Search results for: distributed teams

Authors: Liqin Zhang, Liang Yan

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This paper addresses the speed synchronization control problem for a network-based multi-motor system from the perspective of cluster consensus theory. Each motor is considered as a single agent connected through fixed and undirected network. This paper presents an improved control protocol from three aspects. First, for the purpose of improving both tracking and synchronization performance, this paper presents a distributed leader-following method. The improved control protocol takes the importance of each motor’s speed into consideration, and all motors are divided into different groups according to speed weights. Specifically, by using control parameters optimization, the synchronization error and tracking error can be regulated and decoupled to some extent. The simulation results demonstrate the effectiveness and superiority of the proposed strategy. In practical engineering, the simplified models are unrealistic, such as single-integrator and double-integrator. And previous algorithms require the acceleration information of the leader available to all followers if the leader has a varying velocity, which is also difficult to realize. Therefore, the method focuses on an observer-based variable structure algorithm for consensus tracking, which gets rid of the leader acceleration. The presented scheme optimizes synchronization performance, as well as provides satisfactory robustness. What’s more, the existing algorithms can obtain a stable synchronous system; however, the obtained stable system may encounter some disturbances that may destroy the synchronization. Focus on this challenging technological problem, a state-dependent-switching approach is introduced. In the presence of unmeasured angular speed and unknown failures, this paper investigates a distributed fault-tolerant consensus tracking algorithm for a group non-identical motors. The failures are modeled by nonlinear functions, and the sliding mode observer is designed to estimate the angular speed and nonlinear failures. The convergence and stability of the given multi-motor system are proved. Simulation results have shown that all followers asymptotically converge to a consistent state when one follower fails to follow the virtual leader during a large enough disturbance, which illustrates the good performance of synchronization control accuracy.

Keywords: consensus control, distributed follow, fault-tolerant control, multi-motor system, speed synchronization

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Authors: Maryam Ghoreishi, Christian Larsen

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In this paper, we investigate the effect of stochastic inputs on problem of joint optimal pricing and lot-sizing decisions where the inventory cycle is divided into advance and spot sales periods. During the advance sales period, customer can make reservations while customer with reservations can cancel their order. However, during the spot sales period customers receive the order as soon as the order is placed, but they cannot make any reservation or cancellation during that period. We assume that the inter arrival times during the advance sales and spot sales period are exponentially distributed where the arrival rate is decreasing function of price. Moreover, we assume that the number of cancelled reservations is binomially distributed. In addition, we assume that deterioration process follows an exponential distribution. We investigate two cases. First, we consider two-state case where we find the optimal price during the spot sales period and the optimal price during the advance sales period. Next, we develop a generalized case where we extend two-state case also to allow dynamic prices during the spot sales period. We apply the Markov decision theory in order to find the optimal solutions. In addition, for the generalized case, we apply the policy iteration algorithm in order to find the optimal prices, the optimal lot-size and maximum advance sales amount.

Keywords: inventory control, pricing, Markov decision theory, advance sales system

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Authors: Koel Chakraborty

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This paper critically examines the role of ‘disclosure and visibility’ of sexual minorities in a heteronormative organizational setting. The paper wishes to comment on the importance of promoting ‘visibility’ as an important catalyst in increasing the efficacy of outreach programs as part of diversity management practices as well as increasing the efficacy of teams. The aim of the research is to assess the pitfalls of not bringing ‘one’s authentic or whole self’ to work. In doing so, it will address whether Inclusive Leadership at the top propels employees to come out. The paper finally discusses and recommends strategies that could be helpful toward attaining and improving the visibility factor at a cross-functional level. This is a qualitative research with interviews and surveys conducted in inclusive workplace environments across various private sector companies in India.

Keywords: LGBT, diversity, organisation, leadership

Procedia PDF Downloads 190

Authors: Chih-Chieh Yang, Chung-Neng Huang

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In power system, how to improve the fault diagnosis technology of transmission line has always been the primary goal of power grid operators. In recent years, due to the rise of green energy, the addition of all kinds of distributed power also has an impact on the stability of the power system. Because the smart meters are with the function of data recording and bidirectional transmission, the adaptive Fuzzy Neural inference system, ANFIS, as well as the artificial intelligence that has the characteristics of learning and estimation in artificial intelligence. For transmission network, in order to avoid misjudgment of the fault type and location due to the input of these unstable power sources, combined with the above advantages of smart meter and ANFIS, a method for identifying fault types and location of faults is proposed in this study. In ANFIS training, the bus voltage and current information collected by smart meters can be trained through the ANFIS tool in MATLAB to generate fault codes to identify different types of faults and the location of faults. In addition, due to the uncertainty of distributed generation, a wind power system is added to the transmission network to verify the diagnosis correctness of the study. Simulation results show that the method proposed in this study can correctly identify the fault type and location of fault with more efficiency, and can deal with the interference caused by the addition of unstable power sources.

