Time-based Scheduling Research Articles

An intelligent schedule maintenance method for hydrogen fuel cell vehicles based on deep reinforcement learning considering dynamic reliability

Schedule maintenance activities for hydrogen fuel cell vehicles are aimed to keep them in better condition in long-term operation. In this paper, we develop a reinforcement learning based schedule maintenance strategy, which is used to comprehensively consider the balance between safety and maintenance cost, so as to find the optimal maintenance strategy. A multi-level framework is established with remaining useful life of key components, fault tree analysis, and logistics cost model to generate an exploration environment that can express the operational stability, the failure rate of components, and the cost of repair and storage. The trained agent combines a deep neural network to explore the optimal strategy under the dynamic reliability of key components. Finite steps are the constraints. Safety rates, maintenance cost, and episode of operation are incorporated into a multi-objective reward function. The hydrogen supply circuit of fuel cell vehicle is simulated via Python. The trained agent is compared with traditional time-based schedule maintenance strategy and corrective maintenance strategy. The results show that the reinforcement learning based schedule maintenance agent has optimized the total reward, cost control and accident rate by 77%, 59% and 5%, respectively, compared with the traditional time-based schedule maintenance strategy. In the safe operation management of fuel cell vehicles, efficient and stable decision-making ability has been achieved.

Human factors in aviation maintenance: understanding errors, management, and technological trends

Aircraft maintenance and inspection are complex systems that work on a time-based schedule and require teamwork of different professionals to maintain the airworthiness of aircraft. Errors in maintenance and inspection processes cause in-flight engine shutdowns, flight delays, flight cancellation, sometimes resulting in accidents and incidents that cause significant economic consequences. Due to the substantial impact on both safety and financial aspects of an air carrier, this paper focuses on hangar maintenance as the work is carried out across several shifts by different technicians, addressing various human factor issues that contribute to errors. The paper will also briefly discuss shift work and the health problems of maintenance technicians, human factor issues affecting maintenance, the significance of understanding human factor models, and the impact of maintenance and inspection errors with case studies, particularly aircraft accidents predominantly caused by human factor issues. Additionally, the paper also explores recent advances and trends in aircraft maintenance technologies, highlighting their transformative potential through predictive maintenance, robotics, augmented and virtual reality, big data analytics, blockchain, and additive manufacturing. These technologies promise enhanced efficiency, safety, and effectiveness for maintenance practices. The paper concludes by presenting insights into error management methods and providing recommendations for future research. By integrating traditional insights with cutting-edge technological considerations, this comprehensive analysis aims to significantly contribute to the evolution of safety protocols and practices within the aviation maintenance domain.

Enhancing Task Management in Apache Spark Through Energy-Efficient Data Segregation and Time-Based Scheduling

Enhancing Task Management in Apache Spark Through Energy-Efficient Data Segregation and Time-Based Scheduling

Application of pressure reducing and throttle control valves for optimal pressure management in water distribution networks

ABSTRACT Advanced pressure control facilitates the smart management of water distribution networks (WDNs). In this study, an optimization-based method is proposed to develop a time-based schedule for pressure reducing valves (PRVs). For this purpose, a single-objective optimization problem is initially delineated, seeking to minimize spatiotemporal variations in the nodal pressures. Then, the application of PRVs is addressed in two scenarios, viz. individual (i.e. employing PRVs) and hybrid (i.e. the combined use of PRVs and throttle control valves [TCVs]). The proposed scenarios are subsequently applied to the hydraulic model of synthetic and real WDNs. A set of hydraulic evaluation indices, including variation index (VI), are further measured to spot the optimal pressure management scenario. The study results establish that the hybrid scenario leads to a larger decrease in the VI along with more efficient pressure control (namely, 39.9% and 11.8% in the synthetic and real WDNs, respectively), compared with the individual one.

