Search results for: chemical learning

Authors: Eunice H. Li

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This paper discusses and analyses a small portion of the literature that has been reviewed for research work in Distance Education (DE) pedagogies that I am currently undertaking. It begins by presenting a brief overview of Taylor's (2001) five-generation models of Distance Education. The focus of the discussion will be on the 5th generation, Intelligent Flexible Learning Model. For this generation, educational and other institutions make portal access and interactive multi-media (IMM) an integral part of their operations. The paper then takes a brief look at current trends in technologies – for example smart-watch wearable technology such as Apple Watch. The emergent trends in technologies carry many new features. These are compared to former DE generational features. Also compared is the time span that has elapsed between the generations that are referred to in Taylor's model. This paper is a work in progress. The paper therefore welcome new insights, comparisons and critique of the issues discussed.

Keywords: distance education, e-learning technologies, pedagogy, generational models

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Authors: Zory Marantz

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Hybrid flexible (HyFlex) is a pedagogical methodology whereby an instructor delivers content in three modalities, i.e. live in-person (LIP), live online synchronous (LOS), and non-live online asynchronous (nLOaS). HyFlex is focused on providing the largest level of flexibility needed to achieve a cohesive environment across all modalities and incorporating four basic principles – learner’s choice, reusability, accessibility, and equivalency. Much literature has focused on the advantages of this methodology in providing students with the flexibility to choose their learning modality as best suits their schedules and learning styles. Initially geared toward graduate-level students, the concept has been applied to undergraduate studies, particularly during our national pedagogical response to the COVID19 pandemic. There is still little literature about the practicality and feasibility of HyFlex for hardware laboratory intensive engineering technology programs, particularly in dense, urban commuter institutions of higher learning. During a semester of engineering, a lab-based course was taught in the HyFlex modality, and students were asked to complete a survey about their experience. The data demonstrated that there is no single mode that is preferred by a majority of students and the usefulness of any modality is limited to how familiar the student and instructor are with the technology being applied. The technology is only as effective as our understanding and comfort with its functionality. For HyFlex to succeed in its implementation in an engineering technology environment within an urban commuter institution, faculty and students must be properly introduced to the technology being used.

Keywords: education, HyFlex, technology, urban, commuter, pedagogy

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Jill Steel

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Introduction: Animal-assisted learning (AAL) interventions are increasing exponentially, yet a paucity of quality research in the field exists. The aim of this study was to evaluate how the promotion of emotional wellbeing, through AAL, in this case, a dog, may support children’s engagement with reading in a Primary 1 classroom. Research indicates that dogs can provide emotional support to children; by forming a trusting attachment with a non-critical ‘friend’ who confers unconditional positive regard on the child, confidence may be boosted and anxiety reduced. By promoting emotional wellbeing through interactions with the dog, it is hoped that children begin to associate reading with feelings of wellbeing, which then results in increased engagement with reading. Methodology: A review of the literature was conducted. The relationship between emotional wellbeing and learning was explored, followed by an examination of the literature relating to Animal-Assisted Therapy and AAL. Scottish educational policy and legislation were analysed to establish the extent to which AAL might be suitable for the Scottish pedagogical context. An empirical study was conducted in a mainstream Primary 1 classroom over a four-week period. An inclusive approach was adopted whereby all children that wanted to interact with the dog were given the opportunity to do so, and all 25 children subsequently chose to participate. Children were not withdrawn from the classroom. Primary methods included interviews, observations, and questionnaires. Three focus children were selected for closer study. Main Results: Results were remarkably close to previous research and literature. Children’s emotional wellbeing was boosted, and engagement in reading improved. Principal Conclusions and Implications for Field: It was concluded that AAL could support emotional wellbeing and, in turn, promote children’s engagement with reading. The main limitation of the study was its short-term nature, and a longer randomised controlled trial with a larger sample, currently being undertaken by the author, would provide a fuller answer to the research question. Barriers to AAL include health and safety concerns and steps to ensure the welfare of the dog.

