Search results for: binary logistic

Authors: Harini Chakkera

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Background: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts with hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. Methods: Data on first-time solitary kidney transplantations were retrieved between September 2015 to December 2018. Information was linked to the electronic health record to determine a diagnosis of diabetes mellitus and extract glucometeric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on five bootstrapped partitions of the data to ensure statistical significance. Results: The cohort included 1036 patients who received kidney transplantation, and 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average AUC of 77.3% (95% CI 75.30-79.3%). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, and minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia and recipient and donor BMI values as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. Conclusions: Suboptimal glucose metrics during hospitalization after kidney transplantation is associated with an increased risk for 30-day hospital readmission. Optimizing the hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Keywords: kidney, transplant, diabetes, insulin

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Authors: Getnet Gedefaw, Fentanesh Endalew, Bitewush Azmeraw, Bethelhem Walelign, Eyob Shitie

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Introduction: The issue of menstrual hygiene is often overlooked and has not been sufficiently addressed in the fields of reproductive health in low and middle-income countries. Inadequate menstrual hygiene practices can increase the risk of various infectious and chronic obstetric and gynaecological complications for girls and adolescents. Hence, this study seeks to investigate the knowledge, attitudes, and practices related to menstrual hygiene, along with the factors influencing them, among high school students. Methods: A facility based cross-sectional study was conducted involving a total of 423 study subjects. A systematic random sampling technique was utilized. Data was entered and analyzed through Epi data 3.1 and SPSS 22, respectively. Both univariable and multivariable logistic regression models were employed. A p-value of less than 0.05 was considered statistically significant. Results: This study revealed that 365(89.2%), 200(48.9%) and 196(47.9%) of the study participants have good knowledge, good practice, and good attitudes about menstrual hygiene, respectively. Being higher grade students (grade 10) [AOR=3.96, 95% CI =2.0-7.8] and having good practice of menstrual hygiene (AOR=2.52, 95% CI= 1.26-5) had a positive association with menstrual hygiene knowledge. Whereas maternal education level (AOR=1.86, 95% CI=1.18-2.9) and being a grade 10 student (AOR=2.3, 95% CI=1.48-3.56) were associated factors for practising menstrual hygiene. Additionally, being higher grade students (AOR=1.9, 95% CI=1.2-2.8), age ≥18 years (AOR=1.67, 95% CI=1.09-2.55) were statistically and positively associated with the attitude of menstrual hygiene. Conclusion: The study findings indicated that the knowledge of the study participants regarding menstrual hygiene was high, while their attitudes and practices towards menstrual hygiene were low. It is suggested that raising awareness among reproductive health groups and educating their families and parents could potentially lead to a positive change in their poor practices and attitudes towards menstrual hygiene.

Keywords: menstrual hygiene, menstruation, students, reproductive health

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Authors: Ibrahim Sadek Sedik Ibrahim, Mohamed M. Elgohary

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Sea ports are considered one of the factors affecting the progress of economic globalization and the international trade; consequently, they are considered one of the sources involved in the deterioration of the maritime environment due to the excessive amount of exhaust gases emitted from their activities. The majority of sea ports depend on the national electric grid as a source of power for the domestic and ships’ electric demands. This paper discusses the possibility of shifting ports from relying on the national grid electricity to green power-based ports. Offshore wind turbines and hydrogenic PEM fuel cell units appear as two typical promising clean energy sources for ports. As a case study, the paper investigates the prospect of converting Alexandria Port in Egypt to be an eco-friendly port with the study of technical, logistic, and financial requirements. The results show that the fuel cell, followed by a combined system of wind turbines and fuel cells, is the best choice regarding electricity production unit cost by 0.101 and 0.107 $/kWh, respectively. Furthermore, using of fuel cells and offshore wind turbine as green power concept will achieving emissions reduction quantity of CO₂, NOx, and CO emissions by 80,441, 20.814, and 133.025 ton per year, respectively. Finally, the paper highlights the role that renewable energy can play when supplying Alexandria Port with green energy to lift the burden on the government in supporting the electricity, with a possibility of achieving a profit of 3.85% to 22.31% of the annual electricity cost compared with the international prices.

