Search results for: workforce diversity learning

Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

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Authors: Diana Ruth Caga-Anan

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Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.

Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices

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Authors: Kwadwo Afari Gyan

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Cities are growing at unprecedented levels. Meanwhile, governments in the Global South are already overwhelmed by this growth and are unable to provide infrastructure proactively as expected. As a result, urban residents resort to providing their own infrastructure, such as drainage systems, as part of self-built housing development. Their small-scale, incremental housing practices, which often represent the formation of dense and diverse housing types, styles, and ages, have been identified to affect the planning of drainage systems at the regional scale. Such developments reflect the varied, affordable responses as part of a collective effort to curb regional problems, specifically flooding in this case. However, while some are included in this collective action, others are excluded as they are unable to afford to be included. This phenomenon, in addition to the formation of new autonomous localities, has led to challenges in mitigating flooding and has affected resilience to climate change. Using a qualitative approach, this paper explores how the mismatch between housing development, which occurs at an individual scale, and drainage infrastructure, which is provided at a regional scale, affects a regional effort to mitigate flooding in Tema, Ghana. It seeks to explore and reveal a relationship between affordability and inclusiveness. It also explores how density and diversity influence public infrastructure provision and their connection with affordability.

Keywords: climate change, affordability, inclusivity, equity, contextualization, regionalism

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Authors: Jou-An Chen, Julio Castillo, Errol Duncan Cason, Gabre Kemp, Leana Esterhuizen, Angel Valverde Portal, Esta Van Heerden

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Emerging contaminants (EC) such as pharmaceutical and personal care products have been accumulating for years in water bodies all over the world. However, very little is known about the occurrences, levels, and effects of ECs in South African water resources. This study provides an initial assessment of the distribution of eight ECs (Acetaminophen, Atrazine, Terbuthlyazine, Carbamazepine, Phenyton, Sulfmethoxazole, Nevirapine and Fluconozole) in fifteen water sources from the Free State and Easter Cape provinces of South Africa. Overall, the physiochemical conditions were different in surface and groundwater samples, with concentrations of several elements such as B, Ca, Mg, Na, NO3, and TDS been statistically higher in groundwater. In contrast, ECs levels, quantified at ng/mL using the LC/MS/ESI, were much lower in groundwater samples. The ECs with higher contamination levels were Carbamazepine, Sulfmethoxazole, Nevirapine, and Terbuthlyazine, while the most widespread were Sulfmethoxazole and Fluconozole, detected in all surface and groundwater samples. Fecal and E. coli tests indicated that surface water was more contaminated than groundwater. Microbial communities, assessed using NGS, were dominated by the phyla Proteobacteria and Bacteroidetes, in both surface and groundwater. Actinobacteria, Planctomycetes, and Cyanobacteria, were more dominant in surface water, while Verrucomicrobia were overrepresented in groundwater. In conclusion, ECs contamination is closely associated with human activities (human wastes). The microbial diversity identified can suggest possible biodegradation processes.

Keywords: emerging contaminants, EC, personal care products, pharmaceuticals, natural attenuation process

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Authors: Hassen Hamouda, Mohamed Ouwais Kabaou, Med Salim Bouhlel

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The growth of wireless technology made opportunistic scheduling a widespread theme in recent research. Providing high system throughput without reducing fairness allocation is becoming a very challenging task. A suitable policy for resource allocation among users is of crucial importance. This study focuses on scheduling multiple streaming flows on the downlink of a WiMAX system based on orthogonal frequency division multiple access (OFDMA). In this paper, we take the first step in formulating and analyzing this problem scrupulously. As a result, we proposed a new scheduling scheme based on Round Robin (RR) Algorithm. Because of its non-opportunistic process, RR does not take in account radio conditions and consequently it affect both system throughput and multi-users diversity. Our contribution called MORRA (Modified Round Robin Opportunistic Algorithm) consists to propose a solution to this issue. MORRA not only exploits the concept of opportunistic scheduler but also takes into account other parameters in the allocation process. The first parameter is called courtesy coefficient (CC) and the second is called Buffer Occupancy (BO). Performance evaluation shows that this well-balanced scheme outperforms both RR and MaxSNR schedulers and demonstrate that choosing between system throughput and fairness is not required.

Keywords: OFDMA, opportunistic scheduling, fairness hierarchy, courtesy coefficient, buffer occupancy

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Authors: L. Almulla

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Native plants are better adapted to the local environment providing a more natural effect on landscape projects; their use will both conserve natural resources and produce sustainable greenery. Continuation of evaluation of additional native plants is essential to increase diversity of plant resources for greenery projects. Therefore, in this project an effort was made to study the mass multiplication of further native plants for greenery applications. Standardization of vegetative propagation methods is essential for conservation and sustainable utilization of native plants in restoration projects. Moreover, these simple propagation methods can be readily adapted by the local nursery sector in Kuwait. In the present study, various treatments were used to mass multiply selected plants using vegetative parts to secure maximum rooting and initial growth. Soft or semi-hardwood cuttings of selected native plants were collected from mother plants and subjected to different treatments. Pennisetum divisum can be vegetatively propagated by cuttings/off-shoots. However, Tamarix aucheriana showed maximum number of rooted cuttings and stronger vigor seedlings with the lowest growth hormone concentration. Standardizing the propagation techniques for the native plant species will add to the rehabilitation and landscape revegetation projects in Kuwait.

