Prediction Of Timing Research Articles

Role of Uterine Artery Doppler in Prediction of Preeclampsia: A Prospective Analytical Study

Introduction: Preeclampsia is pregnancy specific hypertensive disorder of pregnancy which is associated with both high maternal and perinatal morbidity and mortality. Timely prediction and management in time, are the two key factors for prevention of these morbidities and mortalities. The aim of present study was to evaluate role of uterine artery doppler in prediction of preeclampsia in second and third trimester of pregnancy in Indian setting. Materials and Methods: This was a prospective analytical study done at department of Obstetrics and Gynaecology, King George Medical university, Lucknow over a period of one year. After ethical approval (Institutional Ethics Committee, KGMU, Lucknow, Ref. Code: 92nd ECM II B- thesis P/29.) and informed consent from every patient, recruitment was done. Group I had 120 normotensive patients and Group 2 had 100 preeclampsia patients at time of enrollment. Bilateral uterine artery doppler was done for every patient and patient were further followed for maternal and perinatal outcome. Results: Total 220 patients were studied. In Group 1, 15 patients developed preeclampsia. Both groups were comparable for maternal age and parity. However, BMI was found to be significantly higher in the patients of Preeclampsia group. Uterine artery doppler, showed statistically significant difference and it was high in preeclampsia group. It had high negative predictive value. Gestation of termination was significantly lower in preeclampsia group. Fetal birth weight was significantly lower in preeclampsia group. Conclusion: Uterine artery doppler is a non-invasive test with high negative predictive value which should be utilized for prediction of preeclampsia for all pregnant patients.

Multi-Time Scale Harmonic Mitigation for High Proportion Electronic Grid

As new energies integrated into grid, the harmonics in the grid are more serious, and the Voltage Detection Active Power Filter (VDAPF) has been used as the mitigation equipment to solve the harmonic problems in power electronic distribution network. The global optimization method generally utilizes 15 min to mitigate harmonic which is named long-time-scale, and it is impossible to mitigate harmonic timely. This paper studies a multi-time-scale harmonic mitigation method which adds 5 min prediction to the global optimization method which is named multi-time-scale. The objective is to correct the deviation of harmonic caused by the long-time-scale harmonic prediction error. This paper utilizes MPC to predict the amplitude and phase variation of the harmonics in short time scale, and utilizes distributed VDAPF to optimize harmonic in long time scale. The IEEE 33 nodes system was used as an example to conduct a comparative analysis of mitigation effects in two different harmonic injection scenarios which injected separately 10 and 20%. The results shows that the mitigating effect of the global optimization method is limited under two different harmonic injection scenarios, and it needs to combine with short time scale harmonic prediction information to ensure the effectiveness and rationality of the mitigation results. When the harmonic current fluctuates, there are unqualified voltage distortion nodes in each region, and the voltage distortion rate is larger. But the MPC can change harmonic prediction in a short time, so every harmonic of each VDAPF conductance values is set bigger, and harmonic conductance of the higher harmonics is relatively lower.

Investor sentiment-aware prediction model for P2P lending indicators based on LSTM.

In recent years, online lending has created many risks while providing lending convenience to Chinese individuals and small and medium-sized enterprises. The timely assessment and prediction of the status of industry indicators is an important prerequisite for effectively preventing the spread of risks in China’s new financial formats. The role of investor sentiment should not be underestimated. We first use the BERT model to divide investor sentiment in the review information of China’s online lending third-party information website into three categories and analyze the relationship between investor sentiment and quantitative indicators of online lending product transactions. The results show that the percentage of positive comments has a positive relationship to the borrowing interest rate of P2P platforms that investors are willing to participate in for bidding projects. The percentage of negative comments has an inverse relationship to the borrowing period. Second, after introducing investor sentiment into the long short-term memory (LSTM) model, the average RMSE of the three forecast periods for borrowing interest rates is 0.373, and that of the borrowing period is 0.262, which are better than the values of other control models. Corresponding suggestions for the risk prevention of China’s new financial formats are made.

Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow

Timely generation of accurate and reliable forecasts of flash flood events is of paramount importance for flood early warning systems in urban areas. Although physically based models are able to provide realistic reproductions of fast-developing inundation maps in high resolutions, the high computational demand of such hydraulic models makes them difficult to be implemented as part of real-time forecasting systems. This paper evaluates the use of a hybrid machine learning approach as a surrogate of a quasi-2D urban flood inundation model developed in PCSWMM for an urban catchment located in Toronto (Ontario, Canada). The capability to replicate the behavior of the hydraulic model was evaluated through multiple performance metrics considering error, bias, correlation, and contingency table analysis. Results indicate that the surrogate system can provide useful forecasts for decision makers by rapidly generating future flood inundation maps comparable to the simulations of physically based models. The experimental tool developed can issue reliable alerts of upcoming inundation depths on traffic locations within one to two hours of lead time, which is sufficient for the adoption of important preventive actions. These promising outcomes were achieved in a deterministic setup and use only past records of precipitation and discharge as input during runtime.

Mental Health Over Time and Financial Concerns Predict Change in Online Gambling During COVID-19.

This study examined online gambling behavior during COVID-19 land-based gambling restrictions and associations with changes in mental health, impacts on household income due to the pandemic, financially focused motivations, and symptoms of gambling problems. A repeated online survey was administered to adult gamblers in Ontario, Canada. Wave 1 was conducted at the beginning of the first COVID-19 lockdown (April 2020) and wave 2 in August 2020. Respondents (N = 940) completed validated self-report questionnaires related to depression and anxiety, financial motivation, financial impacts due to the pandemic, online gambling behaviors, and problem gambling symptomatology. A cluster analysis identified three subgroups: no online gambling (subgroup 1), engagement in a range of gambling games online and change in gambling involvement (i.e., some increased and some decreased gambling; subgroup 2), and predominantly online lottery play with no change in online gambling (subgroup 3). Respondents who reported increased anxiety and depression symptom severity between the two waves and those who experienced greater symptoms of problem gambling and negative impacts on household income due to COVID-19 were most likely to be found in subgroup 2. Greater financial focus was also noted in this group. Results indicate a link between change in online gambling involvement during COVID-19 and increased mental health problems, elevated problem gambling severity, negative impacts on household income, and a greater financially focused self-concept. These results may help generate novel research questions examining short and long-term effects of the pandemic on online behaviors that inform policy and practice.

Integrated pest management strategies for cabbage stem flea beetle (Psylliodes chrysocephala) in oilseed rape.

Oilseed rape (OSR) is the second largest source of vegetable oil globally and the most important biofuel feedstock in the European Union (EU) but the production of this important crop is threatened by a small insect, Psylliodes chrysocephala – the cabbage stem flea beetle (CSFB). The EU ban on use of neonicotinoid seed treatments and resistance of CSFB to pyrethroid insecticides have left farmers with limited control options resulting in drastic reductions in production. Integrated pest management (IPM) may offer a solution. We review the lifecycle of CSFB and the current options available, or in the research pipeline, for the eight IPM principles of the EU Sustainable Use of Pesticides Directive (Directive‐2009/128/EC). A full IPM strategy for CSFB barely exists. Although there are a range of preventative measures, these require scientific validation; critically, resistant/tolerant OSR cultivars are not yet available. Existing monitoring methods are time‐consuming and there are no commercial models to enable decision support based on predictions of migration timing or population size. Available thresholds are not based on physiological tolerances of the plant making it hard to adapt them to changing market prices for the crop and costs of control. Non‐synthetic alternatives tested and registered for use against CSFB are lacking, making resistance management impossible. CSFB control is therefore dependent upon conservation biocontrol. Natural enemies of CSFB are present, but quantification of their effects is needed and habitat management strategies to exploit their potential. Although some EU countries have local initiatives to reduce insecticide use and encourage use of ‘greener’ alternatives, there is no formal process for ranking these and little information available to help farmers make choices. We summarize the main knowledge gaps and future research needed to improve measures for CSFB control and to facilitate development of a full IPM strategy for this pest and sustainable oilseeds production.

Predictability of Exfoliation Time of Deciduous Teeth

Background: The predictability of the eruption time of primary teeth has an important place in clinical applications, forensic medicine, planning of orthodontic treatments and age estimation. Aim: In this study, we tried to contribute to dental literature by trying to prove the predictability of the falling time of primary teeth. Design: From the periapical radiographs taken from the patients who applied to the clinic with the complaint of primary second molars, primary first molars were detected without caries and with 1 to 3 months to fall. The teeth of the patients for whom follow-up was recommended, which were due to fall out and adhered only to the gingiva, were extracted with the help of cotton, according to the patient’s complaint. Fall times were recorded. Results: Null Hypothesis test was applied to estimate the falling time of the teeth followed in the study, and the success rate in the 90-day exfoliation period was statistically significant, but no statistically significant difference was found in the 30-day samples. Conclusion: We think that, besides estimating the falling time of primary teeth in stem cell research, orthodontic treatment, clinical treatment planning and age determination, examining of primary teeth with correct without causing ethical problems will make a significant contribution to the literature. Clinical Benefit: Naturally and physiologically shedding deciduous teeth are unique ex vivo human tissue resources with abundance of multiple cells, including stem cells. The predictability of shedding time of deciduous teeth would be very important while planning sensible stem cell experiments.

