Scheduling Variables Research Articles

An algorithm to build schedule table for schedule-based fieldbus to reduce communication jitter to its minimum

Schedule-based fieldbus is applied widely in industrial control. Building schedule table (ST) can affect the time-critical performance for communication greatly. Communication jitters can cause some unexpected results in bus traffic scheduling and longer jitters can even cause severe delay in time-critical application. Communication jitter and evenness in arrangement of variables to be scheduled in micro-cycles in ST were two important issues dealing with the performance. An algorithm to build schedule table based on numerical co-prime method was proposed in this paper. Communication jitter in ST was investigated. Concept dealing with rank of ST was introduced. Numerical co-prime method to evaluate rank of ST was proposed. The algorithm and procedure to build schedule table based on the proposed numerical co-prime method were presented and exemplified in detail. Performance analysis and comparison of the proposed algorithm to HCF/LCM method was presented. It was proved that the proposed algorithm can reduce communication jitter to its minimum value and the longest time consumed to schedule variable in one micro-cycle, and greatly reduce the possibility the time consumed to schedule variables exceeded micro-cycle.

Loss of Motor Stability After Sports-Related Concussion: Opportunities for Motor Learning Strategies to Reduce Musculoskeletal Injury Risk.

Current best practices to direct recovery after sports-related concussion (SRC) typically require asymptomatic presentation at both rest and during a graduated exercise progression, and cognitive performance resolution. However, this standard of care results in a significantly elevated risk for musculoskeletal (MSK) injury after return-to-sport (RTS). The elevated risk is likely secondary to, in part, residual neurophysiological and dual-task motor stability deficits that remain despite RTS. These deficits present as a loss of autonomous control of gait and posture and an increased need for cognition for motor stability. Thus, the incorporation ofstrategies that can enhance motor stability and restore autonomous control of gait and posture during SRC recovery and RTS progression may facilitate areduction of the elevated risk of secondary MSK injury. We provide a theoretical framework for the application of motor learning principles to restore autonomous gait and postural stability after SRC via incorporation, or targeted manipulation, of external focus, enhanced expectations, autonomy support, practice schedule variability, and dual-task strategies during rehabilitation and RTS training.

647 Adolescent and Young Adult Sleep and Sleep-Related Behaviour Change During the COVID-19 Pandemic

IntroductionSleep disturbance, poor sleep quality, and dissatisfaction with sleep are common among adolescents and young adults (AYAs; e.g., Becker et al., 2018; Hicks et al., 2002; Hysing et al., 2013). Environmental and behavioural factors (e.g., early school start times, evening technology use and social pressures) are barriers to healthy sleep among AYAs that contribute to a “perfect storm” of sleep disturbance during this period (Carskadon, 2011; Crowley et al., 2018). Notwithstanding, few AYAs have access to sleep treatments. The COVID-19 pandemic lockdowns decreased academic and scheduling demands, providing an opportunity to study unconstrained AYA sleep and potentially facilitating better access to sleep interventions (Simpson & Manber, 2020). This study evaluated differences in baseline sleep and sleep-related behaviour change (i.e., how AYAs use an evidence-based app for sleep disturbance) before vs. during the lockdown.MethodsParticipants between the ages of 15 and 24 (M=20.66, SD=2.38) completed a 4-week feasibility study evaluating a free, transdiagnostic sleep self-management app (DOZE) before the lockdown (“Pre-Lockdown”; n=51) or during the lockdown (“Lockdown”; n=29). After 2 weeks of completing baseline sleep diaries, participants could set goals based on feedback and access tips, followed by 2 more weeks of completing sleep diaries.ResultsCompared to Pre-Lockdown, Lockdown demonstrated less variability in their sleep schedules (ps≤.011), less napping (p=.002), but increased time in bed (TIB; p<.001) and total wake time (p=.007). Total sleep time, lingering in bed in the morning, and sleep efficiency did not differ between groups. Relative to Pre-Lockdown, Lockdown showed a greater tendency to set goals to reduce schedule variability (p=.010) and to restrict excessive TIB (p=.005). Rates of goal setting for lingering in bed in the morning, sleepiness, naps, and sleep-interfering substance use did not differ between groups. Rates of accessing tips did not differ between groups.ConclusionEffects of COVID-19 lockdown on AYA sleep included less variability in their schedule and a decreased need for naps, but negative effects on TWT and TIB. As a result, AYAs set different goals during the COVID-19 lockdowns, focusing more on restricting excessive TIB than on schedule variability.Support (if any)Canadian Institutes of Health Research eHealth Innovation Partnership Program (#143551).

