Search results for: active learning

Authors: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

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Authors: R. Ajith, Kamal Bijlani

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Over the past few years, tremendous evolutions have happened in the educational discipline. Serious game, which is regarded as one of the most important inventions is being widely for learning purposes. Serious games can be used to negate the various drawbacks that the current evaluation and assessment methods have, like examination anxiety and the lack of proper feedback given to the learners. This paper proposes serious game as a tool for conducting evaluations and assessments. The examination anxiety faced by learners can be reduced, as they are provided with a game as an examination. The serious game also tracks learner’s actions, records them and provide feedback based on the predefined set of actions according to the course objectives. The appropriate feedback given to the learner will help in developmental activities in the learning process.

Keywords: serious games, evaluation, performance assessment, examination anxiety, performance feedback

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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: Kinda Alsamara

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Foreign language education is a complex endeavor that often presents educators with a range of challenges and difficulties. This study shed light on the specific challenges encountered in the context of teaching Arabic as a foreign language at the Australian National University (ANU). Drawing from real-world experiences and insights, we explore the multifaceted nature of these challenges and discuss strategies that educators have employed to address them. The challenges in teaching the Arabic language encompass various dimensions, including linguistic intricacies, cultural nuances, and diverse learner backgrounds. The complex Arabic script, grammatical structures, and pronunciation patterns pose unique obstacles for learners. Moreover, the cultural context embedded within the language demands a nuanced understanding of cultural norms and practices. The diverse backgrounds of learners further contribute to the challenge of tailoring instruction to meet individual needs and proficiency levels. This study also underscores the importance of technology in tackling these challenges. Technological tools and platforms offer innovative solutions to enhance language acquisition and engagement. Online resources, interactive applications, and multimedia content can provide learners with immersive experiences, aiding in overcoming barriers posed by traditional teaching methods. Furthermore, this study addresses the role of instructors in mitigating challenges. Educators often find themselves adapting teaching approaches to accommodate different learning styles, abilities, and motivations. Establishing a supportive learning environment and fostering a sense of community can contribute significantly to overcoming challenges related to learner diversity. In conclusion, this study provides a comprehensive overview of the challenges faced in teaching Arabic as a foreign language at ANU. By recognizing these challenges and embracing technological and pedagogical advancements, educators can create more effective and engaging learning experiences for students pursuing Arabic language proficiency.

Keywords: Arabic, Arabic online, blended learning, teaching and learning, Arabic language, educational aids, technology

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Authors: Nirmal Kumar Verma, Pallavi Jha

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Terahertz (THz) radiation generation by propagation of two-color laser pulses in plasma is an active area of research due to its potential applications in various areas, including security screening, material characterization and spectroscopic techniques. Due to non ionizing nature and the ability to penetrate several millimeters, THz radiation is suitable for diagnosis of cancerous cells. Traditional THz emitters like optically active crystals when irradiated with high power laser radiation, are subject to material breakdown and hence low conversion efficiencies. This problem is not encountered in laser - plasma based THz radiation sources. The present paper is devoted to the simulation study of the enhanced THz radiation generation by propagation of two-color, linearly polarized laser pulses through magnetized plasma. The two laser pulses orthogonally polarized are co-propagating along the same direction. The direction of the external magnetic field is such that one of the two laser pulses propagates in the ordinary mode, while the other pulse propagates in the extraordinary mode through homogeneous plasma. A transverse electromagnetic wave with frequency in the THz range is generated due to the presence of the static magnetic field. It is observed that larger amplitude terahertz can be generated by mixing of ordinary and extraordinary modes of two-color laser pulses as compared with a single laser pulse propagating in the extraordinary mode.

