Search results for: information warfare techniques

Authors: Wenbo Du, Xiaomei Ma

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With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future.

Keywords: cognitive diagnostic assessment, diagnostic feedback, EFL reading, quantile regression

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Authors: Muhammad Arif Khan, Haroon Idrees, Imran Aziz, Sidra Mushtaq

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The purpose of this study is to investigate the current level of knowledge mapping skills of the public sector employees in Pakistan. National Institute of Management is one of the premiere public sector training organization for mid-career public sector employees in Pakistan. This study is conducted on participants of fourteen weeks long training course called Mid-Career Management Course (MCMC) which is mandatory for public sector employees in order to ascertain how to enhance their knowledge mapping skills. Methodology: Researcher used both qualitative and quantitative approach to conduct this study. Primary data about current level of participants’ understanding of knowledge mapping was collected through structured questionnaire. Later on, Participant Observation method was used where researchers acted as part of the group to gathered data from the trainees during their performance in training activities and tasks. Findings: Respondents of the study were examined for skills and abilities to organizing ideas, helping groups to develop conceptual framework, identifying critical knowledge areas of an organization, study large networks and identifying the knowledge flow using nodes and vertices, visualizing information, represent organizational structure etc. Overall, the responses varied in different skills depending on the performance and presentations. However, generally all participants have demonstrated average level of using both the IT and Non-IT K-mapping tools and techniques during simulation exercises, analysis paper de-briefing, case study reports, post visit presentation, course review, current issue presentation, syndicate meetings, and daily synopsis. Research Limitations: This study is conducted on a small-scale population of 67 public sector employees nominated by federal government to undergo 14 weeks extensive training program called MCMC (Mid-Career Management Course) at National Institute of Management, Peshawar, Pakistan. Results, however, reflects only a specific class of public sector employees i.e. working in grade 18 and having more than 5 years of work. Practical Implications: Research findings are useful for trainers, training agencies, government functionaries, and organizations working for capacity building of public sector employees.

Keywords: knowledge management, km in public sector, knowledge management and professional development, knowledge management in training, knowledge mapping

Procedia PDF Downloads 252

Authors: C. C .D. Kulathilake, M. Jayatilake, T. Takahashi

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The myocardium is composed of specialized muscle which relies mainly on fatty acid and sugar metabolism and it is widely contribute to the heart functioning. The changes of the cardiac energy-producing system during heart failure have been proved using autoradiography techniques. This study focused on evaluating sugar and fatty acid metabolism in myocardium as cardiac energy getting system using heart-accumulated radiopharmaceuticals. Two sets of autoradiographs of heart cross sections of Lewis male rats were analyzed and the time- accumulation curve obtained with use of the MATLAB image processing software to evaluate fatty acid and sugar metabolic functions.

Keywords: autoradiographs, fatty acid, radiopharmaceuticals, sugar

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Authors: Svebor Secak

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This four-year artistic research project hosted by the University of New England, Australia has set the goal to experiment with non-conventional ways of presenting a language-based narrative in dance using insights of recent theoretical writing on performance, addressing the research question: How to transform an archival recording of a neoclassical ballet performance into a new artistic dance video by implementing postmodern philosophical concepts? The Creative Practice component takes the form of a dance video Hamlet Revisited which is a reworking of the archival recording of the neoclassical ballet Hamlet, augmented by new material, produced using resources, technicians and dancers of the Croatian National Theatre in Zagreb. The methodology for the creation of Hamlet Revisited consisted of extensive field and desk research after which three dancers were shown the recording of original Hamlet and then created their artistic response to it based on their reception and appreciation of it. The dancers responded differently, based upon their diverse dancing backgrounds and life experiences. They began in the role of the audience observing video of the original ballet and transformed into the role of the choreographer-performer. Their newly recorded material was edited and juxtaposed with the archival recording of Hamlet and other relevant footage, allowing for postmodern features such as aleatoric content, synchronicity, eclecticism and serendipity, that way establishing communication on a receptive reader-response basis, thus blending the roles of the choreographer, performer and spectator, creating an original work of art whose significance lies in the relationship and communication between styles, old and new choreographic approaches, artists and audiences and the transformation of their traditional roles and relationships. In editing and collating, the following techniques were used with the intention to avoid the singular narrative: fragmentation, repetition, reverse-motion, multiplication of images, split screen, overlaying X-rays, image scratching, slow-motion, freeze-frame and simultaneity. Key postmodern concepts considered were: deconstruction, diffuse authorship, supplementation, simulacrum, self-reflexivity, questioning the role of the author, intertextuality and incredulity toward grand narratives - departing from the original story, thus personalising its ontological themes. From a broad brush of diverse concepts and techniques applied in an almost prescriptive manner, the project focuses on intertextuality that proves to be valid on at least two levels. The first is the possibility of a more objective analysis in combination with a semiotic structuralist approach moving from strict relationships between signs to a multiplication of signifiers, considering the dance text as an open construction, containing the elusive and enigmatic quality of art that leaves the interpretive position open. The second one is the creation of the new work where the author functions as the editor, aware and conscious of the interplay of disparate texts and their sources which co-act in the mind during the creative process. It is argued here that the eclectic combination of the old and new material through constant oscillations of different discourses upon the same topic resulted in a transmodern integrationist recent work of art that might be applied as a model for reconsidering existing choreographic creations.