Keywords: Kuwait desert, landscape, rooting percentage, vegetative propagation

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Authors: C. Annapurna, Rao M. Srinivasa, Bhanu C. H. Vijaya, M. Sivalakshmi, Rao P. V. Surya

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A study on the community structure of meiobenthic fauna was undertaken during three cruises (June 2008, October 2008 and March 2009). Ten stations at depth between 10 and 40 m off Pudimadaka in Visakhapatnam (Lat.17º29′12″N and Long. 83º00′09″), East coast of India were investigated. Ninety species representing 3 major (meiofaunal) taxa namely foraminifera (2), copepoda (9), nematoda (58) and polychaeta (21) were encountered. Overall, meiofaunal (mean) abundance ranged from 2 individuals to 63 ind. 10cm-² with an average of 24.3 ind.10cm-2. The meiobenthic biomass varied between 0.135 to 0.48 mg.10cm-2 with an average 0.27 ± 0.12. On the whole, nematodes constituted 73.62% of the meiofauna in terms of numerical abundance. Shannon –Wiener index values were 2.053 ± 0.64 (June, 2008), 2.477 ± 0.177 (October 2008) and 2.2815±0.24 (March 2009). Multivariate analyses were used to define the most important taxon in nematode assemblages. Three nematode associations could be recognized off Pudimadaka coast, namely Laimella longicaudata, Euchromodora vulgaris and Sabatieria elongata assemblage (June, 2008); Catanema sp. and Leptosomatum sp. assemblage (October 2008) assemblage; Sabatieria sp. and Setosabatieria sp. assemblage (March 2009). Canonical correspondence analysis showed that temperature, organic matter, silt and mean particle diameter were important in controlling nematode community structure.

Keywords: meiofauna, marine nematode, biodiversity, community structure, India

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Authors: Mohammad Hassanzadeh

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As far as incidental vocabulary learning is concerned, the basic contention of the Involvement Load Hypothesis (ILH) is that retention of unfamiliar words is, generally, conditional upon the degree of involvement in processing them. This study examined input modality and incidental vocabulary uptake in a task-induced setting whereby three variously loaded task types (marginal glosses, fill-in-task, and sentence-writing) were alternately assigned to one group of students at Allameh Tabataba’i University (n=2l) during six classroom sessions. While one round of exposure was comprised of the audiovisual medium (TV talk shows), the second round consisted of textual materials with approximately similar subject matter (reading texts). In both conditions, however, the tasks were equivalent to one another. Taken together, the study pursued the dual objectives of establishing a litmus test for the ILH and its proposed values of ‘need’, ‘search’ and ‘evaluation’ in the first place. Secondly, it sought to bring to light the superiority issue of exposure to audiovisual input versus the written input as far as the incorporation of tasks is concerned. At the end of each treatment session, a vocabulary active recall test was administered to measure their incidental gains. Running a one-way analysis of variance revealed that the audiovisual intervention yielded higher gains than the written version even when differing tasks were included. Meanwhile, task 'three' (sentence-writing) turned out the most efficient in tapping learners' active recall of the target vocabulary items. In addition to shedding light on the superiority of audiovisual input over the written input when circumstances are relatively held constant, this study for the most part, did support the underlying tenets of ILH.

Keywords: Keywords— Evaluation, incidental vocabulary learning, input mode, Involvement Load Hypothesis, need, search.

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Authors: Taiebe Shokri, Shahram Yazdani, Leila Afshar, Soleiman Ahmadi

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Introduction: The importance of professionalism in higher education not only determines the appropriate and inappropriate behaviors and guides faculty members in the implementation of professional responsibilities, but also guarantees faculty members' adherence to professional principles and values, ensures the quality of teaching and facilitator will be the teaching-learning process in universities and will increase the commitment to meet the needs of students as well as the development of an ethical culture based on ethics. Therefore, considering the important role of medical education teachers to prepare teachers and students in the future, the need to determine the concept of professional teacher and teacher, and the characteristics of teacher professionalism, we have explained the concept of professionalism in teachers in this study. Methods: The concept analysis method used in this study was Walker and Avant method which has eight steps. Walker and Avant state the purpose of concept analysis as follows: The process of distinguishing between the defining features of a concept and its unrelated features. The process of concept analysis includes selecting a concept, determining the purpose of the analysis, identifying the uses of the concept, determining the defining features of the concept, identifying a model, identifying boundary and adversarial items, identifying the precedents and consequences of the concept, and defining empirical references. is. Results: Professionalism in its general sense, requires deep knowledge, insight, creating a healthy and safe environment, honesty and trust, impartiality, commitment to the profession and continuous improvement, punctuality, criticism, professional competence, responsibility, and Individual accountability, especially in social interactions, is an effort for continuous improvement, the acquisition of these characteristics is not easily possible and requires education, especially continuous learning. Professionalism is a set of values, behaviors, and relationships that underpin public trust in teachers.

