Search results for: activity learning

Authors: Eka Candra Lina, Indah Widhianingrum, Mita Eka Putri, Nur Afni Evalia, Muhammad Makky

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The emulsifiable concentrate (EC) and wettable powder (WP) of Piper aduncum and Tephrosia vogelii mixed formulations were tested for their activities in the laboratory and their effectiveness in the field against cabbage pest Plutella xyostella. Cabbage leaves soaked in six different mixed formulation concentrations were tested to 2ⁿᵈ instar larvae of P. xylostella with six replications. The observation was conducted everyday until larvae reached 4ᵗʰ instar stage. Correlation between concentration and larvae mortality was analyzed using probit (POLO-PC). The survived larvae was observed by looking at the growth and development, as well as the antifeedant effects. Field efficacy test was based on LC₉₅ value from laboratory test result. The experiment used a randomized block design with 5 treatments and 3 replications to test the populations of P. xylostella larvae and insecticide effectivity. The results showed that the EC and WP mixed formulations showed insecticidal activity against P. xylostella larvae, with LC₉₅ value of 0.35% and 0.37%, respectively. The highest antifeedant effect on EC mixed formulation was 85.01% and WP mixed formulation was 86.23%. Both mixed formulations also slowed the development of larvae when compared with control. Field effication result showed that applications of EC mixed formulation were able to restrain the population of P. xylostella, with effectivity value of 71.06%. Insecticide effectivity value of EC mixed formulation was higher than WP mixed formulation and Bacillus thuringiensis formulation.

Keywords: botanical insecticide, efficacy, emulsifiable concentrate (EC), Plutella xylostella, wettable powder (WP)

Procedia PDF Downloads 224

Authors: Mukta Singh, Ratan Kumar Saha, Himadri Saha, Paramveer Singh

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The present study evaluated the effect of sub-lethal doses of antifungal drug miconazole nitrate (MCZ) on immunological responses including immune-related gene expression and its role as a prophylactic drug against S. parasitica in Labeo rohita fingerlings. Fish were fed with sub lethal doses of MCZ i.e., T1- 6.30 mg MCZ kgBW⁻¹, T2- 12.61 mg MCZ kgBW⁻¹ and T3- 25.22 mg MCZ kgBW⁻¹ and sampling was done at different time intervals for 240 h. Immunological parameters viz. lysozyme activity, oxygen radical production and plasma anti-protease activity showed significant enhancement (p < 0.05) in fish fed with T2 and T3 doses. Significant reduction in plasma protein content was observed in all the dietary groups as compared to control. Expression of immune-relevant genes like TLR-22 and β2-M showed significantly higher expression at six h and 24 h of sampling in both liver and head-kidney. However, these genes showed a down-regulation after 120 h of sampling in both the tissues. Preventive efficacy study showed that single dose of MCZ provides protection against oomycetes up to the fourth day of infection. Significantly higher mortality was observed in control diet-fed fish as compared to fish fed with MCZ medicated diet. Thus, from the study, it can be concluded that the MCZ can act as a potent antifungal agent for preventing oomycetes infection as well as to enhance the immune response.

Keywords: antifungal, immune gene, immunological, miconazole nitrate, prophylactic

Procedia PDF Downloads 236

Authors: Tomáš Nejedlý, Dominika Mrázková, Jitka Viktorová

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The blood-brain barrier (BBB) serves as a protective shield, preventing the entry of harmful substances into the central nervous system. On the other hand, it also restricts the transport of neuroactive drugs, such as antiepileptics, which mitigate epileptic seizures. Drug-resistant epilepsy is often associated with the overexpression of efflux transporters, including P-glycoprotein (P-gp) or multidrug resistance protein 1 (MRP1), on the BBB. The aim of this work is to find P-gp and MRP1 inhibitors derived from phytocannabinoids and terpenes. The work evaluates whether these compounds interact directly with P-gp or MRP1 by rhodamine 123 or fluorescein efflux assay. The effect of phytocannabinoids on the gene expression of these transporters is also studied using qPCR and Western blot. These transporters are found in BBB cells; however, we decided to use the human ovarian cancer cell line (A2780ADR) due to its overproduction of P-gp and malignant glioma cell line (U87) due to its overproduction of MRP1. The results showed that while terpenes suppressed the activity of efflux transporters, phytocannabinoids tended to decrease their expression. Terpenes demonstrated an average inhibition of 65%, surpassing phytocannabinoids, which exhibited an average inhibition of approximately 30%. Particularly noteworthy was the modulating effect of (-)-α-bisabolol with the highest activity among the compounds tested. Based on these findings, phytocannabinoids and terpenes emerge as promising natural candidates for addressing drug resistance linked to efflux transporters. Acknowledgment: The project was funded by the Grant No 22-20860S of The Czech Science Foundation.

