Search results for: English learning strategies

Authors: María de los Ángeles Crespo López, Carmen García García

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The highly competitive nature of today's globalised marketplace requires that brands and stores develop effective commercial strategies to ensure their economic survival. Maintaining the loyalty of existing customers constitutes one key strategy that yields the best results. Although the relationship between consumers' self-congruity and their manifestations of loyalty towards a store has been investigated, the role of store personality in this relationship remains unclear. In this study, multiple parallel mediation analysis was used to examine the effect of Store Personality on the relationship between Self-Congruity of consumers and their Manifestations of Loyalty. For this purpose, 457 Spanish consumers of the Fnac store completed three self-report questionnaires assessing Store Personality, Self-Congruity, and Store Loyalty. The data were analyzed using the SPSS macro PROCESS. The results revealed that three dimensions of Store Personality, namely Exciting, Close and Competent Store, positively and significantly mediated the relationship between Self-Congruity and Manifestations of Loyalty. The indirect effect of Competent Store was the greatest. This means that a consumer with higher levels of Self-Congruity with the store will exhibit more Manifestations of Loyalty when the store is perceived as Exciting, Close or Competent. These findings suggest that more attention should be paid to the perceived personality of stores for the development of effective marketing strategies to maintain or increase consumers' manifestations of loyalty towards stores.

Keywords: multiple parallel mediation, PROCESS, self-congruence, store loyalty, store personality

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Authors: Shailaja Thakur

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Managerial literature abounds with discussions on business strategies, success stories as well as cases of failure, which provide an indication of the parameters that should be considered in gauging the dynamism of an entrepreneur. Neoclassical economics has reduced entrepreneurship to a mere factor of production, driven solely by the profit motive, thus stripping him of all creativity and restricting his decision making to mechanical calculations. His ‘dynamism’ is gauged simply by the amount of profits he earns, marginalizing any discussion on the means that he employs to attain this objective. With theoretical backing, we have developed an Index of Entrepreneurial Dynamism (IED) giving weights to the different moves that the entrepreneur makes during his business journey. Strategies such as changes in product lines, markets and technology are gauged as very important (weighting of 4); while adaptations in terms of technology, raw materials used, upgradations in skill set are given a slightly lesser weight of 3. Use of formal market analysis, diversification in related products are considered moderately important (weight of 2) and being a first generation entrepreneur, employing managers and having plans to diversify are taken to be only slightly important business strategies (weight of 1). The maximum that an entrepreneur can score on this index is 53. A semi-structured questionnaire is employed to solicit the responses from the entrepreneurs on the various strategies that have been employed by them during the course of their business. Binary as well as graded responses are obtained, weighted and summed up to give the IED. This index was tested on about 150 tribal entrepreneurs in Mizoram, a state of India and was found to be highly effective in gauging their dynamism. This index has universal acceptability but is devoid of the socio-cultural context, which is very central to the success and performance of the entrepreneurs. We hypothesize that a society that respects risk taking takes failures in its stride, glorifies entrepreneurial role models, promotes merit and achievement is one that has a conducive socio- cultural environment for entrepreneurship. For obtaining an idea about the social acceptability, we are putting forth questions related to the social acceptability of business to another set of respondents from different walks of life- bureaucracy, academia, and other professional fields. Similar weighting technique is employed, and index is generated. This index is used for discounting the IED of the respondent entrepreneurs from that region/ society. This methodology is being tested for a sample of entrepreneurs from two very different socio- cultural milieus- a tribal society and a ‘mainstream’ society- with the hypothesis that the entrepreneurs in the tribal milieu might be showing a higher level of dynamism than their counterparts in other regions. An entrepreneur who scores high on IED and belongs to society and culture that holds entrepreneurship in high esteem, might not be in reality as dynamic as a person who shows similar dynamism in a relatively discouraging or even an outright hostile environment.

