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Fire 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
Pigeons in a flock go cheap: A re-evaluation of the energetics of flying in cluster flocks
No full textLived experiences of students with specific learning disorders in university education in the Czech Republic: a case study
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'A Gendered Media Spectacle for the Digital Era: Nicola Bulley, ‘Missing White Woman Syndrome’ and Middle-Class, Middle-Aged, Menopausal Womanhood on Trial'
No full textUncovering Islamophobia in Higher Education: Supporting the Success of Muslim Students and Staff
No full textA randomized controlled trial of emotionally focused individual therapy (EFIT) for depression and anxiety.
No full textEmotionally focused individual therapy (EFIT; Johnson, 2019) is a newly developed therapeutic modality. In this study, we used a randomized controlled trial (Clinical Trial Registration NCT04719780) intent-to-treat design to examine the effects of 15 sessions of EFIT in comparison with a 15-week wait-list control on general symptom distress and symptoms of depression and anxiety. Eighty-eight participants who met the diagnostic criteria for major depressive disorder and comorbid anxiety, as determined by the Anxiety and Related Disorders Interview Schedule for the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, were randomized to an EFIT treatment group (n = 44) or to a wait-list control group (n = 44). Average age was 35.73 years (SD = 12.28). Sixty-three percent identified as women, and 37% identified as male. In terms of ethnicity, 73% identified as White, 1.3% as Black, 7.7% as Southeast Asian, 7.7% as East Asian, 3.8% as Latinx, and 2.6% as First Nation. Participants completed the Outcome Questionnaire–30.2, the Patient-Reported Outcomes Measurement Information System (PROMIS)-Depression scale, and the PROMIS–Anxiety scale. Multilevel modeling results confirmed a significant difference in growth curves between the treatment group and controls on all measures. Follow-up analyses demonstrated significant reductions in symptom distress (Outcome Questionnaire–30.2) and symptoms of depression and anxiety (PROMIS–Depression and PROMIS–Anxiety) across 15 weeks. Overall, the results of this study suggest that EFIT leads to significant symptom reduction among people with depression and anxiety
Exploring AI-powered Digital Innovations from A Transnational Governance Perspective: Implications for Market Acceptance and Digital Accountability
Get PDFToward Scalable and Sustainable Detection Systems: A Behavioural Taxonomy and Utility-Based Framework for Security Detection in IoT and IIoT
No full textResource-constrained IoT and IIoT systems require detection architectures that balance accuracy with energy efficiency, scalability, and contextual awareness. This paper presents a conceptual framework informed by a systematic review of energy-aware detection systems (XDS), unifying intrusion and anomaly detection systems (IDS and ADS) within a single framework. The proposed taxonomy captures six key dimensions: energy-awareness, adaptivity, modularity, offloading support, domain scope, and attack coverage. Applying this framework to the recent literature reveals recurring limitations, including static architectures, limited runtime coordination, and narrow evaluation settings. To address these challenges, we introduce a utility-based decision model for multi-layer task placement, guided by operational metrics such as energy cost, latency, and detection complexity. Unlike review-only studies, this work contributes both a synthesis of current limitations and the design of a novel six-dimensional taxonomy and utility-based layered architecture. The study concludes with future directions that support the development of adaptable, sustainable, and context-aware XDS architectures for heterogeneous environments