Search results for: financial technology

Authors: Sehrish Qayyum

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In this age of constant technology, asymmetrical warfare could not be won. Attuned psychometric study confirms that screaming sometimes is more productive than active retaliation against strong adversaries. Asymmetric warfare is a game of nerves and thoughts with least vigorous participation for large anticipated losses. It creates the condition of paraplegia with partial but permanent immobility, which effects the core warfare operations, being screams rather than active retaliation. When one’s own power is doubted, it gives power to one’s own doubt to ruin all planning either done with superlative cost-benefit analysis. Strategically calculated estimation of asymmetric warfare since the early WWI to WWII, WWII-to Cold War, and then to the current era in three chronological periods exposits that courage makes nations win the battle of warriors to battle of comrades. Asymmetric warfare has been most difficult to fight and survive due to unexpectedness and being lethal despite preparations. Thoughts before action may be the best-assumed strategy to mix Regional Security Complex Theory and OODA loop to develop the Paraplegic Resilience Policy Plan (PRPP) to win asymmetric warfare. PRPP may serve to control and halt the ongoing wave of terrorism, guerilla warfare, and insurgencies, etc. PRPP, along with a strategic work plan, is based on psychometric analysis to deal with any possible war condition and tactic to save millions of innocent lives such that lost in Christchurch New Zealand in 2019, November 2015 Paris attacks, and Berlin market attacks in 2016, etc. Getting tangled into self-imposed epistemic dilemmas results in regret that becomes the only option of performance. It is a descriptive psychometric analysis of war conditions with generic application of probability tests to find the best possible options and conditions to develop PRPP for any adverse condition possible so far. Innovation in technology begets innovation in planning and action-plan to serve as a rheostat approach to deal with asymmetric warfare.

Keywords: asymmetric warfare, psychometric analysis, PRPP, security

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Baglan Bazylova

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The purpose of our article is to consider the social and psychological conflicts in Lyudmila Petrushevskaya’s stories as an artistic presentation of gender structure of modern society; to reveal originality of the characters’ inner world, the models of their behavior expressing the gender specific nature of modern feminine prose. Gender conflicts have taken the leading place in the modern prose. L. Petrushevskaya represents different types of conflicts including those which are shown in the images of real contradictions in the stories "Narratrix", "Thanks to Life”, "Virgin's Case", "Father and Mother". In the prose of Petrushevskaya the gender conflicts come out in two dimensions: The first one is love relations between a man and a woman. Because of the financial indigence, neediness a woman can’t afford herself even to fall in love and arrange her family happiness. The second dimension is the family conflict because of the male adultery. Petrushevskaya fixed on the unmanifistated conflict in detail. In the real life such gender conflict can appear in different forms but for the writer is important to show it as a life basis, hidden behind the externally safe facade of “the family happiness”. In the stories of L. Petrushevskaya the conflicts reflect the common character of the social and historical situations in which her heroines find themselves, in situations where a woman feels her opposition to the customary mode of life. The types of gender conflicts of these stories differ in character of verbal images. They are presented by the verbal and event ranks creating the conflicts just in operation.

Keywords: gender behavior of heroes, gender conflict, gender picture of the world, gender structure

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Authors: Koko Iwan Agus Kurniawan, Dwi Purnomo, Anas Bunyamin, Arif Rahman Jaya

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Fruters model is a concept of technopreneurship-based on empowerment, in which technology research results were designed to create high value-added products and implemented as a locomotive of collaborative empowerment; thereby, the impact was widely spread. This model still needs to be inventoried and validated concerning the influenced variables in the business growth process. Model validation accompanied by mapping was required to be applicable to Small Medium Enterprises (SMEs) agro-industry based on sustainable social business and existing real cases. This research explained the empowerment model of Yourgood, an SME, which emphasized on empowering the farmers/ breeders in farmers in rural areas, Cipageran, Cimahi, to housewives in urban areas, Bandung, West Java, Indonesia. This research reviewed some works of literature discussing the agro-industrial development associated with the empowerment and social business process and gained a unique business model picture with the social business platform as well. Through the mapped business model, there were several advantages such as technology acquisition, independence, capital generation, good investment growth, strengthening of collaboration, and improvement of social impacts that can be replicated on other businesses. This research used analytical-descriptive research method consisting of qualitative analysis with design thinking approach and that of quantitative with the AHP (Analytical Hierarchy Process). Based on the results, the development of the enterprise’s process was highly affected by supplying farmers with the score of 0.248 out of 1, being the most valuable for the existence of the enterprise. It was followed by university (0.178), supplying farmers (0.153), business actors (0.128), government (0.100), distributor (0.092), techno-preneurship laboratory (0.069), banking (0.033), and Non-Government Organization (NGO) (0.031).