Keywords: index of entrepreneurial dynamism, India, social acceptability, tribal entrepreneurs

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Authors: Vinu Elias Jacob, Manoj Kumar Kini

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Disasters of all types are occurring more frequently and are becoming more costly than ever due to various manmade factors including climate change. A better utilisation of the concept of governance and management within disaster risk reduction is inevitable and of utmost importance. There is a need to explore the role of pre- and post-disaster public policies. The role of urban planning/design in shaping the opportunities of households, individuals and collectively the settlements for achieving recovery has to be explored. Governance strategies that can better support the integration of disaster risk reduction and management has to be examined. The main aim is to thereby build the resilience of individuals and communities and thus, the states too. Resilience is a term that is usually linked to the fields of disaster management and mitigation, but today has become an integral part of planning and design of cities. Disaster resilience broadly describes the ability of an individual or community to 'bounce back' from disaster impacts, through improved mitigation, preparedness, response, and recovery. The growing population of the world has resulted in the inflow and use of resources, creating a pressure on the various natural systems and inequity in the distribution of resources. This makes cities vulnerable to multiple attacks by both natural and man-made disasters. Each urban area needs elaborate studies and study based strategies to proceed in the discussed direction. Cochin in Kerala is the fastest and largest growing city with a population of more than 26 lakhs. The main concern that has been looked into in this paper is making cities resilient by designing a framework of strategies based on urban design principles for an immediate response system especially focussing on the city of Cochin, Kerala, India. The paper discusses, understanding the spatial transformations due to disasters and the role of spatial planning in the context of significant disasters. The paper also aims in developing a model taking into consideration of various factors such as land use, open spaces, transportation networks, physical and social infrastructure, building design, and density and ecology that can be implemented in any city of any context. Guidelines are made for the smooth evacuation of people through hassle-free transport networks, protecting vulnerable areas in the city, providing adequate open spaces for shelters and gatherings, making available basic amenities to affected population within reachable distance, etc. by using the tool of urban design. Strategies at the city level and neighbourhood level have been developed with inferences from vulnerability analysis and case studies.

Keywords: disaster management, resilience, spatial planning, spatial transformations

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Authors: Ximena Rincon Castellanos

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The research target was analyzed the education public policy of Colombia according to the content of the right to education. One problematic element of that content is the principle of progressive implementation of economic, social and cultural rights. The research included a complete study of public documents and other papers; as well as, one focus group with former combatants in a city where is located one of some 'hogares de paz', which hosts these people after leaving the illegal group. This paper presents a critical approach to the public policy strategies to guarantee education to former combatants and its tension with the right to a progressive implementation. Firstly, education is understood as a technology level without considering higher education. Former combatant attends to SENA and private institutions, which offer technology education and it is counted by the Colombian Government as higher education. Therefore, statistics report a high level of attendance of excombatant to that education level, but actually, they do not expect to study a university carrier. Secondly, the budget approved has been invested in private institutions, despite public institutions are able to include this population and they need more money to strengthen the public offer, which has been considered as a better strategy to ensure education as a human right but not a good, by the special rapporteur on the right to education. As a consequence, the progressive implementation should be a guide to change and improve current strategies, invest the budget available into the public system of education in order to give former combatants the chance to access to universities.

Keywords: higher education, progressive implementation, public service, private offering and technology education

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Authors: Geraldine Macdonald, Oliver Perra, Nina O’Neill, Laura Neeson, Kathryn Higgins

