Search results for: evolutionary neural network

Authors: Luisa Mesquita Oliveira Ribeiro, Alexei Manso Correa Machado

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Handwritten signature is a unique form for recognizing an individual, used to discern documents, carry out investigations in the criminal, legal, banking areas and other applications. Signature verification is based on large amounts of biometric data, as they are simple and easy to acquire, among other characteristics. Given this scenario, signature forgery is a worldwide recurring problem and fast and precise techniques are needed to prevent crimes of this nature from occurring. This article carried out a study on the efficiency of the Capsule Network in analyzing and recognizing signatures. The chosen architecture achieved an accuracy of 98.11% and 80.15% for the CEDAR and GPDS databases, respectively.

Keywords: biometrics, deep learning, handwriting, signature forgery

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Authors: Hammad Khalid

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In this paper, a strategy for computerized arranging of Fiber to the Home (FTTH) get to systems is proposed. We presented an efficient methodology for arranging access organize framework. The GIS information and a lot of calculations were utilized to make the arranging procedure increasingly programmed. The technique clarifies various strides of the arranging process. Considering various situations, various designs can be produced by utilizing the technique. It was likewise conceivable to produce the designs in an extremely brief temporal contrast with the conventional arranging. A contextual investigation is considered to delineate the utilization and abilities of the arranging technique. The technique, be that as it may, doesn't completely robotize the arranging however, make the arranging procedure fundamentally quick. The outcomes and dialog are displayed and end is given at last.

Keywords: FTTH, GIS, robotize, plan

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Authors: Ivanka Valova

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This paper is aimed at using a multi-agent approach to monitor and diagnose a biotechnological system in order to validate certain control actions depending on the process development and the operating conditions. A multi-agent system is defined as a network of interacting software modules that collectively solve complex tasks. Remote monitoring and control of biotechnological processes is a necessity when automated and reliable systems operating with no interruption of certain activities are required. The advantage of our approach is in its flexibility, modularity and the possibility of improving by acquiring functionalities through the integration of artificial intelligence.

Keywords: multi-agent approach, artificial intelligence, biotechnological processes, anaerobic biodegradation

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Authors: Ahmed O. Apampa, Yinusa A. Jimoh

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The potential economic viability and environmental benefits of using a biomass waste, such as corn cob ash (CCA) as pozzolan in stabilizing soils for road pavement construction in a sub-tropical region was investigated. Corn cob was obtained from Maya in South West Nigeria and processed to ash of characteristics similar to Class C Fly Ash pozzolan as specified in ASTM C618-12. This was then blended with ordinary Portland cement in the CCA:OPC ratios of 1:1, 1:2 and 2:1. Each of these blends was then mixed with lateritic soil of ASHTO classification A-2-6(3) in varying percentages from 0 – 7.5% at 1.5% intervals. The soil-CCA-Cement mixtures were thereafter tested for geotechnical index properties including the BS Proctor Compaction, California Bearing Ratio (CBR) and the Unconfined Compression Strength Test. The tests were repeated for soil-cement mix without any CCA blending. The cost of the binder inputs and optimal blends of CCA:OPC in the stabilized soil were thereafter analyzed by developing algorithms that relate the experimental data on strength parameters (Unconfined Compression Strength, UCS and California Bearing Ratio, CBR) with the bivariate independent variables CCA and OPC content, using Matlab R2011b. An optimization problem was then set up minimizing the cost of chemical stabilization of laterite with CCA and OPC, subject to the constraints of minimum strength specifications. The Evolutionary Engine as well as the Generalized Reduced Gradient option of the Solver of MS Excel 2010 were used separately on the cells to obtain the optimal blend of CCA:OPC. The optimal blend attaining the required strength of 1800 kN/m2 was determined for the 1:2 CCA:OPC as 5.4% mix (OPC content 3.6%) compared with 4.2% for the OPC only option; and as 6.2% mix for the 1:1 blend (OPC content 3%). The 2:1 blend did not attain the required strength, though over a 100% gain in UCS value was obtained over the control sample with 0% binder. Upon the fact that 0.97 tonne of CO2 is released for every tonne of cement used (OEE, 2001), the reduced OPC requirement to attain the same result indicates the possibility of reducing the net CO2 contribution of the construction industry to the environment ranging from 14 – 28.5% if CCA:OPC blends are widely used in soil stabilization, going by the results of this study. The paper concludes by recommending that Nigeria and other developing countries in the sub-tropics with abundant stock of biomass waste should look in the direction of intensifying the use of biomass waste as fuel and the derived ash for the production of pozzolans for road-works, thereby reducing overall green house gas emissions and in compliance with the objectives of the United Nations Framework on Climate Change.

