Search results for: nursing interventions classification

Authors: G. Calleja-Rodriguez, N. Jimenez-Redondo, J. J. Peralta Escalante

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This research work aims at developing a solution to take advantage of the potential energy saving related to occupants behaviour estimated in between 5-30 % according to existing studies. For that purpose, the following methodology has been followed: 1) literature review and gap analysis, 2) define concept and functional requirements, 3) evaluation and feedback by experts. As result, the concept for a tool-box that implements continuous behavior change interventions named as engagement methods and based on increasing energy literacy, increasing energy visibility, using bonus system, etc. has been defined. These engagement methods are deployed through a set of ICT tools: Building Automation and Control System (BACS) add-ons services installed in buildings and Users Apps installed in smartphones, smart-TVs or dashboards. The tool-box called eTEACHER identifies energy conservation measures (ECM) based on energy behavioral change through a what-if analysis that collects information about the building and its users (comfort feedback, behavior, etc.) and carry out cost-effective calculations to provide outputs such us efficient control settings of building systems. This information is processed and showed in an attractive way as tailored advice to the energy end-users. Therefore, eTEACHER goal is to change the behavior of building´s energy users towards energy efficiency, comfort and better health conditions by deploying customized ICT-based interventions taking into account building typology (schools, residential, offices, health care centres, etc.), users profile (occupants, owners, facility managers, employers, etc.) as well as cultural and demographic factors. One of the main findings of this work is the common failure when technological interventions on behavioural change are done to not consult, train and support users regarding technological changes leading to poor performance in practices. As conclusion, a strong need to carry out social studies to identify relevant behavioural issues and to identify effective pro-evironmental behavioral change strategies has been identified.

Keywords: energy saving, behavioral bhange, building users, engagement methods, energy conservation measures

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Authors: Syed Jamil H. Kazmi, Faiza Sarwar

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The Indus delta of Sindh Province forms the largest creeks zone of Pakistan. The Sindh coast starts from the mouth of Hab River and terminates at Sir Creek area. In this paper, we have considered the major creeks from the site of Bin Qasim Port in Karachi to Jetty of Keti Bunder in Thatta District. A general decline in the mangrove forest has been observed that within a span of last 25 years. The unprecedented human interventions damage the creeks habitat badly which includes haphazard urban development, industrial and sewage disposal, illegal cutting of mangroves forest, reduced and inconsistent fresh water flow mainly from Jhang and Indus rivers. These activities not only harm the creeks habitat but affected the fish stock substantially. Fishing is the main livelihood of coastal people but with the above-mentioned threats, it is also under enormous pressure by fish catches resulted in unchecked overutilization of the fish resources. This pressure is almost unbearable when it joins with deleterious fishing methods, uncontrolled fleet size, increase trash and by-catch of juvenile and illegal mesh size. Along with these anthropogenic interventions study area is under the red zone of tropical cyclones and active seismicity causing floods, sea intrusion, damage mangroves forests and devastation of fish stock. In order to sustain the natural resources of the Indus Creeks, this study was initiated with the support of FAO, WWF and NIO, the main purpose was to develop a Geo-Spatial dataset for fish stock assessment. The study has been spread over a year (2013-14) on monthly basis which mainly includes detailed fish stock survey, water analysis and few other environmental analyses. Environmental analysis also includes the habitat classification of study area which has done through remote sensing techniques for 22 years’ time series (1992-2014). Furthermore, out of 252 species collected, fifteen species from estuarine and marine groups were short-listed to measure the weight, health and growth of fish species at each creek under GIS data through SPSS system. Furthermore, habitat suitability analysis has been conducted by assessing the surface topographic and aspect derivation through different GIS techniques. The output variables then overlaid in GIS system to measure the creeks productivity. Which provided the results in terms of subsequent classes: extremely productive, highly productive, productive, moderately productive and less productive. This study has revealed the Geospatial tools utilization along with the evaluation of the fisheries resources and creeks habitat risk zone mapping. It has also been identified that the geo-spatial technologies are highly beneficial to identify the areas of high environmental risk in Sindh Creeks. This has been clearly discovered from this study that creeks with high rugosity are more productive than the creeks with low levels of rugosity. The study area has the immense potential to boost the economy of Pakistan in terms of fish export, if geo-spatial techniques are implemented instead of conventional techniques.

