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This study develops a specialized technology roadmap for research on protective food products in Thailand, emphasizing targeted and integrated solutions that align with industry and consumer needs. Data were gathered from primary sources through focus group discussions with industry experts, researchers, and policymakers. Content analysis was employed for data analysis, supplemented by secondary data from a comprehensive literature review and bibliometric analysis. The study identifies opportunities for product development based on technological advancements related to protective foods, specifically focusing on digestion, immune system support, and timelines for short-term, medium-term, and long-term goals. The findings offer valuable insights for stakeholders in creating high-nutritional-value protective food products. Furthermore, the study advocates for the use of botanical ingredients and unused materials, underscoring the essential role of technology in advancing functional foods in Thailand. [ABSTRACT FROM AUTHOR]

The investigation of an immune system has long been and will continue to be one of dominant themes in both ecology and biology due to its importance. In this paper, we consider Marchuk's model of an immune system where this model is governed by a system of three differential equations with time. This model has two equilibrium states which are healthy state and chronic state. It is healthy state when the antigen reproduction is small while chronic state is when antigen reproduction rate is large. The objectives of this paper are to analyse the stability of this model, to summarize this stability using bifurcation diagram and to discuss interaction between the healthy and chronic states at stationary solution. The methods involved are stability theory and bifurcation theory. Our results show that healthy states are saddle and only one chronic state is asymptotically stable for a region of parameter considered. For the bifurcation's case, as we increase the value of a parameter in this model, the chronic state shows that there are increment in the number of antigen, plasma cell and the antibody production. [ABSTRACT FROM AUTHOR]

Since the discovery of Stuxnet in 2010, there have been a plethora of Advanced Persistent Threats (APT) that have been discovered in regular IT networks and in critical infrastructure such as Industrial Control Systems (ICS). ICS is a general term for different control systems like Supervisory Control and Data Acquisition (SCADA), Distributed Control Systems (DCS), Process Control Systems (PCS), and Smart grid. ICSs are used in mining industries, transportation systems, and for the distribution of water, natural gas, oil, electricity, and communications, in specialised facilities such as nuclear plants and for automating many other facilities. In a quest to find tangible solutions to the APT problem in information technology systems, many solutions have been designed to detect and protect against, as well as recover from APT. This paper endeavoured to survey the different techniques that have been designed to solve APT problems and their levels of success. This paper therefore discusses APT defence techniques. It is noted that many APTs use social engineering techniques to gain entry into systems; however, technical solutions are being developed to try and correct human errors that enable APTs to infiltrate ICS. Technical solutions include tweaking existing security controls, anomaly detection techniques and defence-in-depth techniques. The researchers believe that the defence-in-depth approach is the future of security and thus, suggest further improvements in defence-in-depth approaches by emulating the immune system which uses a layered defence mechanism to protect the human body from pathogens. Since the main research focused on APTs that are attacking ICS, a control system was setup to find the level of effectiveness of emulating immune system properties. This paper gives some preliminary results on how ICS which exhibit immune system properties perform better than those that are not. [ABSTRACT FROM AUTHOR]

An all-in-one instrumentation system design for immunochromatographic assay with Windows XP Embedded (XPE) Approach is presented in this paper. The proposed system can determine the concentration of target analyte within a few seconds, which can be used in a wide range of applications. This paper also presents a new image analysis algorithm to perform the unsupervised classification for the immunochromatographic assay with high-definition webcam. The overall development scheme is explained with real examples. The Windows XP Embedded approach makes it easier to get our system with up-to-date peripherals to market. That is crucial for short life-cycle peripherals on market. The overall development scheme can be applied to other Windows XP Embedded compatible system design as well. The platform EPIA-P720 is employed as a demonstrating example. The overall system functions are verified by experimental results. [ABSTRACT FROM PUBLISHER]

Opinion mining is used to automate the process of identifying opinion whether it is a positive or negative view. Majority of previous works on this field uses natural language programming techniques to identify the sentiment. This paper reports the use of artificial immune system (AIS) technique in identifying Malaysian online movie reviews. This opinion mining process uses three string similarity functions namely Cosine Similarity, Jaccard Coefficient and Sorensen Coefficient. In addition, AIS performance was compared with other traditional machine learning techniques, which are Support Vector Machine, Naïve Baiyes and k-Nearest Network. The result of the findings are analyzed and discussed in this paper. [ABSTRACT FROM PUBLISHER]

