Search results for: well data integration

Authors: Neli P. Zlatareva

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Ontology validation is an important part of web applications’ development, where knowledge integration and ontological reasoning play a fundamental role. It aims to ensure the consistency and correctness of ontological knowledge and to guarantee that ontological reasoning is carried out in a meaningful way. Existing approaches to ontology validation address more or less specific validation issues, but the overall process of validating web ontologies has not been formally established yet. As the size and the number of web ontologies continue to grow, the necessity to validate and ensure their consistency and interoperability is becoming increasingly important. This paper presents a validation technique intended to test the consistency of independent ontologies utilized by a common application.

Keywords: knowledge engineering, ontological reasoning, ontology validation, semantic web

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Authors: Lazlo Fauth, Andreas Ligocki

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For cost-effective use of harvesters, expensive repairs and unplanned downtimes must be reduced as far as possible. The predictive detection of failing systems and the calculation of intelligent service intervals, necessary to avoid these factors, require in-depth knowledge of the machines' behavior. Such know-how needs permanent monitoring of the machine state from different technical perspectives. In this paper, three approaches will be presented as they are currently pursued in the publicly funded project PreForst at Ostfalia University of Applied Sciences. These include the intelligent linking of workshop and service data, sensors on the harvester, and a special online hydraulic oil condition monitoring system. Furthermore the paper shows potentials as well as challenges for the use of these data in the conception of a predictive maintenance system.

Keywords: predictive maintenance, condition monitoring, forest harvesting, forest engineering, oil data, hydraulic data

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Authors: K. Usanova

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This study contributes to the conceptual and empirical understanding of how gender diversity management (GDM) is integrated into talent management (TM). Following the grounded theory, we interviewed 40 HR managers and talents from the space industry in Luxembourg. We provide a nuanced picture of what attitude on the GDM in TM organizations have, what strategies and practices they conduct, and how they differ from each other. Based on these differences, we developed three types of GDM integration to TM and explained the talents’ view on this issue. To the author's best knowledge, this study is the first empirical investigation of GDM in TM in the space industry that integrates both the TM executives' and TM receivers' views on gender equality in TM.

Keywords: gender diversity management, high-technology industry, human resource management, talent management

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Authors: H. Y. Jung, Ju H. Park, S. M. Lee

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A sampled-data controller is presented for solid oxide fuel cell systems which is expressed by a sector bounded nonlinear model. The sector bounded nonlinear systems, which have a feedback connection with a linear dynamical system and nonlinearity satisfying certain sector type constraints. Also, the sampled-data control scheme is very useful since it is possible to handle digital controller and increasing research efforts have been devoted to sampled-data control systems with the development of modern high-speed computers. The proposed control law is obtained by solving a convex problem satisfying several linear matrix inequalities. Simulation results are given to show the effectiveness of the proposed design method.

Keywords: sampled-data control, fuel cell, linear matrix inequalities, nonlinear control

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Authors: Sungeun Hyun, Sooin Lee, Gyewan Moon

