Search results for: binary logistic regression

Authors: A. Sathiya Susuman, N. Wilson

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Background: The sexual behaviour of youths is believed to play an important role in the spread of sexually transmitted infections (STIs). Method: The data from the Malawi Demographic and Health Survey 2010 and a sample of 16,217 youth’s age 15 to 24 years (with each household 27.2% female and 72.8% male) was the basis for analysis. Bivariate and logistic regression analysis was performed. Results: The result shows married youth were not interested in condom use (94.2%, p<0.05). Those who were living together were 69 times (OR=1.69, 95% CI, 1.26–2.26) more likely to be involved in early sexual activity compared to those who were not living together. Conclusion: This scientific paper will help other researchers, policy makers, and planners to create strategies to encourage these youths to make use of contraception.

Keywords: sexually transmitted infections (STIs), reproductive tract infections (RTIs), condom use, sexual partners, early sexual debut, youths

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Authors: Maria Reichenber Arcilla

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Objective: To determine the risk factors for maternal and neonatal complications associated with operative vaginal deliveries. Methods: A retrospective chart review of 435 patients who underwent operative vaginal deliveries was done. Patient profiles – age, parity, AOG, duration of labor – and outcomes – birthweight, maternal and neonatal complications - were tabulated and multivariable analysis and logistic regression were performed using SPSS® Statistics Base. Results and Conclusion: There was no significant difference in the incidence of maternal and neonatal complications between those that underwent vacuum and forceps extraction. Among the variables analysed, parity and duration of labor reached statistical significance. The odds of maternal complications were 3 times higher among nulliparous patients. Neonatal complications were seen in those whose labor lasted more than 9 hours.

Keywords: operative vaginal deliveries, maternal, neonatal, morbidity

Procedia PDF Downloads 406

Authors: Shokrya Saleh A. Alshqaq, Abdullah Ali H. Ahmadini

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The Schwarz information criterion (SIC) is a popular tool for selecting the best variables in regression datasets. However, SIC is defined using an unbounded estimator, namely, the least-squares (LS), which is highly sensitive to outlying observations, especially bad leverage points. A method for robust variable selection based on SIC for linear regression models is thus needed. This study investigates the robustness properties of SIC by deriving its influence function and proposes a robust SIC based on the MM-estimation scale. The aim of this study is to produce a criterion that can effectively select accurate models in the presence of vertical outliers and high leverage points. The advantages of the proposed robust SIC is demonstrated through a simulation study and an analysis of a real dataset.

Keywords: influence function, robust variable selection, robust regression, Schwarz information criterion

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Authors: Joel Podgorski, Manouchehr Amini, Annette Johnson, Michael Berg

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One-third of the world’s population relies on groundwater for its drinking water. Natural geogenic arsenic and fluoride contaminate ~10% of wells. Prolonged exposure to high levels of arsenic can result in various internal cancers, while high levels of fluoride are responsible for the development of dental and crippling skeletal fluorosis. In poor urban and rural settings, the provision of drinking water free of geogenic contamination can be a major challenge. In order to efficiently apply limited resources in the testing of wells, water resource managers need to know where geogenically contaminated groundwater is likely to occur. The Groundwater Assessment Platform (GAP) fulfills this need by providing state-of-the-art global arsenic and fluoride contamination hazard maps as well as enabling users to create their own groundwater quality models. The global risk models were produced by logistic regression of arsenic and fluoride measurements using predictor variables of various soil, geological and climate parameters. The maps display the probability of encountering concentrations of arsenic or fluoride exceeding the World Health Organization’s (WHO) stipulated concentration limits of 10 µg/L or 1.5 mg/L, respectively. In addition to a reconsideration of the relevant geochemical settings, these second-generation maps represent a great improvement over the previous risk maps due to a significant increase in data quantity and resolution. For example, there is a 10-fold increase in the number of measured data points, and the resolution of predictor variables is generally 60 times greater. These same predictor variable datasets are available on the GAP platform for visualization as well as for use with a modeling tool. The latter requires that users upload their own concentration measurements and select the predictor variables that they wish to incorporate in their models. In addition, users can upload additional predictor variable datasets either as features or coverages. Such models can represent an improvement over the global models already supplied, since (a) users may be able to use their own, more detailed datasets of measured concentrations and (b) the various processes leading to arsenic and fluoride groundwater contamination can be isolated more effectively on a smaller scale, thereby resulting in a more accurate model. All maps, including user-created risk models, can be downloaded as PDFs. There is also the option to share data in a secure environment as well as the possibility to collaborate in a secure environment through the creation of communities. In summary, GAP provides users with the means to reliably and efficiently produce models specific to their region of interest by making available the latest datasets of predictor variables along with the necessary modeling infrastructure.

