Search results for: multiple data

Authors: Frezer Alem Kebede

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The notion of viewing ICT as merely support for the business process has shifted towards viewing ICT as a critical business enabler. As such, the need for ICT devices has increased, contributing to high electronic equipment acquisition and disposal. Hence, its use and disposal must be seen in light of environmental sustainability, i.e., in terms of green use and disposal. However, there are limited studies on green Use and Disposal framework to be used as guiding lens by organizations in developing countries. And this study endeavors to address that need taking one of the largest multinational ICT intensive company in the country. The design and development of this framework passed through several stages, initially factors affecting green use and disposal were identified after quantitative and qualitative data analysis then there were multiple brainstorming sessions for the design enhancement as participative modelling was employed. Given the difference in scope and magnitude of the challenges identified, the proposed framework approaches green use and disposal in four imperatives; strategically, tactically, operationally and through continuous improvement.

Keywords: energy efficiency, green disposal, green ICT, green use, green use and disposal framework, sustainability

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Authors: Thuan Nguyen

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In the present time, data to a data-driven knowledge-based economy is the same as oil to the industrial age hundreds of years ago. Data is everywhere in vast volumes! Big data analytics is expected to help firms not only efficiently improve performance but also completely transform how they should run their business. However, employing the emergent technology successfully is not easy, and assessing the roles of big data in improving firm performance is even much harder. There was a lack of studies that have examined the impacts of big data analytics on organizational performance. This study aimed to fill the gap. The present study suggested using firms’ intellectual capital as a proxy for big data in evaluating its impact on organizational performance. The present study employed the Value Added Intellectual Coefficient method to measure firm intellectual capital, via its three main components: human capital efficiency, structural capital efficiency, and capital employed efficiency, and then used the structural equation modeling technique to model the data and test the models. The financial fundamental and market data of 100 randomly selected publicly listed firms were collected. The results of the tests showed that only human capital efficiency had a significant positive impact on firm profitability, which highlighted the prominent human role in the impact of big data technology.

Keywords: big data, big data analytics, intellectual capital, organizational performance, value added intellectual coefficient

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Authors: Hitesh Tahbildar

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The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.

Keywords: ongest path, saturation point, lmax, kL, kS

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Authors: Surinder Sood, Debajyoti Mukherjee

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Formal verification techniques have become widely popular in pre-silicon verification as an alternate to constrain random simulation based techniques. This paper proposed a novel formal verification-based coverage augmentation technique in verifying complex RTL functional verification faster. The proposed approach relies on augmenting coverage analysis coming from simulation and formal verification. Besides this, the functional qualification framework not only helps in improving the coverage at a faster pace but also aids in maturing and qualifying the formal verification infrastructure. The proposed technique has helped to achieve faster verification sign-off, resulting in faster time-to-market. The design picked had a complex control and data path and had many configurable options to meet multiple specification needs. The flow is generic, and tool independent, thereby leveraging across the projects and design will be much easier

Keywords: COI (cone of influence), coverage, formal verification, fault injection

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Authors: Elvis Munyoka

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While the number of African refugees settling in Australia has significantly increased since the mid-1990s, the marginalisation and exclusion of young people from refugee backgrounds in employment remain a critical challenge. Unemployment or underemployment can negatively impact refugees in multiple areas, such as income, housing, life satisfaction, and social status. Higher rates of unemployment among refugees are linked in part to the intersection of pre-migration and daily challenges like trauma, racism, gender identity, and English language competency, all of which generate multiple employability disadvantages. However, the intersection of gender, race, social class, and age in impacting African refugee youth’s access to employment has received less attention. Using a qualitative case study approach, the paper will explore how gender, race, social class, and age influence African refugee youth graduates’ access to employment in South Australia. The intersectionality theory and capability approach to social justice is used to explore intersecting factors impacting African refugee youth’s access to employment in South Australia. Participants were 16 African refugee graduates aged 18-30 living in South Australia who took part in the study for one year. Based on the trends in the data, the results suggest that long-term unemployment and underemployment, coupled with ongoing racism and marginalisation, have the potential to make refugees more vulnerable to several mental disorders such as depression, hopelessness, and suicidal thoughts. The analysis also reveals that resettlement challenges may limit refugees’ ability to recover from pre-migration trauma. The impact of resettlement challenges on refugee mental health highlights the need for comprehensive policy interventions to address the barriers refugees face in finding employment in resettlement communities. With African refugees constituting such an important part of Australian society, they should have equal access to meaningful employment, as decent work promotes good mental health, successful resettlement, hope, and self-sufficiency.

