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The computational fabrication community is developing an increasing interest in the use of patterned surfaces, which can be designed to show ornamental and unconventional aesthetics or to perform as a proper structural material with a wide range of features. Geometrically designing and controlling the deformation capabilities of these patterns in response to external stimuli is a complex task due to the large number of variables involved. This paper introduces a method for generating sets of tileable and exchangeable flat patterns as well as a model-reduction strategy that enables their mechanical simulation at interactive rates. This method is included in a design pipeline that aims to turn any general flat surface into a pattern tessellation, which is able to deform under a given loading scenario. To validate our approach, we apply it to different contexts, including real-scale 3D printed specimens, for which we compare our results with the ones provided by a ground-truth solver.

Formation of galvanic cells between constituent phases is largely responsible for corrosion in Mg-based alloys. We develop a methodology to calculate the electrochemical potentials of intermetallic compounds and alloys using a simple model based on the Born-Haber cycle. Calculated electrochemical potentials are used to predict and control the formation of galvanic cells and minimize corrosion. We demonstrate the applicability of our model by minimizing galvanic corrosion in Mg-3wt%Sr-xZn alloy by tailoring the Zn composition. The methodology proposed in this work is applicable for any general alloy system and will facilitate efficient design of corrosion resistant alloys.

Natural enzymes can adopt many conformations, widening each state within their rugged energy landscape. This structural flexibility allows for entropic as well as enthalpic reaction rate accelerations. Computational methods, at the forefront of enzyme-related research, are developed to understand enzymatic mechanisms from the ground up. These fundamental principles can then be translated into rational design protocols for synthetic enzymes. Thus far, our design strategies have revolved around the optimization of a single protein conformation. They must be then updated to reflect our current knowledge of a more dynamic picture of enzymatic catalysis.

This paper introduces an integrated computational design framework for the design and realization of arbitrarily-curved bending-active architectural structures. The developed framework consists of a series of methods that enable the production of a complex 3D structures composed of a set of flat 2D panels whose mechanical properties are locally tuned by varying the shape of embedded spiraling patterns. The resulting panels perform as variable stiffness elements, and they are optimized to match a desired target shape once assembled together. The presented framework includes all the steps for the physical delivery of architectural objects, including conception, static assessment, and digital fabrication. The developed framework has been applied to an architectural scale prototype, which demonstrates the potential of integrating architectural design, computational simulation, structural engineering, and digital fabrication, opening up several possible novel applications in the building sector.

Tangible 3D molecular models conceptualize complex phenomena in a stimulating and engaging format. This is especially true for learning environments, where additive manufacturing is increasingly used to produce teaching aids for chemical education. However, the 3D models presented previously are limited in the type of molecules they can represent and the amount of information they carry. In addition, they have little role in representing complex biological entities such as proteins. We present the first complete workflow for the fabrication of soft models of complex proteins of any size. We leverage on molding technologies to generate accurate, soft models which incorporate both spatial and functional aspects of large molecules. Our method covers the whole pipeline from molecular surface preparation and editing to actual 3D model fabrication. The models fabricated with our strategy can be used as aids to illustrate biological functional behavior, such as assembly in quaternary structure and docking mechanisms, which are difficult to convey with traditional visualization methods. We applied the proposed framework to fabricate a set of 3D protein models, and we validated the appeal of our approach in a classroom setting.

Computational design of fully artificial peptides is extensively researched by material scientists and engineers for the construction of novel nanostructures and biomaterials. Such design has yielded a peptide-based building block or bundlemer, a coiled coil peptide assembly that undergoes further physical-covalent interactions to form 1D, 2D and, potentially, 3D hierarchical assemblies and displays targeted and biomimetic material properties. Recombinant expression is a convenient, flexible tool to synthesize such artificial and modified peptides in large quantities while also enabling economical synthesis of isotopically labeled peptides and longer protein-like artificial peptides. This report describes the protocol for recombinant expression of a 31-amino acid, computationally designed bundlemer-forming peptide in Escherichia coli. Peptide yields of 10 mgs per liter of media were achieved which highlights complementary advantages of recombinant expression technique relative to conventional laboratory-scale synthesis, such as solid-phase peptide synthesis.

Burgeoning growth of tall buildings in urban areas around the world is placing new demands on their performance under winds. This involves selection of the building form that minimizes wind loads and structural topologies that efficiently transfer loads. Current practice is to search for optimal shapes, but this limits buildings with static or fixed form. Aerodynamic shape tailoring that consists of modifying the external form of the building has shown great promise in reducing wind loads and associated structural motions as reflected in the design of Taipei 101 and Burj Khalifa. In these buildings, corner modifications of the cross-section and tapering along the height are introduced. An appealing alternative is to design a building that can adapt its form to the changing complex wind environment in urban areas with clusters of tall buildings, i.e., by implementing a dynamic facade. To leap beyond the static shape optimization, autonomous dynamic morphing of the building shape is advanced in this study, which is implemented through a cyber–physical system that fuses together sensing, computing, actuating, and engineering informatics. This approach will permit a building to intelligently morph its profile to minimize the source of dynamic wind load excitation, and holds the promise of revolutionizing tall buildings from conventional static to dynamic facades by taking advantage of the burgeoning advances in computational design.