Keywords: ANFIS, fault diagnosis, power system, smart meter

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Authors: A. Maruf Aytekin

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We present our approach on using continuous delivery pattern for release management. One of the key practices of agile and lean teams is the continuous delivery of new features to stakeholders. The main benefits of this approach lie in the ability to release new applications rapidly which has real strategic impact on the competitive advantage of an organization. Organizations that successfully implement Continuous Delivery have the ability to evolve rapidly to support innovation, provide stable and reliable software in more efficient ways, decrease the amount of resources need for maintenance, and lower the software delivery time and costs. One of the objectives of this paper is to elaborate a case study where IT division of Central Securities Depository Institution (MKK) of Turkey apply Continuous Delivery pattern to improve release management process.

Keywords: automation, continuous delivery, deployment, release management

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Authors: Simon Keller, Rainer Mueller

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Continuous range queries are a common means to handle mobile clients in high-density areas. Most existing approaches focus on settings in which the range queries for location-based services are more or less static, whereas the mobile clients in the ranges move. We focus on a category called dynamic real-time range queries (DRRQ), assuming that both, clients requested by the query and the inquirers, are mobile. In consequence, the query parameters and the query results continuously change. This leads to two requirements: the ability to deal with an arbitrarily high number of mobile nodes (scalability) and the real-time delivery of range query results. In this paper, we present the highly decentralized solution adaptive quad streaming (AQS) for the requirements of DRRQs. AQS approximates the query results in favor of a controlled real-time delivery and guaranteed scalability. While prior works commonly optimize data structures on the involved servers, we use AQS to focus on a highly distributed cell structure without data structures automatically adapting to changing client distributions. Instead of the commonly used request-response approach, we apply a lightweight streaming method in which no bidirectional communication and no storage or maintenance of queries are required at all.

Keywords: approximation of client distributions, continuous spatial range queries, mobile objects, streaming-based decentralization in spatial mobile environments

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Authors: R. Chedid, R. Ghajar

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Lebanon electricity crisis continues to escalate. Rationing hours still apply across the country but with different rates. The capital Beirut is subjected to 3 hours cut while other cities, town and villages may endure 9 to 14 hours of power shortage. To mitigate this situation, private diesel generators distributed illegally all over the country are being used to bridge the gap in power supply. Almost each building in large cities has its own generator and individual villages may have more than one generator supplying their loads. These generators together with their private networks form incomplete and ill-designed and managed microgrids (MG) but can be further developed to become renewable energy-based MG operating in island- or grid-connected modes. This paper will analyze the potential of introducing MG to help resolve the energy crisis in Lebanon. It will investigate the usefulness of developing MG under the prevailing situation of existing private power supply service providers and in light of the developed national energy policy that supports renewable energy development. A case study on a distribution feeder in a rural area will be analyzed using HOMER software to demonstrate the usefulness of introducing photovoltaic (PV) arrays along the existing diesel generators for all the stakeholders; namely, the developers, the customers, the utility and the community at large. Policy recommendations regarding MG development in Lebanon will be presented on the basis of the accumulated experience in private generation and the privatization and public-private partnership laws.

Keywords: decentralized systems, distributed generation, microgrids, renewable energy

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Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj K. Biswas, Frank Ferrese

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This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star topology, fault tolerance, conditional diagnosability, multi-agent system, automated power system

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Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj Biswas, Frank Ferrese

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This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star topology, fault tolerance, conditional diagnosability, multi-agent system, automated power system

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Authors: Bashaiar Alsanaa

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Emerging technologies present human interaction with new challenges. Individuals are required to interact and collaborate to achieve mutual gain. Accomplishing shared goals requires all parties involved to trust others’ commitment to fulfilling their specified obligations. Trust is harder to establish when groups work virtually and members transcend time, space, and culture. This paper identifies the importance of trust in virtual groups of students at Kuwait University by exposing them to electronic projects on which they collaborate. Students respond to a survey to assess their range of trust within their teams and how the outcome is affected. Gender differences and other demographic factors are analyzed to understand results and rates of trust. The paper concludes with summarizing factors influencing trust development and possible implications.