Keywords: drug-resistant epilepsy, efflux transporters, multidrug resistance protein 1, P-glycoprotein, phytocannabinoids, terpens

Procedia PDF Downloads 45

Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

Procedia PDF Downloads 221

Authors: Muna Shafiq

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Growth in international immigration is a reflection of increased migration patterns in Canada and in other parts of the world. Canada continues to promote itself as a bilingual country, yet the bilingual French and English population numbers do not reflect this platform. Each province’s integration policies focus only on second language learning of either English or French. Moreover, since English Canadians outnumber French Canadians, maintaining, much less increasing, English-French bilingualism appears unrealistic. One solution to increasing Canadian bilingualism requires creating intercultural communication initiatives between youth in Quebec and the rest of Canada. Specifically, the focus is on active, experiential learning, where intercultural competencies develop outside traditional classroom settings. The target groups are Generation Y Millennials and Generation Z Linksters, the next generations in the career and parenthood lines. Today, Canada’s education system, like many others, must continually renegotiate lines between programs it offers its immigrant and native communities. While some purists or right-wing nationalists would disagree, the survival of bilingualism in Canada has little to do with reducing immigration. Children and youth immigrants play a valuable role in increasing Canada’s French and English speaking communities. For instance, a focus on more immersion, over core French education programs for immigrant children and youth would not only increase bilingual rates; it would develop meaningful intercultural attachments between Canadians. Moreover, a vigilant increase of funding in French immersion programs is critical, as are new initiatives that focus on experiential language learning for students in French and English language programs. A favorable argument supports the premise that other than French-speaking students in Québec and elsewhere in Canada, second and third generation immigrant students are excellent ambassadors to promote bilingualism in Canada. Most already speak another language at home and understand the value of speaking more than one language in their adopted communities. Their dialogue and participation in experiential language exchange workshops are necessary. If the proposed exchanges take place inter-provincially, the momentum to increase collective regional voices increases. This regional collectivity can unite Canadians differently than nation-targeted initiatives. The results from an experiential youth exchange organized in 2017 between students at the crossroads of Generation Y and Generation Z in Vancouver and Quebec City respectively offer a promising starting point in assessing the strength of bringing together different regional voices to promote bilingualism. Code-switching between standard, international French Vancouver students, learn in the classroom versus more regional forms of Quebec French spoken locally created regional connectivity between students. The exchange was equally rewarding for both groups. Increasing their appreciation for each other’s regional differences allowed them to contribute actively to their social and emotional development. Within a sociolinguistic frame, this proposed model of experiential learning does not focus on hands-on work experience. However, the benefits of such exchanges are as valuable as work experience initiatives developed in experiential education. Students who actively code switch between French and English in real, not simulated contexts appreciate bilingualism more meaningfully and experience its value in concrete terms.

Keywords: experiential learning, intercultural communication, social and emotional learning, sociolinguistic code-switching

Procedia PDF Downloads 126

Authors: Ricky Leung

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Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve the health and well-being of individuals and communities. One way AI can be used to enhance social media in health organizations is through sentiment analysis. This involves analyzing the emotions expressed in social media posts to better understand public opinion and respond accordingly. This can help organizations gauge the impact of their campaigns, track the spread of misinformation, and improve communication with the public. While social media is a useful tool, researchers and practitioners have expressed fear that it will be used for the spread of misinformation, which can have serious consequences for public health. Health organizations must work to ensure that AI systems are transparent, trustworthy, and unbiased so they can help minimize the spread of misinformation. In conclusion, AI and ML have the potential to greatly enhance the use of social media in health organizations. These technologies can help organizations effectively manage large amounts of data and understand stakeholders' sentiments. However, it is important to carefully consider the potential consequences and ensure that these systems are carefully designed to minimize the spread of misinformation.

Keywords: AI, ML, social media, health organizations

Procedia PDF Downloads 74

Authors: Kanika Chowdhary, Satyawati Sharma

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Endophytic microbes are hosted inside plants in a symbiotic and hugely benefitting relationship. Exploring agriculturally beneficial endophytes is quite a prospective field of research. In the present work fungal endophytes associated with aromatic plant Ocimum basicilium L. were investigated for biocontrol potential. The anti-plant pathogenic activity of fungal endophytes was tested against causal agent of stem rot Sclerotinia sclerotiorum. 75 endophytic fungi were recovered through culture-dependent approach. Fungal identification was performed both microscopically and by rDNA ITS sequencing. Curvuaria lunata (Sb-6) and Colletotrichum lindemuthianum (Sb-8) inhibited 86% and 72% mycelia growth of S. sclerotinia on Sabouraud dextrose agar medium at 7.4 pH. Small-scale fermentation was carried out on sterilised oatmeal grain medium. In another set of experiment, fungi were grown in oatmeal grain medium amended with certain biostimulants such as aqueous seaweed extract (10% v/w); methanolic seaweed extract (5% v/w); cow urine (20% v/w); biochar (10% w/w) in triplicate along with control of each to ascertain the degree of metabolic difference and anti-plant pathogenic activity induced. Phytochemically extracts of both the fungal isolates showed the presence of flavanoids, phenols, tannins, alkaloids and terpenoids. Ethylacetate extract of C. lunata and C. lindemuthianum suppressed S. sclerotinia conidial germination at IC50 values of 0.514± 0.02 and 0.913± 0.04 mg/ml. Therefore, fungal endophytes of O. basicilium are highly promising bio-resource agent, which can be developed further for sustainable agriculture.

