Design Assistance Research Articles

The Impact and Process of Transition from Face-to-Face to Online Teaching During a Pandemic: A Grounded Theory Approach

Abstract The COVID-19 pandemic required a “rethinking and retooling” of many educational interactions, especially in schools that offer primarily face-to-face educational models. The sudden necessity for altering teaching formats and styles created challenges and opportunities for faculty and their students. There is little understanding of the impact the abrupt switch had on faculty and students, or the process by which the transition took place. The purpose of this study was to develop a theory about the impact on and the processes used by faculty for implementing online education in response to the pandemic. A total of 24 faculty members from a private southeastern university that primarily uses a face-to-face educational model were interviewed. The grounded theory methods of Corbin and Strauss (2015) and Charmaz (2014) were used to inform the inquiry. The unexpected transition to online teaching impacted faculty and students personally, professionally, and academically. Faculty readiness to teach online was highly dependent on perceptions of self-efficacy, which in turn affected their perceptions of the transition. Study findings reinforce the need for university administrators to provide faculty with structured pedagogical support specifically technological and instructional design assistance, dedicated time to learn and incorporate instructional changes, and mental health services to care for themselves and their students during times of crisis. Determining the process by which the transition took place provides insight regarding both transition facilitators and barriers, which can inform future educational delivery and evaluation.

Authoring training scenarios “from within” in an immersive environment with the CESTATE framework

ABSTRACT Virtual reality (VR) can be used to train for complex, dynamic and dangerous situations, such as disaster response. The aim of this research is to support trainers with an immersive authoring tool that facilitates the implementation of training environments, enabling both the design and execution of complex and dynamic exercises. A systematic literature review was conducted to identify VR-based authoring tools. Each environment was assessed based on variability, reusability and design assistance, with each criterion graded on a 3-point scale. Following a model-driven approach, the work presented in this article introduces the CESTATE (Cascading Effect Simulation To Authoring Training Environments) framework. It uses a metamodel to design training exercises, thus ensuring their reusability. Inheriting from the Physics of Decisions paradigm, this metamodel can be used to describe all types of potentialities and cascading effects. This ensures the dynamic, and therefore, the plausibility of the virtual training environment. In addition, this framework supports the design of immersive training environments, at both design and run time. It is a generic approach. Its applicability is demonstrated in several iterations of a case study involving the training of practitioners on a complex and realistic crisis situation: a fire in a cathedral.

Digital Entrepreneurship: Typography T-shirt Design Production in SMA Negeri 1 Percut Sei Tuan

The purpose of this community service activity is to answer the problems at SMA Negeri 1 Percut Sei Tuan: the lack of interest in digital-based entrepreneurship, the lack of knowledge and skills of students in digital entrepreneurship practices so that students do not always lead to conventional entrepreneurship, limited capital in providing equipment, renting online platforms, and marketing costs, and the lack of commitment in understanding and implementing digital entrepreneurship, both from students and schools. This activity was carried out with three methods, namely equipment procurement, training, design assistance, and assistance in selling T-shirts at the market. The results of the activity include the availability of typography t-shirts with creative writings and then the t-shirts are marketed online in marketplaces, such as Shopee and Tokopedia. Currently, 50 t-shirts with typography designs have been marketed. The typography t-shirt design production development program at SMA Negeri 1 Percut Sei Tuan aims to enhance inclusive industries and encourage innovation through digital entrepreneurship training for students. The main priorities are the procurement of digital platforms, typography t-shirt production training, and the enhancement of digital entrepreneurship interest and knowledge in school.

Parametric Design Method of Buildings Based on Convolutional and Deep Neural Networks

The application of AI technology in architectural software has brought various innovations and changes to the industry. By introducing convolutional neural networks and deep neural networks, architectural design software is expected to automatically identify and generate complex architectural images, optimize the design process, and provide intelligent design assistance. Through literature research, this paper aims to discuss the specific application of CNN and DNN in parametric design and the innovation brought by them, and analyzes the application of these technologies in architectural image recognition, generation, design optimization and other aspects. This paper aims to provide a comprehensive reference for researchers in related fields and promote the innovative application of AI technology in architectural design. The application of AI technology in architectural software has brought numerous innovations and changes to the industry. By introducing convolutional neural networks and deep neural networks, architectural design software is expected to automatically identify and generate complex architectural images, optimize the design process, and provide intelligent design assistance. Through literature research, this paper aims to discuss the specific application of CNN and DNN in parametric design and the innovation brought by them; it also analyzes the application of these technologies in architectural image recognition, generation, design optimization, and other aspects. This paper aims to provide a comprehensive reference for researchers in related fields and promote the innovative application of AI technology in architectural design.

Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.

Digital Documentation and Architectural Heritage Restoration with 3-D Geometry: The Ambulatory of the Cathedral of Santiago de Compostela

ABSTRACT The SFM photogrammetry (Structure from Motion) is a technique developed in the field of computer vision that allows obtaining 3D models from images taken from different angles. Despite its extensive development, this technique faces methodological challenges when applied to complex heritage environments. Typically, heritage restoration documentation focuses on the initial and final stages when works have not yet begun or have already concluded, and the object of study is free of obstacles and workers, with its use in intermediate stages being uncommon. The aim of this article is to present geometric documentation using SFM-MVS photogrammetry across multiple stages of the roof restoration of the Cathedral of Santiago de Compostela (Spain), showcasing an expert application of this technique in a real working scenario with multiple challenges, adapting to complex scenes without interfering with project timelines and providing high-quality outcomes at a low cost, even in areas temporarily obscured by scaffolding or plastic coverings. It provides essential data for geometric assistance in restoration design, adaptation, and stakeout. Moreover, continuous 3D documentation enables the creation of 3D stop-motion models, offering an innovative perspective for understanding and communicating a wide range of restoration and construction projects.

A refined dynamic liquid dehumidification model for the packed dehumidifier considering time-varying thermal mass of the desiccant

Liquid desiccant dehumidification technology is a promising, energy-efficient dehumidification approach. It can flexibly adapt to variations in renewable energy supply and users' demands, making it an inherently dynamic process. However, optimizing this process from a dynamic perspective is often neglected. This is mainly due to the challenges in establishing a dynamic dehumidification model, where the desiccant's thermal mass and heat and mass transfer coefficients should be time-dependent. In this paper, a refined dynamic dehumidification framework using the finite difference method is developed, where governing equations for the mass and species conservations are rebuilt taking the thermal mass and desiccant concentration as time-dependent variables. Auxiliary equations determining evolutions of the desiccant's thermal mass and heat and mass transfer coefficients are derived based on the continuity equation and analogy method, respectively. Subsequently, the experiments are carried out and serve as a benchmark to check the simulations. It suggests the simulated results using the refined model align well with the experimental data and demonstrate around 50 % better predictive accuracy compared to the existing method, indicating the feasibility of the refined model. The refined model also contributes to rapid adaptation of the dehumidification process to meet specific needs. In a specific case, this could result in energy savings of 60 % for the regeneration of the desiccant solution. This study provides a reference for quantifying evolutions of the liquid desiccant's thermal mass based on an understanding of the physical process. It lays a solid foundation for modeling the dynamic dehumidification process at the design stage and assists in operating an energy-saving dehumidification system.

Hyperelasticity of blood clots: Bridging the gap between microscopic and continuum scales

The biomechanical properties of blood clots, which are dictated by their compositions and micro-structures, play a critical role in determining their fates, i.e., occlusion, persistency, or embolization in the human circulatory system. While numerous constitutive models have emerged to describe the biomechanics of blood clots, most of these models have primarily focused on the macroscopic deformation of the clots and the resultant strain-stress correlations without depicting the microscopic contributions from their structural components, such as fibrin fibers, fibrin network and red blood cells. This work addresses the gap in current scientific understanding by quantifying how changes in the microstructure of blood clots affect its mechanical responses under different external stresses. We leverage our previous published work to develop a hyperelastic potential model for blood clots, which incorporates six distinct strain-energy components to describe the alignment of fibers, the entropic and enthalpic stretching of fibrin fibers, the buckling of these fibers, clot densification, and clot jamming. These strain-energy components are represented by a combination of simple harmonic oscillators, one-sided harmonic potentials, and a Gaussian potential. The proposed model, which is C0, C1, and C2 continuous with a total of 13 parameters, has been validated against three datasets: 1) fibrin clot in tension, 2) blood clot in compression, and 3) blood clots in shear, demonstrating its robustness. Subsequent simulations of a microscopic blood clot model are performed to uncover mechanistic correlations for a majority of the hyperelastic potential's stiffness/strain parameters. Our results show that only one proposed term concerning fiber buckling needs further refinement, while the remaining five strain-energy terms appear to describe precisely what they were intended to. In summary, the proposed model provides insight into the behavior of thromboembolisms and assistance in computer-aided design of surgical tools and interventions such as thrombectomy.

