Search results for: real-world learning experiences

Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: Farhaz Amyn Rajabali, Collins Odour

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Mathematics has been there for over 4000 years and has been one of the very first educational topics explored by human civilization. Throughout the years, it has become a complex study and has derived so many other subjects. With advancements in ICT, most of the computation in mathematics is done using powerful computers. In many different countries, the children in primary and secondary schools face difficulties in learning mathematics, and this has many reasons behind it, one being the students don’t engage much with the mathematical concepts hence failing to understand them deeply. The objective of this system is to help the students understand this mathematical concept interactively, which in return will encourage the love for learning and increase thorough understanding of many concepts. Research was conducted among a group of samples and about 50% of respondents replied that they had never used an augmented reality application before. This means that the chances for this system to be accepted in the market are high due to its innovative idea. Around 60% of people did recommend the use of this system to learn mathematics. The study also showed several challenges in an educational system, including but not limited to lack of resources which was chosen by 30% of respondents, the challenge to read from textbooks (34.6%) and how hard it is to visualize concepts (46.2%). The survey question asked what benefits the users see using augmented reality to learn mathematics. The responses that were picked the most were increased student engagement and using real-world examples to understand concepts, both being 65.4% and followed by easy access to learning material at 61.5%, and increased knowledge retention at 50%. This shows that there are plenty of issues with an education system that can be addressed by software applications; now that the newer generation is so enthusiastic about electronic devices, it can actually be used to deliver good knowledge and skills to the upcoming students and mitigate most of the challenges faced currently. The study concludes that the implementation of the system is a best practice for the educational system especially leveraging a new technology that has the ability to attract the attention of many young students and use it to deliver information. It will also give rise to awareness of new technology and on multiple ways it can be implemented. Addressing the educational sector in developing countries using information technology is an imperative task since these kids studying now is the future of the country and will use what they learn and understand during their childhood will help them to make decisions about their lives in the future which will not only affect them personally but also affect the whole society in general.

Keywords: AR, mathematics, system development, augmented reality

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Authors: Gabriele Iannotta

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This paper investigates credit cycles by means of an experiment based on a Kiyotaki & Moore (1997) model with heterogeneous expectations. The aim is to examine how a credit squeeze caused by high lender-level risk perceptions affects the real prices of a collateralised asset, with a special focus on the macroeconomic implications of rising price volatility in terms of total welfare and the number of bankruptcies that occur. To do that, a learning-to-forecast experiment (LtFE) has been run where participants are asked to predict the future price of land and then rewarded based on the accuracy of their forecasts. The setting includes one lender and five borrowers in each of the twelve sessions split between six control groups (G1) and six treatment groups (G2). The only difference is that while in G1 the lender always satisfies borrowers’ loan demand (bankruptcies permitting), in G2 he/she closes the entire credit market in case three or more bankruptcies occur in the previous round. Experimental results show that negative risk-driven supply shocks amplify the volatility of collateral prices. This uncertainty worsens the agents’ ability to predict the future value of land and, as a consequence, the number of defaults increases and the total welfare deteriorates.

Keywords: Behavioural Macroeconomics, Credit Cycle, Experimental Economics, Heterogeneous Expectations, Learning-to-Forecast Experiment

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Authors: Sara Mosteller, Larissa K. Samuelson, Sobanawartiny Wijeakumar, John P. Spencer

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This study was designed to examine how humans are able to teach and learn semantic information as well as cooperate in order to jointly achieve sophisticated goals. Specifically, we are measuring individual differences in how these abilities develop from foundational building blocks in early childhood. The current study adopts a paradigm for novel noun learning developed by Samuelson, Smith, Perry, and Spencer (2011) to a hyperscanning paradigm [Cui, Bryant and Reiss, 2012]. This project measures coordinated brain activity between a parent and child using simultaneous functional near infrared spectroscopy (fNIRS) in pairs of 2.5, 3.5 and 4.5-year-old children and their parents. We are also separately testing pairs of adult friends. Children and parents, or adult friends, are seated across from one another at a table. The parent (in the developmental study) then teaches their child the names of novel toys. An experimenter then tests the child by presenting the objects in pairs and asking the child to retrieve one object by name. Children are asked to choose from both pairs of familiar objects and pairs of novel objects. In order to explore individual differences in cooperation with the same participants, each dyad plays a cooperative game of Jenga, in which their joint score is based on how many blocks they can remove from the tower as a team. A preliminary analysis of the noun-learning task showed that, when presented with 6 word-object mappings, children learned an average of 3 new words (50%) and that the number of objects learned by each child ranged from 2-4. Adults initially learned all of the new words but were variable in their later retention of the mappings, which ranged from 50-100%. We are currently examining differences in cooperative behavior during the Jenga playing game, including time spent discussing each move before it is made. Ongoing analyses are examining the social dynamics that might underlie the differences between words that were successfully learned and unlearned words for each dyad, as well as the developmental differences observed in the study. Additionally, the Jenga game is being used to better understand individual and developmental differences in social coordination during a cooperative task. At a behavioral level, the analysis maps periods of joint visual attention between participants during the word learning and the Jenga game, using head-mounted eye trackers to assess each participant’s first-person viewpoint during the session. We are also analyzing the coherence in brain activity between participants during novel word-learning and Jenga playing. The first hypothesis is that visual joint attention during the session will be positively correlated with both the number of words learned and with the number of blocks moved during Jenga before the tower falls. The next hypothesis is that successful communication of new words and success in the game will each be positively correlated with synchronized brain activity between the parent and child/the adult friends in cortical regions underlying social cognition, semantic processing, and visual processing. This study probes both the neural and behavioral mechanisms of learning and cooperation in a naturalistic, interactive and developmental context.