Keywords: Ballet Hamlet, intertextuality, transformation, transmodern dance video

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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: Fatih Iscan, Ceren Yagci

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Rapid population growth, urbanization and industrialization are known as the most important factors of environment problems. Elimination and management of solid wastes are also within the most important environment problems. One of the main problems in solid waste management is the selection of the best site for elimination of solid wastes. Lately, Geographical Information System (GIS) has been used for easing selection of landfill area. GIS has the ability of imitating necessary economical, environmental and political limitations. They play an important role for the site selection of landfill area as a decision support tool. In this study; map layers will be studied for minimum effect of environmental, social and cultural factors and maximum effect for engineering/economical factors for site selection of landfill areas and using GIS for an decision support mechanism in solid waste landfill areas site selection will be presented in Aksaray/TURKEY city, Güzelyurt district practice.

Keywords: GIS, landfill, solid waste, spatial analysis

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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: Saurabh Rawat, Anushree Sah

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K Maps are generally and ideally, thought to be simplest form for obtaining solution of Boolean equations. Cubical Representation of Boolean equations is an alternate pick to incur a solution, otherwise to be meted out with Truth Tables, Boolean Laws, and different traits of Karnaugh Maps. Largest possible k- cubes that exist for a given function are equivalent to its prime implicants. A technique of minimization of Logic functions is tried to be achieved through cubical methods. The main purpose is to make aware and utilise the advantages of cubical techniques in minimization of Logic functions. All this is done with an aim to achieve minimal cost solution.r

Keywords: K-maps, don’t care conditions, Boolean equations, cubes

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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: 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: 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: Younghoon Kim, Seoung Bum Kim

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One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

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Authors: Okba Belahssen, Lazhar Torchane, Said Benramache, Abdelouahed Chala

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This paper presents corrosion behavior of the plasma-nitrided 32CrMoV13 steel. Different kinds of samples were tested: non-treated, plasma nitrided samples. The structure of layers was determined by X-ray diffraction, while the morphology was observed by scanning electron microscopy (SEM). The corrosion behavior was evaluated by electrochemical techniques (potentiodynamic curves and electrochemical impedance spectroscopy). The corrosion tests were carried out in acid chloride solution (HCl 1M). Experimental results showed that the nitrides ε-Fe2−3N and γ′-Fe4N present in the white layer are nobler than the substrate but may promote, by galvanic effect, a localized corrosion through open porosity. The better corrosion protection was observed for nitrided sample.

Keywords: plasma-nitrided, 32CrMoV13 steel, corrosion, EIS

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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: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Ebrahim Shayesteh, Nikolaos Styliaras, Alin George Raducu, Ozan Sahin, Daniel Pombo VáZquez, Jonas Funkquist, Sotirios Thanopoulos

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One of the important requirements in many code generation projects is defining some of the model parameters tunable. This helps to update the model parameters without performing the code generation again. This paper studies the concept of embedded code generation by MATLAB/Simulink coder targeting the TwinCAT Simulink system. The generated runtime modules are then tested and deployed to the TwinCAT 3 engineering environment. However, defining the parameters tunable in MATLAB/Simulink code generation targeting TwinCAT is not very straightforward. This paper focuses on this subject and reviews some of the techniques tested here to make the parameters tunable in generated runtime modules. Three techniques are proposed for this purpose, including normal tunable parameters, callback functions, and mask subsystems. Moreover, some test Simulink models are developed and used to evaluate the results of proposed approaches. A brief summary of the study results is presented in the following. First of all, the parameters defined tunable and used in defining the values of other Simulink elements (e.g., gain value of a gain block) could be changed after the code generation and this value updating will affect the values of all elements defined based on the values of the tunable parameter. For instance, if parameter K=1 is defined as a tunable parameter in the code generation process and this parameter is used to gain a gain block in Simulink, the gain value for the gain block is equal to 1 in the gain block TwinCAT environment after the code generation. But, the value of K can be changed to a new value (e.g., K=2) in TwinCAT (without doing any new code generation in MATLAB). Then, the gain value of the gain block will change to 2. Secondly, adding a callback function in the form of “pre-load function,” “post-load function,” “start function,” and will not help to make the parameters tunable without performing a new code generation. This means that any MATLAB files should be run before performing the code generation. The parameters defined/calculated in this file will be used as fixed values in the generated code. Thus, adding these files as callback functions to the Simulink model will not make these parameters flexible since the MATLAB files will not be attached to the generated code. Therefore, to change the parameters defined/calculated in these files, the code generation should be done again. However, adding these files as callback functions forces MATLAB to run them before the code generation, and there is no need to define the parameters mentioned in these files separately. Finally, using a tunable parameter in defining/calculating the values of other parameters through the mask is an efficient method to change the value of the latter parameters after the code generation. For instance, if tunable parameter K is used in calculating the value of two other parameters K1 and K2 and, after the code generation, the value of K is updated in TwinCAT environment, the value of parameters K1 and K2 will also be updated (without any new code generation).