Keywords: animal-assisted learning, emotional wellbeing, reading, reading to dogs

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Authors: Eva Tolmeijer, Emmanuelle Peters, Veena Kumari, Liam Mason

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Cognitive behavioral therapy for psychosis (CBTp) is effective in many but not all patients, making it important to better understand the factors that determine treatment outcomes. To date, no studies have examined whether neuroimaging can make clinically useful predictions about who will respond to CBTp. To this end, we used machine learning methods that make predictions about symptom improvement at the individual patient level. Prior to receiving CBTp, 22 patients with a diagnosis of schizophrenia completed a social-affective processing task during functional MRI. Multivariate pattern analysis assessed whether treatment response could be predicted by brain activation responses to facial affect that was either socially threatening or prosocial. The resulting models did significantly predict symptom improvement, with distinct multivariate signatures predicting psychotic (r=0.54, p=0.01) and affective (r=0.32, p=0.05) symptoms. Psychotic symptom improvement was accurately predicted from relatively focal threat-related activation across hippocampal, occipital, and temporal regions; affective symptom improvement was predicted by a more dispersed profile of responses to prosocial affect. These findings enrich our understanding of the neurobiological underpinning of treatment response. This study provides a foundation that will hopefully lead to greater precision and tailoring of the interventions offered to patients.

Keywords: cognitive behavioral therapy, machine learning, psychosis, schizophrenia

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Sweta Rout-Hoolash

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Each year, selected students from around 20 countries begin their degrees at Middlesex University with the International Foundation Program (IFP), developing the skills required for academic study at a UK university. The IFP runs for 30 learning/teaching weeks at Middlesex University Mauritius Branch Campus, which is an international campus of UK’s Middlesex University. Successful IFP students join their degree courses already settled into life at their chosen campus (London, Dubai, Mauritius or Malta) and confident that they understand what is required for degree study. Although part of the School of Science and Technology, in Mauritius it prepares students for undergraduate level across all Schools represented on campus – including disciplines such as Accounting, Business, Computing, Law, Media and Psychology. The researcher has critically reviewed the framework and resources in the curriculum for a particular six week period of IFP study (dedicated group work phase). Despite working together closely for 24 weeks, IFP students approach the final 6 week small group work project phase with mainly inhibitive feelings. It was observed that students did not engage effectively in the group work exercise. Additionally, groups who seemed to be working well did not necessarily produce results reflecting effective collaboration, nor individual members’ results which were better than prior efforts. The researcher identified scope for change and innovation in the IFP curriculum and how group work is introduced and facilitated. The study explores the challenges of groupwork in the context of the Mauritius campus, though it is clear that the implications of the project are not restricted to one campus only. The presentation offers a reflective review on the previous structure put in place for the management of small group assessed projects on the programme from both the student and tutor perspective. The focus of the research perspective is the student voice, by taking into consideration past and present IFP students’ experiences as written in their learning journals. Further, it proposes the introduction of a revised framework to help students take greater ownership of the group work process in order to engage more effectively with the learning outcomes of this crucial phase of the programme. The study has critically reviewed recent and seminal literature on how to achieve greater student ownership during this phase especially under an environment of assessed multicultural group work. The presentation proposes several new approaches for encouraging students to take more control of the collaboration process. Detailed consideration is given to how the proposed changes impact on the work of other stakeholders, or partners to student learning. Clear proposals are laid out for evaluation of the different approaches intended to be implemented during the upcoming academic year (student voice through their own submitted reflections, focus group interviews and through the assessment results). The proposals presented are all realistic and have the potential to transform students’ learning. Furthermore, the study has engaged with the UK Professional Standards Framework for teaching and supporting learning in higher education, and demonstrates practice at the level of ‘fellow’ of the Higher Education Academy (HEA).