Keywords: arsenic, fluoride, groundwater contamination, logistic regression

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Authors: Abm Mostafa, Rumbidzai Mukono, Peijie Wang

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Using 285 respondents from two microfinance institutions, this research aims to assess the impact of microfinance on women’s economic empowerment in Bangladesh. Empirical measures of economic empowerment used in this paper are underpinned by a bargaining theory of household. Questionnaire is used for data collection following purposive sampling. Descriptive statistics, chi-square test, Kruskal-Wallis test, binary, and ordinal logistic regressions are deployed for data analysis. The findings of this study show that around three quarters of respondents have increased household income. They have increased their savings overwhelmingly; nonetheless, many of them are found to have a very small amount of savings. Still, more than half of the respondents are reported to have increased their savings when it is checked against at least 500 BDT per month. On the contrary, the percentage of women is moderate in terms of increasing control over finances. Empirical findings demonstrate the evidence of a relationship between the amount of loan and women’s household income, their savings, and control over finances. Nonetheless, no relationship is found in women’s areas. This study infers that women’s access to financial resources is fundamental to empower them in economic dimension.

Keywords: microfinance, women, economic, empowerment, Bangladesh

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Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

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Authors: Zakiya Shireen, Sujin B. Babu

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Irreversible diffusion-limited cluster aggregation (DLCA) of binary sticky spheres was simulated by modifying the Brownian Cluster Dynamics (BCD). We randomly distribute N spheres in a 3D box of size L, the volume fraction is given by Φtot = (π/6)N/L³. We identify NA and NB number of spheres as species A and B in our system both having identical size. In these systems, both A and B particles undergo Brownian motion. Irreversible bond formation happens only between intra-species particles and inter-species interact only through hard-core repulsions. As we perform simulation using BCD we start to observe binary gels. In our study, we have observed that species B always percolate (cluster size equal to L) as expected for the monomeric case and species A does not percolate below a critical ratio which is different for different volume fractions. We will also show that the accessible volume of the system increases when compared to the monomeric case, which means that species A is aggregating inside the cage created by B. We have also observed that for moderate Φtot the system undergoes a transition from flocculation region to percolation region indicated by the change in fractal dimension from 1.8 to 2.5. For smaller ratio of A, it stays in the flocculation regime even though B have already crossed over to the percolation regime. Thus, we observe two fractal dimension in the same system.

Keywords: BCD, fractals, percolation, sticky spheres

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Authors: Pratima Thapa

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Background: It is very critical to understand that awareness of family planning and proper utilization of contraceptives is an important indicator for reducing maternal and neonatal mortality and morbidity. It also plays an important role in promoting reproductive health of the women in an underdeveloped country like ours. Objective: To assess knowledge, attitude and practices of contraception among married women of reproductive age group in selected wards of Dharan Sub-Metropolitan City. Materials and methods: A cross-sectional descriptive study was conducted among 209 married women of reproductive age. Simple random sampling was used to select the wards, population proportionate sampling for selecting the sample numbers from each wards and purposive sampling for selecting each sample. Semi-structured questionnaire was used to collect data. Descriptive and inferential statistics were used to interpret the data considering p-value 0.05. Results: The mean ± SD age of the respondents was 30.01 ± 8.12 years. Majority 92.3% had ever heard of contraception. Popular known method was Inj. Depo (92.7%). Mass media (85.8%) was the major source of information. Mean percentage score of knowledge was 45.23%.less than half (45%) had adequate knowledge. Majority 90.4% had positive attitude. Only 64.6% were using contraceptives currently. Misbeliefs and fear of side effects were the main reason for not using contraceptives. Education, occupation, and total income of the family was associated with knowledge regarding contraceptives. Results for Binary Logistic Regression showed significant correlates of attitude with distance to the nearest health facility (OR=7.97, p<0.01), education (OR=0.24, p<0.05) and age group (0.03, p<0.01). Regarding practice, likelihood of being current user of contraceptives increased significantly by being literate (OR=5.97, p<0.01), having nuclear family (OR=4.96, p<0.01), living in less than 30 minute walk distance from nearest health facility (OR=3.34, p<0.05), women’s participation in decision making regarding household and fertility choices (OR=5.23, p<0.01) and husband’s support on using contraceptives (OR=9.05, p<0.01). Significant and positive correlation between knowledge-attitude, knowledge-practice and attitude-practice were observed. Conclusion: Results of the study indicates that there is need to increase awareness programs in order to intensify the knowledge and practices of contraception. The positive correlation indorses that better knowledge can lead to positive attitude and hence good practice. Further, projects aiming to increase better counselling about contraceptives, its side effects and the positive effects that outweighs the negative aspects should be enrolled appropriately.

