Search results for: spatial information network

Authors: Hsiaoping Wu

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With the wide use of Internet, learners are exposed to the wider world. This exposure permits learners to discover new information and combine a variety of media in order to reach in-depth and broader understanding of their literacy and the world. Many paper-based teaching, learning and assessment modalities can be transferred to a digital platform. This study examines the use of e-portfolios for ESL (English as a second language) pre-service teacher. The data were collected by reviewing 100 E-portfolio from 2013 to 2015 in order to synthesize meaningful information about e-portfolios for ESL pre-service teachers. Participants were generalists, bilingual and ESL pre-service teachers. The studies were coded into two main categories: learning gains, including assessment, and technical skills. The findings showed that using e-portfolios enhanced and developed ESL pre-service teachers’ teaching and assessment skills. Also, the E-portfolio also developed the pre-service teachers’ technical stills to prepare a comprehensible portfolio to present who they are. Finally, the study and presentation suggested e-portfolios for ecological issues and educational purposes.

Keywords: assessment, e-portfolio, pre-service teacher, reflection

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

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In this paper, we briefly introduce the concept of Poisson attacks in the case of defense/attack strategies where attacks are assumed to be continuous. We suggest a game model in which the attacker will combine both criteria of a sufficient confidence level of a successful attack and a reasonably small size of the estimation error in order to launch an attack. Here, estimation error arises from assessing the system failure upon attack using aggregate data at the system level. The corresponding error is referred to as aggregation error. On the other hand, the defender will attempt to deter attack by making one or both criteria inapplicable. The defender will build his/her strategy by both strengthening the targeted system and increasing the size of error. We will formulate the defender problem based on appropriate optimization models. The attacker will opt for a Bayesian updating in assessing the impact on the improvement made by the defender. Then, the attacker will evaluate the feasibility of the attack before making the decision of whether or not to launch it. We will provide illustrations to better explain the process.

Keywords: attacker, defender, game theory, information

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

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Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Ali Jahanfar, Brajesh Dubey, Bahram Gharabaghi, Saber Bayat Movahed

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Ever increasing population growth of major urban centers and environmental challenges in siting new landfills have resulted in a growing trend in design of mega-landfills some with extraordinary heights and dangerously steep slopes. Landfill failure mobility risk analysis is one of the most uncertain types of dynamic rheology models due to very large inherent variabilities in the heterogeneous solid waste material shear strength properties. The waste flow of three historic dumpsite and two landfill failures were back-analyzed using run-out modeling with DAN-W model. The travel distances of the waste flow during landfill failures were calculated approach by taking into account variability in material shear strength properties. The probability distribution function for shear strength properties of the waste material were grouped into four major classed based on waste material compaction (landfills versus dumpsites) and composition (high versus low quantity) of high shear strength waste materials such as wood, metal, plastic, paper and cardboard in the waste. This paper presents a probabilistic method for estimation of the spatial extent of waste avalanches, after a potential landfill failure, to create maps of vulnerability scores to inform property owners and residents of the level of the risk.

Keywords: landfill failure, waste flow, Voellmy rheology, friction coefficient, waste compaction and type

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Authors: Hanen Ghabi, Youssef Oudhini, Hassen Mnif

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By reference to recent publications dealing with the same problem, and as a follow-up to this research work already published, we propose in this article a new original idea of tele supervision exploiting the opportunities offered by the DVB-H system. The objective is to exploit the RF channels of the DVB-H network in order to insert digital remote monitoring images dedicated to a remote solar power plant. Indeed, the DVB-H (Digital Video Broadcast-Handheld) broadcasting system was designed and deployed for digital broadcasting on the same platform as the parent system, DVB-T. We claim to be able to exploit this approach in order to satisfy the operator of remote photovoltaic sites (and others) in order to remotely control the components of isolated installations by means of video surveillance.

