Search results for: event rules

Authors: Aïssa Rezzoug

Abstract:

This paper is aimed to bring new elements that demonstrate the tide caused the groundwater to rise in the shoreline band, on which the urban areas occurs, especially in the western coastal cities of the Kingdom of Saudi Arabia like Jeddah. The reason for the last events of Jeddah inundation was the groundwater rise in the city coupled at the same time to a strong precipitation event. This paper will illustrate the tide participation in increasing the groundwater level significantly. It shows that the reason for internal groundwater recharge within the urban area is not only the excess of the water supply coming from surrounding areas, due to the human activity, with lack of sufficient and efficient sewage system, but also due to tide effect. The research study follows a quantitative method to assess groundwater level rise risks through many in-situ measurements and mathematical modelling. The proposed approach highlights groundwater level, in the urban areas of the city on the shoreline band, reaching the high tide level without considering any input from precipitation. Despite the small tide in the Red Sea compared to other oceanic coasts, the groundwater level is considerably enhanced by the tide from the seaside and by the freshwater table from the landside of the city. In these conditions, the groundwater level becomes high in the city and prevents the soil to evacuate quickly enough the surface flow caused by the storm event, as it was observed in the last historical flood catastrophe of Jeddah in 2009.

Keywords: Flood, groundwater rise, Jeddah, tide.

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Authors: Lilac A. E. Al-Safadi

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This paper proposes an auto-classification algorithm of Web pages using Data mining techniques. We consider the problem of discovering association rules between terms in a set of Web pages belonging to a category in a search engine database, and present an auto-classification algorithm for solving this problem that are fundamentally based on Apriori algorithm. The proposed technique has two phases. The first phase is a training phase where human experts determines the categories of different Web pages, and the supervised Data mining algorithm will combine these categories with appropriate weighted index terms according to the highest supported rules among the most frequent words. The second phase is the categorization phase where a web crawler will crawl through the World Wide Web to build a database categorized according to the result of the data mining approach. This database contains URLs and their categories.

Keywords: Information Processing on the Web, Data Mining, Document Classification.

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Authors: Sudhir Jagtap, Kodge B. G., Shinde G. N., Devshette P. M

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Numerical analysis naturally finds applications in all fields of engineering and the physical sciences, but in the 21st century, the life sciences and even the arts have adopted elements of scientific computations. The numerical data analysis became key process in research and development of all the fields [6]. In this paper we have made an attempt to analyze the specified numerical patterns with reference to the association rule mining techniques with minimum confidence and minimum support mining criteria. The extracted rules and analyzed results are graphically demonstrated. Association rules are a simple but very useful form of data mining that describe the probabilistic co-occurrence of certain events within a database [7]. They were originally designed to analyze market-basket data, in which the likelihood of items being purchased together within the same transactions are analyzed.

Keywords: Numerical data analysis, Data Mining, Association Rule Mining

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Authors: Suzan Wedyan

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Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed such as Classification based Association (CBA), Classification based on Multiple Association Rules (CMAR), Class based Associative Classification (CACA), and Classification based on Predicted Association Rule (CPAR). This paper surveys major AC algorithms and compares the steps and methods performed in each algorithm including: rule learning, rule sorting, rule pruning, classifier building, and class prediction.

Keywords: Associative Classification, Classification, Data Mining, Learning, Rule Ranking, Rule Pruning, Prediction.

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Authors: Yung-Chien Sun, O. Grant Clark

