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Subjective Well-Being (SWB) is a significant indicator of undergraduate students' life satisfaction and social welfare. Machine learning is gaining increasing prominence as a valuable tool in various fields. However, limited studies have employed machine learning techniques to explore SWB. To predict Subjective Well-Being among undergraduate students, this paper introduces a Heterogeneous Stacked Ensemble classifier model, which combines XGBoost, SVM GridSearch, LR, LightGBM, Bayes Net, and CatBoost classifiers. Additionally, the feature importance index derived from tree models is utilized to uncover shifts in the key factors influencing SWB. The results indicate that the stacking model proposed in this study surpasses traditional models like LR and other standalone machine learning models regarding predictive accuracy. Furthermore, the findings shed light on common factors contributing to SWB over time. The methods employed in this research demonstrate effectiveness, and the outcomes endorse endeavours aimed at fostering a more harmonious society. [ABSTRACT FROM AUTHOR]

Recently, the study of machine learning has gained prominence in the medical field. One of the most challeng-ing tasks in machine learning is creating accurate and reliable classifiers for medical applications. To find the best classi-fier, a comparative analysis of the most prominent machine learning classifiers—SVM, DT, RF, NB, KNN, XGBoost, AdaBoost, and CatBoost—has been done using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The perfor-mance of classifiers is evaluated in terms of accuracy, precision, recall, AUC, confusion matrices, and ROC. The results of the analytical comparison revealed that the SVM and RF classifiers outperformed other classifiers. Furthermore, the WDBC dataset used in this study has a class imbalance issue (malignant 212; benign 357). Imbalanced data sets produce skewed outcomes and deceptive accuracy. Data resampling was performed to fix the problem of class imbalance. [ABSTRACT FROM AUTHOR]

This initiative seeks to reduce the number of fatalities caused by terrorist attacks Therefore, this issue can be resolved by creating an RF-based spy robot with a wireless camera, making it simpler to research rivals as required. This robot can covertly infiltrate hostile terrain and transmit data to us using a wireless camera. The movement of the moving robot is controlled by commands sent from a handheld RF transmitter to an RF receiver mounted on the robot. These robots are utilized to take the place of soldiers in war areas because human life is always regarded highly. Additionally, this spy robot can be used in luxury hotels, shopping centers, and jewelry showrooms. where threats from terrorists or trespassers could exist, it can be used to learn about hostile terrain during conflict, keep track of that information from a safe distance, and securely plan a counterattack and tracking the sites of terrorist officialdoms and then scheduling an attack for the right moment and it can be used for surveillance of any area affected by a disaster that people are unable to access. [ABSTRACT FROM AUTHOR]

Conducted system analysis of implementation of Mealy and Moore control automata made it possible to determine the direction of the study and development in the field of dynamic control of control automata in real time during the transition from the previous state a (t) to the next state a (t + 1). The authors offered to introduce control over the correctness of transitions based on the results of the analysis of the half value of the logical sum of the codes of these states N (t), N (t + 1), determining the concatenation of the value of the logical condition αj ∈ {α} and a specially introduced coefficient δ. The correct concatenation value is found from the reference read only memory. The transition correctness is determined by comparing the result of the read-only memory with the calculated concatenation value. To increase the reliability of ensuring the adoption of the correct decision, not one control read-only memory is used, but its threefold majority reservation too. In this case, the probability of an incorrect value with a correct transition will be negligible, while the value of the concatenations through the second special read-only memory can again obtain a(t)2 and a(t + 1)2 and determine in which code the information was distorted. The offered method for controlling the transitions correctness in control automata is much simpler than the known methods using noise-immune codes of Berger, Rademacher, Hamming, an equilibrium code (kCn) of k ones in n bits, and etc. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is one of the major neurological diseases affecting the nervous system of human body. Till now, there is no proper clinical examination that can diagnose a fully affected PD patient. But, the findings and reports states that the PD patients face disastrous changes in their handwriting. Hence, machine learning experts and research people have proposed different macrograph and computer vision (CV) based methods. Currently, it can take months to get an efficient and proper PD diagnosis, and symptoms that are to be noted and monitored effectively. Even on that note the probability of an improper diagnosis is approx. 20 Percent. I have used the Parkinson's disease handwritten dataset and voice dataset. The results confirmed that handwriting is relevant in diagnosing and monitoring PD. Another set of voice dataset has been used to compare it with handwritten dataset. This is an attempt to find the disease as soon as possible by increasing the accuracy of previous results on the same by other researchers. Here I have used classifiers like LDA, KNN, SVM, RF and DT that can predict PD disease, from that SVM has shown greater result in both dataset but was giving greater accuracy in handwritten dataset. [ABSTRACT FROM AUTHOR]

