Search results for: Learning materials

Authors: Belyihun Muchie

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This study investigates the low English oral communication skills of 5 second-year English major students at Debark University. It aims to identify the key factors contributing to their weaknesses and propose effective interventions to improve their spoken English proficiency. Mixed-methods research will be employed, utilizing observations, questionnaires, and semi-structured interviews to gather data from the participants. To clearly identify these factors, structured and informal observations will be employed; the former will be used to identify their fluency, pronunciation, vocabulary use, and grammar accuracy, and the later will be suited to observe the natural interactions and communication patterns of learners in the classroom setting. The questionnaires will assess their self-perceptions of their skills, perceived barriers to fluency, and preferred learning styles. Interviews will also delve deeper into their experiences and explore specific obstacles faced in oral communication. Data analysis will involve both quantitative and qualitative responses. The structured observation and questionnaire will be analyzed quantitatively, whereas the informal observation and interview transcripts will be analyzed thematically. Findings will be used to identify the major causes of low oral communication skills, such as limited vocabulary, grammatical errors, pronunciation difficulties, or lack of confidence. They are also helpful to develop targeted solutions addressing these causes, such as intensive pronunciation practice, conversation simulations, personalized feedback, or anxiety-reduction techniques. Finally, the findings will guide designing an intervention plan for implementation during the action research phase. The study's outcomes are expected to provide valuable insights into the challenges faced by English major students in developing oral communication skills, contribute to the development of evidence-based interventions for improving spoken English proficiency in similar contexts, and offer practical recommendations for English language instructors and curriculum developers to enhance student learning outcomes. By addressing the specific needs of these students and implementing tailored interventions, this research aims to bridge the gap between theoretical knowledge and practical speaking ability, equipping them with the confidence and skills to flourish in English communication settings.

Keywords: oral communication skills, mixed-methods, evidence-based interventions, spoken English proficiency

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Simon Espigolé, Igor Shalyminov, Helen Hastie

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Deep machine learning techniques including deep neural networks (DNN) have been used to model language and dialogue for conversational agents to perform tasks, such as giving technical support and also for general chit-chat. They have been shown to be capable of generating long, diverse and coherent sentences in end-to-end dialogue systems and natural language generation. However, these systems tend to imitate the training data and will only generate the concepts and language within the scope of what they have been trained on. This work explores how deep neural networks can be used in a task that would normally require human creativity, whereby the human would read the movie description and/or watch the movie and come up with a compelling, interesting movie title. This task differs from simple summarization in that the movie title may not necessarily be derivable from the content or semantics of the movie description. Here, we train a type of DNN called a sequence-to-sequence model (seq2seq) that takes as input a short textual movie description and some information on e.g. genre of the movie. It then learns to output a movie title. The idea is that the DNN will learn certain techniques and approaches that the human movie titler may deploy that may not be immediately obvious to the human-eye. To give an example of a generated movie title, for the movie synopsis: ‘A hitman concludes his legacy with one more job, only to discover he may be the one getting hit.’; the original, true title is ‘The Driver’ and the one generated by the model is ‘The Masquerade’. A human evaluation was conducted where the DNN output was compared to the true human-generated title, as well as a number of baselines, on three 5-point Likert scales: ‘creativity’, ‘naturalness’ and ‘suitability’. Subjects were also asked which of the two systems they preferred. The scores of the DNN model were comparable to the scores of the human-generated movie title, with means m=3.11, m=3.12, respectively. There is room for improvement in these models as they were rated significantly less ‘natural’ and ‘suitable’ when compared to the human title. In addition, the human-generated title was preferred overall 58% of the time when pitted against the DNN model. These results, however, are encouraging given the comparison with a highly-considered, well-crafted human-generated movie title. Movie titles go through a rigorous process of assessment by experts and focus groups, who have watched the movie. This process is in place due to the large amount of money at stake and the importance of creating an effective title that captures the audiences’ attention. Our work shows progress towards automating this process, which in turn may lead to a better understanding of creativity itself.

Keywords: creativity, deep machine learning, natural language generation, movies

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Authors: Sastry Y. Kandukuri, Isak Andersen

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Despite recent advances in the manufacturing sector, quality issues remain a frequent occurrence, and can result in fatal accidents, equipment downtime, and loss of life. Adequate quality is of high importance in high-risk industries such as sea-going vessels and offshore installations in which third party quality assurance and product control play an important essential role in ensuring manufacturing quality of critical components. Classification societies play a vital role in mitigating risk in these industries by making sure that all the stakeholders i.e. manufacturers, builders, and end users are provided with adequate rules and standards that effectively ensures components produced at a high level of quality based on the area of application and risk of its failure. Quality issues have also been linked to the lack of competence or negligence of stakeholders in supply value chain. However, continued actions and regulatory reforms through modernization of rules and requirements has provided additional tools for purchasers and manufacturers to confront these issues. Included among these tools are updated ‘approval of manufacturer class programs’ aimed at developing and implementing a set of standardized manufacturing quality metrics for use by the manufacturer and verified by the classification society. The establishment and collection of manufacturing and testing requirements described in these programs could provide various stakeholders – from industry to vessel owners – with greater insight into the state of quality at a given manufacturing facility, and allow stakeholders to anticipate better and address quality issues while simultaneously reducing unnecessary failures that are costly to the industry. The publication introduces, explains and discusses critical manufacturing and testing requirements set in a leading class society’s approval of manufacturer regime and its rationale and some case studies.

