Search results for: categorical data

Authors: Sami Ayad, Mengshan Lee

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The Sustainable Development Goals (SDGs) were designed to be the best possible blueprint to achieve peace, prosperity, and overall, a better and more sustainable future for the Earth and all its people, and such a blueprint is needed more than ever. The SDGs face many hurdles that will prevent them from becoming a reality, one of such hurdles, arguably, is the chemical pollution and unintended chemical impacts generated through the production of various goods and resources that we consume. Chemical Alternatives Assessment has proven to be a viable solution for chemical pollution management in terms of filtering out hazardous chemicals for a greener alternative. However, the current substitution practice lacks crucial quantitative datasets (exposures and life cycle impacts) to ensure no unintended trade-offs occur in the substitution process. A Chemical Life Cycle Alternative Assessment (CLiCAA) framework is proposed as a reliable and replicable alternative to Life Cycle Based Alternative Assessment (LCAA) as it integrates chemical molecular structure analysis and Chemical Life Cycle Collaborative (CLiCC) web-based tool to fill in data gaps that the former frameworks suffer from. The CLiCAA framework consists of a four filtering layers, the first two being mandatory, with the final two being optional assessment and data extrapolation steps. Each layer includes relevant impact categories of each chemical, ranging from human to environmental impacts, that will be assessed and aggregated into unique scores for overall comparable results, with little to no data. A feasibility study will demonstrate the efficiency and accuracy of CLiCAA whilst bridging both cancer potency and exposure limit data, hoping to provide the necessary categorical impact information for every firm possible, especially those disadvantaged in terms of research and resource management.

Keywords: chemical alternative assessment, LCA, LCAA, CLiCC, CLiCAA, chemical substitution framework, cancer potency data, chemical molecular structure analysis

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Authors: I. Gokasar, G. Bakioglu

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With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: traffic app, real–time information, traffic congestion, regression analysis, dummy variables

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Authors: Magda Milleyde de Sousa Lima, Letícia Lima Aguiar, Marina Guerra Martins, Erika Veríssimo Dias Sousa, Lizandra Sampaio de Oliveira, Lívia Moreira Barros, Joselany Áfio Caetano

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Patients with chronic kidney disease are vulnerable to episodes which make the safety of their health vulnerable, mainly due to the treatment process that exposes them to high rates of interventions during hemodialysis sessions. Some factors associated with health care contribute to the risk of death and complications. However, there are a small number of scientific studies evaluating the level of safety of hemodialysis clinics, and the sociodemographic characteristics of patients and professionals associated with this safety. Therefore, the present study aims to examine the level of patient safety in hemodialysis clinics in the Brazilian capital, to identify the sociodemographic and clinical factors of patients and nursing staff associated with the level of safety. This is an observational, descriptive and quantitative research conducted in three hemodialysis clinics placed in the city of Fortaleza-CE, Brazil, from September to November 2019. The sample was formed after a sample calculation for finite inhabitants of correlation with 200 chronic renal patients, 30 nursing technicians and seven nurses. Conventional sampling was used based on the inclusion criteria: being present at the hemodialysis session on the day the researcher performed the data collection and being 18 years of age or older. Participants who presented communication difficulties to listen to and/or answer the sociodemographic and clinical questionnaire were excluded. Two instruments were applied: sociodemographic and clinical characterization form and Chronic Renal Patient Safety Assessment Scale on Hemodialysis (EASPRCH). The data were analyzed using the Kruskal Walls Test for categorical variables and Spearman correlation coefficient for non-categorical variables, using the Statistical Package SPSS version 20.0. The present study respected the ethical and legal principles determined by resolution 466/2012 of the National Health Council, under the approval of the Ethics and Research Committee with an opinion number: 3,255,635. The results showed that a hemodialysis clinic presented unsafe care practices of 32 points in the EASPRCH (p=0.001). A statistical association was identified between the level of safety and the variables of the patients: level of education (p=0.018), family income (p=0.049), type of employment (p=0.012), venous access site (p=0.009), use of medication during the session (p=0.008) and time of hemodialysis (p=0.002). When evaluating the profile of nurses, a statistical association was evidenced between the level of safety with the variables: marital status (p=0.000), race (p=0.017), schooling (p= 0.000), income (p=0.013), age (p=0.000), clinic workload (p=0.000), time working with hemodialysis (p=0.000), time working in the clinic (p= 0.007) and clinic sizing (p=0.000). In order, the sociodemographic factors of nursing technicians associated with the level of patient safety were: race (p= 0.001) and weekly workload at (p=0.010). Therefore, it is concluded that there is a non-conformity in the level of patient safety in one of the clinics studied and, that sociodemographic and clinical factors of patients and health professionals corroborate the level of safety of the health unit.