Students' critical thinking is a skill for higher-order thinking, in understanding concepts, applying, synthesizing, and evaluating the resulting information. Critical thinking as an important skill for students towards the subject. In the learning process, educators find it difficult to measure the level of students' critical thinking skills. Measurement of critical thinking skills using the Halpern Critical Thinking Test (HCTA) needs to be done to determine learning outcomes. Through the application of machine learning techniques using the K-means Clustering algorithm as a non-hierarchical cluster analysis method to classify critical thinking skills, namely 1) verbal skills, 2) argument analysis skills, 3) thinking skills as hypothesis testing, 4) using probability and uncertainty, 5) decision-making and problem-solving skills in the learning process associated with Solo Taxonomy. The clustering of critical thinking skills based on the solo taxonomy has 5 levels, namely "Extended Abstract", "Relational", "Multistructural", "Unistructural", and "Prestuctural". The results of the analysis show that clustering using the k-means algorithm on 796 students obtained skill levels Critical thinking according to the SOLO Taxonomy includes those in the Extended Abstract cluster at cluster_0 of 20.5%, the relational cluster in cluster_2 at 18%, the Multistructural cluster in cluster_4 at 20.7%, the unistructural cluster in cluster_3 at 19.8%, and the unistructural cluster in cluster_3 at 19.8%. prestructural in cluster_1 by 21%. The results of this study greatly contribute to the field of education to measure the level of students' critical thinking skills so that they can assist educators in taking action in the learning process by paying attention to aspects of critical thinking skills based on the solo taxonomy concept as an indicator of success learn to achieve student competency standards. [ABSTRACT FROM AUTHOR]

The pollution considerations and the economic view of reliable and cheap production have pushed the permanent magnet machines to stand in proceed position in many fields of machines industrial applications. Whereas the sensorless controllers become mature day after day to support and satisfy the beneficial applications for these machines. This paper employs only measurements of the machine terminal voltages to establish a mathematical model whose function is to present sensorless standstill position estimation of one-degree resolution and approximately 5% error estimation. No current measurements are required, therefore the current sensing sensors are eliminated. No position sensors and techniques for magnet polarity detection of the rotor are required as well. The proposed paper strategy was applied on a non-salient synchronous machine and tested by the (MATLAB-Simulink-R-2018). It can be modified to be applicable for any other type of permanent magnet machine. [ABSTRACT FROM AUTHOR]

A mechanical unit, electrical unit, and visual processing section are included in the sugarcane bud cutting system. In order to classify the segments of sugarcane stalks, the centre of the device uses image processing. On the basis of developing a prototype for bud cutting,the system's feasibility and the recognition effect can be better tested. The electrical device consists of an embedded system with sensing and actuating systems that are required. A machine learning model that can be trained on a data set of sugarcane buds performs the identification of nodes in the bud. After cutting, the machine allows the buds to be collected simultaneously by counting the number of buds. The mechanical unit consists of rollers which moves the sugarcane into the cutting region. The rollers are rotated by stepper motors. The steppers motors are actuated by the embedded system for precise portion control. Once the node is sensed, the rotation of the roller is stopped. Then the cutting operation is carried out. Lead screw mechanism is used for moving the cutting blades to be in contact with thesugarcane for cutting operation which is controlled and actuated using electrical unit. [ABSTRACT FROM AUTHOR]

The effective prediction system in stock market helps to the traders and investors by providing the helpful information for the upcoming guidelines about stock market. Machine learning was the recent trend in prediction of stock market technologies, it makes the predictions depends on the values of stock market. To predict stock market indices, in this paper we have used recurrent neural network and long-short-term memory. We have analyzed various parameters used for stock market with the help of three algorithms which are random forest, support vector machine, regression models. [ABSTRACT FROM AUTHOR]

