Design Of Computational Algorithms Research Articles

Computing irregular hypar-based quad-mesh patterns for segmented timber shells

Hyperbolic paraboloids, “hypars,” are special types of ruled surfaces. Their geometric properties provide them with loadbearing and stabilizing capacities, as well as distinct esthetic qualities. These attributes become evident in numerous applications in buildings, in many of which concrete or timber is used for the construction of the hypars. Hypars could also be relevant in the context of circular construction and design for disassembly, and the upcycling of construction waste. Due to the geometric simplicity of straight lines, which generate ruled surfaces, hypar-based structures can be designed and built with relatively simple means. They can consist of self-similar or even identical elements, which could facilitate their reuse.Compared to other types of ruled surfaces, such as conoids, hypars have the advantage of being doubly ruled, meaning that structural grids of straight elements can be formed. This paper investigates another interesting property, which is the possibility of creating flat-quad meshes by diagonally connecting the intersection points of the generatrices. This property has been previously described by other scholars, some of which explored its applicability for glass-clad steel grid shells. In this research, we focus on its potential for segmented timber shells that can serve as stand-alone structures, or as modular and reusable building parts, such as façade or roof components. The reusability of such modular units could be achieved by using reversible joints between them.More specifically, our research investigates the design space of construction systems based on such components via computational design and optimization algorithms, such as the memory limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm with automatic computation of the gradient, within the Julia programming environment. By applying principles and methods of differential geometry, we study hypars with irregular tilings, enabling the integration of panels with diverse proportions, shapes and sizes, as they can occur in wood production waste. By reducing construction waste, the work aims at reducing the negative environmental impact of the building construction sector. Moreover, irregular tilings could enable a more customized design of acoustic qualities and offer visual variety in segmented hypar based timber structures.The here presented studies show that the proposed optimization method provides a good fit of many tiles to rhombi, particularly when the steepness is not too large. We also show that optimizing towards rectangles provides better results. Overall, the results support the initial assumption that irregular rulings could be a means of adapting to both homogeneous and diverse material stocks.

Computer Algorithm Design and Linearity Analysis of Its Data Structures

Linearity optimization of data structure is in an important position in the design of computer algorithms, and the study of its optimization measures and strategies can help to promote the research of computer algorithms in depth. This paper introduces the multithreading technology, designs a multithreaded inverted sorted cited table on the basis of the data chain table structure, and prunes and optimizes the data in the table and its distribution repetitions, and puts forward the linearity optimization strategy. And the GaBP parallel computer algorithm for banded linear equations is designed by combining this strategy. The linearity analysis of the optimized data structure reveals that the linearity is significantly improved, and the overall linearity is smooth and reasonably skewed. In the calculation of large-scale numerical values, the running time of the Partition number of 30 is only 3756.9 s. The average linear solution speed is only 0.063 s when solving the satisfiable SAT instances, which is 4.631 s less than that of the Minisat algorithm, and the optimization strategy of the data structure linearity proposed in this paper provides a reference scheme to accelerate the speed of computer algorithms in solving the difficult problems, and the design of the computer parallel algorithm provides a reference scheme to accelerate the speed of computer algorithms in solving the difficult problems. The designed computer parallel algorithm provides an effective method for the field of large-scale numerical processing and SAT problem solving.