Keywords: agro-industry, small medium enterprises, empowerment, design thinking, AHP, business model canvas, social business

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Authors: Hasmayadi Abdul Majid, Yuzman Yusoff, Noor Shelida Salleh

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This paper presents the development of a single-ended 38.5 kS/s 10-bit programmable reference SAR ADC which is realized in MIMOS’s 0.35 µm CMOS process. The design uses a resistive DAC, a dynamic comparator with pre-amplifier and a SAR digital logic to create 10 effective bits ADC. A programmable reference circuitry allows the ADC to operate with different input range from 0.6 V to 2.1 V. A single ended 38.5 kS/s 10-bit programmable reference SAR ADC was proposed and implemented in a 0.35 µm CMOS technology and consumed less than 7.5 mW power with a 3 V supply.

Keywords: successive approximation register analog-to-digital converter, SAR ADC, resistive DAC, programmable reference

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Authors: Javad Izadi, Sahar Majioud

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This study presents an approach of assessing the choice of performance measures of companies in the United Kingdom after the application of IFRS in 2005. The aim of this study is to investigate the effects of IFRS on the choice of performance evaluation methods for UK companies. We analyse through an econometric model the relationship of the dependent variable, the firm’s performance, which is a nominal variable with the independent ones. Independent variables are split into two main groups: the first one is the group of accounting-based measures: Earning per share, return on assets and return on equities. The second one is the group of market-based measures: market value of property plant and equipment, research and development, sales growth, market to book value, leverage, segment and size of companies. Concerning the regression used, it is a multinomial logistic regression performed on a sample of 130 UK listed companies. Our finding shows after IFRS adoption, and companies give more importance to some variables such as return on equities and sales growth to assess their performance, whereas the return on assets and market to book value ratio does not have as much importance as before IFRS in evaluating the performance of companies. Also, there are some variables that have no impact on the performance measures anymore, such as earning per share. This article finding is empirically important for business in subjects related to IFRS and companies’ performance measurement.

Keywords: performance’s Measure, nominal variable, econometric model, evaluation methods

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Authors: Zahra R. Almansoor

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Background: Selective serotonin reuptake inhibitors (SSRI’s) and cognitive behavioural therapy (CBT) are the main treatment methods used for patients with obsessive compulsive disorder (OCD) under the National Institute of Health and Care Excellence (NICE) guidelines. Yet many patients are left with residual symptoms or remit, so several other therapeutic approaches have been explored. Objective: The objective was to systematically review the available literature regarding the treatment efficacy of current and potential approaches and diagnostic strategies. Method: First, studies were examined concerning diagnosis, prognosis, and influencing factors. Then, one reviewer conducted a systematic search of six databases using stringent search terms. Results of studies exploring the efficacy of treatment interventions were analysed and compared separately for adults and children. This review was limited to randomised controlled trials (RCT’s) conducted from 2016 onwards, and an improved Y-BOCS (Yale- Brown obsessive compulsive scale) score was the primary outcome measure. Results: Technology-based interventions including internet-based cognitive behavioural therapy (iCBT) were deemed as potentially effective. Discrepancy remains about the benefits of SSRI use past one year, but potential medication adjuncts include amantadine. Treatments such as association splitting and family and mindfulness strategies also have future potential. Conclusion: A range of potential therapies exist, either as treatment adjuncts to current interventions or as sole therapies. To further improve efficacy, it may be necessary to remodel the current NICE stepped-care model, especially regarding the potential use of lower intensity, cheaper treatments, including iCBT. Although many interventions show promise, further research is warranted to confirm this.

