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Interior design requires designer creativity and significant workforce investments. Meanwhile, Artificial Intelligence (AI) is crucial for enhancing the creativity and efficiency of interior design. Therefore, this study proposes an innovative method to generate multistyle interior design and videos with AI. First, this study created a new indoor dataset to train an AI that can generate a specified design style. Subsequently, video generation and super-resolution modules are integrated to establish an end-to-end workflow that generates interior design videos from texture-free 3D models. The proposed method utilizes AI to produce diverse interior design videos directly, thus replacing the tedious tasks of texture selection, lighting arrangement, and video rendering in traditional design processes. The research results indicate that the proposed method can effectively provide diverse interior design videos, thereby enriching design presentation and improving design efficiency. Additionally, the proposed workflow is versatile and scalable, thus holding significant reference value for transforming traditional design toward intelligence. [ABSTRACT FROM AUTHOR]

The design of dose–response experiments is an important part of toxicology research. Efficient design of these experiments requires choosing optimal doses and assigning the correct number of subjects to those doses under a given criterion. Optimal design theory provides the tools to find the most efficient experimental designs in terms of cost and statistical efficiency. However, the mathematical details can be distracting and make these designs inaccessible to many toxicologists. To facilitate use of these designs, we present an easy to use web-app for finding two types of optimal designs for models commonly used in toxicology. We include tools for checking the optimality of a given design and for assessing efficiency of any user-supplied design. Using state-of-the-art nature-inspired metaheuristic algorithms, the web-app allows the user to quickly find optimal designs for estimating model parameters or the benchmark dose. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to product design optimization through the synergy of Genetic Algorithms (GA) and specific AI techniques. By iteratively evolving solutions inspired by natural selection principles and utilizing AI for intelligent analysis of vast datasets, designers can efficiently navigate design spaces to identify optimal solutions meeting multiple constraints and objectives. The integration of Convolutional Neural Networks (CNNs) with GA resulted in a 30% faster convergence rate and improved solution quality, facilitating enhanced exploration-exploitation trade-offs. Case studies ranging from automotive engineering to consumer electronics illustrate the effectiveness of these methods in enhancing design efficiency by 25%, reducing costs by 20%, and accelerating time-to-market by 15%. The proposed approach demonstrated superior performance metrics, including improved efficiency, durability, operational safety, and minimized environmental impact. Comparative analysis revealed the advantages of this method over traditional techniques, validating its transformative potential in advancing product design paradigms. The approach is validated through various case studies, highlighting significant improvements in the design process and underscoring its advantages over traditional techniques.

This paper focuses on the functional characteristics of BIM technology, emphasizing the need to develop a combination of BIM technology tailored to the user’s interior design process. We utilize the hardware and software to establish the BIM technology environment, construct the interior design model using the Autodesk Revit software platform, import it into Lumion for color rendering, and utilize Navisworks Simulate software to gather project data and monitor the interior design project’s progress. Autodesk Ecotect software was used to find out how lighting affected the interior design using BIM technology. Next, an interior layout optimization model based on the improved whale algorithm was made to solve the interior layout problem, and the impact of using AI was confirmed. The data show that the error range for the daylighting coefficient using BIM technology and the old method is between 0.04 and 0.91. The old method takes 131 minutes longer than BIM technology. This means that the BIM-based lighting simulation model pre-processing process is still much more efficient than the old method. Furthermore, this paper maintains all the model’s index values above 0.68, demonstrating the model’s ability to achieve a reasonable interior space layout while simultaneously enhancing both the layout speed and the layout effect.

IntroductionIn contrast to other sensory domains, detection of primary olfactory processes using functional magnetic resonance imaging has proven to be notably challenging with conventional block designs. This difficulty arises from significant habituation and hemodynamic responses in olfactory areas that do not appear to align with extended boxcar functions convolved with a generic hemodynamic response model. Consequently, some researchers have advocated for a transition to event-related designs, despite their known lower detection power compared to block designs.MethodsHere, we conducted a block design experiment with 16s of continuous odorant stimulation alternating with 16s of continuous odorless air stimulation in 33 healthy participants. We compared four statistical analyses that relied either on standard block designs (SBD1-2) or on block designs that were modulated by the participants' individual breathing patterns (MBD1-2).ResultsWe found that such modulated block designs were comparatively more powerful than standard block designs, despite having a substantially lower design efficiency. Using whole-brain effect size maps, we observed that the right insular and medial aspects of the left piriform cortex exhibited a preference for a breathing-modulated analysis approach.DiscussionResearch in olfaction that necessitates designs with longer-lasting blocks, such as those employed in the investigation of state-dependent processing, will benefit from the breathing-modulated analyses outlined in this study.