Keywords: African refugee youth, mental health, employment, resettlement, racism

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Authors: Henok Yebyo Henok, Araya Abrha Araya, Alemayehu Bayray Alemayehu, Gelila Goba Gelila

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Background: Every year, an estimated 19–20 million unsafe abortions take place, almost all in developing countries, leading to 68,000 deaths and millions more injured many permanently. Many women throughout the world, experience more than one abortion in their lifetimes. Repeat abortion is an indicator of the larger problem of unintended pregnancy. This study aimed to identify determinants of repeat abortion in Tigray Region, Ethiopia. Methods: Unmatched case-control study was conducted in hospitals in Tigray Region, Northern Ethiopia, from November 2014 to June 2015. The sample included 105 cases and 204 controls, recruited from among women seeking abortion care at public hospitals. Clients having two or more abortions (“repeat abortion”) were taken as cases, and those who had a total of one abortion were taken as controls (“single abortion”). Cases were selected consecutive based on proportional to size allocation while systematic sampling was employed for controls. Data were analyzed using SPSS version 20.0. Binary and multiple variable logistic regression analyses were calculated with 95% CI. Results: Mean age of cases was 24 years (±6.85) and 22 years (±6.25) for controls. 79.0% of cases had their sexual debut in less than 18 years of age compared to 57% of controls. 42.2% of controls and 23.8% of cases cited rape as the reason for having an abortion. Study participants who did not understand their fertility cycle and when they were most likely to conceive after menstruation (adjusted odds ratio [AOR]=2.0, 95% confidence interval [CI]: 1.1-3.7), having a previous abortion using medication(AOR=3.3, CI: 1.83, 6.11), having multiple sexual partners in the preceding 12 months (AOR=4.4, CI: 2.39,8.45), perceiving that the abortion procedure is not painful (AOR=2.3, CI: 1.31,4.26), initiating sexual intercourse before the age of 18 years (AOR=2.7, CI: 1.49, 5.23) and disclosure to a third-party about terminating the pregnancy (AOR=2.1, CI: 1.2,3.83) were independent predictors of repeat abortion. Conclusion: This study identified several factors correlated with women having repeat abortions. It may be helpful for the Government of Ethiopia to encourage women to delay sexual debut and decrease their number of sexual partners, including by promoting discussion within families about sexuality, to decrease the occurrence of repeated abortion.

Keywords: abortion, Ethiopia, repeated abortion, single abortion

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Authors: Luísa Pedro, José Pais-Ribeiro, João Páscoa Pinheiro

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Multiple Sclerosis (MS) is a chronic disease of the central nervous system that affects more often young adults in the prime of his career and personal development, with no cure and unknown causes. The most common signs and symptoms are fatigue, muscle weakness, changes in sensation, ataxia, changes in balance, gait difficulties, memory difficulties, cognitive impairment and difficulties in problem solving. MS is a relatively common neurological disorder in which various impairments and disabilities impact strongly on function and daily life activities. The aim of this study is to examine the implications of the program of self-regulation in the perception of illness and mental health (psychological well-being domain) in MS patients. MS is a relatively common neurological disorder in which various impairments and disabilities impact strongly on function and daily life activities. The aim of this study is to examine the implications of the program of self-regulation in the perception of illness and mental health (psychological well-being domain) in MS patients. After this, a set of exercises was implemented to be used in daily life activities, according to studies developed with MS patients. We asked the subjects the question “Please classify the severity of your disease?” and used the domain of psychological well-being, the Mental Health Inventory (MHI-38) at the beginning (time A) and end (time B) of the program of self-regulation. We used the Statistical Package for the Social Sciences (SPSS) version 20. A non-parametric statistical hypothesis test (Wilcoxon test) was used for the variable analysis. The intervention followed the recommendations of the Helsinki Declaration. The age range of the subjects was between 20 and 58 years with a mean age of 44 years. 58.3 % were women, 37.5 % were currently married, 67% were retired and the mean level of education was 12.5 years. In the correlation between the severity of the disease perception and psychological well before the self-regulation program, an obtained result (r = 0.26, p <0.05), then the self-regulation program, was (r = 0.37, p <0.01), from a low to moderate correlation. We conclude that the program of self-regulation for physical activity in patients with MS can improve the relationship between the perception of disease severity and psychological well-being.

Keywords: psychological well-being, multiple sclerosis, self-regulation, physical activity

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Mei-Hui Liu

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This study examines the effectiveness of a blended professional development program on preservice EFL (English as a foreign language) teachers’ learning to teach speech acts with the advent of Information and Communication Technology, researchers and scholars underscore the significance of integrating online and face-to-face learning opportunities in the teacher education field. Yet, a paucity of evidence has been documented to investigate the extent to which such a blended professional learning model may impact real classroom practice and student learning outcome. This yearlong project involves various stakeholders, including 25 preservice teachers, 5 English professionals, and 45 secondary school students. Multiple data sources collected are surveys, interviews, reflection journals, online discussion messages, artifacts, and discourse completion tests. Relying on the theoretical lenses of Community of Inquiry, data analysis depicts the nature and process of preservice teachers’ professional development in this blended learning community, which triggers and fosters both face-to-face and synchronous/asynchronous online interactions among preservice teachers and English professionals (i.e., university faculty and in-service teachers). Also included is the student learning outcome after preservice teachers put what they learn from the support community into instructional practice. Pedagogical implications and research suggestions are further provided based on the research findings and limitations.

