Search results for: classification of factors

Authors: Jimoh Olumide Yusuf

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Various factors have continued to bedevil the effective teaching and learning of English Language in Nigeria and prominent among these factors are learners’ characteristics. Unfortunately, these particular factors seem to have recorded paucity of research efforts by scholars and the problem of lack of proficiency in the target language continues to linger. This study therefore investigates the relationship between specific learners’ characteristics and effective teaching of English as a Second Language (ESL) in senior secondary schools in Nigeria. To this end, Self-Determination, and Integrative Motivation Theories were applied to investigate motivation, language learning, learners’ characteristics and its relationship to language proficiency. A survey of 500 students and 100 English Language teachers across 20 schools was conducted. Descriptive statistics was used to analyze the data and findings revealed that; specific learners’ characteristics such as learners’ age, learning style and motivation significantly determine the performance of students in English Language. Specifically, students with appropriate school age, visual learning style and intrinsic motivation, demonstrated English Language proficiency; as they performed better than students with extrinsic motivation, audio and kinaesthetic learning styles. Moreover, teachers related factors such as teaching experience; teaching strategies and teachers’ extrinsic motivation also emerged as essential correlates of effective language teaching. The findings conclude that learning characteristics are significant factors that should be considered by the teachers and education planners for adequate, sequential and effective implementation of the ESL curriculum in Nigeria.

Keywords: senior secondary school, English as a second language, intrinsic motivation, Kinaesthetic learning style

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Authors: Michael Tilahun Mulugeta

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Pedestrians are the most vulnerable road users as they are more exposed to the risk of collusion. Pedestrian safety at road intersections still remains the most vital and yet unsolved issue in Addis Ababa, Ethiopia. One of the critical points in pedestrian safety is the occurrence of conflict between turning vehicle and pedestrians at un-signalized intersection. However, a better understanding of the factors that affect the likelihood of the conflicts would help provide direction for countermeasures aimed at reducing the number of crashes. This paper has sorted to explore a model to describe the relation between traffic conflicts and influencing factors using Multiple Linear regression methodology. In this research the main focus is to study the interaction of turning (left & right) vehicle with pedestrian at unsignalized intersections. The specific objectives also to determine factors that affect the number of potential conflicts and develop a model of potential conflict.

Keywords: potential, regression analysis, pedestrian, conflicts

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Authors: Isaac Kofi Mensah, Jianing Mi, Feng Cheng

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This research paper seeks to investigate the factors determining the continuance usage of online mobile payment applications among WECHAT users in China. Technology Acceptance Model (TAM) and the Diffusion of Innovation (DOI) theory would both be applied as the theoretical foundation for this study. A developed instrument would be administered to the targeted sample of 1000 WECHAT Users in the City of Harbin, China, through an online questionnaire administration platform. Factors such as perceived usefulness, perceived ease of use, perceived service quality, social influence, trust in the internet, internet self-efficacy, relative advantage, compatibility, and complexity would be explored to determine its significant impact on the continuance intention to use mobile payment apps. This study is at the development and implementation stage. The successful completion of this research article would not only provide an insightful understanding of the factors influencing the decision of WECHAT users in China to use mobile payment applications but also enrich the e-commerce adoption literature.

Keywords: diffusion of innovation (DOI), e-commerce, mobile payment, technology acceptance model (TAM), WECHAT

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Authors: Musthaya Patchanee

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The aim of this research is to study potential factors relating to the decision of return migration of Myanmar migrant workers in Prachuap Khiri Khan Province by conducting a random sampling of 400 people aged between 15-59 who migrated from Myanmar. The information collected through interviews was analyzed to find a percentage and mean using the Stepwise Multiple Regression Analysis. The results have shown that 33.25% of Myanmar migrant workers want to return to their home country within the next 1-5 years, 46.25%, in 6-10 years and the rest, in over 10 years. The factors relating to such decision can be concluded that the scale of the decision of return migration has a positive relationship with a statistical significance at 0.05 with a conformity with friends and relatives (r=0.886), a relationship with family and community (r=0.782), possession of land in hometown (r=0.756) and educational level (r=0.699). However, the factor of property possession in Prachuap Khiri Khan is the only factor with a high negative relationship (r=0.-537). From the Stepwise Multiple Regression Analysis, the results have shown that the conformity with friends and relatives and educational level factors are influential to the decision of return migration of Myanmar migrant workers in Prachuap Khiri Khan Province, which can predict the decision at 86.60% and the multiple regression equation from the analysis is Y= 6.744+1.198 conformity + 0.647 education.

