Increasing student grades in large online subjects: Combining tutorial support with technology

Three large first-year undergraduate subjects with 240-517 enrolled students were selected to participate in this pilot study. A meeting scheduling tool was embedded in the learning management system and thirty-minute, one-on-one tutorial sessions were available to students in the 2 weeks leading up to the due date of at least one large written task. Thirty one percent (31%) of enrolled students attended at least one appointment with a tutor. There was no difference in the average assessment mark that students obtained before the first tutorial was offered between those who attended a tutorial session for a later assessment item and those who did not. There was a significant increase in the average cumulative grade (10%, p<0.05) of students who attended a tutorial. The novel use of the calendar booking tool combined with online meeting technology provides a simple and convenient method to provide personalised feedback to a large cohort of students.

Dynamic Programming as a Scheduling Tool in Multiprogrammed Computing Systems

<p>A potentially parallel iterative algorithm for the solution of the unconstrained N-stage decision problem of Dynamic Programming is developed. This new solution method, known as Variable Metric Dynamic Programming, is based on the use of variable metric minimisation techniques to develop quadratic approximations to the optimal cost function for each stage. The algorithm is applied to various test problems, and a comparison with an existing similar algorithm proves favourable. The Variable Metric Dynamic Programming solution method is used in the implementation of an adaptive highlevel scheduling mechanism on a multiprogrammed computer in a university environment. This demonstrates a practical application of the new algorithm. More importantly, the application of Variable Metric Dynamic Programming to a scheduling problem illustrates how Mathematical Programming may be used in complex computer scheduling problems to provide in a natural way the required dynamic feedback mechanisms.</p>

Dynamic Programming as a Scheduling Tool in Multiprogrammed Computing Systems

<p>A potentially parallel iterative algorithm for the solution of the unconstrained N-stage decision problem of Dynamic Programming is developed. This new solution method, known as Variable Metric Dynamic Programming, is based on the use of variable metric minimisation techniques to develop quadratic approximations to the optimal cost function for each stage. The algorithm is applied to various test problems, and a comparison with an existing similar algorithm proves favourable. The Variable Metric Dynamic Programming solution method is used in the implementation of an adaptive highlevel scheduling mechanism on a multiprogrammed computer in a university environment. This demonstrates a practical application of the new algorithm. More importantly, the application of Variable Metric Dynamic Programming to a scheduling problem illustrates how Mathematical Programming may be used in complex computer scheduling problems to provide in a natural way the required dynamic feedback mechanisms.</p>

Aircrew Actual vs. Prescriptive Sleep Schedules and Resulting Fatigue Estimates

BACKGROUND: Fatigue is an insidious and costly occurrence in the aviation community, commonly a consequence of insufficient sleep. Some organizations use scheduling tools to generate prescriptive sleep schedules to help aircrew manage their fatigue. It is important to examine whether aircrew follow these prescriptive schedules, especially in very dynamic environments. The current study compares aircrew sleep during missions to prescriptive sleep schedules generated by a mission scheduling tool. METHODS: Participating in the study were 44 volunteers (Mage= 28.23, SDage= 4.23; Proportionmale= 77.27%) from a C-17 mobility squadron providing 25 instances of sleep and mission data (80 flights total). Aircrew wore actigraph watches to measure sleep during missions and prescriptive sleep schedules were collected. Actual and prescriptive sleep was compared with calculated performance effectiveness values per minute across mission flights. RESULTS: Prescriptive schedules generally overestimated effectiveness during missions relative to estimated actual sleep, potentially causing shifts in effectiveness to ranges of increased risk requiring elevated fatigue mitigation efforts. Actual and prescriptive effectiveness estimates tended to increasingly diverge over the course of missions, which magnifies differences on longer missions. DISCUSSION: The current study suggests that aircrew sleep during missions often does not align with prescriptive sleep schedules generated by mission planning software, resulting in effectiveness estimates that are generally lower than predicted. This might discourage aircrew from using mission effectiveness graphs as a fatigue mitigation tool. Additionally, because fatigue estimates factor into overall operational risk management processes, these schedules might underestimate risks to safety, performance, and health. Morris MB, Veksler BZ, Krusmark MA, Gaines AR, Jantscher HL, Gunzelmann G. Aircrew actual vs. prescriptive sleep schedules and resulting fatigue estimates. Aerosp Med Hum Perform. 2021; 92(10):806814.

Objective assessment of nap as a method to improve cognitive performance using a bio-mathematical model

Introduction: Strategic naps are considered as efficacious means of maintaining performance and reducing the individual’s sleep debt. It can reduce subjective feelings of fatigue and improve performance and alertness. However, literature is scant on assessment of naps and associated cognitive performance in the Indian military aviation scenario. This study is an attempt to assess the nap duration and its objective assessment on gain in performance, if any. Material and Methods: In this cross-sectional observational study, sleep data were collected from 23 aviation personnel in a military flying base using actigraphy device. The actigraphic data were then fed into a software called fatigue avoidance scheduling tool. The nap duration and its effect on cognitive parameters were analyzed. Results: About 65.2% of the participant were found to be Day-Time Habitual Nappers. Of the 50 Naps logged by these participants, 11 (22%) naps were less than 30 min, 14 (28%) were between 30 and 60 min, 15 (30%) were between 60 and 120 min, and only 10 (20%) were above 120 min. Post-nap gain in the effectiveness and other cognitive parameters was found to be different in different cognitive domains. Conclusion: Naps more than 30 min had the optimal efficiency. The nap-induced gain in the task effectiveness and cognitive performance was confirmed. While the performance enhancement was significant for the naps more than 30 min, naps more than 60 min did not have any added advantages.

