Search results for: learning through movement

Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

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Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

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Authors: Roseanne Sharon Borromeo, Mariana Carvalho, Mariia Karizhenskaia

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Healthcare providers need to comprehend cultural diversity for optimal patient-centered care, especially near the end of life. Although a universal method for navigating cultural differences would be ideal, culture’s high complexity makes this strategy impossible. Adding cultural humility, a process of self-reflection to understand personal and systemic biases and humbly acknowledging oneself as a learner when it comes to understanding another's experience leads to a meaningful process in palliative care generating respectful, honest, and trustworthy relationships. This study is a systematic review of the literature on cultural humility in palliative care research and best practices. Race, religion, language, values, and beliefs can affect an individual’s access to palliative care, underscoring the importance of culture in palliative care. Cultural influences affect end-of-life care perceptions, impacting bereavement rituals, decision-making, and attitudes toward death. Cultural factors affecting the delivery of care identified in a scoping review of Canadian literature include cultural competency, cultural sensitivity, and cultural accessibility. As the different parts of the world become exponentially diverse and multicultural, healthcare providers have been encouraged to give culturally competent care at the bedside. Therefore, many organizations have made cultural competence training required to expose professionals to the special needs and vulnerability of diverse populations. Cultural competence is easily standardized, taught, and implemented; however, this theoretically finite form of knowledge can dangerously lead to false assumptions or stereotyping, generating poor communication, loss of bonds and trust, and poor healthcare provider-patient relationship. In contrast, Cultural humility is a dynamic process that includes self-reflection, personal critique, and growth, allowing healthcare providers to respond to these differences with an open mind, curiosity, and awareness that one is never truly a “cultural” expert and requires life-long learning to overcome common biases and ingrained societal influences. Cultural humility concepts include self-awareness and power imbalances. While being culturally competent requires being skilled and knowledgeable in one’s culture, being culturally humble involves the sometimes-uncomfortable position of healthcare providers as students of the patient. Incorporating cultural humility emphasizes the need to approach end-of-life care with openness and responsiveness to various cultural perspectives. Thus, healthcare workers need to embrace lifelong learning in individual beliefs and values on suffering, death, and dying. There have been different approaches to this as well. Some adopt strategies for cultural humility, addressing conflicts and challenges through relational and health system approaches. In practice and research, clinicians and researchers must embrace cultural humility to advance palliative care practices, using qualitative methods to capture culturally nuanced experiences. Cultural diversity significantly impacts patient-centered care, particularly in end-of-life contexts. Cultural factors also shape end-of-life perceptions, impacting rituals, decision-making, and attitudes toward death. Cultural humility encourages openness and acknowledges the limitations of expertise in one’s culture. A consistent self-awareness and a desire to understand patients’ beliefs drive the practice of cultural humility. This dynamic process requires practitioners to learn continuously, fostering empathy and understanding. Cultural humility enhances palliative care, ensuring it resonates genuinely across cultural backgrounds and enriches patient-provider interactions.

Keywords: cultural competency, cultural diversity, cultural humility, palliative care, self-awareness

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Authors: Osatuyi Kehinde Micheal

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There is much research that has contributed to an improved understanding of the future of food security, especially during the COVID-19 pandemic. A pathway was developed by using a local community kitchen in Muizenberg in western cape province, cape town, south Africa, a case study to map out the future of food security in times of crisis. This kitchen aims to provide nutritious, affordable, plant-based meals to our community. It is also a place of diverse learning, sharing, empowering the volunteers, and growth to support the local economy and future resilience by sustaining our community kitchen for the community. This document contains an overview of the story of the community kitchen on how we create self-sustainability as a new pathway development to sustain the community and reduce Zero hunger in the regional food system. This paper describes the key elements of how we respond to covid-19 pandemic by sharing food parcels and creating 13 soup kitchens across the community to tackle the immediate response to covid-19 pandemic and agricultural systems by growing home food gardening in different homes, also having a consciousness Dry goods store to reduce Zero waste and a local currency as an innovation to reduce food crisis. Insights gained from our article and outreach and their value in how we create adaptation, transformation, and sustainability as a new development pathway to solve any future problem crisis in the food security system in our society.