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In this paper, the implementation of a rule-based intuitive reasoner is presented. The implementation included two parts: the rule induction module and the intuitive reasoner. A large weather database was acquired as the data source. Twelve weather variables from those data were chosen as the “target variables" whose values were predicted by the intuitive reasoner. A “complex" situation was simulated by making only subsets of the data available to the rule induction module. As a result, the rules induced were based on incomplete information with variable levels of certainty. The certainty level was modeled by a metric called "Strength of Belief", which was assigned to each rule or datum as ancillary information about the confidence in its accuracy. Two techniques were employed to induce rules from the data subsets: decision tree and multi-polynomial regression, respectively for the discrete and the continuous type of target variables. The intuitive reasoner was tested for its ability to use the induced rules to predict the classes of the discrete target variables and the values of the continuous target variables. The intuitive reasoner implemented two types of reasoning: fast and broad where, by analogy to human thought, the former corresponds to fast decision making and the latter to deeper contemplation. . For reference, a weather data analysis approach which had been applied on similar tasks was adopted to analyze the complete database and create predictive models for the same 12 target variables. The values predicted by the intuitive reasoner and the reference approach were compared with actual data. The intuitive reasoner reached near-100% accuracy for two continuous target variables. For the discrete target variables, the intuitive reasoner predicted at least 70% as accurately as the reference reasoner. Since the intuitive reasoner operated on rules derived from only about 10% of the total data, it demonstrated the potential advantages in dealing with sparse data sets as compared with conventional methods.

Keywords: Artificial intelligence, intuition, knowledge acquisition, limited certainty.

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Authors: Sahar Modirzadehbami, Gholamreza Mansourfar

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Different types of Islamic debts have been increasingly utilized as preferred means of debt funding by Malaysian private firms in recent years. This study examines the impact of Islamic debts announcement on private firms- stock returns. Our sample includes forty five listed companies on Bursa Malaysia involved in issuing of Islamic debts during 2005 to 2008. The abnormal returns and cumulative average abnormal returns are calculated and tested using standard event study methodology. The results show that a significant, negative abnormal return occurs one day before announcement date. This negative abnormal return is representing market participant-s adverse attitude toward Islamic private debt announcement during the research period.

Keywords: Announcement effect, Event study, Islamic debts, Malaysia, Sukuk

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Authors: L. Dahmani, S. Drizi, M. Djemai, A. Boudjemia, M. O. Mechiche

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Channel sections are widely used in practice as beams. However, design rules for eccentrically loaded (not through shear center) beams with channel cross- sections are not available in Eurocode 3. This paper compares the ultimate loads based on the adjusted design rules for lateral torsional buckling of eccentrically loaded channel beams in bending to the ultimate loads obtained with Finite Element (FE) simulations on the basis of a parameter study. Based on the proposed design rule, this study has led to a new design rule which conforms to Eurocode 3.

Keywords: ANSYS, Eurocode 3, finite element method, lateral torsional buckling, steel channel beam.

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Authors: Farjana Mumu, Jia Guozho

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Throughout the world, the Islamic way of banking and financing is increasing. The same trend is also visible in Pakistan, where the Islamic banking sector is increasing in size and volume each year. The question immediately arises as why the Pakistanis patronize the Islamic banking system? This study was carried out to find whether following the Islamic rules in finance is the main factor for such selection or whether other factors such as customer service, location, banking hour, physical facilities of the bank etc also have importance. The study was carried by distributing questionnaire and 200 responses were collected from the clients of Islamic banks. The result showed that the service quality and other factors are as important as following the Islamic rules for finance to retain old ustomers and catch new customers. The result is important and Islamic banks can take actions accordingly to look after both the factors

Keywords: Customers' perception, customer satisfaction, customer service, Islamic banking

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Authors: Htay Htay Yi, Zin May Aye

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Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: False negative rate, intrusion detection system, machine learning methods, performance.

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Authors: Nayera E. El-Gharably, Khaled S. El-Kilany, Aziz E. El-Sayed

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A key element of many distribution systems is the routing and scheduling of vehicles servicing a set of customers. A wide variety of exact and approximate algorithms have been proposed for solving the vehicle routing problems (VRP). Exact algorithms can only solve relatively small problems of VRP, which is classified as NP-Hard. Several approximate algorithms have proven successful in finding a feasible solution not necessarily optimum. Although different parts of the problem are stochastic in nature; yet, limited work relevant to the application of discrete event system simulation has addressed the problem. Presented here is optimization using simulation of VRP; where, a simplified problem has been developed in the ExtendSimTM simulation environment; where, ExtendSimTM evolutionary optimizer is used to minimize the total transportation cost of the problem. Results obtained from the model are very satisfactory. Further complexities of the problem are proposed for consideration in the future.

