Search results for: math score

Authors: A. Kamel Abd Elaziz Mohamed, B. Sameh Kamal el Maraghi

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Introduction: Adaptive support ventilation (ASV) is an improved closed-loop ventilation mode that provides both pressure-controlled ventilation and PSV according to the patient’s needs. Aim of the work: To compare the short-term effects of Adaptive support ventilation (ASV), with conventional Pressure support ventilation (PSV) in weaning of intubated COPD patients. Patients and methods: Fifty patients admitted in the intensive care with acute exacerbation of COPD and needing intubation were included in the study. All patients were initially ventilated with control/assist control mode, in a stepwise manner and were receiving standard medical therapy. Patients were randomized into two groups to receive either ASV or PSV. Results: Out of fifty patients included in the study forty one patients in both studied groups were weaned successfully according to their ABG data and weaning indices. APACHE II score showed no significant difference in both groups. There were statistically significant differences between the groups in term of, duration of mechanical ventilation, weaning hours and length of ICU stay being shorter in (group 1) weaned by ASV. Re-intubation and mortality rate were higher in (group 11) weaned by conventional PSV, however the differences were not significant. Conclusion: ASV can provide automated weaning and achieve shorter weaning time for COPD patients hence leading to reduction in the total duration of MV, length of stay, and hospital costs.

Keywords: COPD patients, ASV, PSV, mechanical ventilation (MV)

Procedia PDF Downloads 389

Authors: Qisheng Hu, Jianglei Han, Yue Yang, My Hoa Ha

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When a company is considering purchasing software for commercial use, contract risk assessment is critical to identify risks to mitigate the potential adverse business impact, e.g., security, financial and regulatory risks. Contract risk assessment requires reviewers with specialized knowledge and time to evaluate the legal documents manually. Specifically, validating contracts for a software vendor requires the following steps: manual screening, interpreting legal documents, and extracting risk-prone segments. To automate the process, we proposed a framework to assist legal contract document risk identification, leveraging pre-trained deep learning models and natural language processing techniques. Given a set of pre-defined risk evaluation problems, our framework utilizes the pre-trained transformer-based models for question-answering to identify risk-prone sections in a contract. Furthermore, the question-answering model encodes the concatenated question-contract text and predicts the start and end position for clause extraction. Due to the limited labelled dataset for training, we leveraged transfer learning by fine-tuning the models with the CUAD dataset to enhance the model. On a dataset comprising 287 contract documents and 2000 labelled samples, our best model achieved an F1 score of 0.687.

Keywords: contract risk assessment, NLP, transfer learning, question answering

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Dilara Usta, Fatos Korkmaz

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Background: Individuals who utilise health services maintain relationships with health professionals, insurers and institutions. The nature of these relationships requires service receivers to have trust in the service providers because maintaining health services without reciprocal trust is very difficult. Therefore, individual evaluations of trust within the scope of health services have become increasingly important. Objective: To investigate patients’ trust in the health-care system and their relevant socio-demographical characteristics. Methods: This research was conducted using a descriptive design which included 493 literate patients aged 18-65 years who were hospitalised for a minimum of two days at public university and training&research hospitals in Ankara, Turkey. Patients’ trust in health-care professionals, insurers, and institutions were investigated. Data were collected using a demographic questionnaire and the Multidimensional Trust in Health-Care Systems Scale between September 2015 and April 2016. Results: The participants’ mean age was 47.7±13.1; 70% had a moderate income and 69% had a prior hospitalisation and 63.5% of the patients were satisfied with the health-care services. The mean Multidimensional Trust in Health-Care Systems Scale score for the sample was 61.5±8.3; the provider subscale had a mean of 38.1±5, the insurers subscale had a mean of 12.9±3.7, and institutions subscale had a mean of 10.6±1.9. Conclusion: Patients’ level of trust in the health-care system was above average and the trust level of the patients with higher educational and socio-economic levels was lower compared to the other patients. Health-care professionals should raise awareness about the significance of trust in the health-care system.

