Search results for: teaching and learning empathy

Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Sulemana Ibrahim

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Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

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Authors: Leah Burton, Sara Cumming, Julianne DiSanto

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Homelessness is a widespread and complex problem in Canada and around the globe. Many Canadians will face homelessness at least once in their lifetime, with several experiencing subsequent bouts or cyclical patterns of housing precarity. While a Housing First approach to homelessness is a long-standing and widely accepted best practice, it is also recognized that the acquisition of life skills is an effective way to reduce cycles of homelessness. Indeed, when individuals are provided with a range of life skills—such as (but not limited to) financial literacy, household management, interpersonal skills, critical thinking, and resource management—they are given the tools required to maintain long-term Housing for a lifetime; thus reducing a repetitive need for services. However, there is limited research regarding the best ways to teach life skills, a problem that has been further complicated in a post-pandemic world, where services are being delivered online or in a hybrid model of care. More than this, it is difficult to provide life skills on a large scale without losing a client-centered approach to services. This lack of client-centeredness is also seen in the lack of attention to culturally sensitive life skills, which consider the diverse needs of individuals and imbed equity, diversity, and inclusion (EDI) within the skills being taught. This study aims to fill these identified gaps in the literature by employing a community-engaged (CER) approach. Academic, government, funders, front-line staff, and clients at 15 not-for-profits from across the Greater Toronto Area in Ontario, Canada, collaborated to co-create a virtual, client-centric, EDI-informed life skill learning management system. A triangulation methodology was utilized for this research. An environmental scan was conducted for current best practices, and over 100 front-line staff (including workers, managers, and executive directors who work with homeless populations) participated in two separate Creative Problem Solving Sessions. Over 200 individuals with experience in homelessness completed quantitative and open-ended surveys. All sections of this research aimed to discover the areas of skills that individuals need to maintain Housing and to ascertain what a more client-driven EDI approach to life skills training should include. This presentation will showcase the findings on which life skills are deemed essential for homeless and precariously housed individuals.

Keywords: homelessness, housing first, life skills, community engaged research, client- centered

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Authors: Nizamuddin Zainuddin, Adam Mohd Saifudin, Ahmad Yusni Bahaudin, Mohd Hanizan Zalazilah, Roslan Jamaluddin

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Students’ enhanced experience through simulation is a crucial factor that brings reality to the classroom. The enhanced experience is all about developing, enriching and applications of a generic process flow in the field of logistics and transportations. As educational technology has improved, the effective use of simulations has greatly increased to the point where simulations should be considered a valuable, mainstream pedagogical tool. Additionally, in this era of ongoing (some say never-ending) assessment, simulations offer a rich resource for objective measurement and comparisons. Simulation is not just another in the long line of passing fads (or short-term opportunities) in educational technology. It is rather a real key to helping our students understand the world. It is a way for students to acquire experience about how things and systems in the world behave and react, without actually touching them. In short, it is about interactive pretending. Simulation is all about representing the real world which includes grasping the complex issues and solving intricate problems. Therefore, it is crucial before stimulate the real process of inbound and outbound logistics and transportation a generic process flow shall be developed. The paper will be focusing on the validization of the process flow by looking at the inputs gains from the sample. The sampling of the study focuses on multi-national and local manufacturing companies, third party companies (3PL) and government agency, which are selected in Peninsular Malaysia. A simulation flow chart was proposed in the study that will be the generic flow in logistics and transportation. A qualitative approach was mainly conducted to gather data in the study. It was found out from the study that the systems used in the process of outbound and inbound are System Application Products (SAP) and Material Requirement Planning (MRP). Furthermore there were some companies using Enterprises Resources Planning (ERP) and Electronic Data Interchange (EDI) as part of the Suppliers Own Inventories (SOI) networking as a result of globalized business between one countries to another. Computerized documentations and transactions were all mandatory requirement by the Royal Custom and Excise Department. The generic process flow will be the basis of developing a simulation program that shall be used in the classroom with the objective of further enhanced the students’ learning experience. Thus it will contributes to the body of knowledge on the enrichment of the student’s employability and also shall be one of the way to train new workers in the logistics and transportation filed.

Keywords: enhancement, simulation, process flow, logistics, transportation

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Authors: Malihe Aliasgari, Yousef Nejatbakhsh

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The era of Big Data and the immensity of real-life datasets compels computation tasks to be performed in a distributed fashion, where the data is dispersed among many servers that operate in parallel. However, massive parallelization leads to computational bottlenecks due to faulty servers and stragglers. Stragglers refer to a few slow or delay-prone processors that can bottleneck the entire computation because one has to wait for all the parallel nodes to finish. The problem of straggling processors, has been well studied in the context of distributed computing. Recently, it has been pointed out that, for the important case of linear functions, it is possible to improve over repetition strategies in terms of the tradeoff between performance and latency by carrying out linear precoding of the data prior to processing. The key idea is that, by employing suitable linear codes operating over fractions of the original data, a function may be completed as soon as enough number of processors, depending on the minimum distance of the code, have completed their operations. The problem of matrix-matrix multiplication in the presence of practically big sized of data sets faced with computational and memory related difficulties, which makes such operations are carried out using distributed computing platforms. In this work, we study the problem of distributed matrix-matrix multiplication W = XY under storage constraints, i.e., when each server is allowed to store a fixed fraction of each of the matrices X and Y, which is a fundamental building of many science and engineering fields such as machine learning, image and signal processing, wireless communication, optimization. Non-secure and secure matrix multiplication are studied. We want to study the setup, in which the identity of the matrix of interest should be kept private from the workers and then obtain the recovery threshold of the colluding model, that is, the number of workers that need to complete their task before the master server can recover the product W. The problem of secure and private distributed matrix multiplication W = XY which the matrix X is confidential, while matrix Y is selected in a private manner from a library of public matrices. We present the best currently known trade-off between communication load and recovery threshold. On the other words, we design an achievable PSGPD scheme for any arbitrary privacy level by trivially concatenating a robust PIR scheme for arbitrary colluding workers and private databases and the proposed SGPD code that provides a smaller computational complexity at the workers.