Keywords: blended professional development, preservice EFL teachers, speech act instruction, student learning outcome

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Authors: Hatice Kübra Koç

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For academia, collecting reliable and valid data is one of the most significant issues for researchers. However, it is not the same procedure for all different target groups; meanwhile, during data collection from teenagers, young adults, or adults, researchers can use common data collection tools such as questionnaires, interviews, and semi-structured interviews; yet, for young learners and very young ones, these reliable and valid data collection tools cannot be easily designed or applied by the researchers. In this study, firstly, common data collection tools are examined for ‘very young’ and ‘young learners’ participant groups since it is thought that the quality and efficiency of an academic study is mainly based on its valid and correct data collection and data analysis procedure. Secondly, two different data collection instruments for very young and young learners are stated as discussing the efficacy of them. Finally, a suggested data collection tool – a performance-based questionnaire- which is specifically developed for ‘very young’ and ‘young learners’ participant groups in the field of teaching English to young learners as a foreign language is presented in this current study. The designing procedure and suggested items/factors for the suggested data collection tool are accordingly revealed at the end of the study to help researchers have studied with young and very learners.

Keywords: data collection instruments, performance-based questionnaire, young learners, very young learners

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Authors: Yuen Man Yan

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Peer support has been a rising prevalent mental health service in the globe. The community-based mental health services employ persons with lived experience of mental illness to be peer support workers (PSWs) to provide peer support service to those who are in the progress of recovery (PIRs). It represents the transformation of mental health service system to a recovery-oriented and person-centered care. Literatures proved the feasibility and effectiveness of the peer support service. Researchers have attempted to explore the unique good qualities of peer support service that benefit the PIRs. Empirical researches found that the strength of the relationship between those who sought for change and the change agents positively related to the outcomes in one-to-one therapies across theoretical orientations. However, there is lack of literature on investigating the relationship building between the PSWs and PIRs in the one-to-one community-based peer support service. This study aims to identify and characterise the relationship in the community-based one-to-one peer support service from the perspectives of PSWs and PIRs; and to conceptualize the components of relationship building between PSWs and PIRs in the community-based one-to-one peer support service. The study adopted the constructivist grounded theory approach. 10 pairs of the PSWs and PIRs participated in the study. Data were collected through multiple qualitative methods, including observation of the interaction and exchange of the PSWs and PIRs in the 1ₛₜ, 3ᵣ𝒹 and 9th sessions of the community-based one-to-one peer support service; and semi-structural interview with the PSWs and PIRs separately after the 3ᵣ𝒹and 9ₜₕ session of the peer support service. This presentation is going to report the preliminary findings of the study. PSWs and PIRs identified their relationship as “life alliance”. Empathy was found to be one of key components of the relationship between the PSWs and the PIRs. Unlike the empathy, as explained by Carl Roger, in which the service provider was able to put themselves into the shoes of the service recipients as if he was the service recipients, the intensity of the empathy was much greater in the relationship between PSWs and PIRs because PSWs had the lived experience of mental illness and recovery. The dimensions of the empathy in the relationship between PSWs and PIRs was found to be multiple, not only related to the mental illness but also related to various aspects in life, like family relationship, employment, interest of life, self-esteem and etc.

Keywords: person with lived experience, peer support worker, peer support service, relationship building, therapeutic alliance, community-based mental health setting

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Authors: Anna-Maria Ramezanzadeh