Highlights Models can investigate many more genome designs than laboratory research. Algorithms can search for genomes that optimise specific criteria. Together, models and algorithms can help engineers to design genomes. Algorithm-driven whole-cell modelin silicodesigns could be viableinvivo. The genome design ecosystem needs improved modelling and design tools. Abstract Large-scalein silicogenome designs are on the brink of being engineeredinvivo, offering a potential paradigm shift for cellular research (previous designs relied on fractured available knowledge andinvivoengineering iteration) by integrating computational design,in silicomodels and algorithms, with laboratory construction. However, several challenges remain. Ifinvivoengineering is successful, designing genomes can be used to gain new understanding of cellular life, improve the metabolite production process and reduce the risk of unintended genetic modification and release. Here, we review the progress so far. We suggest improvements on recent models and algorithms, illustrate the next steps for integrating computational and laboratory engineering and offer our opinions on the future of the field.

Purpose This paper reviews an area of interdisciplinary collaboration in the design of healthcare facilities that attempts to optimize hospital space-planning using automated statistical techniques from the discipline of Operations Research (OR). This review articulates Facility Layout Problems (FLPs) as a general class of OR problems. Furthermore, the review highlights limitations of these techniques, which necessitate an ethical and participatory engagement with computerized processes of healthcare architecture. Design/methodology/approach An in-depth critical review was carried out, which revealed a number of common themes, collectively theorized as metamodeling processes, or models of models, through which various FLP modelling techniques can be challenged and debated in terms of their architectural viability, and ethical ramifications. Findings This review provides a methodological basis for the further evaluation of computational models. It was found that most of the reviewed studies are functionally focused on flow efficiency and, in general, do not consider broader contextual, relational, social, or salutogenic design values. Originality/value This review is the first on the subject written from an architectural perspective. It can be used by a broad range of readers as its critical review of past and present hospital layout modelling techniques discusses their capabilities and limitations. As such, it also enables them to consider ethical values while critiquing the epistemology of computational processes hidden beneath algorithmic outputs.

Computation-based approaches in design have emerged in the last decades and rapidly became popular among architects and other designers. Design professionals and researchers adopted different terminologies to address these approaches. However, some terms are used ambiguously and inconsistently, and different terms are commonly used to express the same concept. This paper discusses computational design (CD) and proposes an improved and sound taxonomy for a set of key CD terms, namely, parametric, generative, and algorithmic design, based on an extensive literature review from which different definitions by various authors were collected, analyzed, and compared.

Catalytic upgrading of biowaste to chemicals and fuels is a key step towards circular economy. Computational investigations of catalytic upgrading of biowaste have made valuable contributions towards catalysts design. Recent applications of 1) Density Functional Theory (DFT) and molecular dynamics (MD) simulations, and microkinetic modelling to derive insights on active sites, reactions mechanisms, solvation and catalyst deactivation, 2) DFT and MD simulations, multiscale models, and statistical methods for identification of structure of catalyst nanoparticles, 3) DFT calculations and statistical methods for identification of catalytic descriptors and development of scaling relations, are discussed. A recent trend is to exploit advances in data science and data repositories for catalysis. There is immense potential for combined multiscale computational techniques and machine learning to enable in silico design of catalysts for the upgradation of biowaste.

The advent of laboratory directed evolution yielded a fruitful crosstalk between the disciplines of molecular evolution and bio-engineering. Here, we outline recent developments in both disciplines with respect to how one can identify the best starting points for directed evolution, such that highly efficient and robust tailor-made enzymes can be obtained with minimal optimization. Directed evolution studies have highlighted essential features of engineer-able enzymes: highly stable, mutationally robust enzymes with the capacity to accept a broad range of substrates. Robust, evolvable enzymes can be inferred from the natural sequence record. Broad substrate spectrum relates to conformational plasticity and can also be predicted by phylogenetic analyses and/or by computational design. Overall, an increasingly powerful toolkit is becoming available for identifying optimal starting points including network analyses of enzyme superfamilies and other bioinformatics methods.

