Search results for: hybrid learning algorithm

Authors: Muftahu Jibirin Salihu, Hazri Jamil

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The persisting challenge of access and enrolment to higher education in commonwealth countries has been reported in several studies, including reports of the international organization such as World Bank, UNESCO among others however from the macro perspective. The overarching aim of this study is to examine alternative policy practices towards improving access to university education in Nigeria at meso level of policy practice from evidence base policy studies using one university as a case. The study adopted a qualitative approach to gain insightful understanding on the issue of the study employing a semi-structure interview and policy documents as the means for obtaining the data and other relevant information for the study. The participants of the study were purposively chosen which comprise of a number of individuals from the selected university and other related organization which responsible for the policies development and implementation of Nigerian higher education system. From the findings of the study, several initiatives have been taken at meso level to address this challenge including the introduction of the University Matriculation Program as an alternative route for enhancing to access to the university education. However, the study further provided a number of recommendations which aimed at improving access to university education such as improving the entry requirements, society orientation on university education and the issue of ranking of certificate among the Nigerian higher institutions of learning.

Keywords: policy practice, access, enrolment, university, education, Nigeria

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Authors: Crispin Rakibu Mbamba

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Twenty-two years after the adoption of the United Nation Trafficking Protocol, evidence suggest that child trafficking continues to rise. Community level factors, like poverty which creates the conditions for children’s vulnerability is key to the rise in trafficking cases in Ghana. Albeit, growing evidence suggestthat despite the vulnerabilities, communities have the capacity to prevent and address child trafficking issues. This study contributes to this positive agenda by exploring the ways in which communities (and the key actors) in Ghana contribute to child trafficking interventions.The study objective is explored through in-depth interviews with practitioners (including social workers) from an organization working in trafficking hotspots in Ghana. Interviews wereanalyzed thematically with the help of HyperRESEARCH software. From the in-depth interviews, three themes were identified as the ways in which communities are involved in child trafficking interventions: 1) engagement of community leaders, 2) community-led anti-trafficking committees and 3) knowledge about trafficking. Albeit the cultural differences, evidence on the instrumental role of community chiefs and leaders provide important learning on how to harness trafficking intervention measures and ensure better child protection practices. Based on the findings, we recommend the need to intensify trafficking awareness campaigns in rural communities where education is lacking to contribute to United Nations (UN) promoting Just, Peaceful and Inclusive societies’ mandate.

Keywords: child trafficking, community interventions, knowledge on trafficking, human trafficking intervention

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Authors: Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani

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The development of the method to annotate unknown gene functions is an important task in bioinformatics. One of the approaches for the annotation is The identification of the metabolic pathway that genes are involved in. Gene expression data have been utilized for the identification, since gene expression data reflect various intracellular phenomena. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

Keywords: metabolic pathways, gene expression data, microarray, Kullback–Leibler divergence, KL divergence, support vector machines, SVM, machine learning

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Parimalah Velo, Ahmad Zakaria

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Introduction: Monte Carlo simulations of preclinical imaging systems allow opportunity to enable new research that could range from designing hardware up to discovery of new imaging application. The simulation system which could accurately model an imaging modality provides a platform for imaging developments that might be inconvenient in physical experiment systems due to the expense, unnecessary radiation exposures and technological difficulties. The aim of present study is to validate the Monte Carlo simulation of thyroid phantom imaging using Geant4-GATE for Siemen’s e-cam single head gamma camera. Upon the validation of the gamma camera simulation model by comparing physical characteristic such as energy resolution, spatial resolution, sensitivity, and dead time, the GATE simulation of thyroid phantom imaging is carried out. Methods: A thyroid phantom is defined geometrically which comprises of 2 lobes with 80mm in diameter, 1 hot spot, and 3 cold spots. This geometry accurately resembling the actual dimensions of thyroid phantom. A planar image of 500k counts with 128x128 matrix size was acquired using simulation model and in actual experimental setup. Upon image acquisition, quantitative image analysis was performed by investigating the total number of counts in image, the contrast of the image, radioactivity distributions on image and the dimension of hot spot. Algorithm for each quantification is described in detail. The difference in estimated and actual values for both simulation and experimental setup is analyzed for radioactivity distribution and dimension of hot spot. Results: The results show that the difference between contrast level of simulation image and experimental image is within 2%. The difference in the total count between simulation and actual study is 0.4%. The results of activity estimation show that the relative difference between estimated and actual activity for experimental and simulation is 4.62% and 3.03% respectively. The deviation in estimated diameter of hot spot for both simulation and experimental study are similar which is 0.5 pixel. In conclusion, the comparisons show good agreement between the simulation and experimental data.

