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Interaction Design Strategies for ADHD Learning Attention - A Review
No full textInteraction design and development strategies contain the proficiency to mimic user attention and engagement. These strategies are proven to be beneficial for learning attention while employing various types of digital learning platforms. However, special education need (SEN) learners possess special needs due to their neurodevelopmental disorder and deficiency of proper brain functioning and there-fore giving rise to malfunctioning of inhibitory control, sustained attention, and working memory. Hence, there is a need to develop user-centric interaction design and development strategies and implications to increase the attention span of ADHD (attention deficit hyperactivity disrorder) while learning. In this literature review, we aim to highlight the attention problem of ADHD due to malfunctioning of executive functioning and working memory. We have summarized the existing IxD (interaction design)based solutions for ADHD learning attention. The related limitations, chal-lenges and findings of the literature review are presented along with the future possi-bilities. This paper highlights the need to develop user-centric solutions for ADHD attention improvement during learning and the incorporation of the machine learning and artificial intellegnce based interfaces for the advance user-centric solutions
A replaceable corrugated web shear link for seismic resilience of double-column bridge bent: Experimental, numerical, and theoretical study
No full textThis study introduces an innovative replaceable corrugated steel web (CSW) shear link system for double-column bridge bents, designed to enhance seismic performance and enable rapid post-earthquake recovery. Through a comprehensive experimental program, eight full-scale specimens with varying geometric parameters (span-to-height ratios: 1.46–3.89; corrugation angles: 30–60°; orientation configurations) were subjected to quasi-static testing to evaluate their seismic behaviors, including damage process, energy dissipation, strength, stiffness and ductility. The experimental investigation revealed four characteristic failure modes: (1) CSW tearing, (2) coupled CSW and flange buckling, (3) combined CSW tearing and flange-to-web weld fracture, and (4) endplate-to-CSW connection failure. Key findings demonstrate that specimens with span-to-height ratios below 1.0 and corrugation angles exceeding 45° exhibit superior hysteretic performance, with the vertical-oriented specimen (VL1.89-θ45-a0.29) achieving optimal energy dissipation per unit volume (4.34 × 107J/m3) at the expense of accelerated stiffness degradation (60 % reduction after 3 % drift). Analytical results indicate a nonlinear relationship between ductility enhancement and span-to-height ratios, with measured improvement by 40 % as L/H increased from 1.46 to 3.89. Complementing the experimental work, advanced finite element models incorporating ductile fracture criteria were developed, achieving a 1.06 % correlation with test results. The study further proposes and validates simplified design equations for yield strength and lateral stiffness of CSW links, providing practical tools for engineering implementation. These findings establish a technical foundation for developing resilient bridge systems with rapid recovery capabilities
A simplified numerical simulation of circular CFDST short column with NC, HPC and UHPC under compression
No full textIn this study, axisymmetric models to predict the ultimate strength of circular concrete-filled double-skin steel tube (CFDST) short columns containing Normal Concrete (NC), High-Performance Concrete (HPC), or Ultra-High-Performance Concrete (UHPC) under axial compression are developed. A simplified concrete material model is proposed for these axisymmetric models, offering more convenience compared to the previous axisymmetric model, which was validated only for NC, HPC, and concrete-filled steel tube (CFST) columns. The reliability and accuracy of the new model are verified using experimental data. This study demonstrates that the combination of the axisymmetric model and the simplified concrete model significantly reduces computational time while maintaining acceptable accuracy. The proposed method can generate extensive numerical databases for structural optimization or machine learning-based strength prediction. The reduced computational effort of axisymmetric models, compared to 3D models, allows for a comprehensive parametric study of axial load-displacement curves in circular CFDST short columns, exploring various influencing factors. Additionally, the study evaluates established design codes, including Eurocode 4 (EC4), American Concrete Institute (ACI), and American Institute of Steel Construction (AISC), along with analytical models from the literature, thereby enhancing the understanding of circular CFDST short columns under compression.</p
A data augmentation strategy for deep neural networks with application to epidemic modelling
