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.

Artificial intelligence powered glucose monitoring and controlling system:Pumping module

BACKGROUND Diabetes,a globally escalating health concern,necessitates innovative solutions for efficient detection and management.Blood glucose control is an essential aspect of managing diabetes and finding the most effective ways to control it.The latest findings suggest that a basal insulin administration rate and a single,highconcentration injection before a meal may not be sufficient to maintain healthy blood glucose levels.While the basal insulin rate treatment can stabilize blood glucose levels over the long term,it may not be enough to bring the levels below the post-meal limit after 60 min.The short-term impacts of meals can be greatly reduced by high-concentration injections,which can help stabilize blood glucose levels.Unfortunately,they cannot provide long-term stability to satisfy the postmeal or pre-meal restrictions.However,proportional-integral-derivative(PID)control with basal dose maintains the blood glucose levels within the range for a longer period.AIM To develop a closed-loop electronic system to pump required insulin into the patient's body automatically in synchronization with glucose sensor readings.METHODS The proposed system integrates a glucose sensor,decision unit,and pumping module to specifically address the pumping of insulin and enhance system effectiveness.Serving as the intelligence hub,the decision unit analyzes data from the glucose sensor to determine the optimal insulin dosage,guided by a pre-existing glucose and insulin level table.The artificial intelligence detection block processes this information,providing decision instructions to the pumping module.Equipped with communication antennas,the glucose sensor and micropump operate in a feedback loop,creating a closed-loop system that eliminates the need for manual intervention.RESULTS The incorporation of a PID controller to assess and regulate blood glucose and insulin levels in individuals with diabetes introduces a sophisticated and dynamic element to diabetes management.The simulation not only allows visualization of how the body responds to different inputs but also offers a valuable tool for predicting and testing the effects of various interventions over time.The PID controller's role in adjusting insulin dosage based on the discrepancy between desired setpoints and actual measurements showcases a proactive strategy for maintaining blood glucose levels within a healthy range.This dynamic feedback loop not only delays the onset of steady-state conditions but also effectively counteracts post-meal spikes in blood glucose.CONCLUSION The WiFi-controlled voltage controller and the PID controller simulation collectively underscore the ongoing efforts to enhance efficiency,safety,and personalized care within the realm of diabetes management.These technological advancements not only contribute to the optimization of insulin delivery systems but also have the potential to reshape our understanding of glucose and insulin dynamics,fostering a new era of precision medicine in the treatment of diabetes.

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

Research on the Application Effects of Artificial Intelligence in Personalized Marketing

Artificial Intelligence (AI) technology is profoundly transforming personalized marketing practices. This study employs a mixed-methods approach to explore the application effects of AI in personalized marketing. The research first outlines the primary forms of AI-driven personalized marketing, including intelligent recommendation systems, dynamic pricing, and personalized content generation. Through analysis across multiple industries, the study summarizes best practices and common pitfalls of AI applications. Results indicate that AI can significantly enhance marketing relevance and timeliness but may also raise concerns about privacy and algorithmic discrimination. The study compares the effectiveness of AI-driven and traditional methods in various marketing scenarios. AI excels in handling large-scale, real-time personalization needs but may be less effective than human intervention in scenarios requiring deep emotional connections. Finally, the study discusses the prospects of ethical AI in personalized marketing, emphasizing the importance of transparency and explainability. This research provides theoretical and practical guidance for enterprises to effectively leverage AI technology to improve personalized marketing effectiveness.