Manufacturing Industry Resource Endowment Index and Its Big Data Model

This study aims to demonstrate the application of big data models in the evaluation of manufacturing competitiveness across different countries and economic entities. It involves analyzing algorithms and data structures, designing algorithms for evaluating manufacturing competitiveness suitable for different countries and economic entities, and providing algorithmic and theoretical support for building computer assessment models for manufacturing competitiveness in economic entities. The research adopts qualitative and quantitative methods, comparing and analyzing empirical data on manufacturing resource endowment and competitiveness in various countries, combining theoretical analysis and empirical data. By conducting a literature review and empirical research, relevant information and data are collected to establish the relationship between manufacturing resource endowment index and competitiveness. Through case studies and comparative research, this study explores the main factors affecting manufacturing competitiveness in different countries and their interrelationships. It confirms that the competitiveness of manufacturing industries in different countries is influenced by factors such as resource endowment. Further analysis quantifies the relationship between national manufacturing resource endowment and competitiveness, leading to the design of computer algorithms, data structures, and processing logic. The results demonstrate the significant role of resource endowment in manufacturing competitiveness, serving as a crucial basis for formulating comprehensive national strategies. The innovations of this study include: 1. Introducing the manufacturing resource endowment index as a key variable for evaluating competitiveness, enriching the content of competitiveness big data models. 2. Designing comprehensive data structures, algorithms, and processing logic for assessing manufacturing competitiveness from multiple perspectives. 3. In-depth analysis of the relationship and analytical methods between manufacturing strategies, resource endowment, and competitiveness, providing theoretical and model references for the design of computer big data operation platforms.

Coiled-Coil Protein Hydrogels Engineered with Minimized Fiber Diameters for Sustained Release of Doxorubicin in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) lacks expressed protein targets, making therapy development challenging. Hydrogels offer a promising new route in this regard by improving the chemotherapeutic efficacy through increased solubility and sustained release. Moreover, subcutaneous hydrogel administration reduces patient burden by requiring less therapy and shorter treatment times. We recently established the design principles for the supramolecular assembly of single-domain coiled-coils into hydrogels. Using a modified computational design algorithm, we designed Q8, a hydrogel with rapid assembly for faster therapeutic hydrogel preparation. Q8 encapsulates and releases doxorubicin (Dox), enabling localized sustained release via subcutaneous injection. Remarkably, a single subcutaneous injection of Dox-laden Q8 (Q8•Dox) significantly suppresses tumors within just 1 week. This work showcases the bottom-up engineering of a fully protein-based drug delivery vehicle for improved TBNC treatment via noninvasive localized therapy.

Diversity‐Based Topology Optimization of Soft Robotic Grippers

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.

Design of computer algorithms based on parametric models

Abstract In this paper, the objective function is effectively optimized by improving the fitness function in the computer algorithm. The improvement mainly focuses on adjusting the weighting coefficients of completion time, load balance and execution cost. The article then proceeds to optimize the algorithm’s parameters based on the algorithm’s optimal parameterization criterion, and designs a hybrid hill-climbing-simulated annealing optimization algorithm based on the parameterized model. To verify the safety of the algorithm, avalanche effect experiments were conducted in the study. The experimental results show that when the number of iterations is 14, the number of changed bits reaches 32, indicating that the algorithm is susceptible to the avalanche effect. In addition, for the application effect of the algorithm, the study was tested in the Oliver30 dataset. When the number of iterations reaches 25000 times, the algorithm has reached the optimal value distance 432.82, which shows the superiority in urban path recognition performance. Meanwhile, the algorithm has achieved an accuracy 0.8286 for feature recognition in remote sensing datasets, with a maximum classification accuracy of more than 90%. The study provides critical practical data and theoretical support for further application of the computer algorithm.

The Design Space of Kirchhoff Rods

The Kirchhoff rod model describes the bending and twisting of slender elastic rods in three dimensions and has been widely studied to enable the prediction of how a rod will deform, given its geometry and boundary conditions. In this work, we study a number of inverse problems with the goal of computing the geometry of a straight rod that will automatically deform to match a curved target shape after attaching its endpoints to a support structure. Our solution lets us finely control the static equilibrium state of a rod by varying the cross-sectional profiles along its length. We also show that the set of physically realizable equilibrium states admits a concise geometric description in terms of linear line complexes, which leads to very efficient computational design algorithms. Implemented in an interactive software tool, they allow us to convert three-dimensional hand-drawn spline curves to elastic rods and give feedback about the feasibility and practicality of a design in real time. We demonstrate the efficacy of our method by designing and manufacturing several physical prototypes with applications to interior design and soft robotics.