Keywords: sustainability, food security, community development, adapatation, transformation

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Authors: Penn P. Wu, Jeannie Jo

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Blockchain technology has rapidly gained popularity in industry. This paper attempts to assist academia to answer four questions. First, should community colleges begin offering education to nurture blockchain-literate students for the job market? Second, what are the appropriate topical areas to cover? Third, should it be an individual course? And forth, should it be a technical or management course? This paper starts with identifying the knowledge domains of blockchain technology and the topical areas each domain has, and continues with placing them in appropriate academic territories (Computer Sciences vs. Business) and subjects (programming, management, marketing, and laws), and then develops an evaluation model to determine the appropriate topical area for community colleges to teach. The evaluation is based on seven factors: maturity of technology, impacts on management, real-world applications, subject classification, knowledge prerequisites, textbook readiness, and recommended pedagogies. The evaluation results point to an interesting direction that offering an introductory course is an ideal option to guide students through the learning journey of what blockchain is and how it applies to business. Such an introductory course does not need to engage students in the discussions of mathematics and sciences that make blockchain technologies possible. While it is inevitable to brief technical topics to help students build a solid knowledge foundation of blockchain technologies, community colleges should avoid offering students a course centered on the discussion of developing blockchain applications.

Keywords: blockchain, pedagogies, blockchain technologies, blockchain course, blockchain pedagogies

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Authors: Ganesh B. Shinde, Vijaya B. Musande

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Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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Authors: George Onomah

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Background: The integration of information communication technology (ICT) in Mathematics education faces notable challenges, which this study aimed to dissect and understand. Objectives: The primary goal was to assess the internal and external factors affecting the adoption of ICT by in-service Mathematics teachers. Internal factors examined included teachers' pedagogical beliefs, prior teaching experience, attitudes towards computers, and proficiency with technology. External factors included the availability of technological resources, the level of ICT training received, the sufficiency of allocated time for technology use, and the institutional culture within educational environments. Methods: A descriptive survey design was employed to methodically investigate these factors. Data collection was carried out using a five-point Likert scale questionnaire, administered to a carefully selected sample of 100 in-service Mathematics teachers through a combination of purposive and convenience sampling techniques. Findings: Results from multiple regression analysis revealed a significant underutilization of ICT in Mathematics teaching, highlighting a pronounced deficiency in current classroom practices. Recommendations: The findings suggest an urgent need for educational department heads to implement regular and comprehensive ICT training programs aimed at enhancing teachers' technological capabilities and promoting the integration of ICT in Mathematics teaching methodologies.

Keywords: ICT, Mathematics, integration, barriers

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Authors: Julie Chuah Suan Choo

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Due to the continuous change in workforce and the demands of the global workplace, many employers had lamented that the majority of university graduates were not prepared in the key areas of employment such as critical thinking, writing, self-direction and global knowledge which are most needed for the purposes of promotion. Further, critical thinking skills are deemed as integral parts of transformational pedagogy which aims at having a more informed society. To add to this, literature teaching has recently been advocated for enhancing students’ critical thinking and reasoning. Thus this study explored the effects of incorporating a few strategies in teaching literature, namely a Shakespeare play, into a course design to enhance these skills. An experiment involving a pretest and posttest using the California Critical Thinking Skills Test (CCTST) were administered on 80 first-year students enrolled in the Bachelor of Arts programme who were randomly assigned into the control group and experimental group. For the next 12 weeks, the experimental group was given intervention which included guided in-class discussion with Socratic questioning skills, learning log to detect their weaknesses in logical reasoning; presentations and quizzes. The results of CCTST which included paired T-test using SPSS version 22 indicated significant differences between the two groups. Findings have significant implications on the course design as well as pedagogical practice in using literature to enhance students’ critical thinking skills.