Keywords: coded distributed computation, private information retrieval, secret sharing, stragglers

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Authors: Chana Pavneet

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Background: In more recent years in dentistry, a greater emphasis has been placed on competency-based education (CBE) programmes. Undergraduate and postgraduate curriculums have been reformed to reflect these changes, and adopting a CBE approach has shown to be beneficial to trainees and places an emphasis on continuous lifelong learning. The literature is vast; however, very little work has been done specifically to the assessment of competence in dentistry and even less so in oral surgery. The majority of the literature tends to opinion pieces. Some small-scale studies have been undertaken in this area researching assessment tools which can be used to assess competence in oral surgery. However, there is a lack of general consensus on the preferable assessment methods. The aim of this review is to identify the assessment methods available and their usefulness. Methods: Electronic databases (Medline, Embase, and the Cochrane Database of systematic reviews) were searched. PRISMA guidelines were followed to identify relevant papers. Abstracts of studies were reviewed, and if they met the inclusion criteria, they were included in the review. Papers were reviewed against the critical appraisal skills programme (CASP) checklist and medical education research quality instrument (MERQSI) to assess their quality and identify any bias in a systematic manner. The validity and reliability of each assessment method or tool were assessed. Results: A number of assessment methods were identified, including self-assessment, peer assessment, and direct observation of skills by someone senior. Senior assessment tended to be the preferred method, followed by self-assessment and, finally, peer assessment. The level of training was shown to affect the preferred assessment method, with one study finding peer assessment more useful in postgraduate trainees as opposed to undergraduate trainees. Numerous tools for assessment were identified, including a checklist scale and a global rating scale. Both had their strengths and weaknesses, but the evidence was more favourable for global rating scales in terms of reliability, applicability to more clinical situations, and easier to use for examiners. Studies also looked into trainees’ opinions on assessment tools. Logbooks were not found to be significant in measuring the competence of trainees. Conclusion: There is limited literature exploring the methods and tools which assess the competence of oral surgery trainees. Current evidence shows that the most favourable assessment method and tool may differ depending on the stage of training. More research is required in this area to streamline assessment methods and tools.

Keywords: competence, oral surgery, assessment, trainees, education

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Authors: Păcurar Diana Istina

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The indissoluble link between aesthetics and semiotics, the harmonization and semiotic understanding of the interactions between the viewer and the object being looked at, are the basis of the practical demonstration of the importance of aesthetic perception within the theater performance. The design of a theater performance includes several structures, some considered from the beginning, art forms (i.e., the text), others being represented by simple, common objects (e.g., scenographic elements), which, if reunited, can trigger a certain aesthetic perception. The audience is delivered, by the team involved in the performance, a series of auditory and visual signs with which they interact. It is necessary to explain some notions about the physiological support of the transformation of different types of stimuli at the level of the cerebral hemispheres. The cortex considered the superior integration center of extransecal and entanged stimuli, permanently processes the information received, but even if it is delivered at a constant rate, the generated response is individualized and is conditioned by a number of factors. Each changing situation represents a new opportunity for the viewer to cope with, developing feelings of different intensities that influence the generation of meanings and, therefore, the management of interactions. In this sense, aesthetic perception depends on the detection of the “correctness” of signs, the forms of which are associated with an aesthetic property. Fairness and aesthetic properties can have positive or negative values. Evaluating the emotions that generate judgment and implicitly aesthetic perception, whether we refer to visual emotions or auditory emotions, involves the integration of three areas of interest: Valence, arousal and context control. In this context, superior human cognitive processes, memory, interpretation, learning, attribution of meanings, etc., help trigger the mechanism of anticipation and, no less important, the identification of error. This ability to locate a short circuit produced in a series of successive events is fundamental in the process of forming an aesthetic perception. Our main purpose in this research is to investigate the possible conditions under which aesthetic perception and its minimum content are generated by all these structures and, in particular, by interactions with forms that are not commonly considered aesthetic forms. In order to demonstrate the quantitative and qualitative importance of the categories of signs used to construct a code for reading a certain message, but also to emphasize the importance of the order of using these indices, we have structured a mathematical analysis that has at its core the analysis of the percentage of signs used in a theater performance.

Keywords: semiology, aesthetics, theatre semiotics, theatre performance, structure, aesthetic perception

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: N. Mahon, I. Crute, M. Di Bonito, E. Simmons, M. M. Islam

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Growing interest in the concept of the sustainable intensification (SI) of agriculture has been shown by, national governments, transnational agribusinesses, intergovernmental organizations and research institutes, amongst others. This interest may be because SI is seen as a ‘third way’ for agricultural development, between the seemingly disparate paradigms of ‘intensive’ agriculture and more ‘sustainable’ forms of agriculture. However, there is a lack of consensus as to what SI means in practice and how it should be measured using indicators of change. This has led to growing confusion, disagreement and skepticism regarding the concept, especially amongst civil society organizations, both in the UK and other countries. This has prompted the need for bottom-up, participatory approaches to identify indicators of SI. Our aim is to identify the views of British stakeholders regarding the areas of agreement and disagreement as to what SI is and how it should be measured in the UK using indicators of change. Data for this investigation came from 32 semi-structured interviews, conducted between 2015 and 2016, with stakeholders from throughout the UK food system. In total 110 indicators of SI were identified. These indicators covered a wide variety of subjects including biophysical, social and political considerations. A number of indicators appeared to be widely applicable and were similar to those suggested in the global literature. These include indicators related to the management of the natural resources on which agriculture relies e.g., ‘Soil organic matter’, ‘Number of pollinators per hectare’ and ‘Depth of water table’. As well as those related to agricultural externalities, e.g., ‘Greenhouse gas emissions’ and ‘Concentrations of agro-chemicals in waterways’. However, many of the indicators were much more specific to the context of the UK. These included, ‘Areas of high nature value farmland’, ‘Length of hedgerows per hectare’ and ‘Age of farmers’. Furthermore, tensions could be seen when participants considered the relative importance of agricultural mechanization versus levels of agricultural employment, the pros and cons of intensive, housed livestock systems and value of wild biodiversity versus the desire to increase agricultural yields. These areas of disagreement suggest the need to carefully consider the trade-offs inherent in the concept. Our findings indicate that in order to begin to resolve the confusions surrounding SI it needs to be considered in a context specific manner, rather than as a single uniform concept. Furthermore, both the environmental and the social parameters in which agriculture operates need to be considered in order to operationalize SI in a meaningful way. We suggest that participatory approaches are key to this process, facilitating dialogue and collaborative-learning between all the stakeholders, allowing them to reach a shared vision for the future of agricultural development.

