Predicted Research Articles

Model Predictive Control of a BCDFIG With Active and Reactive Power Control Capability for Grid-Connected Applications

Model Predictive Control of a BCDFIG With Active and Reactive Power Control Capability for Grid-Connected Applications

Naive bayes-correlation based feature weighting technique for sports match result prediction

Naive bayes-correlation based feature weighting technique for sports match result prediction

Providing a Prediction Model for Stress Intensity Factor of Fiber- Reinforced Asphalt Mixtures under Pure Mode III Loading Using the Edge Notched Disc Beam (ENDB)

The use of Edge Notched Disc Beam (ENDB) sample has been proposed as a suitable geometry in performing fracture tests in different loading modes. The most important features of the ENDB samples include easy making, quick and easy sampling, simple testing, and the ability to examine a wide range of pure and combined loading modes. Using a wide range of fracture tests, a statistical model is proposed to predict the stress intensity factors of asphalt mixtures in terms of the pure torsion mode (mode III) loading in this study. To this end, the experiments were carried out at different temperature conditions (-5, -15 and -25 °C), different loading conditions (0.5, 1 and 5 mm/min), and on control and modified asphalt mixtures with different percentages of polyolefin-aramid fibers. The results showed that, with increasing the fiber content and loading rate, the fracture strength increased with average 25%, while an increase in fracture toughness due to lower temperature had an effect of less than 5%. Using the Response Surface Method (RSM), the prediction model of stress intensity coefficients of asphalt mixtures was presented in the pure torsion mode. The results of the proposed models had a good correlation with the results of the conducted fracture tests.

Investigation of the Maternal and Neonatal Factors Affecting the Apgar score of Newborns: A Case-Control Study

Background: Apgar score is a method for clinical evaluation of neonate at birth, and predicts their clinical conditions and physical or mental development. This study aimed to investigate the relationship between some neonatal and maternal factors during pregnancy on the Apgar score. Materials and Methods: This case-control study was conducted on 234 neonates born in Ilam hospitals during 2015-2016. The case group included 81 neonates with Apgar score Results: The mean maternal weights in the case and control groups were 66.8 ± 13.5 and 67.1 ± 11.1 Kg, respectively. The mean± SD for maternal age 29 ± 5 vs. 29 ± 5.7 years, marriage age 22 ± 3 vs. 22.2 ± 4.9 years, BMI 24.5±3.2 vs. 26.1 ± 4.1 and weight gain during pregnancy 13.8 ± 6.2 vs. 12.7 ± 6.8 kg among cases and controls, respectively. The odds ratio for the following factors was significantly higher among cases than the control group: preterm delivery (OR: 3.3, 95% CI: 1.2-17), twin birth (OR: 8.9, 95% CI: 2.6-27.2), abnormal presentation of the fetus (OR: 6, 95% CI: 2.3-15.6), birth weight (OR: 6.9, 95% CI: 1.4-28.6), and placental abruption (OR: 9.4, 95% CI: 2.2-22.5) for Apgar score at five minute. Conclusion: Based on the findings of the present study, preterm labor, multiple birth, and abnormal fetal presentation, and low birth weight, premature rupture of membrane, placental abruption and anemia, led to an increase in Apgar score less than 7.

Smart Phone Use among University Students

Smartphone users have increased to over two and a half billion people and it is predicted to rise to five billion by the year of 2020. These smartphone users have been spending an increasing amount of time, indicating an obsession with a virtual world. Smartphone users include students from secondary school to tertiary levels. Their constant engagement with mobile phones has raised questions of whether it has developed into an addiction that may have negative consequences on academic performance. With regards to these issues, the present study aims to examine the attitude of students towards this intriguing technology, the hours spent, activities involved and possible risk of phone addiction among the university students in the Malaysian context. This study employs a descriptive research design to collect data. Data analysis was conducted using descriptive statistics such as mean, standard deviation, frequency and percentage. Fifty-five out eighty students responded to a survey which was applied randomly among students from a public higher learning institution. The findings indicated that most of the students somewhat agreed that they used smartphones without any compelling reasons at very hour and that it induces emotional stability. The findings also indicated that students were addicted to smartphones to some extent that may affect their academic performance. It is expected that the findings from this study will help higher learning institutions to better understand the pattern of smartphone usage among university students and to reduce or control academically disruptive smart phone addiction.