Keywords: gamma camera, Geant4 application of tomographic emission (GATE), Monte Carlo, thyroid imaging

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Modupe Beatrice Adeyinka

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French, as a foreign language in Nigeria, is pronounced to be the second official language and a compulsory subject in the primary school level; hence, colleges of education across the nation are saddled with the responsibility of training teachers for the subject. However, it has been observed that this policy has not been fully implemented, for French teachers in training, do face many challenges, of which translation is chief. In a bid to investigate the major cause of the perceived translation problem, this study examined French translation problems of pre-service teachers in selected colleges of education in the southwest, Nigeria. This study adopted a descriptive survey research design. The simple random sampling technique was used to select four colleges of education in the southwest, where 100 French students were randomly selected by selecting 25 from each school. The pre-service teachers’ French translation problems’ questionnaire (PTFTPQ) was used as an instrument while four research questions were answered and three null hypotheses were tested. Among others, the findings revealed that students do have problems with false friends, though mainly with its interpretation when attempting French-English translation and vice versa; majority of the students make use of French dictionary as a way out and found the material very useful for their understanding of false friends. Teachers were, therefore, urged to attend in-service training where they would be exposed to new and emerging strategies, approaches and methodologies of French language teaching that will make students overcome the challenge of translation in learning French.

Keywords: false friends, French language, pre-service teachers, source language, target language, translation

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Authors: Poteet Frances, Glovinski Ira

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INTRODUCTION: The interoceptive nervous system continuously senses chemical and anatomical changes and helps you recognize, understand, and feel what’s going on inside your body so it is important for energy regulation, memory, affect, and sense of self. A newborn needs predictable routines rather than confusion/chaos to make connections between internal experiences and emotions. AIM: Current legal protocols of removing babies from drug-addicted mothers impact the critical window of bonding. The newborn’s brain is social and the attachment process influences a child’s development which begins immediately after birth through nourishment, comfort, and protection. DESCRIPTION: Our project aims to educate drug-addicted mothers, and medical, nursing, and social work professionals on interoceptive concepts and practices to sustain the mother/newborn relationship. A mother’s interoceptive knowledge predicts children’s emotion regulation and social skills in middle childhood. CONCLUSION: When mothers develop an awareness of their inner bodily sensations, they can self-regulate and be emotionally available to co-regulate (support their newborn during distressing emotions and sensations). Our project has enhanced relationship preservation (mothers understand how their presence matters) and the overall mother/newborn connection.

Keywords: drug-addiction, interoception, legal, mothers, newborn, self-regulation

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Authors: Paolo Fantozzi, Luigi Laura, Umberto Nanni

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The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses.

Keywords: cooccurrence graph, entity relation graph, unstructured text, weighted distance

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Authors: Diego Giuseppe Romano, Gianvito Apuleo, Jiri Duda