Keywords: hemodialysis, nursing, patient safety, quality improvement

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Oluwayemisi O. Alaba, John O. Olaomi

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The 2013 Nigerian Demographic Health Survey (NDHS) data was used to investigate the determinants of family size in Nigeria using the geo-additive model. The fixed effect of categorical covariates were modelled using the diffuse prior, P-spline with second-order random walk for the nonlinear effect of continuous variable, spatial effects followed Markov random field priors while the exchangeable normal priors were used for the random effects of the community and household. The Negative Binomial distribution was used to handle overdispersion of the dependent variable. Inference was fully Bayesian approach. Results showed a declining effect of secondary and higher education of mother, Yoruba tribe, Christianity, family planning, mother giving birth by caesarean section and having a partner who has secondary education on family size. Big family size is positively associated with age at first birth, number of daughters in a household, being gainfully employed, married and living with partner, community and household effects.

Keywords: Bayesian analysis, family size, geo-additive model, negative binomial

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Authors: Paramita Sekar Ayu, Sunjaya Deni Kurniadi, Yamazaki Chiho, Hilfi Lukman, Koyama Hiroshi

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Social cohesion, or closeness among members of society, is an important determinant of population health. A cohesive society is a crucial societal condition for a positive life evaluation and subjective wellbeing, and people living in a cohesive society are happier and more satisfied with life and achieve better health status. The objective of this study was to compose and validate a questionnaire for measuring social cohesion with Rasch analysis. We develop a set of 13 questions to measure 4 dimensions of social cohesion. Random samples of 166 Bandung citizens’ were selected to answer the questionnaire. To evaluate the questionnaire’s validity and reliability, Rasch analysis (a psychometric model for analyzing categorical data on questionnaire responses) was carried out using Winsteps version 3.75.0. Rasch analysis was performed on the response given to 13 items included in the questionnaire. The reliability coefficient, Cronbach’s alpha was 0.70, model RMSE 0.08, SD 0.54, separation 7.14, and reliability of 0.98. ‘Kohesif-Ques’ is a useful instrument to assess social cohesion.

Keywords: rasch analysis, rasch model, social cohesion, quesionnaire

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Authors: Claudia Borges Colcerniani

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The paper is about a qualitative study whose main objective is to know how labor, as a Brazilian constitutionally established social right, can promote the social inclusion of female heads of one-parent families in a situation of poverty. The participants are six women, mothers, and workers living in Rocinha, a community located in the city of Rio de Janeiro, RJ, Brazil. According to the Brazilian Federal Constitution, social rights are based on the idea that socioeconomic inequalities should not limit or eliminate civil and political rights. In this perspective, labor can be a way to reach social justice, according to the theory of Nancy Fraser, the theoretical framework adopted in this research. Data were collected through socioeconomic questionnaires, and semi-structured interviews applied individually. The results analysis was made using the content analysis/categorical content analysis, according to Bardin's perspective. The results indicate that labor (as a social right) is considered, by the interviewed women, as an opportunity for social inclusion when there are the characteristics of the formality in accordance with the international labor regulations (Decent Work - International Labour Organization/United Nations).

Keywords: female labor, social justice, inclusion, women, decent work

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Authors: Karl Owen L. Suan, Juliet Marie S. Lambayan, Floramay P. Salo-Curato

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Objective: To predict the outcome among patients admitted with community-acquired pneumonia (ages 3 months to 59 months old) admitted in Visayas Community Medical Center using the Respiratory Index of Severity in Children (RISC). Design: A cross-sectional study design was used. Setting: The study was done in Visayas Community Medical Center, which is a private tertiary level in Cebu City from January-June 2013. Patients/Participants: A total of 72 patients were initially enrolled in the study. However, 1 patient transferred to another institution, thus 71 patients were included in this study. Within 24 hours from admission, patients were assigned a RISC score. Statistical Analysis: Cohen’s kappa coefficient was used for inter-rater agreement for categorical data. This study used frequency and percentage distribution for qualitative data. Mean, standard deviation and range were used for quantitative data. To determine the relationship of each RISC score parameter and the total RISC score with the outcome, a Mann Whitney U Test and 2x2 Fischer Exact test for testing associations were used. A p value less of than 0.05 alpha was considered significant. Results: There was a statistical significance between RISC score and clinical outcome. RISC score of greater than 4 was correlated with intubation and/or mortality. Conclusion: The RISC scoring system is a simple combination of clinical parameters and a reliable tool that will help stratify patients aged 3 months to 59 months in predicting clinical outcome.

Keywords: RISC, clinical outcome, community-acquired pneumonia, patients

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, production process optimization, evolutionary algorithms, policy optimization, actor critic approach

