Advanced Design of Soft Robots with Artificial Intelligence

In recent years,breakthrough has been made in the field of artificial intelligence(AI),which has also revolutionized the industry of robotics.Soft robots featured with high-level safety,less weight,lower power consumption have always been one of the research hotspots.Recently,multifunctional sensors for perception of soft robotics have been rapidly developed,while more algorithms and models of machine learning with high accuracy have been optimized and proposed.Designs of soft robots with AI have also been advanced ranging from multimodal sensing,human-machine interaction to effective actuation in robotic systems.Nonethe-less,comprehensive reviews concerning the new developments and strategies for the ingenious design of the soft robotic systems equipped with AI are rare.Here,the new development is systematically reviewed in the field of soft robots with AI.First,background and mechanisms of soft robotic systems are briefed,after which development focused on how to endow the soft robots with AI,including the aspects of feeling,thought and reaction,is illustrated.Next,applications of soft robots with AI are systematically summarized and discussed together with advanced strategies proposed for performance enhancement.Design thoughts for future intelligent soft robotics are pointed out.Finally,some perspectives are put forward.

Research and Application of Artificial Intelligence in Electric Guitar Performance

The article explores the application of artificial intelligence technology in electric guitar playing.It delves into the advantages of artificial intelligence and its seamless integration into electric guitar performance.Additionally,it investigates the application of artificial intelligence technology through an intelligent playing robot.The research aims to offer substantial support for the advancement of artificial intelligence in electric guitar performance.

Towards data-efficient mechanical design of bicontinuous composites usinggenerative AI

The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.

A Generic Intelligent Agent Design Approach Based on Artificial Neural Networks

Artificial intelligence in general and software agents in particular are recognized as computer science disciplines that aim to model or simulate so-called intelligent human behaviors such as perception, decision-making, understanding, learning, etc. This work presents an approach to designing a generic Intelligent Agent that can be used in a multi-agent system to solve a complex problem. The generic agent that is proposed can be instantiated as a concrete agent, which is enabled with learning and autonomy capabilities by using Artificial Neural Networks. To highlight the generic aspect, the proposition is instantiated to be used in agriculture, health and education. The instantiated software agent applied in agriculture can process images in real time and detect defect on plants’ leaf. In the health field, the agent process image to diagnose breast cancer. When applied in Education, the agent can load an image of a student’s script and grade it. The performance of the designed agent system has the same accuracy as that of the respective neural networks used to instantiate them. In the educational field, the software agent has an accuracy of 98.9% and in the health field, it has an accuracy of 99.56% while in the agricultural field, it has an accuracy of 97.2%.

Play by Design:Developing Artificial Intelligence Literacy through Game-based Learning

The paper proposes an innovative approach aimed at fostering AI literacy through interactive gaming experiences.This paper designs a game-based prototype for preparing pre-service teachers to innovate teaching practices across disciplines.The simulation,Color Conquest,serves as a strategic game to encourage educators to reconsider their pedagogical practices.It allows teachers to use and develop various scenarios by customizing maps,giving students agency to engage in the complex decision-making process.Additionally,this engagement process provides teachers with an opportunity to develop students’skills in artificial intelligence literacy as students actively develop strategic thinking,problem-solving,and critical reasoning skills.

The Design and Implementation of the Data Buffer Unit in an Artificial intelligence Computer ITM-1

This paper describes the function,structure and working status of the data buffer unitDBU,one of the most important functional units on ITM-1.It also discusses DBU’s supportto the multiprocessor system and Prolog language.

Research on Multi-Dimensional Aesthetic Perception of Art Design in the Era of Artificial Intelligence

With the rapid development of artificial intelligence technology,the way we interact with products and the environment has undergone great changes compared with the past.While the technology of the artificial intelligence era affects our lives and redefines our relationship with machines,it is also changing our aesthetic and perception of art design.The article mainly starts from the perspective of art design interaction mode and experience innovation,and studies the multi-dimensional aesthetic perception and interaction mode of art design in the era of artificial intelligence in human vision,touch and hearing.