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Background: Despite improvements in recent years, almost one in six children in Northern Ireland (NI) leaves primary school without achieving the expected level in English and Maths. By early adolescence, this ratio is one in five. In 2010-11, around 9000 pupils in NI had failed to achieve the required standard in literacy and numeracy by the time they left full-time education. This paper reports the findings of an experimental evaluation of a programmed designed to improve educational outcomes of a cohort of children starting primary school in areas of high social disadvantage in Northern Ireland. The intervention: ‘Ready to Learn’ comprised two key components: a literacy-rich After School programme (one hour after school, three days per week), and a range of activities and support to promote the engagement of parents with their children’s learning, in school and at home. The intervention was delivered between September 2010 and August 2013. Study aims and objectives: The primary aim was to assess whether, and to what extent, ‘Ready to Learn’ improved the literacy of socially disadvantaged children entering primary schools compared with children in schools without access to the programme. Secondary aims included assessing the programme’s impact on children’s social, emotional and behavioural regulation, and parents’ engagement with their children’s learning. In total, 505 children (almost all) participated in the baseline assessment for the study, with good retention over seven sweeps of data collection. Study design: The intervention was evaluated by means of a cluster randomized trial, with schools as the unit of randomization and analysis. It included a qualitative component designed to examine process and implementation, and to explore the concept of parental engagement. Sixteen schools participated, with nine randomized to the experimental group. As well as outcome data relating to children, 134 semi-structured interviews were conducted with parents over the three years of the study, together with 88 interviews with school staff. Results: Given the children’s ages, not all measures used were direct measures of reading. Findings point to a positive impact of “Ready to Learn” on children’s reading achievement (comprehension and fluency), as assessed by the York Assessment of Reading Comprehension (YARC) and decoding, assessed using the Word Recognition and Phonic Skills (WRaPS3). Effects were not large, but evidence suggests that it is unusual for an after school programme to clearly to demonstrate effects on reading skills. No differences were found on three other measures of literacy-related skills: British Picture Vocabulary Scale (BPVS-II), Naming Speed and Non-word Reading Tests from the Phonological Assessment Battery (PhAB) or Concepts about Print (CAP) – the last due to an age-related ceiling effect). No differences were found between the two groups on measures of social, emotional and behavioural regulation, and due to low levels of participation, it was not possible directly to assess the contribution of the parent component to children’s outcomes. The qualitative data highlighted conflicting concepts of engagement between parents and school staff. Ready to Learn is a promising intervention that merits further support and evaluation.

Keywords: after-school, education, literacy, parental engagement

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Authors: Imran Hashmi, Sipkaduwa Arachchige Sashika Sureni Wickramasooriya

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Gene-drive technology has emerged as a promising tool for disease control by influencing the population dynamics of disease-carrying organisms. Various gene drive mechanisms, derived from global laboratory experiments, aim to strategically manage and prevent the spread of targeted diseases. One prominent strategy involves population replacement, wherein genetically modified mosquitoes are introduced to replace the existing local wild population. To enhance our understanding and aid in the design of effective release strategies, we employ a comprehensive mathematical model. The utilized approach employs agent-based modeling, enabling the consideration of individual mosquito attributes and flexibility in parameter manipulation. Through the integration of an agent-based model and a meta-population spatial approach, the dynamics of gene drive mosquito spreading in a released site are simulated. The model's outcomes offer valuable insights into future population dynamics, providing guidance for the development of informed release strategies. This research significantly contributes to the ongoing discourse on the responsible and effective implementation of gene drive technology for disease vector control.

Keywords: gene drive, agent-based modeling, disease-carrying organisms, malaria

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Authors: Jenny Hall, Alison Jaquet

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Universities’ push toward the production of high quality research is not limited to academic staff and experienced researchers. In this environment of research rich agendas, Higher Degree Research (HDR) students are increasingly expected to engage in the publishing of good quality papers in high impact journals. IFN001: Advanced Information Research Skills (AIRS) is a credit bearing mandatory coursework requirement for Queensland University of Technology (QUT) doctorates. Since its inception in 1989, this unique blended learning program has provided the foundations for new researchers to produce original and innovative research. AIRS was redeveloped in 2012, and has now been evaluated with reference to the university’s strategic research priorities. Our research is the first comprehensive evaluation of the program from the learner perspective. We measured whether the program develops essential transferrable skills and graduate capabilities to ensure best practice in the areas of publishing and data management. In particular, we explored whether AIRS prepares students to be agile researchers with the skills to adapt to different research contexts both within and outside academia. The target group for our study consisted of HDR students and supervisors at QUT. Both quantitative and qualitative research methods were used for data collection. Gathering data was by survey and focus groups with qualitative responses analyzed using NVivo. The results of the survey show that 82% of students surveyed believe that AIRS assisted their research process and helped them learn skills they need as a researcher. The 18% of respondents who expressed reservation about the benefits of AIRS were also examined to determine the key areas of concern. These included trends related to the timing of the program early in the candidature and a belief among some students that their previous research experience was sufficient for postgraduate study. New insights have been gained into how to better support HDR learners in partnership with supervisors and how to enhance learning experiences of specific cohorts, including international students and mature learners.