Keywords: fuel cells, green ports, IMO, national electric grid, offshore wind turbines, port emissions, renewable energy

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Authors: Sarah P. Wuebbolt, Ashlee N. Sawyer-Mays

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Non-medical prescription and other drug (NMPOD) use has been a significant public health issue for the last few decades, with problematic use increasing among university students more recently. The current study focused on associations between NMPOD use and mental health, well-being, and world beliefs among young adults. Young adults (N=513) completed online questionnaires assessing stress, demographic characteristics, self-esteem, NMPOD use, coping mechanisms, and anxiety. A substantial portion of participants reported using cannabis (48.5%, n=249), while smaller portions of participants reported using stimulants (26.7%, n = 137), sedatives (17.2%, n=88), opioids (10.8%, n=55), and hallucinogens (14.4%, n=74). Five hierarchical logistic regressions were performed to determine the independent relationships between mental health, well-being, and world belief factors and NMPOD use for the five classes of substances. After controlling for demographic factors (age, gender, race/ethnicity, sexual orientation, and religious affiliation), depression was associated with increased non-medical stimulant, opioid, and cannabis use; coping self-efficacy was associated with increased hallucinogen use, and attendance of worship services was associated with decreased non-medical cannabis and hallucinogen use. Results suggest that depression was strongly associated with non-medical stimulant, opioid, and cannabis use, and attendance of worship services was protective against cannabis and hallucinogen use. To the best of our knowledge, this is one of the first studies to investigate the relationships between mental health, well-being, world beliefs, and NMPOD use among young adults. The present study illuminates future targets for intervention, such as increased access to mental health diagnosis and treatment and the exploration of the roles of religion and shared community in the prevention of drug use among young adults.

Keywords: cannabis, mental health, non-medical prescription and other drug use, world beliefs

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Authors: Fazle Rabbi, Mashuda Khanom Tithi, Tasnim Mirza, Sanjida Rowshan Anannya, Ahmed Hossain

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Background: Low Back Pain (LBP) is one of the common health problems among the garment workers that causes workers absenteeism from the work. The purpose of the study is to identify the association between occupational factors and LBP among the female garment workers in Bangladesh. Materials and Methods: A cross-sectional study was conducted with 487 female garment workers from three compliant garment factories of Bangladesh. Face-to-face interview on four different LBP measures along with questions on socio-demographic, occupational, and physical factors were used to collect the data. Result: The prevalence rates for LBP lasts for at least one day during the last six months, chronic pain, intense pain, and seeking medical care for LBP were found 63.04%, 38.60%, 13.76%, and 18.89%, respectively among the female garments workers. The multivariate logistic regression analysis indicates that duration of employment (>5 years), regular weight bearing and extended weekly working hours (>48 hours) are positively associated with LBP. Besides, age, BMI, family income, marital status and number of children are also found positively associated with the LBP measures. Conclusion: The prevalence of LBP among female garment workers in Bangladesh is found high. The duration of employment (>5 years), regular weight bearing and extended weekly working hours (>48 hours) play a significant role in developing LBP among the female workers. Factories need to consider training programs on the appropriate technique of weight bearing. It is also important to conduct regular screening programs to identify LBP, especially with married, overweight/obese and older age group to reduce the occurrence of LBP.

Keywords: Bangladesh, garment workers, low back pain, occupational health

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Authors: Xu Yifei, Le Yining, Yang Qingluan, Tu Yanjie

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Objective: This study aims to investigate the distribution features of Traditional Chinese Medicine (TCM) syndromes and syndrome elements in Epstein-Barr virus-associated diseases and then explores the relations between TCM syndromes or syndrome elements and laboratory indicators of Epstein-Barr virus-associated diseases. Methods: A cross-sectional study of 70 patients with EBV infection was described. We assessed the diagnostic information and laboratory indicators of these patients from Huashan Hospital Affiliated to Fudan University between November 2017 and July 2019. The disease diagnosis and syndrome differentiation were based on the diagnostic criteria of EBV-associated diseases and the theory of TCM respectively. Confidence correlation analysis, logistic regression analysis, cluster analysis, and the Sankey diagram were used to analyze the correlation between the data. Results: The differentiation of the 4 primary TCM syndromes in the collected patients was correlated with the indexes of immune function, liver function, inflammation, and anemia, especially the relationship between Qifen syndrome and high lactic acid dehydrogenase level. The common 11 TCM syndrome elements were associated with the increased CD3+ T cell rate, low hemoglobin level, high procalcitonin level, high lactic acid dehydrogenase level, and low albumin level. Conclusion: The changes in immune function indexes, procalcitonin, and liver function-related indexes in patients with EBV-associated diseases were consistent with the evolution law of TCM syndromes. This study provides a reference for judging the pathological stages of these kinds of diseases, predicting their prognosis, and guiding subsequent treatment strategies based on TCM syndrome type.