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Demand for Arabic language is growing worldwide, driven by increased interest in the multifarious purposes the language serves, both for the population of heritage learners and those studying Arabic as a foreign language. The diglossic, or indeed multiglossic nature of the language as used in Arabic speaking communities however, is seldom represented in the content of classroom courses. This disjoint between the nature of provision and students’ expectations can severely impact their engagement with course material, and their motivation to either commence or continue learning the language. The nature of motivation and its relationship to multiglossia is sparsely explored in current literature on Arabic. The theoretical framework here proposed aims to address this gap by presenting a model and instruments for the measurement of Arabic learners’ motivation in relation to the multiple strands of the language. It adopts and develops the Second Language Motivation Self-System model (L2MSS), originally proposed by Zoltan Dörnyei, which measures motivation as the desire to reduce the discrepancy between leaners’ current and future self-concepts in terms of the second language (L2). The tripartite structure incorporates measures of the Current L2 Self, Future L2 Self (consisting of an Ideal L2 Self, and an Ought-To Self), and the L2 Learning Experience. The strength of the self-concepts is measured across three different domains of Arabic: Classical, Modern Standard and Colloquial. The focus on learners’ self-concepts allows for an exploration of the effect of multiple factors on motivation towards Arabic, including religion. The relationship between Islam and Arabic is often given as a prominent reason behind some students’ desire to learn the language. Exactly how and why this factor features in learners’ L2 self-concepts has not yet been explored. Specifically designed surveys and interview protocols are proposed to facilitate the exploration of these constructs. The L2 Learning Experience component of the model is operationalized as learners’ task-based engagement. Engagement is conceptualised as multi-dimensional and malleable. In this model, situation-specific measures of cognitive, behavioural, and affective components of engagement are collected via specially designed repeated post-task self-report surveys on Personal Digital Assistant over multiple Arabic lessons. Tasks are categorised according to language learning skill. Given the domain-specific uses of the different varieties of Arabic, the relationship between learners’ engagement with different types of tasks and their overall motivational profiles will be examined to determine the extent of the interaction between the two constructs. A framework for this data analysis is proposed and hypotheses discussed. The unique combination of situation-specific measures of engagement and a person-oriented approach to measuring motivation allows for a macro- and micro-analysis of the interaction between learners and the Arabic learning process. By combining cross-sectional and longitudinal elements with a mixed-methods design, the model proposed offers the potential for capturing a comprehensive and detailed picture of the motivation and engagement of Arabic learners. The application of this framework offers a number of numerous potential pedagogical and research implications which will also be discussed.

Keywords: Arabic, diglossia, engagement, motivation, multiglossia, sociolinguistics

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Authors: Simon Quick, John Lyle

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Psychology-based research has been a characteristic of empirical enquiry in sport coaching for over fifty years and cognitive function is widely accepted as a fundamental component of sport coaching expertise. Within the academic literature, much empirical research on coaches’ cognitions has tended to adopt retrospective approaches, such as post-session interviews or stimulated recall, thus capturing coaches’ cognitions after the incident, training session or competition. Such methods are prone to a variety of issues, including memory decay and the reordering of accounts. The aim of this research was to overcome the limitations that exist with retrospective approaches and, rather, to capture coaching cognitions in situ using Think Aloud Protocol. Data that were captured was broken down into meaning units and analysed using a Thematic Analysis. Situated in the practice of 6 experienced rugby coaches, findings revealed that Think Aloud Protocol generated rich data, although problematic in a site of enquiry confounded by multiple social interactions and requiring coaches to provide frequent instruction and feedback. The resultant interaction between cognition and action is conceptualised by the tentative offering of a model that situates these elements in conjunction with cognitive triggers and thresholds. The implications of these findings can help academics and coaches to understand the dynamic relationship between types of coaching cognitions and the complexity of the coaching environment.

Keywords: sports coaching, Psychology, Pedagogy, cognition

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Authors: Ahmed Saeed, Sadok Sassi, Mohammad Roshun

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Gears are important components with a vital role in many rotating machines. One of the common gear failure causes is tooth fatigue crack; however, its early detection is still a challenging task. The objective of this study is to develop a numerical model that simulates the effect of teeth cracks on the resulting gears vibrations and permits consequently to perform an early fault detection. In contrast to other published papers, this work incorporates the possibility of multiple simultaneous cracks with different depths. As cracks alter significantly the stiffness of the tooth, finite element software is used to determine the stiffness variation with respect to the angular position, for different combinations of crack orientation and depth. A simplified six degrees of freedom nonlinear lumped parameter model of a one-stage spur gear system is proposed to study the vibration with and without cracks. The model developed for calculating the stiffness with the crack permitted to update the physical parameters of the second-degree-of-freedom equations of motions describing the vibration of the gearbox. The vibration simulation results of the gearbox were by obtained using Simulink/Matlab. The effect of one crack with different levels was studied thoroughly. The change in the mesh stiffness and the vibration response were found to be consistent with previously published works. In addition, various statistical time domain parameters were considered. They showed different degrees of sensitivity toward the crack depth. Multiple cracks were also introduced at different locations and the vibration response along with the statistical parameters were obtained again for a general case of degradation (increase in crack depth, crack number and crack locations). It was found that although some parameters increase in value as the deterioration level increases, they show almost no change or even decrease when the number of cracks increases. Therefore, the use of any statistical parameters could be misleading if not considered in an appropriate way.

