Search results for: impacting student learning outcomes

Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: color moments, visual thing recognition system, SIFT, color SIFT

Procedia PDF Downloads 446

Authors: Abigail Kusi Amponsah, Evans Frimpong Kyei, John Bright Agyemang, Hanson Boakye, Joana Kyei-Dompim, Collins Kwadwo Ahoto, Evans Oduro

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Staff shortages, deficient knowledge, inappropriate attitudes, demanding workloads, analgesic shortages, and low prioritization of pain management have been identified in earlier studies as the nursing-related barriers to optimal children’s pain management. These studies have mainly been undertaken in developed countries, which have different healthcare dynamics than those in developing countries. The current study, therefore, sought to identify and understand the nursing-related barriers to children’s pain management in the Ghanaian context. A descriptive qualitative study was conducted among 28 purposively sampled nurses working in the pediatric units of five hospitals in the Ashanti region of Ghana. Over the course of three months, participants were interviewed on the barriers which prevented them from optimally managing children’s pain in practice. Recorded interviews were transcribed verbatim and deductively analysed based on a conceptual interest in pain assessment and management-related barriers. NVivo 12 plus software guided data management and analyses. The mean age of participating nurses was 30 years, with majority being females (n =24). Participants had worked in the nursing profession for an average of five years and in the pediatric care settings for an average of two years. The nursing-related barriers identified in the present study included communication difficulties in assessing and evaluating pain management interventions with children who have nonfunctional speech, insufficient training, misconceptions on the experience of pain in children, lack of assessment tools, and insufficient number of nurses to manage the workload and nurses’ inability to prescribe analgesics. The present study revealed some barriers which prevented Ghanaian nurses from optimally managing children’s pain. Nurses should be educated, empowered, and supported with the requisite material resources to effectively manage children’s pain and improve outcomes for families, healthcare systems, and the nation. Future studies should explore the facilitators and barriers from other stakeholders involved in pediatric pain management

Keywords: Nursing-Related Barriers, Children, Pain Management, Ghana

Procedia PDF Downloads 159

Authors: Tshilidzi C. Manyanya, Nthaduleni S. Nethengwe, Edmore Kori

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The UNFCCC, FAO, GCF, IPCC and other global structures advocate for agro-ecology do address food security and sovereignty. However, most of the expected outcomes concerning agro-ecological were not empirically tested for universal application. Agro-ecology is theorised to increase crop health over ago-ecological farms and decrease over conventional farms. Increased crop health means increased carbon sequestration and thus less CO2 in the atmosphere. This is in line with the view that global warming is anthropogenically enhanced through GHG emissions. Agro-ecology mainly affects crop health, soil carbon content and yield on the cultivated land. Economic sustainability is directly related to yield capacity, which is theorized to increase by 3-10% in a space of 3 - 10 years as a result of agro-ecological implementation. This study aimed to empirically assess the practicality and validity of these assumptions. The study utilized mainly GIS and RS techniques to assess the effectiveness of agro-ecology in crop health improvement from satellite images. The assessment involved a longitudinal study (2013 – 2015) assessing the changes that occur after a farm retrofits from conventional agriculture to agro-ecology. The assumptions guided the objectives of the study. For each objective, an agro-ecological farm was compared with a conventional farm in the same climatic conditional occupying the same general location. Crop health was assessed using satellite images analysed through ArcGIS and Erdas. This entailed the production of NDVI and Re-classified outputs of the farm area. The NDVI ranges of the entire period of study were thus compared in a stacked histogram for each farm to assess for trends. Yield capacity was calculated based on the production records acquired from the farmers and plotted in a stacked bar graph as percentages of a total for each farm. The results of the study showed decreasing crop health trends over 80% of the conventional farms and an increase over 80% of the organic farms. Yield capacity showed similar patterns to those of crop health. The study thus showed that agro-ecology is an effective strategy for crop-health improvement and yield increase.