Keywords: EBV-associated diseases, traditional Chinese medicine syndrome, syndrome element, diagnostics

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Authors: Thananyada Buapian

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Overnutrition is emerging as a morbid disease in developing and Westernized countries. Because of its comorbidity diseases, it is cost-effective to prevent and manage this disease earlier. In Thailand, this alarming disease has long been studied, but the prevalence is still higher than that in the past. Physicians should recognize it well and have a definite direction to face and combat this dangerous disease. Rapid changes in the tremendous figure of overnutrition students indicate that genetic factors are not the primary determinants since human genes have remained unchanged for a century. This study aims to assess the prevalence of overnutrition students and to investigate the non-genetic factors affecting over nutrition students. A cross-sectional school-based survey was conducted. A two-stage sampling was adopted. Respondents included 1,850 students in grades 4 to 6 in Ayutthaya Province. An anthropometric measurement and questionnaire were developed. Childhood over nutrition was defined as a weight-for-height Z-score above +2SD of NCHS/WHO references. About thirty three percent of the children were over nutrition in Ayutthaya province. Stepwise multiple logistic regression analysis showed that 8 statistically significant non genetic factors explain the variation of childhood over nutrition by 18 percent. Sex is the prime factor to explain the variation of childhood over nutrition, followed by duration of light physical activities, duration of moderate physical activities, having been breastfed, the presence of a healthy role model of the caregiver, number of siblings, birth order, and occupation of the caregiver, respectively. Non genetic factors, especially the subjects’ demographic and physical activities, as well as the caregivers’ background and family environment, should be considered in viable approach to remedy this health imbalance in children.

Keywords: non genetic factors, non-genetic, over nutrition, over nutrition students

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Yepeng Cheng, Yasuhiko Morimoto

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Customer relationship analysis is vital for retail stores, especially for supermarkets. The point of sale (POS) systems make it possible to record the daily purchasing behaviors of customers as an identification point of sale (ID-POS) database, which can be used to analyze customer behaviors of a supermarket. The customer value is an indicator based on ID-POS database for detecting the customer loyalty of a store. In general, there are many supermarkets in a city, and other nearby competitor supermarkets significantly affect the customer value of customers of a supermarket. However, it is impossible to get detailed ID-POS databases of competitor supermarkets. This study firstly focused on the customer value and distance between a customer's home and supermarkets in a city, and then constructed the models based on logistic regression analysis to analyze correlations between distance and purchasing behaviors only from a POS database of a supermarket chain. During the modeling process, there are three primary problems existed, including the incomparable problem of customer values, the multicollinearity problem among customer value and distance data, and the number of valid partial regression coefficients. The improved customer value, Huff’s gravity model, and inverse attractiveness frequency are considered to solve these problems. This paper presents three types of models based on these three methods for loyal customer classification and competitors’ influence analysis. In numerical experiments, all types of models are useful for loyal customer classification. The type of model, including all three methods, is the most superior one for evaluating the influence of the other nearby supermarkets on customers' purchasing of a supermarket chain from the viewpoint of valid partial regression coefficients and accuracy.

Keywords: customer value, Huff's Gravity Model, POS, Retailer

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Rocio Herrero Romero, Lucie Cluver, James Hall, Janina Steinert

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Educational delay and non-completion are major policy concerns in South Africa. However, little research has focused on predictors for educational delay amongst adolescents in disadvantaged areas. This study has two aims: first, to use data integration approaches to compare the educational delay of 599 adolescents aged 16 to 18 from disadvantaged communities to national and provincial representative estimates in South Africa. Second, the paper also explores predictors for educational delay by comparing adolescents out of school (n=64) and at least one year behind (n=380), with adolescents in the age-appropriate grade or higher (n=155). Multinomial logistic regression models using self-report and administrative data were applied to look for significant associations of risk and protective factors. Significant risk factors for being behind (rather than in age-appropriate grade) were: male gender, past grade repetition, rural location and larger school size. Risk factors for being out of school (rather than in the age-appropriate grade) were: past grade repetition, having experienced problems concentrating at school, household poverty, and food insecurity. Significant protective factors for being in the age-appropriate grade (rather than out of school) were: living with biological parents or grandparents and access to school counselling. Attending school in wealthier communities was a significant protective factor for being in the age-appropriate grade (rather than behind). Our results suggest that both personal and contextual factors –family and school- predicted educational delay. This study provides new evidence to the significant effects of personal, family, and school characteristics on the educational outcomes of adolescents from disadvantaged communities in South Africa. This is the first longitudinal and quantitative study to systematically investigate risk and protective factors for post-compulsory educational outcomes amongst South African adolescents living in disadvantaged communities.

