Large-scale Design Research Articles

Research on optimization method of landscape design based on computer algorithms

Traditional landscape design primarily depends on the experience and subjective judgment of designers, lacking systematic and scientific algorithmic support, making it difficult to find the optimal solution in large-scale and complex scenes. This article proposes an interactive genetic algorithm for landscape design, which searches for the optimal solution in large-scale design spaces and improves design efficiency. Collect a large amount of landscape-related data, preprocess it, and ensure its quality, and ensure the quality of the data. Introduce elements such as plants, water bodies, and hard structures to initially design the space, extract features from landscape design images, and perform 3D reconstruction to obtain richer design space information. Generate initial design schemes using genetic algorithms and introduce subjective opinions from designers through interactive processes. The experimental results show that the average aesthetic score of interactive genetic algorithm for landscape design optimization is 9.0, and the average design time of interactive genetic algorithm is 34.5 days. Introducing the subjective opinions of designers into landscape design optimization based on heritage algorithms can effectively improve design aesthetics and shorten the total design time.

Impostor phenomenon short scale (IPSS-3): a novel measure to capture impostor feelings in large-scale and longitudinal surveys

The Imposter Phenomenon (IP) is gaining increasing attention in academia, not only as an overall attractive research topic but also as a concern that especially affects members of minority groups. Nevertheless, there is little evidence for the occurrence and socio-structural correlates of the IP. Against the backdrop of a pressing need to contextualize the IP, this paper provides (1) an overview of the existing empirical evidence on the IP from a perspective that incorporates the role of social contexts, (2) highlights shortcomings in both existing theoretical approaches and methodological tools, (3) introduces the Impostor Phenomenon Short Scale (IPSS-3) as a novel, time-efficient and universally applicable IP measure, and (4) underscores that the IP, in fact, does not occur in a social vacuum but is closely intertwined with socio-structural characteristics. To this end, the paper draws on three distinct data sets gathered among German adolescents and adults for the development of the IPSS-3 (Study 1: n = 271), its validation (Study 2: n = 427), and to assess the IP’s socio-structural correlates (Study 3: n = 865). The findings demonstrate that the IPSS-3 represents the first time-efficient and universally applicable instrument suitable for capturing the IP in large-scale and longitudinal research designs, e.g., initiated in adolescence. Thus, the IPSS-3 can address key open questions related to age effects, the role of transitions in the life course, and systematic variations in IP intensity among different social groups.

Definition of a Baseline Rotor Design for a 25MW Floating Offshore Wind Turbine

Abstract Wind turbine sizes have grown rapidly in recent years with machine ratings of 15-16 MW available from multiple manufacturers for offshore wind turbines. While the industry advances large-scale turbine designs, offshore developers have initially focused on siting in shallow waters on bottom-fixed foundations; however, eyes are also on deep-water locations where floating systems are required. The present study presents the definition for an initial baseline rotor design at 25 MW scale, designed for a floating offshore system. The purpose of this paper is to document this 25 MW blade definition for use as a reference design for future floating wind technology development by industrial and academic researchers and developers. As this is an initial reference design, some opportunities (and plans) for further mass or cost reduction are also noted. In summary, the paper documents the initial baseline rotor design including aerodynamic and structural design of the rotor blades, along with key details about the control system and floating system designs for the floating 25 MW wind turbine system.

Efficacy of acupuncture in reducing accidental injury risk in stroke patients: A national-scale cohort study

BackgroundFalls among stroke patients pose a significant health concern, leading to injuries, reduced mobility, and functional limitations. Recent research has suggested the potential benefits of acupuncture in enhancing balance and motor function in stroke patients. However, large-scale, long-term studies on acupuncture's role in reducing the risk of accidental injuries are scarce. This study aimed to evaluate acupuncture's effectiveness in decreasing the risk of accidental injuries in stroke patients. Materials and methodsThis study employed a large-scale cohort design, utilizing data from the 2000 Longitudinal Generation Tracking Database (LGTD 2000) in Taiwan, covering two million individuals. The cohort comprised stroke patients hospitalized between 2001 and 2012, with follow-up periods of at least six years to estimate accidental injury incidence and risk. Acupuncture treatments during both inpatient and outpatient phases after stroke diagnosis were included. Accidental injury data, including vehicular accidents and falls, were extracted from specialized hospitalization records. Hazard ratios were calculated, adjusting for variables like comorbidities, hypnotic medication usage, and demographic factors. ResultA total of 108,196 newly diagnosed stroke patients were included in the final cohort, with 42,083 receiving acupuncture treatment and 66,113 not receiving acupuncture. The acupuncture group exhibited a significantly lower incidence rate of accidental injuries (10.2 per 1,000 person-years) compared to the non-acupuncture group (15.8 per 1,000 person-years). After adjusting for various covariates, the acupuncture group had a reduced risk of accidental injuries with an adjusted hazard ratio (aHR) of 0.74 (95 % CI: 0.51–0.92). Subgroup analyses revealed consistent protective effects of acupuncture across different patient profiles and treatment characteristics, including age, sex, comorbidities, and hypnotic medication usage. ConclusionThis study provides evidence supporting the potential of acupuncture as an adjunctive therapy to reduce the risk of accidental injuries among stroke patients. Regardless of stroke subtype, age, or comorbidity burden, acupuncture was associated with a lower risk of accidental injuries.