Deformation Characteristics and Safety Evaluation of the Throw Filling Soft Clay Cofferdam under Super‐Historical Flood Conditions

Affected by super‐historical flood, the surface deformation of throw filling soft clay cofferdam is obvious and the stability of the cofferdam slope is difficult to ensure. Relying on the cofferdam project of Jiangxi Xinjiang Shuanggang navigation power junction and according to the data of filed investigation and automatic monitoring, the Plaxis infinite element software is used to analyze the deformation characteristics and stabilities of cofferdam during the super‐historical flood period, and the effective emergency reinforcement measures are presented. The results show that (1) the stability of cofferdam is controlled by downstream face slope of cofferdam during the super‐historical flood period, and there is a most dangerous slide surface interpenetrating through the top to the toe of downstream face slope; (2) the stability of upstream face slope is influenced significantly by the water level fluctuation and it is reduced sharply during the water level decline process; and (3) real‐time remote monitoring can effectively reflect the deformation and failure characteristics of cofferdam under the condition of super‐historical flood level and provide early warning and prediction in time. The results and analyzing methods can be applied to analyze the similar projects excellently.

Rice Disease Diagnosis System (RDDS)

Hitherto, Rice (Oryza Sativa) has been one of the most demanding food crops in the world, cultivated in larger quantities, but loss in both quality and quantity of yield due to abiotic and biotic stresses has become a major concern. During cultivation, the crops are most prone to biotic stresses such as bacterial, viral, fungal diseases and pests. These stresses can drastically damage the crop. Lately and erroneously recognized crop diseases can increase fertilizers costs and major yield loss which results in high financial loss and adverse impact on nation’s economy. The proven methods of molecular biology can provide accurate detection of pathogenic factors, but these methods are not accessible to the majority of the farmers, needs high costs or resources, and require domain knowledge to implement. Expert’s field inspection report provides precise crop diagnosis but continuous field inspection over the remotely placed agriculture fields is not feasible. Therefore, cost effective approach for early detection of diseases can help farmers to take necessary steps in time to boost up the crop production. Precision agriculture makes use of decision support systems built using Machine Learning (ML) or Deep Learning (DL) approaches to cut down heavy costs. Timely crop diagnosis process can be automated with the involvement of Computer Vision, Image Processing and Deep Learning (DL) based methods for more precise prediction in less cost and time. Latest research shows that more accurate image classification can be implemented using Deep Learning based Convolutional Neural Network (CNN) model. In this paper, we have proposed an automated Rice Disease Diagnosis System (RDDS) for timely, more accurate and detailed crop disease diagnosis, which consisting of two modules, they are Leaf Disease Identification (LDI) module for disease detection and Infection Intensity Estimation (IIE) module for disease severity analysis. The LDI module is based on the proposed novel RDD_CNN model that classified the eight most harmful and commonly occurring diseases, it has obtained the best test accuracy of 98.47% when compared to its first three versions. And the IIE module is designed for estimating identified disease’s intensity in terms of extent and stage of infection providing detailed and overall diagnosis report specially designed for Brown Spot disease.

Solar power prediction in real time utilising supervised machine learning algorithms considering Madhya Pradesh region

Solar power prediction in real time utilising supervised machine learning algorithms considering Madhya Pradesh region

A Review of Arctic Sea Ice Climate Predictability in Large-Scale Earth System Models

We provide a high-level review of sea ice models used for climate studies and of the recent advances made with these models to understand sea ice predictability. Models currently in use for the Coupled Model Intercomparison Project and new developments coming online that will enable enhanced predictions are discussed. Previous work indicates that seasonal sea ice can be predicted based on mechanisms associated with long-lived ice thickness or ocean heat anomalies. On longer timescales, internal climate variability is an important source of uncertainty, although anthropogenic forcing is sizable, and studies suggest that anthropogenic signals have already emerged from internal climate noise. Using new analysis from the Multi-Model Large Ensemble, we show that while models differ in the magnitude and timing of predictable signals, many ice predictability characteristics are robust across multiple models. This includes the reemergence of predictable seasonal signals in ice area and the sizable uncertainty in predictions of ice-free Arctic timing associated with internal variability.

