Search results for: logistic regression models

Authors: Rachna Agarwal, R. Naveen, Darpan Thakre, Rohit Shahi, Maryam Abbasi, Upendra Rathore, Latika Gupta

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Aim: Retinal changes are the window to systemic vasculature. Therefore, we explored retinal changes in patients with idiopathic inflammatory myopathies (IIM) as a surrogate for vascular health. Methods: Adult and juvenile IIM patients visiting a tertiary care centre in 2021 satisfying the International Myositis Classification Criteria were enrolled for detailed ophthalmic examination in comparison with healthy controls (HC). Patients with conditions that precluded thorough posterior chamber examination were excluded. Scale variables are expressed as median (IQR). Multivariate analysis (binary logistic regression-BLR) was conducted, adjusting for age, gender, and comorbidities besides factors significant in univariate analysis. Results: 43 patients with IIM [31 females; age 36 (23-45) years; disease duration 5.5 (2-12) months] were enrolled for participation. DM (44%) was the most common diagnosis. IIM patients exhibited frequent attenuation of retinal vessels (32.6% vs. 4.3%, p <0.001), AV nicking (14% vs. 2.2%, p=0.053), and vascular tortuosity (18.6% vs. 2.2%, p=0.012), besides decreased visual acuity (53.5% vs. 10.9%, p<0.001) and immature cataracts (34.9% vs. 2.2%, p<0.001). Attenuation of vessels [OR 10.9 (1.7-71), p=0.004] emerged as significantly different from HC after adjusting for covariates in BLR. Notably, adults with IIM were more predisposed to retinal abnormalities [21 (57%) vs. 1 (16%), p=0.068], especially attenuation of vessels [14(38%) vs. 0(0), p=0.067] than jIIM. However, no difference was found in retinal features amongst the subtypes of adult IIM, nor did they correlate with MDAAT, MDI, or HAQ-DI. Conclusion: Retinal microvasculopathy and diminution of vision occur in nearly one-third to half of the patients with IIM. Microvasculopathy occurs across subtypes of IIM, and more so in adults, calling for further investigation as a surrogate for damage assessment and potentially even systemic vascular health.

Keywords: idiopathic inflammatory myopathies, vascular health, retinal microvasculopathy, arterial attenuation

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Authors: Kang-Mo Jung

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Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers.

Keywords: least absolute deviation, quadratic programming, robustness, support vector machine, weight

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Authors: Sandipika Gupta

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Objective: Due to admirably low 30-day mortality rates for paediatric cardiac surgery, it is now pertinent to turn towards more intermediate-length outcomes such as morbidities closely associated with these surgeries. One such morbidity, major adverse events (MAE) comprises a group of adverse outcomes associated with paediatric cardiac surgery (e.g. cardiac arrest, major haemorrhage). Methods: This is a retrospective study that analysed the incidence and impact of MAE which was the primary outcome in the UK population. The data was collected in 5 centres between October 2015 and June 2017, amassing 3090 surgical episodes. The incidence and risk factors for MAE, were assessed through descriptive statistical analyses and multivariate logistic regression. The secondary outcomes of life status at 6 months and the length of hospital stay were also evaluated to understand the impact of MAE on patients. Results: Out of 3090 episodes, 134 (4.3%) had a postoperative MAE. The majority of the episodes were in: neonates (47%, P<0.001), high-risk cardiac diagnosis groups (20.1%, P<0.001), episodes with longer 5mes on the bypass (72.4%, P<0.001) and urgent surgeries (57.9%, P<0.001). Episodes reporting MAE also reported longer lengths of stay in hospital (29 days vs 9 days, P<0.001). Furthermore, patients experiencing MAE were at a higher risk of mortality at the 6-month life status check (mortality rates: 29.2% vs 2%, P<0.001).Conclusions: Key risk factors were identified. An important negative impact of MAE was found for patients. The identified risk factors could be used to profile and flag at-risk patients. Monitoring of MAE rates and closer investigation into the care pathway before and after individual MAEs in children’s heart units may lead to a reduction in these terrible events.