Keywords: communication, cooperation, development, interaction, neuroscience

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Authors: Micael Sousa

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Board games (BGs) are thriving as new designs emerge from the hobby community to greater audiences all around the world. Although digital games are gathering most of the attention in game studies and serious games research fields, the post-digital movement helps to explain why in the world dominated by digital technologies, the analog experiences are still unique and irreplaceable to users, allowing innovation in new hybrid environments. The BG’s new designs are part of these post-digital and hybrid movements because they result from the use of powerful digital tools that enable the production and knowledge sharing about the BGs and their face-to-face unique social experiences. These new BGs, defined as modern by many authors, are providing innovative designs and unique game mechanics that are still not yet fully explored by the main serious games (SG) approaches. Even the most established frameworks settled to address SG, as fun games implemented to achieve predefined goals need more development, especially when considering modern BGs. Despite the many anecdotic perceptions, researchers are only now starting to rediscover BGs and demonstrating their potentials. They are proving that BGs are easy to adapt and to grasp by non-expert players in experimental approaches, with the possibility of easy-going adaptation to players’ profiles and serious objectives even during gameplay. Although there are many design thinking (DT) models and practices, their relations with SG frameworks are also underdeveloped, mostly because this is a new research field, lacking theoretical development and the systematization of the experimental practices. Using BG as case studies promise to help develop these frameworks. Departing from the Design, Experience, and Play (DPE) framework and considering the Common Design Think Tools (CDST), this paper proposes a new experimental framework for the adaptation and development of modern BG design for DT: the Design, Experience, and Play for Think (DPET) experimental framework. This is done through the systematization of the DPE and CDST approaches applied in two case studies, where two different sequences of adapted BG were employed to establish a DT collaborative process. These two sessions occurred with different participants and in different contexts, also using different sequences of games for the same DT approach. The first session took place at the Faculty of Economics at the University of Coimbra in a training session of serious games for project development. The second session took place in the Casa do Impacto through The Great Village Design Jam light. Both sessions had the same duration and were designed to progressively achieve DT goals, using BGs as SGs in a collaborative process. The results from the sessions show that a sequence of BGs, when properly adapted to address the DPET framework, can generate a viable and innovative process of collaborative DT that is productive, fun, and engaging. The DPET proposed framework intents to help establish how new SG solutions could be defined for new goals through flexible DT. Applications in other areas of research and development can also benefit from these findings.

Keywords: board games, design thinking, methodology, serious games

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Authors: Seyed Kian Haji Seyed Javadi, Aisan Nouri

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Patient satisfaction in dental care is shaped by experiences throughout the treatment journey. Challenges such as fear, lack of trust and poor communication can impact patient contentment and willingness to seek dental care. This narrative review explores these issues and presents innovative solutions to address them by searching PubMed and Scopus data sources. It examines factors affecting patient satisfaction and adherence across three phases—before, during and after treatment—emphasizing the roles of effective communication, payment and follow-up systems, appointment scheduling, welcoming reception and the treatment environment. The factors discussed in this study motivate patients to return for routine check-ups and preventive care, even if their initial visit was for an emergency.

Keywords: patient satisfaction, dentistry, dental access, dental care services

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Authors: Mercedes Sepulveda, Saras Henderson, Dana Farrell, Gaby Heuft

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In Australia, there is a significant number of Culturally and Linguistically Diverse (CALD) Grandparent Carers who are sole carers for their grandchildren. Services in the community such as accessible healthcare, financial support, legal aid, and transport to services can assist Grandparent Carers to continue to live in their own home whilst caring for their grandchildren. Community nurses can play a major role by being aware of the needs of these grandparents and link them to services via information and referrals. The CALD Grandparent Carer experiences have only been explored marginally and may be similar to the general Grandparent Carer population, although cultural aspects may add to their difficulties. This Needs-Gap Analysis aimed to uncover ‘hidden issues’ for CALD Grandparent Carers such as service gaps and actions needed to address these issues. The stakeholders selected for this Needs-Gap Analysis were drawn from relevant service providers such as community and aged care services, child and/or grandparents support services and CALD specific services. One hundred relevant service providers were surveyed using six structured questions via face to face, phone interviews, or email correspondence. CALD Grandparents who had a significant or sole role of being a carer for grandchildren were invited to participate through their CALD community leaders. Consultative Forums asking five questions that focused on the caring role, issues encountered, and what needed to be done, were conducted with the African, Asian, Spanish-Speaking, Middle Eastern, European, Pacific Islander and Maori Grandparent Carers living in South-east Queensland, Australia. Data from the service provider survey and the CALD Grandparent Carer forums were content analysed using thematic principles. Our findings highlighted social determinants of health grouped into six themes. These were; 1) service providers and Grandparent Carer perception that there was limited research data on CALD grandparents as carers; 2) inadequate legal and financial support; 3) barriers to accessing information and advice; 4) lack of childcare options in the light of aging and health issues; 5) difficulties around transport; and 6) inadequate technological skills often leading to social isolation for both carer and grandchildren. Our Needs-Gap Analysis provides insight to service providers especially health practitioners such as doctors and community nurses, particularly on the impact of caring for grandchildren on CALD Grandparent Carers. Furthermore, factors such as cultural differences, English language difficulties, and migration experiences also impacted on the way CALD Grandparent Carers are able to cope. The findings of this Need-Gap Analysis signposts some of the ‘ hidden issues’ that CALD Grandparents Carers face and draws together recommendations for the future as put forward by the stakeholders themselves.