Keywords: Discrete event system simulation, optimization using simulation, vehicle routing problem.

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Authors: Thi Nguyen, Lee Gordon-Brown, Jim Peterson, Peter Wheeler

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An additive fuzzy system comprising m rules with n inputs and p outputs in each rule has at least t m(2n + 2 p + 1) parameters needing to be tuned. The system consists of a large number of if-then fuzzy rules and takes a long time to tune its parameters especially in the case of a large amount of training data samples. In this paper, a new learning strategy is investigated to cope with this obstacle. Parameters that tend toward constant values at the learning process are initially fixed and they are not tuned till the end of the learning time. Experiments based on applications of the additive fuzzy system in function approximation demonstrate that the proposed approach reduces the learning time and hence improves convergence speed considerably.

Keywords: Additive fuzzy system, improving convergence, parameter learning process, unsupervised learning.

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Authors: Huei-Wen Lin, Huei-Fu Lu

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Event sport tourism (EST) has become an especially important economic sector around the world. As the magnitude continues to grow, attracting more tourists, media, and investment for the host community, and many local areas/regions and states have identified the expenditures by visitors as a potential source of economic or employment growth. The main purposes of this study are to investigate stakeholders’ insights into the feature of hosting EST and using them as a regional development strategy. Continuing the focus of previous literature on the regional development and economic benefits by hosting EST, a total of fıve semi-structured interview questions are designed and a thematic analysis is employed to conduct with eight key sport and tourism decision makers in Atlanta during July to August 2016. Through the depth interviews, the study will contribute to a better understanding of stakeholders’ decision-making, identifying benefits and constraints as well as leveraging the impacts of hosting EST. These findings have provided stakeholders’ perspectives of hosting EST and using them as a reference of regional development in emerging sport tourism markets in the US. Additionally, this study examines key considerations and issues that affect and are critical to reliable understanding of the economic impacts of hosting EST on the regional development, and it will be able to benefit future management authorities (i.e. governments and communities) in their sport tourism development endeavors in defining and hosting successful EST. Furthermore, the insights gained from the qualitative analysis could help other cities/regions analyzing the economic impacts of hosting EST and using it as an instrument of city development strategy.

Keywords: Event sport tourism, regional economic development, thematic analysis, stakeholder.

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Authors: Aissam Belghiat, Mustapha Bourahla

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Models are placed by modeling paradigm at the center of development process. These models are represented by languages, like UML the language standardized by the OMG which became necessary for development. Moreover the ontology engineering paradigm places ontologies at the center of development process; in this paradigm we find OWL the principal language for knowledge representation. Building ontologies from scratch is generally a difficult task. The bridging between UML and OWL appeared on several regards such as the classes and associations. In this paper, we have to profit from convergence between UML and OWL to propose an approach based on Meta-Modelling and Graph Grammars and registered in the MDA architecture for the automatic generation of OWL ontologies from UML class diagrams. The transformation is based on transformation rules; the level of abstraction in these rules is close to the application in order to have usable ontologies. We illustrate this approach by an example.

Keywords: ATOM3, MDA, Ontology, OWL, UML

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Authors: Sukir Maryanto, Iyan Mulyana

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Fractal analyses of successive event of explosion earthquake and harmonic tremor recorded at Semeru volcano were carried out to investigate the dynamical system regarding to their generating mechanism. The explosive eruptions accompanied by explosion earthquakes and following volcanic tremor which are generated by continuous emission of volcanic ash. The fractal dimension of successive event of explosion and harmonic tremor was estimated by Critical Exponent Method (CEM). It was found that the method yield a higher fractal dimension of explosion earthquakes and gradually decrease during the occurrence of harmonic tremor, and can be considerably as correlated complexity of the source mechanism from the variance of fractal dimension.

