Search results for: data communication

Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

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Authors: E. A. Krasikov

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Dependence of the materials properties on neutron irradiation intensity (flux) is a key problem while usage data of the accelerated materials irradiation in test reactors for forecasting of their capacity for work in realistic (practical) circumstances of operation. Investigations of the reactor pressure vessel steel radiation degradation dependence on fast neutron fluence (embrittlement kinetics) at low flux reveal the instability in the form of the scatter of the experimental data and wave-like sections of embrittlement kinetics appearance. Disclosure of the steel degradation oscillating is a sign of the steel structure cyclic self-recovery transformation as it take place in self-organization processes. This assumption has received support through the discovery of the similar ‘anomalous’ data in scientific publications and by means of own additional experiments. Data obtained stimulate looking-for ways to management of the structural steel radiation stability (for example, by means of nano - structure modification for radiation defects annihilation intensification) for creation of the intelligent self-recovering material. Expected results: - radiation degradation theory and mechanisms development, - more adequate models of the radiation embrittlement elaboration, - surveillance specimen programs improvement, - methods and facility development for usage data of the accelerated materials irradiation for forecasting of their capacity for work in realistic (practical) circumstances of operation, - search of the ways for creating of the radiation stable self-recovery intelligent materials.

Keywords: degradation, radiation, steel, wave-like kinetics

Procedia PDF Downloads 296

Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Ramanen Sugunesegran, Michael L. Williams

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Over the past two decades surgical approaches for mitral valve (MV) disease have evolved with the advent of minimally invasive techniques. Robotic mitral valve repair (RMVr) safety and efficacy has been well documented, however, mid- to long-term data are limited. The aim of this review was to provide a comprehensive analysis of the available mid- to long-term term data for RMVr. Electronic searches of five databases were performed to identify all relevant studies reporting minimum 5-year data on RMVr. Pre-defined primary outcomes of interest were overall survival, freedom from MV reoperation and freedom from moderate or worse mitral regurgitation (MR) at 5-years or more post-RMVr. A meta-analysis of proportions or means was performed, utilizing a random effects model, to present the data. Kaplan-Meier curves were aggregated using reconstructed individual patient data. Nine studies totaling 3,300 patients undergoing RMVr were identified. Rates of overall survival at 1-, 5- and 10-years were 99.2%, 97.4% and 92.3%, respectively. Freedom from MV reoperation at 8-years post RMVr was 95.0%. Freedom from moderate or worse MR at 7-years was 86.0%. Rates of early post-operative complications were low with only 0.2% all-cause mortality and 1.0% cerebrovascular accident. Reoperation for bleeding was low at 2.2% and successful RMVr was 99.8%. Mean intensive care unit and hospital stay were 22.4 hours and 5.2 days, respectively. RMVr is a safe procedure with low rates of early mortality and other complications. It can be performed with low complication rates in high volume, experienced centers. Evaluation of available mid-term data post-RMVr suggests favorable rates of overall survival, freedom from MV reoperation and freedom from moderate or worse MR recurrence.

Keywords: mitral valve disease, mitral valve repair, robotic cardiac surgery, robotic mitral valve repair

Procedia PDF Downloads 78

Authors: Waraporn Boonchieng, Ekkarat Boonchieng, Wilawan Senaratana, Jaras Singkaew

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Geographical information system (GIS) is a designated system widely used for collecting and analyzing geographical data. Since the introduction of ultra-mobile, 'smart' devices, investigators, clinicians, and even the general public have had powerful new tools for collecting, uploading and accessing information in the field. Epidemiology paired with GIS will increase the efficacy of preventive health care services. The objective of this study is to apply GPS location services that are available on the common mobile device with district health systems, storing data on our private cloud system. The mobile application has been developed for use on iOS, Android, and web-based platforms. The system consists of two parts of district health information, including recorded resident data forms and individual health recorded data forms, which were developed and approved by opinion sharing and public hearing. The application's graphical user interface was developed using HTML5 and PHP with MySQL as a database management system (DBMS). The reporting module of the developed software displays data in a variety of views, from traditional tables to various types of high-resolution, layered graphics, incorporating map location information with street views from Google Maps. Multi-extension exporting is also supported, utilizing standard platforms such as PDF, PNG, JPG, and XLS. The data were collected in the database beginning in March 2013, by district health volunteers and district youth volunteers who had completed the application training program. District health information consisted of patients’ household coordinates, individual health data, social and economic information. This was combined with Google Street View data, collected in March 2014. Studied groups consisted of 16,085 (67.87%) and 47,811 (59.87%) of the total 23,701 households and 79,855 people were collected by the system respectively, in Saraphi district, Chiang Mai Province. The report generated from the system has had a major benefit directly to the Saraphi District Hospital. Healthcare providers are able to use the basic health data to provide a specific home health care service and also to create health promotion activities according to medical needs of the people in the community.

