Prediction Of Test Results Research Articles

Jute-Basalt Reinforced Material Fabrication Experimentation and Prediction of test Results by Machine Learning

Jute-Basalt Reinforced Material Fabrication Experimentation and Prediction of test Results by Machine Learning

Investigation the plastic flows in the metal stamping-drawing process at the die corner

The present investigation covers the study of the influence of metal-plastic flow on the die corner for the deformed state of the blank and energy-power parameters during stamping-drawing. The influence of workpiece size and material on stress intensity and maximum deformation force has also been investigated both experimentally and theoretically. In addition, simulation of the stamping and drawing process was also presented. One type of die, with variations in diameter are considered different in the drawn part size and for each standard size four types of workpiece materials have been considered. The stamping process continued until the part failed structurally. The predicted test results show a dependence of the maximum load and stress intensity on both the diameter of the blank and the yield strength of the alloys and these data are in agreement with the actual measured values. Then, the method for calculating the processes of plastic deformation of metals based on a closed set of equations of continuum mechanics is proposed for the theoretical study of energy-power parameters of the technological processes. Comparison of the results of a full-scale experiment, simulation of metal flow at the die corner and theoretical calculations shows that the proposed theoretical model gives satisfactory results and can be effectively used for engineering calculations.

A Saudi Hospital's Experience in the Management of Well-Appearing Neonates at Increased Risk for Early-Onset Bacterial Sepsis.

Early-onset neonatal bacterial sepsis (EOS) is a serious medical condition where pathogenic bacterial species are isolated from the blood of newborns within the first 72 hours of life. Neonatal healthcare providers face challenges in managing well-appearing newborns born at 35 weeks gestational age or more who are at an increased risk of developing EOS. The American Academy of Pediatrics (AAP) has recommended three approaches for managing EOS. One of these approaches includes enhanced observation to observe the progression of the newborn's clinical condition within the first 48 hours after birth. The AAP recommends that birth centers should adopt institutional approaches that are tailored to their specific local resources and structures. It recommends that the chosen approach is evaluated to identify infrequent negative outcomes and to confirm its effectiveness. To report our experience in managing EOS in newborns born at 35 weeks gestation or later with an increased risk for EOS. This was a review of electronic medical records from the past five years. We included a sample of newborns born at or after 35 weeks gestational age who were at increased risk of EOS and appeared to be healthy. We implemented universal antenatal culture-based screening for Group B streptococcus (GBS). We followed the recommendations of the AAPin 2012 to manage these newborns. We performed a complete blood count (CBC) with differential and C-reactive protein (CRP) tests to predict EOS. We also considered the newborns symptomatic if they displayed any clinical signs of EOS. A total of 806 newborns were included in the study, out of which 27 (3.3%) of them had symptoms of EOS, while the remaining 782 newborns appeared healthy. Predictive blood tests were performed on 281 (34.9%) of the newborns, out of which 126 (44.8%) of them had a positive test result. However, blood cultures were obtained from 134 (16.6%) of the total cohort. Intravenous antibiotics were administered to 33 (4.1%) of the newborns. All symptomatic newborns had a positive predictive blood test result, and two of them had culture-proven EOS. Blood cultures obtained from the remaining 107 asymptomatic newborns were negative. In this context, 140 newborns needed to be pricked for positive predictive blood tests to predict one case of EOS. However, if the positive predictive blood tests were only performed on symptomatic newborns, then only 14 newborns would need to be pricked to predict one case of EOS. Based on the present study, it is advised to follow the current AAP recommendation against predicting EOS by solely relying on CBC with differential or CRP. The study suggests that the enhanced observation approach is a more sensible option for managing EOS, but this needs to be confirmed in a larger study.