Keywords: video surveillance, digital video broadcast-handheld, photovoltaic sites, AVC

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Authors: Yuan Huang, Xiangping Chen, Xiaonan Luo

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Current software are continuously updating. The change between two versions usually involves multiple program entities (e.g., packages, classes, methods, attributes) with multiple purposes (e.g., changed requirements, bug fixing). It is hard for developers to understand which changes are made for the same purpose. Whether two changes are related is not decided by the relationship between this two entities in the program. In this paper, we summarized 4 coupling rules(16 instances) and 4 state-combination types at the class, method and attribute levels for software change. Related Change Vector (RCV) are defined based on coupling rules and state-combination types, and applied to classify related software changes by using Probabilistic Neural Network during a software updating.

Keywords: PNN, related change, state-combination, logical coupling, software entity

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Authors: J. Van Engeland, C. Lavigne, S. De Jaeger

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In the light of the transition towards a more circular economy, recovery of products, parts or materials will gain in importance. Additionally, the EU proximity principle related to waste management and emissions generated by transporting large amounts of end-of-life products, shift attention to local recovery networks. The Flemish inter-communal cooperation for municipal solid waste management Meetjesland (IVM) is currently investigating the set-up of such a network. More specifically, the network encompasses the recycling of polyvinyl chloride (PVC), which is collected in separate containers. When these containers are full, a truck should transport them to the processor which can recycle the PVC into new products. This paper proposes a model to optimize the container collection. The containers are located at different Civic Amenity sites (CA sites) in a certain region. Since people can drop off their waste at these CA sites, the containers will gradually fill up during a planning horizon. If a certain container is full, it has to be collected and replaced by an empty container. The collected waste is then transported to a single processor. To perform this collection and transportation of containers, the responsible firm has a set of vehicles stationed at a single depot and different personnel crews. A vehicle can load exactly one container. If a trailer is attached to the vehicle, it can load an additional container. Each day of the planning horizon, the different crews and vehicles leave the depot to collect containers at the different sites. After loading one or two containers, the crew has to drive to the processor for unloading the waste and to pick up empty containers. Afterwards, the crew can again visit sites or it can return to the depot to end its collection work for that day. All along the collection process, the crew has to respect the opening hours of the sites. In order to allow for some flexibility, a crew is allowed to wait a certain amount of time at the gate of a site until it opens. The problem described can be modelled as a variant to the PVRP-TW (Periodic Vehicle Routing Problem with Time Windows). However, a vehicle can at maximum load two containers, hence only two subsequent site visits are possible. For that reason, we will refer to the model as a model for building tactical waste collection schemes. The goal is to a find a schedule describing which crew should visit which CA site on which day to minimize the number of trucks and the routing costs. The model was coded in IBM CPLEX Optimization studio and applied to a number of test instances. Good results were obtained, and specific suggestions concerning route and truck costs could be made. For a large range of input parameters, collection schemes using two trucks are obtained.

Keywords: container collection, crew scheduling, mixed integer linear programming, waste management

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Authors: Gia Surguladze, Nino Topuria, Lily Petriashvili, Giorgi Surguladze

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Modeling of building processes of a multimodal freight transportation support information system is discussed based on modern CASE technologies. Functional efficiencies of ports in the eastern part of the Black Sea are analyzed taking into account their ecological, seasonal, resource usage parameters. By resources, we mean capacities of berths, cranes, automotive transport, as well as work crews and neighbouring airports. For the purpose of designing database of computer support system for Managerial (Logistics) function, using Object-Role Modeling (ORM) tool (NORMA – Natural ORM Architecture) is proposed, after which Entity Relationship Model (ERM) is generated in automated process. The software is developed based on Process-Oriented and Service-Oriented architecture, in Visual Studio.NET environment.