Keywords: delivery of health care, health care system, nursing, patients, trust

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Authors: Si Chen, Le Zhang, Xian He

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The research explored the influences of public open space system on housing price through hedonic models, in order to support better open space plans and economic policies. We have three initial hypotheses: 1) public open space system has an overall positive influence on surrounding housing prices. 2) Different public open space types have different levels of influence on motivating surrounding housing prices. 3) Walking and driving accessibilities from property to public open spaces have different statistical relation with housing prices. Cook County, Illinois, was chosen to be a study area since data availability, sufficient open space types, and long-term open space preservation strategies. We considered the housing attributes, driving and walking accessibility scores from houses to nearby public open spaces, and driving accessibility scores to hospitals as influential features and used real housing sales price in 2010 as a dependent variable in the built hedonic model. Through ordinary least squares (OLS) regression analysis, General Moran’s I analysis and geographically weighted regression analysis, we observed the statistical relations between public open spaces and housing sale prices in the three built hedonic models and confirmed all three hypotheses.

Keywords: hedonic model, public open space, housing sale price, regression analysis, accessibility score

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Authors: Samira Ali

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Aim: The purpose of this study was to describe nurses’ potential use of power levels to influence the standardization of nursing terminology (SNT) in electronic health records. Also, to examine the relationship between nurses’ use of power levels and variables such as position, communication and the potential goal of achieving SNT in electronic health records. Background: In an era of evidence-based nursing care, with an emphasis on nursing’s ability to measure the care rendered and improve outcomes of care, little is known about the nurse’s potential use of their power to SNT in electronic health records and lack of use of an SNT in electronic health records. Method: This descriptive, correlational, and cross-sectional study was conducted using survey methodology to assess the nurse’s use of power to influence the SNT in electronic health records. The Theory of Group Power within Organizations (TGPO) provided the conceptual framework for this study. A total of (n=232) nurses responded to the survey, posted on three nursing organizations’ websites. Results revealed the mean Cronbach’s alpha of the subscales was .94, suggesting high internal consistency. The mean power capability score was moderately high, at 134.22 (SD = 18.49). Power Capacity was significantly correlated with Power Capability (r = .96, p < .001). Power Capacity subscales were significantly correlated with Power Capacity and Power Capability. Conclusion: The mean Cronbach’s alpha of the subscales was .94 suggestive of reliability of the instrument. Nurses could potentially use power to achieve their goals, such as the implementation of SNT in electronic health records.

Keywords: nurses, power, actualized power, nursing terminology, electronic health records

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Authors: Jaspal Rana

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Oxidative stress has been identified as an underlying cause of ischemia-reperfusion (IR) related cognitive dysfunction and brain damage. Therefore, antioxidant based therapies to treat IR injury are being investigated. Allium cepa L. (onion) is used as culinary medicine and is documented to have marked antioxidant effects. Hence, the present study was designed to evaluate the effect of A. cepa outer scale extract (ACE) against IR induced cognition and biochemical deficit in mice. ACE was prepared by maceration with 70% methanol and fractionated into ethylacetate and aqueous fractions. Bilateral common carotid artery occlusion for 10 min followed by 24 h reperfusion was used to induce cerebral IR injury. Following IR injury, ACE (100 and 200 mg/kg) was administered orally to animals for 7 days once daily. Behavioral outcomes (memory and sensorimotor functions) were evaluated using Morris water maze and neurological severity score. Cerebral infarct size, brain thiobarbituric acid reactive species, reduced glutathione, and superoxide dismutase activity was also determined. Treatment with ACE significantly ameliorated IR mediated deterioration of memory and sensorimotor functions and rise in brain oxidative stress in animals. The results of the present investigation revealed that ACE improved functional outcomes after cerebral IR injury which may be attributed to its antioxidant properties.

