Scheduling Window Research Articles

A rate-responsive duty-cycling protocol for leadless pacemaker synchronization

Dual-chamber leadless pacemakers (LLPMs) consist of two implants, one in the right atrium and one in the right ventricle. Inter-device communication, required for atrioventricular (AV) synchrony, however, reduces the projected longevity of commercial dual-chamber LLPMs by 35–45%. This work analyzes the power-saving potential and the resulting impact on AV-synchrony for a novel LLPM synchronization protocol. Relevant parameters of the proposed window scheduling algorithm were optimized with system-level simulations investigating the resulting trade-off between transceiver current consumption and AV-synchrony. The parameter set included the algorithm’s setpoint for the target number of windows per cardiac cycle and the number of averaging cycles used in the window update calculation. The sensing inputs for the LLPM model were derived from human electrocardiogram recordings in the MIT-BIH Arrhythmia Database. Transceiver current consumption was estimated by combining the simulation results on the required communication resources with electrical measurements of a receiver microchip developed for LLPM synchronization in previous work. The performance ratio given by AV-synchrony divided by current consumption was maximized for a target of one window per cardiac cycle and three averaging cycles. Median transceiver current of both LLPMs combined was 166 nA (interquartile range: 152–183 nA) and median AV-synchrony was 92.5%. This corresponded to median reduction of 18.3% and 3.2% in current consumption and AV-synchrony, respectively, compared to a non-rate-responsive implementation of the same protocol, which prioritized maximum AV-synchrony. In conclusion, adopting a rate-responsive communication protocol may significantly increase device longevity of dual-chamber LLPMs without compromising AV-synchrony, potentially reducing the frequency of device replacements.

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

Reducing the need for users to manually manage the details of work and data distribution is an important goal of high-level many-task runtime systems. For distributed memory platforms this means that the runtime system has to keep track of both fine-grained task dependencies and data residency meta-information. The amount of such meta-information is proportional to the granularity of parallelism which needs to be managed, introducing a trade-off. More precise tracking of data state allows leveraging more opportunities for compute and transfer parallelism, while also introducing more overhead. As such, the fidelity of the information being tracked needs to be managed carefully, ideally without introducing additional latency, communication or substantial compute overhead. We present the “Horizons” approach, designed to fulfill these goals. Specifically, horizons allow for the effective and efficient management of parallelism and the coalescing of previous fine-grained tracking information while maintaining an easily configurable scheduling window with full information precision. As an additional benefit, they provide consistent cluster-wide decision points without requiring any inter-node communication, and effectively cap the size of state tracking data structures even in the presence of problematic access patterns. Experimental evaluation on microbenchmarks and dry runs demonstrates that horizons are effective in keeping the scheduling complexity constant, while their own overhead is negligible—below 10μs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\\, \\upmu {\\rm s}$$\\end{document} per horizon when building a command graph for 512 GPUs. We additionally demonstrate the performance impact of horizons—as well as their low overhead—on a real-world application.

Joint Autoscaling of Containers and Virtual Machines for Cost Optimization in Container Clusters

Autoscaling enables container cluster orchestrators to automatically adjust computational resources, such as containers and Virtual Machines (VMs), to handle fluctuating workloads effectively. This adaptation can involve modifying the amount of resources (horizontal scaling) or adjusting their computational capacity (vertical scaling). The motivation for our work stems from the limitations of previous autoscaling approaches, which are either partial (scaling containers or VMs, but not both) or excessively complex to be used in real systems. This complexity arises from their use of models with a large number of variables and the addressing of two simultaneous challenges: achieving the optimal deployment for a single scheduling window and managing the transition between successive scheduling windows. We propose an Integer Linear Programming (ILP) model to address the challenge of autoscaling containers and VMs jointly, both horizontally and vertically, to minimize deployment costs. This model is designed to be used with predictive autoscalers and be solved in a reasonable time, even for large clusters. To this end, improvements and reasonable simplifications with respect to previous models have been carried out to drastically reduce the size of the resource allocation problem. Furthermore, the proposed model provides an enhanced representation of system performance in comparison to previous approaches. A tool called Conlloovia has been developed to implement this model. To evaluate its performance, we have conducted a comprehensive assessment, comparing it with two heuristic allocators with different problem sizes. Our findings indicate that Conlloovia consistently demonstrates lower deployment costs in a significant number of cases. Conlloovia has also been evaluated with a real application, using synthetic and real workload traces, as well as different scheduling windows, with deployment costs approximately 20% lower than heuristic allocators.