Keywords: attitude, contraceptives, knowledge, practice

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Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

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Authors: Saleem Z. Ramadan

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The material selection problem is concerned with the determination of the right material for a certain product to optimize certain performance indices in that product such as mass, energy density, and power-to-weight ratio. This paper is concerned about optimizing the selection of the manufacturing process along with the material used in the product under performance indices and availability constraints. In this paper, the material selection problem is formulated using binary programming and solved by genetic algorithm. The objective function of the model is to minimize the total manufacturing cost under performance indices and material and manufacturing process availability constraints.

Keywords: optimization, material selection, process selection, genetic algorithm

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Authors: Aishvarya Gupta, Keerthana Anand, Sasirekha Rengaraj, Latha Chathurvedula

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Introduction: Advances in assisted reproductive technology and increase in the proportion of infertile couples have both contributed to the steep increase in the incidence of twin pregnancies in past decades. Maternal and perinatal complications are higher in twins than in singleton pregnancies. Studies comparing the maternal and perinatal outcomes of ART twin pregnancies versus spontaneously conceived twin pregnancies report heterogeneous results making it unclear whether the complications are due to twin gestation per se or because of assisted reproductive techniques. The present study aims to compare both maternal and perinatal outcomes in twin pregnancies which are spontaneously conceived and after assisted conception methods, so that targeted steps can be undertaken in order to improve maternal and perinatal outcome of twins. Objectives: To study perinatal and maternal outcome in twin pregnancies conceived spontaneously as well as with assisted methods and compare the outcomes between the two groups. Setting: Women delivering at JIPMER (tertiary care institute), Pondicherry. Population: 380 women with twin pregnancies who delivered in JIPMER between June 2015 and March 2017 were included in the study. Methods: The study population was divided into two cohorts – one conceived by spontaneous conception and other by assisted reproductive methods. Association of various maternal and perinatal outcomes with the method of conception was assessed using chi square test or Student's t test as appropriate. Multiple logistic regression analysis was done to assess the independent association of assisted conception with maternal outcomes after adjusting for age, parity and BMI. Multiple logistic regression analysis was done to assess the independent association of assisted conception with perinatal outcomes after adjusting for age, parity, BMI, chorionicity, gestational age at delivery and presence of hypertension or gestational diabetes in the mother. A p value of < 0.05 was considered as significant. Result: There was increased proportion of women with GDM (21% v/s 4.29%) and premature rupture of membranes (35% v/s 22.85%) in the assisted conception group and more anemic women in the spontaneous group (71.27% v/s 55.1%). However assisted conception per se increased the incidence of GDM among twin gestations (OR 3.39, 95% CI 1.34 – 8.61) and did not influence any of the other maternal outcomes. Among the perinatal outcomes, assisted conception per se increased the risk of having very preterm (<32 weeks) neonates (OR 3.013, 95% CI 1.432 – 6.337). The mean birth weight did not significantly differ between the two groups (p = 0.429). Though there were higher proportion of babies admitted to NICU in the assisted conception group (48.48% v/s 36.43%), assisted conception per se did not increase the risk of admission to NICU (OR 1.23, 95% CI 0.76 – 1.98). There was no significant difference in perinatal mortality rates between the two groups (p = 0.829). Conclusion: Assisted conception per se increases the risk of developing GDM in women with twin gestation and increases the risk of delivering very preterm babies. Hence measures should be taken to ensure appropriate screening methods for GDM and suitable neonatal care in such pregnancies.