Keywords: CALD grandparents, carer needs, community nurses, grandparent carers

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Authors: Jam Harley

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The COVID-19 epidemic accelerated the speed of change in the workplace. Overnight, several individuals shifted from co-location in an office to hybrid or remote work. The pandemic also expedited and intensified the need to address persistent leadership and management concerns, including digital transformation, remote management, leading through fast change, anxiety, and uncertainty. Nonetheless, many leaders have failed to address the problems left behind by the epidemic. In a fundamental work devoted to comprehending what constitutes a human need, Maslow reiterates similar descriptors in his explanation of belongingness as the human need to be accepted, acknowledged, respected, and appreciated by a community of other individuals. This study aims to investigate the lived experiences of dispersed hybrid team members in order to find leadership best practices that improve team performance and retention through an increased individual’s sense of belonging.

Keywords: organizational change, belonging, diversity, equity

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Nyamsuren Baljinnyam, Narmandakh Khaltar, Otgonbaatar Olzkhuu

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Undergraduate students study English I (elective) and II (compulsory) courses which are included in the General foundation courses in the Teacher Education Curriculum Framework at the Mongolian National University of Education. Teachers at the English Department have designed and developed 2 levels (from pre-intermediate to upper-intermediate) English coursebooks since 2016 and published the second editions of each in 2018 and 2019. Developing coursebooks based on the students’ needs, satisfaction, and dissatisfaction with these instructional materials are essential phenomena in the delivery service of teaching English at the tertiary level. Thus, this study aims at findings from students’ views on English coursebooks which are studied mostly in the first and second semesters of the undergraduate academic program. The purpose of this research project was to determine the overall pedagogical value and suitability of the book to students’ needs and 21st-century teacher education concepts. We have designed a coursebook evaluation checklist with 28 questionnaires, including Morris’s English as a foreign language coursebook evaluation checklist (2017). The study is a 2 phased descriptive survey study that covered 572 and 519 undergraduate students who studied in the spring term of the 2021-2022 academic year and the fall term of the 2022-2023 academic year at 7 branch schools of Mongolian National University of Education (MNUE). Data analysis consists of student responses to each item. Coursebook evaluation data is classified into 3 main categories as “general attributes”, “learning content” and “task evaluation”. Some results of the study indicate the following findings: 97 percent of the total survey participants (in total 1091) have given positive responses to the coursebooks that these are fully aimed at acquiring the students’ language learning skills: reading, writing, listening, and speaking; 78 percent responded that the coursebooks were different from the English Textbooks that they learned in secondary schools; and 91 percent answered that the English coursebooks could give motivation to the students to achieve their self-study.

Keywords: coursebook evaluation, improving English, student satisfaction and dissatisfaction with coursebooks, language learning materials, language tasks, students’ needs

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Authors: Sirine Sayed, Mostapha Tarfaoui, Abdelmalek Toumi, Youssef Qarssis, Mohamed Daly, Chokri Bouraoui

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This paper delves into the combination of additive manufacturing (AM) and artificial intelligence (AI) to solve challenges related to the mechanical behavior of AM-produced parts. The article highlights the fundamentals and benefits of additive manufacturing, including creating complex geometries, optimizing material use, and streamlining manufacturing processes. The paper also addresses the challenges associated with additive manufacturing, such as ensuring stable mechanical performance and material properties. The role of AI in improving the static behavior of AM-produced parts, including machine learning, especially the neural network, is to make regression models to analyze the large amounts of data generated during experimental tests. It investigates the potential synergies between AM and AI to achieve enhanced functions and personalized mechanical properties. The mechanical behavior of parts produced using additive manufacturing methods can be further improved using design optimization, structural analysis, and AI-based adaptive manufacturing. The article concludes by emphasizing the importance of integrating AM and AI to enhance mechanical operations, increase reliability, and perform advanced functions, paving the way for innovative applications in different fields.