Explorations of efficient design solutions for Wire-and-Arc Additive manufacturing in construction

The digitalization of the construction sector could potentially produce more efficient structures, reduce material waste and increase work safety. Current strategies for the realization of automated steel constructions see the application of metal Additive Manufacturing processes (and in particular Wire-and-Arc Additive Manufacturing, WAAM) as an opportunity to build a new generation of efficient steel structures with reduced material use. This, though, requires advanced multidisciplinary knowledge in manufacturing, metallurgy, structural engineering and computational design. Recent effort has been made in order to combine computational design with current digital fabrication procedures to realize resource-efficient steel structures for the future. The present work aims at providing an integrated design approach to develop resource-efficient structural elements combining computational design algorithms with considerations on the WAAM fabrication process, structural considerations and verifications. The idea comes from the preliminary results achieved in terms of new structural optimization theories, fabrication of large-scale elements with WAAM and structural verification of first prototypes. The approach is applied to two classes of structural elements (beam and column). The results aim at increasing the application of metal Additive Manufacturing in construction, through the development a new generation of resource-efficient structural members.

Personalising prosthetics: digital interventions in disability and dance

ABSTRACT This paper emerges from an interdisciplinary collaboration between prosthesis-using disabled dance artists, computer scientists, dance researchers and engineers to explore the transformative potential of digital technologies to co-create aesthetically personalised prosthetics from dance movements. Beginning with the dancers performing improvised movement sequences in motion capture suits, which drove a computational design algorithm, an ‘aesthetic seed’ for each dancer was generated: a kind of personal signature from their movement. These seeds were then algorithmically mapped onto the shapes of prosthetic limb covers that could be 3D printed in a variety of materials. The paper will share some of the reflections from the dancers on how the process generated questions about agency, appropriation, ownership and the political implications of disability as a site of resistance. It will suggest some ways in which digital methods can offer disabled artists different routes towards making and sharing work, whilst foregrounding the importance of inclusion to challenge normative thinking around what ‘connection’ and ‘access’ means in the context of digital innovation.

Recent Advancements in Computational Drug Design Algorithms through Machine Learning and Optimization

The goal of drug discovery is to uncover new molecules with specific chemical properties that can be used to cure diseases. With the accessibility of machine learning techniques, the approach used in this search has become a significant component in computer science in recent years. To meet the Precision Medicine Initiative’s goals and the additional obstacles that they have created, it is vital to develop strong, consistent, and repeatable computational approaches. Predictive models based on machine learning are becoming increasingly crucial in preclinical investigations. In discovering novel pharmaceuticals, this step substantially reduces expenses and research times. The human kinome contains various kinase enzymes that play vital roles through catalyzing protein phosphorylation. Interestingly, the dysregulation of kinases causes various human diseases, viz., cancer, cardiovascular disease, and several neuro-degenerative disorders. Thus, inhibitors of specific kinases can treat those diseases through blocking their activity as well as restoring normal cellular signaling. This review article discusses recent advancements in computational drug design algorithms through machine learning and deep learning and the computational drug design of kinase enzymes. Analyzing the current state-of-the-art in this sector will offer us a sense of where cheminformatics may evolve in the near future and the limitations and beneficial outcomes it has produced. The approaches utilized to model molecular data, the biological problems addressed, and the machine learning algorithms employed for drug discovery in recent years will be the emphasis of this review.

Mechanistic Modelling of Biomass Growth, Glucose Consumption and Ethanol Production by Kluyveromyces marxianus in Batch Fermentation.