Keywords: literature teaching, critical thinking, California critical thinking skills test (CCTST), course design

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Authors: Bin Liu

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MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

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Authors: Shahla Ali

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In the context of promoting green infrastructure development, new opportunities are emerging to re-examine sustainable development practices. This paper presents an initial exploration of the development of community-investor dispute prevention and facilitation mechanisms in the context of the Belt and Road Initiative (BRI) spanning Asia, Africa, and Europe. Given the widescale impact of China’s multi-jurisdictional development initiative, learning how to coordinate with local communities is vital to realizing inclusive and sustainable growth. In the 20 years since the development of the first multilateral community-investor dispute resolution mechanism developed by the International Finance Centre/World Bank, much has been learned about public facilitation, community engagement, and dispute prevention during the early stages of major infrastructure development programs. This paper will explore initial findings as they relate to initiatives underway along the BRI within the Asian Infrastructure Investment Bank and the Asian Development Bank. Given the borderless nature of sustainability concerns, insights from diverse regions are critical to deepening insights into best practices. Drawing on a case-based methodology, this paper will explore the achievements, challenges, and lessons learned in community-investor dispute prevention and resolution for major infrastructure projects in the greater China region.

Keywords: law and development, dispute prevention, sustainable development, mitigation

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Authors: Emma Pope, Demos Parapanos

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Rural tourism has become more obvious and prevalent, with tourists’ increasingly seeking authentic experiences. This movement accelerated post-Covid, putting destinations in danger of reaching levels of saturation called ‘overtourism’. Whereas the phenomenon of overtourism has been frequently discussed in the urban context by academics and practitioners over recent years, it has hardly been referred to in the context of rural tourism, where perhaps it is even more difficult to manage. Rural tourism was historically considered small-scale, marked by its traditional character and by having little impact on nature and rural society. The increasing number of rural areas experiencing overtourism, however, demonstrates the need for new approaches, especially as the impacts and enablers of overtourism are context specific. Cumbria, with approximately 47 million visitors each year, and 23,000 operational enterprises, is one of these rural areas experiencing overtourism in the UK. Using the county of Cumbria as an example, this paper aims to explore better planning and management in rural destinations by clustering the area into rural and ‘urban-rural’ tourism zones. To achieve the aim, this study uses secondary data from a variety of sources to identify variables relating to visitor economy development and demand. These data include census data relating to population and employment, tourism industry-specific data including tourism revenue, visitor activities, and accommodation stock, and big data sources such as Trip Advisor and All Trails. The combination of these data sources provides a breadth of tourism-related variables. The subsequent analysis of this data draws upon various validated models. For example, tourism and hospitality employment density, territorial tourism pressure, and accommodation density. In addition to these statistical calculations, other data are utilized to further understand the context of these zones, for example, tourist services, attractions, and activities. The data was imported into ARCGIS where the density of the different variables is visualized on maps. This study aims to provide an understanding of the geographical context of visitor economy development and tourist behavior in rural areas. The findings contribute to an understanding of the spatial dynamics of tourism within the region of Cumbria through the creation of thematized maps. Different zones of tourism industry clusters are identified, which include elements relating to attractions, enterprises, infrastructure, tourism employment and economic impact. These maps visualize hot and cold spots relating to a variety of tourism contexts. It is believed that the strategy used to provide a visual overview of tourism development and demand in Cumbria could provide a strategic tool for rural areas to better plan marketing opportunities and avoid overtourism. These findings can inform future sustainability policy and destination management strategies within the areas through an understanding of the processes behind the emergence of both hot and cold spots. It may mean that attract and disperse needs to be reviewed in terms of a strategic option. In other words, to use sector or zonal policies for the individual hot or cold areas with transitional zones dependent upon local economic, social and environmental factors.

Keywords: overtourism, rural tourism, sustainable tourism, tourism planning, tourism zones

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Authors: Fstemeh Rais Esmaili, Ehsan Ranjbar