Keywords: disadvantaged communities, quantitative analysis, school delay, South Africa

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Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

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With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: Khalil Ahmad Kakar

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In developing countries increasing the automobile and low capacity public transport (para-transit) which are creating congestion, pollution, noise, and traffic accident are the most critical quandary. These issues are under the analysis of assessors to break down the puzzle and propose sustainable urban public transport system. Kabul city is one of those urban areas that the inhabitants are suffering from lack of tolerable and friendly public transport system. The city is the most-populous and overcrowded with around 4.5 million population. The para-transit is the only dominant public transit system with a very poor level of services and low capacity vehicles (6-20 passengers). Therefore, this study after detailed investigations suggests bus rapid transit (BRT) system in Kabul City. It is aimed to mitigate the role of informal transport and decreases congestion. The research covers three parts. In the first part, aggregated travel demand modelling (four-step) is applied to determine the number of users for para-transit and assesses BRT network based on higher passenger demand for public transport mode. In the second part, state preference (SP) survey and binary logit model are exerted to figure out the utility of existing para-transit mode and planned BRT system. Finally, the impact of predicted BRT system on para-transit is evaluated. The extracted outcome based on high travel demand suggests 10 km network for the proposed BRT system, which is originated from the district tenth and it is ended at Kabul International Airport. As well as, the result from the disaggregate travel mode-choice model, based on SP and logit model indicates that the predicted mass rapid transit system has higher utility with the significant impact regarding the reduction of para-transit.

Keywords: BRT, para-transit, travel demand modelling, Kabul City, logit model

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Authors: Barbara Torres Rives

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Uveitis. Non-infectious anterior uveitis is a polygenic inflammatory eye disease, and it is suggested that mediated processes by the immune system (autoimmune or not) are the main mechanisms proposed in the pathogenesis of this type of uveitis. A relationship between infectious processes, digestive disorders, and a dysbiosis of the microbiome was recently described. In addition, alterations in the immune response associated with the initiation and progression of the disease have been described. Objective: The aim of this study was to identify factors related to the immune system associated with complicated non-infectious anterior uveitis. Methods: A cross-sectional observational analytical study was carried out. The universe consisted of all patients attending the ocular inflammation service of the Cuban Institute of Ophthalmology Ramón Pando Ferrer. The sample consisted of 213 patients diagnosed with non-infectious anterior uveitis. Results: Of the 213 patients with non-infectious anterior uveitis, the development of ophthalmologic complications predominated 56.3% (p=0.0094). In patients with complications was more frequent the presence of human leukocyte antigen-B27 allele (49.2%) (p<0.0001), decreased immunoglobulin G (24.2%, p=0.0124), increased immunoglobulin A (14.2%, p=0.0024), history of recurrent sepsis (59.2%, p=0.0018), recurrent respiratory infections (44.2%, p=0.0003), digestive alterations (40%, p=0.0013) and spondyloarthropathies (30%, p=0.0314). By logistic regression, it was observed that, for each completed year, the elevated risk for developing complicated non-infectious anterior uveitis in human leukocyte antigen-B27 allele positive patients (OR: 4.22, p=0.000), Conclusions: The control of recurrent sepsis at mucosal level and immunomodulation could prevent complications in non-infectious anterior uveitis. Therefore, the microbiome becomes the target of treatment and prevention of complications in non-infectious anterior uveitis.

Keywords: non-infectious anterior uveitis, immune system disorders, recurrent mucosal infections, microbiome

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Sameh A. S. Thabit Alariqi

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Food is widely packaged with plastic materials to prevent microbial contamination and spoilage. Ionizing radiation is widely used to sterilize the food-packaging materials. Sterilization by γ-radiation causes degradation for the plastic packaging materials such as embrittlement, stiffening, softening, discoloration, odour generation, and decrease in molecular weight. Many antioxidants can prevent γ-degradation but most of them are toxic. The migration of antioxidants to its environment gives rise to major concerns in case of food packaging plastics. In this attempt, we have aimed to utilize synergistic mixtures of stabilizers which are approved for food-contact applications. Ethylene-propylene-diene terpolymer (EPDM) have been melt-mixed with hindered amine stabilizers (HAS), phenolic antioxidants and organo-phosphites (hydroperoxide decomposer). Results were discussed by comparing the stabilizing efficiency of mixtures with and without phenol system. Among phenol containing systems where we mostly observed discoloration due to the oxidation of hindered phenol, the combination of secondary HAS, tertiary HAS, organo-phosphite and hindered phenol exhibited improved stabilization efficiency than single or binary additive systems. The mixture of secondary HAS and tertiary HAS, has shown antagonistic effect of stabilization. However, the combination of organo-phosphite with secondary HAS, tertiary HAS and phenol antioxidants have been found to give synergistic even at higher doses of -sterilization. The effects have been explained through the interaction between the stabilizers. After γ-irradiation, the consumption of oligomeric stabilizer significantly depends on the components of stabilization mixture. The effect of the organo-phosphite antioxidant on the overall stability has been discussed.