Keywords: corn cob ash, biomass waste, lateritic soil, unconfined compression strength, CO2 emission

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Authors: Lea Lachaud, Aida Raoult, Marion Trousselard, Francois B. Vialatte

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This paper addresses mindfulness from a psychological and neuroscientific perspective, by studying how it modulates attention. Being mindful defines a state characterized by 1-an attention directed to the subjective experience of present moment, 2-an unconditional acceptance of this experience, and 3-the rejection of systematic rationalization in favor of plain awareness. The aim of this study is to investigate whether perceptual salience filters are lowered in a ‘mindful’ condition by exploring the role of being mindful in focused visual attention. Over the past decade, mindfulness therapies have seen a surge in popularity. While the outcomes of these therapies have been widely discussed, the mechanisms whereby meditation affects the brain remain mostly unknown. To explore the role of mindfulness in focused visual attention, we conducted a change blindness experiment on 24 subjects, 12 of them being mindful according to the Freiburg Mindfulness Inventory (FMI) scale. Our results suggest that mindful subjects are less affected by change blindness than non-mindful subjects. Furthermore, EEG measurements performed during the experiments may expose neural correlates specific to the mindful state on P300 evoked potentials. Finally, the analysis of both amplitude and latency caused by the perception of a change over 864 recordings may reveal biomarkers that are typical of this state. The paper concludes by discussing the implications of these results for further research.

Keywords: EEG, change blindness, mindfulness, p300, perception, visual attention

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Authors: Pablo M. S. Vallejos

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The article aims to analyze the autobiographical practices of users on Instagram, observing the instrumentalization of image resources in the construction of visual narratives that make up that archive and digital diary. Through bibliographical review, discourse exploration and case studies, the research also aims to present a new theoretical perception about everyday records - edited with a collage of filters and aesthetic tools - that permeate that social network, understanding it as a platform fictionalizing and an expansion of life. In this way, therefore, the work reflects on possible futures in the elaboration of representations and identities in the context of digital spaces in the 21st century.

Keywords: visual culture, social media, autobiography, image

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Authors: Mehrnoosh Askarizadeh

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Globalization of the economy, e-business, and introduction of new technologies pose new challenges to all organizations. In recent decades, globalization, outsourcing, and information technology have enabled many organizations to successfully operate collaborative supply networks in which each specialized business partner focuses on only a few key strategic activities For this industries supply network can be acknowledged as a new form of organization. We will study about critical success factors (CSFs) for implementation of SCM in companies. It is shown that in different circumstances e- supply chain management has a higher impact on performance.

Keywords: supply chain management, logistics management, critical success factors, information technology, top management support, human resource

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Authors: Rawaa Maher, Ahmed Allam, Haruichi Kanaya, Adel B. Abdelrahman

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In this paper, a compact, high-efficiency integrated rectenna is presented to operate in the 2.45 GHz band. A comparison between two rectifier topologies is performed to verify the benefits of removing the matching network from the rectifier. A rectifier high conversion efficiency of 74.1% is achieved. To complete the rectenna system, a novel omnidirectional antenna with high gain (3.72 dB) and compact size (25 mm * 29 mm) is designed and fabricated. The same antenna is used with a reflector for raising the gain to nearly 8.3 dB. The simulation and measurement results of the antenna are in good agreement.

