Search results for: international teaching and learning

Authors: Hadeel Abdulatif, Maha Alkhaffaf

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E-government is the use of Information and Communication Technology to enrich the access to and delivery of government services to citizens, business partners and employees, Policy makers and regulatory bodies have to be cognizant of the degree of readiness of a populace in order to design and implement efficient e-government programs. This paper aims to provide a transparent situation analyses for the case of e-government official website in Jordan, it focuses on assessing e-government in Jordan; web site assessment by using international criteria for assessing e-government websites, However, the study analyses the environmental factor consisting of cultural and business environment factors. By reviewing the literature the researchers found that government's efforts towards e-government may vary according to the country's readiness and other key implementation factors which will lead to diverse e-government experience; thus, there is a need to study the impact of key factors to implement e-government in Jordan.

Keywords: e-government, environmental factors, website assessment, readiness

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Innocent Atuhe, Babra Nalwadda, Grace Mulyowa, Annabella Habinka Ejiri

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Background: Atopic Dermatitis (AD) is one of the most prevalent and growing chronic inflammatory skin diseases in African prisons. AD care is limited in African due to a lack of information about the disease amongst primary care workers, limited access to dermatologists, lack of proper training of healthcare workers, and shortage of appropriate treatments. We designed and implemented the Prisons Telederma project based on the recommendations of the International Society of Atopic Dermatitis. We aimed at; i) increase awareness and understanding of teledermatology among prison health workers and ii) improve treatment outcomes of prisoners with atopic dermatitis through increased access to and utilization of consultant dermatologists through teledermatology in Uganda prisons. Approach: We used Store-and-forward Teledermatology (SAF-TD) to increase access to dermatologist-led care for prisoners and prison staff with AD. We conducted five days of training for prison health workers using an adapted WHO training guide on recognizing neglected tropical diseases through changes on the skin together with an adapted American Academy of Dermatology (AAD) Childhood AD Basic Dermatology Curriculum designed to help trainees develop a clinical approach to the evaluation and initial management of patients with AD. This training was followed by blended e-learning, webinars facilitated by consultant Dermatologists with local knowledge of medication and local practices, apps adjusted for pigmented skin, WhatsApp group discussions, and sharing pigmented skin AD pictures and treatment via zoom meetings. We hired a team of Ugandan Senior Consultant dermatologists to draft an iconographic atlas of the main dermatoses in pigmented African skin and shared this atlas with prison health staff for use as a job aid. We had planned to use MySkinSelfie mobile phone application to take and share skin pictures of prisoners with AD with Consultant Dermatologists, who would review the pictures and prescribe appropriate treatment. Unfortunately, the National Health Service withdrew the app from the market due to technical issues. We monitored and evaluated treatment outcomes using the Patient-Oriented Eczema Measure (POEM) tool. We held four advocacy meetings to persuade relevant stakeholders to increase supplies and availability of first-line AD treatments such as emollients in prison health facilities. Results: We have the very first iconographic atlas of the main dermatoses in pigmented African skin. We increased; i) the proportion of prison health staff with adequate knowledge of AD and teledermatology from 20% to 80%; ii) the proportion of prisoners with AD reporting improvement in disease severity (POEM scores) from 25% to 35% in one year; iii) increased proportion of prisoners with AD seen by consultant dermatologist through teledermatology from 0% to 20% in one year and iv)Increased the availability of AD recommended treatments in prisons health facilities from 5% to 10% in one year. Our study contributes to the use, evaluation, and verification of the use of teledermatology to increase access to specialist dermatology services to the most hard to reach areas and vulnerable populations such as that of prisoners.

Keywords: teledermatology, prisoners, reaching, un-reachable

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Authors: Larisa Akhmylovskaia, Andriana Barysh

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The present paper is introducing the translation score developing methodology and methods in the cross-cultural communication. The ideas and examples presented by the authors illustrate the universal character of translation score developing methods under analysis. Personal experience in the international theatre-making projects, opera laboratories, cross-cultural master-classes give more opportunities to single out the conditions, forms, means and principles of translation score developing as well as the translator/interpreter’s functions as cultural liaison for multiethnic collaboration.