Keywords: family and group treatment, mindfulness strategies, novel treatment approaches, standard treatment, technology-based interventions

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Authors: Atish Bagchi, Siva Chandrasekaran

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

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

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Authors: Afsane Amiri

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Electric Vehicle (EV) technology is expected to take a major share in the light-vehicle market in the coming decades. Charging of EVs will put an extra burden on the distribution grid and in some cases adjustments will need to be made. In this paper a review of different plug-in and vehicle to grid (V2G) capable vehicles are given along with their power electronics topologies. The economic implication of charging the vehicle or sending power back to the utility is described in brief.

Keywords: energy storage system, battery unit, cost, optimal sizing, plug-in electric vehicles (PEVs), smart grid

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Authors: Amy Hodges, Deanna Rasmussen, Sherry Ward

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This paper examines the use of ePortfolios in a two-course sequence for ESL (English as a Second Language) students at an international branch campus in Doha, Qatar. ePortfolios support the transfer of language learning, but few have examined the sustainability of that transfer across an ESL program. Drawing upon surveys and interviews with students, we analyze three case studies that complicate previous research on metacognition, language learning, and ePortfolios. Our findings have implications for those involved in ESL programs and assessment of student writing.

Keywords: TESOL, electronic portfolios, assessment, technology

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Authors: Simone Belli, Sergio Minniti, Valeria Santoro

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ICTs and technology transfer can benefit and move a country forward in economic and social development. However, ICT and access to the Internet have been inequitably distributed in most developing countries. In terms of science production and dissemination, this divide articulates itself also through the inequitable distribution of access to scientific knowledge and networks, which results in the exclusion of developing countries from the center of science. Developing countries are on the fringe of Science and Technology (S&T) production due not only to low investment in research but also to the difficulties to access international scholarly literature. In this respect, Open access (OA) initiatives and knowledge infrastructure represent key elements for both producing significant changes in scholarly communication and reducing the problems of developing countries. The spreading of the OA movement in the region, exemplified by the growth of regional and national initiatives, such as the creation of OA institutional repositories (e.g. SciELO and Redalyc) and the establishing of supportive governmental policies, provides evidence of the significant role that OA is playing in reducing the scientific gap between Latin American countries and improving their participation in the so-called ‘global knowledge commons’. In this paper, we map OA publications in Latin America and observe how Latin American countries are moving forward and becoming a leading force in widening access to knowledge. Our analysis, developed as part of the H2020 EULAC Focus research project, is based on mixed methods and consists mainly of a bibliometric analysis of OA publications indexed in the most important scientific databases (Web of Science and Scopus) and OA regional repositories, as well as the qualitative analysis of documents related to the main OA initiatives in Latin America. Through our analysis, we aim at reflecting critically on what policies, international standards, and best practices might be adapted to incorporate OA worldwide and improve the infrastructure of the global knowledge commons.

Keywords: open access, LAC countries, scientific publications, bibliometric analysis

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Authors: Cristina Nieves Perdomo Delgado

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The study consists of the design and use of an application for cell phones called “Me Cuido” that consists of remote control of elderly people who live alone with their families. The objective of the study is to analyze the usability of the application by 40-year-olds using the Questionnaire for User Interaction Satisfaction (QUIS) method. The results highlight that the application has a design adapted to the elderly and that it is easy to use and understand.