Keywords: ethylene-propylene-diene terpolymer, synergistic mixtures, gamma sterilization, gamma stabilization

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Authors: A. S. Strait, J. A. Bryk-Lucy, L. M. Ritchie

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Thrush is a common disease of ungulates primarily affecting the frog and sulci, caused by the anaerobic bacteria Fusobacterium necrophorum. Thrush accounts for approximately 45.0% of hoof disorders in horses. Prevention and treatment of thrush are essential to prevent horses from developing severe infections and becoming lame. Proper knowledge of hoof care and thrush treatments is crucial to avoid financial costs, unsoundness and lost training time. Research on the effectiveness of numerous commercial and homemade thrush treatments is limited in the equine industry. The objective of this study was to compare the effectiveness of three common thrush treatments for horses: weekly application of Thrush Buster, daily dilute bleach solution spray, or Metronidazole pastes every other day. Cases of thrush diagnosed by a veterinarian or veterinarian-trained researcher were given a score, from 0 to 4, based on the severity of the thrush in each hoof (n=59) and randomly assigned a treatment. Cases were rescored each week of the three-week treatment, and the final and initial scores were compared to determine effectiveness. The thrush treatments were compared with Thrush Buster as the reference at a significance level of α=.05. Binomial Logistic Regression Modeling was performed, finding that the odds of a hoof treated with Metronidazole to be thrush-free was 6.1 times greater than a hoof treated with Thrush Buster (p=0.001), while the odds of a hoof that was treated with bleach to be thrush-free was only 0.97 times greater than a hoof treated with Thrush Buster (p=0.970), after adjustment for treatment week. Of the three treatments utilized in this study, Metronidazole paste applied to the affected areas every other day was the most effective treatment for thrush in horses. There are many other thrush remedies available, and further research is warranted to determine the efficacy of additional treatment options.

Keywords: fusobacterium necrophorum, thrush, equine, horse, lameness

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Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

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Authors: Laila Khawaja

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In recent years, an increasing emphasis has been placed on measuring program implementation, in part because of the great variability in how complex interventions are delivered in real-life settings. There is an increased awareness that while conducting process evaluations, one should aim to identify and understand the complexities of intervention if they are to be used for future intervention development or the strategies needed to implement the same intervention in a different setting. Complex interventions are public health interventions that are not drugs or surgical procedures but have many potential active aspects of intervention. In this paper, process evaluations are aligned with MRC guidelines to identify contextual factors related to outcomes to assess the quality of implementation. This paper briefly discusses the perceptions of research team on the implementation of the intervention of ‘Engaging Adolescents in Changing Behaviour’ (EACH-B), a school-based complex intervention study aiming to improve diet and physical activity among adolescents aged 12-13 years. Through qualitative interviews and focus groups with 10 staff members, we aimed to understand their experiences and reflections on implementing the EACH-B trial delivered in 49 Schools around Hampshire, England. Data were uploaded into NVivo, and analysis was conducted using thematic analysis. The investigation revealed two overarching themes: (a) how the communication patterns with teachers were impacted during the delivery of implementation and (b) what were the team’s strategies to keep logistics aligned with the research process that impacted the overall implementation of the trial. The paper informs adaptation strategies used by the research team to establish and maintain effective communication with the teachers as well as the thoughtfulness of the team’s logistic strategy for the successful delivery of the trial.

Keywords: complex interventions, process evaluation, adaptation strategies, randomised controlled trial

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Authors: Teka Haile, Behailu Hawulte, Solomon Alemayehu

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Background: HIV virological failure still remains a problem in HV/AIDS treatment and care. This study aimed to describe the prevalence and identify the factors associated with viral non-suppression among HIV-positive adult patients on antiretroviral therapy in Woliso Town, Oromia, Ethiopia. Methods: A retrospective cross-sectional study was conducted among 424 HIV-positive patient’s attending antiretroviral therapy (ART) in Woliso Town during the period from August 25, 2020 to August 30, 2020. Data collected from patient medical records were entered into Epi Info version 2.3.2.1 and exported to SPSS version 21.0 for analysis. Logistic regression analysis was done to identify factors associated with viral load non-suppression, and statistical significance of odds ratios were declared using 95% confidence interval and p-value < 0.05. Results: A total of 424 patients were included in this study. The mean age (± SD) of the study participants was 39.88 (± 9.995) years. The prevalence of HIV viral load non-suppression was 55 (13.0%) with 95% CI (9.9-16.5). Second-line ART treatment regimen (Adjusted Odds Ratio (AOR) = 8.98, 95% Confidence Interval (CI): 2.64, 30.58) and routine viral load testing (AOR = 0.01, 95% CI: 0.001, 0.02) were significantly associated with virological non-suppression. Conclusion: Virological non-suppression was high, which hinders the achievement of the third global 95 target. The second-line regimen and routine viral load testing were significantly associated with virological non-suppression. It suggests the need to assess the effectiveness of antiretroviral drugs for epidemic control. It also clearly shows the need to decentralize third-line ART treatment for those patients in need.