Keywords: seaport resources, business-processes, multimodal transportation, CASE technology, object-role model, entity relationship model, SOA

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Abdulrazaq Yahaya, Omobola Okoh, Anthony Okoh

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Polychlorinated biphenyls (PCBs) are generated from short emission or leakage from capacitors and electrical transformers, industrial chemicals wastewater discharge and careless disposal of wastes. They are toxic, semi-volatile compounds which can persist in the environment, hence classified as persistent organic pollutants. Their presence in the environmental matrices has become a global concern. In this study, we assessed the concentrations and distribution patterns of 19 polychlorinated biphenyls congeners (PCB 1, 5, 18, 31, 44, 52, 66, 87, 101, 110, 138, 141, 151, 153, 170, 180, 183, 187, and 206) at six sampling points in water along the course of Buffalo River, Eastern Cape, South Africa. Solvent extraction followed by sulphuric acid, potassium permanganate and silica gel cleanup were used in this study. The analysis was done with gas chromatography electron capture detector (GC-ECD). The results of the analysis of all the 19 PCBs congeners ranged from not detectable to 0.52 ppb and 2.5 ppb during summer and autumn periods respectively. These values are generally higher than the World Health Organization (WHO) maximum permissible limit. Their presence in the waterbody suggests an increase in anthropogenic activities over the seasons. In view of their volatility, the compounds are transportable over long distances by air currents away from their point of origin putting the health of the communities at risk, thus suggesting the need for strict regulations on the use as well as save disposal of this group of compounds in the communities.

Keywords: organic pollutants, polychlorinated biphenyls, pollution, solvent extraction

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Authors: M. Münch, R. Brandt

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Martensitic steels with an ultimate tensile strength beyond 2000 MPa are applied in the powertrain of vehicles due to their excellent fatigue strength and high creep resistance. However, the creep controlling mechanism in martensitic steels at ambient temperatures up to 423 K is not evident. The purpose of this study is to review the low temperature creep (LTC) behavior of martensitic steels at temperatures from 363 K to 523 K. Thus, the validity of a logarithmic creep law is reviewed and the stress and temperature dependence of the creep parameters α and β are revealed. Furthermore, creep tests are carried out, which include stepped changes in temperature or stress, respectively. On one hand, the change of the creep rate due to a temperature step provides information on the magnitude of the activation energy of the LTC controlling mechanism and on the other hand, the stress step approach provides information on the magnitude of the activation volume. The magnitude, the temperature dependency, and the stress dependency of both material specific activation parameters may deliver a significant contribution to the disclosure of the nature of the LTC rate controlling mechanism.

Keywords: activation parameters, creep mechanisms, high strength steels, low temperature creep

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Authors: Samuel I. Becher, Tal Z. Zarsky

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The digital world becomes part of our DNA now. The way e-commerce, human behavior, and law interact and affect one another is rapidly and significantly changing. Among others things, the internet equips consumers with a variety of platforms to share information in a volume we could not imagine before. As part of this development, online information flows allow consumers to learn about businesses and their contracts in an efficient and quick manner. Consumers can become informed by the impressions that other, experienced consumers share and spread. In other words, consumers may familiarize themselves with the contents of contracts through the experiences that other consumers had. Online and offline, the relationship between consumers and businesses are most frequently governed by consumer standard form contracts. For decades, such contracts are assumed to be one-sided and biased against consumers. Consumer Law seeks to alleviate this bias and empower consumers. Legislatures, consumer organizations, scholars, and judges are constantly looking for clever ways to protect consumers from unscrupulous firms and unfair behaviors. While consumers-businesses relationships are theoretically administered by standardized contracts, firms do not always follow these contracts in practice. At times, there is a significant disparity between what the written contract stipulates and what consumers experience de facto. That is, there is a crucial gap (“the Gap”) between how firms draft their contracts on the one hand, and how firms actually treat consumers on the other. Interestingly, the Gap is frequently manifested by deviation from the written contract in favor of consumers. In other words, firms often exercise lenient approach in spite of the stringent written contracts they draft. This essay examines whether, counter-intuitively, policy makers should add firms’ leniency to the growing list of firms suspicious behaviors. At first glance, firms should be allowed, if not encouraged, to exercise leniency. Many legal regimes are looking for ways to cope with unfair contract terms in consumer contracts. Naturally, therefore, consumer law should enable, if not encourage, firms’ lenient practices. Firms’ willingness to deviate from their strict contracts in order to benefit consumers seems like a sensible approach. Apparently, such behavior should not be second guessed. However, at times online tools, firm’s behaviors and human psychology result in a toxic mix. Beneficial and helpful online information should be treated with due respect as it may occasionally have surprising and harmful qualities. In this essay, we illustrate that technological changes turn the Gap into a key component in consumers' understanding, or misunderstanding, of consumer contracts. In short, a Gap may distort consumers’ perception and undermine rational decision-making. Consequently, this essay explores whether, counter-intuitively, consumer law should sanction firms that create a Gap and use it. It examines when firms’ leniency should be considered as manipulative or exercised in bad faith. It then investigates whether firms should be allowed to enforce the written contract even if the firms deliberately and consistently deviated from it.