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Work-Family Conflict has been an active research area for the past decades. Work-Family Conflict harms individuals and organizations, it is ultimately expected to bring the cost of losses to the company in the long run. WFC has mainly focused on effects of organizational effectiveness and job attitude such as Job Satisfaction, Organizational Commitment, and Turnover Intention variables. This study is different from consequence variable with previous research. For this purpose, we selected the positive job attitude 'Work Engagement' as a consequence of WFC. This research has its primary research purpose in identifying the negative effects of the Work-Family Conflict, and started out from the recognition of the problem that the research on the direct relationship on the influence of the WFC on Work Engagement is lacking. Based on the COR(Conservation of resource theory) and JD-R(Job Demand- Resource model), the empirical study model to examine the negative effects of WFC with Emotional Exhaustion as the link between WFC and Work Engagement was suggested and validated. Also, it was analyzed how much Positive Psychological Capital may buffer the negative effects arising from WFC within this relationship, and the Mediated Moderation model controlling the indirect effect influencing the Work Engagement by the Positive Psychological Capital mediated by the WFC and Emotional Exhaustion was verified. Data was collected by using questionnaires distributed to 500 employees engaged manufacturing, services, finance, IT industry, education services, and other sectors, of which 389 were used in the statistical analysis. The data are analyzed by statistical package, SPSS 21.0, SPSS macro and AMOS 21.0. The hierarchical regression analysis, SPSS PROCESS macro and Bootstrapping method for hypothesis testing were conducted. Results showed that all hypotheses are supported. First, WFC showed a negative effect on Work Engagement. Specifically, WIF appeared to be on more negative effects than FIW. Second, Emotional exhaustion found to mediate the relationship between WFC and Work Engagement. Third, Positive Psychological Capital showed to moderate the relationship between WFC and Emotional Exhaustion. Fourth, the effect of mediated moderation through the integration verification, Positive Psychological Capital demonstrated to buffer the relationship among WFC, Emotional Exhastion, and Work Engagement. Also, WIF showed a more negative effects than FIW through verification of all hypotheses. Finally, we discussed the theoretical and practical implications on research and management of the WFC, and proposed limitations and future research directions of research.

Keywords: emotional exhaustion, positive psychological capital, work engagement, work-family conflict

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Authors: Daniel Benson, Yundan Gong, Hannah Kirk

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Advancement in national language processing techniques has led to increased data processing speeds, and reduced the need for cumbersome, manual data processing that is often required when processing data from multilateral organizations for specific purposes. As such, using named entity recognition (NER) modeling and the Organisation of Economically Developed Countries (OECD) Creditor Reporting System database, we present the first geotagged dataset of OECD donor Official Development Assistance (ODA) projects on a global, subnational basis. Our resulting data contains 52,086 ODA projects geocoded to subnational locations across 115 countries, worth a combined $87.9bn. This represents the first global, OECD donor ODA project database with geocoded projects. We use this new data to revisit old questions of how ‘well’ donors allocate ODA to the developing world. This understanding is imperative for policymakers seeking to improve ODA effectiveness.

Keywords: international aid, geocoding, subnational data, natural language processing, machine learning

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Authors: Nina V. Zaitseva, Pavel Z. Shur, Nina G. Atiskova

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Nowadays harmonization of sanitary and hygienic standards of environmental quality with international standards is crucial part of integration of Russia into the international community. Harmonization of Russian and international ambient air quality standards may be realized by risk-based standards development. In this paper approaches to risk-based standards development and examples of these approaches implementation are presented.

Keywords: harmonization, health risk assessment, evolutionary modelling, benchmark level, nickel, manganese

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Authors: Guo Wenyu, Qu Youli

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A practical and simple self-indexing data structure, Partitioned Elias-Fano (PEF) - Compressed Suffix Arrays (CSA), is built in linear time for the CSA based on PEF indexes. Moreover, the PEF-CSA is compared with two classical compressed indexing methods, Ferragina and Manzini implementation (FMI) and Sad-CSA on different type and size files in Pizza & Chili. The PEF-CSA performs better on the existing data in terms of the compression ratio, count, and locates time except for the evenly distributed data such as proteins data. The observations of the experiments are that the distribution of the φ is more important than the alphabet size on the compression ratio. Unevenly distributed data φ makes better compression effect, and the larger the size of the hit counts, the longer the count and locate time.

Keywords: compressed suffix array, self-indexing, partitioned Elias-Fano, PEF-CSA