Landmarks in a digital image are the visibly distinguishable objects that can be properly classified by human brain. In the field of computer vision, landmark recognition is an important topic since it involves the basic ability of machines to identify objects in an image. Training a machine to identify all the things in an image will involve training it on the most basic landmarks first. But landmark recognition is not an easy task to accomplish. The widely available datasets in the field of landmark recognition are not only very large to adversely impact the training, but they are also highly noisy and complex. Even the best of pictures clicked tend to contain a lot of noise in the processed data. To tackle the problem, we trained a Convolutional Neural Network (CNN) on a publicly available geographical landmarks dataset and found that both, training and test accuracies of CNNs (98% and 73% respectively),were higher than the classical machine learning techniques. Also, it was seen that overfitting of models was lesser in CNNs. [ABSTRACT FROM AUTHOR]

Sarcasm, a sharp and ironic utterance designed to cut or to cause pain, is often used to express strong emotions, such as contempt, mockery or bitterness. Sarcasm detection is of great importance in understanding people's true sentiments and opinions. Most Existing techniques of detecting sarcasm use speech, writing text, signs as a base to catch percent of sarcasm even with limited accuracy. In this article, a new approach of detecting sarcasm using three techniques of machine learning has been proposed. Eight action units and eight head movements from Facial Action Coding System are considered as a base rule to generate effective features to train and test machine learning techniques. The selection of these units and movements are performed according the advice of some specialist psychiatrists. The simulation results view significant accuracy of diagnosing sarcasm exceed 86% which reflects the robustness of the proposed approach also open the way for further works in this field of research. [ABSTRACT FROM AUTHOR]

Machine learning has helped a lot in the field of cancer research. For the detection and diagnosis of cancer various algorithms like artificial neural networks, Support Vector Machine, Decision Tree, Bayesian Networks, Naive Bayes and K-Nearest Neighbor etc. have been used. Today Machine learning techniques are widely used in distinguishing and characterizing diseases by means of X-ray and CRT (Cathode Ray Tube) images. Using these techniques, one is attempting to anticipate the probability of building up of cancer growth before the disease occur. In this paper, a review has been done on different approaches used to detect different type of cancer using various machine learning techniques. Further the efficiency of these methods is compared on the basis of various parameters like Accuracy, Sensitivity, Specificity, F-measure and ROC (Receiver Operating Characteristics) Curve. [ABSTRACT FROM AUTHOR]

The problem of assessing the reliability of technological machines when performing reclamation works is highlighted. The ways of resource reservation of machines are considered. A model of a variable solution is proposed in the predictive perspective of assessing the real failure risk of individual machine components, with the construction of a time lag of the permissible and critical values of the developments of future periods, considering the factorial influence of the external and internal environment. By analyzing the factor influence, the dust level of air environment is taken as the key factor influencing the intensity of the life loss of machine parts during reclamation work. An assessment of the machine open elements on the wear criticality is given. Using the excavator bucket teeth as an example, a mathematical model is presented for considering the factors of the abrasive medium influence on the wear rate of the working body surface. It is proposed, considering the uncertainty of the intensity of abrasive medium influence on the excavator bucket wear intensity, to introduce the concept of "operational repair" as an impact that does not allow for a decrease in the machine productivity. Methods of restorative repair of bucket teeth are recommended to complete the reserve fund, which ensures a reduction in machine downtime. An assessment of promising technologies of restoration and repair impacts for metal structures of technological machines subjected to abrasive wear is given. [ABSTRACT FROM AUTHOR]

In our contribution we, based on a real-life motivation, propose a modification of the Cartesian composition of quasi-(multi)automata. Because of the lack of space, we consider the composition of two structures only. [ABSTRACT FROM AUTHOR]

In this paper, A new class of fuzzy automata is introduced which is called MK-fuzzy automata over a bimonoid K. Inspired by The construction of fuzzy automata from fuzzy regular expression, a new approach is proposed as the Reorganization of MK-fuzzy regular expression into MK-fuzzy automata applying the follow automata. [ABSTRACT FROM AUTHOR]

Automata theory is study of mathematical properties of abstract computing machine. One of the applications of finite automata is lexical analysis. Researchers proposed rough finite state automata or fuzzy automata as recognizer that accepts imprecise languages. It defines formal languages and decides if any string belongs to the language or not. We have proposed rough fuzzy (final) automata model in this article. This model recognizes rough fuzzy (final) languages. It is suitable as a lexical analyzer when the decision values are fuzzy or can be converted to fuzzy. [ABSTRACT FROM AUTHOR]