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Authors: Paul R Armstrong

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Doubled haploids (DHs) have become an important breeding tool for creating maize inbred lines, although several bottlenecks in the DH production process limit wider development, application, and adoption of the technique. DH kernels are typically sorted manually and represent about 10% of the seeds in a much larger pool where the remaining 90% are hybrid siblings. This introduces time constraints on DH production and manual sorting is often not accurate. Automated sorting based on the chemical composition of the kernel can be effective, but devices, namely NMR, have not achieved the sorting speed to be a cost-effective replacement to manual sorting. This study evaluated a single kernel near-infrared reflectance spectroscopy (skNIR) platform to accurately identify DH kernels based on oil content. The skNIR platform is a higher-throughput device, approximately 3 seeds/s, that uses spectra to predict oil content of each kernel from maize crosses intentionally developed to create larger than normal oil differences, 1.5%-2%, between DH and hybrid kernels. Spectra from the skNIR were used to construct a partial least squares regression (PLS) model for oil and for a categorical reference model of 1 (DH kernel) or 2 (hybrid kernel) and then used to sort several crosses to evaluate performance. Two approaches were used for sorting. The first used a general PLS model developed from all crosses to predict oil content and then used for sorting each induction cross, the second was the development of a specific model from a single induction cross where approximately fifty DH and one hundred hybrid kernels used. This second approach used a categorical reference value of 1 and 2, instead of oil content, for the PLS model and kernels selected for the calibration set were manually referenced based on traditional commercial methods using coloration of the tip cap and germ areas. The generalized PLS oil model statistics were R2 = 0.94 and RMSE = .93% for kernels spanning an oil content of 2.7% to 19.3%. Sorting by this model resulted in extracting 55% to 85% of haploid kernels from the four induction crosses. Using the second method of generating a model for each cross yielded model statistics ranging from R2s = 0.96 to 0.98 and RMSEs from 0.08 to 0.10. Sorting in this case resulted in 100% correct classification but required models that were cross. In summary, the first generalized model oil method could be used to sort a significant number of kernels from a kernel pool but was not close to the accuracy of developing a sorting model from a single cross. The penalty for the second method is that a PLS model would need to be developed for each individual cross. In conclusion both methods could find useful application in the sorting of DH from hybrid kernels.

Keywords: NIR, haploids, maize, sorting

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: George Zhou, Yunchan Chen, Candace Chien

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Kidney replacement therapy is the current standard of care for end-stage renal diseases. In-center or home hemodialysis remains an integral component of the therapeutic regimen. Arteriovenous fistulas (AVF) make up the vascular circuit through which blood is filtered and returned. Naturally, AVF patency determines whether adequate clearance and filtration can be achieved and directly influences clinical outcomes. Our aim was to build a deep learning model for automated AVF stenosis screening based on the sound of blood flow through the AVF. A total of 311 patients with AVF were enrolled in this study. Blood flow sounds were collected using a digital stethoscope. For each patient, blood flow sounds were collected at 6 different locations along the patient’s AVF. The 6 locations are artery, anastomosis, distal vein, middle vein, proximal vein, and venous arch. A total of 1866 sounds were collected. The blood flow sounds are labeled as “patent” (normal) or “stenotic” (abnormal). The labels are validated from concurrent ultrasound. Our dataset included 1527 “patent” and 339 “stenotic” sounds. We show that blood flow sounds vary significantly along the AVF. For example, the blood flow sound is loudest at the anastomosis site and softest at the cephalic arch. Contextualizing the sound with location metadata significantly improves classification performance. How to encode and incorporate categorical metadata is an active area of research1. Herein, we study ordinal (i.e., integer) encoding schemes. The numerical representation is concatenated to the flattened feature vector. We train a vision transformer (ViT) on spectrogram image representations of the sound and demonstrate that using scalar multiples of our integer encodings improves classification performance. Models are evaluated using a 10-fold cross-validation procedure. The baseline performance of our ViT without any location metadata achieves an AuROC and AuPRC of 0.68 ± 0.05 and 0.28 ± 0.09, respectively. Using the following encodings of Artery:0; Arch: 1; Proximal: 2; Middle: 3; Distal 4: Anastomosis: 5, the ViT achieves an AuROC and AuPRC of 0.69 ± 0.06 and 0.30 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 10; Proximal: 20; Middle: 30; Distal 40: Anastomosis: 50, the ViT achieves an AuROC and AuPRC of 0.74 ± 0.06 and 0.38 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 100; Proximal: 200; Middle: 300; Distal 400: Anastomosis: 500, the ViT achieves an AuROC and AuPRC of 0.78 ± 0.06 and 0.43 ± 0.11. respectively. Interestingly, we see that using increasing scalar multiples of our integer encoding scheme (i.e., encoding “venous arch” as 1,10,100) results in progressively improved performance. In theory, the integer values do not matter since we are optimizing the same loss function; the model can learn to increase or decrease the weights associated with location encodings and converge on the same solution. However, in the setting of limited data and computation resources, increasing the importance at initialization either leads to faster convergence or helps the model escape a local minimum.

Keywords: arteriovenous fistula, blood flow sounds, metadata encoding, deep learning

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Authors: Jiaqian Yin, Ruoling Chen, David Churchill, Huijuan Zou, Peipei Guo, Chunmei Liang, Xiaoqing Peng, Zhikang Zhang, Weiju Zhou, Yunxia Cao