Keywords: agro-ecosystem, conventional farm, dialectical, sustainability

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Authors: Madhu Babu Cherukuri, Tamoghna Ghosh

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Factory supply chain management spends billions of dollars every year to procure and manage equipment spare parts. Due to technology -and processes changes some of these spares become obsolete/dead inventory. Factories have huge dead inventory worth millions of dollars accumulating over time. This is due to lack of a scientific methodology to identify them and send the inventory back to the suppliers on a timely basis. The standard approach followed across industries to deal with this is: if a part is not used for a set pre-defined period of time it is declared dead. This leads to accumulation of dead parts over time and these parts cannot be sold back to the suppliers as it is too late as per contract agreement. Our main idea is the time period for identifying a part as dead cannot be a fixed pre-defined duration across all parts. Rather, it should depend on various properties of the part like historical consumption pattern, type of part, how many machines it is being used in, whether it- is a preventive maintenance part etc. We have designed a predictive algorithm which predicts part obsolescence well in advance with reasonable accuracy and which can help save millions.

Keywords: obsolete inventory, machine learning, big data, supply chain analytics, dead inventory

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Authors: Mohd Aderi Che Noh, Tuan Rahayu Tuan Lasan

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Higher order thinking skills (HOTS) is an important skill in the Malaysian education system to produce a knowledgeable generation, able to think critically and creatively in order to face the challenges in the future. Educational challenges of the 21st century require that all students to have the HOTS. Therefore, this study aims to identify the level of knowledge, attitude and readiness of students towards HOTS. The respondents were 127 form four students from schools in the Federal Territory of Putrajaya. This study is quantitative survey using a questionnaire to collect data. Data were analyzed using Statistical Package for the Social Sciences (SPSS) 23.0. The results showed that knowledge, attitudes and readiness of students towards HOTS lam were at a high level. Inferential analysis showed that there was a significant relationship between knowledge with attitude and readiness towards HOTS. This study provides information to the schools and teachers to improve the teaching and learning to increase students HOTS and fulfilling the hope of Ministry of Education to produce human capital who can be globally competitive.

Keywords: high order thinking skills, teaching, education, Malaysia

Procedia PDF Downloads 198

Authors: Tomas Kos

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An increasing body of research has explored patterns of interaction and peer support among young learners. Although some studies suggest that young learners can collaborate and support each other, other studies indicate that young learners may lack the ability to work together and support one another when interacting on classroom tasks. Moreover, despite the claims that peer collaboration is conducive to learning, studies have not paid enough attention to the “how” to enhance peer collaboration on classroom tasks. To fill this gap, this “how-to” article proposes that teaching young learners how to work together is a powerful pedagogical tool that can greatly improve collaborative behavior and a sense of mutuality among young learners. This article will pay particular attention to primary schools and the context of English as a foreign language. It will first review literature related to patterns of interaction and peer support conducted in the cognitive and sociocultural framework. It will then address what it actually means to collaborate. At the heart of the article, it will discuss some practical pedagogical ideas for language teachers, which entail teaching collaborative principles and strategies that will help their students to support each other and engage in communication with each other.

Keywords: young learners, peer collaboration, peer interaction, peer support, patterns of interaction

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Authors: Marie-Josée Morneau

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A literacy-based approach, centred on the use of the language of instruction as a cognitive tool, can increase the L2 communication skills of French immersion students. Academic subject areas such as science and mathematics offer an authentic language learning context where students can become more proficient speakers while using specific vocabulary and language structures to learn, interact and communicate their reasoning, when provided the opportunities and guidance to do so. In this Canadian quasi-experimental study, the effects of teaching specific language elements during mathematic classes through literacy-based activities in Early French Immersion programming were compared between two Grade 7/8 groups: the experimental group, which received literacy-based teaching for a 6-week period, and the control group, which received regular teaching instruction. The results showed that the participants from the experimental group made more progress in their mathematical communication skills, which suggests that targeting L2 language as a cognitive tool can be beneficial to immersion learners who learn mathematic concepts and remind us that all L2 teachers are language teachers.

Keywords: mathematics, French immersion, literacy-based, oral communication, L2

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Authors: Eunice Tan

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The purpose of this presentation is to look at an alternative to the approach and methodologies of working with a child with special needs. The strength-based approach to education embodies a paradigm shift. It is a strategy to move away from a deficit-based methodology which inadvertently may lead to an extensive list of things that the child cannot do or is unable to do. Today, many parents of individuals with special needs are focused on the individual’s deficits rather than on his or her strengths. Even when parents recognise and identify their child’s savant strengths to be valuable and wish to develop their abilities, they face the challenge that there are insufficient programs committed to supporting the development and improvement of such abilities. What is a strength-based approach in education? A strength-based approach in education focuses on students' positive qualities and contributions to class instead of the skills and abilities they may not have. Many schools are focused on the child’s special educational needs rather than the whole child. Parents interviewed have said that they have to engage external tutors to help hone in on their child’s interests and strengths. The strength-based approach to writing statements encourages educators to find out: • What a child can do • What a child can do when he or she is given educational support • Learning more about children with special needs and their strengths and talents will broaden our understanding of how we can help them with language acquisition, social skills, as well as self-help and independence skills.

