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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.

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.

Chinese ice-ray (IR) lattices, known for their intricate and visually fascinating random patterns as decorative elements in traditional 18th-century Chinese window design, exhibit underlying stiffness as latticed window fences. Such unique patterns represent a new morphology within the family of stochastic lattices. However, the latent structural potential within the random patterns of ice-ray lattices remains largely unexplored, particularly in the context of lattice shell design. This study systematically studies the geometric qualities of ice-ray lattice patterns and develops an algorithm to model these patterns for ice-ray lattice shell design. Subsequently, it assesses the structural feasibility and effectiveness of these lattice shells in comparison to conventional gridshells. The practicality of constructing random lattice shells using digital fabrication tools is also explored. Employing fractal geometry as a foundational framework, this research not only offers insights into the potential of ice-ray lattices for innovative lattice shell design but also introduces a new structural morphology to the field, expanding the possibilities of incorporating stochastic patterns in lattice shell design. Ultimately, it opens up new opportunities for innovative lattice shell designs, emphasizing the potential of stochastic patterns in structural applications.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody–antigen interface, which is notoriously difficult in the absence of crystal structures.

Abstract 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.

This paper revisits existing pop-up typologies in architecture to identify opportunities for new shelter models to address current housing demands and future habitation requirements on Mars. It presents advancements in design to production methodologies based on computational and robotic techniques to meet current requirements and affordances while integrating sustainable and adaptive functionalities. The main goal is to advance pop-up architecture by developing methods and technologies for rapidly deployable on- and off-Earth habitats while addressing challenges of carbon-free architecture by means of 3D printing. By reviewing state-of-the-art in-situ vs. prefab 3D printing approaches with a particular focus on Human-Robot Interaction (HRI) supported Design-to-Robotic-Production-Assembly and -Operation (D2RPA&O) methods developed at TU Delft material, process, and energy efficiency using locally sourced materials is achieved.

Abstract 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 Multivalent drugs targeting homo-oligomeric viral surface proteins, such as the SARS-CoV-2 trimeric spike (S) protein, have the potential to elicit more potent and broad-spectrum therapeutic responses than monovalent drugs by synergistically engaging multiple binding sites on viral targets. However, rational design and engineering of nanoscale multivalent protein drugs are still lacking. Here, we developed a computational approach to engineer self-assembling trivalent microproteins that simultaneously bind to the three receptor binding domains (RBDs) of the S protein. This approach involves four steps: structure-guided linker design, molecular simulation evaluation of self-assembly, experimental validation of self-assembly state, and functional testing. Using this approach, we first designed trivalent constructs of the microprotein miniACE2 (MP) with different trimerization scaffolds and linkers, and found that one of the constructs (MP-5ff) showed high trimerization efficiency, good conformational homogeneity, and strong antiviral neutralizing activity. With its trimerization unit (5ff), we then engineered a trivalent nanobody (Tr67) that exhibited potent and broad neutralizing activity against the dominant Omicron variants, including XBB.1 and XBB.1.5. Cryo-EM complex structure confirmed that Tr67 stably binds to all three RBDs of the Omicron S protein in a synergistic form, locking them in the “3-RBD-up” conformation that could block human receptor (ACE2) binding and potentially facilitate immune clearance. Therefore, our approach provides an effective strategy for engineering potent protein drugs against SARS-CoV-2 and other deadly coronaviruses. Graphical Abstract

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.

Design strategies for DNA and RNA nanostructures have developed along parallel lines for the past 30 years, from small structural motifs derived from biology to large ‘origami’ structures with thousands to tens of thousands of bases. With the recent publication of numerous RNA origami structures and improved design methods-even permitting co-transcriptional folding of kilobase-sized structures – the RNA nanotechnolgy field is at an inflection point. Here, we review the key achievements which inspired and enabled RNA origami design and draw comparisons with the development and applications of DNA origami structures. We further present the available computational tools for the design and the simulation, which will be key to the growth of the RNA origami community. Finally, we portray the transition from RNA origami structure to function. Several functional RNA origami structures exist already, their expression in cells has been demonstrated and first applications in cell biology have already been realized. Overall, we foresee that the fast-paced RNA origami field will provide new molecular hardware for biophysics, synthetic biology and biomedicine, complementing the DNA origami toolbox.

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.

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.

