A dataset of 165 fire tests on rectangular concrete columns, spanning five decades, was compiled to evaluate six design methods from five international codes (Eurocode Methods A and B (ECA, ECB), Australian (AS 3600), Chinese (DBJ/T), American (ACI), and Indian (NBC)). A meta-analysis assessed their predictive reliability using historical and recent results. ECA and AS 3600 gave the most consistent and conservative predictions, with ECA performing best overall. For fire resistance ratings (FRR) < 240 min, about 70 % of ECA (2019) predictions were within ±20 % of test outcomes, though accuracy declined at longer durations. AS 3600 produced similar results due to its related formulation. ECB changed notably: 2019 method was unreliable for FRR >140 min, whereas the 2023 version aligned better with recent tests and reduced excessive conservatism. DBJ/T performed adequately on older specimens but overestimated newer ones, while ACI and NBC showed high variability and frequent unconservative predictions. Assessment of the robustness of the ECA (2023) Method with respect to design parameters indicated best performance for 250–300 mm columns, reinforcement ratios of 2.5–3.5 %, effective lengths of 3–5 m, covers of 35–65 mm, and concretes of 20–60 MPa, with reduced accuracy at extreme conditions.

A comparison of small molecule generation methods in structure-based drug design: Quantum-Aided Drug Design (QuADD) vs bond and interaction generating Diffusion model (BInD)

Generation and evaluation mechanism of digital media art pattern design scheme based on interactive genetic algorithm

Configuration and parameter Design of electrified propulsion systems for three-dimensional Transportation: A comprehensive review

Artificial intelligence methods for protein structure and interaction prediction: Recent advances and challenges.

Among existing aptamer design methods, none can address aptamer-target binding energetics during the design phases. A novel aptamer design platform has been proposed to overcome this issue by combining theoretical and computational methods. This is primarily an energy-based platform, focusing on calculating drug-target binding energies (DTBEs) and monitoring the phenomenological energetics of drug-target binding. This relates to understanding the statistical significance of drug-target association/dissociation processes, considering a screened Coulomb interaction (SCI) formalism applied among a distribution of functional charges in a drug-target complex. The interactions of a whole aptamer or any aptamer building block (ABB) (adenine, guanine, cytosine, thymine, or uracil) with the optimal target molecule are considered to calculate DTBEs. Due to various important diagnostic and therapeutic roles in diseases, we chose phosphatidylserine (PS) as our biological target and designed a set of aptamers for it. Using the same technique, concomitantly, we succeeded in discovering aptamers for both lipid and protein biomolecules of therapeutic interest. An aptamer is constructed using a seed-and-grow approach, optimizing SCIs, and selecting the aptamer length based on the trend of DTBE reaching towards equilibrium in a biological environment. This novel aptamer design platform, the 'screened Coulomb interaction approach (SCIA)', ensures the discovery of target-specific aptamers because target specificity is inherently incorporated into the aptamer design phases. Hence, SCIA will significantly enhance aptamer discovery research relevant to dealing with aptamer-based drug discovery for both therapeutic and diagnostic purposes, such as cases where PS is a target biomolecule. Here, our breakthrough achievements are as follows:•An aptamer designing method involving SCIs among charges in a drug-target biomolecule complex to help select ABBs•Nucleic acid aptamers for binding specifically to PS•Aptamer-based therapeutic and diagnostic templates for diseases where PS is a target biomolecule.

Circular valorization of agricultural, industrial, and post-consumer waste into fiber-reinforced composites for environmental sustainability.

Although sample size calculation for open-cohort longitudinal cluster randomized trials (LCRTs) under a fixed design framework was developed by Kasza etal., unifying the closed-cohort and repeated cross-sectional sampling provided in Hooper etal. when a churn rate is constant, there has been no prior efforts in developing optimal open-cohort LCRTs that maximizes the design efficiency. This work assumes a prespecified number of periods and a constant number of replaced individuals at each period in open-cohort LCRTs. We propose algorithms for deriving optimal sample size under a cost-efficiency framework and arrive at the local optimal design (LOD) for fixed correlation parameters and MaxiMin optimal design for addressing uncertainty in correlation parameters. When correlation parameters are known, as the number of replaced individuals increases, for open-cohort PA-LCRTs, the optimal cluster-period size generally decreases and then increases whereas the optimal number of clusters and power under LOD first increase and then decrease. In contrast, for CRXO trials and standard SW-CRTs, the optimal cluster-period size and churn rate under LOD increase whereas the optimal number of clusters and power under LOD decrease. When correlation parameters are unknown, but the parameter space is available, with a small number of replaced individuals, there is no difference in optimal designs between PA-LCRTs and CRXO trials. The number of replaced individuals also has less impact on the optimal cluster-period size than optimal number of clusters. We demonstrate our new optimal design methods using the context of two real-world LCRTs.