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Authors: Shaan Khosla, Jon Krohn

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In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

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Authors: Katalina Grigorova, Kaloyan Mironov

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The paper focuses on the benefits of business process modeling. Although this discipline is developing for many years, there is still necessity of creating new opportunities to meet the ever-increasing users’ needs. Because one of these needs is related to the conversion of business process models from one standard to another, the authors have developed a converter between BPMN and EPC standards using workflow patterns as intermediate tool. Nowadays there are too many systems for business process modeling. The variety of output formats is almost the same as the systems themselves. This diversity additionally hampers the conversion of the models. The presented study is aimed at discussing problems due to differences in the output formats of various modeling environments.

Keywords: business process modeling, business process modeling standards, workflow patterns, converting models

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: Usama Bin Zubair, Muhammad Azeem

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Introduction: Depression has been one of the most commonly diagnosed mental health disorders in Pakistan. A Census conducted by the government of Pakistan in 2017 showed that more than 10000 trans-genders live in Pakistan. HIV, illicit substance use and mental health issues, including depression, have been the main health problems faced by them. Trans-gender population has been suffering from depressive illness more than normal population all over the world. Aim: To assess the prevalence of depression among the transgender population and analyze the relationship of socio-demographic factors with depression. Subjects and Methods: The sample population comprised of one hundred and forty-two transgender people of Rawalpindi and Islamabad. Beck depressive inventory II (BDI-II) was used to record the presence and severity of the depressive symptoms. Depressive symptoms were categorized as mild, moderate and severe. Relationship of the age, smoking, family income, illicit substance use and education were studied with the presence of depressive symptoms among these transgender people of twin cities of Pakistan. Results: A total of 142 transgender people were included in the final analysis. The mean age of the study participants was 39.55 ± 6.18. Out of these, 45.1% had no depressive symptoms while 31.7% had mild, 12.7% had moderate and 10.6% had severe depressive symptomatology. After applying the binary logistic regression, we found that the presence of depressive symptoms had a significant association with illicit substance use among the target population. Conclusion: This study showed a high prevalence of depressive symptoms among the transgender population in the twin cities of Pakistan. Use of illicit substances like tobacco, cannabis, opiates, and alcohol should be discouraged to prevent mental health problems.

Keywords: depression, transgender, prevalence, sociodemographic factors

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Authors: Fatima-Zahra Eddoug, Jamal Benhra, Rajaa Benabbou

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Several project management models exist in the literature and the most used ones are the hybrids for their multiple advantages. Our objective in this paper is to analyze the existing models, which are based on the Lean and Agile approaches and to propose a novel framework with the convenient tools that will allow efficient management of a general project. To create the desired framework, we were based essentially on 7 existing models. Only the Scrum tool among the agile tools was identified by several authors to be appropriate for project management. In contrast, multiple lean tools were proposed in different phases of the project.

Keywords: agility, hybrid project management, lean, scrum

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Authors: Jingjue Bao, Ye Li, Yujie Qi

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The explosive growth of ride-hailing has reshaped residents' travel behavior and plays a crucial role in urban mobility within the built environment. Contributing to the research of the spatial variation of ride-hailing demand and its relationship to the built environment and socioeconomic factors, this study utilizes multi-source data from Haikou, China, to construct a Multi-scale Geographically Weighted Regression model (MGWR), considering spatial scale heterogeneity. The regression results showed that MGWR model was demonstrated superior interpretability and reliability with an improvement of 3.4% on R2 and from 4853 to 4787 on AIC, compared with Geographically Weighted Regression model (GWR). Furthermore, to precisely identify the surrounding environment of sampling point, DeepLabv3+ model is employed to segment street-level images. Features extracted from these images are incorporated as variables in the regression model, further enhancing its rationality and accuracy by 7.78% improvement on R2 compared with the MGWR model only considered region-level variables. By integrating multi-scale geospatial data and utilizing advanced computer vision techniques, this study provides a comprehensive understanding of the spatial dynamics between ride-hailing demand and the urban built environment. The insights gained from this research are expected to contribute significantly to urban transportation planning and policy making, as well as ride-hailing platforms, facilitating the development of more efficient and effective mobility solutions in modern cities.