Keywords: Fractal dimension, Semeru volcano, explosionearthquake, harmonic tremor, Critical Exponent Method

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Authors: Seema Chopra, R. Mitra, Vijay Kumar

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A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.

Keywords: Fuzzy control, neuro-fuzzy techniques, fuzzy subtractive clustering, extraction of rules, and optimization of membership functions.

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Authors: Noor Azryani Auzairy, Rubi Ahmad

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To strengthen the capital market, there is a need to integrate the capital markets within the region by removing legal or informal restriction, specifically, stock market liberalization. Thus the paper is to investigate the effects of the subsequent stock market liberalization on stock market integration in 4 ASEAN countries (Malaysia, Indonesia, Thailand, Singapore) and Korea from 1997 to 2007. The correlation between stock market liberalization and stock market integration are to be examined by analyzing the stock prices and returns within the region and in comparison with the world MSCI index. Event study method is to be used with windows of ±12 months and T-7 + T. The results show that the subsequent stock market liberalization generally, gives minor positive effects to stock returns, except for one or two countries. The subsequent liberalization also integrates the markets short-run and long-run.

Keywords: ASEAN, event method, stock market integration, stock market liberalization.

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Authors: Theodore N.S. Karfakis, Anna Andrade

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The aim of this study was to examine the dynamics of functional composition of a non flooded Amazonian forest in response to drought stress in terms of diameter growth, recruitment and mortality. The survey was carried out in the continuous forest of the Biological dynamics of forest fragments project 90 km outside the city of Manaus, state of Amazonas Brazil. All stems >10 cm dbh where identified to species level and monitored in 18 one hectare permanent sample plots from 1981 to 2004.For statistical analysis all species where aggregated in three ecological guilds. Two distinct drought events occurred in 1983 and 1997. Results showed that more early successional species performed better than later successional ones. Response was significant for both events but for the 1997 event this was more pronounced possibly because of the fact that the event was in the middle of the dry rather than the wet period as was the 1983 one.

Keywords: Brazil, functional composition, drought, Amazonian non flooded forest.

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Authors: Pavel Y. Tabakov, Kevin Duffy

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The present study presents a new approach to automatic data clustering and classification problems in large and complex databases and, at the same time, derives specific types of explicit rules describing each cluster. The method works well in both sparse and dense multidimensional data spaces. The members of the data space can be of the same nature or represent different classes. A number of N-dimensional ellipsoids are used for enclosing the data clouds. Due to the geometry of an ellipsoid and its free rotation in space the detection of clusters becomes very efficient. The method is based on genetic algorithms that are used for the optimization of location, orientation and geometric characteristics of the hyper-ellipsoids. The proposed approach can serve as a basis for the development of general knowledge systems for discovering hidden knowledge and unexpected patterns and rules in various large databases.

Keywords: Classification, clustering, data minig, genetic algorithms.

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Authors: A. Idawaty, O. Mohamed, A. Z. Zuriati

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This paper presents the development of an event based Discrete Event Simulation (DES) for a recovery algorithm known Backward Recovery Global Preemptive Utility Accrual Scheduling (BR_GPUAS). This algorithm implements the Backward Recovery (BR) mechanism as a fault recovery solution under the existing Time/Utility Function/ Utility Accrual (TUF/UA) scheduling domain for multiprocessor environment. The BR mechanism attempts to take the faulty tasks back to its initial safe state and then proceeds to re-execute the affected section of the faulty tasks to enable recovery. Considering that faults may occur in the components of any system; a fault tolerance system that can nullify the erroneous effect is necessary to be developed. Current TUF/UA scheduling algorithm uses the abortion recovery mechanism and it simply aborts the erroneous task as their fault recovery solution. None of the existing algorithm in TUF/UA scheduling domain in multiprocessor scheduling environment have considered the transient fault and implement the BR mechanism as a fault recovery mechanism to nullify the erroneous effect and solve the recovery problem in this domain. The developed BR_GPUAS simulator has derived the set of parameter, events and performance metrics according to a detailed analysis of the base model. Simulation results revealed that BR_GPUAS algorithm can saved almost 20-30% of the accumulated utilities making it reliable and efficient for the real-time application in the multiprocessor scheduling environment.