Keywords: collaborative peer learning, enhancing learning experiences, group work assessment, learning communities, multicultural diverse classrooms, studying abroad

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Authors: Dilek Bilgiç

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Configuration Management (CM) is a discipline assuring the consistency between product information the reality all along the product lifecycle. Although the extensive benefits of this discipline, such as the direct impact on increasing return on investment, reducing lifecycle costs, are realized by most organizations. It is worth evaluating that CM functions might be successfully implemented in some organized anarchies. This paper investigates how to manage ambiguity in CM processes as an opportunity within an environment that has different types of complexities and choice arenas. It is not explained how to establish a configuration management organization in a company; more specifically, it is analyzed how to apply configuration management processes when different types of streams exist. From planning to audit, all the CM functions may provide different organization learning opportunities when those applied with the right leadership methods.

Keywords: configuration management, leadership, organizational analysis, organized anarchy, cm process, organizational learning, organizational maturity, configuration status accounting, leading innovation, change management

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: V. Konjik, J. Schwartz, R. Sandhoff, M. Mack

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The rising number of multi-resistant pathogens demands the development of new antibiotics in order to reduce the lethal risk of infections. Here, we investigate roseoflavin, a vitamin B2 analogue which is produced by Streptomyces davawensis and Streptomyces cinnabarinus. We consider roseoflavin to be a 'Trojan horse' compound. Its chemical structure is very similar to riboflavin but in fact it is a toxin. Furthermore, it is a clever strategy with regard to the delivery of an antibiotic to its site of action but also with regard to the production of this chemical: The producer cell has only to convert a vitamin (which is already present in the cytoplasm) into a vitamin analog. Roseoflavin inhibits the activity of Flavin depending proteins, which makes up to 3.5 % of predicted proteins in organisms sequenced so far. We sequentially knocked out gene clusters and later on single genes in order to find the ones which are involved in the roseoflavin biosynthesis. Consequently, we identified the gene rosB, coding for the protein carrying out the first step of roseoflavin biosynthesis, starting form Flavin mononucleotide. Here we show, that the protein RosB has so far unknown features. It is per se an oxidoreductase, a decarboxylase and an aminotransferase, all rolled into one enzyme. A screen of cofactors revealed needs of oxygen, NAD+, thiamine and glutamic acid to carry out its function. Surprisingly, thiamine is not only needed for the decaboxylation step, but also for the oxidation of 8-demethyl-8-formyl Flavin mononucleotide. We had managed to isolate three different Flavin intermediates with different oxidation states, which gave us a mechanistic insight of RosB functionality. Our work points to a so far new function of thiamine in Streptomyces davawensis. Additionally, RosB could be extremely useful for chemical synthesis. Careful engineering of RosB may allow the site-specific replacement of methyl groups by amino groups in polyaromatic compounds of commercial interest. Finally, the complete clarification of the roseoflavin biosynthesis opens the possibility of engineering cost-effective roseoflavin producing strains.

Keywords: antibiotic, flavin analogue, roseoflavin biosynthesis, vitamin B2

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Authors: O. D. A. N. Perera, H. G. Wanigasinghe

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Tender jackfruit is an underutilized biomass, which still has a good consumer demand. Valorization of this ingredient into meat analog would obtain greater consumer acceptance due to concerns about health, the environment, and living sustainably of mankind have increased significantly in this decade, opening the market for meat substitutes. The objective of this research was to create a plant-based meat substitute with a structure similar to meat products. In this study, three different combinations of tender jackfruit were used to create vegan burger patties, which were then examined for their textural, physico-chemical, and sensory qualities. The developed burger patties have been compared with store-bought chicken patties. The developed vegan burger patties P1, P2, and P3 had a comparable flavor preference to the control and demonstrated considerable general acceptability (p >.05). P3 has a high quantity of protein (17.10 ± 0.02%) and fiber (6.40 ± 0.06%). At the same time, the vegan burger patty resulted in less fat, high fiber, and high protein which meets the vegan consumer requirements.

Keywords: underutilized, high fibre, soya protein isolate, cooking yield

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Authors: Sunico Armie Flores

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Early childhood education (ECE) is pivotal in shaping the future of individuals and society. This paper explores the collaborative efforts required among educators, families, and communities to create a collective impact on young children’s development. It delves into the importance of these partnerships, effective strategies for engagement, and the challenges and opportunities inherent in fostering such collaboration. By examining current research and practices, the paper aims to highlight the essential role of an integrated approach in achieving significant and sustainable improvements in early childhood outcomes.