Robust Operation of Hybrid Solar–Wind Power Plant with Battery Energy Storage System

The intraday continuous electricity market (ICM) is a potential target market for the Dispatchable Hybrid Renewable solar–wind–battery energy storage system (BESS) power plant (DHRB). However, the uncertainty of the electricity price jeopardizes economic justification of BESS operation, an essential component of DHRB. Using the duality theory, this paper proposes a unilevel mixed-integer linear programming rolling-approach-based robust optimal scheduling tool for DHRB that keeps BESS operation optimal should the worst price scenario occur. It reflects BESS’s degradation as penalty factors and also integrates a BESS degradation model in the scheduling tool for better assessment of the available resources through the BESS’s lifetime. This tool aids the DHRB operator to decide the power offer to the ICM in such a way that the BESS’s operation remains optimal. A case study is carried out to demonstrate the application of the proposed tool. Both the long-term and short-term losses/benefits of utilizing this tool for scheduling DHRB in the ICM are investigated at various uncertainty levels. It is shown that there will be a risk of loss of income for the DHRB in the short-term due to increased nondispatchable energy. However, by limiting the use of BESS to only those settlement periods that are either certainly profitable or unavoidable, the lifetime of BESS can potentially be extended. Hence, this can result in more income by the DHRB power plant in the long-term.

Intelligent Sensors for Real-Time Decision-Making

The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This paper proposes the simultaneous integration of information from sensors and business data. The proposal is supported by an industrial implementation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laboratories. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. Results show how the integration of intelligent sensors can improve the online scheduling of tasks. Estimations from system processing times decreased by over 30%. The proposed solution can be extended to other applications such as predictive maintenance, chemical industry, and other industries where scheduling and rescheduling are critical factors for the production.

286 Pilot Sleep During Brazil-to-China COVID-19 Humanitarian Mission Flights Compared to Biomathematical Predictions of Sleep

Introduction n response to the COVID-19 pandemic, Azul Airlines organized and conducted five separate humanitarian missions to China between May and July, 2020. Each mission consisted of 4 flight legs between 11-15 hours long crewed by a team of 8 pilots. Each pilot was given a 9-hour sleep opportunity during the flight period. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model Fatigue Avoidance Scheduling Tool (FAST) was used to predict in-flight time in bed (TIB) and total sleep time (TST). During missions, pilots wore a wrist actigraph and completed a sleep diary. These analyses compare the accuracy of SAFTE-FAST AutoSleep predictions against pilots’ sleep diary and actigraphy from Azul’s COVID-19 humanitarian missions. Methods Pilots wore a sleep-tracking actigraphy device (Zulu Watch, Institutes for Behavior Resources), and reported the TIB and sleep quality of their in-flight rest periods using a sleep diary. Diary TST was estimated from TIB and sleep quality. AutoSleep, diary, and actigraphy measures were compared using paired samples t-tests. Agreement was compared using intraclass correlation coefficients (ICC). Results Twenty (n=20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. AutoSleep predictions of TIB (235±20 minutes) and TST (193±16 minutes) were significantly lower than diary (TIB: 330±123, t=6.80, p≤0.001; TST: 262±108, t=5.60, p≤0.001) and comparable to actigraphy (TIB: 246±127, t=0.78, p=0.43; TST: 212±113, t=1.59, p=0.12). ICC values were &gt;0.90, indicating excellent agreement, for TIB (0.94) and TST (0.91). Conclusion Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances like the COVID-19 pandemic. Pilots may overestimate the amount of sleep that they receive during extreme flights-duty periods, which could constitute a fatigue risk. Support (if any) NA

Automatic Scheduling Tool for Balloon-Borne Planetary Optical Remote Sensing

The balloon-borne Planetary Atmosphere Spectroscopic Telescope (PAST), China’s first planetary optical remote-sensing project, will be launched for testing and conducting scientific flights during 2021 and 2022. Images of the planetary atmosphere and plasma in ultraviolet and visible wavelengths will be used to investigate the diversity of the planetary space environment in the solar system and their different drivers. Because simultaneous observation of multiple target planets in the solar system is possible, effective observation scheduling is critical to acquire high scientific merit spectroscopic imaging data. Herein, we demonstrate an automatic scheduling tool (AST) to aid the planning of observation schedules. The AST is primarily based on a planetary ephemeris and is realized on the basis of the geometrical information and optical requirements of the telescope. The temporal variations of the planetary reference frames can also be obtained to assist in the positioning and data processing of the telescope. As a part of the Chinese deep-space exploration plan, several ground-based planetary optical telescopes will be constructed in China in the future. With the use of the proposed AST, such telescopes can achieve maximum efficiency.