Keywords: Spur gear, cracked tooth, numerical simulation, time-domain parameters

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Authors: Hyun-Woo Cho

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For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

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Authors: Tamadu Perry Egedegu

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Rapid growth in the use of electronic data has affected both the employee and work place. Our experience shows that jobs that have multiple risk factors have a greater likelihood of causing Work Related Musculoskeletal Disorder (WRMSDs), depending on the duration, frequency and/or magnitude of exposure to each. The study investigated musculoskeletal disorder among office workers. Thus, it is important that ergonomic risk factors be considered in light of their combined effect in causing or contributing to WRMSDs. Fast technological growth in the use of electronic system; have affected both workers and the work environment. Awkward posture and long hours in front of these visual display terminals can result in work-related musculoskeletal disorders (WRMSD). The study shall contribute to the awareness creation on the causes and consequences of WRMSDs due to lack of ergonomics training. The study was conducted using an observational cross-sectional design. A sample of 109 respondents was drawn from the target population through purposive sampling method. The sources of data were both primary and secondary. Primary data were collected through questionnaires and secondary data were sourced from journals, textbooks, and internet materials. Questionnaires were the main instrument for data collection and were designed in a YES or NO format according to the study objectives. Content validity approval was used to ensure that the variables were adequately covered. The reliability of the instrument was done through test-retest method, yielding a reliability index at 0.84. The data collected from the field were analyzed with a descriptive statistics of chart, percentage and mean. The study found that the most affected body regions were the upper back, followed by the lower back, neck, wrist, shoulder and eyes, while the least affected body parts were the knee calf and the ankle. Furthermore, the prevalence of work-related 'musculoskeletal' malfunctioning was linked with long working hours (6 - 8 hrs.) per day, lack of back support on their seats, glare on the monitor, inadequate regular break, repetitive motion of the upper limbs, and wrist when using the computer. Finally, based on these findings some recommendations were made to reduce the prevalent of WRMSDs among office workers.

Keywords: work related musculoskeletal disorder, Nigeria, office computer users, ergonomic risk factor

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Authors: Yakubu Suleiman, S. A. Musa

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Nigeria is the largest cowpea producer in the world, accounting for about 45%, followed by Brazil with about 17%. Cowpea is grown in Kano, Bauchi, Katsina, Borno in the north, Oyo in the west, and to the lesser extent in Enugu in the east. This study was conducted to determine the input–output relationship of Cowpea production in Kano, Katsina, and Jigawa states of Nigeria. The data were collected with the aid of 1000 structured questionnaires that were randomly distributed to Cowpea farmers in the three states mentioned above of the study area. The data collected were analyzed using regression analysis (Cobb–Douglass production function model). The result of the regression analysis revealed the coefficient of multiple determinations, R2, to be 72.5% and the F ration to be 106.20 and was found to be significant (P < 0.01). The regression coefficient of constant is 0.5382 and is significant (P < 0.01). The regression coefficient with respect to labor and seeds were 0.65554 and 0.4336, respectively, and they are highly significant (P < 0.01). The regression coefficient with respect to fertilizer is 0.26341 which is significant (P < 0.05). This implies that a unit increase of any one of the variable inputs used while holding all other variables inputs constants, will significantly increase the total Cowpea output by their corresponding coefficient. This indicated that farmers in the study area are operating in stage II of the production function. The result revealed that Cowpea farmer in Kano, Jigawa and Katsina States realized a profit of N15,997, N34,016 and N19,788 per hectare respectively. It is hereby recommended that more attention should be given to Cowpea production by government and research institutions.

Keywords: coefficient, constant, inputs, regression

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Authors: T. Northcut, A. Rai, N. Perkins

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Background: The COVID-19 pandemic has had a serious impact on academia in general and social work education in particular, changing permanently the way in which we approach educating students. The new reality of the pandemic coupled with the much-needed focus on racism across the country inspired and required educators to get creative with their teaching styles in order to disrupt the power imbalance in the classroom and attend to the multiple layers of needs of diverse students in precarious sociological and economic circumstances. This paper highlights research examining educators with distinctive positionalities and approaches to classroom instruction who use feminist and antiracist pedagogies while adapting to online teaching during the pandemic. Despite being feminist scholars, whose ideologies developed during different waves of feminism, our commitment to having student-led classrooms, liberation, and equity of all, and striving for social change, unified our feminist teaching pedagogies as well as provided interpersonal support. Methodology: Following a narrative qualitative inquiry methodology, the five authors of this paper came together to discuss our pedagogical styles and underlying values using Zoom in a series of six conversations. Narrative inquiry is an appropriate method to use when researchers are bound by common stories or personal experiences. The use of feminist pedagogy in the classroom before and during the pandemic guided the discussions. After six sessions, we reached the point of data saturation. All data from the dialogic process was recorded and transcribed. We used in vivo, narrative, and descriptive coding for the data analytic process. Results: Analysis of the data revealed several themes, which included (1) the influence of our positionalities as an intersection of race, sexual orientation, gender, and years of teaching experience in the classroom, (2) the meaning and variations between different liberatory pedagogical approaches, (3) the tensions between these approaches and institutional policies and practices, (4) the role of self-reflection in everyday teaching, (5) the distinctions between theory and practice and its utility for students, and (6) the challenges of applying a feminist-centered pedagogical approach during the pandemic while utilizing an online platform. As a collective, we discussed several challenges that limited the use of our feminist pedagogical approaches due to instruction through Zoom.