Keywords: design, assistive technology, elderly people, independence

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Authors: Herdina Indrijati

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This research aims to describe teenager delinquency behavior (Juvenile Delinquency) of senior high school students in Surabaya, Indonesia. Juvenile Delinquency is a broad range of behaviors start from socially unacceptable behavior (overreact in school), violation (escape from home) to crimes (like stealing). This research uses quantitative descriptive method using 498 students who come from 8 different schools in Surabaya as subjects. Juvenile Delinquency behavior form questionnaire has been completed by subjects and was used to measure and describe the behavior. The result of this research is presented in statistic descriptive forms. Result shows that 169 subjects skip school, 55 subjects get out of home without parent’s permission, 110 subjects engage in smoking behavior, 74 subjects damage other people properties, 32 subjects steal, 16 subjects exploit others and 7 subjects engage in drug abuse. Frequency of the top five mentioned behavior are 1-10 times. It is also found that subject’s peers are most likely to be the victim of Juvenile Delinquency. The reasons teenagers engage in Juvenile Delinquency include (1) feeling tired, bored or lazy – that contributes to their skip school behavior (2) Having a lot of problem with parents - contrives them to run away from home, (3) accidentally damage other people’s properties, (4) financial problems – force them to steal and exploit, (5) feeling like having a lot of life problems – that makes them do drugs (6) trying smoking for experience.

Keywords: juvenile delinquency, senior high school, student

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Authors: Thiago Brito Bezerra

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This paper presents a novel approach to reduce power consumption of digital filters based on dynamic bypassing of partial products in their multipliers. The bypassing elements incorporated into the multiplier hardware eliminate redundant signal transitions, which appear within the carry-save adders when the partial product is zero. This technique reduces the power consumption by around 20%. The circuit implementation was made using the AMS 0.18 um technology. The bypassing technique applied to the circuits is outlined.

Keywords: digital filter, low-power, bypassing technique, low-pass filter

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: B. O. Diyaolu

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Viability and competency during competition is the dream of every team sports so as to have a good result. But it seems factors abound which deter the performance of even a good sports team. Different individuals with different state of mind all come together to perform in team sports with different degree of satisfaction. This study investigated the role of satisfaction on performance among Afe Babalola University team sports. Descriptive survey research design was used and the population consists of all male and female athletes in the team sports that participated in the last 2019 Ekiti State Higher Institution games (ESHIGA). Total enumeration technique was used for the three team sports; football (44), basketball (24) and volleyball (24). A total of 92 participants were involved in the research. The instrument used for the study was a modified Athlete Satisfaction Scale (ASS). The questionnaire was divided into two sections. The Cronbach’s Alpha reliability coefficient of 0.71 was obtained. The hypotheses were tested at 0.05 significant levels. The completed questionnaire was collated, coded, and analyzed using descriptive statistics of frequency counts and percentage and inferential statistics of chi-square (X²). Findings of this study revealed that satisfaction significantly influences team sports performance among Athletes of Afe Babalola University. The responsibility of satisfying athlete lies on the coaches, fans, sports administrators as well as organizers of such event, as it is not only financial reward that gives satisfaction. The performance of a team sports is quiet important and its being determined by the degree of satisfaction of each individual that make up the team. All effort must be made to satisfy athlete in order to guarantee optimum performance.

Keywords: athlete satisfaction, optimum achievement, optimum performance, sports performance and team sports

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Authors: Francis Kwame Bukari, Maclean Acheampong Yeboah

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From collective hunting to contemporary financial markets, the concept of risk sharing in insurance has evolved significantly. In today's insurance landscape, statistical analysis plays a pivotal role in determining premiums and assessing the likelihood of insurance claims. Accurately estimating motor insurance claims remains a challenge, allowing insurance companies to pull much of their money to cover claims, which in the long run will affect their reserves and impact their profitability. Advanced machine learning algorithms can enhance accuracy and profitability. The primary objectives of this study encompassed the prediction of motor insurance claims through the utilization of Artificial Neural Networks (ANN) and Random Forest (RF). Additionally, a comparative analysis was conducted to assess the performance of these two models in the domain of claim prediction. The study drew upon secondary data derived from motor insurance claims, employing a range of techniques, including data preprocessing, model training, and model evaluation. To mitigate potential biases, a random over-sampler was used to balance the target variable within the preprocessed dataset. The Random Forest model outperformed the ANN model, achieving an accuracy rate of 90.33% compared to the ANN model's accuracy of 86.33%. This study highlights the importance of modern data-driven approaches in enhancing accuracy and profitability in the insurance industry.