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Purpose: This study aimed to explore the interaction of male and female age on implantation failure from in vitro fertilisation (IVF)/ intracytoplasmic sperm injection (ICSI) treatments in couples following their first cycles using the Anhui Maternal-Child Health Study (AMCHS). Methods: The AMCHS recruited 2042 infertile couples who were physically fit for in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI) treatment at the Reproductive Centre of the First Affiliated Hospital of Anhui Medical University between May 2017 to April 2021. This prospective cohort study analysed the data from 1910 cohort couples for the current paper data analysis. The multivariate logistic regression model was used to identify the effect of male and female age on implantation failure after controlling for confounding factors. Male age and female age were examined as continuous and categorical (male age: 20-<25, 25-<30, 30-<35, 35-<40, ≥40; female age: 20-<25, 25-<30, 30-<35, 35-<40, ≥40) predictors. Results: Logistic regression indicated that advanced maternal age was associated with increased implantation failure (P<0.001). There was evidence of an interaction between maternal age (30-<35 and ≥ 35) and paternal age (≥35) on implantation failure. (p<0.05). Only when the male was ≥35 years of increased maternal age was associated with the risk of implantation failure. Conclusion: In conclusion, there was an additive effect on implantation failure with advanced parental age. The impact of advanced maternal age was only seen in the older paternal age group. The delay of childbearing in both men and women will be a serious public issue that may contribute to a higher risk of implantation failure in patients needing assisted reproductive technology (ART).

Keywords: parental age, infertility, cohort study, IVF

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Authors: Alida Sandison, Louise A. Stroud

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Narcissism is a personality configuration which has both normal and pathological personality expressions. Narcissism is highly complex, and is linked to a broad field of research. There are both dimensional and categorical conceptualisations of narcissism, and a variety of theoretical formulations that have been put forward to understand the narcissistic personality configuration. Currently, Kernberg’s Object Relations theory is well supported for this purpose. The complexity and particular defense mechanisms at play in the narcissistic personality make it a difficult personality configuration worth further research. Psychobiography as a methodology allows for the exploration of the lived life, and is thus a useful methodology to surmount these inherent challenges. Narcissism has been a focus of academic interest for a long time, and although there is a lot of research done in this area, to the researchers' knowledge, narcissistic dynamics have never been explored within a psychobiographical format. Thus, the primary aim of the research was to explore and describe narcissism in the life of Howard Hughes, with the objective of gaining further insight into narcissism through the use of this unconventional research approach. Hughes was chosen as subject for the study as he is renowned as an eccentric billionaire who had his revolutionary effect on the world, but was concurrently disturbed within his personal pathologies. Hughes was dynamic in three different sectors, namely motion pictures, aviation and gambling. He became more and more reclusive as he entered into middle age. From his early fifties he was agoraphobic, and the social network of connectivity that could reasonably be expected from someone in the top of their field was notably distorted. Due to his strong narcissistic personality configuration, and the interpersonal difficulties he experienced, Hughes represents an ideal figure to explore narcissism. The study used a single case study design, and purposive sampling to select Hughes. Qualitative data was sampled, using secondary data sources. Given that Hughes was a famous figure, there is a plethora of information on his life, which is primarily autobiographical. This includes books written about his life, and archival material in the form of newspaper articles, interviews and movies. Gathered data were triangulated to avoid the effect of author bias, and increase the credibility of the data used. It was collected using Yin’s guidelines for data collection. Data was analysed using Miles and Huberman strategy of data analysis, which consists of three steps, namely, data reduction, data display, and conclusion drawing and verification. Patterns which emerged in the data highlighted the defense mechanisms used by Hughes, in particular that of splitting and projection, in defending his sense of self. These defense mechanisms help us to understand the high levels of entitlement and paranoia experienced by Hughes. Findings provide further insight into his sense of isolation and difference, and the consequent difficulty he experienced in maintaining connections with others. Findings furthermore confirm the effectiveness of Kernberg’s theory in understanding narcissism observing an individual life.

Keywords: Howard Hughes, narcissism, narcissistic defenses, object relations

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Authors: Bediru Abamecha

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Background: Domestic violence is a global problem that occurs regardless of culture, ethnicity or socio-economic class. It is known to be responsible for numerous hospital visits undertaken by women. Violence on pregnant women is a health and social problem that poses particular risks to the woman and her unborn child. Objective: The Objective of this study will be to assess prevalence of domestic violence and its correalates among pregnant women in Manna Woreda of Jimma Zone. Methods: Simple Random Sampling technique will be used to select 12 kebeles (48% of the study area) and Systematic Sampling will be used to reach to the house hold in selected kebeles in manna woreda of Jimma zone, south west Ethiopia from february 15-25, 2011. An in-depth interview will be conducted on Women affairs, police office and Nurses working and minimum of 4FGD with 6-8 members on pregnant women and selected male from the community. SPSS version 16.0 will be used to enter, clean and analyze the data. Descriptive statistics such as mean or median for continuous variables and percent for categorical variables will be made. Bivariate analysis will be used to check the association between independent variables and domestic violence. Variables found to have association with domestic violence will be entered to multiple logistic regressions for controlling the possible effect of confounders and finally the variables which had significance association will be identified on basis of OR, with 95% CI. All statistical significance will be considered at p<0.05. The qualitative data will be summarized manually and thematic analysis will be performed and finally both will be triangulated.