The Findings Of The Governors Of The Turkestan General-Governor’s Office

The Findings Of The Governors Of The Turkestan General-Governor’s Office

Резолюция Совета экспертов «Нормативный регламент и вопросы организации скрининга и прогнозирования пре­эклампсии в РФ»­

Резолюция Совета экспертов «Нормативный регламент и вопросы организации скрининга и прогнозирования пре­эклампсии в РФ»­

Noninvasive programmed ventricular stimulation for prediction of future events in patients with cardioverter-defibrillator implanted for primary prevention of sudden cardiac death

Abstract Funding Acknowledgements Type of funding sources: None. OnBehalf The NIPS-ICD Investigators Background Implantable cardioverter-defibrillator (ICD) offers an opportunity to examine vulnerability to ventricular tachycardia (VT) or ventricular fibrillation (VF) by performing non-invasive programmed ventricular stimulation (NIPS). Whether NIPS can predict mortality in patients with primary prevention ICD, has not yet been examined. Purpose To examine a long-term predictive value of NIPS for identification of patients at increased risk of death during follow up. Methods The study group consisted of consecutive 41 patients with ICD implanted for primary prevention of sudden cardiac death, included in the prospective NIPS-ICD study (ClinicalTrials ID: NCT02373306) (34 males, age 64 ± 11 years). The patients underwent NIPS using the protocol up to three premature extrastimuli at 600, 500 and 400ms drive cycle lengths. The end-point of NIPS was induction of sustained VT or VF or completion of the protocol. Results At baseline NIPS, VT/VF was induced in 8 (20%) patients. After 5 year follow up mortality rate was significantly higher in patients who had VT/VF induced at NIPS vs no VT/VF at NIPS (38% vs 12%, p = 0.04). The difference in mortality was most remarkable at 3 (38% vs 3%, p = 0.001) and 4 years (38% vs 6%, p = 0.007). The NIPS-induced VT/VF had a sensitivity of 37.5%, specificity of 88%, positive predictive value of 43% and negative predictive value of 85% for occurrence of death during follow up. An occurrence of secondary composite endpoint consisting of VT/VF recurrence or death was also most frequent in NIPS-inducible group (75% vs 24%, p = 0.037). In a multivariate Cox-Proportional Hazard model induction of VT/VF at NIPS along with age≥65, and treatment with amiodarone was an independent predictor of the composite endpoint during follow-up Conclusions Inducibility of VF/VF during NIPS in patients with primary prevention ICD is associated with higher mortality and higher incidence of composite endpoint consisting of death or VT/VF during a long term observation. Abstract Figure. Kaplan-Meier survival curves

EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions

Abstract Reliable river streamflow (RSF) forecasting is an important issue due to its impact on planning and operation of the water resources system. In this study, based on Lower Upper Bound Estimation (LUBE), hybrid artificial intelligence methods were used for point and interval prediction of monthly RSF. Two states based on stations' own data and upstream stations' data were considered for RSF modeling of the Housatonic River during the period of 1941–2018. Ensemble Empirical Mode Decomposition (EEMD) and Variational Mode Decomposition (VMD) methods were used for enhancing the streamflow point forecasting accuracy. Interval Prediction (PI) was applied for tolerating increased uncertainty. Results showed that in state 1, the error criterion value for the superior model decreased from 0.155 to 0.082 and 0.09 for the EEMD- and VMD-based models, respectively. Generally, hybrid models increased the modeling accuracy between 20% and 40%. Via the integrated approaches, the upstream stations' data was successfully used for streamflow prediction of stations without data. In this state, the PI coverage probability values for the VMD-based model were approximately 12% higher than the single model. Generally, the VMD-based model led to more desirable results due to having higher PI coverage probability and lower mean PI width values.