Keywords: special needs, strengths, and talents, alternative educational approach, strength based approach

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Authors: Atuhurra Jesse, N'guessan Yves-Roland Douha, Pabitra Lenka

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The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unavailability of high quality labeled training data. Moreover, institutions have been hesitant to share their data related to CSSVD. To fill these gaps, image classifiers to detect CSSVD-infected cacao plants are presented in this study. The classifiers are based on VGG16, ResNet50 and Vision Transformer (ViT). The image classifiers are evaluated on a recently released and publicly accessible KaraAgroAI Cocoa dataset. The best performing image classifier, based on ResNet50, achieves 95.39\% precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20 epochs. There is a +9.75\% improvement in recall when compared to previous works. These results indicate that the image classifiers learn to identify cacao plants infected with CSSVD.

Keywords: CSSVD, image classification, ResNet50, vision transformer, KaraAgroAI cocoa dataset

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Authors: Garima Singh, Kunal Malhotra

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Objective: The expansion of telehealth has been instrumental in increasing access to medical services, especially for underserved and rural communities. In 2020, 14 million patients received virtual care through telemedicine and the global telemedicine market is expected to reach up to $185 million by 2023. It provides a platform and allows a patient to receive primary care as well as specialized care using technology and the comfort of their homes. Telemedicine was particularly useful during COVID-pandemic and the number of telehealth visits increased by 5000% during that time. It continues to serve as a significant resource for patients seeking care and to bridge the gap between the disease and the treatment. Method: As per APA (American Psychiatric Association), Telemedicine is the process of providing health care from a distance through technology. It is a subset of telemedicine, and can involve providing a range of services, including evaluations, therapy, patient education and medication management. It can involve direct interaction between a physician and the patient. It also encompasses supporting primary care providers with specialist consultation and expertise. It can also involve recording medical information (images, videos, etc.) and sending this to a distant site for later review. Results: In our organization, we are using telepsychiatry and serving 25 counties and approximately 1.4 million people. We provide multiple services, including inpatient, outpatient, crisis intervention, Rehab facility, autism services, case management, community treatment and multiple other modalities. With project ECHO (Extension for Community Healthcare Outcomes) it has been used to advise and assist primary care providers in treating mental health. It empowers primary care providers to treat patients in their own community by sharing knowledge. Conclusion: Telemedicine has shown to be a great medium in meeting patients’ needs and accessible mental health. It has been shown to improve access to care in both urban and rural settings by bringing care to a patient and reducing barriers like transportation, financial stress and resources. Telemedicine is also helping with reducing ER visits, integrating primary care and improving the continuity of care and follow-up. There has been substantial evidence and research about its effectiveness and its usage.

Keywords: telehealth, telemedicine, access to care, medical technology

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Authors: Poonam Mishra, Shiv Kumar Sharma, Sanjeev Swami

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Purpose: The Purpose of this paper is to investigate the relationship of percieved organizational politics with three levels of antecedents (i.e., organizational level, work environment level and individual level)and its consequents simultaneously. The study addresses antecedents and consequents of percieved political behavior in the higher education sector of India with specific reference to a central university. Design/ Methodology/ Approach: A conceptual framework and hypotheses were first developed on the basis of review of previous studies on organizational politics. A questionnaire was then developed carrying 66 items related to 8-constructs and demographic characteristics of respondents. Jundegemental sampling was used to select respondents. Primary data is collected through structured questionnaire from 45 faculty members of a central university. The sample constitutes Professors, Associate Professors and Assistant Professors from various departments of the University. To test hypotheses data was analyzed statistically using partial least square-structural equations modeling (PLS-SEM). Findings: Results indicated a strong support for OP’s relationship with three of the four proposed antecedents that are, workforce diversity, relationship conflict and need for power with relationship conflict having the strongest impact. No significant relationship was found between role conflict and perception of organizational politics. The three consequences that is, intention to turnover, job anxiety, and organizational commitment are significantly impacted by perception of organizational politics. Practical Implications– This study will be helpful in motivating future research for improving the quality of higher education in India by reducing the level of antecedents that adds to the level of perception of organizational politics, ultimately resulting in unfavorable outcomes. Originality/value: Although a large number of studies on atecedents and consequents of percieved organizational politics have been reported, little attention has been paid to test all the separate but interdependent relationships simultaneously; in this paper organizational politics will be simultaneously treated as a dependent variable and same will be treated as independent variable in subsequent relationships.