MaSMaker 2.0 aims to contribute to the computational design and fabrication of Triply Periodic Minimal Surface (TPMS) structures. This updated version expands the capabilities of the software in three aspects: (1) the repertoire of available TPMS structures, (2) the possibility of generating graded and hybrid TPMS prisms was added as a basic exploration tool, looking for more complex applications, and (3) introduces the functionality for recommending the resolution to build the TPMS structure depending on the Additive Manufacturing (AM) process to be used. The “isovalue” request has been replaced with a “Volume Fraction” request as a design variable, employing polynomial models derived from wide-ranging characterizations. AM recommendations come from analyzing ratios that consider cell size, cell type, and AM technology limitations to propose a design resolution. MaSMaker 2.0 combines mathematical precision and fabrication feasibility, empowering designers to seamlessly create and manufacture intricate TPMS structures.

Soft grippers are ideal for grasping delicate, deformable objects with complex geometries. Universal soft grippers have proven effective for grasping common objects, however complex objects or environments require bespoke gripper designs. Multi‐material printing presents a vast design‐space which, when coupled with an expressive computational design algorithm, can produce numerous, novel, high‐performance soft grippers. Finding high‐performing designs in challenging design spaces requires tools that combine rapid iteration, simulation accuracy, and fine‐grained optimization across a range of gripper designs to maximize performance, no current tools meet all these criteria. Herein, a diversity‐based soft gripper design framework combining generative design and topology optimization (TO) are presented. Compositional pattern‐producing networks (CPPNs) seed a diverse set of initial material distributions for the fine‐grained TO. Focusing on vacuum‐driven multi‐material soft grippers, several grasping modes (e.g. pinching, scooping) emerging without explicit prompting are demonstrated. Extensive automated experimentation with printed multi‐material grippers confirms optimized candidates exceed the grasp strength of comparable commercial designs. Grip strength, durability, and robustness is evaluated across 15,170 grasps. The combination of fine‐grained generative design, diversity‐based design processes, high‐fidelity simulation, and automated experimental evaluation represents a new paradigm for bespoke soft gripper design which is generalizable across numerous design domains, tasks, and environments.

This work proposes a new formula for predicting riser’s heights in auditoria with variable risers (known as isacoustic curves). Unlike the standard formulae, the proposed one considers the effect of head shape on sightlines. The proposed formula was validated mathematically and computationally. In the mathematical validation, the results of the proposed and standard formulae, in addition to the graphical method, were compared. In the computational validation, parametric design, via Grasshopper and Anemone plugin, was utilized to simulate and calculate the Visibility Ratios VR for the proposed and standard formulae. Results showed that the proposed formula yields a more accurate and a relatively higher VR, while the standard formulae showed a “cumulative error Δ”. This error gradually becomes obvious with the distant tiers and balconies. Results also demonstrated that the enhancement in VR becomes higher in the far rows, but it is much pronounced in the hall level.

The modularity of carbohydrate-active enzymes facilitates that enzymes with different functions have similar fragments. However, because of the complex structure of the enzyme active sites and the epistatic effects of various mutations on enzyme activity, it is difficult to design enzymes with multiple mutation sites using conventional methods. In this study, we designed multi-point mutants by fragment replacement in the donor-acceptor binding pocket of Actinobacillus pleuropneumoniae N-glycosyltransferase (ApNGT) to obtain novel properties. Candidate fragments were selected from a customized glycosyltransferase database. The stability and substrate-binding energy of the three fragment replacement mutants were calculated in comparison with wild-type ApNGT, and mutants with top-ranking stability and middle-ranking substrate-binding energy were chosen for priority experimental verification. We found that a mutant called F13, which increased the glycosylation efficiency of the natural substrate by 1.44 times, the relative conversion of UDP-galactose by 14.2 times, and the relative conversion of UDP-xylose from almost 0 to 78.6%. Most importantly, F13 mutant acquired an entirely new property, the ability to utilize UDP-glucuronic acid. On one hand, this work shows that replacing similar fragments in the donor-acceptor binding pocket of the enzyme might provide new ideas for designing mutants with new properties; on the other hand, F13 mutant is expected to play an important role in targeted drug delivery.

Construction material offcuts is a data problem that can largely be avoided by dimensional coordination during concept design. Besides the environmental benefits, early phase coordination is beneficial to the overall design process as it integrates information not typically considered until later in the design process. However, taking reality-changing actions is often challenged by uncertainty, time constraints, and lack of integration of available tools. Acknowledging the potential of computational design in enabling architects to manage design and coordination complexities and taking plasterboard opportunities for dimensional coordination, the paper presents a review and assessment of the existing methods to interrogate what, when, and how are these adaptable to the task. The study shows that ML-based methods outperform other methods and concludes that leveraging computational design powers to reduce offcuts is not a question of a tool, but one of a strategy. Eventually, the future steps to achieving such a strategy are discussed.

