Large-scale Design Research Articles

Attention-Based Convolutional Neural Network for Ingredients Identification

In recent years, with the development of artificial intelligence, smart catering has become one of the most popular research fields, where ingredients identification is a necessary and significant link. The automatic identification of ingredients can effectively reduce labor costs in the acceptance stage of the catering process. Although there have been a few methods for ingredients classification, most of them are of low recognition accuracy and poor flexibility. In order to solve these problems, in this paper, we construct a large-scale fresh ingredients database and design an end-to-end multi-attention-based convolutional neural network model for ingredients identification. Our method achieves an accuracy of 95.90% in the classification task, which contains 170 kinds of ingredients. The experiment results indicate that it is the state-of-the-art method for the automatic identification of ingredients. In addition, considering the sudden addition of some new categories beyond our training list in actual applications, we introduce an open-set recognition module to predict the samples outside the training set as the unknown ones. The accuracy of open-set recognition reaches 74.6%. Our algorithm has been deployed successfully in smart catering systems. It achieves an average accuracy of 92% in actual use and saves 60% of the time compared to manual operation, according to the statistics of actual application scenarios.

Momentum-Assisted Adjoint Method for Highly Efficient Inverse Design of Large-Scale Digital Nanophotonic Devices

Gradient-descent-based digitized adjoint method offers a way to realize the high-efficiency inverse design of digital nanophotonic devices with diverse functions. However, the vanishing gradient problem encountered in the design of high-dimension devices may lead to significant inefficiencies, making it difficult to integrate novel functions on a single chip. Here, we propose a highly efficient digitized adjoint method for large-scale inverse design, called adaptive gradient-descent with momentum. It uses the first- and second-order momentum, instead of the gradient, to update the device pattern during adjoint optimization. To demonstrate the efficiency of the proposed method, we design a coarse wavelength division multiplexer and a three-mode power divider with design dimensions of 800 and 1360, respectively, which are approximately 2-4 times that of conventional digital nanophotonic devices. The simulation results show that, compared with the conventional gradient descent method, the momentum-assisted adjoint method has about 4-6 times higher efficiency and obtains better optimization performance, which provides a powerful tool for the inverse design of novel digital nanophotonic devices.

Exploring the relationship among traffic, topology, and throughput: towards a traffic-optimal optical network topology design

The design of optical networks for maximum throughput under diverse traffic demands is a long-standing NP-hard problem. In this paper, by parameterizing the relationship between the network topology and traffic demand, a polynomial-time objective function, the demand weighted cost (DWC), is introduced and evaluated for different scale networks and diverse traffic scenarios. It is shown that the proposed DWC is highly correlated to network throughput, while speeding up the topology evaluation process by some five to six orders of magnitude. The DWC was then applied as the optimization target with three different topology optimization algorithms (DWC-selection, genetic algorithm, and our hierarchical topology design), achieving 90% and 460% throughput increases, on average, for small-scale (14-node) and large-scale (100-node) topology designs, respectively. The proposed methods have the potential of maximizing throughput in the design of future optical network topologies.

L-Lysine-Coated Magnetic Core-Shell Nanoparticles for the Removal of Acetylsalicylic Acid from Aqueous Solutions.

Nanotechnologies based on magnetic materials have been successfully used as efficient and reusable strategies to remove pharmaceutical residuals from water. This paper focuses on the fabrication, characterization, and application of ferrite-based magnetic nanoparticles functionalized with L-lysine as potential nanoadsorbents to remove acetylsalicylic acid (ASA) from water. The proposed nanomaterials are composed of highly magnetic and chemically stable core-shell nanoparticles covered with an adsorptive layer of L-lysine (CoFe2O4-γ-Fe2O3-Lys). The nanoadsorbents were elaborated using the coprecipitation method in an alkaline medium, leading to nanoparticles with two different mean sizes (13.5 nm and 8.5 nm). The samples were characterized by XRD, TEM, FTIR, XPS, Zetametry, BET, and SQUID magnetometry. The influence of time, pH, and pollutant concentration was evaluated from batch studies using 1.33 g/L of the nanoadsorbents. The Freundlich isotherm best adjusted the adsorption data. The adsorption process exhibited a pseudo-second-order kinetic behavior. The optimal pH for adsorption was around 4-6, with a maximum adsorption capacity of 16.4 mg/g after 150 min of contact time. Regeneration tests also showed that the proposed nanomaterials are reusable. The set of results proved that the nanoadsorbents can be potentially used to remove ASA from water and provide relevant information for their application in large-scale designs.