Keywords: early childhood education, lifelong learning, cognitive development, socio-emotional development, educators, families, communities, collaborative efforts, collective impact, early learning environments, holistic development, high-quality ECE programs, investment in education

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Authors: Rachel Baruch

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This article describes the implementation of an experimental model for teaching ICT tools and digital environments in teachers training college. In most educational systems in the Western world, new programs were developed in order to bridge the digital gap between teachers and students. In spite of their achievements, these programs are limited due to several factors: The teachers in the schools implement new methods incorporating technological tools into the curriculum, but meanwhile the technology changes and advances. The interface of tools changes frequently, some tools disappear and new ones are invented. These conditions require an experimental model of training the pre-service teachers. The appropriate method for instruction within the domain of ICT tools should be based on exposing the learners to innovations, helping them to gain experience, teaching them how to deal with challenges and difficulties on their own, and training them. This study suggests some principles for this approach and describes step by step the implementation of this model.

Keywords: ICT tools, e-learning, pre-service teachers, new model

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Authors: Jui Afrin, Akhtarul Islam

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In this paper, the solution of glucose, yeast and glucose yeast mixture are being used as sample solution for determining the chemical oxygen demand (COD). In general COD determination method used to determine the different rang of oxidative potential. But in this work has shown to determine the definite oxidative potential for different concentration for known COD value and wanted to see the difference between experimental value and the theoretical value for evaluating the method drawbacks. In this study, made the values of oxidative potential like 400 mg/L, 500 mg/L, 600 mg/L, 700 mg/L and 800mg/L for various sample solutions and determined the oxidative potential according to our developed method. Plotting the experimental COD values vs. sample solutions of various concentrations in mg/L to draw the curve. From these curves see that the curves for glucose solution is not linear; its deviate from linearity for the lower concentration and the reason for this deviation is unknown. If these drawback can be removed this method can be effectively used to determine Oxidative Potential of Industrial wastewater (such as: Leather industry wastewater, Municipal wastewater, Food industry wastewater, Textile wastewater, Pharmaceuticals waste water) that’s why more experiment and study required.

Keywords: bod (biological oxygen demand), cod (chemical oxygen demand), oxidative potential, titration, waste water, development

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Authors: Osagiede Mercy Afe

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After the internet explosion in the 90’s, Technology was immediately integrated into the school system. Technology which symbolizes advancement in human knowledge was seen as a setback by many educators many efforts have been made to help stem this erroneous believes and help educators realize the benefits of technology and ways of implementing it in the classrooms especially in the sciences. This advancement created a constantly expanding gap between the pupil’s perception on the use of technology within the learning atmosphere and the teacher’s perception and limitations hence the focus of this paper is on the need to refocus on the potentials of Science and Technology in enhancing children learning at school especially in science for sustainable development in Nigeria. The paper recommended measures for facilitating the sustenance of science and technology in Nigerian schools so as to enhance the potentials of our children in Science and Technology for a better tomorrow.

Keywords: children, information communication technology (ICT), potentials, sustainable development, science education

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Authors: A. M. Attalla, A. F. lbrahim, Laila Y. Mostaffa

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This study was conducted during three seasons 2010, 2011 and 2012 on Zahhloul date palm cultivar grown in calcareous soil, Alexandria governorate, Egypt. The palms received recommended dose of mineral N only or plus different rates of organic N with or without bio fertilizer to study the effect of such treatments on date palm yield and fruit nitrate and nitrite content due to its negative influence on human, animal and environment. The obtained results clarified that all used treatments of organic and bio fertilizers were effective in improving date palm yield and decreased fruit content of NO2 and NO3 in comparison with 100 % mineral N. It was also noticed that combined treatments of 50 % mineral N + 50 % organic manure with bio fertilizer is the superior treatments for increasing the values of yield and decreasing its content of NO2 and NO3. Hence, it could be concluded that, minimizing the use of chemical nitrogen fertilizer to half of recommended dose through addition of 50 % mineral N + 50 % organic manure with bio fertilizer and also, the utilization of organic and bio fertilizers is considered as a promising alternative for chemical fertilizers to avoid pollution and reduce the costs of mineral fertilizers.

