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Every day, patients access and generate online health content through a variety of channels, creating an ever-expanding sea of digital data. At the same time, proponents of public health have recently called for timely, granular, and actionable data to address a range of public health issues, stressing the need for social listening platforms that can identify and compile this valuable data. Yet previous attempts at social listening in healthcare have yielded mixed results, largely because they have failed to incorporate sufficient context to understand the communications they seek to analyze. Guided by activity theory to design HealthSense, we propose a platform for efficiently sensing and gathering data across the web for real-time analysis to support public health outcomes. HealthSense couples theory-guided content analysis and graph propagation with graph neural networks (GNNs) to assess the relevance and credibility of information, as well as intelligently navigate the complex online channel landscape, leading to significant improvements over existing social listening tools. We demonstrate the value of our artifact in gathering information to support two exemplar public health tasks: (1) performing postmarket drug surveillance for adverse reactions and (2) addressing the opioid crisis by monitoring for potent synthetic opioids released into communities. Our results across data, user, and event experiments show that effective design artifacts can enable better outcomes across both automated and human decision-making contexts, making social listening for public health possible, practical, and valuable. Through our design process, we extend activity theory to address the complexities of modern online communication platforms, where information resides not only in the collection of individual communication activities but also in the complex network of interactions among them. [ABSTRACT FROM AUTHOR]

Background: De-novo-designed synthetic transcriptional regulators have great potential as the genetic parts for constructing complex multilayered gene circuits. The design flexibility afforded by advanced nucleic acid sequence design tools vastly expands the repertoire of regulatory elements for circuit design. In principle, the design space of synthetic regulators should allow for the construction of regulatory circuits of arbitrary complexity; still, the orthogonality and robustness of such components have not been fully elucidated, thereby limiting the depth and width of synthetic circuits. Results: In this work, we systematically explored the design strategy of synthetic transcriptional regulators, termed switchable transcription terminators. Specifically, by redesigning key sequence domains, we created a high-performance switchable transcription terminator with a maximum fold change of 283.11 upon activation by its cognate input RNA. Further, an automated design algorithm was developed for these elements to improve orthogonality for a complex multi-layered circuit construction. The resulting orthogonal switchable transcription terminators could be used to construct a three-layer cascade circuit and a two-input three-layer OR gate. Conclusions: We demonstrated a practical strategy for designing standardized regulatory elements and assembling modular gene circuits, ultimately laying the foundation for the streamlined construction of complex synthetic gene circuits. [ABSTRACT FROM AUTHOR]

The leave-one-out (LOO) green fluorescent protein (GFP) approach to biosensor design combines computational protein design with split protein reconstitution. LOO-GFPs reversibly fold and gain fluorescence upon encountering the target peptide, which can be redefined by computational design of the LOO site. Such an approach can be used to create reusable biosensors for the early detection of emerging biological threats. Enlightening biophysical inferences for nine LOO-GFP biosensor libraries are presented, with target sequences from dengue, influenza, or HIV, replacing beta strands 7, 8, or 11. An initially low hit rate was traced to components of the energy function, manifesting in the over-rewarding of over-tight side chain packing. Also, screening by colony picking required a low library complexity, but designing a biosensor against a peptide of at least 12 residues requires a high-complexity library. This double-bind was solved using a "piecemeal" iterative design strategy. Also, designed LOO-GFPs fluoresced in the unbound state due to unwanted dimerization, but this was solved by fusing a fully functional prototype LOO-GFP to a fiber-forming protein, Drosophila ultrabithorax, creating a biosensor fiber. One influenza hemagglutinin biosensor is characterized here in detail, showing a shifted excitation/emission spectrum, a micromolar affinity for the target peptide, and an unexpected photo-switching ability. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach to architectural visualization using generative artificial intelligence (AI), particularly emphasizing text-to-image technology, to remarkably improve the visualization process right from the initial design phase within the architecture, engineering, and construction industry. By creating more than 10 000 images incorporating an architect's personal style and characteristics into a residential house model, the effectiveness of base AI models. Furthermore, various architectural styles were integrated to enhance the visualization process. This method involved additional training for styles with low similarity rates, which required extensive data preparation and their integration into the base AI model. Demonstrated to be effective across multiple scenarios, this technique markedly enhances the efficiency and speed of production of architectural visualization images. Highlighting the vast potential of AI in design visualization, our study emphasizes the technology's shift toward facilitating more user-centered and personalized design applications. [ABSTRACT FROM AUTHOR]