Interior design requires designer creativity and significant workforce investments. Meanwhile, Artificial Intelligence (AI) is crucial for enhancing the creativity and efficiency of interior design. Therefore, this study proposes an innovative method to generate multistyle interior design and videos with AI. First, this study created a new indoor dataset to train an AI that can generate a specified design style. Subsequently, video generation and super-resolution modules are integrated to establish an end-to-end workflow that generates interior design videos from texture-free 3D models. The proposed method utilizes AI to produce diverse interior design videos directly, thus replacing the tedious tasks of texture selection, lighting arrangement, and video rendering in traditional design processes. The research results indicate that the proposed method can effectively provide diverse interior design videos, thereby enriching design presentation and improving design efficiency. Additionally, the proposed workflow is versatile and scalable, thus holding significant reference value for transforming traditional design toward intelligence.

Minimum projection uniform designs and high efficient designs are two kinds of excellent designs in design of experiment. In this paper, design efficiency for minimum projection uniform designs with q levels is discussed. Firstly, the uniformity pattern of q-level designs is proposed based on the centered L 2 -discrepancy. Secondly, the analytical connection between uniformity pattern and design efficiency is established for the q-level orthogonal arrays with strength 2, and for the orthogonal arrays with strength 3, the minimum projection uniformity criterion is equivalent to the design efficiency criterion. Finally, a tight lower bound of uniformity pattern is presented, which is used as a benchmark for measuring the uniformity of projection designs. [ABSTRACT FROM AUTHOR]

D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem.

Evaluating the product functions and associating them with the manufacturing technique is crucial to reducing the manufacturing cost. Design for Manufacturing and Assembly (DFMA) is a concurrent engineering technique that plays a vital role in redesigning of existing product or service by reducing its manufacturing and assembly cost and time, have the desired level of quality and reliability. In this research work is apply DFMA on existing hair clipper and decrease the net manufacturing cost of the hair clipper. Hair clipper is very popular for at-home use and is seen as an essential tool in hair grooming. The analysis of design for manufacturing and assembly is done using guideline of Boothroyd Dewhurst DFMA. The components of the Hair clipper were undergone alteration by using DFMA and redesigned approaches. In the same platform, the actual model had the redesign. The assembly time for redesign exhibited an enhancement of 49.25% where the assembly time was decreased from 322.91s to 163.81s and design efficiency was improved 7.219% from 7.432% to 14.651%. Hence, the best design of hair clipper was made which is having optimal value is achieved.

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When a model-based approach is appropriate, an optimal design can guide how tocollect data judiciously for making reliable inference at minimal cost. However, findingoptimal designs for a statistical model with several possibly interacting factors can beboth theoretically and computationally challenging, and this issue is rarely discussed inthe literature. We propose nature-inspired metaheuristic algorithms, like particle swarmoptimization (PSO) and its variants, to solve such optimization problems. Wedemonstrate that such techniques, which are easy to implement, can find differenttypes of optimal designs for models with several factors efficiently. To facilitate use ofsuch algorithms, we provide computer codes to generate tailor made optimal designsand evaluate efficiencies of competing designs. As applications, we apply PSO and findBayesian optimal designs for Exponential models useful in HIV studies and re-design acar-refuelling study for a Logistic model with ten factors and some interacting factors.