Keywords: consumer contracts, consumer protection, information flow, law and economics, law and technology, paper deal v firms' behavior

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Authors: Muhammad Ashfaq

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Main theme: This prime focus of this study is on the role of digital technology in crime prevention, with special focus on Cellular Forensic Unit, Capital City Police Peshawar-Khyber Pakhtunkhwa-Pakistan. Objective(s) of the study: The prime objective of this study is to provide statistics, strategies and pattern of analysis used for crime prevention in Cellular Forensic Unit of Capital City Police Peshawar, Khyber Pakhtunkhwa-Pakistan. Research Method and Procedure: Qualitative method of research has been used in the study for obtaining secondary data from research wing and Information Technology (IT) section of Peshawar police. Content analysis was the method used for the conduction of the study. This study is delimited to Capital City Police and Cellular Forensic Unit Peshawar-KP, Pakistan. information technologies. Major finding(s): It is evident that the old traditional approach will never provide solutions for better management in controlling crimes. The best way to control crimes and promotion of proactive policing is to adopt new technologies. The study reveals that technology have transformed police more effective and vigilant as compared to traditional policing. The heinous crimes like abduction, missing of an individual, snatching, burglaries and blind murder cases are now traceable with the help of technology. Recommendation(s): From the analysis of the data, it is reflected that Information Technology (IT) expert should be recruited along with research analyst to timely assist and facilitate operational as well as investigation units of police.A mobile locator should be Provided to Cellular Forensic Unit to timely apprehend the criminals .Latest digital analysis software should be provided to equip the Cellular Forensic Unit.

Keywords: crime prevention, digital technology, pakistan, police

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Khan R. Rahaman, Quazi K. Hassan

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Global changes in climate, environment, economies, populations, governments, institutions, and cultures converge in localities. Changes at a local scale, in turn, contribute to global changes as well as being affected by them. Our hypothesis is built on a consideration that temperature does vary at local level (i.e., termed as local warming) in comparison to the predicted models at the regional and/or global scale. To date, the bulk of the research relating local places to global climate change has been top-down, from the global toward the local, concentrating on methods of impact analysis that use as a starting point climate change scenarios derived from global models, even though these have little regional or local specificity. Thus, our focus is to understand such trends over the southern Alberta, which will enable decision makers, scientists, researcher community, and local people to adapt their policies based on local level temperature variations and to act accordingly. Specific objectives in this study are: (i) to understand the local warming (temperature in particular) trend in context of temperature normal during the period 1961-2010 at point locations using meteorological data; (ii) to validate the data by using specific yearly data, and (iii) to delineate the spatial extent of the local warming trends and understanding influential factors to adopt situation by local governments. Existing data has brought the evidence of such changes and future research emphasis will be given to validate this hypothesis based on remotely sensed data (i.e. MODIS product by NASA).