Keywords: organic and bio fertilizers, mineral fertilizer, nitrate, nitrite, zaghloul date palm cv

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Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling

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Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.

Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Abida Mariam, Anwaar Ahmed, Asif Ahmad, Muhammad Sheeraz Ahmad, Muhammad Akram Khan, Muhammad Mazahir

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The plant olive (Olea europaea L.) is known for its commercial significance due to nutritional and health benefits. Pakistan is ranked 4th among countries who import olive oil whereas, 70% of edible oil is imported to fulfil the needs of the country. There exists great potential for Olea europaea cultivation in Pakistan. The popularity and cultivation of olive fruit has increased in recent past due to its high socio-economic and health significance. There exist almost negligible data on the chemical composition of extra virgin olive oil extracted from cultivars grown in Pothwar, an area with arid climate conducive for growth of olive trees. Keeping in view these factors a study has been conducted to characterize the olive oil extracted from olive cultivars collected from Pothwar regions of Pakistan for their nutritional potential and value addition. Ten olive cultivars (Gemlik, Coratina, Sevillano, Manzanilla, Leccino, Koroneiki, Frantoio, Arbiquina, Earlik and Ottobratica) were collected from Barani Agriculture Research Institute, Chakwal. Extra Virgin Olive Oil (EVOO) was extracted by cold pressing and centrifuging of olive fruits. The highest amount of oil was yielded in Coratina (23.9%) followed by Frantoio (23.7%), Koroneiki (22.8%), Sevillano (22%), Ottobratica (22%), Leccino (20.5%), Arbiquina (19.2%), Manzanilla (17.2%), Earlik (14.4%) and Gemllik (13.1%). The extracted virgin olive oil was studied for various physico- chemical properties and fatty acid profile. The Physical and chemical properties i.e., characteristic odor and taste, light yellow color with no foreign matter, insoluble impurities (≤0.08), fee fatty acid (0.1 to 0.8), acidity (0.5 to 1.6 mg/g acid), peroxide value (1.5 to 5.2 meqO2/kg), Iodine value (82 to 90), saponification value (186 to 192 mg/g) and unsaponifiable matter (4 to 8g/kg), ultraviolet spectrophotometric analysis (k232 and k270), showed values in the acceptable range, established by PSQCA and IOOC set for extra virgin olive oil. Olive oil was analyzed by Near Infra-Red spectrophotometry (NIR) for fatty acids sin olive oils which were found as: palmitic, palmitoleic, stearic, oleic, linoleic and alpha-linolenic. Major fatty acid was Oleic acid in the highest percentage ranging from (55 to 66.1%), followed by linoleic (10.4 to 20.4%), palmitic (13.8 to 19.5%), stearic (3.9 to 4.4%), palmitoleic (0.3 to 1.7%) and alpha-linolenic (0.9 to 1.7%). The results were significant with differences in parameters analyzed for all ten cultivars which confirm that genetic factors are important contributors in the physico-chemical characteristics of oil. The olive oil showed superior physical and chemical properties and recommended as one of the healthiest forms of edible oil. This study will help consumers to be more aware of and make better choices of healthy oils available locally thus contributing towards their better health.