Keywords: groups, students, trust, virtual

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Authors: Amir Alani, Mojtaba Mahmoodian, Anna Romanova, Asaad Faramarzi

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Pipelines are extensively used engineering structures which convey fluid from one place to another. Most of the time, pipelines are placed underground and are encumbered by soil weight and traffic loads. Corrosion of pipe material is the most common form of pipeline deterioration and should be considered in both the strength and serviceability analysis of pipes. The study in this research focuses on concrete pipes in sewage systems (concrete sewers). This research firstly investigates how to involve the effect of corrosion as a time dependent process of deterioration in the structural and failure analysis of this type of pipe. Then three probabilistic time dependent reliability analysis methods including the first passage probability theory, the gamma distributed degradation model and the Monte Carlo simulation technique are discussed and developed. Sensitivity analysis indexes which can be used to identify the most important parameters that affect pipe failure are also discussed. The reliability analysis methods developed in this paper contribute as rational tools for decision makers with regard to the strengthening and rehabilitation of existing pipelines. The results can be used to obtain a cost-effective strategy for the management of the sewer system.

Keywords: reliability analysis, service life prediction, Monte Carlo simulation method, first passage probability theory, gamma distributed degradation model

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Authors: Malay Niraj, Praveen Kumar

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The present study is a new approach for improving equipment life and Overall Equipment Efficiency (O.E.E.) through suitable maintenance practice with the help of value engineering. Value engineering is a one of the most powerful decision-making techniques which depend on many factors. The improvements are the result of recommendations made by multidisciplinary teams representing all parties involved. VE is a rigorous, systematic effort to improve the OEE and optimize the life cycle cost of a facility. The study describes problems in maintenance arising due to the absence of having clear criteria and strong decision constrain how to maintain failing equipment. Using factor comparisons, the study has been made between different maintenance practices and finally best maintenance practice based on value engineering technique has been selected.

Keywords: maintenance strategy, overall equipment efficiency, value engineering, decision-making

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Authors: Erjola Reufi, Thomas Beer

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Plain, unreinforced concrete is a brittle material, with a low tensile strength, limited ductility and little resistance to cracking. In order to improve the inherent tensile strength of concrete there is a need of multi directional and closely spaced reinforcement, which can be provided in the form of randomly distributed fibers. Fiber reinforced concrete (FRC) is a composite material consisting of cement, sand, coarse aggregate, water and fibers. In this composite material, short discrete fibers are randomly distributed throughout the concrete mass. The behavioral efficiency of this composite material is far superior to that of plain concrete and many other construction materials of equal cost. The present experimental study considers the effect of steel fibers and polypropylene fiber on the modulus of elasticity of concrete. Hook end steel fibers of length 5 cm and 3 cm at volume fraction of 0.25%, 0.5% and 1.% were used. Also polypropylene fiber of length 12, 6, 3 mm at volume fraction 0.1, 0.25, and 0.4 % were used. Fifteen mixtures has been prepared to evaluate the effect of fiber on modulus of elasticity of concrete. Ultrasonic pulse velocity (UPV) and resonant frequency methods which are two non-destructive testing techniques have been used to measure the elastic properties of fiber reinforced concrete. This study found that ultrasonic wave propagation is the most reliable, easy and cost effective testing technique to use in the determination of the elastic properties of the FRC mix used in this study.

Keywords: fiber reinforced concrete(FRC), polypropylene fiber, resonance, ultrasonic pulse velocity, steel fiber