Keywords: local warming, climate change, urban area, Alberta, Canada

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Authors: Farhad Ghasemi

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Along with transforming the international system into a complex and chaotic system, the fundamental question arises: how can deterrence be reconstructed conceptually and theoretically in this system model? The deterrence system is much more complex today than it was seven decades ago. This article suggests that the perception of deterrence as a linear system is a fundamental mistake because it does not consider the new dynamics of the international system, including network power dynamics. The author aims to improve this point by focusing on complexity and chaos theories, especially their nonlinearity and cascading failure principles. This article proposes that the perception of deterrence as a linear system is a fundamental mistake, as the new dynamics of the surrounding international system do not take into account. The author recognizes deterrence as a nonlinear system and introduces it as a concept in strategic studies.

Keywords: complexity, international system, deterrence, linear deterrence, nonlinear deterrence

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Authors: Seyed Mohammadbagher Jafari, Elahe Mahdizadeh, Masomeh Ghahremani

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Nowadays almost all organizations depend on information systems to run their businesses. Investment on information systems and their maintenance to keep them always in best situation to support firm business is one of the main issues for every organization. The new concept of cloud computing was developed as a technical and economic model to address this issue. In cloud computing the computing resources, including networks, applications, hardwares and services are configured as needed and are available at the moment of request. However, migration to cloud is not an easy task and there are many issues that should be taken into account. This study tries to provide a comprehensive model to assess a firm readiness to implement cloud computing. By conducting a systematic literature review, four dimensions of readiness were extracted which include technological, human, organizational and environmental dimensions. Every dimension has various criteria that have been discussed in details. This model provides a framework for cloud computing readiness assessment. Organizations that intend to migrate to cloud can use this model as a tool to assess their firm readiness before making any decision on cloud implementation.

Keywords: cloud computing, human readiness, organizational readiness, readiness assessment model

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Authors: Tae Bo Jeon

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Korean air force is undertaking a big project to replace prevailing hundreds of old air fighters such as F-4, F-5, KF-16 etc. The task is to develop and produce domestic fighters equipped with 2 complete-type engines each. A large number of engines, however, will be purchased as products from a foreign engine maker. In addition to the fighters themselves, secure the proper number of spare engines serves a significant role in maintaining combat readiness and effectively managing the national defense budget due to high cost. In this paper, we presented a model dynamically updating spare engine requirements. Currently, the military administration purchases all the fighters, engines, and spare engines at acquisition stage and does not have additional procurement processes during the life cycle, 30-40 years. With the assumption that procurement procedure during the operational stage is established, our model starts from the initial estimate of spare engine requirements based on limited information. The model then performs military missions and repair/maintenance works when necessary. During operation, detailed field information - aircraft repair and test, engine repair, planned maintenance, administration time, transportation pipeline between base, field, and depot etc., - should be considered for actual engine requirements. At the end of each year, the performance measure is recorded and proceeds to next year when it shows higher the threshold set. Otherwise, additional engine(s) will be bought and added to the current system. We repeat the process for the life cycle period and compare the results. The proposed model is seen to generate far better results appropriately adding spare engines thus avoiding possible undesirable situations. Our model may well be applied to future air force military operations.

Keywords: DMSMS, operational availability, METRIC, PRS

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Authors: Prachi Desai, Ankita Gandhi, Mitali Acharya

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Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts.

Keywords: ANN, CNN, supervised learning, machine learning, deep learning

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Authors: Zegrar Ahmed, Ghabi Mohamed