Keywords: characterization, extra virgin olive oil, oil yield, fatty acids

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Authors: Anum Rafiq

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Adolescence is a vulnerable period for youth across the world. It is a period of new learning with opportunities to understand and develop perspectives on health and well-being. With youth beginning to engage in intimate relationships at an earlier age in the 21st century, concentrating on the learning opportunity they have in school is paramount. The nature of what has been deemed important to teach in schools has changed throughout history, and the focus has shifted from home/family skills to teaching youth how to be competitive in the job market. Amidst this emphasis, opportunities for them exist to learn about building healthy intimate relationships, one of the foundational elements of most people’s lives. Using an Institutional Ethnography (IE), the lived experiences of youth in how they understand intimate relationships and how their learning experience is organized through the high school Health and Physical Education (H&PE) course is explored. An empirical inquiry into how the actual work of teachers and youth are socially organized by a biomedical, employment-related, and efficiency-based discourse is provided. Through thirty-two qualitative interviews with teachers and youth, a control of ruling relations such as institutional accountability circuits, performance reports, and timetabling over the experience of teachers and youth is found. One of the facets of the institutional accountability circuit is through the social organization of teaching and learning about healthy intimate relationships being framed through a biomedical discourse. In addition, the role of a hyper-focus on performance and evaluation is found as paramount in situating healthy intimacy discussions as inferior to neoliberally charged productivity measures such as employment skills. Lastly, due to the nature of institutional policies such as regulatory guidelines, teachers are largely influenced to avoid diving into discussions deemed risky or taboo by society, such as healthy intimacy in adolescence. The findings show how texts such as the H&PE curriculum, the Ontario College of Teachers (OCT) guidelines, Ministry of Education Performance Reports, and the timetable organize the day-to-day activities of teachers and students and reproduce different disjunctures for youth. This disjuncture includes some of their experiences being subordinated, difficulty relating to curriculum, and an experience of healthy living discussions being skimmed over across sites. The findings detail that the experience of youth in learning about healthy intimate relationships is not akin to the espoused vision outlined in policy documents such as the H&PE (2015) curriculum policy. These findings have implications for policymakers, activists, and school administration alike, which call for an investigation into who is in power when it comes to youth’s learning needs, as a pivotal period where youth can be equipped with life-changing knowledge is largely underutilized. A restructuring of existing institutional practices that allow for the social and institutional flexibility required to broach the topic of healthy intimacy in a comprehensive manner is required.

Keywords: health policy, intimate relationships, youth, education, ruling relations, sexual education, violence prevention

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Authors: Safaa H. Sindi, Michael Roe

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This project divides supply chain (SC) models into seven Eras, according to the evolution of the market’s needs throughout time. The five earliest Eras describe the emergence of supply chains, while the last two Eras are to be created. Research objectives: The aim is to generate the two latest Eras with their respective models that focus on the consumable goods. Era Six contains the Optimal Multidimensional Matrix (OMM) that incorporates most characteristics of the SC and allocates them into four quarters (Agile, Lean, Leagile, and Basic SC). This will help companies, especially (SMEs) plan their optimal SC route. Era Seven creates an Automated Multidimensional Model (AMM) which upgrades the matrix of Era six, as it accounts for all the supply chain factors (i.e. Offshoring, sourcing, risk) into an interactive system with Heuristic Learning that helps larger companies and industries to select the best SC model for their market. Methodologies: The data collection is based on a Fuzzy-Delphi study that analyses statements using Fuzzy Logic. The first round of Delphi study will contain statements (fuzzy rules) about the matrix of Era six. The second round of Delphi contains the feedback given from the first round and so on. Preliminary findings: both models are applicable, Matrix of Era six reduces the complexity of choosing the best SC model for SMEs by helping them identify the best strategy of Basic SC, Lean, Agile and Leagile SC; that’s tailored to their needs. The interactive heuristic learning in the AMM of Era seven will help mitigate error and aid large companies to identify and re-strategize the best SC model and distribution system for their market and commodity, hence increasing efficiency. Potential contributions to the literature: The problematic issue facing many companies is to decide which SC model or strategy to incorporate, due to the many models and definitions developed over the years. This research simplifies this by putting most definition in a template and most models in the Matrix of era six. This research is original as the division of SC into Eras, the Matrix of Era six (OMM) with Fuzzy-Delphi and Heuristic Learning in the AMM of Era seven provides a synergy of tools that were not combined before in the area of SC. Additionally the OMM of Era six is unique as it combines most characteristics of the SC, which is an original concept in itself.