Keywords: health, public health, GIS, geographic information system

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Authors: Jennifer M. Glennon, Hana D'Souza, Luke Mason, Annette Karmiloff-Smith, Michael S. C. Thomas

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Genetic syndrome groups that feature high rates of autism comorbidity, like Down syndrome (DS) and fragile X syndrome (FXS), have been presented as useful models for understanding risk and protective factors involved in the emergence of autistic traits. Yet despite reaching clinical thresholds, these ‘syndromic’ forms of autism appear to differ in important ways from the idiopathic or ‘non-syndromic’ autism phenotype. To uncover the true nature of these comorbidities, it is necessary to extend definitions of autism to include the cognitive characteristics of the disorder and to then apply this broadened conceptualisation to the study of syndromic autism profiles. The current study employs a variety of well-established eye-tracking paradigms to assess visual attentional performance in children with DS and FXS who reach thresholds for autism on the Social Communication Questionnaire. It investigates whether autism profiles in these children are accompanied by visual orienting difficulties (‘sticky attention’), decreased social attention, and enhanced visual search performance, all of which are characteristic of the idiopathic autism phenotype. Data is collected from children with DS and FXS aged between 6 and 10 years, in addition to two control groups matched on age and intellectual ability (i.e., children with idiopathic autism and neurotypical controls). Cross-sectional developmental trajectory analyses are conducted to enable visuo-attentional profile comparisons. Significant differences in the visuo-attentional processes underpinning autism presentations in children with FXS and DS are hypothesised, supporting notions of syndrome specificity. The study provides insight into the complex heterogeneity associated with syndromic autism presentations and autism per se, with clinical implications for the utility of autism intervention programmes in DS and FXS populations.

Keywords: autism, down syndrome, fragile X syndrome, eye tracking

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Authors: Richard McGregor, Carmen Reaiche, Stephen Boyle

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Cyber threats are a relatively recent phenomenon and offer cyber insurers a dynamic and intelligent peril. As individuals en mass become increasingly digitally dependent, Personal Cyber Insurance (PCI) offers an attractive option to mitigate cyber risk at a personal level. This abstract proposes a literature review that conceptualises a framework for siting Personal Cyber Insurance (PCI) within the context of cyberspace. The lack of empirical research within this domain demonstrates an immediate need to define the scope of PCI to allow cyber insurers to understand personal cyber risk threats and vectors, customer awareness, capabilities, and their associated needs. Additionally, this will allow cyber insurers to conceptualise appropriate frameworks allowing effective management and distribution of PCI products and services within a landscape often in-congruent with risk attributes commonly associated with traditional personal line insurance products. Cyberspace has provided significant improvement to the quality of social connectivity and productivity during past decades and allowed enormous capability uplift of information sharing and communication between people and communities. Conversely, personal digital dependency furnish ample opportunities for adverse cyber events such as data breaches and cyber-attacksthus introducing a continuous and insidious threat of omnipresent cyber risk–particularly since the advent of the COVID-19 pandemic and wide-spread adoption of ‘work-from-home’ practices. Recognition of escalating inter-dependencies, vulnerabilities and inadequate personal cyber behaviours have prompted efforts by businesses and individuals alike to investigate strategies and tactics to mitigate cyber risk – of which cyber insurance is a viable, cost-effective option. It is argued that, ceteris parabus, the nature of cyberspace intrinsically provides characteristic peculiarities that pose significant and bespoke challenges to cyber insurers, often in-congruent with risk attributes commonly associated with traditional personal line insurance products. These challenges include (inter alia) a paucity of historical claim/loss data for underwriting and pricing purposes, interdependencies of cyber architecture promoting high correlation of cyber risk, difficulties in evaluating cyber risk, intangibility of risk assets (such as data, reputation), lack of standardisation across the industry, high and undetermined tail risks, and moral hazard among others. This study proposes a thematic overview of the literature deemed necessary to conceptualise the challenges to issuing personal cyber coverage. There is an evident absence of empirical research appertaining to PCI and the design of operational business models for this business domain, especially qualitative initiatives that (1) attempt to define the scope of the peril, (2) secure an understanding of the needs of both cyber insurer and customer, and (3) to identify elements pivotal to effective management and profitable distribution of PCI - leading to an argument proposed by the author that postulates that the traditional general insurance customer journey and business model are ill-suited for the lineaments of cyberspace. The findings of the review confirm significant gaps in contemporary research within the domain of personal cyber insurance.