Cooling High Power Dissipating Artificial Intelligence (AI) Chips Using Refrigerant

High power dissipating artificial intelligence (AI) chips require significant cooling to operate at maximum performance. Current trends regarding the integration of AI, as well as the power/cooling demands of high-performing server systems pose an immense thermal challenge for cooling. The use of refrigerants as a direct-to-chip cooling method is investigated as a potential cooling solution for cooling AI chips. Using a vapor compression refrigeration system (VCRS), the coolant temperature will be sub-ambient thereby increasing the total cooling capacity. Coupled with the implementation of a direct-to-chip boiler, using refrigerants to cool AI server systems can materialize as a potential solution for current AI server cooling demands. In this study, a comparison of 8 different refrigerants: R-134a, R-153a, R-717, R-508B, R-22, R-12, R-410a, and R-1234yf is analyzed for optimal performance. A control theoretical VCRS model is created to assess variable refrigerants under the same operational conditions. From this model, the coefficient of performance (COP), required mass flow rate of refrigerant, work required by the compressor, and overall heat transfer coefficient is determined for all 8 refrigerants. Lastly, a comprehensive analysis is provided to determine the most optimal refrigerants for cooling applications. R-717, commonly known as Ammonia, was found to have the highest COP value thus proving to be the optimal refrigerant for cooling AI chips and high-performing server applications.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

Artificial Intelligence-Based Sentiment Analysis of Dynamic Message Signs that Report Fatality Numbers Using Connected Vehicle Data

This study presents results from sentiment analysis of Dynamic message sign (DMS) message content, focusing on messages that include numbers of road fatalities. As a traffic management tool, DMS plays a role in influencing driver behavior and assisting transportation agencies in achieving safe and efficient traffic movement. However, the psychological and behavioral effects of displaying fatality numbers on DMS remain poorly understood;hence, it is important to know the potential impacts of displaying such messages. The Iowa Department of Transportation displays the number of fatalities on a first screen, followed by a supplemental message hoping to promote safe driving;an example is “19 TRAFFIC DEATHS THIS YEAR IF YOU HAVE A SUPER BOWL DON’T DRIVE HIGH.” We employ natural language processing to decode the sentiment and undertone of the supplementary message and investigate how they influence driving speeds. According to the results of a mixed effect model, drivers reduced speeds marginally upon encountering DMS fatality text with a positive sentiment with a neutral undertone. This category had the largest associated amount of speed reduction, while messages with negative sentiment with a negative undertone had the second largest amount of speed reduction, greater than other combinations, including positive sentiment with a positive undertone.

Decision Making Support System(DSS)——A Synthesized and Integrated Crystallization of Systems Engineering, Artificial Intelligence and Electronic Technologies

I firmly believe that of systems engineering is the requirement-driven force for the progress ofsoftware engineering, artificial intelligence and electronic technologies. The development ofsoftware engineering, artificial intelligence and electronic technologies is the technical supportfor the progress of systems engineering. INTEGRATION can be considered as "bridging" the ex-isting technologies and the People together into a coordinated SYSTEM.

Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions

The approach for probabilistic rationale of artificial intelligence systems actions is proposed.It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling.The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.The approach includes description of the proposed probabilistic models,optimization methods for rationale actions and incremental algorithms for solving the problems of supporting decision-making on the base of monitored data and rationale robot actions in uncertainty conditions.The approach means practically a proactive commitment to excellence in uncertainty conditions.A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.

A review of medical artificial intelligence

Since the concept of“artificial intelligence”was introduced in 1956,it has led to numerous technological innovations in human medicine and completely changed the traditional model of medicine.In this study,we mainly explain the application of artificial intelligence in various fields of medicine from four aspects:machine learning,intelligent robot,image recognition technology,and expert system.In addition,we discuss the existing problems and future trends in these areas.In recent years,through the development of globalization,various research institutions around the world has conducted a number of researches on this subject.Therefore,medical artificial intelligence has attained significant breakthroughs and will demonstrate wide development prospection in the future.

Artificial Intelligence Embedded Object-Oriented Methodology For Model Based Decision Support

The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strategic decision making lead to the achievemellt of optimal path towardsmarket economy from the central planning situation in China. To meet user's various requirements,a series of innovations in software development have been carried out, such as system formalization with OBFRAMEs in an object-oriented paradigm for problem solving automation and techniques of modules intelligent cooperation, hybrid system of reasoning, connectionist framework utilization,etc. Integration technology has been highly emphasized and discussed in this article and an outlook to future software engineering is given in the conclusion section.