Keywords: risk, insurance claims, artificial neural network, random forest, over-sampler, profitability

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Authors: Jean-Francois Rajotte, Martin Sotir, Frank Gouineau

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The installation and management of traffic monitoring devices can be costly from both a financial and resource point of view. It is therefore important to take advantage of in-place infrastructures to extract the most information. Here we show how low-quality urban road traffic images from cameras already available in many cities (such as Montreal, Vancouver, and Toronto) can be used to estimate traffic flow. To this end, we use a pre-trained neural network, developed for object detection, to count vehicles within images. We then compare the results with human annotations gathered through crowdsourcing campaigns. We use this comparison to assess performance and calibrate the neural network annotations. As a use case, we consider six months of continuous monitoring over hundreds of cameras installed in the city of Montreal. We compare the results with city-provided manual traffic counting performed in similar conditions at the same location. The good performance of our system allows us to consider applications which can monitor the traffic conditions in near real-time, making the counting usable for traffic-related services. Furthermore, the resulting annotations pave the way for building a historical vehicle counting dataset to be used for analysing the impact of road traffic on many city-related issues, such as urban planning, security, and pollution.

Keywords: traffic monitoring, deep learning, image annotation, vehicles, roads, artificial intelligence, real-time systems

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Authors: Ali Haratian, Soroosh Emami Meybodi

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Using viable microbial cultures within hydrocarbon reservoirs so as to the enhancement of oil recovery through metabolic activities is exactly what we recognize as microbial enhanced oil recovery (MEOR). In similar to many other processes in industries, there are some cons and pros following with MEOR. The creation of sulfides such as hydrogen sulfide as a result of injecting the sulfate-containing seawater into hydrocarbon reservoirs in order to maintain the required reservoir pressure leads to production and growth of sulfate reducing bacteria (SRB) approximately near the injection wells, turning the reservoir into sour; however, SRB is not considered as the only microbial process stimulating the formation of sulfides. Along with SRB, thermochemical sulfate reduction or thermal redox reaction (TSR) is also known to be highly effective at resulting in having extremely concentrated zones of ?2S in the reservoir fluids eligible to cause corrosion. Owing to extent of the topic, more information on the formation of ?₂S is going to be put finger on. Besides, confronting the undesirable production of sulfide species in the reservoirs can lead to serious operational, environmental, and financial problems, in particular the transporting pipelines. Consequently, conjuring up reservoir souring control strategies on the way production of oil and gas is the only way to prevent possible damages in terms of environment, finance, and manpower which requires determining the compound’s reactivity, origin, and partitioning behavior. This article is going to provide a comprehensive review of progress made in this field and the possible advent of new strategies in this technologically advanced world of the petroleum industry.

Keywords: corrosion, hydrogen sulfide, NRB, reservoir souring, SRB

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Authors: Tarek M. Alguhane, Ayman H. Khalil, M. N. Fayed, Ayman M. Ismail

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The retrofitting of existing buildings to resist the seismic loads is very important to avoid losing lives or financial disasters. The aim at retrofitting processes is increasing total structure strength by increasing stiffness or ductility ratio. In addition, the response modification factors (R) have to satisfy the code requirements for suggested retrofitting types. In this study, two types of jackets are used, i.e. full reinforced concrete jackets and surrounding steel plate jackets. The study is carried out on an existing building in Madinah by performing static pushover analysis before and after retrofitting the columns. The selected model building represents nearly all-typical structure lacks structure built before 30 years ago in Madina City, KSA. The comparison of the results indicates a good enhancement of the structure respect to the applied seismic forces. Also, the response modification factor of the RC building is evaluated for the studied cases before and after retrofitting. The design of all vertical elements (columns) is given. The results show that the design of retrofitted columns satisfied the code's design stress requirements. However, for some retrofitting types, the ductility requirements represented by response modification factor do not satisfy KSA design code (SBC- 301).