Highly efficient designs and uniform designs are widely applied in many fields because of their good properties. The purpose of this paper is to study the issue of design efficiency for asymmetric minimum projection uniform designs. Based on the centered L 2 discrepancy, the uniformity of the designs with mixed levels is defined, which is used to measure the projection uniformity of the designs. The analytical relationship between the uniformity pattern and the design efficiency is established for mixed-level orthogonal arrays with a strength of two. Moreover, a tight lower bound of the uniformity pattern is presented. The research is relevant in the field of experimental design by providing a theoretical basis for constructing the minimum number of projection uniform designs with a high design efficiency under a certain condition. These conclusions are verified by some numerical examples, which illustrate the theoretical results obtained in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we present a new class of row–column sampling design when a sampling region is presented by non-overlapping quadrats of an rp-by-p row–column grid. Quadrats are rank-ordered on a relevant auxiliary variable, first in the rp rows of the grid and then in the so formed p ranked columns. This ranking enhances the precision of the sampling using ideas from standard rank set sampling methods. The sampling design falls in the traditional sampling framework where the sample selection probabilities are independent of the variables of interest. Selection probabilities are governed by a spatial design and the auxiliary information to facilitate better sampling coverage over the sampling region. The paper constructs unbiased estimators for population mean, total and provides variance estimates for them. It also shows when the new sampling design performs better than its competitors. The new sampling design is applied to a forest and two agricultural field samples to estimate the population means. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Identifying optimal designs for generalized linear models with a binary response can be a challengingtask, especially when there are both discrete and continuous independent factors in the model. Theoreticalresults rarely exist for such models, and for the handful that do, they usually come with restrictive assumptions.In this article, we propose the d-QPSO algorithm, a modified version of quantum-behaved particleswarm optimization, to find a variety of D-optimal approximate and exact designs for experiments withdiscrete and continuous factors and a binary response. We show that the d-QPSO algorithm can efficientlyfind locally D-optimal designs even for experiments with a large number of factors and robust pseudo-Bayesian designs when nominal values for the model parameters are not available. Additionally, we investigaterobustness properties of the d-QPSO algorithm-generated designs to variousmodel assumptions andprovide real applications to design a bio-plastics odor removal experiment, an electronic static experiment,and a 10-factor car refueling experiment. Supplementary materials for the article are available online.

Many problems occur and options emerge during the process of designing the classrooms until the completion of the end product. Decisions should be made so that these problems are solved and the best options are chosen. Decisions made during the design process play a crucial role in students' health, their learning performance, satisfaction levels, and also in terms of the savings that can be made in the lighting, heating and cooling of the classrooms. The model developed within the scope of the study is expected to guide decision-making mechanisms in design using fuzzy logic in architectural design processes. [ABSTRACT FROM AUTHOR]

A product redesign strategy can effectively shorten design lead time and reduce the manufacturing cost of innovative development for the manufacturing industry to stay competitive. Identification of function components is the basis of product redesign. Existing practices to identify the critical component and customer requirements are considered while complaint and historical failure data, crucial for improving product reliability, are frequently ignored. The objective of the article is to develop an integrated framework of product redesign and innovation considering customer requirements and product failure modes. The novel framework integrates the design for manufacturing and assembly (DFMA) and design for reliability (DFR) approaches, as well as finite element analysis to address the cost reduction of product redesign at early-stage, reliability improvement, and higher customer satisfaction. The proposed integrated framework is validated using an example of an Indonesian SME's cooking stove. The redesigned cooking stove showed substantial improvements with 5.46% cost reductions, 20.32% design efficiency, and an 52.81% safety factor. [ABSTRACT FROM AUTHOR]

Kembang Belor Village is one of the villages that have a water resource but are not yet precisely utilized and need a water filter. The current water filter is not suited for users' needs because they cannot afford it. Therefore, this research intends to redesign the water filter. This research aims to reduce production costs and increase the efficiency of design and assembly time. The method employed is Design for Manufacturing and Assembly (DFMA). The results show that the production cost decreased by 50.83%; design efficiency increased by 37.5%; production, handling, and assembly time improved by 51.7%, 33.3%, and 52.7% for each; the number of ppm decreased from 142 to 95. For the contribution, few previous research uses the DFMA method to analyze and redesign the water filter. These findings contribute in helping the user recognize new options to design efficient water filters for future needs. [ABSTRACT FROM AUTHOR]

