Prediction Of Timing Research Articles

The age at first cutaneous manifestation of psoriasis as a prognostic factor in the timing of psoriatic arthritis diagnosis

Background. Psoriatic arthritis is a chronic inflammatory disease of joints, spine and enthesis, which develops in 640% of patients with psoriasis and has a significant impact on the quality of life of patients.
 Aims. To determine the relationship between age of cutaneous manifestation of psoriasis and the timing of psoriatic arthritis diagnosis; to examine the possibility of prediction of timing of psoriatic arthritis diagnosis depending on age of cutaneous manifestation of psoriasis.
 Materials and methods. Data of patients aged 18 years and older with psoriatic arthritis included in the psoriasis patient register were analysed. Inclusion criteria were: patient age 18 years and older; established diagnosis of psoriasis and psoriatic arthritis. Exclusion criteria were: psoriatic arthritis diagnosed before or simultaneously with cutaneous manifestation of psoriasis; the age of the cutaneous manifestation of psoriasis older than 70 years. The sample included 771 patients.
 Spearman's correlation analysis was used to assess the correlation between age of cutaneous manifestation of psoriasis and period from skin psoriasis onset to psoriatic arthritis diagnosis.
 The possibility of prediction of the period till psoriatic arthritis was diagnosed depending on age at cutaneous manifestation of psoriasis was established by means of quantile regression analysis.
 Results. Mean age at cutaneous manifestation of psoriasis (SD) was 26.0 13.5 years. The period from the onset of skin psoriasis to the diagnosis of psoriatic arthritis averaged 13.7 10.3 years, its median [interquartile range] was 12 [619] years.
 According to Spearman's correlation analysis, a moderate (close to strong) negative correlation between varibles was revealed (Spearman's correlation coefficient = 0.490, p 0.0005).
 In accordance to quantile regression analysis, lines of 5, 25, 50, 75 and 95% quantiles were obtained and the dependence of the period till the diagnosis of psoriatic arthritis on the age of onset of skin psoriasis was demonstrated. Based on the quantile equation obtained, a prognostic table for the period from the onset of skin psoriasis to the diagnosis of psoriatic arthritis was compiled.
 Conclusions. Statistically significant inverse relationship between age of cutaneous manifestation of psoriasis and period till psoriatic arthritis diagnosis was established: older age at skin psoriasis onset is associated with shorter period till psoriatic arthritis diagnosis. Based on established relationship, presented as an equation, table and graphically, prediction of period till psoriatic arthritis diagnosis is possible. These results can be used in the clinical practice of dermatovenereologists.

Understanding intraday momentum strategies

AbstractThis paper studies the out‐of‐sample performance of the intraday momentum strategy where the overnight return predicts the return of the last half‐hour of trading. The predictability disappears in the out‐of‐sample period. A Markov‐switching model endogenously identifies two distinct regimes and suggests that the predictability depends on the strength of the signal. Hence, assessing return predictability in calendar time may lead to false conclusions when anomalies feature time‐varying returns. The paper documents that understanding the return dynamics is important for an effective strategy implementation. A strategy with thresholds delivers higher returns than a strategy that is always active.

A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

A Novel Design Approach for Low Speed Recovery of High Performance Fighter Aircraft