Keywords: ischemia-reperfusion, neuroprotective, stroke, antioxidant

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Authors: Kenneth O. Otasowie, Matthew Ikuabe, Clinton Aigbavboa, Ayodeji Oke

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The quantity surveying (QS) profession is one of the professions in the construction industry, and it is saddled with the responsibility of measuring the number of materials as well as the workmanship required to get work done in the industry. This responsibility is vital to the success of a construction project as it determines if a project will be completed on time, within budget, and up to the required standard. However, the practice has been criticised severally for failure to accurately execute her responsibility. The need to reduce errors, inaccuracies and omissions has made the adoption of modern technologies such as building information modeling (BIM) inevitable in its practice. Nevertheless, there are barriers to the adoption of BIM in QS practice in South Africa (SA). Thus, this study aims to investigate these barriers. A survey design was adopted. A total number of one hundred and fifteen (115) questionnaires were administered to quantity surveyors in Guateng Province, SA, and ninety (90) were returned and found suitable for analysis. Collected data were analysed using percentage, mean item score, standard deviation, one-sample t-test, and Kruskal-Wallis. The findings show that lack of BIM expertise, lack of government enforcement, resistance to change, and no client demand for BIM are the most significant barriers to the adoption of BIM in QS practice. As a result, this study recommends that trainings on BIM technology be prioritised, and government must take the lead in BIM adoption in the country, particularly in public projects.

Keywords: barriers, BIM, quantity surveying practice, South Africa

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Authors: Sandesh Achar

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Personalized advertising holds greater potential for higher conversion rates compared to generic advertisements. However, its widespread application in the retail industry faces challenges due to complex implementation processes. These complexities impede the swift adoption of personalized advertisement on a large scale. Personalized advertisement, being a data-driven approach, necessitates consumer-related data, adding to its complexity. This paper introduces an innovative data-driven decision-making framework, D3Advert, which personalizes advertisements by analyzing personalities using a BiLSTM network. The framework utilizes the Myers–Briggs Type Indicator (MBTI) dataset for development. The employed BiLSTM network, specifically designed and optimized for D3Advert, classifies user personalities into one of the sixteen MBTI categories based on their social media posts. The classification accuracy is 86.42%, with precision, recall, and F1-Score values of 85.11%, 84.14%, and 83.89%, respectively. The D3Advert framework personalizes advertisements based on these personality classifications. Experimental implementation and performance analysis of D3Advert demonstrate a 40% improvement in impressions. D3Advert’s innovative and straightforward approach has the potential to transform personalized advertising and foster widespread personalized advertisement adoption in marketing.

Keywords: personalized advertisement, deep Learning, MBTI dataset, BiLSTM network, NLP.

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Authors: Yili Chen, Xiaokun Liang, Zhicheng Zhang, Yaoqin Xie

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In an effort to restore visual perception in retinal diseases, an electronic retinal prosthesis with thousands of electrodes has been developed. The image processing strategies of retinal prosthesis system converts the original images from the camera to the stimulus pattern which can be interpreted by the brain. Practically, the original images are with more high resolution (256x256) than that of the stimulus pattern (such as 25x25), which causes a technical image processing challenge to do large-capacity image information reduction. In this paper, we focus on developing an efficient image processing stimulus pattern extraction algorithm by using a single cue saliency map for extracting salient objects in the image with an optimal trimming threshold. Experimental results showed that the proposed stimulus pattern extraction algorithm performs quite well for different scenes in terms of the stimulus pattern. In the algorithm performance experiment, our proposed SCSPE algorithm have almost five times of the score compared with Boyle’s algorithm. Through experiment s we suggested that when there are salient objects in the scene (such as the blind meet people or talking with people), the trimming threshold should be set around 0.4max, in other situations, the trimming threshold values can be set between 0.2max-0.4max to give the satisfied stimulus pattern.