Keywords: data management, enhancing learning experience, publishing, research higher degree students, doctoral students

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Authors: Faranak Omidian

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This study has been carried out to examine the status of architecture department at bachelor's level of engineering architecture in Islamic Azad University of Dezful in 2012-13 academic year. The present research is a descriptive cross sectional study and in terms of measurement, it is descriptive and analytical, which was done based on 7 steps and in 7 areas with 32 criteria and 169 indicators. The sample includes 201 students, 14 faculty members, 72 graduates and 39 employers. Simple random sampling method, complete enumeration method, network sampling (snowball sampling) were used for students, faculty members and graduates respectively. All sample responded to the questions. After data collection, the findings were ranked on Likert scale from desirable to undesirable with the scores ranging from 1 to 3.The results showed that the department with a score of 1.88 in regard to objectives, organizational status, management and organizations, with a score of 2 in relation to students, with a score of 1.8 in regard to faculty members was in a relatively desirable status. Regarding training courses and curriculum, it gained a score of 2.33 which indicates the desirable status of the department in this regard. It gained scores of 1.75, 2, and 1.8 with respect to educational and research facilities and equipment, teaching and learning strategies, and graduates respectively, all of which shows the relatively desirable status of the department. The results showed that the department of architecture, with an average score of 2.14 in all evaluated areas, was in a desirable situation. Therefore, although the department generally has a desirable status, it needs to put in more effort to tackle its weaknesses and shortages and corrects its defects in order to promote educational quality, taking to the desirable level.

Keywords: internal evaluation, architecture department in Islamic, Azad University, Dezful

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Authors: Kumalbeo Paul Kamani

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Marketing plays a very important role in the success of any firm since it represents the means through which a firm can reach its customers and also promotes its products and services. In fact, marketing aids the firm in identifying customers who the business can competitively serve, and tailoring product offerings, prices, distribution, promotional efforts, and services towards those customers. Unfortunately, in many firms, marketing has been reduced to merely advertisement. For effective marketing, firms must go beyond this often-limited function of advertisement. In the fashion industry in particular, marketing faces challenges due to its peculiar characteristics. Previous research for instance affirms the idiosyncrasy and peculiarities that differentiate the fashion industry from other industrial areas. It has been documented that the fashion industry is characterized seasonal intensity, short product life cycles, the difficulty of competitive differentiation, and long time for companies to reach financial stability. These factors are noted to pose obstacles to the fashion entrepreneur’s endeavours and can be the reasons that explain their low survival rates. In recent times, the fashion industry has been described as a market that is accessible market, has low entry barriers, both in terms of needed capital and skills which have all accounted for the burgeoning nature of startups. Yet as already stated, marketing is particularly challenging in the industry. In particular, areas such as marketing, branding, growth, project planning, financial and relationship management might represent challenges for the fashion entrepreneur but that have not been properly addressed by previous research. It is therefore important to assess marketing strategies of fashion firms and the factors influencing their success. This study generally sought to examine marketing strategies of fashion dealers in Ghana and their critical success factors. The study employed the quantitative survey research approach. A total of 120 fashion dealers were sampled. Questionnaires were used as instrument of data collection. Data collected was analysed using quantitative techniques including descriptive statistics and Relative Importance Index. The study revealed that the marketing strategies used by fashion apparels are text messages using mobile phones, referrals, social media marketing, and direct marketing. Results again show that the factors influencing fashion marketing effectiveness are strategic management, marketing mix (product, price, promotion etc), branding and business development. Policy implications are finally outlined. The study recommends among others that there is a need for the top management executive to craft and adopt marketing strategies that enable that are compatible with the fashion trends and the needs of the customers. This will improve customer satisfaction and hence boost market penetration. The study further recommends that the fashion industry in Ghana should seek to ensure that fashion apparels accommodate the diversity and the cultural setting of different customers to meet their unique needs.