Keywords: Leagile, automation, heuristic learning, supply chain models

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Grazia Disciglio, Annalisa Tarantino, Emanuele Tarantino

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The production system of Foggia province (Apulia, Southern Italy) is characterized by the presence of numerous agro-food industries whose activities include the processing of vegetables products that release large quantities of wastewater. The reuse in agriculture of these wastewaters offers the opportunity to reduce the costs of their disposal and minimizing their environmental impact. In addition, in this area, which suffers from water shortage, the use of agro-industrial wastewater is essential in the very intensive irrigation cropping systems. The present investigation was carried out in years 2009 and 2010 to monitor the physico-chemical and microbiological characteristics of the industrial wastewater (IWW) from the secondary treatment plant of the 'Industrial Area of Foggia'. The treatment plant released on average about 567,000 m3y-1 of IWW, which distribution was not uniform over the year. The monthly values were about 250,000 m3 from November to June and about 90,000 m3 from July to October. The obtained results revealed that IWW was characterized by low values of Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Electrical Conductivity (EC) and Sodium Absorption Rate (SAR). An occasional presence of heavy metal and high concentration of total phosphorus, total nitrogen, ammoniacal nitrogen and microbial organisms (Escherichia coli and Salmonella) were observed. Due to the presence of this pathogenic microorganisms and sometimes of heavy metals, which may raise sanitary and environmental problems in order to the possible irrigation reuse of this IWW, a tertiary treatment of wastewater based on filtration and disinfection in line are recommended. Researches on the reuse of treated IWW on crops (olive, artichoke, industrial tomatoes, fennel, lettuce etc.) did not show significant differences among the irrigated plots for most of the soil and yield characteristics.

Keywords: agroindustrial wastewater, irrigation, microbiological characteristic, physico-chemical characteristics

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Authors: Severino Machingambi

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Most academics entering Higher education as lecturers in South Africa do not have qualifications in Education or teaching. This creates serious problems since they are not sufficiently equipped with pedagogical approaches and theories that inform their facilitation of learning strategies. This, arguably, is one of the reasons why higher education institutions are experiencing high student failure rate. In order to mitigate this problem, it is critical that higher education institutions devise internal academic staff development programmes to capacitate academics with pedagogical skills and competencies so as to enhance the quality of student learning. This paper reported on how the Teaching and Learning Development Centre of a university used design-based research methodology to conceptualise and implement an academic staff development programme for new academics at a university of technology. This approach revolves around the designing, testing and refining of an educational intervention. Design-based research is an important methodology for understanding how, when, and why educational innovations work in practice. The need for a professional development course for academics arose due to the fact that most academics at the university did not have teaching qualifications and many of them were employed straight from industry with little understanding of pedagogical approaches. This paper examines three key aspects of the programme namely, the preliminary phase, the teaching experiment and the retrospective analysis. The preliminary phase is the stage in which the problem identification takes place. The problem that this research sought to address relates to the unsatisfactory academic performance of the majority of the students in the institution. It was therefore hypothesized that the problem could be dealt with by professionalising new academics through engagement in an academic staff development programme. The teaching experiment phase afforded researchers and participants in the programme the opportunity to test and refine the proposed intervention and the design principles upon which it was based. The teaching experiment phase revolved around the testing of the new academics professional development programme. This phase created a platform for researchers and academics in the programme to experiment with various activities and instructional strategies such as case studies, observations, discussions and portfolio building. The teaching experiment phase was followed by the retrospective analysis stage in which the research team looked back and tried to give a trustworthy account of the teaching/learning process that had taken place. A questionnaire and focus group discussions were used to collect data from participants that helped to evaluate the programme and its implementation. One of the findings of this study was that academics joining university really need an academic induction programme that inducts them into the discourse of teaching and learning. The study also revealed that existing academics can be placed on formal study programmes in which they acquire educational qualifications with a view to equip them with useful classroom discourses. The study, therefore, concludes that new and existing academics in universities should be supported through induction programmes and placement on formal studies in teaching and learning so that they are capacitated as facilitators of learning.