Keywords: organizational politics, workforce diversity, relationship conflict, role conflict, need for power, intention to turnover, job anxiety, organizational commitment

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: Duygu Dere, Mert Ergeneci, Kaan Gokcesu

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Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data.

Keywords: adaptive data processing, behavioral finance , convex optimization, online learning, soft minimum thresholding

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Authors: Junqi Zou

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As one of the world’s most tech-ready countries, Singapore has initiated the Smart Nation plan to harness the full power and potential of digital technologies to transform the way people live and work, through the more efficient government and business processes, to make the economy more productive. The key evolutions of digital technology transformation in healthcare and the increasing deployment of Internet of Things (IoTs), Big Data, AI/cognitive, Robotic Process Automation (RPA), Electronic Health Record Systems (EHR), Electronic Medical Record Systems (EMR), Warehouse Management System (WMS in the most recent decade have significantly stepped up the move towards an information-driven healthcare ecosystem. The advances in information technology not only bring benefits to patients but also act as a key force in changing management accounting in healthcare sector. The aim of this study is to investigate the impacts of digital technology transformation on Singapore’s healthcare sector from a management accounting perspective. Adopting a Balanced Scorecard (BSC) analysis approach, this paper conducted an exploratory case study of a newly launched Singapore public hospital, which has been recognized as amongst the most digitally advanced healthcare facilities in Asia-Pacific region. Specifically, this study gains insights on how the new technology is changing healthcare organizations’ management accounting from four perspectives under the Balanced Scorecard approach, 1) Financial Perspective, 2) Customer (Patient) Perspective, 3) Internal Processes Perspective, and 4) Learning and Growth Perspective. Based on a thorough review of archival records from the government and public, and the interview reports with the hospital’s CIO, this study finds the improvements from all the four perspectives under the Balanced Scorecard framework as follows: 1) Learning and Growth Perspective: The Government (Ministry of Health) works with the hospital to open up multiple training pathways to health professionals that upgrade and develops new IT skills among the healthcare workforce to support the transformation of healthcare services. 2) Internal Process Perspective: The hospital achieved digital transformation through Project OneCare to integrate clinical, operational, and administrative information systems (e.g., EHR, EMR, WMS, EPIB, RTLS) that enable the seamless flow of data and the implementation of JIT system to help the hospital operate more effectively and efficiently. 3) Customer Perspective: The fully integrated EMR suite enhances the patient’s experiences by achieving the 5 Rights (Right Patient, Right Data, Right Device, Right Entry and Right Time). 4) Financial Perspective: Cost savings are achieved from improved inventory management and effective supply chain management. The use of process automation also results in a reduction of manpower costs and logistics cost. To summarize, these improvements identified under the Balanced Scorecard framework confirm the success of utilizing the integration of advanced ICT to enhance healthcare organization’s customer service, productivity efficiency, and cost savings. Moreover, the Big Data generated from this integrated EMR system can be particularly useful in aiding management control system to optimize decision making and strategic planning. To conclude, the new digital technology transformation has moved the usefulness of management accounting to both financial and non-financial dimensions with new heights in the area of healthcare management.