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Mobility is one of the most important societal needs for amusement, business activities and health. Thus, transport needs are continuously increasing, with the consequent traffic congestion and pollution increase. Aeronautic effort aims at smarter infrastructures use and in introducing greener concepts. A possible solution to address the abovementioned topics is the development of Small Air Transport (SAT) system, able to guarantee operability from today underused airfields in an affordable and green way, helping meanwhile travel time reduction, too. In the framework of Horizon2020, EU (European Union) has funded the Clean Sky 2 SAT TA (Transverse Activity) initiative to address market innovations able to reduce SAT operational cost and environmental impact, ensuring good levels of operational safety. Nowadays, most of the key technologies to improve passenger comfort and to reduce community noise, DOC (Direct Operating Costs) and pilot workload for SAT have reached an intermediate level of maturity TRL (Technology Readiness Level) 3/4. Thus, the key technologies must be developed, validated and integrated on dedicated ground and flying aircraft demonstrators to reach higher TRL levels (5/6). Particularly, SAT TA focuses on the integration at aircraft level of the following technologies [1]: 1)    Low-cost composite wing box and engine nacelle using OoA (Out of Autoclave) technology, LRI (Liquid Resin Infusion) and advance automation process. 2) Innovative high lift devices, allowing aircraft operations from short airfields (< 800 m). 3) Affordable small aircraft manufacturing of metallic fuselage using FSW (Friction Stir Welding) and LMD (Laser Metal Deposition). 4)       Affordable fly-by-wire architecture for small aircraft (CS23 certification rules). 5) More electric systems replacing pneumatic and hydraulic systems (high voltage EPGDS -Electrical Power Generation and Distribution System-, hybrid de-ice system, landing gear and brakes). 6) Advanced avionics for small aircraft, reducing pilot workload. 7) Advanced cabin comfort with new interiors materials and more comfortable seats. 8) New generation of turboprop engine with reduced fuel consumption, emissions, noise and maintenance costs for 19 seats aircraft. (9) Alternative diesel engine for 9 seats commuter aircraft. To address abovementioned market innovations, two different platforms have been designed: Reference and Green aircraft. Reference aircraft is a virtual aircraft designed considering 2014 technologies with an existing engine assuring requested take-off power; Green aircraft is designed integrating the technologies addressed in Clean Sky 2. Preliminary integration of the proposed technologies shows an encouraging reduction of emissions and operational costs of small: about 20% CO₂ reduction, about 24% NO_x reduction, about 10 db (A) noise reduction at measurement point and about 25% DOC reduction. Detailed description of the performed studies, analyses and validations for each technology as well as the expected benefit at aircraft level are reported in the present paper.

Keywords: affordable, European, green, mobility, technologies development, travel time reduction

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Authors: Shehroz Mukhtar, Maqsood Ahmed, Abdullah Mukhtar, Choudhry Shahid, Waqar Javaid

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Code switching is a common phenomenon, generally observed in multilingual communities across the globe. A critical look at code switching literature reveals that mostly code switching has been studied in classroom in learning and teaching context while code switching outside classroom in settings such as café, hostel and so on have been the least explored areas. Current research investigated the reasons for code switching in the interactive practices of students and their perceptions regarding the same outside the classroom settings. This paper is the study of the common practice that prevails in the Universities of Sialkot that bilinguals mix two languages when they speak in different class room situations. In Pakistani classrooms where Multilingual are in abundance i.e. they can speak two or more than two languages at the same time, the code switching or language combination is very common. The teachers of Sialkot switch from one language to another consciously or unconsciously while teaching English in the class rooms. This phenomenon has not been explored in the Sialkot’s teaching context. In Sialkot private educational institutes does not encourage code-switching whereas the public or government institutes use it frequently. The crux of this research is to investigate and identify the importance of code switching by taking its users in consideration. Survey research method and survey questionnaire will be used to get exact data from teachers and students. We will try to highlight the functions and importance of code switching in foreign language classrooms of Sialkot and will explore why this trend is emerging in Sialkot.

Keywords: code switching, bilingual context, L1, L2

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Authors: Rashmi Gupta

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Rewarding stimuli capture attention involuntarily as a result of an association process that develops quickly during value learning, referred to as the reward or value-driven attentional capture. It is essential to compare reward with punishment processing to get a full picture of value-based modulation in visual attention processing. Hence, the present study manipulated both valence/value (reward as well as punishment) and motivational salience (probability of an outcome: high vs. low) together. Series of experiments were conducted, and there were two phases in each experiment. In phase 1, participants were required to learn to associate specific face stimuli with a high or low probability of winning or losing points. In the second phase, these conditioned stimuli then served as a distractor or prime in a speeded letter search task. Faces with high versus low outcome probability, regardless of valence, slowed the search for targets (specifically the left visual field target) and suggesting that the costs to performance on non-emotional cognitive tasks were only driven by motivational salience (high vs. loss) associated with the stimuli rather than the valence (gain vs. loss). It also suggests that the processing of motivationally salient stimuli is right-hemisphere biased. Together, results of these studies strengthen the notion that our visual attention system is more sensitive to affected by motivational saliency rather than valence, which termed here as motivational-driven attentional capture.