Antimicrobial resistance among canine enterococci in the northeastern United States, 2007-2020.

Antimicrobial resistance (AMR) is a growing and complex One Health concern worldwide, threatening the practice of human and veterinary medicine. Although dogs are a potential reservoir of multidrug-resistant bacteria, there are very few surveillance studies on AMR from the canine population in the United States. Here, we assessed the antimicrobial susceptibility patterns, identified temporal resistance and minimum inhibitory concentration trends, and described associations between resistance phenotypes among canine clinical enterococci in the northeastern United States. Through a large-scale retrospective study design, we collected species identification, minimum inhibitory concentration, and clinical data from 3,659 canine enterococci isolated at the Cornell University Animal Health Diagnostic Center between 2007 and 2020. We used the Mann-Kendall test, Sen's slope, multivariable logistic regression, and survival analysis models to detect the presence of a significant trend in resistance over the study period. Enterococcus faecalis was the most prevalent species (67.1% of isolates), followed by Enterococcus faecium (20.4%). We found high levels of AMR among enterococci to almost all the tested antimicrobials, particularly E. faecium. The lowest percentage of resistance was to vancomycin and chloramphenicol. Multidrug resistance was common (80% of E. faecium and 33% of E. faecalis) and 31 isolates were extensively drug resistant. Multidrug resistance among E. faecium increased over time, but not in E. faecalis. Resistance to penicillins, enrofloxacin, and rifampin increased during the study period, but resistance to tetracyclines is on a downward trajectory compared to AMR data from the last decade. Emerging vancomycin-resistant E. faecalis (0.3%) and E. faecium (0.8%) infections in the canine population are of great concern to both human and animal health. One E. faecium isolate with acquired vancomycin resistance was identified in 2017 and four vancomycin-resistant enterococci isolates were identified in 2020. There is a crucial need to make rational prescribing decisions on the prudent use of antimicrobials and improve the quality of care for patients, especially when empirical antimicrobial treatment for enterococcal infection is common.

An intelligent estimation-based large-scale envelope control for variable cycle engine with packet dropout

The distributed control system is designed to match the advanced variable cycle engine (VCE) with significant advantages, such as the reduced weight and the low cost by introducing the network. In addition to these advantages, the packet dropout follows and may destroy the performances of the controlled systems. Therefore, except for the engine's strong nonlinearity and uncertainty, the effective control design should also possess the ability to handle the packet dropout. The contribution of this study is not only improving the control quality by creatively designing a large-scale intelligent dynamic estimator but also reinforcing the robustness and extending the application of engine control. A novel intelligent estimation-based large-scale envelope (IEBLE) control design for the VCE with packet dropout is proposed. The new framework consists of two parts: high-accuracy estimation and robust control. The intelligent dynamic estimator is developed by deep learning architecture to compensate for the missing information and supply the essential inputs for the control. With the estimated inputs, a model-based large-scale envelope control design is proposed. This separation between information compensation and control design distinguishes our approach from the centralized control of packet dropout. We formulate the engine's nonlinear dynamic in the full envelope as an optimal Takagi-Sugeno system. The engine dynamic and steady parameters are extracted as features. By the intelligent clustering of the features, the rule number and consequent models of each rule are optimized, which renders the resulting optimal system with the high accuracy of the engine's dynamic and steady performances as well as the minimum rules. Finally, the control gain is scheduled based on the fuzzy inference of the optimized system. Afterwards, this design is applied to a VCE as a demonstration. The comparisons between the proposed control and the H∞ and LQR controllers are conducted in different scenarios to evaluate the control quality and robustness. The experimental results show the superior performances, the effectiveness and robustness of the new control, regardless of the packet dropout, large-scale nonlinearity and uncertainty.

Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis

Many-objective truss structure problems from small to large-scale problems with low to high design variables are investigated in this study. Mass, compliance, first natural frequency, and buckling factor are assigned as objective functions. Since there are limited optimization methods that have been developed for solving many-objective truss optimization issues, it is important to assess modern algorithms performance on these issues to develop more effective techniques in the future. Therefore, this study contributes by investigating the comparative performance of eighteen well-established algorithms, in various dimensions, using four metrics for solving challenging truss problems with many objectives. The statistical analysis is performed based on the objective function best mean and standard deviation outcomes, and Friedman's rank test. MMIPDE is the best algorithm as per the overall comparison, while SHAMODE with whale optimisation approach and SHAMODE are the runners-up.•A comparative test to measure the efficiency of eighteen state-of-the-practice methods is performed.•Small to large-scale truss design challenges are proposed for the validation.•The performance is measured using four metrics and Friedman's rank test.