Wavelet-Based Assessment of the Muscle-Activation Frequency Range by EMG Analysis

The assessment of muscle-recruitment timing from electromyography (EMG) signal is relevant in different fields, including clinical gait analysis and robotic systems to interpret user&#x2019;s motion intention. However, available methods typically provide only information in time domain without evaluating muscle-activation frequency content. This study aims to propose a novel adaptative algorithm for detecting muscle activation in time-frequency domain based on continuous wavelet transform (CWT) analysis. Precisely, the novel contribution of the proposed algorithm consists of evaluating the frequency range of every muscle activations detected in time domain. Performances are evaluated on a test bench of 720 simulated and 105 real surface EMG signals, stratified for signal-to-noise ratio (SNR), and then validated against different reference algorithms. Outcomes indicate that the proposed approach can provide an accurate prediction of muscle onset and offset timing in both simulated (mean absolute error, MAE <inline-formula> <tex-math notation="LaTeX">$\approx 10$ </tex-math></inline-formula> ms) and real datasets (MAE &#x003C; 30 ms), minimally affected by the SNR variability and compatible with the timing of EMG-driven assistive devices. Concomitantly, the maximum frequency of the activations is computed, ranging from around 100 Hz up to almost 500 Hz. This suggests a large within-muscle between-muscle variability of the frequency range. In conclusion, the current study introduces a novel reliable wavelet-based algorithm to detect both time and frequency content of muscle activation, suitable in different conditions of signal quality.

Reliability Assessment of Hydro Transport Systems’ Operation

The results of full-scale tests of hydro transport systems operation under real production conditions have been collected in Siberian Transport University. Statistical processing of information has been carried out while creating a database for random variables. The following parameters of hydro transport system operation have been studied: availability factor, coefficient of technical use, efficiency factor, operating time to failure, and coefficient of time use. Indices of technical (coefficient of technical use, the coefficient of readiness, the coefficient of efficiency and operating time to failure) and organizational-technological (coefficient of time use) reliability have been determined. Methodical, mathematical and software methods for estimating reliability of construction machines by the example of systems have been worked out. Using the software, the efficiency and reliability of the operation of systems, complexes, sets and individual machines at specific objects have been substantiated. This allowed the most accurate prediction of timing and cost of production at specific sites. A method for taking into account specific production conditions in a comprehensive assessment of the operation of machines, kits, complexes and systems has been proposed. The use of databases based on the results of field tests of machines made it possible to calculate more accurately the indicators of technical organizational and technological reliability of performing a given volume of work. Taking into account the probability of organizational and technological reliability makes it possible to plan the work of the system under specific operating conditions with a greater validity. It is more accurate to predict the main indicators of work production. The method, which allows taking into account the specific working conditions of the performance of works and forecasting the main indices of the reliability of a particular machine, is proposed. The proposed toolkit of indicators of technical and organizational-technological reliability of hydro transport systems for making informed management decisions has real reliability.

Predicting Travel Times for On-demand Public Transport in Sofia

This document describes the methodical approaches for Module 5 of INNOAIR On-demand Public Transport Platform for route optimization and, in particular, for travel times predictions. The text presents the chosen models of travel time between stops. There are four predictive models presented – all of which have been tested on a particular use-case data. The procedure to implement a prediction model goes through three phases: model identification, parameter estimation/optimization, and prediction, with their corresponding sub-phases. With business understanding and data understanding done already in general for the study, each applied model should be explained in terms of data preparation, data modeling and model validation. In the current text a use-case is used as a numerical experiment to play through the various modeling procedures under study. The input data used in these modeling procedures have two sources – traffic data for times of arrival for 4 consecutive stops of a bus line in Sofia and weather data. Based on the literature review, the researchers have chosen to use several models to predict the target data: Seasonal autoregressive moving average models with exogenous input with Fourier terms; Vector Autoregression and Vector Error-Correction Models; Bayesian Fourier model; Back-Propagation Neural Network models. Root-mean-square error measured in seconds, calculated on the forecast for the test set (last week of observations), is used for comparison of the proposed models.

Numerical analysis for auto-ignition timing prediction affected by rapid compression

Numerical analysis for auto-ignition timing prediction affected by rapid compression

Fault Classification and Timing Prediction Based on Shipment Inspection Data and Maintenance Reports for Semiconductor Manufacturing Equipment

Fault Classification and Timing Prediction Based on Shipment Inspection Data and Maintenance Reports for Semiconductor Manufacturing Equipment

The risk of rupture and abdominal aortic aneurysm morphology: A computational study.