Detent force is an inherent problem of permanent magnet linear synchronous machines (PMLSMs). In this paper, a mesh-level design methodology is proposed to minimize the detent force on PMLSMs. The novelty concerns introducing the inverse-based design to shape the mover ends on PMLSMs, allowing for automatically exploring the ideal mover ends configuration without any pre-given template. To this end, the ON/OFF method is used to express the topology of mover ends, metaheuristics algorithms are used to steer the topological evolution. To clarify the proposed method, two typical PMLSMs are adopted as numerical examples. The resulting mover ends produced by the genetic algorithm and immune algorithm are presented, and the convergence of the two algorithms is discussed. The comprehensive numerical results show that the proposed method is universal and practical as the detent force of the two typical PMLSMs can be suppressed to a low level.

The constitutive proteasome and the immunoproteasome represent validated targets for pharmacological intervention in the context of various diseases, such as cancer, inflammation, and autoimmune diseases. The development of novel chemical scaffolds of non-peptidic nature, capable of inhibiting different catalytically active subunits of both isoforms, is a viable approach against these diseases. Such compounds are also useful as leads for the development of biochemical probes that enable the studies of the roles of both isoforms in various biological contexts. Here, we present a ligand-based computational design of (immuno)proteasome inhibitors, which resulted in the amino-substituted N-arylpiperidine-based compounds that can inhibit different subunits of the (immuno)proteasome in the low micromolar range. The compounds represent a useful starting point for further structure-activity relationship studies that will, hopefully, lead to non-peptidic compounds that could be used in pharmacological and biochemical studies of both proteasomes.

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.

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.

Acoustic materials are widely used for improving interior acoustics based on their sound absorptive or sound diffusive properties. However, common acoustic materials only offer limited options for customizable geometrical features, performance, and aesthetics. This paper focuses on the sound absorption performance of highly customizable 3D-printed Hybrid Acoustic Materials (HAMs) by means of parametric stepped thickness, which is used for sound absorption and diffusion. HAMs were parametrically designed and produced using computational design, 3D-printing technology, and feedstock material with adjustable porosity, allowing for the advanced control of acoustic performance through geometry-related sound absorbing/diffusing strategies. The proposed design methodology paves the way to a customizable large-scale cumulative acoustic performance by varying the parametric stepped thickness. The present study explores the challenges posed by the testing of the sound absorption performance of HAMs in an impedance tube. The representativeness of the test samples (i.e., cylindrical sections) with respect to the original (i.e., rectangular) panel samples is contextually limited by the respective impedance tube’s geometrical features (i.e., cylindrical cross-section) and dimensional requirements (i.e., diameter size). To this aim, an interlaboratory comparison was carried out by testing the normal incidence sound absorption of ten samples in two independent laboratories with two different impedance tubes. The results obtained demonstrate a good level of agreement, with HAMs performing better at lower frequencies than expected and behaving like Helmholtz absorbers, as well as demonstrating a frequency shift pattern related to superficial geometric features.

How can computational tools support the proposition of 15-minute cities? This paper examines the implementation of computer-aided approaches that support the analysis and proposition of urban areas where residents can access all their basic needs within a 15-minute walk, bike ride, or public transportation ride—the ultimate goal of a 15-minute city. Although this concept has gained significant attention as a sustainable and equitable urban development model, more efficient implementations require complex spatial and temporal analysis. At the same time, computational approaches such as those within the generative and parametric logic can utilize algorithms to create and test design solutions quickly and efficiently, allowing designers to address a variety of scenarios and options for producing more walkable, integrated, and resilient urban environments. Exploring a systematic review of the scientific literature (n = 244 peer-reviewed empirical studies) on Scopus and Web of Science databases, this paper synthesizes approaches to support the analysis and design of 15-minute cities, emphasizing the use of computer-aided techniques. The work contributes to developing a knowledge base on computational tools for 15-minute cities, highlighting their strengths, limitations, and potential for further development.

Artificial intelligence and machine learning, in particular, have made rapid advances in image processing. However, their incorporation into architectural design is still in its early stages compared to other disciplines. Therefore, this paper addresses the development of an integrated bottom–up digital design approach and describes a research framework for incorporating the deep convolutional generative adversarial network (GAN) for early stage design exploration and the generation of intricate and complex alternative facade designs for urban interiors. In this paper, a novel facade design is proposed using the architectural style, size, scale, and openings of two adjacent buildings as references to create a new building design in the same neighborhood for urban infill. This newly created building contains the outline, style and shape of the two main buildings. A 2D building design is generated as an image, where (1) neighboring buildings are imported as a reference using the cell phone and (2) iFACADE decodes their spatial neighborhood. It is illustrated that iFACADE will be useful for designers in the early design phase to create new facades in relation to existing buildings in a short time, saving time and energy. Moreover, building owners can use iFACADE to show their preferred architectural facade to their architects by mixing two building styles and creating a new building. Therefore, it is presented that iFACADE can become a communication platform in the early design phases between architects and builders. The initial results define a heuristic function for generating abstract facade elements and sufficiently illustrate the desired functionality of the prototype we developed.

