Simple Prediction Algorithm Research Articles

Home Blood Pressure Variability Risk Prediction Score for Cardiovascular Disease Using Data From the J-HOP Study.

Home blood pressure (BP) is more closely associated with cardiovascular event risk than office BP, but cardiovascular risk prediction based on home BP variability is lacking. This study developed a simple cardiovascular event prediction score, including home BP variability data, from the J-HOP study (Japan Morning Surge-Home Blood Pressure). The J-HOP study extended follow-up from December 2017 to May 2018 generated the study data set (4231 patients). Cardiovascular events included fatal/nonfatal stroke (n=94), coronary heart disease (n=124), heart failure (n=42), and aortic dissection (n=8). Cox proportional hazards models were used to predict overall cardiovascular risk. Potential covariates included age, sex, body mass index, smoking, history of diabetes, statin use, history of cardiovascular disease, total cholesterol:high-density lipoprotein cholesterol ratio, office systolic BP (SBP), mean of morning-evening average (MEave), home SBP, and average real variability of MEave home SBP. A risk score and models were constructed, and model performance was assessed. Model performance was best when average real variability of MEave SBP was included (C statistic, 0.760). The risk score assigns points for age (5-year bands), sex, cardiovascular disease history, high-density lipoprotein cholesterol, mean MEave home SBP, and average real variability of MEave home SBP. Estimated 10-year cardiovascular risk ranged from ≤0.6% (score ≤0) to >32% (score ≥26). Calibration 2 statistics values for the model (2.66) and risk score (5.29) indicated excellent goodness of fit. This simple cardiovascular disease prediction algorithm, including day-by-day home BP variability, could be used as part of a home BP-centered approach to hypertension management in clinical practice.

Predictive Speed Control of a DC Universal Motor Applied to Monitor Electric Vehicle Batteries

This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection.

Forecasting Postoperative Delirium in Older Adult Patients with Fast-and-Frugal Decision Trees.

Postoperative delirium (POD) is associated with increased complication and mortality rates, particularly among older adult patients. However, guideline recommendations for POD detection and management are poorly implemented. Fast-and-frugal trees (FFTrees), which are simple prediction algorithms, may be useful in this context. We compared the capacity of simple FFTrees with two more complex models—namely, unconstrained classification trees (UDTs) and logistic regression (LogReg)—for the prediction of POD among older surgical patients in the perioperative setting. Models were trained and tested on the European BioCog project clinical dataset. Based on the entire dataset, two different FFTrees were developed for the pre-operative and postoperative settings. Within the pre-operative setting, FFTrees outperformed the more complex UDT algorithm with respect to predictive balanced accuracy, nearing the prediction level of the logistic regression. Within the postoperative setting, FFTrees outperformed both complex models. Applying the best-performing algorithms to the full datasets, we proposed an FFTree using four cues (Charlson Comorbidity Index (CCI), site of surgery, physical status and frailty status) for the pre-operative setting and an FFTree containing only three cues (duration of anesthesia, age and CCI) for the postoperative setting. Given that both FFTrees contained considerably fewer criteria, which can be easily memorized and applied by health professionals in daily routine, FFTrees could help identify patients requiring intensified POD screening.