Keywords: concept analysis, medical education, professionalism, faculty members

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Authors: Paul Lam, Kevin Wong, Chi Him Chan

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Constant practicing and accurate, immediate feedback are the keys to improving students’ speaking skills. However, traditional oral examination often fails to provide such opportunities to students. The traditional, face-to-face oral assessment is often time consuming – attending the oral needs of one student often leads to the negligence of others. Hence, teachers can only provide limited opportunities and feedback to students. Moreover, students’ incentive to practice is also reduced by their anxiety and shyness in speaking the new language. A mobile app was developed to use artificial intelligence (AI) to provide immediate feedback to students’ speaking performance as an attempt to solve the above-mentioned problems. Firstly, it was thought that online exercises would greatly increase the learning opportunities of students as they can now practice more without the needs of teachers’ presence. Secondly, the automatic feedback provided by the AI would enhance students’ motivation to practice as there is an instant evaluation of their performance. Lastly, students should feel less anxious and shy compared to directly practicing oral in front of teachers. Technically, the program made use of speech-to-text functions to generate feedback to students. To be specific, the software analyzes students’ oral input through certain speech-to-text AI engine and then cleans up the results further to the point that can be compared with the targeted text. The mobile app has invited English teachers for the pilot use and asked for their feedback. Preliminary trials indicated that the approach has limitations. Many of the users’ pronunciation were automatically corrected by the speech recognition function as wise guessing is already integrated into many of such systems. Nevertheless, teachers have confidence that the app can be further improved for accuracy. It has the potential to significantly improve oral drilling by giving students more chances to practice. Moreover, they believe that the success of this mobile app confirms the potential to extend the AI-assisted assessment to other language skills, such as writing, reading, and listening.

Keywords: artificial Intelligence, mobile learning, oral assessment, oral practice, speech-to-text function

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Authors: Boubacar Niang, Denis Raulin

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This paper presents the design of a dedicated board for learning and programming with ATMEL AVR new generation micro controller’s family. This board designed as a "shield" for the Arduino Uno allows us to focus on the design and programming of basic micro controller functionalities in high level language with a considerable time saving because of dealing with additional components is not required.

Keywords: Arduino, microcontroller, programming, language

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Authors: Sima Modirkhameneh, Mohammad Mohammadpanah

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In the process of EFL teachers’ development, self-led reflection (SLR) is thought to have an imperative role because it may help teachers analyze, evaluate, and contemplate what is happening in their classes. Such contemplations can not only enhance the quality of their instruction and provide better learning environments for learners but also improve the quality of their classroom management (CM). Accordingly, understanding the effect of teachers’ SLR practices may help us gain valuable insights into what possible modifications SLR may bring about in all aspects of EFL teachers' practitioners, especially their CM. The main purpose of this case study was, thus, to investigate the impact of SLR practices of 12 Iranian EFL teachers on their CM based on the universal classroom management checklist (UCMC). In addition, another objective of the current study was to have a clear image of EFL teachers’ perceptions of their own SLR practices and their possible outcomes. By conducting repeated reflective interviews, observations, and feedback of the participants over five teaching sessions, the researcher analyzed the outcomes qualitatively through the process of meaning categorization and data interpretation based on the principles of Grounded Theory. The results demonstrated that EFL teachers utilized SLR practices to improve different aspects of their language teaching skills and CM in different contexts. Almost all participants had positive comments and reactions about the effect of SLR on their CM procedures in different aspects (expectations and routines, behavior-specific praise, error corrections, prompts and precorrections, opportunity to respond, strengths and weaknesses of CM, teachers’ perception, CM ability, and learning process). Otherwise stated, results implied that familiarity with the UCMC criteria and reflective practices contributes to modifying teacher participants’ perceptions about their CM procedure and utilizing the reflective practices in their teaching styles. The results are thought to be valuably beneficial for teachers, teacher educators, and policymakers, who are recommended to pay special attention to the contributions as well as the complexity of reflective teaching. The study concludes with more detailed results and implications and useful directions for future research.

