Search results for: self-regulated learning strategies

Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Tahar Alimi, Rahma Boujebane, Wiem Derouich, Lamia Hadrich Belguith

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Machine translation task of low-resourced languages such as Arabic is a challenging task. Despite the appearance of sophisticated models based on the latest deep learning techniques, namely the transfer learning, and transformers, all models prove incapable of carrying out an acceptable translation, which includes Arabic Dialects (AD), because they do not have official status. In this paper, we present a machine translation model designed to translate Arabic multidialectal content into Modern Standard Arabic (MSA), leveraging both new and existing parallel resources. The latter achieved the best results for both Levantine and Maghrebi dialects with a BLEU score of 64.99.

Keywords: Arabic translation, dialect translation, fine-tune, MSA translation, transformer, translation

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Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Thalia Viveros-Uehara

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The virtues of Corporate Social Responsibility (CSR) are explored widely in the academic literature. However, few studies address its link to human rights, per se; specifically, the right to a safe, clean and healthy environment. Fewer still are the research works in this area that relate to developing countries, where a number of areas are biodiversity hotspots. In Mexico, despite the rise and evolution of CSR schemes, grave episodes of pollution persist, especially those caused by the mining industry. These cases set up the question of the correspondence between the current CSR practices of mining companies in the country and their responsibility to respect the right to a safe, clean and healthy environment. The present study approaches precisely such a bridge, which until now has not been fully tackled in light of Mexico's 2011 constitutional human rights amendment and the United Nation's Guiding Principles on Business and Human Rights (UN Guiding Principles), adopted by the Human Rights Council in 2011. To that aim, it initially presents a contextual framework; it then explores qualitatively the adoption of human rights’ language in the CSR strategies of the three main mining companies in Mexico, and finally, it examines their standing with respect to the UN Guiding Principles. The results reveal that human rights are included in the RSE strategies of the analysed businesses, at least at the rhetoric level; however, they do not embrace the right to a safe, clean and healthy environment as such. Moreover, we conclude that despite the finding that corporations publicly express their commitment to respect human rights, some operational weaknesses that hamper the exercise of such responsibility persist; for example, the systematic lack of human rights impact assessments per mining unit, the denial of actual and publicly-known negative episodes on the environment linked directly to their operations, and the absence of effective mechanisms to remediate adverse impacts.

Keywords: corporate social responsibility, environmental impacts, human rights, right to a safe, clean and healthy environment, mining industry

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Authors: Vytautas Galvanauskas, Rimvydas Simutis, Donatas Levisauskas, Vykantas Grincas, Renaldas Urniezius

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The paper deals with model-based development and implementation of efficient control strategies for recombinant protein synthesis in fed-batch E.coli cultivation processes. Based on experimental data, a kinetic dynamic model for cultivation process was developed. This model was used to determine substrate feeding strategies during the cultivation. The proposed feeding strategy consists of two phases – biomass growth phase and recombinant protein production phase. In the first process phase, substrate-limited process is recommended when the specific growth rate of biomass is about 90-95% of its maximum value. This ensures reduction of glucose concentration in the medium, improves process repeatability, reduces the development of secondary metabolites and other unwanted by-products. The substrate limitation can be enhanced to satisfy restriction on maximum oxygen transfer rate in the bioreactor and to guarantee necessary dissolved carbon dioxide concentration in culture media. In the recombinant protein production phase, the level of substrate limitation and specific growth rate are selected within the range to enable optimal target protein synthesis rate. To account for complex process dynamics, to efficiently exploit the oxygen transfer capability of the bioreactor, and to maintain the required dissolved oxygen concentration, adaptive control algorithms for dissolved oxygen control have been proposed. The developed model-based control strategies are useful in scale-up of cultivation processes and accelerate implementation of innovative biotechnological processes for industrial applications.

