Task Priority Research Articles

Integrating Option Grid Patient Decision Aids in the Epic Electronic Health Record: Case Study at 5 Health Systems.

BackgroundSome researchers argue that the successful implementation of patient decision aids (PDAs) into clinical workflows depends on their integration into electronic health records (EHRs). Anecdotally, we know that EHR integration is a complex and time-consuming task; yet, the process has not been examined in detail. As part of an implementation project, we examined the work involved in integrating an encounter PDA for symptomatic uterine fibroids into Epic EHR systems.ObjectiveThis study aims to identify the steps and time required to integrate a PDA into the Epic EHR system and examine facilitators and barriers to the integration effort. MethodsWe conducted a case study at 5 academic medical centers in the United States. A clinical champion at each institution liaised with their Epic EHR team to initiate the integration of the uterine fibroid Option Grid PDAs into clinician-facing menus. We scheduled regular meetings with the Epic software analysts and an expert Epic technologist to discuss how best to integrate the tools into Epic for use by clinicians with patients. The meetings were then recorded and transcribed. Two researchers independently coded the transcripts and field notes before categorizing the codes and conducting a thematic analysis to identify the facilitators and barriers to EHR integration. The steps were reviewed and edited by an Epic technologist to ensure their accuracy. ResultsIntegrating the uterine fibroid Option Grid PDA into clinician-facing menus required an 18-month timeline and a 6-step process, as follows: task priority negotiation with Epic software teams, security risk assessment, technical review, Epic configuration; troubleshooting, and launch. The key facilitators of the process were the clinical champions who advocated for integration at the institutional level and the presence of an experienced technologist who guided Epic software analysts during the build. Another facilitator was the use of an emerging industry standard app platform (Health Level 7 Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) as a means of integrating the Option Grid into existing systems. This standard platform enabled clinicians to access the tools by using single sign-on credentials and prevented protected health information from leaving the EHR. Key barriers were the lack of control over the Option Grid product developed by EBSCO (Elton B Stephens Company) Health; the periodic Epic upgrades that can result in a pause on new software configurations; and the unforeseen software problems with Option Grid (ie, inability to print the PDA), which delayed the launch of the PDA. ConclusionsThe integration of PDAs into the Epic EHR system requires a 6-step process and an 18-month timeline. The process required support and prioritization from a clinical champion, guidance from an experienced technologist, and a willing EHR software developer team.

Combined dynamics and kinematics networked fuzzy task priority motion planning for underwater vehicle-manipulator systems

The underwater vehicle-manipulator systems (UVMS) face significant challenges in trajectory tracking and motion planning because of external disturbance (current and payload) and kinematic redundancy. Former algorithms can finish the tracking of end-effector (EE) and free of singularity redundancy solution alone. However, only a few analytical studies have been conducted on coordinated motion planning of UVMS considering the dynamics controller. This article introduces a combined dynamics and kinematics networked fuzzy task priority motion planning method to solve the above problems. It avoids the assumption of perfect dynamic control. Firstly, to eliminate the kinematics error, a dynamic transformation method from joint space to task space is proposed. Without chattering, an outer loop sliding mode controller is designed for tracking EE’s trajectory. Further, to ensure the underwater vehicle’ posture stability and joint constraint, a task priority frame with kinematics error is used to planning the coordinated motion of UVMS, in which the posture and joint limits map into the null space of prioritized tasks, and weight gains are adopted to guarantee orthogonality of secondary tasks. On top of that, the gain weighted are updated by the networked fuzzy logic. The proposed algorithm achieves better coordinated motion planning and tracking performance. Effectiveness is validated by numerical simulation.