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Authors: Wanjiku Karanja

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Facebook has monopoly power in the social networking market. It has grown and entrenched its monopoly power through the capture of its users’ data value chains. However, antitrust law’s consumer welfare roots have prevented it from effectively addressing the role of data capture in Facebook’s market dominance. These regulatory blind spots are augmented in Facebook’s proposed Diem cryptocurrency project and its Novi Digital wallet. Novi, which is Diem’s digital identity component, shall enable Facebook to collect an unprecedented volume of consumer data. Consequently, Novi has seismic implications on internet identity as the network effects of Facebook’s large user base could establish it as the de facto internet identity layer. Moreover, the large tracts of data Facebook shall collect through Novi shall further entrench Facebook's market power. As such, the attendant lock-in effects of this project shall be very difficult to reverse. Urgent regulatory action is therefore required to prevent this expansion of Facebook’s data resources and monopoly power. This research thus highlights the importance of data capture to competition and market health in the social networking industry. It utilizes interviews with key experts to empirically interrogate the impact of Facebook’s data capture and control of its users’ data value chains on its market power. This inquiry is contextualized against Novi’s expansive effect on Facebook’s data value chains. It thus addresses the novel antitrust issues arising at the nexus of Facebook’s monopoly power and the privacy of its users’ data. It also explores the impact of platform design principles, specifically data portability and data portability, in mitigating Facebook’s anti-competitive practices. As such, this study finds that Facebook is a powerful monopoly that dominates the social media industry to the detriment of potential competitors. Facebook derives its power from its size, annexure of the consumer data value chain, and control of its users’ social graphs. Additionally, the platform design principles of data interoperability and data portability are not a panacea to restoring competition in the social networking market. Their success depends on the establishment of robust technical standards and regulatory frameworks.

Keywords: antitrust law, data protection law, data portability, data interoperability, digital identity, Facebook

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Authors: Laraba Bala Mohammed

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Education is a very vital tool in enhancement of the general development of individuals in the society who would participate effectively in national development processes, including people with special need, educating children with special needs is one of the greatest challenges of this millennium, this is because professionals in the field of special education are operating in an exciting and rapidly changing phenomenon. Inclusive education in Nigeria is not a new development in the teaching and learning process, but the most important aspect is the utilization and effective integration of people with special needs in the society. This paper focuses on the need of parents, government, professionals in the field of special education and stakeholders to work together for the full implementation of inclusive education in Nigeria.

Keywords: inclusive education, national policy, education, special needs

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Authors: Camille Van Eupen, Dirk Maes, Marc Herremans, Kristijn R. R. Swinnen, Ben Somers, Stijn Luca

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In ecology, species distribution models are commonly implemented to study species-environment relationships. These models increasingly rely on opportunistic citizen science data when high-quality species records collected through standardized recording protocols are unavailable. While these opportunistic data are abundant, uncertainty is usually high, e.g., due to observer effects or a lack of metadata. Data quality filtering is often used to reduce these types of uncertainty in an attempt to increase the value of studies relying on opportunistic data. However, filtering should not be performed blindly. In this study, recommendations are built for data quality filtering of opportunistic species occurrence data that are used as input for species distribution models. Using an extensive database of 5.7 million citizen science records from 255 species in Flanders, the impact on model performance was quantified by applying three data quality filters, and these results were linked to species traits. More specifically, presence records were filtered based on record attributes that provide information on the observation process or post-entry data validation, and changes in the area under the receiver operating characteristic (AUC), sensitivity, and specificity were analyzed using the Maxent algorithm with and without filtering. Controlling for sample size enabled us to study the combined impact of data quality filtering, i.e., the simultaneous impact of an increase in data quality and a decrease in sample size. Further, the variation among species in their response to data quality filtering was explored by clustering species based on four traits often related to data quality: commonness, popularity, difficulty, and body size. Findings show that model performance is affected by i) the quality of the filtered data, ii) the proportional reduction in sample size caused by filtering and the remaining absolute sample size, and iii) a species ‘quality profile’, resulting from a species classification based on the four traits related to data quality. The findings resulted in recommendations on when and how to filter volunteer generated and opportunistically collected data. This study confirms that correctly processed citizen science data can make a valuable contribution to ecological research and species conservation.