Confidence set-membership state estimation for LPV systems with inexact scheduling variables

In this paper, a confidence set-membership state estimator is proposed for a class of polytopic linear parameter varying (LPV) systems with inexact scheduling variables. The set-bounded and Gaussian uncertainties are considered simultaneously in the process disturbances and measurement noises. The purpose of the proposed estimator is to achieve a confidence set of the state with given confidence level. Based on the polytopic LPV uncertain enclosure model, the set-bounded/Gaussian uncertainties of the state are given by using the worst case strategy. The size of the confidence set is minimized to get the optimal gain for the estimator. Meanwhile, the constrained zonotope is adopted to represent set-bounded uncertainties for more accurate results. Finally, a vehicle example is given to illustrate the effectiveness of proposed methods.

Actuator Fault Detection for Discrete-Time Descriptor Systems via a Convex Unknown Input Observer with Unknown Scheduling Variables

This paper presents actuator fault detection of discrete-time nonlinear descriptor systems by means of nonlinear unknown input observers. The approach is based on the exact factorization of the estimation error in order to overcome the well-known problem of unmeasurable scheduling variables within the observation of convex models, thus avoiding the use of Lipschitz constants, differential mean value theorem, or robust techniques. As a result, the designing conditions are cast in terms of linear matrix inequalities and efficiently solved via commercially available software. Numerical as well as academic setups are provided to illustrate the advantages and performance of the proposal.

Economic model predictive control of nonlinear systems using a linear parameter varying approach

AbstractThis article proposes an economic model predictive control (EMPC) approach for linear parameter varying (LPV) systems. An efficient implementation of the associated MPC optimization problem is introduced based on transforming the LPV model into a linear time‐varying one by using an estimation of the scheduling variables along the prediction horizon. This estimation is based on the optimal states/inputs determined from the solution of the previous optimization problem while running the receding horizon strategy. Using this approach, the solution of the proposed LPV‐based EMPC scheme would be possible by solving a series of quadratic programming problems at each time instant. This approach allows reducing the computational burden compared with the solution of a nonlinear optimization problem that would result naturally from the LPV‐based formulation. The stability of the proposed approach is guaranteed by forcing the terminal state to converge towards the optimal equilibrium/working point of the system. Moreover, the terminal constraint is relaxed by using a constraint set around the terminal state instead of a constraint value and adding a penalty on the terminal state into the cost function. Besides, strict dissipativity is established as a sufficient condition to prove stability. Finally, the effectiveness of the LPV‐based EMPC strategy is shown by controlling a small‐scale pasteurization system in simulation. The comparison between the proposed control approach and standard MPC approaches is performed. Results show the advantages of the proposed LPV‐based EMPC controller in terms of economic cost minimization.

National Quality Measure Compliance for Palliative Bone Radiation Among Patients With Metastatic Non-Small Cell Lung Cancer.

There exists wide practice variability in palliative treatment schedules for bone metastases. In an effort to reduce variation and promote high-quality, cost-conscious care, the National Quality Forum (NQF) endorsed measure 1822 in 2012. This measure recommends the use of 30 Gy in 10 fractions, 24 Gy in 6 fractions, 20 Gy in 5 fractions, or 8 Gy in a single fraction for palliative radiation for bone metastases. We report on longitudinal compliance with this measure. Using the National Cancer Database, patients with metastatic thoracic non-small cell lung cancer diagnosed between 2004 and 2016 who received radiation therapy for bony sites of metastatic disease were identified. Treatment courses fitting 1 of the 4 recommended schedules under NQF 1822 were coded as compliant. Rates of compliance by patient, tumor, and treatment characteristics were analyzed. A total of 42,685 patients met the criteria for inclusion. Among all patients, 60.2% of treatment courses were compliant according to NQF 1822. Compliance increased over time and was highest for treatments to the extremity (69.8%), lowest for treatments to the skull or head (48.8%), and higher for academic practice (67.1%) compared with community (56.0%) or integrated network facilities (61.2%). On multivariable analysis, predictors of NQF 1822 compliance included year of diagnosis after 2011, treatment to an extremity, or treatment at an academic facility. Of noncompliant treatment courses, extended fractionation (≥11 fractions) occurred in 62.6% and was more common before 2012, in community practice, and for treatments of the skull or head. Among patients treated for metastatic non-small cell lung cancer, compliance with NQF 1822 increased over time. Although extended fractionation constituted a majority of noncompliant treatment courses, a substantial proportion also involved shorter courses.

