Search results for: mortality prediction

Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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Authors: Yuwaratu Syafira, Wahyu K. Y. Putra, Kusharisupeni Djokosujono

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Background/Objective: Body Mass Index (BMI) serves various purposes, including measuring the prevalence of obesity in a population, and also in formulating a patient’s diet at a hospital, and can be calculated with the equation = body weight (kg)/body height (m)². However, the BMI of an individual with difficulties in carrying their weight or standing up straight can not necessarily be measured. The aim of this study was to form a prediction model for the BMI of young adult students of Public Health Faculty of University of Indonesia. Subject/Method: This study used a cross sectional design, with a total sample of 132 respondents, consisted of 58 males and 74 females aged 21- 30. The dependent variable of this study was BMI, and the independent variables consisted of sex and anthropometric measurements, which included ulna length, arm length, tibia length, knee height, mid-upper arm circumference, and calf circumference. Anthropometric information was measured and recorded in a single sitting. Simple and multiple linear regression analysis were used to create the prediction equation for BMI. Results: The male respondents had an average BMI of 24.63 kg/m² and the female respondents had an average of 22.52 kg/m². A total of 17 variables were analysed for its correlation with BMI. Bivariate analysis showed the variable with the strongest correlation with BMI was Mid-Upper Arm Circumference/√Ulna Length (MUAC/√UL) (r = 0.926 for males and r = 0.886 for females). Furthermore, MUAC alone also has a very strong correlation with BMI (r = 0,913 for males and r = 0,877 for females). Prediction models formed from either MUAC/√UL or MUAC alone both produce highly accurate predictions of BMI. However, measuring MUAC/√UL is considered inconvenient, which may cause difficulties when applied on the field. Conclusion: The prediction model considered most ideal to estimate BMI is: Male BMI (kg/m²) = 1.109(MUAC (cm)) – 9.202 and Female BMI (kg/m²) = 0.236 + 0.825(MUAC (cm)), based on its high accuracy levels and the convenience of measuring MUAC on the field.

Keywords: body mass index, mid-upper arm circumference, prediction model, ulna length

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Authors: Ejaz Ahmed, Huang Yong

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The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

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Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

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We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

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Authors: Tomas Vampola, Michael Valášek

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On the basis of simulation-generated failure states of structural elements of a turboprop engine suitable for the busy-jet class of aircraft, an algorithm for early prediction of damage or reduction in functionality of structural elements of the engine is designed and verified with real data obtained at dynamometric testing facilities of aircraft engines. Based on an expanding database of experimentally determined data from temperature and pressure sensors during the operation of turboprop engines, this strategy is constantly modified with the aim of using the minimum number of sensors to detect an inadmissible or deteriorated operating mode of specific structural elements of an aircraft engine. The assembled algorithm for the early prediction of reduced functionality of the aircraft engine significantly contributes to the safety of air traffic and to a large extent, contributes to the economy of operation with positive effects on the reduction of the energy demand of operation and the elimination of adverse effects on the environment.

Keywords: detectability of malfunction, dynamometric testing, prediction of damage, turboprop engine

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Authors: Kefaya Qaddoum

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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

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Authors: Sherif Sabri, Suzy Fawzi, Sanaa Abdelshafy, Ayman Nagah