Design and application of garment art patterns based on visual sample generation

High-performance chiral metasurface sensors optimized by a target-driven active learning framework

• Outdoor-facing facade tests were compared to hot box tests and R-value calculations. • Cavity ventilation found to increase effective R-values in sunny, calm conditions. • Standard hot box tests are unable to predict this ventilative cooling effect. • R-value calculation methods do not take cavity ventilative cooling into account. • Hot box tests of facades with and without cavities are compared. Ventilation of cavities behind facade cladding can have varied effects on the facade thermal performance. However, not all standard R-value calculation and test methods can accurately predict these effects. In this study, facades with various cavity details were tested in a guarded hot box and in outdoor-facing experiments, and results were compared with R-value calculation methods prescribed in ISO 6946, NZS 4214, and AS/NZS 4859.2. While hot box tests and calculations indicated that facades with ventilated cavities have lower effective R-values compared to sealed cavities, the outdoor tests demonstrated that under sunny conditions with low wind speeds, cavity ventilation can increase the facade effective R-value; reducing membrane temperatures by an average of 2.0–3.2 °C (maximum measured reduction: 7.0 °C). Tracer-gas measurements revealed that such cooling was achieved by ventilation air flow rates in the order of 2 L s −1 m −1 (90 air changes per hour). R-value calculations overpredicted the magnitude of apparent R-value reduction due to cavity ventilation by almost an order of magnitude relative to hot box tests. However, in some cases, this compensated for other inaccuracies, resulting in overall mean absolute errors of 5.5–11 %. Hot box tests showed that, even without capturing the potential benefits of cavity ventilation, battened-out ventilated cavities increased facade effective R-values by 0.17–0.42 m 2 K W −1 , exceeding the improvement provided by R0.2 thermal break strips. This study demonstrates that current design methods misrepresent the effects of cavity ventilation under realistic conditions and should potentially be revised to more accurately model real facades.

The automated design of three-dimensional (3D) molecular structures is a rapidly advancing field with major applications in drug discovery, catalysis, and materials science. Despite this progress, there remains a lack of standardized benchmarks for objectively evaluating and comparing generative methods for 3D molecular design beyond the domain of organic drug-like compounds. Here, we present 3DOpt, the first benchmark designed to assess the ability of generative methods to identify optimal 3D structures across the full chemical spectrum, including organometallic and transition-metal complexes. Each benchmark task is defined by a target 3D structure, a rigorously curated starting population of pre-evaluated candidate molecules, and a scoring function that quantifies similarity to the target in terms of both geometry and composition. We demonstrate the utility of 3DOpt by applying baseline generative methods and provide reference performance metrics for widely used molecular design strategies. Overall, 3DOpt establishes a general-purpose framework for the systematic evaluation of 3D molecular generative design methods across diverse chemical spaces.

ABSTRACT Design methods for extra-large-diameter welded hollow spherical joints (WHSJs; diameter > 900 mm) are essential for engineering applications. This study presents a parametric analysis of the ultimate axial compressive capacity of such joints, utilising finite element models calibrated against experimental data. The investigation focuses on the effect of sphere diameter, tube diameter, wall thickness, stiffener height, and stiffener thickness, on failure modes and ultimate capacity. The results reveal that the failure mode of extra-large-diameter WHSJs under axial compression is characterised by a circumferential plastic hinge at the tube-sphere junction following substantial plastic deformation. For unstiffened joints, the ultimate capacity is positively correlated with the tube-to-sphere diameter ratio. Increasing the height and thickness of stiffeners significantly enhances the load-bearing capacity of the hollow-sphere wall. To facilitate design optimisation, the joint capacity is decomposed into two components: (1) the enhanced capacity of the stiffeners, and (2) the bearing capacity of the stiffener. Building on this decomposition model, an analytical formula is proposed to evaluate the ultimate compressive capacity of extra-large-diameter WHSJs. The accuracy of this formula is subsequently validated against parametric analysis results.

A Patient-Clinician Co-Designed Infographic for Diabetic Ketoacidosis Education in Type 1 Diabetes.