Keywords: balanced scorecard, digital technology transformation, healthcare ecosystem, integrated information system

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Authors: Beeto W. C. Leung, Nicole C. Y. Lee

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It was well known that college students experienced a high level of stress and anxiety. College athletes, a special group of college students, may even encounter a higher level of pressure and distress due to their dual endeavors in academic and athletic settings. Due to the high demands and costs of mental health services, easily accessible, web-based self-help interventions are getting more popular. The aim of the present experimental study was to examine the potential intervention effect of a mindfulness-based self-help mobile App, called 'Smiling Mind', on mindfulness and psychological well-being. Forty-six college athletes, recruited from athletic teams of two local universities in Hong Kong, were randomly assigned to the Mindfulness App Group (MAG) and the Control Group (CG). All participants were administered the Mindful Attention Awareness Scale, Geriatric Depression Scale, and Perceived Stress Scale-10 before the study (Time 1, T1) and after the 4-week intervention (Time 2, T2). MAG was requested to use the app and follow the instructions every day for at least 5 days per week. Participants were also asked to record their daily app usage time. Results showed that, for MAG, from T1 to T2, mindfulness has been increased from 3.25 to 3.92; depressive symptoms and stress has been significantly decreased from 8.6 to 5.1 and 24.8 to 13.5 respectively while for the CG, mindfulness has been decreased slightly from 3.29 to 3.13; depressive symptoms and stress has been slightly increased from 7.1 to 7.3 and 24.1 to 27.1 respectively. Three mixed-design ANOVAs with time (T1, T2) as the within-subjects factor and intervention group (MAG, CG) as the between-subjects factor revealed a main effect of time on mindfulness, F(1, 41) = 10.28, p < 0.01, depressive symptoms, F(1, 41) = 6.55, p < 0.02 and stress, F(1, 41) = 16.96, p < 0.001 respectively. Both predicted interaction between time and intervention group on mindfulness, F(1, 41) = 26.6, p < 0.001, ηp 2 =0.39, depressive symptoms, F(1, 41) = 8.00, p < 0.01, ηp 2 =0.16 and Stress F(1, 41) = 49.3, p < 0.001, ηp 2 =0.55 were significant meaning that participants using the Mindfulness Mobile App in the intervention did experienced a significant increase in mindfulness and significant decrease in depressive symptoms and perceived level of stress after the 4-week intervention when compared with the control group. The present study provided encouraging empirical support for using Smiling Mind, a self-help mobile app, to promote mindfulness and mental health in a cost-effective way. Further studies should examine the potential use of Smiling Mind in different samples, including children and adolescence, as well as, investigate the lasting effects of using the app on other psychosocial outcomes such as emotional regulations.

Keywords: college athletes, experimental study, mindfulness mobile apps, psychological well-being

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Authors: Hyunbin Choi, Jihyeon Noh, Changwon Lim

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In this paper, we propose SiamMask++, an architecture that performs layer-wise aggregation and depth-wise cross-correlation and introduce multi-RPN module and multi-MASK module to improve EAO (Expected Average Overlap), a representative performance evaluation metric for Visual Object Tracking (VOT) challenge. The proposed architecture, SiamMask++, has two versions, namely, bi_SiamMask++, which satisfies the real time (56fps) on systems equipped with GPUs (Titan XP), and rf_SiamMask++, which combines mask refinement modules for EAO improvements. Tests are performed on VOT2016, VOT2018 and VOT2019, the representative datasets of Visual Object Tracking tasks labeled as rotated bounding boxes. SiamMask++ perform better than SiamMask on all the three datasets tested. SiamMask++ is achieved performance of 62.6% accuracy, 26.2% robustness and 39.8% EAO, especially on the VOT2018 dataset. Compared to SiamMask, this is an improvement of 4.18%, 37.17%, 23.99%, respectively. In addition, we do an experimental in-depth analysis of how much the introduction of features and multi modules extracted from the backbone affects the performance of our model in the VOT task.

Keywords: visual object tracking, video, deep learning, layer wise aggregation, Siamese network

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Authors: Yasaman Ashrafi, Stephen Kajewski, Annastiina Silvennoinen, Madhav Nepal