Clearing the plate: a strategic approach to mitigate well-to-well contamination in large-scale microbiome studies.

Understanding dynamic microbial communities typically requires large-scale studies. However, handling large numbers of samples introduces many challenges, with cross-contamination being a major issue. It not only complicates analysis but also leads to sample loss and increased costs and restricts diverse study designs. The prevalent use of 96-well plates for nucleic acid and metabolite extractions exacerbates this problem due to their wells having little separation and being connected by a single plate seal. To address this, we propose a new strategy using barcoded Matrix Tubes, showing a significant reduction in cross-contamination compared to conventional plate-based approaches. Additionally, this method facilitates the extraction of both nucleic acids and metabolites from a single tubed sample, eliminating the need to collect separate aliquots for each extraction. This innovation improves large-scale study design by shortening processing times, simplifying analysis, facilitating metadata curation, and producing more reliable results.

Volume-based branch-and-cut algorithm for large-scale fixed-charge multicommodity network design

Volume-based branch-and-cut algorithm for large-scale fixed-charge multicommodity network design

Interpretable reduced-order optimization research for improving indoor environmental quality in buildings based on dimensional analysis and surrogate-based optimization

Implementing large-scale optimization designs and identifying optimal design variables in fields such as architectural engineering is crucial for improving indoor environmental quality. However, the high-dimensional nonlinear characteristics complicate the understanding of the optimization process and increase the difficulty of finding optimal solutions. This study proposes an interpretable reduced-order optimization (ROO) method based on dimensional analysis, which integrates numerous variables into a single reduced-order coefficient through a power form. The goal is to develop a reduced-order and approximately one-dimensional nonlinear relationship during the optimization process and to reveal the mechanisms of single-objective and multi-objective optimization, thereby enhancing the interpretability of the optimization process. The reliability of the ROO is validated through single-objective optimization of building thermal bridges and multi-objective optimization of squirrel cage fans. The study shows that the power in the reduced-order coefficient can be used to determine the sensitivity and impact of design variables on optimization targets. The nonlinear relationship between the composite structural parameters of the building thermal bridge and the total heat flux can be reduced to an approximately one-dimensional linear relationship, significantly reducing the total heat flux of the thermal bridge by 10.3 %. The ROO accurately describes the aerodynamic performance of the fan and its high-dimensional nonlinear relationship with eight design variables, eliminating optimization conflicts among multiple operating conditions and significantly enhancing the aerodynamic performance. The ROO method proposed in this study offers a new approach to making the optimization process more interpretable, playing a crucial role in optimization design and revealing optimization mechanisms in engineering applications.

A design method of large-scale partial similar model test for cementing casing string system

When the cementing casing string system (CCSS) is subjected to a large-scale equal-scale model test, the huge aspect ratio of the system makes the wall thickness of the model casing string too thin to be manufactured. Therefore, this paper proposes a design method for model test with unequal scale of axial size and radial size of the CCSS. Firstly, the dynamic model of the CCSS is established and solved by the transfer matrix method (TMM), and the modal test is carried out and the results show that the numerical calculation error of the first five natural frequencies of the system is less than 4 %. Then, based on the dynamic model, the similarity laws of the complete similarity model and the partial similarity model of the CCSS are derived by equation analysis method, and the design process of the similarity model test is given. The advantages of the research are elaborated in details through examples. To verify the correctness of the similarity model’s similarity laws of the CCSS, similarity models with different parameters are designed, and the prediction errors of prototype response are analyzed based on numerical calculations. The results show that the maximum prediction error of the first eight natural frequencies of the prototype system is less than 3 %, and the response prediction results of the system are also accurate. Finally, considering uncertain factors of the partial similarity test, the similarity model test with the axial size of double scale is designed, and the results show that the maximum prediction error of the first three natural frequencies is 7.06 %, which meets the engineering requirements. This study can provide useful theoretical guidance for the large-scale model test design of the CCSS.

