Search results for: predictive index

Authors: Jacky Liu

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This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

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Authors: Vishwesh Kulkarni, Nikhil Bellarykar

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Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets.

Keywords: synthetic gene network, network identification, optimization, nonlinear modeling

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Authors: Dong Liu

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By sending a high-frequency probe wave and a low-frequency pump wave to a specimen, the vibroacoustic method evaluates the defect’s severity according to the modulation index of the received signal. Many studies experimentally proved the significant sensitivity of the modulation index to the tiny contact type defect. However, it has also been found that the modulation index was highly affected by the frequency of probe or pump waves. Therefore, the chirp signal has been introduced to the VAM method since it can assess multiple frequencies in a relatively short time duration, so the robustness of the VAM method could be enhanced. Consequently, the signal processing method needs to be modified accordingly. Various studies utilized different algorithms or combinations of algorithms for processing the VAM signal method by chirp excitation. These signal process methods were compared and used for processing a VAM signal acquired from the steel samples.

Keywords: vibroacoustic modulation, nonlinear acoustic modulation, nonlinear acoustic NDT&E, signal processing, structural health monitoring

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Authors: Mehmet Yildiz, Mehmet Kale

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The primary aim of the present study was to identify the bilateral balance asymmetries when comparing the dominant (DL) vs. the non-dominant leg (NDL) in the collegian soccer players. The secondary aim was to compare the inter-limb asymmetry index (ASI) when differentiating by kicking preference (right-dominant vs. left-dominant). 34 right-dominant leg (RightDL) (age:21.12±1.85, height:174.50±5.18, weight:69.42±6.86) and 23 left-dominant leg (LeftDL), (age:21.70±2.03, height:176.2±6.27, weight:68.73±5.96) collegian football players were tested for bilateral static and dynamic balance. Balance ability was assessed by measuring centre of pressure deviation on a single leg. Single leg static and dynamic balance scores and inter-limb asymmetry index (ASI) were determined. Student t tests were used for the comparison of dominant and nondominant leg balance scores and RightDL and LeftDL football players’ inter-limb asymmetry index of the balance scores. The results showed that there were significant differences in the dynamic balance scores in favour of the nondominant leg, (DL:738±211 vs. NDL:606±226, p < 0.01). Also, it has been seen that LeftDL players have significantly higher inter-limb asymmetry index when compared with rightDL players for both static (rightDL:-7.07±94.91 vs. leftDL:-183.19±354.05, p < 0.01) and dynamic (rightDL: 1.73±49.65 vs. leftDL:27.08±23.34, p < 0.05) balance scores. In conclusion, bilateral dynamic balance asymmetries may be affected using single leg predominantly in the mobilization workouts. Because of having higher inter-limb asymmetry index, left-dominant leg players may be screened and trained to minimize balance asymmetry.

Keywords: bilateral balance, asymmetries, dominant leg, leg preference

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Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

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The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

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Authors: Haonan Hu, Shuge Lei, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Jijun Tang

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This paper focuses on the classification task of breast ultrasound images and conducts research on the reliability measurement of classification results. A dual-channel evaluation framework was developed based on the proposed inference reliability and predictive reliability scores. For the inference reliability evaluation, human-aligned and doctor-agreed inference rationals based on the improved feature attribution algorithm SP-RISA are gracefully applied. Uncertainty quantification is used to evaluate the predictive reliability via the test time enhancement. The effectiveness of this reliability evaluation framework has been verified on the breast ultrasound clinical dataset YBUS, and its robustness is verified on the public dataset BUSI. The expected calibration errors on both datasets are significantly lower than traditional evaluation methods, which proves the effectiveness of the proposed reliability measurement.