Keywords: ante natal care, ethiopian demographic and health survey, domestic violence, statistical package for social science

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Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

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Authors: Noelene Rose

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The DSM has been plagued with conceptual issues since its inception, not least discriminant validity and comorbidity issues. An attempt to remain a-theoretical in the divide between the psycho-dynamicists and the behaviourists contributed to much of this, in particular relating to the Personality Disorders. With the DSM-5, although the criterion have remained unchanged, major conceptual and structural directions have been flagged and proposed in section III. The biggest changes concern the Personality Disorders. While Narcissistic Personality Disorder (NPD) was initially tagged for removal, instead the addition of section III proposes a move away from a categorical approach to a more dimensional approach, with a measure of Global Function of Personality. This global measure is an assessment of impairment of self-other relations; a measure of trait narcissism. In the same way mainstream psychology has struggled in its diagnosis of narcissism, so too in its treatment. Kohut’s self psychology represents the most significant inroad in theory and treatment for the narcissistic disorders. Kohut had moved away from a categorical system, towards disorders of the self. According to this theory, disorders of the self are the result of childhood trauma (impaired attunement) resulting in a developmental arrest. Self-psychological, Psychodynamic treatment of narcissism, however, is expensive, in time and money and outside the awareness or access of most people. There is more than a suggestion that narcissism is on the increase, created in trauma and worsened by a fearful world climate. A dimensional model of narcissism, from mild to severe, requires cut off points for diagnosis. But where do we draw the line? Mainstream psychology is inclined to set it high when there is some degree of impairment in functioning in daily life. Transpersonal Psychology is inclined to set it low, with the concept that we all have some degree of narcissism and that it is the point and the path of our life journey to transcend our focus on our selves. Mainstream psychology stops its focus on trait narcissism with a healthy level of self esteem, but it is at this point that Transpersonal Psychology can complement the discussion. From a Transpersonal point of view, failure to begin the process of self-transcendence will also create emotional symptoms of meaning or purpose, often later in our lives, and is also conceived of as a developmental arrest. The maps for this transcendence are hidden in plain sight; in the chakras of kundalini yoga, in the sacraments of the Catholic Church, in the Kabbalah tree of life of Judaism, in Maslow’s hierarchy of needs, to name a few. This paper outlines some proposed research exploring the use of daily practices that can be incorporated into the therapy room; practices that utilise meditation, visualisation and imagination: that are informed by spiritual technology and guided by the psychodynamic theory of Self Psychology.

Keywords: narcissism, self-psychology, self-practice, self-transcendence

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Authors: Zahra Tolou-Ghamari

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Objective: Health care associated with multiresistant pathogens is rising globally. It is well known that nosocomial infections increase hospital stay, morbidity, mortality, and disability. Therefore, the aim of this study was to define the occurrence of nosocomial infections in a tertiary hospital in Isfahan/Iran. Materials and Methods: The data were extracted from the official database of hospital nosocomial infections records that included 9152 vertical rows. For each patient, the reported infections were coded by number as UTI-SUTI; Code 55, VAE-PVAP; Code 56, BSI-LCBI Code 19, SSI-DIP; Code 14, and so on. For continuous variables, mean ± standard deviation and for categorical variables, the frequency was used. Results: The study population was 5542 patients, comprised of males (n=3282) and females (n=2260). With a minimum of 15 and a maximum of 99, the mean age in 5313 patients was 58.5 ± 19.1 years old. The highest reported nosocomial infections (n= 77%) were associated with the ages 30-80 years old. Sites of nosocomial infections in 87% were as: VAE-PVAP; 27.3%, VAE-IVAC; 7.7, UTI-SUTI; 29.5%, BSI-LCBI; 12.9%, SSI-DIP; 9.5% and other individual infection (13%) with the main pathogens klebsiella pneumonia, acinetobacter baumannii and staphylococcus. Conclusions: For an efficient surveillance system, adopting pharmacotherapy used antibiotics in terms of monotherapy or polypharmacy control policy, in addition to advanced infection control programs at regional and national levels in Iran recommended.

Keywords: infection, nosocomial, ventilator, blood stream, Isfahan, Iran

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Authors: Esra Polat

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Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and consists a classical Partial Least Squares Regression (PLSR) in which the dependent variable is a categorical one expressing the class membership of each observation. PLSDA can be applied in many cases when classical discriminant analysis cannot be applied. For example, when the number of observations is low and when the number of independent variables is high. When there are missing values, PLSDA can be applied on the data that is available. Finally, it is adapted when multicollinearity between independent variables is high. The aim of this study is to determine the economic and/or demographic indicators, which are effective in grouping the 28 European Union (EU) member countries and 7 candidate countries (including potential candidates Bosnia and Herzegovina (BiH) and Kosova) by using the data set obtained from database of the World Bank for 2014. Leaving the political issues aside, the analysis is only concerned with the economic and demographic variables that have the potential influence on country’s eligibility for EU entrance. Hence, in this study, both the performance of PLSDA method in classifying the countries correctly to their pre-defined groups (candidate or member) and the differences between the EU countries and candidate countries in terms of these indicators are analyzed. As a result of the PLSDA, the value of percentage correctness of 100 % indicates that overall of the 35 countries is classified correctly. Moreover, the most important variables that determine the statuses of member and candidate countries in terms of economic indicators are identified as 'external balance on goods and services (% GDP)', 'gross domestic savings (% GDP)' and 'gross national expenditure (% GDP)' that means for the 2014 economical structure of countries is the most important determinant of EU membership. Subsequently, the model validated to prove the predictive ability by using the data set for 2015. For prediction sample, %97,14 of the countries are correctly classified. An interesting result is obtained for only BiH, which is still a potential candidate for EU, predicted as a member of EU by using the indicators data set for 2015 as a prediction sample. Although BiH has made a significant transformation from a war-torn country to a semi-functional state, ethnic tensions, nationalistic rhetoric and political disagreements are still evident, which inhibit Bosnian progress towards the EU.