Keywords: decision of return migration, factors of return migration, Myanmar migrant workers, Prachuap Khiri Khan Province

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Authors: Jiang Yuqi

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Aims: The aim is to analyze the facilitators and barriers of family resilience in cancer patients based on the theoretical domain framework, provide a basis for intervention in the family resilience of cancer patients, and identify the progress and enlightenment of existing intervention projects. Methods: NVivo software was used to code the influencing factors using the framework of 14 theoretical domains as primary nodes; secondary nodes were then refined using thematic analysis, and specific influencing factors were aggregated and analyzed for evaluator reliability. Data sources: PubMed, Embase, CINAHL, Web of Science, Cochrane Library, MEDLINE, CNKI, and Wanfang (search dates: from construction to November 2023). Results: A total of 35 papers were included, with 142 coding points across 14 theoretical domains and 38 secondary nodes. The three most relevant theoretical domains are social influences (norms), the environment and resources, and emotions (mood). The factors with the greatest impact were family support, mood, confidence and beliefs, external support, quality of life, economic circumstances, family adaptation, coping styles with illness, and management. Conclusion: The factors influencing family resilience in cancer patients cover most of the theoretical domains in the Theoretical Domains Framework and are cross-cutting, multi-sourced, and complex. Further in-depth exploration of the key factors influencing family resilience is necessary to provide a basis for intervention research.

Keywords: cancer, survivors, family resilience, theoretical domains framework, literature review

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Authors: Rosa Hendijani, Mohammadhesam Hajighasemi

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As of late, the sharing economy has become an important type of business model. Online taxi services are one example that has grown rapidly around the world. This study examines the factors influencing the use of online taxis as one form of IT-enabled sharing services based on the theory of planned behavior (TPB). Based on the theory of planned behavior, these factors can be divided into three categories, including the ones related to attitude (e.g., image and perceived usefulness), normative believes (e.g., subjective norms), and behavioral control (e.g., technology facilitating conditions and self-efficacy). Three other factors were also considered based on the literature, including perceived economic benefits, openness towards using shared services, and perceived availability. The effect of all these variables was tested both directly and indirectly through intention as the mediating variable. A survey method was used to test the research hypotheses. In total, 361 individuals partook in the study. The results of a multiple regression analysis on behavior showed that perceived economic benefits, compatibility, and subjective norms were important factors influencing behavior among online taxi users. In addition, intention partially mediated the effect of perceived economic benefits and compatibility on behavior. It can be concluded that perceived economic benefits, compatibility, and subjective norms are the three main factors that influence behavior among online taxi users.

Keywords: collaborative consumption, IT-enabled sharing services model, online taxi, sharing economy, theory of planned behavior

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Authors: Salwa Hagag Abdelaziz, Naglaa Fathy Youssef, Nadia Abdellatif Hassan, Rasha Wesam Abdelrahman

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Breast cancer is considered as a substantial health concern and practicing mammography screening [MS] is important in minimizing its related morbidity. So it is essential to have a better understanding of breast cancer screening behaviors of women and factors that influence utilization of them. The aim of this study is to identify the factors that are linked to MS behaviors among the Egyptian women. A cross-sectional descriptive design was carried out to provide a snapshot of the factors that are linked to MS behaviors. A convenience sample of 311 women was utilized and all eligible participants admitted to the Women Imaging Unit who are 40 years of age or above, coming for mammography assessment, not pregnant or breast feeding and who accepted to participate in the study were included. A structured questionnaire was developed by the researchers and contains three parts; Socio-demographic data; Motivating factors associated with MS; and association between MS and model of behavior change. The analyzed data indicated that most of the participated women (66.6 %) belonged to the age group of 40-49.A high proportion of participants (58.1%) of group having previous MS influenced by their neighbors to practice MS, whereas 32.7 % in group not having previous MS were influenced by family members which indicated significant differences (P <0.05). Doctors and media are shown to be the least influence of others to practice MS. Women with intention to have a future mammogram had higher OR (1.404) for practicing MS compared with women with no intention. Further studies are needed to examine the relation between Trans-theoretical Model [TTM] and practicing MS.