Keywords: cyberspace, personal cyber risk, personal cyber insurance, customer journey, business model

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Authors: Mostafa Aminzadeh, Min Deng

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Modeling loss data is an important part of actuarial science. Actuaries use models to predict future losses and manage financial risk, which can be beneficial for marketing purposes. In the insurance industry, small claims happen frequently while large claims are rare. Traditional distributions such as Normal, Exponential, and inverse-Gaussian are not suitable for describing insurance data, which often show skewness and fat tails. Several authors have studied classical and Bayesian inference for parameters of composite distributions, such as Exponential-Pareto, Weibull-Pareto, and Inverse Gamma-Pareto. These models separate small to moderate losses from large losses using a threshold parameter. This research introduces a computational approach using a nonlinear regression model for loss data that relies on multiple predictors. Simulation studies were conducted to assess the accuracy of the proposed estimation method. The simulations confirmed that the proposed method provides precise estimates for regression parameters. It's important to note that this approach can be applied to datasets if goodness-of-fit tests confirm that the composite distribution under study fits the data well. To demonstrate the computations, a real data set from the insurance industry is analyzed. A Mathematica code uses the Fisher information algorithm as an iteration method to obtain the maximum likelihood estimation (MLE) of regression parameters.

Keywords: maximum likelihood estimation, fisher scoring method, non-linear regression models, composite distributions

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Authors: Mattias Bergstrom

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Significance of the Study: This research will provide new insights into the risks with digital embedded infrastructure. Through this research, we will analyze each risk and its potential negation strategies, especially for AI and autonomous automation. Moreover, the analysis that is presented in this paper will convey valuable information for future research that can create more stable, secure, and efficient autonomous systems. To learn and understand the risks, a large IoT system was envisioned, and risks with hardware, tampering, and cyberattacks were collected, researched, and evaluated to create a comprehensive understanding of the potential risks. Potential solutions have then been evaluated on an open source IoT hardware setup. This list shows the identified passive and active risks evaluated in the research. Passive Risks: (1) Hardware failures- Critical Systems relying on high rate data and data quality are growing; SCADA systems for infrastructure are good examples of such systems. (2) Hardware delivers erroneous data- Sensors break, and when they do so, they don’t always go silent; they can keep going, just that the data they deliver is garbage, and if that data is not filtered out, it becomes disruptive noise in the system. (3) Bad Hardware injection- Erroneous generated sensor data can be pumped into a system by malicious actors with the intent to create disruptive noise in critical systems. (4) Data gravity- The weight of the data collected will affect Data-Mobility. (5) Cost inhibitors- Running services that need huge centralized computing is cost inhibiting. Large complex AI can be extremely expensive to run. Active Risks: Denial of Service- It is one of the most simple attacks, where an attacker just overloads the system with bogus requests so that valid requests disappear in the noise. Malware- Malware can be anything from simple viruses to complex botnets created with specific goals, where the creator is stealing computer power and bandwidth from you to attack someone else. Ransomware- It is a kind of malware, but it is so different in its implementation that it is worth its own mention. The goal with these pieces of software is to encrypt your system so that it can only be unlocked with a key that is held for ransom. DNS spoofing- By spoofing DNS calls, valid requests and data dumps can be sent to bad destinations, where the data can be extracted for extortion or to corrupt and re-inject into a running system creating a data echo noise loop. After testing multiple potential solutions. We found that the most prominent solution to these risks was to use a Peer 2 Peer consensus algorithm over a blockchain to validate the data and behavior of the devices (sensors, storage, and computing) in the system. By the devices autonomously policing themselves for deviant behavior, all risks listed above can be negated. In conclusion, an Internet middleware that provides these features would be an easy and secure solution to any future autonomous IoT deployments. As it provides separation from the open Internet, at the same time, it is accessible over the blockchain keys.