Keywords: code generation, MATLAB, tunable parameters, TwinCAT

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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: Zhi Liu, Hui Jiang, Jin Li, Kunhao Chen, Langfang Zhang

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Fast acquisition of the seismic information and accurate assessment of the earthquake disaster is the key problem for emergency rescue after a destructive earthquake. In order to meet the requirements of the earthquake emergency response and rescue for the cities and counties, a quasi real-time comprehensive evaluation system for earthquake disaster is developed. Based on monitoring data of Micro-Electro-Mechanical Systems (MEMS) strong motion network, structure database of a county area and the real-time disaster information by the mobile terminal after an earthquake, fragility analysis method and dynamic correction algorithm are synthetically obtained in the developed system. Real-time evaluation of the seismic disaster in the county region is finally realized to provide scientific basis for seismic emergency command, rescue and assistant decision.

Keywords: quasi real-time, earthquake disaster data collection, MEMS accelerometer, dynamic correction, comprehensive evaluation

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Authors: Cristina Costa-Lobo, Ana Martins, Maria Das Dores Formosinho, Ana Campina, Filomena Ponte

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This paper is a report on the findings of a study conducted at an international leading food group. Documentary analysis and discourse analysis techniques were used to examine how corporate social responsibility and career education are valued by this international group. The Survey on Corporate Social Responsibility and Career Education was used, with 18 open-ended questions, the first six related to Corporate Social Responsibility and the last 12 related to Education for the Career. The Survey on the Social Emergency Fund was made up of 16 open-ended questions. The Social Welfare Survey was used to investigate the contribution of social workers in this area, as well as to understand their status. The sample of this investigation is composed by the Director of the development area, by the Coordinator and two Social Assistants of the Social Emergency Fund. Their collaboration was the provision of information in the form of an interview where the two main axes of this study were explored: Corporate Social Responsibility and Career Education. With regard to the analysis of data obtained from interviews, it was accomplished through the content analysis according to the Bardin's method (2004), through the pre-analytical, exploratory and qualitative treatment and interpretation of responses. Critical review of documents was also used. The success and effectiveness of this international group are marked by ambition, ability to resist difficulties, sharing of values, spirit of unity and team sense that is shared in its different companies, its leadership position is also due to the concern to see reinforced and developed values of work, discipline, rigor and competence, its management is geared towards responding to immediate challenges from a Corporate Social Responsibility perspective that is characteristic of it, incorporating concerns about impacts both in the medium and long term. In addition to internal training, it directs investments for external training by promoting actions such as participation in seminars and congresses worldwide and the creation of partnerships in various areas of management with prestigious teaching entities. Findings indicate the creation of a training school, with initiatives for internal and external training, in partnerships with prestigious teaching entities. Of particular note is the Management Trainees Program, developed for more than 25 years, characterized by building a career by obtaining knowledge and skills acquired in the combination of on-the-job experience and a training program.