Recent literature has seen a surge in artificial intelligence (AI) and machine learning (ML) for computational design and discovery of novel materials. Most of these studies focus on inverse design or property prediction through composition–structure–property relationships. However, a crucial aspect that has received less attention is the materials' processing route and testing conditions. Here, we present a framework combining text-mining with ML, enabling extraction of processing and testing conditions in addition to the material composition for improved property prediction. We demonstrate this framework by predicting the hardness of inorganic glasses with composition, annealing temperature, and loading conditions as input. Specifically, we analyze the text associated with research articles to obtain the process parameters of oxide glasses with various input components. We show that process parameters and composition together as input yield superior predictions of hardness. Interpreting the predictions using a game-theoretic approach, namely SHAP, we show that the processing and testing parameters indeed play a significant role in controlling the hardness of glasses. Finally, we show that the proposed approach can outperform heuristic approaches for glass synthesis, allowing scientists to choose the most appropriate synthesis route for developing glasses with targeted properties.

Designing new nanoporous materials with high methane delivery capacity (DC) is crucial to achieve US Department of Energy (DOE) targets of 315 v/v and 0.5 g/g for the transportation applications. We have performed extensive computational studies of methane adsorption in around 9000 multilayer frameworks (MFs) to achieve these targets. Our analysis shows that close placement of methane binding sites, optimum range of framework interaction, and pore size give rise to high methane DC in nanoporous materials. We found the highest DC of 319 v/v in general multilayer frameworks (GMFs) at 298 K. Further, we probed MFs made of graphene sheets and observed high DC of 297 v/v and 0.42 g/g in MFs with 11 and 20 A of interlayer separation, respectively. These values are close to DOE target; however, experimental realization of such MFs is difficult. Therefore, we explored methane adsorption in experimentally feasible pillared graphene frameworks (PGFs) where graphene layers are stacked with the help of suitable linker. Among the various linkers probed, PGF with cubane linker has significantly higher uptake and DC of 317 and 261 v/v, respectively. Further, we observed that in these frameworks changing adsorption and desorption temperature attains DOE target at lower pressures.

Building on a large scale with Additive Manufacturing (AM) is one of the biggest manufacturing challenges of our time. In the last decade, the proliferation of 3D printing has allowed architects and engineers to imagine and develop constructions that can be produced additively. However, questions about the convenience of using this technology, and whether additive large-scale constructions can be feasible, efficient and sustainable are still open. In this research 3D printing is considered not as a question, but as an answer to the increasing scarcity of material resources in the construction industry. This paper illustrates the overarching process from concept to the realisation of the Trabeculae Pavilion, a load-responsive architecture that is entirely designed and optimized for 3D printing, using Fused Filament Fabrication (FFF) - one of the most cost-effective additive techniques of production. The research methodology is based on a multi-scale computational workflow that integrates several aspects, such as material testing, bio-inspired design algorithms, multi-criteria optimization, and production management. The work culminates in the construction process of a full-scale architectural prototype; an anticlastic shell that features a cellular structure with increased material and structural efficiency.

GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery.

Lithium–sulfur (Li–S) batteries are considered as promising candidates for next-generation energy storage devices due to their ultrahigh theoretical gravimetric energy density, cost-effectiveness, and environmental friendliness. However, the application of Li–S batteries remains challenging, mainly due to a lack of understanding of the complex chemical reactions and associated equilibria occurring in a working Li–S system. In this review, the typical applications of computational chemistry in Li–S battery studies, correlating to characterization techniques, such as X-ray diffraction, infra-red & Raman spectra, X-ray absorption spectroscopy, binding energy, and nuclear magnetic resonance, are reviewed. In particular, high-accuracy calculations and large-scale models, materials genome, and machine-learning approaches are expected to further advance computational design for the development of Li–S batteries and related fields.

Computational design with supersecondary structures as building blocks has proven effective in the construction of new proteins with controlled geometries. So far, this approach has primarily exploited amplification, effectively harnessing the internal folding propensity of self-compatible fragments to achieve sufficient enthalpy for folding. Here we exploit an interface-driven strategy to depart from the repeat design realm, constructing an asymmetric, globular domain from heterologous supersecondary structures. We report the successful design of a dRP lyase domain fold, which agrees with the experimental NMR structure at atomic accuracy (backbone RMSD of 0.94 A). Our results show that the residual folding information within conserved fragments, combined with efficient interface-directed sampling, can effectively yield globular proteins with novel sequences and biophysical properties.

Research has been conducted previously to reduce the energy consumption and costs of energy systems by focusing on the optimization of building designs. However, the operation method significantly affects the results of the design optimization. Therefore, the aim of this study was to optimize the operation of thermal energy storage, which is particularly difficult, and the load dispatch to the heat source equipment. The optimization was achieved using a hybrid strategy involving three methods: epsilon differential evolution with random jumping (eDE-RJ) for design optimization, dynamic programming for heat storage tank optimization, and Lagrange's multiplier method for load dispatch optimization. Based on this method, 28% and 8% reductions were achieved in the life cycle costs associated with operation and design optimization, as compared to those incurred using the conventional method and the design optimization approach without operation optimization, respectively. The results show that optimal systems can be realized by simultaneously optimizing the operation and design of energy systems.