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Xia Fang

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Arid and semiarid areas of China (ASAC) have experienced significant land-use/cover changes (LUCC), along with intensified climate change. However, LUCC and climate changes and their individual and interactive effects on carbon stocks have not yet been fully understood in the ASAC. This study analyses the carbon stocks in the ASAC during 1980 - 2020 using the specific arid ecosystem model (AEM), and investigates the effects of LUCC and climate change on carbon stock trends. The results indicate that in the past 41 years, the ASAC carbon pool experienced an overall growth trend, with an increase of 182.03 g C/m2. Climatic factors (+291.99 g C/m2), especially the increase in precipitation, were the main drivers of the carbon pool increase. LUCC decreased the carbon pool (-112.27 g C/m2), mainly due to the decrease in grassland area (-2.77%). The climate-induced carbon sinks were distributed in northern Xinjiang, on the Ordos Plateau, and in Northeast China, while the LUCC-induced carbon sinks mainly occurred on the Ordos Plateau and the North China Plain, resulting in a net decrease in carbon sequestration in these regions according to carbon pool measurements. The study revealed that the combination of climate variability, LUCC, and increasing atmospheric CO2 concentration resulted in an increase of approximately 182.03 g C/m2, which was mainly distributed in eastern Inner Mongolia and the western Qinghai-Tibet Plateau. Our findings are essential for improving theoretical guidance to protect the ecological environment, rationally plan land use, and understand the sustainable development of arid and semiarid zones.

Keywords: AEM, climate change, LUCC, carbon stocks

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Authors: Jairson Barbosa Rodrigues, Paulo Romero Martins Maciel, Germano Crispim Vasconcelos

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This paper presents an investigation of the performance impacts regarding the variation of five factors (input data size, node number, cores, memory, and disks) when applying a distributed implementation of Naïve Bayes for text classification of a large Corpus on the Spark big data processing framework. Problem: The algorithm's performance depends on multiple factors, and knowing before-hand the effects of each factor becomes especially critical as hardware is priced by time slice in cloud environments. Objectives: To explain the functional relationship between factors and performance and to develop linear predictor models for time and cost. Methods: the solid statistical principles of Design of Experiments (DoE), particularly the randomized two-level fractional factorial design with replications. This research involved 48 real clusters with different hardware arrangements. The metrics were analyzed using linear models for screening, ranking, and measurement of each factor's impact. Results: Our findings include prediction models and show some non-intuitive results about the small influence of cores and the neutrality of memory and disks on total execution time, and the non-significant impact of data input scale on costs, although notably impacts the execution time.

Keywords: big data, design of experiments, distributed machine learning, natural language processing, spark

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Authors: Shosh Kazaz, Shmuel Banai, Vered Zilberberg

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Optimizing high business performance and employee's well-being simultaneously often challenges organizations. 'TIP' intervention enables achieving them both as the given project demonstrates. Increasing outcomes and improving performance were the initial motivators for this explorative project, followed by a request of the head of the Cardiology department: 'I know we are the best at our clinical practice, but we need to take it further and break our own glass ceiling.' Two guided interventions were conducted in two different units within the department, designed to implement advanced managerial and business-oriented tools, along with 'soft tools' based on coaching psychology and particularly wellness coaching. The organ department multi-disciplinary teams were assembled, aiming to manage and lead the process: mapping the patients' flow, creating solutions, implementing, assessing, improving and assimilating them. Approximately four months later, without additional external resources, meaningful results emerged by the teams in terms of business and performance: shortening the hospitalization length at a given procedure (from 7 to 2.1 days); increasing the availability of Catheterization laboratory by 16% daily – resulting profitability raise; improving patients' journey and experience. A year later, those results are maintained. Furthermore, interviews with the participants revealed positive perceptions regarding the department; a higher sense of joyfulness, connectedness, belonging and a better department climate were reported. Additionally, participants reported a higher sense of fulfillment as opposed to their earliest skepticism and cynicism about their ability to enhance outcomes without more resources (budget and/or manpower), experiencing a mindset change toward the possibility of leading personal and professional growth processes. These reports were supported by analyzing a set of questionnaires that the participants completed, parallel to a control group of non-participating colleagues. Although the assessment was taken a year after the completion of the project and during 'covid-19th-3rd national quarantine, the results indicated a significant impact on several personal parameters associated with wellness, compared to the control group. The participants were higher in self-efficacy and organizational commitment; men were higher in resilience and optimism and women were higher in well-being. In conclusion, the 'TIP' relatively short intervention integrates advanced managerial and wellness coaching tools, empowers organizational resources: Team, Individual and Process and by that generates multi-impact measurable results in terms of employee's wellness parameters along with business performance and patient care.