An algorithm based on a delayed constraint generation method for solving semi-infiniteprograms for constructing minimax optimal designs for nonlinear models is proposed.The outer optimization level of the minimax optimization problem is solved using asemidefinite programming based approach that requires the design space be discretized. Anonlinear programming solver is then used to solve the inner program to determine thecombination of the parameters that yields the worst-case value of the design criterion.The proposed algorithm is applied to find minimax optimal designs for the logistic model,the flexible 4-parameter Hill homoscedastic model and the general nth order consecutivereaction model, and shows that it (i) produces designs that compare well with minimaxD−optimal designs obtained from semi-infinite programming method in the literature;(ii) can be applied to semidefinite representable optimality criteria, that include thecommon A−, E−, G−, I− and D-optimality criteria; (iii) can tackle design problems witharbitrary linear constraints on the weights; and (iv) is fast and relatively easy to use.

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

China's urban housing demand has directly influenced urbanization development. To stabilize the level of urbanization, it is urgent to optimize the whole life-cycle efficiency of construction and the preliminary design as the first step is even more significant. Building Information Modeling (BIM) is widely used as information technology in the construction industry to promote the implementation and management of projects. However, the traditional preliminary design approach still occupies the mainstream market without forming a systematic BIM workflow, which causes inefficiency. To address this issue, this research aims to construct a BIM-assisted workflow to enhance the preliminary design efficiency of architecture. This study creates traditional and BIM-assisted workflows for comparative analysis to capture duration data with a questionnaire and validate by practical simulation. The findings show that the BIM-assisted workflow consumes less time than the traditional workflow. This research indicates that the BIM-assisted workflow can significantly reduce operation time to enhance preliminary design efficiency and deserves to be strongly promoted in the Chinese Architecture, Engineering, and Construction (AEC) industry. [ABSTRACT FROM AUTHOR]

Optimal sampling frameworks attempt to identify the most efficient sampling plans to achieve an adequate statistical power. Although such calculations are theoretical in nature, they are critical to the judicious and wise use of funding because they serve as important starting points that guide practical discussions around sampling tradeoffs and requirements. Conventional optimal sampling frameworks, however, often identify sub-optimal designs because they typically presume the costs of sampling units are equal across treatment conditions. In this study, we develop a more flexible framework that allows costs to differ by treatment conditions and derive the optimal sample size formulas for three-level multisite cluster-randomized trials. We find that the proposed optimal sampling schemes are driven by the differences in costs between treatment conditions, cross-level sampling cost ratios and cross-level variance decomposition ratios. We illustrate the utility of the proposed framework by comparing it to a conventional framework and find that the proposed framework frequently identifies more efficient designs. The proposed optimal sampling framework has been implemented in the R package odr. [ABSTRACT FROM AUTHOR]

Big Data are huge amounts of digital information that rarely result from properly planned surveys; as a consequence they often contain redundant observations. When the aim is to answer particular questions of interest, we suggest selecting a subsample of units that contains the majority of the information to achieve this goal. Selection methods driven by the theory of optimal design incorporate the inferential purposes and thus perform better than standard sampling schemes. [ABSTRACT FROM AUTHOR]

Highly efficient designs and uniform designs are widely applied in many fields because of their good properties. The purpose of this paper is to study the issue of design efficiency for asymmetric minimum projection uniform designs. Based on the centered L2 discrepancy, the uniformity of the designs with mixed levels is defined, which is used to measure the projection uniformity of the designs. The analytical relationship between the uniformity pattern and the design efficiency is established for mixed-level orthogonal arrays with a strength of two. Moreover, a tight lower bound of the uniformity pattern is presented. The research is relevant in the field of experimental design by providing a theoretical basis for constructing the minimum number of projection uniform designs with a high design efficiency under a certain condition. These conclusions are verified by some numerical examples, which illustrate the theoretical results obtained in this paper.