Extensive molecular dynamics (MD) simulations on designed and evolved enzymes for the Kemp Elimination were performed. Over thirty different systems including combinations of three protein scaffolds used in computational design (KE70, KE59 and HG3), several directed evolution variants and ligands (5‐nitrobenzo[d]isoxazole, and 6‐nitro‐1H‐benzo[d][1,2,3]triazole), were screened computationally. The study was focused mostly on computational and evolved variants for which X‐ray structures are available. MD simulations were used to monitor the organization of the active sites over time periods of 100 ns. A general trend was observed in the simulations: the optimal organization of active site residues, corresponding to the most active catalysts, is only maintained with polarized, TS‐like substrates, reinforcing the crucial role of protein dynamics and electrostatic preorganization for efficient biocatalysis. Implications for enzyme design protocols are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a morphological study of Islamic geometric patterns (IGP) and their role in application of formal grammar in computational modeling of IGPs. Through a comprehensive literature review and data collection, we analyze the morphological properties of these patterns using techniques such as geometric transformations, pattern classification, and symmetry analysis. Based on our findings, we explore how these properties can be used in constructing a formal grammar through string rewriting system for a CAD application. Building on a study on the potential of a string rewriting system for modeling IGPs, the current research suggests an update to the previous system and introduces a new morphological structure for IGPs. The new method has an expanded sample set and is tested on a class of 5-fold star patterns with 12 members and demonstrates successful development. The results are implemented in a Grasshopper add-on, providing a flexible platform to generate the patterns through strings and to control their parameters. This tool opens up new possibilities to bridge traditional patterns with contemporary technologies and make them more accessible. Furthermore, it contributes to the preservation of IGPs as a significant cultural and architectural heritage, while also advancing the evolution of these patterns to a new and contemporary generation. [ABSTRACT FROM AUTHOR]

As we strive to improve efficiency and minimise environmental impact in the construction industry, it is important to consider how these objectives impact the work and productivity of designers. This article presents a tool to assist designers in matching reclaimed building products with design intent. The demonstration is based on an actual design project of a two-storey public building made of reclaimed timber from disassembled barns. The algorithm's objective is to propose optimal matching in terms of a structure's environmental impact without overloading the user with additional work or excess information. The impact includes emissions embodied in material and related to the transportation from the donor site. The algorithm is integrated into the Grasshopper interface for easier use. All the inputs and objectives are quantitative and fully customisable. The user can still assess qualitative characteristics thanks to a graphical representation of the result. The demonstration shows how automation can simplify the design process with reclaimed elements, support traditional design workflows and enable improvements towards a more sustainable construction industry. [ABSTRACT FROM AUTHOR]

Partition walls play a crucial role in buildings, influencing their aesthetics, functionality, and integration with other architectural elements. However, the selection process for partition wall types is often challenging due to the multitude of options available, varying decision criteria, and inadequate decision-making practices in the Architecture, Engineering, and Construction (AEC) industry. To address these challenges and improve decision-making, a hybrid Decision Support System (DSS) named PartitionWall Pro is proposed. This tool combines both document-driven and model-driven approaches to assist in the selection and design of partition walls. The document-driven aspect utilizes a choosing-by-advantages (CBA) model to compare the advantages of different partition wall options, while the model-driven component employs computational design models to analyze the structural integrity of unreinforced masonry partition walls. Validation procedures ensure the reliability and accuracy of the DSS in practical applications. Through case studies involving a warehouse and a school, the study demonstrates how the DSS simplifies decision-making processes and encourages the adoption of cost-effective partition wall solutions. The results underline the potential of the DSS to enhance efficiency, foster stakeholder discussions, and improve communication in building design projects, thereby offering valuable insights for researchers and industry professionals alike, ultimately transforming partition wall design practice. [ABSTRACT FROM AUTHOR]