Keywords: medical imaging, ultrasound imaging, XAI, uncertainty measurement, trustworthy AI

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Authors: Mariela Gonzalez, Zoraide Lugli, Eleonora Vivas, Rosana Guzmán

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The objective was to determine the predictive capacity of self-efficacy perceived for weight control, locus of weight control and skills of weight self-management in the dissatisfaction of the body image in obese people who attend treatment. Sectional study conducted in the city of Maracay, Venezuela, with 243 obese who attend to treatment, 173 of the feminine gender and 70 of the male, with ages ranging between 18 and 57 years old. The sample body mass index ranged between 29.39 and 44.14. The following instruments were used: The Body Shape Questionnaire (BSQ), the inventory of body weight self-regulation, The Inventory of self-efficacy in the regulation of body weight and the Inventory of the Locus of weight control. Calculating the descriptive statistics and of central tendency, coefficients of correlation and multiple regression; it was found that a low ‘perceived Self-efficacy in the weight control’ and a high ‘Locus of external control’, predict the dissatisfaction with body image in obese who attend treatment. The findings are a first approximation to give an account of the importance of the personal control variables in the study of the psychological grief on the overweight individual.

Keywords: dissatisfaction with body image, obese people, personal control, psychological variables

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Authors: Nasim Nosoudi, Amir Zadeh, Hunter White, Joshua Conrad, Joon W. Shim

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De novo Malignancy has become one of the major causes of death after transplantation, so early cancer diagnosis and detection can drastically improve survival rates post-transplantation. Most previous work focuses on using artificial intelligence (AI) to predict transplant success or failure outcomes. In this work, we focused on predicting de novo malignancy after liver transplantation using AI. We chose the patients that had malignancy after liver transplantation with no history of malignancy pre-transplant. Their donors were cancer-free as well. We analyzed 254,200 patient profiles with post-transplant malignancy from the US Organ Procurement and Transplantation Network (OPTN). Several popular data mining methods were applied to the resultant dataset to build predictive models to characterize de novo malignancy after liver transplantation. Recipient's bilirubin, creatinine, weight, gender, number of days recipient was on the transplant waiting list, Epstein Barr Virus (EBV), International normalized ratio (INR), and ascites are among the most important factors affecting de novo malignancy after liver transplantation

Keywords: De novo malignancy, bilirubin, data mining, transplantation

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Authors: Hamid Karimi, Naegar Mahdavi, Hossein Tayefi Nasrabadi

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Spermatozoids production rate and its quality are more important factors in the diagnosis of infertility. Also, spematozids activity have a more important role in fertilization. Some medicinal plants as Asparagus(Asparagus officinalis) has many antioxidant component. Therefore, They can affect testes tissue to production more and high-quality spermatozoids. In this survey, Asparagus root extract is studied on spermatogenesis index in the laboratory mouse testes. Hydro-alcoholic extract of asparagus root is prepared and examined on four group of the mature male mouse. Blank group without extract, group 1,100ml/kg dose, group 2, 200 ml/kg dose and group 3, 300ml/kg dose. Then, mice are euthanized, and testes are removed. Testes are weighted, and paraffinized blocks are prepared. TDI(Tubular Differentiation Index) and SPI(Spermiation Index) are studied on histological sections by light microscope. This study results were showed that TDI and SPI in treatments groups with 200 and 300 ml/kg dose had significant enhancement (P<0.05). Consequently, Extract of Asparagus root can enhance spermatozoid production and, therefore, cause improve fertility in male laboratory mice.

Keywords: histology, spermatozoid, ASP [aragus, testes

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Authors: Vladyslav Povoroznyuk, Galina Dubetska, Roksolana Povoroznyuk