Keywords: two-color laser pulses, terahertz radiation, magnetized plasma, ordinary and extraordinary mode

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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: Ekta Pandey

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The Indian Pharmaceutical Industry is ranked third largest by volume and fourteenth by value. It thus accounts for 10% of world’s production by volume and 1.5% by value according to Department of Pharmaceuticals, Government of India. The industry has shown phenomenal growth over past few years, moving from US $ 1 billion turnover in 1990 to a turnover of around US $30 billion in 2015. The Indian pharmaceutical sector is ranked seventeenth in terms of export value of active pharmaceutical ingredients and dosage forms to more than 200 countries around the globe. It has shown tremendous changes especially after Trade Related Aspects of Intellectual Property Rights (TRIPS) agreement. Recognizing the immense potential for growth and its direct impact on Indian economy, it is important to look up the industrial policies adopted since Indian independence which turnaround the Indian pharmaceutical industry. A systematic review of changes in market structure of Indian pharmaceutical industry due to shift in policy regimes is done from 1850 to 2015 using secondary peer reviewed published research work. The aim is to understand the impact of anti-trust laws, intellectual property rights, industry competition acts and regulations are quite crucial in determining effective economic policy and have overall lasting effects on international trade and ties. The proposed paper examines the position of Indian domestic firms relative to multinational pharmaceutical firms tries to throw some light on the growth curve of Indian pharmaceutical sector.

Keywords: active pharmaceutical ingredients, competition act, pharmaceutical industry, TRIPS

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Authors: Sami M. Alshareef

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The rapid growth of smart grid technology has brought significant advancements to the power industry. However, with the increasing interconnectivity and reliance on information and communication technologies, smart grids have become vulnerable to cyber-attacks, posing significant threats to the reliable operation of power systems. Among the critical components of smart grids, the Automatic Generation Control (AGC) system plays a vital role in maintaining the balance between generation and load demand. Therefore, protecting the AGC system from cyber threats is of paramount importance to maintain grid stability and prevent disruptions. Traditional security measures often fall short in addressing sophisticated and evolving cyber threats, necessitating the exploration of innovative approaches. Machine learning, with its ability to analyze vast amounts of data and learn patterns, has emerged as a promising solution to enhance AGC system security. Therefore, this research proposal aims to address the challenges associated with detecting and mitigating cyber-attacks on AGC in smart grids by leveraging machine learning techniques on automatic generation control of two-area power systems. By utilizing historical data, the proposed system will learn the normal behavior patterns of AGC and identify deviations caused by cyber-attacks. Once an attack is detected, appropriate mitigation strategies will be employed to safeguard the AGC system. The outcomes of this research will provide power system operators and administrators with valuable insights into the vulnerabilities of AGC systems in smart grids and offer practical solutions to enhance their cyber resilience.

Keywords: machine learning, cyber-attacks, automatic generation control, smart grid

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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: Ursula D'Almeida, Danielly Aliprandi

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A car-based development is observed in our cities, what leads to social and environmental impacts, such as air pollution, excessive noises and the substitution of public open spaces for roads and parking lots. Concerning the efforts to promote a sustainable development, a key issue is the reduction of fossil fuels consumption. An alternative to the pollutant gases emission, especially from urban transportation, is the incentive for active transport. The promoting of non-motorized travels and locomotion ways that only depend on human propulsion meets the sustainable mobility notion. Walking is one of the healthiest, cleanest, most natural and economical means to move around. Also, it integrates part of public transportation travels. Since walking demands physical effort, it is sensitive to environmental conditions. In urban space, not always we come across pedestrian friendly road structures. Based on the theory of walkability, the present paper aims to discuss the walking conditions in city centers by analyzing the distribution of urban services and uses, and this also regarding sidewalks quality. The case study presented is the urban center in the medium-sized Brazilian city Campos dos Goytacazes, in Rio de Janeiro State. The study also brings contributions to the recovering of underused public open spaces, especially by encouraging their use and appropriation through valuing non-motorized travels conditions.

Keywords: active transport, historical center, sustainable mobility, walking

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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: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, 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: Naira Sahakyan, Margarit Petrosyan, Armen Trchounian