OPTIMASI FORMULA HAIR TONIC MIKROEMULSI BIOTIN (VITAMIN B7) DENGAN METODE FACTORIAL DESIGN

Loss becomes a problem in the hair that causes baldness. Biotin is a vitamin that nourishes hair and prevents hair loss. Biotin which is formulated in microemulsion tends to be transparent, has low viscosity and high solubility. Formula optimization was carried out to determine the concentration of span 80, tween 80, and PEG 400 in biotin microemulsion hair tonic. The research was conducted on an experimental basis using a factorial design method with 3 factors, namely span 80, tween 80, and PEG 400. The optimum microemulsion formula was determined using Design Expert 13 software using the Factorial Design method. Testing the physical quality of the preparation is carried out at the optimum formula. Optimization of biotin microemulsion hair tonic showed results of span 80 (20%), tween 80 (9.466%) and PEG 400 (5%) with a predicted viscosity of 46.75 cPs and globule size of 2.98 µm. Prediction testing and test results compared to the One Sample T-test showed results that were not significantly different with a significance value of >0.05. The optimum formula has a viscosity value of 47.67 cPs and a globule size of 2.4 µm. The results obtained concluded that the optimal composition composition of the biotin microemulsion formula from the combination of span 80, tween 80 and PEG 400 which produces hair tonic with good effects and physical quality is span 80 20%, tween 80 9.47% and PEG 400 5%.

Automatic Ship Object Detection Model Based on YOLOv4 with Transformer Mechanism in Remote Sensing Images

Despite significant advancements in object detection technology, most existing detection networks fail to investigate global aspects while extracting features from the inputs and cannot automatically adjust based on the characteristics of the inputs. The present study addresses this problem by proposing a detection network consisting of three stages: preattention, attention, and prediction. In the preattention stage, the network framework is automatically selected based on the features of the images’ objects. In the attention stage, the transformer structure is introduced. Taking into account the global features of the target, this study combines a self-attention module in the transformer model and convolution operation to integrate image features from global to local and for detection, thus improving the ship target accuracy. This model uses mathematical methods to obtain results of predictive testing in the prediction stage. The above improvements are based on the You Only Look Once version 4 (YOLOv4) framework, named “Auto-T-YOLO”. The model achieves the highest accuracy of 96.3% on the SAR Ship Detection dataset (SSDD) compared to the other state-of-the-art (SOTA) model. It achieves 98.33% and 91.78% accuracy in the offshore and inshore scenes, respectively. The experimental results verify the practicality, validity, and robustness of the proposed model.

Investigation of the effect of fly ash content on the bonding performance of CFRP-concrete interface in sulfate environment

The present study focuses on the investigation of the interfacial bond behavior of carbon fiber-reinforced polymer (CFRP)-concrete under dry–wet sulfate cycles by double-sided shear testing. Besides, the effects of fly ash content on the interfacial failure characteristics, interfacial debonding bearing capacity, CFRP strain distribution, and interfacial shear stress peak were analyzed. The interfacial debonding capacity, maximum CFRP strain, and peak value of interfacial shear stress of the CFRP-concrete interface decreased with increasing erosion time under the sulfate dry–wet cycle's action, according to the sulfate dry–wet cycle test results. The sulfate resistance of the CFRP-concrete interface increased after the addition of fly ash. However, the final decrease amplitude of interfacial debonding capacity, CFRP maximum strain, and maximum interfacial shear stress all reduced as the fly ash content increased. The effective bond length of the interface gradually increased with increasing erosion time; however, the change in fly ash content had little effect on the effective bond length, and the final effective bond length of the samples with different fly ash content was the same. Moreover, the CFRP-concrete interfacial bearing capacity model under the sulfate dry–wet cycle was established by introducing sulfate's comprehensive influence coefficient and considering fly ash content's influence. In conclusion, the comparative analysis of the prediction model and test results revealed that the prediction model could well reflect the degradation law of interfacial debonding bearing capacity with sulfate attack time.

Models for individualized COVID-19 diagnostic prediction

Since the COVID-19 pandemic, the world has experienced a large incidence of infections in short periods of time, giving rise to waves of contagion caused by the different variations of SARS-COV-2. Health services, as well as personnel, have been overwhelmed, especially in the poorest countries. Currently and after two years, the pandemic continues and according to experts it is here to stay, which highlights the importance of vaccines and methods of detecting the disease, to curb the number of infections and avoid that the pandemic continues to spread and thus the virus continues to mutate. Detection tests have been scarce and expensive for most of the population, so alternative methods to laboratory ones could be a decisive factor so that people can self-isolate before continuing to infect more people. One of the most effective methods have been statistical predictions of the diagnosis of COVID-19 in a patient, based on certain variables. In this article, it was identified that the most common prediction models were developed from logistic regression and machine-learning, which have shown high percentages of predicting test results for COVID-19. The most important predictor variables in the different models developed in various regions of the world were identified and the opportunities, limitations and perspectives of this prediction method are discussed.