Critical Disciplinary Thinking and Curricular Design in Games

Abstract This article details a large-scale curricular design project in creating and implementing an MS/PhD in “Critical Game Design.” Curricular design and critical scholarship in the analysis and design of games are co-constitutive. Institutional structures build individual and institutional capacity, they legitimize scholarship, define boundaries of expertise, and contribute to imaginations of disciplinary purview. We reflect on what is at stake beyond the discipline itself in wider digital culture, particularly the spread of disinformation, related growth of anti-academic sentiment, and testing of the foundations of democracy. We examine our own complicity and articulate the space of the games classroom as a site of potential transformation.

Codesign for Extreme Heterogeneity: Integrating Custom Hardware With Commodity Computing Technology to Support Next-Generation HPC Converged Workloads

The future of high-performance computing (HPC) will be driven by the convergence of physical simulation, artificial intelligence, machine learning, and data science computing capabilities. While computational performance gains afforded by technology scaling, as predicted by Moore's Law, have enabled large-scale HPC system design and deployment using commodity CPU and GPU processing components, emerging technologies will be required to effectively support such converged workloads. These emerging technologies will integrate commodity computing components with custom processing and networking accelerators into ever-more heterogeneous architectures resulting in a diverse ecosystem of industry technology developers, university, and U.S. Government researchers. In this article, we describe efforts at the U.S. Department of Energy's Pacific Northwest National Laboratory to construct an end-to-end codesign framework that lays a groundwork for such an ecosystem, including notable outcomes, remaining challenges, and future opportunities.

Practical Appropriate Fidelity Optimization for Large-scale Multidisciplinary Aircraft Design

Practical Appropriate Fidelity Optimization for Large-scale Multidisciplinary Aircraft Design

BIM in infrastructure projects

Building Information Modeling (BIM) has made one of the most significant and fundamental enhancements in the construction industry and has led to more in-depth collaboration among project stakeholders. Large infrastructure projects differ from other construction projects, mainly because of size, location, levels of interfacing, complexity, and time frame. Coordinating such intense projects challenge, particularly across multiple teams and numerous stakeholders However, Building Information Modeling (BIM) helped to resolve some of these difficulties by facilitating a collaborative environment. BIM for infrastructure projects helps ensure the efficiency and accuracy of large-scale infrastructure projects design and implementation phases, in addition to the management of these projects. This research is aimed to explore the benefits and importance of implementing BIM in an infrastructure project for stakeholders through analyzing Five international projects and investigating how the utilization of BIM enhanced the processes during projects’ stages from design, construction, and operation. It was found that, amongst the various obstacles of implementing BIM practices the prime factor is the rarity of qualified BIM personnel in construction field; however, the near future promises to use BIM systems on a large scale in infrastructure projects.

Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations

Meta-heuristic algorithms have been effectively employed to tackle a wide range of optimisation issues, including structural engineering challenges. The optimisation of the shape and size of large-scale truss structures is difficult due to the nonlinear interplay between the cross-sectional and nodal coordinate pressures of structures. Recently, it was demonstrated that the newly proposed Marine Predator Algorithm (MPA) performs very well on mathematical challenges. The MPA is a meta-heuristic that simulates the essential hunting habits of natural marine predators. However, this algorithm has some disadvantages, such as becoming locked in locally optimal solutions and not exhibiting a high level of exploratory behaviour. This paper proposes two hybrid marine predator algorithms, Nonlinear Marine Predator (HNMPA) and Nonlinear-Chaotic Marine Predator Algorithm (HNCMPA), as improved variations of the marine predator algorithm paired with a hill-climbing (HC) technique for truss optimisation on form and size. The major advantage of these techniques are that they seek to overcome the MPA’s disadvantages by using nonlinear values and prolonging the exploration phase with chaotic values; also, the HC algorithm has been used to avoid locally optimum solutions. In terms of truss optimisation performance, the proposed algorithm is compared to fourteen well-known meta-heuristics, including the Dragonfly Algorithm (DA), Henry Gas Solubility optimisation (HGSO), Arithmetic optimisation Algorithm (AOA), Generalized Normal Distribution Optimisation (GNDO), Salp Swarm Algorithm (SSA), Marine Predators Algorithm (MPA), Neural Network Algorithm (NNA), Water Cycle Algorithm (WCA), Artificial Gorilla Troops Optimiser (GTO), Gray Wolf Optimiser (GWO), Moth Flame Optimiser (MFO), Multi-Verse Optimiser (MVO), Equilibrium Optimiser (EO), and Cheetah Optimiser (CO). Furthermore, seven algorithms were chosen to test HNCMPA performance on benchmark optimisation sets, including MPA, MVO, PSO, MFO, SSA, GWO, and WOA. The results of the experiment demonstrate that the optimisation techniques put forth surpass previously established meta-heuristics in the field of optimisation, encompassing both traditional and CEC problems, by a margin of over 95% in terms of attaining a superior ultimate solution. Additionally, with regards to solving truss optimisation difficulties as a large-scale real-world engineering challenge, the outcomes indicate a performance boost of over 65% in obtaining significantly better solutions for problems involving 260-bar and 314-bar; conversely, in the case of 340-bar issues, the improvement rate is slightly lower at almost 25%.