Keywords: adaptive algorithms, model-based control, recombinant E. coli, scale-up of bioprocesses

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Authors: Zhongde Wang, Fanwei Meng, Ling Yang

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Today, social media deeply engages in urban spatial production in a ‘Disembedding’ form, allowing the public to ‘foresight’ physical spaces through online platforms and subsequently engage in corresponding ‘sight’ and visits, thus leading to the emergence of ‘Internet Celebrity Spots’. This paper delves into the laws of action of online media, focusing on experiences. From the perspectives of the public, space, and media, it thoroughly analyzes the experiential design strategies of Chongqing's Liziba Light Rail Station, including the construction of the experiential mainline capturing the matrix of public behavior, the creation of experiential sidelines leveraging spatial advantages, and the deepening of experiential touchpoints to promote media resonance. This analysis aims to provide insights and references for similar urban spaces to transition from ‘internet-famous’ to ‘real-famous’ attractions.

Keywords: online media, urban space, disembedding, internet celebrity spots, experience design

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Authors: Setyarini Sri, Nursalim Agus

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Integrating critical thinking and digital based learning is one of demands in teaching English in 21st century. Child-friendly digital storytelling (CFDS) is an innovative learning model to promote young learners’ critical thinking. Therefore, this study aims to (1) investigate how child-friendly digital storytelling is implemented to promote young learners’ critical thinking in speaking English; (2) find out the benefits gained by the students in their learning based on CFDS. Classroom Action Research (CAR) took place in two cycles in which each of the cycle covered four phases namely: Planning, Acting, Observing, and Evaluating. Three classes of seventh graders were selected as the subjects of this study. Data were collected through observation, interview with some selected students as respondents, and document analysis in the form individual recorded storytelling. Sentences, phrases, words found in the transcribed data were identified and categorized based on Bloom taxonomy. The findings from the first cycle showed that the students seemed to speak critically that can be seen from the way they understood the story and related the story to their real life. Meanwhile, the result investigated from the second cycle likely indicated their higher level of critical thinking since the students spoke in English critically through comparing, questioning, analyzing, and evaluating the story by giving arguments, opinions, and comments. Such higher levels of critical thinking were also found in the students’ final project of individual recorded digital story. It is elaborated from the students’ statements in the interview who claimed CFDS offered opportunity to the students to promote their critical thinking because they comprehended the story deeply as they experienced in their real life. This learning model created good learning atmosphere and engaged the students directly so that they looked confident to retell the story in various perspectives. In term of the benefits of child-friendly digital storytelling, the students found it beneficial for some enjoyable classroom activities through watching beautiful and colorful pictures, listening to clear and good sounds, appealing moving motion and emotionally they were involved in that story. In the interview, the students also stated that child-friendly digital storytelling eased them to understand the meaning of the story as they were motivated and enthusiastic to speak in English critically.

Keywords: critical thinking, child-friendly digital storytelling, English speaking, promoting, young learners

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Authors: Wu Hong, Pan Cuilian, Wu Panzifan

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The student’s attention during the class is one of the most important indicators for the learning evaluation; the level of attention is directly related to the results of primary education. In recent years, extensive research has been conducted across China on improving primary school students’ attention during class. During the specific teaching activities in primary school, students have the characteristics of short concentration periods, high probability of distraction, and difficulty in long-term immersive learning. In physical education teaching, where there are mostly outdoor activities, this characteristic is particularly prominent due to the large changes in the environment and the strong sense of freshness among students. It is imperative to overcome this characteristic in a targeted manner, improve the student’s experience in the course, and raise the degree of systematization. There are many ways to improve the systematization of teaching and learning, but most of them lack quantitative indicators, which makes it difficult to evaluate the improvements before and after changing the teaching methods. Based on the situation above, we use the case analysis method, combined with a literature review, to study the negative impact of low systematization levels in primary school physical education teaching, put forward targeted improvement suggestions, and make a quantitative evaluation of the method change.

Keywords: attention, adolescent, evaluation, systematism, training-method

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Authors: Mangala. Sadasivan, Mary Hughes, Bryan Kelly