Keywords: attention, distractors, motivational salience, valence

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Baohua Yu

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In line with wider tends in the expansion of international student mobility, the number of international higher degree research students has grown at a significant rate in recent years. In particular, Australia has become a hub for attracting international higher degree research students from around the world. However, research has identified that international higher degree research students often encounter a wide range of academic and socio-cultural challenges in adapting to their new environment. Moreover, this can have a significant bearing on their levels of satisfaction with their studies. This paper outlines the findings of a mixed method study exploring the experiences and perceptions of international higher degree research students in Australia. Findings revealed that IRHD students’ overall and academic satisfaction in Australia were highly related to each other, and they were strongly influenced by their learning and research, moderately influenced by co-national support and intercultural contact ability. Socio-cultural satisfaction seemed to belong to a different domain from academic satisfaction because it was explained by a different set of variables such as living and adaptation and intercultural contact ability. In addition, the most important issues in terms of satisfaction were not directly related to academic studies. Instead, factors such as integration into the community, interacting with other students, relationships with supervisors, and the provision of adequate desk space were often given the greatest weight. Implications for how university policy can better support international doctoral students are discussed.

Keywords: international higher degree research students, academic adaptation, socio-cultural adaptation, student satisfaction

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Authors: Renee Moten

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Introduction: Knee pain becomes chronic because the therapy used focuses only on the symptoms of knee pain and not the causes of knee pain. Preventing knee injuries is not in the toolbox of the traditional practitioner. This research was done to show that we must reduce the inflammation (holistically), reduce the swelling and regain flexibility before considering any type of exercise. This method of performing the correct exercise stops the bowing of the knee, corrects the walking gait, and starts to relieve knee, hip, back, and shoulder pain. Method: The holistic method that is used to heal knees is called the Knee Pain Recipe. It’s a six step system that only uses alternative medicine methods to reduce, relieve and restore knee joint mobility. The system is low cost, with no hospital bills, no physical therapy, and no painkillers that can cause damage to the kidneys and liver. This method has been tested on 200 women with knee, back, hip, and shoulder pain. Results: All 200 women reduce their knee pain by 50%, some by as much as 90%. Learning about ankle and foot flexibility, along with understanding the kinetic chain, helps improve the walking gait, which takes the pressure off the knee, hip and back. The knee pain recipe also has helped to reduce the need for a cortisone injection, stem cell procedures, to take painkillers, and surgeries. What has also been noted in the research was that if the women's knees were too far gone, the Knee Pain Recipe helped prepare the women for knee replacement surgery. Conclusion: It is believed that the Knee Pain Recipe, when performed by men and women from around the world, will give them a holistic alternative to drugs, injections, and surgeries.

Keywords: knee, surgery, healing, holistic

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Authors: Periklis Brakatsoulas

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Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

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Authors: Carmen Avilés Palacios, Camilo Muñoz Arenas, Joaquín García Alfonso, Jesús González Arteaga, Alberto Alcalde Calonge

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There is a need for review of the consumption and production models of industrialized states in accordance with the Paris Agreement and the Sustainable Development Goals (1) (OECD, 2016). This constitutes the basis of the bioeconomy (2) that is focused on striking a balance between economic development, social development and environmental protection. Bioeconomy promotes the adequate use and consumption of renewable natural resources (3) and involves developing new products and services adapted to the principles of circular economy: more sustainable (reusable, biodegradable, renewable and recyclable) and with a lower carbon footprint (4). In this context, Urban Forest Innovation Lab (UFIL) grows, an Urban Laboratory for experimentation focused on promoting entrepreneurship in forest bioeconomy (www.uiacuenca.es). UFIL generates local wellness taking sustainable advantage of an endogenous asset, forests. UFIL boosts forest bioeconomy opening its doors of knowledge to pioneers in this field, giving the opportunity to be an active part of a change in the way of understanding the exploitation of natural resources, discovering business, learning strategies and techniques and incubating business ideas So far now, 100 entrepreneurs are incubating their nearly 30 new business plans. UFIL has promoted an ecosystem to connect the rural-urban world that promotes sustainable rural development around the forest.