Development of window scheduler algorithm exploiting natural ventilation and thermal mass for building energy simulation and smart home controls

Building energy simulations often rely on abstract assumptions when it comes to natural ventilation, such as ‘windows always open [or closed]’ or ‘windows open when outdoor temperature is below a certain threshold.’ However, simulations based on these assumptions fail to fully exploit the cooling potential of natural ventilation, as its effectiveness can be enhanced or diminished by various factors, including the presence of thermal mass. This issue also extends to smart home controls, where determining the window schedule becomes challenging without information about the building's response to outdoor conditions. To address these issues, this study has developed an analytical model for window operation schedules that leverages the passive cooling from natural ventilation. The analytical model was validated against a Modelica simulation. A case study utilizing the BESTEST model of ANSI/ASHRAE Standard 140 underwent validation with EnergyPlus simulations, showing strong concordance. The algorithm provides window schedule recommendations adapted to various airflow rates, thermal masses, and climate variations. The case study demonstrated that proper window scheduling could reduce indoor temperature by up to 8 °C under the given simulation settings, thereby improving resilience and indicating potential energy savings. Furthermore, the paper explores the potential opportunities and challenges this approach presents, especially for building simulation and smart home applications.

Dynamic Digital Twin and Online Scheduling for Contact Window Resources in Satellite Network

The contact window scheduling technology supports various space mission requirements of the satellite information network. The continuous emergence of new space missions challenges the scarce satellite contact window scheduling. In this paper, We propose a dynamically updated micro-cloud structure based on digital twin and multi-agent system technology. Then, we build an on-satellite autonomous scheduling model based on the micro-cloud structure and design an efficient solution algorithm to solve the maximum revenue problem of inefficient task scheduling caused by the dynamic change of task priority and satellite position over time. Simulation experiments show that the scheme proposed in this paper can significantly improve task scheduling revenue and resource utilization, and have specific enlightening significance for the innovation of satellite task scheduling methods in the future.

Patient Attitudes and Barriers to Endoscopy During the COVID-19 Pandemic.

After COVID-19 restrictions on nonessential procedures were lifted and safety protocols established, utilization rates of endoscopic procedures remained reduced. This study assessed patient attitudes and barriers to scheduling endoscopy during the pandemic. A survey was administered to patients with ordered procedures at a hospital-based setting (7/21/2020-2/19/2021) collecting demographic data, body mass index, COVID-19 relevant comorbidities, level of procedural urgency (defined by recommended scheduling window), scheduling and attendance, concerns, and awareness of safety measures. The average respondent was female (63.8%), age 57.6 ± 14, White (72.3%), married (76.7%), insured (99.3%), affluent English speakers (92.3%) and highly educated (at least college 90.2%). Most reported moderate to excellent COVID-19 knowledge (96.6%). Of 1039 procedures scheduled, emergent cases accounted for 5.1%, urgent 55.3% and elective 39.4%. Respondents identified appointment convenience (48.53%) as the most frequent factor impacting scheduling, also noting concern for results (28.4%). Age (p = .022), native language (p = .04), education (p = .007), self-reported COVID knowledge (p = .002), and a desire to be COVID tested pre-procedure (p = .023) were associated with arrival, more commonly in an ambulatory surgical center than hospital (p = .008). Diabetes mellitus (p = .004) and an immunocompromised state (p = .009) were adversely related to attendance. Attitudes towards safety protocols did not affect scheduling. Multivariate analysis demonstrated age, education and COVID knowledgeability were associated with procedure completion. Safety protocols and urgency levels were not associated with procedure completion. Pre-pandemic barriers to endoscopy persisted as dominant factors amid pandemic concerns.

Customizing Treatment Scheduling Windows with a Time Margin Recipe: A Single-institutional Study.

Rising cancer incidences, complex treatment techniques, and workflows have all impacted the radiotherapy scheduling process. Intelligent appointment scheduling is needed to help radiotherapy users adapt to new practices. We utilized van Herk's safety margin formula to determine the radiotherapy department's treatment scheduling window (TSW). In addition, we examined the influence of in-room imaging on linac occupancy time (LOT). Varian Aria™ software version 15.1 was used to collect retrospective data on LOT, treatment site, intent, techniques, special protocol, and in-room imaging. Treatment scheduling windows varied across treatment sites. The mean TSW using van Herk's formalism was 31.5 min, significantly longer than the current TSW of 15 min (P = 0.036), with the pelvic site having the longest (43.8 min) and the brain site having the shortest (12 min). 28% of patients exceeded the in-practice TSW of 15 min. 46.2% of patients had multiple images per fraction, with the proportion being highest in pelvic patients (33%). Patients treated with palliative intent, intensity-modulated radiotherapy, special protocols (bladder protocol and gating), and multiple in-room images per fraction had significantly higher LOT. High treatment time uncertainty was observed in the pelvic and thorax sites, indicating the impact of in-room imaging frequency and on-couch treatment decisions on overall treatment time and indicating that current treatment practices should be reviewed and modified if necessary. The time margin recipe can customize the treatment scheduling window and improve treatment practices. This formalism can help manage the radiotherapy department's workload and reduce patient wait times.