Keywords: fish stock, geo-spatial, productivity analysis, risk

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Authors: Damian Czubak

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The Białowieża Forest is a very valuable natural area, included in the World Natural Heritage at UNESCO, where, due to infestation by the bark beetle (Ips typographus), norway spruce (Picea abies) have deteriorated. This catastrophic scenario led to an increase in fire danger. This was due to the occurrence of large amounts of dead wood and grass cover, as light penetrated to the bottom of the stands. These factors in a dry state are materials that favour the possibility of fire and the rapid spread of fire. One of the objectives of the study was to monitor the quantitative and qualitative changes of combustible material on the permanent decay plots of spruce stands from 2012-2022. In addition, the size of the area with highly flammable vegetation was monitored and a classification of the stands of the Białowieża Forest by flammability classes was made. The key factor that determines the potential fire hazard of a forest is combustible material. Primarily its type, quantity, moisture content, size and spatial structure. Based on the inventory data on the areas of forest districts in the Białowieża Forest, the average fire load and its changes over the years were calculated. The analysis was carried out taking into account the changes in the health status of the stands and sanitary operations. The quantitative and qualitative assessment of fallen timber and fire load of ground cover used the results of the 2019 and 2021 inventories. Approximately 9,000 circular plots were used for the study. An assessment was made of the amount of potential fuel, understood as ground cover vegetation and dead wood debris. In addition, monitoring of areas with vegetation that poses a high fire risk was conducted using data from 2019 and 2021. All sub-areas were inventoried where vegetation posing a specific fire hazard represented at least 10% of the area with species characteristic of that cover. In addition to the size of the area with fire-prone vegetation, a very important element is the size of the fire load on the indicated plots. On representative plots, the biomass of the land cover was measured on an area of 10 m2 and then the amount of biomass of each component was determined. The resulting element of variability of ground covers in stands was their flammability classification. The classification developed made it possible to track changes in the flammability classes of stands over the period covered by the measurements.

Keywords: classification, combustible material, flammable vegetation, Norway spruce

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Authors: Malik D. Halidu, Cathrine F. Moe, Tommy Haugan

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Purpose: The demand for effective school-based interventions in shaping adolescents' unmet mental health needs is growing. Grounding in the functional contextualism approach, this study investigates the role of school satisfaction (SS) in serving as a buffer to school absenteeism (SAB) among adolescents experiencing social anxiety (SA). Methods: A unique and large population-based sample of adolescents (upper secondary school pupils; n= 1864) from the Young-HUNT 3 survey dataset merged with the national educational registry from Norway. Moderation regression analysis was performed using Stata 17. Results: We find a statistically significant moderating role of school satisfaction on the relationship between social anxiety and school absenteeism (β=-0.109,p<0.01) among upper secondary school pupils. Among socially anxious adolescents associated with a higher perceived quality of school life, it functions as a buffer by reducing the positive relationship between SA and SAB. But, there was no statistically significant difference between social anxiety and school absenteeism for adolescents with low school satisfaction. Conclusion: Overall, the study's hypothesis model was statistically supported and contributes to the discourse that school satisfaction as a target of school-based interventions can effectively improve school outcomes (e.g., reduced absenteeism) among socially anxious pupils.

Keywords: social anxiety, school satisfaction, school absenteeism, Norwegian adolescent

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Authors: Royan Dawud Aldian, Endah Purwanti, Soegianto Soelistiono

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In this research we have been developed an automatic investigation to classify normal children voice or autistic by using modern computation technology that is computation based on artificial neural network. The superiority of this computation technology is its capability on processing and saving data. In this research, digital voice features are gotten from the coefficient of linear-predictive coding with auto-correlation method and have been transformed in frequency domain using fast fourier transform, which used as input of artificial neural network in back-propagation method so that will make the difference between normal children and autistic automatically. The result of back-propagation method shows that successful classification capability for normal children voice experiment data is 100% whereas, for autistic children voice experiment data is 100%. The success rate using back-propagation classification system for the entire test data is 100%.

Keywords: autism, artificial neural network, backpropagation, linier predictive coding, fast fourier transform

