Novel design concepts for network intrusion systems based on dendritic cells processes

An abstraction and an investigation to the worth of dendritic cells (DCs) ability to collect, process and present antigens are presented. Computationally, this ability is shown to provide a feature reduction mechanism that could be used to reduce the complexity of a search space, a mechanism for development of highly specialized detector sets as well as a selective mechanism used in directing subsets of detectors to be activated when certain danger signals are present. It is shown that DCs, primed by different danger signals, provide a basis for different anomaly detection pathways. Different antigen-peptides are developed based on different danger signals present, and these peptides are presented to different adaptive layer detectors that correspond to the given danger signal. Experiments are then undertaken that compare current approaches, where a full antigen structure and the whole repertoire of detectors are used, with the proposed approach. Experiment results indicate that such an approach is feasible and can help reduce the complexity of the problem by significant levels. It also improves the efficiency of the system, given that only a subset of detectors are involved during the detection process. Having several different sets of detectors increases the robustness of the resulting system. Detectors developed based on peptides are also highly discriminative, which reduces the false positives rates, making the approach feasible for a real time environment.

Liver replacement therapy with extracorporeal blood purification techniques current knowledge and future directions

Clinically,it is highly challenging to promote recovery in patients with acute liver failure(ALF)and acute-on-chronic liver failure(ACLF).Despite recent advances in understanding the underlying mechanisms of ALF and ACLF,standard medical therapy remains the primary therapeutic approach.Liver transplantation(LT)is considered the last option,and in several cases,it is the only intervention that can be lifesaving.Unfortunately,this intervention is limited by organ donation shortage or exclusion criteria such that not all patients in need can receive a transplant.Another option is to restore impaired liver function with artificial extracorporeal blood purification systems.The first such systems were developed at the end of the 20th century,providing solutions as bridging therapy,either for liver recovery or LT.They enhance the elimination of metabolites and substances that accumulate due to compromised liver function.In addition,they aid in clearance of molecules released during acute liver decompensation,which can initiate an excessive inflammatory response in these patients causing hepatic encephalopathy,multiple-organ failure,and other complications of liver failure.As compared to renal replacement therapies,we have been unsuccessful in using artificial extracorporeal blood purification systems to completely replace liver function despite the outstanding technological evolution of these systems.Extracting middle to high-molecular-weight and hydrophobic/protein-bound molecules remains extremely challenging.The majority of the currently available systems include a combination of methods that cleanse different ranges and types of molecules and toxins.Furthermore,conventional methods such as plasma exchange are being re-evaluated,and novel adsorption filters are increasingly being used for liver indications.These strategies are very promising for the treatment of liver failure.Nevertheless,the best method,system,or device has not been developed yet,and its probability of getting developed in the near future is also low.Furthermore,little is known about the effects of liver support systems on the overall and transplant-free survival of these patients,and further investigation using randomized controlled trials and meta-analyses is needed.This review presents the most popular extracorporeal blood purification techniques for liver replacement therapy.It focuses on general principles of their function,and on evidence regarding their effectiveness in detoxification and in supporting patients with ALF and ACLF.In addition,we have outlined the basic advantages and disadvantages of each system.

Revolutionizing Treatment:AI-Driven Noninvasive Approaches for ODD and ADHD

Oppositional Defiant Disorder(ODD)and Attention Deficit/Hyperactivity Disorder(ADHD)are mental health conditions that have traditionally been managed through behavioral therapies and medication.However,the integration of Artificial Intelligence(AI)has brought about a revolutionary shift in treatment approaches.This article explores the role of AI-driven noninvasive treatments for ODD and ADHD.AI offers personalized treatment plans,predictive analytics,virtual therapeutic platforms,and continuous monitoring,enhancing the effectiveness and accessibility of interventions.Ethical considerations and the need for a balanced approach are discussed.As technology evolves,collaborative efforts between mental health professionals and technologists will shape the future of mental health care for individuals with ODD and ADHD.