Keywords: Time Utility Function/ Utility Accrual (TUF/UA) scheduling, Real-time system (RTS), Backward Recovery, Multiprocessor, Discrete Event Simulation (DES).

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Authors: Le Hieu Giang, Truong Nguyen Luan Vu, Le Linh

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In this paper, the analytical tuning rules of IMC-PID controller are presented for the multivariable Smith predictor that involved the ideal decoupling. Accordingly, the decoupler is first introduced into the multivariable Smith predictor control system by a well-known approach of ideal decoupling, which is compactly extended for general nxn multivariable processes and the multivariable Smith predictor controller is then obtained in terms of the multiple single-loop Smith predictor controllers. The tuning rules of PID controller in series with filter are found by using Maclaurin approximation. Many multivariable industrial processes are employed to demonstrate the simplicity and effectiveness of the presented method. The simulation results show the superior performances of presented method in compared with the other methods.

Keywords: Ideal decoupler, IMC-PID controller, multivariable Smith predictor, Maclaurin approximation.

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Authors: Rima Grati, Khouloud Boukadi, Hanene Ben-Abdallah

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Due to the dynamic nature of the Cloud, continuous monitoring of QoS requirements is necessary to manage the Cloud computing environment. The process of QoS monitoring and SLA violation detection consists of: collecting low and high level information pertinent to the service, analyzing the collected information, and taking corrective actions when SLA violations are detected. In this paper, we detail the architecture and the implementation of the first step of this process. More specifically, we propose an event-based approach to obtain run time information of services developed as BPEL processes. By catching particular events (i.e., the low level information), our approach recognizes the run-time execution path of a monitored service and uses the BPEL execution patterns to compute QoS of the composite service (i.e., the high level information).

Keywords: Monitoring of Web service composition, Cloud environment, Run-time extraction of execution path of BPEL.

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Authors: Jae-Min Yu, Hyoung-Ho Doh, Ji-Su Kim, Dong-Ho Lee, Sung-Ho Nam

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A reconfigurable manufacturing system (RMS) is an advanced system designed at the outset for rapid changes in its hardware and software components in order to quickly adjust its production capacity and functionally. Among various operational decisions, this study considers the scheduling problem that determines the input sequence and schedule at the same time for a given set of parts. In particular, we consider the practical constraints that the numbers of pallets/fixtures are limited and hence a part can be released into the system only when the fixture required for the part is available. To solve the integrated input sequencing and scheduling problems, we suggest a priority rule based approach in which the two sub-problems are solved using a combination of priority rules. To show the effectiveness of various rule combinations, a simulation experiment was done on the data for a real RMS, and the test results are reported.

Keywords: Reconfigurable manufacturing system, scheduling, priority rules, multiple process plans, pallets/fixtures

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Authors: Hassan M. Elragal

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This paper discusses a method for improving accuracy of fuzzy-rule-based classifiers using particle swarm optimization (PSO). Two different fuzzy classifiers are considered and optimized. The first classifier is based on Mamdani fuzzy inference system (M_PSO fuzzy classifier). The second classifier is based on Takagi- Sugeno fuzzy inference system (TS_PSO fuzzy classifier). The parameters of the proposed fuzzy classifiers including premise (antecedent) parameters, consequent parameters and structure of fuzzy rules are optimized using PSO. Experimental results show that higher classification accuracy can be obtained with a lower number of fuzzy rules by using the proposed PSO fuzzy classifiers. The performances of M_PSO and TS_PSO fuzzy classifiers are compared to other fuzzy based classifiers

Keywords: Fuzzy classifier, Optimization of fuzzy systemparameters, Particle swarm optimization, Pattern classification.