Keywords: classification, demographic indicators, economic indicators, European Union, partial least squares discriminant analysis

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Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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Authors: Yen Ling Liu, Shu-Fen Wu, Chen-Fuh Lam, I-Ling Tsai, Chia-Yu Chen

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Purpose: Nurse anesthetists are confronting extraordinarily high job stress in their daily practice, deriving from the fast-track anesthesia care, risk of perioperative complications, routine rotating shifts, teaching programs and interactions with the surgical team in the operating room. This study investigated the influence of work stress on the burnout and turnover intention of nurse anesthetists in a regional general hospital in Southern Taiwan. Methods: This was a descriptive correlational study carried out in 66 full-time nurse anesthetists. Data was collected from March 2017 to June 2017 by in-person interview, and a self-administered structured questionnaire was completed by the interviewee. Outcome measurements included the Practice Environment Scale of the Nursing Work Index (PES-NWI), Maslach Burnout Inventory (MBI) and nursing staff turnover intention. Numerical data were analyzed by descriptive statistics, independent t test, or one-way ANOVA. Categorical data were compared using the chi-square test (x²). Datasets were computed with Pearson product-moment correlation and linear regression. Data were analyzed by using SPSS 20.0 software. Results: The average score for job burnout was 68.7916.67 (out of 100). The three major components of burnout, including emotional depletion (mean score of 26.32), depersonalization (mean score of 13.65), and personal(mean score of 24.48). These average scores suggested that these nurse anesthetists were at high risk of burnout and inversely correlated with turnover intention (t = -4.048, P < 0.05). Using linear regression model, emotional exhaustion and depersonalization were the two independent factors that predicted turnover intention in the nurse anesthetists (19.1% in total variance). Conclusion/Implications for Practice: The study identifies that the high risk of job burnout in the nurse anesthetists is not simply derived from physical overload, but most likely resulted from the additional emotional and psychological stress. The occurrence of job burnout may affect the quality of nursing work, and also influence family harmony, in turn, may increase the turnover rate. Multimodal approach is warranted to reduce work stress and job burnout in nurse anesthetists to enhance their willingness to contribute in anesthesia care.

Keywords: anesthesia nurses, burnout, job, turnover intention

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Authors: Warsa Melles

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This study examines the way second-generation Eritrean immigrants living in the French-speaking part of Switzerland behave linguistically and culturally. The aim of this research is to demonstrate how the participants deal with their bilingualism (Tigrinya and French). More precisely, how does their language use correlates with their socio-cultural attitudes and how do these aspects (re)construct their identity? Data for this research was collected via, questionnaires and semi-structured interviews. Participants were asked to answer questions regarding their linguistic habits, their perception on being bilingual and their cultural identity. The major findings demonstrate that generation 2 relates more with the host country’s language since French is used as the main language in their daily interactions. On the other hand, due to the fact that they have never lived in Eritrea yet were raised by Eritrean born parents in a foreign country, it is more difficult for them to unanimously identify with just one culture. In that sense, intergenerational transmission plays a major role in the perception of identity. All the participants have at least a basic knowledge of Tigrinya, but the use of languages varies according to the purpose. Proficiency in the native language and sense of belonging can be correlated with the frequency of visits to Eritrea. In conclusion, the question of identity in the second-generation Eritrean community cannot be given a categorical and clear-cut answer instead, the new-self image that this social group aims to build is shaped by different factors that are essential to take into consideration.

Keywords: biculturalism, identity, language, migration

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Authors: Pandega Gama Mahardika, Muhammad Rifki Al Iksan, Datuk Fachrul Razy

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Oil And Gas Tanker Driver is a high-risk jobdesc. drivers drive with sleep circadian rhythm disturbances. Therefore, FAMOUS (Fatigue Management Online Ultimate System) conducted a diagnostic test on the effectiveness and accuracy of the Psychomotor vigilance test (PVT) in the field to capture the fatigue level of Oil And Gas Tanker Driver. Fatigue examination with the PVP method for 3 minutes using the Pertamina FAMOUS system (Fatigue Management Online Ultimate System). The research sample was Oil And Gas Tanker Driver Elnusa petrofin drivers as many as 2205 people. PVT is categorical data that states a driver has a low or high fatigue level. The quality of sleep at home was recorded by filling in a score of 1 = not well, 2 = not well, 3 = well, per person. A total of 1852 (84%) driver had a low fatigue level, while 353 (16%) driver had a high fatigue level. Poor sleep quality was experienced by 68 (79%) driver who had a high fatigue level. Oil And Gas Tanker Driver who slept soundly at home as many as 1804 (87%) had a low fatigue level. The correlation coefficient of sleep quality home and fatigue level is significant because it shows a probability value of 0.00 (p <5%). Fatigue level can be diagnosed through examining sleep quality, using FAMOUS Program for occupational medicine, particularly in the oil and gas sector.