Keywords: citizen science, data quality filtering, species distribution models, trait profiles

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Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: string classification, data quality, feature selection, probability distribution, string length

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Authors: Jiongbin Liu, William Yeoh, Shang Gao, Xiaoliang Meng, Yuhan Zhu

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The metaverse concept has sparked significant interest in both academic and industrial spheres. As educational institutions increasingly adopt this technology, understanding its implementation becomes crucial. In response, we conducted a comprehensive case study at a large university, systematically analyzing the nine stages of metaverse development initiatives. Our study unveiled critical insights into the planning, assessment, and execution processes, offering invaluable guidance for stakeholders. The findings highlight both the opportunities for enhanced learning experiences and the challenges related to technological integration and social interaction in higher education.

Keywords: metaverse, metaverse development framework, higher education, case study

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Authors: Salam Khalifa, Naveed Ahmed

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We present a new method to reconstruct a temporally coherent 3D animation from single or multi-view RGB-D video data using unbiased feature point sampling. Given RGB-D video data, in form of a 3D point cloud sequence, our method first extracts feature points using both color and depth information. In the subsequent steps, these feature points are used to match two 3D point clouds in consecutive frames independent of their resolution. Our new motion vectors based dynamic alignment method then fully reconstruct a spatio-temporally coherent 3D animation. We perform extensive quantitative validation using novel error functions to analyze the results. We show that despite the limiting factors of temporal and spatial noise associated to RGB-D data, it is possible to extract temporal coherence to faithfully reconstruct a temporally coherent 3D animation from RGB-D video data.

Keywords: 3D video, 3D animation, RGB-D video, temporally coherent 3D animation

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Authors: Kiwon Yeom

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Change points are abrupt variations in a data sequence. Detection of change points is useful in modeling, analyzing, and predicting time series in application areas such as robotics and teleoperation. In this paper, a change point is defined to be a discontinuity in one of its derivatives. This paper presents a reliable method for detecting discontinuities within a three-dimensional trajectory data. The problem of determining one or more discontinuities is considered in regular and irregular trajectory data from teleoperation. We examine the geometric detection algorithm and illustrate the use of the method on real data examples.

Keywords: change point, discontinuity, teleoperation, abrupt variation

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Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: Norbert Szulc, Cezary Wieczorkowski, Igor Baranowski

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In this paper, a control system that bridges the gap in support for Unmanned Surface Vessels in the PX4 Opensource Autopilot was developed. The system is designed for an omnidirectional water craft with four motors. A modular autopilot architecture design centred around publish-subscribe interprocess communication was used. The paper presents the implementation and integration process of a generic surface vehicle controller capable of driving any configuration of motors through the recently introduced in control allocator in PX4 autopilot. The proposed approach was successfully tested in a case study through implementation on the ASV Perkoz.

Keywords: control system, PX4, drones, rovers, surface vessels, omnidirectional

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Authors: K. Umbleja, M. Ichino, H. Yaguchi

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In this paper, we propose a coloring method for multivariate data visualization by using parallel coordinates based on dissimilarity and tree structure information gathered during hierarchical clustering. The proposed method is an extension for proximity-based coloring that suffers from a few undesired side effects if hierarchical tree structure is not balanced tree. We describe the algorithm by assigning colors based on dissimilarity information, show the application of proposed method on three commonly used datasets, and compare the results with proximity-based coloring. We found our proposed method to be especially beneficial for symbolic data visualization where many individual objects have already been aggregated into a single symbolic object.

Keywords: data visualization, dissimilarity-based coloring, proximity-based coloring, symbolic data