Investigation of impact behavior of innovative non-curing steel fiber geopolymer composites

One of the most important factors influencing the performance of geopolymer composites is the curing temperature. The dependence of geopolymerization process on activator under high temperature is a drawback need to overcome and research. This study focuses on designing of the new and innovative geopolymer composites mixtures designs with an ambient non-curing periods and improving the capacity of this materials, particularly in terms of mechanical properties and impact resistance. Experiments were conducted to measure impact resistance based on ACI committee 544, as well as impact energy absorption, compressive strength, splitting tensile strength, and flexural strength. The experiments were carried out by using fly ash from 20% to 80%, slag from 40% to 60% to replace cement as a main binder, liquid/solid ratios of 0.7–0.8 and 0–1.5% of steel fiber. Comparing with cured-geopolymer composites, the compressive strength, splitting tensile strength, and flexural strength are improved of 18.63%, 30.14%, 10% and non-curing mixture could be applied widely in production and construction site. The liquid/solid material ratio of 0.8 and 1.5% of hooked end steel fiber contribute to the significant enhancement of geopolymer composites without curing. Besides, the experimental result was examined with analysis results using Ansys software showed good agreement with a slight and acceptable difference under 10%, confirming the numerical method can be used to predict test results with acceptable accuracy.

Axial compressive behaviour of tapered CFDST stub columns with large void ratio

An experimental and numerical investigation on tapered concrete-filled double skin steel tubular (CFDST) stub columns with large void ratio under axial compression is presented in this paper. Twelve tapered CFDST specimens with different parameters, namely the void ratio and the diameter-to-thickness ratio of outer steel tube, are tested. The load versus deformation curves, failure modes, effects of different parameters are studied, respectively. It is found that tapered CFDST specimens with large void ratio experience the strength failure, and severely outward buckling occurs near the column top. The diameter-to-thickness ratio of outer steel tube and the void ratio engender a significant influence on the ultimate capacity and ductility of tapered CFDST columns. A finite element (FE) model is developed to predict test results, and to further analyze the working mechanism of tapered columns. The result shows that changing the tapered angle and void ratio has distinct effects on the position of failure section and distribution of peak load of tapered columns. A method considering the influence of failure section is finally proposed for calculating the ultimate capacity of tapered CFDST columns and a good predicted result is presented.

Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities.

This study is conducted to build a multi-criteria text mining model for COVID-19 testing reasons and symptoms. The model is integrated with a temporal predictive classification model for COVID-19 test results in rural underserved areas. A dataset of 6895 testing appointments and 14 features is used in this study. The text mining model classifies the notes related to the testing reasons and reported symptoms into one or more categories using look-up wordlists and a multi-criteria mapping process. The model converts an unstructured feature to a categorical feature that is used in building the temporal predictive classification model for COVID-19 test results and conducting some population analytics. The classification model is a temporal model (ordered and indexed by testing date) that uses machine learning classifiers to predict test results that are either positive or negative. Two types of classifiers and performance measures that include balanced and regular methods are used: (1) balanced random forest and (2) balanced bagged decision tree. The balanced or weighted methods are used to address and account for the biased and imbalanced dataset and to ensure correct detection of patients with COVID-19 (minority class). The model is tested in two stages using validation and testing sets to ensure robustness and reliability. The balanced classifiers outperformed regular classifiers using the balanced performance measures (balanced accuracy and G-score), which means the balanced classifiers are better at detecting patients with positive COVID-19 results. The balanced random forest achieved the best average balanced accuracy (86.1%) and G-score (86.1%) using the validation set. The balanced bagged decision tree achieved the best average balanced accuracy (83.0%) and G-score (82.8%) using the testing set. Also, it was found that the patient history, age, testing reasons, and time are the key features to classify the testing results.