Keywords: classroom management, EFL teachers, reflective practices, self-led reflection

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Temitope Ogungbemi

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Jama'atu Ahlis Sunna Lidda'awati wal-Jihad (also called Boko Haram) in the North-East of Nigeria has facilitated ideological binarity in discourses on the crisis. Since its proliferation, media representation of the crisis has facilitated identity contamination and ideological struggle through which other critical issues, such as religious intolerance, ethnic diversity and other forms of class conflict in the Nigerian state, are brought to public notice. Though Boko Haram insurgency is ideological laden, the manifestation of the inherent ideologies requires extensive scholarly attention in order deconstruct the veiled ideologies. Therefore, the thrust of this study is to critically investigate identity (mis)representation as a basis for ideological mapping in discourses on Boko Haram in Nigeria, adopting critical discourse analytical tools supported with insights from systemic functional linguistics and critical discourse analysis. The data for this study consist of articles on Boko Haram in Nigerian newspapers published in English. The data selection is purposive and aimed at responding to challenges that are inherent in Nigeria's multifaithism and multiculturalism, and their effects on the construction of narratives on Boko Haram. The study reveals that identity manipulation is a constructive device for ideological mapping, realised through labeling, agency activation, and transitivity. Identity representation in discourses on Boko Haram depicted four dichotomous binarities using exclusion, generalisation, contrasting and attribution.

Keywords: identity representation, ideology, Boko Haram, newspapers

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Authors: Shenlun Chen, Leonard Wee

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Tumor grading is an essential reference for colorectal cancer (CRC) staging and survival prognostication. The widely used World Health Organization (WHO) grading system defines histological grade of CRC adenocarcinoma based on the density of glandular formation on whole slide images (WSI). Tumors are classified as well-, moderately-, poorly- or un-differentiated depending on the percentage of the tumor that is gland forming; >95%, 50-95%, 5-50% and <5%, respectively. However, manually grading WSIs is a time-consuming process and can cause observer error due to subjective judgment and unnoticed regions. Furthermore, pathologists’ grading is usually coarse while a finer and continuous differentiation grade may help to stratifying CRC patients better. In this study, a deep learning based automatic differentiation grading algorithm was developed and evaluated by survival analysis. Firstly, a gland segmentation model was developed for segmenting gland structures. Gland regions of WSIs were delineated and used for differentiation annotating. Tumor regions were annotated by experienced pathologists into high-, medium-, low-differentiation and normal tissue, which correspond to tumor with clear-, unclear-, no-gland structure and non-tumor, respectively. Then a differentiation prediction model was developed on these human annotations. Finally, all enrolled WSIs were processed by gland segmentation model and differentiation prediction model. The differentiation grade can be calculated by deep learning models’ prediction of tumor regions and tumor differentiation status according to WHO’s defines. If multiple WSIs were possessed by a patient, the highest differentiation grade was chosen. Additionally, the differentiation grade was normalized into scale between 0 to 1. The Cancer Genome Atlas, project COAD (TCGA-COAD) project was enrolled into this study. For the gland segmentation model, receiver operating characteristic (ROC) reached 0.981 and accuracy reached 0.932 in validation set. For the differentiation prediction model, ROC reached 0.983, 0.963, 0.963, 0.981 and accuracy reached 0.880, 0.923, 0.668, 0.881 for groups of low-, medium-, high-differentiation and normal tissue in validation set. Four hundred and one patients were selected after removing WSIs without gland regions and patients without follow up data. The concordance index reached to 0.609. Optimized cut off point of 51% was found by “Maxstat” method which was almost the same as WHO system’s cut off point of 50%. Both WHO system’s cut off point and optimized cut off point performed impressively in Kaplan-Meier curves and both p value of logrank test were below 0.005. In this study, gland structure of WSIs and differentiation status of tumor regions were proven to be predictable through deep leaning method. A finer and continuous differentiation grade can also be automatically calculated through above models. The differentiation grade was proven to stratify CAC patients well in survival analysis, whose optimized cut off point was almost the same as WHO tumor grading system. The tool of automatically calculating differentiation grade may show potential in field of therapy decision making and personalized treatment.

Keywords: colorectal cancer, differentiation, survival analysis, tumor grading

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Authors: Sara T. K. Li, Belinda H. J. Chung, Jeffery C. N. Yip, Janet H. Hsiao