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Authors: Alina Murtishcheva

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The development of an effective system of administrative supervision is a prerequisite for the functioning of local self-government on the basis of the rule of law. Administrative supervision of local self-government is of particular importance in the EU countries due to the influence of integration processes. The central authorities act on the international level; however, subnational authorities also have to implement European legislation in order to strengthen integration. Therefore, the central authority, being the connecting link between supranational and subnational authorities, should bear responsibility, including financial responsibility, for possible mistakes of subnational authorities. Consequently, the state should have sufficient mechanisms of control over local and regional authorities in order to correct their mistakes. At the same time, the control mechanisms do not deny the autonomy of local self-government. The paper analyses models of administrative supervision of local self-government in Ukraine, Poland, Lithuania, Belgium, Great Britain, Italy, and France. The research methods used in this paper are theoretical methods of analysis of scientific literature, constitutions, legal acts, Congress of Local and Regional Authorities of the Council of Europe reports, and constitutional court decisions, as well as comparative and logical analysis. The legislative basis of administrative supervision was scrutinized, and the models of administrative supervision were classified, including a priori control and ex-post control or their combination. The advantages and disadvantages of these models of administrative supervision are analysed. Compliance with Article 8 of the European Charter of Local Self-Government is of great importance for countries achieving common goals and sharing common values. However, countries under study have problems and, in some cases, demonstrate non-compliance with provisions of Article 8. Such non-conformity as the endorsement of a mayor by the Flemish Government in Belgium, supervision with a view to expediency in Great Britain, and the tendency to overuse supervisory power in Poland are analysed. On the basis of research, the tendencies of administrative supervision of local authorities’ activities in selected European countries are described. Several recommendations for Ukraine as a country that had been granted EU candidate status are formulated. Having emphasised its willingness to become a member of the European community, Ukraine should not only follow the best European practices but also avoid the mistakes of countries that have long-term experience in developing the local self-government institution. This project has received funding from the Research Council of Lithuania (LMTLT), agreement № S-PD-22-65.

Keywords: administrative supervision, decentralisation, legality, local authorities, local self-government

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Authors: Hina Kausher, Sangita Srivastava

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In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which covers the variety of figure proportions in both height and girth. 3,000 data has been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. This data is used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from a large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: anthropometric data, data mining, decision tree, garments manufacturing, sizing systems, ready-made garments

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

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Authors: Diego Caballero Vélez

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In 1997 the Schengen Convention was incorporated in the mainstream of EU law by the Amsterdam Treaty. It came into effect in 1999 with the abolition of internal border controls in the EU, a milestone in the European integration project. In the meantime, due to the Yugoslav wars, nearly 700,000 asylum applications were filed in the European countries provoking a major refugee crisis. During this period, the opening of Eastern Europe fostered more cooperation and policy-making at the EU level in migration issues. Currently, a similar migratory crisis is taking place in Europe. The Syrian war has caused the most massive influx of immigrants in Europe since World War II. Nevertheless, the EU is adopting different migration policies from those implemented during the Yugoslav migration crisis. The current crisis has not led to a common European position but national responses have been offered on migration policies and responsibility for border security and asylum-seekers. A lot of factors can explain this change from a cooperation scenario to a no cooperation one, such as the economic crisis, but this research is focused on the premise that 'threat perception' lies at the core of some states grand strategies towards migration and it also influences in multilateral or unilateral responses. Migration rests at the nexus of three dimensions of security, including geopolitical interests, material production, and internal security. According to some scholars, migration policy is an 'integral instrument' of state grand strategy in that context. Political integration at the EU might be altered with the emergence of existential threats. In other words, some areas of the European cooperation can be transformed when a 'critical juncture' occurs, for instance a migration crisis. In that instance, Member states could see migration as a matter of threat that modifies their national interests and willingness to embrace international cooperation. This research will focus on EU Member states´ perceptions of the 90´s migration crisis and the current one. The goal is to evaluate to what extent the perceptions of threat are one of the main factors for explaining the transition from a cooperation scenario to a no-cooperation one in European asylum and security policies. To analyze threat perception in both migration crisis, some relevant Member states are treated as cases of study and a comparative analysis is carried out based on public opinion polls, public and policy discourse in migration, voting practices and deconstruction of the migration policies themselves both at EU level and a national one.

