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This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors.

The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection.

The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM.

The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results.

This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.

This research aims to develop conceptual phase building project cost forecasting models by exploring the relationship of existing plan shape complexity indices and general design morphology parameters with total construction cost.

Plan shape indices proposed to date by the literature for measuring building design complexity are critically reviewed. Building morphology is also dictated by town planning restrictions such as plot coverage ratio or number of storeys. This study analyses historical data collected from 49 residential building projects to develop multiple linear regression (MLR) and artificial neural network (ANN) models for forecasting construction cost. Existing plan shape coefficients are calculated to evaluate the geometrical complexity of sampled projects. Ten regression-based cost estimating equations are totally derived from stepwise backward and forward methods, and their predictive accuracy is contrasted: to performance levels reported in past studies and to ANN models developed in this research with multilayer perceptron architecture.

Analysis of plan shape indices revealed that 85.7% of examined past projects possess a high degree of design complexity, hence resulting in expensive initial decisions. This highlights the need for more effective early design stage decision-making by developing new building economic tools. The most accurate regression model, with a mean absolute percentage error (MAPE) of 19.2%, predicts the log of total cost from wall to floor index and total building envelope surface. Other explanatory variables resulting in MAPE values in the order of 20%–22% are total volume, volume above ground level, gross floor area below ground level, gross floor area per storey and total number of storeys. The overall MAPE of regression-based equations is 24.3% whilst ANN models are slightly more accurate with MAPE scores of 21.8% and 21.6% for one hidden and two hidden layers, respectively. The most accurate forecasting model in the research is the ANN with two hidden layers and the sigmoid activation function which predicts total building cost from total building volume (19.1%).

This paper introduces MLR-based and ANN-based conceptual construction cost forecasting models which are founded solely on building morphology design parameters and compare favourably with previous studies with an average predictive accuracy less than 25%. This paper is expected to be beneficial to both practitioners and academics in the built environment towards more effective cost planning of building projects. The methodology suggested can further be implemented in other countries provided that accurate and relevant data from historical projects are used.

The choice of a particular architectural solution when designing a building considerably influences the costs of its construction. The aim of this paper is to present the indexes which may relate to the complexity of the building shape, and which were then specified and compared on a particular example.

The article analyses various indices of building shape evaluation, their influence on the changes in construction costs and their sensitivity to the changes in the dimensions of the building. The methodology adopted in this article concerns the analysis of the selected simple factors assessing the shape of the building against the cost‐effectiveness of constructing the basic elements of the building.

The authors established the best building shape in relation to the costs of constructing the walls and foundations (square), and to the layout of the inside of the building (rectangle). They believe that the best evaluations of building shape were achieved by the LBI index, which evaluated the shape correctly in all of the analysed examples and which is the most sensitive to the changes in building parameters in correct proportions to the changes in construction costs.

The article presents the indexes which may relate to the complexity of the building shape. The authors established the best building shape in relation to the costs of constructing. The authors provide the degree of the floor area usability in buildings of various shapes.

The purpose of this paper is to convert real-world raster data into vector format and evaluate loss of accuracy in the conversion process. Open-source Geographic Information System (GIS) is used in this process and system resource utilizations were measured for conversion and accuracy analysis methods. Shape complexity attributes were analyzed in co-relation to the observed conversion errors.

The paper empirically evaluated the challenges and overheads involved in the format conversion algorithms available in open-source GIS with real-world land use and land cover (LULC) map data of India. Across the different LULC categories, geometric errors of varying density were observed in Quantum GIS (QGIS) algorithm. Area extents of original raster data were compared to the vector forms and the shape attributes such as average number of vertices and shape irregularity were evaluated to explore the possible correlation.

The results indicate that Geographic Resources Analysis Support System provides near error-free conversion algorithm. At the same time, the overall time taken for the conversion and the system resource utilizations were optimum as compared to the QGIS algorithm. Higher vector file sizes were generalized and accuracy loss was tested.

Complete shape complexity analysis could not be achieved, as the weight factor for the irregularity of the shapes is to be varied based on the demography as well as on the LULC category.

Because of the higher system resource requirements of topological checker tool, positional accuracy checks for the converted objects could not be completed.

This paper addresses the need of accuracy analysis of real-world spatial data conversions from raster to vector format along with experimental setups challenges and impact of shape complexity.

Neo-institutional theories and their constructs have so far only received limited attention in supply chain management literature. As recent supply chain disruptions and their ripple effects affect actors on a broader institutional level, supply chains are confronted with multiple new and emerging, often conflicting, institutional demands. This study aims to unpack the notion of institutional complexity behind supply chain disruptions and present a novel institutional framework to lower supply chain susceptibility and increase supply chain resilience.

The authors identify the patterns of complexity that shape the supply chain susceptibility, namely, distance, diversity and ambiguity, and present three institutional responses to susceptibility to increase supply chain resilience, namely, institutional entrepreneurship, institutional alignment and institutional layering.

This paper analyses the current situational relevance to better understand the various and patterned ways how logics influence both supply chain susceptibility and the supply chain resilience. The authors derive six propositions on how complexity can be reduced for supply chain susceptibility and can be increased for supply chain resilience.