As respiratory pathogens, influenza B viruses (IBVs) cause a significant socioeconomic burden each year. Vaccine and antiviral development for influenza viruses has historically viewed IBVs as a secondary concern to influenza A viruses (IAVs) due to their lack of animal reservoirs compared to IAVs. However, prior to the global spread of SARS-CoV-2, the seasonal epidemics caused by IBVs were becoming less predictable and inducing more severe disease, especially in high-risk populations. Globally, researchers have begun to recognize the need for improved prevention strategies for IBVs as a primary concern. This review discusses what is known about IBV evolutionary patterns and the effect of the spread of SARS-CoV-2 on these patterns. We also analyze recent advancements in the development of novel vaccines tested against IBVs, highlighting the promise of computational vaccine design strategies when used to target both IBVs and IAVs and explain why these novel strategies can be employed to improve the effectiveness of IBV vaccines. [ABSTRACT FROM AUTHOR]

Background and Aim: Nipah virus (NiV) poses a threat to human and animal health, particularly swine, which serve as primary vectors for human transmission. Despite its severe risks, no NiV vaccine currently exists for humans or animal hosts; thus, innovative vaccine development approaches that address cross-species transmission are required. This study was computationally designed to evaluate a multi-epitope messenger RNA (mRNA) vaccine targeting NiV for human and swine immunization. Materials and Methods: B and T lymphocyte epitopes were identified from NiV structural proteins using multiple epitope prediction tools. All epitopes were linked to form a multi-epitope construct, and various adjuvant combinations were analyzed for physicochemical properties and immune simulation. Molecular docking and dynamics were employed to visualize the construct’s interaction with a host immune receptor. Signal peptides were added to the construct, and mRNA sequences were generated using LinearDesign. The minimum free energies (MFEs) and codon adaptation indices (CAI) were used to select the final mRNA sequence of the vaccine construct. Results: Computational tools predicted 10 epitopes within NiV structural proteins that can be recognized by human and swine immune receptors. The construct with β-defensin 2 adjuvant was selected as the final immunogenic region after showing favorable immunogenicity profiles and physicochemical properties. The final vaccine sequence had higher MFE and CAI compared to the BioNTech/Pfizer BNT162b2 and Moderna mRNA-1273 vaccines. Conclusion: The multi-epitope mRNA vaccine designed in this study shows promising results as a potential NiV vaccine candidate. Further in vivo and in vitro studies are required to confirm the efficacy.

Abstract Background De-novo-designed synthetic transcriptional regulators have great potential as the genetic parts for constructing complex multilayered gene circuits. The design flexibility afforded by advanced nucleic acid sequence design tools vastly expands the repertoire of regulatory elements for circuit design. In principle, the design space of synthetic regulators should allow for the construction of regulatory circuits of arbitrary complexity; still, the orthogonality and robustness of such components have not been fully elucidated, thereby limiting the depth and width of synthetic circuits. Results In this work, we systematically explored the design strategy of synthetic transcriptional regulators, termed switchable transcription terminators. Specifically, by redesigning key sequence domains, we created a high-performance switchable transcription terminator with a maximum fold change of 283.11 upon activation by its cognate input RNA. Further, an automated design algorithm was developed for these elements to improve orthogonality for a complex multi-layered circuit construction. The resulting orthogonal switchable transcription terminators could be used to construct a three-layer cascade circuit and a two-input three-layer OR gate. Conclusions We demonstrated a practical strategy for designing standardized regulatory elements and assembling modular gene circuits, ultimately laying the foundation for the streamlined construction of complex synthetic gene circuits.

This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications. [ABSTRACT FROM AUTHOR]

It is in the preliminary design phase of a project that the designer makes decisions concerning the global geometry of the structure. When working with space frames, the choice of the frame topology is key for the structural behavior. It is difficult to find manuals that provide guidance on which of the most common topologies is the right one for the project, let alone in wood construction. In response to this shortcoming, the use of parametric software is proposed (Grasshopper build1.0.0007 and Karamba 3D 2.2.0.16-220828). The aim is to create a dynamic catalog that responds instantaneously to changes in the parameters to provide information on structural behavior, pre-dimensioning and metrics. With the display of all this information, the architect will have enough technical argumentation to choose or reject options. The proposal is developed through a case study: the early design and analysis stages of flat double-layer timber spatial frames as for rectangular medium-span roofs. [ABSTRACT FROM AUTHOR]