Designers constantly and consistently draft and develop both general concepts and directions to identify the solution that best fits the styling objectives of the lead designer. Designers often confront design fixations that cognitively clash to explore different design combinations. As design teams explore the range of possible design spaces of a certain design strategy, there is an opportunity for computational approaches to improve the styling process. By implementing product appearance similarity and styling strategy in computational design synthesis, it is possible to discover combinations that would otherwise remain unexplored by human designers. Numerous studies on design synthesis have been conducted. However, there has been no focus on the morphological synthesis of designs with strategic styling decisions. Considering this, the proposed study develops a method to synthesize car styling based on product appearance similarity for effective design exploration in the concept generation phase. The similarities of products across different generations, product portfolios, and competitors’ products are calculated to evaluate the strategic styling decision. The results of the strategic styling decision are used to formulate a fitness function. Car styling is then synthesized with a genetic algorithm based on this fitness function to generate car styling in accordance with the target strategic styling decision. In this respect, designers can computationally synthesize novel design alternatives that consider both homogeneity (family look in design) and heterogeneity (design trend in the market) by pinpointing the desired design exploration area. Ultimately, the style synthesis methodology proposed in this research can help designers to utilize the gradual visualization of styling strategies for more effective and efficient managerial design decisions. To do this, we conduct five major tasks: first, car design data are collected for design synthesis; second, the product appearance similarity is calculated to measure the strategic styling decision; third, synthesis validation is conducted to test whether the proposed methodology can create outside-the-box designs; fourth, a genetic algorithm is used to synthesize car designs in consideration of the strategic styling decision; finally, a series of in-depth interviews with experts and validation experiments are conducted with in-house automobile designers to examine the impact of the proposed methodology. The results showed that designers can quantitatively measure and compare the styling strategies of each car brand, then implement design upgrades, while still maintaining that specific style. Correspondingly, computationally generated design alternatives improve the satisfaction in ease, time, objective reflection and novelty of design outcomes when formulating design strategies in the concept generation phase.

This paper presents an overview on the application of CALPHAD (CALculation of Phase Diagrams) methodology in the design and development of advanced lightweight metallic materials including magnesium, aluminum, titanium, aluminum-based metal matrix composites, and high entropy alloys. In this work, CALPHAD methodology has been established and summarized from the construction of databases describing thermodynamics, atomic mobility, kinetics, thermo-physical properties (such as viscosity) to the application of computational design of lightweight materials. The examples in this paper have demonstrated the effectiveness and capability of CALPHAD methodology in accelerating the design of advanced lightweight materials by optimizing the compositions and various heat-treatment conditions, modifying the evolution of microstructures during processing, and finally predicting the mechanical properties (e.g., yield strength and hardness) of the lightweight components. Although the examples are given in lightweight alloys for structural applications, the fundamental methodology and modeling principles are applicable to all materials and engineering applications. Thus, the future of the advanced material design will be strongly based on development of CALPHAD methodology such as the construction of reliable databases coupled with CALPHAD-based models for various applications.

Computational protein design strategies can be used to increase enzyme stability without the need for high-throughput screening. In this report, computational methods were used to redesign cephalosporin C acylase from Pseudomonas strain SE83 to enhance its stability by repacking the hydrophobic core regions and reconstructing the protein-protein interactions in the segment interface regions. A nine-fold mutant with enhanced catalytic activity in the hydrolysis of cephalosporin C to 7-aminocephalosporanic acid, but with low stability, was used as a starting point. A computational enzyme design strategy was used to identify target regions to increase the protein melting temperature (Tm). Single point mutations Asn2βThr, Asn2βVal, Cys470βSer, Leu154βPhe, and Leu180βPhe in hydrophobic core regions, and Ala100αSer and Ala37βSer in segment-segment interface regions, increased the Tm by 4.7–19.7° C, while combining these confirmed single mutations increased the Tm by up to 20.5° C.

Antibody design aims to create new antibodies with biological activity that can be used in therapy and research. Traditional methods for antibody discovery, such as animal immunization and large-scale library screening, generate antibodies that bind to the target of interest, but do not necessarily have the desired functional effect. Computational methods can be utilized as a means to guide the search for biologically relevant antibodies, focusing on specificity and affinity determinants to target a particular region of the antigen. Such an approach would allow for the design of epitope-specific antibodies that will have the desired effect on the function of the targeted protein.