Keywords: methodology of translation score developing, pre-production, analysis, production, post-production, ethnic scene theory, theatre anthropology, laboratory, master-class, educational project, academic project, participant observation, super-objective

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: Prashanth Pillai, Purnaprajna Mangsuli

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Oil exploration and production companies invest considerable time and efforts to extract essential well attributes (like well status, surface, and target coordinates, wellbore depths, event timelines, etc.) from unstructured data sources like technical reports, which are often non-standardized, multimodal, and highly domain-specific by nature. It is also important to consider the context when extracting attribute values from reports that contain information on multiple wells/wellbores. Moreover, semantically similar information may often be depicted in different data syntax representations across multiple pages and document sources. We propose a hierarchical multi-source fact extraction workflow based on a deep learning framework to extract essential well attributes at scale. An information retrieval module based on the transformer architecture was used to rank relevant pages in a document source utilizing the page image embeddings and semantic text embeddings. A question answering framework utilizingLayoutLM transformer was used to extract attribute-value pairs incorporating the text semantics and layout information from top relevant pages in a document. To better handle context while dealing with multi-well reports, we incorporate a dynamic query generation module to resolve ambiguities. The extracted attribute information from various pages and documents are standardized to a common representation using a parser module to facilitate information comparison and aggregation. Finally, we use a probabilistic approach to fuse information extracted from multiple sources into a coherent well record. The applicability of the proposed approach and related performance was studied on several real-life well technical reports.

Keywords: natural language processing, deep learning, transformers, information retrieval

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Muhammad Naeem, Syed M. Moeen-ud-Din Raheel, Muhammad Arshad, Muhammad Naeem Qaisar, Muhammad Khalid, Muhammad Zubair Ahmed, Muhammad Ashraf

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Iron, Copper, Cadmium, Zinc, Manganese, Chromium levels were estimated to study the risk of trace elements on human consumption. The area of collection was Dera Ghazi Khan, Pakistan and was evaluated by means of flame atomic absorption spectrophotometer. The standards find in favor of the six heavy metals were in accordance with the threshold edge concentrations on behalf of fish meat obligatory by European and other international normative. Regressions were achieved for both size (length and weight) and condition factor with concentrations of metal present in the fish body.

Keywords: Oreochromis mossambica, toxic analysis, body size, condition factor

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Authors: Giselle Martinez Negrette

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Language and culture mutually shape and reflect the human experience. In the learning process, this connection creates and sustains the shared world of learners and educators. Dual Language (DL) programs exemplify this relationship by placing language and culture at the center of their educational approach. These programs, originally conceived to advance social justice in education, aim to foster bilingualism, biliteracy, academic development and sociocultural competence, emphasizing the inseparability of linguistic and cultural growth. Furthermore, because DL programs serve children from diverse cultural, ethnic, and socioeconomic backgrounds, they operate as spaces where linguistic skills and sociocultural understandings are actively cultivated, negotiated, and celebrated. Against this background, this paper examines how two DL teachers see language and culture shaping and reflecting the educational experience, and how their understandings of the relationship influence their mediation of sociocultural competence in their classrooms. This qualitative study employs critical discourse analysis to study in detail participants’ narratives seeking to uncover their perspectives on the “politics” surrounding language use and cultural understandings in their school contexts. Our findings show that these educators are not only keenly aware of the pivotal role that language and culture play in multilingual students’ learning journeys, but they have identified the sociolinguistic “games” taking place in their classrooms. We contend these understandings are pivotal for the critical development of sociocultural competence in DL programs. This study provides DL educators with important conceptual and pedagogical insights regarding the intersection between language and culture in their classrooms and seeks to encourage them to analyze their roles as supporters or opponents of transformative rupture opportunities to contest inequities in education