Keywords: additive manufacturing, mechanical behavior, artificial intelligence, machine learning, neural networks, reliability, advanced functionalities

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Authors: Alfia Sari

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The implementation of the Content Language Integrated Learning (CLIL) approach in Indonesia has shown positive impacts in several educational institutions. Several studies have proven the benefits of implementing the CLIL approach, including the development of students’ language and content subject knowledge. Interestingly, one primary school in Surabaya, Indonesia, has been successfully implementing the CLIL approach. The students achieved high content and language subject scores, and the school was accredited A. A study on how the CLIL approach was practiced is important to investigate how teachers implemented it and how students benefited from it. Therefore, this present study attempted to investigate the implementation of the CLIL approach in this school to characterize good practices that can be implemented in other schools. A case study was conducted to observe its implementation in the third-grade classes (English, Science, and Math) by using the Protocol for Language Arts Teaching Observation (PLATO). The findings indicated that the CLIL teaching in this school accommodated the content and language well (scores 3-4). The content and language were clearly integrated, and the teachers successfully carried out the subjects in English. Teachers offered students opportunities to listen, speak, read, and write using the target language. This study described some characteristics of CLIL teaching in primary school that can be used as examples for future CLIL teachers to integrate the content and language in their teaching practices.

Keywords: CLIL, ELT, young learners, case study

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Authors: Olufemi Sunday Onabajo

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Radio according to communication scholars is a veritable instrument of mass education. However, its full potentials in boosting higher education have not been realized because of the commercial nature of radio stations in Nigeria. The licensing of campus radio for disseminating information on university curricular is aimed at reinforcing information shared during face to face teaching. This study anchored on Agenda Setting and Technology determinism theories seeks to find out the extent to which university students in Lead City University and University of Ibadan, Nigeria have keyed-in to the philosophy of their campus radio – Lead City FM and Diamond FM in making information dissemination in their domiciled universities less cumbersome. The study employs both qualitative and quantitative methods though the use of depth interview for ten (10) academic staff and five (5) radio personnel of both radio stations; and a questionnaire addressed to 200 students of both institutions using the systematic random sampling technique. The data collected was analyzed using simple percentage and chi-square one tail test, and it was discovered that students of both universities and their radio personnel are yet to realize the potentials of campus radio as a resource alternative to effective learning, and recommends the coming together of all stakeholders to articulate the way forward.

Keywords: disseminating information, effective learning, resource alternative, utilizing radio

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Authors: Linda Fellag, Arlene Caney

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E-learning has earned an essential place in academia because it promotes learner autonomy, student engagement, and technological aptitude, and allows for flexible learning. However, despite advantages, educators have been slower to embrace e-learning for ESL and other non-traditional students for fear that such students will not succeed without the direct faculty contact and academic support of face-to-face classrooms. This study aims to determine if a non-traditional student-friendly online course can produce student retention and performance rates that compare favorably with those of students in standard online sections of the same course aimed at traditional college-level students. One Music faculty member is currently collaborating with an English instructor to redesign an online college-level Music Appreciation course for non-traditional college students. At Community College of Philadelphia, Introduction to Music Appreciation was recently designated as one of the few college-level courses that advanced ESL, and developmental English students can take while completing their language studies. Beginning in Fall 2017, the course will be critical for international students who must maintain full-time student status under visa requirements. In its current online format, however, Music Appreciation is designed for traditional college students, and faculty who teach these sections have been reluctant to revise the course to address the needs of non-traditional students. Interestingly, presenters maintain that the online platform is the ideal place to develop language and college readiness skills in at-risk students while maintaining the course's curricular integrity. The two faculty presenters describe how curriculum rather than technology drives the redesign of the digitized music course, and self-study materials, guided assignments, and periodic assessments promote independent learning and comprehension of material. The 'scaffolded' modules allow ESL and developmental English students to build on prior knowledge, preview key vocabulary, discuss content, and complete graded tasks that demonstrate comprehension. Activities and assignments, in turn, enhance college success by allowing students to practice academic reading strategies, writing, speaking, and student-faculty and peer-peer communication and collaboration. The course components facilitate a comparison of student performance and retention in sections of the redesigned and existing online sections of Music Appreciation as well as in previous sections with at-risk students. Indirect, qualitative measures include student attitudinal surveys and evaluations. Direct, quantitative measures include withdrawal rates, tests of disciplinary knowledge, and final grades. The study will compare the outcomes of three cohorts in the two versions of the online course: ESL students, at-risk developmental students, and college-level students. These data will also be compared with retention and student outcomes data of the three cohorts in f2f Music Appreciation, which permitted non-traditional student enrollment from 1998-2005. During this eight-year period, the presenter addressed the problems of at-risk students by adding language and college success support, which resulted in strong retention and outcomes. The presenters contend that the redesigned course will produce favorable outcomes among all three cohorts because it contains components which proved successful with at-risk learners in f2f sections of the course. Results of their study will be published in 2019 after the redesigned online course has met for two semesters.