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Pedestrian streets as one type of urban public spaces have an important role in improving the quality of urban life. In Iran, planning and designing of pedestrian streets is in its primary steps. In spite of starting this approach in Iran, and designing several pedestrian streets, there are still not organized studies about quality assessment of pedestrian streets. As a result, the strength and weakness points of the initial experiences have not been utilized. This inattention to quality assessment have caused designing pedestrian streets to be limited to just vehicles traffic control and preliminary actions like paving; so that, special potentials of pedestrian streets for creating social, livable and dynamic public spaces have not been used. This article, as an organized study about quality assessment of pedestrian streets in Iran, tries to reach two main goals: first, introducing a framework for quality assessment of pedestrian streets in Iran, and second, creating a context for improving the quality of pedestrian streets especially for further experiences. The main research methods are description and context analyzing. With respect to comparative analysis of ideas about quality, considering international and local case studies and analyzing existing condition of Saf Pedestrian Street, a particular model for quality assessment has been introduced. In this model, main components and assessment criteria have been presented. On the basis of this model, questionnaire and checklist for assessment have been prepared. The questionnaire and interview have been used to assess qualities which are in direct contact with people and the checklist has been used for analyzing visual qualities by authors through observation. Some results of questionnaire and checklist show that 7 of 11 primary components, diversity, flexibility, cleanness, legibility and imaginably, identity, livability, form and physical setting are rated low and very low in quality degree. Three components, efficiency, comfort and distinctiveness, have medium and low quality degree and one component, access, linkage and permeability has high quality degree. Therefore, based on implemented analyzing process, Saf Pedestrian Street needs to be improved and these quality improvement priorities are determined based on presented criteria. Adaption of final results with existing condition illustrates the shortage of services for satisfying user’s needs, inflexibility and impossibility of using spaces in various times, lack of facilities for different climatic conditions, lack of facilities such as drinking fountain, inappropriate designing of existing urban furniture like garbage cans, and creating pollution and unsuitable view, lack of visual attractions, neglecting disabled persons in designing entrances, shortage of benches and their undesirable designing, lack of vegetation, absence of special characters making it different from other streets, preventing people taking part in the space causing lack of affiliation, lack of appropriate elements for leisure time and lack of exhilaration in the space. On the other hand, these results present high access and permeability, high safety, less sound pollution and more relief, comfortable movement along the way due to suitable pavement and economic efficiency, as the strength points of Saf pedestrian street.

Keywords: pedestrian streets, quality assessment, quality criteria, Saf Pedestrian Street

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

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Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Smah Almotiri

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One of the most common fields of natural language processing (NLP) is sentimental analysis. The inferred feeling in the text can be successfully mined for various events using sentiment analysis. Twitter is viewed as a reliable data point for sentimental analytics studies since people are using social media to receive and exchange different types of data on a broad scale during the COVID-19 epidemic. The processing of such data may aid in making critical decisions on how to keep the situation under control. The aim of this research is to look at how sentimental states differed in a single geographic region during the lockdown at two different times.1162 tweets were analyzed related to the COVID-19 pandemic lockdown using keywords hashtags (lockdown, COVID-19) for the first sample tweets were from March 23, 2020, until April 23, 2020, and the second sample for the following year was from March 1, 2020, until April 4, 2020. Natural language processing (NLP), which is a form of Artificial intelligence, was used for this research to calculate the sentiment value of all of the tweets by using AFINN Lexicon sentiment analysis method. The findings revealed that the sentimental condition in both different times during the region's lockdown was positive in the samples of this study, which are unique to the specific geographical area of New Zealand. This research suggests applying machine learning sentimental methods such as Crystal Feel and extending the size of the sample tweet by using multiple tweets over a longer period of time.

Keywords: sentiment analysis, Twitter analysis, lockdown, Covid-19, AFINN, NodeJS

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Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Huicong Jia, Donghua Pan

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As a key component of disaster reduction management, disaster emergency relief and reconstruction is an important process. Based on disaster system theory, this study analyzed the Jiangsu tornado from the formation mechanism of disasters, through to the economic losses, loss of life, and social infrastructure losses along the tornado disaster chain. The study then assessed the emergency relief and reconstruction efforts, based on an analytic hierarchy process method. The results were as follows: (1) An unstable weather system was the root cause of the tornado. The potentially hazardous local environment, acting in concert with the terrain and the river network, was able to gather energy from the unstable atmosphere. The wind belt passed through a densely populated district, with vulnerable infrastructure and other hazard-prone elements, which led to an accumulative disaster situation and the triggering of a catastrophe. (2) The tornado was accompanied by a hailstorm, which is an important triggering factor for a tornado catastrophe chain reaction. (3) The evaluation index (EI) of the emergency relief and reconstruction effect for the ‘‘6.23’’ tornado disaster in Yancheng was 91.5. Compared to other relief work in areas affected by disasters of the same magnitude, there was a more successful response than has previously been experienced. The results provide new insights for studies of disaster systems and the recovery measures in response to tornado catastrophe in China.