Comprehensive evaluation of the influence of PEM water electrolyzers structure on mass transfer performance based on entropy weight method

The proton exchange membrane water electrolyzer (PEMWE) has broad prospects in hydrogen production due to its green and efficient advantages. This paper comprehensively analyzes the performance and water-gas transport characteristics of PEMWE with different cell structures, in which polarization curves, flow velocity distribution, oxygen concentration, temperature, liquid water saturation, and pressure are investigated. Simultaneously, the study explores the effects of variations in inlet velocity, inlet temperature, porosity, and thickness of the anode porous transport layer (APTL) on the four flow fields. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on entropy weight method is employed for a comprehensive evaluation of the flow fields. The results indicate that, compared to parallel flow field (PFF), sinusoidal wave flow field (SWFF), intercepted flow field (ICFF), and single-serpentine flow field (SSFF) exhibit stronger exhaust capabilities, with temperature uniformity increasing by 83.65 %, 59.24 %, and 70.15 %, respectively. However, the longer flow channel of SSFF results in a high pressure drop and lower liquid water saturation. An increase in inlet velocity leads to a decrease in temperature, causing an increase in voltage, as higher temperatures reduce activation and ohmic overpotentials. Additionally, an increase in the porosity and thickness of APTL deteriorates the polarization performance of PEMWE. The research results of this study can provide valuable assistance for the future design of PEMWE.

Development and evaluation of parallel simulated annealing algorithm for reactor core optimization problems

Background The optimization of core loading patterns in nuclear reactors is one of the most studied optimization problems in nuclear engineering due to the enormous economical and safety benefits. Various algorithms such as Genetic Algorithms (GA), Simulated Annealing (SA), and Parallel Simulated Annealing (PSA) have been used in the past for such problems. Methods In this work, a PSA algorithm was developed and integrated into the Modularly Implemented Design Assistance Suite (MIDAS), a framework developed at North Carolina State University to solve nuclear engineering problems. The effectiveness of PSA was compared against the GA and SA algorithms available in MIDAS for a Pressurized Water Reactor first cycle core loading pattern optimization problem. Results PSA consistently generates more optimal solutions than SA and GA by having the higher average fitness, and showing less variance in its performance and thus being more robust. This provides confidence in the PSA implementation within MIDAS. The obtained loading pattern positions high reactive fuel in peripheral locations and low reactive fuel towards the centers in a strategy resembling both Out-In-Checkboard and L3P loading pattern approaches. Conclusions Future studies will involve applying the PSA algorithm to other optimization studies in larger combinatorial spaces, such as in multi-cycle optimization problems.

Homologous Design and Three-Dimensional Quantitative Structure-Activity Relationship Study of Acaricidal 2,4-Diphenyloxazolines Containing Different Heteroatoms and Alkyl Chains.

In order to speculate the three-dimensional structure of the potential binding pocket of the chitin synthase inhibitor, a series of 2,4-diphenyloxazoline derivatives with different lengths of alkyl chains and heteroatoms were designed and synthesized by a homologous strategy. The bioassay results indicate that both the length of the alkyl chains and the type of substituents can affect the acaricidal activity against mite eggs. Compounds containing chloropropyl, alkoxyalkyl, and para-substituted phenoxyalkyl or phenylthioalkyl groups exhibit good activity, while those containing steric hindrance substituents or carbonyl substituents on the benzene ring exhibit reduced activity. Three-dimensional quantitative structure-activity relationship (3D-QSAR) study showed that there may be a narrow hydrophobic region deep in the pocket, and the steric effect plays a more important role than the electrostatic effect. The current work will provide assistance for future molecular design and target binding research.

Research on Digital Transformation and System Reconstruction of AIGC-Enabled Landscape Architecture

Landscape architecture is a huge and complex discipline with strong cross-fertilization, which needs to be supported by massive resource databases and strong Analysis and processing capabilities. The traditional design methods need help. The digital landscape has become an important method of landscape architecture, and the development of Artificial Intelligence Generated Content (AIGC) provides new opportunities for the digitalization process. Through the introduction of related concepts, the article takes the dilemma faced by traditional landscape architecture as the main entry point, explores the application form of AIGC in landscape architecture workflow from three aspects: information analysis, design assistance, and innovation demand, and identifies that AIGC-enabled development is the road to the future of landscape architecture. Finally, it discusses the possible misconceptions of the process, aiming to provide new ideas for landscape architecture digital transformation and system restructuring.