Keywords: IoT, security, infrastructure, SCADA, blockchain, AI

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Authors: Myoungsoon You, Heejung Son

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Background: In the current study, we investigated the effects of emotions induced by an infectious disease outbreak on the various protective behaviors taken during the crisis and on the perception after the crisis. The investigation was based on two psychological theories of appraisal tendency and action tendency. Methods: A total of 900 participants in South Korea who experienced the 2015 Middle East Respiratory Syndrome outbreak were sampled by a professional survey agency. To assess the influence of the emotions fear and anger, a regression approach was used. The effect of emotions on various protective behaviors and perceptions was observed using a hierarchical regression method. Results: Fear and anger induced by the infectious disease outbreak were both associated with increased protective behaviors during the crisis. However, the differences between the emotions were observed. While protective behaviors with avoidance tendency (adherence to recommendations, self-mitigation), were raised by both fear and anger, protective behaviors with approach tendency (information-seeking) were increased by anger, but not fear. Regarding the effect of emotion on the risk perception after the crisis, only fear was associated with a higher level of risk perception. Conclusions: This study confirmed the role of emotions in crisis protective behaviors and post-crisis perceptions regarding an infectious disease outbreak. These findings could enhance understanding of the public’s protective behaviors during infectious disease outbreaks and afterward risk perception corresponding to emotions. The results also suggested strategies for communicating with the public that takes into account emotions that are prominently induced by crises associated with disease outbreaks.

Keywords: crisis communication, emotion, infectious disease outbreak, protective behavior, risk perception

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Authors: Oscar E. Cariceo, Claudia V. Casal

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Machine learning offers a set of techniques to promote social work interventions and can lead to support decisions of practitioners in order to predict new behaviors based on data produced by the organizations, services agencies, users, clients or individuals. Machine learning techniques include a set of generalizable algorithms that are data-driven, which means that rules and solutions are derived by examining data, based on the patterns that are present within any data set. In other words, the goal of machine learning is teaching computers through 'examples', by training data to test specifics hypothesis and predict what would be a certain outcome, based on a current scenario and improve that experience. Machine learning can be classified into two general categories depending on the nature of the problem that this technique needs to tackle. First, supervised learning involves a dataset that is already known in terms of their output. Supervising learning problems are categorized, into regression problems, which involve a prediction from quantitative variables, using a continuous function; and classification problems, which seek predict results from discrete qualitative variables. For social work research, machine learning generates predictions as a key element to improving social interventions on complex social issues by providing better inference from data and establishing more precise estimated effects, for example in services that seek to improve their outcomes. This paper exposes the results of a classification algorithm to predict cyberbullying among adolescents. Data were retrieved from the National Polyvictimization Survey conducted by the government of Chile in 2017. A logistic regression model was created to predict if an adolescent would experience cyberbullying based on the interaction and behavior of gender, age, grade, type of school, and self-esteem sentiments. The model can predict with an accuracy of 59.8% if an adolescent will suffer cyberbullying. These results can help to promote programs to avoid cyberbullying at schools and improve evidence based practice.

Keywords: cyberbullying, evidence based practice, machine learning, social work research

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Authors: Awdhesh Narayan Sharma

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The use of herbal medicines for the cure of various ailments among the tribal population is as old as human origin itself. Most of the tribal populations of Madhya Pradesh inhabit in remote and inaccessible ecological setup. From long back, tribals and forests are interrelated to each other. They use an enormous range of wild plants for their basic needs and medicines. The tribal developed a unique understanding with wild plants, herbs, etc., and earned specialized knowledge of disease pattern and curative therapy-through hard experiences, common sense, trial, and error methods. They have passed this knowledge through traditions, taboos, totems, folklore by words of mouth from generation to generation. Here, an attempt has been made to study the possible aspects of herbal medicine for the cure of Jaundice among the tribal populations of Madhya Pradesh, India, through primary data as well as available secondary data. The data have been collected from the 305 Bharias of Patalkot, Madhya Pradesh, India, and included available secondary source of data by various investigators. It may be concluded that a sizable herbal medicinal plants' wealth exists in Madhya Pradesh, India, which still awaits for scientific exploration. The existing herbal medicines used for the cure of jaundice need an extensive investigation from the pharmaceutical point of view.

Keywords: Bharias, herbal medicine, tribal, Madhya Pradesh

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Authors: Tsitsi Nyukurayi Muparari, Walter Timo De Vries, Jaap Zevenbergen