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Authors: Sue S. Feldman

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As clinical and nursing informatics mature, an area that has gotten a lot of attention is the value capstone projects. Capstones are meant to address authentic and complex domain-specific problems. While capstone projects have not always been essential in graduate clinical and nursing informatics education, employers are wanting to see evidence of the prospective employee's knowledge and skills as an indication of employability. Capstones can be organized in many ways: a single course over a single semester, multiple courses over multiple semesters, as a targeted demonstration of skills, as a synthesis of prior knowledge and skills, mentored by one single person or mentored by various people, submitted as an assignment or presented in front of a panel. Because of the potential for capstones to enhance the educational experience, and as a mechanism for application of knowledge and demonstration of skills, a rigorous capstone can accelerate a graduate's potential in the workforce. In 2016, the capstone at the University of Alabama at Birmingham (UAB) could feel the external forces of a maturing Clinical and Nursing Informatics discipline. While the program had a capstone course for many years, it was lacking the depth of knowledge and demonstration of skills being asked for by those hiring in a maturing Informatics field. Since the program is online, all capstones were always in the online environment. While this modality did not change, other contributors to instruction modality changed. Pre-2016, the instruction modality was self-guided. Students checked in with a single instructor, and that instructor monitored progress across all capstones toward a PowerPoint and written paper deliverable. At the time, the enrollment was few, and the maturity had not yet pushed hard enough. By 2017, doubling enrollment and the increased demand of a more rigorously trained workforce led to restructuring the capstone so that graduates would have and retain the skills learned in the capstone process. There were three major changes: the capstone was broken up into a 3-course sequence (meaning it lasted about 10 months instead of 14 weeks), there were many chunks of deliverables, and each faculty had a cadre of about 5 students to advise through the capstone process. Literature suggests that the chunking, breaking up complex projects (i.e., the capstone in one summer) into smaller, more manageable chunks (i.e., chunks of the capstone across 3 semesters), can increase and sustain learning while allowing for increased rigor. By doing this, the teaching responsibility was shared across faculty with each semester course being taught by a different faculty member. This change facilitated delving much deeper in instruction and produced a significantly more rigorous final deliverable. Having students advised across the faculty seemed like the right thing to do. It not only shared the load, but also shared the success of students. Furthermore, it meant that students could be placed with an academic advisor who had expertise in their capstone area, further increasing the rigor of the entire capstone process and project and increasing student knowledge and skills.

Keywords: capstones, clinical informatics, health informatics, informatics

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Authors: Abiola Falilat Ibraheem, Akinyimika Sowunmi, Valerie Otti

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Background: Introduction of innovative cancer clinical trials to Nigeria is a critical step in addressing global inequities of cancer burden. Low health and clinical trial literacy among Nigerian patients have been sighted as a significant barrier to ensuring that patients enrolled in clinical trials are truly informed. Video intervention has been shown to be the most proactive method to improving patient’s clinical trial knowledge. In the US, video interventions have been successful at improving education about cancer clinical trials among minority patients. Thus, this study aimed to apply and adapt video interventions addressing attitudinal barriers peculiar to Nigerian patients. Methods: A hospital-based representative mixed-method study was conducted at the Lagos State University Teaching Hospital (LASUTH) from July to December 2020, comprising of cancer patients aged 18 and above. Patients were randomly selected during every clinic day, of which 63 patients volunteered to participate in this study. We first administered a cancer literacy survey to determine patients’ knowledge about clinical trials. For patients who had prior knowledge, a pre-intervention test was administered, after which a 15-minute video (attitudes and intention to enroll in therapeutic clinical trials (AIET)) to improve patients’ knowledge, perception, and attitudes towards clinical trials was played, and then ended by administering a post-intervention test to the patients. For patients who had no prior knowledge, the AIET video was played for them, followed by the post-intervention test. Results: Out of 63 patients sampled, 43 (68.3%) had breast cancer. On average, patients agreed to understand their cancer diagnosis and treatment very well. 84.1% of patients had never heard about cancer clinical trials, and 85.7% did not know what cancer clinical trials were. There was a strong positive relationship (r=0.916) between the pretest and posttest, which means that the intervention improved patients’ knowledge, perception, and attitudes about cancer clinical trials. In the focus groups, patients recommended adapting the video in Nigerian settings and representing all religions in order to address trust in local clinical trialists. Conclusion: Due to the small size of patients, change in clinical trial knowledge was not statistically significant. However, there is a trend suggesting that culturally adapted video interventions can be used to improve knowledge and perception about cancer clinical trials.

Keywords: clinical trials, culturally targeted intervention, patient education, video intervention

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Authors: Mkpoikanke Sunday Otu

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Introduction: Career anxiety is a common issue among university students, particularly in developing countries like Nigeria. This anxiety can significantly impact students' academic performance, overall well-being, and future career prospects. Therefore, it is crucial to explore effective interventions that can alleviate career anxiety among university students. The primary aim of this study was to determine the effectiveness of a psychosocial intervention in reducing career anxiety among Nigerian university students. The study employed a group randomized trial research design to further analyze the impact on career anxiety. Methodology: A total of 306 university students from various universities in Akwa Ibom State, Nigeria, were recruited for this study. The participants were purposively selected to ensure diversity and represent a range of academic disciplines. A group randomized trial research design was employed, with participants randomly assigned to either the treatment group or the control group. The treatment group received a comprehensive psychosocial intervention, while the control group served as a comparison group. The Career Anxiety Questionnaire (CAQ) was used to assess career anxiety levels among the participants. The CAQ is a validated and reliable tool that assesses various aspects of career-related anxiety, including uncertainty, fear, and self-doubt. It was administered to the participants at baseline (before the intervention), immediately after the intervention, and at follow-up (after the intervention). Results: Data analysis was conducted using statistical techniques, including analysis of variance (ANOVA). The results demonstrated that the treatment group showed a significantly lower mean score of career anxiety compared to the control group (p-value<0.05). This finding suggests that the psychosocial intervention was effective in reducing the career anxiety levels of the participants at post-test and follow-up. Conclusion: The findings of this study provide compelling evidence that psychosocial interventions have a significant impact on the reduction of career anxiety among Nigerian university students. The treatment group demonstrated a significant reduction in career anxiety scores, indicating the effectiveness of this intervention. Additionally, this study highlights the importance of addressing the career anxiety challenges faced by university students. By implementing targeted interventions, educational institutions can play a vital role in supporting the overall well-being and success of their students, both academically and professionally.

