Design Factors Research Articles

Exploring interactivity effects on learners’ sense of agency, cognitive load, and learning outcomes in immersive virtual reality: A mixed methods study

This study explored the effects of IVR interactivity on learners’ sense of agency, cognitive load, and learning outcomes. The research questions were: 1. “How does interactivity influence the learners’ sense of agency?” and 2. “How does interactivity influence the learners’ cognitive load and learning outcomes?” A mixed-methods experimental design was applied. Safety training interventions, including individually performed IVR scenarios, were held for 68 participants in groups of two to four persons in two work organizations. Single- and repeated-measure questionnaires were the main source of the quantitative data. Qualitative data collection by video recordings and stimulated recall interviews was carried out on 23 persons in total. The results indicate that high interactivity enables a stronger sense of agency for the learners and yields learning benefits by supporting generative cognitive processing. Based on the results, interactivity and learner involvement may be particularly important for achieving affective training goals. Considering the sociocultural and individual factors in training design and pre-briefing the learners are also recommended.

Revolutionizing tire engineering: Finite element analysis and design of an X-shaped spoke structure for non-pneumatic military tires

In this study, we developed a non-pneumatic tire (NPT) specifically designed for military vehicles, such as the Humvee. Our aim was to enhance performance on challenging terrains crucial for mission success. We selected a novel X-shaped polygonal structure for the spoke design owing its exceptional load-carrying capacity and durability. The equivalent modulus, which is the key factor in spoke design, was analytically derived and formulated. This calculated modulus served as a benchmark for achieving the desired vertical stiffness of the NPT. We manufactured a prototype NPT and evaluated its mechanical properties by using a polyurethane (PU) spoke structure. Multiple finite element models of the novel NPT, considering different parameters such as the number of spoke sections, thickness, and modulus of elasticity, were developed while maintaining a consistent equivalent modulus value. Dynamic finite element analysis was then performed to assess the rolling performance of the NPT. We found that the maximum dynamic stress at the spoke was 2.387 MPa, which is 0.398 times the allowable yield strength of the PU spoke. This finding confirms the durability of the novel NPT under dynamic usage in military vehicles, with promising implications for overcoming challenging terrains.

Enhancing the performance of zero energy buildings with boosted coyote optimization and elman neural networks

This research paper aims to optimize electrical system modeling to minimize CO2 emissions, improve reliability, and reduce costs. It proposes integrating Elman Neural Network (ENN) and metaheuristic algorithm to evaluate environmental impact and optimize system performance. The goal is to create a mathematical model considering renewable energy sources, grid energy, CO2 emissions, and maintenance costs, and use metaheuristic algorithms to determine the optimal combination of wind turbines and photovoltaic panels. To train the ENN model and optimize the system, a three-stage approach is proposed. Quasi-random numbers are generated using Sobol techniques to create input data, which is then processed by TRNSYS for simulation. The proposed approach incorporates an ENN with Boosted version of the Coyote optimization algorithm to accurately model and predict the behavior of Hybrid Renewable Energy Systems for Zero Energy Buildings. The results demonstrate the effectiveness of the proposed approach. Our method significantly reduces energy consumption, costs, and system reliability, achieving a 25% reduction in energy consumption and 12% reduction in system cost, and is effective in optimizing hybrid solar/wind and hydrogen storage systems. The optimal system configuration significantly reduces CO2 emissions, achieves a high level of system reliability, and minimizes costs. Comparative analysis with previous studies showcases the superiority of the proposed method in achieving comprehensive optimization. In conclusion, this study combines AI techniques and metaheuristic algorithms to optimize electrical system design, considering environmental, technical, and economic factors. The proposed approach overcomes limitations in previous studies and offers an effective solution to address the optimization problem.

The impact of IEQ in the university lecture theatres on students’ concentration levels in London

Purpose University students’ lecture theatre concentration levels are significantly related to indoor environmental quality (IEQ). The purpose of this study is to investigate the relationship between indoor environmental quality (IEQ) and the self-reported concentration levels of university students during the winter at University College London (UCL), UK. Design/methodology/approach A questionnaire survey and physical measurements were used to assess the IEQ factors affecting students’ concentration levels. Findings The lecture theatre design factor was the most significant factor influencing students’ concentration levels, and the facility environment was more important than the thermal environment, indoor air quality, and acoustic environment in influencing students’ concentration levels in this winter investigation at UCL, UK. Additionally, students prefer a colder thermal environment. The concentration level of students was positively correlated with the indoor air quality and negatively correlated with the acoustic environment. Practical implications Based on model application, this research could provide lecture theatre IEQ design. This research additionally provides an acceptable indoor thermal environment temperature range based on a large sample, which can be used to calibrate a student performance benchmark. Originality/value As this study evaluates the IEQ factors that influence the concentration levels of university students, interior designers and engineers should consider the rational layout of these factors. Therefore, this study may provide a reference for the interior environmental design of lecture theatres in educational buildings.