By expanding and extending research on institutional complexity to supply chains, the authors broaden how researchers in supply chain management view supply chain susceptibility, thereby providing managers with theory to think differently about supply chains and its resilience.

Children with Autism Spectrum Disorder (ASD) show reduced attention to social stimuli. The reasons for these impairments are still being debated by researchers. The aim of this study is to analyse if reduced attention towards social stimuli is determined by initial underlying difficulties in the control of visual attention. Among the variables that could produce these difficulties, the authors considered geometric complexity and typology of geometric figures.

To test this hypothesis, in this paper, an eye-tracker paradigm was used for assessing visual exploration and recognition memory towards geometric figures (curved vs rectilinear) with two levels of geometric complexity (low and high) in 17 children with ASD matched with 17 children with typical development (TD).

The results showed that the ASD group seemed indifferent to both the geometric complexity and the typology of figures (curved and rectilinear), whereas the TD group showed higher performances with highly complex and curved geometric figures than with low complex and rectilinear geometric figures.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed hypotheses further.

This paper includes implications upon the presence of an unspecified visual attention deficit that is present from the early stages of the processing of stimuli.

The understanding of this deficit from the early stages of the processing of stimuli can help educators to intervene at an early stage when disturbances in social relationships are starting.

This study contributes to understanding the presence of dysfunctional perceptual antecedents that could determine general difficulties in paying attention to social stimuli in ASD subjects.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

This study aims to systematically review the economic complexity literature to advance the knowledge on its contribution to building regional competitiveness.

In this study, we did a systematic review of 111 relevant papers. In this regard, we did a thematic analysis on all the collected papers, which led to a two-level processed approach. In the first level, the contributions of the reviewed articles have been classified into three main streams. In the second level, the findings under each contribution category are analyzed and explained. This approach led to a thematic network demonstrating economic complexity and the dynamics of regional competitiveness and a set of managerial and policy implications. We followed a multiple processed approach for the systematic review of 95 papers that reveals considerable contributions in three categories, including measurement techniques, criticisms and exploratory studies.

Despite some critiques and the undertaken evolution in measurement techniques of complexity, economic complexity has become a well-known method mainly for regions' competitiveness dynamics. Our review demonstrates a nested network of economic complexity dynamics that drives policy advice concerning countries' status in their development path. The provided set of policies includes guidelines for underdeveloped and developing countries and general policy implications, applicable for all regional contexts for building competitiveness dynamics.

This research contributes to the literature on competitiveness from the window of economic complexity. The study allows a deep understanding of regions' productive structure role in their development and competitiveness. A set of policies for building regional competitiveness is provided concerning the study's findings. The literature gaps are identified, and future research ideas are provided for using economic complexity methodologically and logically to boost regional competitiveness.

The morphology of a building describes its outline and influences its architectural aesthetics and the cost directly. However, the literature on the impact of morphology on the aesthetics and cost of urban detached residential buildings is scarce. Thus, this study is significant because its aim was to identify the critical building morphology factors and the relationship that each of them maintains with the cost and aesthetics of urban detached residential buildings.

The multi-method qualitative approach was used to collect the required empirical data through interviews and case studies and to identify the effect of the morphology factors on the aesthetics and construction cost of urban residences in Sri Lanka, respectively. The collected data were analyzed using manual content analysis and descriptive statistics.

The study findings revealed that the morphology factors such as the roof and circulation spaces, open spaces and voids have a high impact on both the building cost and aesthetics. These findings will assist building designers in making effective design decisions on building costs and aesthetics so that a successful design outcome satisfying both the clients and design team could be obtained.

Although morphology has an impact on the cost and aesthetic of buildings, literature on the subject is scarce. Thus, this study is significant in that it aimed at identifying the significant building morphology factors in urban detached residential buildings and identifying their relationship with the cost and aesthetic of those buildings.

Complexity has been called the 21st-century supply chain (SC) challenge. Most SC managers view it as a necessary evil, ever-present, costly and tough to manage, and few prioritize it. Still, anecdotes suggest some leverage it to drive operational excellence. This study aims to explore how they do it, delving into the development of a complexity management capability, under what circumstances it emerges and its effect on competitiveness.

To better understand why, and how, companies develop (or not) a distinctive SC complexity management capability, this study employed an inductive study of 10 leading European companies, each operating a complex SC.

Although SC complexity raises costs, increases disruptions and makes decision-making difficult, few companies have made complexity management a priority. Among those, most focus on reducing or absorbing complexity to improve operational excellence. A few invest to develop a distinctive SC complexity management capability. They manage complexity for market success. The interaction among competitive pressures, managerial attitudes and investments delineate a dynamic capability development process.

Despite extensive research on complexity drivers, the tools used to manage SC complexity and the impact of SC complexity on performance, the interplay among factors that promote, or hinder, the development of an SC complexity capability continues to be poorly understood. By mapping the complexity capability development process, this study explicates a more nuanced approach to managing SC complexity that can yield a competitive edge.

SC complexity prevails because the dynamic, iterative complexity capability development process is overlooked. Managers can use the complexity capability roadmap to assess the cost/benefits of pursuing a distinctive complexity management capability more accurately.

This study demystifies the development of a complexity management capability, showing how some companies develop the capability to distinguish between value-added and value-dissipating complexity and thus become empowered to leverage SC complexity for competitive advantage.