Keywords: classification society, manufacturing, materials processing, materials testing, quality control

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Authors: Hamza Ouachtouk, Amine Harbi, Said Azerblou, Youssef Naimi, El Mostafa Tace

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This study delves into the structural, vibrational, magnetic, and electronic properties of La₂MMnO₆ double perovskites, where M denotes Ni, Co, and Zn. Recognized for their versatile ionic configurations within the A and B sub-lattices, double perovskite oxides have attracted considerable interest due to their extensive array of physical properties, which include multiferroic behavior, colossal magnetoresistance, and ferroelectric/piezoelectric functionalities. These materials are pivotal for energy-related technologies like solid oxide fuel cells and water-splitting catalysis, attributed to their superior oxygen ion transport and storage capabilities. This research places particular emphasis on La₂NiMnO₆ and La₂CoMnO₆, known for their distinct magnetic, electric, and multiferroic properties, and extends the investigation to La₂ZnMnO₆, synthesized via high-temperature solid-state chemistry. This addition aims to ascertain the impact of zinc substitution on these properties. Structural analysis through X-ray diffraction has confirmed a monoclinic structure within the P2₁/n space group. Comprehensive vibrational studies utilizing infrared and Raman spectroscopy, alongside additional XRD assessments, provide a detailed examination of the dynamic and electronic behaviors of these compounds. The results underscore the significant role of chemical composition in modulating their functional properties. Comparatively, this study highlights that zinc substitution notably alters the electronic and magnetic responses, which could enhance the applicability of these materials in advanced energy technologies. This expanded analysis not only reinforces our understanding of La₂MMnO₆'s physical characteristics but also highlights its potential applications in the next generation of energy solutions.

Keywords: double perovskites, structural analysis, vibrational spectroscopy, magnetic properties, electronic properties, high-temperature solid-state chemistry, La₂MMnO₆, monoclinic structure, x-ray diffraction

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Authors: Mohammed Tahir, Jonas Lagergren

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Vancron 40, a nitrided powder metallurgical tool Steel, is used in cold work applications where the predominant failure mechanisms are adhesive wear or galling. Typical applications of Vancron 40 are among others fine blanking, cold extrusion, deep drawing and cold work rolls for cluster mills. Vancron 40 positive results for cold work rolls for cluster mills and as a tool for some severe metal forming process makes it competitive compared to other type of work rolls that require higher precision, among others in cold rolling of thin stainless steel, which required high surface finish quality. In this project, three roll materials for cold rolling of stainless steel strip was examined, Vancron 40, Narva 12B (a high-carbon, high-chromium tool steel alloyed with tungsten) and Supra 3 (a Chromium-molybdenum tungsten-vanadium alloyed high speed steel). The purpose of this project was to study the depth profiles of the ironed stainless steel strips, emergence of galling and to study the lubrication performance used by steel industries. Laboratory experiments were conducted to examine scratch of the strip, galling and surface roughness of the roll materials under severe tribological conditions. The critical sliding length for onset of galling was estimated for stainless steel with four different lubricants. Laboratory experiments result of performance evaluation of resistance capability of rolls toward adhesive wear under severe conditions for low and high reductions. Vancron 40 in combination with cold rolling lubricant gave good surface quality, prevents galling of metal surfaces and good bearing capacity.

Keywords: Vancron 40, cold rolling, adhesive wear, galling, surface finish, lubricant, stainless steel

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Authors: Jihee Jang, Seowon Yoon, Gaeun Son, Minjung Kang, Joon Yeon Choeh, Kee-Hong Choi

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Background: Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple-choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological constructs to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that psychology due to small data sets and unvalidated modeling practices. Aims: The current article introduces the study method and procedure of phase II, which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods: The phase I (pilot) study was conducted on fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 425 Korean adults were recruited using a convenience sampling method via an online survey. The text data collected from interviews were analyzed using natural language processing. The results of the online survey, including demographic data, depression, anxiety, and personality inventories, were analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress (phase 2).