Keywords: coaching, health and wellness, health management, leadership and well-being

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Authors: Belal Mohamadi Kalesar, Raouf Hasanpour

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Proper placement of Distributed Generation (DG) units such as wind turbine generators in distribution system are still very challenging issue for obtaining their maximum potential benefits because inappropriate placement may increase the system losses. This paper proposes Particle Swarm Optimization (PSO) technique for optimal placement of wind based DG (WDG) in the primary distribution system to reduce energy losses and voltage profile improvement with four different wind levels modeling in year duration. Also, wind turbine is modeled as a DFIG that will be operated at unity power factor and only one wind turbine tower will be considered to install at each bus of network. Finally, proposed method will be implemented on widely used 69 bus power distribution system in MATLAB software environment under four scenario (without, one, two and three WDG units) and for capability test of implemented program it is supposed that all buses of standard system can be candidate for WDG installing (large search space), though this program can consider predetermined number of candidate location in WDG placement to model financial limitation of project. Obtained results illustrate that wind speed increasing in some months will increase output power generated but this can increase / decrease power loss in some wind level, also results show that it is required about 3MW WDG capacity to install in different buses but when this is distributed in overall network (more number of WDG) it can cause better solution from point of view of power loss and voltage profile.

Keywords: wind turbine, DG placement, wind levels effect, PSO algorithm

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Zeeshan Khan, Faisal Nouroz, Shumaila Noureen

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European or common rabbit (Oryctolagus cuniculus) belongs to class Mammalia, order Lagomorpha of family Leporidae. They are distributed worldwide and are native to Europe (France, Spain and Portugal) and Africa (Morocco and Algeria). LTR retrotransposons are major Class I mobile genetic elements of eukaryotic genomes and play a crucial role in genome expansion, evolution and diversification. They were mostly annotated in various genomes by conventional approaches of homology searches, which restricted the annotation of novel elements. Present work involved de novo identification of LTR retrotransposons by LTR_FINDER in haploid genome of rabbit (2247.74 Mb) distributed in 22 chromosomes, of which 7,933 putative full-length or partial copies were identified containing 69.38 Mb of elements, accounting 3.08% of the genome. Highest copy numbers (731) were found on chromosome 7, followed by chromosome 12 (705), while the lowest copy numbers (27) were detected in chromosome 19 with no elements identified from chromosome 21 due to partially sequenced chromosome, unidentified nucleotides (N) and repeated simple sequence repeats (SSRs). The identified elements ranged in sizes from 1.2 - 25.8 Kb with average sizes between 2-10 Kb. Highest percentage (4.77%) of elements was found in chromosome 15, while lowest (0.55%) in chromosome 19. The most frequent tRNA type was Arginine present in majority of the elements. Based on gained results, it was estimated that rabbit exhibits 15,866 copies having 137.73 Mb of elements accounting 6.16% of diploid genome (44 chromosomes). Further molecular analyses will be helpful in chromosomal localization and distribution of these elements on chromosomes.

Keywords: rabbit, LTR retrotransposons, genome, chromosome

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Authors: Jasmin Cowin

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Augmented reality digital technologies, big data, and the need for a teacher workforce able to meet the demands of a knowledge-based society are poised to lead to major changes in the field of education. This paper explores applications and educational use cases of augmented reality digital technologies for educational organizations during the Fourth Industrial Revolution. The Fourth Industrial Revolution requires vision, flexibility, and innovative educational conduits by governments and educational institutions to remain competitive in a global economy. Educational organizations will need to focus on teaching in and for a digital age to continue offering academic knowledge relevant to 21st-century markets and changing labor force needs. Implementation of contemporary disciplines will need to be embodied through learners’ active knowledge-making experiences while embracing ubiquitous accessibility. The power of distributed ledger technology promises major streamlining for educational record-keeping, degree conferrals, and authenticity guarantees. Augmented reality digital technologies hold the potential to restructure educational philosophies and their underpinning pedagogies thereby transforming modes of delivery. Structural changes in education and governmental planning are already increasing through intelligent systems and big data. Reimagining and redesigning education on a broad scale is required to plan and implement governmental and institutional changes to harness innovative technologies while moving away from the big schooling machine.