Keywords: travel behavior, ride-hailing, spatial relationship, built environment, street-level image

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Authors: Hsien Hua Kuo, Wen Chun Lin, Chia Chi Hsu, Hsien Wen Kuo

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Objective: Despite the high prevalence of musculoskeletal disorders (MSDs) among nurses, which has been consistently observed in the studies of Western countries, very little information regarding intensity of workload and work-related quality of life (WRQOL) related to MSDs among nurses is available in Taiwan. The objective of this study is to investigate the factors affecting musculoskeletal disorders in nurses from a hospital. Methods: 550 nurses from a hospital in Taoyuan were interviewed using a modified standardized Nordic Musculoskeletal (NMQ) questionnaire which contained the demographic information, workplace condition and musculoskeletal disorders. Results: Response rate of nurses were 92.5% from a teaching hospital. Based on medical diagnosis by physician, neck of musculoskeletal disorders had the highest percentage in nine body portions. The higher percentage of musculoskeletal disorders in nurses found from wards of internal and surgery. Severity and symptoms of musculoskeletal disorders diagnosed by self-reported questionnaire significantly correlated with WRQOL, job satisfaction and intensity of workload among nurses based on the logistic regression model. Conclusion: The severity and symptoms of musculoskeletal disorders among nurses showed a dose-dependent with WRQOL and workload. When work characteristics in hospital were modified, the severity of musculoskeletal disorders among nurses will be decreased and alleviated. Comment: Multifaceted ergonomic intervention programme to reduce the prevalence of MSDs among nurses was by encouraging nurses to do more physical activity which will make them more flexible and increase their strength. Therefore, the head nurse should encourage nurses to regularly physical activity and to modify unfitting ergonomic environment in order to reduce the prevalence of MSDs.

Keywords: musculoskeletal disorders, nurse, WRQOL, job satisfaction

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Authors: Marjan Mahdavi-Roshan, Arsalan Salari, Mahbobeh Gholipour

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Hypertension is one of the important reasons of morbidity and mortality in countries, including Iran. It has been shown that hypertension is a consequence of the interaction of genetics and environment. Nutrients have important roles in the controlling of blood pressure. We assessed dietary habit and anthropometric status in patients with hypertension in the north of Iran, and that have special dietary habit and according to their culture. This study was conducted on 127 patients with newly recognized hypertension and the 120 normotensive participants. Anthropometric status was measured and demographic characteristics, and medical condition were collected by valid questionnaires and dietary habit assessment was assessed with 3-day food recall (two weekdays and one weekend). The mean age of participants was 58 ± 6.7 years. The mean level of energy intake, saturated fat, vitamin D, potassium, zinc, dietary fiber, vitamin C, calcium, phosphorus, copper and magnesium was significantly lower in the hypertensive group compared to the control (p < 0.05). After adjusting for energy intake, positive association was observe between hypertension and some dietary nutrients including; Cholesterol [OR: 1.1, P: 0.001, B: 0.06], fiber [OR: 1.6, P: 0.001, B: 1.8], vitamin D [OR: 2.6, P: 0.006, B: 0.9] and zinc [OR: 1.4, P: 0.006, B: 0.3] intake. Logistic regression analysis showed that there was not significant association between hypertension, weight and waist circumference. In our study, the mean intake of some nutrients was lower in the hypertensive individuals compared to the normotensive individual. Health training about suitable dietary habits and easier access to vitamin D supplementation in patients with hypertension are cost-effective tools to improve outcomes in Iran.

Keywords: hypertension, north of Iran, dietary intake, weight

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Authors: M. Ouassaf, S. Belaidi

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Aromatase is an estrogen biosynthetic enzyme belonging to the cytochrome P450 family, which catalyzes the limiting step in the conversion of androgens to estrogens. As it is relevant for the promotion of tumor cell growth. A set of thirty 1,2,3-triazole derivatives was used in the quantitative structure activity relationship (QSAR) study using regression multiple linear (MLR), We divided the data into two training and testing groups. The results showed a good predictive ability of the MLR model, the models were statistically robust internally (R² = 0.982) and the predictability of the model was tested by several parameters. including external criteria (R²pred = 0.851, CCC = 0.946). The knowledge gained in this study should provide relevant information that contributes to the origins of aromatase inhibitory activity and, therefore, facilitates our ongoing quest for aromatase inhibitors with robust properties.