TB and HIV infection have the effect of deeply on assault the immune system, since they can afford to weaken host immune respone through a mechanism that has not been fully understood. HIV co-infection is the stongest risk factor for progression of M. tuberculosis to active TB disease in HIV individuals ,as well as TB has been accelerated to progression HIV infection. In this paper we create a model of transmission co-infection TB in HIV community, dynamic system with ten compartments built in here . Dynamic analysis in this paper mentioned ranging from disease free equilibrium conditions, endemic equilibrium conditions , basic reproduction ratio, stability analysis and numerical simulation. Basic reproductive ratio were obtained from spectral radius the next generation matrix of the model. Numerical simulations are built to justify the results of the analysis and to see the changes in the dynamics of the population in each compartment. The sensitivity analysis indicates that the parameters affecting the population dynamics of TB in people with HIV infection is parameters rate of progression of individuals from the exposed TB class to the active TB, treatment rate of exposed TB individuals, treatment rate of infectious (active TB) individuals and probability of transmission of TB infection from an infective to a susceptible per contact per unit time. We can conclude that growing number of infections carried by infectious TB in people with HIV infection can lead to increased spread of disease or increase in endemic conditions. [ABSTRACT FROM AUTHOR]

Brakes are one of the most important components in automobiles because they allow the vehicle to stop or slow down. It requires extra caution in terms of safety and dependability. As a result, it is critical to monitor the brake system's condition in order to assure safety. Vibration signals play an important function in detecting brake system faults. A machine learning approach was employed in this work to identify brake defects under various scenarios. A piezoelectric type transducer and data collecting system were used to collect vibration signals. The vibration signals were used to obtain the relevant histogram features. The feature selection and feature classification were done using the vibration signals obtained from the transducer. An artificial immune recognition system was used to classify the extracted features (AIRS). The classification accuracy as well as the classifier's performance level have been reported. [ABSTRACT FROM AUTHOR]

Artificial Immune System (AIS) has become known as an area of computer science and engineering that uses immune system's metaphors to solve problems with a novel solution. The immune system has an interesting area for computer scientists with its uniqueness and fascinating computational system that has evolved to solve a unique problem. A deeper understanding of the immune system, in part through the use of modeling techniques, which will lead to the development of richer, more effective immune inspired engineered systems. It can suggest new solutions to computer science problems, or at least give us new ways of looking at these problems. This paper shows that bio-systems like immune system can be modeled using the object oriented perspective. We use Unified Modeling Language to represent the behavior of immune system and the interaction between immune system elements. The introduction motivates the need of immune system modeling at different levels of abstraction. Then the UML diagrams are used to illustrate the static and the dynamic behavior of immune system. There are an examples model for elementary clonal selection that involved B-cell, antibodies, and antigen correspondences. Since there are 14 (fourteen) diagrams of UML, this paper uses some of the diagrams. There are the use-case diagram to show functionality of immune system, activity diagrams to show the global abstractions of system and the class diagram to show the structure of immune system elements. At the conclusion, we can see that OO perspectives are promising the better understanding for complex bio-systems such as immune system. This is lead to get the better bio-inspired computation solutions to solve computer science problems. [ABSTRACT FROM PUBLISHER]

Danger Theory is a method of biological immunology. It presents an effective method to reduce false positive and improve the efficiency of AIS. Cloud model is an effective tool to transform qualitative concepts into quantitative expressions for uncertain problems. Change is potential causes of system imbalance, and is the basis and sample for danger analysis. This paper uses the concept of Cloud model to estimate systematic parameters and consequently presents the definition of danger signal. We construct an artificial immune model with danger theory based on changes, "dangerous" work with "non self" to stimulate the immune response. [ABSTRACT FROM PUBLISHER]

This paper discusses obtaining further results looking at the implementation of a full-wave rectifier based on phase-modulation. Phase modulation is used to obtain a low-voltage DC supply by controlling phase-angle through changes in a variable resistor and capacitor. This property allows for AC rectification along with an inherent voltage regulation feature. The response of the circuit is recorded in proportion to changes in resistance and capacitance over its range. The ability for the circuit to rectify and regulate is analyzed with respect to its ripple voltage factor which in this paper is recorded as low as 0.25mV/V. [ABSTRACT FROM PUBLISHER]