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Medical Education must focus on bridging the gap between teaching and learning when training pre-clinical year students in skills needed to keep up with medical knowledge and to meet the demands of health care in the future. The authors were interested in showing that a 3-S Model (building strength, developing stamina, and increasing speed) using a bridged curriculum design helps connect teaching and learning and improves students’ retention of basic science and clinical knowledge. The authors designed three learning modules using the 3-S Model within a systems course in a pre-clerkship medical curriculum. Each module focused on a bridge (concept map) designed by the instructor for specific content delivered to students in the course. This with-in-subjects design study included 304 registered MSU osteopathic medical students (3 campuses) ranked by quintile based on previous coursework. The instructors used the bridge to create self-directed learning exercises (building strength) to help students master basic science content. Students were video coached on how to complete assignments, and given pre-tests and post-tests designed to give them control to assess and identify gaps in learning and strengthen connections. The instructor who designed the modules also used video lectures to help students master clinical concepts and link them (building stamina) to previously learned material connected to the bridge. Boardstyle practice questions relevant to the modules were used to help students improve access (increasing speed) to stored content. Unit Examinations covering the content within modules and materials covered by other instructors teaching within the units served as outcome measures in this study. This data was then compared to each student’s performance on a final comprehensive exam and their COMLEX medical board examinations taken some time after the course. The authors used mean comparisons to evaluate students’ performances on module items (using 3-S Model) to non-module items on unit exams, final course exam and COMLEX medical board examination. The data shows that on average, students performed significantly better on module items compared to non-module items on exams 1 and 2. The module 3 exam was canceled due to a university shut down. The difference in mean scores (module verses non-module) items disappeared on the final comprehensive exam which was rescheduled once the university resumed session. Based on Quintile designation, the mean scores were higher for module items than non-module items and the difference in scores between items for Quintiles 1 and 2 were significantly better on exam 1 and the gap widened for all Quintile groups on exam 2 and disappeared in exam 3. Based on COMLEX performance, all students on average as a group, whether they Passed or Failed, performed better on Module items than non-module items in all three exams. The gap between scores of module items for students who passed COMLEX to those who failed was greater on Exam 1 (14.3) than on Exam 2 (7.5) and Exam 3 (10.2). Data shows the 3-S Model using a bridge effectively connects teaching and learning

Keywords: bridging gap, medical education, teaching and learning, model of learning

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Authors: Jeffrey M. Byford

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The conceptualized theory of decision making on historical events often does not conform to uniform beliefs among students. When presented the opportunity, many students have differing opinions and rationales associated with historical events and outcomes. The intent of this paper was to provide students with the economic, social and political dilemmas associated with the autonomy of East Berlin. Students ranked seven possible actions from the most to least acceptable. In addition, students were required to provide both positive and negative factors for each decision and relative ranking. Results from this activity suggested that while most students chose a financial action towards West Berlin, some students had trouble justifying their actions.

Keywords: content-centered learning, cold war, Berlin, decision-making

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Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

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We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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

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

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

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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Authors: Seedwell Sithole

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One of the persistent problems in accounting education is how to effectively support students’ learning. A promising technique to this issue is to investigate the extent that learning is determined by the design of instructional material. This study examines the academic performance of students using three instructional designs in financial accounting. Student’s performance scores and reported mental effort ratings were used to determine the instructional effectiveness. The findings of this study show that accounting students prefer graph and text designs that are integrated. The results suggest that spatially separated graph and text presentations in accounting should be reorganized to align with the requirements of human cognitive architecture.

Keywords: accounting, cognitive load, education, instructional preferences, students

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Xiongxiong You, Zhanwen Niu

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Choosing an appropriate surrogate model plays an important role in surrogates-assisted evolutionary algorithms (SAEAs) since there are many types and different kernel functions in the surrogate model. In this paper, an adaptive selection of the best suitable surrogate model method is proposed to solve different kinds of expensive optimization problems. Firstly, according to the prediction residual error sum of square (PRESS) and different model selection strategies, the excellent individual surrogate models are integrated into multiple ensemble models in each generation. Then, based on the minimum root of mean square error (RMSE), the best suitable surrogate model is selected dynamically. Secondly, two methods with dynamic number of models and selection strategies are designed, which are used to show the influence of the number of individual models and selection strategy. Finally, some compared studies are made to deal with several commonly used benchmark problems, as well as a rotor system optimization problem. The results demonstrate the accuracy and robustness of the proposed method.