Keywords: endophytic fungi, ocimum basicilium, sclerotinia sclerotiorum, biostimulants

Procedia PDF Downloads 163

Authors: Mun Yee Chan, Sin Ming Goh, Lisa Gaik Ai Ong

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Approximately 10,000 different types of dyes and pigments are being used in various industrial applications yearly, which include the textile and printing industries. However, these dyes are difficult to degrade naturally once they enter the aquatic system. Their high persistency in natural environment poses a potential health hazard to all form of life. Hence, there is a need for alternative dye removal strategy in the environment via bioremediation. In this study, fungi laccase is investigated via commercial agar dyes plates and submerged fermentation to explore the application of fungi laccase in textile dye wastewater treatment. Two locally isolated basidiomycetes were screened for laccase activity using media added with commercial dyes such as 2, 2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS), guaiacol and Remazol Brillant Blue R (RBBR). Isolate TBB3 (1.70±0.06) and EL2 (1.78±0.08) gave the highest results for ABTS plates with the appearance of greenish halo on around the isolates. Submerged fermentation performed on Isolate TBB3 with the productivity 3.9067 U/ml/day, whereas the laccase activity for Isolate EL2 was much lower (0.2097 U/ml/day). As isolate TBB3 showed higher laccase production, it was subjected to molecular characterization by DNA isolation, PCR amplification and sequencing of ITS region of nuclear ribosomal DNA. After being compared with other sequences in National Center for Biotechnology Information (NCBI database), isolate TBB3 is probably from species Trametes hirsutei. Further research work can be performed on this isolate by upscale the production of laccase in order to meet the demands of the requirement for higher enzyme titer for the bioremediation of textile dyes.

Keywords: bioremediation, dyes, fermentation, laccase

Procedia PDF Downloads 339

Authors: Yasàlu Haruna

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In every transaction, the use of language is central regardless of form or complexity if any meaning is expected to be harvested therefrom. Students constituting a population group in the learning landscape of Nigeria occupy a central position with a propensity to excel or otherwise in the context of communication, especially in the learning process and social interaction. The nature or quantum of anxiety or confidence in speaking a second language is not only peculiar to societies where the second language is not an official language but to a degree, the linguistic gap created by adoption and adaptation syndrome manifests in created anxiety or lack of confidence especially where mastery of a spoken language becomes a major challenge. This paper explores the manner in which linguistic complexity and cultural barriers combine to widen the adaptation and adoption gap. In much the same way, typical issues of pronouncement, intonation and accent difficulties are vital variables that explain the root cause of anxiety. Using a combination of primary and secondary sources of data expressed in questionnaires, key informant interviews and other available data, the paper concludes that the non-integration of anxiety possibility into the education delivery framework has left a lot to be needed in cultivating second language speakers among students of Nigerian Colleges of Education. In addition, cultural barriers and the absence of integration interfaces in the course of learning within and outside the classroom contribute to further widening the gap. Again, colleagues/mates/conversation partners' mastery of a second language remains a contributory factor largely due to the quality of the preparatory school system in many parts of the country. The paper recommends that national policies and frameworks must be reviewed to consider integration windows where culture and conversation partner deficiencies can be remedied through educational events such as debates, quizzes and symposia; improvements can be attained while commercial advertisements are tailored towards seeking for adoption of second language in commerce and major cultural activities.

Keywords: cultural barriers, integration, college of education and adaptation, second language

Procedia PDF Downloads 68

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

Procedia PDF Downloads 28

Authors: Valery Yakubovich, Shuping wu

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Organization theorists have long claimed that organizational innovations are nontechnological, in part because they are unpatentable. The claim rests on the assumption that organizational innovations are abstract ideas embodied in persons and contexts rather than in context-free practical tools. However, over the last three decades, organizational knowledge has been increasingly embodied in digital tools which, in principle, can be patented. To provide the first empirical evidence regarding the patentability of organizational innovations, we trained two machine learning algorithms to identify a population of 205,434 patent applications for organizational technologies (OrgTech) and, among them, 141,285 applications that use organizational innovations accumulated over the 20th century. Our event history analysis of the probability of patenting an OrgTech invention shows that ideas from organizational innovations decrease the probability of patent allowance unless they describe a practical tool. We conclude that the present-day digital transformation places organizational innovations in the realm of high tech and turns the debate about organizational technologies into the challenge of designing practical organizational tools that embody big ideas about organizing. We outline an agenda for patent-based research on OrgTech as an emerging phenomenon.

Keywords: organizational innovation, organizational technology, high tech, patents, machine learning

Procedia PDF Downloads 106

Authors: Yael Leibovitch

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Teaching academic writing is a source of concern for secondary schools. Many students struggle to meet the basic standards of literacy while teacher confidence in this arena remains low. These issues are compounded by the conventionally prescriptive character of writing instruction, which fails to engage student writers. At the same time, a growing body of research on dialogic teaching has highlighted the powerful role of talk in student learning. With the intent of enhancing pedagogical capability, this paper shares finding from a co-inquiry case study that investigated how teachers think about and negotiate classroom discourse to position students as effective academic writers and thinkers. Using a range of qualitative methods, this project closely documents the iterative collaboration of educators as they sought to create more opportunities for dialogic engagement. More specifically, it triangulates both teacher and student data regarding the efficacy of interdependent thinking and collaborative reasoning as organizing principals for literacy learning. Findings indicate that a dialogic teaching repertoire helps to develop the cognitive and metacognitive skills of adolescent writers. In addition, they underscore the importance of sustained professional collaboration to the uptake of new writing pedagogies.