Keywords: personality prediction, psychological distress prediction, natural language processing, machine learning, the five-factor model of personality

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Authors: Mohammad Khatib, Salam Hadid

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Introduction: Developing nurses' cultural competence begins in their basic training and requires them to participate in an array of activities which raise their awareness and stimulate their interest, desire and curiosity about different cultures, by creating opportunities for intercultural meetings promoting the concept of 'culture' and its components, including recognition of cultural diversity and the legitimacy of the other. Importantly, professionals need to acquire specific cultural knowledge and thorough understanding of the values, norms, customs, beliefs and symbols of different cultures. Similarly, they need to be given opportunities to practice the verbal and non-verbal communication skills of other cultures according to their cultural codes. Such a system is being implemented as part of nursing studies at Zefat Academic College in two study frameworks; firstly, a course integrating nursing theory and practice in multicultural nursing; secondly, a course in learning the languages spoken in Israel focusing on medical and nursing terminology. Methods: Students participating in the 'Transcultural Nursing' course come from a variety of backgrounds: Jews, or Arabs, religious, or secular; Muslim, Christian, new immigrants, Ethiopians or from other cultural affiliations. They are required to present and discuss cultural practices that affect health. In addition, as part of the language course, students learn and teach their friends 5 spoken languages (Arabic, Russian, Amharian, Yidish, and Sign language) focusing on therapeutic interaction and communication using the vocabulary and concepts necessary for the therapeutic encounter. An evaluation of the process and the results was done using a structured questionnaire which includes series of questions relating to the contributions of the courses to their cultural knowledge, awareness and skills. 155 students completed the questionnaire. Results: A preliminary assessment of this educational system points an increase in cultural awareness and knowledge among the students as well as in their willingness to recognize the other's difference. A positive atmosphere of multiculturalism is reflected in students' mutual interest and respect was created. Students showed a deep understanding of cultural issues relating to health and care (consanguinity and genetics, food customs; cultural events, reincarnation, traditional treatments etc.). Most of the students were willing to recommend the courses to others and suggest some changes relating learning methods (more simulations, role playing and activities).

Keywords: cultural competence, nursing education, culture, language

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Authors: Mohammad H. Yarmohammadian, Mehdi Nasr Isfahani, Elham Anbari

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Introduction: The awareness of using chemical, biological, and nuclear agents in everyday industrial and non-industrial incidents has increased recently; release of these materials can be accidental or intentional. Since hospitals are the forefronts of confronting Chemical, Biological, Radiological and Nuclear( CBRN) incidents, the goal of the present research was to provide a generic protocol for CBRN incidents through a comparative review of CBRN protocols and guidelines of different countries and reviewing various books, handbooks and papers. Method: The integrative approach or research synthesis was adopted in this study. First a simple narrative review of programs, books, handbooks, and papers about response to CBRN incidents in different countries was carried out. Then the most important and functional information was discussed in the form of a generic protocol in focus group sessions and subsequently confirmed. Results: Findings indicated that most of the countries had various protocols, guidelines, and handbooks for hazardous materials or CBRN incidents. The final outcome of the research synthesis was a 50 page generic protocol whose main topics included introduction, definition and classification of CBRN agents, four major phases of incident and disaster management cycle, hospital response management plan, equipment, and recommended supplies and antidotes for decontamination (radiological/nuclear, chemical, biological); each of these also had subtopics. Conclusion: In the majority of international protocols, guidelines, handbooks and also international and Iranian books and papers, there is an emphasis on the importance of incident command system, determining the safety degree of decontamination zones, maps of decontamination zones, decontamination process, triage classifications, personal protective equipment, and supplies and antidotes for decontamination; these are the least requirements for such incidents and also consistent with the provided generic protocol.

Keywords: hospital, CBRN, decontamination, generic protocol, CBRN Incidents

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Authors: Valencia Fernandes, Dharmendra Kumar Khatri, Shashi Bala Singh

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Background and Aim: Epigenetics are the inaudible signatures of several pathological processes in the brain. This study understands the influence of DNA methylation, a major epigenetic modification, in the prefrontal cortex and hippocampus of the diabetic brain and its notable effect on the cellular chaperones and synaptic proteins. Method: Chronic high fat diet and STZ-induced diabetic mice were studied for cognitive dysfunction, and global DNA methylation, as well as DNA methyltransferase (DNMT) activity, were assessed. Further, the cellular chaperones and synaptic proteins were examined using DNMT inhibitor, 5-aza-2′-deoxycytidine (5-aza-dC)-via intracerebroventricular injection. Moreover, % methylation of these synaptic proteins were also studied so as to correlate its epigenetic involvement. Computationally, its interaction with the DNMT enzyme were also studied using bioinformatic tools. Histological studies for morphological alterations and neuronal degeneration were also studied. Neurogenesis, a characteristic marker for new learning and memory formation, was also assessed via the BrdU staining. Finally, the most important behavioral studies, including the Morris water maze, Y maze, passive avoidance, and Novel object recognition test, were performed to study its cognitive functions. Results: Altered global DNA methylation and increased levels of DNMTs within the nucleus were confirmed in the cortex and hippocampus of the diseased mice, suggesting hypermethylation at a genetic level. Treatment with AzadC, a global DNA demethylating agent, ameliorated the protein and gene expression of the cellular chaperones and synaptic fidelity. Furthermore, the methylation analysis profile showed hypermethylation of the hsf1 protein, a master regulator for chaperones and thus, confirmed the epigenetic involvement in the diseased brain. Morphological improvements and decreased neurodegeneration, along with enhanced neurogenesis in the treatment group, suggest that epigenetic modulations do participate in learning and memory. This is supported by the improved behavioral test battery seen in the treatment group. Conclusion: DNA methylation could possibly accord in dysregulating the memory-associated proteins at chronic stages in type 2 diabetes. This could suggest a substantial contribution to the underlying pathophysiology of several metabolic syndromes like insulin resistance, obesity and also participate in transitioning this damage centrally, such as cognitive dysfunction.