Dengue epidemiological characteristic in Kuala Lumpur and Selangor, Malaysia

In the last few decades, dengue remains a leading public health problem in Malaysia. The problem persists with increasing trend of cases despite implementation of various interventions by the Ministry of Health Malaysia. The difficulty in eradicating dengue disease throughout the world is related to the complexity of the dengue phenomena. With no access to a safe, effective and affordable dengue vaccine, the only intervention strategy is to control the mosquito vector. In reality, data on mosquitoes is limited, and in most cases, the dynamic of mosquito is unobservable. In this study, based on the dengue incidence data in Malaysia−Kuala Lumpur and Selangor−from the year 2011 to 2014, a transmission model of type SI-SIR is constructed. Transmission rates in the two locations are estimated from the data fitting of the dynamic of infected human to dengue incidence. The average infection rate and the estimated average reproduction number for Selangor are estimated to be 0.1674 week−1 and 1.025, respectively. An enclave in Selangor, the capital city Kuala Lumpur has slightly lower average dengue infection rate of 0.1656 week−1 and estimated average reproduction number of 1.014. Further sensitivity analysis indicated that the dengue infection rate is the critical parameter of dengue transmission in Kuala Lumpur and Selangor. This implies that dengue control strategies should focus on reducing the dengue infection rate. Numerical simulations are also performed to validate the estimated dengue infection rate, to illustrate the dengue dynamics and to determine the effects of dengue control strategies in the two selected locations. The predictive test results of this method also provide good results in capturing the pattern of dengue cases both in Kuala Lumpur and in Selangor. Numerical simulations on instantaneous reproduction number reveal that the instantaneous reproduction number of Kuala Lumpur varies between 0.8 and 1.28 while the instantaneous reproduction number of Selangor varies between 0.7 and 1.2. The insights and results presented in this paper are useful to the relevant authorities in analyzing the effect of mosquito intervention for reducing the prevalence of dengue in Malaysia.

A study of using back-propagation neural model in automatic lubrication installation for the feeding system of computer numerical control machine tool

Nowadays, the feeding systems of computer numerical control machine tools are lubricated by periodic oil supply or fixed stroke, the lubrication is insufficient in the case of high load and high-speed movement, and the lubrication is excessive during finishing and low feed rate. This study discusses the optimum lubrication timing of the feeding system. When the feeding system is moving, the servomotor torque value and current, accuracy, and oil film thickness are measured by sensors. Moreover, the lubrication characteristic model is validated and built by using the sensor values, and the optimal lubrication state estimation is obtained by using the back-propagation neural model. Then analyzed and feedback to the machine tool controller, to intelligent the lubrication system. According to the test, when the feed rate is increased by 5 m/min, the friction coefficient increases with rate, increasing the output of the frictional value of the work table by 6.90%. When the load is increased by 175 kg, the friction coefficient decreases with load, reducing the frictional value output of table movement by 6.71%. In the relationship between oil film thickness and current, the accuracy difference between the prediction and actual test results is less than 10%; in the case of the same accuracy, the oil supply frequency is reduced by 80%, and environmentally friendly machine tool has been achieved.

Resting Left Ventricular Global Longitudinal Strain to Identify Silent Myocardial Ischemia in Asymptomatic Patients with Diabetes Mellitus

Screening for silent coronary artery disease in asymptomatic patients with diabetes mellitus (DM) is challenging and controversial. In this context, it seems crucial to identify early markers of coronary artery disease. The aim of this study was to investigate the incremental value of resting left ventricular (LV) global longitudinal strain (GLS) for the prediction of positive results on stress (exercise or dobutamine) transthoracic echocardiography in 273 consecutive asymptomatic high-risk patients with DM. Positive results on stress transthoracic echocardiography were defined as stress-induced LV wall motion abnormalities (new or worsening preexisting abnormalities). Compared with patients with negative stress results, those with positive stress results (n=28 [10%]) more frequently had cardiovascular risk factors, complications of DM, vascular disease, moderate and severe calcification of the aortic valve and mitral annulus, and worse resting LV GLS (-16.7±2.9% vs -19.0±1.9%, P<.001). On multivariable logistic regression analysis, DM duration > 10years, diabetic retinopathy, LV hypertrophy, and impaired LV GLS (odds ratio, 1.39 [95% CI, 1.14-1.70] per percentage increase; odds ratio, 5.16 [95% CI, 1.96-13.59] for LV GLS worse than -18%) were independently associated with positive results on stress transthoracic echocardiography. The area under the curve to predict positive results was 0.74 for LV GLS with a cutoff of -18.0% (sensitivity 68%, specificity 78%). The area under the curve of the multivariable model to predict test results was improved by the addition of LV GLS (P<.001), with a bias-corrected area under the curve after bootstrapping of 0.842 [95% CI, 0.753-0.893]. The present findings show that resting LV GLS is associated with the presence of silent ischemia and could be useful to better identify asymptomatic patients with DM who might benefit from coronary artery disease screening.