Keywords: assisted conception, maternal outcomes, perinatal outcomes, twin gestation

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Authors: Fatemeh Valizadeh Gamchi, Edward L. Boone

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This research focuses on Lyme disease, a widespread infectious condition in the United States caused by the bacterium Borrelia burgdorferi sensu stricto. It is critical to identify environmental and economic elements that are contributing to the spread of the disease. This study examined data from Virginia to identify a subset of explanatory variables significant for Lyme disease case numbers. To identify relevant variables and avoid overfitting, linear poisson, and regularization regression methods such as a ridge, lasso, and elastic net penalty were employed. Cross-validation was performed to acquire tuning parameters. The methods proposed can automatically identify relevant disease count covariates. The efficacy of the techniques was assessed using four criteria on three simulated datasets. Finally, using the Virginia Department of Health’s Lyme disease data set, the study successfully identified key factors, and the results were consistent with previous studies.

Keywords: lyme disease, Poisson generalized linear model, ridge regression, lasso regression, elastic net regression

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Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

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Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct

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Authors: Muhammad Bahrul Ilmi

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The purpose of this research is to analyze the influence of Islamic financing and work relation founding simultaneously and partially towards non-performing financing in Islamic banks. This research was regression quantitative field research, and had been done in Muammalat Indonesia Bank and Islamic Danamon Bank in 3 months. The populations of this research were 15 account officers of Muammalat Indonesia Bank and Islamic Danamon Bank in Surakarta, Indonesia. The techniques of collecting data used in this research were documentation, questionnaire, literary study and interview. Regression analysis result shows that Islamic financing and work relation founding simultaneously has positive and significant effect towards non performing financing of two Islamic Banks. It is obtained with probability value 0.003 which is less than 0.05 and F value 9.584. The analysis result of Islamic financing regression towards non performing financing shows the significant effect. It is supported by double linear regression analysis with probability value 0.001 which is less than 0.05. The regression analysis of work relation founding effect towards non-performing financing shows insignificant effect. This is shown in the double linear regression analysis with probability value 0.161 which is bigger than 0.05.

Keywords: Syariah financing, work relation founding, non-performing financing (NPF), Islamic Bank

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Authors: Shamsa Al Sharji, Athar Al Jabri, Haitham Al Dughaishi, Mirfat Al Barwani, Raja Al Rawahi, Raiya Al Rajhi, Shurooq Al Ruqaishi, Thamreen Al Zadjali, Iman Al Humaidi

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Background: External Ventricular Drains (EVDs) are critical in managing traumatic brain injuries and hydrocephalus by controlling intracranial pressure, but they carry a high risk of infection. Infection rates vary globally, ranging from 5% to 45%, leading to increased morbidity, prolonged hospital stays, and higher healthcare costs. Nursing protocols play a pivotal role in reducing these infection rates. This study investigates the impact of a structured nursing protocol on EVD-associated infections in adult neurosurgery patients at the Directorate General of Khoula Hospital, Oman, from January to September 2024. Methods: A cluster-randomized trial was conducted across neurosurgery wards and the ICU. The intervention group followed a comprehensive nursing protocol, including strict sterile insertion, standardized dressing changes, infection control training, and regular clinical audits. The control group received standard care. The primary outcome was the incidence of EVD-associated infections, with secondary outcomes including protocol compliance, infection severity, recovery times, length of stay, and 30-day mortality. Statistical analysis was conducted using Chi-square tests, paired t-tests, and logistic regression to assess the differences between groups. Results: The study involved 75 patients, with an overall infection rate of 13.3%. The intervention group showed a reduced infection rate of 8.9% compared to 20% in the control group. Compliance rates for key nursing actions were high, with 89.7% for hand hygiene and 86.2% for wound dressing. The relative risk of infection was 0.44 in the intervention group, reflecting a 55.6% reduction. Logistic regression identified obesity as a significant predictor of EVD infections. Although mortality rates were slightly higher in the intervention group, the number needed to treat (NNT) of 9 suggests that the nursing protocol may improve survival outcomes. Conclusion: This study demonstrates that structured nursing protocols can reduce EVD-related infections and improve patient outcomes in neurosurgery. While the findings are promising, further research with larger sample sizes is needed to confirm these results and optimize infection control strategies in neurosurgical care.