An artificial neural network model for determining stress concentration factors for fatigue design of tubular T-joint under compressive loads

PurposeThe stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.Design/methodology/approachA mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.FindingsUsing real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.Practical implicationsEngineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.Originality/valuePrecisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

Performances of the sandwich panel structures under fire accident due to hydrogen leaks: Consideration of structural design and environment factor using FE analysis

Abstract This study presents a comprehensive analysis using the finite element method (FEM) to examine and evaluate the behavior of fuel carrier ship structure. Sandwich panels are effective structures for use in ship structures due to their lightweight yet robust nature. Sandwich panels used in ship structures have various core shapes, such as hexagonal, circular, and square, as needed. The sandwich panel structure can be widely implemented in ship construction, for example, on the deck, hull, and bulkhead of the ship’s cabin. Hydrogen is an alternative fuel that can replace fossil fuels. In this modern era, hydrogen is high valuable energy commodity, so accidents involving carrier ships could have significant consequences. Hydrogen is produced from liquefied natural gas (LNG), so if a leak occurs, for instance, in a storage tank or fuel tank, it has the potential to lead to accidents such as fires. Corrosion is a significant concern for the maritime industry, as it can jeopardize the structural integrity of these vessels and pose substantial safety and environmental risks. In this research, FEM was utilized to model and simulate the effects of corrosion on hydrogen carrier ships when exposed to fire, considering various environmental and operational factors. Through a systematic investigation, it is aimed to gain insights into the impact of corrosion on ship structural components during fires, such as hulls and storage tanks. The result of this study will contribute to improving corrosion and fire mitigation strategies, ensuring the safety and longevity of hydrogen and LNG carrier ships, and supporting the sustainable transportation of hydrogen to meet global energy demands. No research has been conducted on the structural behavior resulting from hydrogen fires and corrosion simultaneously. To achieve this, it is assumed to use the corrosion properties of steel in heavily traveled ship routes such as the Panama Canal Zone, Barent Sea, North Sea, and Suez Canal Zone. This study utilized an approach by modeling corrosion using shell thickness in Abaqus Quasi-Static and applying boundary conditions in the form of temperature increase up to 800°C and subsequent cooling back to the initial temperature. At the maximum temperature, the most significant mid-span displacement occurred in the circular core sandwich panel, with a value of 4 mm. The axial force in the structure was inversely proportional to the mid-span displacement. In the case of the circular core sandwich panel, the axial force was 96 kN. The most resilient core type was hexagonal because it experiences the least deformation when compared to circular and square cores.

Quantifying the impacts of green roof design factors on microclimate regulation: A case study in Washington, D.C.

This study explores and quantifies the impacts of green roofs (GR) design factors on microclimate regulation. Against rapid urbanization and intensifying climate changes, green infrastructure has received increasing attention due to its contribution to dealing with the urban heat islands effect (UHIE) and increasing urban sustainability. GR is one example of green infrastructure and is widely used in cities. However, its microclimate regulation benefits have not been thoroughly explored and require more exploration. There are insufficient studies on quantifying GR design factors impacts on microclimate regulation. Meanwhile, little research has explored the impact of regulating microclimate when accessibility is considered as a design factor. Using a GR in Washington, D.C., as a case study, this study models 14 roof scenarios with several common GR design factors, generates their simulated microclimate patterns, and quantifies each design factors impact based on statistical analyses. Results present quantitative data on how studied design factors change microclimate patterns and find that accessibility does affect some microclimate aspects but only to a limited rate. This study provides suggestions and insights to optimize GR design guidelines. Overall, this study contributes to the refinement of GR research, promotes better GR design, and responds to urbanization and climate issues.