Hunting down the dominating subclone of cancer stem cells as a potential new therapeutic target in multiple myeloma: An artificial intelligence perspective

The development of single-cell subclones,which can rapidly switch from dormant to dominant subclones,occur in the natural pathophysiology of multiple myeloma(MM)but is often"pressed"by the standard treatment of MM.These emerging subclones present a challenge,providing reservoirs for chemoresistant mutations.Technological advancement is required to track MM subclonal changes,as understanding MM's mechanism of evolution at the cellular level can prompt the development of new targeted ways of treating this disease.Current methods to study the evolution of subclones in MM rely on technologies capable of phenotypically and genotypically characterizing plasma cells,which include immunohistochemistry,flow cytometry,or cytogenetics.Still,all of these technologies may be limited by the sensitivity for picking up rare events.In contrast,more incisive methods such as RNA sequencing,comparative genomic hybridization,or whole-genome sequencing are not yet commonly used in clinical practice.Here we introduce the epidemiological diagnosis and prognosis of MM and review current methods for evaluating MM subclone evolution,such as minimal residual disease/multiparametric flow cytometry/next-generation sequencing,and their respective advantages and disadvantages.In addition,we propose our new single-cell method of evaluation to understand MM's mechanism of evolution at the molecular and cellular level and to prompt the development of new targeted ways of treating this disease,which has a broad prospect.

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.

Implications of Implementation of Artificial Intelligence in the Banking Business with Correlation to the Human Factor

Banks daily interact with a vast number of customers and are still depending on a legacy system. With today’s advances in technology, regarding lifting almost all processes to automation, from start of production to finish, there is a need for revolution in archaic monetary management institutes. By not being in tune with the contemporary trends and times, banks are losing on an opportunity to transform some of their business models and relieve humans of repetitive work, prevent frauds, make better decisions and consequently gain losses. Banks can engage in implementation of new Virtual Assistants and Artificial Intelligence (A.I.) machine learning technologies, just as the other industries have engaged in modernizing i.e. medical checks, medical reports and evaluations, and this research paper will elaborate and emphasize the impact of artificial intelligence implementation on the banking sector processes. This research is based on both quantitative and model-based proofs of system performance by using several analytical tools, such as SPSS. The automation process helps institutions to enhance profitability, performance and to reduce human dependency. In a nutshell, Virtual Assistants powered with Artificial Intelligence improve the business process performance in every sector of business, especially the banking sector making it fast, reliable and not human dependent.

An Assessment Tool for Land Reuse with Artificial Intelligence Method

This paper focuses on land resource consumption due to urban sprawl. Special attention is given to shrinking regions, characterized by economic decline, demographic change, and high unemployment rates. In these regions, vast terrain is abandoned and falls derelict. A geographic information system （GIS） based multi-criteria decision tool is introduced to determine the reuse potential of derelict terrain, to investigate the possible reuse options （housing, business and trade, industry, services, tourism and leisure, and re-greening）, and to visualize the best reuse options for groups of sites on a regional scale. Achievement functions for attribute data are presented to assess the best reuse options based on a multi-attribute technique. The assessment tool developed is applied to a model region in Germany. The application of the assessment tool enables communities to become aware of their resources of derelict land and their reuse potential.

Repairing the human with artificial intelligence in oncology

Artificial intelligence is a groundbreaking tool to learn and analyse higher features extracted from any dataset at large scale.This ability makes it ideal to facing any complex problem that may generally arise in the biomedical domain or oncology in particular.In this work,we envisage to provide a global vision of this mathematical discipline outgrowth by linking some other related subdomains such as transfer,reinforcement or federated learning.Complementary,we also introduce the recently popular method of topological data analysis that improves the performance of learning models.