Social media plays a predominant role in this new era. Hence, the performance metrics of social media are usually evaluated by using statistical methods and machine learning approach. The core impetus of this research is to formulate a robust classification paradigm by integrating metaheuristic and neural network as a single paradigm. In this paper, artificial immune system algorithm incorporated with Hopfield neural network (HFB-3SATAIS) is proposed to evaluate the Facebook performance metrics data set. The proposed approach outperforms the standard Hopfield neural network with exhaustive search (HFB-3SATES) in terms of root mean square error (RMSE), mean absolute error (MAE), sum of squared error (SSE), symmetric mean absolute percentage error (SMAPE), Bayesian information criterion (BIC) and the CPU time. Thus, the accuracy, stability and robustness of the proposed network demonstrated a solid performance compared to the standard model. In addition, the work can be expanded by evaluating different class of benchmark data set such as time series data set. [ABSTRACT FROM AUTHOR]

To minimize makespan for scheduling independent tasks in cloud computing, an improved estimation of distribution algorithm (IEDA) is proposed to tackle the investigated problem in this paper. Considering that the problem is concerned with multi-dimensional discrete problems, an improved population-based incremental learning (PBIL) algorithm is applied, which the parameter for each component is independent with other components in PBIL. In order to improve the performance of PBIL, on the one hand, the integer encoding scheme is used and the method of probability calculation of PBIL is improved by using the task average processing time; on the other hand, an effective adaptive learning rate function that related to the number of iterations is constructed to trade off the exploration and exploitation of IEDA. In addition, both enhanced Max-Min and Min-Min algorithms are properly introduced to form two initial individuals. In the proposed IEDA, an improved genetic algorithm (IGA) is applied to generate partial initial population by evolving two initial individuals and the rest of initial individuals are generated at random. Finally, the sampling process is divided into two parts including sampling by probabilistic model and IGA respectively. The experiment results show that the proposed IEDA not only gets better solution, but also has faster convergence speed. [ABSTRACT FROM AUTHOR]

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study. [ABSTRACT FROM AUTHOR]

In the paper we continue our study on the problem of the induction of minimal nondeterministic finite automata consistent with given sample. The sample is composed of a set of examples, i.e. words to be accepted by the automaton, and counterexamples, i.e. words to be rejected by the automaton. The goal of the current research is to investigate the impact of the set of final states on the quality of the induction results. The quality is expressed here by: (i) the execution times of the applied parallel algorithm, and (ii) the number of transitions in the induced automaton. [ABSTRACT FROM AUTHOR]

The problem of bringing control system in space Rn to a target set at a given time moment is considered in the paper. System's behavior is described by the system of di_erential equations on which standard conditions of existence, uniqueness and extendability of a solution of the system are implied for a given time period. The 2-stage method is proposed for finding approximate solution. Sets of solvability in a discrete model are constructed on the first stage. On the second stage procedure of constructing the solving control takes place. This procedure utilizes the points of constructed sets as guides. Program complex based on proposed method was implemented in order to provide numerical experiments using computers. The article contains the results of one of these experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a model of a robot's behaviour described by finite automata. We split robot's knowledge into several knowledge bases which are used by the inference mechanism of the robot's expert system to make a logic deduction. Each knowledgebase is dedicated to the particular behaviour domain and the finite automaton helps us switching among these knowledge bases with the respect of actual situation. Our goal is to simplify and reduce complexity of one big knowledgebase splitting it into several pieces. The advantage of this model is that we can easily add new behaviour by adding new knowledgebase and add this behaviour into the finite automaton and define necessary states and transitions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to find an optimal replacement of equipment in the warehouse of the company. Real data for the machines are used and are processed with appropriate software. The mathematical model is made for this problem as problem for the shortest path from graph theory. The solution is made by Bellman's principle. [ABSTRACT FROM AUTHOR]

The shallow water equations are considered. In particular, we solve steady state problems of water flows in an open channel. The shallow water equations are simplified into a cubic equation for modelling a steady state problem. We solve the cubic equation using a modified Newton's method. We obtain that the modified Newton's solution is accurate up to the machine precision for only several iterations. Comparison between the modified Newton's, the standard Newton's, and the bisection iterations is provided in this paper. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem. [ABSTRACT FROM AUTHOR]