Keywords: aromatase inhibitors, QSAR, MLR, 1, 2, 3-triazole

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Authors: A. Abdullah, N. Faghih Mirzaei, S. Hany Haron

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Neighbourhood environment walkability on reported physical activity (PA) levels of students of Universiti Sains Malaysia (USM) in Malaysia. Compared with previous generations, today’s young people spend less time playing outdoors and have lower participation rates in PA. Research suggests that negative perceptions of neighbourhood walkability may be a potential barrier to adolescents’ PA. The sample consisted of 200 USM students (to 24 years old) who live outside of the main campus and engage in PA in sport halls and sport fields of USM. The data were analysed using the t-test, binary logistic regression, and discriminant analysis techniques. The present study found that youth PA was affected by neighbourhood environment walkability factors, including neighbourhood infrastructures, neighbourhood safety (crime), and recreation facilities, as well as street characteristics and neighbourhood design variables such as facades of sidewalks, roadside trees, green spaces, and aesthetics. The finding also illustrated that active students were influenced by street connectivity, neighbourhood infrastructures, recreation facilities, facades of sidewalks, and aesthetics, whereas students in the less active group were affected by access to destinations, neighbourhood safety (crime), and roadside trees and green spaces for their PAs. These results report which factors of built environments have more effect on youth PA and they message to the public to create more awareness about the benefits of PA on youth health.

Keywords: fear of crime, neighbourhood built environment, physical activities, street characteristics design

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Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

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Authors: A. Kaeotawee, N. Bunmas, W. Chomthong

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Background: The cancellation of elective surgery is a major indicator of poor operating room efficiency. Furthermore, it is recognized as a major cause of emotional trauma to patients as well as their families. This study was carried out to assess the incidence and causes of elective surgery cancellation in our setting and to find the appropriate solutions for better quality management. Objective: To determine the incidence and causes of elective surgery cancellations in Songklanagarind Hospital. Material and Method: A prospective survey was conducted from September to November 2012. All patients who had their scheduled elective operations cancelled were assessed. Data was collected on the following 2 components: (1) patient demographics;(2) main reasons for cancellations, which were grouped into patient-related factors and organizational-related factors. Data are reported as a percentage of patients whose operations were cancelled. The association between cancellation status and patient demographics was assessed using univariate logistic regression. Results: 2,395 patients were scheduled for elective surgery and of these 343 (14.3%) had their operations cancelled. Cardiothoracic surgery had the highest rate of cancellations (28.7%) while the least number of cancellations occurred in ophthalmology (10.1%). The main reasons for cancellations were related to the unit's organization (53.6%), due to the surgeon (48.4%). Patient related causes (46.4%), due to non medical reasons (32.1%). The most common cause of cancellation by the surgeon was lack of theater time (21.3%), by patients due to the patient’s nonappearance (25.1%). Cancellation was significantly associated with type of patient, health insurance, type of anesthesia and specialties (p<0.05). Conclusion: Surgery cancellations by surgeons relating to a lack of theater time was a significant problem in our setting. Appropriate solutions for better quality improvement are needed.

Keywords: elective cases, surgery cancellation, quality management, appropriate solutions

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Authors: Joseph Marfo Boaheng, Daniel Ansong, Eugenia Amporfo