Artificial visual-tactile perception array for enhanced memory and neuromorphic computations

The emulation of human multisensory functions to construct artificial perception systems is an intriguing challenge for developing humanoid robotics and cross-modal human–machine interfaces.Inspired by human multisensory signal generation and neuroplasticity-based signal processing,here,an artificial perceptual neuro array with visual-tactile sensing,processing,learning,and memory is demonstrated.The neuromorphic bimodal perception array compactly combines an artificial photoelectric synapse network and an integrated mechanoluminescent layer,endowing individual and synergistic plastic modulation of optical and mechanical information,including short-term memory,long-term memory,paired pulse facilitation,and“learning-experience”behavior.Sequential or superimposed visual and tactile stimuli inputs can efficiently simulate the associative learning process of“Pavlov's dog”.The fusion of visual and tactile modulation enables enhanced memory of the stimulation image during the learning process.A machine-learning algorithm is coupled with an artificial neural network for pattern recognition,achieving a recognition accuracy of 70%for bimodal training,which is higher than that obtained by unimodal training.In addition,the artificial perceptual neuron has a low energy consumption of~20 pJ.With its mechanical compliance and simple architecture,the neuromorphic bimodal perception array has promising applications in largescale cross-modal interactions and high-throughput intelligent perceptions.

Research Progress of Parallel Control and Management

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex systems.This paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application demonstration.The future research,application directions,and suggestions are also discussed.

Optimal approximation of linear systems by artificial immune response

This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.

Novel D-galactosamine-induced cynomolgus monkey model of acute liver failure

AIM To establish a simplified, reproducible D-galactosamineinduced cynomolgus monkey model of acute liver failure having an appropriate treatment window. METHODS Sixteen cynomolgus monkeys were randomly dividedinto four groups(A, B, C and D) after intracranial pressure(ICP) sensor implantation. D-galactosamine at 0.3, 0.25, 0.20 + 0.05(24 h interval), and 0.20 g/kg body weight, respectively, was injected via the small saphenous vein. Vital signs, ICP, biochemical indices, and inflammatory factors were recorded at 0, 12, 24, 36, 48, 72, 96, and 120 h after D-galactosamine administration. Progression of clinical manifestations, survival times, and results of H&E staining, TUNEL, and Masson staining were recorded. RESULTS Cynomolgus monkeys developed different degrees of debilitation, loss of appetite, and jaundice after D-galactosamine administration. Survival times of groups A, B, and C were 56 ± 8.7 h, 95 ± 5.5 h, and 99 ± 2.2 h, respectively, and in group D all monkeys survived the 144-h observation period except for one, which died at 136 h. Blood levels of ALT, AST, CK, LDH, TBi L, Cr, BUN, and ammonia, prothrombin time, ICP, endotoxin, and inflammatory markers [(tumor necrosis factor(TNF)-α, interleukin(IL)-1β, and IL-6)] significantly increased compared with baseline values in different groups(P < 0.05). Pathological results showed obvious liver cell necrosis that was positively correlated with the dose of D-galactosamine.CONCLUSION We successfully established a simplified, reproducible D-galactosamine-induced cynomolgus monkey model of acute liver failure, and the single or divided dosage of 0.25 g/kg is optimal for creating this model.

Multimodal Evolution Approach to Multidimensional Intrusion Detection

An artificial immunity based multimodal evolution algorithm is developed to generate detectors with variable coverage for multidimensional intrusion detection. In this algorithm, a proper fitness function is used to drive the detectors to fill in those detection holes close to self set or among self spheres, and genetic algorithm is adopted to reduce the negative effects that different distribution of self imposes on the detector generating process. The validity of the algorithm is tested with spherical and rectangular detectors, respectively, and experiments performed on two real data sets （machine learning database and DAPRA99） indicate that the proposed algorithm can obtain good results on spherical detectors, and that its performances in detection rate, false alarm rate, stabih＇ty, time cost, and adaptability to incomplete training set on spherical detectors are all better than on rectangular ones.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

Experimental Studies of Artificial Conscious Systems

Human consciousness is the most interesting and mysterious phenomenon inthe world. In this paper, the results of the computational study and simulationof the conscious behaviour, such as the learning of 1anguage and image pat-terns, traditional conditioning, association, imagination and dream, have beenpresented. Based on these results, an experimental conscious system - CON-SCITRON, has been developed. Further discussion on development of artificialconscious systems is also provided.