The distribution characteristics of the deviatoric stress tensor (DST) field in the surrounding rock of the end-mining retreat roadway (ERR) are intricate, exerting a substantial influence on the stability of the ERR's surrounding rock. Taking the fully mechanized working face and its retreat roadway in Wutong Coal Mine as the engineering backdrop, initially, a numerical model of the DST in the end-mining coal pillar (ECP) was established to analyze its evolutionary pattern. Based on the response surface methodology (RSM) model, the significance of each influencing factor was examined. Subsequently, the three dimensional stress expression above the ECP was deduced, and the invariants and distortion energy (DE) of the DST at any position within the ECP were ascertained. Then, the expressions for the cutting height and cutting angle were derived, and a novel time step control technology (TSCT) for the ERR's surrounding rock was put forward, based on the real-time evolution characteristics of DST, dynamic matching of support strategies and roof cutting parameters is achieved through two stages of passive reinforcement and active roof cutting to achieve collaborative control. Compared with traditional static design or single time step methods, this technology reduces the deformation rate of surrounding rock by about 84% through step-by-step and timely application of support and cutting. Ultimately, according to the field measured data, it was demonstrated that this technology can effectively mitigate the deformation of the ERR's surrounding rock.

Designing freeform optics for extended light sources remains a challenge in illumination design, since conventional design methods for zero-étendue sources are difficult to extend to general, finite-étendue sources. As an alternative to these conventional methods, this work introduces a framework for direct prediction of zero-étendue freeform illumination surfaces. A multi-stage training strategy is presented for a deep neural network enabling near-instant prediction of smooth freeform geometries for point sources and random target irradiance distributions of varying sizes. The predicted designs achieve high irradiance fidelity and serve as effective initialization for differentiable fine-tuning, requiring only a couple of optimization iterations to reach ultra-precise irradiance control. This represents the first, to the best of our knowledge, deep learning framework for nonimaging freeform illumination design with zero-étendue sources. While this work focuses on zero-étendue sources, the multi-configuration capability of the framework provides a fundamental base that can be extended to eventually capture the full spatial and angular emission characteristics of finite-étendue sources.

Traditionally recognized for its role in calcium homeostasis and bone metabolism, vitamin D is increasingly acknowledged for its broader physiological effects, particularly in cardiovascular health. One area of growing interest is its influence on cardiac autonomic function. Cardiac autonomic dysfunction, characterized by an imbalance between sympathetic and parasympathetic activity, is a well-established predictor of adverse cardiovascular outcomes. This narrative review aims to synthesize the current literature on the association between vitamin D status and cardiac autonomic function. Accumulating observational data indicate that individuals with vitamin D deficiency exhibit poorer cardiac autonomic function, reflected by reduced heart rate variability indices, compared to vitamin D-sufficient individuals. Interventional trials, though limited, suggest that vitamin D supplementation may improve autonomic balance, especially in those with baseline deficiency. The relationship appears to be affected by factors such as glycemic control, disease state, and vitamin D binding protein levels. Vitamin D may modulate autonomic regulation largely via the vitamin D receptor, which activation influences multiple downstream pathways, including central nervous system effects, neurotrophic properties, regulation of the renin-angiotensin-aldosterone system, calcium-parathyroid hormone axis, and immune modulation. However, inconsistencies in study design, population characteristics, and assessment methods limit the strength of current evidence. Further longitudinal and interventional studies are warranted to establish causality and determine whether vitamin D repletion can reduce cardiovascular risk by restoring autonomic balance.

Generative AI expands opportunities for embodied agents in HCI, yet a gap persists between human-centered AI principles and practical design methods, particularly for pedagogical agents’ (PAs) co-speech gestures. Automated text-to-gesture systems lack the instructional nuance needed for effective teaching. To address this, we used a Research-through-Design approach to develop a human-centered framework that translates pedagogical intent into gesture specifications for embodied AI teachers. The framework includes four iterative stages: Preparation, which analyzes gesture patterns and instructional functions; Human PA Acting, where educators and designers rehearse gestures through performance; Embodied PA Acting, which transfers human motion to agents using video-based pose estimation; and EPA-assisted Course Delivery, which evaluates student experiences through interviews. Findings indicate that these gestures enhanced students’ perception of the PA’s professionalism, approachability, and instructional rhythm, while boosting overall engagement. This work contributes a design framework and insights for pedagogical gesture design, and exploratory guidance for generative-AI prompting.