Keywords: academic staff, pedagogy, programme, staff development

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Authors: Martin K. Steiger, Lukas Heisler, Hans-Georg Brachtendorf

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Deep neural networks and their variants form the backbone of many AI applications. Based on the so-called residual networks, a continuous formulation of such models as ordinary differential equations (ODEs) has proven advantageous since different techniques may be applied that significantly increase the learning speed and enable controlled trade-offs with the resulting error at the same time. For the evaluation of such models, high-performance numerical differential equation solvers are used, which also provide the gradients required for training. However, whether classical gradient-based methods are even applicable or which one yields the best results has not been discussed yet. This paper aims to redeem this situation by providing empirical results for different applications.

Keywords: deep neural networks, gradient-based learning, image processing, ordinary differential equation networks

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Authors: Shavindrie Cooray

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It is vital that students are supported in having balanced conversations about topics that might be controversial. This process is crucial to the development of critical thinking skills. This can be difficult to attain in e-learning environments, with some research finding students report a perceived loss in the quality of knowledge exchange and performance. This research investigated if Systems Theory could be applied to structure the discussion, improve information sharing, and reduce conflicts when students are working in online environments. This research involved 160 participants across four categories of student groups at a college in the Northeastern US. Each group was provided with a shared problem, and each group was expected to make a proposal for a solution. Two groups worked face-to-face; the first face to face group engaged with the problem and each other with no intervention from a facilitator; a second face to face group worked on the problem using Systems tools to facilitate problem structuring, group discussion, and decision-making. There were two types of virtual teams. The first virtual group also used Systems tools to facilitate problem structuring and group discussion. However, all interactions were conducted in a synchronous virtual environment. The second type of virtual team also met in real time but worked with no intervention. Findings from the study demonstrated that the teams (both virtual and face-to-face) using Systems tools shared more information with each other than the other teams; additionally, these teams reported an increased level of disagreement amongst their members, but also expressed more confidence and satisfaction with the experience and resulting decision compared to the other groups.

Keywords: e-learning, virtual teams, systems approach, conflicts

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Authors: Thandie Veronicah Sima, Moatlhodi Wiseman Letshwenyo

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Discharge of wastewater is one of the major sources of phosphorus entering streams, lakes and other water bodies causing undesired environmental problem such as eutrophication. This condition not only puts the ecosystem at risk but also causes severe economic damages. Stringent laws have been developed globally by different bodies to control the level of phosphorus concentrations into receiving environments. In order to satisfy the constraints, a high degree of tertiary treatment or at least a significant reduction of phosphorus concentration is obligatory. This comprehensive review summarizes phosphorus removal technologies, from the most commonly used conventional technologies such as chemical precipitation through metal addition, membrane filtration, reverse osmosis and enhanced biological phosphorus removal using activated sludge system to passive systems such as constructed wetlands and filtration systems. Trends, perspectives and scientific procedures conducted by different researchers have been presented. This review critically evaluates the advantages and limitations behind each of the technologies. Enhancement of passive systems using reactive media such as industrial wastes to provide additional uptake through adsorption or precipitation is also discussed in this article.

Keywords: adsorption, chemical precipitation, enhanced biological phosphorus removal, phosphorus removal

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Authors: Tanima Chowdhury, Sanjay Chattopadhaya, Narayan Ch. Saha

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Khoa is an indigenous heat desiccated milk product having very poor shelf life. At ambient condition, shelf-life of khoa is normally only 2 days. The aim of present study was to determine the effect of benzoic acid as preservative as well as modified atmosphere packaging (MAP) technology to enhance shelf life of khoa at 27±2°C and 65% RH. During storage, analysis of chemical, sensory as well as microbiological characteristics were taken into consideration to mark distinguishable changes between the package of modified atmosphere technology (MAP) and ordinarily packed khoa (with and without preservative) samples. The results indicated a significant decrease of moisture content, pH and sensory scores and increase in titratable acidity, standard plate count and yeast and mould count during storage, irrespective of the type of packaging conditions. However, the rate of changes in characteristics of product packed in modified atmosphere was found to be slow. The storage study indicated that the khoa packed in ordinary packaging, with and without preservative, was acceptable for 4 and 8 days, respectively, whereas for modified atmosphere packed samples, it was consumable up to 8 and 12 days, respectively.

Keywords: benzoic acid, khoa, modified atmosphere packaging, shelf life

Procedia PDF Downloads 316