Keywords: dialogic teaching, writing, teacher professional development, student literacy

Procedia PDF Downloads 201

Authors: Mahsa Jalili, Trude Johansen, Signe Dille Lovmo, Turid Rustad, Rolf Erik Olsen, Atle M. Bones

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The quality of fish muscle is a key factor in fish industry, and dietary ingredients can influence fish quality. The aim of this study was to examine the impact of krill meal, soybean meal, Bactocell® and butyrate fortified feeds and control diet on characteristics of salmon fillet. Thirty Atlantic salmon (6 per each group) were farmed for 12 weeks. All the fish were killed and frozen immediately. The white muscle from top posterior part of dorsal fin was dissected to analyze fat content, carotenoid content, content of water-soluble and salt-soluble proteins, cathepsin B and cathepsin B-L activities. ANOVA test was used to analyze mean and standard error of mean values at 0.05 significance level. There were significant difference in cathepsin B activity, water-soluble proteins and salt-soluble proteins (p-value= 0.005, 0.009 and 0.002). The mean values of other factors were not significantly different among the groups. Cathepsin B activity was higher in soymeal group. Water-soluble proteins were reported higher in soy meal and krill groups and salt-soluble proteins were significantly higher in soy meal and butyrate rich diets. Although soy meal has proven effect on enteritis, it results in higher percentage of protein in fillets. On the other hand, this feeding may have role in textural deterioration of fillets owing to higher values of endogenous cathepsin B in soymeal group.

Keywords: aquaculture, food quality, Krill protein extract, prebiotics, probiotics, Salmo salar, soy

Procedia PDF Downloads 195

Authors: Nunuy Nuraeni, Vera Amalia Lestari, Atri Laranova, Viena Nissa Mien Fadhillah, Mutia, Muhammad Ikhlas Abdian Putra

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Introduction: Obesity is a common disease with high prevalence especially in developing countries including Indonesia. The obesitygenic lifestyle such as excessive intake of food, sedentary lifestyle is the major environmental etiologies of obesity. Obesity is also as one of burden disease with high morbidity due to its complication, such as diabetes mellitus and hypertension. The objective of this literature review is to know how the Epigallocathecin-3-Gallate of Green tea or Camellia sinensis effect as anti-obesity agent and reduce the complication of obesity. Material and Methods: This study based on the secondary data analysis complemented by primary data collection from several journal and textbook. We identified the effect of Epigallocathecin-3-Gallate of Green tea or Camellia sinensis as adjuvant therapy for management obesity and to prevent the complications of obesity. Results: Based on the result, Green tea or Camellia sinensis contain Epigallocathecin-3-Gallate (EGCG) that has anti-obesity effect such as induce apoptosis, inhibit adipogenesis, increasing lipolytic activity, increasing fat oxidation and thermogenesis. Discussion: EGCG are naturally distributed in green tea, that contains a biological activity that has a potential effect to treat obesity. Conclusion: EGCG are capable to treat obesity. By consuming EGCG can prevent obesity in normal health person and prevent complication in patient with obesity.

Keywords: adjuvant therapy, anti-obesity effect, complication, epigallocathecin-3-gallate, obesity

Procedia PDF Downloads 261

Authors: Ali H.Alyami, Hussam Darraj, Faisal Hakami, Mohammed Awaf, Sulaiman Hamdi, Nawaf Bakri, Abdulaziz Saber, Khalid Hakami, Almuhanad Alyami, Mohammed khashab

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Introduction: Adolescents frequently self-report pain, according to epidemiological research. The knee is one of the sites where the pain is most common. One of the main factors contributing to the number of years people spend disabled and having substantial personal, societal, and economic burdens globally are musculoskeletal disorders. Adolescents may have knee pain due to an abrupt, traumatic injury or an insidious, slowly building onset that neither the adolescent nor the parent is aware of. Objectives: The present study’s authors aimed to estimate the prevalence of knee pain in Saudi adolescents. Methods: This cross-sectional survey, carried out from June to November 2022, included 676 adolescents ages 10 to 18. Data are presented as frequencies and percentages for categorical variables. Analysis of variance (ANOVA) was used to compare means between groups, while the chi-square test was used for the comparison of categorical variables. Statistical significance was set at P< 0.05.Result: Adolescents were invited to take part in the study. 57.5% were girls, and 42.5% were males,68.8% were 676 aged between 15 and 18. The prevalence of knee pain was considerably high among females (26%), while it was 19.2% among males. Moreover, age was a significant predictor for knee pain; also BMI was significant for knee pain. Conclusion: Our study noted a high rate of knee pain among adolescents, so we need to raise awareness about risk factors. Adolescent knee pain can be prevented with conservative methods and some minor lifestyle/activity modifications.