Keywords: breast cancer, mammography, screening behaviors, morbidity

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Authors: C. Brandt, R. Christopher, N. Damons

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Background: Soccer is one of the most common sports in the world. It is associated with a significant chance of injury either during training or during the course of an actual match. Studies on the epidemiology of soccer injuries have been widely conducted, but methodological appraisal is lacking to make evidence-based decisions. Objectives: The purpose of this study was to conduct a systematic review of common injuries in soccer and their risk factors. Methods: A systematic review was performed based on the Joanna Briggs Institute procedure for conducting systematic reviews. Databases such as SPORT Discus, Cinahl, Medline, Science Direct, PubMed, and grey literature were searched. The quality of selected studies was rated, and data extracted and tabulated. Plot data analysis was done, and incidence rates and odds ratios were calculated, with their respective 95% confidence intervals. I² statistic was used to determine the proportion of variation across studies. Results: The search yielded 62 studies, of which 21 were screened for inclusion. A total of 16 studies were included for the analysis, ten for qualitative and six for quantitative analysis. The included studies had, on average, a low risk of bias and good methodological quality. The heterogeneity amongst the pooled studies was, however, statistically significant (χ²-p value < 0.001). The pooled results indicated a high incidence of soccer injuries at an incidence rate of 6.83 per 1000 hours of play. The pooled results also showed significant evidence of risk factors and the likelihood of injury occurrence in relation to these risk factors (OR=1.12 95% CI 1.07; 1.17). Conclusion: Although multiple studies are available on the epidemiology of soccer injuries and risk factors, only a limited number of studies were of sound methodology to be included in a review. There was also significant heterogeneity amongst the studies. The incidence rate of common soccer injuries was found to be 6.83 per 1000 hours of play. This incidence rate is lower than the values reported by the majority of previous studies on the occurrence of common soccer injuries. The types of common soccer injuries found by this review support the soccer injuries pattern reported in existing literature as muscle strain and ligament sprain of varying severity, especially in the lower limbs. The risk factors that emerged from this systematic review are predominantly intrinsic risk factors. The risk factors increase the risk of traumatic and overuse injuries of the lower extremities such as hamstrings and groin strains, knee and ankle sprains, and contusion.

Keywords: incidence, prevalence, risk factors, soccer injuries

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Authors: Wenqi Liu, Reginald Bailey

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This study explores an advanced approach to enhancing B2B sales forecasting by integrating machine learning models with a rule-based decision framework. The methodology begins with the development of a machine learning classification model to predict conversion likelihood, aiming to improve accuracy over traditional methods like logistic regression. The classification model's effectiveness is measured using metrics such as accuracy, precision, recall, and F1 score, alongside a feature importance analysis to identify key predictors. Following this, a machine learning regression model is used to forecast sales value, with the objective of reducing mean absolute error (MAE) compared to linear regression techniques. The regression model's performance is assessed using MAE, root mean square error (RMSE), and R-squared metrics, emphasizing feature contribution to the prediction. To bridge the gap between predictive analytics and decision-making, a rule-based decision model is introduced that prioritizes customers based on predefined thresholds for conversion probability and predicted sales value. This approach significantly enhances customer prioritization and improves overall sales performance by increasing conversion rates and optimizing revenue generation. The findings suggest that this combined framework offers a practical, data-driven solution for sales teams, facilitating more strategic decision-making in B2B environments.

Keywords: sales forecasting, machine learning, rule-based decision model, customer prioritization, predictive analytics

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Authors: Marta Maria Aguilar Carceles, Ginesa Torrente Hernandez

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The goal of the current study is focused on giving an interdisciplinary comprehension of an increased phenomenon in recent years: violence against parents. Violence can take different forms depending on the context and the vulnerability of the victims, but in this kind of situations, the relationship between parents and young people can become abusive and uncontrollable. Taking a sample from the Spanish Criminal Courts, this study explores those psychological and sociological factors that can contribute to the appearance and continuity of this kind of behaviors in minors. It is considered factors like the type of offence, presence or absence of psychopathology in the subjects, family aspects, or sociodemographic factors, getting a criminal profile of the minor and evaluating which measures are more efficient or adequate in each particular case. Finally, it will be discussed on how getting effective interventions and restorative responses to address teen violence against their parents within the Spanish Legal System.