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The nature of Land administration accommodates diversity in terms of both spatial data handling activities and the expertise involved, which supposedly aims to satisfy the unpredictable demands of land data and the diverse demands of the customers arising from the land. However, it is known that strategic decisions of restructuring are in most cases repelled in favour of complex structures that strive to accommodate professional diversity and diverse roles in the field of Land administration. Yet despite of this widely accepted knowledge, there is scanty theoretical knowledge concerning the psychological methodologies that can extract the deeper perceptions from the diverse spatial expertise in order to explain the invisible control arm of the polarised reception of the ideas of change. This paper evaluates Q methodology in the context of a cadastre and land registry merger (under one agency) using the Swedish cadastral system as a case study. Precisely, the aim of this paper is to evaluate the effectiveness of Q methodology towards modelling the diverse psychological perceptions of spatial professionals who are in a widely contested decision of merging the cadastre and land registry components of Land administration using the Swedish cadastral system as a case study. An empirical approach that is prescribed by Q methodology starts with the concourse development, followed by the design of statements and q sort instrument, selection of the participants, the q-sorting exercise, factor extraction by PQMethod and finally narrative development by logic of abduction. The paper uses 36 statements developed from a dominant competing value theory that stands out on its reliability and validity, purposively selects 19 participants to do the Qsorting exercise, proceeds with factor extraction from the diversity using varimax rotation and judgemental rotation provided by PQMethod and effect the narrative construction using the logic abduction. The findings from the diverse perceptions from cadastral professionals in the merger decision of land registry and cadastre components in Sweden’s mapping agency (Lantmäteriet) shows that focus is rather inclined on the perfection of the relationship between the legal expertise and technical spatial expertise. There is much emphasis on tradition, loyalty and communication attributes which concern the organisation’s internal environment rather than innovation and market attributes that reveals customer behavior and needs arising from the changing humankind-land needs. It can be concluded that Q methodology offers effective tools that pursues a psychological approach for the evaluation and gradations of the decisions of strategic change through extracting the local perceptions of spatial expertise.

Keywords: cadastre, factor extraction, land administration merger, land registry, q-methodology, rotation

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Authors: Yamba Dabone, Tounwendyam Frédéric Ouedraogo, Pengwendé Justin Kouraogo, Oumarou Sie

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Reliable data transport over the Internet is one of the goals of researchers in the field of computer science. Data such as videos and audio files are becoming increasingly large. As a result, transporting them over the Internet is becoming difficult. Therefore, it has been important to establish a method to locally interconnect autonomous systems (AS) with each other to facilitate traffic exchange. It is in this context that Internet Exchange Points (IXPs) are set up to facilitate local and even regional traffic. They are now the lifeblood of the Internet. Therefore, it is important to think about the factors that can characterize IXPs. However, other more quantifiable characteristics can help determine the quality of an IXP. In addition, these characteristics may allow ISPs to have a clearer view of the exchange node and may also convince other networks to connect to an IXP. To that end, we define five new IXP characteristics: the attraction rate (τₐₜₜᵣ); and the peering rate (τₚₑₑᵣ); the target rate of an IXP (Objₐₜₜ); the number of IXP links (Nₗᵢₙₖ); the resistance rate τₑ𝒻𝒻 and the attraction failure rate (τ𝒻).

Keywords: characteristic, autonomous system, internet service provider, internet exchange point, rate

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Authors: Apollinaire Barme, Simon Tamayo, Arthur Gaudron

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This paper presents a statistical approach to identify explanatory variables linearly related to e-commerce sales. The proposed methodology allows specifying a regression model in order to quantify the relevance between openly available data (economic and demographic) and national e-commerce sales. The proposed methodology consists in collecting data, preselecting input variables, performing regressions for choosing variables and models, testing and validating. The usefulness of the proposed approach is twofold: on the one hand, it allows identifying the variables that influence e- commerce sales with an accessible approach. And on the other hand, it can be used to model future sales from the input variables. Results show that e-commerce is linearly dependent on 11 economic and demographic indicators.

Keywords: e-commerce, statistical modeling, regression, empirical research

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Authors: Li Qiang, Yang Ze-Ming, Liu Bao-Xu, Jiang Zheng-Wei

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With the increasing complexity of cyberspace security, the cyber-attack attribution has become an important challenge of the security protection systems. The difficult points of cyber-attack attribution were forced on the problems of huge data handling and key data missing. According to this situation, this paper presented a reasoning method of cyber-attack attribution based on threat intelligence. The method utilizes the intrusion kill chain model and Bayesian network to build attack chain and evidence chain of cyber-attack on threat intelligence platform through data calculation, analysis and reasoning. Then, we used a number of cyber-attack events which we have observed and analyzed to test the reasoning method and demo system, the result of testing indicates that the reasoning method can provide certain help in cyber-attack attribution.

Keywords: reasoning, Bayesian networks, cyber-attack attribution, Kill Chain, threat intelligence

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Authors: Y. Atilgan Şengül

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Health information technologies promise higher quality, safer care and much more for both patients and professionals. Despite their promise, they are costly to develop and difficult to implement. On the other hand, user acceptance and usage determine the success of implemented information technology in healthcare. This study provides a model to understand health professionals’ perception and expectation of health information technology. Extensive literature review has been conducted to determine the main factors to be measured. A questionnaire has been designed as a measurement model and submitted to the personnel of an in vitro fertilization clinic. The respondents’ degree of agreement according to five-point Likert scale was 72% for convenient access to data and 69.4% for the importance of data security. There was a significant difference in acceptance of electronic data storage for female respondents. Also, other significant differences between professions were obtained.