Keywords: feminist, pedagogy, COVID, zoom

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Authors: Anes Enakoa, Yawei Liang

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Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task.

Keywords: information retrieval, machine learning, natural language processing, question answering, textual entailment

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Authors: Avirup Chowdhury, Angus K. McFadyen, Linsey Batchelor

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Blood cultures (BCs) are an important aspect of management of the septic patient, identifying the underlying pathogen and its antibiotic sensitivities. However, while the current literature outlines indications for initial BCs to be taken, there is little guidance for repeat sampling in the following 5-day period and little information on how antibiotic use can affect the usefulness of this investigation. A retrospective cohort study was conducted using inpatients who had undergone 2 or more BCs within 5 days between April 2016 and April 2017 at a 400-bed hospital in the west of Scotland and received antibiotic therapy between the first and second BCs. The data for BC sampling was collected from the electronic microbiology database, and cross-referenced with data from the hospital electronic prescribing system. Overall, 283 BCs were included in the study, taken from 92 patients (mean 3.08 cultures per patient, range 2-10). All 92 patients had initial BCs, of which 83 were positive (90%). 65 had a further sample within 24 hours of commencement of antibiotics, with 35 positive (54%). 23 had samples within 24-48 hours, with 4 (17%) positive; 12 patients had sampling at 48-72 hours, 12 at 72-96 hours, and 10 at 96-120 hours, with none positive. McNemar’s Exact Test was used to calculate statistical significance for patients who received blood cultures in multiple time blocks (Initial, < 24h, 24-120h, > 120h). For initial vs. < 24h-post BCs (53 patients tested), the proportion of positives fell from 46/53 to 29/53 (one-tailed P=0.002, OR 3.43, 95% CI 1.48-7.96). For initial vs 24-120h (n=42), the proportions were 38/42 and 4/42 respectively (P < 0.001, OR 35.0, 95% CI 4.79-255.48). For initial vs > 120h (n=36), these were 33/36 and 2/36 (P < 0.001,OR ∞). These were also calculated for a positive in initial or < 24h vs. 24-120h (n=42), with proportions of 41/42 and 4/42 (P < 0.001, OR 38.0, 95% CI 5.22-276.78); and for initial or < 24h vs > 120h (n=36), with proportions of 35/36 and 2/36 respectively (P < 0.001, OR ∞). This data appears to show that taking an initial BC followed by a BC within 24 hours of antibiotic commencement would maximise blood culture yield while minimising the risk of false negative results. This could potentially remove the need for as many as 46% of BC samples without adversely affecting patient care. BC yield decreases sharply after 48 hours of antibiotic use, and may not provide any clinically useful information after this time. Further multi-centre studies would validate these findings, and provide a foundation for future health policy generation.

Keywords: antibiotics, blood culture, efficacy, inpatient

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Authors: Gul Rahim Rahimi

Abstract:

The IoT-based soil moisture monitoring system for indoor plants is designed to address the challenges of maintaining optimal moisture levels in soil for plant growth and health. The system utilizes sensor technology to collect real-time data on soil moisture levels, which is then processed and analyzed using machine learning algorithms. This allows for accurate and timely monitoring of soil moisture levels, ensuring plants receive the appropriate amount of water to thrive. The main objectives of the system are twofold: to keep plants fresh and healthy by preventing water deficiency and to provide users with comprehensive insights into the water content of the soil on a daily and hourly basis. By monitoring soil moisture levels, users can identify patterns and trends in water consumption, allowing for more informed decision-making regarding watering schedules and plant care. The scope of the system extends to the agriculture industry, where it can be utilized to minimize the efforts required by farmers to monitor soil moisture levels manually. By automating the process of soil moisture monitoring, farmers can optimize water usage, improve crop yields, and reduce the risk of plant diseases associated with over or under-watering. Key technologies employed in the system include the Capacitive Soil Moisture Sensor V1.2 for accurate soil moisture measurement, the Node MCU ESP8266-12E Board for data transmission and communication, and the Arduino framework for programming and development. Additionally, machine learning algorithms are utilized to analyze the collected data and provide actionable insights. Cloud storage is utilized to store and manage the data collected from multiple sensors, allowing for easy access and retrieval of information. Overall, the IoT-based soil moisture monitoring system offers a scalable and efficient solution for indoor plant care, with potential applications in agriculture and beyond. By harnessing the power of IoT and machine learning, the system empowers users to make informed decisions about plant watering, leading to healthier and more vibrant indoor environments.