Emotion-aware mobile edge computing system: A case study

Recently, great progress has been witnessed in the application of mobile cloud computing in the field of health care such as online medical inquiries. However, due to the limitation of cognitive intelligence, QoE (Quality of Experience) is hampered by two problems, the first of which is that the traffic pressure of the core network cannot well meet the requirements of delay-sensitive emotional services, especially for users with different emergencies, while the second is that current applications cannot provide personalized service for different users. Based on the two problems, we propose an emotion-aware mobile edge computing architecture based on emotional task priority to guide the allocation of edge resources and to provide intelligent and personalized emotional services with higher QoE. Specifically, we first introduce the entities involved in the proposed architecture of emotion-aware mobile edge computing system. Next, we describe our optimal computing resource allocation strategy, including important concepts and a detailed algorithm. Finally, we build a test platform and conduct experiments, which show that the proposed architecture obtains better performance in terms of system utility compared with baseline methods.

A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system

This study deals with the task assignment problem of heterogeneous unmanned aerial vehicle (UAV) system with the limited resources and task priority constraints. The optimization model which comprehensively considers the resource consumption, task completion effect, and workload balance is formulated. Then, a concept of fuzzy elite degree is proposed to optimize and balance the transmission of good genes and the variation strength of population during the operations of algorithm. Based on the concept, we propose the fuzzy elite strategy genetic algorithm (FESGA) to efficiently solve the complex task assignment problem. In the proposed algorithm, two unlock methods are presented to solve the deadlock problem in the random optimization process; a sudden threat countermeasure (STC) mechanism is presented to help the algorithm quickly respond to the change of task environment caused by sudden threats. The simulation results demonstrate the superiority of the proposed algorithm. Meanwhile, the effectiveness and feasibility of the algorithm in workload balance and task priority constraints are verified.

Memory-Aware Scheduling Parallel Real-Time Tasks for Multicore Systems

Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling policies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose parallel real-time tasks scheduling (PRTTS) policy on multicore platforms. Each set of tasks is represented as a directed acyclic graph (DAG). The priorities of tasks are assigned according to task periods Rate Monotonic (RM). Each task is composed of three phases. The first phase is read memory stage, the second phase is execution phase and the third phase is write memory phase. The tasks use locks and critical sections to protect data access. The global scheduler maintains the task pool in which tasks are ready to be executed which can run on any core. PRTTS scheduling policy consists of two levels: the first level scheduling schedules ready real-time tasks in the task pool to cores, and the second level scheduling schedules real-time tasks on cores. Tasks can preempt the core on running tasks of low priority. The priorities of tasks which want to access memory are dynamically increased above all tasks that do not access memory. When the data accessed by a task is in the cache, the priority of the task is raised to the highest priority, and the task is scheduled immediately to preempt the core on running the task not accessing memory. After accessing memory, the priority of these tasks is restored to the original priority and these tasks are pended, the preempted task continues to run on the core. This paper analyzes the schedulability of PRTTS scheduling policy. We derive an upper-bound on the worst-case response-time for parallel real-time tasks. A series of extensive simulation experiments have been performed to evaluate the performance of proposed PRTTS scheduling policy. The results of simulation experiment show that PRTTS scheduling policy offers better performance in terms of core utilization and schedulability rate of tasks.

Learning context-adaptive task constraints for robotic manipulation

Constraint-based control approaches offer a flexible way to specify robotic manipulation tasks and execute them on robots with many degrees of freedom. However, the specification of task constraints and their associated priorities usually requires a human-expert and often leads to tailor-made solutions for specific situations. This paper presents our recent efforts to automatically derive task constraints for a constraint-based robot controller from data and adapt them with respect to previously unseen situations (contexts). We use a programming-by-demonstration approach to generate training data in multiple variations (context changes) of a given task. From this data we learn a probabilistic model that maps context variables to task constraints and their respective soft task priorities. We evaluate our approach with 3 different dual-arm manipulation tasks on an industrial robot and show that it performs better than comparable approaches with respect to reproduction accuracy in previously unseen contexts.