Keywords: adaptive selection, expensive optimization, rotor system, surrogates assisted evolutionary algorithms

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Authors: Dominic Kwaku Danso Mensah

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The study examined conflict management strategies among head teachers and teachers in selected basic schools in Okai-Koi sub metro in the greater region of Ghana. In all, 270 participants were engaged in the study, comprising 237 teachers, 32 head teachers, and one officer in charge of the Metropolis. The study employed descriptive survey while using purposive and simple random sampling techniques to sample participants. Interview guides and questionnaires were the main instruments used for gathering primary data. The study found that conflict is inevitable in the schools. Conflicts in schools are usually subtle and hardly noticed by outsiders even though they occur on daily basis. The causes of conflict include among other things, high expectation from head teachers, inability to attain goals set, communication from head teachers and power struggle. The study found out that, in managing and resolving conflicts, issues such as identifying and focusing on the problem, building of trust and cooperation, clarifying goals and objectives were seen to be effective means of managing conflict and recommended that management should design and develop conflict management strategies to quickly resolve conflict.

Keywords: basic education, conflict management, organizational harmony, power

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Authors: Cansu Civelek

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The flexible and uneven character of neoliberalism has provided adaptation of urban strategies into the constantly changing circumstances in order to renew and reproduce the neoliberal accumulation model. Instrumentalization of catastrophic events to this end has been one of those global urban strategies. Regarding Turkey, exploitation of natural disasters has been the latest tactic of the Justice and Development Party (JDP) government to achieve radical economic goals. ‘Unhealthy’ and ‘risky’ structures of squatter settlements have often been articulated while the regenerations, expropriations, and exclusions have been sugarcoated through the discourses of ‘reintegrating the shanty zones into the cities’, ‘supplying healthy housing’, and ‘win-win’ character of the projects. Being the first regeneration project of Eskişehir, the Karapınar Regeneration Project has been initiated in 2011 by the partnership of the Odunpazarı Municipality of the JDP and the Mass Housing Organization. Discourses around the forthcoming disasters, ‘risky structures’ of the squatters, and the importance of the ‘security of life and property’ have been utilized, even though the zone is situated on a geotechnically stable area. Yet, many of the locals are worried about the payments while some have already decided to move elsewhere at the outskirts of the city.

Keywords: neoliberal urbanism, urban regeneration, illegal settlements, discourses

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Authors: Peter Akayuure, Michael Johnson Nabie

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Similar to many colonized nations in the world, one indelible mark left by colonial masters after Ghana’s independence in 1957 has been the fact that many contexts used to teach statistics and probability concepts are often alien and do not resonate with the social domain of our indigenous Ghanaian child. This has seriously limited the understanding, discoveries, and applications of mathematics for national developments. With the recent curriculum demands of making the Ghanaian child mathematically literate, this qualitative study involved video recordings and mathematical analysis of play sessions of an indigenous girl game called Ampe with the aim to demystify the concepts in probability theorem, which is applied in mathematics related fields of study. The mathematical analysis shows that the game of Ampe, which is widely played by school girls in Ghana, is suitable for learning concepts of the probability theorems. It was also revealed that as a girl game, the use of Ampe provides good lessons to educators, textbook writers, and teachers to rethink about the selection of mathematics tasks and learning contexts that are sensitive to gender. As we undertake to transform teacher education and student learning, the use of indigenous games should be critically revisited.

Keywords: Ampe, mathematical analysis, probability theorem, Ghanaian girl game

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Authors: Olakunle Felix Adekunle

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In the recent years several non-tariff provisions have been regarded as means holding back transboundary environmental damages. Affected countries have then increasingly come up with trade policies to compensate for or to In recent years, several non‐tariff trade provisions have been regarded as means of holding back transboundary environmental damages. Affected countries have then increasingly come up with trade policies to compensate for or to enforce the adoption of environmental policies elsewhere. These non‐tariff trade constraints are claimed to threaten the freedom of trading across nations, as well as the harmonization sought towards the distribution of income and policy measures. Therefore the ‘greening’ of world trade issues essentially ranges over whether there ought or ought not to be a trade‐off between trade and environmental policies. The impacts of free trade and environmental policies on major economic variables (such as trade flows, balances of trade, resource allocation, output, consumption and welfare) are thus studied here, and so is the EKC hypothesis, when such variables are played against the resulting emission levels. The policy response is seen as a political game, played here by two representative parties named North and South. Whether their policy choices, simulated by four scenarios, are right or wrong depends on their policy goals, split into economic and environmental ones.