Enhancing Accessibility to Data in Data-Intensive Web Applications by Using Intelligent Web Prefetching Methodologies

Data-intensive Web Applications built using client–server architectures usually provide prefetching mechanisms to enhance data accessibility. Prefetching is a strategy of retrieving data before it is requested so that it can be ready when the user requests it. Prefetching reduces the load on the web server by making data available before the user requests it. Prefetching can be used for static content, such as images and web pages, as well as dynamic content, such as search results. Prefetching can also be used to improve the performance of web applications, as the data is available quickly. There are several scheduling methods, such as time-based scheduling, event-based scheduling and priority-based scheduling, for prefetching to ensure that essential data is always ready when the user requests it. In this study, we focus on time-based scheduling for prefetching. We introduce time-based scheduling methodologies using sequential pattern mining techniques and long-term short memory-based deep learning strategies. To show the usefulness of these strategies, we develop a prototype application. We conduct an extensive experimental study to evaluate the performance of the proposed time-based scheduling methodologies using both performance and accuracy metrics. Based on the computed metrics, using proposed prefetching methods provided a promising cache hit rate when using the optimal cache size. The results show that the proposed prefetching methodologies are useful in data-intensive web applications for enhancing data accessibility. Work remains to investigate the use of attention-based sequence-to-sequence models in the web prefetching domain.

Effects of and preference for fixed-ratio and time-based exchange-production schedules in a token system.

Effects of and preference for fixed-ratio and time-based exchange-production schedules in a token system.

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System.

The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

Dynamic cooling strategy based on individual animal response mitigated heat stress in dairy cows

Technological progress enables individual cow's temperatures to be measured in real time, using a bolus sensor inserted into the rumen (reticulorumen). However, current cooling systems often work at a constant schedule based on the ambient temperature and not on monitoring the animal itself. This study hypothesized that tailoring the cooling management to the cow's thermal reaction can mitigate heat stress. We propose a dynamic cooling system based on in vivo temperature sensors (boluses). Thus, cooling can be activated as needed and is thus most efficacious. A total of 30 lactating cows were randomly assigned to one of two groups; the groups received two different evaporative cooling regimes. A control group received cooling sessions on a preset time-based schedule, the method commonly used in farms; and an experimental group, which received the sensor-based (SB) cooling regime. Sensor-based was changed weekly according to the cow's reaction, as reflected in the changes in body temperatures from the previous week, as measured by reticulorumen boluses. The two treatment groups of cows had similar milk yields (44.7 kg/d), but those in the experimental group had higher milk fat (3.65 vs 3.43%), higher milk protein (3.23 vs 3.13%), higher energy corrected milk (ECM, 42.84 vs 41.48 kg/d), higher fat corrected milk 4%; (42.76 vs 41.34 kg/d), and shorter heat stress duration (5.03 vs 9.46 h/day) comparing to the control. Dry matter intake was higher in the experimental group. Daily visits to the feed trough were less frequent, with each visit lasting longer. The sensor-based cooling regime may be an effective tool to detect and ease heat stress in high-producing dairy cows during transitional seasons when heat load can become severe in arid and semi-arid zones.

Ecological Location of a Water Source and Spatial Dynamics of Behavior Under Temporally Scheduled Water Deliveries in a Modified Open-Field System: An Integrative Approach.

It has been reported in non-contingent schedules that the variety of patterns of behavior is affected by the temporal variation of water deliveries. While temporal variation is accomplished by delivering water at fixed or variable times, spatial variation is usually accomplished by varying the number of dispensers and distance among them. Such criteria do not consider the possible ecological relevance of the location of water dispensers. Nevertheless, it is plausible to suppose that the intersection of the programed contingencies (e.g., time-based schedules), the ecological differentiated space (e.g., open vs. closed zones), and the relative location of relevant objects and events (e.g., location of the water source—peripherical vs. center zone) could set up an integrated system with the behavioral patterns of the organism. In the present study, we evaluated the eco-functional relevance of two locations of the dispensers upon behavioral dynamics in Wistar rats using fixed and variable time schedules in a modified open-field system. In Experiment 1, three subjects were exposed to a fixed time 30-s water delivery schedule. In the first condition, the water dispenser was located at the center of the experimental chamber. In the second condition, the water dispenser was located at the center of a wall of the experimental chamber. Each location was present for 20 sessions. In Experiment 2, conditions were the same, but a variable time schedule was used. Routes, distance to the dispenser, recurrence patterns, time spent in zones, entropy, and divergence were analyzed. Our findings suggest a robust differential relevance of the location of the dispensers that should be considered in studies evaluating behavioral dynamics. Results are discussed from an integrative, ecological-parametric framework.