Anxiety, depression and post-traumatic stress disorder and related factors among Chinese population during the COVID-19 pandemic: A cross-sectional study.

The sudden outbreak of coronavirus disease 2019 (COVID-19) has deep and wide negative mental impacts on the public, and studies on the impact of COVID-19 on social and mental well-being are necessary. This study aimed to evaluate mental distress, including anxiety, depression, and post-traumatic stress disorder (PTSD), and its related risk factors in Chinese adults in the early stages of the COVID-19 pandemic. This study used a large-scale cross-sectional design. A total of 2067 adult participants completed the online survey via REDcap from 1st to 15th of March 2020 during the COVID-19 outbreak in China. Anxiety, depression, PTSD, and related risk factors, including self-efficacy, coping style, and social support, were measured using valid and reliable instruments. The data were analyzed using multiple linear regression. We found that 201 (9.7%) participants reported moderate-to-severe anxiety, 669 (33.8%) reported depression, and 368 (17.8%) reported symptoms of PTSD. Self-efficacy, coping style, and social support significantly affected anxiety, depression, and PTSD symptoms. Participants' sociodemographic characteristics, COVID-19 pandemic-related factors, low self-efficacy, low social support, and negative coping were predictors of mental distress during the COVID-19 pandemic. Our study will help healthcare professionals carry out early predictions and identification of high-risk groups and provide appropriate interventions to target groups during public health emergencies that plague the world.

A dimensionality reduction and layout optimization scheme for grid-stiffened structures

ABSTRACT When directly solving the layout optimization design problem of grid-stiffened structures with large-scale discrete design variables, gradient-based methods and intelligent optimization algorithms are rendered ineffective owing to multilocal optimal traps and the discrete variable space with extremely large dimensionality. A new layout optimization design scheme for grid-stiffened structures is proposed in this study, based on the continuous representation of discrete variables and a dimensionality reduction method. In this method, first, a discrete stiffener layout is described by a bounded field with space correlation and transformed into continuous design variables using material interpolation. Then, to significantly reduce the dimensionality of the design space, a series expansion approach is adopted. Finally, a sequential optimization strategy combined with the kriging surrogate model is used to successfully solve optimization problems. Layout optimization designs, including a stiffened plate and a space-borne antenna reflector, are proposed to demonstrate the validity and applicability of the proposed method.

Parallel BESO framework for solving high-resolution topology optimisation problems

The bi-directional evolutionary structural optimisation (BESO) has attracted much interest in recent decades. However, the high computational cost of the topology optimisation method hinders its applications in large-scale industrial designs. In this study, a parallel BESO method is developed to solve high-resolution topology optimisation problems. An open-source computing platform, FEniCS, is used to parallelise the finite element analysis (FEA) and optimisation steps. Significant improvements in efficiency have been made to the FEA and the filtering process. An iterative solver, a reanalysis approach and a hard-kill option in BESO have been developed to reduce the computational cost of the FEA. A PDE-based isotropic filter scheme is used to eliminate the time-consuming elemental adjacency search process. The efficiency and effectiveness of the developed method are demonstrated by a series of numerical examples in both 2D and 3D. It is shown that the parallel BESO can efficiently solve problems with more than 100 million tetrahedron elements on a 14-cores CPU server. This work holds great potential for high-resolution design problems in engineering and architecture.