Keywords: internet of things, integrated rectenna, rectenna, RF energy harvesting, wireless sensor networks(WSN)

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Authors: Isabel Freitas, Paula Remoaldo, Olga Matos, Ricardo Goja, Juliana Araujo, Vitor Ribeiro, Miguel Pereira

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Several destinations have been experiencing a transition from a massified cultural tourism to a creative tourism approach. In this new segment of tourism, urban territories have been the focus for several decades. Urban studies on creative industries and initiatives have been taking place in big cities marginalizing small towns and more specifically rural areas. This paper envisages evaluating the differences between rural and urban institutions/platforms, mostly certified by the Creative Tourism Network, in what concerns the practices and initiatives in creative tourism worldwide. In the research carried out between March 2017 and March 2018, we had three levels of primary data and qualitative analysis: i) research on Google (web) by using several keywords like 'creative tourism initiatives', 'creative cities', 'best practices in creative tourism' (from March to August 2017). With the help of the certification of institutions/platforms by the Creative Tourism Network, 24 institutions were found and declared to be developing creative initiatives. It was decided to try to unravel the type of activities and some practices and initiatives carried out by these institutions and the analysis of the differences between rural and urban initiatives. A database of 20 items (e.g., institutions in charge of implementing the initiatives, year of implementation, site, activities developed, place of development, country of origin, type of partners chosen) was created for each institution/platform; ii) A deeper analysis was made on the websites’ information on the institutions (from September to December 2017). The type of professionals involved in the activities, the language used in the activities and the type of activity performed were some of the data analysed and iii) To complement these data, semi-structured interviews were done to representatives of the institutions, conducted mainly by Skype from July 2017 to April 2018. The interviews consisted of 17 questions. In the present paper, these interviews are used to complement the analysis of the same items. Some of the qualitative analysis was supported by the narratives of the leaders of the twenty-four institutions that were surveyed. The results indicate that creative tourism is more active and diverse in urban areas. Some more consolidated communication strategies and partnerships are needed for these activities to become economically more sustainable. The findings of this research provide researchers and practitioners with a better understanding of creative tourism and give some information of how creative tourism is developed in rural and urban areas, the gaps and lack of information, and all the possible directions towards the development of the creative tourism industry.

Keywords: creative tourism, practices of creative tourism, rural areas, urban areas

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Authors: Roland Lachmayer, Mahtab Afsari

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Research is the cornerstone for advancement of human communities. So that it is one of the indexes for evaluating advancement of countries. Research projects are usually cost and time-consuming and do not end in result in short term. Project scheduling is one of the integral parts of project management. The present article offers a new method by using C# and Matlab software to solve Fuzzy GERT networks for Exclusive-OR kind of nodes to schedule the network. In this article we concentrate on flowcharts that we used in Matlab to show how we apply Matlab to schedule Exclusive-OR nodes.

Keywords: research projects, fuzzy GERT, fuzzy CPM, CPM, α-cuts, scheduling

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Authors: Minjung Park, Sangmi Chai, Myoung Jun Lee