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Mega projects either in construction, urban development or energy sectors are one of the key drivers that build the foundation of wealth and modern civilizations in regions and nations. Such projects require economic justification and substantial capital investment, often derived from individual and corporate investors as well as governments. Cost overruns and time delays in these mega projects demands a new approach to more accurately predict project costs and establish realistic financial plans. The significance of this paper is that the cost efficiency of megaprojects will improve and decrease cost overruns. This research will assist Project Managers (PMs) to make timely and appropriate decisions about both cost and outcomes of ongoing projects. This research, therefore, examines the oil and gas industry where most mega projects apply the classic methods of Cost Performance Index (CPI) and Schedule Performance Index (SPI) and rely on project data to forecast cost and time. Because these projects are always overrun in cost and time even at the early phase of the project, the probabilistic methods of Monte Carlo Simulation (MCS) and Bayesian Adaptive Forecasting method were used to predict project cost at completion of projects. The current theoretical and mathematical models which forecast the total expected cost and project completion date, during the execution phase of an ongoing project will be evaluated. Earned Value Management (EVM) method is unable to predict cost at completion of a project accurately due to the lack of enough detailed project information especially in the early phase of the project. During the project execution phase, the Bayesian adaptive forecasting method incorporates predictions into the actual performance data from earned value management and revises pre-project cost estimates, making full use of the available information. The outcome of this research is to improve the accuracy of both cost prediction and final duration. This research will provide a warning method to identify when current project performance deviates from planned performance and crates an unacceptable gap between preliminary planning and actual performance. This warning method will support project managers to take corrective actions on time.

Keywords: cost forecasting, earned value management, project control, project management, risk analysis, simulation

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Authors: Bjorn Kierulf, Arun Chundru

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Over the past decade, collegiate rocketry teams have emerged across the country with various goals: space, liquid-fueled flight, etc. A critical piece of the development of knowledge within a club is the use of so-called "sounding rockets," whose goal is to take in-flight measurements that inform future rocket design. Common measurements include acceleration from inertial measurement units (IMU's), and altitude from barometers. With a properly tuned filter, these measurements can be used to find velocity, but are susceptible to noise, offset, and filter settings. Instead, velocity can be measured more directly and more instantaneously using a pitot tube, which operates by measuring the stagnation pressure. At supersonic speeds, an additional thermodynamic property is necessary to constrain the upstream state. One possibility is the stagnation temperature, measured by a thermocouple in the pitot tube. The routing of the pitot tube from the nosecone tip down to a pressure transducer is complicated by the nosecone's structure. Commercial-off-the-shelf (COTS) nosecones come with a removable metal tip (without a pitot tube). This provides the opportunity to make custom tips with integrated measurement systems without making the nosecone from scratch. The main design constraint is how the nosecone tip is held down onto the nosecone, using the tension in a threaded rod anchored to a bulkhead below. Because the threaded rod connects into a threaded hole in the center of the nosecone tip, the pitot tube follows a winding path, and the pressure fitting is off-center. Two designs will be presented in the paper, one with a curved pitot tube and a coaxial design that eliminates the need for the winding path by routing pressure through a structural tube. Additionally, three manufacturing methods will be presented for these designs: bound powder filament metal 3D printing, stereo-lithography (SLA) 3D printing, and traditional machining. These will employ three different materials, copper, steel, and proprietary resin. These manufacturing methods and materials are relatively low cost, thus accessible to student researchers. These designs and materials cover multiple use cases, based on how fast the sounding rocket is expected to travel and how important heating effects are - to measure and to avoid melting. This paper will include drawings showing key features and an overview of the design changes necessitated by the manufacture. It will also include a look at the successful use of these nosecone tips and the data they have gathered to date.

Keywords: additive manufacturing, machining, pitot tube, sounding rocketry

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Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

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Authors: Botirjon Kosimov

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This work sheds light on the reforms towards the independence of the judiciary in Uzbekistan, as well as issues of further ensuring judicial independence in the country based on international values, particularly the legal practice of the United States. In every democratic state infringed human rights are reinstated and violated laws are protected by the help of justice based on the strict principle of judicial independence. The realization of this principle in Uzbekistan has been paid much attention since the proclamation of its independence. In the country, a series of reforms have been implemented in the field of the judiciary in order to actualize the principle of judicial independence. Uzbekistan has been reforming the judiciary considering both international and national values and practice of foreign countries. While forming a democratic state based on civil society, Uzbekistan shares practice with the most developed countries in the world. The United States of America can be a clear example which is worth learning how to establish and ensure an independent judiciary. It seems that although Uzbekistan has reformed the judiciary efficiently, it should further reform considering the legal practice of the United States.