Keywords: China, disaster system, emergency relief, tornado catastrophe

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

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Authors: Tracy L. F. Worley

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This presentation explores the environment that has limited leadership opportunities for Black women in cinema and advocates for autonomy among Black women filmmakers that is facilitated by strong internal and external networks and cooperative opportunities. Early images of African Americans in motion pictures were often conceptualized from the viewpoint of a White male director and depicted by White actors. The black film evolved in opposition to this context, leading to a Black film aesthetic. The oppositional context created in response to racist, misogynistic, and sexist representations in motion pictures sets the tone for female filmmakers of every hue – but especially for African American women. For them, the context of a male gaze, and for all intents and purposes, a White male gaze, forces them to create their own aesthetic. Theoretically, men and women, filmmakers and spectators have different perspectives across race, ethnicity, and gender. Two feminist theorists, bell hooks and Mary Ann Doane, suggest that female filmmakers are perceived as disparate from male filmmakers and that women, in general, are defined by what men see. Mary Ann Doane, a White feminist film theorist, has focused extensively on female spectatorship and women (White) in general as the object of the male gaze. Her discussion of the female body, male perception of it, and feminism in the motion picture industry support the suggestion that comprehending the organization and composition of Hollywood is critical to understanding women’s roles in the industry. Although much of her research addresses the silent film era and women’s roles then, Doane suggests that across cinematic periods, the theory assigned to “cinematic apparatus” is formulated within a context of sexuality. Men and women are viewed and treated differently in cinema (in front of and behind the camera), with women’s attractiveness and allure photographed specifically for the benefit of the “spectatorial desire” of the male gaze. Bell Hooks, an African American feminist writer and theorist with more than 30 published books and articles on race, gender, class, and culture in feminism and education, suggests that women can overcome the male gaze by using their “oppositional gaze” to transform reality and establish their own truth. She addresses gender within the context of race by acknowledging the realities faced by African American women and the fact that the feminist movement was never intended to include Black women. A grounded theory study led to the development of a leadership theory that explains why African American women are disproportionately represented in a mainstream motion picture leadership. The study helped to reveal the barriers to entry and illuminated potential strategies that African American female motion picture directors might pursue to reduce this inequity. Using semi-structured interviews as the primary means for data collection, the lived experiences of African American female directors and organizational leadership’s perceived role in the perpetuation of negative female imagery in major motion pictures led to the identification of support strategies for African American female motion picture directors that counter social stereotyping and validate the need for social networking in the mainstream.

Keywords: African American, cinema, directors, filmmaking, leadership, women

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Authors: Ruwan Gunawardane

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This paper attempts to emphasize the benefits of conducting an intensive course in English for university entrants. In the Sri Lankan university context, an intensive course in English is usually conducted amidst various obstacles. In the 1970s and 1980s, undergraduates had intensive programmes in English for two to three months. Towards the end of the 1990s, a programme called General English Language Training (GELT) was conducted for the new students, and it was done outside universities before they entered their respective universities. Later it was not conducted, and that also resulted in students’ poor performance in English at university. However, having understood its importance, an eight week long intensive course in English was conducted for the new intake of the Faculty of Science, University of Ruhuna. As the findings show, the students heavily benefited from the programme. More importantly, they had the opportunity to refresh their knowledge of English gained at school and private institutions while gaining new knowledge. Another advantage was that they had plenty of time to enjoy learning English since the learners had adequate opportunities to carry out communicative tasks and the course was not exam-oriented, which reduced their fear of making mistakes in English considerably. The data was collected through an open-ended questionnaire given to 60 students, and their oral feedback was also taken into consideration. In addition, a focus group interview with 6 teachers was also conducted to get an idea about their experience and perception. The data were qualitatively analyzed. The findings suggest that an intensive programme in English undoubtedly lays a good foundation for the students’ academic career at university.