Keywords: sociocultural competence, critical discourse analysis, dual language programs, language, culture

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Authors: Thassanant Unnanantn, Suttipong Boonphadung

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This research study aimed to survey and analyze the attitudes of pre-service teachers’ the analytical thinking development based on Miller’s Model. The informants of this study were 22 third year teacher students majoring in Thai. The course where the instruction was conducted was English for Academic Purposes in Thai Language 2. The instrument of this research was an open-ended questionnaire with two dimensions of questions: academic and satisfaction dimensions. The investigation revealed the positive attitudes. In the academic dimension, the majority of 12 (54.54%), the highest percentage, reflected that the method of teaching analytical thinking and language simultaneously was their new knowledge and the similar percentage also belonged to text cohesion in writing. For the satisfaction, the highest frequency count was from 17 of them (77.27%) and this majority favored the openness or friendliness of the teacher.

Keywords: analytical thinking development, Miller’s Model, attitudes, pre-service teachers

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Authors: David A. Harness

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Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: automated theorem proving, constructive quantum field theory, information theory, neural networks

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Authors: A. Martins, I. Martins, O. Pereira

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The purpose of this research is to focus on the importance of distributed leadership in universities and Higher Education Institutions (HEIs). The research question is whether there a significant finding in self-reported ratings of leadership styles of those respondents that are studying management. The study aims to further discover whether students are encouraged to become responsible and proactive citizens, to develop their skills set, specifically shared leadership and higher-level skills to inspire creation knowledge, sharing and distribution thereof. Contemporary organizations need active and responsible individuals who are capable to make decisions swiftly and responsibly. Leadership influences innovative results and education play a dynamic role in preparing graduates. Critical reflection of extant literature indicates a need for a culture of leadership and innovation to promote organizational sustainability in the globalised world. This study debates the need for HEIs to prepare the graduate for both organizations and society as a whole. This active collaboration should be the very essence of both universities and the industry in order for these to achieve responsible sustainability. Learning and innovation further depend on leadership efficacy. This study follows the pragmatic paradigm methodology. Primary data collection is currently being gathered via the web-based questionnaire link which was made available on the UKZN notice system. The questionnaire has 35 items with a Likert scale of five response options. The purposeful sample method was used, and the population entails the undergraduate and postgraduate students in the College of Law and Business, University of KwaZulu-Natal, South Africa. Limitations include the design of the study and the reliance on the quantitative data as the only method of primary data collection. This study is of added value for scholars and organizations in the innovation economy.

Keywords: knowledge creation, learning, performance, sustainability

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Sabry A. El Azazy

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The archaeological heritage sites are one of the most important touristic attractions worldwide. Egypt has various archaeological sites and historical locations that are classified within the list of the archaeological heritage destinations in the world, such as Cairo, Luxor, Aswan, Alexandria, and Sinai. This study focuses on how to manage the archaeological sites and provide them with all services according to the traveler's needs. Tourism management depends on strategic planning for supporting the national economy and sustainable development. Additionally, tourism management has to utilize highly effective standards of security, promotion, advertisement, sales, and marketing while taking into consideration the preservation of monuments. In Egypt, the archaeological heritage sites must be well-managed and protected, which would assist tourism management, especially in times of crisis. Recently, the monumental places and archeological heritage sites were affected by unstable conditions and were threatened. It is essential to focus on preserving our heritage. Moreover, more efforts and cooperation between the tourism organizations and ministry of archaeology have to be done in order to protect the archaeology and promote the tourism industry. Methodology: Qualitative methods have been used as the overall approach to this study. Interviews and observations have provided the researcher with the required in-depth insight to the research subject. The researcher was a lecturer of tourist guidance that allows visiting all historical sites in Egypt. Additionally, the researcher had the privilege to communicate with tourism specialists and attend meetings, conferences, and events that were focused on the research subject. Objectives: The main purpose of the research was gaining information in order to develop theoretical research on how to effectively benefit out of those historical sights both economically and culturally, and pursue further researches and scientific studies to be well-suited for tourism and hospitality sector. The researcher works hard to present further studies in a field related to tourism and archaeological heritage using previous experience. Pursing this course of study enables the researcher to acquire the necessary abilities and competencies to achieve the set goal successfully. Results: The professional tourism management focus on making Egypt one of the most important destinations in the world, and provide the heritage and archaeological sites with all services that will place those locations into the international map of tourism. Tourists interested in visiting Egypt and making tourism flourish supports and strengths Egypt's national economy and the local community, taking into consideration preserving our heritage and archaeology. Conclusions: Egypt has many tourism attractions represented in the heritage, archaeological sites, and touristic places. These places need more attention and efforts to be included in tourism programs and be opened for visitors from all over the world. These efforts will encourage both local and international tourism to see our great civilization and provide different touristic activities.