A Hybrid Josephson Transmission Line and Passive Transmission Line Routing Framework for Single Flux Quantum Logic

The single flux quantum (SFQ) logic family is a novel digital logic as it provides ultrafast and energy-efficient circuits. For large-scale SFQ circuit design, specialized electronic design automation (EDA) tools are required due to the differences in logic type, timing constraints, and circuit architecture, in contrast to the Complementary metal–oxide–semiconductor (CMOS) logic. In order to improve the overall performance of an SFQ circuit, an efficient routing algorithm should be applied during the layout design to perform accurate timing adjustment for fixing hold violations and optimizing critical paths. Thus, a hybrid Josephson transmission line and passive transmission line routing framework is proposed. It consists of four main modules and an exploration of the potential timing performance based on the given layout placement. The proposed routing tool is demonstrated on seven testbench circuits. The obtained results demonstrate that the operating frequency is greatly improved, and all the hold violations are eliminated for each circuit.

Optimal and systematic design of large-scale electrodes for practical Li–air batteries

The battery market to enable the broad uses of electric vehicles and renewable energy sources is undergoing a rapid expansion given the requirements of light, safe, and low-cost materials. Li–air batteries are considered suitable candidates that can overcome the existing challenges in this field. Continuous progress in performance improvements triggered by the structure and design evolution of battery electrodes is essential to utilize Li–air batteries in future applications. In this study, an optimal design process is devised for the practical application of Li–air batteries with large-scale area and capacity levels. The primary difficulty in the large-scale design for practical use originates from the design complexity caused by the one-side (air cathode) open-cell structure for the proper air flow. To examine problems related to practical cell manufacturing, we attempted to explore the large-scale electrode on the charge/discharge performance with two current collectors, specifically Ni foam and a gas diffusion layer (GDL), in single and stacked Li–air cells. A large-scale stacked 1.07 Ah cell was manufactured using commercial carbon mixtures with tabless current collector of Ni foam. The single-cell capacity of tabless Ni foam presented approximately 440 mAh at full-depth scan, showing that the cell-specific gravimetric and volumetric energy densities were 385 Wh/kg and 428 Wh/L, respectively. In the case of tabless GDL, the cell capacity was 630 mAh at full-depth scan. The cell-specific gravimetric and volumetric energy densities of tabless GDL were 680 Wh/kg and 756 Wh/L, respectively. However, the use of a GDL current collector resulted in structural deformation during the charge/discharge reaction due to a self-reaction in spite of the 1.56 times higher energy density than that of Ni foam. Although the GDL helps to enhance the reactivity and performance by increasing the capacity when used with an active material, the extended cycle stability of the Li–air cell is hindered due to the self-reaction of the current collector. These results present case‐based evidence that extends our knowledge about the trade‐offs between capacity enhancements and long-cycle stability regarding the choices of materials for the manufacturing of Li-air cells, thus proving practical insights with regard to the fabrication of large-scale Li–air cells for commercialization.

Wing jig shape optimisation with gradient-assisted metamodel building in a trust-region optimisation framework

Significant computational resources are required to obtain an optimised wing jig shape by solving a high-fidelity large-scale aero-structural design optimisation problem. Gradient-based methods are efficient; however, some of the features of real-life engineering problems including numerical noise that pollutes the function values and occurrences of failed evaluations in the optimisation may limit their performance. To address these issues, this paper presents the latest developments in the multipoint approximation method (MAM) based on a gradient-assisted metamodel assembly technique within a trust-region optimisation framework. The proposed method is tested by a benchmark case first, and then, an aircraft wing jig shape optimisation problem is offered to demonstrate its performance. The gradient-based optimisation is used as a benchmark case, and the metamodel-based optimisation utilises the latest developments in MAM to solve the same problem. The results show that the proposed method can achieve the same design goal as the gradient-based method but with enhanced robustness and efficient performance. In the wing jig shape optimisation, the difference in the design objective, the global equivalent drag coefficient, between the two aforementioned optimisation approaches is 0.20 counts, whose relative difference is approximately 0.10%. Three approximate sub-optimisations have been conducted in every iteration of the metamodel-based optimisation to reduce the possibility of local optimality, while the overall elapsed time of the metamodel-based optimisation is approximately 1.98 times that of one gradient-based optimisation, which confirms the competitiveness of the proposed method bearing in mind the added safeguards for numerical noise, failed evaluations and possible local optimality.

An adaptive and scalable artificial neural network-based model-order-reduction method for large-scale topology optimization designs

An adaptive and scalable artificial neural network-based model-order-reduction method for large-scale topology optimization designs

Retracted: Large-Scale Shopping Mall Architectural Design Based on Intelligent BIM Technology

Retracted: Large-Scale Shopping Mall Architectural Design Based on Intelligent BIM Technology