A Score to Assess Mortality After Percutaneous Mitral Valve Repair

BackgroundRisk stratification for transcatheter edge-to-edge mitral valve repair (TEER) is paramount in the decision-making process for treating severe mitral regurgitation (MR). ObjectivesThis study sought to create and validate a user-friendly score (MitraScore) to predict the risk of mortality in patients undergoing TEER. MethodsThe derivation cohort was based on a multicentric international registry that included 1,119 patients referred for TEER between 2012 and 2020. Score discrimination was assessed using Harrell’s c-statistic, and the calibration was evaluated with the Gronnesby and Borgan goodness-of-fit test. An external validation was carried out in 725 patients from the GIOTTO registry. ResultsAfter multivariate analysis, we identified 8 independent predictors of mortality during the follow-up (2.1 ± 1.8 years): age ≥75 years, anemia, glomerular filtrate rate <60 mL/min/1.73 m2, left ventricular ejection fraction <40%, peripheral artery disease, chronic obstructive pulmonary disease, high diuretic dose, and no therapy with renin-angiotensin system inhibitors. The MitraScore was derived by assigning 1 point to each independent predictor. The c-statistic was 0.70. Per each point of the MitraScore, the relative risk of mortality increased by 55% (HR: 1.55; 95% CI: 1.44-1.67; P < 0.001). The discrimination and calibration for mortality prediction was better than those of EuroSCORE II (c-statistic 0.61) or Society of Thoracic Surgeons score (c-statistic 0.57). The MitraScore maintained adequate performance in the validation cohort (c-statistic 0.66). The score was also predictive for heart failure rehospitalization and was correlated with the probability of clinical improvement. ConclusionsThe MitraScore is a simple prediction algorithm for the prediction of follow-up mortality in patients treated with TEER.

Risk Factors for Reattempt and Suicide Within 6 Months After an Attempt in the French ALGOS Cohort

Objective: Understanding the cumulative effect of several risk factors involved in suicidal behavior is crucial for the development of effective prevention plans. The objective of this study is to provide clinicians with a simple predictive model of the risk of suicide attempts and suicide within 6 months after suicide attempt. Methods: A prospective observational cohort of 972 subjects, included from January 26, 2010, to February 28, 2013, was used to perform a survival tree analysis with all sociodemographic and clinical variables available at inclusion. The results of the decision tree were then used to define a simple predictive algorithm for clinicians. Results: The results of survival tree analysis highlighted 3 subgroups of patients with an increased risk of suicide attempt or death by suicide within 6 months after suicide attempt: patients with alcohol use disorder and a previous suicide attempt with acute alcohol use (risk ratio = 2.92; 95% CI, 2.08 to 4.10), patients with anxiety disorders (RR = 0.98; 95% CI, 0.69 to 1.39), and patients with a history of more than 2 suicide attempts in the past 3 years (RR = 2.11; 95% CI, 1.25 to 3.54). The good prognosis group comprised all other patients. Conclusions: By using a data-driven method, this study identified 4 clinical factors interacting together to reduce or increase the risk of recidivism. These combinations of risk factors allow for a better evaluation of a subject’s suicide risk in clinical practice. Trial Registration: ClinicalTrials.gov identifier: NCT01123174

Determining chaotic characteristics and forecasting tall tower wind speeds in Missouri using empirical dynamical modeling (EDM)

The chaotic characteristics of the tall tower wind speed data within Missouri was investigated using both quantitative and qualitative methodologies. The phase space diagrams were constructed using the method of time delay. The two parameters needed in the construction of the attractor are the embedding dimension and the time delay. The former was determined using the Cao Algorithm and the latter by Average Mutual Information (AMI). Qualitatively, the phase portraits display chaos for all the wind speed time series for the various stations and height levels. They did not illustrate periodicity nor were they random motions, rather, they depicted a single attractor representative of chaos. Quantitatively the Largest Lyapunov Exponent (LLE) was evaluated. It was determined that for the Columbia station the wind speeds display chaotic characteristics representative of the positive LLEs. However, the increasing level of chaos characteristics did not coincide with the increasing height levels of the tall tower. Thereafter, a simple non-linear prediction algorithm was used to forecast wind speeds using a moving window. The attractor was constructed using the first 56 days and the subsequent 6 h or 36 (10 min) time steps were predicted. The preceding forecast was done when the attractor was reconstructed using the training data of 56 days starting from a 6-h delay from the previous run. The RMSE, MAE and Correlation were investigated for the model with the errors evaluated cumulatively for all of the 1st through 36st predictions. It was determined that the errors increase as the forecasting steps increased for all stations and height levels. The RMSE plateaus at higher wind speeds for increasing height levels with the exception of the station, Neosho, where it plateaued at all height levels at approximately 3.0 ms−1. For Columbia at all height levels, after the 20th time step or 3.33 h, the model's normalized errors exceeds 1 or 100%. However, using a 50% normalized error cap, it was noted that these values occurred for Columbia's height levels after the 1st, 2nd and 3rd time steps respectively. For Blanchard, this value was given by the 2nd time step for both heights whilst for Neosho, at all heights this percentage occurred after at most, 2 time steps. From the Lyapunov exponent, the prediction horizons or the time limits to obtain accurate predictions from the chaotic system were determined to be 6 time steps for all the height levels in the Columbia station using a 95% confidence band. When a range of confidence bands was used, it was shown that for the 90% confidence, this value was decreased to 4 time steps. This model was compared to the benchmark model of persistence where it was determined that the EDM is comparable to persistence and it beats it in the very short-term range of one time step for Columbia and Blanchard. Seasonality and diurnal cycle analyses were also accomplished. Seasonality was investigated by slicing the results every 6 h or extracting every 36th forecast error. It was shown that four of the eight stations' height levels had the season of summer incurring the lowest magnitude of average errors and standard deviations. The diurnal cycle was examined by extracting every four of the 6 time slices done previously. The time of day was analysed by lagging these slices by 6, 12 and 18 h. It was determined that there was no evident trend where a particular time of day the model incurred more errors and had greater standard deviations for all stations and heights.