Keywords: EVD, CSF, nursing protocol, EVD infection

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Authors: P. K. Singhal, R. Naresh, V. Sharma

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This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: artificial bee colony algorithm, economic dispatch, unit commitment, wind power

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Authors: L. Khalfa, M. Bagane, M. L. Cervera, S. Najjar

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The aim of this work is to present a low cost adsorbent for removing toxic heavy metals from aqueous solutions. Therefore, we are interested to investigate the efficiency of natural clay minerals collected from south Tunisia and their modified form using sulfuric acid in the removal of toxic metal ions: Zn(II) and Pb(II) from synthetic waste water solutions. The obtained results indicate that metal uptake is pH-dependent and maximum removal was detected to occur at pH 6. Adsorption equilibrium is very rapid and it was achieved after 90 min for both metal ions studied. The kinetics results show that the pseudo-second-order model describes the adsorption and the intraparticle diffusion models are the limiting step. The treatment of natural clay with sulfuric acid creates more active sites and increases the surface area, so it showed an increase of the adsorbed quantities of lead and zinc in single and binary systems. The competitive adsorption study showed that the uptake of lead was inhibited in the presence of 10 mg/L of zinc. An antagonistic binary adsorption mechanism was observed. These results revealed that clay is an effective natural material for removing lead and zinc in single and binary systems from aqueous solution.

Keywords: heavy metal, activated clay, kinetic study, competitive adsorption, modeling

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Authors: Josine Beek, S. Agten, R. Del Pozo, B. Balis

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The objective of the present study was to compare the safety of intramuscular administration of Zuprevo® (tildipirosin, 40 mg/mL) with Draxxin® (tulathromycin, 100 mg/mL) in the treatment of swine respiratory disease at weaning age. The trial was carried out in two farrow-to-finish farms with 300 sows (farm A) and 500 sows (farm B) in a batch-production system. Farm A had no history of respiratory problems, whereas farm B had a history of respiratory outbreaks with increased mortality ( > 2%) in the nursery. Both farms were positive to Pasteurella multocida, Bordetella bronchiseptica, Actinobacillus pleuropneumoniae and Haemophilus parasuis. From each farm, one batch of piglets was included (farm A: 644 piglets; farm B: 963 piglets). One day before weaning (day 0; 18-21 days of age), piglets were identified by an individual ear tag and randomly assigned to a treatment group. At day 0, Group 1 was treated with a single intramuscular injection with Zuprevo® (tildipirosin, 40 mg/mL; 1 mL/10 kg) and group 2 with Draxxin® (tulathromycin, 100 mg/mL; 1 mL/40 kg). For practical reasons, dosage of the product was adjusted according to three weight categories: < 4 kg, 4-6 kg and > 6 kg. Within each farm, piglets of both groups were comingled at weaning and subsequently managed and located in the same facilities and with identical environmental conditions. Our study involved the period from day 0 until 10 weeks of age. Safety of treatment was evaluated by 1) visual examination for signs of discomfort directly after treatment and after 15 min, 1 h and 24 h and 2) mortality rate within 24 h after treatment. Efficacy of treatment was evaluated based on mortality rate from day 0 until 10 weeks of age. Each piglet that died during the study period was necropsied by the herd veterinarian to determine the probable cause of death. Data were analyzed using binary logistic regression and differences were considered significant if p < 0.05. The pig was the experimental unit. In total, 848 piglets were treated with tildipirosin and 759 piglets with tulathromycin. In farm A, one piglet with retarded growth ( < 1 kg at 18 days of age) showed an adverse reaction after injection of tildipirosin: lateral recumbence and dullness for ± 30 sec. The piglet recovered after 1-2 min. This adverse reaction was probably due to overdosing (12 mg/kg). No adverse effect of treatment was observed in any other piglet. There was no mortality within 24 h after treatment. No significant difference was found in mortality rate between both groups from day 0 until 10 weeks of age. In farm A, overall mortality rate was 0.3% (2/644). In farm B, mortality rate was 0.2% (1/502) in group 1 (tildipirosin) and 0.9% (4/461) in group 2 (tulathromycin)(p=0.60). The necropsy of piglets that died during the study period revealed no macroscopic lesions of the respiratory tract. In conclusion, Zuprevo® (tildipirosin, 40 mg/mL) was shown to be a safe and efficacious alternative to Draxxin® (tulathromycin, 100 mg/mL) for the early treatment of swine respiratory disease at weaning age.