Keywords: environmental, policies, strategies, constraint

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Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Rajae Tahri, Omar Chokairi, Asmae Saadi, Souad Chaouir

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Background: The factors motivating students to choose a medical career is a long-standing topic of publication and discussion. To our knowledge, no national study on the motivation for choosing medical studies has been published to date. Population and methods: This is an observational, descriptive, and cross-sectional study of first-year medical students at the Faculty of Medicine and Pharmacy of Rabat. An anonymous questionnaire comprising 16 questions was developed and distributed to students during Embryology tutorials. The students were free to fill it in or not. The number of students who consented to participate in the survey was 266. The variables studied are the socio-demographic variables of the students and the reasons for choosing medical studies. Results: The most strongly and frequently chosen reasons for choice by our students were saving lives (64.9%), helping others (62.1%), love of medicine (57%), and reducing suffering (56.5%). The comparison of the results according to gender showed a significant difference between the degree of self-motivation of girls compared to that of boys (p <0.001). The reason that stood out the most for them was teamwork. The presence of a health professional in the family was associated with strong extrinsic motivation (p = 0.005). Conclusion: Understanding medical student career choices would improve our knowledge of the factors that influence medical student learning and performance. This knowledge will make it possible to adapt the educational strategies to maintain the motivation of the students throughout their course as well as during their exercise.

Keywords: motivation, motivational profiles, medical studies, Morocco

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Authors: Julian Thrash, Douglas Folk, Michael Ciracy, Audrey C. Tseng, Kristen M. Stromsodt, Amber Younggren, Christopher Maciolek

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The cardiopulmonary system is comprised of the heart, lungs, and many dynamic feedback mechanisms that control its function based on a multitude of variables. The next generation of cardiopulmonary medical devices will involve adaptive control and smart pacing techniques. However, testing these smart devices on living systems may be unethical and exceedingly expensive. As a solution, a comprehensive computational model of the cardiopulmonary system was implemented in Simulink. The model contains over 240 state variables and over 100 equations previously described in a series of published articles. Simulink was chosen because of its ease of introducing machine learning elements. Initial results indicate that physiologically correct waveforms of pressures and volumes were obtained in the simulation. With the development of a comprehensive computational model, we hope to pioneer the future of predictive medicine by applying our research towards the initial stages of smart devices. After validation, we will introduce and train reinforcement learning agents using the cardiopulmonary model to assist in adaptive control system design. With our cardiopulmonary model, we will accelerate the design and testing of smart and adaptive medical devices to better serve those with cardiovascular disease.

Keywords: adaptive control, cardiopulmonary, computational model, machine learning, predictive medicine

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Authors: Mahmoud Almasri, Xavier Marjou, Fanny Parzysz

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The demand for services provided by Unmanned Aerial Vehicles (UAVs) is increasing pervasively across several sectors including potential public safety, economic, and delivery services. As the number of applications using UAVs grows rapidly, more and more powerful, quality of service, and power efficient computing units are necessary. Recently, cellular technology draws more attention to connectivity that can ensure reliable and flexible communications services for UAVs. In cellular technology, flying with a high speed and altitude is subject to several key challenges, such as frequent handovers (HOs), high interference levels, connectivity coverage holes, etc. Additional HOs may lead to “ping-pong” between the UAVs and the serving cells resulting in a decrease of the quality of service and energy consumption. In order to optimize the number of HOs, we develop in this paper a Q-learning-based algorithm. While existing works focus on adjusting the number of HOs in a static network topology, we take into account the impact of cells deployment for three different simulation scenarios (Rural, Semi-rural and Urban areas). We also consider the impact of the decision distance, where the drone has the choice to make a switching decision on the number of HOs. Our results show that a Q-learning-based algorithm allows to significantly reduce the average number of HOs compared to a baseline case where the drone always selects the cell with the highest received signal. Moreover, we also propose which hyper-parameters have the largest impact on the number of HOs in the three tested environments, i.e. Rural, Semi-rural, or Urban.