Keywords: college readiness, e-learning, music appreciation, online courses

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Authors: Paul Dankers, Juliet Stoltenkamp, Carolynne Kies

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In May 2005, the University of the Western Cape (UWC) established an eLearning Division (ED) that, over the past 18 years, accelerated into the institutionalization of an efficient eLearning Centre. The initial objective of the ED was to incessantly align itself with emerging technologies caused by digital transformation, which progressively impacted Higher Education Institutions (HEIs) globally. In this paper, we present how the UWC eLearning Division (ED) first evolved into the eLearning Development and Support Unit (EDUS), currently called the ‘Centre for Innovative Education and Communication Technologies (CIECT). CIECT was strategically separated from the Department of Information and Communication Services (ICS) in 2009 and repositioned as an independent structure at UWC. Using a comparative research method, we highlight the transformative eLearning landscape at UWC by doing a detailed account of the shift in practices. Our research method will determine the initial vision and outcomes of institutionalizing an eLearning division. The study aims to compare across space or time the eLearning division’s rate of growth. By comparing the progressive growth of the UWCs eLearning division over the years, we will be able to document the successes and achievements of the eLearning division precisely. This study’s outcomes will act as a reference for novel research subjects on formalising eLearning. More research that delves into the effectiveness of having an eLearning division at HEIs in support of students’ teaching and learning is needed.

Keywords: eLearning, institutionalization, teaching and learning, transformation

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Authors: Ai Chin Wong, Wan Ahmad Jaafar Wan Yahaya, Balakrishnan Muniandy

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This study aims to explore the impact of Virtual Reality (VR) using visual signaling principles in learning about the science laboratory safety guide; this study involves students with different spatial ability. There are two types of science laboratory safety lessons, which are Virtual Reality with Signaling (VRS) and Virtual Reality Non Signaling (VRNS). This research has adopted a 2 x 2 quasi-experimental factorial design. There are two types of variables involved in this research. The two modes of courseware form the independent variables with the spatial ability as the moderator variable. The dependent variable is the students’ performance. This study sample consisted of 141 students. Descriptive and inferential statistics were conducted to analyze the collected data. The major effects and the interaction effects of the independent variables on the independent variable were explored using the Analyses of Covariance (ANCOVA). Based on the findings of this research, the results exhibited low spatial ability students in VRS outperformed their counterparts in VRNS. However, there was no significant difference in students with high spatial ability using VRS and VRNS. Effective learning in students with different spatial ability can be boosted by implementing the Virtual Reality with Signaling (VRS) in the design as well as the development of Virtual Science Laboratory (ViSLab).

Keywords: spatial ability, science laboratory safety, visual signaling principles, virtual reality

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Authors: Tsega Asresa Mengistu, Getahun Tigistu

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Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.

Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network

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Authors: Zhuang Hou, Xiaolei Cao

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The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contactless devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contactless mini program HealthyPai’s heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the real-life environment), the mean absolute error (MAE) ± standard deviation was 1.4827 ± 1.7452 in the lab, 6.9231 ± 5.6426 in the real-life setting. In the SpO2 study of 24 samples, the MAE ± standard deviation of the measurement was 1.0375 ± 0.7745. Our results validated that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS) framework, can accurately measure HR and SpO2, providing the test quality at least comparable to other FDA-approved wearable devices in the market and surpassing the consumer-grade and research-grade wearable standards.

Keywords: remote photoplethysmography, heart rate, oxygen saturation, contactless measurement, mini program

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Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

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The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

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Authors: K. Kalieva, G. Ibraimova

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In literature, poetry stands as a profound genre that bridges the life experiences of everyday people, transcending language and culture to unite people through the universal language of emotion and human connection. This paper explores the collaborative efforts of translators in creating the anthology of post-Soviet Kyrgyz women’s poetry, a project spanning over ten years. This compelling anthology brings together the works of fifty prominent female poets from Kyrgyzstan during the post-Soviet era. It includes the original poems in Kyrgyz and provide English translations, sharing the rich and diverse voices of Kyrgyz women with a global audience and fostering a deep appreciation for the beauty of their words. The paper highlights the unique perspectives on life, love, and identity offered by each poet, and emphasizes the role of translation in making these voices accessible worldwide. Each poet's unique voice offers a glimpse into the rich cultural and literary landscape of Kyrgyzstan, highlighting themes that resonate universally. Methodology of the paper employs a combination of qualitative content analysis, semiotic analysis, and quantitative thematic analysis to examine the translation strategies, and the cultural and emotional peculiarities captured in the translations, as well as the themes explored by the poets in their poems. Through the art of translation, the paper explores the lyrical world of Kyrgyz women poets. Although Kyrgyz poets’ names and poems are unfamiliar to many, their words resonate with an emotional depth that is sure to leave a lasting impression. Kyrgyz women's poetry translated into English celebrates the distinctive voices of women in the contemporary world. It serves as a reminder that poetry possesses the power to transcend life's obstacles, foster mutual understanding, and inspire positive change. The poems created by Kyrgyz women are envisioned to serve as a source of inspiration for readers. The paper proposes a poetic journey created by Kyrgyz women, offering readers an opportunity to experience Kyrgyz landscapes, traditions, and universal human themes through their verses. The paper provides an in-depth analysis of the poem translations, exploring the beauty and depth of the poets' thoughts and feelings. Through these translations, readers are invited to explore the world of Kyrgyz women poets, enriching their understanding of the language, culture, and the profound human experiences conveyed in the poetry. The hypotheses of the paper is that analyzing these translations through translation studies theories and linguistic and semiotic frameworks will reveal the complexities and challenges involved in translating poetry across languages and cultures.