A comprehensive review of optimization research concerning the design and proportioning of concrete mixtures is presented herein. Mixture design optimization is motivated by an ever-increasing need for designers and decision-makers to proportion concrete mixtures that satisfy multiple – oftentimes competing – performance requirements, including cost, workability, mechanical properties, durability, and environmental sustainability. In this review, we first discuss common mathematical problem formulations, decisions, objectives, and constraints pertaining to concrete mixture design optimization. Subsequently, we examine the types of models employed to approximate properties of concrete, which include a variety of linear combination, statistical, machine learning, and physics-based models that are required to optimize the proportions of a mixture. We then review and discuss computational methods used to optimize concrete mixtures in the context of surveyed literature. Finally, we highlight and discuss current trends and opportunities for advancing the field of concrete mixture design optimization in context of the current state of the art.

Hospital facilities are known as functionally complex buildings. There are usually configurational problems that lead to inefficient transportation processes for patients, medical staff, and/or logistics of materials. The Quadratic Assignment Problem (QAP) is a well-known problem in the field of Operations Research from the category of the facility's location/allocation problems. However, it has rarely been utilized in architectural design practice. This paper presents a formulation of such logistics issues as a QAP for space planning processes aimed at renovation of existing hospitals, a heuristic QAP solver developed in a CAD environment, and its implementation as a computational design tool designed to be used by architects. The tool is implemented in C# for Grasshopper (GH), a plugin of Rhinoceros CAD software. This tool minimizes the internal transportation processes between interrelated facilities where each facility is assigned to a location in an existing building. In our model, the problem of assignment is relaxed in that a single facility may be allowed to be allocated within multiple voxel locations, thus alleviating the complexity of the unequal area assignment problem. The QAP formulation takes into account both the flows between facilities and distances between locations. The distance matrix is obtained from the spatial network of the building by using graph traversal techniques. The developed tool also calculates spatial geodesic distances (walkable, easiest, and/or shortest paths for pedestrians) inside the building. The QAP is solved by a heuristic optimization algorithm, called Iterated Local Search. Using one exemplary real test case, we demonstrate the potential of this method in the context of hospital layout design/re-design tasks in 3D. Finally, we discuss the results and possible further developments concerning a generic computational space planning framework.

The present review provide a brief overview of the basic aspects of molecular imprinting technology covering template molecules, functional monomers, crosslinking agents, initiators, characterization methods and application by emphasizing on the novel polymer materials. Compare to other reviews, various aspects of computational designing of imprinted polymers, carbon based nano-imprinting and stimuli-responsive imprinted polymers are highlighted here in the present review. This review comprises a critical account of the most recent developments in the field of molecular imprinting. It is directed towards a broad spectrum of readers like separation science, polymer material, synthetic, analytical and bioorganic chemists who make use of computational aspects and photoresponsive materials for specific purpose.

Opioids such as morphine are important pain-relieving drugs but also carry a risk of harmful side effects including addiction. Morphine is active in both healthy and inflamed tissue, however, decreasing the pKa of the biochemically-active amine group can promote selective binding in the more acidic conditions of inflamed tissue and reduce harmful side effects associated with opioids. Herein, we explore the impact of fluorination on the pKa of fluoromorphine derivatives to identify which will bind selectively in inflamed tissue. Theoretical pKa values are determined at the M06-2X(SMD)/aug-cc-pVDZ level of theory by calculating the Δ G aq values for the amine deprotonation reactions.

Computational design of menu systems has been solved in limited cases such as the linear menu (list) as an assignment task, where commands are assigned to menu positions while optimizing for for users selection performance and distance of associated items. We show that this approach falls short with larger, hierarchically organized menu systems, where one must also take into account how users navigate hierarchical structures. This paper presents a novel integer programming formulation that models hierarchical menus as a combination of the exact set covering problem and the assignment problem. It organizes commands into ordered groups of ordered groups via a novel objective function based on information foraging theory. It minimizes, on the one hand, the time required to select a command whose location is known from previous usage and, on the other, the time wasted in irrelevant parts of the menu while searching for commands whose location is not known. The convergence of these two factors yields usable, well-ordered command hierarchies from a single model. In generated menus, the lead (first) elements of a group or tab are good indicators of the remaining contents, thereby facilitating the search process. In a controlled usability evaluation, the performance of computationally designed menus was 25 faster than existing commercial designs with respect to selection time. The algorithm is efficient for large, representative instances of the problem. We further show applications in personalization and adaptation of menu systems., Comment: 22 pages paper. 6 pages appendix. 12 Figures

As one large family of intermetallic compounds, Heusler compounds offer a wide playground for novel materials design because of their wide range of compositions and tunable materials properties. In recent years, the high-throughput computational design has emerged as one efficient approach to search for target materials with desired properties thanks to the development of the automatic framework for materials discovery and open-access quantum materials database. In this article, we present a review of the current research progress of the high-throughput computational design of Heusler-based functional materials, including the selection of appropriate materials descriptors and common methods for evaluating materials stability, along with comprehensive lists of the predicated Heusler compounds with various types of target properties. We conclude the review with a discussion of the potential future research directions in the field.