Keywords: retinal prosthesis, image processing, region of interest, saliency map, trimming threshold selection

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Authors: Chintida Vichitsophaphan, Piyapat Chitpirom, Chaichana Nimnuan, Teerakiat Jareonsettasin

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The purpose of this research is to study the influence of a growth mindset, as defined by Prof. Dweck, in Thai teachers and Thai students’ achievements and success. The participants of the study were teachers of schools in Office of the Basic Education Commission in Bangkok. The teachers were recruited from high achievement schools and low achievement schools (based on average National Standard Test Score). Participants were divided into two groups: 85 teachers in 3 high achievement schools and 213 teachers in 6 low achievement schools. They were asked to complete the Carol Dweck’s Implicit Theories Scale – (Adults) 8 items. Data were analyzed including the use of mean, standard deviation and t-test to test hypothesis. The finding of this study revealed that teachers who were in the high achievement schools have higher scores in Carol Dweck’s Implicit Theories Scale (x ̅= 35.5, SE = .58) than teachers who were in the low achievement schools (x ̅= 33.9, SE = .35) at .05 level. The difference is statistically significant (t (296) = 2.44, p = .015) with the effect size of 0.31. In conclusion, teachers’ growth mindset from high achievement schools have higher scores than teachers’ growth mindset from low achievement schools, and this is statistically significant. From the study, it can be concluded that growth mindset development for teachers has a tendency to increase students’ achievements. For these reasons, it is necessary to implement such training and development in our education system in larger scale, and even nationwide policies.

Keywords: fixed mindset, growth mindset, students’ achievement, teachers’ growth mindset

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Authors: Vikrum Malhotra, Manpreet Kaur, Ayesha Ghotto

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Background: Cardiac registries such as ACC Percutaneous Coronary Intervention Registry require high quality data to be abstracted, including data elements such as nuclear cardiology, diagnostic coronary angiography, and PCI. Introduction: The audit tool created is used by data abstractors to provide data audits and assess the accuracy and inter-rater reliability of abstraction performed by the abstractors for a health system. This audit tool solution has been developed across 13 registries, including ACC/NCDR registries, PCI, STS, Get with the Guidelines. Methodology: The data audit tool was used to audit internal registry abstraction for all data elements, including stress test performed, type of stress test, data of stress test, results of stress test, risk/extent of ischemia, diagnostic catheterization detail, and PCI data elements for ACC/NCDR PCI registries. This is being used across 20 hospital systems internally and providing abstraction and audit services for them. Results: The data audit tool had inter-rater reliability and accuracy greater than 95% data accuracy and IRR score for the PCI registry in 50 PCI registry cases in 2021. Conclusion: The tool is being used internally for surgical societies and across hospital systems. The audit tool enables the abstractor to be assessed by an external abstractor and includes all of the data dictionary fields for each registry.

Keywords: abstraction, cardiac registry, cardiovascular registry, registry, data

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Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

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Authors: Stephen Vera, Tabitha Black, Charalambos Cleanthous, Ryan Sain

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A gender comparison of USA commercial pilots’ demographics and views of CRM, social desirability and locus of control were surveyed. The Aviation safety locus of control (ASLOC) was used to measure external (ASLOC-E) or internal (ASLOC-I) aviation safety locus of control. The gender differences were explored using the ASLOC scores as a categorical variable. A differential comparison of crew resource management (CRM), based on the Federal Aviation Administration’s (FAA) guidelines was conducted. The results indicated that the proportion of female to male respondents matches the current ratio of USA commercial pilots. Moreover, there were no significant differences regarding overall education and the total number of communication classes one took. Regarding CRM issues, there were no significant differences on their views regarding the roles of the PIC, stress, time management, and managing a flight team. The females scored significantly lower on aeronautical decision making (ADM) and communications. There were no significant differences on either the Balanced Inventory of Desirable Responding (BIDR) impression management (IM) or self-deceptive enhancement (SDE). Although there were no overall significant differences on the ASLOC, the females did score higher on the internal subscale than did the males. An additional comparison of socially desirable responding indicates that all scores may be invalid, especially from the female respondents.