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One of the tasks in contemporary biotechnology, pharmacology and other fields of human activities is to obtain biologically active substances from plants. They are very essential in the treatment of many diseases due to their actually high therapeutic value without visible side effects. However, sometimes the possibility of obtaining the metabolites is limited due to the reduction of wild-growing plants. That is why the plant cell cultures are of great interest as alternative sources of biologically active substances. Besides, during the monitored cultivation, it is possible to obtain substances that are not synthesized by plants in nature. Isolated culture of Ajuga genevensis with high growth activity and ability of regeneration was obtained using MS nutrient medium. The agar-diffusion method showed that aqueous extracts of callus culture revealed high antimicrobial activity towards various gram-positive (Bacillus subtilis A1WT; B. mesentericus WDCM 1873; Staphylococcus aureus WDCM 5233; Staph. citreus WT) and gram-negative (Escherichia coli WKPM M-17; Salmonella typhimurium TA 100) microorganisms. The broth dilution method revealed that the minimal and half maximal inhibitory concentration values against E. coli corresponded to the 70 μg/mL and 140 μg/mL concentration of the extract respectively. According to the photochemiluminescent analysis, callus tissue extracts of leaf and root origin showed higher antioxidant activity than the same quantity of A. genevensis intact plant extract. A. genevensis intact plant and callus culture extracts showed no cytotoxic effect on K-562 suspension cell line of human chronic myeloid leukemia. The GC-MS analysis showed deep differences between the qualitative and quantitative composition of callus culture and intact plant extracts. Hexacosane (11.17%); n-hexadecanoic acid (9.33%); and 2-methoxy-4-vinylphenol (4.28%) were the main components of intact plant extracts. 10-Methylnonadecane (57.0%); methoxyacetic acid, 2-tetradecyl ester (17.75%) and 1-Bromopentadecane (14.55%) were the main components of A. genevensis callus culture extracts. Obtained data indicate that callus culture of A. genevensis can be used as an alternative source of biologically active substances.

Keywords: Ajuga genevensis, antibacterial activity, antioxidant activity, callus cultures

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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: Elena G. Salina, Laurent R. Chiarelli, Maria R. Pasca, Vadim A. Makarov

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Tuberculosis is one of the most challenging threats to human health, confronted by the problem of drug resistance. Evidently, new drugs for tuberculosis are urgently needed. Thienopyrimidine TP053 is one of the most promising new antitubercular prodrugs. Mycothiol-dependent reductase Mrx2, encoded by rv2466c, is known to be a TP053 activator; however, the precise mode of action of this compound remained unclear. Being highly active against both replicating and non-replicating tuberculosis bacilli, TP053 also revealed dose-escalating activity for M. tuberculosis-infected murine macrophages. The chemical structure of TP053 is characterized by the presence of NO₂ group which was suggested to be responsible for the toxic effects of the activated compound. Reduction of a nitroaromatic moiety of TP53 by Mrx2 was hypothesized to result in NO release. Analysis of the products of enzymatic activation of TP053 by Mrx2 by the Greiss reagent clearly demonstrated production of nitric oxide in a time-dependent manner. Mass-spectra of cell lysates of TP-treated M. tuberculosis bacilli demonstrated the transformation of TP053 to its non-active metabolite with Mw=261 that corresponds NO release. The mechanism of NO toxicity for bacteria includes DNA damage and degradation of iron-sulfur centers, especially under oxygen depletion. Thus, TP-053 drug-like scaffold is prospective for further development of novel anti-TB drug. This work was financially supported by the Russian Foundation for Basic Research (Grant 17-04-00342).

Keywords: drug discovery, M. tuberculosis, nitric oxide, NO donors

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Authors: Peikun Zhang, Chunhua Cui

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Photoelectrochemical (PEC) water splitting provides a promising pathway to convert solar energy into renewable fuels. However, the main and seemingly insurmountable obstacle is that the sluggish kinetics of oxygen evolution reaction (OER) severely jeopardizes the overall efficiency, thus exploring highly active, stable, and appreciable catalysts is urgently requested. Herein a competitive coordination strategy was demonstrated to form a reversible hybrid homo-heterogeneous catalyst for efficient OER in alkaline media. The dynamic process involves an in-situ anchoring of soluble nickel–bipyridine pre-catalyst to a conductive substrate under OER and a re-dissolution course under open circuit potential, induced by the competitive coordination between nickel–bipyridine and nickel-hydroxyls. This catalyst allows to elaborately self-modulate a charge-transfer layer thickness upon the catalytic on-off operation, which affords substantially increased active sites, yet remains light transparency, and sustains the stability of over 200 hours of continuous operation. The integration of this catalyst with exemplified state-of-the-art Ni-sputtered Si photoanode can facilitate a ~250 mV cathodic shift at a current density of 20 mA cm-2. This finding helps the understanding of catalyst from a “dynamic” perspective, which represents a viable alternative to address remaining hurdles toward solar-driven water oxidation.