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The degradation of arid and semi-arid ecosystems in Algeria has become a palpable fact that only hinders progress and rural development. In these exceptionally fragile environments, the decline of vegetation is done according to an alarming increase and wind erosion dominates. The ecosystem is subjected to a long hot dry season and low annual average rainfall. The urgency of the fight against desertification is imposed by the very nature of the process that tends to self-accelerate, resulting when human intervention is not forthcoming the irreversibility situations, preventing any possibility of restoration state of these zones. These phenomena have led to different degradation processes, such as the destruction of vegetation, soil erosion, and deterioration of the physical environment. In this study, the work is mainly based on the criteria for classification and identification of physical parameters for spatial analysis and multi-sources to determine the vulnerability of major steppe formations and their impact on desertification. we used Landsat data with two different dates March 2010 and November 2014 in order to determine the changes in land cover, sand moving and land degradation for the diagnosis of the desertification Phenomenon. The application, through specific processes, including the supervised classification was used to characterize the main steppe formations. An analysis of the vulnerability of plant communities was conducted to assign weights and identify areas most susceptible to desertification. Vegetation indices are used to characterize the steppe formations to determine changes in land use.

Keywords: remote sensing, SIG, ecosystem, degradation, desertification

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Authors: Excellent Hansda

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Over the past years, a lot of research has been on the ethnic enclaves from historical, sociological and economic point of view. However there exist a research gap in the built environment and spatial layout of these areas. When immigrants (People) assimilate in a different place, they struggle to preserve their original identity to maintain their heritage. Then there is the Place, which is the physical manifestation of the heritage, shown through streetscape and architecture. Together 'People and Place' form a relationship with the authenticity of the enclave. As immigrants come in the host country, they try to bring their culture into the place, but at the same time, the culture of the host country also affects the immigrants. This creates conflicts not only in the lifestyle and culture of the immigrants, but also the built characteristics of the place. In the midst of such conflicts, one may easily question the authenticity of an ethnic enclave. In Malaysia, a number of ethnic enclaves emerged due to trade during the medieval times. Little India is one among the other ethnic enclaves present in Chulia Street in Malaysia. The study investigates the factors of 'Place and People', affecting the authenticity of a little India, in the context of an evolving state of Penang in Malaysia. The study is carried through extensive literature review of existing data, followed by observations drawn by visual analysis, discussions and interviews with the stakeholders of the study area. The findings of this research suggest the contribution of 'people and places' in the process of place making in an ethnic enclave. The findings are essential for conservation and further development of ethnic enclaves.

Keywords: conservation, ethnic enclaves, heritage, identity

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Authors: Chansiri Singhtaun

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This paper proposes a mathematical model and examines the performance of an exact algorithm for a location–transportation problems in humanitarian relief. The model determines the number and location of distribution centers in a relief network, the amount of relief supplies to be stocked at each distribution center and the vehicles to take the supplies to meet the needs of disaster victims under capacity restriction, transportation and budgetary constraints. The computational experiments are conducted on the various sizes of problems that are generated. Branch and bound algorithm is applied for these problems. The results show that this algorithm can solve problem sizes of up to three candidate locations with five demand points and one candidate location with up to twenty demand points without premature termination.

Keywords: disaster response, facility location, humanitarian relief, transportation

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Authors: Khatia Tavidashvili, Nino Jamrishvili, Valerian Omsarashvili

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Studies of the vertical distribution of the air temperature in the atmosphere have great value for the solution of different problems of meteorology and climatology (meteorological forecast of showers, thunderstorms, and hail, weather modification, estimation of climate change, etc.). From the end of May 2015 in Kakheti after 25-year interruption, the work of anti-hail service was restored. Therefore, in connection with climate change, the need for the detailed study of the contemporary regime of the vertical distribution of the air temperature above this territory arose. In particular, the indicated information is necessary for the optimum selection of rocket means with the works on the weather modification (fight with the hail, the regulation of atmospheric precipitations, etc.). Construction of the detailed maps of the potential damage distribution of agricultural crops from the hail, etc. taking into account the dimensions of hailstones in the clouds according to the data of radar measurements and height of locality are the most important factors. For now, in Georgia, there is no aerological probing of atmosphere. To solve given problem we processed information about air temperature profiles above Telavi, at 27 km above earth's surface. Information was gathered during four observation time (4, 10, 16, 22 hours with local time. After research, we found vertical distribution of the average monthly values of the air temperature above Kakheti in ‎2012-2017 from January to December. Research was conducted from 0.543 to 27 km above sea level during four periods of research. In particular, it is obtained: -during January the monthly average air temperature linearly diminishes with 2.6 °C on the earth's surface to -57.1 °C at the height of 10 km, then little it changes up to the height of 26 km; the gradient of the air temperature in the layer of the atmosphere from 0.543 to 8 km - 6.3 °C/km; height of zero isotherm - is 1.33 km. -during July the air temperature linearly diminishes with 23.5 °C to -64.7 °C at the height of 17 km, then it grows to -47.5 °C at the height of 27 km; the gradient of the air temperature of - 6.1 °C/km; height of zero isotherm - is 4.39 km, which on 0.16 km is higher than in the sixties of past century.