Keywords: social desirability, safety locus of control, crew resource management, commercial pilots

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Authors: N. Hadthamard, P. Chaumpluk, M. Buanong, P. Boonyaritthongchai, C. Wongs-Aree

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Ripe ‘Nam Dok Mai’ mango (Mangifera indica L.) is an important exported fruit of Thailand, but rapidly declined in the quality attributes mainly by infection of anthracnose and stem end rot diseases. Multilayer coating is considered as a developed technique to maintain the postharvest quality of mangoes. The utilization of alternated coating by matching oppositely electrostatic charges between 0.1% chitosan and 0.1% polystyrene sulfonate (PSS) was studied. A number of the coating layers (layer by layer) were applied on mature green ‘Nam Dok Mai No.4’ mangoes prior to storage at 25 ^oC, 65-70% relative humidity (RH). There were significant differences in some quality attributes of mangoes coated by 3½ layers, 4½ layers and 5½ layers. In comparison to coated mangoes, uncoated fruits were higher in weight loss, total soluble solids, respiration rate, ethylene production and disease incidence except the titratable acidity. Coating fruit at 3½ layers exhibited the ripening delay and reducing disease infection without off flavour. On the other hand, fruit coated with 5½ layers comprised the lowest acceptable score, caused by exhibiting disorders from fermentation at the end of storage. As a result, multilayer coating between chitosan and PSS could effectively maintain the postharvest quality of mango, but number of coating layers should be thoroughly considered.

Keywords: multilayer, chitosan, polystyrene sulfonate, Nam Dok Mai No.4

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Authors: Jongsung Kim, Daegun Han, Myungjin Lee, Soojun Kim, Hung Soo Kim

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Recently, the frequency and magnitude of natural disasters are gradually increasing due to climate change. Especially in Korea, large-scale damage caused by heavy rainfall frequently occurs due to rapid urbanization. Therefore, this study proposed a Heavy rain Damage Risk Index (HDRI) using PSR (Pressure – State - Response) structure for heavy rain risk assessment. We constructed pressure index, state index, and response index for the risk assessment of each local government in Gyeonggi-do province, and the evaluation indices were determined by principal component analysis. The indices were standardized using the Z-score method then HDRIs were obtained for 31 local governments in the province. The HDRI is categorized into three classes, say, the safest class is 1st class. As the results, the local governments of the 1st class were 15, 2nd class 7, and 3rd class 9. From the study, we were able to identify the risk class due to the heavy rainfall for each local government. It will be useful to develop the heavy rainfall prediction function by risk class, and this was performed in this issue. Also, this risk class could be used for the decision making for efficient disaster management. Acknowledgements: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B3005695).

Keywords: natural disaster, heavy rain risk assessment, HDRI, PSR

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Authors: Mahvish Anjum

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India is a land of craftsmen and a hub of many popular embroidery clusters. Chikankari is the name given to the delicate art of hand embroidery, traditionally practiced in the city of Lucknow and its environs. Chikankari not only provide employment to 250,000 artisans of different crafts but people from non-craft base also earn their livelihood by associating themselves with this craft. People working in this sector are exploited in term of working hours, low and irregular income, unsatisfactory work conditions, no legal protection and exposed to occupational health hazards. The present paper is an attempt to analyse occupational profile of workers engaged in Chikan embroidery industry. Being an empirical study, the entire work is based upon primary sources of data which have collected through field survey. Purposive random sampling has used for selection of data. Total 150 workers have surveyed through questionnaire technique in Lucknow city during October-November, 2017. For analysis of data Z-score, ANOVA, and Pearson correlation techniques are used. The result of present study indicates that artisans are exploited by the middle man and face the problem of late payment and long working hours because they are not directly associated with the manufacturers. Work conditions of the workers are quite poor such as improper ventilation, poor light and unhygienic conditions that adversely affect the health of workers.

Keywords: artisans, socio-economic status, unorganized industry, work condition

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Authors: Bhaskar Chakravorti, Christopher Tunnard, Ravi Shankar Chaturvedi