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Since there are growing concerns on online privacy, enterprises could involve various personal privacy infringements cases resulting legal causations. For companies that are involving online business, it is important for them to pay extra attentions to protect users’ privacy. If firms can aware consequences from possible online privacy invasion cases, they can more actively prevent future online privacy infringements. This study attempts to predict the probability of ruling types caused by various invasion cases under U.S Personal Privacy Act. More specifically, this research explores online privacy invasion cases which was sentenced guilty to identify types of criminal punishments such as penalty, imprisonment, probation as well as compensation in civil cases. Based on the 853 U.S judicial cases ranged from January, 2000 to May, 2016, which related on data privacy, this research examines the relationship between personal information infringements cases and adjudications. Upon analysis results of 41,724 words extracted from 853 regal cases, this study examined online users’ privacy invasion cases to predict the probability of conviction for a firm as an offender in both of criminal and civil law. This research specifically examines that a cause of privacy infringements and a judgment type, whether it leads a civil or criminal liability, from U.S court. This study applies network text analysis (NTA) for data analysis, which is regarded as a useful method to discover embedded social trends within texts. According to our research results, certain online privacy infringement cases caused by online spamming and adware have a high possibility that firms are liable in the case. Our research results provide meaningful insights to academia as well as industry. First, our study is providing a new insight by applying Big Data analytics to legal cases so that it can predict the cause of invasions and legal consequences. Since there are few researches applying big data analytics in the domain of law, specifically in online privacy, this study suggests new area that future studies can explore. Secondly, this study reflects social influences, such as a development of privacy invasion technologies and changes of users’ level of awareness of online privacy on judicial cases analysis by adopting NTA method. Our research results indicate that firms need to improve technical and managerial systems to protect users’ online privacy to avoid negative legal consequences.

Keywords: network text analysis, online privacy invasions, personal information infringements, predicting judgements

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Authors: Jun Wang, Ge Zhang

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Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample.

Keywords: machine learning, ETF prediction, dynamic trading, asset allocation

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Authors: Koyangbo Guere Monguia Michel Alex Emmanuel

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In e-government and especially recruitment, many researches have been conducted to build a trustworthy and reliable online or application system capable to process users or job applicant files. In this research (Government Recruitment Agency), cloud computing, identity management and public key encryption have been used to management domains, access control authorization mechanism and to secure data exchange between entities for reliable procedure of processing files.

Keywords: cloud computing network, identity management systems, public key encryption, access control and authorization

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Mahdi Pirmoradian, Olayinka Adigun, Christos Politis

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Cooperative spectrum sensing is a crucial challenge in cognitive radio networks. Cooperative sensing can increase the reliability of spectrum hole detection, optimize sensing time and reduce delay in cooperative networks. In this paper, an efficient central capacity optimization algorithm is proposed to minimize cooperative sensing time in a homogenous sensor network using OR decision rule subject to the detection and false alarm probabilities constraints. The evaluation results reveal significant improvement in the sensing time and normalized capacity of the cognitive sensors.

Keywords: cooperative networks, normalized capacity, sensing time

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Authors: Giorgi Sulashvili, Vladimer Kekenadze, Olga Khutsishvili, Bela Khotenashvili, Tsiuri Phkhakadze, Besarion Tsikhelashvili

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The article dealt with the South Caucasus is a complex economic policy, which consists of strands: The process of deepening economic integration in the South Caucasus region; deepening economic integration with the EU in the framework of "Neighbourhood policy with Europe" and in line with the Maastricht criteria; the development of bilateral trade and economic relations with many countries of the world community; the development of sufficient conditions for the integration of the South Caucasus region in the world to enter the market. According to the author, to determine the place of Georgia in the regional policy of the South Caucasus, it is necessary to consider two views about Georgia: The first is the view of Georgia, as a part of global economic and political processes and the second look at Georgia, as a country located in the geo-economic and geopolitical space of the South Caucasus. Such approaches reveal the place of Georgia in two dimensions; in the global and regional economies. In the countries of South Caucasus, the tourism has been developing fast and has a great social and economic importance. Tourism influences deeply on the social and economic growth of the regions of the country. Tourism development formulates thousand new jobs, fixes the positions of small and middle businesses, ensures the development of the education and culture of the population. In the countries of South Caucasus, the Tourist Industry can be specified as the intersectoral complex, which consists of travel transport and it’s technical service network, tourist enterprises which are specialized in various types, wide network services. Tourists have a chance to enjoy all of these services. At the transitional stage of shifting to the market economy, tourism is among the priorities in the development of the national economy of our country. It is true that the Georgian tourism faces a range of problems at present, but its recognition and the necessity for its development may be considered as a fact. Besides, we would underline that the revitalization of the Georgian tourism is not only the question of time. This area can bring a lot of benefits as to private firms, as to specific countries. It also has many negative effects were conducted fundamental research and studies to consider both, positive and negative impacts of tourism. In the future such decisions will be taken that will bring, the maximum benefit at minimum cost, in order for tourism to take its place in Georgia it is necessary to understand the role of the tourism sector in the economic structure.