WORK-LIFE INTEGRATION AND FEMALE LECTURERS’ TASK PERFORMANCE IN HIGHER EDUCATIONAL INSTITUTIONS IN IMO STATE, NIGERIA

This study examined work-life integration and female lecturers’ tasks performance in government-owned higher educational institutions in Imo state. The correlational research design was adopted for the study. Two research questions were answered while two hypotheses were tested. The population of the study comprised 1292 female lecturers in government-owned higher educational institutions in Imo State. The sample size of the study was determined using Taro Yamane formula. A two-stage sampling of proportionate and stratified random sampling technique was used to draw 406 respondents from the strata. Two sets of instruments titled; Female Lecturers’ Work-Life Integration Questionnaire (FLWLIQ) and Female Lecturers’ Task Performance Questionnaire (FLTPQ) were used for data collection. Face and content validities were ensured by experts including the researcher’s supervisor. The reliability coefficient of the FLWLIQ and FLTPQ was calculated to be 0.97 and 0.95 with the help of Cronbach Alpha. Multiple and simple regressions were used to answer the research questions. ANOVA associated with multiple regression and t-test associated with simple regression were used to test the null hypotheses at 0.05 alpha level. It was found that self-management skills predicts female lecturers’ tasks performance by 54% in government-owned higher educational institutions in Imo state. Based on the findings, it was concluded that work-life integration is positively and moderately significant to female lecturers’ tasks performance in higher educational institutions in Imo state. It was therefore recommended among others that female lecturers should adopt SMART goal setting so as to manage their energy properly. Article visualizations:

Accuracy of Prediction Methods for Sound Insulation of Homogeneous Single Baffles

Accuracy of Prediction Methods for Sound Insulation of Homogeneous Single Baffles

Genome Sequence of Hardyhisp2, a Gammapleolipovirus Infecting Haloarcula hispanica.

Hardyhisp2 virus infects the halophilic archaeon Haloarcula hispanica DSM 4426T and is closely related to His2 (Pleolipoviridae family). The viral genome is 16,133 bp long, with terminal inverted repeats of 599 bp. The predicted spike protein is only weakly similar (32% amino acid identity) to that of His2.

Calculation of Equivalent Axle Load Factor Based on Artificial Intelligence

In most road pavements design methods, a solution is required to transform the traffic spectrum to standard axle load with using equivalent axle load factor (EALF). The EALF depends on various parameters, but in existing design methods, only the axle type (single, tandem, and tridem) and pavement structure number were considered. Also, the EALF only determined for experimental axles and axle details (i.e., axle weight, length, pressure), wheel type (single or dual wheel) plus pavement properties were overlooked which may cause inaccuracy and unusable for the new axle. This paper presented a developed model based on Artificial Neural Network (ANN) for calculation of EALFs considering axle type, axle length, contact area, pavement structure number (SN), tire pressure, speed, and final serviceability. Backpropagation architecture was selected for the model for the EALF prediction based on fatigue criteria. Finally, among all reviewed ANN configuration, a network with 7-13-1 was selected for the optimum network.

Invited perspectives: Landslide populations – can they be predicted?

Invited perspectives: Landslide populations – can they be predicted?

Laboratory study and predictive modeling for thaw subsidence in deep permafrost

Laboratory study and predictive modeling for thaw subsidence in deep permafrost

Soft computing for anomaly detection and prediction to mitigate IoT-based real-time abuse

Soft computing for anomaly detection and prediction to mitigate IoT-based real-time abuse

Optimization of Bacteriocin Production by Lactobacillus fermentum Strain COE20 from Fermenting Pentaclethra macrophylla Benth Using Response Surface Methodology