Artificial Intelligence in Pharmaceutical Sciences

Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.

Artificial intelligence improves accuracy, efficiency, and reliability of a handheld infrared eccentric autorefractor for adult refractometry

AIM: To evaluate the accuracy, efficiency, and reliability of a handheld infrared eccentric autorefractor(h ICA) with artificial intelligence(AI) by comparing its refraction measurements to those recorded using h ICA and a clinical table-mounted automatic refractor(TAR).METHODS: A cross-sectional study using three optometers, including h ICA with or without AI and TAR, for refractometry of adults(aged 19-49 years old) with no signs of ocular disease or trauma in the absence of cycloplegia. Right and left eye refraction data were recorded, including the spherical equivalent(SE), diopter of spherical power(DS), diopter of cylindrical power(DC) decomposed into vectors J0 and J45, and measurement times. To avoid analytical difficulties associated with the interdependence of observations between eyes from the same individual, the Generalized Estimation Equation was used to compare the SE, DS, J0 and J45 measurements, and the times thereof, among the different groups. The intraclass correlation coef f icient(ICC) and Spearman’s rank correlation coefficient were used to evaluate correlations among the measurements recorded by the three different instruments. Bland-Altman were used to analyze the precision of the equipment by the agreement. RESULTS: A total of 70 patients(140 eyes;mean age: 31.37 y;range: 19-49 y) were assessed using refractometry. In a brightly lit environment, there was no significant difference between the mean SE recorded using TAR and that recorded using h ICA with AI or without AI(both P>0.05). In an intense-light environment, h ICA equipped with AI showed a better detection rate than without AI. Light intensity had a greater effect on dioptric measurements recorded using h ICA without AI(P<0.001) than on those recorded using the one equipped with AI(P<0.05). Measurement times varied significantly between the different light intensities and instruments(P<0.05).CONCLUSION: For the normal human eyes, AI may improve the accuracy, efficiency, and reliability of measurements recorded using h ICA in various light environments.

Intelligent Agent Based Mapping of Software Requirement Specification to Design Model

Automatically mapping a requirement specification to design model in Software Engineering is an open complex problem. Existing methods use a complex manual process that use the knowledge from the requirement specification/modeling and the design, and try to find a good match between them. The key task done by designers is to convert a natural language based requirement specification (or corresponding UML based representation) into a predominantly computer language based design model—thus the process is very complex as there is a very large gap between our natural language and computer language. Moreover, this is not just a simple language conversion, but rather a complex knowledge conversion that can lead to meaningful design implementation. In this paper, we describe an automated method to map Requirement Model to Design Model and thus automate/partially automate the Structured Design (SD) process. We believe, this is the first logical step in mapping a more complex requirement specification to design model. We call it IRTDM (Intelligent Agent based requirement model to design model mapping). The main theme of IRTDM is to use some AI (Artificial Intelligence) based algorithms, semantic representation using Ontology or Predicate Logic, design structures using some well known design framework and Machine Learning algorithms for learning over time. Semantics help convert natural language based requirement specification (and associated UML representation) into high level design model followed by mapping to design structures. AI method can also be used to convert high level design structures into lower level design which then can be refined further by some manual and/or semi automated process. We emphasize that automation is one of the key ways to minimize the software cost, and is very important for all, especially, for the “Design for the Bottom 90% People” or BOP (Base of the Pyramid People).

Teaching Design of Course Building Decoration Materials Based on Generative Artificial Intelligence

With the digital transformation of global education and China's emphasis on education digital,generative AI technology has been widely used in the field of higher education.In this paper,the development of generative AI technology and its potential in personalized learning,interactive content creation and adaptive assessment in education were introduced firstly.Then,the application case of generative AI tools in teaching content creation,scenario-based teaching content development,visual teaching content development,complex concept deconstruction and analogy,student-led application practice and other aspects in the teaching of Building Decoration Materials was discussed.Through the teaching experiment and effect evaluation,the positive influence of generative AI technology on the improvement of students'learning effect and teaching efficiency was verified.Finally,some thoughts and inspirations on the combination of educational theory and generative AI technology,the integration of teaching design and generative AI technology,and the practice cases and effect evaluation were put forward,and the importance of teacher role transformation and personalized learning path design was emphasized to provide theoretical and practical support for the innovative development of higher education.