The Motif Tracking Algorithm

The search for patterns or motifs in data represents a problem area of key interest to finance and economic researchers. In this paper, we introduce the motif tracking algorithm （MTA）, a novel immune inspired （IS） pattern identification tool that is able to identify unknown motifs of a non specified length which repeat within time series data. The power of the algorithm comes from the fact that it uses a small number of parameters with minimal assumptions regarding the data being examined or the underlying motifs. Our interest lies in applying the algorithm to financial time series data to identify unknown patterns that exist. The algorithm is tested using three separate data sets. Particular suitability to financial data is shown by applying it to oil price data. In all cases, the algorithm identifies the presence of a motif population in a fast and efficient manner due to the utilization of an intuitive symbolic representation. The resulting population of motifs is shown to have considerable potential value for other applications such as forecasting and algorithm seeding.

Artificial cognitive systems: the next generation of the digital twin. An opinion. [version 2;peer review: 2 approved]

The digital twin is often presented as the solution to Industry 4.0 and,while there are many areas where this may be the case,there is a risk that a reliance on existing machine learning methods will not be able to deliver the high level cognitive capabilities such as adaptability,cause and effect,and planning that Industry 4.0 requires.As the limitations of machine learning are beginning to be understood,the paradigm of strong artificial intelligence is emerging.The field of artificial cognitive systems is part of the strong artificial intelligence paradigm and is aimed at generating computational systems capable of mimicking biological systems in learning and interacting with the world.This paper presents an argument that artificial cognitive systems offer solutions to the higher level cognitive challenges of Industry 4.0 and that digital twin research should be driven in the direction of artificial cognition accordingly.This argument is based on the inherent similarities between the digital twin and artificial cognitive systems,and the insights that can already be seen in aligning the two approaches.

Effects of artificially induced complete mixing on dissolved organic matter in a stratified source water reservoir

Naturally complete mixing promotes the spontaneous redistribution of dissolved oxygen(DO),representing an ideal state for maintaining good water quality,and conducive to the biomineralization of organic matter.Water lifting aerators(WLAs)can extend the periods of complete mixing and increase the initial mixing temperature.To evaluate the influence of artificial-induced continuously mixing on dissolved organic matter(DOM)removal performance,the variations of DOM concentrations,optical characteristic,environmental factors were studied after approaching the total mixing status via WLAs operation.During this process,the dissolved organic carbon reduced by 39.18%,whereas the permanganate index decreased by 20.47%.The optical properties indicate that the DOM became more endogenous and its molecular weight decreased.Based on the results of the Biolog Eco Plates,the microorganisms were maintained at a relatively high metabolic activity in the early stage of induced mixing when the mixing temperature was relatively high,whereas DOM declined at a high rate.With the continuous decrease in the water temperature,both the metabolic capacity and the diversity of aerobic microorganisms significantly decreased,and the rate of organic matter mineralization slowed down.The results of this study demonstrate that the artificial induced mixing largely enhanced the removal DOM performance by providing a long period of aerobic conditions and higher initial temperature.

Intelligent multi-user detection using an artificial immune system

Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

Enabling data-driven anomaly detection by design in cyber-physical production systems

Designing and developing distributed cyber-physical production systems(CPPS)is a time-consuming,complex,and error-prone process.These systems are typically heterogeneous,i.e.,they consist of multiple components implemented with different languages and development tools.One of the main problems nowadays in CPPS implementation is enabling security mechanisms by design while reducing the complexity and increasing the system’s maintainability.Adopting the IEC 61499 standard is an excellent approach to tackle these challenges by enabling the design,deployment,and management of CPPS in a model-based engineering methodology.We propose a method for CPPS design based on the IEC 61499 standard.The method allows designers to embed a bio-inspired anomaly-based host intrusion detection system(A-HIDS)in Edge devices.This A-HIDS is based on the incremental Dendritic Cell Algorithm(iDCA)and can analyze OPC UA network data exchanged between the Edge devices and detect attacks that target the CPPS’Edge layer.This study’s findings have practical implications on the industrial security community by making novel contributions to the intrusion detection problem in CPPS considering immune-inspired solutions,and cost-effective security by design system implementation.According to the experimental data,the proposed solution can dramatically reduce design and code complexity while improving application maintainability and successfully detecting network attacks without negatively impacting the performance of the CPPS Edge devices.