Keywords: agriculture, indicators, participatory approach, sustainable intensification

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Authors: Henry Memczak, Marc Hovestaedt, Bernhard Ay, Sandra Saenger, Thorsten Wolff, Frank F. Bier

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The influenza viruses cause flu epidemics every year and serious pandemics in larger time intervals. The only cost-effective protection against influenza is vaccination. Due to rapid mutation continuously new subtypes appear, what requires annual reimmunization. For a correct vaccination recommendation, the circulating influenza strains had to be detected promptly and exactly and characterized due to their antigenic properties. During the flu season 2016/17, a wrong vaccination recommendation has been given because of the great time interval between identification of the relevant influenza vaccine strains and outbreak of the flu epidemic during the following winter. Due to such recurring incidents of vaccine mismatches, there is a great need to speed up the process chain from identifying the right vaccine strains to their administration. The monitoring of subtypes as part of this process chain is carried out by national reference laboratories within the WHO Global Influenza Surveillance and Response System (GISRS). To this end, thousands of viruses from patient samples (e.g., throat smears) are isolated and analyzed each year. Currently, this analysis involves complex and time-intensive (several weeks) animal experiments to produce specific hyperimmune sera in ferrets, which are necessary for the determination of the antigen profiles of circulating virus strains. These tests also bear difficulties in standardization and reproducibility, which restricts the significance of the results. To replace this test a peptide-based assay for influenza virus subtyping from corresponding virus samples was developed. The differentiation of the viruses takes place by a set of specifically designed peptidic recognition molecules which interact differently with the different influenza virus subtypes. The differentiation of influenza subtypes is performed by pattern recognition guided by machine learning algorithms, without any animal experiments. Synthetic peptides are immobilized in multiplex format on various platforms (e.g., 96-well microtiter plate, microarray). Afterwards, the viruses are incubated and analyzed comparing different signaling mechanisms and a variety of assay conditions. Differentiation of a range of influenza subtypes, including H1N1, H3N2, H5N1, as well as fine differentiation of single strains within these subtypes is possible using the peptide-based subtyping platform. Thereby, the platform could be capable of replacing the current antigenic characterization of influenza strains using ferret hyperimmune sera.

Keywords: antigenic characterization, influenza-binding peptides, influenza subtyping, influenza surveillance

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Authors: Jared R. Chapman

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This study introduces a type of motivational information system called an educational engagement information system (EEIS). An EEIS draws on principles of behavioral economics, motivation theory, and learning cognition theory to design information systems that help students want to improve their performance. This study compares academic outcomes for course sections taught by part- and full-time instructors both with and without an EEIS. Without an EEIS, students in the part-time instructor's course sections demonstrated significantly higher failure rates (a 143.8% increase) and dropout rates (a 110.4% increase) with significantly fewer students scoring a B- or higher (39.8% decrease) when compared to students in the course sections taught by a full-time instructor. It is concerning that students in the part-time instructor’s course without an EEIS had significantly lower academic outcomes, suggesting less understanding of the course content. This could impact retention and continuation in a major. With an EEIS, when comparing part- and full-time instructors, there was no significant difference in failure and dropout rates or in the number of students scoring a B- or higher in the course. In fact, with an EEIS, the failure and dropout rates were statistically identical for part- and full-time instructor courses. When using an EEIS (compared with not using an EEIS), the part-time instructor showed a 62.1% decrease in failures, a 61.4% decrease in dropouts, and a 41.7% increase in the number of students scoring a B- or higher in the course. We are unaware of other interventions that yield such large improvements in academic performance. This suggests that using an EEIS such as Delphinium may compensate for part-time instructors’ limitations of expertise, time, or rewards that can have a negative impact on students’ academic outcomes. The EEIS had only a minimal impact on failure rates (7.7% decrease) and dropout rates (18.8% decrease) for the full-time instructor. This suggests there is a ceiling effect for the improvements that an EEIS can make in student performance. This may be because experienced instructors are already doing the kinds of things that an EEIS does, such as motivating students, tracking grades, and providing feedback about progress. Additionally, full-time instructors have more time to dedicate to students outside of class than part-time instructors and more rewards for doing so. Using adjunct and other types of part-time instructors will likely remain a prevalent practice in higher education management courses. Given that using part-time instructors can have a negative impact on student graduation and persistence in a field of study, it is important to identify ways we can augment part-time instructors’ performance. We demonstrated that when part-time instructors use an EEIS, it can result in significantly lower students’ failure and dropout rates and an increase in the rate of students earning a B- or above; and bring their students’ performance to parity with the performance of students taught by a full-time instructor.

Keywords: gamification, engagement, motivation, academic outcomes

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Authors: Xining Wang

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After-school educational service has been proven that it could benefit children’s academic performance, socio-emotional skills, and physical health level. However, there is little research demonstrating parents’ perspectives on the choice of after-school educational service from a level of cross-cultural backgrounds. China and Ireland are typical representatives of collectivist countries (e.g., estimated individualism score is 20) and individualist countries (e.g., estimated individualism score is 70) according to Hofstede's cultural dimensions theory. Living in countries with distinguished cultural backgrounds, there is an evident discrepancy in parents’ attitudes towards domestic after-school education and parents’ motivations for choosing after-school educational services. Through conducting a semi-structured interview with 15 parents from China and 15 parents from Ireland, using thematic analysis software (ATLAS) to extract the key information, and applying a comparative approach to process data analysis; results present polarization of Chinese and Irish parents' perspectives and motivations on after-school educational service. For example, Chinese parents tend to view after-school education as a complement to school education. It is a service they purchased for their children to acquire extra knowledge and skills so that they could adapt to the highly competitive educational setting. Given the fact that children’s education is a priority for Chinese families, most parents believe that their children would succeed in the future through massive learning. This attitude reflects that Chinese parents are more likely to apply authoritarian parenting methods and having a strong expectations for their children. Conversely, Irish parents' choice of after-school educational service is a consideration that primarily based on their own situation, secondly, for their family. For instance, with the expansion of the labor market, there is a change in household structure. Irish mothers are more likely to seek working opportunities instead of looking after the family. Irish parents view that after-school educational service is an essential need for themselves and a beneficial component for their family due to the external pressure (e.g., the growing work intensity and extended working hours, increasing numbers of separated families, as well as parents’ pursuit of higher education and promotion). These factors are fundamental agents that encourage Irish parents to choose after-school educational services. To conclude, the findings could provide readers with a better understanding of parents’ disparate and contrasting perspectives on after-school educational services from a multi-culture level.