Adaptive model predictive scheduling of flexible interconnected low-voltage distribution networks considering charging preferences of electric vehicles

DC interconnection at the second side of distribution transformers helps achieve power sharing among nearby low-voltage distribution networks (LVDNs) and promote integration of intermittent inverter-based distributed generators (DGs). This paper proposes an adaptive model predictive scheduling method for flexible interconnected LVDNs considering charging preferences of electric vehicles (EVs). Firstly, the steady-state models of flexible resources including voltage source converters, energy storage systems along with AC and DC power flow models are established. Then, a model predictive control (MPC)-based rolling optimization model is formulated aiming to minimize the daily energy loss considering uncertainties of DGs, load and each charging station as a whole. To further explore the flexibility and dispatchability of each charging station, an adaptive MPC-based rolling optimization model is built considering three types of EVs with different charging preferences, i.e., uncontrollable EVs, charging-only EVs and vehicle-to-grid EVs. The scheduling window of the adaptive MPC-based scheduling is dynamically updated according to the maximum departure time of currently charging EVs to fulfill expected energy requirements of all EVs. Simulation results on a typical flexible LVDN show that the daily energy loss and total load fluctuation can be further reduced through real-time scheduling of controllable EVs in addition to existing flexible resources.

Beyond 100 Gbit/s Pipeline Decoders for Spatially Coupled LDPC Codes

Low-density parity-check (LDPC) codes are a well-established class of forward error correction codes that provide excellent error correction performance for large code block sizes. However, for throughputs toward 1 Tbit/s, as expected for B5G systems, state-of-the-art LDPC soft decoders are restricted to short code block sizes of several hundred to thousands of bits due to routing congestion challenges, limiting the overall communications performance of the transmission system. Spatially coupled LDPC (SC-LDPC) codes and respective sliding window decoding methods show the potential to overcome these block size restrictions. However, in contrast to conventional LDPC codes little literature exists on the efficient hardware implementation of respective high-throughput decoders. In this work, we present the first in-depth investigation on the implementation of SC-LDPC decoders for throughputs beyond 100 Gbit/s. For an N=51328, R=0.8 terminated SC-LDPC code with sub-block size c=640 and coupling width m_s=1, we explore various design trade-offs, including row- and column-wise decoding, non-overlapping and overlapping window scheduling, and processor pipelining. To the best of our knowledge, this is the first description of a column-wise SC-LDPC decoding architecture in the literature. We complement the algorithmic investigation with the virtual silicon implementation of all presented decoders in a 22nm FD-SOI technology.

Dynamic fault-tolerant VLIW processor with heterogeneous Function Units

Instruction Level Parallelism (ILP) of applications is typically limited and variant in time, thus during application execution some processor Function Units (FUs) may not be used all the time. Therefore, these idle FUs can be used to execute replicated instructions, improving reliability. However, existing approaches either schedule the execution of replicated instructions based on compiler schedule or consider processors with identical FUs, able to execute any instruction type. The former approach has a negative impact on performance, whereas the later approach is not applicable on processors with heterogeneous FUs. This work presents a hardware mechanism for processors with heterogeneous FUs that dynamically replicates instructions and schedules both original and replicated instructions considering space and time scheduling. The proposed approach uses a small scheduling window of two cycles, leading to a hardware mechanism with small hardware area. In order to perform such a flexible dynamic instruction scheduling, switches are required, which, however, increase the hardware area. To reduce the area overhead, a cluster-based approach is proposed, enabling scalability for larger hardware designs. The proposed mechanism is implemented on VEX VLIW processor. The obtained results show an average speed-up of 24.99% in performance with an almost 10% area and power overhead, when time scheduling is also considered on top of space scheduling. Compared to the unprotected version, the instruction reliability has increased by 2.2×.