Improving Performance Covertly and Remotely with Tactile Stimulation.

Tactile stimulation can be used to privately prompt or provide feedback for a variety of behaviors. However, technological limitations have primarily resulted in narrow investigations of tactile stimulation delivered on a time-based schedule by a device like a MotivAider or WatchMinder. Recent advances in smartphone and watch technologies have created new ways for practitioners to deliver tactile stimulation privately and remotely to improve behavior. The purpose of this Brief Report is to provide a case study and a description of the advantages and applications of utilizing this technology, including preserving the privacy and dignity of clients by covertly improving performance; prompting behavior based on environmental events independent of scheduled times; delivering feedback quickly, quietly, and remotely without interrupting interactions; and fading dependencies on tactile prompting and feedback.

Evaluation of Testing Procedures for Weather-Based Irrigation Controllers

HighlightsThe SWAT test was deficient in terms of independent reproducibility.Performance results increased for the virtual test but decreased when testing irrigation schedules from an actual landscape by switching to an hourly version of the daily soil water balance.There was lack of transferability in performance results as evidenced by the dichotomous model outcomes.Abstract. Weather-based irrigation controllers (WBICs) are modern technologies designed to schedule irrigation applications with automatic in-ground irrigation systems based on plant water requirements. The Irrigation Association developed the Smart Water Application Technologies (SWAT) testing protocol as a pseudo standard for evaluating the scheduling abilities of these WBICs. SWAT test scores provide an estimate of irrigation adequacy and scheduling efficiency as measures of under- and over-irrigation, respectively. The EPA WaterSense Program adopted the SWAT test as part of its criteria for identifying water-efficient devices for consumer labeling. The objectives of this study were to evaluate the rigor of the SWAT test in terms of (1) reproducibility of the evaluation, (2) transferability of performance results when tested in a different climate, and (3) whether an hourly soil water balance that better reflects natural soil water movement would significantly alter performance results and improve the relationship between performance scores and water conservation potential. Both hourly and daily soil water balances were evaluated using data from a virtual test, modeled after original SWAT testing conditions, and a field test conducted in southwest Florida that evaluated the same brands of WBICs to assess water conservation potential against time-based schedules. During the exploratory process, it was determined that the SWAT test was not independently reproducible due to deficiencies in protocol documentation, controller setup and programming procedures, order of operations, and weather data availability. In the virtual test, both irrigation adequacy and scheduling efficiency increased by as much as 11.8 and 9.6 percentage points, respectively, for the hourly model compared to the daily model for each WBIC. Scheduling efficiency scores from the field test declined by as much as 12.9 percentage points when switching to the hourly model, with almost perfect irrigation adequacy in every season. These results indicate over-irrigation by WBICs in practice, with few instances when scores met the minimum criteria for achieving a label by WaterSense. Over-irrigation by WBICs in the field test was partially attributed to the inability to effectively incorporate rainfall into scheduling decisions. The dichotomy in the results of these two datasets highlights deficiencies in the transferability of results. Intermittent rainfall created transitional periods, both before and after the rain event, when the controller must make smart decisions and should be included in any evaluation of a smart controller. Additionally, the connection between water conservation potential and performance results must be explored in more depth. Keywords: ET controllers, Smart Water Application Technologies, Soil water balance, SWAT, Testing standards.

Irrigation Savings from Smart Irrigation Technologies and a Smartphone App on Turfgrass