Harking back to the age of early electronic computers, Mario Carpo recounts the history of cybernetics, and how enthusiasm for a new technology drove experiments in forms of responsive architecture, often overlooking the distance between imagination and actual computational capacities. The moral of this story is all the more poignant in the current frenzy over tools like AI or ChatGPT: change in architecture doesn’t always come in the form we expect or is underpinned by the technologies we expect it to be driven by.

Three-dimensional porous scaffolds are substitutes for traditional bone grafts in bone tissue engineering (BTE) applications to restore and treat bone injuries and defects. The use of computational modelling is gaining momentum to predict the parameters involved in tissue healing and cell seeding procedures in perfusion bioreactors to reach the final goal of optimal bone tissue growth. Computational modelling based on finite element method (FEM) and computational fluid dynamics (CFD) are two standard methodologies utilised to investigate the equivalent mechanical properties of tissue scaffolds, as well as the flow characteristics inside the scaffolds, respectively. The success of a computational modelling simulation hinges on the selection of a relevant mathematical model with proper initial and boundary conditions. This review paper aims to provide insights to researchers regarding the selection of appropriate finite element (FE) models for different materials and CFD models for different flow regimes inside perfusion bioreactors. Thus, these FEM/CFD computational models may help to create efficient designs of scaffolds by predicting their structural properties and their haemodynamic responses prior to in vitro and in vivo tissue engineering (TE) applications.

Interlocking metasurfaces (ILMs) are a new class of mechanical metasurfaces built from architected arrays of interlocking features that can serve as a nonpermanent, robust joining technology. An ILM's strength is governed by the structural material, orientation, and topology of its latching unit cells. The presented work optimized the topologies of ILM unit cells to maximize strength in tensile and shear loading using gradient-based parametric optimization and genetic algorithms. Experimental validation confirmed that the optimized designs achieved considerable strength increases compared to a human intuitive design. In several cases, the optimized designs were approximately double the effective interfacial strength compared to that achieved via expert intuition alone. The strength improvement was seen for isolated unit cells and arrays of interacting unit cells (metasurfaces). An analysis of the topologies of the optimized designs showed that tall dendritic geometric features with large contact surfaces yield robust solutions in tension, while short and broad geometric features with large contact surfaces yield better results in shear loading. This study revealed the importance of shape optimization to maximize ILM effectiveness under single- and multi-objective scenarios.

Abstract Research is revealing an increasing number of positive effects of nature for humans. At the same time, biodiversity in cities, where most humans live, is often low or in decline. Tangible solutions are needed to increase urban biodiversity. Architecture is a key discipline that has considerable influence on the built‐up area of cities, thereby influencing urban biodiversity. In general, architects do not design for biodiversity. Conversely, urban conservation planning generally focuses on the limited space free of buildings and does not embrace architecture as an important discipline for the creation of urban green infrastructure. In this paper, we argue that the promotion of biodiversity needs to become a key driving force of architectural design. This requires a new multi‐species design paradigm that considers both human and non‐human needs. Such a design approach needs to maintain the standards of the architectural profession, including the aim to increase the well‐being of humans in buildings. Yet, it also needs to add other stakeholders, organisms such as animals, plants and even microbiota. New buildings designed for humans and other inhabitants can then increase biodiversity in cities and also increase the benefits that humans can derive from close proximity to nature. We review the challenges that this new design approach poses for both architecture and ecology and show that multi‐species‐design goes beyond existing approaches in architecture and ecology. The new design approach needs to make ecological knowledge available to the architectural design process, enabling practitioners to find architectural solutions that can facilitate synergies from a multi‐species perspective. We propose that a first step in creating such a multi‐species habitat is the design of buildings with an ecolope, a multi criteria‐designed building envelope that takes into account the needs of diverse organisms. Because there is no framework to design such an ecolope, we illustrate how multi‐species design needs to draw on knowledge from ecology, as well as architecture, and design computation. We discuss how architectures designed via a multi‐species approach can be an important step in establishing beneficial human–nature relationships in cities, and contribute to human well‐being and biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