Keywords: cooperation, migration crisis, national responses, threat perception

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Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

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Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence is invaluable in identifying crime. It has been observed that an algorithm based on artificial intelligence (AI) is highly effective in detecting risks, preventing criminal activity, and forecasting illegal activity. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. Researchers and other authorities have used the available data as evidence in court to convict a person. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISA). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The MADIK is implemented using the Java Agent Development Framework and implemented using Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISA and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5 percent of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: artificial intelligence, computer science, criminal investigation, digital forensics

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Authors: Ahmed Slami, Med El Amine Brixi Nigassa, Nassima Labdelli, Sofiane Soulimane, Arnaud Pothier

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Many people suffer from diabetes, a disease marked by abnormal levels of sugar in the blood; 285 million people have diabetes, 6.6% of the world adult population (in 2010), according to the International Diabetes Federation. Insulin medicament is invented to be injected into the body. Generally, the injection requires the patient to do it manually. However, in many cases he will be unable to inject the drug, saw that among the side effects of hyperglycemia is the weakness of the whole body. The researchers designed a medical device that injects insulin too autonomously by using micro-pumps. Many micro-pumps of concepts have been investigated during the last two decades for injecting molecules in blood or in the body. However, all these micro-pumps are intended for slow infusion of drug (injection of few microliters by minute). Now, the challenge is to develop micro-pumps for fast injections (1 microliter in 10 seconds) with accuracy of the order of microliter. Recently, studies have shown that only piezoelectric actuators can achieve this performance, knowing that few systems at the microscopic level were presented. These reasons lead us to design new smart microsystems injection drugs. Therefore, many technological advances are still to achieve the improvement of materials to their uses, while going through their characterization and modeling action mechanisms themselves. Moreover, it remains to study the integration of the piezoelectric micro-pump in the microfluidic platform features to explore and evaluate the performance of these new micro devices. In this work, we propose a new micro-pump model based on piezoelectric actuation with a new design. Here, we use a finite element model with Comsol software. Our device is composed of two pumping chambers, two diaphragms and two actuators (piezoelectric disks). The latter parts will apply a mechanical force on the membrane in a periodic manner. The membrane deformation allows the fluid pumping, the suction and discharge of the liquid. In this study, we present the modeling results as function as device geometry properties, films thickness, and materials properties. Here, we demonstrate that we can achieve fast injection. The results of these simulations will provide quantitative performance of our micro-pumps. Concern the spatial actuation, fluid rate and allows optimization of the fabrication process in terms of materials and integration steps.

Keywords: COMSOL software, piezoelectric, micro-pump, microfluidic

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Authors: Ioanna H. Lioliou, Despoina D. Zavraka

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The radical reformation that Greek urban space, has undergone over the last century, due to the socio-historical developments, technological development and political–geographic factors, has left its imprint on the urban landscape. While the big cities struggle to regain urban landscape balance, small towns are considered to offer high quality lifescapes, ensuring sustainable development potential. However, their unplanned urbanization process led to the loss of significant areas of nature, lack of essential infrastructure, chaotic built environment, incompatible land uses and urban cohesiveness. Natural environment reference points, such as springs, streams, rivers, forests, suburban greenbelts, and etc.; seems to be detached from urban space, while the public, open and green spaces, unequally distributed in the built environment, they are no longer able to offer a complete experience of nature in the city. This study focuses on Greek mainland, a small town Elassona, and aims to restore spatial coherence between the city’s homonymous river and its urban space surroundings. The existence of a linear aquatic ecosystem, is considered a precious greenway, also referred as blueway, able to initiate natural penetrations and ecosystems empowering. The integration of disconnected natural ecosystems forms the basis of a strategic intervention scheme, where the river becomes the urban integration tool / feature, constituting the main urban corridor and an indispensible part of a wider green network that connects open and green spaces, ensuring the function of all the established networks (transportation, commercial, social) of the town. The proposed intervention, introduces a green network highlighting the old stone bridge at the ‘entrance’ of the river in the town and expanding throughout the town with strategic uses and activities, providing accessibility for all the users. The methodology used, is based on the collection of design tools used in related urban river-design interventions around the world. The reinstallation/reactivation of the balance between natural and urban landscape, besides the environmental benefits, contributes decisively to the illustration/projection of urban green identity and re-enhancement of the quality of lifescape qualities and social interaction.