Vaccination has been one of the most successful breakthroughs in medical history. In recent years, epitope-based subunit vaccines have been introduced as a safer alternative to traditional vaccines. However, they suffer from limited immunogenicity. Nanotechnology has shown value in solving this issue. Different kinds of nanovaccines have been employed, among which virus-like nanoparticles (VLPs) and self-assembled peptide nanoparticles (SAPNs) seem very promising. Recently, SAPNs have attracted special interest due to their unique properties, including molecular specificity, biodegradability, and biocompatibility. They also resemble pathogens in terms of their size. Their multivalency allows an orderly repetitive display of antigens on their surface, which induces a stronger immune response than single immunogens. In vaccine design, SAPN self-adjuvanticity is regarded an outstanding advantage, since the use of toxic adjuvants is no longer required. SAPNs are usually composed of helical or β-sheet secondary structures and are tailored from natural peptides or de novo structures. Flexibility in subunit selection opens the door to a wide variety of molecules with different characteristics. SAPN engineering is an emerging area, and more novel structures are expected to be generated in the future, particularly with the rapid progress in related computational tools. The aim of this review is to provide a state-of-the-art overview of self-assembled peptide nanoparticles and their use in vaccine design in recent studies. Additionally, principles for their design and the application of computational approaches to vaccine design are summarized.

Office buildings in contemporary architecture usually have a high value of window to wall ratio (WWR) determining transparent facade as the most important part of the building facade to control solar gains, thermal losses and visual comfort. Therefore, in order to fulfill strict National energy regulations and LEED requirements, facades require a proper balance between transparent and opaque or shaded surfaces to avoid overheating, optimizing daylighting aspects and outdoor perception. Concrete is usually used in building sector for primary structure or secondary structure of cladding solutions. With new concrete materials and innovative digital fabrication process is possible to rediscover concrete for high performance facade/shading solutions. The aim of this paper is the development of high performance concrete static shading system using computational design approach and its optimization, by genetic algorithms, based on several parameters such as radiation control, outdoor view and daylight indexes and energy performance. After the development of a geometry definition managed with a parametric modelling approach (Rhinoceros and Grasshopper), genetic algorithm optimization were used in order to define the openness's sizes and their cutting angle to minimize solar radiation entrance during year, maintaining outdoor view. The possible geometric alternatives were part of a sensitivity analysis with Radiance and Energy-plus engine to assess the shading system performance in terms of daylighting and energy use. In order to evaluate their performances a study case, a single office unit placed in Milan (Italy) was defined, considered with wall facing outdoors, having a WWR of 86%, and five surfaces adiabatic. The results shows that using this approach is possible to develop an effectiveness shading solution able to control radiation over the year and likewise guarantee a high performance regarding the outdoor perception and visual comfort.

Designing personalized cancer nanomedicines is a challenging process. The emerging field of nanoinformatics can facilitate this process by enabling computational design of nanocarrier-encapsulated drugs. Recent data show that quantitative structure–nanoparticle assembly calculations predict particle formation and size, and can lead to safer and more effective personalized cancer therapeutics.

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Recent developments in computational design and digital fabrication with concrete enable the realization of freeform geometries that optimize material use. 3D Concrete extrusion Printing (3DCP) is presently one of the most utilized digital fabrication methods with concrete. The expected advantages of 3DCP result from shaping concrete without formwork and from placing material only where functionally required. Although these advantages were pointed out more than 20 years ago, it is difficult to find competitive examples and their usage in real buildings. Consequently, the nonspecific character of the process acts as a shortcoming by opening up extensive possibilities without a clear direction. This paper proposes an automated 3DCP prefabrication platform for customized columns. The process-specific parameters are, therefore, fine-tuned for high-quality products with diverse forms and textures. Additionally, this paper proposes an evaluation method for geometric complexity and identifies the types of column typologies that may benefit from a 3DCP prefabrication platform., Automation in Construction, 122, ISSN:0926-5805

When dealing with complex curved geometries and massive datasets linked to linear infrastructures, manual generation and maintenance of related multidisciplinary BIM models and documentation are yet to be fully automated. This research focused on the integration of BIM and computational design into an intuitive approach for the generation of as-built models of mechanised tunnelling projects, leveraging the use of the real-time data collected by TBM. A preliminary study of the parameters was conducted to describe the curved geometry of the tunnel, then their mutual alignment was followed by the identification of additional relevant information. To automate the tunnel's BIM-based modelling process, a system of four coupled algorithms was developed and tested with a sample TBM dataset. The results demonstrate how the adoption of computational design methods drastically enhances the modelling process for infrastructure projects, allowing for the on-demand generation of as-built BIM models, reducing time and errors.