Keywords: concrete jackets, steel jackets, RC buildings, pushover analysis, non-Linear analysis

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Authors: Aurélia Jandot

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With the oil and economic crises which began in the 1970’s, it has progressively appeared necessary to convince the French “general public“ that a use of new energy sources was essential. In this field, nuclear energy represented the future and concentrated lots of hopes. However, the discourse about nuclear energy has progressively seen negative arguments growing in the French media. The gradual changes in the perception of nuclear energy will be studied here through the arguments given in the main French weekly newsmagazines, which had a great impact on the readers, thus on the “general public“, from the 1970’s to the end of the 1980’s. Indeed, to understand better these changes will be taken into account the major international events, the reorientations of the French domestic policy, and the evolutions of the nuclear technology. As this represents a considerable amount of copies and thus of information, will be selected here the main articles which emphasize the “mental images“ aiming to direct the thought of the readers, and which have led the public awareness and acceptance to evolve. From the 1970’s to the end of the 1980’s, two dichotomous trends are in confrontation : one is promoting the perception of the nuclear energy, the other is discrediting it. Moreover, these two trends are organized in two axes. The first axis is about the engineerings evolutions, such as the main French media represent them, with its approximations, its exaggerations, its fictions sometimes. Is added the will to make accessible to the “general public“ some concepts which are quite difficult to understand for the largest number. The second axis rests on the way the major accidents of the period are approached, including those of Three Mile Island and Chernobyl. Thanks to these accidents and because of the international relations evolutions, the ecologist movements and their impacts have progressively grown, with evident consequences on the public perception of nuclear energy and on the way the successive governments can implement new power plants in France. Then, in both cases, over the period considered, the language has changed, as the perceptible objectives of the communication, allowing to discern the deepest intentions of the newsmagazines editing. This is all these changes that will be emphasized, over a period where the nuclear energy technology, to there a field for specialists, bearing mystery and secret, has become a social issue seemingly open to all.

Keywords: social issues, public acceptance, mediatization, discourse changes

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Authors: Elson Szeto

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Over the past 15 years, digital technology has ubiquitously penetrated societies around the world. New values of e-learning are emerging in the preparation of future talents, while e-learning is a key driver of widening participation and knowledge transfer in Chinese higher education. As a vibrant, Chinese society in Asia, Hong Kong’s new generation university students, perhaps the digital natives, have been learning with e-learning since their basic education. They can acquire new knowledge with the use of different forms of e-learning as a generic competence. These students who embrace this competence further their study journeys in higher education. This project reviews the Government’s policy of Information Technology in Education which has largely put forward since 1998. So far, primary to secondary education has embraced advantages of e-learning capability to advance the learning of different subject knowledge. Yet, e-learning capacity in higher education is yet to be fully examined in Hong Kong. The study reported in this paper is a pilot investigation into e-learning capacity in Chinese higher education in the region. By conducting a qualitative case study of Hong Kong, the investigation focuses on (1) the institutional ICT settings in general; (2) the pedagogic responses to e-learning in specific; and (3) the university students’ satisfaction of e-learning. It is imperative to revisit the e-learning capacity for promoting effective learning amongst university students, supporting new knowledge acquisition and embracing new opportunities in the 21st century. As a pilot case study, data will be collected from individual interviews with the e-learning management team members of a university, teachers who use e-learning for teaching and students who attend courses comprised of e-learning components. The findings show the e-learning capacity of the university and the key components of leveraging e-learning capability as a university-wide learning settings. The findings will inform institutions’ senior management, enabling them to effectively enhance institutional e-learning capacity for effective learning and teaching and new knowledge acquisition. Policymakers will be aware of new potentials of e-learning for the preparation of future talents in this society at large.

Keywords: capability, e-learning, higher education, student learning

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Authors: Jeisson A. Estrella, Laura C. Herrera, Cristian A. Arenas

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The Colombian electricity sector has been transforming through the insertion of new energy sources to generate electricity, one of them being solar energy, which is being promoted by companies interested in photovoltaic technology. The study of this technology is important for electricity generation in general and for the planning of the sector from the perspective of energy complementarity. Precisely in this last approach is where the project is located; we are interested in answering the concerns about the reliability of the electrical system when climatic phenomena such as El Niño occur or in defining whether it is viable to replace or expand thermoelectric plants. Reliability of the electrical system when climatic phenomena such as El Niño occur, or to define whether it is viable to replace or expand thermoelectric plants with renewable electricity generation systems. In this regard, some difficulties related to the basic information on renewable energy sources from measured data must first be solved, as these come from automatic weather stations. Basic information on renewable energy sources from measured data, since these come from automatic weather stations administered by the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) and, in the range of study (2005-2019), have significant amounts of missing data. For this reason, the overall objective of the project is to complete the global solar irradiance datasets to obtain time series to develop energy complementarity analyses in a subsequent project. Global solar irradiance data sets to obtain time series that will allow the elaboration of energy complementarity analyses in the following project. The filling of the databases will be done through numerical and statistical methods, which are basic techniques for undergraduate students in technical areas who are starting out as researchers technical areas who are starting out as researchers.

