Background Venous thromboembolism (VTE) is a significant cause of morbidity in children, particularly among hospitalized patients and those with chronic medical conditions. There is a lack of consensus on anticoagulant prophylaxis strategies. Objective These evidence-based guidelines from the American Society of Hematology (ASH) and the International Society on Thrombosis and Haemostasis (ISTH) are intended to support patients and health care professionals in decisions about anticoagulant prophylaxis for pediatric VTE prevention. Methods ASH formed a multidisciplinary guideline panel that included 1 patient representative. The University of Kansas Health System supported the guideline development process, including systematic evidence reviews up to April 2025. Clinical questions and outcomes were prioritized according to their importance for clinicians and patients. The panel used the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach to assess certainty in the evidence and make recommendations. Results The panel agreed on 12 recommendations. For pediatric patients with solid cancer, who have experienced trauma, or who are critically ill, the panel issued conditional recommendations suggesting no anticoagulant prophylaxis. For pediatric patients with antiphospholipid antibody syndrome, or those on long-term total parenteral nutrition, the panel issued conditional recommendations suggesting the use of anticoagulant prophylaxis. Other pediatric subgroups addressed included patients with acute lymphoblastic leukemia or lymphoma, surgical and hospitalized patients, and those with a central venous access device. Conclusions High-quality data on anticoagulant prophylaxis for pediatric VTE prevention are scarce. Key research priorities include the development and validation of subgroups-specific VTE risk assessment models, and evaluation of the safety and efficacy of risk-stratified anticoagulant prophylaxis strategies across different pediatric subgroups.
Sugarcrete, as a sustainable building material, was derived from sugarcane bagasse, incorporating other natural minerals. This study investigated the effects of bagasse particle size, silica fume addition, and curing time on mechanical strength, thermal properties, and durability. Material characterization revealed the formation of calcium silicate hydrate and aluminosilicate phases, which enhance structural integrity. The use of finer bagasse particles and silica fume improved compressive and flexural strength, reduced water absorption, and increased fire resistance and thermal insulation, with optimal formulations achieving compressive strengths of up to 4.5 MPa and 10.49 MPa, more than 130
This paper develops a framework for fast online inference on semiparametric models with large sample sizes and possibly many covariates. The computational algorithm itself is the object of statistical study: after a globally consistent warm start in the first phase, the path of averaged online iterates generated in the second phase automatically delivers estimators with optimal convergence rates and valid confidence sets. Both phases require only a single pass over the data stream and are well suited to streaming data or to settings with storage/privacy constraints. For semiparametric monotone index models, the averaged trajectory of the second phase lead to estimators that are automatically orthogonalized and satisfy the laws of the iterated logarithms, and policy functionals are updated along the same trajectory at negligible additional cost. The averaged trajectories satisfy functional central limit theorems, which yield fast online inference via random scaling and bypass the explicit variance estimation that complicates inference for semiparametric models. Applied to a fixed large sample, our online algorithm achieves substantial computational gains over corresponding offline procedures without sacrificing statistical performance. Monte Carlo experiments show adequate behavior. Our methods are applied to 19 million traffic-stop online records from the North Carolina State Patrol (Pierson et al. 2020) and to the international trade data of Helpman et al. (2008) with over 300 regressors. We also compare our estimator to its parametric benchmark in both empirical illustrations
We propose a test-time defense mechanism against adversarial attacks: Unlike existing methods that rely on feature filtering or smoothing, which can lead to information loss, we propose to ``combat noise with noise'' by leveraging stochastic resonance to enhance robustness while minimizing information loss. Our approach introduces small translational perturbations to the input image, aligns the transformed feature embeddings, and aggregates them before mapping back to the original reference image. This can be expressed in a closed-form formula, which can be deployed on diverse existing network architectures without introducing additional network modules or fine-tuning for specific attack types. The resulting method is entirely training-free, architecture-agnostic, and attack-agnostic. Empirically, the method achieves state-of-the-art robustness on image classification and provides the first generic test-time defense for dense prediction tasks, including stereo matching, optical flow, and monocular depth estimation. Across adversarial attacks, it recovers up to 68.1% of the accuracy loss on image classification, 71.9% on stereo matching (MAE), 29.2% on optical flow (EPE), 68.3% on depth estimation (SqRel), and 83.8% on vision-language alignment (cosine similarity).
The HEXACO-60 is a widely used 60-item instrument assessing the six-factor model of personality, yet its psychometric robustness remains largely untested in the South Caucasus. This study evaluated the Azerbaijani version of the HEXACO-60 in a large multi-university sample of university students (N = 1178; 654 males, 524 females; M age = 20.9, SD = 2.1). Confirmatory factor analysis supported the hypothesized six-factor structure with acceptable model fit (χ2 (1684) = 4165.20, CFI = .930, TLI = .923, RMSEA = .045, SRMR = .048). All dimensions exhibited satisfactory internal consistency (Cronbach’s alpha = .72–.85; McDonald’s omega = .74–.87). Convergent validity was supported through theoretically consistent associations with self-compassion and self-esteem: Honesty–Humility and Emotionality showed the strongest positive associations with self-compassion (r = .42 and .35, respectively), whereas Extraversion demonstrated the strongest positive association with self-esteem (r = .42). These findings provide the first comprehensive psychometric evaluation of the Azerbaijani HEXACO-60 and support the cross-cultural applicability of the HEXACO model in an understudied South Caucasus context.
