antibody-based regimens are standard care for newly diagnosed multiple myeloma (NDMM), direct comparative efficacy and comprehensive real-world safety data remain scarce. Methods: We conducted a systematic review and Bayesian network meta-analysis (NMA) of randomized controlled trials (RCTs). Efficacy was assessed using hazard ratios (HRs) for progression-free survival and odds ratios (ORs) for response rates, with treatment rankings evaluated by Surface Under the Cumulative Ranking (SUCRA) values. Separately, adverse event reports for daratumumab, bortezomib, lenalidomide, and dexamethasone (D_VRd) regimens were extracted from the US FDA Adverse Event Reporting System (FAERS) (Q1 2015–Q2 2025). Statistical analyses were performed using R (4.3.3) and STATA (16.0). Results: The NMA included 33 RCTs. For the primary efficacy endpoints, compared to the standard bortezomib, lenalidomide, and dexamethasone (VRd) regimen, both D_VRd (OR = 3.21, 95% CI: 2.46–4.26; HR = 0.48, 95% CI: 0.38–0.63) and isatuximab plus VRd (Isa_VRd) (OR = 1.71, 95% CI: 1.25–2.32; HR = 0.66, 95% CI: 0.51–0.85) regimens demonstrated superior efficacy. Subsequent pharmacovigilance analysis of D_VRd identified 11,714 FAERS reports, yielding 197 significant adverse drug event signals (64 unlabeled). These signals primarily affected elderly males and showed a bimodal distribution pattern. Conclusions: Combination regimens containing anti-CD38 monoclonal antibodies demonstrate superiority in achieving deep remission and survival benefits, with D_VRd and Isa_VRd regimens showing particularly outstanding performance. However, efficacy and safety profiles vary across different combination regimens. Real-world data analysis further indicates that the D_VRd regimen carries several safety risk signals that remain underappreciated and exhibits a bimodal time distribution pattern. These findings provide new evidence to guide clinical decision-making and risk-stratified monitoring. Full article

Taiwan’s strategic focus in digital healthcare has been officially integrated into national industrial policy and identified as a crucial application area for artificial intelligence (AI) and next-generation communication technologies. As the healthcare sector undergoes rapid digital transformation, digital healthcare technologies have emerged as essential tools for improving medical quality and efficiency. Leveraging the extensive coverage of its National Health Insurance (NHI) system and its strengths in Information and Communications Technology (ICT), Taiwan also benefits from the robust research capacity of universities and hospitals. Government-driven regulatory reforms and infrastructure initiatives are further accelerating the advancement of the NHI MediCloud system and the broader digital healthcare ecosystem. This article provides a comprehensive overview of smart healthcare development, highlighting government policy support and the R&D capabilities of universities, research institutes, and hospitals. It also examines the ICT industry’s participation in the development of smart healthcare ecosystems, such as Foxconn, Quanta, Acer, ASUS, Wistron, Qisda, etc. With strong data assets, technological expertise, and policy backing, Taiwan demonstrates significant potential in both AI innovation and smart healthcare applications, steadily positioning itself as a key player in the global healthcare market. Full article