Keywords: psychosocial intervention, career anxiety, psychoeducation, university students

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Alberto Piatto, Zaza Nadja Lee Hansen, Peter Jacobsen

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This paper examines the quantitative and qualitative factors influencing the buyer-supplier relationship. Understanding and acting on the right factors influencing supplier relationship management is crucial when a company outsource an important part of its business as it can be for engineering to order (ETO) company executing only the designing part in-house. Acting on these factors increase the quality of the relationship obtaining for both parties what they want and expect from an improved relationship. Best practices in supplier relationship management are considered and a case study of a large global company, called Cement A/S, operating in the cement business is carried out. One study is conducted including a large international company and hundreds of its suppliers. Data from the company is collected using semi-structured interviews and data from the suppliers is collected using a survey. Based on these inputs and an extensive literature review a classification of factors influencing the relationship buyer-supplier is presented and discussed. The results show that different managers among the company are assessing supplier from various perspectives, a standard approach to measure the performance of suppliers does not exist. The factors used nowadays in the company to measure performances of the suppliers are mostly related to time and cost. Quality is a key factor, but it has not been addressed properly since no data are available in the system. From a practical perspective, managers can learn from this paper which factors to consider when applying best practices of Supplier Relationship Management. Furthermore, from a theoretical perspective, this paper contributes with new knowledge in the area as limited research in collaboration with the company has been conducted. For this reason, a company, its suppliers and few studies for this type of industry have been conducted. For further research, it is suggested to define the correlation of factors to the profitability of the company and calculate its impact. When conducting this analysis it is important to focus on the efficient and effective use of factors that can be measurable and accepted from the supplier.

Keywords: buyer-supplier relationship, cement industry, classification of factors, ETO

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Authors: Delies L. Alejo

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This study explores the psychosocial influences of Spinal Cord Injury (SCI) on patients in the Philippine Orthopedic Center Hospital in the Philippines, examining their social functioning and proposing interventions for reintegration. Quantitative data were collected through surveys using a concurrent triangulation research design, while qualitative insights were obtained via interviews. Findings revealed significant psychosocial challenges among SCI patients, impacting relationships, family dynamics, work, friendships, parenting, education, and self-care. Demographic profiles indicated variations in psychosocial functioning. The study underscores the importance of tailored interventions for SCI patients based on age, marital status, gender, education, and occupation. Triangulation of data enhanced understanding, revealing four themes: ‘Resilient Navigation of Intimacy and Connection,’ ‘Family Dynamics and Care Challenges,’ ‘Occupational Hurdles and Work Engagement,’ and ‘Social and Community Integration Obstacles.’ The study proposes a holistic intervention plan, addressing emotional challenges, creating support networks, implementing vocational rehabilitation, promoting community engagement, and sustaining collaboration with healthcare professionals.

Keywords: spinal cord injury, psychosocial influences, social functioning, concurrent triangulation, intervention plan

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Authors: N. Dunleavy, E. Lovemore, K. Siljeur, D. Jackson, M. Hendricks, M. Hoosain, N. Plastow, S. Marais

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Background: The work requirements of sewing machine operators cause physical and emotional strain. Past ergonomic interventions have been provided to alleviate physical concerns; however, a holistic, multimodal intervention was needed to improve these factors. Aim: The study aimed to examine the effect of an occupational therapy programme on sewing machine operators’ pain, mental health, and productivity within a factory in the South African context. Methods: A pilot randomised control trial was conducted with 22 sewing machine operators within a single factory. Stratified randomisation was used to determine the experimental (EG) and control groups (CG), using measures for pain intensity, level of depression (mental health), and productivity rates as stratification variables. The EG received the multimodal intervention, incorporating education, seating adaptations, and mental health intervention. In three months, the CG will receive the same intervention. Pre- and post-intervention testing have occurred with upcoming three- and six-month follow-ups. Results: Immediate results indicate a statistically significant decrease in pain in both experimental and control groups; no change in productivity scores and depression between the two groups. This may be attributed to external factors. The values for depression further showed no statistical significance between the two groups and within pre-and post-test results. The Statistical Program for Social Sciences (SPSS) version-24 was used as the data analysis testing, where all the tests will be evaluated at a 5% significance level. Contribution of research: The research adds to the body of knowledge informing the Occupational Therapy role in work settings, providing evidence on the effectiveness of workplace-based multimodal interventions. Conclusion: The study provides initial data on the effectiveness of a pilot randomised control trial on pain and mental health in South Africa. Results indicated no quantitative change between the experimental and control groups; however, qualitative data suggest a clinical significance of the findings.