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This report introduces the Digital Evolution Index (DEI) as a way to gauge the transformation of economies in the advanced and developing world from traditional brick-and-mortar to digitally enabled. The DEI measures the digital trajectories of 50 countries to provide actionable, data-informed insights for businesses, investors and policymakers. Created by The Fletcher School, in collaboration with MasterCard Worldwide and DataCash, the DEI analyzes the key underlying drivers and barriers that govern a country’s evolution into a digital economy: Demand, Supply, Institutional Environment and Innovation. A longitudinal analysis of these four drivers during the years 2008 to 2013 reveals both the current state of a country’s digital economy, as well as changes over time. Combining these two measures allows us to assign each country to one of four Trajectory Zones: • Stand Out countries have shown high levels of digital development in the past and continue to remain on an upward trajectory. • Stall Out countries have achieved a high level of evolution in the past but are losing momentum and risk falling behind. • Break Out countries have the potential to develop strong digital economies. Though their overall score is still low, they are moving upward and are poised to become Stand Out countries in the future. • Watch Out countries face significant opportunities and challenges, with low scores on both current level and upward motion of their DEI. Some may be able to overcome limitations with clever innovations and stopgap measures, while others seem to be stuck.

Keywords: e-commerce, digital evolution, digital commerce ecosystems, e-consumer

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Ebtsam Aly Abou Hashish, Sharifah Abdulmuttalib Alsayed, Hend Abdu Alnajjar

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Background: Academic nurse educators play a crucial role in the educational environment, but the demands of their profession can lead to workaholism, which could result in an imbalance between work and personal life. Purpose: The study aimed to explore workaholism and life balance among academic nursing educators, as well as investigate the factors associated with workaholism. Methods: A mixed-methods design based on the ‘concurrent triangulation’ approach was employed. A convenience sample of 76 nurse educators completed the Dutch Work Addiction Scale (DUWAS) and the Life Balance Inventory (LBI), while a purposive sample of 20 nurse educators participated in semi-structured interviews. Inferential statistics and thematic analysis were used to analyze the data. Results: The researchers found a notable prevalence of workaholism among nurse educators, with 59.0 % reporting a mean score above 2.5 and 86.8 % perceiving an unbalanced life. Regression analysis indicated that workaholism negatively predicted life balance (B = 􀀀 0.404, p < 0.001). The qualitative findings derived three themes as determinants of workaholism: antecedents, consequences, and personal and institutional strategies to mitigate workaholism among nursing educators. Conclusion: Educational institutions should develop comprehensive approaches to support and develop their academicians, fostering a positive work environment, work-life balance, employee well-being, and professional development.

Keywords: workaholism, life balance, academic nurse educators, mixed-method

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Authors: Diana Ruth Caga-Anan

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Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.

Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices

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Authors: Jiao Sun, Li Pan, Shijun Liu

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Recommender systems have been widely used in contemporary industry, and plenty of work has been done in this field to help users to identify items of interest. Collaborative Filtering (CF, for short) algorithm is an important technology in recommender systems. However, less work has been done in automobile recommendation system with the sharp increase of the amount of automobiles. What’s more, the computational speed is a major weakness for collaborative filtering technology. Therefore, using MapReduce framework to optimize the CF algorithm is a vital solution to this performance problem. In this paper, we present a recommendation of the users’ comment on industrial automobiles with various properties based on real world industrial datasets of user-automobile comment data collection, and provide recommendation for automobile providers and help them predict users’ comment on automobiles with new-coming property. Firstly, we solve the sparseness of matrix using previous construction of score matrix. Secondly, we solve the data normalization problem by removing dimensional effects from the raw data of automobiles, where different dimensions of automobile properties bring great error to the calculation of CF. Finally, we use the MapReduce framework to optimize the CF algorithm, and the computational speed has been improved times. UV decomposition used in this paper is an often used matrix factorization technology in CF algorithm, without calculating the interpolation weight of neighbors, which will be more convenient in industry.