In many randomized controlled trials, treatment groups are of equal size, but this is not necessarily the best choice. This paper provides a methodology to calculate optimal treatment allocations for longitudinal trials when we wish to compare multiple treatment groups with a placebo group, and the comparisons may have unequal importance. The focus is on trials with a survival endpoint measured in discrete time. We assume the underlying survival process is Weibull and show that values for the parameters in the Weibull distribution have an impact on the optimal treatment allocation scheme in an interesting way. Additionally, we incorporate different cost considerations at the subject and measurement levels and determine the optimal number of time periods. We also show that when many events occur at the beginning of the trial, fewer time periods are more efficient. As an application, we revisit a risperidone maintenance treatment trial in schizophrenia and use our proposed methodology to redesign it and compare merits of our optimal design.

A product redesign strategy can effectively shorten design lead time and reduce the manufacturing cost of innovative development for the manufacturing industry to stay competitive. Identification of function components is the basis of product redesign. Existing practices to identify the critical component and customer requirements are considered while complaint and historical failure data, crucial for improving product reliability, are frequently ignored. The objective of the article is to develop an integrated framework of product redesign and innovation considering customer requirements and product failure modes. The novel framework integrates the design for manufacturing and assembly (DFMA) and design for reliability (DFR) approaches, as well as finite element analysis to address the cost reduction of product redesign at early-stage, reliability improvement, and higher customer satisfaction. The proposed integrated framework is validated using an example of an Indonesian SME’s cooking stove. The redesigned cooking stove showed substantial improvements with 5.46% cost reductions, 20.32% design efficiency, and an 52.81% safety factor.

In this paper, we investigate a class of optimal circulant { 0 , 1 } -arrays other than the previously known class of optimal designs for fMRI experiments with a single type of stimulus. We suppose throughout the paper that n ≡ 2 (mod 4) and discuss the asymptotic optimality and the D-efficiency of k × n circulant almost orthogonal arrays (CAOAs) with 2 levels (presence/absence of the stimulus), strength 2 and bandwidth 1, denoted by CAOA (n , k , 2 , 2 , 1). We show that for n ≡ 2 (mod 4) the largest possible value of k for statistically optimal CAOA (n , k , 2 , 2 , 1) cannot exceed n ∕ 2. We also clarify that CAOA (n , k , 2 , 2 , 1) with high D-efficiency and k greater than n ∕ 2 can be obtained via perfect binary sequences. By applying algebraic constructions for perfect binary sequences and by computer search, lists of such efficient CAOAs and the new class of optimal CAOAs are provided. • A new class of optimal CAOA (n , k , 2 , 2 , 1) for n ≡ 2 (mod 4) with larger k is provided. • Asymptotic optimality of CAOA (n , k , 2 , 2 , 1) for n ≡ 2 (mod 4) and k < n is shown. • The relationship between a class of CAOAs and perfect sequences is established. • Efficient CAOAs are obtained from perfect sequences via algebraic constructions. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy logic algorithm was created in order to grade and classify the design efficiencies of classrooms selected from Süleyman Demirel University. The existing classrooms were examined on site and the orientation of the classrooms, the number of people, the classroom area and the window area of the classroom were calculated. As the input variables, the orientation of the classrooms, the number of people, the area per-capita and the ratio of window area to the classroom area were modelled. The design efficiencies of the classrooms as the output variables were obtained by the rules formed among the input variables. In the model, fuzzy model as the Mamdani type and "weighted average" method as the clarification method were used. For fuzzy logic model, 180 fuzzy rules have been formed in the type of IF, which are associated with the facade of the classroom, the number of people, the area per capita and the ratio of the window area of the classrooms to the design efficiency of the classroom. Design efficiency of the classrooms were created; the design efficiency classes and the average design efficiencies of classrooms in faculties were compared and concluded according to faculties. The efficiency of the classrooms, which is the main place of the educational buildings, depends on the decisions taken during the design phase. With the model created in this paper, more efficient designs will be provided by having knowledge about the design efficiency through making use of the decision-making process of the classrooms during the design process.