A novel multiclass priority algorithm for task scheduling in cloud computing

Task scheduling is an attractive research topic in cloud computing nowadays. This process is very challenging and well known as NP-complete problem. Due to the dynamic and heterogeneous nature of user’s request and provider’s resource in cloud computing, the scheduling process still needs intelligent algorithms to achieve an efficient cloud resource allocation and to guarantee a good Quality of Service (QoS) for the users and their request classes. An important aspect for meeting these objectives is to design an effective task scheduling scheme which can not only satisfy users’ varying priorities and QoS requirements, but also enhance providers’ profit and system performances. In this paper, we introduce a new strategy to address the priority issue in both users’ requests and providers’ resources. We propose an efficient priority tasks scheduling called MCPTS, where the priority is adjusted according to four tasks’ parameters including length, waiting time, deadline and burst time. MCPTS scheme consists of three sub-models such as tasks priority, task queueing priority and resources priority. A new hybrid multi-criteria decision-making (MCDM) method, namely ELECTRE III, and a meta-heuristic algorithm called differential evolution are proposed to evaluate and determine tasks’ priorities. Further, we introduce a novel dynamic priority-queue algorithm based on queueing model. Furthermore, we adjust dynamically the resources priority based on tasks priority model in order to design an efficient and flexible relation between both resources and tasks classes. The proposed algorithm is validated through the CloudSim simulator. The experimental results indicate the superiority of MCPTS algorithm compared to other existing algorithms. Also, it shows the effectiveness of our algorithm in providing good system performance, satisfying users’ priorities as well as QoS requirements, enhancing load balancing and improving resources utilization.

Relative position coordinated control for spacecraft formation flying with obstacle/collision avoidance

The problem of the relative position coordinated control for spacecraft formation flying with a leader spacecraft under the obstacle environment is the focus of this paper. To avoid obstacle/collision and maintain the formation configuration, the Null-Space-Based behavioral control architecture is built by defining the priorities of the basic tasks and computing the corresponding velocity vectors. Through the null-space projection, the desired velocity of each follower spacecraft can be calculated by merging the basic tasks. Moreover, due to the partial access to the dynamic leader spacecraft’s states, the distributed estimators are presented for each follower spacecraft. Then, based on the desired velocity, the adaptive coordinated tracking control algorithm incorporated with the barrier Lyapunov function is designed such that the states satisfy the time-varying constraints, even subject to uncertainties and unknown disturbances. Finally, numerical simulations are performed to illustrate the main results.

A hybrid list-based task scheduling scheme for heterogeneous computing

Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task grid represented using a Directed Acyclic Graph (DAG). Solving such DAG representing a scheduling problem is an NP-complete task. The primary concern in this problem domain is to reduce the schedule length with minimum complexity. This work presents a Hybrid List-based Task Scheduling using Duplication Scheme (HLTSD) algorithm for heterogeneous processors. The proposed HLTSD algorithm has the same time complexity as that of the recent state-of-the-art algorithms. However, it produces a minimum cost schedule in comparison with other related methods. This work also presents a mathematical formulation to find task priorities. The processor selection phase is improved by utilizing the techniques, like entry task duplication, insertion-based policy, duplication of parent task on other levels, and balancing the load on each processor. The current proposal minimizes the overall makespan of execution by reasonable levels. Performance of the proposed algorithm is evaluated using DAGs adopted from various state-of-the-art algorithms, real-world problems, like Gaussian elimination (GE) and fast Fourier transformation (FFT) task graph and randomly generated graphs with diverse characteristics. The proposed scheme is compared with four state-of-the-art list-based scheduling algorithms, namely Heterogeneous Earliest Finish Time (HEFT), Predict Earliest Finish Time (PEFT), Heterogeneous Scheduling with Improved task Priority (HSIP), and Task Scheduling for Heterogeneous Computing Systems (TSHCS). Based on the best quality schedule, the obtained results suggest that HLTSD has better results in 87% cases.

Biped Walking Based on Stiffness Optimization and Hierarchical Quadratic Programming.