Keywords: archaeology, archaeological sites, heritage, ministry of archaeology, national economy, touristic attractions, tourism management, tourism organizations

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Authors: Po-Kun Tsai

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The issue of climate change is essential in the 21st century. State governments have launched types of strategic industrial policies to encourage more widespread R&D in green technology. Research also indicates that technology is an essential tool to mitigate some of extreme situations. However, one could learn from several prominent cases in international trade area that they have been easily argued and disputed by the foreign counterparts. Thus, how to justify the public sector’s R&D measures under the current world trading system and how to promote the transfer of environmentally sound technologies (EST) to developing states are crucial. The study is to undertake a preliminary examination of the current R&D research area in green technology in Taiwan. Through selective interviews and comparative approach, it tries to identify the loopholes under the current legal framework in Taiwan. It would be, as a basis, for further legal and policy recommendations for the benefits of mankind.

Keywords: government, R&D, innovation, environmentally sound technology (EST)

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Authors: Fradreck Jockonia Mujuru

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The effects of landmine residues from the Zimbabwean liberation war are persisting. Landmines are violently maiming and killing people and animals, causing certain areas inaccessible for agriculture and habitation, instilling fear, and even inducing forced migration. A significant gap in landmines literature is that they are mainly treated as a humanitarian issue and less scholarly. This paper engaged in theorising landmines using postcolonial literature as an epistemology. The results exhibit three issues. One, postcolonial literature provides a timeframe, a process, a space, and an attitude towards modernity on the inquiry of landmines. Two, landmines are understood in the context of war and were further decolonised to pick unique principles studied. Lastly, some of the unique principles found in landmines after decolonising are their ability to provide surveillance, repression and violent fate to all who cross the set boundaries. Therefore, theorising landmines can also be pushed further to be understood through repression. This article concluded that landmines can be theorised outside mainstream International Relations theories using postcolonial literature.

Keywords: landmines, postcolonial, repression, security, violence

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Authors: Rob Waddle

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We study an economy with weak justice and security systems and with weak public policy and regulation or little capacity to implement them, and with high barriers to profitable sectors. We look at growth and development opportunities based on the derived demand. We show that there is hope for such an economy to grow up and to generate a win-win situation for all stakeholders if the derived demand is supplied. We then investigate conditions that could stimulate the derived demand supply. We show that little knowledge of public, private and international expenditures in the economy and academic tools are enough to trigger the derived demand supply. Our model can serve as guidance to donor and NGO working in developing countries, and show to media the best way to help is to share information about existing and accessible opportunities. It can also provide direction to vocational schools and universities that could focus more on providing tools to seize existing opportunities.