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Authors: Muhammed Ordu, Eren Demir, Chris Tofallis

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Over the past decade, the non-elective admissions in the UK have increased significantly. Taking into account limited resources (i.e. beds), the related service managers are obliged to manage their resources effectively due to the non-elective admissions which are mostly admitted to inpatient specialities via A&E departments. Geriatric medicine is one of specialities that have long length of stay for the non-elective admissions. This study aims to develop a discrete event simulation model to understand how possible increases on non-elective demand over the next 12 months affect the bed occupancy rate and to determine required number of beds in a geriatric medicine speciality in a UK hospital. In our validated simulation model, we take into account observed frequency distributions which are derived from a big data covering the period April, 2009 to January, 2013, for the non-elective admission and the length of stay. An experimental analysis, which consists of 16 experiments, is carried out to better understand possible effects of case studies and scenarios related to increase on demand and number of bed. As a result, the speciality does not achieve the target level in the base model although the bed occupancy rate decreases from 125.94% to 96.41% by increasing the number of beds by 30%. In addition, the number of required beds is more than the number of beds considered in the scenario analysis in order to meet the bed requirement. This paper sheds light on bed management for service managers in geriatric medicine specialities.

Keywords: Bed management, bed occupancy rate, discrete event simulation, geriatric medicine, non-elective admission.

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Authors: H.S. Kim

Abstract:

In this study, control performance of a smart base isolation system consisting of a friction pendulum system (FPS) and a magnetorheological (MR) damper has been investigated. A fuzzy logic controller (FLC) is used to modulate the MR damper so as to minimize structural acceleration while maintaining acceptable base displacement levels. To this end, a multi-objective optimization scheme is used to optimize parameters of membership functions and find appropriate fuzzy rules. To demonstrate effectiveness of the proposed multi-objective genetic algorithm for FLC, a numerical study of a smart base isolation system is conducted using several historical earthquakes. It is shown that the proposed method can find optimal fuzzy rules and that the optimized FLC outperforms not only a passive control strategy but also a human-designed FLC and a conventional semi-active control algorithm.

Keywords: Fuzzy logic controller, genetic algorithm, MR damper, smart base isolation system

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Authors: Chyn Liaw, Chun-Wei Tung, Shinn-Jang Ho, Shinn-Ying Ho

Abstract:

The γ-turns play important roles in protein folding and molecular recognition. The prediction and analysis of γ-turn types are important for both protein structure predictions and better understanding the characteristics of different γ-turn types. This study proposed a physicochemical property-based decision tree (PPDT) method to interpretably predict γ-turn types. In addition to the good prediction performance of PPDT, three simple and human interpretable IF-THEN rules are extracted from the decision tree constructed by PPDT. The identified informative physicochemical properties and concise rules provide a simple way for discriminating and understanding γ-turn types.

Keywords: Classification and regression tree (CART), γ-turn, Physicochemical properties, Protein secondary structure.

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Authors: Arvind Srivastav, Tarun Kanti Bhattacharyya

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The north-eastern, Himalayan, and Eastern Ghats Belt of India comprise of earthquake-prone, remote, and hilly terrains. Earthquakes have caused enormous damages in these regions in the past. A wireless sensor network based earthquake early warning system (EEWS) is being developed to mitigate the damages caused by earthquakes. It consists of sensor nodes, distributed over the region, that perform majority voting of the output of the seismic sensors in the vicinity, and relay a message to a base station to alert the residents when an earthquake is detected. At the heart of the EEWS is a low-power two-stage seismic sensor that continuously tracks seismic events from incoming three-axis accelerometer signal at the first-stage, and, in the presence of a seismic event, triggers the second-stage P-wave detector that detects the onset of P-wave in an earthquake event. The parameters of the P-wave detector have been optimized for minimizing detection time and maximizing the accuracy of detection.Working of the sensor scheme has been verified with seven earthquakes data retrieved from IRIS. In all test cases, the scheme detected the onset of P-wave accurately. Also, it has been established that the P-wave onset detection time reduces linearly with the sampling rate. It has been verified with test data; the detection time for data sampled at 10Hz was around 2 seconds which reduced to 0.3 second for the data sampled at 100Hz.