Carbon disulfide (CS2) is an environmental contaminant, which is deadly hazardous to the workers under chronic or acute exposure. However, the toxicity mechanisms of CS2 are still unclear due to the scarcity of biocompatible donors, which can release CS2 in cells. Here we developed the first bioorthogonal CS2 delivery system based on the "click‐and‐release" reactions between mesoionic 1,3‐thiazolium‐5‐thiolates (TATs) and strained cyclooctyne exo‐BCN‐OH. We successfully realized intracellular CS2 release and investigated the causes of CS2‐induced hepatotoxicity, including oxidative stress, proteotoxic stress and copper‐dependent cell death. It is found that CS2 can be copper vehicles bypassing copper transporters after reacting with nucleophiles in cytoplasm, and extra copper supplementation will exacerbate the loss of homeostasis of cells and ultimately cell death. These findings inspired us to explore the anticancer activity of CS2 in combination with copper by introducing a copper chelating group in our CS2 delivery system. [ABSTRACT FROM AUTHOR]

The limited success of current targeted therapies for pancreatic cancer underscores an urgent demand for novel treatment modalities. The challenge in mitigating this malignancy can be attributed to the digestive organ expansion factor (DEF), a pivotal yet underexplored factor in pancreatic tumorigenesis. The study uses a blend of in vitro and in vivo approaches, complemented by the theoretical analyses, to propose DEF as a promising anti‐tumor target. Analysis of clinical samples reveals that high expression of DEF is correlated with diminished survival in pancreatic cancer patients. Crucially, the depletion of DEF significantly impedes tumor growth. The study further discovers that DEF binds to p65, shielding it from degradation mediated by the ubiquitin‐proteasome pathway in cancer cells. Based on these findings and computational approaches, the study formulates a DEF‐mimicking peptide, peptide‐031, designed to disrupt the DEF‐p65 interaction. The effectiveness of peptide‐031 in inhibiting tumor proliferation has been demonstrated both in vitro and in vivo. This study unveils the oncogenic role of DEF while highlighting its prognostic value and therapeutic potential in pancreatic cancer. In addition, peptide‐031 is a promising therapeutic agent with potent anti‐tumor effects. [ABSTRACT FROM AUTHOR]

Technological developments have enabled the acceleration of a shift from Computer Aided Design to Computational Design. The productive structure of Computational Design plays an important role in revealing the mechanisms that make up traditional design thinking. The traditional design thinking approach lacks an explicit structure for understanding and formulating the design process, the mechanisms involved, and the formation of design knowledge and representation. As a result, it is difficult to develop productive and evaluative knowledge. In contrast, the computational design approach facilitates the discovery of tacit knowledge, leading to the creation of productive and evaluative knowledge. The aim of this study is to create a learning framework for using the productive and evaluative knowledge discovered through the computational design approach in urban design. The study will discuss how to handle Cellular Automata, one of the most frequently used generative systems that reflects computational design thinking, in the context of urban design. This section will analyze the contributions of the generative method in the context of urban design, after discussing the computational thinking and approach methods used in Cellular Automata studies. Cellular Automata approaches are useful in urban development scenarios as they allow for the recognition of relationships and patterns between parts, and rediscovery of them during and after the generative process. [ABSTRACT FROM AUTHOR]

We present a novel technique for designing molds suitable for desktop thermoforming, specifically for creating packaging such as blister packs. Our molds, PackMolds, feature neither undercuts nor negative draft angles, facilitating their easy release from thermoformed plastic sheets. In this study, we optimize the geometry of PackMolds to comply with user-specified draft angle constraints. Instead of simulating the traditional thermoforming process, which necessitates time discretization and specifying detailed parameters for both material properties and machine configuration to achieve an accurate simulation result, we formulate our problem as a constrained geometric optimization problem and solve it using a gradient-based solver. Additionally, in contrast to industrial thermoforming, which benefits from advanced tools, desktop thermoforming lacks such sophisticated resources. Therefore, we introduce a suite of assistive tools to enhance the success of desktop thermoforming. Furthermore, we demonstrate its wide applicability by showcasing its use in not only designing blister packs but also in creating double-sided blister packs and model stands. [ABSTRACT FROM AUTHOR]