Keywords: growth, development, monopoly, oligopoly, inequality

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Authors: Dielleza Namani, Laureta Kadrijaj-Qerimi

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Neurolinguistics studies the relationship between the brain and language. It is a subject not often found in the syllabus of universities in the Balkans but more spread in Europe and especially the United States of America. The purpose of this study is to see what importance this subject has for studies in the English language department. It contains an analysis of other research papers written regarding neurolinguistics, a questionnaire made for professors and deans at private and public universities in Kosovo, and an interview with a neurolinguistics professor in England. Since this subject is not found in the syllabus of any of the universities in Kosovo, the researchers wanted to find out why this happens but, at the same time, provide reasons why they should consider having it in the future. The results showed that for this subject, there had been researching made, but not enough so far, which gives more information and feedback on why it needs to be in the syllabus, and how linguists can use the knowledge they receive from this subject in their workplace. Also, the professors and deans see this subject as too medical for their students to learn and not necessary for their future jobs. Hopefully, in the near future, there will be more research done on why this is important and how English language students can benefit from it.

Keywords: English language department, neurolinguistics, second language acquisition, teaching methods

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Authors: Donald Wright

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The ultimate goal of this study is to reinvigorate the philosophical underpinnings the study of urbanization with scientific data with the goal of circumventing what seems an inevitable future clash between rural and urban populations. To that end urban infrastructure must be sustainable economically, politically and ecologically over the course of several generations as cities continue to grow with the incorporation of climate refugees. Our research will provide data concerning the projected increase in population over the coming two decades in Morocco, and the population will shift from rural areas to urban centers during that period of time. As a result, urban infrastructure will need to be adapted, developed or built to fit the demand of future internal migrations from rural to urban centers in Morocco. This paper will also examine how past experiences of internally displaced people give insight into the challenges faced by future migrants and, beyond the gathering of data, how people react to internal migration. This study employs four different sets of research tools. First, a large part of this study is archival, which involves compiling the relevant literature on the topic and its complex history. This step also includes gathering data bout migrations in Morocco from public data sources. Once the datasets are collected, the next part of the project involves populating the attribute fields and preprocessing the data to make it understandable and usable by machine learning algorithms. In tandem with the mathematical interpretation of data and projected migrations, this study benefits from a theoretical understanding of the critical apparatus existing around urban development of the 20th and 21st centuries that give us insight into past infrastructure development and the rationale behind it. Once the data is ready to be analyzed, different machine learning algorithms will be experimented (k-clustering, support vector regression, random forest analysis) and the results compared for visualization of the data. The final computational part of this study involves analyzing the data and determining what we can learn from it. This paper helps us to understand future trends of population movements within and between regions of North Africa, which will have an impact on various sectors such as urban development, food distribution and water purification, not to mention the creation of public policy in the countries of this region. One of the strengths of this project is the multi-pronged and cross-disciplinary methodology to the research question, which enables an interchange of knowledge and experiences to facilitate innovative solutions to this complex problem. Multiple and diverse intersecting viewpoints allow an exchange of methodological models that provide fresh and informed interpretations of otherwise objective data.

Keywords: climate change, machine learning, migration, Morocco, urban development

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Hozaifa Zaki, Ghada Soliman

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In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

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Authors: Nathalie Gagnon, Andréanne Gagné, Julie Courcy