Keywords: marketing, fashion, industry, success factors

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Authors: Elizabeth D. Ramos

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Peace education supports a holistic appreciation for the value of life and the interdependence of all living systems. Peace education aims to build a culture of peace. One way of building a culture of peace is through environmental stewardship. This study sought to find out the environmental stewardship practices in selected Higher Education Institutions (HEIs) in the Philippines and how these environmental stewardship practices lead to building a culture of peace. The findings revealed that there is still room for improvement in implementing environmental stewardship in schools through academic service learning. In addition, the following manifestations are implemented very satisfactorily in schools: 1) waste reduction, reuse, and recycling, 2) community service, 3) clean and green surroundings. Administrators of schools in the study lead their staff and students in implementing environmental stewardship. It could be concluded that those involved in environmental stewardship display an acceptable culture of peace, particularly, solidarity, respect for persons, and inner peace.

Keywords: academic service learning, environmental stewardship, leadership support, peace, solidarity

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Borja Ruiz de Arbulo Alonso

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This paper analyzes the impact of flipped instruction in adult learners of Spanish as a second language in a face-to-face course at Boston University. Given the limited amount of contact hours devoted to studying world languages in the American higher education system, implementing strategies to free up classroom time for communicative language practice is key to ensure student success in their learning process. In an effort to improve the way adult learners acquire a second language, this paper examines the role that regular pre-class and web-based exposure to Spanish grammar plays in student performance at the end of the academic term. It outlines different types of web-based pre-class activities and compares this approach to more traditional classroom practice. To do so, this study works for three months with two similar groups of adult learners in an intermediate-level Spanish class. Both groups use the same course program and have the same previous language experience, but one receives an additional set of instructor-made online materials containing a variety of grammar explanations and online activities that need to be reviewed before attending class. Since the online activities cover material and concepts that have not yet been studied in class, students' oral and written production in both groups is measured by means of a writing activity and an audio recording at the end of the three-month period. These assessments will ascertain the effects of exposing the control group to the grammar of the target language prior to each lecture throughout and demonstrate where flipped instruction helps adult learners of Spanish achieve higher performance, but also identify potential problems.

Keywords: educational technology, flipped classroom, second language acquisition, student success

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Authors: Christian Luarca

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The study aims to define the efficiency of e-Trainings by the use of cloud platform as part of the onboarding process for IT support engineers. Traditional lecture based trainings involves human resource to guide and assist new hires as part of onboarding which takes time and effort. The use of electronic medium as a platform for training provides a two-way basic communication that can be done in a repetitive manner. The study focuses on determining the most efficient manner of learning the basic knowledge on IT support in the shortest time possible. This was determined by conducting the same set of knowledge transfer categories in two different approaches, one being the e-Training and the other using the traditional method. Performance assessment will be done by the use of Service Tracker Assessment (STA) Tool and Service Manager. Data gathered from this ongoing study will promote the utilization of e-Trainings in the IT onboarding process.

Keywords: cloud platform, e-Training, efficiency, onboarding

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Authors: Khader Musbah Titi

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In this era, traditional education systems do not meet the new challenges created by emerging technologies. On the other hand, eLearning offers all the necessary tools to meet these challenges. Using the Internet has brought numerous benefits to most educational institutions; it has also stretched traditional problems of plagiarism, cheating, stealing, vandalism, and spying into the cyberspace. This research discusses these issues in an eLearning environment. It attempts to provide suggestions and possible solutions to some of these issues. The main aim of this research is to conduct a survey at Irbid National University (INU), one of the oldest and biggest universities in Jordan, to study information related to moral and ethical issues in e-learning environment that affect the construction of the students’ characters in the future. The study will focus on student’s behavior and actions through the Internet using Learning Management System (LMS). Another aim of this research is to analyze the opinions of the instructors and last year students at INU about ethical behavior and interaction through LMS. The results show that educational institutes that use LMS should focus on student character development along with field knowledge. According to disadvantages, the results of the study showed that most of students behave unethically in their online activities (cheating, plagiarism, copy/paste etc.) while studying online courses through LMS. The result showed that instructors play a major role in the character development of students. The result also showed that academic institute must have variant mechanisms and strict policy in LMS to control unethical actions of students.