The world in the current era is standing at the big door of a scientific and technological development for use of computers in the design process, and this matter has greatly contributed to the progress of generations by helping them acquire the necessary tools and skills that help them easily access innovative and contemporary designs, and encourage them to distinguished scientific production and make The learning process is more enjoyable by linking the course applications to the field of specialization. The modern educational process requires keeping pace with the labor market and meeting the graduate's needs for tools that enable him to excel in his field of work. Students must practice educational science closely related to the requirements of specialization, that is, be able to do the work that they do, and university education aims to achieve many things, the most important of which is the acquisition of skills that enable the student to keep pace with the requirements of the labor market.The curricula of the glass program at the College of Applied Arts are divided in the four years of the study in terms of objectives into courses that develop the creative side and other courses aimed at building the scientific basis for the student, and other materials that qualify the student to acquaint the student scientifically and practically with the different production methods of glass, in addition to the courses that help build the innovative side of The student.

This paper explores the creation of architectural structures through parametric design tools. The proposed methodology presents an alternative framework of using digital tools for the product design development from the design thinking point of view. The main core of the suggested design process includes a great number of theoretical issues from design identity, design language and architectural design areas. As a result, designers can develop constructions for public spaces that are based on innovative morphologies by using new digital design techniques such as computational design (Rhino3D © and Grasshopper ©). The development of an architectural structure that aims to bring greenery into the city and increase the rest and action spaces is presented. A valuable aspect of this exploration is in positioning the proposed design framework inside - to aid in the creative process and better leverage downstream outcomes. The final steps of the proposed methodology include the production of the final architectural structure through rapid prototyping, laser cutting and engraving techniques.

The paper envisions the use of Augmented Reality (AR) as an interactive and communication tool utilized in the architectural design research, education, and practice. It summarises the current knowledge and various applications of this immersive technology in both the theoretical and practical field and focuses on a particular type of the AR implementation – tangible user interfaces (TUI) – in a computational design context. The outcome of the research is an adaptation of the originally GRASS-GIS-powered Tangible Landscape tool into Grasshopper 3D environment, which is more accurate and suitable for the architectural design workflow with respect to 3D computation, algorithmic modelling and different scale management. The newly prototyped tool is reactive to the modifications of the physical model and projects the computed additional information on it in real time and thus can communicate with the designer or observer, which results in a more interactive, haptic man-machine interface. The projected and visualised data on the physical model are the outcome of the computing algorithm designed in Grasshopper that allows for a wide range of applications, including the visualisation of shadows and solar potential analysis and thus depicts the physical model in multiple dimensions. Furthermore, the article discusses the potential and further development of this tool as well as the possibilities of layering different AR technologies in the subsequent research.

The paper envisions the use of Augmented Reality (AR) as an interactive and communication tool utilized in the architectural design research, education, and practice. It summarises the current knowledge and various applications of this immersive technology in both the theoretical and practical field and focuses on a particular type of the AR implementation – tangible user interfaces (TUI) – in a computational design context. The outcome of the research is an adaptation of the originally GRASS-GIS-powered Tangible Landscape tool into Grasshopper 3D environment, which is more accurate and suitable for the architectural design workflow with respect to 3D computation, algorithmic modelling and different scale management. The newly prototyped tool is reactive to the modifications of the physical model and projects the computed additional information on it in real time and thus can communicate with the designer or observer, which results in a more interactive, haptic man-machine interface. The projected and visualised data on the physical model are the outcome of the computing algorithm designed in Grasshopper that allows for a wide range of applications, including the visualisation of shadows and solar potential analysis and thus depicts the physical model in multiple dimensions. Furthermore, the article discusses the potential and further development of this tool as well as the possibilities of layering different AR technologies in the subsequent research.

Patient-specific osteosynthesis plates can be used to reduce complications related to bone fracture treatment, such as infection, malocclusion and fatigue fractures of plates and screws. However, the implant design process is tedious. We propose a semi-automatic workflow to computationally design patient-specific titanium osteosynthesis plates for mandibular angle fractures. In this process, the plate stiffness is maximized while the mass is reduced. Two plate designs with different numbers of screw holes (implant #1 with four holes, implant #2 with eight holes) were generated with identical topology optimization settings and compared in a finite element model simulating various biomechanical masticatory loads. Differences in von Mises stresses in the implants and screws were observed. The load case of clenching the jaw on the opposite side of the fracture showed the highest stress distribution in implant #1 and higher peak stresses in implant #2. Stress concentrations were observed in sharp corners of the implant and could be reduced using local stress-based topology optimization. We conclude that the design process is an effective method to generate patientspecific implants.