This paper presents a new technique for indentifying optimal network reconfiguration. The proposed technique was developed based on the hybridization of quantum mechanics concepts with the Artificial Immune System (AIS) optimization and it is named as Quantum-inspired Artificial Immune System (QI-AIS). Network reconfiguration is performed by altering the topological structure of the distribution feeder. It provides an efficient way to control the tie-line and sectionalizing switches. By reconfiguring the network, voltage stability can be improved and at the same time system total losses can also be minimized for particular set of loads in a distribution system. Throughout the research, the total loss minimization and voltage stability improvement are the objective functions used to indicate the optimal network reconfiguration. This paper utilized IEEE 69-bus system in this research. Other than having loss minimization and voltage stability improvement as the objective function, this research also looked into multi objective function which is combination of loss minimization and voltage stability improvement using the weighted sum technique. Simulation results show that QI AIS optimization technique gave a better total loss minimization and it also provides a better computation time compared to AIS optimization technique. The results from the multi objective function also outperformed the loss minimization and voltage stability improvement considered individually. [ABSTRACT FROM PUBLISHER]

This paper applies the data mining process to predict hypertension from patient medical records with eight other diseases. A sample with the size of 9862 cases has been studied. The sample was extracted from a real world Healthcare Information System database containing 309383 medical records. We observed that the distribution of patient diseases in the medical database is imbalanced. Under-sampling technique has been applied to generate training data sets, and data mining tool Weka has been used to generate the Naïve Bayesian and J-48 classifiers. In addition, an ensemble of five J-48 classifiers was created trying to improve the prediction performance, and rough set tools were used to reduce the ensemble based on the idea of second-order approximation. Experimental results showed a little improvement of the ensemble approach over pure Naïve Bayesian and J-48 in accuracy, sensitivity, and F-measure. [ABSTRACT FROM PUBLISHER]

The producing mechanism for danger signals is the most important in the danger theory used in AIS. To determine whether a system is "dangerous" needs more considerations. This paper uses the cloud model to describe the changes of system continuous parameters. For discontinuous change parameters, we use the changes of each parameter as a separate danger signal. The continuous and discontinuous parameters of danger signals together constitute the overall rule library. we use the method that calculating the triggered rules to determine the "danger" or "security" of system, and realize the collaborative identification of danger signals. [ABSTRACT FROM PUBLISHER]

This paper presents an approach for detecting network traffic anomalies using detectors generated by a genetic algorithm with deterministic crowding Niching technique. Particularly, the suggested approach is inspired by the negative selection mechanism of the immune system that can detect foreign patterns in the complement (non-self) space. In our paper, we run a number of experiments on the relatively new NSL-KDD data set which was never tested against this algorithm before our work. We run the test using different values for the involved parameters, to find out which values give the best detection rates, so we can give recommendations for future application of the algorithm. Also, Formal Concept Analysis is applied on the generated rules to visualize the relation among attributes. We will show in the results that the algorithm have very good results through the analysis, compared to other machine learning approaches. [ABSTRACT FROM PUBLISHER]

The interoperability of healthcare information system is an important issue in medical informatics field. It is important to increase the life expectancy, reduce medical errors and provide more medical information for medical personal. For achieving the interoperability of healthcare information systems is important to have a standardized communication. This paper presents a control for Visual Studio .NET 2010 toolbox; it can be used for ASP. NET pages and in the final it will be used to improve interoperability issues. It has the possibility to show the tables and fields from different database and it can be integrated with a cloud application. Further is reveled the use of this control in a Pediatrics application on Azure platform. CC (cloud computing) is a technology that supports flexibility, seamless care, and can reduced costs of the medical act. [ABSTRACT FROM PUBLISHER]

This paper overviews private pediatric practice clinical data management needs and discusses the basic visit types. It then models the patients' trajectory in the course of a visit as he or she sees the nurse for routine measurements then the doctor for age-based well visit interviews or ad-hoc sick visit consultations. For both the nurse and the doctor, we consider conventional text-based electronic health records (EHR) data entry forms as well as an alternative, visually-based, user interface. We discuss the impacts of the alternative visual interface and projected benefits. It is our hope that the findings are generalizable to other medical specialties. [ABSTRACT FROM PUBLISHER]

In this paper we propose a new method for cells segmentation in HEp-2 images addressing and overcoming the main limitations of the existing approaches. The proposed method adopts image reconstruction for a preliminary image segmentation and, then, it employs a sort of classifier-controlled dilation for better determining the structure of the cells, where the classifier is trained using data of the image at hand. We compare the performance of the proposed method with the most representative approaches from the scientific literature on a common and publicly available dataset of HEp-2 images. [ABSTRACT FROM PUBLISHER]

Breast cancer is the second most common cancer in females, after lung cancer in the world. In Taiwan, there are about 7500 female suffering from breast cancer every year. The incidence of breast cancer has exceeded cervical cancer and has become the most common female cancer. Immunohisto chemistry (IHC) image is widely applied to the diagnosis of breast cancer, but it requires a great deal of manpower and time. Therefore, in this paper, we proposed a method to assess the grade of breast cancer in IHC images. The proposed method consists of four steps, including ROI extraction, feature extraction, feature selection, and a SVM classifier. According to the experimental results, the proposed method can automatically and effectively asses the score of IHC images. [ABSTRACT FROM PUBLISHER]