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Music notation and text reading both involve statistical learning of music or linguistic structures. However, it remains unclear how music reading expertise influences text reading behavior. The present study examined this issue through an eye-tracking study. Chinese-English bilingual musicians and non-musicians read English sentences, Chinese sentences, musical phrases, and sentences in Tibetan, a language novel to the participants, with their eye movement recorded. Each set of stimuli consisted of two conditions in terms of structural regularity: syntactically correct and syntactically incorrect musical phrases/sentences. They then completed a sentence comprehension (for syntactically correct sentences) or a musical segment/word recognition task afterwards to test their comprehension/recognition abilities. The results showed that in reading musical phrases, as compared with non-musicians, musicians had a higher accuracy in the recognition task, and had shorter reading time, fewer fixations, and shorter fixation duration when reading syntactically correct (i.e., in diatonic key) than incorrect (i.e., in non-diatonic key/atonal) musical phrases. This result reflects their expertise in music reading. Interestingly, in reading Tibetan sentences, which was novel to both participant groups, while non-musicians did not show any behavior differences between reading syntactically correct or incorrect Tibetan sentences, musicians showed a shorter reading time and had marginally fewer fixations when reading syntactically correct sentences than syntactically incorrect ones. However, none of the musicians reported discovering any structural regularities in the Tibetan stimuli after the experiment when being asked explicitly, suggesting that they may have implicitly acquired the structural regularities in Tibetan sentences. This group difference was not observed when they read English or Chinese sentences. This result suggests that music reading expertise facilities reading texts in a novel language (i.e., Tibetan), but not in languages that the readers are already familiar with (i.e., English and Chinese). This phenomenon may be due to the similarities between reading music notations and reading texts in a novel language, as in both cases the stimuli follow particular statistical structures but do not involve semantic or lexical processing. Thus, musicians may transfer their statistical learning skills stemmed from music notation reading experience to implicitly discover structures of sentences in a novel language. This speculation is consistent with a recent finding showing that music reading expertise modulates the processing of English nonwords (i.e., words that do not follow morphological or orthographic rules) but not pseudo- or real words. These results suggest that the modulation of music reading expertise on language processing depends on the similarities in the cognitive processes involved. It also has important implications for the benefits of music education on language and cognitive development.

Keywords: eye movement behavior, eye-tracking, music reading expertise, sentence reading, structural regularity, visual processing

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Authors: Lik Hang Gary Wong

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Background: Practice learning is crucial for preparing the healthcare profession to meet the real challenge upon graduation. Students are required to demonstrate their competence in managing interpersonal challenges, such as teamwork with other professionals and communicating well with the service users, during the placement. Such competence precedes clinical practice, and it may eventually affect students' actual performance in a clinical context. Unfortunately, there were limited studies investigating how such competence affects students' performance in practice learning. Objectives: The aim of this study is to investigate how self-rated interpersonal competence affects students' actual performance during clinical placement. Methods: 40 occupational therapy students from Hong Kong were recruited in this study. Prior to the clinical placement (level two or above), they completed an online survey that included the Interpersonal Communication Competence Scale (ICCS) measuring self-perceived competence in interpersonal communication. Near the end of their placement, the clinical educator rated students’ performance with the Student Practice Evaluation Form - Revised edition (SPEF-R). The SPEF-R measures the eight core competency domains required for an entry-level occupational therapist. This study adopted the cross-sectional observational design. Pearson correlation and multiple regression are conducted to examine the relationship between students' interpersonal communication competence and their actual performance in clinical placement. Results: The ICCS total scores were significantly correlated with all the SPEF-R domains, with correlation coefficient r ranging from 0.39 to 0.51. The strongest association was found with the co-worker communication domain (r = 0.51, p < 0.01), followed by the information gathering domain (r = 0.50, p < 0.01). Regarding the ICCS total scores as the independent variable and the rating in various SPEF-R domains as the dependent variables in the multiple regression analyses, the interpersonal competence measures were identified as a significant predictor of the co-worker communication (R² = 0.33, β = 0.014, SE = 0.006, p = 0.026), information gathering (R² = 0.27, β = 0.018, SE = 0.007, p = 0.011), and service provision (R² = 0.17, β = 0.017, SE = 0.007, p = 0.020). Moreover, some specific communication skills appeared to be especially important to clinical practice. For example, immediacy, which means whether the students were readily approachable on all social occasions, correlated with all the SPEF-R domains, with r-values ranging from 0.45 to 0.33. Other sub-skills, such as empathy, interaction management, and supportiveness, were also found to be significantly correlated to most of the SPEF-R domains. Meanwhile, the ICCS scores correlated differently with the co-worker communication domain (r = 0.51, p < 0.01) and the communication with the service user domain (r = 0.39, p < 0.05). It suggested that different communication skill sets would be required for different interpersonal contexts within the workplace. Conclusion: Students' self-perceived interpersonal communication competence could predict their actual performance during clinical placement. Moreover, some specific communication skills were more important to the co-worker communication but not to the daily interaction with the service users. There were implications on how to better prepare the students to meet the future challenge upon graduation.