Pulmonary fibrosis in non-mutation carriers of families with short telomere syndrome gene mutations.

Diagnostic and predictive genetic testing for disease cause and risk estimation is common in many countries. For genetic diseases, predictive test results are commonly straightforward: presence of the mutation involves increased risk for disease and absence of the mutation involves no inherit risk for disease. Germline mutations in telomere-related genes (TRGs) can lead to telomere shortening and are associated with short telomere syndrome (STS). Telomere length is heritable, and in families with STS due to a TRG mutation, progeny with and without the TRG mutation is known to have shorter than average telomeres. We hypothesize that progeny of TRG mutation carriers who did not inherit the TRG mutation may still develop pulmonary fibrosis. A genetic screen of 99 unrelated families with familial pulmonary fibrosis revealed five patients with features of pulmonary fibrosis but without carrying the familial disease-causing TRG mutation. Features of STS were present in each family, including short telomeres in blood and tissue of the non-mutation carrying patients. Additional genetic, clinical or environmental risk factors for pulmonary fibrosis were present in each non-mutation carrying patient. Our study shows that non-mutation carrying first-degree relatives in families with STS are at increased risk for pulmonary fibrosis. Disease development may be triggered by inherited short telomeres and additional risk factors for disease. This observation has profound consequences for genetic counselling. Unlike any other genetic syndrome, absence of the mutation does not imply absence of disease risk. Therefore, clinical follow-up is still urged for non-mutation carrying first-degree family members.

A new approach for prediction of the remaining strength of pipeline with external defects

Dent and gouge defects are affected by external load on the outer perimeter of the pipelines. The external defects occur both separately and in combination, reducing the remaining strength of the pipelines. Such defects with plastic strain and metal losses lead to stress concentrations. Plastic deformation depends on the initial deflection, springback, and re-rounding behavior of the pipelines. In this research, the semiempirical technique is applied to assess the springback and re-rounding behavior of a pipeline with external defects under internal pressure and the predicted remaining strength of defected pipelines. For this purpose, elastoplastic multi-linear isotropic hardening FE simulation on high-strength carbon steel was utilized to assess the impacts of several factors, such as strain hardening coefficient, pipeline geometry, and dent and gouge geometry on the re-rounding coefficient and remaining strength of defected pipeline. Finally, new methods are proposed for predicting the re-rounding coefficient and the burst pressure of the pipelines with external defects. Full-scale burst tests are implemented to validate the proposed models. Using the proposed formula, the predicted test results of the high- and low-strength pipes demonstrated a good correlation with experimental tests.

Penerapan Metode Penghalusan Eksponensial Tunggal pada Prediksi Penjualan Air Minum dalam Kemasan

The Bottled Drinking Water Industry has grown rapidly to various provinces and cities throughout Indonesia now. The development of this industry is caused by high demand from consumers and the lower quality of raw water in springs and wells. PT. Cahaya Bumi Intanpari (CBI) is a company that produces gallons, bottles, and glasses bottled drinking water under the brand “AirMu”. Sales of bottled water every month always fluctuate, the management of PT. Cahaya Bumi Intanpari requires an estimate of the amount of production of each type of bottled water to market demand in the future. Based on the description of the background, in this study, a desktop-based application was designed and built. The research method used includes data collection and data analysis. Data collection includes observation, interviews, and literature study. While data analysis includes making data flow diagrams. The forecasting model is used in forecasting the sales of bottled drinking water at PT. Cahaya Bumi Intanpari is a time series or it can be called a time series, and the forecasting method used is single exponential smoothing. From the results of calculation testing, it is proven that the Single Exponential Smoothing method can be implemented in the AMDK sales forecasting system. Based on the validity test, it is found that the prediction test results of bottled drinking water sales below 20% are included in the good criteria.