In terms of weighted scale-free networks' properties, this paper proposes edges immunization based on discrete particle swarm optimization algorithm. We translate the virus immunization problem to a multi-objective optimization problem in this paper. After a set of edges are immunized, the network is divided into sub-networks whose nodes strength and average path length are as small as possible. Experiment results show that the proposed algorithm has a higher spread threshold and better performance than the traditional immunization strategies. [ABSTRACT FROM PUBLISHER]

The paper contains the study of Multi skill training to the employees, which includes equalizing the work pressure among the employees and managing the shortage of employees in the manufacturing process. A mathematical model was formed with an objective of minimizing the total expected cost of the employees in the manufacturing process. An Artificial Immune system algorithm was used to stimulate the solutions. The results showed that the multi skill training program yields a better result compared to non-trained employees. [ABSTRACT FROM AUTHOR]

This paper proposes a big data integrated framework to assist with prevention and control of HIV/AIDS, TB and silicosis (HATS) in the mining industry. The linkage between HATS presents a major challenge to the mining industry globally. When the immune system is compromised by HIV/AIDS and silicosis, it makes it easier for tuberculosis to infect the body. In addition, the silica dust which affects the lungs may also cause silicosis and tuberculosis. The objective of this paper is to posit a big data integrated framework to assist in the prevention and control of HATS in the mining industry. Literature was reviewed in order to build a conceptual framework. Although this study is not the first to apply big data in healthcare, to the researcher's knowledge, it is the first to apply big data in understanding the linkage between HATS in the mining industry. The literature review indicates only a few studies using big data in healthcare with no research found on big data and HATS. It therefore makes a contribution to existing body of literature on the control of HATS. The proposed big data framework has the potential of addressing the needs of predictive epidemiology which is important in forecasting and disease control in the mining industry. The paper therefore lays a foundation for the use of viable systems model and big data to address the challenges of HATS in the mining industry. As part of future work, the framework will be validated using sequential explanatory mixed methods case study approach in mining organizations. [ABSTRACT FROM AUTHOR]

This paper concerns the optimal control of a mathematical model of a growing tumor and its interaction with the immune system. This model consists of four populations-tumor cells, dendritic cells (as an innate immune system), cytotoxic T cells, and helper T cells (as a specific immune system)-in the form of a system of ordinary differential equations. Some tumors present dendritic cell and such cells have a potential role in regulating the immune system. In this model, we assume that dendritic cells can activate cytotoxic T cells and, in turn, can clear out tumor cells. Furthermore, by adding controls as a treatment to the model, we minimize both the tumor cell population and the cost of treatment. We do this by applying the optimal control for this problem. First, Pontryagin's Principle is used to characterize the optimal control. Then, the optimal system is solved numerically using the Forward-Backward Runge- Kutta method. Finally, the effect of each treatment is investigated. The numerical results show that these controls are effective in reducing the number of tumor cells. [ABSTRACT FROM AUTHOR]

In this paper a brief review of the Methodology for Developing Virtual Reality Projects with Haptic Rendering (HAREVIR) is presented, subsequently a case study based on it is shown. The methodology and the case study are focused on the task of rendering and manipulating virtual objects through different algorithms that exist and are related to haptic rendering. The proposed case study is for an informative virtual environment about Non-Hodgkin lymphoma (NHL) that is a type of cancer of the immunologic system. In the case study, the issues associated to the design process are divided into four main parts. In the first part of the case study, the requirements for its development are analyzed. In the second one, activities, algorithms, languages and tools are selected. In the third one, the case study is implemented and finally contributions are presented. [ABSTRACT FROM AUTHOR]

The human immune system has characteristics such as self-organisation, robustness and adaptivity that may be useful in the development of adaptive systems. One suitable application area for adaptive systems is Information Filtering (IF).Within the context of IF, learning and adapting user profiles is an important research area. In this paper, we proposed an architecture for adaptive IF incorporating ideas from Artificial Immune Systems (AIS). We then extract general features of potential immune inspired profile adaptation based on scenario-based modeling. The richness of scenario usage which we have encountered in this work indicates the importance of comprehensive process guidance [ABSTRACT FROM AUTHOR]