Keywords: virological non-suppression, HIV-positive, ART, Woliso town, Ethiopia

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Authors: Akanimo Emene, Robert Edyvean

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In developing countries, a low cost method of wastewater treatment is highly recommended. Adsorption is an efficient and economically viable treatment process for wastewater. The utilisation of this process is based on the understanding of the relationship between the growth environment and the metal capacity of the biomaterial. Luffa cylindrica (LC), a plant material, was used as an adsorbent in adsorption design system of heavy metals. The chemically modified LC was used to adsorb heavy metals ions, lead and cadmium, from aqueous environmental solution at varying experimental conditions. Experimental factors, adsorption time, initial metal ion concentration, ionic strength and pH of solution were studied. The chemical nature and surface area of the tissues adsorbing heavy metals in LC biosorption systems were characterised by using electron microscopy and infra-red spectroscopy. It showed an increase in the surface area and improved adhesion capacity after chemical treatment. Metal speciation of the metal ions showed the binary interaction between the ions and the LC surface as the pH increases. Maximum adsorption was shown between pH 5 and pH 6. The ionic strength of the metal ion solution has an effect on the adsorption capacity based on the surface charge and the availability of the adsorption sites on the LC. The nature of the metal-surface complexes formed as a result of the experimental data were analysed with kinetic and isotherm models. The pseudo second order kinetic model and the two-site Langmuir isotherm model showed the best fit. Through the understanding of this process, there will be an opportunity to provide an alternative method for water purification. This will be provide an option, for when expensive water treatment technologies are not viable in developing countries.

Keywords: adsorption, luffa cylindrica, metal-surface complexes, pH

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Authors: D. Miroud, H. Mokaddem, M. Tata, N. Foucha

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The body of PDC (polycrystalline diamond compact) drill bit can be manufactured from two different materials, steel and tungsten carbide matrix. Commonly the steel body is produced by machining, thermal spraying a bonding layer and hardfacing of Ni-based matrix reinforced with fused eutectic tungsten carbide (WC/W2C). The matrix body bit is manufactured by infiltrating tungsten carbide particles, with a Copper binary or ternary alloy. By erosion-corrosion mechanisms, the PDC drill bits matrix undergoes severe damage, occurring particularly around the PDC inserts and near injection nozzles. In this study, we investigated the possibility to repair the damaged matrix regions by hardfacing technic. Ni-based hardfacing alloy reinforced with fused eutectic tungsten carbide is deposited on infiltrated WC-W-Ni substrate by oxyacetylene welding (OAW). The microstructure at the hardfacing / matrix interface is characterized by SEM- EDS, XRD and micro hardness Hv0.1. The hardfacing conditions greatly affect the dilution phenomenon and the distribution of carbides at the interface, without formation of transition zone. During OAW welding deposition, interdiffusion of atoms occurs: Cu and Sn diffuse from infiltrated matrix substrate into hardfacing and simultaneously Cr and Si alloy elements from hardfacing diffuse towards the substrate. The dilution zone consists of a nickel-rich phase with a heterogeneous distribution of eutectic spherical (Ni-based hardfacing alloy) and irregular (matrix) WC/W2C carbides and a secondary phase rich in Cr-W-Si. Hardfacing conditions cause the dissolution of banding around both spherical and irregular carbides. The micro-hardness of interface is significantly improved by the presence of secondary phase in the inter-dendritic structure.

Keywords: dilution, dissolution, hardfacing, infiltrated matrix, PDC drill bits

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: William Thomas Worster

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Diplomatic assurances issued by states declaring that they will not mistreat individuals returned to them occupy a strange middle ground between being legal and non-legal obligations. States assert that they are non-binding, yet at other times that they are binding. However, this assertion may not be the end of the discussion. The International Court of Justice and other tribunals have concluded that similar instruments were binding, states have disagreed that certain similar instruments were binding, and the Vienna Convention on the Law of Treaties and its travaux prépératoires do not appear to contemplate non-binding instruments. This paper is a case study of diplomatic assurances but, by necessity, touches on the delicate question of whether certain texts are treaties, promises, or non-binding political statements. International law, and law in general, requires a binary approach to obligation. All communications must be binding or not, even if the fit is not precise. Through this study, we will find that some of the obligations in certain assurances can be understood as legal and some not. We will attempt to state the current methodology for determining which obligations are legal under the law of treaties and law on binding unilateral promises. The paper begins with some background of the legal environment of diplomatic assurances and their use in cases of expulsion. The paper then turns to discuss the legal nature of diplomatic assurances, proceeding to address various possibilities for legal value as treaties and as binding unilateral statements. This paper will not examine the legal value of diplomatic assurances solely under customary international law other than the way in which customary international law might further refine the treaty definition. In order to identify whether any assurances are contained in legal acts, this study identifies a pool of relevant assurances and qualitatively analyzes whether any of those are contained in treaties or binding unilateral statements. To the author’s best knowledge, this study is the first large-scale, qualitative qualitative analysis of assurances as a group of instruments that accounts for their heterogenous nature. It is also the first study to identify the indicators of whether an instrument is a treaty or promise.