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Quebec's vocational education teachers experience an atypical induction process into the workplace and thus face unique challenges. In contrast to elementary and high school teachers, who must undergo initial teacher training in order to access the profession, vocational education teachers, in most cases, are hired based on their professional expertise in the trade they are teaching, without prior pedagogical training. In addition to creating significant stress, which does not foster the acquisition of teaching roles and skills, this approach also forces recruits into a particular posture during their practical training: that of juggling their dual identities as teacher and trainee simultaneously. Recruits are supported by Cooperating Teachers (CPs) who, as experienced educators, take a critical and constructive look at their practices, observe them in the classroom, give them constructive feedback, and encourage them in their reflective practice. Thus, the vocational setting CP also assumes a distinctive posture and role due to the characteristics of the trainees they support. Although it is recognized that preparation, training, and supervision of CPs are essential factors in improving the support provided to trainees, there is little research about how CPs develop their support skills, and very little research focuses on the distinct posture they occupy. However, in order for them to be properly equipped for the important role they play in recruits’ practical training, it is vital to know more about their experience. An individual’s competencies cannot be studied without first examining what characterizes their experience, how they experience any given situation on cognitive, emotional, and motivational levels, in addition to how they act and react in situ. Depending on its nature, the experience will or will not promote the development of a specific competency. The research from which this communication originates focuses on describing the overall experience of vocational education CP in an effort to better understand the mechanisms linked to the development of their mentoring competencies. Experience and competence were, therefore, the two main theoretical concepts leading the research. As per methodology choices, case study methods were used since it proves to be adequate to describe in a rich and detailed way contemporary phenomena within contexts of life. The set of data used was collected from semi-structured interviews conducted with 15 vocational education CP in Quebec (Canada), followed by the use of a data-driven semi-inductive analysis approach to let the categories emerge organically. Focusing on the development needs of vocational education CP to improve their mentoring skills, this paper presents the results of our research, namely the importance of adequate training, better support offered by university supervisors, greater recognition of their role, and specific time slots dedicated to trainee support. The knowledge resulting from this research could improve the quality of support for trainee teachers in vocational education settings and to a more successful induction into the workplace. This communication also presents recommendations regarding the development of training systems that meet the specific needs of vocational education CP.

Keywords: development of competencies, cooperating teacher, mentoring trainee teacher, practical training, vocational education

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Authors: Nino Pailodze, Malkhaz Sulashvili, Vladimer Kekenadze, Tea Khutsishvili, Irma Makharashvili, Aleksandre Kekenadze

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In the nearest future among the important problems which Georgia has solve the most important is economic stability, that bases on fiscal policy and the proper definition of the its directions. The main source of the Budget revenue is the national income. The State uses taxes, loans and emission in order to create national income, were the principal weapon are taxes. As well as fiscal function of the fulfillment of the budget, tax systems successfully implement economic and social development and the regulatory functions of foreign economic relations. A tax is a mandatory, unconditional monetary payment to the budget made by a taxpayer in accordance with this Code, based on the necessary, nonequivalent and gratuitous character of the payment. Taxes shall be national and local. National taxes shall be the taxes provided for under this Code, the payment of which is mandatory across the whole territory of Georgia. Local taxes shall be the taxes provided for under this Code, introduced by normative acts of local self-government representative authorities (within marginal rates), the payment of which is mandatory within the territory of the relevant self-governing unit. National taxes have the leading role in tax systems, but also the local taxes have an importance role in tax systems. Exactly in the means of local taxes, the most part of the budget is formatted. National taxes shall be: income tax, profit tax, value added tax (VAT), excise tax, import duty, property tax shall be a local tax The property tax is one of the significant taxes in Georgia. The paper deals with the taxation mechanism that has been operated in Georgia. The above mention has the great influence in financial accounting. While comparing foreign legislation towards Georgian legislation we discuss the opportunity of using their experience. Also, we suggested recommendations in order to improve the tax system in financial accounting. In addition to accounting, which is regulated according the International Accounting Standards we have tax accounting, which is regulated by the Tax Code, various legal orders / regulations of the Minister of Finance. The rules are controlled by the tax authority, Revenue Service. The tax burden from the tax values are directly related to expenditures of the state from the emergence of the first day. Fiscal policy of the state is as well as expenditure of the state and decisions of taxation. In order to get the best and the most effective mobilization of funds, Government’s primary task is to decide the kind of taxation rules. Tax function is to reveal the substance of the act. Taxes have the following functions: distribution or the fiscal function; Control and regulatory functions. Foreign tax systems evolved in the different economic, political and social conditions influence. The tax systems differ greatly from each other: taxes, their structure, typing means, rates, the different levels of fiscal authority, the tax base, the tax sphere of action, the tax breaks.