Keywords: drones connectivity, reinforcement learning, handovers optimization, decision distance

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Authors: Han Chih Feng, Yi-Cheng Hou, Jing-Huei Wu

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Diabetes is a chronic disease. Nutrition knowledge and skills enable individuals with type 2 diabetes to optimize metabolic self-management and quality of life. This research studies the effect of nutritional education on diabetes inpatients in terms of their cognition, behavior, and attitude. The participants are inpatients diagnosed with diabetes at Taipei Tzu Chi Hospital. A total of 103 participants, 58 male, and 45 females, enrolled in the research between January 2018 and July 2018. The research evaluates cognition, behavior, and attitude level before and after nutritional education conducted by dietitians. The result shows significant improvement in actual consumption (2.5 ± 1.4 vs 3.8 ± 0.7; p<.001), diet control motivation (2.7 ± 0.8 vs 3.4 ± 0.6; p<.001), correct nutrition concept (1.2± 0.4 vs 2.4 ± 0.5; p<.001), learning willingness (2.7± 0.9 vs 3.4 ± 0.6; p<.001), cognitive behaviors (1.4 ± 0.5 vs 2.9 ± 0.7; p<.001). AC sugar (278.5 ± 321.5 vs 152.2 ± 49.1; p<.001) and HbA1C (10.3 ± 2.6 vs 8.6 ± 1.9; p<.001) are significant improvement after nutritional education. After nutritional education, participants oral hypoglycemic agents increased from 16 (9.2%) to 33 (19.0%), insulin decreased from 75 (43.1%) to 68 (39.1%), and hypoglycemic drugs combined with insulin decreased from 83 (47.7%) to 73 (42.0%).Further analysis shows that female inpatients have significant improvement in diet control motivation (3.91 ± 0.85 vs 4.44 ± 0.59; p<0.000), correct nutrition concept (3.24± 0.48 vs 4.47± 0.51; p<0.000), learning willingness (3.89 ± 0.86 vs 4.44 ± 0.59; p<0.000) and cognitive behaviors (2.42 ± 0.58 vs 4.02 ± 0.69; p<0.000); male inpatients have significant improvement in actual food intake (4.41± 0.92 vs 3.97 ± 0.42; p<0.000), diet control motivation (3.62 ± 0.86 vs 4.29 ± 0.62; p<0.000), correct nutrition concept (3.26 ± 0.44 vs 4.36 ± 0.49; p<0.000), learning willingness (3.72± 0.93 vs 4.33± 0.63; p<0.000) and cognitive behaviors (2.45± 0.54 vs 4.03± 0.77; p<0.000). In conclusion, nutritional education proves effective, regardless of gender, in improving an inpatient’s cognition, behavior, and attitude toward diabetes self-management.

Keywords: diabetes, nutrition education, actual consumption, diet control motivation, nutrition concept, learning willingness, cognitive behaviors

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Authors: Parvin Goli, Amirhosein Kefayat, Rezvan Goli

Abstract:

Background: It is well established that parents can influence their offspring's neurodevelopment. It is shown that paternal environment and lifestyle is beneficial for the progeny's fitness and might affect their metabolic mechanisms; however, the effects of paternal exercise on the brain in the offspring have not been explored in detail. Objective: This study aims to review the impact of paternal physical exercise on memory and learning, neuroplasticity, as well as DNA methylation levels in the off-spring's hippocampus. Study design: In this systematic review and meta-analysis, an electronic literature search was conducted in databases including PubMed, Scopus, and Web of Science. Eligible studies were those with an experimental design, including an exercise intervention arm, with the assessment of any type of memory function, learning ability, or any type of brain plasticity as the outcome measures. Standardized mean difference (SMD) and 95% confidence intervals (CI) were computed as effect size. Results: The systematic review revealed the important role of environmental enrichment in the behavioral development of the next generation. Also, offspring of exercised fathers displayed higher levels of memory ability and lower level of brain-derived neurotrophic factor. A significant effect of paternal exercise on the hippocampal volume was also reported in the few available studies. Conclusion: These results suggest an intergenerational effect of paternal physical activity on cognitive benefit, which may be associated with hippocampal epigenetic programming in offspring. However, the biological mechanisms of this modulation remain to be determined.