3D Pop-Ups: Omnidirectional image visual saliency prediction based on crowdsourced eye-tracking data in VR

The prediction of the visual saliency of omnidirectional images in VR is valuable for understanding visual behaviors. However, the equipment cost, software setups, hardware operation, and other constraints in acquiring eye-tracking data of omnidirectional images for visual saliency prediction would lead to a low training efficiency and prediction performance. Therefore, this paper proposed a crowdsourcing method based on recall fixations, which was used to collect and construct an omnidirectional image with eye-tracking dataset called CrowdSourcing360, which contained 16,200 pieces of data on 180 images. Using this dataset, a visual saliency prediction model CSnet360 was trained. Experiments demonstrated that the visual saliency prediction performance of the CSnet360 outperformed most existing models even without using actual gaze fixations. Finally, a VR interior design assistance prototype system was built and the preliminary study results indicated that the system could help designers to improve the quality of their design solutions.

Performance evaluation and optimization of compact tube-fin latent heat storage system in phase change nanoemulsion discharge mode

The low thermal conductivity of phase change materials (PCMs) and the reduced heat transfer temperature difference along a tube both limit the thermal performance of latent heat thermal energy storage (LHTES). Herein, a compact tube-fin LHTES system based on phase change nanoemulsions was developed and the heat transfer process of nanoemulsion and PCM was numerically investigated. The effects of the heat transfer structure of the heat exchanger and the thermophysical parameters of the heat transfer fluid (HTF) on the cold energy discharge performance of the system were quantified. The optimized model reduced the discharge time by 24.4 % in nanoemulsion mode relative to water. Heat transfer improvement on the PCM side could be achieved by decreasing the fin spacing and tube spacing and by increasing the fin thickness. Such improvement reinforced the heat transfer enhancement of the nanoemulsion. The heat transfer mechanism was explained by the temperature distributions of the HTF and PCM along the tube. The phase-change thermostatic properties of the nanoemulsion and PCM contributed to the formation of dual-phase-change coupled heat transfer (i.e., the heat transfer process when the fluid inside the tube and the PCM filled outside the tube undergo a simultaneous phase change) within the LHTES, increasing the heat transfer temperature difference and the magnitude of the driving force. Increasing the phase change temperature difference accelerated the onset of dual-phase-change heat transfer, while increasing the PCM content prolonged the process. Briefly, this study provided operational and design assistance for LHTES systems in nanoemulsion mode with the intention to accelerate heat transfer rates.

Systematic literature review: DOGBL in enhancing EFL students’ motivation

This systematic literature review (SLR) aimed to investigate the potential of digital online game-based learning (DOGBL) to enhance motivation in English as a foreign language (EFL). Online gaming has grown in popularity among students, opening up the possibility of using games as powerful instructional resources. Academic achievement depends on motivation, and this study, led by self-determination theory (SDT), explored how external rules, like rewards and recognition, could increase motivation in EFL utilizing DOGBL. The study used the SLR method, examining databases and choosing articles based on predetermined criteria. The chosen publications were examined in-depth, and a preferred reporting items for systematic reviews and meta-analyses (PRISMA) diagram was employed for analysis. For results, DOGBL could enhance teaching EFL by providing flexible and interesting learning environments. Key elements in motivating in DOGBL included game design, personalization, social engagement, curricular integration, and instructor assistance. As a promising method to improve EFL instruction, game-based learning, especially DOGBL, saw considerable developments between 2018 and 2023. Thus, these ground-breaking techniques transformed the way people learn English vocabulary and provided a fun and engaging way to learn the language. For educators and students, the potential for DOGBL to change EFL education is still exciting as technology develops.

The Interweaving of Artificial Intelligence in the Fashion Industry

The Interweaving of Artificial Intelligence in the Fashion Industry

Frizzled receptors (FZDs) in Wnt signaling: potential therapeutic targets for human cancers.