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Introduction: Static derangements in lactate homeostasis during ICU stay have become established as a clinically useful marker of increased risk of hospital and ICU mortality. Lactate indices or kinetic alteration of the anaerobic metabolism make it a potential parameter to evaluate disease severity and intervention adequacy. This is an inexpensive and simple clinical parameter that can be obtained by a minimally invasive means. Aim of work: Comparing the predictive value of dynamic indices of hyperlactatemia in the first twenty four hours of intensive care unit (ICU) admission with other static values are more commonly used. Patients and Methods: This study included 40 critically ill patients above 18 years old of both sexes with Hyperlactamia (≥ 2 m mol/L). Patients were divided into septic group (n=20) and low oxygen transport group (n=20), which include all causes of low-O2. Six lactate indices specifically relating to the first 24 hours of ICU admission were considered, three static indices and three dynamic indices. Results: There were no statistically significant differences among the two groups regarding age, most of the laboratory results including ABG and the need for mechanical ventilation. Admission lactate was significantly higher in low-oxygen transport group than the septic group [37.5±11.4 versus 30.6±7.8 P-value 0.034]. Maximum lactate was significantly higher in low-oxygen transport group than the septic group P-value (0.044). On the other hand absolute lactate (mg) was higher in septic group P-value (< 0.001). Percentage change of lactate was higher in the septic group (47.8±11.3) than the low-oxygen transport group (26.1±12.6) with highly significant P-value (< 0.001). Lastly, time weighted lactate was higher in the low-oxygen transport group (1.72±0.81) than the septic group (1.05±0.8) with significant P-value (0.012). There were statistically significant differences regarding lactate indices in survivors and non survivors, whether in septic or low-oxygen transport group. Conclusion: In critically ill patients, time weighted lactate and percent in lactate change in the first 24 hours can be an independent predictive factor in ICU mortality. Also, a rising compared to a falling blood lactate concentration over the first 24 hours can be associated with significant increase in the risk of mortality.

Keywords: critically ill patients, lactate indices, mortality in intensive care, anaerobic metabolism

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Phawichsak Prapassornpitaya, Wanida Jinsart

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Daily monitoring PM₁₀ and PM₂.₅ data from 2007 to 2017 were analyzed to provide baseline data for prediction of the air pollution in Bangkok in the period of 2018 -2020. Two statistical models, Autoregressive Integrated Moving Average model (ARIMA) were used to evaluate the trends of pollutions. The prediction concentrations were tested by root means square error (RMSE) and index of agreement (IOA). This evaluation of the traffic PM₂.₅ and PM₁₀ were studied in association with the regulatory control and emission standard changes. The emission factors of particulate matter from diesel vehicles were decreased when applied higher number of euro standard. The trends of ambient air pollutions were expected to decrease. However, the Bangkok smog episode in February 2018 with temperature inversion caused high concentration of PM₂.₅ in the air environment of Bangkok. The impact of traffic pollutants was depended upon the emission sources, temperature variations, and metrological conditions.

Keywords: fine particulate matter, ARIMA, RMSE, Bangkok

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Mikhail Gritskevich, Sebastian Hohenstein

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The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: discrete holes film cooling, Reynolds Averaged Navier-Stokes (RANS), Reynolds stress tensor anisotropy, turbulent heat transfer

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Al Asyary, Meita Veruswati, Dian Ayyubi

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Sukabumi municipal has highest rank for maternal mortality in Indonesia with 102 by 100,000 live birth with almost 80% of birth were not attended by skilled birth attendant (SBA). Although universal health coverage has been implemented, availability and sufficiency of SBA, such as midwife in this developing country, are problematic agenda for the quality of public healthcare as well as decreasing maternal mortality rate. This study aims to describe the equal distribution of midwives in Sukabumi municipal as support of government’s program named Millennium Development Goals (MDGs) that suppressed maternal mortality rate in Indonesia. We conducted an observational study with Workload Indicator of Staffing Need (WISN) analysis to present the dispersion of midwives by their activities and workloads in 37 Puskesmas. We also generated in-depth interview with several executive chief of health sections, including chief of health offices in Sukabumi municipal. It resulted inferentially that several activities in midwives’ program were differed at once of existing than needed condition ideally (ρ value = 0.002 < 0.05). Meanwhile, decision for midwives’ procurement and placement were held by un-systematically procedure such as based on where the midwife was staying, and it also progressed by neighborhood issue priorities. The absence of formal regulation in local government is a serious problem that indicated poor political commitment, while access to SBA shall be focused carefully.

Keywords: developing country, health professional resources, health policy, need assessment

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Authors: A. Hakimiyan, H. Namazi

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Human behavior is defined as a range of behaviors exhibited by humans who are influenced by different internal or external sources. Human behavior is the subject of much research in different areas of psychology and neuroscience. Despite some advances in studies related to forecasting of human behavior, there are not many researches which consider the effect of the time delay between the presence of stimulus and the related human response. Analysis of EEG signal as a fractal time series is one of the major tools for studying the human behavior. In the other words, the human brain activity is reflected in his EEG signal. Artificial Neural Network has been proved useful in forecasting of different systems’ behavior especially in engineering areas. In this research, a time delay neural network is trained and tested in order to forecast the human EEG signal and subsequently human behavior. This neural network, by introducing a time delay, takes care of the lagging time between the occurrence of the stimulus and the rise of the subsequent action potential. The results of this study are useful not only for the fundamental understanding of human behavior forecasting, but shall be very useful in different areas of brain research such as seizure prediction.