Keywords: diplomatic assurances, deportation, extradition, expulsion, non-refoulement, torture, persecution, death penalty, human rights, memorandum of understanding, promises, secret, monitoring, compliance, enforcement

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Ksenija Milošević, Davor Lončarević, Tihana Mudrinić, Jasmina Dostanić

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Heterogeneous photocatalytic processes have gained growing interest as an efficient method to generate hydrogen by using clean energy sources and degrading various organic pollutants. The main obstacles that restrict efficient photoactivity are narrow light-response range and high rates of charge carrier recombination. The formation of heterojunction by combining a semiconductor with low VB and a semiconductor with high CB and a suitable band gap was found to be an efficient method to prepare more sensible materials with improved charge separation, appropriate oxidation and reduction ability, and enhanced visible-light harvesting. In our research, various binary heterojunction systems based on the wide-band gap (TiO₂) and narrow bandgap (g-C₃N₄, CuO, and Co₂O₃) photocatalyst were studied. The morphology, optical, and electrochemical properties of the photocatalysts were analyzed by X-ray diffraction (XRD), scanning electron microscopy (FE-SEM), N₂ physisorption, diffuse reflectance measurements (DRS), and Mott-Schottky analysis. The photocatalytic performance of the synthesized catalysts was tested in single and simultaneous systems. The synthesized photocatalysts displayed good adsorption capacity and enhanced visible-light photocatalytic performance. The mutual interactions of pollutants on their adsorption and degradation efficiency were investigated. The interfacial connection between photocatalyst constituents and the mechanism of the transport pathway of photogenerated charge species was discussed. A radical scavenger study revealed the interaction mechanisms of the photocatalyst constituents in single and multiple pollutant systems under solar and visible light irradiation, indicating the type of heterojunction system (Z scheme or type II).

Keywords: bandgap alignment, heterojunction, photocatalysis, reaction mechanism

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Authors: Anthropometric, Physical Fitness Ability Profile of Elite, Non-Elite Boxers of Manipur

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Background: Boxing is one of the oldest combat sports where different anthropological and fitness ability parameters determine performance. It is characterized by short duration, high intensity bursts of activity. The purpose of this research was to determine anthropometric and physical fitness profile of male elite and non-elite boxers of Manipur and to compare the two groups. Materials and Methods: Nineteen subjects were selected as elite boxers and twenty-four were non-elite boxers of Manipur. A cross-sectional study was conducted on anthropometric measurements and physical fitness ability tests on 33 subjects (elite and non-elite boxers). Statistical analysis was done using descriptive statistics, t-test and logistic regression with the help of SPSS version 15 software. Results: Results showed elite boxers have significantly reduced neck girth and calf girth as compare to non-elite boxers. Elite boxers have significantly lower sub scapular skin fold (SSF) and supra iliac skin fold (SISF) than their counterparts. Higher stature, larger BTB and lower percent fat are associated with higher performance in boxing. Sit ups (SU), standing Broad Jump (SBJ), Plat taping (PT), Sit and reach (SAR) and Harvard Step Test (HST) are predicted as most contributing factors enhancing performance level among the physical fitness components. Elite boxers are found to have more functional strength (sit ups), higher explosive strength (SBJ), more agility (PT), cardio-vascular endurance and flexibility (SAR) than non-elite boxers. Conclusion: In conclusion, lower fat, higher lean body mass, larger bi-trochantric breadth, high explosive strength, agility and flexibility are significantly associated with higher performance and chance of becoming elite boxers.