Keywords: after-school, China, family studies, Ireland, parents

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Authors: Tahamina Begum, Wan Nor Azlen Wan Mohamad, Faruque Reza, Wan Rosilawati Wan Rosli

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To assess auditory perception and cognitive function during pregnancy is necessary as the pregnant women need extra effort for attention mainly for their executive function to maintain their quality of life. This study aimed to investigate neural correlates of cognitive and behavioral processing during mid trimester pregnancy. Event-Related Potentials (ERPs) were studied by using 128-sensor net and PAS or COWA (controlled Oral Word Association), WCST (Wisconsin Card Sorting Test), RAVLTIM (Rey Auditory Verbal and Learning Test: immediate or interference recall, delayed recall (RAVLT DR) and total score (RAVLT TS) were tested for neuropsychology assessment. In total 18 subjects were recruited (n= 9 in each group; control and pregnant group). All participants of the pregnant group were within 16-27 (mid trimester) weeks gestation. Age and education matched control healthy subjects were recruited in the control group. Participants were given a standardized test of auditory cognitive function as auditory oddball paradigm during ERP study. In this paradigm, two different auditory stimuli (standard and target stimuli) were used where subjects counted silently only target stimuli with giving attention by ignoring standard stimuli. Mean differences between target and standard stimuli were compared across groups. N100 (auditory sensory ERP component) and P300 (auditory cognitive ERP component) were recorded at T3, T4, T5, T6, Cz and Pz electrode sites. An equal number of electrodes showed non-significantly shorter amplitude of N100 component (except significantly shorter at T3, P= 0.05) and non-significant longer latencies (except significantly longer latency at T5, P= 0.008) of N100 component in pregnant group comparing control. In case of P300 component, maximum electrode sites showed non-significantly higher amplitudes and equal number of sites showed non-significant shorter latencies in pregnant group comparing control. Neuropsychology results revealed the non-significant higher score of PAS, lower score of WCST, lower score of RAVLTIM and RAVLTDR in pregnant group comparing control. The results of N100 component and RAVLT scores concluded that auditory perception is mildly impaired and P300 component proved very mild cognitive dysfunction with good executive functions in second trimester of pregnancy.

Keywords: auditory perception, pregnancy, stimuli, trimester

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Authors: Mohkammad Nur Cahyadi, Hepi Hapsari Handayani, Agus Budi Raharjo, Ronny Mardianto, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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PDAM (local water company) determines customer charges by considering the customer's building or house. Charges determination significantly affects PDAM income and customer costs because the PDAM applies a subsidy policy for customers classified as small households. Periodic updates are needed so that pricing is in line with the target. A thorough customer survey in Surabaya is needed to update customer building data. However, the survey that has been carried out so far has been by deploying officers to conduct one-by-one surveys for each PDAM customer. Surveys with this method require a lot of effort and cost. For this reason, this research offers a technology called moblie mapping, a mapping method that is more efficient in terms of time and cost. The use of this tool is also quite simple, where the device will be installed in the car so that it can record the surrounding buildings while the car is running. Mobile mapping technology generally uses lidar sensors equipped with GNSS, but this technology requires high costs. In overcoming this problem, this research develops low-cost mobile mapping technology using a webcam camera sensor added to the GNSS and IMU sensors. The camera used has specifications of 3MP with a resolution of 720 and a diagonal field of view of 78⁰. The principle of this invention is to integrate four camera sensors, a GNSS webcam, and GPS to acquire photo data, which is equipped with location data (latitude, longitude) and IMU (roll, pitch, yaw). This device is also equipped with a tripod and a vacuum cleaner to attach to the car's roof so it doesn't fall off while running. The output data from this technology will be analyzed with artificial intelligence to reduce similar data (Cosine Similarity) and then classify building types. Data reduction is used to eliminate similar data and maintain the image that displays the complete house so that it can be processed for later classification of buildings. The AI method used is transfer learning by utilizing a trained model named VGG-16. From the analysis of similarity data, it was found that the data reduction reached 50%. Then georeferencing is done using the Google Maps API to get address information according to the coordinates in the data. After that, geographic join is done to link survey data with customer data already owned by PDAM Surya Sembada Surabaya.

Keywords: mobile mapping, GNSS, IMU, similarity, classification

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Authors: Belbaly Nassim

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Virtual teams (VT) initiatives, in which teams are geographically dispersed and communicate via modern computer-driven technologies, have attracted increasing attention from researchers and professionals. The growing need to examine how to balance and optimize VT is particularly important given the exposure experienced by companies when their employees encounter globalization and decentralization pressures to monitor VT performance. Hence, organization is regularly limited due to misalignment between the behavioral capabilities of the team’s dispersed competences and knowledge capabilities and how trust issues interplay and influence these VT dimensions and the effects of such exchanges. In fact, the future success of business depends on the extent to which VTs are managing efficiently their dispersed expertise, skills and knowledge to stimulate VT creativity. Transactive memory system (TMS) may enhance VT creativity using its three dimensons: knowledge specialization, credibility and knowledge coordination. TMS can be understood as a composition of both a structural component residing of individual knowledge and a set of communication processes among individuals. The individual knowledge is shared while being retrieved, applied and the learning is coordinated. TMS is driven by the central concept that the system is built on the distinction between internal and external memory encoding. A VT learns something new and catalogs it in memory for future retrieval and use. TMS uses the role of information technology to explain VT behaviors by offering VT members the possibility to encode, store, and retrieve information. TMS considers the members of a team as a processing system in which the location of expertise both enhances knowledge coordination and builds trust among members over time. We build on TMS dimensions to hypothesize the effects of specialization, coordination, and credibility on VT creativity. In fact, VTs consist of dispersed expertise, skills and knowledge that can positively enhance coordination and collaboration. Ultimately, this team composition may lead to recognition of both who has expertise and where that expertise is located; over time, the team composition may also build trust among VT members over time developing the ability to coordinate their knowledge which can stimulate creativity. We also assess the reciprocal relationship between TMS dimensions and VT creativity. We wish to use TMS to provide researchers with a theoretically driven model that is empirically validated through survey evidence. We propose that TMS provides a new way to enhance and balance VT creativity. This study also provides researchers insight into the use of TMS to influence positively VT creativity. In addition to our research contributions, we provide several managerial insights into how TMS components can be used to increase performance within dispersed VTs.