Keywords: ergonomics programme, occupational therapy, sewing machine operators, workplace-based multimodal interventions

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Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

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Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: Aytakin Huseynli

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The paper reports findings of the study which examined under-five mortality rate among resource-rich countries. Typically when countries obtain wealth citizens gain increased wellbeing. Societies with new wealth create equal opportunities for everyone including vulnerable groups. But scholars claim that this is not the case for developing resource-rich countries and natural resources become the curse for them rather than the blessing. Spillovers from natural resource curse affect the social wellbeing of vulnerable people negatively. They get excluded from the mainstream society, and their situation becomes tangible. In order to test this hypothesis, the study compared under-5 mortality rate among resource-rich countries by using independent sample one-way ANOVA. The data on under-five mortality rate came from the World Bank. The natural resources for this study are oil, gas and minerals. The list of 67 resource-rich countries was taken from Natural Resource Governance Institute. The sample size was categorized and 4 groups were created such as low, low-middle, upper middle and high-income countries based on income classification of the World Bank. Results revealed that there was a significant difference in the scores for low, middle, upper-middle and high-income countries in under-five mortality rate (F(3(29.01)=33.70, p=.000). To find out the difference among income groups, the Games-Howell test was performed and it was found that infant mortality was an issue for low, middle and upper middle countries but not for high-income countries. Results of this study are in agreement with previous research on resource curse and negative effects of resource-based development. Policy implications of the study for social workers, policy makers, academicians and social development specialists are to raise and discuss issues of marginalization and exclusion of vulnerable groups in developing resource-rich countries and suggest interventions for avoiding them.

Keywords: children, natural resource, extractive industries, resource-based development, vulnerable groups

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Authors: Ramón Aparicio-García, Gustavo Juárez Gracia, Jesús Álvarez Cedillo

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A research line of the computer science that involve the study of the Human-Computer Interaction (HCI), which search to recognize and interpret the user intent by the storage and the subsequent analysis of the electrical signals of the brain, for using them in the control of electronic devices. On the other hand, the affective computing research applies the human emotions in the HCI process helping to reduce the user frustration. This paper shows the results obtained during the hardware and software development of a Brain Computer Interface (BCI) capable of recognizing the human emotions through the association of the brain electrical activity patterns. The hardware involves the sensing stage and analogical-digital conversion. The interface software involves algorithms for pre-processing of the signal in time and frequency analysis and the classification of patterns associated with the electrical brain activity. The methods used for the analysis and classification of the signal have been tested separately, by using a database that is accessible to the public, besides to a comparison among classifiers in order to know the best performing.

Keywords: affective computing, interface, brain, intelligent interaction

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Authors: Ülle Parm, Kata Pedamäe, Jaak Jürimäe, Evelin Lätt, Aivar Orav, Anna-Liisa Tamm

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Vegetarianism has gained popularity across the world. It`s being chosen for multiple reasons, but among Estonians, these have remained unknown. Previously, attention to bone health and probable nutrient deficiency of vegetarians has been paid and in vegetarians lower body mass index (BMI) and blood cholesterol level has been found but the results are inconclusive. The goal was to explain reasons for choosing vegetarian diet in Estonia and impact of vegetarianism to body composition – BMI, fat percentage (fat%), fat mass (FM), and fat free mass (FFM). The study group comprised of 68 vegetarians and 103 omnivorous. The determining body composition with DXA (Hologic) was concluded in 2013. Body mass (medical electronic scale, A&D Instruments, Abingdon, UK) and height (Martin metal anthropometer to the nearest 0.1 cm) were measured and BMI calculated (kg/m2). General data (physical activity level included) was collected with questionnaires. The main reasons why vegetarianism was chosen were the healthiness of the vegetarian diet (59%) and the wish to fight for animal rights (72%) Food additives were consumed by less than half of vegetarians, more often by men. Vegetarians had lower BMI than omnivores, especially amongst men. Based on BMI classification, vegetarians were less obese than omnivores. However, there were no differences in the FM, FFM and fat percentage figures of the two groups. Higher BMI might be the cause of higher physical activity level among omnivores compared with vegetarians. For classifying people as underweight, normal weight, overweight and obese both BMI and fat% criteria were used. By BMI classification in comparison with fat%, more people in the normal weight group were considered; by using fat% in comparison with BMI classification, however, more people categorized as overweight. It can be concluded that the main reasons for vegetarianism chosen in Estonia are healthiness of the vegetarian diet and the wish to fight for animal rights and vegetarian diet has no effect on body fat percentage, FM and FFM.