Intelligent design and construction of novel APN-based theranostic probe driven by advanced computational methods

Multifunctional molecules with both optical signal and pharmacological activity play an important role in drug development,disease diagnosis,and basic theoretical research.Aminopeptidase N(APN),as a representative tumor biomarker with anti-tumor potential,still lacks a high-precision theranostic probe specifically targeting it.In this study,a novel quaternity design strategy for APN theranostic probe was developed.This proposed strategy utilizes advanced machine learning and molecular dynamics simulations,and cleverly employs the strategy of conformation-induced fluorescence recovery to achieve multi-objective optimization and integration of functional fragments.Through this strategy,a unique“Off-On”theranostic probe,ABTP-DPTB,was ingeniously constructed to light up APN through fluorescence restoration,relying on conformation-induced effects and solvent restriction.Differ from the common diagnostic probes,the intelligent design with non-substrated linkage makes ABTP-DPTB for long-term in-situ imaging.The fabricated probe was used for detecting and inhibiting APN in various environments,with a better in vitro inhibitory than golden-standard drug bestatin.

Design and regulation of the surface and interfacial behavior of protein molecules

Surface and interfacial behavior of protein molecules are crucial for the protein function involved in many biochemical processes and biomedical products such as enzyme design,bio-separation,drug design and delivery.This article is devoted to an overview of design and regulation of the surface and interfacial behavior of protein molecules.The improvement of enzyme surface such as the directed evolution and the rational design of enzymes is introduced at first,followed by the rational design of protein interface for the protein assembly.Thereafter,the design of micro-environment and ligands are described as two examples for the design guided by protein surface.Then the design of protein surface and interface with the help of artificial intelligence will be discussed.

An artificial intelligence-aided design (AIAD) of ship hull structures

Ship-hull design is a complex process because the any slight local alteration in ship hull structure may significantly change the hydrostatic and hydrodynamic performances of a ship.To find the optimum hull shape under the design requirements,the state-of-art of ship hull design combines computational fluid dynamics computation with geometric modeling.However,this process is very computationally intensive,which is only suitable at the final stage of the design process.To narrow down the design parameter space,in this work,we have developed an AI-based deep learning neural network to realize a real-time prediction of the total resistance of the ship-hull structure in its initial design process.In this work,we have demonstrated how to use the developed DNN model to carry out the initial ship hull design.The validation results showed that the deep learning model could accurately predict the ship hull’s total resistance accurately after being trained,where the average error of all samples in the testing dataset is lower than 4%.Simultaneously,the trained deep learning model can predict the hip’s performances in real-time by inputting geometric modification parameters without tedious preprocessing and calculation processes.The machine learning approach in ship hull design proposed in this work is the first step towards the artificial intelligence-aided design in naval architectures.

Optimization of the Design of Structures in Coverage Using the Genetic Algorithm Method

In this research work,an optimization of the design of steel structures is developed through Genetic Algorithms(Gas)with the objective of reducing the amount of material and with them the cost,for which the programming of the GAs tool in the Fortran programming language with the SAP2000 Application Programming Interface(API)v.19 using Visual Studio Professional 2015-Intel?Visual Fortran with Parallel Studio XE.The GAs method consists of randomly searching the Objective Function(OF)of the optimal variables with restrictions.The OF defined by the weight of the structure,the variables in relation to the cross sections of the structural elements and the constraints are the design of resistance using the method of Load and Resistance Factor Design(LRFD),restriction of displacements and restriction of the ratio of slenderness considered in the National Building Regulations(RNE),whose GAs search parameters are given by probability of crossing 70%by simple crossing strategy,probability of mutation 20%,elitism of 5%and maximum number of generations according to the profile.The examined metal structure is a cover with five double-body warren arches with 32 m light connected with 24 joists both with welded connections,that is,rigid.The metal structure was evaluated on demand for loads(dead load,live load,snow load,wind load and earthquake loads).The structural design was carried out with the following profiles:circular,tubular,square,angular and Normal Profile with I-shaped section(IPN)of which the most optimal was the tubular profile.The outer diameters of the cross section are:diagonal with a diameter of 1 in,lower flange with a diameter of 23/8 in,upper flange with a diameter of 11/2 in,inner belt with a diameter of 1 in and belt.outside with a diameter of 11/2 in.