Organophosphates (OPs) are a class of neurotoxic acetylcholinesterase inhibitors including widely used pesticides as well as nerve agents such as VX and VR. Current treatment of these toxins relies on reactivating acetylcholinesterase, which remains ineffective. Enzymatic scavengers are of interest for their ability to degrade OPs systemically before they reach their target. Here we describe a library of computationally designed variants of phosphotriesterase (PTE), an enzyme that is known to break down OPs. The mutations G208D, F104A, K77A, A80V, H254G, and I274N broadly improve catalytic efficiency of VX and VR hydrolysis without impacting the structure of the enzyme. The mutation I106 A improves catalysis of VR and L271E abolishes activity, likely due to disruptions of PTE's structure. This study elucidates the importance of these residues and contributes to the design of enzymatic OP scavengers with improved efficiency. [ABSTRACT FROM AUTHOR]

The design representations of lattice structures are fundamental to the development of computational design approaches. Current applications of lattice structures are characterized by ever-growing demand on computational resources to solve difficult optimization problems or generate large datasets, opting for the development of efficient design representations which offer a high range of possible design variants, while at the same time generating design spaces with attributes suitable for computational methods to explore. In response, the focus of this work is to propose a parametric design representation based on crystallographic symmetries and investigate its implications for the computational design of lattice structures. The work defines design rules to support the design of functionally graded structures using crystallographic symmetries such that the connectivity between individual members in a structure with varying geometry is guaranteed and investigates how to use the parametrization in the context of optimization. The results show that the proposed parametrization achieves a compact design representation to benefit the computational design process by employing a small number of design variables to control a broad range of complex geometries. The results also show that the design spaces based on the proposed parametrization can be successfully explored using a direct search-based method. [ABSTRACT FROM AUTHOR]

The paper seeks to investigate novel potentials for building design and structure generation that arise at the intersection of computational design and AI-generated 3D geometries. Although the use of AI technologies is exponentially increasing inside the architectural discipline, the design of spatial building configurations using AI-generated 3D geometries is still limited in its applications and represents an ongoing field of investigation in advanced architectural research. In this regard, several questions still need to be answered: how can we design new building typologies from AI-generated 3D geometries? And how can we use these typologies to shape both the real and the virtual world? The paper proposes a new approach to architectural design where artificial intelligence is used as the starting point for design exploration, while computational design procedures are employed to convert AI-generated 3D geometries into building elements – such as columns, beams, horizontal and vertical surfaces. The paper starts with a general overview of the current use of artificial intelligence inside the architectural discipline, and then it moves towards the explanation of specific AI generative models for 3D geometry reconstruction and representation. Subsequently, the proposed working pipeline is analysed in more detail – from the creation of 3D geometries using generative AI models to the conversion of such geometries into building elements that can be further designed and optimised using computational design tools and methods. The results shown in the paper are achieved using Shap-E as the main AI model, though the proposed pipeline can be implemented with multiple AI models. The paper ends by showing some of the generated results, finally adding some considerations to the relationship between human and artificial creativity inside the architectural discipline. The work presented in the paper suggests that the use of computational design tools and methods combined with the tectonics of the latent space opens new opportunities for topological and typological explorations. In a time where traditional architectural typologies are moving towards stagnation due to their inability to satisfy new human needs and ways of living, exploring AI-based working pipelines related to architectural design allows the definition of new design solutions for the generation of new architectural spaces. In doing so, the serendipitous aspect of AI biases is used as an auxiliary force to inform design decisions, promoting the discovery of a new inbuilt dynamism between human and artificial creativity. In a time where AI is everywhere, understanding the measure of such dynamism represents a key aspect for the future of the architectural discipline. [ABSTRACT FROM AUTHOR]

Computational design together with the digitization of most fabrication processes play an important role in many research areas. Digital tools such as 3D modeling and computational design have been increasingly used. Computational design combines traditional 3D product design together with programming a general-purpose CAD system in order to promote system integration. In essence, using CAD-based textual or visual programming languages a series of products can be designed with accuracy and take advantage of product customization and automation of downstream applications. The present paper aims at customizing furniture design based on automating both the design and the fabrication procedures. The customer is able to define a series of geometrical characteristics, i.e., width, length, internal dimensions, and various other properties. The outcome consists of automating a great deal of processes, i.e., 3D modeling and assembling, visualization, creating the bill of materials (BOM), producing assembly instructions for the user, drawings and prototyping files, weight estimation. [ABSTRACT FROM AUTHOR]