Inferring room-level use of domestic space heating from room temperature and humidity measurements using a deep, dilated convolutional network

Time series data about when heating is on and off in homes can be useful for research on building energy use and occupant behaviours, particularly data at room level and at a granularity of minutes. Direct methods which measure the temperature of radiators and other heaters can be effective at producing such data, but are expensive. Indirect methods, which infer heating on- and off-times from ambient room temperature data, can be cheaper but produce more error-prone data. Existing indirect methods have however utilised relatively simple prediction algorithms based on changes in ambient temperature between closely adjacent time points. In the method presented here we have implemented several refinements to this approach:•An Artificial Neural Network algorithm is applied to the prediction task: a deep, dilated convolutional network.•A wider range of input features is utilised to base predictions upon: ambient room temperature and humidity, and external temperature and humidity.•Predictions for each time point are based on data from a wider, 600-minute, time window.•We evaluate model performance on a dataset with 10 min granularity and achieve mean precision and recall during the heating season of >=0.74 for individual time points, and >=0.82 for full heating events, outperforming comparator methods.

On the prediction of long-lived bugs: An analysis and comparative study using FLOSS projects

Software evolution and maintenance activities in today’s Free/Libre Open Source Software (FLOSS) rely primarily on information extracted from bug reports registered in bug tracking systems. Many studies point out that most bugs that adversely affect the user’s experience across versions of FLOSS projects are long-lived bugs. However, proposed approaches that support bug fixing procedures do not consider the real-world lifecycle of a bug, in which bugs are often fixed very fast. This may lead to useless efforts to automate the bug management process. This study aims to confirm whether the number of long-lived bugs is significantly high in popular open-source projects and to characterize the population of long-lived bugs by considering the attributes of bug reports. We also aim to conduct a comparative study evaluating the prediction accuracy of five well-known machine learning algorithms and text mining techniques in the task of predicting long-lived bugs. We collected bug reports from six popular open-source projects repositories (Eclipse, Freedesktop, Gnome, GCC, Mozilla, and WineHQ) and used the following machine learning algorithms to predict long-lived bugs: K-Nearest Neighbor, Naïve Bayes, Neural Networks, Random Forest, and Support Vector Machines. Our results show that long-lived bugs are relatively frequent (varying from 7.2% to 40.7%) and have unique characteristics, confirming the need to study solutions to support bug fixing management. We found that the Neural Network classifier yielded the best results in comparison to the other algorithms evaluated. Research efforts regarding long-lived bugs are needed and our results demonstrate that it is possible to predict long-lived bugs with a high accuracy (around 70.7%) despite the use of simple prediction algorithms and text mining methods.

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.