Keywords: healthcare, health informatics, medical record system, questionnaire

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Authors: Fatemeh Yazdanmehr, Iulian Nistor

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The scope of the current study is the evaluation of the electric field effect on electrostatic desalting mathematical modeling with laboratory data. This research study was focused on developing a model for an existing operation desalting unit of one of the Iranian heavy oil field with a 75 MBPD production capacity. The high temperature of inlet oil to dehydration unit reduces the oil recovery, so the mathematical modeling of desalter operation parameters is very significant. The existing production unit operating data has been used for the accuracy of the mathematical desalting plant model. The inlet oil temperature to desalter was decreased from 110 to 80°C, and the desalted electrical field was increased from 0.75 to 2.5 Kv/cm. The model result shows that the desalter parameter changes meet the water-oil specification and also the oil production and consequently annual income is increased. In addition to that, changing desalter operation conditions reduces environmental footprint because of flare gas reduction. Following to specify the accuracy of selected electrostatic desalter electrical field, laboratory data has been used. Experimental data are used to ensure the effect of electrical field change on desalter. Therefore, the lab test is done on a crude oil sample. The results include the dehydration efficiency in the presence of a demulsifier and under electrical field (0.75 Kv) conditions at various temperatures. Comparing lab experimental and electrostatic desalter mathematical model results shows 1-3 percent acceptable error which confirms the validity of desalter specification and operation conditions changes.

Keywords: desalter, electrical field, demulsification, mathematical modeling, water-oil separation

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Authors: Swati Kishor Zode, Rahul Ambekar

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Data mining is the extraction of fascinating examples on the other hand information from enormous measure of information and choice is made as indicated by the applicable information extracted. As of late, with the dangerous advancement in internet, stockpiling of information and handling procedures, privacy preservation has been one of the major (higher) concerns in data mining. Various techniques and methods have been produced for protection saving data mining. In the situation of Clinical Decision Support System, the choice is to be made on the premise of the data separated from the remote servers by means of Internet to diagnose the patient. In this paper, the fundamental thought is to build the precision of Decision Support System for multiple diseases for different maladies and in addition protect persistent information while correspondence between Clinician side (Client side) also, the Server side. A privacy preserving protocol for clinical decision support network is proposed so that patients information dependably stay scrambled amid diagnose prepare by looking after the accuracy. To enhance the precision of Decision Support System for various malady C5.0 classifiers and to save security, a Homomorphism encryption algorithm Paillier cryptosystem is being utilized.

Keywords: classification, homomorphic encryption, clinical decision support, privacy

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Authors: Anant Sharma, Ashwin Rajan

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Customer experience is measured today based on defining a set of metrics and KPIs, setting up thresholds and defining triggers across those thresholds. While this is an effective way of measuring against a Key Performance Indicator ( referred to as KPI in the rest of the paper ), this approach cannot capture the various nuances that make up the overall customer experience. Customers consume a product or service at various levels, which is not reflected in metrics like Customer Satisfaction or Net Promoter Score, but also across other measurements like recurring revenue, frequency of service usage, e-learning and depth of usage. Here we explore an alternative method of measuring customer experience by flipping the traditional views. Rather than rolling customers up to a metric, we roll up metrics to hierarchies and then measure customer experience. This method allows any team to quantify customer experience across multiple touchpoints in a customer’s journey. We make use of various data sources which contain information for metrics like CXSAT, NPS, Renewals, and depths of service usage collected across a customer lifecycle. This data can be mined systematically to get linkages between different data points like geographies, business groups, products and time. Additional views can be generated by blending synthetic contexts into the data to show trends and top/bottom types of reports. We have created a framework that allows us to measure customer experience using the above logic.

Keywords: analytics, customers experience, BI, business operations, KPIs, metrics

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Authors: Rhadinia Tayag-Relanes, Felina C. Young

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This study was conducted to analyze the noodle production process at Yan Hu Food Manufacturing for the basis of production improvement. The study utilized the PDCA approach and record review in the gathering of data for the calendar year 2019 from August to October data of the noodle products miki, canton, and misua. Causal-comparative research was used in this study; it attempts to establish cause-effect relationships among the variables such as descriptive statistics and correlation, both were used to compute the data gathered. The study found that miki, canton, and misua production has different cycle time sets for each production and has different production outputs in every set of its production process and a different number of wastages. The company has not yet established its allowable rejection rate/ wastage; instead, this paper used a 1% wastage limit. The researcher recommended the following: machines used for each process of the noodle product must be consistently maintained and monitored; an assessment of all the production operators by checking their performance statistically based on the output and the machine performance; a root cause analysis for finding the solution must be conducted; and an improvement on the recording system of the input and output of the production process of noodle product should be established to eliminate the poor recording of data.