Keywords: transitional stage, national economy, Georgian tourism, positive and negative impacts

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Authors: Nesrine Berber, Hafid Haffaf, Abdel Madjid Meghabar

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In the last decade, technology has continued to grow and has changed the structure of our society. Today, new technologies including the information and communication (ICT) play a main role which importance continues to grow, now it's become indispensable to the economic, social and cultural. Thus, ICT technology has proven to be as a promising intervention in the area of road transport. The supervision model of class of train of intelligent and autonomous vehicles leads us to give some defintions about IAV and the different technologies used for communication between them. Our aim in this work is to present an hypergraph modeling a class of train of Intelligent and Autonomous Vehicles (IAV).

Keywords: intelligent transportation system, intelligent autonomous vehicles, Ad Hoc network, wireless technologies, hypergraph modeling, supervision

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Authors: Sandoval H. Leyla Angélica, Casani Fernando

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The objective of this study is to analyse how enterprises in developed countries use cooperation agreements to expand into developing countries. Starting from the literature review, seven theoretical prepositions were derived. The qualitative methodology used includes case study, through interviews conducted with eight enterprises from Spain and Colombia. Results show that the cooperation agreements have provided a quick and solid connection that facilitates internationalization, bearing in mind aspects such as: strategic factors, partners, network, technology, experience, communication methods, social benefit and the connection between these aspects and allied enterprises.

Keywords: internationalisation, firms, cooperation agreement, case study, Spain, Colombia

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Authors: S. Cheng, C. Catallo

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Introduction: Canada's healthcare and social services systems are failing high risk, vulnerable older adults. Care for vulnerable older Canadians (65 and older) is not optimal in Canada. It does not address the care needs of vulnerable, high risk adults using a holistic approach. Given the growing aging population, and the care needs for seniors with complex conditions is one of the highest in Canada's health care system, there is a sense of urgency to optimize care. Integration of health and social services is an emerging trend in Canada when compared to European countries. There is no common and universal understanding of healthcare and social services integration within the country. Consequently, a clear understanding and definition of integrated health and social services are absent in Canada. Objectives: A study was undertaken to develop a case definition for integrated health and social care initiatives that serve older adults, which was then tested against three Canadian integrated initiatives. Methodology: A limited literature review was undertaken to identify common characteristics of integrated health and social care initiatives that serve older adults, and comprised both scientific and grey literature, in order to develop a case definition. Three Canadian integrated initiatives that are located in the province of Ontario, were identified using an online search and a screening process. They were surveyed to determine if the literature-based integration definition applied to them. Results: The literature showed that there were 24 common healthcare and social services integration characteristics that could be categorized into ten themes: 1) patient-care approach; 2) program goals; 3) measurement; 4) service and care quality; 5) accountability and responsibility; 6) information sharing; 7) Decision-making and problem-solving; 8) culture; 9) leadership; and 10) staff and professional interaction. The three initiatives showed agreement on all the integration characteristics except for those characteristics associated with healthcare and social care professional interaction, collaborative leadership and shared culture. This disagreement may be due to several reasons, including the existing governance divide between the healthcare and social services sectors within the province of Ontario that has created a ripple effect in how professions in the two different sectors interact. In addition, the three initiatives may be at maturing levels of integration, which may explain disagreement on the characteristics associated with leadership and culture. Conclusions: The development of a case definition for healthcare and social services integration that incorporates common integration characteristics can act as a useful instrument in identifying integrated healthcare and social services, particularly given the emerging and evolutionary state of this phenomenon within Canada.