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In recent years, the problem of hyperuricemia is getting a particular importance due to its increased incidence in the world population. The aim of this study was to determine uriс acid level in blood serum, incidence of hyperuricemia among women in postmenopausal period and their association with body mass index and some components of metabolic syndrome (triglyceride, cholesterol, systolic and diastolic pressure). We examined 412 women in postmenopausal period. They were divided in to the following groups: I group (BMI = 18,5-24,9), II group (BMI = 25,0-29,9), III group (BMI = 30,0-34,9), IV group (BMI > 35). We determined uric acid level among women during postmenopausal period depending on their body mass index. The higher level of uric acid was found in patients with the maximal body mass index (BMI > 35). In the I group it was 277,52 ± 8,40; in the II group – 286,81 ± 7,79; in the III group – 291,81 ± 7,56; in the IV group – 327,17 ± 12,17. Incidence of hyperuricemia among women in the I group was 10,2%, in the II group – 15,9%; in the III group – 21,2%, in the IV group – 34,2%. We found an interdependence between an uric acid level and BMI in the examined women (r = 0,21, p < 0,05). We determined that the highest level of triglyceride (F = 18,62, p < 0,05), cholesterol (F = 3,64, p < 0,05), atherogenic coefficient (F = 22,64, p < 0,05), systolic (F = 10,5, p < 0,05) and diastolic pressure (F = 4,30, p < 0,05) was among women with hyperuricemia. It was an interdependence between an uric acid level and triglyceride (r = 0,26, p < 0,05), atherogenic coefficient (r = 0,24, p < 0,05) among women in postmenopausal period.

Keywords: hyperuricemia, uric acid, body mass index, women

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Authors: Gizem Akcan, Erdinc Ozturk

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The purpose of the study was to determine some psychosocial variables that predict dissociation in university students. These psychosocial variables were perceived childhood trauma, alexithymia, and gender. 150 (75 males, 75 females) university students (bachelor, master and postgraduate) were enrolled in this study. They were chosen from universities in Istanbul at the education year of 2016-2017. Dissociative Experiences Scale (DES), Childhood Trauma Questionnaire (CTQ) and Toronto Alexithymia Scale were used to assess related variables. Demographic Information Form was given to students in order to have their demographic information. Frequency Distribution, Linear Regression Analysis, and t-test analysis were used for statistical analysis. Childhood trauma and alexithymia were found to have predictive value on dissociation among university students. However, physical abuse, physical neglect and emotional neglect sub dimensions of childhood trauma and externally-oriented thinking sub dimension of alexithymia did not have predictive value on dissociation. Moreover, there was no significant difference between males and females in terms of dissociation scores of participants.

Keywords: childhood trauma, dissociation, alexithymia, gender

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Authors: Mustafa M. Donma, Orkide Donma

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Anthropometric obesity indices such as waist circumference (WC), indices derived from anthropometric measurements such as waist-to-hip ratio (WHR), and indices created from body fat mass composition such as trunk-to-leg fat ratio (TLFR) are commonly used for the evaluation of mild or severe forms of obesity. Their clinical utilities are being compared using body mass index (BMI) percentiles to classify obesity groups. The best of them is still being investigated to make a clear-cut discrimination between healthy normal individuals (N-BMI) and overweight or obese (OB) or morbid obese patients. The aim of this study is to derive a new index, which best suits the purpose for the discrimination of children with N-BMI from OB children. A total of eighty-three children participated in the study. Two groups were constituted. The first group comprised 42 children with N-BMI, and the second group was composed of 41 OB children, whose age- and sex- adjusted BMI percentile values vary between 95 and 99. The corresponding values for the first group were between 15 and 85. This classification was based upon the tables created by World Health Organization. The institutional ethics committee approved the study protocol. Informed consent forms were filled by the parents of the participants. Anthropometric measurements were taken and recorded following a detailed physical examination. Within this context, weight, height (Ht), WC, hip C (HC), neck C (NC) values were taken. Body mass index, WHR, (WC+HC)/2, WC/Ht, (WC/HC)/Ht, WC*NC were calculated. Bioelectrical impedance analysis was performed to obtain body’s fat compartments in terms of total fat, trunk fat, leg fat, arm fat masses. Trunk-to-leg fat ratio, trunk-to-appendicular fat ratio (TAFR), (trunk fat+leg fat)/2 ((TF+LF)/2) were calculated. Fat mass index (FMI) and diagnostic obesity notation model assessment-II (D2I) index values were calculated. Statistical analysis of the data was performed. Significantly increased values of (WC+HC)/2, (TF+LF)/2, D2I, and FMI were observed in OB group in comparison with those of N-BMI group. Significant correlations were calculated between BMI and WC, (WC+HC)/2, (TF+LF)/2, TLFR, TAFR, D2I as well as FMI both in N-BMI and OB groups. The same correlations were obtained for WC. (WC+HC)/2 was correlated with TLFR, TAFR, (TF+LF)/2, D2I, and FMI in N-BMI group. In OB group, the correlations were the same except those with TLFR and TAFR. These correlations were not present with WHR. Correlations were observed between TLFR and BMI, WC, (WC+HC)/2, (TF+LF)/2, D2I as well as FMI in N-BMI group. Same correlations were observed also with TAFR. In OB group, correlations between TLFR or TAFR and BMI, WC as well as (WC+HC)/2 were missing. None was noted with WHR. From these findings, it was concluded that (WC+HC)/2, but not WHR, was much more suitable as an anthropometric obesity index. The only correlation valid in both groups was that exists between (WC+HC)/2 and (TF+LF)/2. This index was suggested as a link between anthropometric and fat-based indices.