Keywords: collaborative filtering, recommendation, data normalization, mapreduce

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Ari Istiany, Guspri Devi Artanti, M. Si

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Based on the results Riskesdas, it is known that breastfeeding awareness about the importance of exclusive breastfeeding is still low at only 15.3 %. These conditions resulted in a very infant at risk for infectious diseases, such as diarrhea and acute respiratory infection. Therefore, the aim of this study to evaluate the video development about exclusive breastfeeding as a nutrition education media for posyandu cadre. This research used development methods for making the video about exclusive breastfeeding. The study was conducted in urban areas Rawamangun, East Jakarta. Respondents of this study were 1 media experts from the Department of Educational Technology - UNJ, 2 subject matter experts from Department of Home Economics - UNJ and 20 posyandu cadres to assess the quality of the video. Aspects assessed include the legibility of text, image display quality, color composition, clarity of sound, music appropriateness, duration, suitability of the material and language. Data were analyzed descriptively likes frequency distribution table, the average value, and deviation standard. The result of this study showed that the average score assessment according to media experts, subject matter experts, and posyandu cadres respectively was 3.43 ± 0.51 (good), 4.37 ± 0.52 (very good) and 3.6 ± 0.73 (good). The conclusion is on exclusive breastfeeding video as feasible as a media for nutrition education. While suggestions for the improvement of visual media is multiply illustrations, add material about the correct way of breastfeeding and healthy baby pictures.

Keywords: exclusive breastfeeding, posyandu cadre, video, nutrition education

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Authors: Mst Shapna Akter, Hossain Shahriar

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Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

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Authors: Ashfaque Hossain Khan, Brian Thomson, Ratan Debnath, Abhishek Motayed, Mulpuri V. Rao

Abstract:

Though the efforts had been made, the problem of cross-sensitivity for a single metal oxide-based sensor can’t be fully eliminated. In this work, a sensor array has been designed and fabricated comprising of platinum (Pt), copper (Cu), and silver (Ag) decorated TiO2 and ZnO functionalized GaN nanowires using industry-standard top-down fabrication approach. The metal/metal-oxide combinations within the array have been determined from prior molecular simulation study using first principle calculations based on density functional theory (DFT). The gas responses were obtained for both single and mixture of NO2, SO2, ethanol, and H2 in the presence of H2O and O2 gases under UV light at room temperature. Each gas leaves a unique response footprint across the array sensors by which precise discrimination of cross-sensitive gases has been achieved. An unsupervised principal component analysis (PCA) technique has been implemented on the array response. Results indicate that each gas forms a distinct cluster in the score plot for all the target gases and their mixtures, indicating a clear separation among them. In addition, the developed array device consumes very low power because of ultra-violet (UV) assisted sensing as compared to commercially available metal-oxide sensors. The nanowire sensor array, in combination with PCA, is a potential approach for precise real-time gas monitoring applications.

Keywords: cross-sensitivity, gas sensor, principle component analysis (PCA), sensor array

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Authors: A. Sathiya Susuman, Hamisi F. Hamisi

Abstract:

Background: Many socio-economic and demographic data are age-sex attributed. However, a variety of irregularities and misstatement are noted with respect to age-related data and less to sex data because of its biological differences between the genders. Noting the misstatement/misreporting of age data regardless of its significance importance in demographics and epidemiological studies, this study aims at assessing the quality of 2012 Tanzania Population and Housing Census Results. Methods: Data for the analysis are downloaded from Tanzania National Bureau of Statistics. Age heaping and digit preference were measured using summary indices viz., Whipple’s index, Myers’ blended index, and Age-Sex Accuracy index. Results: The recorded Whipple’s index for both sexes was 154.43; male has the lowest index of about 152.65 while female has the highest index of about 156.07. For Myers’ blended index, the preferences were at digits ‘0’ and ‘5’ while avoidance were at digits ‘1’ and ‘3’ for both sexes. Finally, Age-sex index stood at 59.8 where sex ratio score was 5.82 and age ratio scores were 20.89 and 21.4 for males and female respectively. Conclusion: The evaluation of the 2012 PHC data using the demographic techniques has qualified the data inaccurate as the results of systematic heaping and digit preferences/avoidances. Thus, innovative methods in data collection along with measuring and minimizing errors using statistical techniques should be used to ensure accuracy of age data.