Frizzled receptors (FZDs) are key contributors intrinsic to the Wnt signaling pathway, activation of FZDs triggering the Wnt signaling cascade is frequently observed in human tumors and intimately associated with an aggressive carcinoma phenotype. It has been shown that the abnormal expression of FZD receptors contributes to the manifestation of malignant characteristics in human tumors such as enhanced cell proliferation, metastasis, chemotherapy resistance as well as the acquisition of cancer stemness. Given the essential roles of FZD receptors in the Wnt signaling in human tumors, this review aims to consolidate the prevailing knowledge on the specific status of FZD receptors (FZD1-10) and elucidate their respective functions in tumor progression. Furthermore, we delineate the structural basis for binding of FZD and its co-receptors to Wnt, and provide a better theoretical foundation for subsequent studies on related mechanisms. Finally, we describe the existing biological classes of small molecule-based FZD inhibitors in detail in the hope that they can provide useful assistance for design and development of novel drug candidates targeted FZDs.

Optimization of laser annealing parameters based on bayesian reinforcement learning

AbstractDeveloping new semiconductor processes consumes tremendous time and cost. Therefore, we applied Bayesian reinforcement learning (BRL) with the assistance of technology computer-aided design (TCAD). The fixed or variable prior BRL is tested where the TCAD prior is fixed or is changed by the experimental sampling and decays during the entire RL procedure. The sheet resistance (Rs) of the samples treated by laser annealing is the optimization target. In both cases, the experimentally sampled data points are added to the training dataset to enhance the RL agent. The model-based experimental agent and a model-free TCAD Q-Table are used in this study. The results of BRL proved that it can achieve lower Rs minimum values and variances at different hyperparameter settings. Besides, two action types, i.e., point to state and increment of levels, are proven to have similar results, which implies the method used in this study is insensitive to the different action types.

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

This study explores the integration of Generative Design Assistants (GDAs), specifically machine learning based tools, in the architectural design process. It investigates how these tools, once confined to experimental realms, are now influencing mainstream architectural practice, particularly among novice architects. The research focuses on third and fourth-year architecture students, examining how they adapt to and integrate these advanced AI tools into their design workflows. Through an empirical online workshop, the study collected data of design process recordings, design output success scores of students by an independent jury, and post-experiment surveys. This approach provided insights into the timing, frequency, and sequence of GDA usage, as well as the influence of specific GDA features on design success. The research reveals that three primary strategies emerged in students' GDA usage: continuous use throughout the design process, selective problem-solving use, and initial ideation use followed by traditional methods. However, an over-reliance on GDAs was noted to potentially limit the designer’s interpretive and developmental input. The survey shows that different GDAs have distinct strengths and impacts on the design process. In terms of selected GDAs for the experiment, ArchiGAN aids in discovery and ideation, while HouseGAN excels in reframing design problems. In conclusion, the study underscores the transformative potential and challenges of GDAs in architectural design and highlights the need for balanced GDA integration. The research outputs show that future research should focus on the long-term implications of GDAs in architectural education. This research aims to guide the effective integration of AI in architecture, enhancing the human designer's role rather than overshadowing it.

Dual-stage theoretical model of magnetorheological dampers and experimental verification

The theoretical model for predicting the damping characteristics of magnetorheological dampers (MRDs) not only facilitates the optimization of MRD parameters, but also provides assistance for the theoretical design of MRDs. However, some existing models have limitations in fully characterizing the damping characteristics of MRDs. In this paper, the working stage of MRDs was categorized into yield and pre-yield stages based on whether the internal magnetorheological fluid attains the dynamic shear yield state or not, and the Herschel–Bulkley model with pre-yield viscosity (HBPV) and improved polynomial model (IPOL) were employed to respectively characterize the yield and pre-yield stages of MRDs. Subsequently, the HBPV-IPOL model was proposed to characterize the complete damping characteristics of MRDs in low-frequency vibration conditions, with considering the local loss effect of the fluid in the model. To accurately characterize the magnetic induction intensity in the MRD damping channel, employing the steady-state finite element method for magnetic field analysis; on this basis, dividing the damping channel to investigate the variation trends of the magnetic induction intensity in different regions. Simultaneously, the zero-field region hypothesis was proposed to quantitatively consider the influence of minute magnetic induction intensity in the traditional zero-field regions on the damping characteristics of MRDs. Finally, integrating the impact trends of currents in different regions, and employing the HBPV model to determine the impact magnitude of each region within the damping channel on the damping characteristics of the MRD in the yield stage. In the pre-yield stage, polynomial curves were fitted to experimental damping force–velocity curves, and the obtained polynomials were employed to predict the damping characteristics. Extensive experiments have been conducted on MRD samples to assess the predictive performance of the model on MRD damping characteristics under sinusoidal displacement excitation vibration conditions with different excitation currents, vibration frequencies and vibration amplitudes.