D-optimal designs are frequently used in controlled experiments to obtain the most accurate estimate of model parameters at minimal cost. Finding them can be a challenging task, especially when there are many factors in a nonlinear model. As the number of factors becomes large and interacts with one another, there are many more variables to optimize and the D-optimal design problem becomes high-dimensional and non-separable. Consequently, premature convergence issues arise. Candidate solutions get trapped in the local optima, and the classical gradient-based optimization approaches to search for the D-optimal designs rarely succeed. We propose a specially designed version of differential evolution (DE), which is a representative gradient-free optimization approach to solve such high-dimensional optimization problems. The proposed specially designed DE uses a new novelty-based mutation strategy to explore the various regions in the search space. The exploration of the regions will be carried out differently from the previously explored regions, and the diversity of the population can be preserved. The proposed novelty-based mutation strategy is collaborated with two common DE mutation strategies to balance exploration and exploitation at the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as the evolution proceeds. Using the logistic models with several factors on various design spaces as examples, our simulation results show that our algorithm can find the D-optimal designs efficiently and the algorithm outperforms its competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experiment with discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able to consistently outperform the other algorithms and find a more efficient D-optimal design for the problem.

Design activity is the initial step in manufacturing. In smart factory, these activities should be interconnected. The final cost of a product is affected by decisions which are made in these activities. Since 1980, Design for Manufacturing and Assembly (DFMA) approaches has been developed to guide planners to produce products which are easy to fabricate and assemble. Designing products for recyclability is constrained by environmental and economic targets. Numerous Design for Assembly (DFA) laws and parameters can be employed to measure the recyclability index of product designs. Parallel assembly and disassembly techniques using human and robot collaboration can also improve the design quality by choosing the shorter way to completely dismantle the product or to dismantle wear parts. The implementation of these approaches has created a revolution in today's industry, by many improvements as reduce product cost, shorter time to market, better quality, and few suppliers of standard parts. In this paper, a novel method to parallel dismantle a product is presented. To improve the design quality of the product, a set of assessment metrics were considered as recyclability for reusing aspect, total disassembly time of the product, parallelism aspect, and design efficiency. A comparative example is used to demonstrate the added value of the proposed approach. [ABSTRACT FROM AUTHOR]

China’s urban housing demand has directly influenced urbanization development. To stabilize the level of urbanization, it is urgent to optimize the whole life-cycle efficiency of construction and the preliminary design as the first step is even more significant. Building Information Modeling (BIM) is widely used as information technology in the construction industry to promote the implementation and management of projects. However, the traditional preliminary design approach still occupies the mainstream market without forming a systematic BIM workflow, which causes inefficiency. To address this issue, this research aims to construct a BIM-assisted workflow to enhance the preliminary design efficiency of architecture. This study creates traditional and BIM-assisted workflows for comparative analysis to capture duration data with a questionnaire and validate by practical simulation. The findings show that the BIM-assisted workflow consumes less time than the traditional workflow. This research indicates that the BIM-assisted workflow can significantly reduce operation time to enhance preliminary design efficiency and deserves to be strongly promoted in the Chinese Architecture, Engineering, and Construction (AEC) industry.

In recent years, prefabricated structures have attracted a lot of attention because of their advantages of multifunctional design, fast construction speed, and high degree of standardization. However, they also entail increased design information complexity. To achieve a simplified and efficient design process, this study proposes to develop a common library of prefabricated structural components using graphic media mapping. The study integrates digital graphic information of building information modeling (BIM) with engineering and production data. Based on the Industry Foundation Classes (IFC) data standard, semantic data related to the geometry, processes, resources, production costs, construction sites, and other design information relevant for prefabricated components are assigned to a component's graphic representation. Thus, the visible geometric representation and the background engineering information are integrated into a unified database. A collection of common structural components forms a library that can be reused in prefabricated design projects. The development of a component library and its application to a more efficient design process is demonstrated through a case study of an urban viaduct project. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) experiments help to render correct statistical inferences on brain function. However, few theoretical constructions of efficient designs for these crucial experiments exist in the literature. Recent works on the construction of circulant orthogonal arrays using algebraic difference sets show promise. However, such arrays are limited by the assumption that any interactions between the effects of a hemodynamic response function (HRF) and its residual effects are negligible in magnitude estimations of BOLD signals collected from an fMRI. Therefore, we propose a theoretical construction of a circulant orthogonal array of high strength using an extension of the complete difference system. FMRI experiments conducted using our proposed design show that the main effects of the individual HRF on the signal are not biased by the main effects of other HRFs, or by the interaction with its residual effects. Several properties of this new class of designs are studied, and the statistical regression model associated with this class of designs is presented. [ABSTRACT FROM AUTHOR]