Keywords: virtual team creativity, transactive memory systems, specialization, credibility, coordination

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Authors: Paulina Ojeda, Damaris Estrella, Hector Rubio

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Insomnia is a disorder characterized by difficulty falling asleep, staying asleep or both. It negatively affects the life quality of people, it hinders the concentration, attention, memory, motor skills, among other abilities that complicate work or learning. Some studies show that women are more susceptible to insomnia. Medicine curricula usually involve a great deal of theoretical and memory content, especially in the early years of the course. The way to accredit a university course is to demonstrate the level of competence or acquired knowledge. In Mexico the most widely used form of measurement is written exams, with numerical scales results. The prevalence of sleep disorders in university students is usually high, so it is important to know if insomnia has an effect on school performance in men and women. A cross-sectional study was designed that included a probabilistic sample of 118 regular students from the School of Medicine of the Autonomous University of Yucatan, Mexico. All on legally age. The project was authorized by the School of Medicine and all the ethical implications of the case were monitored. Participants completed anonymously the following questionnaires: Pittsburgh Sleep Quality Index, Insomnia Severity Index, AUDIT test, epidemiological and clinical data. Academic performance was assessed by the average number of official grades earned on written exams, as well as the number of approved or non-approved courses. These data were obtained officially through the corresponding school authorities. Students with at least one unapproved course or average less than 70 were considered to be poor performers. With all courses approved and average between 70-79 as regular performance and with an average of 80 or higher as a good performance. Statistical analysis: t-Student, difference of proportions and ANOVA. 65 men with a mean age of 19.15 ± 1.60 years and 53 women of 18.98 ± 1.23 years, were included. 96% of the women and 78.46% of the men sleep in the family home. 16.98% of women and 18.46% of men consume tobacco. Most students consume caffeinated beverages. 3.7% of the women and 10.76% of the men complete criteria of harmful consumption of alcohol. 98.11% of the women and 90.76% of the men are perceived with poor sleep quality. Insomnia was present in 73% of women and 66% of men. Women had higher levels of moderate insomnia (p=0.02) compared to men and only one woman had severe insomnia. 50.94% of the women and 44.61% of the men had poor academic performance. 18.86% of women and 27% of men performed well. Only in the group of women we found a significant association between poor performance with mild (p= 0.0035) and moderate (p=0.031) insomnia. The medical students reported poor sleep quality and insomnia. In women, levels of insomnia were associated with poor academic performance.

Keywords: scholar-average, sex, sleep, university

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Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: M. Joris, O. Agirdag

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For over two decades, calls for citizenship education (CE) have been rising to the top of educational policy agendas in Europe. The main motive for the current treatment of CE as a key topic is a sense of crisis: social and political threats that go beyond the reach of nations and require action at the international and European level. On the one hand, this context has triggered abundant attention to the promotion of citizenship through education. On the other hand, the ubiquity of citizenship and education in policy language is paired with a self-evident manner of using the concepts: the more we call for citizenship in and through education, the less the concepts seem to be made explicit or be defined. Research and reflection on the normativity of the concepts of citizenship and CE in Europe are scarce. Departing from the idea that policies are always normative, this study, therefore, investigates the normativity of the current concepts of citizenship and education, in ’key’ European CE policy texts. The study consists of a content analysis of these texts, based on a normative framework developed around the different dimensions of citizenship as status, identity, virtues and agency. The framework also describes the purposes of education and its learning processes, content and practices, based on the assumption that good education always includes, next to qualification and socialisation, a purpose of emancipation: of helping young people become autonomous and independent subjects. The analysis shows how contemporary European citizenship is conceptualised around the dimension of competences. This focus on competences is also visible in the normative framing of education and its relationship to citizenship in the texts: CE should help young people learn how to become good citizens by acquiring a toolkit of competences, consisting of knowledge, skills, values and attitudes that can be predetermined, measured and evaluated. This ideal of citizenship-as-competence entails a focus on the educational purposes of socialisation and qualification. Current policy texts thus seem to leave out the educational purpose of emancipating young people, allowing them to take on citizenship as something to which they can determine their own relation and position. It is, however, this purpose of CE that seems increasingly important in our current context. Young people are stepping out of school and onto the streets by the thousands in Belgium and throughout Europe, protesting for more and better environmental policies. They are making use of existing modes of citizenship, exactly to indicate to policymakers how these are falling short and are claiming their right and entitlement to a future that established practices of politics are putting at risk. The importance of citizenship education might then lie, now more than ever, not in the fact that it would prepare young people for competent citizenship, but in offering them a possibility, an emancipatory experience of being able to do something new. It seems that this is what we might want to expect from the school if we want it to educate our truly future citizens.

Keywords: citizenship education, normativity, policy, purposes of education

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Authors: Kais Amir Kadhim

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Context: The study focuses on the rhetorical structure of chapters in engineering final year projects, specifically the Introduction chapter, written by UMT (University of Marine Technology) engineering students. Existing research has explored the use of genre-based approaches to analyze the writing of final year projects in various disciplines. Research Aim: The aim of this study is to investigate the rhetorical structure of Introduction chapters in engineering final year projects by UMT students. The study aims to identify the frequency of communicative moves and their constituent steps within the Introduction chapters, as well as understand how students justify their research projects. Methodology: The research design will utilize a mixed method approach, combining both quantitative and qualitative methods. Forty Introduction chapters from two different fields in UMT engineering undergraduate programs will be selected for analysis. Findings: The study intends to identify the types of moves present in the Introduction chapters of engineering final year projects by UMT students. Additionally, it aims to determine if these moves and steps are obligatory, conventional, or optional. Theoretical Importance: The study draws upon Bunton's modified CARS (Creating a Research Space) model, which is a conceptual framework used for analyzing the introduction sections of theses. By applying this model, the study contributes to the understanding of the rhetorical structure of Introduction chapters in engineering final year projects. Data Collection: The study will collect data from forty Introduction chapters of engineering final year projects written by UMT engineering students. These chapters will be selected from two different fields within UMT's engineering undergraduate programs. Analysis Procedures: The analysis will involve identifying and categorizing the communicative moves and their constituent steps within the Introduction chapters. The study will utilize both quantitative and qualitative analysis methods to examine the frequency and nature of these moves. Question Addressed: The study aims to address the question of how UMT engineering students structure and justify their research projects within the Introduction chapters of their final year projects. Conclusion: The study aims to contribute to the knowledge of rhetorical structure in engineering final year projects by investigating the Introduction chapters written by UMT engineering students. By using a mixed method research design and applying the modified CARS model, the study intends to identify the types of moves and steps employed by students and explore their justifications for their research projects. The findings have the potential to enhance the understanding of effective academic writing in engineering disciplines.