Keywords: human behavior, EEG signal, time delay neural network, prediction, lagging time

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Authors: Hadura Abu Hasan, Zairi Jaal, P. J. McCall

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Dengue is a serious problem in Malaysia, particularly in high-density urban communities with lower socio-economic levels. This study evaluated the susceptibility of local populations of Aedesaegypti (Linnaeus), Aedesalbopictus (Skuse) and Culexquinquefasciatus (Say) from the traditional community of BaganDalam, Penang, Malaysia to lambdacyhalothrin and pirimiphos-methyl using standard World Health Organization (WHO) adult bioassay test. Unfed female mosquitoes aged 3-5 days were exposed to WHO recommended dosages of insecticides over fixed time periods with results presented as knock-down time (KT50) for each strain.The insecticide susceptible VCRU laboratory strain was usedas control. All three specieswere highly resistant to lambda-cyhalothrin with less than 10% mortality at 24 hours after treatment. In contrast, Ae.aegypti and Ae. albopictus were susceptible to pirimiphos-methyl, showing 100% mortality recorded 24 hoursafter treatment. Cx. quinquefasciatuswasclassed as ‘suspected resistant’ to pirimiphos-methyl as mortality recorded 24 hours after treatment was 94-96%. The results indicate that organophosphates such as pirimiphos-methyl might be used as alternative to pyrethroid for dengue vector control in this dengue-prone area.

Keywords: vector control, aedes aegypti, aedes albopictus, dengue, culex quinquefasciatus, residuals insecticides, pyrethroid, organophosphate, resistant, mosquito

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Bao Thach Nguyen, Abbas Mohajerani

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The California bearing ratio (CBR) has been acknowledged as an important parameter to characterize the bearing capacity of earth structures, such as earth dams, road embankments, airport runways, bridge abutments, and pavements. Technically, the CBR test can be carried out in the laboratory or in the field. The CBR test is time-consuming and is infrequently performed due to the equipment needed and the fact that the field moisture content keeps changing over time. Over the years, many correlations have been developed for the prediction of CBR by various researchers, including the dynamic cone penetrometer, undrained shear strength, and Clegg impact hammer. This paper reports and discusses some of the results from a study on the prediction of CBR. In the current study, the CBR test was performed in the laboratory on some fine-grained subgrade soils collected from various locations in Victoria. Based on the test results, a satisfactory empirical correlation was found between the CBR and the physical properties of the experimental soils.

Keywords: California bearing ratio, fine-grained soils, soil physical properties, pavement, soil test

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Authors: S. Ben Fredj, H. Ghali, M. Ben Rejeb, S. Layouni, S. Khefacha, L. Dhidah, H. Said