Keywords: hippocampal plasticity, learning ability, memory, parental exercise

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Authors: Xinyi Xu

Abstract:

This study conducts a detailed examination of Asian stock markets over the period from 2008 to 2023, with a focus on the dynamics of cointegration and their relevance for long-term investment strategies. Specifically, we assess the co-movement and potential for pairs trading—a strategy where investors take opposing positions on two stocks, indices, or financial instruments that historically move together. For example, we explore the relationship between the Nikkei 225 (N225), Japan’s benchmark stock index, and the Straits Times Index (STI) of Singapore, as well as the relationship between the Korea Composite Stock Price Index (KS11) and the STI. The methodology includes tests for normality, stationarity, cointegration, and the application of Vector Error Correction Modeling (VECM). Our findings reveal significant long-term relationships between these pairs, indicating opportunities for pairs trading strategies. Furthermore, the research underscores the challenges posed by model instability and the influence of major global incidents, which are identified as structural breaks. These findings pave the way for further exploration into the intricacies of financial market dynamics.

Keywords: normality tests, stationarity, cointegration, VECM, pairs trading

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Authors: Ryo Shikimoto, Hidehito Niimura, Hisashi Kida, Kota Suzuki, Yukiko Miyasaka, Masaru Mimura

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Introduction: In Japan, the most aged country in the world, it is important to explore predictive factors of cognitive function among the elderly. Coping behavior relieves chronic stress and improves lifestyle, and consequently may reduce the risk of cognitive impairment. One of the most widely investigated frameworks evaluated in previous studies is approach-oriented and avoidance-oriented coping strategies. The purpose of this study is to investigate the relationship between cognitive function and coping strategies among elderly residents in urban areas of Japan. Method: This is a part of the cross-sectional Arakawa geriatric cohort study for 1,099 residents (aged 65 to 86 years; mean [SD] = 72.9 [5.2]). Participants were assessed for cognitive function using the Mini-Mental State Examination (MMSE) and diagnosed by psychiatrists in face-to-face interviews. They were then investigated for their each coping behaviors and coping strategies (approach- and avoidance-oriented coping) using stress and coping inventory. A multiple regression analysis was used to investigate the relationship between MMSE score and each coping strategy. Results: Of the 1,099 patients, the mean MMSE score of the study participants was 27.2 (SD = 2.7), and the numbers of the diagnosis of normal, mild cognitive impairment (MCI), and dementia were 815 (74.2%), 248 (22.6%), and 14 (1.3%), respectively. Approach-oriented coping score was significantly associated with MMSE score (B [partial regression coefficient] = 0.12, 95% confidence interval = 0.05 to 0.19) after adjusting for confounding factors including age, sex, and education. Avoidance-oriented coping did not show a significant association with MMSE score (B [partial regression coefficient] = -0.02, 95% confidence interval = -0.09 to 0.06). Conclusion: Approach-oriented coping was clearly associated with neurocognitive function in the Japanese population. A future longitudinal trial is warranted to investigate the protective effects of coping behavior on cognitive function.

Keywords: approach-oriented coping, cognitive impairment, coping behavior, dementia

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Authors: Tuji Jemal Ahmed

Abstract:

This paper is to provides a comprehensive review of current research works on foodborne illness and food safety, including the risks associated with foodborne illnesses, the latest research on food safety, and the mitigation strategies used to prevent and control foodborne illnesses. Foodborne illness is a major public health concern that affects millions of people every year. As foodborne illnesses have grown more common and dangerous in recent years, it is vital that we research and build upon methods to ensure food remains safe throughout consumption. Additionally, this paper will discuss future directions for food safety research, including emerging technologies, changes in regulations and standards, and collaborative efforts to improve food safety. The first section of the paper provides an overview of the risks of foodborne illness, including a definition of foodborne illness, the causes of foodborne illness, the types of foodborne illnesses, and high-risk foods for foodborne illness, Health Consequences of Foodborne Illness. The second section of the paper focuses on current research on food safety, including the role of regulatory agencies in food safety, food safety standards and guidelines, emerging food safety concerns, and advances in food safety technology. The third section of the paper explores mitigation strategies for foodborne illness, including preventative measures, hazard analysis and critical control points (HACCP), good manufacturing practices (GMPs), and training and education. Finally, this paper examines future directions for food safety research, including hurdle technologies and their impact on food safety, changes in food safety regulations and standards, collaborative efforts to improve food safety, and research gaps and areas for further exploration. In general, this work provides a comprehensive review of current research and future directions in food safety and understanding the risks associated with foodborne illness. The implications of the assessment for food safety and public health are discussed, as well as recommended for research scholars.

Keywords: food safety, foodborne illness, technologies, mitigation

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Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

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Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

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