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Health Insurance is proposed to provide financial protection against catastrophic health care cost arising from disease. Ghana has had a National Health Insurance Scheme (NHIS) since 2003 with the current enrolment/retention rate of 36%. The main goal of the scheme is to provide equity in the health sector as well as ensuring affordable health care for the poor. However, the current payment system is not flexible to attract significant proportion of the poor informal sector onto the scheme. Looking at the extensive use of mobiles in the Ghana where about 29,220,602.00 registered mobile phone lines are actively in used as of June 2014, paying health insurance premium through mobile phone could be feasible to attract larger proportion of the informal sector onto the scheme. Methodology: The quantitative cross-sectional survey was used to solicit the required information from 877 respondents living in Kumasi, the second capital city of Ghana. The magnitude of the effect of Pro-rata system (flexible payment terms) on NHIS enrollment rate was estimated with binary logistic regression model. Results: The odds for an individual to enroll onto NHIS with mobile phone increases about 2 times more when payment of insurance premium is on pro-rata basis ie. flexible payment terms (p=0.008, CI=1.212-3.565). Conclusion: The study advocates the National Health Insurance Authority consider this alternative payment system that has the potential of attracting a greater proportion of the informal sector to be enrolled or retained onto the scheme.

Keywords: enrollment, health insurance, mobile phone, pro-rata

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Authors: Z. Bayat

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A quantitative structure–property relationship (QSPR) study was performed to develop models those relate the structures of 65 Kovats retention index (RI) of adamantane derivatives. Molecular descriptors derived solely from 3D structures of the molecular compounds. The usefulness of the quantum chemical descriptors, calculated at the level of the DFT theories using 6-311+G** basis set for QSAR study of adamantane derivatives was examined. The use of descriptors calculated only from molecular structure eliminates the need to experimental determination of properties for use in the correlation and allows for the estimation of RI for molecules not yet synthesized. The prediction results are in good agreement with the experimental value. A multi-parametric equation containing maximum Four descriptors at B3LYP/6-31+G** method with good statistical qualities (R2train=0.913, Ftrain=97.67, R2test=0.770, Ftest=3.21, Q2LOO=0.895, R2adj=0.904, Q2LGO=0.844) was obtained by Multiple Linear Regression using stepwise method.

Keywords: DFT, adamantane, QSAR, Kovat

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Authors: O. S. Asaolu, A. Bolorunduro

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Background: Unwanted, mistimed and unintended pregnancy is an important public health issue and the most common cause of maternal mortality in developing countries. Unintended pregnancy is a potential hazard for every sexually active woman as it most times ends in unsafe abortion. The study aimed at assessing the pre-conception contraceptive use, prevalence of unintended pregnancies and the non-contraceptive factors associated with unintended pregnancy amongst currently married women in Osun state. Methodology: A descriptive cross-sectional study among randomly selected 341 currently married pregnant women attending antenatal clinics in Ilesa town of Osun state was conducted in 5 health facilities. A random selection of 5 of the 22 health facilities in the state was done. Data was collected through a self-administered questionnaire and all completed questionnaires were analyzed with SPSS. Result: About two-fifth of the currently pregnant women (40%) who has never used an FP method reported that their current pregnancy was unintended. The results indicate that age of women, age at first sex, substance use, total children ever born of children, religion, and extramarital affairs were key predictors of unintended pregnancy. Women who have higher parity are more likely to experience unintended pregnancy compared to women with lower parity (odds ratio, 0.25). Furthermore, those women who don’t engage in extra marital affairs were less likely to experience unintended pregnancy (odds ratio, 0.3) compared to those who do not. Contribution to knowledge: The predicted probability, using logistic regression, has shown that women who engage in extramarital affairs and women with high parity are more likely to have unintended pregnancy. Conclusion: Behaviour change programs should aim to reduce unintended pregnancy by focusing mostly on identified factors so that the need for abortion is decreased and the overall well-being of the family is maintained and enhanced.

Keywords: unintended pregnancy, factors, pregnant women, Nigeria

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Authors: Iman Fakhrian, Ali Salehi Manzari

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Consistent Profitability is one of the most important priorities in manufacturing companies. One of the fundamental factors for increasing the companies profitability is cost management. Isfahan's mobarakeh steel company is one of the largest producers of the slab product grades in the middle east. Raw material cost constitutes about 70% of the company's expenditures. The costs of the ferro alloys have a remarkable contribution of the raw material costs. This research aims to determine the ferro alloys which have significant effect on the variability of the standard cost of the slab product grades. Used data in this study were collected from standard costing system of isfahan's mobarakeh steel company in 2022. The results of conducting the regression analysis model show that expense items: 03020, 03045, 03125, 03130 and 03150 have dominant role in variability of the standard cost of the slab product grades. In other words, the mentioned ferro alloys have noticeable and significant role in variability of the standard cost of the slab product grades.