Keywords: IoT-based, soil moisture monitoring, indoor plants, water management

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Authors: I. Farid, K. Zouari

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An attempt has been made to examine the effect of water storage structures on groundwater recharge in a semi-arid agroclimatic setting in Jeloula Basin (Central Tunisia). In this area, surface water in rivers is seasonal, and therefore groundwater is the perennial source of water supply for domestic and agricultural purposes. Three pumped storage water power plants (PSWPP) have been built to increase the overall water availability in the basin and support agricultural livelihoods of rural smallholders. The scale and geographical dispersion of these multiple lakes restrict the understanding of these coupled human-water systems and the identification of adequate strategies to support riparian farmers. In the present review, hydrochemistry and isotopic tools were combined to get an insight into the processes controlling mineralization and recharge conditions in the investigated aquifer system. This study showed a slight increase in the groundwater level, especially after the artificial recharge operations and a decline when the water volume moves down during drought periods. Chemical data indicate that the main sources of salinity in the waters are related to water-rock interactions. Data inferred from stable isotopes in groundwater samples indicated recharge with modern rainfall. The investigated surface water samples collected from the PSWPP are affected by a significant evaporation and reveal large seasonal variations, which could be controlled by the water volume changes in the open surface reservoirs and the meteorological conditions during evaporation, condensation, and precipitation. The geochemical information is comparable to the isotopic results and illustrates that the chemical and isotopic signatures of reservoir waters differ clearly from those of groundwaters. These data confirm that the contribution of the artificial recharge operations from the PSWPP is very limited.

Keywords: Jeloula basin, recharge, hydrochemistry, isotopes

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Authors: Hsien-Tsen Wang

Abstract:

Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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Authors: Pallav Singh, Rajesh Yabaji, Rajesh Dhir, Chanakya Hridaya

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Supply Chain Costs for the Indian Industries have been on an exponential trend due to steep inflation on fundamental cost factors – Fuel, Labour, Rents. In this changing context organizations have been focusing on adopting multiple approaches to keep logistics costs under control to protect the profit margins. The lever of ‘Business to Business (B2B) collaboration’ can be used by organizations to garner higher value. Given the context of Indian Logistics Industry the penetration of B2B Collaboration initiatives have been limited. This paper outlines a structured framework for adoption of B2B collaboration through discussion of a successful initiative between ITC’s Leaf Tobacco Business and a leading Indian Media House. Multiple barriers to such a collaborative process exist which need to be addressed through comprehensive structured approaches. This paper outlines a generic framework approach to B2B collaboration for the Indian Logistics Space, outlining the guidelines for arriving at potential opportunities, identification of collaborators, effective tie-up process, design of operations and sustenance factors. The generic methods outlined can be used in any other industry and also builds a foundation for further research on many topics.

Keywords: business to business collaboration, reverse haul logistics, transportation cost optimization, exports logistics

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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This study aimed to address the problem of improving the confidence of key points by fusing multi-view information, thereby estimating human posture more accurately. We first obtained multi-view image information and then used the MvP algorithm to fuse this multi-view information together to obtain a set of high-confidence human key points. We used these as the input for the Spatio-Temporal Graph Convolution (ST-GCN). ST-GCN is a deep learning model used for processing spatio-temporal data, which can effectively capture spatio-temporal relationships in video sequences. By using the MvP algorithm to fuse multi-view information and inputting it into the spatio-temporal graph convolution model, this study provides an effective method to improve the accuracy of human posture estimation and provides strong support for further research and application in related fields.

Keywords: multi-view, pose estimation, ST-GCN, joint fusion

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Authors: Brad Curabba

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Utilising active, process-based learning methods to improve critical thinking and writing skills of second language (L2) writers brings unique challenges. To comprehensively satisfy different learners' needs, when commenting on student work, instructors can embed multiple feedback methods so that the capstone of their abilities as writers can be achieved. This research project assesses faculty and student perceptions regarding the effectiveness of various feedback practices used in process-based writing classrooms with L2 students at the American University of Sharjah (AUS). In addition, the research explores the challenges encountered by faculty during the provision of feedback practices. The quantitative research findings are based on two concurrent electronically distributed anonymous surveys; one aimed at students who have just completed a process-based writing course, and the other at instructors who delivered these courses. The student sample is drawn from multiple sections of Academic Writing I and II, and the faculty survey was distributed among the Department of Writing Studies (DWS) faculty. Our findings strongly suggest that all methods of feedback are deemed equally important by both students and faculty. Students, in particular, find process writing and its feedback practices to have greatly contributed to their writing proficiency.