Keywords: criminality, legal system, parents, tyrant sons, violence

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Authors: Faucal Marie Providence Idufashe, Rafał Katamay

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Background: Cases of foreign students who do not return to their home countries after their graduation have been reported. Over the past years, More and more young Rwandans choose to study in Poland, appreciating the high level of education in Polish universities. However, the majority of them tend to stay there after their studies or move to other nearby countries. Therefore, this study aims at identifying factors contributing to a career choice abroad among Rwandan students in Poland. Methods: This was a cross-sectional, observational, survey-based study and targeted the Rwandan community living in Poland. All the analyses were done in SPSS. A total of 219 respondents completed the online survey within two months from July to September 2022. Results: The prevalence of migration intention among Rwandan student in Poland was estimated at 79.91%. Only religion was statistically significant, whereas other social demographic factors such as age, residence, education, and marital status did not contribute to the decision of a career choice in Poland among respondents, Rwandans in Poland. Furthermore, perceived connection to co-workers, employment company's culture and respect were the significant socio-economic factors contributed to the decision of a career choice in Poland among those studied. The level of income did not contribute. Conclusion: A high proportion expressed migration intention in our study. These intentions were attracted by opportunities in Poland in addition to the welcoming culture. Going forward, we recommend exploring those factors using in-depth interviews for more insights.

Keywords: career, choice, abroad, Poland, students, Rwandan

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Authors: Lloyd A. Taylor

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An increase in the prevalence of Attention Deficit-Hyperactivity Disorder (ADHD) highlights the need to consider factors that may be exacerbating symptom presentation. Traditional diagnostic criteria provide a little framework for healthcare providers to consider as they attempt to diagnose and treat children with behavioral problems. In fact, aside from exclusion criteria, limited alternative considerations are available, and approaches fail to consider the impact of outside factors that could increase or decrease the likelihood of appropriate diagnosis and success of interventions. This paper will consider specific systems-based factors that influence behavior and intervention successes that, when not considered, could account for the upsurge of diagnoses. These include understanding (1) challenges in the healthcare system, (2) the influence and impact of educators and the educational system, (3) technology use, and (4) patient and parental attitudes about the diagnosis of ADHD. These factors must be considered both individually and as a whole when considering both the increase in diagnoses and the subsequent increases in prescriptions for psychostimulant medication. A theoretical model based on a risk management approach will be presented. Finally, data will be presented that demonstrates pediatric provider satisfaction with this approach to diagnoses and treatment of ADHD as it relates to practice trends.

Keywords: ADHD, diagnostic criteria, risk management model, pediatricians

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Authors: Amirhossein Karamoozian, Desheng Wu, Behzad Abbasnejad

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Building information modelling (BIM) is a new technology to enhance the efficiency of project management in the construction industry. In addition to the potential benefits of this useful technology, there are various risks and obstacles to applying it in construction projects. In this study, a decision making approach is presented for risk assessment in BIM adoption in construction projects. Various risk factors of exerting BIM during different phases of the project lifecycle are identified with the help of Delphi method, experts’ opinions and related literature. Afterward, Shannon’s entropy and Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) are applied to derive priorities of the identified risk factors. Results indicated that lack of knowledge between professional engineers about workflows in BIM and conflict of opinions between different stakeholders are the risk factors with the highest priority.

Keywords: risk, BIM, fuzzy TOPSIS, construction projects

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Authors: Emad A. M. Osman, Ahmed M. Abu-Bakr

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Compacted granular fill under shallow foundation is one of the oldest, cheapest, and easiest techniques to improve the soil characteristics to increase the bearing capacity and decrease settlement under footing. There are three main factors affecting the design of soil replacement to gain these advantages. These factors are the type of replaced soil, characteristics, and thickness. The first two factors can be easily determined by laboratory and field control. This paper emphasizes on how to determine the thickness accurately for footing under centric axial load by limit state design method. The advantages of the method are the way of determining the thickness (independent of experience) and it takes into account the replaced and original or underneath soil characteristics and reaches the goals of replaced soils economically.