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Background: The Intensive Care Unit (ICU) provides continuous care and uses a high level of treatment technologies. Although developed country hospitals allocate only 5–10% of beds in critical care areas, approximately 20% of nosocomial infections (NI) occur among patients treated in ICUs. Whereas in the developing countries the situation is still less accurate. The aim of our study is to assess mortality rates in ICUs and to determine its predictive factors. Methods: We carried out a nested case-control study in a 630-beds public tertiary care hospital in Eastern Tunisia. We included in the study all patients hospitalized for more than two days in the surgical or medical ICU during the entire period of the surveillance. Cases were patients who died before ICU discharge, whereas controls were patients who survived to discharge. NIs were diagnosed according to the definitions of ‘Comité Technique des Infections Nosocomiales et les Infections Liées aux Soins’ (CTINLIS, France). Data collection was based on the protocol of Rea-RAISIN 2009 of the National Institute for Health Watch (InVS, France). Results: Overall, 301 patients were enrolled from medical and surgical ICUs. The mean age was 44.8 ± 21.3 years. The crude ICU mortality rate was 20.6% (62/301). It was 35.8% for patients who acquired at least one NI during their stay in ICU and 16.2% for those without any NI, yielding an overall crude excess mortality rate of 19.6% (OR= 2.9, 95% CI, 1.6 to 5.3). The population-attributable fraction due to ICU-NI in patients who died before ICU discharge was 23.46% (95% CI, 13.43%–29.04%). Overall, 62 case-patients were compared to 239 control patients for the final analysis. Case patients and control patients differed by age (p=0,003), simplified acute physiology score II (p < 10-3), NI (p < 10-3), nosocomial pneumonia (p=0.008), infection upon admission (p=0.002), immunosuppression (p=0.006), days of intubation (p < 10-3), tracheostomy (p=0.004), days with urinary catheterization (p < 10-3), days with CVC ( p=0.03), and length of stay in ICU (p=0.003). Multivariate analysis demonstrated 3 factors: age older than 65 years (OR, 5.78 [95% CI, 2.03-16.05] p=0.001), duration of intubation 1-10 days (OR, 6.82 [95% CI, [1.90-24.45] p=0.003), duration of intubation > 10 days (OR, 11.11 [95% CI, [2.85-43.28] p=0.001), duration of CVC 1-7 days (OR, 6.85[95% CI, [1.71-27.45] p=0.007) and duration of CVC > 7 days (OR, 5.55[95% CI, [1.70-18.04] p=0.004). Conclusion: While surveillance provides important baseline data, successful trials with more active intervention protocols, adopting multimodal approach for the prevention of nosocomial infection incited us to think about the feasibility of similar trial in our context. Therefore, the implementation of an efficient infection control strategy is a crucial step to improve the quality of care.

Keywords: intensive care unit, mortality, nosocomial infection, risk factors

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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: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: C. Romero, R. Ortega, P. Sánchez-Camacho, P. Aguilar, V. Segur, J. Ruiz, G. Gutiérrez

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Introduction: Breast cancer is a leading cause of death in CLM. (2.8% of all deaths in women and 13,8% of deaths from tumors in womens). It is the most tumor incidence in CLM region with 26.1% from all tumours, except nonmelanoma skin (Cancer Incidence in Five Continents, Volume X, IARC). Cancer registries are a good information source to estimate cancer incidence, however the data are usually available with a lag which makes difficult their use for health managers. By contrast, mortality and survival statistics have less delay. In order to serve for resource planning and responding to this problem, a method is presented to estimate the incidence of mortality and survival data. Objectives: To estimate the incidence of breast cancer by age group in CLM in the period 1991-2013. Comparing the data obtained from the model with current incidence data. Sources: Annual number of women by single ages (National Statistics Institute). Annual number of deaths by all causes and breast cancer. (Mortality Registry CLM). The Breast cancer relative survival probability. (EUROCARE, Spanish registries data). Methods: A Weibull Parametric survival model from EUROCARE data is obtained. From the model of survival, the population and population data, Mortality and Incidence Analysis MODel (MIAMOD) regression model is obtained to estimate the incidence of cancer by age (1991-2013). Results: The resulting model is: Ix,t = Logit [const + age1*x + age2*x2 + coh1*(t – x) + coh2*(t-x)2] Where: Ix,t is the incidence at age x in the period (year) t; the value of the parameter estimates is: const (constant term in the model) = -7.03; age1 = 3.31; age2 = -1.10; coh1 = 0.61 and coh2 = -0.12. It is estimated that in 1991 were diagnosed in CLM 662 cases of breast cancer (81.51 per 100,000 women). An estimated 1,152 cases (112.41 per 100,000 women) were diagnosed in 2013, representing an increase of 40.7% in gross incidence rate (1.9% per year). The annual average increases in incidence by age were: 2.07% in women aged 25-44 years, 1.01% (45-54 years), 1.11% (55-64 years) and 1.24% (65-74 years). Cancer registries in Spain that send data to IARC declared 2003-2007 the average annual incidence rate of 98.6 cases per 100,000 women. Our model can obtain an incidence of 100.7 cases per 100,000 women. Conclusions: A sharp and steady increase in the incidence of breast cancer in the period 1991-2013 is observed. The increase was seen in all age groups considered, although it seems more pronounced in young women (25-44 years). With this method you can get a good estimation of the incidence.