Keywords: epigenetics, cognition, chaperones, DNA methylation

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Authors: Johanna Sagner, María José Sandoval

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At the early stages of adolescence, young people, regardless of a disability or not, start to establish closer friendship ties. Unlike previous developmental phases, these ties are rather reciprocal, more committed, and require more time. Friendship ties during adolescence allow the development of social and personal skills, specifically the skills to start constructing identity. In an inclusive context that incorporates young people with a disability, friendship among peers also takes place. Nonetheless, the relation is shaped, among others, by the alterity construction about the other with disability. Research about peers’ relation between young people with and without disability in an inclusive context has shown that the relation tends to become a helper-helpee relation, where those with a disability are seen as people in need. Prejudices about the others’ condition or distancing from the other because of his/hers disability are common. In this sense, the helper-helpee relation, as a non-reciprocal and protective relation, will not promote friendship between classmates, but a rather asymmetric alterity. Our research is an explorative case study that wants to know how the relation between peers is shaped within a different inclusive program, were also the integrated group has special educational needs. Therefore, we analyze from a qualitative and quantitative approach the data of an inverted inclusive program. This is a unique case of a special public school for visual disability in Germany that includes young people from a mainstream school who had learning difficulties. For the research, we analyze data from interviews, focal interviews and open-ended questions with an interpretative phenomenological analysis approach. The questionnaires include a five point Likert scale, for which we calculate the acceptance rate. The findings show that the alterity relation between pupils is less asymmetrical and represents a rather horizontal alterity. The helper-helpee relation is marked by exchange, since both groups have special educational needs and therefore, those with visual disability and those with learning difficulties help each other indistinctly. Friendship is more present among classmates. The horizontal alterity peers’ relation is influenced by a sort of tie, where none of the groups need more or less help than other groups. Both groups identify that they themselves and the other have special needs. The axiological axe of alterity is not of superiority or inferiority, recognizing each other’s differences and otherness. Another influential factor relates with the amount of time they spend together, since the program does not have a resource room or a teacher who teaches parallel lessons. Two probable causes for that rather equal peer relation might be the constellation of fewer pupils per classroom and the differentiated lessons taught by teachers with a special educational formation.

Keywords: alterity, disability, inverted inclusion, peers’ relation

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Authors: Juan A. Ferriz-Papi, Allan L. Nantel, Talib E. Butt

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Buildings are thought to consume about 50% of the total energy in the UK. The use stage in a building life cycle has the largest energy consumption, although different assessments are showing that the construction can equal several years of maintenance and operations. The selection of materials with lower embodied energy is very important to reduce this consumption. For this reason, timber is one adequate material due to its low embodied energy and the capacity to be used as carbon storage. The use of timber in the construction industry is very significant. Sawn wood, for example, is one of the top 5 construction materials consumed in the UK according to National Statistics. Embodied energy for building products considers the energy consumed in extraction and production stages. However, it is not the same consideration if this product is produced locally as when considering the resource produced further afield. Transport is a very relevant matter that profoundly influences in the results of embodied energy. The case of timber use in the UK is important because the balance between imports and exports is far negative, industry consuming more imported timber than produced. Nearly 80% of sawn softwood used in construction is imported. The imports-exports deficit for sawn wood accounted for more than 180 million pounds during the first four-month period of 2016. More than 85% of these imports come from Europe (83% from the EU). The aim of this study is to analyze climate change impact due to transport for timber products consumed in the UK. An approximate estimation of energy consumed and carbon emissions are calculated considering the timber product’s import origin. The results are compared to the total consumption of each product, estimating the impact of transport on the final embodied energy and carbon emissions. The analysis of these results can help deduce that one big challenge for climate change is the reduction of external dependency, with the associated improvement of internal production of timber products. A study of different types of timber products produced in the UK and abroad is developed to understand the possibilities for this country to improve sustainability and self-management. Reuse and recycle possibilities are also considered.