Keywords: design of soil replacement, LSD method, soil replacement, soil improvement

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Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda

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This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.

Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure

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Authors: Mahdi Fakoor, Seyed Mohammad Navid Ghoreishi

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Initiation and propagation of cracks may cause catastrophic failures in rotating disks, and hence determination of fracture parameter in rotating disks under the different working condition is very important issue. In this paper, a comprehensive study of stress intensity factors in rotating disks containing 3D semi-elliptical cracks under the different working condition is investigated. In this regard, after verification of modeling and analytical procedure, the effects of mechanical properties, rotational velocity, and orientation of cracks on Stress Intensity Factors (SIF) in rotating disks under centrifugal loading are investigated. Also, the effects of using composite patch in reduction of SIF in rotating disks are studied. By that way, the effects of patching design variables like mechanical properties, thickness, and ply angle are investigated individually.

Keywords: stress intensity factor, semi-elliptical crack, rotating disk, finite element analysis (FEA)

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Authors: Kouorsh Zarea, Fatemeh Hassani, Samira Beiranvand, Akram Mohamadi

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Background and Objectives: Medication error in hospitals is a major cause of the errors which disrupt the health care system. The aim of this study was to assess the nurses’ medication errors and related factors. Material and methods: This was a descriptive study on 225 nurses in various hospitals, selected through multistage random sampling. Data was collected by three researcher made tools; demographic, medication error and related factors questionnaires. Data was analyzed by descriptive statistics, Chi-square, Kruskal-Wallis, One-way analysis of variance. Results: Based on the results obtained, the type of medication errors giving drugs to patients later or earlier (55.6%), multiple oral medication together regardless of their interactions (36%) and the postoperative analgesic without a prescription (34.2%), respectively. In addition, factors such as the shortage of nurses to patients’ ratio (57.3%), high load functions (51.1%) and fatigue caused by the extra work (40.4%), were the most important factors affecting the incidence of medication errors. The fear of legal issues (40%) are the most important factor is the lack of reported medication errors. Conclusions: Based on the results, effective management and promotion motivate nurses. Therefore, increasing scientific and clinical expertise in the field of nursing medication orders is recommended to prevent medication errors in various states of nursing intervention. Employing experienced staff in areas with high risk of medication errors and also supervising less-experienced staff through competent personnel are also suggested.

Keywords: medication error, nurse, clinical care, drug errors

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Authors: Tianyou Wang

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Students' own intrinsic motivation is the main source of energy for learning activities, and their self-efficacy becomes a key factor affecting the learning effect. In today's increasingly common situation of online vocal music teaching, virtualized teaching scenarios have brought a considerable impact on students' personal efficacy. Since personal efficacy is the result of the interaction between environmental factors and subject characteristics, an empirical study was conducted to investigate the changes in students' self-efficacy, influencing factors, and characteristics in online vocal teaching scenarios based on the three dimensions of teachers, students, and technology. One hundred valid questionnaires were studied through a quantitative survey. The results showed that students' personal efficacy was significantly lower in online learning environments compared to offline vocal teaching and showed significant differences due to factors such as gender and class type; students' self-efficacy in online vocal teaching was significantly affected by factors such as technological environment, teaching style, and information technology ability. Based on the results of the study, it is recommended to pay attention to inquiry and practice in the teaching design, use singing projects as the teaching organization, grasp the learning process with the orientation of problem-solving, push the applicable vocal music teaching resources in time, lead students to explore and refine the problems and push students to learn independently according to the goals and plans.

Keywords: vocal pedagogy, self-efficacy, online learning, intrinsic motivation, information technology

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Authors: Luljeta Gashi, Naser Ramadani, Zana Deva, Dafina Gexha-Bunjaku