Surface-based inflatables are composed of two thin layers of nearly inextensible sheet material joined together along carefully selected fusing curves. During inflation, pressure forces separate the two sheets to maximize the enclosed volume. The fusing curves restrict this expansion, leading to a spatially varying in-plane contraction and hence metric frustration. The inflated structure settles into a 3D equilibrium that balances pressure forces with the internal elastic forces of the sheets. We present a computational framework for analyzing and designing surface-based inflatable structures with arbitrary fusing patterns. Our approach employs numerical homogenization to characterize the behavior of parametric families of periodic inflatable patch geometries, which can then be combined to tessellate the sheet with smoothly varying patterns. We propose a novel parametrization of the underlying deformation space that allows accurate, efficient, and systematical analysis of the stretching and bending behavior of inflated patches with potentially open boundaries. We apply our homogenization algorithm to create a database of geometrically diverse fusing patterns spanning a wide range of material properties and deformation characteristics. This database is employed in an inverse design algorithm that solves for fusing curves to best approximate a given input target surface. Local patches are selected and blended to form a global network of curves based on a geometric flattening algorithm. These fusing curves are then further optimized to minimize the distance of the deployed structure to target surface. We show that this approach offers greater flexibility to approximate given target geometries compared to previous work while significantly improving structural performance. [ABSTRACT FROM AUTHOR]

This study aims to analyze how architectural forms recently designed with computational design are perceived by architects and non-architects and to measure the perceptual differences between the groups. For this purpose, an experimental study was designed. In the experimental study, in parallel with the literature, 4th year architecture students were categorized as 'architects' and 4th year civil engineering students were categorized as 'nonarchitects'. For the survey study, questionnaires were conducted with a total of 84 students, 42 participants from Konya Technical University Department of Architecture 4th year students in the architect category and 42 participants from Konya Technical University Department of Civil Engineering 4th year students in the civil engineer category, and the perceptual differences of the participants towards the architectural forms of computational design products were measured. All the buildings selected within the scope of the field study are iconic forms designed by computational design method in the 21st century. The participants were asked to evaluate each of the building images with the help of a 10-point semantic differential scale (1: positive, 10: negative) in terms of the specified adjective pairs. The data were analyzed, the analyses were interpreted, findings were presented, and recommendations were made. [ABSTRACT FROM AUTHOR]

Flexible housing resolves the fundamental conflicts between the long-standing structure and the evolving demands. We propose a computational method of optimizing the structural layout of high-rise residential buildings. Chinese high-rise apartment buildings have widely employed shear wall-frame structure in which one big room or multiple small rooms could occupy the same span. Fitting multiple floor plans into a fixed sparse scheme of shear walls and columns is feasible. We developed a computational framework to seek flexible structural schemes. A building scheme consists of a circulation core, shear walls, columns, and boundaries. The computer program automatically adapts floor plans to any drawn or generated scheme. Based on a large dataset of apartment layouts, the number of apartments that fit into a building scheme statistically reflects the flexibility of the scheme. If many hypothetical plans can fit into a wall-frame structure in computer simulation, this structure could probably support several generations of unknown plans. Such a data-driven computational method provides the possibility of creating a one-to-many mapping between permanent structure and evolving apartment plans. [ABSTRACT FROM AUTHOR]

This study focuses on the traditional weaving technique, which has its origins in Anatolia. It is aimed to uncover the plain, twill, and satin weaving techniques within the context of computational design approaches. The objectives of this study are to analyse and decipher the weaving patterns through a computational model. The core idea of this study consists of five steps that represent a cycle of the weaving from traditional processes to digital and digital fabrication. Eight parameters are defined to integrate the weaving technique into the computational model. A computational weaving model was developed which transforms the two-dimensional pattern into a three-dimensional model. Describing weaving patterns using parameters is crucial in today's world for cultural transfer of traditional weaving. Thus, weaving techniques can be used for designing architectural elements, such as shells, panels, structural elements. [ABSTRACT FROM AUTHOR]