Keywords: embodied energy, climate change, CO2 emissions, timber, transport

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Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

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Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Inger Eriksson, Lisbeth Ohlsson, Jeremias Rosenqvist

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Ingress: Focus in the study is directed towards phenomena and concepts of segregation, integration, and inclusion of students attending a special school form in Sweden, namely compulsory school for pupils with learning disabilities (in Swedish 'särskola') as an alternative to mainstream compulsory school. Aim: The aim of the study is to examine the school situation for students attending särskola from a historical perspective focussing the 1980s, 1990s and the 21st century, from an integration perspective, and from a perspective of power. Procedure: Five sub-studies are reported, where integration and inclusion are looked into by observation studies and interviews with school leaders, teachers, special and remedial teachers, psychologists, coordinators, and parents in the special schools/särskola. In brief, the study about special school students attending mainstream classes from 1998 takes its point of departure in the idea that all knowledge development takes place in a social context. A special interest is taken in the school’s role for integration generally, and the role of special education particularly and on whose conditions the integration is taking place – the special school students' or the other students,' or may be equally, in the class. Pedagogical and social conditions for so called individually integrated special school students in elementary school classes were studied in eleven classes. Results: The findings are interpreted in a power perspective supported by Foucault and relationally by Vygotsky. The main part of the data consists of extensive descriptions of the eleven cases, here called integration situations. Conclusions: In summary, this study suggests that the possibilities for a special school student to get into the class community and fellowship and thereby be integrated with the class are to a high degree dependant on to what extent the student can take part in the pedagogical processes. The pedagogical situation for the special school student is affected not only by the class teacher and the support and measures undertaken but also by the other students in the class as they, in turn, are affected by how the special school student is acting. This mutual impact, which constitutes the integration process in itself, might result in a true integration if the special school student attains the status of being accepted on his/her own terms not only being cared for or cherished by some classmates. A special school student who is not accepted even on the terms of the class will often experience severe problems in the contacts with classmates and the school situation might thus be a mere placement.

Keywords: integration/inclusion, mainstream school, power, special school students

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Authors: Goodluck Akaoma Ordu

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Ruthium (Rth) represents a theoretical superheavy element with an atomic number of 119, proposed within the context of advanced materials science and nuclear physics. The conceptualization of Rth involves theoretical frameworks that anticipate its atomic structure, including a hypothesized stable isotope, Rth-320, characterized by 119 protons and 201 neutrons. The synthesis of Ruthium (Rth) hinges on intricate nuclear fusion processes conducted in state-of-the-art particle accelerators, notably utilizing Calcium-48 (Ca-48) as a projectile nucleus and Einsteinium-253 (Es-253) as a target nucleus. These experiments aim to induce fusion reactions that yield Ruthium isotopes, such as Rth-301, accompanied by neutron emission. Theoretical predictions outline various physical and chemical properties attributed to Ruthium (Rth). It is envisaged to possess a high density, estimated at around 25 g/cm³, with melting and boiling points anticipated to be exceptionally high, approximately 4000 K and 6000 K, respectively. Chemical studies suggest potential oxidation states of +2, +3, and +4, indicating a versatile reactivity, particularly with halogens and chalcogens. The atomic structure of Ruthium (Rth) is postulated to feature an electron configuration of [Rn] 5f^14 6d^10 7s^2 7p^2, reflecting its position in the periodic table as a superheavy element. However, the creation and study of superheavy elements like Ruthium (Rth) pose significant challenges. These elements typically exhibit very short half-lives, posing difficulties in their stabilization and detection. Research efforts are focused on identifying the most stable isotopes of Ruthium (Rth) and developing advanced detection methodologies to confirm their existence and properties. Specialized detectors are essential in observing decay patterns unique to Ruthium (Rth), such as alpha decay or fission signatures, which serve as key indicators of its presence and characteristics. The potential applications of Ruthium (Rth) span across diverse technological domains, promising innovations in energy production, material strength enhancement, and sensor technology. Incorporating Ruthium (Rth) into advanced energy systems, such as the Arc Reactor concept, could potentially amplify energy output efficiencies. Similarly, integrating Ruthium (Rth) into structural materials, exemplified by projects like the NanoArc gauntlet, could bolster mechanical properties and resilience. Furthermore, Ruthium (Rth)--based sensors hold promise for achieving heightened sensitivity and performance in various sensing applications. Looking ahead, the study of Ruthium (Rth) represents a frontier in both fundamental science and applied research. It underscores the quest to expand the periodic table and explore the limits of atomic stability and reactivity. Future research directions aim to delve deeper into Ruthium (Rth)'s atomic properties under varying conditions, paving the way for innovations in nanotechnology, quantum materials, and beyond. The synthesis and characterization of Ruthium (Rth) stand as a testament to human ingenuity and technological advancement, pushing the boundaries of scientific understanding and engineering capabilities. In conclusion, Ruthium (Rth) embodies the intersection of theoretical speculation and experimental pursuit in the realm of superheavy elements. It symbolizes the relentless pursuit of scientific excellence and the potential for transformative technological breakthroughs. As research continues to unravel the mysteries of Ruthium (Rth), it holds the promise of reshaping materials science and opening new frontiers in technological innovation.