Keywords: Canada, case definition, healthcare and social services integration, integration, seniors health, services delivery

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Authors: M. Abbasi Layegh, S. Haghipour, K. Athari, R. Khosravi, M. Tafkikialamdari

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An approach to find the optimum mel-frequency cepstral coefficients (MFCCs) for the Radif of Mirzâ Ábdollâh, which is the principal emblem and the heart of Persian music, performed by most famous Iranian masters on two Iranian stringed instruments ‘Tar’ and ‘Setar’ is proposed. While investigating the variance of MFCC for each record in themusic database of 1500 gushe of the repertoire belonging to 12 modal systems (dastgâh and âvâz), we have applied the Fuzzy C-Mean clustering algorithm on each of the 12 coefficient and different combinations of those coefficients. We have applied the same experiment while increasing the number of coefficients but the clustering accuracy remained the same. Therefore, we can conclude that the first 7 MFCCs (V-7MFCC) are enough for classification of The Radif of Mirzâ Ábdollâh. Classical machine learning algorithms such as MLP neural networks, K-Nearest Neighbors (KNN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and Support Vector Machine (SVM) have been employed. Finally, it can be realized that SVM shows a better performance in this study.

Keywords: radif of Mirzâ Ábdollâh, Gushe, mel frequency cepstral coefficients, fuzzy c-mean clustering algorithm, k-nearest neighbors (KNN), gaussian mixture model (GMM), hidden markov model (HMM), support vector machine (SVM)

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Authors: Ivan Belik

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In terms of social networks split graphs correspond to the variety of interpersonal and intergroup relations. In this paper we analyse the interaction between the cliques (socially strong and trusty groups) and the independent sets (fragmented and non-connected groups of people) as the basic components of any split graph. Based on the Semi-Lagrangean relaxation for the k-cardinality assignment problem we show the way of how to minimize the socially risky interactions between the cliques and the independent sets within the social network.

Keywords: cliques, independent sets, k-cardinality assignment, social networks, split graphs

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Authors: Christian Angerer, Markus Lienkamp

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Electric vehicles offer a high variety of possible drivetrain topologies with up to 4 motors. Multi-motor-designs can have several advantages regarding traction, vehicle dynamics, safety and even efficiency. With a rising number of motors, the whole drivetrain becomes more complex. All permutations of gearings, drivetrain-layouts, motor-types and –sizes lead up in a very large solution space. Single elements of this solution space can be analyzed by simulation methods. In addition to longitudinal vehicle behavior, which most optimization-approaches are restricted to, also lateral dynamics are important for vehicle dynamics, stability and efficiency. In order to compete large solution spaces and to find an optimal result, genetic algorithm based optimization is state-of-the-art. As lateral dynamics simulation is way more CPU-intensive, optimization takes much more time than in case of longitudinal-only simulation. Therefore, this paper shows an approach how to create meta-models from a 14-degree of freedom vehicle model in order to enable a numerically efficient drivetrain-layout optimization process under consideration of lateral dynamics. Different meta-modelling approaches such as neural networks or DoE are implemented and comparatively discussed.

Keywords: driving dynamics, drivetrain layout, genetic optimization, meta-modeling, lateral dynamicx

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Authors: Priti Kumari, Tricha Anjali

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Body Area Networks (BANs) have emerged as the most promising technology for pervasive health care applications. Since they facilitate communication of very sensitive health data, information leakage in such networks can put human life at risk, and hence security inside BANs is a critical issue. Safe distribution and periodic refreshment of cryptographic keys are needed to ensure the highest level of security. In this paper, we focus on the key distribution techniques and how they are categorized for BAN. The state-of-art pre-shared key distribution algorithms are surveyed. Possible attacks on algorithms are demonstrated with examples.