Keywords: consistent profitability, ferro alloys, slab product grades, regression analysis

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Authors: Narges Ebadi, Davod Ahmadi, Hiliary Monteith, Hugo Melgar-Quinonez

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Migration cannot necessarily relieve pressure on water resources in origin communities, and male out-migration can increase the water management burden of women. However, inflows of financial remittances seem to offer possibilities of investing in improving drinking-water access. Therefore, remittances may be an important pathway for migrants to support water security. This paper explores the association between water access and the receipt of remittances in households in sub-Saharan Africa. Data from round 6 of the 'Afrobarometer' surveys in 2016 were used (n= 49,137). Descriptive, bivariate and multivariate statistical analyses were carried out in this study. Regardless of country, findings from descriptive analyses showed that approximately 80% of the respondents never received remittance, and 52% had enough clean water. Only one-fifth of the respondents had piped water supply inside the house (19.9%), and approximately 25% had access to a toilet inside the house. Bivariate analyses revealed that even though receiving remittances was significantly associated with water supply, the strength of association was very weak. However, other factors such as the area of residence (rural vs. urban), cash income frequencies, electricity access, and asset ownership were strongly associated with water access. Results from unadjusted multinomial logistic regression revealed that the probability of having no access to piped water increased among remittance recipients who received financial support at least once a month (OR=1.324) (p < 0.001). In contrast, those not receiving remittances were more likely to regularly have a water access concern (OR=1.294) (p < 0.001), and not have access to a latrine (OR=1.665) (p < 0.001). In conclusion, receiving remittances is significantly related to water access as the strength of odds ratios for socio-demographic factors was stronger.

Keywords: remittances, water access, SSA, migration

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Authors: Navid Daryasafar, Nima Farshidfar

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In this article, we make attempt to hide error in video with an emphasis on the time-wise use of autoregressive (AR) models. To resolve this problem, we assume that all information in one or more video frames is lost. Then, lost frames are estimated using analogous Pixels time information in successive frames. Accordingly, after presenting autoregressive models and how they are applied to estimate lost frames, two general methods are presented for using these models. The first method which is the same standard method of autoregressive models estimates lost frame in unidirectional form. Usually, in such condition, previous frames information is used for estimating lost frame. Yet, in the second method, information from the previous and next frames is used for estimating the lost frame. As a result, this method is known as bidirectional estimation. Then, carrying out a series of tests, performance of each method is assessed in different modes. And, results are compared.

Keywords: error steganography, unidirectional estimation, bidirectional estimation, AR linear estimation

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

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Authors: I Putu Yuda Hananta, Inke Kusumastuti

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The latest Indonesian Biology and Behavior Surveillance (IBBS) conducted by Indonesian Ministry of Health reported a large proportion of men who have sex with men (MSM) engaging in oral sex in their recent sexual history. While it is considered as a pleasuring and safe, oral sex might facilitate the transmission of various sexually transmitted infection (STI) pathogens. This study was aimed to investigate the oral sex practice among MSM in Indonesian urban settings to help delineate demographic and behavior determinants of such practice. In 2014, 501 MSM in 8 clinic-based and outreach STI services were recruited in Jakarta, Yogyakarta and Denpasar, Indonesia. Respondents completed a self-administered questionnaire inquiring about their demographics and sexual history. Median age (interquartile range) of the respondents was 27 (24-30) years; most completed senior high school (54.3%), worked in informal jobs (57.9%), and single (60.9%); and 32.3% reported receiving money in exchange for sex. Oral sex was practiced by most respondents: insertive only (10.0%), receptive only (6.0%), and both (82.4%). A separate multivariable analysis was performed using logistic regression to identify the determinants for receptive and insertive oral sex. Factors associated with receptive oral sex were having more than 10 sex partner(s) in the preceding 6 months vs 1 partner, adjusted odds ratio (aOR) [95% CI]=3.40 [1.22-9.42], p=0.03; and history of receptive-insertive anal sex vs no history, aOR=4.37 [1.76-10.82], p=0.01. Factors associated with insertive oral sex were receiving money for sex vs. not receiving, aOR=2.98 [1.10-8.04], p=0.02; and history of receptive-insertive anal sex vs. no history, aOR=2.10 [0.51-8.74], p<0.001. Only a few respondents reported consistent condom use (11.6% and 12.0% for receptive and insertive oral sex, respectively). Our findings demonstrated that while oral sex is a common practice among MSM, the consistency of condom use in oral sex is very low. In addition, certain sex behavior (number of sex partners, sex work and history of anal sex) were associated with oral sex, and this might need to be addressed during health promotion efforts on STI prevention through oral-genital contact.