Keywords: superheavy element, nuclear fusion, bombardment, particle accelerator, nuclear physics, particle physics

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Authors: Vadanasundari Vedarethinam, Kun Qian

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The metabolic analysis is more distal over proteomics and genomics engaging in clinics and needs rationally distinct techniques, designed materials, and device for clinical diagnosis. Conventional techniques such as spectroscopic techniques, biochemical analyzers, and electrochemical have been used for metabolic diagnosis. Currently, there are four major challenges including (I) long-term process in sample pretreatment; (II) difficulties in direct metabolic analysis of biosamples due to complexity (III) low molecular weight metabolite detection with accuracy and (IV) construction of diagnostic tools by materials and device-based platforms for real case application in biomedical applications. Development of chips with nanomaterial is promising to address these critical issues. Mass spectroscopy (MS) has displayed high sensitivity and accuracy, throughput, reproducibility, and resolution for molecular analysis. Particularly laser desorption/ ionization mass spectrometry (LDI MS) combined with devices affords desirable speed for mass measurement in seconds and high sensitivity with low cost towards large scale uses. We developed a plasmonic chip for clinical metabolic fingerprinting as a hot carrier in LDI MS by series of chips with gold nanoshells on the surface through controlled particle synthesis, dip-coating, and gold sputtering for mass production. We integrated the optimized chip with microarrays for laboratory automation and nanoscaled experiments, which afforded direct high-performance metabolic fingerprinting by LDI MS using 500 nL of serum, urine, cerebrospinal fluids (CSF) and exosomes. Further, we demonstrated on-chip direct in-vitro metabolic diagnosis of early-stage lung cancer patients using serum and exosomes without any pretreatment or purifications. To our best knowledge, this work initiates a bionanotechnology based platform for advanced metabolic analysis toward large-scale diagnostic use.

Keywords: plasmonic chip, metabolic fingerprinting, LDI MS, in-vitro diagnostics

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Authors: Carlos Monge López, Patricia Gómez Hernández

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Educational innovation is a quality factor of teaching-learning processes and institutional accreditation. There is an increasing of these change processes, especially after 2000. However, the publications about this topic are more associated with ICTs in currently century. The main aim of the study was to determine the tendency of educational innovations around ICTs. The used method was mixed research design (content analysis, review of scientific literature and descriptive, comparative and correlation study) with 649 papers. In summary, the results indicated that, progressively, the educational innovation is associated with ICTs, in comparison with this type of change processes without ICTs. In conclusion, although this tendency, scientific literature must divulgate more kinds of pedagogical innovation with the aim of deepening in other new resources.

Keywords: descriptive study, knowledge society, pedagogical innovation, technologies

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Authors: Maurício N. Kleinberg, Ana P. R. N. Barroso, Frederico R. Silva, Natasha l. Gomes, Rodrigo F. Guimarães, Marcelo M. V. Parente, Jackson Q. Malveira

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Increased production of biofuels, especially biodiesel, as an option to replace the diesel derived from oil is already a reality in countries seeking a renewable and environmentally friendly fuel, as is the case in Brazil. However, it is known that the use of fuels, renewable or not, implies that it is in contact with various metallic materials which may cause corrosion. In the search for more corrosion resistant materials has been experimentally observed that the addition of molybdenum in ferritic steels increases their protective character without significantly burdening the cost of production. In order to evaluate the effect of adding molybdenum, samples of commercial steel (austenitic, ferritic and carbon steel) and the experimental ferritic alloy with a high molybdenum content (5.3%) were immersed separately into biodiesel derived from transesterification of soy oil to monitor the corrosion process of these metal samples, and in parallel to analyze the oxidative degradation of biodiesel itself. During the immersion time of 258 days, biodiesel samples were taken for analysis of acidity, kinematic viscosity, density and refraction. Likewise, the metal samples were taken from the biodiesel to be weighed and microstructurally analyzed by light microscopy. The results obtained at the end of 258 days shown that biodiesel presented a considerable increase on the values of the studied parameters for all the samples. However, this increase was not able to produce significant mass loss in metallic samples. As regards the microstructural analysis, it showed the onset of surface oxidation on the carbon steel sample. As for the other samples, no significant surface changes were shown. These results are consistent with literature for short immersion times. It is concluded that the increase in the values of the studied parameters is not significant yet, probably due to the low time of immersion and exposure of the samples. Thus, it is necessary to continue the tests so that the objectives of this work are achieved.

Keywords: biodiesel, corrosion, immersion, experimental alloy

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Authors: Bahieddine. Bouabdellah, Benameur. Amiri, Abdelkader.nouri

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Europium-doped gallium nitride (EuGaN) is a promising material for optoelectronic and thermoelectric devices. This study investigates its optical properties using density functional theory (DFT) with the FP-LAPW method and MBJ+U correction. The simulation substitutes a gallium atom with europium in a hexagonal GaN lattice (6% doping). Distinct absorption peaks are observed in the optical analysis. These results highlight EuGaN's potential for various applications and pave the way for further research on rare earth-doped materials.