Keywords: Kyrgyz poetry, post-soviet literature, translation, women poets.

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Authors: Nishaben Dholakiya, Anirban Roy, Ranjan Dey

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The unprecedented rise in global temperatures has triggered complex changes in marine ecosystems, necessitating a deeper understanding of seawater's thermophysical properties by experimentally measuring ultrasonic velocity and density at varying temperatures and salinity. This study investigates the critical relationship between temperature variations and molecular-level interactions in Arabian Sea surface waters, specifically focusing on internal pressure (π) and cohesion energy density (CED) as key indicators of ecosystem disruption. Our experimental findings reveal that elevated temperatures significantly reduce internal pressure, weakening the intermolecular forces that maintain seawater's structural integrity. This reduction in π correlates directly with decreased habitat stability for marine organisms, particularly affecting pressure-sensitive species and their physiological processes. Similarly, the observed decline in cohesion energy density at higher temperatures indicates a fundamental shift in water molecule organization, impacting the dissolution and distribution of vital nutrients and gases. These molecular-level changes cascade through the ecosystem, affecting everything from planktonic organisms to complex food webs. By employing advanced machine learning techniques, including Stacked Ensemble Machine Learning (SEML) and AdaBoost (AB), we developed highly accurate predictive models (>99% accuracy) for these thermophysical parameters. The results provide crucial insights into the mechanistic relationship between climate warming and marine ecosystem degradation, offering valuable data for environmental policymaking and conservation strategies. The novelty of this research serves as no such thermodynamic investigation has been conducted before in literature, whereas this research establishes a quantitative framework for understanding how molecular-level changes in seawater properties directly influence marine ecosystem stability, emphasizing the urgent need for climate change mitigation efforts.

Keywords: thermophysical properties, Arabian Sea, internal pressure, cohesion energy density, machine learning

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Authors: Pooja Khanal, Huaming Zhang

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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

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Authors: José María Renteria, Vania Salas

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This study explores the impact of inclusive education on the educational outcomes of students without Special Educational Needs (non-SEN) in Peru, utilizing official Ministry of Education data and implementing cross-sectional regression analyses. Inclusive education is a complex issue that, without appropriate adaptations and comprehensive understanding, can present substantial challenges to the educational community. While prior research from developed nations offers diverse perspectives on the effects of inclusive education on non-SEN students, limited evidence exists regarding its impact in developing countries. Our study addresses this gap by examining inclusive education in Peru and its effects on non-SEN students, thereby contributing to the existing literature. the findings reveal that, on average, the presence of SEN students in regular classrooms does not significantly affect their non-SEN counterparts. However, we uncover heterogeneous effects contingent on the specific type of SEN and students’ academic placement. These results emphasize the importance of targeted resources, specialized teachers, and parental involvement in facilitating successful inclusive education, particularly for specific SEN types and students positioned at the lower end of the academic achievement spectrum. In summary, this study underscores the need for tailored strategies and additional resources to foster the success of inclusive education and calls for further research in this field to expand our understanding and enhance educational policy.

Keywords: inclusive education, special educational needs, learning achievement, Peru, Basic education

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Authors: Marcia Figueira

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Context: Positivist transformations in agriculture were responsible for top-down – often coercive – mechanisms of uniformed modernization that weathered local diversities and agency. New development pathways need to now shift according to comprehensive integrations of knowledge - scientific, indigenous, and local, and to be sustained on political interventions, bottom-up change, and social learning if climate goals are to be met – both in mitigation and adaptation. Objectives The objectives of this research are to understand how social-ecological and socio-technical systems characterisation can be nested to bridge scientific research/knowledge into a local context and knowledge system; and, with it, stem sustainable innovation. Methods To do so, we conducted a scoping review to explore theoretical and empirical works linked to Ostrom’s Social-Ecological Systems framework and Geels’ multi-level perspective of socio-technical systems transformations in the context of agriculture. Results As a result, we were able to identify key variables and connections to 1- understand the rules in use and the community attributes influencing resource management; and 2- how they are and have been shaped and shaping systems innovations. Conclusion Based on these results, we discuss how to leverage action research for mutual learning toward a replicable but highly place-based agriculture transformation frame.

Keywords: agriculture systems innovations, social-ecological systems, socio-technical systems, action research

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Authors: Shan Zhang, Peter Suechting