Keywords: knee pain, prevalence of knee pain, exercise training, physical activity

Procedia PDF Downloads 90

Authors: Evrim Genc Kumtepe, Elif Toprak, Aylin Ozturk, Gamze Tuna, Hakan Kilinc, Irem Aydin Menderis

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Support services are very significant elements for all educational institutions in general; however, for distance learners, these services are more essential than traditional (face-to-face) counterparts. One of the most important reasons for this is that learners and instructors do not share the same physical environment and that distance learning settings generally require intrapersonal interactions rather than interpersonal ones. Some learners in distance learning programs feel isolated. Furthermore, some fail to feel a sense of belonging to the institution because of lack of self-management skills, lack of motivation levels, and the need of being socialized, so that they are more likely to fail or drop out of an online class. In order to overcome all these problems, support services have emerged as a critical element for an effective and sustainable distance education system. Within the context of distance education support services, it is natural to include technology-based and web-based services and also the related materials. Moreover, institutions in education sector are expected to use information and communication technologies effectively in order to be successful in educational activities and programs. In terms of the sustainability of the system, an institution should provide distance education services through ICT enabled processes to support all stakeholders in the system, particularly distance learners. In this study, it is envisaged to develop a model based on the current support services literature in the field of open and distance learning and the applications of the distance higher education institutions. Specifically, content analysis technique is used to evaluate the existing literature in the distance education support services, the information published on websites, and applications of distance higher education institutions across the world. A total of 60 institutions met the inclusion criteria which are language option (English) and availability of materials in the websites. The six field experts contributed to brainstorming process to develop and extract codes for the coding scheme. During the coding process, these preset and emergent codes are used to conduct analyses. Two coders independently reviewed and coded each assigned website to ensure that all coders are interpreting the data the same way and to establish inter-coder reliability. Once each web page is included in descriptive and relational analysis, a model of support services is developed by examining the generated codes and themes. It is believed that such a model would serve as a quality guide for future institutions, as well as the current ones.

Keywords: support services, open education, distance learning, support model

Procedia PDF Downloads 185

Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

Procedia PDF Downloads 29

Authors: Amelia R. Anshar, Dini Kurnia, Muh A. Bahar

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Nowadays, there are so many incidences had been reported in pet animals regarding drug overdose caused by incorrect doses of a non-steroidal anti-inflammatory drug (NSAID), for instance, meloxicam. As supporting treatment, the avocado is used in traditional medicine to treat or prevent some health cases. The study was aimed at providing the basis for the antioxidant activity of avocado puree in animal medicine. Experimental animals used in this study were 24 male rats that were randomly divided into 4 groups (n=6). Control Group I got 1 ml CMC 1% and control II got meloxicam 30 mg/kgBB and 1 ml CMC 1%. Treatment group I got meloxicam 30 mg/kgBB and avocado 5 g/kgBB/day and treatment II got meloxicam 30 mg/kgBB and avocado 10 g/kgBB/day. The study was conducted over 8 days, then the level of Blood Urea Nitrogen and creatinine of the white rats were examined in 1st day and 8th day. The results were analyzed by ANOVA Two Way With Replication, then followed by T-test (α = 0,05) if there were a difference. Comparison test among the four groups after treatment with avocado using Anova Two Way With Replication test showed that there were significant differences between the mean of the four groups either decreased levels of Blood Urea Nitrogen and creatinine with p < 0,05. Treatment group I and II received treatment showed remarkable (p < 0,05) decreases ini Blood Urea Nitrogen level by 27,17 mg/dl and 17,83 mg/dl respectively. There was also significant decrease in the values of creatinine in Treatment group I and treatment group II by 0,983 mg/dl and 0,713 mg/dl respectively. The conclusion of this study was that avocado decreases level of Blood Urea Nitrogen and creatinine in white rats which are exposed to toxic doses of meloxicam.

Keywords: avocado, blood urea nitrogen, creatinine, meloxicam

Procedia PDF Downloads 290

Authors: Mesbah Khaled, Bouraoui Ouissal, Benkiniouar Rachid, Belkhiri Lotfi

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Alstonia, which are tropical plants with a wide geographical distribution, have been divided into different sections by different authors based on previous studies of several species within the genus. Monachino divides Alstonia into 5 sections, while Pichon divides it into 3 sections. Several plants belonging to this genus, such as Alstonia brassii, have been used in traditional folk medicine to treat ailments such as fever, malaria and dysentery]. Previous studies focusing on the chemical composition of these plants have successfully identified indol alkaloids with cytotoxic, anti-diabetic and anti-inflammatory properties. The newly discovered monomers are structurally similar to the backbones of picralin, affinisin and macrolin. On the other hand, all recently isolated dimeric compounds have a macrolin moiety. In this study, a computational analysis was performed on a series of novel molecules, including both monomeric and dimeric compounds with different structural frameworks. This investigation represents the first computational study of these molecules using an in silico approach incorporating 2D-QSAR data. The analysis involved various computational techniques, including 2D-QSAR modelling, molecular docking studies and subsequent validation by molecular dynamics simulation and assessment of ADMET properties. The chemical composition was identified by 1D and 2D NMR. Eight new alkaloids were isolated, 5 monomers and 3 dimers. In this section, we focus on the biological activity of 4 new alkaloids belonging to two different skeletons, the affinisine skeleton.

Keywords: affinisine, talcarpine, macroline, cytotoxicity, alkaloids

Procedia PDF Downloads 257

Authors: Neha Singh, Ananya Arora

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Facial expression measurement plays a crucial role in the identification of emotion. Facial expression plays a key role in psychophysiology, neural bases, and emotional disorder, to name a few. The Facial Action Coding System (FACS) has proven to be the most efficient and widely used of the various systems used to describe facial expressions. Coders can manually code facial expressions with FACS and, by viewing video-recorded facial behaviour at a specified frame rate and slow motion, can decompose into action units (AUs). Action units are the most minor visually discriminable facial movements. FACS explicitly differentiates between facial actions and inferences about what the actions mean. Action units are the fundamental unit of FACS methodology. It is regarded as the standard measure for facial behaviour and finds its application in various fields of study beyond emotion science. These include facial neuromuscular disorders, neuroscience, computer vision, computer graphics and animation, and face encoding for digital processing. This paper discusses the conceptual basis for FACS, a numerical listing of discrete facial movements identified by the system, the system's psychometric evaluation, and the software's recommended training requirements.