Keywords: career education, corporate social responsibility, training school, management trainees program

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Authors: Thaveesha Dheerasekera, Dileeka Sandamali Alwis

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The escalating prevalence of mental health issues, such as depression and suicidal ideation, is a matter of significant global concern. It is plausible that a variety of factors, such as life events, social isolation, and preexisting physiological or psychological health conditions, could instigate or exacerbate these conditions. Traditional approaches to diagnosing depression entail a considerable amount of time and necessitate the involvement of adept practitioners. This underscores the necessity for automated systems capable of promptly detecting and diagnosing symptoms of depression. The PsyVBot system employs sophisticated natural language processing and machine learning methodologies, including the use of the NLTK toolkit for dataset preprocessing and the utilization of a Long Short-Term Memory (LSTM) model. The PsyVBot exhibits a remarkable ability to diagnose depression with a 94% accuracy rate through the analysis of user input. Consequently, this resource proves to be efficacious for individuals, particularly those enrolled in academic institutions, who may encounter challenges pertaining to their psychological well-being. The PsyVBot employs a Long Short-Term Memory (LSTM) model that comprises a total of three layers, namely an embedding layer, an LSTM layer, and a dense layer. The stratification of these layers facilitates a precise examination of linguistic patterns that are associated with the condition of depression. The PsyVBot has the capability to accurately assess an individual's level of depression through the identification of linguistic and contextual cues. The task is achieved via a rigorous training regimen, which is executed by utilizing a dataset comprising information sourced from the subreddit r/SuicideWatch. The diverse data present in the dataset ensures precise and delicate identification of symptoms linked with depression, thereby guaranteeing accuracy. PsyVBot not only possesses diagnostic capabilities but also enhances the user experience through the utilization of audio outputs. This feature enables users to engage in more captivating and interactive interactions. The PsyVBot platform offers individuals the opportunity to conveniently diagnose mental health challenges through a confidential and user-friendly interface. Regarding the advancement of PsyVBot, maintaining user confidentiality and upholding ethical principles are of paramount significance. It is imperative to note that diligent efforts are undertaken to adhere to ethical standards, thereby safeguarding the confidentiality of user information and ensuring its security. Moreover, the chatbot fosters a conducive atmosphere that is supportive and compassionate, thereby promoting psychological welfare. In brief, PsyVBot is an automated conversational agent that utilizes an LSTM model to assess the level of depression in accordance with the input provided by the user. The demonstrated accuracy rate of 94% serves as a promising indication of the potential efficacy of employing natural language processing and machine learning techniques in tackling challenges associated with mental health. The reliability of PsyVBot is further improved by the fact that it makes use of the Reddit dataset and incorporates Natural Language Toolkit (NLTK) for preprocessing. PsyVBot represents a pioneering and user-centric solution that furnishes an easily accessible and confidential medium for seeking assistance. The present platform is offered as a modality to tackle the pervasive issue of depression and the contemplation of suicide.

Keywords: chatbot, depression diagnosis, LSTM model, natural language process

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Authors: H. Kadi-Hanifi, F. Amghar

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Steppes of arid Mediterranean zones are deeply threatened by desertification. To stop or alleviate ecological and economic problems associated with this desertification, management actions have been implemented since the last three decades. The struggle against desertification has become a national priority in many countries. In Algeria, several management techniques have been used to cope with desertification. This study aims at investigating the effect of exclosure on floristic diversity and chemical soil proprieties after four years of implementation. 167 phyto-ecological samples have been studied, 122 inside the exclosure and 45 outside. Results showed that plant diversity, composition, vegetation cover, pastoral value and soil fertility were significantly higher in protected areas.

Keywords: Algeria, arid, desertification, pastoral management, soil fertility

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Authors: Gennady P. Ginkul, Sergey Yu. Soloviov

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The article proposes to use so-called "quotation-based algorithm" for simulation of decision making process in distributed expert systems and multi-agent systems. The idea was adopted from the techniques for group decision-making. It is based on the assumption that one expert system to perform its logical inference may use rules from another expert system. The application of the algorithm was demonstrated on the example in which the consolidated decision is the decision that requires minimal quotation.

Keywords: backward chaining inference, distributed expert systems, group decision making, multi-agent systems

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Authors: R. Berrached, H. Ait Mahamed, A. Iddou

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Water pollution is nowadays a serious problem, due to the increasing scarcity of water and thus to the impact induced by such pollution on the human health. Various techniques are made use of to deal with water pollution. Among the most used ones, some can be enumerated: the bacterian bed, the activated sludge, lagoons as biological processes and coagulation-flocculation as a physic-chemical process. These processes are very expensive and a decreasing in efficiency treatment with the increase of the initial pollutants concentration. This is the reason why research has been reoriented towards the use of adsorption process as an alternative solution instead of the other traditional processes. In our study, we have tempted to explore the characteristics of hydroxides of Al and Fe to purify contaminated water by two industrial dyes SBL blue and SRL-150 orange. Results have shown the efficiency of the two materials on the blue SBL dye.

Keywords: metallic hydroxydes, dyes, purification, adsorption

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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: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Petros Dlamini, Bethiweli Malambo, Maggie Masenya

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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

Procedia PDF Downloads 87