Keywords: children, hip circumference, obesity, waist circumference

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Authors: Cenk Aksit, Suleyman Kodal, Yusuf Ersoy Yildirim

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Normalized Difference Vegetation Index (NDVI) is a widely used index in the world that provides reference information, such as the health status of the plant, and the density of the vegetation in a certain area, by making use of the electromagnetic radiation reflected from the plant surface. On the other hand, the chlorophyll index provides reference information about the chlorophyll density in the plant by making use of electromagnetic reflections at certain wavelengths. Chlorophyll concentration is higher in healthy plants and decreases as plant health decreases. This study, it was aimed to determine the changes in Normalize Difference Vegetation Index (NDVI) and Normalize Difference Chlorophyll (NDCI) of silage corn irrigated with subsurface drip irrigation systems under different irrigation levels. In 5 days irrigation interval, the daily potential plant water consumption values were collected, and the calculated amount was applied to the full irrigation and 3 irrigation water levels as irrigation water. The changes in NDVI and NDCI of silage corn irrigated with subsurface drip irrigation systems under different irrigation levels were determined. NDVI values have changed according to the amount of irrigation water applied, and the highest NDVI value has been reached in the subject where the most water is applied. Likewise, it was observed that the chlorophyll value decreased in direct proportion to the amount of irrigation water as the plant approached the harvest.

Keywords: NDVI, NDCI, sub-surface drip irrigation, silage corn, deficit irrigation

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Model assessment, in the Bayesian context, involves evaluation of the goodness-of-fit and the comparison of several alternative candidate models for predictive accuracy and improvements. In posterior predictive checks, the data simulated under the fitted model is compared with the actual data. Predictive model accuracy is estimated using information criteria such as the Akaike information criterion (AIC), the Bayesian information criterion (BIC), the Deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). The goal of an information criterion is to obtain an unbiased measure of out-of-sample prediction error. Since posterior checks use the data twice; once for model estimation and once for testing, a bias correction which penalises the model complexity is incorporated in these criteria. Cross-validation (CV) is another method used for examining out-of-sample prediction accuracy. Leave-one-out cross-validation (LOO-CV) is the most computationally expensive variant among the other CV methods, as it fits as many models as the number of observations. Importance sampling (IS), truncated importance sampling (TIS) and Pareto-smoothed importance sampling (PSIS) are generally used as approximations to the exact LOO-CV and utilise the existing MCMC results avoiding expensive computational issues. The reciprocals of the predictive densities calculated over posterior draws for each observation are treated as the raw importance weights. These are in turn used to calculate the approximate LOO-CV of the observation as a weighted average of posterior densities. In IS-LOO, the raw weights are directly used. In contrast, the larger weights are replaced by their modified truncated weights in calculating TIS-LOO and PSIS-LOO. Although, information criteria and LOO-CV are unable to reflect the goodness-of-fit in absolute sense, the differences can be used to measure the relative performance of the models of interest. However, the use of these measures is only valid under specific circumstances. This study has developed 11 models using normal, log-normal, gamma, and student’s t distributions to improve the PCR stutter prediction with forensic data. These models are comprised of four with profile-wide variances, four with locus specific variances, and three which are two-component mixture models. The mean stutter ratio in each model is modeled as a locus specific simple linear regression against a feature of the alleles under study known as the longest uninterrupted sequence (LUS). The use of AIC, BIC, DIC, and WAIC in model comparison has some practical limitations. Even though, IS-LOO, TIS-LOO, and PSIS-LOO are considered to be approximations of the exact LOO-CV, the study observed some drastic deviations in the results. However, there are some interesting relationships among the logarithms of pointwise predictive densities (lppd) calculated under WAIC and the LOO approximation methods. The estimated overall lppd is a relative measure that reflects the overall goodness-of-fit of the model. Parallel log-likelihood profiles for the models conditional on equal posterior variances in lppds were observed. This study illustrates the limitations of the information criteria in practical model comparison problems. In addition, the relationships among LOO-CV approximation methods and WAIC with their limitations are discussed. Finally, useful recommendations that may help in practical model comparisons with these methods are provided.