人工智能在土木工程领域的应用研究现状及展望

作为新一代产业变革的核心驱动力,人工智能是全面提高土木工程领域数字化、自动化、信息化和智能化的重要方法。为全面了解人工智能在土木工程中的发展及应用,定性分析了人工智能的基本研究领域,定量分析了人工智能在土木工程设计、制造、养维护阶段的研究现状,利用CiteSpace可视化工具深入挖掘人工智能在土木工程中存在的问题、发展瓶颈和研究趋势,并给出相应的解决办法及研究思路。通过文献综述发现,土木工程领域已展开了大量人工智能研究,但各阶段智能化发展不均衡,实际应用也存在一定局限性,需深入探索神经网络、大数据、深度学习等智能技术在土木工程全生命周期的交叉融合,促进土木工程领域人工智能研究的协同发展。

Application of Feature Curves and Shape Blending on Yacht Designing

The global yacht market share and industry might expand continually since increasing people are willing to enjoy the yacht life in lower price nowadays while yacht activities were regards as a luxury acitivity in the past.Additionally,Taiwan Residents yacht manufacturers are well-known worldwide.They show excellent performances on the international annual rankings which implies Taiwan has excellent manufacturing technologies.However,Taiwan Residents manufacturers so far do not have a mature local design team.Therefore,this study goals to developing a systematic and objective design method for hull designing,which facilitates designers to design innovative yachts or create a series product with brand recognition.This study is divided into three parts:the first part is the investigation of the yachts market;the second is establishing a shape blending platform;the third is 3D forming.Finally,it is used the existing ship CAD software to design an innovative yacht based on the blended curves and then calculates its basic hydrostatic performance.This study provides a quantitative method to create a new form and to preserve the features for a brand.The exist yacht combines with other graphics to create a new form and maximizes the features of the original graphics.

Design methods for antimicrobial peptides with improved performance

The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult.In this regard,developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority.Antimicrobial peptides(AMPs),a ubiquitous class of naturally occurring compounds with broadspectrum antipathogenic activity,hold significant promise as an effective solution to the current antimicrobial resistance(AMR)crisis.Several AMPs have been identified and evaluated for their therapeutic application,with many already in the drug development pipeline.Their distinct properties,such as high target specificity,potency,and ability to bypass microbial resistance mechanisms,make AMPs a promising alternative to traditional antibiotics.Nonetheless,several challenges,such as high toxicity,lability to proteolytic degradation,low stability,poor pharmacokinetics,and high production costs,continue to hamper their clinical applicability.Therefore,recent research has focused on optimizing the properties of AMPs to improve their performance.By understanding the physicochemical properties of AMPs that correspond to their activity,such as amphipathicity,hydrophobicity,structural conformation,amino acid distribution,and composition,researchers can design AMPs with desired and improved performance.In this review,we highlight some of the key strategies used to optimize the performance of AMPs,including rational design and de novo synthesis.We also discuss the growing role of predictive computational tools,utilizing artificial intelligence and machine learning,in the design and synthesis of highly efficacious lead drug candidates.

Applications and prospects of cryo-EM in drug discovery

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Aided innovative design of bathroom products based on artificial intelligence technology

Artificial intelligence technology, mainly refers to strengthening the artificial way, so as to combinecomputer technology with product design. Firstly, the auxiliary innovation of bathroomproducts based on artificial intelligence technology is proposed, then the user characteristicsare analysed, and the auxiliary design framework of bathroom products is designed. Finally, theinnovation model is established to realise the auxiliary innovation design of bathroom products.Experimental results show that the proposed method can effectively improve the interactionefficiency and response time, and reduce the false response.