Keywords: Earthquake early warning system, EEWS, STA/LTA, polarization, wavelet, event detector, P-wave detector.

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Authors: Saimir Heta, Kers Kapa, Rialda Xhizdari, Ilma Robo

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Despite the existence of different dental specialties, the dentist-patient relationship is unique, in the very fact that the treatment is performed by one doctor and the patient identifies the malpractice presented as part of that doctor's practice; this is in complete contrast to cases of medical treatments where the patient can be presented to a team of doctors, to treat a specific pathology. The rules of dental ethics are almost the same as the rules of medical ethics. The appearance of dental malpractice affects exactly this two-party relationship, created on the basis of professionalism, without deviations in this direction, between the dentist and the patient, but with very narrow individual boundaries, compared to cases of medical malpractice. Malpractice can have different reasons for its appearance, starting from professional negligence, but also from the lack of professional knowledge of the dentist who undertakes the dental treatment. It should always be seen in perspective that we are not talking about the individual - the dentist who goes to work with the intention of harming their patients. Malpractice can also be a consequence of the impossibility, for anatomical or physiological reasons of the tooth under dental treatment, to realize the predetermined dental treatment plan. On the other hand, the dentist himself is an individual who can be affected by health conditions, or have vices that affect the systemic health of the dentist as an individual, which in these conditions can cause malpractice. So, depending on the reason that led to the appearance of malpractice, the method of treatment from a legal point of view also varies, for the dentist who committed the malpractice, evaluating the latter if the malpractice came under the conditions of applying the rules of dental ethics. The deviation from the predetermined dental plan is the minimum sign of malpractice and the latter should not be definitively related only to cases of difficult dental treatments. The identification of the reason for the appearance of malpractice is the initial element, which makes the difference in the way of its treatment, from a legal point of view, and the involvement of the dentist in the assessment of the malpractice committed, must be based on the legislation in force, which must be said to have their specific changes in different states. Malpractice should be referred to, or included in the lectures or in the continuing education of professionals, because it serves as a method of obtaining professional experience in order not to repeat the same thing several times, by different professionals.

Keywords: Dental ethics, malpractice, negligence, legal basis, continuing education, dental treatments.

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Authors: W. Louhichi, M.Berrima, N. Ben Rajeb Robbana

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Elasticity is the essential property of cloud computing. As the name suggests, it constitutes the ability of a cloud system to adjust resource provisioning in relation to fluctuating workloads. There are two types of elasticity operations, vertical and horizontal. In this work, we are interested in horizontal scaling, which is ensured by two mechanisms; scaling in and scaling out. Following the sizing of the system, we can adopt scaling in the event of over-supply and scaling out in the event of under-supply. In this paper, we propose a formal model, based on temporized and colored Petri nets (TdCPNs), for the modeling of the duplication and the removal of a virtual machine from a server. This model is based on formal Petri Nets (PNs) modeling language. The proposed models are edited, verified, and simulated with two examples implemented in colored Petri nets (CPNs)tools, which is a modeling tool for colored and timed PNs.

Keywords: Cloud computing, elasticity, elasticity controller, petri nets, scaling in, scaling out.

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Authors: Andreas Jede, Frank Teuteberg

Abstract:

Cloud computing (CC) has already gained overall appreciation in research and practice. Whereas the willingness to integrate cloud services in various IT environments is still unbroken, the previous CC procurement processes run mostly in an unorganized and non-standardized way. In practice, a sufficiently specific, yet applicable business process for the important acquisition phase is often lacking. And research does not appropriately remedy this deficiency yet. Therefore, this paper introduces a field-tested approach for CC procurement. Based on an extensive literature review and augmented by expert interviews, we designed a model that is validated and further refined through an in-depth real-life case study. For the detailed process description, we apply the event-driven process chain notation (EPC). The gained valuable insights into the case study may help CC research to shift to a more socio-technical area. For practice, next to giving useful organizational instructions we will provide extended checklists and lessons learned.

Keywords: Cloud Procurement Process, IT-Organization, Event-driven Process Chain, In-depth Case Study.

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