Keywords: cross-validation, importance sampling, information criteria, predictive accuracy

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Authors: Patel Kailash P, Patel Parimal M

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The effect of coal fly ash treatment on the chromosomes of Raphanus sativus L. was investigated. The seeds of Raphanus sativusL. were placed in petri dishes in three replicates and allowed to germinate for five days in different concentration of coal fly ash solution. The root was treated with the diluted, semidiluted, and concentrated solution of fly ash while the control group had distilled water.The total aberration were examined. The mitotic index was calculated and the results were statically evaluated by the analysis of variance 5% significant level. The mitotic index decreased as the concentration increased. The highest mitotic index value was diluted fly ash solution while the least was concentrated fly ash treatment. The results show the most frequent chromosomal abnormalities observed included: chromatid bridge, c-mitosis, and stickiness. Concentrated fly ash solution is much more genotoxic than semidiluted fly ash solution, as it induced more aberrations having percentage abnormalities for the highest concentration tested. Increased fly ash pollution can lead to some irreversible cytogenetic effect in plants. The study is an attempt to corroborate the toxic effect of coal fly ash of thermal power plant on the chromosome of plants. These results will be useful in environmental monitoring of the cytotoxicity of coal fly ash.

Keywords: coal fly-ash, genotoxic, cytogenetic, mitotic index, Raphanus sativus L.

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Authors: Shreedevi Moharana, Subashisa Dutta

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The present study focuses on the estimation of crop biophysical parameters like crop chlorophyll, nitrogen and water stress at plot scale in the crop fields. To achieve these, we have used high-resolution satellite LISS IV imagery. A new methodology has proposed in this research work, the spectral shape function of paddy crop is employed to get the significant wavelengths sensitive to paddy crop parameters. From the shape functions, regression index models were established for the critical wavelength with minimum and maximum wavelengths of multi-spectrum high-resolution LISS IV data. Moreover, the functional relationships were utilized to develop the index models. From these index models crop, biophysical parameters were estimated and mapped from LISS IV imagery at plot scale in crop field level. The result showed that the nitrogen content of the paddy crop varied from 2-8%, chlorophyll from 1.5-9% and water content variation observed from 40-90% respectively. It was observed that the variability in rice agriculture system in India was purely a function of field topography.