Keywords: interpersonal competence, clinical education, healthcare professional education, occupational therapy, occupational therapy students

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Authors: Norhan El Dallal

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The MENA region is a challenging, rapid urbanizing region, with a special profile; culturally, socially, economically and environmentally. Despite the diversity between different countries of the MENA region they all share similar urban challenges where extensive interventions are crucial. A climate sensitive region as the MENA region requires special attention for development, adaptation and mitigation. Integrating climatic and environmental parameters into the planning process to create a responsive urban form is the aim of this research in which “Parametric Urbanism” as a trend serves as a tool to reach a more sustainable urban morphology. An attempt to parameterize the relation between the climate and the urban form in a detailed manner is the main objective of the thesis. The aim is relating the different passive approaches suitable for the MENA region with the design guidelines of each and every part of the planning phase. Various conceptual scenarios for the network pattern and block subdivision generation based on computational models are the next steps after the parameterization. These theoretical models could be applied on different climatic zones of the dense communities of the MENA region to achieve an energy efficient neighborhood or city with respect to the urban form, morphology, and urban planning pattern. A final criticism of the theoretical model is to be conducted showing the feasibility of the proposed solutions economically. Finally some push and pull policies are to be proposed to help integrate these solutions into the planning process.

Keywords: parametric urbanism, climate responsive, urban form, urban and regional studies

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Authors: Obasa Joshua Ifeoluwa

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Phonetics is the foundation and most important part of language learning. This article, through an acoustic experiment as well as using Praat software, uses Yoruba students’ Chinese consonants, vowels, and tones pronunciation to carry out a visual comparison with that of native Chinese speakers. This article is aimed at Yoruba native speakers learning Chinese phonetics; therefore, Yoruba students are selected. The students surveyed are required to be at an elementary level and have learned Chinese for less than six months. The students selected are all undergraduates majoring in Chinese Studies at the University of Lagos. These students have already learned Chinese Pinyin and are all familiar with the pinyin used in the provided questionnaire. The Chinese students selected are those that have passed the level two Mandarin proficiency examination, which serves as an assurance that their pronunciation is standard. It is discovered in this work that in terms of Mandarin’s consonants pronunciation, Yoruba students cannot distinguish between the voiced and voiceless as well as the aspirated and non-aspirated phonetics features. For instance, while pronouncing [ph] it is clearly shown in the spectrogram that the Voice Onset Time (VOT) of a Chinese speaker is higher than that of a Yoruba native speaker, which means that the Yoruba speaker is pronouncing the unaspirated counterpart [p]. Another difficulty is to pronounce some affricates like [tʂ]、[tʂʰ]、[ʂ]、[ʐ]、 [tɕ]、[tɕʰ]、[ɕ]. This is because these sounds are not in the phonetic system of the Yoruba language. In terms of vowels, some students find it difficult to pronounce some allophonic high vowels such as [ɿ] and [ʅ], therefore pronouncing them as their phoneme [i]; another pronunciation error is pronouncing [y] as [u], also as shown in the spectrogram, a student pronounced [y] as [iu]. In terms of tone, it is most difficult for students to differentiate between the second (rising) and third (falling and rising) tones because these tones’ emphasis is on the rising pitch. This work concludes that the major error made by Yoruba students while pronouncing Chinese sounds is caused by the interference of their first language (LI) and sometimes by their lingua franca.

Keywords: Chinese, Yoruba, error analysis, experimental phonetics, consonant, vowel, tone

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Authors: Folasade R. Sulaiman

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Pupils’ health and nutrition are basically important to their schooling. The preponderance of avoidable deaths among children in Africa (WHO, 2000) may not be unconnected with the nutritional and health education status of families and communities that have their children as school clients. This study adopted a descriptive survey design focusing on the assessment of the level of nutritional and health education of families and community members in the rural riverine areas of Ogun State. Two research questions were raised. The Nutritional and Health Education of Families and Communities Inventory (NHEFCI) was used to collect data from 250 rural child-bearing aged women, and 0.73 test-retest reliability coefficient was established to determine the strength of the instrument. Data collected were analysed using descriptive statistics of frequency counts, percentages and mean in accordance with research questions raised in the study. The findings revealed amongst others: that 65% of the respondents had low level of nutritional and health education among the families and community members; while 72% had low level of awareness of the possible influence of nutritional and health education on the learning outcomes of the children. Based on the findings, it was recommended among others that government should intensify efforts on sensitization, mass literacy campaign etc.; also improve upon the already existing School Feeding Programme in Nigerian primary schools to provide at least one balanced diet for children while in school; community health workers, social workers, Non-Governmental Organizations (NGO) should collaborate with international Organizations like UNICEF, UNESCO, WHO etc. to organize sensitization programmes for members of the rural riverine communities on the importance of meeting the health and nutritional needs of their children in order to attain their educational potentials.