Keywords: body composition, body fat percentage, body mass index, vegetarianism

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Elissar Mansour, Emily Chen, Tracee Fernandez, Mariam Basheti, Christopher Gordon, Bandana Saini

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Sleep is an essential process for the maintenance of several neurobiological processes such as memory consolidation, mood, and metabolic processes. It is known that sleep patterns vary with age and is affected by multiple factors. While non-pharmacological strategies are generally considered first-line, sedatives are excessively used in the older population. This study aimed to explore the management of sleep in residential aged care facilities (RACFs) by nurse professionals and to identify the key factors that impact provision of optimal sleep health care. An inductive thematic qualitative research method was employed to analyse the data collected from semi-structured interviews with registered nurses working in RACF. Seventeen interviews were conducted, and the data yielded three themes: 1) the nurses’ observations and knowledge of sleep health, 2) the strategies employed in RACF for the management of sleep disturbances, 3) the organizational barriers to evidence-based sleep health management. Nurse participants reported the use of both non-pharmacological and pharmacological interventions. Sedatives were commonly prescribed due to their fast action and accessibility despite the guidelines indicating their use in later stages. Although benzodiazepines are known for their many side effects, such as drowsiness and oversedation, temazepam was the most commonly administered drug. Sleep in RACF was affected by several factors such as aging and comorbidities (e.g., dementia, pain, anxiety). However, the were also many modifiable factors that negatively impacted sleep management in RACF. These include staffing ratios, nursing duties, medication side effects, and lack of training and involvement of allied health professionals. This study highlighted the importance of involving a multidisciplinary team and the urge to develop guidelines and training programs for healthcare professionals to improve sleep health management in RACF.

Keywords: registered nurses, residential aged care facilities, sedative use, sleep

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Authors: Mahesh Balan U, Rohith Srinivaas Mohanakrishnan, Venkat Subramanian

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Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning.

Keywords: PU learning, semi-supervised learning, pseudo f measure, classification

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Authors: R. L. David, E. C. P. Tan, M. A. Ferenczi

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This study aimed to investigate the effect of a 'one-time' optional Information Literacy (IL) workshop to enhance Year 1 medical students' literature search, writing, and citation management skills as directed by a customized five-year IL framework developed for LKC Medicine students. At the end of the IL workshop, the overall rated 'somewhat difficult' when finding, citing, and using information from sources. The study method is experimental using a standardized IL test to study the cohort effect of a 'one-time' optional IL workshop on Year 1 students; experimental group in comparison to Year 2 students; control group. Test scores from both groups were compared and analyzed using mean scores and one-way analysis of variance (ANOVA). Unexpectedly, there were no statistically significant differences between group means as determined by One-Way ANOVA (F₁,₁₉₃ = 3.37, p = 0.068, ηp² = 0.017). Challenges and shortfalls posed by 'one-time' interventions raised a rich discussion to adopt an 'embedded librarianship' approach, which shifts the medial librarians' role into the curriculum and uses Team Based Learning to teach IL skills to medical students. The customized five-year IL framework developed for LKC Medicine students becomes a useful librarian-faculty model for embedding and bringing IL into the classroom.

Keywords: information literacy, 'one-time' interventions, medical students, standardized tests, embedded librarianship, curriculum, medical librarians

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Authors: Gülcan Taşkıran, Ülkü Tatar Baykal