Keywords: process writing, feedback, formative feedback, composition, reflection

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Authors: John Q. Todd

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Given that modern equipment can provide comprehensive health, status, and error condition data via built-in sensors, maintenance organizations have a new and valuable source of insight to take advantage of. This presentation will expose what these data payloads might look like and how they can be filtered, visualized, calculated into metrics, used for machine learning, and generate alerts for further action.

Keywords: condition based maintenance, equipment data, metrics, alerts

Procedia PDF Downloads 188

Authors: Phatthanan Chaiyabut

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This research paper investigates whether the development of environmentally friendly practices by luxury hotel resorts can be used as a strategy for gaining competitive advantage through differentiation, and suggests ways to do it. The focus is on luxury hotel resorts in Samut Songkram Province, Thailand. A questionnaire was utilized as a tool to collect data. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. Findings indicate that environmentally friendly development of hotel resorts in Samut Songkram Province has a very limited use as a corporate strategy. Only two luxury hotel resorts had it incorporated in their strategy, it is not much used in marketing indicating environmental issues are not seen as important. This was confirmed through the interviews with the managers that it is not seen as important issue to promote.

Keywords: competitive advantage, competitive strategy, Samut Songkram Province, hotel and resort

Procedia PDF Downloads 277

Authors: Mesfin Abebe Molla

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This research aims to examine the practices of inclusion of the hard of hearing and deaf students in regular schools. It also focuses on exploring strategies for optimal benefits of students with Hard of Hearing and Deaf (HH-D) from inclusion. Concurrent mixed methods research design was used to collect quantitative and qualitative data. The instruments used to gather data for this study were questionnaire, semi- structured interview, and observations. A total of 102 HH-D students and 42 primary and High School teachers were selected using simple random sampling technique and used as participants to collect quantitative data. Non-probability sampling technique was also employed to select 14 participants (4-school principals, 6-teachers and 4-parents of HH-D students) and they were interviewed to collect qualitative data. Descriptive and inferential statistical techniques (independent sample t-test, one way ANOVA and Multiple regressions) were employed to analyze quantitative data. Qualitative data were also analyzed qualitatively by theme analysis. The findings reported that there were individual principals’, teachers’ and parents’ strong commitment and efforts for practicing inclusion of HH-D students effectively; however, most of the core values of inclusion were missing in both schools. Most of the teachers (78.6 %) and HH-D students (75.5%) had negative attitude and considerable reservations about the feasibility of inclusion of HH-D students in both schools. Furthermore, there was a statistically significant difference of attitude toward to inclusion between the two school’s teachers and the teachers’ who had taken and had not taken additional training on IE and sign language. The study also indicated that there was a statistically significant difference of attitude toward to inclusion between hard of hearing and deaf students. However, the overall contribution of the demographic variables of teachers and HH-D students on their attitude toward inclusion is not statistically significant. The finding also showed that HH-D students did not have access to modified curriculum which would maximize their abilities and help them to learn together with their hearing peers. In addition, there is no clear and adequate direction for the medium of instruction. Poor school organization and management, lack of commitment, financial resources, collaboration and teachers’ inadequate training on Inclusive Education (IE) and sign language, large class size, inappropriate assessment procedure, lack of trained deaf adult personnel who can serve as role model for HH-D students and lack of parents and community members’ involvement were some of the major factors that affect the practicing inclusion of students HH-D. Finally, recommendations are made to improve the practices of inclusion of HH-D students and to make inclusion of HH-D students an integrated part of Ethiopian education based on the findings of the study.

Keywords: deaf, hard of hearing, inclusion, regular schools

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Authors: Khalid Soussi

Abstract:

The study at hand has attempted to describe the acquisition of three EFL lexical knowledge aspects - meaning, synonymy and collocation – across three academic levels: Baccalaureate, second year and fourth year university levels in Morocco. The research also compares the development of the three lexical knowledge aspects between knowledge (reception) and use (production) and attempts to trace their order of acquisition. This has led to the use of three main data collection tasks: translation, acceptability judgment and multiple choices. The study has revealed the following findings. First, L1 and EFL mental lexicons are connected at the lexical knowledge depth. Second, such connection is active whether in language reception or use. Third, the connectivity between L1 and EFL mental lexicons tends to relatively decrease as the academic level of the learners increases. Finally, the research has revealed a significant 'order' of acquisition between the three lexical aspects, though not a very strong one.

Keywords: vocabulary acquisition, EFL lexical knowledge, mental lexicon, vocabulary knowledge depth

Procedia PDF Downloads 283