Keywords: bioeconomy, forestry, innovation, entrepreneurship

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Authors: Fabianna Bacil, Zenathan Hasannundin, Clovis Freire

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The green transition opens green windows of opportunity – temporary moments in which there are lower barriers and shorter learning periods for developing countries to enter emerging technologies and catch-up. However, taking advantage of these windows requires capabilities in national sectoral systems to adopt and develop technologies linked to green sectors as well as strong responses to build the required knowledge, skills, and infrastructure and foster the growth of targeted sectors. This paper uses UNCTAD’s frontier technology readiness index to analyse the current position of Latin America and the Caribbean to use, adopt, and adapt frontier technologies, examining the preconditions in the region to take up windows of opportunity that arise with the green transition. The index highlights the inequality across countries in the region, as well as gaps in capabilities dimensions, especially in terms of R&D. Moving to responses, it highlights industrial policies implemented to foster the growth of green technologies, emphasising the essential role played by the state to build and strengthen capabilities and provide infant industry protection that enables the growth of these sectors. Overall, while there are exceptions, especially in the Brazilian case, countries in Latin America and the Caribbean should focus on strengthening their capabilities to be better positioned, especially in terms of knowledge creation, infrastructure, and financing availability.

Keywords: Green technologies, Industrial policy, Latin America, windows of opportunity

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Authors: Hsin-Yi Lo, Huang-Ju Jiun, Yu-Chiao Chu

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Hemodynamic monitoring is an important in the intensive care unit. Advances in medical technology in recent years, more diversification of intensive care equipment, there are many kinds of instruments available for monitoring of hemodynamics, Edwards Lifesciences Hemodynamic Monitoring (FloTrac) is one of them. The recent medical safety incidents in parameters were changed, nurses have not to notify doctor in time, therefore, it is hoped to analyze the current problems and find effective improvement strategies. In August 2021, the survey found that only 74.0% of FloTrac correctness of operation, reasons include lack of education, the operation manual is difficulty read, lack of audit mechanism, nurse doesn't know those numerical changes need to notify doctor, work busy omission, unfamiliar with operation and have many nursing records then omissions. Improvement methods include planning professional nurse education, formulate the secret arts of FloTrac, enacting an audit mechanism, establish FloTrac action learning, make「follow the sun」care map, hold simulated training and establish monitoring data automatically upload nursing records. After improvement, FloTrac correctness of operation increased to 98.8%. The results are good, implement to the ICU of the hospital.

Keywords: hemodynamic monitoring, edwards lifesciences hemodynamic monitoring, multiple strategies, intensive care

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Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: B. Likar, K. Širok

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The paper presents a new method for efficient innovation process management. Even though the innovation management methods, tools and knowledge are well established and documented in literature, most of the companies still do not manage it efficiently. Especially in SMEs the front end of innovation - problem identification, idea creation and selection - is often not optimally performed. Our eMIPS methodology represents a sort of "umbrella methodology"- a well-defined set of procedures, which can be dynamically adapted to the concrete case in a company. In daily practice, various methods (e.g. for problem identification and idea creation) can be applied, depending on the company's needs. It is based on the proactive involvement of the company's employees supported by the appropriate methodology and external experts. The presented phases are performed via a mixture of face-to-face activities (workshops) and online (eLearning) activities taking place in eLearning Moodle environment and using other e-communication channels. One part of the outcomes is an identified set of opportunities and concrete solutions ready for implementation. The other also very important result is connected to innovation competences for the participating employees related with concrete tools and methods for idea management. In addition, the employees get a strong experience for dynamic, efficient and solution oriented managing of the invention process. The eMIPS also represents a way of establishing or improving the innovation culture in the organization. The first results in a pilot company showed excellent results regarding the motivation of participants and also as to the results achieved.