This paper presents results concerning mechanistic modeling to describe the dynamics and interactions between biomass growth, glucose consumption and ethanol production in batch culture fermentation by Kluyveromyces marxianus (K. marxianus). The mathematical model was formulated based on the biological assumptions underlying each variable and is given by a set of three coupled nonlinear first-order Ordinary Differential Equations. The model has ten parameters, and their values were fitted from the experimental data of 17 K. marxianus strains by means of a computational algorithm design in Matlab. The latter allowed us to determine that seven of these parameters share the same value among all the strains, while three parameters concerning biomass maximum growth rate, and ethanol production due to biomass and glucose had specific values for each strain. These values are presented with their corresponding standard error and 95% confidence interval. The goodness of fit of our system was evaluated both qualitatively by in silico experimentation and quantitative by means of the coefficient of determination and the Akaike Information Criterion. Results regarding the fitting capabilities were compared with the classic model given by the logistic, Pirt, and Luedeking-Piret Equations. Further, nonlinear theories were applied to investigate local and global dynamics of the system, the Localization of Compact Invariant Sets Method was applied to determine the so-called localizing domain, i.e., lower and upper bounds for each variable; whilst Lyapunov's stability theories allowed to establish sufficient conditions to ensure asymptotic stability in the nonnegative octant, i.e., R+,03. Finally, the predictive ability of our mechanistic model was explored through several numerical simulations with expected results according to microbiology literature on batch fermentation.

Fitness functions for RNA structure design.

An RNA design algorithm takes a target RNA structure and finds a sequence that folds into that structure. This is fundamentally important for engineering therapeutics using RNA. Computational RNA design algorithms are guided by fitness functions, but not much research has been done on the merits of these functions. We survey current RNA design approaches with a particular focus on the fitness functions used. We experimentally compare the most widely used fitness functions in RNA design algorithms on both synthetic and natural sequences. It has been almost 20years since the last comparison was published, and we find similar results with a major new result: maximizing probability outperforms minimizing ensemble defect. The probability is the likelihood of a structure at equilibrium and the ensemble defect is the weighted average number of incorrect positions in the ensemble. We find that maximizing probability leads to better results on synthetic RNA design puzzles and agrees more often than other fitness functions with natural sequences and structures, which were designed by evolution. Also, we observe that many recently published approaches minimize structure distance to the minimum free energy prediction, which we find to be a poor fitness function.

Computational design and experimental verification of pseudoknotted ribozymes.

The design of new RNA sequences that retain the function of a model RNA structure is a challenge in bioinformatics because of the structural complexity of these molecules. RNA can fold into its secondary and tertiary structures by forming stem-loops and pseudoknots. A pseudoknot is a set of base pairs between a region within a stem-loop and nucleotides outside of this stem-loop; this motif is very important for numerous functional structures. It is important for any computational design algorithm to take into account these interactions to give a reliable result for any structures that include pseudoknots. In our study, we experimentally validated synthetic ribozymes designed by Enzymer, which implements algorithms allowing for the design of pseudoknots. Enzymer is a program that uses an inverse folding approach to design pseudoknotted RNAs; we used it in this study to design two types of ribozymes. The ribozymes tested were the hammerhead and the glmS, which have a self-cleaving activity that allows them to liberate the new RNA genome copy during rolling-circle replication or to control the expression of the downstream genes, respectively. We demonstrated the efficiency of Enzymer by showing that the pseudoknotted hammerhead and glmS ribozymes sequences it designed were extensively modified compared to wild-type sequences and were still active.

Sequence patterns and signatures: Computational and experimental discovery of amyloid-forming peptides.