Keywords: dependent judges, independent judges, judicial independence, judicial reforms, judicial life tenure, obstacles to judicial independence

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Authors: Sinethemba Yakobi, Lindiwe Zuma, Ofentse Pooe

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Gonococcal infections present a notable public health issue, and the major approach for treatment involves using β-lactam antibiotics that specifically target penicillin-binding protein 2 (PBP2) in Neisseria gonorrhoeae. This study examines the influence of flavonoids, namely rutin, on the structural changes of PBP2 in both penicillin-resistant (FA6140) and penicillin-susceptible (FA19) strains. The research clarifies the structural effects of particular mutations, such as inserting an aspartate residue at position 345 (Asp-345a) in the PBP2 protein. The strain FA6140, which is resistant to penicillin, shows specific changes that lead to a decrease in penicillin binding. These mutations, namely P551S and F504L, significantly impact the pace at which acylation occurs and the stability of the strain under high temperatures. Molecular docking analyses investigate the antibacterial activities of rutin and other phytocompounds, emphasizing its exceptional binding affinity and potential as an inhibitor of PBP2. Quercetin and protocatechuic acid have encouraging antibacterial effectiveness, with quercetin displaying characteristics similar to those of drugs. Molecular dynamics simulations offer a detailed comprehension of the interactions between flavonoids and PBP2, highlighting rutin's exceptional antioxidant effects and strong affinity for the substrate binding site. The study's wider ramifications pertain to the pressing requirement for antiviral treatments in the context of the ongoing COVID-19 epidemic. Flavonoids have a strong affinity for binding to PBP2, indicating their potential as inhibitors to impair cell wall formation in N. gonorrhoeae. Ultimately, this study provides extensive knowledge on the interactions between proteins and ligands, the dynamics of the structure, and the ability of flavonoids to combat penicillin-resistant N. gonorrhoeae bacteria. The verified simulation outcomes establish a basis for creating potent inhibitors and medicinal therapies to combat infectious illnesses.

Keywords: phytochemicals, penicillin-binding protein 2, gonococcal infection, ligand-protein interaction, binding energy, neisseria gonorrhoeae FA19, neisseria gonorrhoeae FA6140, flavonoids

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: Sevim Ugur, Cemile Kutmec Yilmaz, Omer Us, Sevdenur Koksaldi

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Aim: This study aims to investigate the effect of digital game playing and Internet addiction on perceived stress levels in university students. Method: The descriptive study was conducted through face-to-face interview method with a total of 364 university students studying at Aksaray University between November 15 and December 30, 2017. The research data were collected using personal information form, a questionnaire to determine the characteristics of playing digital game, the Internet addiction scale and the perceived stress scale. In the evaluation of the data, Mann-Whitney U test was used for two-group comparison of the sample with non-normal distribution, Kruskal-Wallis H-test was used in the comparison of more than two groups, and the Spearman correlation test was used to determine the relationship between Internet addiction and the perceived stress level. Results: It was determined that the mean age of the students participated in the study was 20.13 ± 1.7 years, 67.6% was female, 35.7% was sophomore, and 62.1% had an income 500 TL or less. It was found that 83.5% of the students use the Internet every day and 70.6% uses the Internet for 5 hours or less per day. Of the students, 12.4% prefers digital games instead of spending time outdoors, 8% plays a game as the first activity in leisure time, 12.4% plays all day, 15.7% feels anger when he/she is prevented from playing, 14.8% prefers playing games to get away from his/her problems, 23.4% had his/her school achievement affected negatively because of game playing, and 8% argues with family members due to the time spent for gaming. Students who play games on the computer for a long time were found to feel back pain (30.8%), headache (28.6%), insomnia (26.9%), dryness and pain in the eyes (26.6%), pain in the wrist (21.2%), feeling excessive tension and anger (16.2%), humpback (12.9), vision loss (9.6%) and pain in the wrist and fingers (7.4%). In our study, students' Internet addiction scale mean score was found to be 45.47 ± 16.1 and mean perceived stress scale score was 28.56 ± 2.7. A significant and negative correlation (p=0.037) was found between the total score of the Internet addiction scale and the total score of the perceived stress scale (r=-0.110). Conclusion: It was found in the study that Internet addiction and perceived stress of the students were at a moderate level and that there was a negative correlation between Internet addiction and perceived stress levels. Internet addiction was found to increase with the increasing perceived stress levels of students, and students were found to have health problems such as back pain, dryness in the eyes, pain, insomnia, headache, and humpback. Therefore, it is recommended to inform students about different coping methods other than spending time on the Internet to cope with the stress they perceive.