This paper discusses a finite group associated to a machine called a lamplighter, having ?? lamps with ?? settings arranged in a circle around a single lighter. The properties of the group are discussed. Criteria for determining whether group elements are conjugates, commutators or in the center are established. [ABSTRACT FROM AUTHOR]

This paper discusses a finite group associated to a machine called a lamplighter, having ?? lamps with ?? settings arranged in a circle around a single lighter. The properties of the group are discussed. Criteria for determining whether group elements are conjugates, commutators or in the center are established. [ABSTRACT FROM AUTHOR]

The paper deals with the designing and verification of distributed logic controllers. The control system is initially modelled with Petri nets and formally verified against structural and behavioral properties with the application of the temporal logic and model checking technique. After that it is decomposed into separate sequential automata that are working concurrently. Each of them is re-verified and if the validation is successful, the system can be finally implemented. [ABSTRACT FROM AUTHOR]

Alvis is a formal modelling language that enables possibility of verification of distributed concurrent systems. An Alvis model semantics finds expression in an LTS graph (labelled transition system). Execution of any language statement is expressed as a transition between formally defined states of such a model. An LTS graph is generated using a middle-stage Haskell representation of an Alvis model. Moreover, Haskell is used as a part of the Alvis language and is used to define parameters' types and operations on them. Thanks to the compiler's modular construction many aspects of compilation of an Alvis model may be modified. Providing new plugins for Alvis Compiler that support languages like Java or C makes possible using these languages as a part of Alvis instead of Haskell. The paper presents the compiler internal model and describes how the default specification language can be altered by new plugins. [ABSTRACT FROM AUTHOR]

Logic controller behavior can be specified using various techniques, including UML activity diagrams and control Petri nets. Each technique has its advantages and disadvantages. Application of both specification types in one project allows to take benefits from both of them. Additional elements of UML models make it possible to divide a specification into some parts, considered from other point of view (logic controller, user or system). The paper introduces an idea to use UML activity diagrams with swimlanes to increase the understandability of design models. [ABSTRACT FROM AUTHOR]

Given the discrete state-space system, the set cover problem is defined as selection of the minimal number of global states to cover all the local states. Commonly known methods base on the matrix reduction, boolean function transformation or heuristics ideas. Most of them are inefficient because of computational/memory complexity or non-optimal results. We propose an application of xt-hypergraphs to compute the solution in case where the discrete system can be represented by an xt-hypergraph. Recognition, as well as computation of exact cover in case of xt-hypergraphs is bounded by a polynomial in the number of local states. Therefore, the whole cover process problem turns out to be polynomial. [ABSTRACT FROM AUTHOR]

In the presented paper technique for derivation of adaptive model-based predictive control is introduced. The derived controller is built up by using the methods of subspace identification, receding window technique and modelbased predictive control. The presented control concept is equipped by inspection mechanism increasing reliability of the concept. The controller is meant to be used in the area of machine tools, where machines with significantly reduced masses are taking the new trend. Those machines can easily suffer from low stiffness or nonlinearities due to new kinematic structures. For such machines presented controller could be an alternative replacing the cascade controller that has been used in the area of machine tools for decades. Capabilities of the new controller are evaluated by simulation on a simple machine tool axis with reduced masses represented by a two-mass model. Results of simulation using derived adaptive predictive controller are compared with results obtained using typical cascade controller. [ABSTRACT FROM AUTHOR]

We studied the optimal control of an inventory-production system with deterioration and defective items. Our objective is to develop an optimal inventory control model with Gamma distributed deterioration and beta distributed defective item. The explicit solution of the inventory-production model is derived under continuous review policy using the Pontryagin maximum principle. The optimality conditions are derived from the dynamic of the inventory-production level. Moreover, the simulation and sensitivity analysis results are illustrated numerically in this optimal control model with different demand patterns. The results of the inventory system are analyzed against different parametric values of Beta and Gamma distributions. As a result, the optimal total production strategy is increasing with increase the value of the Beta distribution parameter and decreasing with an increase in the value of the Gamma distribution parameter. [ABSTRACT FROM AUTHOR]