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The official HIV/AIDS data in Kosovo are based on HIV case reporting from health-care services, the blood transfusion system and Voluntary Counselling and Testing centres. Between 1986 and 2014, are reported 95 HIV and AIDS cases, of which 49 were AIDS, 46 HIV and 40 deaths. The majority (69%) of cases were men, age group 25 to 34 (37%) and route of transmission is: heterosexual (90%), MSM (7%), vertical transmission (2%) and IDU (1%). Based on existing data and the UNAIDS classification system, Kosovo is currently still categorised as having a low-level HIV epidemic. Even though with a low HIV prevalence, Kosovo faces a number of threatening factors, including increased number of drug users, a stigmatized and discriminated MSM community, high percentage of youth among general population (57% of the population under the age of 25), with changing social norms and especially the sexual ones. Methods: Data collection was done using self administered structured questionnaires amongst 249 high school students. Data were analysed using the Statistical Package for Social Sciences (SPSS). Results: The findings revealed that 68% of students know that HIV transmission can be reduced by having sex with only one uninfected partner who has no other partners, 94% know that the risk of getting HIV can be reduced by using a condom every time they have sex, 68% know that a person cannot get HIV from mosquito bites, 81% know that they cannot get HIV by sharing food with someone who is infected and 46% know that a healthy looking person can have HIV. Conclusions: Seventy one percent of high school students correctly identify ways of preventing the sexual transmission of HIV and who reject the major misconceptions about HIV transmission. The findings of the study indicate a need for more health education and promotion.

Keywords: Kosovo, KPAR, HIV, high school

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Authors: Puti F. Marzuki, Adrianto Oktavianus, Almerinda Regina

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Interface problems among interacting parties in Indonesian construction projects have most often led to low productivity and completion delay. In the midst of this country’s needs to accelerate construction of public infrastructure providing connectivity among regions and supporting economic growth as well as better living quality, project delays have to be seriously addressed. This paper identifies potential causes factors of interface problems experienced by construction projects in Indonesia. Data are collected through a survey involving the main actors of six important public infrastructure construction projects including railway, LRT, sports stadiums, apartment, and education building construction projects. Five of these projects adopt the design-build project delivery method and one applies the design-bid-build scheme. Interface problems’ potential causes are categorized into contract, management, technical experience, coordination, financial, and environmental factors. Research results reveal that, especially in railway and LRT projects, potential causes of interface problems are mainly technical and managerial in nature. These relate to complex construction execution in highly congested areas. Meanwhile, coordination cause factors are mainly found in the education building construction project with loan from a foreign donor. All of the six projects have to resolve interface problems caused by incomplete or low-quality contract documents. This research also shows that the design-bid-build delivery method involving more parties in construction projects tends to induce more interface problem cause factors than the design-build scheme.

Keywords: cause factors, construction delays, project delivery method, contract documents

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Elucir Gir, Myllena Nilce de Freitas Surmano, Laelson Rochelle Milanês Sousa, Mayra Gonçalves Menegueti, Ana Cristina de Oliveira E Silva, Renata Karina Reis

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Objective: To analyze the factors associated with the worsening of the quality of life of health professionals in the Southeast region of Brazil during the COVID-19 pandemic and its associated factors. Method: Analytical cross-sectional study carried out with health professionals from the southeastern region of Brazil. Data collection took place through an online survey with a form stored on the Survey Monkey platform. Bivariate analysis was used, and the chi-square test was adopted, followed by the multiple binary logistic regression model based on the stepwise method. Results: 3,493 health professionals participated in the study. Factors associated with worsening quality of life were: Professional Category (Nursing assistant) [OR 1.851 (95%CI 1.035-3.311) p= 0.038]; types of people who provided care (people in general) [OR 1.445 (95%CI 1.072-1.945) p=0.015]; Supply of good quality PPE by the institution where he works (no) [OR 1.595 (CI 95% 1.144-2.223) p= 0.006] and Supply of good quality PPE by the institution where he works (in part) [OR 1.563 (CI 95% 1.257-1.943) p < 0.001]. Conclusion: The factors associated with the worsening of the quality of life of health professionals during the COVID-19 pandemic were: Professional Category (Nursing assistant); types of people who provided assistance (people in general); Supply of sufficient PPE by the institution where you work (no) and Supply of good quality PPE by the institution where you work (in part). Future studies should investigate to what extent QoL can be improved based on modifiable factors.