In this paper, a novel design approach for low-speed recovery of a high-performance fighter aircraft is presented. It is shown that the phugoid mode has an important bearing on the problem of low-speed departure. Based on the analysis of the phugoid mode trajectories, a novel low-speed protection algorithm is presented in this paper. The proposed low speed recovery is achieved in three phases. The first phase consists of detecting the incipient departure followed in the second phase by the application of suitable recovery controls and finally the third phase ends with the transfer of controls to the pilot. The design of the first and the third phase consist of choosing the correct trigger conditions which ensures safe recovery of the aircraft in all conditions. The proposed Automatic low speed recovery is triggered when the aircraft trajectory crosses a fixed boundary in the region spanned by the dynamic pressure and its rate of decrease. It is observed that this boundary is approximately a straight line, implying that it is equivalent to a forward prediction in time to indicate when the aircraft will reach the lowest controllable airspeed. This Automatic Low Speed Recovery with Forward Prediction (ALSR-FP) algorithm is found to be simpler than other existing design methods and effective in preventing low speed departure for a variety of pilot inputs that result in the aircraft losing airspeed leading to stall. In the second phase control inputs are chosen to align the velocity vector to the direction of local gravity. The recovery phase is considered complete after the aircraft reaches the dynamic pressure which is approximately 10 % higher than the minimum dynamic pressure for control. Performance of the ALSR-FP is demonstrated using the high-performance fighter aircraft ADMIRE model which has a delta wing configuration, canards and multiple redundant controls. It is also shown that the proposed algorithm can be easily implemented on board for any other fighter and civil aircraft.

A novel approach to joint prediction of preeclampsia and delivery timing using semicompeting risks

Preeclampsia is a pregnancy complication that contributes substantially to perinatal morbidity and mortality worldwide. Existing approaches to modeling and prediction of preeclampsia typically focus either on predicting preeclampsia risk alone, or on the timing of delivery following a diagnosis of preeclampsia. As such, they are misaligned with typical healthcare interactions during which the 2 events are generally considered simultaneously. This study aimed to describe the "semicompeting risks" framework as an innovative approach for jointly modeling the risk and timing of preeclampsia and the timing of delivery simultaneously. Through this approach, one can obtain, at any point during the pregnancy, clinically relevant summaries of an individual's predicted outcome trajectories in 4 risk categories: not developing preeclampsia and not having delivered, not developing preeclampsia but having delivered because of other causes, developing preeclampsia but not having delivered, and developing preeclampsia and having delivered. To illustrate the semicompeting risks methodology, we presented an example analysis of a pregnancy cohort from the electronic health record of an urban, academic medical center in Boston, Massachusetts (n=9161 pregnancies). We fit an illness-death model with proportional-hazards regression specifications describing 3 hazards for timings of preeclampsia, delivery in the absence of preeclampsia, and delivery following preeclampsia diagnosis. The results indicated nuanced relationships between a variety of risk factors and the timings of preeclampsia diagnosis and delivery, including maternal age, race/ethnicity, parity, body mass index, diabetes mellitus, chronic hypertension, cigarette use, and proteinuria at 20 weeks' gestation. Sample predictions for a diverse set of individuals highlighted differences in projected outcome trajectories with regard to preeclampsia risk and timing, and timing of delivery either before or after preeclampsia diagnosis. The semicompeting risks framework enables characterization of the joint risk and timing of preeclampsia and delivery, providing enhanced, meaningful information regarding clinical decision-making throughout the pregnancy.

Neutrosophic Cost Pattern of Inventory System with Novel Demand Incorporating Deterioration and Discount on Defective Items Using Particle Swarm Algorithm.

The potential to obtain defective or damaged items with non-defective commodities is common to experience at the production unit or when shipping products from one layer to another. This research focuses on the faulty things that retailers receive from suppliers. The retailer has set a restriction on the percentage of defective things, and the retailer receives a discount on the cost of purchasing defective items. The proposed inventory system handles the uncertainty in inventory costs and also considers the demand and deterioration of items with prioritized maximum product life. This work minimizes total inventory cost when demand rate as a function of reliability and power pattern of time under a crisp and triangular neutrosophic environment. The inventory system for degrading items considers the predictability and power pattern of time with a reasonable payment delay. The interest charges are applied only after a specific permissible time limit in the proposed inventory system. The neutrosophic number that defines three different kinds of membership functions representing the truth, hesitation, and falseness is applied in the inventory model in handling the uncertainty of the cost pattern. The proposed inventory model is investigated using a particle swarm optimization algorithm, and the results are validated using a numerical example and a sensitivity analysis for various parameters.

Dynamic models of obstructive sleep apnea provide robust prediction of respiratory event timing and a statistical framework for phenotype exploration.