Along with the circular bioeconomy principles, alternative ways of utilizing biomass waste streams are considered viable approaches to reaching sustainability goals. Accordingly, a growing body of literature is exploring new materials utilizing biomass in 3D-printing applications. This article presents early-stage research that initially investigates the usability of bamboo fibers and dust with bio-based binders in 3D printing towards its use in the design and production of the built environments. The research delves into solutions through a material tinkering approach to develop a bio-based composite material that can be used in fused deposition modeling (FDM). It includes mechanical strength analyses of printed specimens to understand the effects of different infill designs on the structural performance of objects printed using bamboo-based composite. Then, it demonstrates a design-to-production workflow that integrates a mechanically informed infill pattern within a self-supporting wall design that can be produced by 3D printing with bamboo. The workflow is presented with a partial demonstrator produced through robotic 3D printing. The article concludes with discussions and recommendations for further research. [ABSTRACT FROM AUTHOR]

Space layout design is a critical aspect of architectural design, influencing functionality and aesthetics. The inherent combinatorial nature of layout design poses challenges for traditional planning approaches; thus, it demands the exploration of novel methods. This paper presents a novel framework that leverages the potential of deep reinforcement learning (RL) algorithms to optimize space layouts. RL has demonstrated remarkable success in addressing complex decision-making problems, yet its application in the design process remains relatively unexplored. We argue that RL is particularly well-suited for the design process due to its ability to accommodate offline tasks and seamless integration with existing computer-aided design software, effectively acting as a simulator for design exploration. Framing space layout design as an RL problem and employing RL methods allows for the automated exploration of the expansive design space, thereby enhancing the discovery of innovative solutions. This paper also elucidates the synergy between the design process and the RL problem, which opens new avenues for exploring the potential of RL algorithms in design. We aim to foster experimentation and collaboration within the RL and architecture communities. To facilitate our research, we have developed SpaceLayoutGym , an environment specifically designed for space layout design tasks. SpaceLayoutGym serves as a customizable environment that encapsulates the essential elements of the layout design process within an RL framework. To showcase the effectiveness of SpaceLayoutGym and the capabilities of RL as an artificial space layout designer, we employ the Proximal Policy Optimization (PPO) algorithm to train the RL agent in selected design scenarios with both geometrical constraints and topological objectives. The study further extends to contrast the effectiveness of PPO agents with that of genetic algorithms, and also includes a comparative analysis with existing layouts. Our results demonstrate the potential of RL to optimize space layouts, offering a promising direction for the future of artificial intelligence-aided design. [ABSTRACT FROM AUTHOR]

The invention of the wheel is widely credited as a pivotal moment in human history, yet the details surrounding its discovery are shrouded in mystery. There remains no scholarly consensus on key questions such as where, how and by whom this technology was originally invented. In this study, we employ state-of-the-art techniques from computational structural mechanics to shed light on this long-standing puzzle. Based on this analysis, we propose a probable path along which the wheel evolved via a sequence of three major innovations. We also introduce an original computational design algorithm that autonomously generates a wheel-and-axle system using an evolutionary process that offers insight into the way in which the first wheels likely evolved nearly 6000 years ago. Our analysis provides new supporting evidence for the recently advanced theory that the wheel was invented by Neolithic miners harvesting copper ore from the Carpathian Mountains as early as 3900 BC. Moreover, we show how the discovery of the wheel was made possible by the unique physical features of the mine environment, whose impact was analogous to the selective environmental pressures that drive biological evolution.

Several research initiatives across the European Union investigated the application of off-site prefabrication strategies for energy refurbishment of buildings. Although the results obtained, none of these projects offer a solution for reducing the overall cost.This paper presents the results of the ‘LegnAttivo’ project, an applied research initiative that investigates applications of Mass Customization and Design for Manufacturability to reduce the cost of prefabricated timber-based façades for energy refurbishment. Applying a Design Science Research approach, the research develops a design prototype of timber-based façade elements for the energy refurbishment of the Italian building stock. Relying on a single case study which is a starting point for future generalizations and extensions of the method, results highlight that applications of Design for Manufacturability combined with Computational Design techniques provide effective and efficient solutions, even under extremely customized requirements, and bring to considerable saves, aligned with the ones measured by the scientific community.