Keywords: green network, rehabilitation scheme, urban landscape, urban streams

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Authors: Eva de Mingo-Fernández, Lourdes Rubio Rico, Carmen Ortega-Segura, Montserrat Querol-García, Raúl González-Jauregui

Abstract:

Today, one of the great challenges that the university faces is to get closer to society and transfer knowledge. The competency-based training approach favours a continuous interaction between practice and theory, which is why it is essential to establish real experiences with reflection and debate and to contrast them with personal and professional knowledge. Service-learning (SL) consists of an integration of academic learning with service in the community, which enables teachers to transfer knowledge with social value and students to be trained on the basis of experience of real needs and problems with the aim of solving them. SLE combines research, teaching, and social value knowledge transfer with the real social needs and problems of a community. Goal: The objective of this study was to design, implement, and evaluate a service-learning program in the subject of adult nursing for second-year nursing students. Methodology: After establishing collaboration with eight associations of people with different pathologies, the students were divided into eight groups, and each group was assigned an association. The groups were made up of 10-12 students. The associations willing to participate were for the following conditions: diabetes, multiple sclerosis, cancer, inflammatory bowel disease, fibromyalgia, heart, lung, and kidney diseases. The methodological design consisting of 5 activities was then applied. Three activities address personal and individual reflections, where the student initially describes what they think it is like to live with a certain disease. They then express their reflections resulting from an interview conducted by peers, in person or online, with a person living with this particular condition, and after sharing the results of their reflections with the rest of the group, they make an oral presentation in which they present their findings to the other students. This is followed by a service task in which the students collaborate in different activities of the association, and finally, a third individual reflection is carried out in which the students express their experience of collaboration. The evaluation of this activity is carried out by means of a rubric for both the reflections and the presentation. It should be noted that the oral presentation is evaluated both by the rest of the classmates and by the teachers. Results: The evaluation of the activity, given by the students, is 7.80/10, commenting that the experience is positive and brings them closer to the reality of the people and the area.

Keywords: academic learning integration, knowledge transfer, service-learning, teaching methodology

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Authors: M. Iommi, G. Losco

Abstract:

Current building sector is focused on reduction of energy requirements, on renewable energy generation and on regeneration of existing urban areas. These targets need to be solved with a systemic approach, considering several aspects simultaneously such as climate conditions, lighting conditions, solar radiation, PV potential, etc. The solar access analysis is an already known method to analyze the solar potentials, but in current years, simulation tools have provided more effective opportunities to perform this type of analysis, in particular in the early design stage. Nowadays, the study of the solar access is related to the easiness of the use of simulation tools, in rapid and easy way, during the design process. This study presents a comparison of three simulation tools, from the point of view of the user, with the aim to highlight differences in the easy-to-use of these tools. Using a real urban context as case study, three tools; Ecotect, Townscope and Heliodon, are tested, performing models and simulations and examining the capabilities and output results of solar access analysis. The evaluation of the ease-to-use of these tools is based on some detected parameters and features, such as the types of simulation, requirements of input data, types of results, etc. As a result, a framework is provided in which features and capabilities of each tool are shown. This framework shows the differences among these tools about functions, features and capabilities. The aim of this study is to support users and to improve the integration of simulation tools for solar access with the design process.

Keywords: energy building design tools, solar access analysis, solar potential, urban planning

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Authors: F. Boudet, V. Galano, D. Gmira, L. Munoz, A. Reina

Abstract:

Commodities pricing needs a specific approach as they are often linked to each other and so are expectedly doing their prices. They are called co-integrated when at least one stationary linear combination exists between them. Though widespread in economic literature, and even if many equilibrium relations and co-movements exist in the economy, this principle of co-movement is not developed in derivatives field. The present study focuses on the following problem: How can the price of a forward agreement on a commodity be simulated, when it is co-integrated with other ones? Theoretical analysis is developed from Gibson-Schwartz model and an analytical solution is given for short maturities contracts and under risk-neutral conditions. The application has been made to crude oil and heating oil energy commodities and result confirms the applicability of proposed method.

Keywords: co-integration, commodities, forward pricing, Gibson-Schwartz

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

Abstract:

Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

Procedia PDF Downloads 105