Measuring perception on multimedia-based agro-advisory: A scale construction

Access to information and effective delivery can be improved by using multimedia as a tool for advisory services. Various factors contribute to the development of an effective multimedia-based agro-advisory model. Stakeholders’ perception plays a major role to design and validate it properly. To measure stakeholders’ perception towards multimedia-based agro-advsiory (Pusa Samachar), a multi-dimensional perception scale was developed using Polychoric Principal Component Analysis (PCA). The data pertaining to this study were collected from 150 farmers using Google forms in 2021 and from 225 farmers in 2022. These farmers were sampled using stratified two-stage sampling from five districts each from Uttar Pradesh, Haryana and Punjab states. The majority of the farmers (68.6%) reported watching full weekly episode of agro-advisory telecasted as Pusa Samachar. Notably, farmers of Uttar Pradesh (54.67%) and Haryana (60.0%) showed affirmative perception; while Punjab (50.83%) had neutral perception towards Pusa Samachar model. Analysis of average perception score of farmers revealed that technical factor ranked I followed by linguistic factor (II), content and design factor (III) and timeliness factor (IV). Audio-visual quality, graphics, time duration of content, language, accent, and style of presentation with quality content could be considered as prime parameters for developing multimedia-based content. Location-specific, farmers’ centric language-based, and farmer participatory multimedia-based content should be created for better information availability and acceptance among farming community.

Influence of Surface Roughness on Symmetric Airfoil Flow Properties and Reynolds Number

Collaborative Aircraft Design Optimization: Investigating Roughness Management of Wings to Reduce Drag and Improve Performance This study investigates the potential of roughness control of aircraft wings based on joint design optimization. By adding roughness points to specific wings, our goal is to reduce drag and improve the overall performance of the aircraft. A multi-modal design optimization (MDO) framework combining aerodynamics, modelling, and performance analysis was used in this study. We measure the effect of tissue on the boundary layer, the progression of laminar flow, and the potential for weight savings due to reduced friction. This study investigates how roughness control interacts with other design factors such as flow components or blade materials to achieve optimal performance. The ultimate goal is to create a collaborative design that uses tissue management to contribute to next-generation fuel efficiency and high performance. Keywords – CFD, Surface Roughness, Wind Tunnel, Aerodynamic Efficiency.

TOPOLOGY OF GALVANIC ISOLATED BATTERY CHARGING UNIT WITH POWER FACTOR CORRECTION

The present study investigates topology for battery charging units (BCU) with simple design and power factor correction. It has a galvanic isolation between the grid and the battery, which is one of the law and standard requirements. The impulse transformer ensures galvanic isolation, which makes the topology suitable for onboard electric vehicles (EV) charging units (OBC). The study also presents a simulation model a simulation model with the help of which a simulation is conducted and a result is obtained.

Physical activity to address mental health in a remote Australian community: community readiness assessment

Purpose The mental and physical health of those residing in Australian rural and remote communities is poorer compared to major cities. Physical health comorbidities contribute to almost 80% of premature mortality for people living with mental illness. Leisure time physical activity (LTPA) is a well-established intervention to improve physical and mental health. To address the physical and mental health of rural and remote communities through LTPA, the community’s level of readiness should be first determined. This study aims to use the community readiness model (CRM) to explore community readiness in a remote Australian community to address mental health through LTPA. Design/methodology/approach Individual semi-structured interviews were conducted using the CRM on LTPA to address mental health. Quantitative outcomes scored the community’s stage of readiness for LTPA programmes to address mental health using the CRM categories of one (no awareness) to nine (high level of community ownership). Qualitative outcomes were thematically analysed, guided by Braun and Clark. Findings The community scored six (initiation) for community efforts and knowledge of LTPA programmes and seven (stabilisation) for leadership. The community’s attitude towards LTPA and resources for programmes scored four (pre-planning), and knowledge of LTPA scored three (vague awareness). Originality/value To the best of the authors’ knowledge, this is the first Australian study to use CRM to examine community readiness to use LTPA to improve mental health in a remote community. The CRM was shown to be a useful tool to identify factors for intervention design that might optimise community empowerment in using LTPA to improve mental health at the community level.

Study on the Impact of Design Factors of Piloti Forms on the Thermal Environment in Residential Quarters

Study on the Impact of Design Factors of Piloti Forms on the Thermal Environment in Residential Quarters

Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?