There are tremendous works in biotechnology especially in area of DNA molecules. The computer society is attempting to develop smaller computing devices through computational models which are based on the operations performed on the DNA molecules. A Watson-Crick automaton, a theoretical model for DNA based computation, has two reading heads, and works on double-stranded sequences of the input related by a complementarity relation similar with the Watson-Crick complementarity of DNA nucleotides. Over the time, several variants of Watson-Crick automata have been introduced and investigated. However, they cannot be used as suitable DNA based computational models for molecular stochastic processes and fuzzy processes that are related to important practical problems such as molecular parsing, gene disease detection, and food authentication. In this paper we define new variants of Watson-Crick automata, called weighted Watson-Crick automata, developing theoretical models for molecular stochastic and fuzzy processes. We define weighted Watson-Crick automata adapting weight restriction mechanisms associated with formal grammars and automata. We also study the generative capacities of weighted Watson-Crick automata, including probabilistic and fuzzy variants. We show that weighted variants of Watson-Crick automata increase their generative power. [ABSTRACT FROM AUTHOR]

Splicing system is known as a mathematical model that initiates the connection between the study of DNA molecules and formal language theory. In splicing systems, languages called splicing languages refer to the set of double-stranded DNA molecules that may arise from an initial set of DNA molecules in the presence of restriction enzymes and ligase. In this paper, some splicing languages resulted from their respective splicing systems are shown. Since all splicing languages are regular, languages which result from the splicing systems can be further investigated using grammars and automata in the field of formal language theory. The splicing language can be written in the form of regular languages generated by grammar. Besides that, splicing systems can be accepted by automata. In this research, two restriction enzymes are used in splicing systems namely BfuCI and NcoI. [ABSTRACT FROM AUTHOR]

A finite automaton is one of the classic models of recognition devices, which is used to determine the type of language a string belongs to. A string is said to be recognized by a finite automaton if the automaton "reads" the string from the left to the right starting from the initial state and finishing at a final state. Another type of automata which is a counterpart of sticker systems, namely Watson-Crick automata, is finite automata which can scan the double-stranded tapes of DNA strings using the complimentary relation. The properties of groups have been extended for the recognition of finite automata over groups. In this paper, two variants of automata, modified deterministic finite automata and modified deterministic Watson-Crick automata are used in the study of Abelian groups. Moreover, the relation between finite automata diagram over Abelian groups and the Cayley table is introduced. In addition, some properties of Abelian groups are presented in terms of automata. [ABSTRACT FROM AUTHOR]

The Berkeley Open Infrastructure for Network Computing (BOINC) has become the standard open source solution for grid computing in the Internet. Volunteers use their computers to complete an small part of the task assigned by a dedicated server. We have developed a BOINC project called Neurona@Home whose objective is to simulate a cellular automata random network with, at least, one million neurons. We consider a cellular automata version of the integrate-and-fire model in which excitatory and inhibitory nodes can activate or deactivate neighbor nodes according to a set of probabilistic rules. Our aim is to determine the phase diagram of the model and its behaviour and to compare it with the electroencephalographic signals measured in real brains. [ABSTRACT FROM AUTHOR]

When we replace addition and multiplication of real numbers by the operations of taking the maximum of two numbers and of adding two numbers respectively, we obtain the so-called max-algebra which offers an attractive language to deal with certain problems in automata theory, scheduling theory, discrete event systems, manufacturing systems, telecommunication networks, parallel processing systems and traffic control. The aim of this paper is to employ max-algebra as platforms for secret key establishment between two individuals whose only means of communication is a public channel. The proposed new cryptographic protocols are based on the difficulty of solving matrix equations since matrices over max-semirings are generally not invertible. [ABSTRACT FROM AUTHOR]

The stable (Hurwitz) polynomials are important in the study of differential equations systems and control theory (see [7] and [19]). A property of these polynomials is related to Hadamard product. Consider two polynomials p,q ∈ R[x]:p(x) = anxn+an-1xn-1+...+a1x+a0q(x) = bmxm+bm-1xm-1+...+b1x+b0the Hadamard product (p × q) is defined as (p×q)(x) = akbkxk+ak-1bk-1xk-1+...+a1b1x+a0b0where k = min(m,n). Some results (see [16]) shows that if p,q ∈R[x] are stable polynomials then (p×q) is stable, also, i.e. the Hadamard product is closed; however, the reciprocal is not always true, that is, not all stable polynomial has a factorization into two stable polynomials the same degree n, if n> 4 (see [15]).In this work we will give some conditions to Hadamard factorization existence for stable polynomials. [ABSTRACT FROM AUTHOR]