Keywords: eugan, fp-lapw, dft, wien2k, mbj hubbard

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Authors: Introducing Principles of Land Surveying by Assigning a Practical Project

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A practical project is used in an engineering surveying course to expose sophomore and junior civil engineering students to several important issues related to the use of basic principles of land surveying. The project, which is the design of a two-lane rural highway to connect between two arbitrary points, requires students to draw the profile of the proposed highway along with the existing ground level. Areas of all cross-sections are then computed to enable quantity computations between them. Lastly, Mass-Haul Diagram is drawn with all important parts and features shown on it for clarity. At the beginning, students faced challenges getting started on the project. They had to spend time and effort thinking of the best way to proceed and how the work would flow. It was even more challenging when they had to visualize images of cut, fill and mixed cross sections in three dimensions before they can draw them to complete the necessary computations. These difficulties were then somewhat overcome with the help of the instructor and thorough discussions among team members and/or between different teams. The method of assessment used in this study was a well-prepared-end-of-semester questionnaire distributed to students after the completion of the project and the final exam. The survey contained a wide spectrum of questions from students' learning experience when this course development was implemented to students' satisfaction of the class instructions provided to them and the instructor's competency in presenting the material and helping with the project. It also covered the adequacy of the project to show a sample of a real-life civil engineering application and if there is any excitement added by implementing this idea. At the end of the questionnaire, students had the chance to provide their constructive comments and suggestions for future improvements of the land surveying course. Outcomes will be presented graphically and in a tabular format. Graphs provide visual explanation of the results and tables, on the other hand, summarize numerical values for each student along with some descriptive statistics, such as the mean, standard deviation, and coefficient of variation for each student and each question as well. In addition to gaining experience in teamwork, communications, and customer relations, students felt the benefit of assigning such a project. They noticed the beauty of the practical side of civil engineering work and how theories are utilized in real-life engineering applications. It was even recommended by students that such a project be exercised every time this course is offered so future students can have the same learning opportunity they had.

Keywords: land surveying, highway project, assessment, evaluation, descriptive statistics

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Authors: H. M. Wilhelm, P. O. Fernandes, L. P. Dill, C. Steffens, K. G. Moscon, S. M. Peres, V. Bender, T. Marchesan, J. B. Ferreira Neto

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Deterioration of insulating oil is a natural process that occurs during transformers operation. However, this process can be accelerated by some factors, such as oxygen, high temperatures, metals and, moisture, which rapidly reduce oil insulating capacity and favor transformer faults. Parts of building materials of a transformer can be degraded and yield soluble compounds and insoluble particles that shorten the equipment life. Physicochemical tests, dissolved gas analysis (including propane, propylene and, butane), volatile and furanic compounds determination, besides quantitative and morphological analyses of particulate are proposed in this study in order to correlate transformers building materials degradation with insulating oil characteristics. The present investigation involves tests of medium temperature overheating simulation by means of an electric resistance wrapped with the following materials immersed in mineral insulating oil: test I) copper, tin, lead and, paper (heated at 350-400 °C for 8 h); test II) only copper (at 250 °C for 11 h); and test III) only paper (at 250 °C for 8 h and at 350 °C for 8 h). A different experiment is the simulation of electric arc involving copper, using an electric welding machine at two distinct energy sets (low and high). Analysis results showed that dielectric loss was higher in the sample of test I, higher neutralization index and higher values of hydrogen and hydrocarbons, including propane and butane, were also observed. Test III oil presented higher particle count, in addition, ferrographic analysis revealed contamination with fibers and carbonized paper. However, these particles had little influence on the oil physicochemical parameters (dielectric loss and neutralization index) and on the gas production, which was very low. Test II oil showed high levels of methane, ethane, and propylene, indicating the effect of metal on oil degradation. CO₂ and CO gases were formed in the highest concentration in test III, as expected. Regarding volatile compounds, in test I acetone, benzene and toluene were detected, which are oil oxidation products. Regarding test III, methanol was identified due to cellulose degradation, as expected. Electric arc simulation test showed the highest oil oxidation in presence of copper and at high temperature, since these samples had huge concentration of hydrogen, ethylene, and acetylene. Particle count was also very high, showing the highest release of copper in such conditions. When comparing high and low energy, the first presented more hydrogen, ethylene, and acetylene. This sample had more similar results to test I, pointing out that the generation of different particles can be the cause for faults such as electric arc. Ferrography showed more evident copper and exfoliation particles than in other samples. Therefore, in this study, by using different combined analytical techniques, it was possible to correlate insulating oil characteristics with possible contaminants, which can lead to transformers failure.

Keywords: Ferrography, gas analysis, insulating mineral oil, particle contamination, transformer failures

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Kaewmart Pongakkasira

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This study examines whether construction of LEGO reflects affective states and creativity as the clue to develop effective learning resources for classrooms. For this purpose, participants are instructed to complete a hands-on activity by using LEGO. Prior to the experiment, participants’ affective states and creativity are measured by the Positive and Negative Affect Schedule (PANAS) and the Alternate Uses Task (AUT), respectively. Then, subjects are asked to freely combine LEGO as unusual as possible versus constraint LEGO combination and named the LEGO products. Creativity of the LEGO products is scored for originality and abstractness of titles. It is hypothesized that individuals’ mood and creativity may affect product creativity. If so, there might be correlation among the three parameters.