Keywords: breast cancer, incidence, cancer registries, castilla-la mancha

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Authors: Qiming Zhang, Youda Ye, Qinxue Jiang

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Accurate prediction of external flowfield and aero-heating at the wall of hypersonic vehicle is very crucial for the design of aircrafts. Unstructured/hybrid meshes have more powerful advantages than structured meshes in terms of pre-processing, parallel computing and mesh adaptation, so it is imperative to develop high-resolution numerical methods for the calculation of aerothermal environment on unstructured/hybrid meshes. The inviscid flux scheme is one of the most important factors affecting the accuracy of unstructured/ hybrid mesh heat flux calculation. Here, a new hybrid flux scheme is developed and the approach of interface type selection is proposed: i.e. 1) using the exact Riemann scheme solution to calculate the flux on the faces parallel to the wall; 2) employing Sterger-Warming (S-W) scheme to improve the stability of the numerical scheme in other interfaces. The results of the heat flux fit the one observed experimentally and have little dependence on grids, which show great application prospect in unstructured/ hybrid mesh.

Keywords: aero-heating prediction, computational fluid dynamics, hybrid meshes, hybrid schemes

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Authors: Rehan Waheed, Abdul Shakoor

Abstract:

Distortion produced during welding of thin metal plates is a problem in many industries. The purpose of this research was to study distortion produced during welding in 2mm Mild Steel plate by simulating the welding process using Finite Element Analysis. Simulation of welding process requires a couple field transient analyses. At first a transient thermal analysis is performed and the temperature obtained from thermal analysis is used as input in structural analysis to find distortion. An actual weld sample is prepared and the weld distortion produced is measured. The simulated and actual results were in quite agreement with each other and it has been found that there is profound deflection at center of plate. Temperature dependent material properties play significant role in prediction of weld distortion. The results of this research can be used for prediction and control of weld distortion in large steel structures by changing different weld parameters.

Keywords: welding simulation, FEA, welding distortion, temperature dependent mechanical properties

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Authors: Hyunchul Ahn, William X. S. Wong

Abstract:

Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Abdelkader Hocine, Abdelhakim Maizia

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The exponential growth of reinforced fibers composite materials use has prompted researchers to step up their work on the prediction of their reliability. Owing to differences between the properties of the materials used for the composite, the manufacturing processes, the load combinations and types of environment, the prediction of the reliability of composite materials has become a primary task. Through failure criteria, TSAI-WU and the maximum stress, the reliability of multilayer tubular structures under pressure is the subject of this paper, where the failure probability of is estimated by the method of Monte Carlo.

Keywords: composite, design, monte carlo, tubular structure, reliability

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Authors: Mohhamed El Habib Labdouni

Abstract:

Introduction: Co-infection with VIH and tuberculosis poses one of the major ongoing challenges for global TB and AIDS prevention and control. The objective of this study is to raise the issue of the resurgence of TB, in People living with VIH supported in a referent center in western Algeria. Its epidemiological, clinical, biological and radiological new trends, and to compare the mortality rate between early and delayed ART. Methods: It was a prospective study, during 36 months from the 01st/01/2012 to 31st/12/2014, by identifying and analyzing cases of TB-VIH co-infection. Our population was devised in two groups/ early ART and delayed ART. The primary and secondary endpoints were analyzed with Kaplan-Meier curves and log-rank test the period of follow up, which was fixed at 300 weeks. Results: Sixty cases of co-infection TB -VIH were enrolled in our study: 78.3% had pulmonary tuberculosis associated with extra-pulmonary, 13.3% had only pulmonary tuberculosis and 08.3% presented strictly extra-pulmonary TB. The clinical particularity of this co-infection is the frequency of serious localization such us: pleural 23.3%, peritoneal 31.7%, and meningeal suffusion 13.3%.y-.biologicaly we notice the predominance both of pancytopenia and leucoanemia, hyponatremia in 38,6% and hypokalemia in 19,3%. By analyzing Kaplan-Meier survival curves, we notice that early ART initiation is associated with a significant reduction of all-cause mortality (p = 0,000), and we have identified several prognostic factors such as hypokalemia hyponatremia, leukocytosis thrombopenemia leucothrombopenia (p = 0,005). Conclusion: Our study confirms most of the results reported in the literature. Early ART initiation reduces the rate of all-cause mortality, despite the probability of the occurrence of TB-IRIS.