Keywords: antibiotic treatment, safety, swine respiratory disease, tildipirosin

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Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

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In urban area, several landmarks may affect housing price and rents, hedonic analysis should employ distance variables corresponding to each landmarks. Unfortunately, the effects of distances to landmarks on housing prices are generally not consistent with the true price. These distance variables may cause magnitude error in regression, pointing a problem of spatial multicollinearity. In this paper, we provided some approaches for getting the samples with less bias and method on locating the specific sampling area to avoid the multicollinerity problem in two specific landmarks case.

Keywords: landmarks, hedonic regression, distance variables, collinearity, multicollinerity

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

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Authors: Amjad Abdullah, Amjad Yahya, Bushra Aljohani, Amani Alghamdi

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Change point detection is an important part of data analysis. The presence of a change point refers to a significant change in the behavior of a time series. In this article, we examine the detection of multiple change points of parameters of the exponential Lomax distribution, which is broad and flexible compared with other distributions while fitting data. We used the Schwarz information criterion and binary segmentation to detect multiple change points in publicly available data on the average temperature in the ozone layer. The change points were successfully located.

Keywords: binary segmentation, change point, exponentialLomax distribution, information criterion

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Authors: Waleed El-Said Abou El-Amaiem

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Sole hemorrhages are considered as a main cause for sub clinical laminitis. In this study we aimed at discussing the most prominent risk factors associated with sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows. The final multivariate logistic regression model showed, a significant association between sub acute ruminal acidosis (P< 0.05), limb affected (P< 0.05) and weight (P< 0.05) and sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows. According to our knowledge, this is the first paper to discuss the risk factors associated with sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows.

Keywords: lameness, buffalo, sole hemorrhages, breed cows

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Authors: Rajeshwar Singh

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The presence of multicollinearity is common in handling with several explanatory variables simultaneously due to exhibiting a linear relationship among them. A great problem arises in understanding the impact of explanatory variables on the dependent variable. Thus, the method of least squares estimation gives inexact estimates. In this case, it is advised to detect its presence first before proceeding further. Using the ridge regression degree of its occurrence is reduced but principal components regression gives good estimates in this situation. This paper discusses well-known techniques of the ridge and principal components regressions and applies to get the estimates of coefficients by both techniques. In addition to it, this paper also discusses the conflicting claim on the discovery of the method of ridge regression based on available documents.

Keywords: conflicting claim on credit of discovery of ridge regression, multicollinearity, principal components and ridge regressions, variance inflation factor

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Authors: Aynur Aker, Hasan Kaya

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Al-12.6wt.%Si-%2wt.Co alloy was prepared in a graphite crucible under vacuum atmosphere. The samples were directionally solidified upwards with different growth rate at constant temperature gradient using by Bridgman–type growth apparatus. The values of microstructures (λ) was measured from transverse sections of the samples. The microhardness (HV), ultimate tensile strength (σ) and electrical resistivity (ρ) of the directional solidification samples were also measured. Influence of the growth rate and spacings on microhardness, ultimate tensile strength and electrical resistivity were investigated and the relationships between them were experimentally obtained by using regression analysis. The results obtained in present work were compared with the previous similar experimental results obtained for binary and ternary alloys.

Keywords: directional solidification, Al-Si-Co alloy, mechanical properties, electrical properties

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Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

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This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

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Authors: K. Y. Tseng, C. Y. Wu

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This paper employs a heuristic algorithm to solve engineering problems including truss structure optimization and optimal chiller loading (OCL) problems. Two different type algorithms, real-valued differential evolution (DE) and modified binary differential evolution (MBDE), are successfully integrated and then can obtain better performance in solving engineering problems. In order to demonstrate the performance of the proposed algorithm, this study adopts each one testing case of truss structure optimization and OCL problems to compare the results of other heuristic optimization methods. The result indicates that the proposed algorithm can obtain similar or better solution in comparing with previous studies.