Keywords: digital game, internet addiction, student, stress level

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Authors: Eftah Bte. Moh Hj Abdullah, Izazol Binti Idris, Abd Aziz Bin Abd Shukor

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This study aims to identify the understanding expectations of school administrators concerning school assessment. The researcher utilized a qualitative descriptive study on 19 administrators from three secondary schools in the North Kinta district. The respondents had been interviewed on their understanding expectations of school assessment using the focus group discussion method. Overall findings showed that the administrators’ understanding expectations of school assessment was weak; especially in terms of content focus, articulation across age and grade, transparency and fairness, as well as the pedagogical implications. Findings from interviews indicated that administrators explained their understanding expectations of school assessment from the aspect of school management, and not from the aspect of instructional leadership or specifically as assessment leaders. The study implications from the administrators’ understanding expectations may hint at the difficulty of the administrators to function as assessment leaders, in order to reduce their focus as manager, and move towards their primary role in the process of teaching and learning. The administrator, as assessment leaders, would be able to reach assessment goals via collaboration in identifying and listing teacher assessment competencies, how to construct assessment capacity, how to interpret assessment correctly, the use of assessment and how to use assessment information to communicate confidently and effectively to the public.

Keywords: assessment leaders, assessment goals, instructional leadership, understanding expectation of assessment

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Monika Paleczna, Barbara Szmigielska

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The period of adolescence is a time when young people are becoming more and more active and conscious users of the digital world. One of the most frequently undertaken activities by them is computer games. Young players can choose from a wide range of games, including action, adventure, strategy, and logic games. The main aim of this study is to answer the question about the motivation of teenage players. The basic question is what motivates young players to play computer games and what motivates them to play a particular game. Fifty adolescents aged 15-17 participated in the study. They completed a questionnaire in which they determined what motivates them to play, how often they play computer games, and what type of computer games they play most often. It was found that entertainment and learning English are among the most important motives. The most important specific features related to a given game are the knowledge of its previous parts and the ability to play for free. The motives chosen by the players will be described in relation to the concepts of internal and external as well as approach and avoidance motivation. An additional purpose of this study is to present data concerning preferences regarding the type of games and the amount of time they spend playing.

Keywords: computer games, motivation, game preferences, adolescence

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: Guerriche Amina

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The recent trends in foreign language teaching -influenced widely by the process of globalization, interculturalism, and global flows and migration- are leaning towards adopting an intercultural perspective to help in developing students who are global citizens able to effectively function across diverse boundaries (cultural, social, geographical). Researchers call for intercultural learning and teaching perspective that would foster and increase intercultural awareness and understanding (e.g., Guilherme, 2002; Byram et al., 2002). The present research aims at unfolding whether including the cultural dimension in foreign language instruction can help in developing students’ intercultural understanding and awareness. In doing so, a cultural pedagogical experiment was designed and conducted for the period of one year at the level of the university. Data were collected qualitatively and analyzed thematically. Results help in drawing important implications for educational institutions, foreign language teachers, and syllabus designers about the importance and effectiveness of perceiving foreign language instruction as a social activity that can nurture interculturally competent individuals who adequately respond to the demands of today’s intercultural and globalized societies.

Keywords: foreign language teaching, intercultural awareness, language and culture, intercultural understanding

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Authors: Daniel James Pitchforth, Timothy James Rogers, Ulf Tyge Tygesen, Elizabeth Jane Cross

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Wave loading is a primary cause of fatigue within offshore structures and its quantification presents a challenging and important subtask within the SHM framework. The accurate representation of physics in such environments is difficult, however, driving the development of data-driven techniques in recent years. Within many industrial applications, empirical laws remain the preferred method of wave loading prediction due to their low computational cost and ease of implementation. This paper aims to develop an approach that combines data-driven Gaussian process models with physical empirical solutions for wave loading, including Morison’s Equation. The aim here is to incorporate physics directly into the covariance function (kernel) of the Gaussian process, enforcing derived behaviors whilst still allowing enough flexibility to account for phenomena such as vortex shedding, which may not be represented within the empirical laws. The combined approach has a number of advantages, including improved performance over either component used independently and interpretable hyperparameters.

Keywords: offshore structures, Gaussian processes, Physics informed machine learning, Kernel design

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