A mechanics-based data-free Problem Independent Machine Learning (PIML) model for large-scale structural analysis and design optimization

Machine learning (ML) enhanced fast structural analysis and design recently attracted considerable attention. In most related works, however, the generalization ability of the ML model and the massive cost of dataset generation are the two most criticized aspects. This work combines the advantages of the universality of the substructure method and the superior predictive ability of the operator learning architecture. Specifically, using a novel mechanics-based loss function, lightweight neural network mapping from the material distribution inside a substructure and the corresponding continuous multiscale shape function is well-trained without preparing a dataset. In this manner, a problem machine learning model (PIML) that is generally applicable for efficient linear elastic analysis and design optimization of large-scale structures with arbitrary size and various boundary conditions is proposed. Several examples validate the effectiveness of the present work on efficiency improvement and different kinds of optimization problems. This PIML model-based design and optimization framework can be extended to large-scale multiphysics problems.

Efficient Inverse Design of Large-Scale, Ultrahigh-Numerical-Aperture Metalens

Efficient design methods for large-scale metalenses are crucial for various applications. The conventional phase-mapping method shows a weak performance under large phase gradients, thus limiting the efficiency and quality of large-scale, high-numerical-aperture metalenses. While inverse design methods can partially address this issue, existing solutions either accommodate only small-scale metalenses due to high computational demands or compromise on focusing performance. We propose an efficient large-scale design method based on an optimization approach combined with the adjoint-based method and the level-set method, which first forms a one-dimensional metalens and then extends it to two dimensions. Taking fabrication constraints into account, our optimization method for large-area metalenses with a near-unity numerical aperture (NA = 0.99) has improved the focusing efficiency from 42% to 60% in simulations compared to the conventional design method. Additionally, it has reduced the deformation of the focusing spot caused by the ultrahigh numerical aperture. This approach retains the benefits of the adjoint-based method while significantly reducing the computational burden, thereby advancing the development of large-scale metalenses design. It can also be extended to other large-scale metasurface designs.

NSGA-II-Based Hierarchical Irregular Subarray Tiling for Large-Scale Phased Array Design

NSGA-II-Based Hierarchical Irregular Subarray Tiling for Large-Scale Phased Array Design

Optimizing large-scale CO2 pipeline networks using a geospatial splitting approach

CO2 transport infrastructure is the backbone of carbon capture and storage (CCS) technology for the mitigation of carbon emissions and project deployment viability. In conventional large-scale CO2 pipeline network designs, the storage sites are generally assumed as the centroids of the major geologic basins, however, this approach might provide suboptimal solutions since the large extension of some storage formations significantly increases the length of the CO2 transportation networks. To address this situation and obtain optimal pipeline routes, we present a novel geospatial splitting framework that partitions large basins into multiple sub-sinks. In our approach, we used a large number of reservoir models varying petrophysical properties and CO2 injection rates to compute pressure plumes through numerical simulations, leading to the calculation of the number of subregions for each basin as a function of the extension of pressure interference areas and boundaries. Finally, we applied K-means clustering and Voronoi polygon algorithms to partition large basins into subregions and obtain their sink coordinates. To demonstrate the capability of the developed workflow, we investigated two CO2 pipeline network modeling case studies using our splitting approach: one regional case study focusing on the Intermountain West (I-West) region and one nationwide case study covering the lower 48 states in the U.S. In both case studies, we compared the optimal pipeline routes using the original and new storage locations and examined the major differences. The use of the developed geospatial approach resulted in both cases in a shortening of the total pipeline network length by 13% and 10%, compared to the pipeline modeling with the original basins, leading to cost reductions of 25% and 17%, respectively, demonstrating that the location of point sinks has a critical impact on the length and expenses of pipelines to efficiently transport CO2 to distant storage sites. Therefore, the workflow presented here contributes to the proper and realistic modeling of case studies that support decision-making in CCS deployment.