Keywords: molecular catalyst, oxygen evolution reaction, solar energy, transition metal complex, water splitting

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Authors: Andrew Anis Fakhrey Mosaad

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Objectives: Physiotherapy in the intensive care unit (ICU) improves patient outcomes. We aimed to determine the characteristics of physiotherapy practice and critical barriers to applying physiotherapy in ICUs. Materials and Methods: A 54-item survey for determining the characteristics physiotherapists and physiotherapy applications in the ICU was developed. The survey was electronically sent to potential participants through the Turkish Physiotherapy Association network. Sixty-five physiotherapists (47F and 18M; 23–52 years; ICU experience: 6.0±6.2 years) completed the survey. The data were analyzed using quantitative and qualitative methods. Results: The duration of ICU practice was 3.51±2.10 h/day. Positioning (90.8%), active exercises (90.8%), breathing exercises (89.2%), passive exercises (87.7%), and percussion (87.7%) were the most commonly used applications. The barriers were related to physiotherapists (low level of employment and practice, lack of shift); patients (unwillingness, instability, participation restriction); teamwork (lack of awareness and communication); equipment (inadequacy, non-priority to purchase); and legal (reimbursement, lack of direct physiotherapy access, non-recognition of autonomy) procedures. Conclusion: The most common interventions were positioning, active, passive, breathing exercises, and percussion. Critical barriers toward physiotherapy are multifactorial and related to physiotherapists, patients, teams, equipment, and legal procedures. Physiotherapist employment, service maintenance, and multidisciplinary teamwork should be considered for physiotherapy effectiveness in ICUs.

Keywords: intensive care units, physical therapy, physiotherapy, exercises

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Authors: Eylem Tas

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This empirical study aims to explore the development of students' entrepreneurial skills through problem-based learning within the context of university-industry collaborations (UICs) in curriculum co-design and co-delivery (CDD). The research question guiding this study is: "How do problem-based learning and university-industry collaborations influence the development of students' entrepreneurial skills in the context of curriculum co-design and co-delivery?” To address this question, the study was conducted in a big city in Germany and involved interviews with stakeholders from various industries, including the private sector, government agencies (govt), and non-governmental organizations (NGOs). These stakeholders had established collaborative partnerships with the targeted university for projects encompassing entrepreneurial development aspects in CDD. The study sought to gain insights into the intricacies and subtleties of UIC dynamics and their impact on fostering entrepreneurial skills. Qualitative content analysis, based on Mayring's guidelines, was employed to analyze the interview transcriptions. Through an iterative process of manual coding, 442 codes were generated, resulting in two main sections: "the role of problem-based learning and UIC in fostering entrepreneurship" and "challenges and requirements of problem-based learning within UIC for systematical entrepreneurship development.” The chosen experimental approach of semi-structured interviews was justified by its capacity to provide in-depth perspectives and rich data from stakeholders with firsthand experience in UICs in CDD. By enlisting participants with diverse backgrounds, industries, and company sizes, the study ensured a comprehensive and heterogeneous sample, enhancing the credibility of the findings. The first section of the analysis delved into problem-based learning and entrepreneurial self-confidence to gain a deeper understanding of UIC dynamics from an industry standpoint. It explored factors influencing problem-based learning, alignment of students' learning styles and preferences with the experiential learning approach, specific activities and strategies, and the role of mentorship from industry professionals in fostering entrepreneurial self-confidence. The second section focused on various interactions within UICs, including communication, knowledge exchange, and collaboration. It identified key elements, patterns, and dynamics of interaction, highlighting challenges and limitations. Additionally, the section emphasized success stories and notable outcomes related to UICs' positive impact on students' entrepreneurial journeys. Overall, this research contributes valuable insights into the dynamics of UICs and their role in fostering students' entrepreneurial skills. UICs face challenges in communication and establishing a common language. Transparency, adaptability, and regular communication are vital for successful collaboration. Realistic expectation management and clearly defined frameworks are crucial. Responsible data handling requires data assurance and confidentiality agreements, emphasizing the importance of trust-based relationships when dealing with data sharing and handling issues. The identified key factors and challenges provide a foundation for universities and industrial partners to develop more effective UIC strategies for enhancing students' entrepreneurial capabilities and preparing them for success in today's digital age labor market. The study underscores the significance of collaborative learning and transparent communication in UICs for entrepreneurial development in CDD.