Keywords: affective states, creativity, hands-on activity, LEGO

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Authors: Trupti Diwan

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Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute.

Keywords: classification, educational data mining, student failure, grammar-based genetic programming

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Authors: Salah A. Aly, Mohammed A. Ibrahim, Mostafa M. khttab

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The amount of construction and demolition (C&D) waste has increased considerably over the last few decades. From the viewpoint of environmental preservation and effective utilization of resources, crushing C&D concrete waste to produce coarse aggregate (CA) with different replacement percentage for the production of new concrete is one common means for achieving a more environment-friendly concrete. In the study presented herein, the investigation was conducted in two phases. In the first phase, the selection of the materials was carried out and the physical, mechanical and chemical characteristics of these materials were evaluated. Different concrete mixes were designed. The investigation parameter was Recycled Concrete Aggregate (RCA) ratios. The mechanical properties of all mixes were evaluated based on compressive strength and workability results. Accordingly, two mixes have been chosen to be used in the next phase. In the second phase, the study of the structural behavior of the concrete beams was developed. Sixteen beams were casted to investigate the effect of RCA ratios, the shear span to depth ratios and the effect of different locations and reinforcement of openings on the shear behavior of the tested specimens. All these beams were designed to fail in shear. Test results of the compressive strength of concrete indicated that, replacement of natural aggregate by up to 50% recycled concrete aggregates in mixtures with 350 Kg/m3 cement content led to increase of concrete compressive strength. Moreover, the tensile strength and the modulus of elasticity of the specimens with RCA have very close values to those with natural aggregates. The ultimate shear strength of beams with RCA is very close to those with natural aggregates indicating the possibility of using RCA as partial replacement to produce structural concrete elements. The validity of both the Egyptian Code for the design and implementation of Concrete Structures (ECCS) 203-2007 and American Concrete Institute (ACI) 318-2011Codes for estimating the shear strength of the tested RCA beams was investigated. It was found that the codes procedures gives conservative estimates for shear strength.

Keywords: construction and demolition (C&D) waste, coarse aggregate (CA), recycled coarse aggregates (RCA), opening

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Authors: Moustafa A. Fadel, Omar Al Shehhi, Mohsin Al Mussabi, Abdullah Al Ameri

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Hydroponics represents an extraordinary promising technique if used efficiently in arid regions where water resources are extremely scarce where a great portion of the used water should be recycled and saved. Available research publications studying the production of seedlings for such purpose are limited. This research paper focuses on investigating the effect of using various substrate materials on the development of seedlings for ornamental plants. Bermuda grass, Petunia (Compacta Enana Rosa) and Epipremnum aureum are used widely in landscape design. Bermuda is used as a turf grass; Petunia is used as a flowering plant and Epipremnum aureum as an indoor ornamental plant in hydroponics. Three substrate materials were used to germinate and propagate the first two and the cuttings of the third one. Synthetic sponge (Polyurethane sponge), Rockwool and sterilized cotton were used as the substrate material in each case where an experimental water-circulating apparatus was designed and installed to execute the test. An experimental setup of closed hydroponic apparatus was developed to carry out the experiment equipped with water recycling circuit and an aeration mechanism pumping air in reservoir in order to increase oxygen levels in the recycled water. Water pumping was programmed in different regimes to allow better aeration for seeds and cuttings under investigation. Results showed that Bermuda grass germinated in Rockwool reached a germination rate of 70% while it did not exceed 50% when sponge and medically treated cotton were used after 15 days. On the other hand the highest germination rate of Petunia was observed when treated cotton was used where it recorded about 30% while it was 22%, and 7% after 20 days where Rockwool and sponge were utilized respectively. Cuttings propagation of Epipremnum aureum developed the highest number of shoots when treated cotton was used where it gave 10 shoots after 10 days while it gave just 7 shoots when Rockwool and sponge were used as the propagation substrate.

Keywords: hydroponics, germination, seedlings, cuttings

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Authors: Ronit German, Alexandra Beytenbrat

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Although English seems to be prevalent in every sphere of Israeli daily life, not many Israeli students have a sufficient level of writing and speaking in English which is necessary for academic studies. The inadequate level of English among Israeli students is because the sole focus is on teaching reading comprehension, and the need to adapt to the trends of the professional worldwide demands triggered a reform that requires implementing Common European Framework of Reference (CEFR) and English as a Medium of Instruction (EMI) courses in the Israeli academic institutions. However, it will be argued that this reform is challenging to implement. The fact that modern Hebrew is a revived language, and that English is L3 for more than 30% of the population, the diverse social and cultural students’ background, and psychological factors stand in the way of the new reform.

Keywords: CEFR, cultural diversity, EMI courses, English in Israel, reform

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

Procedia PDF Downloads 196