Keywords: cohesive markers, learning, meaning, students

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Authors: Cherry Yieng Siang Ling, Joong Hee Lee, Myung Hwan Yun

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The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations.

Keywords: usability, qualitative data, text-processing algorithm, natural language processing

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Authors: Shohreh Hashemi

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For the past two years, applications of Generative AI (GenAI) have been generating world-wide interest and concern in almost all aspects of life, including business, research, art, and, more specifically, education. Students are using GenAI tools such as ChatGPT to produce coursework. McGraw Hill, major publishing company, has reported that according to their survey, 35% of students have used Chat GPT and various chatbots to start or complete their assignments. Furthermore, as of September 2023, Tyton Partners, a consulting firm focusing on the global knowledge sector and education, reported the 22% of faculty use AI technologies in classrooms and 75% of students who already use GenAI indicate that they will continue to use it even if the use of technology is prohibited. Moreover, employers are integrating GenAI tools into their daily business activities to improve employee productivity. For example, according to Matt Abbott, Chief Enterprise Transformation Officer at Chemours, a chemistry company, has been training its employees in the use of AI tools. While many businesses, faculty and students embrace the use of GenAI tools, the many concerns associated with GenAI tools remain. These concerns include GenAI limitations, plagiarism, copy right, legal issues, to name a few. Despite these concerns, many institutions of higher education realize that the GenAI technologies and tools are here to stay and agree that one way to address these concerns is to educate the students on responsible ways to use the technology by embedding the use of GenAI tools in coursework and providing students opportunities to harvest the capabilities of GenAI tools to better prepare for their careers. For example, Dr. Markman, Vice Provost for Academic Affairs at University of Texas in Austin (UT) states that ““UT is focusing on using new tools like ChatGPT to learn and teach most effectively rather than focusing on finding students who are cheating.” The author agrees and proposes that to better prepare the workforce of the future, today’s students from all disciplines must understand the capabilities and limitations of GenAI tools and know how to use them. Engineering precise prompts, aligning prompts, and drilling down into the tool’s knowledge set are the key skills for efficient use of the tools and major requirements for GenAI savvy users of these tools. This article discusses ways to include coverage of AI and GenAI tools in the curriculum, steps to amplify the transformative power of prompt engineering, coursework to promote students’ critical thinking skills, boost their creativity, and enhance their learning experiences. In addition, this article provides an extensive guide for crafting GenAI prompts that would result in appropriate, unbiased, and hallucination-free responses.

Keywords: artificial intelligence, ChatGPT, Chatbot, education, generative AI, GenAI tools, prompts

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Authors: Yonwaba Atyosi, Moses Cho, Abel Ramoelo, Nobuhle Majozi, Cecilia Masemola, Yoliswa Mkhize

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Spectroscopy has been widely used to understand the hyperspectral remote sensing of soils. Accurate and efficient measurement of soil moisture is essential for precision agriculture. The aim of this study was to understand the spectral behavior of soil at different soil water content levels and identify the significant spectral bands for soil moisture content retrieval at field-scale. The study consisted of 60 soil samples from a maize farm, divided into four different treatments representing different moisture levels. Spectral signatures were measured for each sample in laboratory under artificial light using an Analytical Spectral Device (ASD) spectrometer, covering a wavelength range from 350 nm to 2500 nm, with a spectral resolution of 1 nm. The results showed that the absorption features at 1450 nm, 1900 nm, and 2200 nm were particularly sensitive to soil moisture content and exhibited strong correlations with the water content levels. Continuum removal was developed in the R programming language to enhance the absorption features of soil moisture and to precisely understand its spectral behavior at different water content levels. Statistical analysis using partial least squares regression (PLSR) models were performed to quantify the correlation between the spectral bands and soil moisture content. This study provides insights into the spectral behavior of soil at different water content levels and identifies the significant spectral bands for soil moisture content retrieval. The findings highlight the potential of spectroscopy for non-destructive and rapid soil moisture measurement, which can be applied to various fields such as precision agriculture, hydrology, and environmental monitoring. However, it is important to note that the spectral behavior of soil can be influenced by various factors such as soil type, texture, and organic matter content, and caution should be taken when applying the results to other soil systems. The results of this study showed a good agreement between measured and predicted values of Soil Moisture Content with high R2 and low root mean square error (RMSE) values. Model validation using independent data was satisfactory for all the studied soil samples. The results has significant implications for developing high-resolution and precise field-scale soil moisture retrieval models. These models can be used to understand the spatial and temporal variation of soil moisture content in agricultural fields, which is essential for managing irrigation and optimizing crop yield.