Keywords: COVID-19, quality of life, health professionals, respiratory infections

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Authors: Mohammad Qasim Mohammadi, Mohammad Arif Rohman

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Although socially sustainable building is becoming increasingly popular worldwide, past studies indicate that when policymakers support sustainable building development, the social dimension is often given insufficient attention or entirely disregarded. There are not many studies that focus on the problems of socially sustainable buildings in Afghanistan. This research investigates the factors that may hinder social sustainability implementation in residential building construction. The study will gather data from construction professionals by purposive sampling and employ Exploratory Factor Analysis (EFA) and Varimax for analysis. The results will undergo rigorous examination and thorough discussion. The expected results in this research will analyze the underlying barrier structure (factors) that hinder social sustainability, and each of these factors will represent a set of observed variables. In addition, the factor loadings show which barriers pose the greatest challenges. The primary goal of this study is to provide valuable insights into the impediment factors of social sustainability within the residential building environment, aiming to inform decision-making in the industry and encourage the adoption of more socially sustainable construction practices.

Keywords: social sustainability, residential building, barriers, drivers, afghanistan, factor analysis

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Authors: Rahul Thakur, Onkar Chandel

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Red Hat is the world leader in providing open source software and solutions. A global enterprise, like Red Hat, has unique issues of connecting employees with content because of distributed offices, multiple teams spread across geographies, multiple languages, and different cultures. Employees, of a global company, create content that is distributed across departments, teams, regions, and countries. This makes finding the best content difficult since owners keep iterating on the existing content. When employees are unable to find the content, they end up creating it once again and in the process duplicating existing material and effort. Also, employees may not find the relevant content and spend time reviewing obsolete duplicate, or irrelevant content. On an average, a person spends 15 minutes/day in failed searches that might result in missed business opportunities, employee frustration, and substandard deliverables. Red Hat Knowledge Management Office (KMO) applied 'open source strategy' to solve the above problems. Under the Open Source Strategy, decisions are taken collectively. The strategy aims at accomplishing common goals with the help of communities. The objectives of this initiative were to save employees' time, get them authentic content, improve their content search experience, avoid duplicate content creation, provide context based search, improve analytics, improve content management workflows, automate content classification, and automate content upload. This session will describe open source strategy, its applicability in content management, challenges, recommended solutions, and outcome.

Keywords: content classification, content management, knowledge management, open source

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Authors: A. Swetha

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This study aims to prioritize and explore critical risks in construction project assessment, employing the Weighted Average Index method and Principal Component Analysis (PCA). Through extensive literature review and expert interviews, project assessment risk factors were identified across Budget and Cost Management Risk, Schedule and Time Management Risk, Scope and Planning Risk, Safety and Regulatory Compliance Risk, Resource Management Risk, Communication and Stakeholder Management Risk, and Environmental and Sustainability Risk domains. A questionnaire was distributed to stakeholders involved in construction activities in Hyderabad, India, with 180 completed responses analyzed using the Weighted Average Index method to prioritize risk factors. Subsequently, PCA was used to understand relationships between these factors and uncover underlying patterns. Results highlighted dependencies on critical resources, inadequate risk assessment, cash flow constraints, and safety concerns as top priorities, while factors like currency exchange rate fluctuations and delayed information dissemination ranked lower but remained significant. These insights offer valuable guidance for stakeholders to mitigate risks effectively and enhance project outcomes. By adopting systematic risk assessment and management approaches, construction projects in Hyderabad and beyond can navigate challenges more efficiently, ensuring long-term viability and resilience.

Keywords: construction project assessment risk factor, risk prioritization, weighted average index, principal component analysis, project risk factors

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

Abstract:

Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Yananda Siraphatthada

Abstract:

The objectives of this research were to study the level of influencing factors, leadership, supply chain management, innovation, competitive advantages, business success, and affecting factors to the business success of the mobile phone system service providers in Bangkok Metropolitan. This research was done by the quantitative approach and the qualitative approach. The quantitative approach was used for questionnaires to collect data from the 331 mobile service shop managers franchised by AIS, Dtac and TrueMove. The mobile phone system service providers/shop managers were randomly stratified and proportionally allocated into subgroups exclusive to the number of the providers in each network. In terms of qualitative method, there were in-depth interviews of 6 mobile service providers/managers of Telewiz and Dtac and TrueMove shop to find the agreement or disagreement with the content analysis method. Descriptive Statistics, including Frequency, Percentage, Means and Standard Deviation were employed; also, the Structural Equation Model (SEM) was used as a tool for data analysis. The content analysis method was applied to identify key patterns emerging from the interview responses. The two data sets were brought together for comparing and contrasting to make the findings, providing triangulation to enrich result interpretation. It revealed that the level of the influencing factors – leadership, innovation management, supply chain management, and business competitiveness had an impact at a great level, but that the level of factors, innovation and the business, financial success and nonbusiness financial success of the mobile phone system service providers in Bangkok Metropolitan, is at the highest level. Moreover, the business influencing factors, competitive advantages in the business of mobile system service providers which were leadership, supply chain management, innovation management, business advantages, and business success, had statistical significance at .01 which corresponded to the data from the interviews.