A Review of Carbon Anode Materials for Sodium-Ion Batteries: Key Materials, Sodium-Storage Mechanisms, Applications, and Large-Scale Design Principles.

Sodium-ion batteries (SIBs) have been proposed as a potential substitute for commercial lithium-ion batteries due to their excellent storage performance and cost-effectiveness. However, due to the substantial radius of sodium ions, there is an urgent need to develop anode materials with exemplary electrochemical characteristics, thereby enabling the fabrication of sodium-ion batteries with high energy density and rapid dynamics. Carbon materials are highly valued in the energy-storage field due to their diverse structures, low cost, and high reliability. This review comprehensively summarizes the typical structure; energy-storage mechanisms; and current development status of various carbon-based anode materials for SIBs, such as hard carbon, soft carbon, graphite, graphene, carbon nanotubes (CNTs), and porous carbon materials. This review also provides an overview of the current status and future development of related companies for sodium-ion batteries. Furthermore, it offers a summary and outlook on the challenges and opportunities associated with the design principles and large-scale production of carbon materials with high-energy-density requirements. This review offers an avenue for exploring outstanding improvement strategies for carbon materials, which can provide guidance for future application and research.

Porphyrin photosensitizer molecules as effective medicine candidates for photodynamic therapy: electronic structure information aided design.

Traditional photosensitizers (PS) in photodynamic therapy (PDT) have restricted tissue penetrability of light and a lack of selectivity for tumor cells, which diminishes the efficiency of PDT. Our aim is to effectively screen porphyrin-based PS medication through computational simulations of large-scale design and screening of PDT candidates via a precise description of the state of the light-stimulated PS molecule. Perylene-diimide (PDI) shows an absorption band in the near-infrared region (NIR) and a great photostability. Meanwhile, the insertion of metal can enhance tumor targeting. Therefore, on the basis of the original porphyrin PS segments, a series of metalloporphyrin combined with PDI and additional allosteric Zn-porphyrin-PDI systems were designed and investigated. Geometrical structures, frontier molecular orbitals, ultraviolet-visible (UV-vis) absorption spectra, adiabatic electron affinities (AEA), especially the triplet excited states and spin-orbit coupling matrix elements (SOCME) of these expanded D-A porphyrin were studied in detail using the density functional theory (DFT) and time-dependent density functional theory (TDDFT) methods. PS candidates, conforming type I or II mechanism for PDT, have been researched carefully by molecular docking which targeted Factor-related apoptosis (Fas)/Fas ligand (Fasl) mediated signaling pathway. It was found that porphyrin-PDI, Fe2-porphyrin-PDI, Zn-porphyrin-PDI, Mg-porphyrin-PDI, Zn-porphyrin combined with PDI through single bond (compound 1), and two acetylenic bonds (compound 2) in this work would be proposed as potential PS candidates for PDT process. This study was expected to provide PS candidates for the development of novel medicines in PDT.

A hyper-heuristic algorithm via proximal policy optimization for multi-objective truss problems

This paper proposes a hyper-heuristic evolutionary algorithm via proximal policy optimization, named HHEA-PPO, for solving multi-objective truss optimization problems. HHEA-PPO has a two-layer structure: a high-level strategy and low-level heuristics. The high-level strategy consists of proximal policy optimization, while the low-level heuristics consist of ten predefined heuristic operators. During the iteration process, the high-level strategy selects the most promising low-level heuristic according to the state of the individuals and the population. To maintain the convergence and distribution of the external Pareto archive, a dynamic crowding distance mechanism is employed. HHEA-PPO is applied to eight multi-objective truss optimization problems and compared with thirteen state-of-the-art optimization algorithms in terms of success rate, average computation duration, and average fitness evaluations to evaluate its performance. The results show that HHEA-PPO has higher search efficiency and greater stability, demonstrating its ability to solve large-scale engineering design problems.

AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria

Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (<102), much smaller than public polymer datasets (>105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking β-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.

Indirect drive ICF design study for a 3 MJ NIF enhanced yield capability

A proposed upgrade to the National Ignition Facility is under consideration that would ultimately increase the maximum operating envelope for the laser to 3.0 MJ with a peak power of 450 TW. This upgrade would provide opportunities to address an expanded set of data needs for NNSA’s Stockpile Stewardship mission, including the potential to generate fusion yields ≥30 megajoules. A simplified model of ignition and burn is used to scope the theoretical maximum target yield as a function of laser driver energy. We examine two indirect drive ICF target designs that make use of the 3 MJ laser drive using a common model for integrated laser-hohlraum simulations. These two designs compare and contrast the impacts of two different ablator materials, pure carbon and CH. Additionally, the potential for increased backscatter from these larger scale designs is discussed.