Keywords: facial action, action units, coding, machine learning

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Authors: Sousan Arafeh

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Educational systems and schools worldwide are increasingly reliant on information and communication technology (ICT). Unfortunately, most educational leadership development programs do not offer formal curricular and/or field experiences that prepare students for managing ICT resources, personnel, and processes. The result is a steep learning curve for the leader and his/her staff and dissipated organizational energy that compromises desired outcomes. To address this gap in education leaders’ development, Arafeh’s Integrated Technology Leadership Model (AITLM) was created. It is a conceptual model and tool that educational leadership students can use to better understand the ICT ecology that exists within their schools. The AITL Model consists of six 'infrastructure types' where ICT activity takes place: technical infrastructure, communications infrastructure, core business infrastructure, context infrastructure, resources infrastructure, and human infrastructure. These six infrastructures are further divided into 16 key areas that need management attention. The AITL Model was created by critically analyzing existing technology/ICT leadership models and working to make something more authentic and comprehensive regarding school leaders’ purview and experience. The AITL Model then served as a tool when it was distributed to over 150 educational leadership students who were asked to review it and qualitatively share their reactions. Students said the model presented crucial areas of consideration that they had not been exposed to before and that the exercise of reviewing and discussing the AITL Model as a group was useful for identifying areas of growth that they could pursue in the leadership development program and in their professional settings. While development in all infrastructures and key areas was important for students’ understanding of ICT, they noted that they were least aware of the importance of the intangible area of the resources infrastructure. The AITL Model will be presented and session participants will have an opportunity to review and reflect on its impact and utility. Ultimately, the AITL Model is one that could have significant policy and practice implications. At the very least, it might help shape ICT content in educational leadership development programs through curricular and pedagogical updates.

Keywords: education leadership, information and communications technology, ICT, leadership capacity building, leadership development

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Authors: Ricardo Pinto Mario Covele, Patricio V. Langa, Patrick Swanzy

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The English language has been recognized as a universal medium of instruction in academia, especially in Higher Education Institutions (HEIs) hence exerting enormous influence within the context of research and publication. By extension, the English Language has been embraced by scholars from non-English speaking countries. The purpose of this review was to synthesize the discussions using four databases. Discussion in the English language in the career development of academics, particularly in non-English speaking universities, is largely less visible. This paper seeks to fill this gap and to improve the visibility of the English language in the career development of academics focusing on non-English language speaking universities by undertaking a systematic literature review. More specifically, the paper addresses the language policy, English language learning model as a second language, sociolinguistic field and career development, methods, as well as its main findings. This review analyzed 75 relevant resources sourced from Western Cape’s Library, Scopus, Google scholar, and web of science databases from November 2020 to July 2021 using the PQRS framework as an analytical lens. The paper’s findings demonstrate that, while higher education continues to be under-challenges of English language usage, literature targeting non-English speaking universities remains less discussed than it is often described. The findings also demonstrate the dominance of English language policy, both for knowledge production and dissemination of literature challenging emerging scholars from non-English speaking HEIs. Hence, the paper argues for the need to reconsider the context of non-English language speakers in the English language in the career development of academics’ research, both as empirical fields and as emerging knowledge producers. More importantly, the study reveals two bodies of literature: (1) the instrumentalist approach to English Language learning and (2) Intercultural approach to the English Language for career opportunities, classified as the appropriate to explain the English language learning process and how is it perceived towards scholars’ academic careers in HEIs.

Keywords: English language, public and private universities, language policy, career development, non-English speaking countries

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Authors: Şakir Bingöl, Abdullah Emre Aydemir, Abdullah Saado, Ahmet Akıl, Elif Canbaz, Feyza Nur Bulgurcu, Gizem Uzun, Günsu Bilge Dal, Muhammedcan Pirinççi

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Smart contracts, which can be applied in many different areas, from financial applications to the internet of things, come to the fore with their security, low cost, and self-executing features. In this paper, it is focused on peer-to-peer (P2P) energy trading and the implementation of the smart contract on the Ethereum blockchain. It is assumed a microgrid consists of consumers and prosumers that can produce solar and wind energy. The proposed architecture is a system where the prosumer makes the purchase or sale request in the smart contract and the maximum price obtained through the distribution system operator (DSO) by forecasting. It is aimed to forecast the hourly maximum unit price of energy by using deep learning instead of a fixed pricing. In this way, it will make the system more reliable as there will be more dynamic and accurate pricing. For this purpose, Istanbul's energy generation, energy consumption and market clearing price data were used. The consistency of the available data and forecasting results is observed and discussed with graphs.