Design of LPV control for autonomous vehicles using the contributions of big data analysis

The paper deals with a robust autonomous path-following functionality, in which the safe velocity and the motion profile of the vehicle with varying tyre-road contact must be guaranteed. The integration of Linear Parameter-Varying (LPV) control and the results of the machine learning-based analysis on the big data of autonomous vehicles is proposed. The integration is achieved in two steps. First, an estimation method on the adhesion coefficient through decision trees is presented. The result of the estimation is incorporated in the robust LPV control through a scheduling variable. During the control design, the error of the machine-learning algorithm is incorporated. Second, an optimisation method of the longitudinal velocity on a predicted horizon is proposed, which is aided with the machine learning-based reachability set approximation of the steering intervention. The effectiveness of the control strategy is illustrated through CarSim simulations.

Adaptive Semi-Active Suspension and Cruise Control through LPV Technique

Several studies exist on topics of semi-active suspension and vehicle cruise control systems in the literature, while many of them just consider actual road distortions and terrain characteristics, these systems are not adaptive and their subsystems designed separately. This study introduces a new method where the integration of look-ahead road data in the control of the adaptive semi-active suspension, where it is possible to the trade-off between comfort and stability orientation. This trade-off is designed by the decision layer, where the controller is modified based on prehistorical passive suspension simulations, vehicle velocity and road data, while the behavior of the controller can be modified by the use of a dedicated scheduling variable. The adaptive semi-active suspension control is designed by using Linear Parameter Varying (LPV) framework. In addition to this, it proposes designing the vehicle velocity for the cruise controller by considering energy efficiency and comfort together. TruckSim environment is used to validate the operation of the proposed integrated cruise and semi-active suspension control system.

Actuator and sensor fault estimation based on a proportional multiple‐integral sliding mode observer for linear parameter varying systems with inexact scheduling parameters

AbstractThis article proposes an approach for the estimation of states, actuator, and sensor faults in nonlinear systems represented by a polytopic linear parameter varying (LPV) system with inexact scheduling parameters. In the traditional LPV approaches, the scheduling variables are considered to be perfectly known. However, in practical applications, their measurement may contain precision and calibration errors or noise that can affect the performance of the diagnostic systems. Therefore, this work proposes the design of a proportional multiple‐integral sliding mode observer for fault diagnosis (FD) that copes with LPV systems with inexact scheduling parameters. Due to the introduction of some nonlinear functions, the proposed observer is a nonlinear parameter varying system for which stability and robustness performance are formulated using the Lyapunov technique and a H∞ performance criterion. It is shown that the design conditions boil down to a set of linear matrix inequalities whose solution allows computing the observer gain matrix along with the tunable parameters of the nonlinear functions. Results obtained using the simulator of an octocopter‐type unmanned aerial vehicle are used to demonstrate the applicability and performance of the proposed FD scheme.

Precarious Work, Marital Quality, and Divorce: A Gendered Dyadic Analysis of Aging Couples

Precarious work – work that is unstable and insecure – is often stressful and may contribute to marital strain and dissolution among midlife adults. However, prior research has not considered how precarious work spills over to spouses. Using longitudinal dyadic data of midlife couples from the Health and Retirement Study, I examine whether having a spouse in precarious work is associated with marital strain and dissolution, with attention to differences by gender. I find that indicators of precarious work (job insecurity, schedule variability) are associated with a heightened risk of marital strain and divorce in midlife. These patterns depend on the gender of the spouse experiencing precarious work. Understanding the implications of precarious work for marriage is important because poor marital quality and divorce hasten health declines at older ages. Thus, this study suggests that precarious work may be a risk factor for divorce and poor health among aging adults.

Affine linear parameter‐varying embedding of non‐linear models with improved accuracy and minimal overbounding

In this study, automated generation of linear parameter-varying (LPV) state-space models to embed the dynamical behaviour of non-linear systems is considered, focusing on the trade-off between scheduling complexity and model accuracy and the minimisation of the conservativeness of the resulting embedding. The LPV state-space model is synthesised with affine scheduling dependency, while the scheduling variables themselves are non-linear functions of the state and input variables of the original system. The method allows to generate complete or approximative embedding of the non-linear system model and also it can be used to minimise the complexity of existing LPV embeddings. The capabilities of the method are demonstrated on simulation examples and also in an empirical case study where the first-principle motion model of a three degrees of freedom (3DOF) control moment gyroscope is converted by the proposed method to an LPV model with low scheduling complexity. Using the resulting model, a gain-scheduled controller is designed and applied on the gyroscope, demonstrating the efficiency of the developed approach.