Keywords: fourth industrial revolution, artificial intelligence, big data, education, augmented reality digital technologies, distributed ledger technology

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Authors: Maria Florencia Ferrer, Saulo Fabiano Amancio-Vieira

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The use of the blockchain in the public sector is present today and no longer the future of disruptive institutional and technological models. There are still some cultural barriers and resistance to the proper use of its potential. This research aims to present the strengths and weaknesses of using a public-permitted and distributed network in the context of the public sector. Therefore, bibliographical/documentary research was conducted to raise the main aspects of the studied platform, focused on the use of the main demands of the public sector. The platform analyzed was LACChain, which is a global alliance composed of different actors in the blockchain environment, led by the Innovation Laboratory of the Inter-American Development Bank Group (IDB Lab) for the development of the blockchain ecosystem in Latin America and the Caribbean. LACChain provides blockchain infrastructure, which is a distributed ratio technology (DLT). The platform focuses on two main pillars: community and infrastructure. It is organized as a consortium for the management and administration of an infrastructure classified as public, following the ISO typologies (ISO / TC 307). It is, therefore, a network open to any participant who agrees with the established rules, which are limited to being identified and complying with the regulations. As benefits can be listed: public network (open to all), decentralized, low transaction cost, greater publicity of transactions, reduction of corruption in contracts / public acts, in addition to improving transparency for the population in general. It is also noteworthy that the platform is not based on cryptocurrency and is not anonymous; that is, it is possible to be regulated. It is concluded that the use of record platforms, such as LACChain, can contribute to greater security on the part of the public agent in the migration process of their informational applications.

Keywords: blockchain, LACChain, public sector, technological disruptions

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Authors: Neda Seyyedi, Reza Berangi

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Voice over IP (VOIP) network, also known as Internet telephony, is growing increasingly having occupied a large part of the communications market. With the growth of each technology, the related security issues become of particular importance. Taking advantage of this technology in different environments with numerous features put at our disposal, there arises an increasing need to address the security threats. Being IP-based and playing a signaling role in VOIP networks, Session Initiation Protocol (SIP) lets the invaders use weaknesses of the protocol to disable VOIP service. One of the most important threats is denial of service attack, a branch of which in this article we have discussed as flooding attacks. These attacks make server resources wasted and deprive it from delivering service to authorized users. Distributed denial of service attacks and attacks with a low rate can mislead many attack detection mechanisms. In this paper, we introduce a mechanism which not only detects distributed denial of service attacks and low rate attacks, but can also identify the attackers accurately. We detect and prevent flooding attacks in SIP protocol using Shannon (FDP-S), Renyi (FDP-R) and Tsallis (FDP-T) entropy. We conducted an experiment to compare the percentage of detection and rate of false alarm messages using any of the Shannon, Renyi and Tsallis entropy as a measure of disorder. Implementation results show that, according to the parametric nature of the Renyi and Tsallis entropy, by changing the parameters, different detection percentages and false alarm rates will be gained with the possibility to adjust the sensitivity of the detection mechanism.

Keywords: VOIP networks, flooding attacks, entropy, computer networks

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Authors: Jihad Daba, Jean-Pierre Dubois

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Multi path fading noise degrades the performance of cellular communication, most notably in femto- and pico-cells in 3G and 4G systems. When the wireless channel consists of a small number of scattering paths, the statistics of fading noise is not analytically tractable and poses a serious challenge to developing closed canonical forms that can be analysed and used in the design of efficient and optimal receivers. In this context, noise is multiplicative and is referred to as stochastically local fading. In many analytical investigation of multiplicative noise, the exponential or Gamma statistics are invoked. More recent advances by the author of this paper have utilized a Poisson modulated and weighted generalized Laguerre polynomials with controlling parameters and uncorrelated noise assumptions. In this paper, we investigate the statistics of multi-diversity stochastically local area fading channel when the channel consists of randomly distributed Rayleigh and Rician scattering centers with a coherent specular Nakagami-distributed line of sight component and an underlying doubly stochastic Poisson process driven by a lognormal intensity. These combined statistics form a unifying triply stochastic filtered marked Poisson point process model.

Keywords: cellular communication, femto and pico-cells, stochastically local area fading channel, triply stochastic filtered marked Poisson point process

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Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

Procedia PDF Downloads 115

Authors: Shreya Saxena, Felix Miller-Molloy, Phillipa Bowen, Greg Fellows, Elizabeth Bowen