Keywords: psychomotor vigilance test, fatigue, sleep, oil and gas tanker driver drivers, pertamina FAMOUS

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Authors: Eric Muñoz de la Torre, Julián González Trinidad, Efrén González Ramírez

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Rain varies greatly in its duration, intensity, and spatial coverage, it is important to have sub-daily rainfall data for various applications, including risk prevention. However, the ground measurements are limited by the low and irregular density of rain gauges. An alternative to this problem are the Satellite Precipitation Products (SPPs) that use passive microwave and infrared sensors to estimate rainfall, as IMERG, however, these SPPs have to be validated before their application. The aim of this study is to evaluate the performance of the IMERG: Integrated Multi-satellitE Retrievals for Global Precipitation Measurament final run V06B SPP in a semi-arid region of Mexico, using 4 automatic rain gauges (pluviographs) sub-daily data of October 2019 and June to September 2021, using the Minimum inter-event Time (MIT) criterion to separate unique rain events with a dry period of 10 hrs. for the purpose of evaluating the rainfall properties (depth, duration and intensity). Point to pixel analysis, continuous, categorical, and volumetric statistical metrics were used. Results show that IMERG is capable to estimate the rainfall depth with a slight overestimation but is unable to identify the real duration and intensity of the rain events, showing large overestimations and underestimations, respectively. The study zone presented 80 to 85 % of convective rain events, the rest were stratiform rain events, classified by the depth magnitude variation of IMERG pixels and pluviographs. IMERG showed poorer performance at detecting the first ones but had a good performance at estimating stratiform rain events that are originated by Cold Fronts.

Keywords: IMERG, rainfall, rain gauge, remote sensing, statistical evaluation

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Authors: Siddhartha Dhungana

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This article primarily explores dimensions in English language teacher's professionalism so that a teacher could reflect and make a strategic professional devotion to implement effective educational programs for the present and the future. The paper substantially incorporates the eastern Hindu practices, especially life values from the Bhagavad Gita, as a basis of teacher’s professional enrichment. Basically, it applies three categorical practices, i.e., Karma Yoga, Jnana Yoga, and Bhakti Yoga, in teachers’ professionality to illustrate, ignite further ahead and sharpen academic journey, professional journey, and professional devotion reflecting common practices. In this journey, a teacher comes to a stage of professional essence as s/he surpasses Karma Yoga, Jnana Yoga, and Bhakti Yoga with their basic quality formation. To illustrate their essence-making process, the three narrative stories for each category mentioned above are analyzed. The data collected from a research participant who has a high level of professional success and who inspires all English Language teachers in Nepal to develop stories for narrative analysis. The narrative analysis is based on eastern themes that are supported by Vygotsky's concept of developmental psychology. Moreover, the structural analysis is based on Gary Barkhuizen's narrative analysis.

Keywords: Karma Yoga, Jnana Yoga, Bhakti Yoga, Vygotsky's concepts, narrative analysis

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Authors: Nighat Bakhtiar, Ali Raza Khan, Shahid Khan Khattak, Aamir Ali Syed

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Introduction: Chemoradiation followed by resection has been the standard therapy for resectable (cT1-4aN0-3M0) esophageal carcinoma. The optimal surgical approach remains a matter of debate. Therefore, the purpose of this study was to share our experiences of minimal invasive esophagectomies concerning morbidity, mortality and oncological quality. This study aims to enlighten the world about the surgical outcomes after minimally invasive esophagectomy at Shaukat Khanum Hospital Lahore. Objective: The purpose of this study is to review an institutional experience of Surgical outcomes of Minimal Invasive esophagectomies for esophageal cancer. Methodology: This retrospective study was performed after ethical approval at Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC) Pakistan. Patients who underwent Minimal Invasive esophagectomies for esophageal cancer from March 2018 to March 2023 were selected. Data was collected through the human information system (HIS) electronic database of SKMCH&RC. Data was described using mean and median with minimum and maximum values for quantitative variables. For categorical variables, a number of observations and percentages were reported. Results: A total of 621 patients were included in the study, with the mean age of the patient was 39 years, ranging between 18-58 years. Mean Body Mass Index of patients was 21.2.1±4.1. Neo-adjuvant chemoradiotherapy was given to all patients. The mean operative time was 210.36 ± 64.51 minutes, and the mean blood loss was 121 milliliters. There was one mortality in 90 days, while the mean postoperative hospital stay was 6.58 days with a 4.64 standard deviation. The anastomotic leak rate was 4.2%. Chyle leak was observed in 12 patients. Conclusion: The minimal invasive technique is a safe approach for esophageal cancers, with minimal complications and fast recovery.

