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Dynamic low-altitude airspace partitioning and management strategy optimization based on graph neural networks and spatial cognitive constraints
Polymer-free all-glass millimeter-Wave antenna-in-package platform with vertical-transition-free aperture-coupled feeding at 28 GHz
An automatic RQD analysis method based on borehole image edge threshold segmentation
Data-driven sustainable design of automobile seats: integrating surrogate modeling and multi-objective optimization
Development and external validation of a lightweight, explainable clinical decision support system for personalized perioperative risk stratification using electronic health records
Abstract Late-preoperative risk stratification after final surgical scheduling may support perioperative risk communication, monitoring escalation, and resource coordination, yet many established calculators are difficult to automate within structured EHR workflows. We developed and externally validated LiteSurgFormer, a lightweight explainable MLP–attention risk-stratification model, in a retrospective multicenter cohort of 58,630 adult grade III–IV surgical patients treated at six Chinese sites from 2021 to 2024. Predictions were anchored after final operating-room schedule confirmation and primary surgical-team assignment but before incision, using 25 structured patient, disease, procedure, and surgical-team/scheduling predictors objectively available at that timestamp; intraoperative and postoperative variables were excluded. Zhejiang sites were used for model development and temporal internal validation, whereas a geographically external Xinjiang Alar affiliated-center cohort was isolated for final validation without refitting, recalibration, or domain adaptation. The primary endpoint was a 30-day composite of clinically significant postoperative adverse events. In external validation ( n = 9772; 1308 events), LiteSurgFormer achieved an AUC of 0.912, Brier score of 0.070, and Hosmer–Lemeshow P = 0.227, with higher decision-curve net benefit than machine-learning comparators and implementable clinical baselines across clinically relevant threshold probabilities. It outperformed ASA-only logistic regression (AUC 0.656) and a 21-predictor core-clinical logistic regression benchmark (AUC 0.895). In the ≥ 20% predicted-risk stratum, observed and predicted risks were closely aligned (48.5% vs. 48.0%; calibration slope 1.036; Hosmer–Lemeshow P = 0.422). Discriminative performance was retained after removing surgical-team variables (AUC 0.896) or clinician-derived composite scores (AUC 0.886). These findings demonstrate retrospectively that LiteSurgFormer provides workflow-aligned risk stratification in comparable structured EHR settings, but portability to administratively unrelated health systems and clinical effectiveness after active deployment remain unproven. Clinical trial number Not applicable.
Preliminary evaluation of palmar tri-radii-related measurements for sex estimation in an Arabian Gulf population sample
Abstract This cross-sectional study assessed the utility of the distance between palmar tri-radii for sex estimation in an Arabian Gulf population. We enrolled 125 citizens of the Arabian Gulf residing in Alexandria Governorate, Egypt, aged 18–30 years. The palmprints were obtained using fingerprint ink strips. Software (AutoCAD 2025) was used to analyze the 600 dots per inch (dpi)-scanned images of the inked palmprints. We investigated five palmar tri-radii present at the base of the fingers (a, b, c, d) and axial tri-radius ‘t’ situated near the base of the fourth metacarpal. Four distances were measured, including the a-t, b-t, c-t, and d-t distances. Combined abcd-t distance was calculated for each palmprint. With excellent intra- and interobserver agreement, the measured variables exhibited significant sexual dimorphism, with males having larger values. Sex could be predicted from individual measurements, with modest performance. The most significant sex-predictive model incorporated bilateral palmprint measurements: log-odds of being male =-7.710 + 1.074 × (Right d-t) − 0.946 × (Right b-t) + 0.774× (Left a-t) + 0.590 × (Left c-t) − 0.334 × (Left d-t). The model achieved 82.7% sensitivity and an area under the curve of 76.2%. The palmar tri-radii-related measurements could guide sex estimation, alongside other evidence.