No full textIn this work, we integrate the predictive capabilities of compartmental disease dynamics models with machine learning’s ability to analyze complex, high-dimensional data and uncover patterns that conventional models may overlook. Specifically, we present a proof of concept demonstrating the application of data-driven methods and deep neural networks to a recently introduced Susceptible-Infected-Recovered type model with social features, including a saturated incidence rate, to improve epidemic prediction and forecasting. Our results show that a robust data augmentation strategy trough suitable data-driven models can improve the reliability of Feed-Forward Neural Networks and Nonlinear Autoregressive Networks, providing a complementary strategy to Physics-Informed Neural Networks, particularly in settings where data augmentation from mechanistic models can enhance learning. This approach enhances the ability to handle nonlinear dynamics and offers scalable, data-driven solutions for epidemic forecasting, prioritizing predictive accuracy over the constraints of physics-based models. Numerical simulations of the lockdown and post-lockdown phase of the COVID-19 epidemic in Italy and Spain validate our methodology.</p
Seismic Performance and Mechanical Behavior Assessment of Demountable Diagonal Connection RCS Joints:A Numerical Simulation Study
No full textTo evaluate the performance of the newly designed demountable reinforced concrete column-steel beam (RCS) joint, numerical simulations were performed using the finite element software ABAQUS. The analysis results show that the proposed demountable RCS joint offers enhanced load-bearing capacity and improved ductility relative to conventional cast-in-place joints. A parametric analysis was performed to further investigate the seismic behavior of these joints, focusing on factors such as axial compression ratio, steel beam web strength, stirrup ratio, flange thickness, and Y-shaped connecting plate thickness. Additionally, an analysis of the seismic force transfer mechanism of the proposed joints was conducted. The existing shear capacity calculation formula for RCS joints was improved by considering the components within the joint domain. The improved formula demonstrated a more accurate assessment of the shear capacity of the novel joints, providing a theoretical foundation for future research on this type of joint.</p
Optimal design of the Side Sensitive Modified Group Runs Double Sampling (SSMGRDS) X̅ scheme with estimated process parameters
No full textThe previous studies on the side sensitive modified group runs double sampling (SSMGRDS) X̅ scheme focused on the known process parameters assumption (Case-K). However, the process parameters in real-life scenarios are frequently undisclosed and require estimation using an appropriate in-control (IC) reference sample. Unfortunately, prior research works have revealed that a substantial quantity of reference samples is necessary for the scheme with unknown process parameters assumption (Case-U) to attain a comparable performance as the Case-K scheme. Given the challenges of obtaining a large number of IC samples, we resort to exploring optimal designs for the Case-U SSMGRDS X̅ scheme, focusing on minimizing the average number of observations to signal (ANOS) in situations where the shift size is known. Moreover, we also investigate the expected ANOS (EANOS) since the shift size is commonly unknown in advance. The obtained optimal parameters for the SSMGRDS X̅ scheme under Case-U ensure its performance is equivalent to the Case-K scheme, without requiring an extensive number of reference samples. Our study demonstrates the effectiveness of the SSMGRDS X̅ scheme under Case-U in monitoring the silicon epitaxial process
Acceleration of Digitalisation in Manufacturing SMEs Through Capability Maturity Assessment
No full textDigitalisation enables organisations to redesign their value propositions, processes and systems. The value created by digitalisation makes it attractive for many other organisations; however, despite the undeniable benefits, numerous manufacturing SMEs continue to struggle with adopting digital tools to create value for their business. Consequently, the level of digitalisation in the manufacturing sector lags behind that of other industries. Various maturity models exist to assess organisations’ level of digitalisation, such as SIRI, IMPULS, and DDX. These maturity models are extensively designed and utilised to assess digitalisation in larger organisations. This study applied a tailored digital capability maturity assessment tool to evaluate ten manufacturing SMEs in the United Kingdom. The objective was to develop firm-specific digitalisation roadmaps aligned with each organisation’s strategic goals and operational context. The assessment revealed diverse digital maturity profiles across the firms. Some SMEs had invested in digital infrastructure but lacked the skills or processes to generate value. In contrast, others demonstrated strong data-driven decision-making capabilities but lacked the infrastructure to scale digital initiatives. These contrasting patterns underscore the importance of a balanced digital capacity and maturity approach. As an outcome, all ten SMEs received customised digital transformation roadmaps. Notably, six firms began implementing capability-building initiatives, including investments in digital infrastructure and workforce development. These findings suggest that effective SME digitalisation requires both technological readiness and organisational alignment and highlight the need for maturity models that accommodate SME-specific constraints and strategic priorities. This study employed a digital capability maturity assessment tool.</p