Obstructive sleep apnea (OSA), in which breathing is reduced or ceased during sleep, affects at least 10% of the population and is associated with numerous comorbidities. Current clinical diagnostic approaches characterize severity and treatment eligibility using the average respiratory event rate over total sleep time (apnea-hypopnea index). This approach, however, does not characterize the time-varying and dynamic properties of respiratory events that can change as a function of body position, sleep stage, and previous respiratory event activity. Here, we develop a statistical model framework based on point process theory that characterizes the relative influences of all these factors on the moment-to-moment rate of event occurrence. Our results provide new insights into the temporal dynamics of respiratory events, suggesting that most adults have a characteristic event pattern that involves a period of normal breathing followed by a period of increased probability of respiratory event occurrence, while significant differences in event patterns are observed among gender, age, and race/ethnicity groups. Statistical goodness-of-fit analysis suggests consistent and substantial improvements in our ability to capture the timing of individual respiratory events using our modeling framework. Overall, we demonstrate a more statistically robust approach to characterizing sleep disordered breathing that can also serve as a basis for identifying future patient-specific respiratory phenotypes, providing an improved pathway towards developing individualized treatments.

A Machine Learning Approach to Real-Time Uncertainty Assessment of SAGD Forecasts

Summary Field development planning and economic analysis require reliable forecasting of bitumen production. Forecasting at the field level may be done using reservoir simulations, type curve analysis, and other (semi-)analytical techniques. Performing reservoir simulation is usually computationally expensive, and the nonuniqueness of a history-matched solution leads to uncertainty in the model predictions and production forecasts. Analytical proxies, such as Butler’s model and its various improvements, allow for sensitivity studies on input parameters and forecasting under multiple operational scenarios and geostatistical realizations to be conducted rather quickly, despite being less accurate than reservoir simulation. Similar to their reservoir simulation counterparts, proxy models can also be tuned or updated as more data are obtained. Type curves also facilitate efficient reservoir performance prediction; however, in practice, the performance of many steam-assisted gravity drainage (SAGD) well pairs tends to deviate from a set of predefined type curves. Historical well data is a digital asset that can be utilized to develop machine learning (ML) or data-driven models for production forecasting. These models involve lower computational effort than numerical simulators and can offer better accuracy compared to proxy models based on Butler’s equation. Furthermore, these data-driven models can be used for automated optimization, quantification of geological uncertainties, and “What If” scenario analysis due to their lower computational cost. This paper presents a novel ML workflow that includes a predictive model development using the random forest algorithm, clustering (to group well pairs by geological properties), Bayesian updating, and Monte Carlo sampling (for uncertainty quatification) for the forecasting of real-world SAGD injection and production data. The training data set consists of field data from 152 well pairs, including approximately 3 years of operational data. Each well pair’s data set involves data that are typically available for an SAGD well pair (e.g., operational data, geological, and well design parameters). This ML workflow can update predictions in real time and be applied for quantifying the uncertainties associated with the forecasts, making it an important step for development planning. To the best of the author’s knowledge, this is the first time ML algorithms have been applied to an SAGD field data set of this size.

Combination of Decadal Predictions and Climate Projections in Time: Challenges and Potential Solutions

AbstractThis study presents an approach to provide seamless climate information by concatenating decadal climate predictions and climate projections in time. Results for near‐surface air temperature over 29 regions indicate that such an approach has potential to provide meaningful information but can also introduce significant inconsistencies. Inconsistencies are often most pronounced for relatively extreme quantiles of the CMIP6 multi‐model ensemble distribution, whereas they are generally smaller and mostly insignificant for quantiles close to the median. The regions most affected are the North Atlantic, Greenland and Northern Europe. Two potential ways to reduce inconsistencies are discussed, including a simple calibration method and a weighting approach based on model performance. Calibration generally reduces inconsistencies but does not eliminate all of them. The impact of model weighting is minor, which is found to be linked to the small size of the decadal climate prediction ensemble, which in turn limits the applicability of that method.