Keywords: attacks, body area network, key distribution, key refreshment, pre-shared keys

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Authors: Ying Bo Xie, Hisa Yuki Kurokawa

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Due to the fast development, China's express delivery industry has involved in a dilemma that the service quality are keeping decreasing while the construction rate of delivery network cannot meet the customers’ demand. In order to get out of this dilemma and enjoy a succession development rate, it is necessary to understand the current situation of China's express delivery industry. Firstly, the evolution of China's express delivery industry was systematical presented. Secondly, according to the number of companies and the amount of parcels they has dealt each year, the merits and faults of tow kind of operating pattern was analyzed. Finally, based on the characteristics of these express companies, the problems of China's express delivery industry was divided into several types and the countermeasures were given out respectively.

Keywords: China, express delivery industry, status, problem

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Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: credit network, case portfolio, binary tree, priority queue, stack

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Authors: N. Zerzouri, N. Benalia, N. Bensiali

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This work is devoted to an analysis of the operation of a doubly fed induction generator (DFIG) integrated with a wind system. The power transfer between the stator and the network is carried out by acting on the rotor via a bidirectional signal converter. The analysis is devoted to the study of a fault in the converter due to an interruption of the control of a semiconductor. Simulation results obtained by the MATLAB / Simulink software illustrate the quality of the power generated at the default.

Keywords: doubly fed induction generator (DFIG), wind power generation, back to back PWM converter, default switching

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Authors: Marianna Constantinou, Hana Burianova, Ala Yankouskaya

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Episodic memory retrieval benefits from arousal, with better performance linked to arousing to-be-remembered information. However, the enduring impact of arousal on subsequent memory processes, particularly for non-arousing stimuli, remains unclear. This functional Magnetic Resonance Imaging (fMRI) study examined the effects of arousal on episodic memory processes in young and old adults, focusing on memory of neutral information following arousal exposure. Neural activity was assessed at three distinct timepoints: during exposure to arousing and non-arousing stimuli, memory consolidation (with or without arousing stimulus exposure), and during memory retrieval (with or without arousing stimulus exposure). Behavioural results show that across both age groups, participants performed worse when retrieving episodic memories about a video preceded by a highly arousing negative image. Our fMRI findings reveal three key findings: i) the extension of the influence of negative arousal beyond encoding; ii) the presence of this influence in both young and old adults; iii) and the differential treatment of positive arousal between these age groups. Our findings emphasise valence-specific effects on memory processes and support the enduring impact of negative arousal. We further propose an age-related alteration in the old adult brain in differentiating between positive and negative arousal.

Keywords: episodic memory, ageing, fmri, arousal, valence

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Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

Abstract:

The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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Authors: Richard Joseph, Nerey Mvungi

Abstract:

An electric power system includes a generating, a transmission, a distribution and consumers subsystems. An electrical power network in Tanzania keeps growing larger by the day and become more complex so that, most utilities have long wished for real-time monitoring and remote control of electrical power system elements such as substations, intelligent devices, power lines, capacitor banks, feeder switches, fault analyzers and other physical facilities. In this paper, the concept of automation of management of power systems from generation level to end user levels was determined by using Power System Simulator for Engineering (PSS/E) version 30.3.2.

Keywords: automation, distribution subsystem, generating subsystem, PSS/E, TANESCO, transmission subsystem

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Authors: Ebrahim Alrashed, Yousef Rafique

Abstract:

Mobile devices have drastically changed the way we do things on the move. They are being extremely relied on to perform tasks that are analogous to desktop computer capability. There has been a rapid increase of computational power on these devices; however, battery technology is still the bottleneck of evolution. The primary modern approach day approach to tackle this issue is offloading computation to the cloud, proving to be latency expensive and requiring high network bandwidth. In this paper, we explore efforts to perform barter-based mobile-to-mobile offloading. We present define a protocol and present an architecture to facilitate the development of such a system. We further highlight the deployment and security challenges.

Keywords: computational offloading, power conservation, cloud, sandboxing

Procedia PDF Downloads 379