Keywords: nutritional and health education, learning capacities, school-age children, universal basic education, rural riverine areas

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Authors: Joyce Joas Kahembe

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The implementation of education assessment reforms in developing countries has been claimed to be problematic and difficult. The socio-economic teaching and learning environment has pointed to constraints in the education reform process. Nevertheless, there are existing assessment practices that if enhanced, can have potential to foster formative assessment practices in those contexts. The present study used the sociocultural perspective to explore teachers’ assessment practices and factors influencing them in Tanzania. Specifically, the sociocultural perspective helped to trace social, economic and political histories imparted to teachers’ assessment practices. The ethnographic oriented methods like interviews, observations and document reviews was used in this exploration. Teachers used assessment practices, such as questioning and answering, tests, assignments and examinations, for evaluating, monitoring and diagnosing students’ understanding, achievement and performance and standards and quality of instruction practices. The obtained assessment information functioned as feedback for improving students’ understanding, performance, and the standard and quality of teaching instruction and materials. For example, teachers acknowledged, praised, approved, disapproved, denied, graded, or marked students’ responses to give students feedback and aid learning. Moreover, teachers clarified and corrected or repeated students’ responses with worded/added words to improve students’ mastery of the subject content. Teachers’ assessment practices were influenced by the high demands of passing marks in the high stakes examinations and the contexts of the social economic teaching environment. There is a need to ally education assessment reforms with existing socio-economic teaching environments and society and institutional demands of assessment to make assessment reforms meaningful and sustainable. This presentation ought to contribute on ongoing strategies for contextualizing assessment practices for formative uses.

Keywords: assessment, feedback, practices, formative assessment

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Authors: L. Bendifallah, S. Doumandji, K. Louadi, S. Iserbyt, F. Acheuk

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Due to their ecological key position and diversity, plant-bee relationships constitute excellent models to understand the processes of food specialisation. The purpose of this study is to define and identify the most important species of bees foraging broadbean flowers, we estimated morphological, phonological and behavioural features. We discuss the results by considering the food specialisation level of the visitor. In the studied populations (Algiers, Algeria), visiting bees belong to four different genus: Apis, Andrena, Eucera and Xylocopa. Eucera is foraging broad beans flowers during months of April, May. The genus Andrena and Xylocopa were found on weeds after the flowering period of beans. The two species have not a preferred type of vegetation compared to Eucera. The main pollinators were generalist bees such as Apis mellifera L. and Xylocopa pubescens Spinola (Apidae), and specialist bees such Eucera numida Lep. (Apidae). The results show that no one of the studied species, neither the specialist, nor the generalist ones, share adaptative morphological or behavioural features that may improve foraging on Vicia faba. However, there is a narrow synchronisation between the daily and yearly phenologies of Eucera numida and those of V. faba. This could be an adaptation of the specialist bee to its host plant. Thus, the food specialisation of Eucera numida, as for most specialist bees, would be more linked to its adapted phenology than to an adapted morphology.

Keywords: Vicia faba, bees, pollinators, Algeria

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Talar Agopian

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Teaching English as a Second Language (ESL) is a common practice in many Lebanese schools. However, ESL teaching is done in traditional ways. Methods such as constructivism are seldom used, especially in villages. Here lies the significance of this research which joins constructivism and Piaget’s theory of cognitive development in ESL classes in Lebanese villages. The purpose of the present study is to explore the effects of applying constructivist student-centered strategies in teaching grammar, reading comprehension, and poetry on students in elementary ESL classes in two villages in Lebanon, Zefta in South Lebanon and Boqaata in Mount Lebanon. 20 English teachers participated in a training titled “Teaching English with Fun”, which focused on strategies that create a student-centered class where active learning takes place and there is increased learner engagement and autonomy. The training covered three main areas in teaching English: grammar, reading comprehension, and poetry. After participating in the training, the teachers applied the new strategies and methods in their ESL classes. The methodology comprised two phases: in phase one, practice-based research was conducted as the teachers attended the training and applied the constructivist strategies in their respective ESL classes. Phase two included the reflections of the teachers on the effects of the application of constructivist strategies. The results revealed the educational benefits of constructivist student-centered strategies; the students of teachers who applied these strategies showed improved engagement, positive attitudes towards poetry, increased motivation, and a better sense of autonomy. Future research is required in applying constructivist methods in the areas of writing, spelling, and vocabulary in ESL classrooms of Lebanese villages.

Keywords: active learning, constructivism, learner engagement, student-centered strategies

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: Rupamoni Thakur, Madhusmita Dehingia, Narayan C. Talukdar, Mojibur R. Khan

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The human gut is an extremely active fermentation site and is inhabited by diverse bacterial species. Consumption of alcoholic beverages has been shown to substantially modulate the human gut bacterial profile (GBP) of an individual. Assam, a major north-eastern state of India, is home to a number of tribal populations of which the tea-tribes form a major community. These tea-tribal communities are known to prepare and consume a locally-prepared alcoholic beverage from fermented jaggery, whose chemical composition is unknown. In this study, we demonstrate the effect of daily intake of the locally-prepared alcoholic beverage on the GBP of the tea-tribal communities and correlate it with the changes in the biochemical biomarkers of the population. The fecal bacterial diversity of 40 drinkers and 35 non-drinking healthy individuals were analyzed by polymerase chain reaction (PCR)–denaturing gradient gel electrophoresis (DGGE). The results suggested that the GBP was significantly modulated in the fermented-beverage consuming subjects. Significant difference was also observed in the serum biochemical parameters such as triglyceride, total cholesterol and the liver marker enzymes (ASAT/ALAT and GGT). Further studies to identify the GBP of drinkers vs non-drinkers through Next-generation Sequencing (NGS) analysis and to correlate the changes with the biochemical biomarkers of the population is underway.