Keywords: LMS, cyber ethics, e-learning, IT ethics, students’ behaviors

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Authors: Reham Niazi, Marwa Helmy, Susanne Rizzo

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This classroom action research is designed to explore the possibility of adding effective online content to supplement and add learning value to the current reading module. The aim of this research was two-fold, first to investigate students’ acceptance of and interactivity with online components, chosen to orient students with the content, and to pave the way for more in-class activities and skill practice. Secondly, the instructor aimed to examine students’ willingness to have the course contact hours remain the same with some online components to be done at home (flipped approach) or if students were open to turn the class into a blended format with two scenarios; either to have the current contact hours and apply the blended and in this case the face to face component will be less or keep the number of face to face classes the same and add more online structured classes as part of the course hours.

Keywords: blended learning, flipped classroom, graduate students, education

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Authors: Napasri Suwanajote

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The purposes of this research were to analyse and evaluate successful factors in OTOP production process for the developing of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The research has been designed as a qualitative study to gather information from 30 OTOP producers in Bangkontee District, Samudsongkram Province. They were all interviewed on 3 main parts. Part 1 was about the production process including 1) production 2) product development 3) the community strength 4) marketing possibility and 5) product quality. Part 2 evaluated appropriate successful factors including 1) the analysis of the successful factors 2) evaluate the strategy based on Sufficiency Economic Philosophy and 3) the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The results showed that the production did not affect the environment with potential in continuing standard quality production. They used the raw materials in the country. On the aspect of product and community strength in the past 1 year, it was found that there was no appropriate packaging showing product identity according to global market standard. They needed the training on packaging especially for food and drink products. On the aspect of product quality and product specification, it was found that the products were certified by the local OTOP standard. There should be a responsible organization to help the uncertified producers pass the standard. However, there was a problem on food contamination which was hazardous to the consumers. The producers should cooperate with the government sector or educational institutes involving with food processing to reach FDA standard. The results from small group discussion showed that the community expected high education and better standard living. Some problems reported by the community included informal debt and drugs in the community. There were 8 steps in developing the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality.

Keywords: production process, OTOP, sufficiency economic philosophy, marketing management

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Authors: Samatha Ss Sutton, Kaouther Kooli

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In recent years social media influences (SMI) have become integrated into many companies marketing strategies to create buzz, target new and younger markets and further expand social media coverage in business (Lim et al 2017). SMI’s can be defined as online personalities with a substantial number of followers, across one or more social media platforms, with influence on their followers (Lou and Yuan 2018). Recently expenditure on influencer marketing has increased exponentially becoming an important area for marketing opportunities and strategies in the future (Lou and Yuan 2018). In order to market products and brands effectively through SMI’s it is important for business to understand the attributes of SMI that effect purchase intention (Lim et al 2017) of their followers and whether or not these attributes vary across generations so to market effectively to their specific segment or target market. The present study involves quantitative research to understand the attributes by which influence differs across generations namely Generation X and Millennials and its effects on purchase intentions of these generational groups. A survey will be conducted using an online questionnaire. Structural Equation Modelling and Multi group analysis will be applied. The study provides insight to marketers/decision makers on how to use influencers accordingly with their target consumer.

Keywords: social media marketing, social media influencers, attitude towards social media influencers, intention to purchase

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Lei Zhu, Nan Li

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Cold stamping has been widely applied in the automotive industry for the mass production of a great range of automotive panels. Predicting the springback to ensure the dimensional accuracy of the cold-stamped components is a critical step. The main approaches for the prediction and compensation of springback in cold stamping include running Finite Element (FE) simulations and conducting experiments, which require forming process expertise and can be time-consuming and expensive for the design of cold stamping tools. Machine learning technologies have been proven and successfully applied in learning complex system behaviours using presentative samples. These technologies exhibit the promising potential to be used as supporting design tools for metal forming technologies. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the springback fields for variable U-shape cold bending geometries. A dataset is created based on the U-shape cold bending geometries and the corresponding FE simulations results. The dataset is then applied to train the CNN surrogate model. The result shows that the surrogate model can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.