Predischarge cerebral oxygenation and psychomotor outcome in very preterm infants: is there an association?

This observational study aimed to investigate whether predischarge cerebral oxygenation (CrSO2), monitored by near-infrared spectroscopy, correlates with later psychomotor outcome in very preterm infants. Infants <32 weeks’ gestation or <1500 g without evidence of major brain lesions underwent a 3-h continuous CrSO2 monitoring before hospital discharge. Psychomotor development was assessed at 6, 12, 18, and 24 months using the Griffiths Mental Developmental Scales. The developmental quotients (DQ) at each follow-up appointment were correlated with predischarge CrSO2. Significant correlations were adjusted for possible confounders. Sixty-three infants were enrolled. A significant correlation between CrSO2 and DQ was observed at 6 months ca (p=0.010), but not at later psychomotor assessments. This correlation was confirmed significant (b=0.274, p=0.038) even after the adjustment for relevant covariates. Conclusion: According to these preliminary findings, the association between predischarge CrSO2 and psychomotor development over the first 24 months in preterm infants without major brain lesions is time-limited. Hence, this parameter may not represent an effective predictor for medium-term neurodevelopment.What is Known:• Prematurity is a major risk factor for adverse neurodevelopment. • The validation of clinical tools for psychomotor outcome prediction may aid to identify high-risk preterm infants who might benefit from early interventions.What is New:• In infants without major brain lesions, predischarge CrSO2correlates with psychomotor outcome at 6 months ca but not later, indicating a short time predictability.

Hierarchical Control of Heavy-Duty Trucks Through Signalized Intersections With Non-Deterministic Signal Timing

This paper presents a hierarchical Green-Light Approach Speed (h-GLAS) strategy for controlling heavy-duty trucks traveling through urban/suburban environments, where future intersection signal phase and timing (SPaT) information is non-deterministic. Through vehicle-to-infrastructure communication, past SPaT information is sent to the vehicle and is used to forecast future predictions of the signal timing using a Gaussian Process (GP) model. The h-GLAS strategy uses the predicted SPaT information to generate an efficient desired velocity profile that navigates through intersections where the probability of a green light is maximized. This velocity profile is tracked by a convex model predictive controller (MPC) that simultaneously minimizes mechanical energy expenditure and braking effort over its prediction horizon. Downstream from the MPC, we implement a command governor (CG) that adjusts the MPC output by the minimum amount necessary to maintain safe vehicle following and ensure that the vehicle stops at all red lights. Using a proprietary medium-fidelity Simulink model provided by Volvo Trucks, we characterize the h-GLAS strategy’s performance over a real suburban route, consisting of 10 actuated signalized intersections, using actual SPaT information provided by the NC Department of Traffic (DOT). Simulation results demonstrate a 30-43% reduction in fuel consumption, as compared to a baseline control strategy, which is attributable primarily to avoiding the massive energy losses associated with braking. Furthermore, we evaluate the computational efficiency of our approach by assessing average simulation execution times.

A Non‐Parametric Circular Statistics‐Based Framework for Predicting Peakflow Seasonality at Ungauged Sites

AbstractRegional frequency analysis (RFA) serves as a useful tool for flood estimation at a regional level, to improve the precision of estimated flood quantiles or for prediction in ungauged basins. RFA is used extensively in hydrologic literature for estimation of the magnitude of the hydrologic variable (annual maximum flows), pooling data from several sites within a homogeneous region. However, in areas that experience a strong seasonality of the climate, the timing of a hazardous event, such as flood, may be equally important. Therefore, in such regions, improved estimation of annual peakflow seasonality is imperative for efficient management of water infrastructure or for undertaking timely preventive measures against floods. In this study, we propose a novel RFA framework for estimation of annual peakflow seasonality based on circular statistics. The framework consists of (a) selection of attributes based on catchment similarity using seasonality descriptors as a response, (b) formation of homogeneous regions based on the region of influence method, (c) homogeneity tests adapted to directional data on timing of annual peakflows, and (d) probabilistic prediction of annual peakflow seasonality using the non‐parametric regional circular density. Applicability of the proposed approach is first demonstrated on two synthetically generated homogeneous regions, with unimodal and bimodal annual peakflow seasonality, respectively. The proposed homogeneity test outperforms the existing test yielding H value &lt; 1 for homogeneous regions. Further, real‐world applications are illustrated on prediction of flood peak timing in the Northwest USA, revealing nearly 75% of the sites with absolute bias &lt;16 days.