We present parallel algorithms with optimal cache complexity for the kernel routine of many real root isolation algorithms, namely, Taylor shift, targeting multicore processors. We then report an efficient multithreaded implementation for isolating the real roots of univariate polynomials based on the parallel Taylor shift algorithms. For processing some well-known benchmark examples with sufficiently large size, our software tool reaches linear speedup on a 8-core machine. In addition, we show that our software is able to fully utilize the many cores and the memory space of a 32-core machine to tackle large problems that are out of reach for a desktop implementation. [ABSTRACT FROM AUTHOR]

The present paper introduces a new variant of a scattered context grammar, called an LL leftmost κ-linear scattered context grammar. It is an ordinary scattered context grammar without erasing rules, where (1) every scattered context rule is composed of κ-linear rules, (2) if we take the first components of every rule, the resulting context-free grammar is an LL grammar, and (3) every rule is applied in a leftmost way. We study the generative power of this variant and its parsing properties, including time and space complexity. In the conclusion, several remarks regarding the achieved results are made. [ABSTRACT FROM AUTHOR]

An outlined subquantum theory that might be considered a possible extension of Bohmian mechanics is further discussed. In this theory a fundamental physical system is modelled as a particle endowed with a methodological probabilistic classical Turing machine, which characterizes systems as data processing devices, making of information a crucial physical concept in the determination of the system dynamics. The main idea of this theory resides in proposing a Darwinian evolutionary mechanism-therefore based on natural selection-as the central element that would determine the emergence of quantum mechanics from an evolutionarily stable strategy (ESS) whose essentials are supposedly captured in three regulating principles. The Darwinian character of these regulating principles is explored. [ABSTRACT FROM AUTHOR]

We have studied a quantum algorithm for several years, and developed some applications for difficult problems, NPC problems and NP intermidiate problems. In order to discuss the computational complexity of quantum algorithm, we defined a generalized quantum Turing machine using a density operator on a Hilbert space and quantum channels on it. Since the properity of quantum channel, this mathematical model can describe not only unitary process of quantum algorithm but also measurement process and non-linear dynamical process. Then we can calculate the computational complexity of quantum algorithm more regorously. In this paper, we review our results, and discuss why quantum algorithms are more effective than classical ones. Moreover, we propose a quantum algorotihm for EXPTIME problem. [ABSTRACT FROM AUTHOR]

The paper is focused on application of multi-scale 2D method. Model approach consists of Cellular Automata (CA) model of microstructure development and the finite element code to solve thermo-mechanical problem. Dynamic recrystallization phenomenon is taken into account in 2D CA model which takes advantage of explicit representation of microstructure, including individual grains and grain boundaries. Flow stress is the main material parameter in mechanical part of FE and is calculated on the basis of average dislocation density obtained from CA model. The results attained from the model were validated with the experimental data. In the present study, austenitic steel X3CrNi18-10 was investigated. The examination of microstructure for the initial and final microstructures was carried out, using light microscopy and transmission electron microscopy. [ABSTRACT FROM AUTHOR]

One of the most prospective new methodologies for modelling is the so-called cellular automata (CA) approach. According to this paradigm, the models are built from simple elements connected into regular structures with local interaction between neighbours. The patterns of connections usually have a simple geometry (lattices). As one of the classical examples of CA we mention the game 'Life' by J. Conway. This paper presents two examples of CA with anticipation property. These examples include a modification of the game 'Life' and a cellular model of crowd movement. [ABSTRACT FROM AUTHOR]