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Predicting trade patterns is critical to decision-making in public and private domains, especially in the current context of trade disputes among major economies. In the past, U.S. recycling has relied heavily on strong demand for recyclable materials overseas. However, starting in 2017, a series of new recycling policies (bans and higher inspection standards) was enacted by multiple countries that were the primary importers of recyclables from the U.S. prior to that point. As the global trade flow of recycling shifts, some new importers, mostly developing countries in South and Southeast Asia, have been overwhelmed by the sheer quantities of scrap materials they have received. As the leading exporter of recyclable materials, the U.S. now has a pressing need to build its recycling industry domestically. With respect to the global trade in scrap materials used for recycling, the interest in this paper is (1) predicting how the export of recyclable materials from the U.S. might vary over time, and (2) predicting how international trade flows for recyclables might change in the future. Focusing on three major recyclable materials with a history of trade, this study uses data-driven and machine learning (ML) algorithms---supervised (shrinkage and tree methods) and unsupervised (neural network method)---to decipher the international trade pattern of recycling. Forecasting the potential trade values of recyclables in the future could help importing countries, to which those materials will shift next, to prepare related trade policies. Such policies can assist policymakers in minimizing negative environmental externalities and in finding the optimal amount of recyclables needed by each country. Such forecasts can also help exporting countries, like the U.S understand the importance of healthy domestic recycling industry. The preliminary result suggests that gravity models---in addition to particular selection macroeconomic predictor variables--are appropriate predictors of the total export value of recyclables. With the inclusion of variables measuring aspects of the political conditions (trade tariffs and bans), predictions show that recyclable materials are shifting from more policy-restricted countries to less policy-restricted countries in international recycling trade. Those countries also tend to have high manufacturing activities as a percentage of their GDP.

Keywords: environmental economics, machine learning, recycling, international trade

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Authors: Vichada Chokesikarin

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We propose that the ability to create the business success requires Adversity Quotient (AQ) and Human Capital (HC). The aims of the present study are to investigate adversity quotient, human capital and entrepreneurial success of accommodation entrepreneurs in Pranakorn, Bangkok and to examine the relationship between AQ, HC and Entrepreneurial Success. The participants of this study were 112 entrepreneurs in accommodation business in the Khao San/Grand Palace, the location nearby demonstration area in 2014. Specifically, we focus on higher adversity which provides a measure of one’s perceived capacity to prevail in the face of adversity and the effects of human capital on success. Results indicated that there is significant relationship between human capital and entrepreneurial success, while adversity quotient was found to partially mediate the entrepreneurial success. Moreover, our findings showed that the human capital -experience and skills- are more important than adversity quotient. This suggests that the entrepreneurial success should rely on their skill and experiences.

Keywords: accommodation business, adversity quotient, entrepreneurial success, human capital

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Authors: Felicia Titilayo Olanrewaju

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Folktales are a subclass of folklores which are verbally told and passed down from one generation to another, from the elderly ones to their children, usually at moonlight. These tales are heavily laden with moral lessons of what should be done and what not within the society. Though these are oftentimes heavily embellished yet are related to guide, guard, train, and dishing out moral attributes and mores worthwhile for ethical progression of the young minds within our traditional settings. With the rapid advancement of technological know-how, the existence of most of these moral-inclined stories becomes questionable; hence this study appraised the influences of these traditional storytellings have in the upgrading of moral learning of ethical behavioral traits acceptable among the Yoruba people. Oral interviews couples with recording gadgets were used to collate both sample parents' and children’s responses within a particular community in Owo (ute) local government area of Owo Ondo State, Nigeria. Findings reveal that diverse tales told at moonlight periods have an untold impact on the speedy growth of the children intellectually than the modern happenings around them. These telltale stories become powerful aids in learning goodly traits and eschewing bad manners. It is recommended that folk stories be told within the household among the family after hard labour in the evenings as this would help develop human relationships and brings about a strong sense of community bindings.

Keywords: folktales, folklores, impact, advancement, ethical progression

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Authors: Sacha Joseph-Mathews, Leili Javadpour

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In the tourism industry, hoteliers spend millions annually on marketing and positioning efforts for their respective hotels, all in an effort to create a specific image in the minds of the consumer. Yet despite extensive efforts to seduce potential hotel guests with sophisticated advertising messages generated by hotel entities, consumers continue to mistrust corporate branding, preferring instead to place their trust in the reviews of their consumer peers. In today’s complex and cluttered marketplace, online reviews can serve as a mediator for consumers who do not have actual knowledge and experiences with the brand, but are in the process of deciding whether or not to engage in a consumption exercise. Traditionally, consumers have used online reviews as a source of comfort and confirmation of a product/service’s positioning. But today, very few customers make any purchase decisions without first researching existing user reviews, making reviews more of a necessity, rather than a luxury in the purchase decision process. The influence of user generated content (UGC) is amplified in the tourism industry; as more than a third of potential hotel guests will not book a room without first reading a review. As corporate branding becomes less relevant and online reviews become more important, how much of the consumer’s stay expectations are being dictated by existing UGC? Moreover, as hotel guest experience a hotel through the lens of an existing review, how much of their stay and in turn their review, would have been influenced by those reviews that they read? Ultimately, there is the potential for UGC to dictate what potential guests will be most critical about, and or most focused on during their stay. If UGC is a stronger influencer in the purchase decision process than corporate branding, doesn’t it have the potential to dictate, the entire stay experience by influencing the expectations of the guest prior to them arriving on the property? For example, if a hotel is an eco-destination and they focus their branding on their website around sustainability and the retreat nature of the hotel. Yet, guest reviews constantly discuss how dissatisfactory the service and food was with no mention of nature or sustainability, will future reviews then focus primarily on the food? Using text analysis software to examine over 25,000 online reviews, we explore the extent to which new reviews are influenced by wording used in previous reviews for a hotel property, versus content generated by corporate positioning. Additionally, we investigate how distinct hotel related UGC is across different types of tourism destinations. Our findings suggest that UGC can have a greater impact on future reviews, than corporate branding and there is more cohesiveness across UGC of different types of hotel properties than anticipated. A model of User Generated Content Influence is presented and the managerial impact of the power of online reviews to trump corporate branding and shape future user experiences is discussed.