Keywords: soil moisture content retrieval, precision agriculture, continuum removal, remote sensing, machine learning, spectroscopy

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Authors: Pauline Cuenin, Marcelo Leles Romarco Oliveira

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Based on their localities and following their ecological rationality, family-based farmers have experimented, adapted and innovated to improve their production systems continuously for millennia. With the technological package transfer processes of the so-called Green Revolution for agricultural holdings, farmers have become increasingly dependent on ready-made "recipes" built from so-called "universal" and global knowledge to face the problems that emerge in the management of local agroecosystems, thus reducing their creative and experiential capacities. However, the production of novelties within farms is fundamental to the transition to more sustainable agro food systems. In fact, as the fruits of local knowledge and / or the contextualization of exogenous knowledge, novelties are seen as seeds of transition. By presenting new techniques, new organizational forms and epistemological approaches, agroecology was pointed out as a way to encourage and promote the creative capacity of farmers. From this perspective, this theoretical work aims to analyze how agroecology encourages the innovative capacity of farmers, and in general, the production of novelties. For this, an analysis was made of the theoretical and methodological bases of agroecology through a literature review, specifically looking for the way in which it articulates the local with the global, complemented by an analysis of agro ecological Brazilian experiences. It was emphasized that, based on the peasant way of doing agriculture, that is, on ecological / social co-evolution or still called co-production (interaction between human beings and living nature), agroecology recognizes and revalues peasant involves the deep interactions of the farmer with his site (bio-physical and social). As a "place science," practice and movement, it specifically takes into consideration the local and empirical knowledge of farmers, which allows questioning and modifying the paradigms that underpin the current agriculture that have disintegrated farmers' creative processes. In addition to upgrade the local, agroecology allows the dialogue of local knowledge with global knowledge, essential in the process of changes to get out of the dominant logic of thought and give shape to new experiences. In order to reach this articulation, agroecology involves new methodological focuses seeking participatory methods of study and intervention that express themselves in the form of horizontal spaces of socialization and collective learning that involve several actors with different knowledge. These processes promoted by agroecology favor the production of novelties at local levels for expansion at other levels, such as the global, through trans local agro ecological networks.

Keywords: agroecology, creativity, global, local, novelty

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Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

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Authors: Margo O'Sullivan, Xuefeng Chen, Qi Zhao, J. Jiang, Ning Fu

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Early childhood development, or ECD, is about the 'whole child' – the physical, social and emotional, cognitive thinking and language progression of each young individual. Overwhelming evidence is now available to support investment in Early Childhood Development internationally, attendance at ECD leads to: improved learning outcomes; improved completion and reduced less dropout rates; and most notably, Professor Heckman, Nobel Laureate’s, findings that for every dollar invested, there is an economic return of up to 17%. Notably, ECD has been included in the 2015-2030 Sustainable Development Goals. The Government of China (GOC) has embraced this research and in 2010, State Council, announced focus on ECD setting a target to provide access to ECD for 85% of 3-6 year olds by 2020; to date, the target has surpassed expectations and reached 70.4%. GoC is also increasingly focusing on the even more critical 0-3 age group, when the plasticity of the brain is at its peak and neurons form connections as fast as 1,000 per second. Key to ECD are parents and caregivers of young children, with parental education critical to fully exploiting the significant potential of the early years of children. In China, with such vast numbers, one in seven pre-school age children in the world live in China, the Ministry of Education (MoE) and the National Centre for Education Technology, explored how to best provide parental education and provide key child developmental related knowledge to parents and caregivers. In response, MoE and UNICEF created a resource for parenting information that began with a computer website in 2012, followed by piloting a kiosk service in 2013 for parents in remote areas without access to the internet, and then a mobile phone application in 2014. The resource includes 269 ECD messages and 200 micro-videos covering critical issues of early childhood development from birth to age 6 years: daily care, nutrition and feeding, disease prevention, immunization, development and education, and safety and protection. To date, there have been 397,599 unique views on the website, and data for the mobile app currently being analysed (Links: http://yuer.cbern.gov.cn/; App: https://appsto.re/cn/OiKPZ.i). This paper will explore the development of this resource, its use by parents and the public, efforts to assess the effectiveness in improving parenting and child development, and future plans to roll an updated version in 2016 to all parents.

Keywords: early childhood development, mobile apps for education, parental education, China

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Authors: Titilope F. Onaolapo, Christiana A. Breed, Maya Pasgaard, Kristine E. Jensen, Peta Brom

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In the face of fast-growing urbanization in most of the world's developing countries, there is a need to understand and address the risk and consequences involved in the indiscriminate use of urban green space. Tshwane city in South Africa has the potential to become one of the world's top biodiversity cities as South Africa is ranked one of the mega countries in biodiversity conservation, and Tshwane metropolitan municipality is the city with the wealthiest biodiversity with grassland biomes. In this study, we focus on the potentials and challenges of urban green transitioning from the Global South perspective with Tshwane city as the case study. We also address the issue of management conflicts that have resulted in informal and illegal activities in and around green spaces, with consequences such as land degradation, loss of livelihoods and biodiversity, and socio-ecological imbalances. A desk study review of eight policy frameworks related to green urban planning and development was done based on four GI principles: multifunctionality, connectivity, interdisciplinary and social inclusion. We interviewed 15 key informants in related departments in the city and administered 200 survey questionnaires among residents. We also had several workshops the other researchers and experts on biodiversity and ecosystem. We found out there is no specific document dedicated to green space management, and where green infrastructure was mentioned, it was focused on as an approach to climate mitigation and adaptation. Also, residents perceive green and open spaces as extra land that could be developed at will. We demonstrated the use of collaborative learning approaches in ecological and development research and the tying research to the existing frameworks, programs, and strategies. Based on this understanding. We outlined the need to incorporate principles of green infrastructure in policy frameworks on spatial planning and environmental development. Furthermore, we develop a model for co-management of green infrastructures by stakeholders, such as residents, developers, policymakers, and decision-makers, to maximize benefits. Our collaborative, interdisciplinary projects pursue SDG multifunctionality of goals 11 and 15 by simultaneously addressing issues around Sustainable Cities and Communities, Climate Action, Life on Land, and Strong Institutions, and halt and reverse land degradation and biodiversity.

Keywords: governance, green infrastructure, South Africa, sustainable development, urban planning, Tshwane

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Authors: Mona Rashidirad, Hamid Salimian