Fresh Moringa Stenopetala leaves consumption and its determinants among pregnant women in southern Ethiopia.

A woman's health and nutritional status has significant impact on her pregnancy situation. However, many pregnant women are undernourished. Moringa stenopetala is a plant consumed worldwide in various forms, and its consumption showed a reduction in the incidence of malnutrition. Although Moringa stenopetala is rich in essential macro- and micronutrients, there is little evidence on the proportion and determinants of fresh Moringa stenopetala leaf intake among pregnant women. The objective of this study was to fill this gap in the littérature and provide a baseline evidence for further research or intervention by investigation the proportion and determinants of fresh Moringa stenopetala leaf intake among pregnant women in the Gamo zone, south Ethiopian region. A community-based cross-sectional study was conducted among 623 randomly selected pregnant women using a pre-tested and structured questionnaire via a face-to-face interview. The consumption pattern was assessed based on a self-reported dietary history over the last 30 days before data collection. Multivariable logistic regression model was fitted using STATA version 14. An adjusted odds ratio with a 95% confidence interval was reported to show an association between the dependent and independent variables with level of statistical significance at a p-value of <0.05. The proportion of fresh Moringa stenopetala leaves intake among pregnant women was 49.60% (95% CI: 45.67, 53.52%). The déterminants of fresh moringa leaf intake were being below 24 years old (AOR: 2.92; 95% CI: 1.51, 5.63), rural résidence (AOR: 1.97; 95% CI: 1.10, 3.50), antenatal care attendance (AOR: 2.08; 95% CI: 1.03, 4.21), history of contraceptive use (AOR: 1.88; 95% CI: 1.03, 3.55), and having a good knowledge about the importance of moringa Stenopetala (AOR: 9.76; 95% CI: 5.30, 17.95). The study showed that almost half of the pregnant women consumed fresh Moringa stenopetala leaves. Women's age, place of residence, prenatal care, history of contraceptive use, and knowledge of the benefits of Moringa Stenopetala were positively associated with the consumption of fresh Moringa Stenopetala leaves. Therefore, health authorities and stakeholders involved in maternal and child health need to target older women, and urban residents and promote the benefits of consumption by strengthening uptake of maternal health services and raising awareness about Moringa Stenopetela. Future studies involving large scale and longitudinal designs evidence are required to further validate the findings so that this nutritious diet can be promoted widely among pregnant women in the study area and Ethiopia at large.

Saline Dreams

This text examines the engineered ecology and resultant aesthetic implications of the Los Angeles Department of Water and Power’s Dust Mitigation Project at Owens Lake, California with an eye toward its aesthetic implications and potential for understanding large-scale landscape design in the Anthropocene. Imagined here is a near-future evolution of this infrastructure toward strange new landscapes, turning radically empirical environmental geoengineering techniques toward an emergent, more expansive aesthetic dimension.

Automated design of hybrid halide perovskite monolayers for band gap engineering

The recent experimental confirmation of perovskite monolayers has sparked ongoing efforts in their prediction and synthesis, showcasing their flexible tunable band gap and potential in advanced functional devices. Although large-scale computational designs have been successfully performed for inorganic perovskite monolayers, the complexity introduced by organic cations hinders the same approaches applied to the hybrid halide perovskite monolayers. To address this challenge, we have proposed a high-throughput first-principles computational workflow that automates the design of hybrid halide perovskite monolayers. We strategically reduce the complexity of the configurations by analyzing the orientation of cations and the structural symmetry. Over 400 hybrid halide perovskite monolayers have been designed, and their structures and fundamental properties are stored in the database. Correlation analyses show a strong correlation between band gaps and metal-halogen-metal bond angles or metal-halogen bond lengths, consistent with prior studies for bulk and layered perovskites. The underlying physics that the band gap is modulated by the antibonding in the metal-halogen bond makes band gap engineering of hybrid halide perovskite monolayers feasible. Accordingly, initial research on lateral heterojunctions and solar cells has been conducted to explore the potential practical applications of the designed hybrid halide perovskite monolayers. Our study lays the foundation for further exploration of hybrid halide perovskite monolayers and highlights promising opportunities for their potential applications in electronic and optical devices.