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Background: Complex fluid balance abnormalities are well-established post-neurosurgery and traumatic brain injury (TBI). The triple-phase response requires fluid management strategies reactive to urine output and sodium homeostasis as patients shift between Diabetes Insipidus (DI) and Syndrome of Inappropriate ADH (SIADH). It was observed, at a tertiary paediatric center, a relatively high prevalence of the above complications within a cohort of paediatric post-neurosurgical and TBI patients. An audit of the clinical practice against set institutional guidelines was undertaken and analyzed to understand why this was occurring. Based on those results, new guidelines were developed with structured educational packages for the specialist teams involved. This was then reaudited, and the findings were compared. Methods: Two independent audits were conducted across two time periods, pre and post guideline change. Primary data was collected retrospectively, including both qualitative and quantitative data sets from the CQUIN neurosurgical database and electronic medical records. All paediatric patients post posterior fossa (PFT) or supratentorial surgery or with a TBI were included. A literature review of evidence-based practice, initial audit data, and stakeholder feedback was used to develop new clinical guidelines and nursing standard operation procedures. Compliance against these newly developed guidelines was re-assessed and a thematic, trend-based analysis of the two sets of results was conducted. Results: Audit-1 January2017-June2018, n=80; Audit-2 January2020-June2021, n=30 (reduced operative capacity due to COVID-19 pandemic). Overall, improvements in the monitoring of both fluid balance and electrolyte trends were demonstrated; 51% vs. 77% and 78% vs. 94%, respectively. The number of clear fluid management plans documented postoperatively also increased (odds ratio of 4), leading to earlier recognition and management of evolving fluid-balance abnormalities. The local paediatric endocrine team was involved in the care of all complex cases and notified sooner for those considered to be developing DI or SIADH (14% to 35%). However, significant Na fluctuations (>12mmol in 24 hours) remained similar – 5 vs six patients – found to be due to complex pituitary hypothalamic pathology – and the recommended adaptive fluid management strategy was still not always used. Qualitative data regarding useability and understanding of fluid-balance abnormalities and the revised guidelines were obtained from health professionals via surveys and discussion in the specialist teams providing care. The feedback highlighted the new guidelines provided a more consistent approach to the post-operative care of these patients and was a better platform for communication amongst the different specialist teams involved. The potential limitation to our study would be the small sample size on which to conduct formal analyses; however, this reflects the population that we were investigating, which we cannot control. Conclusion: The revised clinical guidelines, based on audited data, evidence-based literature review and stakeholder consultations, have demonstrated an improvement in understanding of the neuro-endocrine complications that are possible, as well as increased compliance to post-operative monitoring of fluid balance and electrolytes in this cohort of patients. Emphasis has been placed on preventative rather than treatment of DI and SIADH. Consequently, this has positively impacted patient safety for the center and highlighted the importance of educational awareness and multi-disciplinary team working.

Keywords: post-operative, fluid-balance management, neuro-endocrine complications, paediatric

Procedia PDF Downloads 76

Authors: Arjun Goutham, Chaitanya Sridhar, Sunita Maheshwari, Robin Uthappa, Prasanna Gopinath

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In alignment with the growth in the sporting industry, a number of people playing and competing in sports are growing exponentially across the globe. However, the number of sports psychology experts are not growing at a similar rate, especially in the Asian and more so, Indian context. Hence, the access to actionable mental training solutions specific to individual athletes is limited. Also, the time constraint an athlete faces due to their intense training schedule makes one-on-one sessions difficult. One of the means to bridge that gap is through technology. Technology makes individualization possible. It allows for easy access to specific-qualitative content/information and provides a medium to place individualized assessments, analysis, solutions directly into an athlete's hands. This enables mental training awareness, education, and real-time actionable solutions possible for athletes in-spite of the limitation of available sports psychology experts in their region. Furthermore, many athletes are hesitant to seek support due to the stigma of appearing weak. Such individuals would prefer a more discreet way. Athletes who have strong mental performance tend to produce better results. The mobile application helps to equip athletes with assessing and developing their mental strategies directed towards improving performance on an ongoing basis. When an athlete understands their strengths and limitations in their mental application, they can focus specifically on applying the strategies that work and improve on zones of limitation. With reports, coaches get to understand the unique inner workings of an athlete and can utilize the data & analysis to coach them with better precision and use coaching styles & communication that suits better. Systematically capturing data and supporting athletes(with individual-specific solutions) or teams with assessment, planning, instructional content, actionable tools & strategies, reviewing mental performance and the achievement of objectives & goals facilitate for a consistent mental skills development at all levels of sporting stages of an athlete's career. The mobile application will help athletes recognize and align with their stable attributes such as their personalities, learning & execution modalities, challenges & requirements of their sport, etc and help develop dynamic attributes like states, beliefs, motivation levels, focus etc. with practice and training. It will provide measurable analysis on a regular basis and help them stay aligned to their objectives & goals. The solutions are based on researched areas of influence on sporting performance individually or in teams.