Round-Trip Emergency Supply Distribution Model Based on Nonfixed Routes

In the face of increasing natural or man-made disasters, rapid and effective emergency dispatch and organization are of great significance to ensure the life safety of people and reduce social losses. In view of the long duration, strong demand urgency, and relatively limited transportation capacity after catastrophic events, this paper proposes a round-trip emergency supply distribution model based on nonfixed routes. This model includes two main features: (1) round trip: emergency vehicles can travel back and forth to distribute supplies; (2) unfixed routes: distribution routes of the same emergency vehicle could be variable in different trips. In order to ensure the timeliness and fairness of the supply distribution scheme, the model objective function is set to minimize the total supplies’ waiting time at all demand points. According to model features, 4 constraints are set, including flow balance, capacity, vehicle scheduling, and time window. On this basis, a compound algorithm combining 2-opt and tabu search is designed to obtain the optimal plan of the model. To verify the effectiveness and superiority of the model and solution method, a case study based on the Sioux Falls network is carried out. Compared with the traditional method, the objective function is optimized by 11.92%. In fact, under the control of multiple constraint conditions, the model well fits the actual application scenarios, which can provide theoretical guidance and decision support for the distribution of relevant emergency supplies.

Studying the appointment scheduling window considering patient no-show behavior in one public and one private outpatient clinics

PurposeThis study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized.Design/methodology/approachIn this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model.FindingsIn this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment.Originality/valueToday, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

Appointment window scheduling with wait-dependent abandonment for elective inpatient admission

ABSTRACT In this study, we propose a new appointment window scheduling (AWS) approach of informing customers of an admission window (AW) rather than the traditional appointment time. We provide a formal description of this AWS problem for only one kind of customer and propose a dedicated chance-constrained policy to assign AWs dynamically under the condition with fixed service capacity, different scales as well as status in different waiting stages, and wait-dependent abandonment. Numerical experiments show that customer satisfaction can be significantly improved (by reducing over 60% of wait-but-abandon events and by reducing 90% of departures caused by waiting beyond the AW), and server utilisation is slightly improved. And the improvements are more significant when systems are overloaded, and customers are more sensitive to online waiting than offline waiting. The AWS scenario can also be applied to other queueing systems as long as it is possible and profitable to let customers wait outside of the waiting area.

Perdas de grãos na cadeia logística do transporte rodoviário

A research aims to study and analyze which factors contribute to grain losses in the transport logistics chain. For this purpose, a descriptive research was carried out, through the application of a questionnaire, involving aspects related to transport logistics. The results indicate that, for the interviewees, the factors that most contribute to the loss of grains in the transport logistics chain are the use of vehicles in inadequate transport conditions, and that do not have adequate maintenance of the bodies. In addition, the factors least related to losses are the lack of tire calibration and the short scheduling windows for unloading grains at ports.

Intelligent approach for residential load scheduling

Residential load scheduling has been introduced in recent years as a result of implementing demand response programmes in the residential sector. Up to now, studies have focussed on developing algorithms for scheduling the residential loads with the objective of reducing the electricity payment while satisfying the users’ comfort level. To aim at this goal, the user should determine the desired scheduling window for each appliance to initiate the scheduling process. Although these algorithms designed to benefit the end-users, determining the scheduling window for each appliance at the beginning of the scheduling process can be challenging. In this study, an intelligent load scheduling model is proposed to perform the scheduling with the least user intervention. The proposed model predicts the preferred scheduling window for each appliance and initiates the scheduling process. Furthermore, the degradation cost of the battery pack in the vehicle-to-grid mode is considered in the cost function. The model is applied to a case study containing plug-in hybrid electric vehicle and flexible loads such as a washing machine and dishwasher under inclining block rates and hourly pricing tariffs. The results indicate that implementing the model in a residential building can decrease the electricity payment while performing the residential load scheduling with the least user intervention.

Highly Multiplex Targeted Proteomics Enabled by Real-Time Chromatographic Alignment

Targeted mass spectrometry methods produce high-quality quantitative data in terms of limits of detection and dynamic range, at the cost of a substantial compromise in throughput compared to methods such as data independent and data dependent acquisition. The logistical and experimental issues inherent to maintaining assays of even several hundred targets are significant. Prominent among these issues is the drift in analyte retention time as liquid chromatography (LC) columns wear, forcing targeted scheduling windows to be much larger than LC peak widths. If these problems could be solved, proteomics assays would be capable of targeting thousands of peptides in an hour-long experiment, enabling large cohort studies to be performed without sacrificing sensitivity and specificity. We describe a solution in the form of a new method for real-time chromatographic alignment and demonstrate its application to a 56 min LC-gradient HeLa digest assay with 1489 targets. The method is based on the periodic acquisition of untargeted survey scans in a reference experiment and alignment to those scans during subsequent experiments. We describe how the method enables narrower scheduled retention time windows to be used. The narrower scheduling windows enables more targets to be included in the assay or proportionally more time to be allocated to each target, improving the sensitivity. Finally, we point out how the procedure could be improved and how much additional target multiplexing could be gained in the future.