Predicting cyanobacteria occurrence using climatological and environmental controls

The occurrence of algal bloom results in deterioration of water quality, undesirable sights, tastes and odors, and the possibility of infections to humans and fatalities to livestock, wildlife and pets. Earlier studies have identified a range of factors including water temperature, flow, and nutrient concentrations that could affect cyanobacterial proliferation. Lack of enough data, independence in data across multiple sampling time steps, as well as the presence of more than one causative factors, each with different levels of influence on the response, has resulted in limited progress in the development of generalized prediction frameworks for cyanobacteria. In this study, a prediction model for cyanobacteria occurrences was developed using only three dominant environmental variables; water temperature, velocity and phosphorus concentration. These environmental variables were selected due to not only direct or joint contribution to algal bloom but also the ease of their availability either through direct measurements or as modelled responses in the river location of interest. In order to apply bacterial growth dynamic to the model, weight functions which quantify the importance assigned to the three variables depending on the cell number at the preceding time, were formulated. An extensive dataset spanning from 2013 to 2018 at 16 representative locations across the four major rivers in South Korea was used to develop and validate the model. Through cross-validation, this model was shown to have more than 75% forecasting accuracy despite the use of a relatively simple predictive algorithm. As the developed model makes use of commonly available environmental variables, it can easily be extended to locations across the country where very limited or no prior information about cyanobacteria bloom is available.

Predicting coronary heart disease using risk assessment charts and risk factor categories

Cardiovascular disease is the leading cause of death and disability in Egypt. An appropriate estimation of risk is important to improve cardiovascular outcomes and ensure efficient allocation of resources used to support disease prevention. Risk prediction charts have been developed to provide clinicians with a simple tool to estimate the absolute risk of developing coronary heart disease (CHD). To test the accuracy of the World Health Organization/International Society of Hypertension (WHO/ISH) risk prediction charts in predicting and identifying people at risk of developing CHD and to propose an alternative risk assessment instrument based on simple laboratory tests, routine examination, and reported personal and medical data. A cross-sectional screening survey was conducted at an emergency department of a health insurance hospital in Alexandria, Egypt. A total of 350 enrolled patients were evaluated clinically. We applied the WHO/ISH risk assessment chart for the Eastern Mediterranean region to test its accuracy in predicting CHD. The validation statistics indicated that the WHO/ISH risk prediction charts identified cases with CHD with 60.0% sensitivity and 93.2% specificity. At least nine risk factors for developing CHD were detected in all patients in the study cohort. Hypertension was the strongest predictor of CHD [odds ratio (OR) (95% confidence interval (CI)): 20.8 (5.6–76.9)]. Silent cardiovascular risk factors were ascertained in about 30% of the studied population, a finding that did not differ significantly by sex. We propose a different risk assessment model for general practice that incorporates standard cardiovascular risk factors, with or without laboratory tests. Standard cardiovascular risk factors included in a simple risk prediction algorithm can predict multivariate CHD risk in apparently healthy individuals. Non-laboratory-based risk assessment models have no superior advantage over those employing laboratory investigations.

Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability

Time series prediction has wide applications ranging from stock price prediction, product demand estimation to economic forecasting. In this article, we treat the taxi and Uber demand in each location as a time series, and reduce the taxi and Uber demand prediction problem to a time series prediction problem. We answer two key questions in this area. First, time series have different temporal regularity. Some are easy to be predicted and others are not. Given a predictive algorithm such as LSTM (deep learning) or ARIMA (time series), what is the maximum prediction accuracy that it can reach if it captures all the temporal patterns of that time series? Second, given the maximum predictability, which algorithm could approach the upper bound in terms of prediction accuracy? To answer these two question, we use temporal-correlated entropy to measure the time series regularity and obtain the maximum predictability. Testing with 14 million data samples, we find that the deep learning algorithm is not always the best algorithm for prediction. When the time series has a high predictability a simple Markov prediction algorithm (training time 0.5s) could outperform a deep learning algorithm (training time 6 hours). The predictability can help determine which predictor to use in terms of the accuracy and computational costs. We also find that the Uber demand is easier to be predicted compared the taxi demand due to different cruising strategies as the former is demand driven with higher temporal regularity.