A methodology is proposed to build a run-time image for an instrumentation system with Windows Embedded Standard 7 (WES7) operating system. The proposed system can determine the concentration of target analyte within a few seconds, which can be used in a wide range of applications. This paper also presents a new image analysis algorithm to perform the unsupervised classification for the immunochromatographic assay with high-definition webcam. The overall development scheme to complete an instrumentation system is explained with real examples. The proposed methodology makes it easier to get our system with up-to-date peripherals to market. That is crucial for short life-cycle peripherals on market. The proposed methodology is applied to design a medical instrumentation system, and the Tablet is employed as a demonstrating example. The proposed methodology suits any WES7 compatible system as well. The overall system functions are verified by experimental results. [ABSTRACT FROM PUBLISHER]

This paper provides an introduction to and an overview of type-2 fuzzy Logic system and its optimal computation. The difference between a type-2 fuzzy set (T2 FS) and a type-1 fuzzy set (T1 FS) is discussed. The need of optimal design for the type-2 fuzzy Logic system is presented. The important advance in the optimal computation for interval type-2 fuzzy sets (IT2 FLS) and systems is demonstrated. The opinion that the artificial immune algorithm (AIS) is a novel optimistic method in the study area of type-2 fuzzy logic system is presented. Further work that needs to be done with the AIS and other versions of intelligent algorithms is pointed out. [ABSTRACT FROM PUBLISHER]

Nowadays, computers and networks have become the infrastructure of people's daily lives. As the Internet becomes more open to public, most people and organizations prefer to use online services. At the same time, the Internet security problem is becoming more severe. An effective and feasible protection measure for network security is necessary. Intrusion detection system (IDS) is a typical measure used to provide protection for information systems. In order to detect unknown attacks, anomaly detection technique is a commonly used method. Meanwhile, many scholars are studying more effective and easy-to-use anomaly detection methods. In this paper, a new intrusion detection approach inspired by the function of biological memory cell is proposed. The detectors in this approach (which called memory detector) are more effective than common detectors. The generation method of memory detector is detailed. In order to analyze and evaluate the performance of this approach, comparative experiments are implemented with training and testing data from “Knowledge Discovery and Data mining cup 1999” (KDD99) dataset. The results show that our approach provides better performance than ordinary anomaly detection approaches with higher true positive rate and lower false positive rate. [ABSTRACT FROM PUBLISHER]

Immunostimulatory complexes (ISCOMs), composed of antigen, saponin, cholesterol, and phospholipid, are the typical nano materials. It has the ability to hold antigen to delivery system, while ISCOMATRIX is a particulate adjuvant comprising saponin, cholesterol and phospholipid but antigen absence. The misture of antigens with ISCOMATRIX is called an ISCOMATRIX vaccine. In this paper, a new material (DC-Chol) to form ISCOMATRIX by the method of dialysis. The ISCOMATRIX is a nano materials and have the targenting effect. It has the advantage of taking the antigen to targeting cell(APCs). [ABSTRACT FROM PUBLISHER]

In this paper, we propose a Neighbourhood Clonal Selection Learning Algorithm(N-Clonalg), which is combined with the idea of artificial immune system and Agent technology. Based on the grid environment, the individuals in N-Clonalg do local and global searching operators. Unlike other artificial immune algorithms, N-Clonalg has three main search operators, i.e. Neighbourhood Clonal Selection, Neighbourhood Competition and Self-learning. Combining the global and local search, N-Clonalg overcomes the phenonmena of premature and slow convergence, and can effectively get the global optimals. Distillation resources optimization shows that it has better search performance. [ABSTRACT FROM PUBLISHER]

In this paper authors presented the research of textile electroconductive materials, which can be used to construction sensory textronic shirt to breath frequency measurement. The paper describes a method of measuring resistance of contact point and contact line of two electro conductive threads in textile knitted structures, which is known as transition resistance. This resistance is the input value to the modeling. The full paper also will be present some results of measurements carried out on unique measuring stands. The model of resistance connection in knitted structure in Matlab program will been also describe. [ABSTRACT FROM PUBLISHER]

In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and significantly reduce the false positive rate. The proposed HPSOWM works especially well for the efficient classification of unknown behaviors and malicious attacks. [ABSTRACT FROM PUBLISHER]

The amazing nature of biological immune systems on protecting humans from pathogens inspired people to develop artificial immune systems. Designed to simulate the functionalities of biological immune systems, artificial immune systems are suggested to be mainly applied in the domain of computer security. In this paper, we propose an artificial immune system for phishing detection. The system is to detect phishing emails through memory detectors and mature detectors. The memory detectors are generated from the training data set, which, in turn, contains the phishing emails previously seen by the system. The immature detectors are reproduced through the system's mutation process. To the best of our knowledge, this is the first time such a system is ever proposed. We believe that the system is more adaptive than any other existing phishing detection techniques. [ABSTRACT FROM PUBLISHER]