Towards an energy-efficient quarter-clairvoyant mixed-criticality system

Mode switch is the key strategy in mixed-criticality systems, enabling a dynamic balance between system performance and safety. Mode switch in conventional MCS frameworks is always triggered by over-execution of a task, i.e., a task overrunning the less pessimistic worst-case execution time. In cyber–physical systems, the data volume generated by I/O affects and even dominates task execution time. Based on this observation, we propose a novel MCS framework, named Pythia-MCS, which predicts task execution time according to I/O run-time behaviors. With the new feature of future-prediction, Pythia-MCS provides more timely, but still accurate, mode switches. We specifically introduce the Pythia-MCS design methods, including different allocations of I/O monitoring and an efficient energy management framework. We present a new theoretical model (quarter-clairvoyance), which guarantees the timing predictability of the design, and a new schedulability analysis for Pythia-MCS, which demonstrates improved schedulability compared to conventional MCS frameworks. In addition, Pythia-MCS is comprehensively evaluated using a number of metrics.

Prediction of Forming Forces for Incremental Micro-Forming Using Finite Element Analysis

As incremental forming is a relatively new sheet metal forming process, very limited analytical and finite element prediction models are available in literature to study the process mechanics and improve its performance. Thus, most studies involve many trial-and-error iterations to optimize the processing conditions in order to take advantage of high process flexibility and material formability. However, reducing efforts of trial-and-error iterations is of utmost importance to make a process financially viable. Therefore, an FE model is developed and experimentally validated to predict the forming forces involved in incremental micro-forming process. Different mass scaling factors and element-types are used to optimize and develop the model for accurate prediction in the least possible computation time.

Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data

Wake interactions between wind turbines have a great impact on the overall performance of a wind farm. In this work, a novel deep learning method, called Bilateral Convolutional Neural Network (BiCNN), is proposed and then employed to accurately model dynamic wind farm wakes based on flow field data generated by high-fidelity simulations. Different from the existing machine-learning-based dynamic wake models where dimensionality reduction is essential, the proposed BiCNN is designed to directly process the different types of inputs through a background path and a foreground path, thus avoiding the errors due to dimensionality reduction. Substantial results show that the developed machine learning based wake model can achieve accurate wake predictions in real time, i.e. it captures the spatial variations of the dynamic wakes similarly as high-fidelity wake models and runs as fast as low-fidelity static wake models. The overall prediction error of the developed model is 3.7% with respect to the freestream wind speed. Furthermore, the results for a test farm consisting of 25 turbines show that the developed model can predict the dynamic wind farm wakes within several seconds using a standard laptop, while the same scenario using high-fidelity numerical models would consume tens of thousands of CPU hours.

Scaled Vecchia Approximation for Fast Computer-Model Emulation

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia’s ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.

Muscle Co-Contraction Detection in the Time-Frequency Domain.