Keywords: anthropometry, elite and non-elite boxers, Manipur, physical fitness

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Authors: Nichole Patricia Pedrina, Karl Jasper Sumande, Alice Joan Ferrer

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Nogas Island situated in the municipality of Anini-y in the province of Antique is endowed with natural resources especially a thriving marine ecosystem that attracts tourists all year round. But despite its beauty and emerging popularity, the island and its surrounding waters remain vulnerable to degradation brought about by anthropocentric activities. An emphasis on the protection and conservation is paramount in order to ensure environmental sustainability over time. This study was conducted in order to determine the willingness-to-pay (WTP) of the local residents of Anini-y, Antique for the conservation of Nogas Island and its surrounding waters. The Contingent Valuation Method (CVM) was used to determine the WTP of the study participants. In addition, the study also described the socio-demographic and economic characteristics, the level of awareness, knowledge and attitude towards the conservation and the reasons for the willingness to pay off the residents for the conservation of the island and its surrounding waters. A pilot-tested interview schedule was used to collect data from 320 randomly selected study participants in 8 barangays in the municipality of Anini-y from January to December 2017. Binary logit regression was conducted in order to identify factors affecting the study participants’ WTP. The results revealed that 54.69 percent of the study participants were willing to pay (with adjustment to the level of certainty) for the conservation program. The sex, monthly household income, randomly assigned bid price and the knowledge index were the variables that affected the willingness-to-pay of the study participants for both with and without adjustment to the level of certainty. The monthly mean WTP of the study participants with and without adjustment to the level of certainty were P115 and P104.5, respectively. This study can serve as a guide for the municipality of Anini-y in creating a policy or program that aims to conserve and protect Nogas Island and its surrounding waters.

Keywords: economic valuation, environmental conservation, total economic value, willingness to pay

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Authors: Shayli Varasteh Moradi, Wayne A. Johnston, Dejan Gagoski, Kirill Alexandrov

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The demand for a rapid and more efficient technique to identify protein-protein interaction particularly in the areas of therapeutics and diagnostics development is growing. The method described here is a rapid in vitro protein-protein interaction analysis approach based on AlphaLISA technology combined with Leishmania tarentolae cell-free protein production (LTE) system. Cell-free protein synthesis allows the rapid production of recombinant proteins in a multiplexed format. Among available in vitro expression systems, LTE offers several advantages over other eukaryotic cell-free systems. It is based on a fast growing fermentable organism that is inexpensive in cultivation and lysate production. High integrity of proteins produced in this system and the ability to co-express multiple proteins makes it a desirable method for screening protein interactions. Following the translation of protein pairs in LTE system, the physical interaction between proteins of interests is analysed by AlphaLISA assay. The assay is performed using unpurified in vitro translation reaction and therefore can be readily multiplexed. This approach can be used in various research applications such as epitope mapping, antigen-antibody analysis and protein interaction network mapping. The intra-viral protein interaction network of Zika virus was studied using the developed technique. The viral proteins were co-expressed pair-wise in LTE and all possible interactions among viral proteins were tested using AlphaLISA. The assay resulted to the identification of 54 intra-viral protein-protein interactions from which 19 binary interactions were found to be novel. The presented technique provides a powerful tool for rapid analysis of protein-protein interaction with high sensitivity and throughput.

Keywords: AlphaLISA technology, cell-free protein expression, epitope mapping, Leishmania tarentolae, protein-protein interaction

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Authors: A. Rezazadeh, N. Omidvar, H. Eini-Zinab

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Food insecurity (FI) influences may result in poor physical and mental health outcomes. Minor ethnic group may experience higher level of FI, and this situation may be related with higher depression prevalence. The aim of this study was to determine the association of depression with food security status in major (Azeri) and minor (Kurdish) ethnicity living in Urmia, West Azerbaijan, north of Iran. In this cross-sectional study, 723 participants (427 women and 296 men) aged 20–64 years old, from two ethnic groups (445 Azeri and 278 Kurdish), were selected through a multi stage cluster systematic sampling. Depression rate was assessed by “Beck” short form questionnaire (validated in Iranians) through interviews. Household FI status (HFIS) was measured using adapted HFI access scale through face-to-face interviews at homes. Multinomial logistic regression was used to estimate odds ratios (OR) of depression across HFIS. Higher percent of Kurds had moderate and severe depression in comparison with Azeri group (73 [17.3%] vs. 86 [27.9%]). There were not any significant differences between the two ethnicities in mild depression. Also, of all the subjects, moderate-to-sever FI was more prevalent in Kurds (28.5%), compared to Azeri group (17.3%) [P < 0.01]. Kurdish ethnic group living in food security or mild FI households had lower chance to have symptom of severe depression in comparison to those with sever FI (OR=0.097; 95% CI: 0.02-0.47). However, there was no significant association between depression and HFI in Azeri group. Findings revealed that the severity of HFI was related with severity depression in minor studied ethnic groups. However, in Azeri ethnicity as a major group, other confounders may have influence on the relation with depression and FI, that were not studied in the present study.

Keywords: depression, ethnicity, food security status, Iran

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