A generalized neural solver based on LLM-guided heuristic evoluation framework for solving diverse variants of vehicle routing problems
No full textVehicle Routing Problems (VRPs) are key combinatorial optimization challenges with broad applications in logistics. While neural solvers based on attention mechanisms offer promising results, they require retraining for each VRP variant, limiting scalability. Existing expert-designed and LLM-based heuristic methods often suffer from limited exploration ability and premature convergence. We propose the Unified VRP Neural Solver (UNS), an LLM-enabled framework that dynamically adjusts attention scores by generating variant-specific heuristics without requiring retraining of neural model parameters. At its core, the LLM-Guided Heuristic Evolution (LHE) algorithm, which is inspired by population-based Differential Evolution (DE) frameworks, iteratively refines heuristics through Mutation, Global Crossover, and Local Crossover to enhance diversity and avoid local optima. Extensive experiments across 16 VRP variants show that LHE outperforms state-of-the-art neural solvers and LLM-based approaches. The similarity analysis of heuristic populations reveals that LHE maintains higher diversity and avoids premature convergence. Additional evaluations on CVRP and TSP, along with ablation studies, validate the effectiveness and generalizability of LHE
A simplified numerical simulation of circular CFDST short column with NC, HPC and UHPC under compression
No full textIn this study, axisymmetric models to predict the ultimate strength of circular concrete-filled double-skin steel tube (CFDST) short columns containing Normal Concrete (NC), High-Performance Concrete (HPC), or Ultra-High-Performance Concrete (UHPC) under axial compression are developed. A simplified concrete material model is proposed for these axisymmetric models, offering more convenience compared to the previous axisymmetric model, which was validated only for NC, HPC, and concrete-filled steel tube (CFST) columns. The reliability and accuracy of the new model are verified using experimental data. This study demonstrates that the combination of the axisymmetric model and the simplified concrete model significantly reduces computational time while maintaining acceptable accuracy. The proposed method can generate extensive numerical databases for structural optimization or machine learning-based strength prediction. The reduced computational effort of axisymmetric models, compared to 3D models, allows for a comprehensive parametric study of axial load-displacement curves in circular CFDST short columns, exploring various influencing factors. Additionally, the study evaluates established design codes, including Eurocode 4 (EC4), American Concrete Institute (ACI), and American Institute of Steel Construction (AISC), along with analytical models from the literature, thereby enhancing the understanding of circular CFDST short columns under compression.</p
Origins and fate of polycyclic aromatic hydrocarbons (PAHs) in sustainable drainage systems (SuDS) in a Scottish urban area: Implications for groundwater systems
No full textIncreasing urbanisation and the effects of climate change have resulted in a decrease in water quality and availability worldwide. At the same time, flooding in urban areas has become one of the most prevalent natural disasters worldwide. Sustainable drainage systems (SuDS) are resilient stormwater management solutions that can help reduce flooding by mimicking natural drainage processes and promoting infiltration. Numerous studies have focused on the benefits SuDS provide. But, to date, studies fail to investigate the risks that detention basins pose to groundwater quality, particularly the potential for infiltration of stormwater pollutants such as polycyclic aromatic hydrocarbons (PAHs). To address this important knowledge gap, this study combines gas chromatography–mass spectrometry (GC–MS) techniques for PAHs characterisation with numerical modelling tools to investigate organic pollutant infiltration patterns. We find that high levels of PAHs originating from urban areas are temporarily stored in SuDS basins and are likely to reach shallow water tables within one year. Multiple factors such as vegetation, precipitation, drainage area size, total organic carbon content in the soil and soil saturation influence the PAHs infiltration rates within the basins. More broadly, this study highlights the need for more research regarding SuDS dynamics to prevent both flooding and groundwater deterioration.<br/
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