Design Science (United Kingdom)

Vortex-induced vibration (VIV) is a typical nonlinear fluid–structure interaction phenomenon. Although the semi-empirical model is a theoretical and efficient manner, it is difficult to accurately predict the above nonlinear phenomena due to the incomplete mathematical expressions and uncertain parameters. In this article, the recursive radial basis function neural network (RRBFNN) is proposed to modify the typical vortex induced force (VIF) model. The RRBFNN VIF model was first constructed, and the dynamic equation for calculating VIV response was created, based on the significant nonlinear fluid–structure interaction characteristics of VIV. The aerodynamic forces of the two-edge box girders under various reduced velocities and vibration amplitudes were calculated via computational fluid dynamics (CFD) in order to obtain the mentioned VIF model parameters and verify the efficacy of several RRBFNN learning methods. Ultimately, the amplitude of vortex-induced vibration was predicted using the earlier-mentioned effective training method, and the aerodynamic decoupling method was used to investigate the influence of multiple order aerodynamic forces on the vortex-induced vibration response. As a result, the RRBFNN accurately simulates the aerodynamic force in the time domain and frequency domain. The orthogonal least squares (OLS) and exponential basis function are well-suited to the RRBFNN VIF model. The decisive factor of VIV is the fundamental frequency signal in the aerodynamic force. The second harmonic wave can significantly affect the VIV amplitude, while the influence of the third harmonic wave can be ignored.

Disaster risk reduction (DRR) strategies must actively include people with disabilities (PWD) to ensure equitable and effective preparedness. Although awareness of disability inclusion in DRR is growing, the literature remains fragmented, and no prior structured literature review has focused specifically on participatory methods involving PWD. This review addresses that gap by identifying and synthesizing evidence on how PWD have been engaged in disaster preparedness and contingency planning. Following a structured process inspired by PRISMA and using the PICO framework, searches in Scopus and Web of Science yielded 720 articles, of which 20 peer-reviewed studies from 12 countries were included. The studies employed diverse participatory methods such as interviews, focus groups, co-design workshops, photovoice, and multi-stakeholder consultations. These approaches led to increased preparedness, empowerment, and leadership among PWD, while also strengthening community networks and collaboration with DRR personnel. However, challenges such as communication barriers, limited resources, and exclusion of marginalized groups were common. To conclude, this review offers the first comprehensive synthesis of participatory methods for disability-inclusive DRR, highlighting both their transformative potential and the need for more inclusive, tailored strategies in future research and practice. • This review explores disability inclusion in disaster risk reduction through participatory practices. • Identified benefits of inclusion include capacity building, empowerment and enhanced collaboration. • Communication barriers and resource constraints are significant barriers to effective inclusion in disaster risk reduction. • Exclusion of certain groups remain a critical challenge in achieving full disability inclusion in disaster risk reduction. • This review identifies a shortage of studies on disability-inclusive disaster risk reduction, emphasizing the need for further exploration in this area.

Purpose: Growing climate pressures and the limitations of prevailing sustainability frameworks have intensified the need to reconsider how architecture responds to environmental and social challenges. Many existing approaches separate climate performance, equity, and systems thinking into discrete categories, leading to fragmented and short-term outcomes. This paper introduces the Six Climate-Conscious Design Actions (SCDA), namely Climate, Site, Design, Decarbonisation, Systems, and Community, as an integrated framework to support early-stage design decision-making beyond the 2030 Agenda. Design/Methodology/Approach: Drawing on a critical synthesis of peer-reviewed research, professional benchmarks, and practice-based evidence, the framework aligns each design action with environmental and social performance objectives. International references, including the Sustainable Development Goals (SDGs), the RIBA 2030 Climate Challenge, the Whole Building Design Guide, and principles of regenerative and inclusive design, inform the analytical structure. Findings: Application of the SCDA framework addresses key gaps in contemporary practice by strengthening early interdisciplinary coordination. It enables whole-life carbon reduction, improves passive design performance, and embeds social value considerations, supporting a transition from compliance-led processes toward more transformative sustainability outcomes. Originality/Value: Rather than prioritising isolated environmental metrics or context-specific solutions, the SCDA offers a unified and scalable approach. By linking environmental and social dimensions of sustainability, the framework provides design teams with a practical structure for operationalising post-2030 priorities and reframing architectural responsibility in relation to climate action, equity, and wellbeing. Research Limitations/Practical Implications: Although conceptual in scope, the framework establishes a clear foundation for practical validation. Further research should examine empirical case studies, performance evaluation, and stakeholder engagement to assess implementation across diverse architectural and cultural contexts. Citation: Elhussein, S. (2025): Tracking Climate-Conscious design for the SDGs: A post-2030 framework for sustainable architecture. Sustainability Tracker (ST), Vol. 1, Nos. 1/2, pp. 43-59.