Lithium-ion batteries (LIBs) and lithium-sulfur (Li–S) batteries are two types of energy storage systems with significance in both scientific research and commercialization. Nevertheless, the rational design of electrode materials for overcoming the bottlenecks of LIBs and Li–S batteries (such as low diffusion rates in LIBs and low sulfur utilization in Li–S batteries) remain the greatest challenge, while two-dimensional (2D) electrodes materials provide a solution because of their unique structural and electrochemical properties. In this article, from the perspective of ab-initio simulations, we review the design of 2D electrode materials for LIBs and Li–S batteries. We first propose the theoretical design principles for 2D electrodes, including stability, electronic properties, capacity, and ion diffusion descriptors. Next, classified examples of promising 2D electrodes designed by theoretical simulations are given, covering graphene, phosphorene, MXene, transition metal sulfides, and so on. Finally, common challenges and a future perspective are provided. This review paves the way for rational design of 2D electrode materials for LIBs and Li–S battery applications and may provide a guide for future experiments.

Simulations have become increasingly important to understand and design organic optoelectronic devices, such as organic light emitting diodes (OLEDs) and to optimize their performance by selecting appropriate materials and layer arrangements. To achieve accurate device simulations, it is crucial to consider the interplay between material properties, device architecture, and operating conditions and to incorporate physical processes such as charge injection, transport, recombination, and exciton decay. Simulations can provide insights into device bottlenecks and streamline optimization cycles, eliminating the need for physical prototyping and rationalizing OLED design. In this study, we investigated three heuristic OLED architectures with a 3D kinetic Monte Carlo (kMC) model and compared their quantum efficiency at different operation voltages. Our investigation focused on examining the effects of various layer arrangements on charge and exciton dynamics in OLED devices and establishing design principles for achieving high efficiency, which are consistent with experimental observations. Notably, we find that increasing the thickness of the emissive layer (EML) led to higher luminance efficiency, and that an emitter concentration of approximately 5% results in optimal performance. By using this model, it is possible to rapidly study the influence of many device parameters and explore a broad range of parameter and architecture space within a reasonable time-frame.

The provision of national housing to citizens in the United Arab Emirates (UAE) is considered a crucial topic. Over the past four decades, the process of developing national housing has emerged into multiple housing programs and schemes, all with the same aim of offering affordable and high-quality housing to citizens, in addition to meeting the needs of local families regarding spatial configurations while maintaining cultural values. However, despite all these efforts, the question has always remained: are the offered housing practices suited for family needs, socioeconomic trends, and environmental challenges? This study aims to offer an alternative approach for the design and delivery of national housing practices in the UAE. The proposed process is structured based on the following ethos: first, a conceptual approach for design flexibility toward offering customization while maintaining contextual and cultural qualities for inhabitants; second, a computational design strategy for facade optimization that illustrates the significance of incorporating environmentally conscious design strategies in response to local climatic conditions toward enhancing overall building performance; and third, a hybrid production model that relies on a prefabricated building approach that combines precast concrete systems with 3D printing technology. The efforts described in this article represent a significant phase of an ongoing research endeavor that explores how technological capacities could help rethink national housing in the UAE.

Prefabricated modules are being used to renovate the building envelope. However, compared to manual methods, the design and prefabricated module’s definition is time consuming. Therefore, it is necessary to improve the efficiency of the prefabricated layout definition processes by incorporating automation and computational design. The purpose of this paper is to present a semi-automated definition of the layout of the prefabricated modules with the only input of the existing building facade being the Point Cloud. In this research, a novel step-by-step workflow was developed. More precisely, an algorithm was developed that processes the coordinates of each point of the cloud and generates the layout of the prefabricated modules. To validate the workflow and the algorithm, four facades were tested, considering two parameters: (a) working time and (b) output accuracy. According to the results, it was concluded that spending more time achieving an accurate laser data acquisition can be a good strategy to obtain the primary layout with sufficient precision.

The studies on design and design methods increased towards the end of the twentieth century in industrialized societies, however, the act of design started to be carried out with different auxiliary tools. The digital and algorithmic structure behind the computer has begun to provide the designer with different possibilities outside of the traditional drawing environment. After this period, the queries and discussions on the content of the design, its components, the intellectual process of the designer, and similar issues gradually increased. Within this scope, new theories and methods have emerged. In today's design, especially with digital technologies, transformations in theory and practice in the design process have brought new methods with them. Architects and designers have now become design tool developers rather than using use of the design tool. Based on this, the assistive tools that determine the current architectural design style is aimed to be explored in this paper. The study contributes to the field by (i) exploring these tools and their latent features, (ii) assessing the pros and cons of these tools, and (iii) last, implementing these design tools on the case studies.