Risk dynamics for marine systems:towards a bio-inspired framework for dynamic risk assessment

The development of innovative, complex marine systems, such as autonomous ship concepts, has led to risk-based approaches indesign and operation that provide safety level quantification and continuous risk assessment. The existing approaches to dynamicrisk assessmentmainly aim at updating accident probabilities for specific risk scenarios, based on knowledge of system operation andfailure, aswell as on past accident and failure information. However, for innovative marine systems that include complex interactions,our ability to identify anything that might go wrong is very limited, which may lead to unidentified risks, and failure data may not beavailable. This paper presents the foundations of a framework for dynamic risk assessment, which is equally applicable to mannedand autonomous ships and mainly relies on information about the safe operational envelope and real-time information regardingdeviations from safety. Inspiration is drawn from how the biological immune system identifies the risk of infection in a dynamicenvironment. The objective is to show the feasibility and benefits of our approach for quantifying the operational risk of marinesystems. This paper provides the conceptual basis for developing ship specific applications and describes a process for dynamic riskassessment that is methodologically based on artificial immune systems. To demonstrate the implementation of our framework, wedescribed, an illustrative example that involves a ship in a grounding scenario. The results show that the bio-inspired assessmentprocess and risk description can reflect the changes of the risk level of a marine system.

A survey of the pursuit-evasion problem in swarm intelligence

For complex functions to emerge in artificial systems,it is important to understand the intrinsic mechanisms of biological swarm behaviors in nature.In this paper,we present a comprehensive survey of pursuit–evasion,which is a critical problem in biological groups.First,we review the problem of pursuit–evasion from three different perspectives:game theory,control theory and artificial intelligence,and bio-inspired perspectives.Then we provide an overview of the research on pursuit–evasion problems in biological systems and artificial systems.We summarize predator pursuit behavior and prey evasion behavior as predator–prey behavior.Next,we analyze the application of pursuit–evasion in artificial systems from three perspectives,i.e.,strong pursuer group vs.weak evader group,weak pursuer group vs.strong evader group,and equal-ability group.Finally,relevant prospects for future pursuit–evasion challenges are discussed.This survey provides new insights into the design of multi-agent and multi-robot systems to complete complex hunting tasks in uncertain dynamic scenarios.

集成智能触觉系统用于机器人闭环反馈的可控抓握

人工智能与机器人技术的融合,使智能机器人的应用范围从工业制造领域扩展到日常生活和医疗健康等领域.目前,智能机器人除了视觉、听觉和嗅觉等感知外,还需具备触觉反馈的感知能力才能在复杂的环境中帮助人类完成更精细的任务.本文基于触觉感知阵列模块、多通道数据采集和信息处理芯片以及闭环反馈控制模块,构建了集成智能触觉系统(IITS),实现了机器人手或假肢手对轻柔物体灵活可控抓握.通过设定适当的压力阈值,机器人手在抓取物体的过程中,一旦触觉感知阵列上的抓握值达到设定阈值,则会停止抓握,进而实现一个闭环反馈的可控抓指令.实验证明,集成有IITS的机器人可以灵活抓取气球、纸杯、软饼、活蚕等各种物体,该智能系统在假肢、服务型机器人和深海探索机器人的设计方面具有重要的潜在应用价值.

2D-materials-based optoelectronic synapses for neuromorphic applications

Optoelectronic artificial synapses(OEASs)are essential for realizing artificial neural networks(ANNs)in nextgeneration information processing that has high transmission speed,high bandwidth,and low power consumption.Two-dimensional(2D)materials endowed with strong light-matter interactions and atomically thin dangling-bond-free surfaces are candidates for achieving versatile optoelectronics.Developing 2D OEASs for future neuromorphic applications is significant to break the bottleneck of von Neumann architecture and achieve future artificial intelligence systems.This review primarily focuses on recent developments in advanced 2D OEASs,discussing their working mechanism as well as potential applications.Common materials,device structures,and their synthesis and construction methods are also summarized.Finally,the prospects for future 2D OEASs from the perspectives of materials,performance,and applications are briefly described.

Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.