Keywords: behavior, Indonesia, men who have sex with men, oral sex

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Authors: Débora V. Busman, Venerando E. Amaro, Mattheus da C. Prudêncio

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Prognostic studies of the shoreline are of utmost importance for Ponta Negra Beach, located in Natal, Northeastern Brazil, where the infrastructure recently built along the shoreline is severely affected by flooding and erosion. This study compares shoreline predictions using three linear regression methods (LMS, LRR and WLR) and tries to discern the best method for different shoreline position scenarios. The methods have shown erosion on the beach in each of the scenarios tested, even in less intense dynamic conditions. The WLA_A with confidence interval of 95% was the well-adjusted model and calculated a retreat of -1.25 m/yr to -2.0 m/yr in hot spot areas. The change of the shoreline on Ponta Negra Beach can be measured as a negative exponential curve. Analysis of these methods has shown a correlation with the morphodynamic stage of the beach.

Keywords: coastal erosion, prognostic model, DSAS, environmental safety

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Authors: Jeongmin Kim, Eunju Lee, Kwang Ryel Ryu

Abstract:

This paper addresses the problem of predictive control for energy management of large-scaled exhibition halls, where a lot of energy is consumed to maintain internal atmosphere under certain required conditions. Predictive control achieves better energy efficiency by optimizing the operation of air-conditioning facilities with not only the current but also some future status taken into account. In this paper, we propose to use predictive models learned from past sensor data of hall environment, for use in optimizing the operating plan for the air-conditioning facilities by simulating future environmental change. We have implemented an emulator of an exhibition hall by using EnergyPlus, a widely used building energy emulation tool, to collect data for learning environment-change models. Experimental results show that the learned models predict future change highly accurately on a short-term basis.

Keywords: predictive control, energy management, machine learning, optimization

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Authors: Seyedtaghi Mirmohammadi, Jamshid Yazdani, Syavash Etemadi Nejad

Abstract:

One of the main indoor air parameters in classrooms is dust pollution and it depends on the particle size and exposure duration. However, there is a lake of data about the exposure level to PM2.5 concentrations in rural area classrooms. The objective of the current study was exposure assessment for PM2.5 for students in the classrooms. One year monitoring was carried out for fifteen schools by time-series sampling to evaluate the indoor air PM2.5 in the rural district of Sari city, Iran. A hygrometer and thermometer were used to measure some psychrometric parameters (temperature, relative humidity, and wind speed) and Real-Time Dust Monitor, (MicroDust Pro, Casella, UK) was used to monitor particulate matters (PM2.5) concentration. The results show the mean indoor PM2.5 concentration in the studied classrooms was 135µg/m3. The regression model indicated that a positive correlation between indoor PM2.5 concentration and relative humidity, also with distance from city center and classroom size. Meanwhile, the regression model revealed that the indoor PM2.5 concentration, the relative humidity, and dry bulb temperature was significant at 0.05, 0.035, and 0.05 levels, respectively. A statistical predictive model was obtained from multiple regressions modeling for indoor PM2.5 concentration and indoor psychrometric parameters conditions.