The spring-loaded inverted pendulum model is similar to human walking in terms of the center of mass (CoM) trajectory and the ground reaction force. It is thus widely used in humanoid robot motion planning. A method that uses a velocity feedback controller to adjust the landing point of a robot leg is inaccurate in the presence of disturbances and a nonlinear optimization method with multiple variables is complicated and thus unsuitable for real-time control. In this paper, to achieve real-time optimization, a CoM-velocity feedback controller is used to calculate the virtual landing point. We construct a touchdown return map based on a virtual landing point and use nonlinear least squares to optimize spring stiffness. For robot whole-body control, hierarchical quadratic programming optimization is used to achieve strict task priority. The dynamic equation is given the highest priority and inverse dynamics are directly used to solve it, reducing the number of optimizations. Simulation and experimental results show that a force-controlled biped robot with the proposed method can stably walk on unknown uneven ground with a maximum obstacle height of 5 cm. The robot can recover from a 5 Nm disturbance during walking without falling.

A Unifying Task Priority Approach for Autonomous Underwater Vehicles Integrating Homing and Docking Maneuvers

This research proposes a unified guidance and control framework for Autonomous Underwater Vehicles (AUVs) based on the task priority control approach, incorporating various behaviors such as path following, terrain following, obstacle avoidance, as well as homing and docking to stationary and moving stations. The integration of homing and docking maneuvers into the task priority framework is thus a novel contribution of this paper. This integration allows, for example, to execute homing maneuvers close to uneven seafloor or obstacles, ensuring the safety of the AUV, as safety tasks can be given the highest priority. Furthermore, another contribution shown in the paper is that the proposed approach tackles a wide range of scenarios without ad hoc solutions. Indeed, the proposed approach is well suited for both the emerging trend of resident AUVs, which stay underwater for a long period inside garage stations, exiting to perform inspection and maintenance missions and homing back to them, and for AUVs that are required to dock to moving stations such as surface vehicles, or towed docking stations. The proposed techniques are studied in a simulation setting, taking into account the rich number of aforementioned scenarios.

Towards addressing dynamic multi-agent task allocation in law enforcement

Police officers conduct routine patrols and perform tasks in response to reported incidents. The importance of each task varies from low (e.g. noise complaint) to high (e.g. murder). The workload associated with each task, indicating the amount of work to be completed for the incident to be processed, may vary as well. Multiple officers with heterogeneous skills may work together on important tasks to share the workload and improve response time. To deal with the underlying law enforcement problem (LEPH), one needs to allocate police officers to dynamic tasks whose locations, arrival times, and importance levels are unknown a priori. Addressing this challenge and inspired by real police logs, this research aims to solve the LEPH problem by using and comparing three methods: Fisher market-based FMC_TAH+, swarm intelligence HDBA, and Simulated Annealing SA algorithms. FMC_TAH+ is implemented, using agents as buyers and tasks as goods, to compute fair allocations (i.e. envy-free), and efficient (i.e. Pareto-optimal) in a polynomial or pseudo-polynomial time. FMC_TAH+ allocations are heuristically scheduled, considering inter-agent constraints on shared tasks. HDBA, a probabilistic swarm intelligence algorithm inspired by the emergent behavior of social bees, was previously implemented to allocate agents to tasks based on agent performance, task priorities, and distances between agents and task-execution locations. SA is a meta-heuristic for approximating the global optimums in large optimization problems. The three methods were compared in this study for five different performance measures that are commonly used by law enforcement authorities. The results indicate an advantage for FMC_TAH+ both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TAH+ leads to 34% and 32% higher team utility in the highest shift workload.

Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing

In virtualized cloud computing systems, energy reduction is a serious concern since it can offer many major advantages, such as reducing running costs, increasing system efficiency, and protecting the environment. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping cloud resources to user requests to achieve good performance by minimizing the energy consumption of cloud resources within a user-defined deadline is a huge challenge. This paper proposes Energy and Performance-Efficient Task Scheduling Algorithm (EPETS) in a heterogeneous virtualized cloud to resolve the issue of energy consumption. There are two stages in the proposed algorithm: initial scheduling helps to reduce execution time and satisfy task deadlines without considering energy consumption, and the second stage task reassignment scheduling to find the best execution location within the deadline limit with less energy consumption. Moreover, to make a reasonable balance between task scheduling and energy saving, we suggest an energy-efficient task priority system. The simulation results show that, compared to current energy-efficient scheduling methods of RC-GA, AMTS, and E-PAGA, the proposed solution helps to reduce significant energy consumption and improve performance by 5%–20% with deadline constraint satisfied.

КОМУНІКАЦІЯ В ПРОЄКТНІЙ ДІЯЛЬНОСТІ ПЕДАГОГІЧНИХ ПРАЦІВНИКІВ ЗАКЛАДІВ ПРОФЕСІЙНОЇ ОСВІТИ

Актуальність. Проєктна комунікація спрямовується на здійснення постійної взаємодії педагогічних працівників закладів професійної освіти під час реалізації завдань проєктів. Комунікація в управлінні проєктною діяльністю передбачає усвідомлення всіма учасниками проєктної діяльності вимог та прогресу поетапного виконання проєктних завдань. Проєктна діяльність передбачає забезпечення комунікації шляхом реалізації функцій та принципів проєктної взаємодії. Мета: обґрунтувати сучасні підходи до організації комунікації в проєктній діяльності педагогічних працівників закладів професійної освіти. Методи: теоретичний аналіз – для з’ясування проблеми дослідження у науковій літературі та стану проєктної комунікації в закладах професійної освіти, визначення напрямів наукового пошуку; порівняння – з метою вивчення різних наукових підходів щодо розв’язання проблеми; аналіз і синтез – для обґрунтування функцій комунікації та форм вираження проєктної взаємодії. Результати. Комунікація в проєктній діяльності педагогічних працівників закладів професійної освіти посідає важливе місце особливо в частині координації та проєктної взаємодії. Комунікація в проєктній діяльності реалізується через форми вираження проєктної взаємодії, а саме: функціональну, соціальну, юридичну, адміністративну, асоціативну, територіальну, нав’язану, залежну та дотичну. На якості результатів проєктної діяльності позначаються конфліктні ситуації, що виникають під час взаємодії учасників і керівників проєкту: розклад роботи, фінансування, розподіл навантаження, проєктне управління, технічне (технологічне) залучення, пріоритетність виконання, розподіл ресурсів, надмірне навантаження учасників проєкту тощо. Проєктна комунікація ґрунтується на врахуванні положень таких принципів організації проєктної діяльності: відкритість проєкту упродовж усього життєвого циклу; співпраця (кооперація) в проєктному середовищі (функціональна, соціальна, юридична, асоціативна, територіальна, дотична, ієрархічна тощо); відповідальність проєктних виконавців за якість виконання завдань. Висновки. Комунікація в проєктній діяльності педагогічних працівників закладів професійної освіти забезпечується учасниками проєкту спільно з зацікавленими сторонами та стейкхолдерами. Успіх проєкту залежить від компетентності їх керівників та наявності в них: технічних знань, критичного мислення, комунікативних навичок, здатності до співпраці, навичок роботи в команді та координації виконання проєктних завдань, досвіду управління ресурсами, лідерських якостей та ін.