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Aim: To identify nurses’ perceived competencies for disaster preparedness. Background: Recently, the number of disasters has increased worldwide. Since disasters often strike without warning, healthcare providers, especially nurses must be prepared with appropriate competencies for disaster procedures. Nurses’ perceptions of their own competencies for disaster preparedness need to be evaluated to aid in the creation of effective national plans and educational programs. Design: This study was conducted with a descriptive and cross-sectional design. Methods: Nurses’ perceptions were assessed using the 13-item Demographic Profile Questionnaire that is based on previous literature and the 45-item Nurses’ Perception of Core Competencies for Disaster Preparedness Scale (NPCDPS). Data were collected from June to September 2014 from 406 (79.9% return rate) Turkish nurses working in the western region of Turkey. Results: At the end of the study, it was found that out of the nurses whose mean age was 31.27 ± 5.86 and mean of working time was 8.07 ± 6.60 by the time vast majority of the nurses were women (85.7%), married (59.4%), bachelor’s degree holder (88.2%) and service nurses (56.2%). The most potential disaster that nurses think is an earthquake (70.9%) by the time majority of nurses consider having a role as a nurse at every stage of disasters. The mean total point score of nurses’ perception of disaster preparedness was 4.62. The mean total point score of the nurses from the Nurses’ Perception of Core Competencies for Disaster Preparedness Scale was 133.96. When the subscales’ mean scores are examined, the highest average of the mean score is for Technical Skills (44.52), and the lowest is for Critical Thinking Skills (10.47). When the subscales of Nurses’ Perception of Core Competencies for Disaster Preparedness Scale compared with sex, marital status and education level out of independent variable of nurses there is no significant difference (p > 0.05); compared with age group, working years, duty and being with a disaster out of independent variable of nurses there is a significant difference (p ≤ 0.05). Conclusion: Nurses generally perceive themselves as sufficient at a ‘medium level’ in terms of meeting the core competencies that are required for disaster preparedness. Nurses are not adequately prepared for disasters, but they are aware of the need for such preparation and disaster education. Disaster management training should be given to all nurses in their basic education.

Keywords: disaster competencies, disaster management, disaster nursing, disaster preparedness, nursing, nursing administration, Turkish nurses

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: S. Abed, S. O’Neill

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This research explored ward nurses’ views about the characteristics of effective nurse leaders in the context of Iraq as a developing country, where the delivery of health care continues to face disruption and change. It is well established that the provision of modern health care requires effective nurse leaders, but in countries such as Iraq the lack of effective nurse leaders is noted as a major challenge. In a descriptive quantitative study, a survey questionnaire was administered to 210 ward nurses working in two public hospitals in a major city in the north of Iraq. The participating nurses were of the opinion that the effectiveness of their nurse leaders was evident in their ability to demonstrate: good clinical knowledge, effective communication and managerial skills. They also viewed their leaders as needing to hold high-level nursing qualifications, though this was not necessarily the case in practice. Additionally, they viewed nurse leaders’ personal qualities as important, which included politeness, ethical behaviour, and trustworthiness. When considered against the issues raised in interviews with a smaller group (20) of senior nurse leaders, representative of the various occupational levels, implications identify the need for professional development that focuses on how the underpinning competencies relate to leadership and how transformational leadership is evidenced in practice.

Keywords: health care, nurse education, nursing in Iraq, nurse leadership

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving the army, moving convoys etc. The radar operator selects one of the promising targets into single target tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper, we present a technique using mathematical and statistical methods like fast fourier transformation (FFT) and principal component analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, FFT, principal component analysis, eigenvector, octave-notes, DSP

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Authors: Khawlah Al-Khalidi

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Prevalence of lower third molar impactions and angulations among Yemeni population The purpose of this study was to look into the prevalence of lower third molars in a sample of patients from Ibb University Affiliated Hospital, as well as to study and categorise their position by using Pell and Gregory classification, and to look into a possible correlation between their position and the indication for extraction. Materials and methods: This is a retrospective, observational study in which a sample of 200 patients from Ibb University Affiliated Hospital were studied, including patient record validation and orthopantomography performed in screening appointments in people aged 16 to 21. Results and discussion: Males make up 63% of the sample, while people aged 19 to 20 make up 41.2%. Lower third molars were found in 365 of the 365 instances examined, accounting for 91% of the sample under study. According to Pell and Gregory's categorisation, the most common position is IIB, with 37%, followed by IIA with 21%; less common classes are IIIA, IC, and IIIC, with 1%, 3%, and 3%, respectively. It was feasible to determine that 56% of the lower third molars in the sample were recommended for extraction during the screening consultation. Finally, there are differences in third molar location and angulation. There was, however, a link between the available space for third molar eruption and the need for tooth extraction.

Keywords: lower third molar, extraction, Pell and Gregory classification, lower third molar impaction

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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The aim of the present work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. Based on feature selection in different phases, in this research, we design a neural network system that has optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each ROI, 6 distinct set of texture features are extracted such as first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. We show that with the injection of liquid and the analysis of more phases the high relevant features in each region changed. Our results show that for detecting HCC tumor phase3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between these two classes according to our method, relates to first order histogram parameters with the accuracy of 85% in phase 1, 95% phase 2, and 95% in phase 3.