Keywords: classrooms, concentration, humidity, particulate matters, regression

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Authors: Vivian Correa

Abstract:

Hypothesis There are typical patterns - phenotypes - of sleep behaviors by age and biological sex groups of parasomnia patients where wearable devices could avoid injuries. Materials and methods We analyzed public video records on sleep-related behaviors likely representing parasomnias, looking for phenotypes in different groups. We searched public internet databases using the keywords “sleepwalking”, “sleep eating,” “sleep sex”, and “aggression in sleep” in six languages. Poor-quality vide-records and those showing apparently faked sleep behaviors were excluded. We classified the videos into estimated sex and age (children, adults, elderly) groups; scored the activity types by a self-made scoring scale; and applied binary logistic regression for analyzing the association between sleep behaviors versus the groups by STATA package providing 95% confidence interval and the probability of statistical significance. Results 224 videos (102 women) were analyzed. The odds of sleepwalking and related dangerous behaviors were lower in the elderly than in adults (P<0.025). Females performed complex risky behaviors during sleepwalking more often than males (P<0.012). Elderly people presented emotional behaviors less frequently than adults (P<0.004), and females showed them twice often as males. Elderly males had 40-fold odds compared to adults and children to perform aggressive movements and 70-fold odds of complex movements in the bed compared to adults. Conclusion Unlike other groups, the high chances of adults being sleepwalkers and elderly males performing intense and violent movements in bed showed us the importance of developing wearable parasomnia devices to prevent injuries.

Keywords: parasomnia, wearable devices, sleepwalking, RBD

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Authors: Min Wei

Abstract:

The traditional prediction methods of the forecast for tourism market are paid more attention to the accuracy of the forecasts, ignoring the results of the feasibility of forecasting and predicting operability, which had made it difficult to predict the results of scientific testing. With the application of Linear Regression Model, this paper attempts to construct a scientific evaluation system for predictive value, both to ensure the accuracy, stability of the predicted value, and to ensure the feasibility of forecasting and predicting the results of operation. The findings show is that a scientific evaluation system can implement the scientific concept of development, the harmonious development of man and nature co-ordinate.

Keywords: linear regression model, tourism market, forecast, tourism economics

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Authors: Josip Vasilj, Petar Sarajcev, Damir Jakus

Abstract:

One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind power generation. For this purpose, simulation models are required, reproducing the performance of wind power forecasts. This paper presents a wind power forecast error simulation models which are based on the stochastic process simulation. Proposed models capture the most important statistical parameters recognized in wind power forecast error time series. Furthermore, two distinct models are presented based on data availability. First model uses wind speed measurements on potential or existing wind power plant locations, while the seconds model uses statistical distribution of wind speeds.

Keywords: wind power, uncertainty, stochastic process, Monte Carlo simulation

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Authors: Sudip Kumar Kundu, Charu Singh

Abstract:

As a great physiographic divide, the Himalayas affecting a large system of water and air circulation which helps to determine the climatic condition in the Indian subcontinent to the south and mid-Asian highlands to the north. It creates obstacles by defending chill continental air from north side into India in winter and also defends rain-bearing southwesterly monsoon to give up maximum precipitation in that area in monsoon season. Nowadays extreme weather conditions such as heavy precipitation, cloudburst, flash flood, landslide and extreme avalanches are the regular happening incidents in the region of North Western Himalayan (NWH). The present study has been planned to investigate the suitable model(s) to find out the rainfall pattern over that region. For this investigation, selected models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and Coupled Model Intercomparison Project Phase 5 (CMIP5) has been utilized in a consistent framework for the period of 1976 to 2000 (historical). The ability of these driving models from CORDEX domain and CMIP5 has been examined according to their capability of the spatial distribution as well as time series plot of rainfall over NWH in the rainy season and compared with the ground-based Indian Meteorological Department (IMD) gridded rainfall data set. It is noted from the analysis that the models like MIROC5 and MPI-ESM-LR from the both CORDEX and CMIP5 provide the best spatial distribution of rainfall over NWH region. But the driving models from CORDEX underestimates the daily rainfall amount as compared to CMIP5 driving models as it is unable to capture daily rainfall data properly when it has been plotted for time series (TS) individually for the state of Uttarakhand (UK) and Himachal Pradesh (HP). So finally it can be said that the driving models from CMIP5 are better than CORDEX domain models to investigate the rainfall pattern over NWH region.

Keywords: global warming, rainfall, CMIP5, CORDEX, NWH

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