Keywords: energy trading smart contract, deep learning, microgrid, forecasting, Ethereum, peer to peer

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Authors: D. Zahran, M. AlKhazindar, M. Khalil, E. T. A. Sayed

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Streptomyces scabies is a pathogenic actinomycete that infects potato crop causing severe production losses. Actinophages affect the composition and diversity of the bacterial population, thereby, can be used as a biological control. Samples of actinomycetes and phages were collected from different cultivated soils including farms of Faculty of Science, Faculty of Agriculture and different locations in Giza, Egypt. Actinomycetes were identified by using biochemical, morphological tests and molecular studies using 16S rRNA sequencing. Two specific phages (E1 and E2) against Streptomyces scabies and other hosts were isolated. Phages were identified using dilution end point (DEP), longevity in vitro (LIV), thermal inactivation point (TIP), host range and electron microscopy. PhageE1 was characterized by 10-8 (DEP),180 days(LIV), 95°C(TIP), narrow host range and electron microscopy showed ahead (59.9 nm) and neck (10.4nm). On the other hand phageE2 had 10-20 (DEP),180 days(LIV), 90°C(TIP), and the size of head was (67.2 nm) and tail (114nm). Antiviral activity was also studied using different chemicals (NaCL, KCL, CaCL2, BaCL2, CoCL2, AgNO3, ALCL3and HgCL2) with different concentrations and different plant extracts with different concentrations (star anise, tea, tillia, peppermint, ginger, cumin, chamomile, turmeric cinnamon, marjoram and black cumin). Both Phage E1and phage E2 were vulnerable to (cumin, ginger, chamomile, guavas leaves and star anise) but resistant to (Tillie, marjoram, fennelflower seeds, peppermint, and cinnamon).

Keywords: potato scab, actinophages, biological control, electron microscopy, TIP, DEP, LIV, antiviral activity

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: M. Thuanthong, W. Youravong, N. Sirinupong

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Nile tilapia (Oreochromis niloticus) is one of fish species cultured in Thailand with a high production volume. A lot of skin is generated during fish processing. In addition, there are many research reported that fish skin contains abundant of collagen. Thus, the use of Nile tilapia skin as collagen source can increase the benefit of industrial waste. In this study, Acid soluble collagen (ASC) was extracted at 5, 15 or 25 ˚C with 0.5 M acetic acid then the acid was removed out and collagen was concentrated by ultrafiltration-diafiltration (UFDF). The triple helix collagen from UFDF process was used as substrate to produce collagen peptides by alcalase hydrolysis in an enzymatic membrane reactor (EMR) coupling with 1 kDa molecular weight cut off (MWCO) polysulfone hollow fiber membrane. The results showed that ASC extracted at high temperature (25 ˚C) with 0.5 M acetic acid for 5 h still preserved triple helix structure. In the UFDF process, the acid removal was higher than 90 % without any effect on ASC properties, particularly triple helix structure as indicated by circular dichroism spectrum. Moreover, Collagen from UFDF was used to produce collagen peptides by EMR. In EMR, collagen was pre-hydrolyzed by alcalase for 60 min before introduced to membrane separation. The EMR operation was operated for 10 h and provided a good of protein conversion stability. The results suggested that there is a successfulness of UF in application for acid removal to produce ASC with desirable preservation of its quality. In addition, the EMR was proven to be an effective process to produce low molecular weight peptides with ACE-inhibitory activity properties.

Keywords: acid soluble collagen, ultrafiltration-diafiltration, enzymatic membrane reactor, ace-inhibitory activity

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Authors: Hyun Young Kim, Min Jung Kim, Ji Hyun Kim, Sanghyun Lee, Eun Ju Cho

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Oxidative stress that results from overproduction of free radicals can lead to pathogenesis of human diseases including cancer, neurodegenerative diseases, and cardiovascular disease. Aster yomena (Kitam.) Honda (A. yomena) belonging to Compositae family is a perennial plant, and it has anti-inflammatory, anti-asthmatic and anti-obesity effects. In this study, we investigated the antioxidative effect of A. yomena by measuring 2, 2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl radical (˙OH) and superoxide radical (O₂⁻) scavenging activities in vitro. A. yomena was extracted with ethanol and then partitioned with n-hexane, methylene chloride (CH₂Cl₂), ethyl acetate (EtOAc) and n-butanol (n-BuOH). In DPPH radical scavenging assay, the concentration of A. yomena from 10 to 100μg/mL dose-dependently raised the inhibition of DPPH oxidation. Especially, EtOAc fraction of A. yomena showed the highest DPPH radical scavenging activity among other fractions. The ˙OH radical scavenging activities of the extract and four fractions of A. yomena were increased by over 80% at a concentration of 50μg/mL. Especially, the IC50 value of EtOAc fraction was 0.03 μg/mL that is the lowest value compared with the values of other fractions. In addition, we found that the EtOAc fraction of A. yomena was showed to be better at O₂⁻ radical scavenging than other fractions. Taken together these results, we suggested that A. yomena, especially EtOAc fraction, can be used as a natural antioxidant against free radicals. Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2016R1D1A1B03931593).