Keywords: user generated content, UGC, corporate branding, online reviews, hotels and tourism

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Authors: Christopher Thornton, Niina Kolehmainen, Kianoush Nazarpour

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Background: Accelerometers are widely used to measure physical activity behavior, including in children. The traditional method for processing acceleration data uses cut points, relying on calibration studies that relate the quantity of acceleration to energy expenditure. As these relationships do not generalise across diverse populations, they must be parametrised for each subpopulation, including different age groups, which is costly and makes studies across diverse populations difficult. A data-driven approach that allows physical activity intensity states to emerge from the data under study without relying on parameters derived from external populations offers a new perspective on this problem and potentially improved results. We evaluated the data-driven approach in a diverse population with a range of rapidly evolving physical and mental capabilities, namely very young children (9-38 months old), where this new approach may be particularly appropriate. Methods: We applied an unsupervised machine learning approach (a hidden semi-Markov model - HSMM) to segment and cluster the accelerometer data recorded from 275 children with a diverse range of physical and cognitive abilities. The HSMM was configured to identify a maximum of six physical activity intensity states and the output of the model was the time spent by each child in each of the states. For comparison, we also processed the accelerometer data using published cut points with available thresholds for the population. This provided us with time estimates for each child’s sedentary (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data on the children’s physical and cognitive abilities were collected using the Paediatric Evaluation of Disability Inventory (PEDI-CAT). Results: The HSMM identified two inactive states (INS, comparable to SED), two lightly active long duration states (LAS, comparable to LPA), and two short-duration high-intensity states (HIS, comparable to MVPA). Overall, the children spent on average 237/392 minutes per day in INS/SED, 211/129 minutes per day in LAS/LPA, and 178/168 minutes in HIS/MVPA. We found that INS overlapped with 53% of SED, LAS overlapped with 37% of LPA and HIS overlapped with 60% of MVPA. We also looked at the correlation between the time spent by a child in either HIS or MVPA and their physical and cognitive abilities. We found that HIS was more strongly correlated with physical mobility (R²HIS =0.5, R²MVPA= 0.28), cognitive ability (R²HIS =0.31, R²MVPA= 0.15), and age (R²HIS =0.15, R²MVPA= 0.09), indicating increased sensitivity to key attributes associated with a child’s mobility. Conclusion: An unsupervised machine learning technique can segment and cluster accelerometer data according to the intensity of movement at a given time. It provides a potentially more sensitive, appropriate, and cost-effective approach to analysing physical activity behavior in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive across diverse populations.

Keywords: physical activity, machine learning, under 5s, disability, accelerometer

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Authors: Charles Masulani Mwale

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Background: It is argued that 85% of children with the disability live in resource-poor countries where there are few available disability services. A majority of these children, including their parents, suffer a lot as a result of the disability and its associated stigmatization, leading to a marginalized life. These parents also experience more stress and mental health problems such as depression, compared with families of normal developing children. There is little research from Africa addressing these issues especially among parents of intellectually disabled children. WHO encourages research on the impact that child with a disability have on their family and appropriate training and support to the families so that they can promote the child’s development and well-being. This study investigated the parenting experiences, mechanisms of coping with these challenges and psychosocial needs while caring for children with intellectual disabilities in both rural and urban settings of Lilongwe and Mzuzu. Methods: This is part of a larger Mixed-methods study aimed at developing a contextualized psychosocial intervention for parents of intellectually disabled children. 16 focus group discussions and four in-depth interviews were conducted with parents in catchments areas for St John of God and Children of Blessings in Mzuzu and Lilongwe cities respectively. Ethical clearance was obtained from COMREC. Data were stored in NVivo software for easy retrieval and management. All interviews were tape-recorded, transcribed and translated into English. Note-taking was performed during all the observations. Data triangulation from the interviews, note taking and the observations were done for validation and reliability. Results: Caring for intellectually disabled children comes with a number of challenges. Parents experience stigma and discrimination; fear for the child’s future; have self-blame and guilt; get coerced by neighbors to kill the disabled child; and fear violence by and to the child. Their needs include respite relief, improved access to disability services, education on disability management and financial support. For their emotional stability, parents cope by sharing with others and turning to God while other use poor coping mechanisms like alcohol use. Discussion and Recommendation: Apart from neighbors’ coercion to eliminate the child life, the findings of this study are similar to those done in other countries like Kenya and Pakistan. It is recommended that parents get educated on disability, its causes, and management to array fears of unknown. Community education is also crucial to promote community inclusiveness and correct prevailing myths associated with disability. Disability institutions ought to intensify individual as well as group counseling services to these parents. Further studies need to be done to design culturally appropriate and specific psychosocial interventions for the parents to promote their psychological resilience.

Keywords: psychological distress, intellectual disability, psychosocial interventions, mental health, psychological resilience, children

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