Decision diagram-based integer programming for the paired job scheduling problem

The paired job scheduling problem seeks to schedule n jobs on a single machine, each job consisting of two tasks for which there is a mandatory minimum waiting time between the completion of the first task and the start of the second task. We provide complexity results for problems defined by three commonly used objective functions. We propose an integer programming formulation based on a decision diagram decomposition that models the objective function and some of the challenging constraints in the space of the flow variables stemming from the diagrams while enforcing the simpler constraints in the space of the original scheduling variables. We then show how to simplify our reformulation by projecting out a subset of the flow variables, resulting in a lifted reformulation for the problem that can be obtained without building the decision diagrams. Computational results show that our proposed model performs considerably better than a standard time-indexed formulation over a set of randomly generated instances.

Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections

The aim of the paper is to describe a multicriteria model predictive control method for autonomous vehicles at non-signalized intersections. The centralized controller aims to describe control action for each autonomous vehicle to guarantee collision free passage. At the same time, performances are defined for the centralized Model Predictive Controller, namely the minimization of traveling time and energy consumption. Since these control goals are often conflicting, a scheduling variable is introduced to create a balance between them. Hence, the centralized controller can be tuned based on the importance of each control goal, which can be useful in urban environment where traffic densities may vary heavily depending on the period of the day. The effectiveness of the proposed centralized multicriteria controller is demonstrated through a complex simulation example in CarSim simulation environment using different tpye of autonomous vehicles.

Decentralized User Scheduling for Rate-Constrained Sum-Utility Maximization in the MIMO IBC

While the problems of sum-rate maximization and sum-power minimization subject to quality of service (QoS) constraints in the multiple input multiple output interference broadcast channel (MIMO IBC) have been widely studied, most of the proposed solutions have neglected the user scheduling aspect assuming that a feasible set of users has been previously selected. However, ensuring QoS for each user in the MIMO IBC involves the joint optimization of transmit/receive beamforming vectors, transmit powers, and user scheduling variables. To address the full problem, we propose a novel formulation of a rate-constrained sum-utility maximization problem which allows to either deactivate users or minimize the QoS degradation for some scheduled users in infeasible scenarios. Remarkably, this is achieved avoiding the complexity of traditional combinatorial formulations, but rather by introducing a novel expression of the QoS constraints that allows to solve the problem in a continuous domain. We propose centralized and decentralized solutions, where the decentralized solutions focus on practical design and low signaling overhead. The proposed solutions are then compared with benchmarking algorithms, where we show the effectiveness of the joint scheduling and transceiver design as well as the flexibility of the proposed solution performing advantageously in several MIMO IBC scenarios.

Quantifying the effects of spatial-temporal variability of soil properties on crop growth in management zones within an irrigated maize field in Northwest China

Spatial and temporal variability of soil properties, irrigation schedules, and fertilization management play an important role in crop growth and yield in agricultural systems. However, how to improve and accurately predict crop yield in different management zones (MZs) within a large-scale field are important but rarely discussed. In this study, coefficient of variation, linear correlations, partial least squares discriminant analysis and quantile regression were used to quantify relationships between crop and soil properties, and determine key explanatory factors of yield, and to identify suitable ways to improve crop yield among MZs. At the temporal scale, coefficients of variation of crop growth parameters (LAI and biomass), and correlations of crop growth parameters with soil water content and available nitrogen varied during crop growth stages in MZs across years. Crop–soil correlations varied with time and were most pronounced in heading and filling stages when LAI and biomass approached maximum values, respectively. At the spatial scale, 22 explanatory factors belonging to four categories: soil physical properties, initial soil properties, soil water and nitrogen dynamics, and topography were compared. Results showed that among the four categories, initial soil properties had the greatest influence on yield across the whole field, whereas soil water and nitrogen dynamics were the most influential for individual MZs. Key explanatory factors of yield varied across MZs in 2017 and 2018. Although key explanatory factors varied across years within a MZ, some remained unchanged, especially soil physical properties. Based on the quantile regression results, the same key explanatory factors might appear in MZs, but the reference ranges were different. For more sustainable and efficient agricultural production, distributed integrated management, including soil amelioration before sowing, and irrigation and N fertilization scheduling during the crop growth period should be implemented in MZs based on the spatial-temporal variability of soil properties.