ISNR-PQC: isometry noise resilience post quantum cryptography primitive
Viscoelastic finite element modeling of asphalt pavement behavior under overloaded traffic conditions
Analgesic effect of robotic stroking with speech
Abstract In medical and nursing care settings, stroking with speech, which combines physical touch with verbal communication, is extensively employed to enhance positive outcomes, such as anxiety relief and analgesia. Although human–robot interaction studies have demonstrated that robotic stroking with speech elicits stronger positive emotions than stroking or speaking alone, only a few studies have quantitatively verified its analgesic effects. This study investigates whether robotic stroking with speech modulates pain perception. Using the gate control theory as an interpretive framework, we examined whether this multimodal interaction increases pain thresholds and exceeds the effect of stroking only. In an experiment with 37 healthy volunteers, pain thresholds were measured under conditions of No Interaction, Stroking Only, and Stroking with Speech via electrical stimulation. The results revealed that Stroking Only and Stroking with Speech significantly increased the pain thresholds compared with the No Interaction condition. Notably, Stroking with Speech produced a significantly higher threshold than Stroking Only. These findings demonstrate that the benefits of robotic stroking with speech extend to the modulation of pain perception, highlighting its potential as an effective nonpharmacological intervention for pain relief.
SuperiorGAT: graph attention networks for sparse LiDAR point cloud reconstruction in autonomous systems
Characteristic expression patterns of PROX1, GLAST, GFAP, and PLP1 in the cochlea of the common marmoset
Sequential mediation linking perceived government autonomy support and public-sphere water conservation intentions via autonomy satisfaction and intrinsic motivation in karst regions
Tailoring the performance of polypropylene nanocomposites with hybrid fillers for high-voltage electrical applications
OCPP integrated artificial intelligence for forecasting scheduling and anomaly detection in city scale electric vehicle charging under urban tariffs
Interactive recommendation algorithm based on intelligent assistance for project application
A phase II study of alpelisib and capecitabine in patients with previously treated metastatic colorectal cancer (KCSG-CO21-04)
Steroid hormone responses to three exercise modalities assessed by liquid chromatography tandem mass spectrometry in a randomized crossover trial
Abstract Exercise disrupts hormonal balance, driving physiological adaptations. However, inconsistent findings exist due to methodological limitations. To address this, this study simultaneously profiled 16 steroid hormones via LC-MS/MS in response to three distinct exercise modalities – moderate intensity continuous exercise (MICE), resistance exercise (RE), and High-intensity Intermittent Exercise (HIIE) – using a randomized crossover design. Ten healthy adult males completed HIIE (> 80% V̇O 2max ), MICE (< 65% V̇O 2max ) and whole-body RE (3 sets×10 reps at 70% 1RM with 90 s rest) in a randomized sequence. Blood samples were collected pre-, 10 min post-, and 2 h post-exercise, and analyzed for glucose, lactate, and steroid hormones. DHEA and androstenedione increased post-exercise following MICE, HIIE (both P < 0.01), and RE ( P < 0.05), but declined below rest by 2 h. Estrone increased 2 h post-RE vs. post-exercise ( P < 0.01), and 2 h post-HIIE vs. rest ( P < 0.01). Only HIIE elevated cortisol ( P < 0.001) and cortisone ( P < 0.01) post-exercise, while cortisol fell below rest at 2 h across all modalities ( P < 0.001). HIIE also raised corticosterone ( P < 0.001), exceeding post-MICE ( P < 0.01) which showed sub-resting corticosterone at 2 h post-exercise ( P < 0.01). All exercise modalities increased aldosterone ( P < 0.01), with greater responses in MICE and HIIE than RE. At 2 h, all protocols elevated the testosterone: cortisol ratio ( P < 0.05). RE and HIIE altered sex hormone ratios, decreasing testosterone:17b-estradiol and androgens: estrogens, and increasing estrogens: progesterone ( P < 0.05). These exploratory findings demonstrate mode-specific acute hormonal responses in young males. Future studies should explore circulating hormonal tissue interactions to clarify potential functional adaptations.