Screening amino acid sequence space via experiments to discover peptides that self-assemble into amyloid fibrils is challenging. We have developed a computational peptide assembly design (PepAD) algorithm that enables the discovery of amyloid-forming peptides. Discontinuous molecular dynamics (DMD) simulation with the PRIME20 force field combined with the FoldAmyloid tool is used to examine the fibrilization kinetics of PepAD-generated peptides. PepAD screening of ∼10,000 7-mer peptides resulted in twelve top-scoring peptides with two distinct hydration properties. Our studies revealed that eight of the twelve in silico discovered peptides spontaneously form amyloid fibrils in the DMD simulations and that all eight have at least five residues that the FoldAmyloid tool classifies as being aggregation-prone. Based on these observations, we re-examined the PepAD-generated peptides in the sequence pool returned by PepAD and extracted five sequence patterns as well as associated sequence signatures for the 7-mer amyloid-forming peptides. Experimental results from Fourier transform infrared spectroscopy (FTIR), thioflavin T (ThT) fluorescence, circular dichroism (CD), and transmission electron microscopy (TEM) indicate that all the peptides predicted to assemble in silico assemble into antiparallel β-sheet nanofibers in a concentration-dependent manner. This is the first attempt to use a computational approach to search for amyloid-forming peptides based on customized settings. Our efforts facilitate the identification of β-sheet-based self-assembling peptides, and contribute insights towards answering a fundamental scientific question: "What does it take, sequence-wise, for a peptide to self-assemble?".

From evidence to assessment: Developing a scenario-based computational design algorithm to support informed decision-making in primary care clinic design workflow

This paper describes a method to tailor computational design algorithms to evaluate human-centric outputs in architectural projects by presenting a case study that evaluates teamwork affordance for primary care clinics. We argue that computational design assessment techniques fall short of evaluating those human experiences that stem from multiple interactions between individuals and the surrounding environment. This research suggests that future computational design algorithms could benefit from incorporating scenario-based methods developed in the field of evidence-based design, in which studies are concerned about improving qualitative goals. Through this case study, we describe the process of prototyping a computational design algorithm based on the Functional Scenario Analysis approach, applying the algorithm to evaluate case studies, visualizing findings, and extracting design strategies based on the analysis results. This method offers a new vision of how computational design can benefit informed decision-making processes and generate design alternatives aligned with a project’s design goals.

Design based on availability: Generative design and robotic fabrication workflow for non-standardized sheet metal with variable properties

This paper presents the use of a computational design algorithm in combination with robotic fabrication and sensing to augment the design and construction process for non-standardized material. Although reusing reclaimed material can significantly reduce the environmental impact in building construction, current design processes are not set up for this shift in thinking. Contrary to conventional practices, designing within the constraints of available material means that geometry and topology cannot be fully pre-determined. This paper introduces a design methodology for corrugated shell structures from folded sheet metal of variable geometries and properties, in which the design goal adapts to available material. It follows a two-fold approach of digital algorithm development and scanning and physical prototyping for robotic fabrication. The scanned materials database is classified based on object geometry data and material properties; such as thickness, type of metal, and spring-back values for fabrication purposes. Together with a target surface, it is an input for a generative design algorithm consisting of surface generation and optimization. The surface generation tries to approximate the target through a translation of search algorithm results for object placement into a 2D mesh graph which is then linked to 3D particle spring based form-finding. The optimization consists of evaluation of structural, fabrication, and design criteria, with finally user selection. Robotic fabrication included object recognition, metal sheet folding and consideration of different metal spring back behavior. These methods were tested on a single curved arch surface and applied to a double curved cantilever canopy as a final demonstrator. The algorithm results showed a generation of different corrugated shell topologies based on iterated object placement. As a demonstrator, a part of the selected canopy was robotically fabricated from local industrial leftovers.