Shear and Flexural Strengthening of Steel Beams with Thick Carbon Fiber Reinforced Polymer Laminate

In this paper, shear and flexural behavior of structural steel beams strengthened by high modulus carbon fiber reinforced polymer (CFRP) laminates are presented. Totally, 18 steel specimens including 6 un-strengthened beams as control specimens and 12 strengthened steel beams with simple supports were tested under 3-point bending test set-up. All specimens were strengthened using the bonded system. Influence of different parameters including length of steel beams, section size of specimens, number of CFRP laminates, and location of CFRP laminates were studied. Based on anticipated failure modes, the bonded laminates were implemented on the surface of tension flange, compression flange, and web of beams. Three failure modes of flexural, shear, and lateral-torsional buckling failures were observed in the tested beams. The main goal of these experiments was to evaluate the enhancement in load capacity, beam ductility, and initial stiffness. The results showed that the yield load, ultimate load capacity, and energy absorption of strengthened steel beams improved up to 15, 29 and 28 percent, respectively. Finally, in order to predict test results and compare the actual and predicted valves, analytical and numerical studies were carried out.

Genetic information, insurance and a pluralistic approach to justice

The use of genetic testing has prompted the question of whether insurance companies should be able to use predictive genetic test results (GTRs) in their risk classification of clients. While...

The use of modified wood in the construction of wastewater treatment plants in coal mines of Kuzbass

PurposeThe purpose of the study is to show an improvement in resistance (permeability) and durability (residual strength) of modified wood compared to natural wood.Design/methodology/approachA relevance way to increase the durability and resistance of wood is processing with polymers. Novosibirsk State Technical University, Novosibirsk State University of Architecture and Civil Engineering (Sibstrin) and the Norilsk State Industrial Institute researched the resistance of elements of mine treatment plants made of wood modified with synthetic polymers. The study was carried out according to V.A. Kucherenko. Modification of such species as birch and pine can significantly increase wood resources by extending its service life and improving its physical and mechanical properties. Latex-organosilicon modifier was used as a modifier.FindingsLatex clogs the capillaries of wood, preventing the penetration of aggressive solutions into it, and potassium methyl silicate interacts with hydrophilic OH groups, increasing the water-repellent properties of the surface of cells and wood capillaries.Originality/valueThe results of long-term and predictive tests of modified wood in aggressive environments of treatment facilities are presented. As a result, diffusion of aggressive media into the wood becomes more difficult and its resistance and durability increase.

Evaluation of lap-splices in NSM FRP rods for retrofitting RC members

Externally Bonded FRP (EB-FRP) and Near Surface Mounted (NSM) FRP are the two common forms of utilizing FRP material for flexural strengthening of Reinforced Concrete (RC) members. While the design and construction of EB-FRP is well established, there are several gaps in the current knowledge base regarding strengthening using NSM FRP. One area where research in NSM FRP is limited is on splicing of the NSM FRP rods/strips. Splicing becomes extremely advantageous when strengthening bridge beams spanning over multi-lane traffic lanes, or when strengthening long spans where it may be more economical to carry out the retrofit in stages. The present study evaluates the feasibility of lap-splicing NSM FRP rods using finite element analysis (FEA). Several experimentally evaluated NSM FRP strengthened RC beams available in literature were modeled using finite elements. The developed FE models are validated based on a comparative study of the experimental and FEA predicted test results. The calibrated FE models are then utilized to evaluate different methods of splicing configurations for NSM FRP rods. The development length for each configuration is evaluated and the best methods identified. A parametric study is then carried out varying geometric and material properties to evaluate their effect on the development length and beam behavior. The required splice length is found to be a function of FRP rod diameter and FRP modulus, but invariant of concrete compressive strength. A design splice length of 110 times the FRP rod diameter is recommended for concrete strengths between 15 and 45 MPa and FRP rod diameters between 6 and 10 mm.