Background: Muscle co-contraction plays a significant role in motion control. Available detection methods typically only provide information in the time domain. The current investigation proposed a novel approach for muscle co-contraction detection in the time–frequency domain, based on continuous wavelet transform (CWT). Methods: In the current study, the CWT-based cross-energy localization of two surface electromyographic (sEMG) signals in the time–frequency domain, i.e., the CWT coscalogram, was adopted for the first time to characterize muscular co-contraction activity. A CWT-based denoising procedure was applied for removing noise from the sEMG signals. Algorithm performances were checked on synthetic and real sEMG signals, stratified for signal-to-noise ratio (SNR), and then validated against an approach based on the acknowledged double-threshold statistical algorithm (DT). Results: The CWT approach provided an accurate prediction of co-contraction timing in simulated and real datasets, minimally affected by SNR variability. The novel contribution consisted of providing the frequency values of each muscle co-contraction detected in the time domain, allowing us to reveal a wide variability in the frequency content between subjects and within stride. Conclusions: The CWT approach represents a relevant improvement over state-of-the-art approaches that provide only a numerical co-contraction index or, at best, dynamic information in the time domain. The robustness of the methodology and the physiological reliability of the experimental results support the suitability of this approach for clinical applications.

A predictive Markov decision process for optimizing inspection and maintenance strategies of partially observable multi-state systems

Optimizing both inspection and maintenance strategies for multi-state systems is challenging, especially when the inspected conditions contain uncertainties. One classic approach for addressing this issue is the Partially Observable Markov Decision Process (POMDP). However, the POMDP often considers the system is periodically inspected, resulting in a waste of inspection resources (cost and manpower) in the early stage of the system. To predictively optimize the inspection strategies, we formulate a new model-Predictive Markov Decision Process (PMDP). It extends the POMDP by embedding the Forward algorithm for inspection timing prediction and the Baum–Welch algorithm for model parameters estimation. Therefore, it could harvest the inspection information for predicting the successive inspection timing in an online updating scheme based on the new observation. In this manner, maintenance actions can take place at the predicted inspection timing that reduces unnecessary inspections. The PMDP manifests the power of predictive maintenance. As illustrated by the case study, the PMDP outperforms the POMDP under routine inspection by saving 26.3% of the cost on average.

Using CNN-LSTM to predict signal phasing and timing aided by High-Resolution detector data

This paper proposes a real-time signal timing prediction based on deep learning algorithms that takes various traffic flow parameters as input and predicts signal timing parameters. Signal retiming methods have been traditionally used to improve traffic flow at intersections by reducing delay and improving the level of service at signalized intersections. This can often be a lengthy process that includes several iterations of various optimization methods. In this paper, we have used detector data to calculate the traffic flow metrics at several intersections. This processed data is then used to predict the signal timing and phasing for the next six cycles with reasonable accuracy. Seventeen intersections from two distinct corridors have been used in this study. One of the corridors runs adaptive signal control and the other corridor runs actuated signal control. The proposed CNN-LSTM model shows that cycle length can be accurately predicted with an MAE of 7 to 16 s and phase duration can be predicted with an MAE of 3 to 8 s. The trained model was also successfully validated at five different unknown intersections.

Hardware Performance Monitoring Methodology at Near-Threshold Computing and Advanced Technology Nodes: From Design to Postsilicon

Near-threshold computing is essential for energy-efficient operation of VLSI systems, but wide performance variation and nonlinearity to process variations block the proliferation. To cope with this, in this article, we propose a holistic hardware performance monitoring methodology for accurate timing prediction in a near-threshold voltage regime. Precisely, 1) we formulate the problem of finding an efficient configuration of monitoring circuits into an instance of optimal experiment design problem and 2) propose a new timing prediction flow, consisting of statistical estimation of FEOL and BEOL process variations and a neural network-based timing inference model. For accurate control of timing margin and overcoming simulation-silicon discrepancies, 3) we introduce uncertainty learning in the prediction model construction and calibrate it through transfer learning. Furthermore, 4) we avoid time-consuming SPICE simulations in our methodology by employing efficient but accurate surrogate models. Through simulations using a 28-nm industry PDK and DK characterized at 0.6-V operation, it is shown that our methodology is highly effective, reducing the average prediction pessimism of maximum delay by 77.9% over conventional signoff results while respecting target prediction yield. Besides, for test chips fabricated using a 10-nm process, we demonstrated that our holistic approach from design to postsilicon phase in conjunction with adaptive voltage scaling reduces dynamic power consumption by 28.2%–28.8% on average, in comparison with typical supply voltage operation.