Keywords: alcoholic beverage, gut bacterial profile, PCR-DGGE analysis, tea-tribes of India

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Authors: Mehrdad Kamali-Sanzighi, Maziar Kamali-Sanzighi

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The aim of this research was to study zooplankton community density, diversity and Saprobi index in carp ponds at Golestan province, Gonbade Kavous city, Iran. Zooplankton sampling was done monthly in each pond during one carp culture time. Our analysis showed 27 genus from 4 groups (Protozoa 12, Rotatoria 8, Copepoda 4 and Cladocera 3). The highest and lowest frequency of zooplankton groups were belongs to Rotatoria, Copepoda, Cladocera and Protozoa with 46, 28, 23 and 3 percent, respectively. No significant differences between saprobi index of six carp ponds (P>0.05) were observed. Saprobi index indicated Class ßmesosaprob for six analysis ponds. There was a general tendency to decrease and significantly in the saprobi index with the value range of 1.52-1.70 from the beginning to end of the culture season (P<0.05). Also, gradual improvement of water quality observed toward the end of culture period and these reasons are partly a result of natural and management processed such as seasons changes (climate), water exchange (replacement of water) and pause of introduce of fertilizer materials to the ponds. According to the ability of saprobi index in monitoring of water quality condition and health of different water resources, focus to similar kind of effective research is necessary in future time.

Keywords: zooplankton, saprobi pollution index, water quality, fish pond, east of Golestan Province

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Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

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Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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Authors: Karolina Wieczorek, Zofia Przypaśniak

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Climate change is a rapidly growing threat to global health, and part of the responsibility to combat it lies within the healthcare sector itself, including adequate education of future medical professionals. To mitigate the consequences, the General Medical Council (GMC) has equipped medical schools with a list of outcomes regarding sustainability teaching. Students are expected to analyze the impact of the healthcare sector’s emissions on climate change. The delivery of the related teaching content is, however, often inadequate and insufficient time is devoted for exploration of the topics. Teaching curricula lack in-depth exploration of the learning objectives. This study aims to assess the extent and characteristics of climate change and sustainability subjects teaching in the curriculum of a chosen UK medical school (Barts and The London School of Medicine and Dentistry). It compares the data to the national average scores from the Climate Change and Sustainability Teaching (C.A.S.T.) in Medical Education Audit to draw conclusions about teaching on a regional level. This is a single-center audit of the timetabled sessions of teaching in the medical course. The study looked at the academic year 2020/2021 which included a review of all non-elective, core curriculum teaching materials including tutorials, lectures, written resources, and assignments in all five years of the undergraduate and graduate degrees, focusing only on mandatory teaching attended by all students (excluding elective modules). The topics covered were crosschecked with GMC Outcomes for graduates: “Educating for Sustainable Healthcare – Priority Learning Outcomes” as gold standard to look for coverage of the outcomes and gaps in teaching. Quantitative data was collected in form of time allocated for teaching as proxy of time spent per individual outcomes. The data was collected independently by two students (KW and ZP) who have received prior training and assessed two separate data sets to increase interrater reliability. In terms of coverage of learning outcomes, 12 out of 13 were taught (with the national average being 9.7). The school ranked sixth in the UK for time spent per topic and second in terms of overall coverage, meaning the school has a broad range of topics taught with some being explored in more detail than others. For the first outcome 4 out of 4 objectives covered (average 3.5) with 47 minutes spent per outcome (average 84 min), for the second objective 5 out of 5 covered (average 3.5) with 46 minutes spent (average 20), for the third 3 out of 4 (average 2.5) with 10 mins pent (average 19 min). A disproportionately large amount of time is spent delivering teaching regarding air pollution (respiratory illnesses), which resulted in the topic of sustainability in other specialties being excluded from teaching (musculoskeletal, ophthalmology, pediatrics, renal). Conclusions: Currently, there is no coherent strategy on national teaching of climate change topics and as a result an unstandardized amount of time spent on teaching and coverage of objectives can be observed.

Keywords: audit, climate change, sustainability, education

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Authors: Łukasz Bis

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The author is going to discuss virtual reality and its importance for new didactic methods. It has been known for years that experience-based education gives much better results in terms of long-term memory than theoretical study. However, practice is expensive - virtual reality allows the use of an empirical approach to learning, with minimized production costs. The author defines what makes a given VR experience appropriate (adequate) for the didactic and cognitive process. The article is a kind of a list of guidelines and their importance for the VR experience under development.

Keywords: virtual reality, education, universal design, guideline

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