Keywords: TB-HIV co-infection, early ART, hyponatremia, extrapulmonary tuberculosis

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Authors: D. Zahraddeen, N. M. Lemu, P. P. Barje, I. S. R. Butswat

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This study was conducted to investigate some of the factors affecting the optimum reproductive capacity of the indigenous breeds of sheep in Nigeria. A total of 767 sheep of different breeds were investigated. The reproductive indices considered were birth/weaning weights, litter size, parity, mortality, reproductive problems/disorders, body condition score (BCS), as well as growth traits. The results showed that litter size, parity, and BCS had significant (p < 0.05) effects on birth/weaning weights, mortality rates and growth traits of the sheep breeds studied. Similarly, the rearing method/system significantly (p < 0.05) influenced other reproductive traits such as birth/weaning weights, mortality, growth performance of lambs. However, the major reproductive problems/disorders in the ewes were dystocia (30.94%), retained placenta (16.91%), mastitis (15.83), pregnancy toxaemia (11.51%), uterine prolapse (6.48%) and vaginal prolapse (3.24%). In the rams, the incidence of reproductive problems included cryptorchidism (1.08%), orchitis (2.87%) and scrotal dermatophilosis (1.79%), among others. This study concludes that the four breeds of sheep (Balami, Yankasa, Uda, and West African Dwarf sheep) and their crosses exhibited varied genetic make-up and potentials. However, the large number of sheep farmers practicing the extensive production system might be responsible for the low reproductive performance of this species in the country. It is, therefore, recommended that significant improvement could be achieved through enhanced management practices of these animals.

Keywords: sheep, breeds, reproduction, disorders

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

Abstract:

Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Muhammad Mamoon-Ur-Rashid, Gul Rehman

Abstract:

Entomopathogenic fungi are considered effective bio-control agents for the management of a range of insect pests including red palm weevil. The research studies were conducted under laboratory and field conditions against 5th and 6th instars larvae and adults of [Rhynchophorus ferrugineus (Olivier)] at the faculty of Agriculture, Gomal University Dera Ismail Khan (KPK) Pakistan. The 5th instar larvae were used under field conditions whereas, the 6th instar larvae and newly emerged adults were used under lab conditions. Conidial suspensions were used at five different concentrations of 1×10⁴, 1×10⁵, 1×10⁶, 1×10⁷ and 1×10⁸, conidia per ml. The data were recorded on the mortality, total larval duration, weight of larvae, pre-pupal and pupal durations, percent pupal formation, pupal weight, percent adult emergence, and adult longevity (♂ and ♀) of red palm weevil. The B. bassiana had varying degrees of pathogenicity against different developmental stages of red palm weevil. The maximum larval duration (113.40 days) was noted when 5th instar larvae were treated with the maximum concentration (1 × 10⁸) of B. bassiana, whereas; the minimum total larval duration of 87.20 days was recorded on the lowest concentration (1 × 10⁴) of B. bassiana. The maximum pre-pual and pupal durations were noted at the maximum concentration. The maximum life span of adult male and females were noted at the lowest concentration, whereas; the minimum values were noted at the maximum concentration. The earliest mortality of red palm weevil was observed 1-day after treatment at higher concentrations of 1 × 10⁷ and 1 × 10⁸, whereas; it was recorded 3 and 4 days after treatment at lower concentrations of 1 × 10⁵ and 1 × 10⁴. At 10 days after treatment, the entomopathogenic fungus caused > 80% cumulative mortality of 5th and 6th instar larvae and adult weevils at the maximum concentrations which were more than double than those recorded at the lowest concentration. Overall, the 5th instar larvae of red palm weevils were most susceptible to the fungus compared to the 6th instar larvae and adult weevils. Based on current findings, it is suggested that entomopathogenic fungi could be used for the safer management of red palm weevil.

Keywords: entomopathogenic nematodes, mortality, red palm weevil, sub-lethal effects

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