Keywords: international accounting standards, financial accounting, tax systems, financial obligations

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Authors: Noga Bregman

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Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

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Authors: Anna-Maria Saarela

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At the Savonia University of Applied Sciences (UAS), curriculum and studies have been improved by applying an Open Innovation Space approach (OIS). It is based on multidisciplinary action learning. The key elements of OIS-ideology are work-life orientation, and student-centric communal learning. In this approach, every participant can learn from each other and innovations will be created. In this social innovation educational approach, all practices are carried out in close collaboration with enterprises in real-life settings, not in classrooms. As an example, in this paper, Savonia UAS’s Future Food RDI hub (FF) shows how OIS practices are implemented by providing food product development and consumer research services for enterprises in close collaboration with academicians, students and consumers. In particular one example of OIS experimentation in the field is provided by a consumer research carried out utilizing verbal analysis protocol combined with audio-visual observation (VAP-WAVO). In this case, all co-learners were acting together in supermarket settings to collect the relevant data for a product development and the marketing department of a company. The company benefitted from the results obtained, students were more satisfied with their studies, educators and academicians were able to obtain good evidence for further collaboration as well as renewing curriculum contents based on the requirements of working life. In addition, society will benefit over time as young university adults find careers more easily through their OIS related food science studies. Also this knowledge interaction model re-news education practices and brings working-life closer to educational research institutes.

Keywords: collaboration, education, food science, industry, knowledge transfer, RDI, student

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Authors: Ehsan Daryadel, Farhad Talaie

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Wetlands are one of the most important ecosystems on Earth. Nevertheless, various challenges threaten these ecosystems and disrupt their ecological character. Among these, the effects of human-based threats are more devastating. Following mass degradation of wetlands during 1970s, the Ramsar Convention on Wetlands (Ramsar, Iran, 1971) was concluded to conserve wetlands of international importance and prevent destruction and degradation of such ecosystems through wise use of wetlands as a mean to achieve sustainable development in all over the world. Therefore, in this paper, efforts have been made to analyze threats to wetlands and then investigate solutions in the framework of the Ramsar Convention. Finally, in order to operate these mechanisms, this study concludes that all states should in turn make their best effort to improve and restore global wetlands through preservation of environmental standards and close contribution and also through taking joint measures with other states effectively.

Keywords: Ramsar Convention, threats, wetland wcosystems, wise use

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Authors: German Ricci

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The research aims to analyse the symbolic power of the International Monetary Fund (IMF) in its relationship with a borrowing country, drawing upon Pierre Bourdieu’s Field Theory. This theory of power, typical of constructivist structuralism, has been minor used in international relations. Thus, selecting this perspective offers a new understanding of how the IMF's power operates and is structured. The IMF makes periodic economic reviews in which the staff evaluates the Government's performance. It also offers “last instance” loans when private external credit is not accessible. This relationship generates great expectations in financial agents because the IMF’s statements indicate the capacity of the Nation-State to meet its payment obligations (or not). Therefore, it is argued that the IMF is a legitimate actor for financial agents concerned about a government facing an economic crisis both for the effects of its immediate economic contribution through loans and the promotion of adjustment programs, helpful to guarantee the payment of the external debt. This legitimacy implies a symbolic power relationship in addition to the already known economic power relationship. Obtaining the IMF's consent implies that the government partially puts its political-economic decisions into play since the monetary policy must be agreed upon with the Fund. This has consequences at the local level. First, it implies that the debtor state must establish a daily relationship with the Fund. This everyday interaction with the Fund influences how officials and policymakers internalize the meaning of political management. On the other hand, if the Government has access to the IMF's seal of approval, the State will be again in a position to re-enter the financial market and go back into debt to face external debt. This means that private creditors increase the chances of collecting the debt and, again, grant credits. Thus, it is argued that the borrowing country submits to the relationship with the IMF in search of the latter's economic and symbolic capital. Access to this symbolic capital has objective and subjective repercussions at the national level that might tend to reproduce the relevance of the financial market and legitimizes the IMF’s intervention during economic crises. The paper has Argentina as its case study, given its historical relationship with the IMF and the relevance of the current indebtedness period, which remains largely unexplored. Argentina’s economy is characterized by recurrent financial crises, and it is the country to which the Fund has lent the most in its entire history. It surpasses more than three times the second, Egypt. In addition, Argentina is currently the country that owes the most to the Fund after receiving the largest loan ever granted by the IMF in 2018, and a new agreement in 2022. While the historical strong association with the Fund culminated in the most acute economic and social crisis in the country’s contemporary history, producing an unprecedented political and institutional crisis in 2001, Argentina still recognized the IMF as the only way out during economic crises.