This paper proposes an artificial immune system based algorithm for solving constrained optimization problems, inspired by the principle of the vertebrate immune system. The analogy between the mechanism of vertebrate immune system and constrained optimization formulation is first given. The population is divided into two groups- feasible individuals and infeasible individuals. The infeasible individuals are viewed as the inactivated immune cells approaching the feasible regions by decreasing the constraint violations whereas the feasible individuals are treated as activated immune cells searching for the optima. The interaction between them through the extracted directional information is facilitated mimicking the functionality of T cells. This mechanism not only encourages infeasible individuals approaching feasibility regions, but facilitates exploring the boundary between the feasible and infeasible regions in which optima are often located. This approach is validated and performance is quantified by the benchmark functions used in related researches through statistical means with those of the state-of-the-art from various branches of evolutionary computation paradigms. The performance obtained is fairly competitive and in some cases even better. [ABSTRACT FROM PUBLISHER]

The evolution in the use of digital identities has brought several advancements. However, this evolution has also contributed to the rise of the identity theft. An alternative to curb identity theft is by the identification of anomalous user behavior on the computer, what is known as behavioral intrusion detection. Among the features to be extracted from the user behavior, this paper focuses on keystroke dynamics, which analysis the user typing rhythm. This work uses a neural network to recognize users by keystroke dynamics and draws a comparison among several training algorithms: single backpropagation, three approaches based on genetic algorithms and three approaches based on immune algorithms. [ABSTRACT FROM PUBLISHER]

Agent-based modeling and simulation is a way to model the behavior of populations of components and their interactions within a system. The key of this approach is to model the components of the system as autonomous agents and to simulate their behavior for evaluating the system as a whole. That is very useful for observing the emergence of properties in social, biological, environmental or financial systems, among others. Artificial immune systems, which is a sub field of artificial intelligence, comprises systems modeled by simplifying models from the biological immune system. If agent-based modeling and simulation is used as a laboratory for understanding the biological immune system then, it can also be used for transferring the observed principles into artificial immune system models or for evaluating models that have been already adapted for solving technical problems. This paper presents first, a methodology for transferring principles of the biological immune system into the field of artificial immune systems. Then, it presents a brief explanation of the behavior of the cells of the biological immune system, which are treated as internal agents inside a biological organism. Afterwards, the modeling of some selected type of cells and the simulation of the whole simplified system are presented. In the end, the principles of that simplified system are transferred into the design of an alarm management system for the smart grid. [ABSTRACT FROM PUBLISHER]

The Artificial Hormone System (AHS) is a self organizing system which allocates tasks to processing elements. It works in a distributed way, is able to hold real-time conditions and can run in a mixed-signal chip environment significantly increasing system reliability. Yet the hormone mechanisms offer new ways for malicious attacks which can affect the correct functioning of the AHS. Such attacks may cause severe damage if the AHS is used in an embedded (maybe real-time) environment. Therefore, this paper deals with analyzing malicious attacks on the AHS. We present several ways of attacking the AHS and resulting detection and defense strategies. We also evaluate these strategies in the paper and demonstrate that they can help to protect the AHS from attacks. [ABSTRACT FROM PUBLISHER]

In this paper, we compare the performance of Artificial Immune System (AIS)-based classification algorithms to the performance of Gaussian kernel-based Support Vector Machines (SVM) in problems with a high degree of class imbalance. Our experimentation indicates that the AIS-based classification paradigm has the intrinsic properly of dealing more efficiently with highly skewed datasets. Specifically, our experimental results indicate that AIS-based classifiers identify instances from the minority class quite efficiently. [ABSTRACT FROM PUBLISHER]

Currently used flow equation of rotameter was obtained by analogy of orifice meter, which is not rigorous in theoretic. In this paper, according to the fluid motion characteristics in rotameter, the integrity form flow equation of rotameter is derived based on flow around a body, and the tapered tube rotameter flow equation with dead zone error is also derived, the experimental results related flow coefficient are explained very well by means of drag coefficient of flow around a body. The experimental results show that new flow equation derived in this paper is correct. Meanwhile, the reason that current used flow equation has been using ever since is explained. These working are very important to perfect rotameter theory and guide design. [ABSTRACT FROM PUBLISHER]