Validation of simple prediction algorithms to consistently achieve CD3+ and postselection CD34+ targets with leukapheresis.

Cellular therapies using engineered T cells, haploidentical transplants, and autologous gene therapy are increasing. Specified CD3+ or high CD34+ doses are typically required for subsequent manufacturing, manipulation, or CD34+ selection. Simple, practical, and reliable lymphocyte and hematopoietic progenitor cell (HPC) collection algorithms accounting for subsequent CD34+ selection have not been published. In this analysis of 15 haploidentical donors undergoing tandem lymphocyte and HPC collections, we validated one-step, practical prediction algorithms (Appendix S1, available as supporting information in the online version of this paper) that use conservative facility-specific collection efficiencies, CD34+ selection efficiency, and donor-specific peripheral counts to reliably achieve the target CD3+ and CD34+ product doses. These algorithms expand on our previously published work regarding predictive HPC collection algorithms. Ninety-three percent of lymphocyte and 93% of CD34+ collections achieved the final target CD3+ and CD34+ product dose when our algorithm-calculated process volumes were used. Linear regression analysis of our algorithms for CD3+, preselection CD34+, and postselection CD34+ showed statistically significant models with R2 of 0.80 (root mean square error [RMSE], 31.3), 0.72 (RMSE, 385.7), and 0.56 (RMSE, 326.0), respectively, all with p values less than 0.001. Because achievement of CD3+ or CD34+ dose targets may be critical for safety and efficacy of cell therapies, these simple, practical, and reliable prediction algorithms for lymphocyte and HPC collections should be very useful for collection facilities.

Protein-protein binding supersites.

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

Postdischarge Mortality Prediction in Sub-Saharan Africa.

Although the burden of postdischarge mortality (PDM) in low-income settings appears to be significant, no clear recommendations have been proposed in relation to follow-up care after hospitalization. We aimed to determine the burden of pediatric PDM and develop predictive models to identify children who are at risk for dying after discharge. Deaths after hospital discharge among children aged <15 years in the last 17 years were reviewed in an area under demographic and morbidity surveillance in Southern Mozambique. We determined PDM over time (up to 90 days) and derived predictive models of PDM using easily collected variables on admission. Overall PDM was high (3.6%), with half of the deaths occurring in the first 30 days. One primary predictive model for all ages included young age, moderate or severe malnutrition, a history of diarrhea, clinical pneumonia symptoms, prostration, bacteremia, having a positive HIV status, the rainy season, and transfer or absconding, with an area under the curve of 0.79 (0.75-0.82) at day 90 after discharge. Alternative models for all ages including simplified clinical predictors had a similar performance. A model specific to infants <3 months old was used to identify as predictors being a neonate, having a low weight-for-age z score, having breathing difficulties, having hypothermia or fever, having oral candidiasis, and having a history of absconding or transfer to another hospital, with an area under the curve of 0.76 (0.72-0.91) at day 90 of follow-up. Death after discharge is an important although poorly recognized contributor to child mortality. A simple predictive algorithm based on easily recognizable variables could readily be used to identify most infants and children who are at a high risk of dying after discharge.

A dual strategy to optimize hematopoietic progenitor cell collections: validation of a simple prediction algorithm and use of collect flow rates guided by mononuclear cell count.