Bi-Objective Workflow Scheduling on Heterogeneous Computing Systems Using a Memetic Algorithm

Due to the high power bills and the negative environmental impacts, workflow scheduling with energy consciousness has been an emerging need for modern heterogeneous computing systems. A number of approaches have been developed to find suboptimal schedules through heuristics by means of slack reclamation or trade-off functions. In this article, a memetic algorithm for energy-efficient workflow scheduling is proposed for a quality-guaranteed solution with high runtime efficiency. The basic idea is to retain the advantages of population-based, heuristic-based, and local search methods while avoiding their drawbacks. Specifically, the proposed algorithm incorporates an improved non-dominated sorting genetic algorithm (NSGA-II) to explore potential task priorities and allocates tasks to processors by an earliest finish time (EFT)-based heuristic to provide a time-efficient candidate. Then, a local search method integrated with a pruning technique is launched with a low possibility, to exploit the feasible region indicated by the candidate schedule. Experimental results on workflows from both randomly-generated and real-world applications suggest that the proposed algorithm achieves bi-objective optimization, improving makespan, and energy saving by 4.9% and 24.3%, respectively. Meanwhile, it has a low time complexity compared to the similar work HECS.

Biased random-key genetic algorithm for cobot assignment in an assembly/disassembly job shop scheduling problem

Abstract Nowadays many manufacturing companies try to improve the performance of their processes by including innovative available technologies such as collaborative robots. Collaborative robots are robots where no safety distance is necessary, through cooperation with human workers they can increase production speed. In this paper we consider the collaborative robot assignment combined with the job shop scheduling problem. To solve this problem, we propose a genetic algorithm with a biased random-key encoding. The objective function for the optimization is a weighted function that factors in production cost and makespan that should be minimized. We propose a special encoding of the solution: the assignment of cobots to workstations, the assignment of tasks to different workstations and the priority of tasks. The results show how much the weighted objective function can be decreased by the deployment of additional collaborative robots in a real-world production line. Additionally, the biased random-key encoded results are compared to typical integer encoded solution. With the biased random-key encoding, we were able to find better results than with the standard integer encoding.

Adaptive scheduling strategy of fog computing tasks with different priority for intelligent production lines

Abstract Fog computing has been widely deployed in intelligent production lines to provide real-time computing services for terminal devices to alleviate the transmission problem between cloud and terminal. Nevertheless, when there are many services provided in a single fog node, how to choose an optimal data processing path is a challenge to minimize optimization latency and power consumption while ensuring the reliable transmission of data with different priorities. To address the problems as mentioned above in this paper, we first propose a joint evaluation model of time latency and power consumption based on task priority. Then, we formulate a fog computing adaptive scheduling algorithm (FCAS) based on dynamic programming to obtain the optimal path for processing data with different priorities at fog nodes. Experimental results illustrate that our proposed evaluation model and algorithm have lower power consumption, high efficiency, and higher reliability.

Analysis of Priorities of Policy Implementation Tasks for Revitalizing Virtual Reality(VR) and Augmented Reality(AR) Industries

Analysis of Priorities of Policy Implementation Tasks for Revitalizing Virtual Reality(VR) and Augmented Reality(AR) Industries

Study and Prototype Implementation of Basic Non-CAN and CAN Based Communication in Context with Automotive Application(s)

Controller Area Networks (CAN) are frequently utilized in vehicles to provide communication between Electronic Control Units (ECUs) and sensors. The use of CAN implementations enables message-based communication among different sections of vehicles. This results in the reduction of complex and dedicated wiring for communication. The CAN have five significant benefits such as low-cost application because multiple nodes communicate via a single bus, the centralized system reduces the probabilities of error, CAN bus provides robustness, reliability, and flexibility with task priority management, and CAN bus provides robustness, reliability, and flexibility with task priority management. This paper focuses on the study of Non-CAN based system which was used earlier inside the automotive applications. Also, the study of open-source hardware like Arduino along with CAN Controllers like MCP 2515. In later part of the paper described laboratory experimentation of Non-CAN-based communication, CAN-based communication by using ARM as well as Arduino. The prototype is successfully implemented and verified the outputs with the help of a serial monitor. The visual output is observed and reported on LCD and LEDs.

Task Priority Control Method based on the Characteristics of Applications in CFS

Task Priority Control Method based on the Characteristics of Applications in CFS