Keywords: hail, Kakheti, meteorology, vertical distribution of the air temperature

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Authors: Ali Ben Abbes, ImedRiadh Farah, Vincent Barra

Abstract:

Remotely sensed data are a significant source for monitoring and updating databases for land use/cover. Nowadays, changes detection of urban area has been a subject of intensive researches. Timely and accurate data on spatio-temporal changes of urban areas are therefore required. The data extracted from multi-temporal satellite images are usually non-stationary. In fact, the changes evolve in time and space. This paper is an attempt to propose a methodology for changes detection in urban area by combining a non-stationary decomposition method and stochastic modeling. We consider as input of our methodology a sequence of satellite images I₁, I₂, … I_n at different periods (t = 1, 2, ..., n). Firstly, a preprocessing of multi-temporal satellite images is applied. (e.g. radiometric, atmospheric and geometric). The systematic study of global urban expansion in our methodology can be approached in two ways: The first considers the urban area as one same object as opposed to non-urban areas (e.g. vegetation, bare soil and water). The objective is to extract the urban mask. The second one aims to obtain a more knowledge of urban area, distinguishing different types of tissue within the urban area. In order to validate our approach, we used a database of Tres Cantos-Madrid in Spain, which is derived from Landsat for a period (from January 2004 to July 2013) by collecting two frames per year at a spatial resolution of 25 meters. The obtained results show the effectiveness of our method.

Keywords: multi-temporal satellite image, urban growth, non-stationary, stochastic model

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Authors: Bharat P. Modi, Jayesh M. Patel

Abstract:

Mobile Internet is acting as a major source of data. As the number of web pages continues to grow the Mobile web provides the data miners with just the right ingredients for extracting information. In order to cater to this growing need, a special term called Mobile Web mining was coined. Mobile Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web Usage mining deals with understanding the behavior of users by making use of Mobile Web Access Logs that are generated on the server while the user is accessing the website. A Web access log comprises of various entries like the name of the user, his IP address, a number of bytes transferred time-stamp etc. A variety of Log Analyzer tools exists which help in analyzing various things like users navigational pattern, the part of the website the users are mostly interested in etc. The present paper makes use of such log analyzer tool called Mobile Web Log Expert for ascertaining the behavior of users who access an astrology website. It also provides a comparative study between a few log analyzer tools available.

Keywords: mobile web access logs, web usage mining, web server, log analyzer

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Authors: Yatin Wadhawan, Clifford Neuman, Anas Al Majali

Abstract:

In this paper, we evaluate the resilience of the smart grid system in the presence of multiple cyber-physical attacks on its distinct functional components. We discuss attack-defense scenarios and their effect on smart grid resilience. Through contingency simulations in the Network and PowerWorld Simulator, we analyze multiple cyber-physical attacks that propagate from the cyber domain to power systems and discuss how such attacks destabilize the underlying power grid. The analysis of such simulations helps system administrators develop more resilient systems and improves the response of the system in the presence of cyber-physical attacks.