The growing deployment of deep learning in Indus-trial Internet of Things (IIoT) applications demands efficient, low-latency inference mechanisms on resource-constrained edge devices. This paper presents a distributed real-time object detection framework implemented using a master-worker architecture across three edge configurations: a single Raspberry Pi, a Raspberry Pi cluster, and a high-performance Intel i7 system. The system performs inference offloading using socket-based communication and streams annotated results via a lightweight Flask server. We evaluate key performance metrics including frame rate, latency, CPU/RAM usage, and processing time under Ethernet-based communication. Results show that while the multi-Pi setup marginally improves throughput over a single board, the i7 system significantly outperforms both in latency and overall efficiency. Our analysis highlights the trade-offs between cost, scalability, and real-time responsiveness in IIoT environments. Future work will extend this framework with heterogeneous edge support, adaptive task scheduling, and integration of 5G networks to further enhance performance and deployment flexibility.

Air pollution is increasingly recognized as a key factor in lung health. Robust experimental exposure models are thus essential for studying the involved molecular mechanisms. We aimed to study the effects of aerosolized fine particulate matter (PM2.5) in naïve rats using chronic and acute exposure. <bold>Methods:</bold> Male Sprague-Dawley rats were exposed to well characterized PM2.5 (<italic>Bergman F. 2024</italic>), collected at an urban site, then aerosolized in exposure chambers. The chronic exposure group (CG) underwent exposure for 12 weeks (5 days/week, 5 h/day) at a concentration of 250 µg/m³, while the acute exposure group (AG) received a single 10-h exposure at 1000 µg/m³. At termination, bronchoalveolar lavage (BAL) fluid was collected and analysed for leukocytes and proteins. Lung tissue was assessed for histology and proteins analysed by Western Blot. <bold>Results:</bold> In the CG, BAL macrophages decreased (p=0.05) and had changed morphology, while lymphocytes showed an increasing trend (p=0.06) following PM2.5 exposure, while no change in BAL cells was observed in the AG. Inflammatory markers; BAL uric acid (p=0.017), LDH (p=0.07) and SOD2 (p=0.005) were significantly upregulated in the CG, but no significant changes were observed in the AG. ZO-1 and E-cadherin were significantly elevated in both exposure groups compared to controls (p=0.03–0.008). <bold>Conclusion:</bold> Aerosolized PM2.5 of different duration and concentration exposure led to similar effects on barrier integrity while distinct effects on oxidative stress and leukocyte populations were observed between the chronic and acute exposure groups, highlighting the complex kinetics of PM2.5-induced lung responses.

Most common lung function measurements do not adequately account for the distal airways, and imaging techniques can be complex, costly, or involve radiation exposure. Airspace Dimension Assessment (AiDA) was developed to measure distal airway radius by analysis of in- and exhaled nanoparticles. This method has potential as a safe and fast diagnostic tool for diseases where the distal airways may be enlarged. Distal airway radius (<italic>r</italic>_AIDA) was measured in six groups: healthy adults, healthy adolescents, adults with chronic airway limitation (CAL), chronic obstructive pulmonary disease (COPD), or asthma, and adolescents with bronchopulmonary dysplasia (BPD). AiDA also provides R₀, a parameter thought to reflect small conductive airway dysfunction. Compared to adult controls (<italic>r</italic>_AIDA = 283±36 µm), <italic>r</italic>_AIDA was larger in individuals with CAL (313±61 µm) and COPD (345±64 µm), as well as in adolescents with BPD (313±52 µm) vs. healthy adolescents (231±12 µm). Healthy adolescents had smaller, less variable airway radii than adults. <italic>r</italic>_AIDA did not differ significantly between healthy individuals and those with asthma (285±46 µm). R₀ was reduced in CAL (0.47±0.18 µm), COPD (0.35±0.15), and asthma (0.45±0.21), but not in BPD. AiDA shows promise as a tool for early detection and severity assessment of emphysema. Adolescents with BPD had airway radii comparable to adults with COPD. <fig><object-id>erj;66/suppl_69/PA420/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>