Keywords: creativity, distance learning, front end, innovation, problem

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Authors: Prachi Buche Marathe

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Karanja Laad is a city located in the Vidarbha region in the state of Maharashtra, India. It has a huge amount of tangible and intangible heritage in the form of monuments, precincts, a group of structures, festivals and procession route, which is neglected and lost with time. Three different religions Hinduism, Islam and Jainism along with associations of being a birthplace of Swami Nrusinha Saraswati, an exponent of Datta Sampradaya sect and the British colonial layer have shaped the culture and society of the place over the period. The architecture of the town Karanja Laad has enhanced its unique historic and cultural value with a combination of all these historic layers. Karanja Laad is also a traditional trading historic town with unique hybrid architectural style and has a good potential for developing as a tourist place along with the present image of a pilgrim destination of Datta Sampradaya. The aim of the research is to prepare a conservation proposal for the historic town along with the management framework. Objectives of the research are to study the evolution of Karanja town, to identify the cultural resources along with issues of the historic core of the city, to understand Datta sampradaya, and contribution of Saint Nrusinha Saraswati in the religious sect and his association as an important personality with Karanja. The methodology of the research is site visits to the Karanja city, making field surveys for documentation and discussions and questionnaires with the residents to establish heritage and identify potential and issues within the historic core thereby establishing a case for conservation. Field surveys are conducted for town level study of land use, open spaces, occupancy, ownership, traditional commodity and community, infrastructure, streetscapes, and precinct activities during the festival and non-festival period. Building level study includes establishing various typologies like residential, institutional commercial, religious, and traditional infrastructure from the mythological references like waterbodies (kund), lake and wells. One of the main issues is that the loss of the traditional footprint as well as the traditional open spaces which are getting lost due to the new illegal encroachments and lack of guidelines for the new additions to conserve the original fabric of the structures. Traditional commodities are getting lost since there is no promotion of these skills like pottery and painting. Lavish bungalows like Kannava mansion, main temple Wada (birthplace of the saint) have a huge potential to be developed as a museum by adaptive re-use which will, in turn, attract many visitors during festivals which will boost the economy. Festival procession routes can be identified and a heritage walk can be developed so as to highlight the traditional features of the town. Overall study has resulted in establishing a heritage map with 137 heritage structures identified as potential. Conservation proposal is worked out on the town level, precinct level and building level with interventions such as developing construction guidelines for further development and establishing a heritage cell consisting architects and engineers for the upliftment of the existing rich heritage of the Karanja city.

Keywords: built heritage, conservation, Datta Sampradaya, Karanja Laad, Swami Nrusinha Saraswati, procession route

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Authors: Hendri Purwadi, Christine McCloud

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Triage is a complex decision-making process that aims to categorize a victim’s level of acuity and the need for medical assistance. Two common triage systems have been widely used in Mass Casualty Incidents (MCIs) and disaster situation are START (Simple triage algorithm and rapid treatment) and SALT (sort, asses, lifesaving, intervention, and treatment/transport). There is currently controversy regarding the effectiveness of SALT over START triage system. This systematic review aims to investigate and compare the effectiveness between SALT and START triage system in disaster and MCIs setting. Literatures were searched via systematic search strategy from 2009 until 2019 in PubMed, Cochrane Library, CINAHL, Scopus, Science direct, Medlib, ProQuest. This review included simulated-based and medical record -based studies investigating the accuracy and applicability of SALT and START triage systems of adult and children population during MCIs and disaster. All type of studies were included. Joana Briggs institute critical appraisal tools were used to assess the quality of reviewed studies. As a result, 1450 articles identified in the search, 10 articles were included. Four themes were identified by review, they were accuracy, under-triage, over-triage and time to triage per individual victim. The START triage system has a wide range and inconsistent level of accuracy compared to SALT triage system (44% to 94. 2% of START compared to 70% to 83% of SALT). The under-triage error of START triage system ranged from 2.73% to 20%, slightly lower than SALT triage system (7.6 to 23.3%). The over-triage error of START triage system was slightly greater than SALT triage system (START ranged from 2% to 53% compared to 2% to 22% of SALT). The time for applying START triage system was faster than SALT triage system (START was 70-72.18 seconds compared to 78 second of SALT). Consequently; The START triage system has lower level of under-triage error and faster than SALT triage system in classifying victims of MCIs and disaster whereas SALT triage system is known slightly more accurate and lower level of over-triage. However, the magnitude of these differences is relatively small, and therefore the effect on the patient outcomes is not significance. Hence, regardless of the triage error, either START or SALT triage system is equally effective to triage victims of disaster and MCIs.