Keywords: Aster yomena (Kitam.) Honda (A. yomena), free radicals, antioxidant, EtOAc fraction

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Authors: Rajender Dahiya

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Investigation outcomes of major process incidents have been consistent for decades and validate that the causes and consequences are often identical. The debate remains as we continue to experience similar process incidents even with enormous development of new tools, technologies, industry standards, codes, regulations, and learning processes? The objective of this paper is to investigate the most common causes of major industrial incidents and reveal industry challenges and best practices to prevent such incidents. The author, in his current role, performs audits and inspections of a variety of high-hazard industries in North America, including petroleum refineries, chemicals, petrochemicals, manufacturing, etc. In this paper, he shares real life scenarios, examples, and case studies from high hazards operating facilities including key challenges and best practices. This case study will provide a clear understanding of the importance of near miss incident investigation. The incident was a Safe operating limit excursion. The case describes the deficiencies in management programs, the competency of employees, and the culture of the corporation that includes hazard identification and risk assessment, maintaining the integrity of safety-critical equipment, operating discipline, learning from process safety near misses, process safety competency, process safety culture, audits, and performance measurement. Failure to identify the hazards and manage the risks of highly hazardous materials and processes is one of the primary root-causes of an incident, and failure to learn from past incidents is the leading cause of the recurrence of incidents. Several investigations of major incidents discovered that each showed several warning signs before occurring, and most importantly, all were preventable. The author will discuss why preventable incidents were not prevented and review the mutual causes of learning failures from past major incidents. The leading causes of past incidents are summarized below. Management failure to identify the hazard and/or mitigate the risk of hazardous processes or materials. This process starts early in the project stage and continues throughout the life cycle of the facility. For example, a poorly done hazard study such as HAZID, PHA, or LOPA is one of the leading causes of the failure. If this step is performed correctly, then the next potential cause is. Management failure to maintain the integrity of safety critical systems and equipment. In most of the incidents, mechanical integrity of the critical equipment was not maintained, safety barriers were either bypassed, disabled, or not maintained. The third major cause is Management failure to learn and/or apply learning from the past incidents. There were several precursors before those incidents. These precursors were either ignored altogether or not taken seriously. This paper will conclude by sharing how a well-implemented operating management system, good process safety culture, and competent leaders and staff contributed to managing the risks to prevent major incidents.

Keywords: incident investigation, risk management, loss prevention, process safety, accident prevention

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Authors: Saule Shazhanbayeva, Denise van der Merwe

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Study hypothesis: Nazarbayev Intellectual School of Kyzylorda’s Biology teachers can use STEM-integrated learning to improve students' problem-solving ability and responsibility as global citizens. The significance of this study is to indicate how the use of STEM integrative learning during Biology lessons could contribute to forming globally-minded students who are responsible community members. For the purposes of this study, worldview is defined as a view that is broader than the country of Kazakhstan, allowing students to see the significance of their scientific contributions to the world as global citizens. The context of worldview specifically indicates that most students have never traveled outside of their city or region within Kazakhstan. In order to broaden student understanding, it is imperative that students are exposed to different world views and contrasting ideas within the educational setting of Biology as the science being used for the research. This exposure promulgates students understanding of the significance they have as global citizens alongside the obligations which would rest on them as scientifically minded global citizens. Integrative learning should be Biological Science - with Technology and engineering in the form of problem-solving, and Mathematics to allow improved problem-solving skills to develop within the students of Nazarbayev Intellectual School (NIS) of Kyzylorda. The school's vision is to allow students to realise their role as global citizens and become responsible community members. STEM allows integrations by combining four subject skills to solve topical problems designed by educators. The methods used are based on qualitative analysis: for students’ performance during a problem-solution scenario; and Biology teacher interviews to ascertain their understanding of STEM implementation and willingness to integrate it into current lessons. The research indicated that NIS is ready for a shift into STEM lessons to promote globally responsible students. The only additional need is for proper STEM integrative lesson method training for teachers.

Keywords: global citizen, STEM, Biology, high-school

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Authors: Rashad R. Al-Hindi

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The aim of this study was to isolate and identify lactic acid bacteria and evaluate their therapeutic and food preservation importance. Ninety-three suspected lactic acid bacteria (LAB) were isolated from thirteen different raw and fermented milk of indigenous sources in the Kingdom of Saudi Arabia. The identification of forty-six selected LAB strains and genetic relatedness were performed based on 16S rDNA gene sequence comparison. The LAB counts in certain samples were higher under microaerobic than anaerobic conditions. The identified LAB belonged to genera Enterococcus (16 strains), Lactobacillus (9 strains), Weissella (10 strains), Streptococcus (8 strains) and Lactococcus (3 strains). Phylogenetic tree generated from the full-length (~1.6 kb) sequences confirmed previous findings. Utilization of shorter 16S rDNA sequences (~1.0 kb) also discriminated among strains of which V2 region was the most effective. None of the strains exhibited resistance to clinically relevant antibiotics or undesirable hemolytic activity, while they differed in other probiotic characteristics, e.g., tolerance to acidic pH, resistance to bile salt, and antibacterial activity. In conclusion, the isolates Lactobacillus casei MSJ1, Lactobacillus casei Dwan5, Lactobacillus plantarum EyLan2 and Enterococcus faecium Gail-BawZir8 are likely the best probiotic LAB and we speculate that studying the synergistic effects of bacterial combinations might result in the occurrence of more effective probiotic potential. We argue that the raw and fermented milk of animals hosted in Saudi Arabia, especially stirred yogurt (Laban) made from camel milk, are rich in LAB with promising probiotics potential.

Keywords: fermented foods, lactic acid bacteria, probiotics, Saudi Arabia

Procedia PDF Downloads 183