Formula One (F1) World Championship has become one of the most successful sport tournaments over the last decade. Races take place in modern-day closed racing circuits, whose design plays a key role in racing results. This paper proposes a framework for the design efficiency assessment of the more representative racing circuits that hosted Grands Prix during the recent F1 seasons. The proposed approach considers two basic circuit features (namely, circuit length and number of turns) and combines car performance and race safety data. The methodology used is based on data envelopment analysis (DEA). The number of inefficient circuits is small, five in the case of variable returns to scale and nine (out of 21) when constant returns to scale are assumed. Potential improvements in terms of speed, fuel consumption and safety targets are computed. For each inefficient circuit its reference set is identified. Also, a second-stage DEA fractional regression analysis is carried out to study the influence of the circuit type (race or street circuit), the track orientation (clockwise or anticlockwise) and the number of red-flagged races due to rainfall on the circuits' efficiency. The results indicate that all three variables are significant. The implications of the results for track designers and F1 organizers are also discussed. [ABSTRACT FROM AUTHOR]

Landscape performance evaluation plays an important role in Landscape Architecture's transformation to an evidence-based science. Most of existing relevant studies focus on the selection of evaluation indicators and methods, or description of the sustainability characteristics of completed projects, while in-depth theoretical discussion on its development processes and essential connotation is still in shortage. By tracing back and comparing the theoretical characteristics and development relationships of the three major systems -- POE, SITES, and LPS, this paper clarifies the evolution of landscape performance evaluation towards serving evidence-based design, and further expounds its connotation of discovering the causality of design strategies and benefit results. Finally, two research interests, "feedback analysis of design efficiency" and "producing practical and operable knowledge" are proposed with significance as a key to support high-performance landscape design practices with reliable evidences. [ABSTRACT FROM AUTHOR]

Response surface designs (RSDs) are a core component of the response surface methodology, which is widely used in the context of product and process optimization. In this contribution, we consider three-level RSDs, which can be viewed as matrices with entries equal to { − 1 , 0 , 1 } . Each column of an RSD corresponds to a factor and each row to an experimental test. We define a new family of orthogonal RSDs, for which there is no aliasing between the main effects and the second-order effects (two-factor interactions and quadratic effects). Using integer programming techniques, we construct a database of 55,531 such RSDs for 3–7 factors. We name these designs orthogonal minimally aliased RSDs (or OMARS designs). Each design in the catalog is extensively characterized in terms of efficiency, power, fourth-order correlations, fraction of design space plots, projection capabilities, etc. We identify interesting designs and investigate trade-offs between different quality criteria. Finally, we present a multiattribute decision algorithm to select designs from the catalog. An important result of our study is that we discovered some novel and interesting designs that challenge standard RSDs. [ABSTRACT FROM AUTHOR]

Introduction: In contrast to other sensory domains, detection of primary olfactory processes using functional magnetic resonance imaging has proven to be notably challenging with conventional block designs. This difficulty arises from significant habituation and hemodynamic responses in olfactory areas that do not appear to align with extended boxcar functions convolved with a generic hemodynamic response model. Consequently, some researchers have advocated for a transition to event-related designs, despite their known lower detection power compared to block designs., Methods: Here, we conducted a block design experiment with 16s of continuous odorant stimulation alternating with 16s of continuous odorless air stimulation in 33 healthy participants. We compared four statistical analyses that relied either on standard block designs (SBD1-2) or on block designs that were modulated by the participants' individual breathing patterns (MBD1-2)., Results: We found that such modulated block designs were comparatively more powerful than standard block designs, despite having a substantially lower design efficiency. Using whole-brain effect size maps, we observed that the right insular and medial aspects of the left piriform cortex exhibited a preference for a breathing-modulated analysis approach., Discussion: Research in olfaction that necessitates designs with longer-lasting blocks, such as those employed in the investigation of state-dependent processing, will benefit from the breathing-modulated analyses outlined in this study., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Alahäivälä, Thaploo, Wein, Seidel, Riebel, Hummel and Schwarzbach.)