Keywords: multi-phasic liver images, texture analysis, neural network, hidden layer

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Authors: F. Fatoye, C. E. Mbada, T. Gebrye, A. O. Ogunsola, C. Fatoye, O. Oyewole

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Musculoskeletal disorders (MSDs) are a major cause of pain and disability. It is likely to become a greater economic and public health burden that is unnecessary. Thus, reliable prevalence figures are important for both clinicians and policy-makers to plan health care needs for those affected with the disease. This study estimated hospital based real-world prevalence of MSDs in Nigeria. A review of medical charts for adult patients attending Physiotherapy Outpatient Clinic at the Obafemi Awolowo University Teaching Hospitals Complex, Osun State, Nigeria between 2009 and 2018 was carried out to identify common MSDs including low back pain (LBP), cervical spondylosis (CSD), post immobilization stiffness (PIS), sprain, osteoarthritis (OA), and other conditions. Occupational class of the patients was determined using the International Labour Classification (ILO). Data were analysed using descriptive statistics of frequency and percentages. Overall, medical charts of 3,340 patients were reviewed within the span of ten years (2009 to 2018). Majority of the patients (62.8%) were in the middle class, and the remaining were in low class (25.1%) and high class (10.5%) category. An overall prevalence of 47.35% of MSD was found within the span of ten years. Of this, the prevalence of LBP, CSD, PIS, sprain, OA, and other conditions was 21.6%, 10%, 18.9%, 2%, 6.3%, and 41.3%, respectively. The highest (14.2%) and lowest (10.5%) prevalence of MSDs was recorded in the year of 2012 and 2018, respectively. The prevalence of MSDs is considerably high among Nigerian patients attending outpatient a physiotherapy clinic. The high prevalence of MSDs underscores the need for clinicians and decision makers to put in place appropriate strategies to reduce the prevalence of these conditions. In addition, they should plan and evaluate healthcare services to improve the health outcomes of patients with MSDs. Further studies are required to determine the economic burden of the condition and examine the clinical and cost-effectiveness of physiotherapy interventions for patients with MSDs.

Keywords: musculoskeletal disorders, Nigeria, prevalence, real world

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Authors: Na Li, Yunwei Zhang, Yuhong Niu

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The study discussed the patent protections of health system and enterprises in Shanghai. The comparisons of technical distribution and scopes of patent protections between Shanghai health system and enterprises were used by the methods of IPC classification, co-words analysis and visual social network. Results reflected a decreasing order within IPC A61 area, namely A61B, A61K, A61M, and A61F. A61B required to be further investigated. The highest authorized patents A61B17 of A61B of IPC A61 area was found. Within A61B17, fracture fixation, ligament reconstruction, cardiac surgery, and biopsy detection were regarded as common concerned fields by Shanghai health system and enterprises. However, compared with cardiac closure which Shanghai enterprises paid attention to, Shanghai health system was more inclined to blockages and hemostatic tools. The results also revealed that the scopes of patent protections of Shanghai enterprises were relatively centralized. Shanghai enterprises had a series of comprehensive strategies for protecting core patents. In contrast, Shanghai health system was considered to be lack of strategic patent protections for core patents.

Keywords: co-words analysis, IPC classification, patent protection, technical distribution

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Authors: Ayse Pekrioglu Balkis, Sawash Mecid

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Due to continued growth and rapid development of road construction in worldwide, road sub-layers consist of soil layers, therefore, identification and recognition of type of soil and soil behavior in different condition help to us to select soil according to specification and engineering characteristic, also if necessary sometimes stabilize the soil and treat undesirable properties of soils by adding materials such as bitumen, lime, cement, etc. If the soil beneath the road is not done according to the standards and construction will need more construction time. In this case, a large part of soil should be removed, transported and sometimes deposited. Then purchased sand and gravel is transported to the site and full depth filled and compacted. Stabilization by cement or other treats gives an opportunity to use the existing soil as a base material instead of removing it and purchasing and transporting better fill materials. Classification of soil according to AASHTOO system and USCS help engineers to anticipate soil behavior and select best treatment method. In this study soil classification and the relation between soil classification and stabilization method is discussed, cement stabilization with different percentages have been selected for soil treatment based on NCHRP. There are different parameters to define the strength of soil. In this study, CBR will be used to define the strength of soil. Cement by percentages, 0%, 3%, 7% and 10% added to soil for evaluation effect of added cement to CBR of treated soil. Implementation of stabilization process by different cement content help engineers to select an economic cement amount for the stabilization process according to project specification and characteristics. Stabilization process in optimum moisture content (OMC) and mixing rate effect on the strength of soil in the laboratory and field construction operation have been performed to see the improvement rate in strength and plasticity. Cement stabilization is quicker than a universal method such as removing and changing field soils. Cement addition increases CBR values of different soil types by the range of 22-69%.

Keywords: California Bearing Ratio, cement stabilization, clayey soil, mechanical properties

Procedia PDF Downloads 380