Precision dosing requires selecting the appropriate population pharmacokinetic model, which can be assessed through external evaluations (EEs). The lack of understanding of how different study design factors influence EE study outcomes makes it challenging to select the most suitable model for clinical use. This study aimed to evaluate the impact of sample size, sampling strategy, and handling of concentrations below thelowerlimit of quantification (BLQ) on the outcomes of EE for four population pharmacokinetic models using vancomycin and tobramycin as examples. Three virtual patient populations undergoing vancomycin or tobramycin therapy were simulated with varying sample size and sampling scenarios. The three approaches used to handle BLQ data were to (1) discard them, (2) impute them as LLOQ/2, or (3) use a likelihood-based approach. EEs were performed with NONMEM and R. Sample size did not have an important impact on the EE results for a given scenario. Increasing the number of samples per patient did not improve predictive performance for two out of the three evaluated models. Evaluating a model developed with rich sampling did not result in better performance than those developed with regular therapeutic drug monitoring. A likelihood-based method to handle BLQ samples impacted the outcomes of the EE with lower bias for predicted troughs. This study suggests that a large sample size may not be necessary for an EE study, and models selected based on TDM may be more generalizable. The study highlights the need for guidelines for EE of population pharmacokinetic models for clinical use.

Minimal Social Cues, Body Proportion, Head Shape, and Participant’s Gender on the Trustworthiness of Social Robots

Exploring and validating factors that may influence the trustworthiness of social robots is of great significance to improving the effect of human-robot interaction. The purpose of this study was to explore the impact of several design factors on people’s perceptions of the trustworthiness of social robots. The research variables were social cues (ie, watching-eyes configuration, control configuration, and neutral configuration), head shape (ie, round head and rectangular head), body proportion (ie, low body proportion and high body proportion), and participant’s gender (ie, male and female). Twelve static synthetic social robots were created and presented to the participants as digital pictures. A total of 175 participants took part in the experiment through the online survey via the convenience sampling method. The generated results were as follows: (1) The robots with round heads appear to be more trustworthy than those with rectangular heads. (2) Robots with high body proportion are considered to be more trustworthy than robots with low body proportion. (3) Minimal social cues and body proportion of robots also have a significant interaction effect on improving the trustworthiness of robots. In the watching-eyes configuration, high body proportion is considered more trustworthy than low body proportion. On one hand, the results of this study can provide a good reference for the appearance form of social robots in human-robot interaction to serve human beings, and on the other hand, it also lays a foundation for further exploring the influence of social cues elicited by a series of social robot appearances and social behaviors regarding the trustworthiness of social robots.

Evaluating fit indices in a multilevel latent growth model with unbalanced design: a Monte Carlo study

This study informed researchers about the performance of different level-specific and target-specific model fit indices in the Multilevel Latent Growth Model (MLGM) with unbalanced design. As the use of MLGMs is relatively new in applied research domain, this study helped researchers using specific model fit indices to evaluate MLGMs. Our simulation design factors included three levels of number of groups (50, 100, and 200) and three levels of unbalanced group sizes (5/15, 10/20, and 25/75), based on simulated datasets derived from a correctly specified MLGM. We evaluated the descriptive information of the model fit indices under various simulation conditions. We also conducted ANOVA to calculated the extent to which these fit indices could be influenced by different design factors. Based on the results, we made recommendations for practical and theoretical research about the fit indices. CFI- and TFI-related fit indices performed well in the MLGM and could be trustworthy to use to evaluate model fit under similar conditions found in applied settings. However, RMSEA-related fit indices, SRMR-related fit indices, and chi square-related fit indices varied by the factors included in this study and should be used with caution for evaluating model fit in the MLGM.

Investigating Space Utilization in Skyscrapers Designed with Prismatic Form

The enduring appeal of prismatic shapes, historically prevalent in office building designs, persists in contemporary skyscraper architecture, which is attributed particularly to their advantageous aspects concerning cost-efficiency and optimal space utilization. Space efficiency is a crucial factor in prismatic skyscraper design, carrying substantial implications for sustainability. However, the current academic literature lacks a complete exploration of space efficiency in supertall towers with prismatic forms, despite their widespread use. This paper seeks to address this significant gap by conducting a comprehensive analysis of data gathered from a carefully selected set of 35 case studies. The primary discoveries presented in this paper are outlined as follows: (i) average space efficiency stood at approximately 72%, covering a range that extended from 56% to 84%; (ii) average core to gross floor area ratio averaged around 24%, spanning a spectrum that ranged from 12% to 36%; (iii) the majority of prismatic skyscrapers utilized a central core approach, mainly customized for residential use; (iv) the dominant structural system observed in the analyzed cases was the outriggered frame system, with concrete being the commonly utilized material for the structural components; and (v) the impact of diverse structural systems on space efficiency showed no significant deviation, although differences in function led to variations in average space efficiency. The authors expect that these findings will provide valuable guidance, especially for architects, as they strive to enhance the sustainable planning of prismatic towers.