Highly selective molecularly imprinted polymers (MIPs) towards benzyl methyl ketone (BMK) were synthesized for application as recognition elements in a capacitive sensor. A computational approach was employed to select the most appropriate monomers and cross-linkers. Using the selected compounds, different polymerization techniques and protocols were compared in order to study the effect on the MIP performance and characteristics. MIPs synthesized by bulk polymerization using itaconic acid and 1-vinylimidazole as monomers and p-divinylbenzene as cross-linker possess the highest affinity towards the target analyte. Prior to capacitive analysis, the developed particles were immobilized on the surface of gold transducers using tyramine as a linker. The validity of the developed sensor was checked by the BMK detection in spiked tap water and real water samples. A linear working range from 50 to 1000 μM was found while the limit of detection (LOD) was determined to be 1 μM in tap water. To the best of our knowledge, both the developed MIPs towards BMK and the electrochemical sensor for its detection have not been published or marketed to date.

Cyclic peptides (CPs) are promising modulators of protein-protein interactions (PPIs), but their application remains challenging. It is currently difficult to predict the structures and bioavailability of CPs. The ability to design CPs using computer modeling would greatly facilitate the development of CPs as potent PPI modulators for fundamental studies and as potential therapeutics. Herein, we describe computational methods to generate CP libraries for virtual screening, as well as current efforts to accurately predict the conformations adopted by CPs. These advances are making it possible to envision robust computational design of active CPs. However, unique properties of CPs pose significant challenges associated with sampling CP conformational space and accurately describing CP energetics. These major obstacles to structure prediction likely must be solved before robust design of active CPs can be reliably achieved.

Bioprinting provides a fully automated and advanced platform that facilitates the simultaneous and highly specific deposition of multiple types of skin cells and biomaterials, a process that is lacking in conventional skin tissue-engineering approaches. Here, we provide a realistic, current overview of skin bioprinting, distinguishing facts from myths. We present an in-depth analysis of both current skin bioprinting works and the cellular and matrix components of native human skin. We also highlight current limitations and achievements, followed by design considerations and a future outlook for skin bioprinting. The potential of bioprinting with converging opportunities in biology, material, and computational design will eventually facilitate the fabrication of improved tissue-engineered (TE) skin constructs, making bioprinting skin an impending reality.

Protein self-assembly is extensively used in nature to build functional biomolecules and provides a general approach to design molecular complexes with many intriguing applications. Although computational design of protein-protein interfaces remains difficult, much progress has recently been made in de novo design of protein assemblies with cyclic, helical, cubic, internal and lattice symmetries. Here, we discuss some of the underlying biophysical principles of self-assembly that influence the design problem and highlight methodological advances that have made self-assembly design a fruitful area of protein design.

The benefits of using pin heat sinks (PHSs) with single, rectangular slotted or notched pin perforations, are explored computationally, using a conjugate heat transfer model. Results show that the heat transfer increases monotonically while the pressure drop decreases monotonically as the size of the rectangular perforation increases. Performance comparisons with PHSs with multiple circular perforations show favourable heat transfer and pressure drop characteristics. However, the reduced manufacturing complexity of rectangular notched pins in particular provide strong motivation for their use in practical applications. Detailed parameterisation and optimisation studies into the benefits of single rectangular notch perforations demonstrate the scope for improving heat transfer and reducing mechanical fan power consumption yet further by careful design of pin density and pin perforations in PHSs.

In the last two decades, architecture in the Arab world has undergone dramatic changes. Such changes were driven by three major paradigms; globalization, environment, and technology. However, these changes seem to ignore the rich architectural heritage of the region, especially Islamic architecture, trying to use technology breakthroughs in design and construction to replicate the same western building forms and concepts. This research discusses the current state of contemporary architecture in the Arab world, then reviews computational design as an innovative design tool, highlights its potentials to revisit regional architectural heritage in a contemporary way.

Using the first-principles calculations method based on the density functional theory, the adsorption and dissociation of water on clean and oxygen-covered Cu (111) surface have been investigated systematically. According to the theoretical calculations results, it was found that H 2 O prefers to adsorb on the top site, while OH, O and H are favorable on the fcc site. In addition, the structures of the transition states, the corresponding energy barriers and reaction energies were determined. A feasible mechanism on oxygen-covered Cu (111) surface for complete dissociation of H 2 O was also put forward. The results showed that the activation barrier for H 2 O dissociation decreases obviously with the aid of the oxygen atom. It was proposed that the presence of oxygen atom on the Cu (111) surface can promote the dissociation of H 2 O. Compared with our previous reports on Pd and Au surfaces, It was found that a negative correlation exists between the promoted effect of pre-adsorbed oxygen atom and the adsorption energies on the corresponding surfaces. The results may be useful for computational design, modification and optimization of Cu-based catalysts.