Keywords: age heaping, digit preference/avoidance, summary indices, Whipple’s index, Myer’s index, age-sex accuracy index

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Authors: Sharareh Hekmat, Michelle Lane

Abstract:

Pregnancy during adolescence affects both the growth and development of mother and baby, particularly in low socioeconomic and food insecure areas. This mixed methods study is aimed at discovering a need for a community-based probiotic yogurt program to assist pregnant women in the Mukuru slum Nairobi, Kenya. Surveys were conducted with pregnant women (14-25 years old, n=43), which included questionnaires on dietary intake, food access, and health/quality of life perception. The frequency and means procedure was used to analyze maternal characteristics, Women’s Dietary Diversity Score (WDDS) and Household Hunger Scale. 24-hour recalls were analyzed via ESHA Food Processor, and median nutrient intakes were reported as a percent of recommendations. An environmental scan was conducted to assess food availability, accessibility, and quality. WDDS reflected a low-moderate diet variation (3.86 food groups out of 9, SD ± 1.3) among the women. The 24-hour recall suggested an inadequate intake of many nutrients, most significantly B12, potassium and calcium. 86% of women reported little to no household hunger. However, the environmental scan revealed low quality and poor sanitation of food. This study provides evidence that a probiotic program would be desirable, and contribute to the nutritional status of women in the Mukuru community.

Keywords: dietary diversity, pregnant women, probiotics, urban slum, Kenya

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Authors: Eldah Ephraim Buba, Sanusi Abubakar Sadiq

Abstract:

Habitat can be degraded by tourism leisure activities for example wildlife viewing can bring abrupt stress for animals and alter their natural behaviors when tourist come too close and wildlife watching have degradation effects on the habitats as they often are accompanied by the noise and commotion created by tourist as they chase wild animals. It is observed that Jos Wild Life Park is usually congested during on-peak periods which causes littering and contamination of the environment by tourist which may lead to changes in the soil nutrient. The issue of unauthorized feeding of animals by a tourist in which the food might be dangerous and harmful to their health and making them be so aggressive is also observed. The aim of the study is to investigate the environmental impact of tourists’ activities in Jos Wild Life Park, Nigeria. The study used survey questionnaires to both tourists and the staff of the wildlife park. One hundred questionnaires were self-administered to randomly selected tourists as the visit the park and some staff. The average mean score of the response was used to show agreement or disagreement. Major findings show the negative impact of tourist’s activities to the environment as air pollution, overcrowding, and congestion, solid littering of the environment, distress to animals and alteration of the ecosystem. Furthermore, the study found the positive impact of tourists activities on the environment to be income generation through tourists activities and infrastructural development. It is recommended that the impact of tourism should be minimized through admitting the right carrying capacity and impact assessment.

Keywords: environmental, impact, investigation, tourists, activities

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Authors: P. Efstathiou, E. Kouskouni, S. Papanikolaou, K. Karageorgou, Z. Manolidou, Tseroni Maria, A. Efstathiou, V. Karyoti, I. Agrafa

Abstract:

Aim: Aim of this study was to evaluate the reduction on Intensive Care Unit (ICU) microbial flora after the antimicrobial copper alloy (Cu+) implementation as well as the effect on financial-epidemiological operation parameters. Methods: Medical, epidemiological and financial data in two time periods, before and after the implementation of copper (Cu 63% - Zn 37%, low lead) were recorded and analyzed in a general ICU. The evaluated parameters were: the importance of patients' admission (Acute Physiology and Chronic Health Evaluation - APACHE II and Simplified Acute Physiology Score - SAPS), microbial flora's record in the ICU before and after the implementation of Cu+ as well as the impact on epidemiological and ICU's operation financial parameters. Results: During December 2010 and March 2011 and respectively during December 2011 and March 2012 comparative results showed statistically significant reduction on the microbial flora (CFU/ml) by 95% and the use of antimicrobial medicine (per day per patient) by 30% (p = 0,014) as well as patients hospitalization time and cost. Conclusions: The innovative implementation of antimicrobial copper in ICUs contributed to their microbial flora significant reduction and antimicrobial drugs use reduction with the apparent positive effect (decrease) in both patient’s hospitalization time and cost. Under the present circumstances of economic crisis, survey results are of highest importance and value.

Keywords: antimicrobial copper, financial benefits, ICU, cost reduction

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