Keywords: smart grid, gas pipeline, cyber- physical attack, security, resilience

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Authors: Petros Dlamini, Bethiweli Malambo, Maggie Masenya

Abstract:

Knowledge management practices are very important in public libraries, especial in the era of the information society. The success of public libraries depends on the recognition and application of knowledge management practices. The study investigates the value and challenges of knowledge management practices in public libraries. Three research objectives informed the study: to identify knowledge management practices in public libraries, understand the value of knowledge management practices in public libraries, and determine the factors hampering knowledge management practices in public libraries. The study was informed by the interpretivism research paradigm, which is associated with qualitative studies. In that light, the study collected data from eight librarians and or library heads, who were purposively selected from public libraries. The study adopted a social anthropological approach, which thoroughly evaluated each participant's response. Data was collected from the respondents through telephonic semi-structured interviews and assessed accordingly. Furthermore, the study used the latest content concept for data interpretation. The chosen data analysis method allowed the study to achieve its main purpose with concrete and valid information. The study's findings showed that all six (100%) selected public libraries apply knowledge management practices. The findings of the study revealed that public libraries have knowledge sharing as the main knowledge management practice. It was noted that public libraries employ many practices, but each library employed its practices of choice depending on their knowledge management practices structure. The findings further showed that knowledge management practices in public libraries are employed through meetings, training, information sessions, and awareness, to mention a few. The findings revealed that knowledge management practices make the libraries usable. Furthermore, it has been asserted that knowledge management practices in public libraries meet users’ needs and expectations and equip them with skills. It was discovered that all participating public libraries from Umkhanyakude district municipality valued their knowledge management practices as the pillar and foundation of services. Noticeably, knowledge management practices improve users ‘standard of living and build an information society. The findings of the study showed that librarians should be responsible for the value of knowledge management practices as they are qualified personnel. The results also showed that 83.35% of public libraries had factors hampering knowledge management practices. The factors are not limited to shortage of funds, resources and space, and political interference. Several suggestions were made to improve knowledge management practices in public libraries. These suggestions include improving the library budget, increasing libraries’ building sizes, and conducting more staff training.

Keywords: knowledge management, knowledge management practices, storage, dissemination

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Authors: Abdolreza Noroozi Chakoli

Abstract:

This study aims to examine the impact of five effective factors on the success of the managers of distance knowledge sharing centers in Iran. To conduct it, 3 centers, including the National Library and Archives of Iran (NLAI), Scientific Information Database Center (SID), and Islamic World Science Citation Center (ISC), were selected to study the effect of five factors 'infrastructure of information technology', 'experienced staff', 'specialized staff', 'employee public relations' and 'the geographical location of the establishment' on the success of the centers. ANOVA test, Scheffe test, and Pearson's correlation test were used to analyze the data. The findings confirmed the effect of all 5 factors on the success of these centers. However, their effects are not the same on each factor. The results show each of these factors is not only individually but also together affect the success of centers for distance knowledge sharing. Moreover, it was demonstrated that there is a correlation between these factors. The results of this study show what factors determine the success of the centers and their efficiency in distance knowledge sharing in Iran.

Keywords: distance knowledge sharing centers, Iran’s knowledge centers, knowledge sharing centers, staff success

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Authors: Iyad Ismail, Fathilatul Abdul Hamid

Abstract:

This research was mainly investigated to recognize the extent of big data analysis by external auditors. This paper adopts grounded theory as a framework for conducting a series of semi-structured interviews with eighteen external auditors. The research findings comprised the availability extent of big data and big data analysis usage by the external auditors in Palestine, Gaza Strip. Considering the study's outcomes leads to a series of auditing procedures in order to improve the external auditing techniques, which leads to high-quality audit process. Also, this research is crucial for auditing firms by giving an insight into the mechanisms of auditing firms to identify the most important strategies that help in achieving competitive audit quality. These results are aims to instruct the auditing academic and professional institutions in developing techniques for external auditors in order to the big data analysis. This paper provides appropriate information for the decision-making process and a source of future information which affects technological auditing.

Keywords: big data analysis, external auditors, audit reliance, internal audit function

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