Prediction of rupture and optimal timing for abdominal aortic aneurysm (AAA) surgical intervention remain wanting even after decades of clinical, histological, and numerical research. Although studies estimating rupture from AAA geometrical features from CT imaging showed some promising results, they are still not being used in practice. Patient-specific numerical stress analysis introduced too many assumptions about wall structure for the related rupture potential index (RPI) to be considered reliable. Growth and remodeling (G&R) numerical models eliminate some of these assumptions and thus might have the most potential to calculate mural stresses and RPI and increase our understanding of rupture. To recognize numerical models as trustworthy, it is necessary to validate the computed results with results derived from imaging. Elastin degradation function is one of the main factors that determine idealized aneurysm sac shape. Using a hundred different combinations of variables defining AAA geometry or influences AAA stability (elastin degradation function parameters, collagen mechanics, and initial healthy aortic diameters), we investigated the relationship between AAA morphology and RPI and compared numerical results with clinical findings. Good agreement of numerical results with clinical expectations from the literature gives us confidence in the validity of the numerical model. We show that aneurysm morphology significantly influences the stability of aneurysms. Additionally, we propose new parameters, geometrical rupture potential index (GRPI) and normalized aneurysm length (NAL), that might predict rupture of aneurysms without thrombus better than currently used criteria (i.e., maximum diameter and growth rate). These parameters can be computed quickly, without the tedious processing of CT images.

Optimal steroid exposure for women eligible for late preterm steroids

Late preterm antenatal corticosteroid (ACS) administration is standard of care since publication of Antenatal Betamethasone for Women at Risk for Late Preterm Delivery (ALPS) trial. Previous data suggests incomplete or nonoptimal steroid exposure is associated with decreased efficacy, but prediction of delivery timing remains a challenge. Our aim was to investigate if the obstetric indication for steroids is predictive of optimal steroid exposure.

Cortical mu rhythms during action and passive music listening.

Brain systems supporting body movement are active during music listening in the absence of overt movement. This covert motor activity is not well understood, but some theories propose a role in auditory timing prediction facilitated by motor simulation. One question is how music-related covert motor activity relates to motor activity during overt movement. We address this question using scalp electroencephalogram by measuring mu rhythms-cortical field phenomena associated with the somatomotor system that appear over sensorimotor cortex. Lateralized mu enhancement over hand sensorimotor cortex during/just before foot movement in foot versus hand movement paradigms is thought to reflect hand movement inhibition during current/prospective movement of another effector. Behavior of mu during music listening with movement suppressed has yet to be determined. We recorded 32-channel EEG (n = 17) during silence without movement, overt movement (foot/hand), and music listening without movement. Using an independent component analysis-based source equivalent dipole clustering technique, we identified three mu-related clusters, localized to left primary motor and right and midline premotor cortices. Right foot tapping was accompanied by mu enhancement in the left lateral source cluster, replicating previous work. Music listening was accompanied by similar mu enhancement in the left, as well as midline, clusters. We are the first, to our knowledge, to report, and also to source-resolve, music-related mu modulation in the absence of overt movements. Covert music-related motor activity has been shown to play a role in beat perception (Ross JM, Iversen JR, Balasubramaniam R. Neurocase 22: 558-565, 2016). Our current results show enhancement in somatotopically organized mu, supporting overt motor inhibition during beat perception.NEW & NOTEWORTHY We are the first to report music-related mu enhancement in the absence of overt movements and the first to source-resolve mu activity during music listening. We suggest that music-related mu modulation reflects overt motor inhibition during passive music listening. This work is relevant for the development of theories relating to the involvement of covert motor system activity for predictive beat perception.

Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow

AbstractTo understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations generally measure bulk quantities. Where internal snowpack measurements are made, they tend to be destructive and unsuitable for continuous monitoring. Here, we present a novel method for in situ monitoring of water flow in seasonal snow using the electrical self-potential (SP) geophysical method. A prototype geophysical array was installed at Col de Porte (France) in October 2018. Snow hydrological and meteorological observations were also collected. Results for two periods of hydrological interest during winter 2018–19 (a marked period of diurnal melting and refreezing, and a rain-on-snow event) show that the electrical SP method is sensitive to internal water flow. Water flow was detected by SP signals before it was measured in conventional snowmelt lysimeters at the base of the snowpack. This initial feasibility study shows the utility of the SP method as a non-destructive snow sensor. Future development should include combining SP measurements with a high-resolution snow physics model to improve prediction of melt timing.