Keywords: collaborative learning, curriculum co-design and co-delivery, entrepreneurial skills, problem-based learning, university-industry collaborations

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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: 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: 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: Abhit Kumar

Abstract:

The world is full of master slave Telemanipulator where the doctor’s masters the console and the surgical arm perform the operations, i.e. these robots are passive robots, what the world needs to focus is that in use of these passive robots we are acquiring doctors for operating these console hence the utilization of the concept of robotics is still not fully utilized ,hence the focus should be on active robots, Auto Surgical-Emissive Hand use the similar concept of active robotics where this anthropomorphic hand focuses on the autonomous surgical, emissive and scanning operation, enabled with the vision of 3 way emission of Laser Beam/-5°C < ICY Steam < 5°C/ TIC embedded in palm of the anthropomorphic hand and structured in a form of 3 way disc. Fingers of AS-EH (Auto Surgical-Emissive Hand) as called, will have tactile, force, pressure sensor rooted to it so that the mechanical mechanism of force, pressure and physical presence on the external subject can be maintained, conversely our main focus is on the concept of “emission” the question arises how all the 3 non related methods will work together that to merged in a single programmed hand, all the 3 methods will be utilized according to the need of the external subject, the laser if considered will be emitted via a pin sized outlet, this radiation is channelized via a thin channel which further connect to the palm of the surgical hand internally leading to the pin sized outlet, here the laser is used to emit radiation enough to cut open the skin for removal of metal scrap or any other foreign material while the patient is in under anesthesia, keeping the complexity of the operation very low, at the same time the TIC fitted with accurate temperature compensator will be providing us the real time feed of the surgery in the form of heat image, this gives us the chance to analyze the level, also ATC will help us to determine the elevated body temperature while the operation is being proceeded, the thermal imaging camera in rooted internally in the AS-EH while also being connected to the real time software externally to provide us live feedback. The ICY steam will provide the cooling effect before and after the operation, however for more utilization of this concept we can understand the working of simple procedure in which If a finger remain in icy water for a long time it freezes the blood flow stops and the portion become numb and isolated hence even if you try to pinch it will not provide any sensation as the nerve impulse did not coordinated with the brain hence sensory receptor did not got active which means no sense of touch was observed utilizing the same concept we can use the icy stem to be emitted via a pin sized hole on the area of concern ,temperature below 273K which will frost the area after which operation can be done, this steam can also be use to desensitized the pain while the operation in under process. The mathematical calculation, algorithm, programming of working and movement of this hand will be installed in the system prior to the procedure, since this AS-EH is a programmable hand it comes with the limitation hence this AS-EH robot will perform surgical process of low complexity only.

Keywords: active robots, algorithm, emission, icy steam, TIC, laser

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Authors: Mohammad Hajjartabar, Tahereh Kermani Ranjbar

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P. aeruginosa EF2 was isolated and identified from human infection sources before in our previous study. The present study was performed to determine the characteristics and activity role of bioflocculant produced by the bacterium in flocculation of the wastewater active sludge treatment. The bacterium was inoculated and then was grown in an orbital shaker at 250 rpm for 5 days at 35 °C under TSB and peptone water media. After incubation period, culture broths of the bacterial strain was collected and washed. The concentration of the bacteria was adjusted. For the extraction of the bacterial bioflocculant, culture was centrifuged at 6000 rpm for 20 min at 4 °C to remove bacterial cells. Supernatant was decanted and pellet containing bioflocculant was dried at 105 °C to a constant weight according to APHA, 2005. The chemical composition of the extracted bioflocculant from the bacterial sample was then analyzed. Wastewater active sludge sample obtained from aeration tank from one of wastewater treatment plants in Tehran, was first mixed thoroughly. After addition of bioflocculant, improvements in floc density were observed with an increase in bioflocculant. The results of this study strongly suggested that the extracted bioflucculant played a significant role in flocculation of the wastewater sample. The use of wild bacteria and nutrient regulation techniques instead of genetic manipulation opens wide investigation area in the future to improve wastewater treatment processes. Also this may put a new path in front of us to attain and improve the more effective bioflocculant using the purified microbial culture in wastewater treatment.

Keywords: wastewater treatment, P. aeruginosa, sludge treatment

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

Procedia PDF Downloads 131