Set‐based gain‐scheduled control via quasi‐convex difference inclusions

SummaryA nonlinear system with sector‐bounded nonlinearities may be expressed as a quasi‐linear parameter‐varying (LPV) system (convex combination of linear models), being this a well‐known fact. The convex difference inclusion (CDI) modeling framework proposed by Fiacchini and coworkers in several of their works generalizes the quasi‐LPV modeling procedure and proposes robust controllers enlarging polytopic domain of attraction estimates. This works further generalizes the CDI approach to a gain‐scheduled case including, also, some quasi‐convex cases. Controller design is based on convexity properties of two set valued maps describing (with some uncertainty) the state evolution and the state‐dependent set where scheduling variables take values. As most set‐based approaches, the proposal is tractable in low‐dimensional cases. The presented results encompass prior quasi‐LPV and CDI models as particular cases.

Comparison Between Actigraphy Records and Parental Reports of Child's Sleep.

Given the impact of sleep in several domains of a child's development, the comparison between actigraphy and parental questionnaires is of great importance in preschool-aged children, an understudied group. While parental reports tend to overestimate sleep duration, actigraphy boosts the frequency of night-waking's. Our primary goal was to compare actigraphy data and parental reports (Children's Sleep Habits Questionnaire, CSHQ), regarding bedtime, wake-up time, sleep duration, and wake after sleep onset (WASO), using the Bland–Altman technique. Forty-six children, age 3–6 years, and their parents participated. Results suggest that, despite existing associations between sleep schedule variables measured by both methods (from r = 0.57 regarding bedtime at weekends to r = 0.86 regarding wake-up time during the week, ps), differences between them were significant and agreements were weak, with parents overestimating bedtimes and wake-up times in relation to actigraphy. Differences between actigraphy and CSHQ were ± 52 min for weekly bedtime, ± 38 min for weekly wake-up time, ±159 min for total sleep time, and ± 62 min for WASO, indicating unsatisfactory agreement between methods. Correlations between actigraphy data and CSHQ dimensions are also explored. Our study contributes to the knowledge of the characteristics of each instrument, along with their tendency to overestimate and underestimate certain sleep parameters. We conclude that a complementary use of both instruments would better inform clinical practice and research on a child's sleep.

Optimal robust fault detection of discrete‐time LPV systems with measurement error‐affected scheduling variables combining ZKF and pQP

SummaryOptimal robust state estimation (SE) and fault detection (FD) methods of discrete‐time linear parameter varying systems with measurement error‐affected scheduling variables are proposed under the boundedness assumption of system uncertainties. By using the weighted Frobenius norm of the generator matrix of SE zonotope to characterize the set size, the optimal observer gain can be computed by a zonotopic Kalman filter (ZKF) procedure for the purpose of observation. Meanwhile, by minimizing the influence of system uncertainties while maximizing that of faults on SE to enhance the sensitivity of FD, the optimal FD criterion is characterized based on an on‐line fractional programming problem, which can be equivalently transformed into a parametric quadratic programming (pQP) problem. The pQP problem can be efficiently solved by searching the root of its nonlinear characteristic equation using secant method. In general, as long as sensors with sufficiently high precision are equipped to measure the scheduling variables, the bounds of measurement errors of scheduling variables can be less conservative than those direct bounds of scheduling variables, which can reduce the conservatism of FD or SE in this way. At the end of this article, a case study based on a practical circuit model is used to illustrate the effectiveness of the proposed method.

A Method for Balancing Provider Schedules in Outpatient Specialty Clinics.

Background. Variability in outpatient specialty clinic schedules contributes to numerous adverse effects including chaotic clinic settings, provider burnout, increased patient waiting times, and inefficient use of resources. This research measures the benefit of balancing provider schedules in an outpatient specialty clinic. Design. We developed a constrained optimization model to minimize the variability in provider schedules in an outpatient specialty clinic. Schedule variability was defined as the variance in the number of providers scheduled for clinic during each hour the clinic is open. We compared the variance in the number of providers scheduled per hour resulting from the constrained optimization schedule with the actual schedule for three reference scenarios used in practice at M Health Fairview’s Clinics and Surgery Center as a case study. Results. Compared to the actual schedules, use of constrained optimization modeling reduced the variance in the number of providers scheduled per hour by 92% (1.70–0.14), 88% (1.98–0.24), and 94% (1.98–0.12). When compared with the reference scenarios, the total, and per provider, assigned clinic hours remained the same. Use of constrained optimization modeling also reduced the maximum number of providers scheduled during each of the actual schedules for each of the reference scenarios. The constrained optimization schedules utilized 100% of the available clinic time compared to the reference scenario schedules where providers were scheduled during 87%, 92%, and 82% of the open clinic time, respectively. Limitations. The scheduling model’s use requires a centralized provider scheduling process in the clinic. Conclusions. Constrained optimization can help balance provider schedules in outpatient specialty clinics, thereby reducing the risk of negative effects associated with highly variable clinic settings.