Keywords: disaster, effectiveness, mass casualty incidents, START triage system, SALT triage system

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Authors: Shehroz Mukhtar, Maqsood Ahmed, Abdullah Mukhtar, Choudhry Shahid, Waqar Javaid

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Code switching is a common phenomenon, generally observed in multilingual communities across the globe. A critical look at code-switching literature reveals that mostly code-switching has been studied in the classrooms in learning and teaching contexts, while code-switching outside the classroom in settings such as café, hostels and so on has been the least explored areas. The current research investigated the reasons for code-switching in the interactive practices of students and their perceptions regarding the same outside the classroom settings. This paper is the study of the common practice that prevails in the Universities of Sialkot that bilinguals mix two languages when they speak in different classroom situations. In Pakistani classrooms where Multilingual is in abundance, i.e. they can speak two or more two languages at the same time, code-switching or language combination is very common. The teachers of Sialkot switch from one language to another consciously or unconsciously while teaching English in the classrooms. This phenomenon has not been explored in Sialkot’s teaching context. In Sialkot, private educational institutes do not encourage code-switching, whereas public or government institutes use it frequently. The crux of this research is to investigate and identify the importance of code-switching by taking its users into consideration. The survey research method and survey questionnaire will be used to get exact data from teachers and students. We will try to highlight the functions and importance of code switching in foreign language classrooms of Sialkot and will explore why this trend is emerging in Sialkot.

Keywords: code switching, foreign language classrooms, bilingual context, use of L1, importance of L2.

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Authors: Daodu Joshua Rotimi

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This study investigated the Efficacy of Self-Assessment Metacognitive Strategy on Academic performance in Energy Concepts among Upper Basic Science Students in Ankpa, Kogi State, Nigeria. The research design adopted for the study was a Quasi-experimental control group design which employed a pretest, posttest of the experimental and control groups. The population of the study consisted of one hundred and twenty-four (124) JSSII Students; sixty-five (65) for the experimental group and (59) for the control group. The instrument used for the study was the Energy Concept Performance Test (ECPT), with a reliability coefficient of 0.80. Two research questions were answered using descriptive statistics of mean and standard deviation, while two hypotheses were tested using a t-test at P≤0.05 level of significance. The findings of the study revealed that the use of the Self-Assessment Metacognitive Strategy has a positive effect on students’ performance in energy concepts among upper Basic Science Students leading to high academic performance; also, there is no significant difference in the mean Academic Performance scores between Male and Female students taught Energy Concept using Self-Assessment Metacognitive Strategy. Based on the research findings, recommendations were made, which include that Secondary school teachers should be encouraged the use Self-Assessment Metacognitive strategy so as to make the learning process attractive, interactive and enriching to the learners.

Keywords: metacognition, self-assessment, performance, efficacy

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Authors: Ahmed Soliman Nagiub, Mahmoud Fayez, Heba Khaled, Said Ghoniemy

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Accurate perception is a critical component in enabling autonomous vehicles to understand their driving environment. The acquisition of 3D information about objects, including their location and pose, is essential for achieving this understanding. This survey paper presents a comprehensive review of 3D object detection techniques specifically tailored for autonomous vehicles. The survey begins with an introduction to 3D object detection, elucidating the significance of the third dimension in perceiving the driving environment. It explores the types of sensors utilized in this context and the corresponding data extracted from these sensors. Additionally, the survey investigates the different types of datasets employed, including their formats, sizes, and provides a comparative analysis. Furthermore, the paper categorizes and thoroughly examines the perception methods employed for 3D object detection based on the diverse range of sensors utilized. Each method is evaluated based on its effectiveness in accurately detecting objects in a three-dimensional space. Additionally, the evaluation metrics used to assess the performance of these methods are discussed. By offering a comprehensive overview of 3D object detection techniques for autonomous vehicles, this survey aims to advance the field of perception systems. It serves as a valuable resource for researchers and practitioners, providing insights into the techniques, sensors, and evaluation metrics employed in 3D object detection for autonomous vehicles.