Artificial intelligence for modeling uveal melanoma

Understanding of the cellular signaling pathways involved in cancer disease is of great importance.These complex biological mechanisms can be thoroughly revealed by their structure,dynamics,and control methods.Artificial intelligence offers rule-based models that favor the research of human signaling processes.In this paper,we give an overview of the advantages of the formalism of symbolic models in medical biology and cell biology of the uveal melanoma.A language is described that allows us:(1)To define the system states and elements with their alterations;(2)To model the dynamics of the cellular system;and(3)To perform inference-based analysis with the logical tools of the language.

Study on the Use of Artificial Intelligence for Cybersecurity in Companies: Case of Companies in Burkina Faso

Poorly secured connected objects can compromise the security of an entire company, or even paralyze others. As useful as they are, they can be open doors for computer attacks against the company. To protect themselves, large companies set up expensive infrastructures to analyze the data that circulates inside and outside the company. They install a SOC, a Security Operation Center whose objective is to identify and analyze, using various tools, the level of protection of a company and, if necessary, to alert on vulnerabilities and leaks of security data. However, the attack detection capabilities of traditional systems are based on a base of known signatures. Problem is that it is increasingly rare to have to face threats whose signature is unknown. Artificial intelligence, on the contrary, does not look for fingerprints in the packets carrying the attack, but rather analyzes how these packets are arranged. The objective of this study is to show that the use of artificial intelligence in companies may be low and to show the positive impacts of its use compared to the traditional system used in companies. We also simulate an attack on a system equipped with artificial intelligence to highlight the advantages of AI in a computer attack. This research is important because it highlights the risks that companies expose themselves to by always remaining secure in their systems based on traditional techniques. The aim of this research is to show the advantages that AI offers on cyber security compared to the traditional security system. The expected result is to show the existing issues regarding the rate of use of AI on cybersecurity in Burkina Faso. .

Adoption of Artificial Intelligence for Diagnosis and Treatment of <i>Staphylococcus aureus</i>Infections Disease on Humans

The aim of this paper is to develop an expert system that could aid medical practitioner to effectively diagnose and treat Staphylococcus aureus infections disease on a human. The objective of the research includes to develop an expert system for quick diagnosis and detection of Staphylococcus aureus bacteria on human skin, a system that aids in accurate treatment of staph infectious diseases by doctors, helps in quick decision making in the hospital, improves accuracy in drug prescription, and a system that will bring about computerized storage process, and to enlighten the knowledge workers on how to implement a computer based decision support systems and importance of it in the health care. The research was motivated due to delay in diagnosis and identification of Staphylococcus aureus bacteria and the fast rate at which infectious disease is spreading, delay in treatment of these bacteria, increase of guess work by health practitioners leading to delay in decision making and lack of electronic storage facility in the hospitals. Top down approach was used in the system design of this research while adopting expert system as the methodology and the programming language used was Java and database design used was MySQL. The result after design was a computerized standalone application that assists health practitioners (Doctor’s) in quick identification, diagnosis, prescription and treatment of Staphylococcus aureus bacteria on human skin. The expert system will facilitate quick decision making in the clinic.

Complexity at Mesoscales:A Common Challenge in Developing Artificial Intelligence

Exploring the physical mechanisms of complex systems and making effective use of them are the keys to dealing with the complexity of the world.The emergence of big data and the enhancement of computing power,in conjunction with the improvement of optimization algorithms,are leading to the development of artificial intelligence(AI)driven by deep learning.However,deep learning fails to reveal the underlying logic and physical connotations of the problems being solved.Mesoscience provides a concept to understand the mechanism of the spatiotemporal multiscale structure of complex systems,and its capability for analyzing complex problems has been validated in different fields.This paper proposes a research paradigm for AI,which introduces the analytical principles of mesoscience into the design of deep learning models.This is done to address the fundamental problem of deep learning models detaching the physical prototype from the problem being solved;the purpose is to promote the sustainable development of AI.