Keywords: time series, global solar irradiance, imputed data, energy complementarity

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Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

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Authors: Falguni Vasavada, Pradyumna Malladi

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The research focuses on studying the ecosystem of the youth using Ogilvy's 3E model, Ethnography and Thematic Analysis. It has been found that urban Indian youth today is an honest generation, hungry for success, living life by the moment, fiercely independent, are open about sex, sexuality and embrace individual differences. Technology and social media dominate their life. However, they are also phobic about commitments, often drifting along life and engage in unsubstantiated brave-talk.

Keywords: ethnography, youth, culture, track, buyer behavior

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Authors: Sahil Kumar, Rajaram Ghadge, Ramesh Bhujade

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Historically, lipid extraction from dried algal biomass remained a focus area of the algal research. It has been realized over the past few years that the lipid-centric approach and conversion technologies that require dry algal biomass have several challenges. Algal culture in cultivation systems contains more than 99% water, with algal concentrations of just a few hundred milligrams per liter ( < 0.05 wt%), which makes harvesting and drying energy intensive. Drying the algal biomass followed by extraction also entails the loss of water and nutrients. In view of these challenges, focus has shifted toward developing processes that will enable oil production from wet algal biomass without drying. Hydrothermal liquefaction (HTL), an emerging technology, is a thermo-chemical conversion process that converts wet biomass to oil and gas using water as a solvent at high temperature and high pressure. HTL processes wet algal slurry containing more than 80% water and significantly reduces the adverse cost impact owing to drying the algal biomass. HTL, being inherently feedstock agnostic, i.e., can convert carbohydrates and proteins also to fuels and recovers water and nutrients. It is most effective with low-lipid (10--30%) algal biomass, and bio-crude yield is two to four times higher than the lipid content in the feedstock. In the early 2010s, research remained focused on increasing the oil yield by optimizing the process conditions of HTL. However, various techno-economic studies showed that simply converting algal biomass to only oil does not make economic sense, particularly in view of low crude oil prices. Making the best use of every component of algae is a key for economic viability of algal to oil process. On investigation of HTL reactions at the molecular level, it has been observed that sequential HTL has the potential to recover value-added products along with biocrude and improve the overall economics of the process. This potential of sequential HTL makes it a most promising technology for converting wet waste to wealth. In this presentation, we will share our experience on the techno-economic and engineering aspects of sequential HTL for conversion of algal biomass to algal bio-oil and co-products.

Keywords: algae, biomass, lipid, protein

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Authors: Anna Paola Micheli, Anna Maria Calce, Loris Di Nallo

Abstract:

Sustainable finance identifies the process that leads, in the adoption of investment decisions, to take into account environmental and social factors, with the aim of orienting investments towards sustainable and long-term activities. Considering that the topic is at the center of the interest of national agendas, long-term investments will no longer be analyzed only by looking at financial data, but environmental, social, and governance (ESG) factors will be increasingly important and will play a fundamental role in determining the risk and return of an investment. Although this perspective does not deny the orientation to profit, ESG mutual funds represent sustainable finance applied to the world of mutual funds. So the goal of this paper is to verify this attitude, in particular in the energy sector. The choice of the sector is not casual: ESG is the acronym for environmental, social, and governance, and energy companies are strictly related to the environmental theme. The methodology adopted leads to a comparison between a sample of ESG funds and a sample of ESG funds with similar characteristics, using the most important indicators of literature: yield, standard deviation, and Sharpe index. The analysis is focused on equity funds. Results that are partial, due to the lack of historicity, show a good performance of ESG funds, testifying how a sustainable approach does not necessarily mean lower profits. It is clear that these first findings do not involve an absolute preference for ESG funds in terms of performance because the persistence of results is requested. Furthermore, these findings are to be verified in other sectors and in bond funds.