Mono-Material Wood Wall

The project seeks to create a building envelope that functions as structure, enclosure, and insulation,which is assembled from one solid timber construction element type. Wood has clear environmentalbenefits when compared to other standard construction materials such as steel and concrete, a goodstrength-to-weight ratio, relatively high thermal insulation, and low production costs. This researchseeks to leverage these characteristics to simultaneously reduce the number of material layers intimber building envelopes while improving the building physics performance. Thus, the environmentalimpact of buildings can be reduced during planning, construction, operation, and disposal. The projectproposes a system that reduces material layers and improves envelope performance by combiningcontemporary fabrication technologies with traditional woodworking techniques. Design tools shouldallow for compelling formal opportunities and facilitate fabrication and construction. The systemmanifests as a free-form, curvilinear log-cabin. Solid timber beams are used to minimise binders andfillers found in composite wood products, and the entire primary construction is achieved with purewood joinery. CNC machining allows for the precise joining of members to achieve robust, easy-toassemble, structural and airtight facades. By sawing deep slits into solid timber beams, the resulting airchambers improve thermal insulation values up to 30% compared to comparable solid wood assemblieswhile also relieving naturally occurring internal stresses. Computational design algorithms generatetoolpaths and construction data directly from simple input curves, enabling direct coordination ofarchitects, engineers, and contractors. To evaluate the system, multiple prototypes are fabricated totest constructability, thermal conductance, and airtightness, including a demonstrator building to testfull-scale implementation. Laboratory tests and the successful completion of the IBA: Timber PrototypeHouse demonstrate the potential for this renewable material to fulfil the requirements of contemporarybuilding envelopes and open the door for the development of all-wood multi-storey facades.

Experimental pairwise entanglement estimation for an N-qubit system

Designing and implementing algorithms for medium- and large-scale quantum computers is not easy. In the previous work, we have suggested, and developed, the idea of using machine learning techniques to train a quantum system such that the desired process is “learned,” thus obviating the algorithm design difficulty. This works quite well for small systems. But the goal is macroscopic physical computation. Here, we implement our learned pairwise entanglement witness on Microsoft’s Q#, one of the commercially available gate model quantum computer simulators; we perform statistical analysis to determine reliability and reproducibility; and we show that after training the system in stages for an incrementing number of qubits (2, 3, 4, ...) we can infer the pattern for mesoscopic N from simulation results for three-, four-, five-, six-, and seven-qubit systems. Our results suggest a fruitful pathway for general quantum computer algorithm design and for practical computation on noisy intermediate-scale quantum devices.

A Turing Test for Molecular Generators.

Machine learning approaches promise to accelerate and improve success rates in medicinal chemistry programs by more effectively leveraging available data to guide a molecular design. A key step of an automated computational design algorithm is molecule generation, where the machine is required to design high-quality, drug-like molecules within the appropriate chemical space. Many algorithms have been proposed for molecular generation; however, a challenge is how to assess the validity of the resulting molecules. Here, we report three Turing-inspired tests designed to evaluate the performance of molecular generators. Profound differences were observed between the performance of molecule generators in these tests, highlighting the importance of selection of the appropriate design algorithms for specific circumstances. One molecule generator, based on match molecular pairs, performed excellently against all tests and thus provides a valuable component for machine-driven medicinal chemistry design workflows.

THE VIRTUAL LABORATORY IN THE ANALYSIS OF ALGORITHMS AS DIDACTIC STRATEGY

The objective of the present work was to apply a methodology in the teaching of the Techniques of Design and Analysis of Computational Algorithms, for the construction of optimal algorithms in the solution of problems. The methodology followed in the investigation began with the construction and selection of measurement tools. The subject of the Techniques of Design and Analysis of Computational Algorithms in Computer Science is very important, since they are a tool for the optimal and effective solution in the solution of problems through the design of computer algorithms, being necessary to investigate what ideas students have or preconceived ideas through diagnostic tests. This document first describes the methodology followed and the results obtained when applied through the virtual material implemented with Gowin's UVE and the conceptual maps (MMCC) to achieve significant learning in the topic of algorithm analysis. The data recorded at the beginning, during and at the end of the course were analyzed qualitatively and quantitatively in a comparative manner. This research was carried out in the School of Mechanical and Electrical Engineering of the Culhuacán Unit of the National Polytechnic Institute of Mexico in the course of the analysis of algorithms of the fifth semester of.