Calibration of resistance factors for reliability-based design of driven steel piles considering separate factors for toe and shaft resistances

ABSTRACT The LRFD method employs the geotechnical resistance factor (GRF) to design pile foundations. Different resistance mechanisms at the end and side of the pile lead to the requirement of separate GRF values for the shaft and toe capacities. The main aim of this study is to establish regionally separate GRFs for the toe and shaft resistances of pile foundations using different reliability analysis methods. A comprehensive database was gathered from field tests conducted in Alberta, Canada, involved 623 Pile Driving Analyzer (PDA) tests and 28 static load tests. Two criteria called ‘70% rule’ and ‘R ratio’ were used to evaluate the results. Due to certain limitations in the definition of the ‘70% rule’, the ‘R ratio’ method yielded more realistic results. The results indicated that employing separate GRFs for shaft and toe resistances can lead to a significant increase in the factored pile resistance when compared to the values suggested by the Canadian Foundation Engineering Manual (CEFM) in the case of redundant piles.

Analyzing a series of ligands against malaria through the application of molecular docking, molecular quantum similarity, and reactivity indices

Background The primary goal of this research is to underscore the significance of molecular docking in the context of malaria drug discovery. Molecular docking plays a crucial role in comprehending the interactions between prospective drugs and the target proteins found in Plasmodium parasites. The study delves into the docking interactions of various compounds, emphasizing the necessity of stabilizing the active site to formulate potent and selective drugs. Methods The research focuses on highlighting compound-specific interactions with residues, stressing the importance of stabilizing the active site to design drugs tailored to specific target proteins. Inhibiting the function of these target proteins disrupts the life cycle of the malaria parasite. Quantum Similarity Analysis, utilizing Overlap and Coulomb operators, is employed to identify electronic similarities. The resulting quantum similarity values guide subsequent chemical reactivity analysis. Global reactivity indices such as chemical potential, hardness, softness, and electrophilicity contribute to drug design by showcasing compound-specific indices that underscore the significance of stability and electrophilicity. Fukui functions are utilized to visualize regions for stabilization, providing insights crucial for potential malaria treatment. Results The enhancement of drug-target binding affinity is observed through stabilizing interactions in the active site. Understanding electrophilicity at the active site emerges as a critical factor in drug design and selectivity. The rational manipulation of electrophilic interactions holds promise for developing potent and selective drugs against malaria. Consequently, the integration of molecular docking, quantum similarity analysis, and chemical reactivity indices offers a comprehensive approach to malaria drug discovery. Conclusions The study identifies potential lead compounds, emphasizing the crucial role of stabilizing the active site. Additionally, it sheds light on electronic considerations vital for the design of effective and resistance-resistant drugs. The insights provided by Fukui functions into regions susceptible to -H bond formation make these compounds promising candidates for malaria treatment.

Design of high-performance entangling logic in silicon quantum dot systems with Bayesian optimization

Device engineering based on computer-aided simulations is essential to make silicon (Si) quantum bits (qubits) be competitive to commercial platforms based on superconductors and trapped ions. Combining device simulations with the Bayesian optimization (BO), here we propose a systematic design approach that is quite useful to procure fast and precise entangling operations of qubits encoded to electron spins in electrode-driven Si quantum dot (QD) systems. For a target problem of the controlled-X (CNOT) logic operation, we employ BO with the Gaussian process regression to evolve design factors of a Si double QD system to the ones that are optimal in terms of speed and fidelity of a CNOT logic driven by a single microwave pulse. The design framework not only clearly contributes to cost-efficient securing of solutions that enhance performance of the target quantum operation, but can be extended to implement more complicated logics with Si QD structures in experimentally unprecedented ways.

Development of an induction heating reactor for rapid catalytic CF4 decomposition

A novel catalytic reactor configuration based on the induction heating technique is proposed for the decomposition of CF4. To demonstrate the feasibility of this concept, a commercial catalyst and induction heating module are employed. The experimental results demonstrated that the reactor provides a fast response time (approximately 3 min) in the decomposition of CF4 (roughly 100 ppm), without the requirement for insulation or preheating. In addition, introduction of the induction heating technique was found to allow precise temperature control. Furthermore, empirical guidelines for optimizing the catalyst bed are established in terms of two design factors, namely the length/diameter ratio (≥ 1.48) of the catalyst bed, and 1/3 of the blending of an additional inductive heating medium in the catalyst bed. The fast response time exhibited by this system is expected to permit connection of the reactor with electric power generated by renewable energy.