Keywords: athletes, mental training, mobile application, performance, sports

Procedia PDF Downloads 248

Authors: António J. Gano, Carmen Rangel

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Currently, electric energy storage systems for stationary applications have known an increasing interest, namely with the integration of local renewable energy power sources into energy communities. Li-ion batteries are considered the leading electric storage devices to achieve this integration, and Battery Management Systems (BMS) are decisive for their control and optimum performance. In this work, the advancement of a smart BMS (sBMS) prototype with a modular distributed topology is described. The system, still under development, has a distributed architecture with modular characteristics to operate with different battery pack topologies and charge capacities, integrating adaptive algorithms for functional state real-time monitoring and management of multicellular Li-ion batteries, and is intended for application in the context of a local energy community fed by renewable energy sources. This sBMS system includes different developed hardware units: (1) Cell monitoring units (CMUs) for interfacing with each individual cell or module monitoring within the battery pack; (2) Battery monitoring and switching unit (BMU) for global battery pack monitoring, thermal control and functional operating state switching; (3) Main management and local control unit (MCU) for local sBMS’s management and control, also serving as a communications gateway to external systems and devices. This architecture is fully expandable to battery packs with a large number of cells, or modules, interconnected in series, as the several units have local data acquisition and processing capabilities, communicating over a standard CAN bus and will be able to operate almost autonomously. The CMU units are intended to be used with Li-ion cells but can be used with other cell chemistries, with output voltages within the 2.5 to 5 V range. The different unit’s characteristics and specifications are described, including the different implemented hardware solutions. The developed hardware supports both passive and active methods for charge equalization, considered fundamental functionalities for optimizing the performance and the useful lifetime of a Li-ion battery package. The functional characteristics of the different units of this sBMS system, including different process variables data acquisition using a flexible set of sensors, can support the development of custom algorithms for estimating the parameters defining the functional states of the battery pack (State-of-Charge, State-of-Health, etc.) as well as different charge equalizing strategies and algorithms. This sBMS system is intended to interface with other systems and devices using standard communication protocols, like those used by the Internet of Things. In the future, this sBMS architecture can evolve to a fully decentralized topology, with all the units using Wi-Fi protocols and integrating a mesh network, making unnecessary the MCU unit. The status of the work in progress is reported, leading to conclusions on the system already executed, considering the implemented hardware solution, not only as fully functional advanced and configurable battery management system but also as a platform for developing custom algorithms and optimizing strategies to achieve better performance of electric energy stationary storage devices.

Keywords: Li-ion battery, smart BMS, stationary electric storage, distributed BMS

Procedia PDF Downloads 80

Authors: Silvia Franco, Marcos Sarasola

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The Faculty of Business Administration at the Catholic University of Uruguay is performing an important educational innovation, unique in the country. In preparing future professionals in companies, teamwork competence is very important. However, there is no often a systematic and specific training in the acquisition of this competence in undergraduate students. For this reason, we have designed and implemented an educational innovation through an intervention method to improve teamwork competence for undergraduate students of business studies. Students’ teams are integrated according to the complementary roles of Belbin; changes in teamwork competence during training period are measured with CCSAC tool; classroom methodology in the prio-border teamwork by Team-Based Learning. Methodology also integrates coaching and support team performance during the first two semesters.

Keywords: business students, teamwork, learning, competences

Procedia PDF Downloads 347

Authors: Amrane Meriem, Oukid Salyha

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The presence of defects or bugs in software can lead to costly setbacks, operational inefficiencies, and compromised user experiences. The integration of Machine Learning(ML) techniques has emerged to predict and preemptively address software defects. ML represents a proactive strategy aimed at identifying potential anomalies, errors, or vulnerabilities within code before they manifest as operational issues. By analyzing historical data, such as code changes, feature im- plementations, and defect occurrences. This en- ables development teams to anticipate and mitigate these issues, thus enhancing software quality, reducing maintenance costs, and ensuring smoother user interactions. In this work, we used a recommendation system to improve the performance of ML models in terms of predicting the code severity and effort estimation.

Keywords: software engineering, machine learning, bugs detection, effort estimation

Procedia PDF Downloads 67