Keywords: intensive course, English, teachers, undergraduates, experience, perception

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Authors: Mahfoodh Saleh Al Sabbagh, Venkoba Rao

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There is plenty of research, both in academic and consultancy circles, about the importance and benefits of employee engagement and customer engagement and how it gives organization an opportunity to reduce variability and improve performance. Customer engagement is directly related to the engagement level of the organization's employees. It is therefore important to measure both. This research drawing from the work of Human Sigma by Fleming and Asplund, attempts to assess engagement level of customer and employees - the human systems of business - in an educational setup. Student is important to an educational institute and is a customer to be served efficiently and effectively. Considering student as customer and faculty as employees serving them, in–depth interviews were conducted to analyze the relationship between faculty and student engagement in two leading colleges in Oman, one from private sector and another from public sector. The study relied mainly on secondary data sources to understand the concept of engagement. However, the search of secondary sources was extensive to compensate the limited primary data. The results indicate that high faculty engagement is likely to lead to high student engagement. Engaged students were excited about learning, loved the feeling of they being cared as a person by their faculty and advocated the organization to other. The interaction truly represents an opportunity to build emotional connection to the organization. This study could be of interest to organizations interest in building and maintaining engagement with employees and customers.

Keywords: customer engagement, consumer psychology, strategy, educational institutes

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Authors: Abhijeet Ostawal, Parmjit Lall

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The run times for a large strategic model that we were managing had become too long leading to delays in project delivery, increased costs and loss in productivity. Software developers are continuously working towards developing more efficient tools by changing their algorithms and processes. The issue faced by our team was how do you apply the latest technologies on validated existing models which are based on much older versions of software that do not have the latest software capabilities. The multi-model transport model that we had could only be run in sequential assignment order. Recent upgrades to the software now allowed the assignment to be run in parallel, a concept called parallelization. Parallelization is a Python script working only within the latest version of the software. A full model transfer to the latest version was not possible due to time, budget and the potential changes in trip assignment. This article is to show the method to adapt and update the Python script in such a way that it can be used in older software versions by calling the latest version and then recalling the old version for assignment model without affecting the results. Through a process of trial-and-error run time savings of up to 30-40% have been achieved. Assignment results were maintained within the older version and through this learning process we’ve applied this methodology to other even older versions of the software resulting in huge time savings, more productivity and efficiency for both client and consultant.

Keywords: model run time, demand model, parallelisation, python scripting

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: Ebenezer Joseph, Veena Easvaradoss, S. Sundar Manoharan, David Chandran, Sumathi Chandrasekaran, T. R. Uma

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Perceptual reasoning is the ability that incorporates fluid reasoning, spatial processing, and visual motor integration. Several theories of cognitive functioning emphasize the importance of fluid reasoning. The ability to manipulate abstractions and rules and to generalize is required for reasoning tasks. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 1-year chess training on the perceptual reasoning of children. A pretest–posttest with control group design was used, with 43 (28 boys, 15 girls) children in the experimental group and 42 (26 boys, 16 girls) children in the control group. The sample was selected from children studying in two private schools from South India (grades 3 to 9), which included both the genders. The experimental group underwent weekly 1-hour chess training for 1 year. Perceptual reasoning was measured by three subtests of WISC-IV INDIA. Pre-equivalence of means was established. Further statistical analyses revealed that the experimental group had shown statistically significant improvement in perceptual reasoning compared to the control group. The present study clearly establishes a correlation between chess learning and perceptual reasoning. If perceptual reasoning can be enhanced in children, it could possibly result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess, cognition, intelligence, perceptual reasoning

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Authors: Waleed Alanzi

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The purpose of this qualitative study was to add to the existing literature and provide a more detailed understanding of the individual experiences and perceptions of 15 Deans at the University of Kuwait regarding their first year of planning, developing, and implementing crisis distance education (CDE) in response to the COVID-19 epidemic. An interpretative phenomenological approach was applied, using the thematic analysis of interview transcripts to describe the challenging journeys taken by each of the Deans from the first-person point of view. There was objective evidence, manifested by four primary themes (“Obstacles to the implementation of CDE”; “Planning for CDE”; “Training for CDE,” and “Future Directions”) to conclude that the faculty members, technical staff, administrative staff, and students generally helped each other to overcome the obstacles associated with planning and implementing CDE. The idea that CDE may turn homes into schools and parents into teachers was supported. The planning and implementation of CDE were inevitably associated with a certain amount of confusion, as well as disruptions in the daily routines of staff and students, as well as significant changes in their responsibilities. There were contradictory ideas about the future directions of distance education after the pandemic. Previous qualitative research on the implementation of CDE at higher education institutions in the Arab world has focused mainly on the experiences and perceptions of students; however, little is known about the experiences and perceptions of the students at the University of Kuwait during the COVID19 pandemic, providing a rationale and direction for future research.