Keywords: continuous improvement, process, operations, PDCA

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Authors: Parveen Kumar

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The cope the rapid change of technology and to control the ephemeral rate of information generation, librarians along with their professional colleagues need to equip themselves as per the requirement of the electronic information society. E-learning is purely based on computer and communication technologies. The terminologies like computer based learning. It is the delivery of content via all electronic media through internet, internet, Extranets television broadcast, CD-Rom documents, etc. E-learning poses lot of issues in the transformation of literature or knowledge from the conventional medium to ICT based format and web based services.

Keywords: e-learning, digital libraries, online learning, electronic information society

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Authors: Jorge Duarte de Sá

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The appearance of the new media technologies has opened new fields of intervention in architecture creating a new dynamic communication in the relationship between public and space, where are present technological devices that enable a new sensory experience, aesthetic and even spiritual. This connection makes relevant the idea of rehabilitate architectonic spaces with new media technologies such as sacred spaces. This research aims to create a media project integrated in sacred spaces that combine Architecture, Art and New Technologies, exploring new perspectives and different dynamics in space.

Keywords: media, architecture, religious spaces, projections, contemplation

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Authors: Jamilatuzzahro, Rezzy Eko Caraka

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The yield curve is the plot of the yield to maturity of zero-coupon bonds against maturity. In practice, the yield curve is not observed but must be extracted from observed bond prices for a set of (usually) incomplete maturities. There exist many methodologies and theory to analyze of yield curve. We use two methods (the Nelson-Siegel Method, the Svensson Method, and the SVR method) in order to construct and compare our zero-coupon yield curves. The objectives of this research were: (i) to study the adequacy of NSS model and SVR to Indonesian government bonds data, (ii) to choose the best optimization or estimation method for NSS model and SVR. To obtain that objective, this research was done by the following steps: data preparation, cleaning or filtering data, modeling, and model evaluation.

Keywords: support vector regression, Nelson-Siegel-Svensson, yield curve, Indonesian government

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Authors: Fuad A. A. Trayek, Tunku Badariah Tunku Ahmad, Mohamad Sahari Nordin, Mohammed AM Dwikat

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This paper reports on the results of an exploratory factor analysis procedure applied on the e-learning readiness data obtained from a survey of four hundred and seventy-nine (N = 479) teachers from secondary schools in Nablus, Palestine. The data were drawn from a 23-item Likert questionnaire measuring e-learning readiness based on Chapnick's conception of the construct. Principal axis factoring (PAF) with Promax rotation applied on the data extracted four distinct factors supporting four of Chapnick's e-learning readiness dimensions, namely technological readiness, psychological readiness, infrastructure readiness and equipment readiness. Together these four dimensions explained 56% of the variance. These findings provide further support for the construct validity of the items and for the existence of these four factors that measure e-learning readiness.

Keywords: e-learning, e-learning readiness, technological readiness, psychological readiness, principal axis factoring

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Authors: Adel Al Hashlan

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Parent and family engagement plays a crucial role in supporting the success of students with special needs in educational settings. This paper explores the significance of parental involvement in special education, examining its impact on academic achievement, social-emotional development, and overall well-being. Meaningful collaboration between educators, parents, and families can promote positive outcomes for students with diverse learning needs. The study employs a mixed-methods approach, incorporating both qualitative and quantitative techniques. Data were collected through structured interviews, focus groups, and surveys involving students with special needs, their parents, and educators across diverse educational settings. The analysis identifies patterns, themes, and correlations to understand the impact of parent and family engagement on student outcomes. Major findings reveal that effective parent and family involvement initiatives, characterized by strong communication strategies, collaboration frameworks, and partnership-building approaches, significantly enhance students’ academic performance, social-emotional development, and overall well-being. The study also identifies common barriers to parental involvement, such as cultural differences and accessibility issues, and suggests strategies for overcoming these challenges. In conclusion, the study underscores the importance of systemic support and resource allocation to facilitate meaningful partnerships between schools and families. Ongoing research and professional development are crucial to enhancing the effectiveness of parent and family engagement initiatives in special education, ultimately maximizing student achievement and well-being.