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Dynamic capabilities, as the most considerable capabilities of firms in the current fast-moving economy may not be sufficient for performance improvement, but their contribution to performance is undeniable. While much of the extant literature investigates the impact of dynamic capabilities on organisational performance, little attention has been devoted to understand whether and how dynamic capabilities create value. Dynamic capabilities as the mirror of competitive strategies should enable firms to search and seize new ideas, integrate and coordinate the firm’s resources and capabilities in order to create value. A careful investigation to the existing knowledge base remains us puzzled regarding the relationship among competitive strategies, dynamic capabilities and value creation. This study thus attempts to fill in this gap by empirically investigating the impact of dynamic capabilities on value creation and the mediating impact of competitive strategy on this relationship. We aim to contribute to dynamic capability view (DCV), in both theoretical and empirical senses, by exploring the impact of dynamic capabilities on firms’ value creation and whether competitive strategy can play any role in strengthening/weakening this relationship. Using a sample of 491 firms in the UK telecommunications market, the results demonstrate that dynamic sensing, learning, integrating and coordinating capabilities play a significant role in firm’s value creation, and competitive strategy mediates the impact of dynamic capabilities on value creation. Adopting DCV, this study investigates whether the value generating from dynamic capabilities depends on firms’ competitive strategy. This study argues a firm’s competitive strategy can mediate its ability to derive value from its dynamic capabilities and it explains the extent a firm’s competitive strategy may influence its value generation. The results of the dynamic capabilities-value relationships support our expectations and justify the non-financial value added of the four dynamic capability processes in a highly turbulent market, such as UK telecommunications. Our analytical findings of the relationship among dynamic capabilities, competitive strategy and value creation provide further evidence of the undeniable role of competitive strategy in deriving value from dynamic capabilities. The results reinforce the argument for the need to consider the mediating impact of organisational contextual factors, such as firm’s competitive strategy to examine how they interact with dynamic capabilities to deliver value. The findings of this study provide significant contributions to theory. Unlike some previous studies which conceptualise dynamic capabilities as a unidimensional construct, this study demonstrates the benefits of understanding the details of the link among the four types of dynamic capabilities, competitive strategy and value creation. In terms of contributions to managerial practices, this research draws attention to the importance of competitive strategy in conjunction with development and deployment of dynamic capabilities to create value. Managers are now equipped with solid empirical evidence which explains why DCV has become essential to firms in today’s business world.

Keywords: dynamic capabilities, resource based theory, value creation, competitive strategy

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Authors: Sajjad Hassanpour, Vicente A. González, Yang Zou, Jiamou Liu

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Earthquakes pose a significant threat to both structural and non-structural elements in buildings, putting human lives at risk. Effective post-earthquake evacuation is critical for ensuring the safety of building occupants. However, current design practices often neglect the integration of post-earthquake evacuation considerations into the early-stage architectural design process. To address this gap, this paper presents a novel automated internal architectural layout generation tool that optimizes post-earthquake evacuation performance. The tool takes an initial plain floor plan as input, along with specific requirements from the user/architect, such as minimum room dimensions, corridor width, and exit lengths. Based on these inputs, firstly, the tool randomly generates different architectural layouts. Secondly, the human post-earthquake evacuation behaviour will be thoroughly assessed for each generated layout using the advanced Agent-Based Building Earthquake Evacuation Simulation (AB2E2S) model. The AB2E2S prototype is a post-earthquake evacuation simulation tool that incorporates variables related to earthquake intensity, architectural layout, and human factors. It leverages a hierarchical agent-based simulation approach, incorporating reinforcement learning to mimic human behaviour during evacuation. The model evaluates different layout options and provides feedback on evacuation flow, time, and possible casualties due to earthquake non-structural damage. By integrating the AB2E2S model into the automated layout generation tool, architects and designers can obtain optimized architectural layouts that prioritize post-earthquake evacuation performance. Through the use of the tool, architects and designers can explore various design alternatives, considering different minimum room requirements, corridor widths, and exit lengths. This approach ensures that evacuation considerations are embedded in the early stages of the design process. In conclusion, this research presents an innovative automated internal architectural layout generation tool that integrates post-earthquake evacuation simulation. By incorporating evacuation considerations into the early-stage design process, architects and designers can optimize building layouts for improved post-earthquake evacuation performance. This tool empowers professionals to create resilient designs that prioritize the safety of building occupants in the face of seismic events.

Keywords: agent-based simulation, automation in design, architectural layout, post-earthquake evacuation behavior

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Authors: Yinghua Huang, Tsu-Hong Yen

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The latest advance in leadership research has revealed that leadership is co-created through the combined action of leading and following. The role of followers is as important as leaders in the leadership process. However, the previous leadership studies often conceptualize leadership as a leader-centric process, while the role of followers is largely neglected in the literature. Until recently, followership studies receives more attention because the character and behavior of followers are as vital as the leader during the leadership process. Yet, there is a dearth of followership research in the context of tourism and hospitality industries. Therefore, this study seeks to fill in the gap of knowledge and investigate the followership styles in the U.S. hospitality workforce. In particular, the objectives of this study are to identify popular followership practices among hospitality employees and evaluate hospitality employees' followership styles using Kelley’s followership typology framework. This study also compared the generational differences in followership styles among hospitality employees. According to the U.S. Bureau of Labor Statistics, the workforce in the lodging and foodservice sectors consists of around 12% baby boomers, 29% Gen Xs, 23% Gen Ys, and 36% Gen Zs in 2019. The diversity of workforce demographics in the U.S. hospitality industry calls for more attention to understand the generational differences in followership styles and organizational performance. This study conducted an in-depth interview and a questionnaire survey to collect both qualitative and quantitative data. A snowball sampling method was used to recruit participants working in the hospitality industry in the San Francisco Bay Area, California, USA. A total of 120 hospitality employees participated in this study, including 22 baby boomers, 32 Gen Xs, 30 Gen Ys, and 36 Gen Zs. 45% of the participants were males, and 55% were female. The findings of this study identified good followership practices across the multi-generational participants. For example, a Gen Y participant said that 'followership involves learning and molding oneself after another person usually an expert in an area of interest. I think of followership as personal and professional development. I learn and get better by hands-on training and experience'. A Gen X participant said that 'I can excel by not being fearful of taking on unfamiliar tasks and accepting challenges.' Furthermore, this study identified five typologies of Kelley’s followership model among the participants: 45% exemplary followers, 13% pragmatist followers, 2% alienated followers, 18% passive followers, and 23% conformist followers. The generational differences in followership styles were also identified. The findings of this study contribute to the hospitality human resource literature by identifying the multi-generational perspectives of followership styles among hospitality employees. The findings provide valuable insights for hospitality leaders to understand their followers better. Hospitality leaders were suggested to adjust their leadership style and communication strategies based on employees' different followership styles.

Keywords: followership, hospitality workforce, generational diversity, Kelley’s followership topology

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Authors: Madhuka G. P. D. Udantha, Gihan V. Dias, Surangika Ranathunga

Abstract:

Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc.

Keywords: anomaly detection, clustering, pattern recognition, web sessions

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