Keywords: mutual funds, ESG, performance, energy

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

Abstract:

In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Young Nam Chun, Soo Hyuk Yun, Byeo Ri Jeong

Abstract:

The recent gradual increase in the energy demand is mostly met by fossil fuel, but the research on and development of new alternative energy sources is drawing much attention due to the limited fossil fuel supply and the greenhouse gas problem. Biomass is an eco-friendly renewable energy that can achieve carbon neutrality. The conversion of the biomass sludge wastes discharged from a wastewater treatment plant to clean energy is an important green energy technology in an eco-friendly way. In this NRF study, a new type of microwave thermal treatment was developed to apply the biomass-CCS technology to sludge wastes. For this, the microwave dielectric heating characteristics were examined to investigate the energy conversion mechanism for the combined drying-pyrolysis/gasification of the dewatered wet sludge. The carbon dioxide gasification was tested using the CO2 captured from the pre-combustion capture process. In addition, the results of the pyrolysis and gasification test with the wet sludge were analyzed to compare the microwave energy conversion results with the results of the use of the conventional heating method. Gas was the largest component of the product of both pyrolysis and gasification, followed by sludge char and tar. In pyrolysis, the main components of the producer gas were hydrogen and carbon monoxide, and there were some methane and hydrocarbons. In gasification, however, the amount of carbon monoxide was greater than that of hydrogen. In microwave gasification, a large amount of heavy tar was produced. The largest amount of benzene among light tar was produced in both pyrolysis and gasification. NH3 and HCN which are the precursors of NOx, generated as well. In microwave heating, the sludge char had a smooth surface, like that of glass, and in the conventional heating method with an electric furnace, deep cracks were observed in the sludge char. This indicates that the gas obtained from the microwave pyrolysis and gasification of wet sewage sludge can be used as fuel, but the heavy tar and NOx precursors in the gas must be treated. Sludge char can be used as solid fuel or as a tar reduction adsorbent in the process if necessary. This work supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1R1A2A2A03003044).

Keywords: microwave heating, pyrolysis gasification, precombustion CCS, sewage sludge, biomass energy

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Authors: N. Ismail, O. Thammajinda, U. Thongpanya

Abstract:

Games-based learning (GBL) has become increasingly important in teaching and learning. This paper explains the first two phases (analysis and design) of a GBL development project, ending up with a prototype design based on students’ and teachers’ perceptions. The two phases are part of a full cycle GBL project aiming to help secondary school students in Thailand in their study of Comprehensive Sex Education (CSE). In the course of the study, we invited 1,152 students to complete questionnaires and interviewed 12 secondary school teachers in focus groups. This paper found that GBL can serve students in their learning about CSE, enabling them to gain understanding of their sexuality, develop skills, including critical thinking skills and interact with others (peers, teachers, etc.) in a safe environment. The objectives of this paper are to outline the development of GBL variables from the research question(s) into the developers’ flow chart, to be responsive to the GBL beneficiaries’ preferences and expectations, and to help in answering the research questions. This paper details the steps applied to generate GBL variables that can feed into a game flow chart to develop a GBL prototype. In our approach, we detailed two models: (1) Game Elements Model (GEM) and (2) Game Object Model (GOM). There are three outcomes of this research – first, to achieve the objectives and benefits of GBL in learning, game design has to start with the research question(s) and the challenges to be resolved as research outcomes. Second, aligning the educational aims with engaging GBL end users (students) within the data collection phase to inform the game prototype with the game variables is essential to address the answer/solution to the research question(s). Third, for efficient GBL to bridge the gap between pedagogy and technology and in order to answer the research questions via technology (i.e. GBL) and to minimise the isolation between the pedagogists “P” and technologist “T”, several meetings and discussions need to take place within the team.

Keywords: games-based learning, engagement, pedagogy, preferences, prototype

Procedia PDF Downloads 171