Keywords: springback, cold stamping, convolutional neural networks, machine learning

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Authors: Aboagye Boye Gilbert

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Teaching and learning (Education), not only in Ghana but the whole world is regarded as the (Stepping stone) vehicle to accelerate the country’s economy, development and social growth. Basically the ingredients for human development and the country in general is Vocational and Technical education and this has been stressed in Ghana’s education system since Pre-independence. To this effect, this research seeks to determine using demonstration method of Teachings sewing to improve the skills of form 3 Fashion Designing students of Baworo Integrated Community Centre for Employable Skills. In this research, reviewed literature on opinions of other researchers and what other people have done and said on related articles or topics, analyzed the research design used, translate the data gathered in the study. The study was design to gather information from the school on how they use Teaching methods to teach sewing. The targeted respondent contacted to give assistance Consist of students from BICCES, fashion teachers and tailored garment makers. The sample size consisted of 5 teachers, 20 students and 5 tailors were selected to answer questionnaire items that were used to gather the data for the study. The study revealed that most teachers and students agreed to the fact that demonstration, teaching and learning materials had a positive attitude towards the students in learning sewing. The study recommends that there should be more mechanisms in place to serve as a guide.

Keywords: VOTEC, BECE, BICCES, SHS

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Authors: Ramesh Kumar Behera, Md. Izhar Hassan

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Accident investigation and analysis are useful to identify deficiencies in plant, process, and management practices and formulate preventive strategies for injury elimination. In India and other parts of the world, industrial accidents are investigated to know the causes and also to fulfill legal compliances. However, findings of investigation are seldom used appropriately to strengthen Occupational Safety and Health (OSH) in expected lines. The mineral rich state of Odisha in eastern coast of India; known as a hub for Iron and Steel industries, witnessed frequent accidents during 2005-2009. This article based on study of 982 fatal ‘factory-accidents’ occurred in Odisha during the period 2001-2016, discusses the ‘turnaround-story’ resulting in reduction of fatal accident from 122 in 2009 to 45 in 2016. This paper examines various factors causing incidents; accident pattern in steel and chemical sector; role of climate and harsh weather conditions on accident causation. Software such as R, SQL, MS-Excel and Tableau were used for analysis of data. It is found that maximum fatality is caused due to ‘fall from height’ (24%); steel industries are relatively more accident prone; harsh weather conditions of summer increase chances of accident by 20%. Further, the study suggests that enforcement of partial work-restriction around lunch time during peak summer, screening and training of employees reduce accidents due to fall from height. The study indicates that learning from accident investigation and analysis can be used as a method to reduce work related accidents in the journey towards ‘Vision Zero’.

Keywords: accident investigation and analysis, fatal accidents in India, fall from height, vision zero

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Authors: A. Maghoul, A. Rostampouryasouri, MR. Maghami

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The development of an urban design and power network planning has been gaining momentum in recent years. The integration of renewable energy with urban design has been widely regarded as an increasingly important solution leading to climate change and energy security. Through the use of passive strategies and solar integration with Urban Building Energy Modeling (UBEM) tools, architects and designers can create high-quality designs that meet the needs of clients and stakeholders. To determine the most effective ways of combining renewable energy with urban development, we analyze the relationship between urban form and renewable energy production. The procedure involved in this practice include passive solar gain (in building design and urban design), solar integration, location strategy, and 3D models with a case study conducted in Tehran, Iran. The study emphasizes the importance of spatial and temporal considerations in the development of sector coupling strategies for solar power establishment in arid and semi-arid regions. The substation considered in the research consists of two parallel transformers, 13 lines, and 38 connection points. Each urban load connection point is equipped with 500 kW of solar PV capacity and 1 kWh of battery Energy Storage (BES) to store excess power generated from solar, injecting it into the urban network during peak periods. The simulations and analyses have occurred in EnergyPlus software. Passive solar gain involves maximizing the amount of sunlight that enters a building to reduce the need for artificial lighting and heating. Solar integration involves integrating solar photovoltaic (PV) power into smart grids to reduce emissions and increase energy efficiency. Location strategy is crucial to maximize the utilization of solar PV in an urban distribution feeder. Additionally, 3D models are made in Revit, and they are keys component of decision-making in areas including climate change mitigation, urban planning, and infrastructure. we applied these strategies in this research, and the results show that it is possible to create sustainable and energy-efficient urban environments. Furthermore, demand response programs can be used in conjunction with solar integration to optimize energy usage and reduce the strain on the power grid. This study highlights the influence of ancient Persian architecture on Iran's urban planning system, as well as the potential for reducing pollutants in building construction. Additionally, the paper explores the advances in eco-city planning and development and the emerging practices and strategies for integrating sustainability goals.