One way of studying social processes is through the use of simulations. The use of simulations for this purpose has been established as its own field, social simulations, and has been used for studying a variety of phenomena. A simulation of a social setting can serve as an aid for thinking about that social setting, and for experimenting with different parameters and studying the outcomes caused by them. When using the simulation as an aid for thinking and experimenting, the chosen simulation approach will implicitly steer the simulationist towards thinking in a certain fashion in order to fit the model. To study the implications of model choice on the understanding of a setting where human anticipation comes into play, a simulation scenario of a coffee room was constructed using three different simulation approaches: Cellular Automata, Systems Dynamics and Agent-based modeling. The practical implementations of the models were done in three different simulation packages: Stella for Systems Dynamic, CaFun for Cellular automata and SesAM for Agent-based modeling. The models were evaluated both using Randers' criteria for model evaluation, and through introspection where the authors reflected upon how their understanding of the scenario was steered through the model choice. Further the software used for implementing the simulation models was evaluated, and practical considerations for the choice of software package are listed. It is concluded that the models have very different strengths. The Agent-based modeling approach offers the most intuitive support for thinking about and modeling a social setting where the behavior of the individual is in focus. The Systems Dynamics model would be preferable in situations where populations and large groups would be studied as wholes, but where individual behavior is of less concern. The Cellular Automata models would be preferable where processes need to be studied from the basis of a small set of very simple rules. It is further concluded that in most social simulation settings the Agent-based modeling approach would be the probable choice. This since the other models does not offer much in the way of supporting the modeling of the anticipatory behavior of humans acting in an organization. [ABSTRACT FROM AUTHOR]

Reactive behavior is still considered and the exact opposite for the anticipatory one. Despite the advances on the field of anticipation there are little thoughts on relation with the reactive behavior, the similarities and where the boundary is. In this article we will present our viewpoint and we will try to show that reactive and anticipatory behavior can be combined. This is the basic ground of our unified theory for anticipatory behavior architecture. We still miss such compact theory, which would integrate multiple aspects of anticipation. My multi-level anticipatory behavior approach is based on the current understanding of anticipation from both the artificial intelligence and biology point of view. As part of the explanation we will also elaborate on the topic of weak and strong artificial life. Anticipation is not matter of a single mechanism in a living organism. It was noted already that it happens on many different levels even in the very simple creatures. What we consider to be important for our work and what is our original though is that it happens even without voluntary control. We believe that this is novelty though for the anticipation theory. Naturally research of anticipation was in the beginning of this decade focused on the anticipatory principles bringing advances on the field itself. This allowed us to build on those, look at them from higher perspective, and use not one but multiple levels of anticipation in a creature design. This presents second original though and that is composition of the agent architecture that has anticipation built in almost every function. In this article we will focus only on first two levels within the 8-factor anticipation framework. We will introduce them as defined categories of anticipation and describe them from theory and implementation algorithm point of view. We will also present an experiment conducted, however this experiment serves more as explanatory example. These first two levels may seem trivial, but growing interest in implicit anticipation and reactive anticipation in last two years suggest that this is worthwhile topic. The simplicity of them is intentional to demonstrate our way of thinking and understanding of the topic. The comparison with other results is possible only when these levels are integrated into the complex architecture, as will be shown below that can also act as standalone. [ABSTRACT FROM AUTHOR]

2-dimensional finite cellular automata defined by local rule based on hexagonal cell structure are studied. Rule matrix of the hexagonal finite cellular automaton is obtained. The rank of rule matrices related to hexagonal finite cellular automata via an algorithm is computed. By using the matrix algebra it is shown that the hexagonal finite cellular automata are reversible, if the number of columns is even and the hexagonal finite cellular automata are not reversible, if the number of the columns is odd. [ABSTRACT FROM AUTHOR]

Fuzzy approach has been applied to automata theory to obtain fuzzy automaton in the very early age of fuzzy set theory. Fuzzy automaton is the mathematical model, recognizing fuzzy regular languages. Myhill-Nerode theorem for finite automaton is a very powerful tool for minimizing the number of states in a finite automaton. Interval-valued fuzzy automaton is one of the generalized fuzzy automata. In this paper, an extension of Myhill-Nerode theorem for Interval-valued fuzzy regular language is established. Also, the problem of minimizing the finite automaton with Interval-valued fuzzy (final) states is explored. [ABSTRACT FROM AUTHOR]

In the hypecompositional structures, unlike the classical ones, there exist several types of identities. This paper deals with two main types of identities, the scalar and strong ones and with the hypergroups in which they appear. Certain such hypergroups e.g. the fortified join hypergroup and the join polysymmetric hypergroup derived from the theory of formal languages and automata. [ABSTRACT FROM AUTHOR]