HighlightsAll tested smart irrigation technologies regulated irrigation based on real-time soil moisture and weather conditions.Treatments based on SMS, ET, or smartphone app applied less water than the comparison treatment by 51% to 63%, 28% to 66%, and 51% to 63%, respectively.The payback period for the different technologies ranged between 0 and 12 months.Abstract. A plot study comparing a variety of irrigation scheduling technologies was conducted in Gainesville, Florida, from 2015 to 2017. Our study objectives were to: (1) compare the ability of different irrigation scheduling technologies to bypass scheduled irrigation cycles and/or decrease water application; and (2) compare water savings. Twelve irrigation scheduling treatments were investigated, with each replicated three or four times. Treatments included time-based schedules without sensor feedback (WOS), with a rain sensor, deficit irrigation with a rain sensor, and non-irrigated. Also included were three soil moisture sensor (SMS) based treatments, three weather or ET-based treatments (ET), and two smartphone app-based treatments. Significant differences in turfgrass quality among all treatments (including non-irrigated) were not observed during the testing periods, which tended to be wetter than normal. Compared to WOS, the SMS, ET, and smartphone app treatments achieved water savings of 51% to 63%, 28% to 66%, and 51% to 63%, respectively, depending on treatment specifics. Inclusion of additional practices, such as a split irrigation strategy (half in the morning and half in the evening) and seasonal deficit irrigation, were shown to be advantageous in an area where rainfall is frequent and a substantial contributor to plant water needs. The SMS treatments saved water by bypassing scheduled irrigation cycles, the ET controllers saved water mainly through lower application depths, while the app-based treatments saved water through a combination of bypassing and applying lower irrigation depths. The payback period for the evaluated scheduling technologies ranged between 0 and 12 months. Financial and practical considerations should be included when recommending or acquiring one of these irrigation scheduling technologies. Keywords: Evapotranspiration controllers, Smart irrigation controllers, Smartphone apps, Soil moisture sensors.

Applying Standards of Effectiveness to Noncontingent Reinforcement: A Systematic Literature Review.

Noncontingent reinforcement is a time-based schedule of reinforcement that has been shown to decrease problem behavior. Although the intervention is considered well established, there exist concerns that much of the supporting research has been conducted under highly controlled experimental conditions that may lack ecological validity. That is, although the efficacy has been demonstrated, the effectiveness in less controlled settings has not. To evaluate this concern, we analyzed research on noncontingent reinforcement between 1993 and 2017. Standards of evidence for effectiveness were adapted from prevention science and applied to noncontingent reinforcement literature. We specifically focused on generalizability across populations and settings, the conditions under which the intervention was applied, specific treatment parameters, opportunity cost, and social validity. Our results indicate several areas where evidence of noncontingent reinforcement effectiveness in applied settings is limited. We identify these limitations and provide a range of recommendations for future research to promote more widespread dissemination of the procedure.

Time-aware Traffic Shaper using Time-based Packet Scheduling on Intel I210

By 2015, the Institute of Electrical and Electronics Engineers (IEEE) time-sensitive networking (TSN) task group has released several TSN standards. Amongst them is 802.1Qbv, also known as a time-aware shaper, aiming to provide performance assurances of latency and delivery variation to enable applications in a TSN network. While there are several products and evaluation kits that employ 802.1Qbv in the market now, it is still not widely adopted yet due to the maturity of the standard. Hardware-enabled 802.1Qbv use hardware queues and timers to achieve accurate transmission of packets in the switch and bridge. This research aims to investigate the feasibility of using an existing end-station Ethernet controller, Intel I210, and its launch time control feature (commonly known as time-based packet scheduling) to shape traffic compatible to 802.1Qbv-enabled network bridges. A software solution is developed by implementing a software configurable gate-control list and employing open-source Linux RFC patches for per-packet transmit time specification. By configuring the kernel and mapping kernel-layer traffic classes to the hardware queues, packets can be transmitted out at precise times while attaching 802.1Q VLAN tags, required by bridges to identify packets. Through analysis, it is found that this solution will require an additional 30 μs transmit offset to be used effectively. That is 55% more time is needed to transmit a packet in a back-to-back connection and 17.6% on a 3-switch network to improve period peak jitter performance to just 8.9 μs compared to 1 ms on solutions that send packets out periodically using software sleep functions.

An Adaptive Time-Based Kiln Schedule using Forward Chaining Approach

An Adaptive Time-Based Kiln Schedule using Forward Chaining Approach

Comparison of Pumping Regimes for Water Distribution Systems to Minimize Cost and Greenhouse Gases

AbstractA single-objective optimization model has been developed for water distribution system (WDS) pumping operations, considering five different types of pump operating regimes. These regimes use tank trigger levels, scheduling, and a combination of both to control pumps. A new toolkit development to alter rule-based controls in hydraulic simulation software has allowed more complex pump operating regimes than have previously been considered to be optimized. The performance of each of the regimes is compared with respect to two different objectives: cost and greenhouse gas (GHG) emissions, which were optimized separately to allow the comparison of regimes to be made more clearly. Two case study networks, including one that represents a segment of the South Australian WDS, illustrate the effectiveness of the model. Time-based scheduling operating strategies were found to perform better than the other types of pump operating regimes. Significant cost savings were achieved for the South Australian case st...