Keywords: parental involvement, special education, student success, collaborative partnerships

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Authors: Murtadha Kareem, Oliver Faust

Abstract:

Wearable biosensor systems can be designed and developed for health monitoring. There is much interest in both scientific and industrial communities established since 2007. Fundamentally, the cost of healthcare has increased dramatically and the world population is aging. That creates the need to harvest technological improvements with small bio-sensing devices, wireless-communication, microelectronics and smart textiles, that leads to non-stop developments of wearable sensor based systems. There has been a significant demand to monitor patient's health status while the patient leaves the hospital in his/her personal environment. To address this need, there are numerous system prototypes which has been launched in the medical market recently, the aim of that is to provide real time information feedback about patient's health status, either to the patient himself/herself or direct to the supervising medical centre station, while being capable to give a notification for the patient in case of possible imminent health threatening conditions. Furthermore, wearable health monitoring systems comprise new techniques to address the problem of managing and monitoring chronic heart diseases for elderly people. Wearable sensor systems for health monitoring include various types of miniature sensors, either wearable or implantable. To be specific, our proposed system able to measure essential physiological parameter, such as heart rate signal which could be transmitted through Bluetooth to the cloud server in order to store, process, analysis and visualise the data acquisition. The acquired measurements are connected through internet of things to a central node, for instance an android smart phone or tablet used for visualising the collected information on application or transmit it to a medical centre.

Keywords: Wearable sensor, Heart rate, Internet of things, Chronic heart disease

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Authors: Rafat Alwazna

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Translation teaching is a particular activity that includes translators and interpreters training either inside or outside institutionalised settings, such as universities. It can also serve as a means of teaching other fields, such as foreign languages. Translation teaching began in the twentieth century. Teachers of translation hold the responsibilities of educating students, developing their translation competence and training them to be professional translators. The activity of translation teaching involves various tasks, including curriculum design, course delivery, material writing as well as application and implementation. The present paper addresses translation teaching during COVID-19 outbreak, seeking to find out the challenges encountered by translation teachers in online translation teaching and the discoveries/solutions arrived at to resolve them. The paper makes use of a comprehensive questionnaire, containing closed-ended and open-ended questions to elicit both quantitative as well as qualitative data from about sixty translation teachers who have been teaching translation at BA and MA levels during COVID-19 outbreak. The data shows that about 40% of the participants evaluate their online translation teaching experience during COVID-19 outbreak as enjoyable and exhilarating. On the contrary, no participant has evaluated his/her online translation teaching experience as being not good, nor has any participant evaluated his/her online translation teaching experience as being terrible. The data also presents that about 23.33% of the participants evaluate their online translation teaching experience as very good, and the same percentage applies to those who evaluate their online translation teaching experience as good to some extent. Moreover, the data indicates that around 13.33% of the participants evaluate their online translation teaching experience as good. The data also demonstrates that the majority of the participants have encountered obstacles in online translation teaching and have concurrently proposed solutions to resolve them.

Keywords: online translation teaching, electronic learning platform, COVID-19 outbreak, challenges, solutions

Procedia PDF Downloads 217

Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

Abstract:

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

Procedia PDF Downloads 183

Authors: Nathan Reeves

Abstract:

Aim: The aim of this study was to evaluate if there was an impact on student learning when peer role play was substituted for a professional actor in the role of simulated patient in a simulation-based scenario. Method: Third-year exercise science students enrolled in a field project course in 2015 (n=24), and 2016 (n=20) participated in a simulation-based case scenario designed to develop their client-centred exercise prescription skills. During the simulation, students were provided with feedback from the simulated patients. In 2015, three professional actors played the part of the simulated patient, and in 2016 one of the simulated patients was a student from another exercise science cohort (peer role play). The student learning experience, consistency in case fidelity and feedback provided by the simulated patients was evaluated using a 5-point Likert scale survey and collecting phenomenological data. Results: Improvements to student pre and post confidence remained constant between the 2015 and 2016 cohorts (1.04 and 0.85). The perceived usefulness and enjoyability also remained high across the two cohorts (4.96 and 4.71). The feedback provided by all three simulated patients in 2016 was seen to strongly support student learning experience (4.82), and was of a consistent level (4.47). Significance of the findings to allied health: Simulation-based education is rapidly expanding in the curricula across the allied health professions. The simulated patient methodology continues to receive support as a pedagogy to develop a range of clinical skills including communication, engagement and client-centeredness. Upskilling students to peer role play can be a reasonable alternative to engaging paid actors.

Keywords: exercise science, simulation-based learning, simulated patient, peer role play

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