Keywords: crop parameters, index model, LISS IV imagery, plot scale, shape function

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Authors: Cuong V Nguyen, Ralph Horne, John Fien, France Cheong

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Social vulnerability to climate change is increasingly being acknowledged, and proposals to measure and manage it are emerging. Building upon this work, this paper proposes an approach to social vulnerability assessment using a new mechanism to aggregate and account for causal relationships among components of a Social Vulnerability Index (SVI). To operationalize this index, the authors propose a means to develop an appropriate primary dataset, through application of a specifically-designed household survey questionnaire. The data collection and analysis, including calibration and calculation of the SVI is demonstrated through application in case study city in central coastal Vietnam. The calculation of SVI at the fine-grained local neighbourhood scale provides high resolution in vulnerability assessment, and also obviates the need for secondary data, which may be unavailable or problematic, particularly at the local scale in developing countries. The SVI household survey is underpinned by the results of a Delphi survey, an in-depth interview and focus group discussions with local environmental professionals and community members. The research reveals inherent limitations of existing SVIs but also indicates the potential for their use in assessing social vulnerability and making decisions associated with responding to climate change at the local scale.

Keywords: climate change, local scale, social vulnerability, social vulnerability index

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Authors: Abdull Halim Abdul, Muhaimin Sulam

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This paper is about a subsurface study of Taman Pudu Ulu, Cheras, Kuala Lumpur with emphasize of Geo mechanic properties based on stratum index factor in humanities environment. Subsurface drilling and seismic data were used to understand the subsurface condition of the study area such as the type and thickness of the strata. Borehole and soil samples were recovered Geo mechanic properties of the area by conducting number of experiments. Taman Pudu Ulu overlies the Kuala Lumpur Limestone formation that is known for its karstic features such as caves and cavities. Hence by knowing the Geo mechanic properties such as the normal strain and shear strain we can plan a safer and economics construction that is plan at the area in the future.

Keywords: stratum, index factor, geo mechanic properties, humanities environment

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Authors: Larry V. Aceres, Jesryl B. Paulite, Emelie M. Pelicano, J. B. Anciano, J. A. Esteban

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Enhancing the population of beneficial arthropods under less diverse agroecosystem is the most sought by many researchers and plant growers. This strategy was done through the establishment of pintoi peanut, Arachis pintoi as live mulch or cover crop in pummelo orchard of the University of Southeastern Philippines (USeP), Mabini, Compostela Valley Province, Philippines. This study was conducted to compare and compute population dynamics and diversity of beneficial arthropods in pummelo in with and without Arachis pintoi cover crop. Data collections were done for the 12-month period (from June 2013 to May 2014) at the pummelo orchard of USeP Mabini Campus, COMVAL Province, Philippines and data were analyzed using the Independent Samples T-Test to compare the effect of the presence and absence of Arachis pintoi on beneficial arthropods incidence in pummelo orchard. Moreover, diversity and family richness analyses were computed using the Margalef’s diversity index for family richness; the Shannon index of general diversity and the evenness index; and the Simpson index of dominance. Results revealed numerically and statistically higher density of important beneficial arthropods such as microhymenopterans, macrohymenopterans, spiders, tachinid flies and ground beetles were recorded in pummelo orchard with Arachis pintoi than from without Arachis pintoi cover crop for the 12-month observation period. Further, the result of the study revealed the high family richness and diversity index with more or less even distribution of individuals within the family and low dominance index were documented in pummelo with Arachis pintoi cover crop than from pummelo without Arachis pintoi cover crop. The study revealed that planting A. pintoi in pummelo orchard could enhance natural enemy populations.

Keywords: Arachis pintoi, cover crop, beneficial arthropods, pummelo

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Authors: Masood Roohi, Amir Taghavipour

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This paper describes a strategy to develop an energy management system (EMS) for a charge-sustaining power-split hybrid electric vehicle. This kind of hybrid electric vehicles (HEVs) benefit from the advantages of both parallel and series architecture. However, it gets relatively more complicated to manage power flow between the battery and the engine optimally. The applied strategy in this paper is based on nonlinear model predictive control approach. First of all, an appropriate control-oriented model which was accurate enough and simple was derived. Towards utilization of this controller in real-time, the problem was solved off-line for a vast area of reference signals and initial conditions and stored the computed manipulated variables inside look-up tables. Look-up tables take a little amount of memory. Also, the computational load dramatically decreased, because to find required manipulated variables the controller just needed a simple interpolation between tables.