Keywords: computer vision, 3D object detection, autonomous vehicles, deep learning

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Authors: Sukrita Mukherjee, Anindita Chaudhuri

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Soft skills are a crucial aspect for employees, and these skills are not confined to any particular field rather, it guarantees further career growth and job opportunities for employees who are seeking growth. Soft skills are also regarded as personality-specific skills that are observable and are qualitative in nature, which determines an employee’s strengths as a leader. When an employee intends to hold his job, then the person must make effective use of his personal resources, that, in turn, impacts his employability in a positive manner. An employee at his workplace is expected to make effective use of his personal resources. The resources that are to be used by the employee are generally of two types. First type of resources are occupation related, which is related with the educational background of the employee, and the second type of resources are the psychological resources of the employee, such as self-knowledge, career orientation awareness, sense of purpose and emotional literacy, that are considered crucial for an employee in his workplace. The present study is a qualitative study which includes 10 individuals working in IT Sector and Service Industry, respectively. For IT sector, graduate people are considered, and for the Service Industry, individuals who have done a Professional course in order to get into the industry are considered. The emerging themes from the findings after thematic analysis reveal that different aspect of Soft skills such as communication, decision making, constant learning, keeping oneself updated with the latest technological advancement, emotional intelligence are some of the important factors that helps an employee not only to sustain his job, but also grow in his workplace.

Keywords: employabiliy, soft skils, employees, resources, workplace

Procedia PDF Downloads 56

Authors: Peien Ma

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Bilingual effect/advantage theorized the positive effect of being bilingual on general cognitive abilities, but it was unknown which factors tend to modulate these bilingualism effects on working memory capacity. This study imposed empirical field research on a group of low-SES emerging bilinguals, Miao people, in the hill tribes of rural China to investigate whether bilingualism affected their verbal working memory performance. 20 Miao-Chinese bilinguals (13 girls and 7 boys with a mean age of 11.45, SD=1.67) and 20 Chinese monolingual peers (13 girls and 7 boys with a mean age of 11.6, SD=0.68) were recruited. These bilingual and monolingual juveniles, matched on age, sex, socioeconomic status, and educational status, completed a language background questionnaire and a standard forward and backward digit span test adapted from Wechsler Adult Intelligence Scale-Revised (WAIS-R). The results showed that bilinguals earned a significantly higher overall mean score of the task, suggesting the superiority of working memory ability over the monolinguals. And bilingual cognitive benefits were independent of proficiency levels in learners’ two languages. The results suggested that bilingualism enhances working memory in sequential bilinguals from low SES backgrounds and shed light on our understanding of the bilingual advantage from a psychological and social perspective.

Keywords: bilingual effects, heritage language, Miao/Hmong language Mandarin, working memory

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Authors: Aamna Tufail, Catherine Anyanwu

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Introduction/Aims: Neurosensory deficits following third molar removal affect the quality of life markedly. The purpose of this audit was to evaluate the incidence of IDN and LN damage and to compare departmental rates to an established standard. A secondary objective was to provide a descriptive summary of identified cases for clinical learning. Materials and Methods: A retrospective audit was conducted by a telephone survey of 101 patients who had third molar extractions performed under LA, IVS, or GA from January 2019 to June 2020 at a District General Hospital. The results were compared to a clinical standard identified as Cheng et al1. Data collection included mode of surgery, mode of anaesthesia, grade of clinician, assessment of difficulty, severity, and duration of symptoms. Results/Statistics: A total of 101 patients had 136 third molars extracted. Age range was 18-84 years. 44% extractions were under LA, 52% under GA, and 4% under IV sedation. 30% were simple extractions, 68% were surgical removals, 2% were unspecified. 89% extractions were performed by an Associate Specialist, 5% by a consultant, and 6% by unspecified grade of clinician. The rate of IDN injuries was 2.9% (n=4), higher than standard (0.3%). The rate of LN injuries was 0.7% (n=1), same as standard (0.7%). The 5 cases of neurosensory deficits are discussed in detail. Conclusions/Clinical Relevance: The rate of ID nerve injuries was higher than the standard. The rate of LN complications was lower than the standard.

Keywords: inferior dental nerve, lingual nerve, nerve injuries, third molars

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Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

Procedia PDF Downloads 240