Keywords: distance learning, qualitative research, COVID-19 epidemic, Kuwait university

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Christopher Leung, Tony Kim, Rebecca Lendzion, Scott Mackenzie

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Retroperitoneal abscesses are a rare but serious condition with often delayed diagnosis, non-specific symptoms, multiple causes and high morbidity/mortality. With the advent of more readily available cross-sectional imaging, retroperitoneal abscesses are treated earlier and better outcomes are achieved. Occasionally, a retroperitoneal abscess is present as a huge retroperitoneal abscess, as evident in this 53-year-old male. With a background of chronic renal disease and left partial nephrectomy, this gentleman presented with a one-month history of left flank pain without any other symptoms, including fevers or abdominal pain. CT abdomen and pelvis demonstrated a huge retroperitoneal abscess spanning from the diaphragm, abutting the spleen, down to the iliopsoas muscle and abutting the iliac vessels at the pelvic brim. This large retroperitoneal abscess required open drainage as well as drainage by interventional radiology. A long course of intravenous antibiotics and multiple drainages was required to drain the abscess. His blood culture and fluid culture grew Proteus species suggesting a urinary source, likely from his non-functioning kidney, which had a partial nephrectomy. Such a huge retroperitoneal abscess has rarely been described in the literature. The learning point here is that the basic principle of source control and antibiotics is paramount in treating retroperitoneal abscesses regardless of the size of the abscess.

Keywords: retroperitoneal abscess, retroperitoneal mass, sepsis, genitourinary infection

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Authors: Greg Turner, Bin Lu, Cheer-Sun Yang

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As computing technology advances, smartphone applications can assist in student learning in a pervasive way. For example, the idea of using a mobile apps for the PA Common Trees, Pests, Pathogens, in the field as a reference tool allows middle school students to learn about trees and associated pests/pathogens without bringing a textbook. In the past, some researches study the mobile software Mobile Application Software Development Life Cycle (MADLC) including traditional models such as the waterfall model, or more recent Agile Methods. Others study the issues related to the software development process. Very little research is on the development of three heterogenous mobile systems simultaneously in a small university where the availability of developers is an issue. In this paper, we propose to use a hybride model of Waterfall Model and the Agile Model, known as the Relay Race Methodology (RRM) in practice, to reflect the concept of racing and relaying for scheduling. Based on the development project, we observe that the modeling of the transition between any two phases is manifested naturally. Thus, we claim that the RRM model can provide a de fecto rather than a de jure basis for the core concept in the MADLC. In this paper, the background of the project is introduced first. Then, the challenges are pointed out followed by our solutions. Finally, the experiences learned and the future work are presented.

Keywords: agile methods, mobile apps, software process model, waterfall model

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Authors: Soofia Malik

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The purpose of this study is to develop an instrument to measure undergraduate students’ attitudes toward mathematics (MAT) and to assess the data collected from the instrument for validity and reliability. The instrument is developed using five subscales: anxiety, enjoyment, self-confidence, value, and technology. The technology dimension is added as the fifth subscale of attitude toward mathematics because of the recent trend of incorporating online homework in mathematics courses as well as due to heavy reliance of higher education on using online learning management systems, such as Blackboard and Moodle. The sample consists of 163 (M = 82, F = 81) undergraduates enrolled in College Algebra course in the summer 2017 semester at a university in the USA. The data is analyzed to answer the research question: if and how do undergraduate students’ attitudes toward mathematics load using Principal Components Analysis (PCA)? As a result of PCA, three subscales emerged namely: anxiety/self-confidence scale, enjoyment, and value scale. After deleting the last five items or the last two subscales from the initial MAT scale, the Cronbach’s alpha was recalculated using the scores from 20 items and was found to be α = .95. It is important to note that the reliability of the initial MAT form was α = .93. This means that employing the final MAT survey form would yield consistent results in repeated uses. The final MAT form is, therefore, more reliable as compared to the initial MAT form.

Keywords: college algebra, Cronbach's alpha reliability coefficient, Principal Components Analysis, PCA, technology in mathematics

Procedia PDF Downloads 114