Keywords: hybrid electric vehicles, energy management system, nonlinear model predictive control, real-time

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: Warda Ajaz

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This study uses the ‘Community Capitals Framework’ (CCF) to develop a community resilience index that can serve as a useful tool for measuring resilience of communities in diverse contexts and backgrounds. CCF is an important analytical tool to assess holistic community change. This framework identifies seven major types of community capitals: natural, cultural, human, social, political, financial and built, and claims that the communities that have been successful in supporting healthy sustainable community and economic development have paid attention to all these capitals. The framework, therefore, proposes to study the community development through identification of assets in these major capitals (stock), investment in these capitals (flow), and the interaction between these capitals. Capital based approaches have been extensively used to assess community resilience, especially in the context of natural disasters and extreme events. Therefore, this study identifies key indicators for estimating each of the seven capitals through an extensive literature review and then develops an index to calculate a community resilience score. The CCF-based community resilience index presents an innovative way of operationalizing the concept of community resilience and will contribute toward decision-relevant research regarding adaptation and mitigation of community vulnerabilities to climate change-induced, as well as other adverse events.

Keywords: adverse events, community capitals, community resilience, climate change, economic development, sustainability

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Authors: Mohammed Abed, Rita Nemes

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In this study different amounts of waste cellular concrete powder (WCCP) as replacement of cement have been investigated as an attempt to produce green binder, which is useful for sustainable construction applications. From zero to up to 60% of WCCP by mass replacement amounts of cement has been conducted. Consistency, compressive strength, bending strength and the activity index of WCCP through seven to ninety days old specimens have been examined, where the optimum WCCP replacement was up to 30%, depending on which the activity index still increased to the end of test period (90 days) and this could be an evidence for its continuity to increase for longer age. Also up to 30% of WCCP increased the bending strength to be higher than the control one. The main point in the present study that there is a possibility of replacing cement by 30% of WCCP, however, it is preferable to be less than this amount.

Keywords: cellular concrete powder, waste cellular concrete powder (WCCP), supplementary cementatious material, SCM, activity index, mechanical properties

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Authors: Birhanu Kassa

Abstract:

The knowledge of temperature effect on index properties and, thus, the understanding of its behavior may be essential for a complete understanding of the various cases of Geotechnical Engineering problems and for conducting meaningful practical research, analysis, and design in tropical regions, such as the Ethiopian environment. The scarcity of the proper geotechnical information on the subsoil makes foundation and engineering works risk able, difficult, and sometimes hazardous. Seasonal variations, environmental effects, terrain challenges, and temperature effects all affect the quality of soil. Simada is a city which is found in south Gondar and it is developing rapidly both in horizontal and vertical construction. Rapid urbanization in the city area has led to an increased interest in the basic properties of soils that are present within the city area. There has been no previous research that looks into the effect of temperature on the investigation of clay soil index qualities in Simada. This work focuses mainly on investigating the Index and some other properties of soil in Simada Town with varying temperatures. To explore the influence of temperature change, samples were collected from various regions of the city, and routine laboratory tests were performed on the collected samples at various temperatures. Disturbed samples were taken at intervals where an average depth of 1.5-2m depths below natural ground level. The standard laboratory tests performed on all twenty-four soil samples were the water content, gradation analysis, Atterberg limits, specific gravity, and compaction test. All specimens were tested at different temperatures (25°C, 35 °C, 45 °C, 65 °C,75 and 105 °C). The variation of the plasticity characteristics of the soils has been determined based on the temperature variation. From the test result, we can conclude that temperature has a significant effect on the index properties of clay soil, in our case, red clay soil.