Keywords: minimal invasive, esophagectomy, laparscopic, cancer

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Authors: Mayuko Matsuoka

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This presentation reports the relationships among EFL(English as a Foreign Language) students’ story comprehension in reading a story written in English and Japanese and empathic reactions. The main focus is put on their self-reflection on past study experiences, one of the empathic reactions after reading a story. One hundred fifty-five first-year university students in Japan read three SRL-themed stories written in English (their foreign language) and those written in Japanese (their mother tongue). The levels of the stories are equivalent, at CEFR(Common European Framework of Reference for Languages) B2 level. The result of categorical correlation analysis shows significant moderate correlations among three empathic reactions in a group reading English versions: having similar emotions as a protagonist, reflecting on their past study experiences, and getting lessons from a story. In addition, the result of logistic regression analysis for the data in a group reading English versions shows the chance of getting lessons from a story significantly approximately doubles if participants’ scores of a comprehension test increases by one, while it approximately triples if participants’ self-reflection occurs. These results do not appear in a group reading Japanese versions. The findings imply that self-reflection may support their comprehension of the English texts and leads to the participants’ getting lessons about SRL.

Keywords: comprehension, lesson, self-reflection, SRL

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Authors: Silas Webb, Joseph Hartland

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Background: Long-term retention of patients on ART has become a major health challenge in Sub-Saharan Africa (SSA). In 2010 a systematic review of 39 papers found that 30% of patients were no longer taking their ARTs two years after starting treatment. In the same review, it was noted that there was a paucity of data as to why patients become lost to follow-up (LTFU) in SSA. This project was performed in Mulanje Mission Hospital in Malawi as part of Swindon Academy’s Global Health eSSC. The HIV prevalence for Malawi is 10.3%, one of the highest rates in the world, however prevalence soars to 18% in the Mulanje. Therefore it is essential that patients at risk of being LTFU are identified early and managed appropriately to help them continue to participate in the service. Methodology: All patients on adult antiretroviral formulations at MMH, who were classified as ‘defaulters’ (patients missing a scheduled follow up visit by more than two months) over the last 12 months were included in the study. Demographic varibales were collected from Mastercards for data analysis. A comparison group of patients currently not lost to follow up was created by using all of the patients who attended the HIV clinic between 18th-22nd July 2016 who had never defaulted from ART. Data was analysed using the chi squared (χ²) test, as data collected was categorical, with alpha levels set at 0.05. Results: Overall, 136 patients had defaulted from ART over the past 12 months at MMH. Of these, 43 patients had missing Mastercards, so 93 defaulter datasets were analysed. In the comparison group 93 datasets were also analysed and statistical analysis done using Chi-Squared testing. A higher proportion of men in the defaulting group was noted (χ²=0.034) and defaulters tended to be younger (χ²=0.052). 94.6% of patients who defaulted were taking Tenofovir, Lamivudine and Efavirenz, the standard first line ART therapy in Malawi. The mean length of time on ART was 39.0 months (RR: -22.4-100.4) in the defaulters group and 47.3 months (RR: -19.71-114.23) in the control group, with a mean difference of 8.3 less months in the defaulters group (χ ²=0.056). Discussion: The findings in this study echo the literature, however this review expands on that and shows the demographic for the patient at most risk of defaulting and being LTFU would be: a young male who has missed more than 4 doses of ART and is within his first year of treatment. For the hospital, this data is important at it identifies significant areas for public health focus. For instance, fear of disclosure and stigma may be disproportionately affecting younger men, so interventions can be aimed specifically at them to improve their health outcomes. The mean length of time on medication was 8.3 months less in the defaulters group, with a p-value of 0.056, emphasising the need for more intensive follow-up in the early stages of treatment, when patients are at the highest risk of defaulting.

Keywords: anti-retroviral therapy, ART, HIV, lost to follow up, Malawi

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Authors: Faisal Kalota

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Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: big data, learning analytics, analytics, big data in education, Hadoop

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Authors: Alexander A. Tokmakov

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Cell-free protein synthesis is widely used to synthesize recombinant proteins. It allows genome-scale expression of various polypeptides under strictly controlled uniform conditions. However, only a minor fraction of all proteins can be successfully expressed in the systems of protein synthesis that are currently used. The factors determining expression success are poorly understood. At present, the vast volume of data is accumulated in cell-free expression databases. It makes possible comprehensive bioinformatics analysis and identification of multiple features associated with successful cell-free expression. Here, we describe an approach aimed at identification of multiple physicochemical and structural properties of amino acid sequences associated with protein solubility and aggregation and highlight major correlations obtained using this approach. The developed method includes: categorical assessment of the protein expression data, calculation and prediction of multiple properties of expressed amino acid sequences, correlation of the individual properties with the expression scores, and evaluation of statistical significance of the observed correlations. Using this approach, we revealed a number of statistically significant correlations between calculated and predicted features of protein sequences and their amenability to cell-free expression. It was found that some of the features, such as protein pI, hydrophobicity, presence of signal sequences, etc., are mostly related to protein solubility, whereas the others, such as protein length, number of disulfide bonds, content of secondary structure, etc., affect mainly the expression propensity. We also demonstrated that amenability of polypeptide sequences to cell-free expression correlates with the presence of multiple sites of post-translational modifications. The correlations revealed in this study provide a plethora of important insights into protein folding and rationalization of protein production. The developed bioinformatics approach can be of practical use for predicting expression success and optimizing cell-free protein synthesis.

Keywords: bioinformatics analysis, cell-free protein synthesis, expression success, optimization, recombinant proteins

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