Keywords: energy-efficient urban planning, sustainable architecture, solar energy, sustainable urban design

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Hammad Riaz, Abubakr Saeed

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Environmental Management System (EMS), i.e., ISO 14001 helps to build corporate reputation, legitimacy and can also be considered as firms’ strategic response to institutional pressure to reduce the impact of business activity on natural environment. The financial outcomes of certifying with ISO 14001 are still unclear and equivocal. Drawing on institutional and market learning theories, the impact of ISO 14001 on firms’ market performance is examined for Chinese firms. By employing rigorous event study approach, this paper compared ISO 14001 certified firms with non-certified counterpart firms based on different matching criteria that include size, return on assets and industry. The results indicate that the ISO 14001 has been negatively signed by the investors both in the short and long-run. This paper suggested implications for policy makers, managers, and other nonprofit organizations.

Keywords: ISO 14001, legitimacy, institutional forces, event study approach, emerging markets

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Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

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Authors: Rachelly Ashwall, Ephraim Tabory

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In an attempt to reduce the level of a major identity-based conflict in Israel between Ultra-orthodox and secular Jews, several initiatives in recent years have tried to bring members of the two societies together in facilitated joint discussion forums. Our study analyzes the impact of two types of such programs: joint mediation training classes and confrontation-based learning programs that are designed to facilitate discussions over controversial issues. These issues include claims about an unequal shouldering of national obligations such as military service, laws requiring public observance of the Sabbath, and discrimination against women, among others. The study examines the factors that enabled the two groups to reduce their social distance, and increase their understanding of each other, and develop a recognition and tolerance of the other group's particular social identity. The research conducted over a course of two years involved observations of the activities of the groups, interviews with the participants, and analysis of the social media used by the groups. The findings demonstrate the progression from a mutual initial lack of knowledge about habits, norms, and attitudes of the out-group to an increasing desire to know, understand and more readily accept the identity of a previously rejected outsider. Participants manifested more respect, concern for and even affection for those whose identity initially led them to reject them out of hand. We discuss the implications for seemingly intractable identity-based conflict in fragile societies.

Keywords: identity-based conflict, intergroup relations, joint mediation learning, out-group recognition, social identity

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Authors: J. S. Dhillon, K. K. Dhaliwal

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In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.

Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization

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Authors: L. Freeborn

Abstract:

Recent years have seen an increasing number of neuroimaging studies related to language learning as imaging techniques such as fMRI and EEG have become more widely accessible to researchers. By using a variety of structural and functional neuroimaging techniques, these studies have already made considerable progress in terms of our understanding of neural networks and processing related to first and second language acquisition. However, the methodological designs employed in neuroimaging studies to test language learning have been questioned by applied linguists working within the field of second language acquisition (SLA). One of the major criticisms is that tasks designed to measure language learning gains rarely have a communicative function, and seldom assess learners’ ability to use the language in authentic situations. This brings the validity of many neuroimaging tasks into question. The fundamental reason why people learn a language is to communicate, and it is well-known that both first and second language proficiency are developed through meaningful social interaction. With this in mind, the SLA field is in agreement that second language acquisition and proficiency should be measured through learners’ ability to communicate in authentic real-life situations. Whilst authenticity is not always possible to achieve in a classroom environment, the importance of task authenticity should be reflected in the design of language assessments, teaching materials, and curricula. Tasks that bear little relation to how language is used in real-life situations can be considered to lack construct validity. This paper first describes the typical tasks used in neuroimaging studies to measure language gains and proficiency, then analyses to what extent these tasks can validly assess these constructs.

Keywords: neuroimaging studies, research design, second language acquisition, task validity

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