A computational and experimental study has been carried out to produce highly value-added {100} textured steel sheets. This study is based on an idea that an anisotropic surface segregation tendency of impurity atoms would cause an inversion of relative surface energy between {110} and {100} surfaces in bcc Fe and may induce the formation of {100} texture in steel sheets during grain coarsening. A phase field model that can consider surface segregation of impurity atoms (and resultant decrease of surface energy) and grain growth simultaneously is newly developed. The phase field model and surface property data obtained from an atomistic approach are used to simulate grain coarsening behavior of phosphorus-containing iron and to find an optimum process condition that can induce the {100} texture in steel sheets. The phase field simulation indicates that the surface segregation and resultant change in the relative difference in surface energy should be accomplished before the start of grain coarsening for an effective formation of the {100} texture. A strong {100} texture is indeed generated experimentally in phosphorus-containing bcc Fe-3.5wt% Si steel sheets through a two-step annealing process, one at a relatively low temperature where only surface segregation can occur but not the grain coarsening and the other at a relatively high temperature where the grain coarsening can also occur.

Systems of motile microscopic particles can exhibit behaviors that resemble those of living microorganisms, including cooperative motion, self-organization, and adaptability to changing environments. Using mesoscale computational modeling, we design synthetic microswimmers and microcapsules that undergo controllable, self-propelled motion in solution. Stimuli-responsive hydrogels are used to actuate the microswimmers and to enable their navigation and chemotaxing behavior. The self-propelled motion of microcapsules on solid surfaces is achieved by the release of encapsulated solutes that alter the surface adhesiveness. These signaling solutes also enable interactions among multiple microcapsules that lead to complex, cooperative behavior. Our findings provide guidelines for creating microscopic devices and machines able to autonomously move and mimic the communication and chemotaxis of biological microorganisms.

Music and architecture have made use of mathematical proportions throughout the history for the purpose of creating acoustic and visual forms. The reason for this is the aesthetic pursuit of both disciplines since centuries. Mathematics is one of the most important factors that influence aesthetic results. While forming their abstract aesthetic compositions the musicians use the musical notes that have definite frequency values. Each of these frequency values are defined by one integer. Every classical music artist uses the fractal sequencing of these frequencies. On the other hand we encounter hundreds of silent formats which are produced using mathematical ideas. In this context if we think of the interdisciplinary interaction between music and architecture no form is ever silent. In this study, the intersection of two disciplines will be examined in the perspective of architecture; a stumper and interrogative start for pursuit of architectural forms of the present day with the transformation of auditory forms to visual forms will be made; and a basis will be provided to be able to discuss the innovations that the spaces, structures and auditory experiences which can be formed by obtaining musical codes bring.

Recently, multiple researches of solvent accessibility on protein are well established. However, research on solvent accessibility of RNA faces a few challenges such as instability and diversity of RNA tertiary structure. Nowadays, no study has examined the predicting performance from different datasets built from multi-models and multi-attributes, but it is an important part of measuring the overall performance of modeling. Therefore, we performed a comprehensive comparison for predicting RNA solvent accessibility based on two datasets. 15,923 (12,229 ​+ ​3694) samples and 512(336 ​+ ​176) attributes were generated for attribute selection, finally 336 models were built for predicting. 12 modeling methods and 2 attribute selection methods were used for modeling and evaluating. This work provided a strategy for getting stable expectation when predicting RNA solvent accessibility. These results would be useful in further experimental or computational design for RNA solvent accessibility predicting and we hope this work could help the related researches which need a workflow for stable prediction.

Small kitchen appliances have become very popular with increasing needs. One of the most common household appliances is the toast grills. The grills are available in every home or restaurant. It is necessary to reach a high temperature for the operation of these grills. However, some contact surfaces should also be kept within a certain temperature range for safety. In this paper, development and assessment of some design changes to reduce the high temperature on the upper (contact) surface of the grill and the overall performance effect are studied. Compliance with EU small domestic appliances standard (SDA), which states conditions that could threaten user health, overheating and electrical accidents constitute the A13 standard. Compliance of the new design proposals are also reported in this paper. Laboratory tests at several locations of the grill and infrared examination are carried out to analyze the results of the proposed changes. Temperature drops of up to 20 ℃ are observed on the top cover surfaces with the application of insulation as part of the new design proposal. Savings obtained in accordance with Electrotechnical Commission (ICE) standards and heat and energy efficiency gain are reported. Computational model of the toast grill developed in this paper is found in line with experimental and infrared results. The model may be used to improve new designs for further studies.