Keywords: airdried, oven dried, soils index properties, compaction test

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Authors: Mahmut Yildirim

Abstract:

This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Gabriella Tjondro, Julita Dortua Laurentina Nainggolan

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Introduction: There are several things affecting menstrual cycle, namely, nutritional status, diet, financial status of one’s household and exercises. The most commonly used parameter to calculate the fat in a human body is body mass index. Therefore, it is necessary to do research to prevent complications caused by menstrual disorder in the future. Design Study: This research is an observational analytical study with the cross-sectional-case control approach. Participants (n = 124; median age = 19.5 years ± SD 3.5) were classified into 2 groups: normal, NM (n = 62; BMI = 18-23 kg/m²) and obese, OB (n = 62; BMI = > 25 kg/m²). BMI was calculated from the equation; BMI = weight, kg/height, m². Results: There were 79.10% from obese group who experienced menstrual cycle disorders (n=53, 79.10%; p value 0.00; OR 5.25) and 20.90% from normal BMI group with menstrual cycle disorders. There were several factors in this research that also influence the menstrual cycle disorders such as stress (44.78%; p value 0.00; OR 1.85), sleep disorders (25.37%; p value 0.00; OR 1.01), physical activities (25.37%; p value 0.00; OR 1.24) and diet (10.45%; p value 0.00; OR 1.07). Conclusion: There is a significant relation between body mass index (obese) and menstrual cycle disorders. However, BMI is not the only factor that affects the menstrual cycle disorders. There are several factors that also can affect menstrual cycle disorders, in this study we use stress, sleep disorders, physical activities and diet, in which none of them are dominant.

Keywords: menstrual disorders, menstrual cycle, obesity, body mass index, stress, sleep disorders, physical activities, diet

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Authors: Mukesh Ranjan, Varsha Nagargoje

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Applying the exploratory factor analysis (EFA), Women Employment Participation Perception Index (WEPPI) has been made through 13 components. The basic purpose of the WEPPI is to develop an index or search for the latent factors which will capture the attitude or perception of the Mumbai’s slum women towards women’s employment participation in the job market through primary survey based on 160 observations. Majority of the response analyzed under various socio-economic and demographic characteristics falls in the strongly agree or agree category. It means whether it is age wise, marital status-wise, caste, religion or economic dimension-wise women responded that they should participate in employment in Mumbai. Value of KMO test was 0.544 and chronbac’s alpha value was between 0.5-0.6, so the index falls in poor category and can be improved upon by adding more number of items.

Keywords: WEPPI, exploratory factor analysis, KMO test, Chronbac alpha

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Authors: Ayşe Dilek Maden

Abstract:

For a given a simple connected graph, we present some new bounds via a new approach for a special topological index given by the sum of the real number power of the non-zero normalized Laplacian eigenvalues. To use this approach presents an advantage not only to derive old and new bounds on this topic but also gives an idea how some previous results in similar area can be developed.

Keywords: degree Kirchhoff index, normalized Laplacian eigenvalue, spanning tree, simple connected graph

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Authors: Rabia Munsaf Khan, Eshrat Fatima

Abstract:

The analysis of the spatial extent and temporal change of vegetation cover using remotely sensed data is of critical importance to agricultural sciences. Pakistan, being an agricultural country depends on this resource as it makes 70% of the GDP. The case study is of Lal Suhanra National Park, which is not only the biggest forest reserve of Pakistan but also of Asia. The study is performed using different temporal images of Landsat. Also, the results of Landsat are cross-checked by using Sentinel-2 imagery as it has both higher spectral and spatial resolution. Vegetation can easily be detected using NDVI which is a common and widely used index. It is an important vegetation index, widely applied in research on global environmental and climatic change. The images are then classified to observe the change occurred over 15 years. Vegetation cover maps of 2000 and 2016 are used to generate the map of vegetation change detection for the respective years and to find out the changing pattern of vegetation cover. Also, the NDVI values aided in the detection of percentage decrease in vegetation cover. The study reveals that vegetation cover of the area has decreased significantly during the year 2000 and 2016.

Keywords: Landsat, normalized difference vegetation index (NDVI), sentinel 2, Greenland monitoring

Procedia PDF Downloads 306