Acute kidney injury (AKI) is a common complication following pediatric cardiac surgery, frequently leading to poor outcomes and even death in severe cases. Early prevention remains the primary intervention strategy. Studies have developed prediction models to identify at-risk children at an early stage. This study systematically evaluate existing AKI prediction models to support their clinical utility and future refinement. PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang and SinoMed were searched from inception to 31 December, 2024. The search of references from included studies, as well as the manual search, extended until November 30, 2025. Literature searching, screening, and data extraction were done by two authors. Quality evaluation according to prediction model risk of bias assessment tool (PROBAST). Area under the receiver operating characteristic curve (AUROC) was pooled using a random-effects model to summarize the overall performance of existing models, exploring sources of heterogeneity of performance through subgroup analysis and meta-regression. Sensitivity analysis and Egger's method were used to analyze the stability of the included studies and to identify publication bias. This study was registered with PROSPERO (CRD42024593112) and reported following the Transparent Reporting of Multivariable Prediction Models for Individual Prognosis or Diagnosis: Checklist for Systematic Reviews and Meta-Analysis (TRIPOD-SRMA). A total of 2189 studies were screened which represented the total number of studies retrieved from the database search, the search of references from included studies, and the manual search. Nineteen studies were included in this review. Included studies differed in study design, AKI definition, predictor screening, model development and validation and model performance. The overall pooled AUROC was 0.850 (95% CI, 0.810-0.890), but all studies were evaluated as high risk of bias using the PROBAST. Heterogeneity in model performance was high, and study design and development methods were identified as possible sources of heterogeneity in pooled AUROC. Included studies were stable and free of publication bias. This systematic review suggested that machine learning models for predicting postoperative AKI in pediatric cardiac surgery indicated good discriminative ability. However, the high risk of bias across all included studies and the significant heterogeneity in model performance indicated that the reported performance may be overestimated. The high heterogeneity observed highlights the substantial variability in model performance, which is likely driven by differences in study design and development methods. The clinical utility of these models was currently limited due to the lack of external validation in most studies and the methodological limitations identified. Future research must incorporate rigorous study design, transparent reporting based on the TRIPOD guidelines, and external validation to develop prediction models with clinical utility.

Nutrition apps offer scalable opportunities to support dietary behavior change and prevent chronic diseases. Their success depends on sustained user engagement, which is essential yet challenging to achieve and, consequently impacts the long-term effectiveness of these digital tools. Engagement strategies have been widely explored in digital health, but a comprehensive synthesis focusing on nutrition apps for adults is lacking. This scoping review aimed to map the current engagement approaches and metrics implemented in nutrition apps targeting adults and to identify how user engagement is defined across studies. We conducted a search of the PubMed, Scopus, Cochrane, and Web of Science databases for relevant studies published from January 1, 2013, to June 30, 2024. The inclusion criteria included original adult interventional or observational studies that evaluated nutrition apps and reported user‑engagement strategies or metrics. Two reviewers independently screened records in Covidence, with discrepancies resolved by a third reviewer. Data were charted across study characteristics, engagement strategies, and engagement metrics and then synthesized narratively. A total of 59 studies that used apps to improve dietary behaviors were included in our analysis, including randomized controlled trials, observational trials, and mixed methods studies. Most of these apps were designed for adults who were overweight and obese. The studies were primarily conducted in North America and Europe and were randomized controlled trials or nonrandomized intervention studies, with varying durations and sample sizes. Engagement strategies varied widely, and engagement was typically measured by frequency of specific function use and frequency of app use, followed by retention rate. The most common engagement strategies reported in studies were push notifications (n=29, 49%), behavioral theory integration (n=24, 41%), personalization and customization (n=19, 32%), and goal‑setting features (n=18, 31%). Only 31% (n=18) of studies provided an explicit definition of "user engagement," and definitions were highly heterogeneous. Engagement measurement was dominated by quantitative system‑recorded metrics, including time and frequency of using specific functions (n=38, 64%), app use frequency (n=34, 58%), and retention (n=17, 29%). Few studies assessed qualitative or long‑term engagement dimensions, and long‑duration studies rarely integrated adaptive or contextualized engagement mechanisms. Research apps more frequently used theory‑driven strategies compared with commercial apps, which tended to emphasize streamlined user experience. Although several engagement strategies are commonly used, their implementation is inconsistent and often lacks grounding in conceptual frameworks. Research in the future needs to prioritize the use of common definitions for user engagement and measurement criteria while implementing user-centered design methods and using multiple research approaches to study the complex patterns of user engagement. The evidence base for engagement strategies needs strengthening because it will support the development of sustainable nutrition mobile health interventions.