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The Old Guard's (2020) New Order: Gina Prince-Bythewood, Andy & Nile, and 'the opportunity to put a young Black female hero into the world'
Get PDFFire performance and design of LSF wall panels with 3D printed concrete and steel lipped channel sections
No full textPurpose Conventional plasterboard linings impose a hard limit on the fire resistance of light steel frame (LSF) walls because gypsum rapidly degrades at high temperature. This study analyses whether substituting those linings with 3D-printed concrete (3DPC) can enhance load bearing fire rating (LFR) and insulation fire rating (IFR) under both standard and severe hydrocarbon fire exposures. Design/methodology/approach Eighty-eight finite-element models simulated LSF walls combining steel lipped channels and 3DPC facings. Parameters varied were 3DPC thickness (25–100 mm), cavity-insulation type (rockwool or glass fibre) and infill ratio (20–100%). Critical outputs were time to reach steel temperatures of 320 °C, 490 °C and 640 °C (load ratios 0.6, 0.4, 0.2) and time to 160/200 °C on the unexposed face. Findings Replacing 25 mm panels (IFR = 18 min in hydrocarbon fire) with 100 mm 3DPC panels extended insulation fire resistance beyond the 240-min analysis window; under the standard curve, 50 mm panels already sustained the 0.2 load ratio for over four hours. Rockwool increased IFR by up to 55% and added more than 60 min to LFR. Regression models linking thickness, fill, fire severity and insulation type achieved R2 values to 0.992. Originality/value This is the first systematic investigation of 3DPC-LSF walls under both rapid-rise hydrocarbon and standard fires. It supplies design-ready regression models and shows that 3DPC walls = 50 mm, especially with rockwool, deliver multi-hour structural and insulation fire resistance, up to 50% higher than plasterboard, making them a viable, fire-robust alternative for fire-safe LSF construction. Highlight
Enhancing Corporate Sustainability Performance Through Supply Chain Low‐Carbon Transformation: A Stakeholder Theory Perspective
No full textSupply chain low‐carbon transformation (SCLCT) is regarded as an important strategy for corporate sustainability, but whether SCLCT can enhance corporate sustainability performance (CSP) remains unclear. To fill this research gap, this study investigates the effect of SCLCT on CSP. Grounded in stakeholder theory and organizational legitimacy theory, this study employs fixed effects models to analyze 2011–2023 panel data on Chinese A‐share listed manufacturing firms. The results demonstrate that SCLCT has a positive effect on CSP. Specifically, SCLCT improves CSP through three potential mechanisms: by reducing external transaction costs, fostering green innovation, and increasing employment opportunities. Furthermore, both supply chain concentration and media attention positively moderate the relationship between SCLCT and CSP. This study aims to provide theoretical insights and practical guidance for managers and policymakers seeking to enhance CSP through SCLCT strategies
Corticospinal Excitability During Explosive Voluntary Contractions and Its Association With Rapid Torque Production
Get PDFWe investigated relationships between rapid torque and corticospinal excitability (denoted by motor‐evoked potential; MEP) and inhibition (denoted by silent period duration; SPD) during explosive voluntary contractions, as well as differences in MEP and SPD between different phases of explosive contractions and at maximum voluntary contraction (MVC) plateau. In 14 adults, and across multiple repeated trials, quadriceps muscle MEP and SPD were measured at the early, middle and late phases of knee‐extensor isometric explosive contractions, and at the MVC plateau, using transcranial magnetic stimulation (TMS). Torque at equivalent time points was also measured on trials without TMS. Using repeated measures correlation applied to early phase data from TMS trials, we found MEP and torque (measured just prior to MEP) were correlated across trials within participants (r = 0.43, p < 0.001). Using Spearman rho correlations to investigate correlations across participants for each phase, we found MEP (averaged across phases up to the phase of interest) and torque (measured on non‐TMS trials) to be significantly correlated for the middle phase only (rho = 0.73, p = 0.004). Linear mixed effects models were used to investigate the effect of phase (three explosive phases and MVC plateau) on MEP and SPD. Absolute MEP, MEP normalised to maximal M‐wave and SPD all increased across the phases of explosive contraction and up to MVC plateau (fixed effects of phase, p < 0.025). Our results suggest corticospinal excitability may be an important determinant of rapid torque. Further, corticospinal inhibition and excitability both increase throughout the rising torque‐time curve and up to MVC plateau
Evaluating the Effectiveness of SAST Tools: A Comparative Study on Vulnerability Detection, Reporting, and Usability
No full textEvaluating the Effectiveness of SAST Tools: A Comparative Study on Vulnerability Detection, Reporting, and Usability
No full textOptimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective
Get PDFAlignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to achieve high accuracy and reliability in CVD diagnostics due to complex clinical data and the selection and modeling process of useful features. Therefore, this paper studies advanced AI-based feature selection techniques and the application of AI technologies in the CVD classification. It uses methodologies such as Chi-square, Info Gain, Forward Selection, and Backward Elimination as an essence of cardiovascular health indicators into a refined eight-feature subset. This study emphasizes ethical considerations, including transparency, interpretability, and bias mitigation. This is achieved by employing unbiased datasets, fair feature selection techniques, and rigorous validation metrics to ensure fairness and trustworthiness in the AI-based diagnostic process. In addition, the integration of various Machine Learning (ML) models, encompassing Random Forest (RF), XGBoost, Decision Trees (DT), and Logistic Regression (LR), facilitates a comprehensive exploration of predictive performance. Among this diverse range of models, XGBoost stands out as the top performer, achieving exceptional scores with a 99% accuracy rate, 100% recall, 99% F1-measure, and 99% precision. Furthermore, we venture into dimensionality reduction, applying Principal Component Analysis (PCA) to the eight-feature subset, effectively refining it to a compact six-attribute feature subset. Once again, XGBoost shines as the model of choice, yielding outstanding results. It achieves accuracy, recall, F1-measure, and precision scores of 98%, 100%, 98%, and 97%, respectively, when applied to the feature subset derived from the combination of Chi-square and Forward Selection methods