This study evaluated the effect of varied culture conditions (Temperature, pH, and Sodium Chloride concentration) on bacteriocin production by Lactobacillus fermentum strain COE20 isolated from fermenting African oil bean seeds (Pentaclethra macrophylla Benth) using Response Surface Methodology (RSM). A Central Composite Design (CCD) was adopted with the interest of estimating the optimal conditions for its production using the response surface regression model, which estimated the linear, squared, and interactive relationship between the response variables. The Analysis of Variance (ANOVA) showed that the coefficient of determination in terms of predicted R2 was 0.8697 which was in close agreement with an adjusted R2 of 0.7393 and was accounted for by the predictors suggesting that the model was adequate. Optimal culture condition for bacteriocin production by L. fermentum strain COE20 was found at approximately 31°C, pH 5.9, 1.9% NaCl concentration at Y = 11.75mm. Y represents the response (zone of inhibition) against Staphylococcus aureus ATCC 19095 using the agar well diffusion assay method.

Evaluation of longitudinal / transverse dispersion coefficients and prediction of concentration at river confluence in two-dimensional solute transport analysis

Evaluation of longitudinal / transverse dispersion coefficients and prediction of concentration at river confluence in two-dimensional solute transport analysis

Efficient metaheuristic-retrofitted techniques for concrete slump simulation

Due to the benefits of the early prediction of concrete slump, introducing an efficient model for this purpose is of great importance. Considering this motivation, four strong metaheuristic algorithms, namely electromagnetic field optimization (EFO), water cycle algorithm (WCA), teaching-learning-based optimization (TLBO), and multi-tracker optimization algorithm (MTOA) are used to supervise a neural predictive system in analyzing the slump pattern. This supervision protects the network against computational issues like pre-mature convergence. The overall results (e.g., Pearson correlation indicator larger than 0.839 and 0.807 for the training and testing data, respectively) revealed the competency of the proposed models. However, investigating the rankings of the models pointed out the superiority of the WCA (MAEtrain = 3.3080 vs. 3.7821, 3.5782, and 3.6851; and MAEtest = 3.8443 vs. 4.0326, 4.1417, and 4.0871 obtained for the EFO, TLBO, and MTOA, respectively). Moreover, the high efficiency of the EFO in terms of model complexity and convergence rate, as well as the adequate accuracy of prediction, demonstrated the suitability of the corresponding ensemble. Therefore, the neural systems trained by these two algorithms (i.e., the WCA and EFO) are efficient slump evaluative models and can give an optimal design of the concrete mixture for any desirable slump.

Recent advances in preoperative assessment of hepatic functional reserve for hepatectomy

Hepatectomy represents the first choice of treatment for primary and secondary liver malignancies.However,post-hepatectomy liver failure(PHLF) remains a significant cause of morbidity and mortality after liver resection.Inadequate remnant liver volume and function are the determining factors of PHLF.Therefore,preoperative assessment of hepatic functional reserve is of paramount importance for prevention of PHLF.There are two main conventional assessment systems of preoperative hepatic functional reserve.The first is remnant liver volume(RLV) and its derivative indicators such as residual liver volume ratio,standardized residual liver volume ratio,RLV/body weight,and RLV/body surface area,calculated by imaging-based methods such as CT,that assess whether the volume of residual liver meets the requirements of safe hepatectomy.However,RLV is not an appropriate indicator of functional liver remnant,since the function of liver within equal volume among different persons is unequal due to the underlying liver diseases.Functional imaging techniques,such as 99Tcm-GSA,99Tcm-IDA and Gd-EOB-DTPA-enhanced MRI can simultaneously evaluate residual liver volume and function,leading to be a more appropriate indicator of functional liver remnant.The second is liver function tests,including serum biochemical parameters,clinical scoring systems and quantitative liver function tests.However,it can merely evaluate the overall liver function preoperatively,but not the residual one postoperatively.The residual liver function can be accurately measured by intraoperative indocyanine green clearance with the extrahepatic and intrahepatic blood flow of liver to be resected blocked.In addition,methods such as preoperative portal vein embolization and associating liver partition and portal vein ligation for staged hepatectomy,can be used for patients with a predicted inadequate RLV.Due to the unequal liver function within an equal volume increased by different methods,functional imaging techniques are more appropriate in predicting the increasing functional liver than traditional methods which just assess the increasing liver volume.