Molecular solar thermal (MOST) systems are working their way as a possible technology to store solar light and release it when necessary. Such systems could, in principle, constitute a solution to the energy storage problem characteristic of solar cells and are conceived, at a first instance, as simple molecular photoswitches. Nevertheless, the optimization of their different required properties is presently limiting their technological scale up. From the chemical perspective, we need to design a novel MOST system based on unconventional photoswitches. Here, by applying multi-configurational quantum chemistry methods, we unravel the potentialities of ad hoc-designed molecular photoswitches, which aim to photoproduce cubane or cubadiene as high-energy isomers that can be thermally (or eventually catalytically) reverted to the initial structure, releasing their stored energy. Specifically, while cubane can be photoproduced via different paths depending on the reactant tricycle diene conformation, an undesired bicyclic by-product limits its application to MOST systems. An evolution of this starting design toward cubadiene formation is therefore proposed, avoiding conformational equilibria and by-products, considerably red shifting the absorption to reach the visible portion of the solar spectrum and maintaining an estimated storage density that is expected to overcome the current MOST reference system (norbornadiene/quadricyclane), although consistently increasing the photoisomerization energy barrier.

The Digital era ended bequeathing remarkable technological advancements and numerous queries related to the critical evolution between productive activities and the survival of human beings on planet Earth. In a scenario where the fashion industry is considered one of the relevant players, postdigital fashion emerge with interesting design strategies and fabrication tools that open up new possibilities for more equal and sustainable development. To outline desirable future directions, the research PostDigital Manufacturing Processes. Body Hacking for Productive Systems has examined fifty designer cases studies. In these selected design studios, computational design, wearable technologies, biomaterials, and virtual interfaces are involved in redesigning the fashion role and the whole concept of the human body as a hybrid in the intersection between physical and digital realms.

This article presents a project that explores the potential of Additive Manufacturing (AM) for designing and fabricating multi-functional building components for improved climate performance. In this project, an innovative façade wall design was developed by using a computational method in an attempt to integrate a displacement ventilation system into the wall. A robotic AM solution is integrated into the workflow as a potentially feasible fabrication method for the resulting wall design with an intricate geometry. Clay is proposed as the AM material, being a potential low-carbon building material. To this end, a material exploration of clay was conducted to develop an appropriate composite for AM. A displacement ventilation system was developed to achieve better indoor air quality by using a Computational Fluid Dynamics (CFD) model. Subsequently, an AM solution was integrated into the workflow to automate the fabrication phase. Finally, a partial prototype of the design was made through AM with clay to demonstrate the feasibility and observe the material qualities of the final product. The proposed workflow proves applicable, highlighting directions for future research.

The environmental impact of the construction industry (CI) is no longer a matter of debate. On the other hand, society is in a constant race to build bigger and more audaciously. To mitigate these competing directions, CI is turning to geometry and computation to reduce new material consumption and to improve the structural properties of buildings. Aluminum, with its remarkable weight to stiffness ratio and its natural anticorrosive properties, is at the forefront of this endeavor. For structural applications, aluminum is mainly used in its extruded form, while the rolled (sheet) material is preferred for architectural finishings and non-structural applications due to its low stiffness. However, with the advent of computational design and ubiquitous 2d CNC cutting, sheet metal can be used for bespoke architectural applications that combine aesthetic, structural, and environmental innovations. This paper will present two research projects developed in an academic setting at University of Montreal’s School of Architecture that use low thickness aluminum sheets to build bespoke architectural structures. The first project highlights an innovative use of surface discretization and assembly tabs to induce stiffness in large-scale doubly curved architectural surfaces and thus produces complex, free-standing aluminum surfaces with no support structure. The second project introduces a new folded low-tech ultralight aluminum node (300–400 gr) that is used to produce complex reticular wooden structures with nonstandard angles using reclaimed 2 × 4 wooden studs. Both projects culminated in full-scale research demonstrators with architectural pavilions exhibited on the university campus in 2021 and 2022.