Inductive coupling link is widely used in transcutaneous power transmission to transfer data and power to the implanted biomedical devices such as implanted micro-sensor system. The inductive coupling variables play a strong rule in coupling efficiency. In this paper the coupling link variables is investigated and analyzed. The resonant frequency 13.56 MHz was used according to industrial, scientific and medical (ISM) band. Results show that the voltage gain Vgain of the inductive links is depended of coupling factor K and resistive load Rload (implanted device). The voltage gain increase with increasing the implanted resistance with constant K. Simulation show that when Rload=400Ω, the Vgain will be in maximum value, and when Rload=200Ω, the Vgain will be in minimum value and approximately 4v. With these results, the system operation is satisfied the requirement of the implanted devices power. Theoretical and simulation is done using software OrCAD-P spice 16.2. [ABSTRACT FROM PUBLISHER]

Immunocytochemistry is a common laboratory technique in which the quality of interaction between antibody antigen is evaluated on a cell surface. The amount of binding between antibodies and cancerous cells in Immunocytochemical images indicates how much the antibody is effective for treating the cancerous cell. In this paper, an automated system has been proposed for the first time, to measure such binding, utilizing well-known image processing methods. The experiments are simulated using the collected reference images in this research which can be used as a benchmark in the future researches. Proposing this method leads to an accurate and fast evaluation of the antigens' expression along with more reduced human-error factors. [ABSTRACT FROM PUBLISHER]

Behavioral performances of our legged robots are still far behind those of biological systems. Energy efficiency and locomotion velocity of our robots, for example, are orders of magnitude lower than those of animals, and in order to fill the gap, it requires a radically new approach in the design and control processes. From this perspective, we have been exploring a novel approach to design and control of legged robots which makes use of free vibration of elastic curved beams. We found that this approach not only simplifies the design and manufacturing processes of locomotion robots, but also substantially improves their energy efficiency, which is comparable to those of animals. In this paper, we explain the novelty and principles of this approach through the four representative case studies that we have been exploring, and discuss challenges and perspectives toward the future. [ABSTRACT FROM PUBLISHER]

With the advent of implantable hearing aids, acoustic sensing strategy of the implantable microphone becomes an important issue. In this paper, placing the microphone in middle ear cavity (MEC) is proposed and researched feedback characteristic when both the implantable microphone and transducer were in the MEC. Analytical and finite element analysis were conducted and implemented the microphone to have a natural frequency of 7 kHz. The implemented microphone showed good characteristics of SNR, sensitivity, and vibration immunity. For the verification, small animal in vitro experiments (n=4) conducted. From the experiments, the gain margin was measured and showed feasibility to use. [ABSTRACT FROM PUBLISHER]

In this paper we present a model of tumor based on the use of an autonomous system of ordinary differential equations (ODE). The model assumes that normal cells and cancer cells coexist in an environment as two different species which compete for nutrients and space. The immune system and the tumor cells fight against each other. The analysis of the linear stability of the fixed points of the model yields to two groups of solutions. In the first one, the immune system wins against the tumor cells, so the cancer disappears. In the second one, the cancer grows until some fixed level and then stabilizes. [ABSTRACT FROM AUTHOR]

Immune system plays a vital role in controlling the tumor growth. Therefore, this paper proposes a new mathematical model that describes tumor-immune interaction, focusing on the role of natural killer (NK) cell and CD8+ T cell. The tumor population is subdivided into two different phases, namely interphase and mitosis. This model used Ordinary Differential Equations (ODEs) and the functions involved in the model represents tumor-immune growth, responses and interaction between the cells. The stability and analysis of the model are carried out. From the analysis, the stability curve limits tumor growth region is shown. The curve from the model lie below the curve of the model with single immune response (CD8+ T cell). This result concluded that the proposed model with involvement of NK cell suppression will lower the tumor growth region. [ABSTRACT FROM AUTHOR]

In this paper we compare the dynamics of three tumor growth models that include an immune system and a drug administration therapy using optimal control. The objective is to minimize a combined function of the total of tumor cells over time and a chemotherapeutic drug administration. [ABSTRACT FROM AUTHOR]