Keywords: mobile service providers, facilitating factors, Bangkok Metropolitan, business success

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Authors: Elham Dehghan, A. Shirzadi Javid, Mohsen Tadayon

Abstract:

Labor productivity is very important and gained special concerns among professionals in the construction industry, worldwide. Productivity improvements on labors achieve higher cost savings with minimal investment. Due to the fact that profit margins are small on construction projects, cost savings associated with productivity are crucial to become a successful contractor. This research program studies and highlights the factors affecting labor productivity in Iranian construction industry. A questionnaire was used to gather the relevant data from respondents who involve in managing various types of projects in wide areas in Iran. It involved ranking 57 predefined factors divided into 5 categories: Human/Labor; Financial; Management; Equipments/Materials and Environmental. Total 62 feedbacks were analyzed through the Relative Importance Index (RII) technique. The top ten factors affecting construction labor productivity in Iran are: 1) Professional capability of contractor project manager, 2) skills of contractor’s project management team, 3) professional capability of owner project manager, 4) professional capability of Consulting Project manager, 5) discipline working, 6) delay payments by the owner, 7) material shortages, 8) delays in delivery of materials, 9) turnover power of the owner, 10) poor site management. Recommendations have been made in the study to address these factors. The research has direct benefits to key stakeholders in Iranian construction industry.

Keywords: Iranian construction projects, labor, productivity, relative importance index

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Authors: Elvira Mustikawati P.H., Iis Dewi Ratih, Dita Amelia

Abstract:

Bojonegoro has proclaimed a policy of zero malnutrition. Therefore, as an effort to solve the cases of malnutrition children in Bojonegoro, this study used the approach geographically Mixed Weighted Regression (MGWR) to determine the factors that influence the percentage of malnourished children under five in which factors can be divided into locally influential factor in each district and global factors that influence throughout the district. Based on the test of goodness of fit models, R2 and AIC values in GWR models are better than MGWR models. R2 and AIC values in MGWR models are 84.37% and 14.28, while the GWR models respectively are 91.04% and -62.04. Based on the analysis with GWR models, District Sekar, Bubulan, Gondang, and Dander is a district with three predictor variables (percentage of vitamin A, the percentage of births assisted health personnel, and the percentage of clean water) that significantly influence the percentage of malnourished children under five.

Keywords: GWR, MGWR, R2, AIC

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Authors: C. Noknoi, W. Boripunt, K. Boomid, S. Suwitphanwong

Abstract:

This research studies the factors influencing the wages of native workers in Thailand's construction industry. The sample used comprised some 156 native construction workers from Songkhla Province, Thailand. The utilized research instrument was a questionnaire, with the data being analyzed according to frequency, percentage, and regression analysis. The results revealed that in general, native Thai construction workers are generally married males aged between 26 and 37 years old. They typically have four to six years of education, are employed as laborers with an average salary of 4,000–9,200 baht per month, and have fewer than five years of work experience. Most Thai workers work five days a week. Each establishment typically has 10–30 employees, with fewer than 10 of these being migrant workers in general. Most Thai workers are at a 20% to 40% risk from work, and they have never changed employer. The average wage of Thai workers was found to be 10,843.03 baht per month with a standard deviation of 4,898.31 baht per month. Hypothesis testing revealed that position, work experience, and the number of times they had switched employer were the factors most affecting the wages of native Thai construction workers. These three factors alone explain the salaries of Thai construction workers at 51.9%.  

Keywords: construction industry, native workers, Thailand, wages

Procedia PDF Downloads 230