Previous prediction algorithms to achieve target CD34+ goals have not been widely adopted, with many centers still using a set volume to process for hematopoietic progenitor cell collections. This may be because previous algorithms are challenging to implement. Additionally, no study has yet examined the utility of adjusting the collect flow rate (CFR) based on the donor's preprocedure total mononuclear cell (MNC) count, which correlates with CD34+ yield. In this retrospective analysis of mobilized allogeneic donors collected using MNC (COBE Spectra, Terumo BCT) or continuous mononuclear cell collection (CMNC) (Spectra Optia, Terumo BCT) procedures, we validated a one-step prediction algorithm to achieve the target CD34+ product dose (Appendix S1, available as supporting information in the online version of this paper). The COBE Spectra MNC Collect Flow Tool (Appendix S2, available as supporting information in the online version of this paper) was used to select the collect flow rate for both MNC and CMNC procedures. Procedural collection efficiency (CE) was compared to that of historical procedures utilizing fixed CFRs (1.0-1.5 mL/min). Ninety-three percent of collections achieved the target CD34+ goal using our algorithm-calculated process volumes. The remaining 7% of cases had CEs lower than the algorithm CE (0.40), and thus were below goal. Second, an MNC-based CFR improved MNC and CD34+ CEs in patients with higher MNC counts compared to our historical controls. We validated that this simple, single-step prediction algorithm achieves the target CD34+ goal in most HPC collections. Secondly, we showed that an MNC-based CFR for hematopoietic progenitor cell collections improves CE at higher MNCs; this may be preferable to a WBC-based CFR because of variability of MNC counts at a given WBC count.

CLINICAL USEFULNESS OF PRECISE DAPT SCORE TO PREDICT CARDIOVASCULAR OUTCOMES IN PATIENTS WITH ATRIAL FIBRILLATION UNDERGOING PERCUTANEOUS CORONARY INTERVENTION WITH STENTING

Oral anticoagulant therapy (OAC) with dual antiplatelet therapy (DAPT) increases the risk of hemorrhagic complications in patients with AF undergoing percutaneous coronary intervention (PCI) with stenting. The PRECISE-DAPT score is a simple five-item prediction algorithm for the prediction of out-of

An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series

Recently, the study of the coherent noise model has led to a simple (binary) prediction algorithm for the forthcoming earthquake magnitude in aftershock sequences. This algorithm is based on the concept of natural time and exploits the complexity exhibited by the coherent noise model. Here, using the relocated catalogue from Southern California Seismic Network for 1981 to June 2011, we evaluate the application of this algorithm for the aftershocks of strong earthquakes of magnitudeM≥6. The study is also extended by using the Global Centroid Moment Tensor Project catalogue to the case of the six strongest earthquakes in the Earth during the last almost forty years. The predictor time series exhibits the ubiquitous1/fnoise behavior.

A low-complexity image compression approach with single spatial prediction mode and transform

The well-known low-complexity JPEG and the newer JPEG-XR systems are based on block-based transform and simple transform-domain coefficient prediction algorithms. Higher complexity image compression algorithms, obtainable from intra-frame coding tools of video coders H.264 or HEVC, are based on multiple block-based spatial-domain prediction modes and transforms. This paper explores an alternative low-complexity image compression approach based on a single spatial-domain prediction mode and transform, which are designed based on a global image model. In our experiments, the proposed single-mode approach uses an average 20.5 % lower bit-rate than a standard low-complexity single-mode image coder that uses only conventional DC spatial prediction and 2-D DCT. It also does not suffer from blocking effects at low bit-rates.

A Simple Fatigue Life Prediction Algorithm Using the Modified NASGRO Equation

A simple fatigue life prediction algorithm using the modified NASGRO equation is proposed in this paper. The NASGRO equation is modified by introducing the concept of intrinsic effective threshold stress intensity factor (SIF) rangeΔKeffth. One advantage of the proposed method is that the complex growth behavior analysis of small cracks can be avoided, and then the fatigue life can be calculated by directly integrating the crack growth model from the initial defect size to the critical crack size. The fatigue limit and the intrinsic effective threshold SIF rangeΔKeffthare used to calculate the initial defect size or initial flaw size. The value ofΔKeffthis determined by extrapolating the crack propagation rate curves. Instead of using the fatigue limit determined by the fatigue strength at the specific fatigue life, the fatigue limit is selected based on the horizontal tendency of theS-Ncurve. The calculated fatigue lives are compared to the experimental data of two different alloys. The predictedS-Ncurves agree with the test data well. Besides, the prediction results are compared with that calculated using the FASTRAN code. Results indicate that the proposed life prediction algorithm is simple and efficient.