The immune system has an ability to recognize tumor as non-self antigen, and initiates inflammatory response to eliminate tumor. A dendritic cell (DCs) population is one of immune cell subsets that specifically uptakes foreign antigen and then presents to T cells. Dendritic cell boost ex vivo is operated to enhance immune response against tumor that in general comes to fail due to several complex reasons. Although dendritic cell therapy has been operated in clinical trials by boosting tumor immune responses, less is known about dynamic behaviors generated by interactions among immune cell subsets and tumor cells. In this paper, we construct and analyze a mathematical model describing tumor killing by T cells activated by dendritic cells. A handling time representing a waiting time required for T cells to be activated during antigen presentation is incorporated in our model. Mathematical analyses imply that successful tumor elimination depends on the amount of T cells activated ex vivo when introduced. Moreover, numerical simulations imply that an immune escape basin in which tumor can escape from T cell responses increases when the handling time increases, indicating that effcient tumor elimination might result in immediate T cell inactivation due to rapid decline of antigenic stimulation. [ABSTRACT FROM AUTHOR]

ABSTRACT We explored who wrote the milestone articles in the innovation processes of immune-checkpoints CTLA-4 and PD-1. In order to map the processes, a literature inter-citation network analysis was made through HistCite and Pajek. We found knowledge flow through citations between topics (clusters), transverse knowledge diffusion between topics at the same basic research level and knowledge translation from basic research to clinical research. Betweenness Centrality showed a good performance in selecting key nodes of knowledge flow. [ABSTRACT FROM AUTHOR]

Socio-economic development and the associated demand for increasingly higher standard of living has led to the degradation and disintegration of the natural environment on a global scale. The soil environment (mainly with heavy metals, radioactive nuclides, nitrates and pesticides), water and atmospheric air are contaminated. Air pollution, despite the direct impact, leading to the depletion of the ozone layer and global warming, as well as the deposition of pollutant load parts into the soil environment. The consequences of human intervention in the functioning of the natural environment may be irreversible and threaten the future of biological life, including human existence. The so-called environmental diseases include, among others, cancer, impaired liver function, certain diseases of the immune system, asthma and impaired reproduction (miscarriage, infertility, malformations). The deposition are processes of the removal of atmospheric air components including pollution on the surface of the soil and the ground. Removal of pollutants from the atmosphere and their deposition on the surface of the soil and the ground during the atmospheric precipitation is called wet deposition. Wet deposition is usually acidic, when the air is removed from the acid or acids which give compounds react with water, hydroxyl radicals or oxygen atoms. Dry deposition is called deposition on the surface of the soil and the earth gases, aerosols and particulates. Dry deposition may be acidic, if it contains compounds of sulphur and / or nitrogen. Measurements of the concentration of gaseous pollutants in the atmosphere and the content of these compounds in the soil are used to determine the degree of air pollution and soil. The size of air pollution getting into the soil environment in the dry and wet deposition is extremely to determine the state of soil quality. The paper presents the concentration of sulphur and nitrogen compounds and the distribution of their content in the soil in the Mazowieckie Province. For this purpose variogram graphs for each set of data and the created regular grid values so called gridding process were analyzed. For the better understanding of the research developed and generated air pollution and soil maps. The methodology for creating maps with extrapolated beyond the boundaries of the region in the programs Surfer and ArcMap also has been described. [ABSTRACT FROM AUTHOR]

2 Satisfiability (2SAT) logic programming has been a prominent logical rule that defines the structure of Radial Basis Function Neural Network. Training Radial Basis Function Neural Network with logic 2 Satisfiability is an optimization task since it is desired to find the optimal output weights during the training process. In this paper, artificial immune system (AIS) algorithm will be introduced to facilitate the training of RBFNN-2SAT. AIS is used for updating the output weights during training RBFNN-2SAT. In this study, the effectiveness of our hybrid computing paradigm, namely RBFNN-2SATAIS can be estimated by evaluating its testing data result using the root mean square error (RMSE) and computation time (CT). The obtained findings show that the proposed method was effective for achieving acceptable results for 2SAT logic rule. [ABSTRACT FROM AUTHOR]

Most mathematical models for malaria transmission use the SEIR model in human population. However, the model is no longer suitable for malaria cases due to some medical results that the individuals who have recovered from malaria still have the plasmodium parasites in their bodies. Therefore, malaria has ability to relapse without any contact with mosquitos when the individual's immune system is decreased. In our case, we group the individuals who still have plasmodium in their bodies to the susceptible individuals with plasmodium (Sp) compartment. The Sp compartments is important to accommodate the presence of plasmodium in the human who have been infected by malaria. In this paper, we consider a mathematical model of malaria in human population by assuming that the infection rate of the disease in human population is constant and it depends only with the number of infected human. Furthermore, our analysis are also focused on the existence of a disease-free and the endemic equilibrium points, and also the local stability for both equilibrium points and the global stability for disease-free equilibrium point. We also do numerical simulations by using the fourth order Runge-Kutta method to describe the behavior of the malaria disease for certain parameters. [ABSTRACT FROM AUTHOR]