Keywords: differential evolution, Truss structure optimization, optimal chiller loading, modified binary differential evolution

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Authors: César Garza

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In supervised binary classification and regression problems, it is well-known that learnability is equivalent to a uniform convergence of the hypothesis class, and if a problem is learnable, it is learnable by empirical risk minimization. For the general learning setting of unsupervised learning tasks, there are non-trivial learning problems where uniform convergence does not hold. We present here the task of learning centers of mass with an extra feature that “activates” some of the coordinates over the unit ball in a Hilbert space. We show that the learning problem is learnable under a stable RLM rule. We introduce a family of distributions over the domain space with some mild restrictions for which the sample complexity of uniform convergence for these problems must grow logarithmically with the dimension of the Hilbert space. If we take this dimension to infinity, we obtain a learnable problem for which the uniform convergence property fails for a vast family of distributions.

Keywords: statistical learning theory, learnability, uniform convergence, stability, regularized loss minimization

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Authors: Tuji Mohammed Sani

Abstract:

Back ground: An open tibial fracture is an injury where the fractured bone directly communicates with the outside environment. Due to the specific anatomical features of the tibia (limited soft tissue coverage), more than quarter of its fractures are classified as open, representing the most common open long-bone injuries. Open tibial fractures frequently cause significant bone comminution, periosteal stripping, soft tissue loss, contamination and are prone to bacterial entry with biofilm formation, which increases the risk of deep bone infection. Objective: The main objective of the study was to determine Prevalence of infection and its associated factors in surgically treated open tibial fracture in Addis Ababa Burn Emergency and Trauma (AaBET) center. Method: A facility based retrospective cross-sectional study was conducted among patient treated for open tibial fracture at AaBET center from September 2018 to September 2021. The data was collected from patient’s chart using structured data collection form, and Data was entered and analyzed using SPSS version 26. Bivariable and multiple binary logistic regression were fitted. Multicollinearity was checked among candidate variables using variance inflation factor and tolerance, which were less than 5 and greater than 0.2, respectively. Model adequacy were tested using Hosmer-Lemeshow goodness of fitness test (P=0.711). AOR at 95% CI was reported, and P-value < 0.05 was considered statistically significant. Result: This study found that 33.9% of the study participants had an infection. Initial IV antibiotic time (AOR=2.924, 95% CI:1.160- 7.370) and time of wound closure from injury (AOR=3.524, 95% CI: 1.798-6.908), injury to admission time (AOR=2.895, 95% CI: 1.402 – 5.977). and definitive fixation method (AOR=0.244, 95% CI: 0.113 – 0.4508) were the factors found to have a statistically significant association with the occurrence of infection. Conclusion: The rate of infection in open tibial fractures indicates that there is a need to improve the management of open tibial fracture treated at AaBET center. Time from injury to admission, time from injury to first debridement, wound closure time, and initial Intra Venous antibiotic time from the injury are an important factor that can be readily amended to improve the infection rate. Whether wound closed before seven days or not were more important factor associated with occurrences of infection.

Keywords: infection, open tibia, fracture, magnitude

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

Abstract:

In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

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Authors: Waqas Ali

Abstract:

Concrete is broadly used as a structural material in the construction of buildings. When the concrete is exposed to elevated temperature, its strength evaluation is very necessary in the existing structure. In this study, the effect of temperature and the reliability of the core test has been evaluated. For this purpose, the cylindrical cores were extracted from High strength concrete (HSC) specimens that were exposed to the temperature ranging from 300 ℃ to 900 ℃ with a constant duration of 4 hr. This study compares the difference between the standard heated cylinders and the cores taken from them after curing of 90 days. The difference of cylindrical control and binary mix samples and extracted cores revealed that there is 12.19 and 12.38% difference at 300℃, while this difference was found to increase up to 12.89%, 13.03% at 500 ℃. Furthermore, this value is recorded as 12.99%, 13.57% and 14.40%, 14.38% at 700 ℃ and 900 ℃, respectively. A total of four equations were developed through a regression model for the prediction of the strength of concrete for both standard cylinders and extracted cores whose R square values were 0.9733, 0.9627 and 0.9473, 0.9452, respectively.

Keywords: high strength, temperature, core, reliability

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