Modeling and Analysis of Data Harvesting Architecture Based on Unmanned Aerial Vehicles

This paper explores an emerging wireless Internet-of-things (IoT) architecture based on unmanned aerial vehicles (UAVs). We consider a network where a fleet of UAVs at a fixed altitude flies on planned trajectories and IoT devices on the ground are scheduled to transmit their data to the UAVs when the latter are nearby. In such a system, the UAVs’ motion triggers the uplink transmissions of the IoT devices. As a result, network performance is determined by the geometric and dynamic characteristics of the system. We propose a joint stationary model for UAVs and IoT devices and then evaluate the interference, the coverage probability, and the data rate of the typical UAV. To assess the harvesting capability of the proposed architecture, we derive a formula for the amount of data uploaded from each IoT device to a UAV. We also establish a linear relationship between the UAV coverage and the harvesting capability of the network, which provides insights into the design of the proposed harvesting scheme. In addition, we use our analytical results to numerically show that there exists a trade-off between the uploaded data and the size of the IoT scheduling window. Specifically, for a given UAV and IoT geometry, there exists an optimal scheduling window that maximizes the harvesting capability of the proposed network.

Simulation optimization approach for patient scheduling at destination medical centers

This study presents a simulation optimization approach for scheduling medical tourists who travel to destination medical centers (DMCs). The objective of this research is two-fold: (1) to minimize deviations from a patient's preferred start day and (2) to minimize the flow time of patients at the DMC. The duration of treatment is considered uncertain, and patients are scheduled over a multiday scheduling window. This problem is modeled as a flow-shop scheduling problem (FSP) where each patient should undergo a specific number of stages to complete treatment. The goal is to assign patients to different days within the time horizon such that the minimum deviation from the patient's preferred start day and the minimum average patient flow time are achieved. Due to the complex and stochastic nature of the problem, the simulation-optimization approach is used. This approach includes simulation evaluation at each iteration of the optimization procedure. The optimization procedures used in this research are tabu search (TS) and simulated annealing (SA). Finally, several test problems are used to evaluate the performance of the proposed simulation optimization approach. Based on our results, the total cost of the proposed model is significantly less than that of the problem without any admission planning and patient sequencing.

Improved antibody-guided surgery with a near-infrared dye on a pegylated linker for CEA-positive tumors.

.Real-time intraoperative image-guided cancer surgery promises to improve oncologic outcomes. Tumor-specific antibodies conjugated with near-infrared (NIR) fluorophores have demonstrated the potential to enhance visualization of solid tumor margins and metastatic disease; however, multiple challenges remain, including improvement in probe development for clinical utility. We have developed an NIR-IR800 dye on a PEGylated linker (sidewinder) conjugated to the humanized anti-carcinoembryonic antigen (CEA) antibody (M5A) with extended in vivo serum and tumor persistence. The anti-CEA M5A-sidewinder has a high dye-to-antibody ratio (average of 7 per antibody) that allows, in an orthotopic implanted human pancreatic cancer mouse model increased tumor fluorescence, higher tumor-to-background ratio and extends the surgical scheduling window compared to current antibody dye conjugates. These preclinical results demonstrate the potential of this probe for fluorescence-guided surgery of CEA-positive gastrointestinal cancers.

A Bayesian Real-Time Electric Vehicle Charging Strategy for Mitigating Renewable Energy Fluctuations

A novel pricing and scheduling mechanism is proposed in this paper for plug-in electric vehicles (PEVs) charging/discharging to track and synchronize with a renewable power generation pattern. Moreover, the proposed mechanism can be used in the demand-side management and ancillary service applications for the peak shaving and frequency regulation responding, respectively. We design a fully distributed stochastic optimization mechanism using a Bayesian pure strategic repeated game by which the PEVs optimally schedule their demands. We also use a mixed Bayesian–diffusion–Kalman filtering strategy for the customers to collaboratively estimate and track the stochastic price and regulation signals for the upcoming scheduling window. In this paper, all the characteristics of the PEVs as well as the uncertainty about their deriving patterns are considered. As our framework converges to an equilibrium even with incomplete information, it is agent-based, and the agents share the information only with their optional neighbors, it is scale free, robust, and secure.