Keywords: IMF, fields theory, symbolic power, Argentina, Bourdieu

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Authors: Jamilah Ahmad

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In 2023, Malaysia's International Trade and Industry Ministry (MITI) introduced the i-ESG framework to establish environmental, social, and governance (ESG) standards to enhance organisations' ethical conduct and social responsibility. The role of education, particularly universities, is crucial in preparing future generations to understand the significance of ESG and confront sustainability challenges. This study employs a mixed-methods approach, utilizing semi-structured interviews with university leaders and students to investigate the implementation of University Social Responsibility (USR) in Malaysian universities and the impact of ESG on sustainable development in alignment with Malaysia's SDG Agenda 2030. The findings underscore the critical role of USR and ESG in fostering sustainable development, ensuring that graduates are well-equipped to address environmental and sustainability issues in Malaysia.

Keywords: USR, ESG, sustainability, higher education in Malaysia, Malaysia

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Authors: Palak Sadhwani, Susie Pryor

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This paper investigates interprofessional collaboration (IPC) in the context of entrepreneurship education. Collaboration has been found to enhance problem solving, leverage expertise, improve resource allocation, and create organizational efficiencies. However, research suggests that successful collaboration is hampered by individual and organizational characteristics. IPC occurs when two or more professionals work together to solve a problem or achieve a common objective. The necessity for this form of collaboration is particularly prevalent in cross-disciplinary fields. In this study, we utilize social exchange theory (SET) to examine IPC in the context of an entrepreneurship living learning community (LLC) at a large university in the Western United States. Specifically, we explore these research questions: How are rules or norms established that govern the collaboration process? How are resources valued and distributed? How are relationships developed and managed among and between parties? LLCs are defined as groups of students who live together in on-campus housing and share similar academic or special interests. In 2007, the Association of American Colleges and Universities named living communities a high impact practice (HIP) because of their capacity to enhance and give coherence to undergraduate education. The entrepreneurship LLC in this study was designed to offer first year college students the opportunity to live and learn with like-minded students from diverse backgrounds. While the university offers other LLC environments, the target residents for this LLC are less easily identified and are less apparently homogenous than residents of other LLCs on campus (e.g., Black Scholars, LatinX, Women in Science and Education), creating unique challenges. The LLC is a collaboration between the university’s College of Business & Public Administration and the Department of Housing and Residential Education (DHRE). Both parties are contributing staff, technology, living and learning spaces, and other student resources. This paper reports the results an ethnographic case study which chronicles the start-up challenges associated with the co-creation of the LLC. SET provides a general framework for examining how resources are valued and exchanged. In this study, SET offers insights into the processes through which parties negotiate tensions resulting from approaching this shared project from very different perspectives and cultures in a novel project environment. These tensions occur due to a variety of factors, including team formation and management, allocation of resources, and differing output expectations. The results are useful to both scholars and practitioners of entrepreneurship education and organizational management. They suggest probably points of conflict and potential paths towards reconciliation.

Keywords: case study, ethnography, interprofessional collaboration, social exchange theory

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