The discovery of new antimicrobials is necessary to combat multidrug-resistant (MDR) bacteria, especially those that infect wounds and form prodigious biofilms, such as Acinetobacter baumannii. Antimicrobial peptides (AMPs) are a promising class of new therapeutics against drug-resistant bacteria, including gram-negatives. Here, we utilized a computational AMP design strategy combining database filtering technology plus positional analysis to design a series of novel peptides, named HRZN, designed to be active against A. baumannii. All of the HRZN peptides we synthesized exhibited antimicrobial activity against three MDR A. baumannii strains with HRZN-15 being the most active (MIC 4 µg/mL). This peptide also inhibited and eradicated biofilm of A. baumannii strain AB5075 at 8 and 16 µg/mL, which is highly effective. HRZN-15 permeabilized and depolarized the membrane of AB5075 rapidly, as demonstrated by the killing kinetics. HRZN 13 and 14 peptides had little to no hemolysis activity against human red blood cells, whereas HRZN-15, -16, and -17 peptides demonstrated more significant hemolytic activity. HRZN-15 also demonstrated toxicity to waxworms. Further modification of HRZN-15 could result in a new peptide with an improved toxicity profile. Overall, we successfully designed a set of new AMPs that demonstrated activity against MDR A. baumannii using a computational approach.

People with Alzheimer’s disease (PWAD) are impacted by their surroundings, and their performance improves in therapeutic environments designed to meet their specific individual needs, are adjustable in terms of their health status, and are created to accommodate their abilities. A literature review of the field revealed scarce knowledge in using a combination of building information modeling (BIM) and the Internet of Things (IoT) for the purpose of understanding the daily needs and self-orientation ability of PWAD, as well as the architectural barriers they face in their rooms in long-term healthcare centers. In this context, this paper proposes a framework based on computational design approaches to assess the existing therapeutic environment for PWAD using BIM–IoT sensors-based monitoring. The proposed framework used the user experience design concept (UX) and the design thinking framework to evaluate the resident rooms of PWAD. The UX design concept and the design thinking framework core allow for the adoption of user-centered methods to provide a comprehensive image of the issues that affect PWAD in their therapeutic environment. The proposed framework-structured approach will enable healthcare architects/designers to (1) digitalize old building architecture plans using BIM; (2) strategize IoT sensor selection; (3) recognize the activities performed by PWAD and detect any anomaly; and (4) integrate IoT real-time data into the BIM system. The proposed framework supports three types of professionals: (1) architects in decision-making processes, (2) researchers in collecting/analyzing accurate data for shadow observations, and (3) neurologists in following up the health statuses of PWAD.

In order to reach a Zero-Energy-consuming building stock, it is necessary to insulate and add renewable energy sources on top of existing building envelopes. Off-site prefabricated modules have been used for covering building facades, but manual on-site installation procedures are still more competitive than prefabricated ones. Renovation with prefabricated modules requires high precision in order to obtain airtight and waterproof conditions. For that, an accurate installation of the anchors on top of the facade is crucial. With current techniques, this is a time-consuming operation. One of the attempts to solve the above-mentioned issue was to place the part of the anchor on top of a building facade with high tolerances and to use an interface to correct the deviations. In previous research, this concept, named Matching Kit, was validated, but improvements needed to be made to make it more competitive. In this paper, thanks to novel algorithms and the use of Point Clouds, an improved version is presented. The results show a reduction in working time and an increase in precision. With this research, the interface is closer to being used in the construction industry.

Novel technologies, fabrication methods, controllers and computational methods are rapidly advancing the capabilities of soft robotics. This is creating the need for design techniques and methodologies that are suited for the multi-disciplinary nature of soft robotics. These are needed to provide a formalized and scientific approach to design. In this paper, we formalize the scientific questions driving soft robotic design; what motivates the design of soft robots, and what are the fundamental challenges when designing soft robots? We review current methods and approaches to soft robot design including bio-inspired design, computational design and human-driven design, and highlight the implications that each design methods has on the resulting soft robotic systems. To conclude, we provide an analysis of emerging methods which could assist robot design, and we present a review some of the necessary technologies that may enable these approaches.

This research investigates the potential of additive manufacturing and digital planning tools for the creation of location-specific façade redesigns that can host cavity-dependent animal species and develops methods for their realization. The proposed approach is explored based on a case study of a student dormitory in need of renovation in the urban area of Munich. Based on theoretical knowledge and design experimentations that link the fields of architecture, climate-responsive design, terrestrial ecology, and digital fabrication, a set of design principles for the additive manufacturing of inhabitable ceramic tiles is conceived and transferred into a computational design tool. The conception of single tiles and the overall façade design are developed in terms of their positive climatic impact on both the animal species and humans, their nesting opportunities, their structural feasibility, and their integrability with standard ceramic façade systems. To verify the fabricability of the proposed design, a façade fragment was additively manufactured as a prototype in 1:1 scale. The initial findings presented in this paper provide a glimpse of how emerging digital technologies could provide new ways to expand current habitual architectural planning and fabrication tools, to enable the creation of site-specific solutions, and to bring together human and animal needs.