INTELLIGENT IRRIGATION IN VEGETABLE CROP (TOMATO): NOVEL APPROACH FOR WATER RESOURCE USE OPTIMIZATION

Field experiments have been conducted in King Saud University, Riyadh- Saudi Arabia to study the weather-based irrigation controllers (WBICs) affecting agronomical characteristics and water use of irrigated. The assessed WBICs technologies were Weathermatic SL1600 and Hunter pro C controllers under subsurface drip irrigation system. The study investigated the effect of these technologies and its suitability for agricultural applications. The main results in this study can be summarized as the WBICs had significantly affected the tomato yield, water use efficiency and water savings. The WBICs could save irrigation water by 32.4 % and increased water use efficiency greatly up to 50.8 %, while maintaining competing yield as compared to a time-based irrigation schedules (control). Furthermore, the agronomical characteristics (vegetative growth, fruit quality and fruit yield traits) confirmed the priority of the WBICs when they compared with various time-based irrigation schedules.

Performance evaluation of urban turf irrigation smartphone app

Data and technology are available to support a real-time irrigation smartphone app for turf that would result in more efficient irrigation scheduling which is needed to reduce water volumes applied and increase irrigation water conservation. Objectives were to (1) develop a turf irrigation smartphone app for warm season turf that would generate real-time irrigation schedules for users to program automatic timers and (2) evaluate app performance in regards to turf quality and water volumes applied with a field plot study. A smartphone app was developed and tested in a plot study in Homestead, Florida, USA, from December 2013 to November 2014. Study treatments included different irrigation scheduling methods: time-based schedule, smartphone app, and two on-site evapotranspiration (ET) controllers. Results indicated that the app and ET controllers resulted in significantly lower irrigation depths compared to the time-based treatment, ranging in water savings from 42% to 57%. The difference among the app and ET controllers was how rainfall was integrated into the schedule. Use of the seasonal water conservation model in the smartphone app is recommended to compensate for the lack of on-site rainfall measurements in the generated irrigation schedule.

Modeling the Effect of Cistern Size, Soil Type, and Irrigation Scheduling on Rainwater Harvesting as a Stormwater Control Measure

Urban stormwater runoff could have negative impacts on water resources and the environment. Rainwater Harvesting (RWH) can serve both as a stormwater control and water conservation measure. Cistern size and irrigation scheduling are two of the factors that directly impact the total runoff from a residential unit with a RWH system and the amount of potable water used for irrigation. The effectiveness of RWH was evaluated for four soil types; Sand, Sandy Loam, Loamy Sand, and Silty Clay, with a root zone of 15.2 cm using three irrigation scheduling methods (Evapotranspiration (ET)-based, soil moisture-based, and time-based), and five cistern sizes. Total runoff volumes and total supplemental potable water used were compared among the three irrigation scheduling systems and a control treatment without RWH. A model was developed to simulate the daily water balance for the treatments. Irrigation and runoff volumes were compared for the various scenarios. Silty clay soil resulted with 83 % more runoff than Sandy soil, while Sandy soil required on average 58 % more supplemental water than Silty Clay soil. On average, the 833 L cistern resulted with 41 % savings in water supply and 45 % reduction in total runoff. Results showed that the greatest volumes of runoff predicted were for the silty clay soil Control Treatment using a time-based irrigation scheduling method, while the least volumes calculated were for the sandy loam soil time-based irrigation scheduling treatment with 833 L cistern size. The greatest volumes of total supplemental water predicted were for sandy loam soil Control Treatment, while the least volumes were for silty clay soil ET-based irrigation scheduling treatment with 833 L cistern size. Regression equations were developed to allow for users to select a RWH cistern size based on the amount of water they want to save or runoff to reduce.