Total Number Of Tasks Research Articles

Utilization of a Voluntary Reporting System in Quantitative Risk Assessment for Medical Tasks in a Hospital Setting‐with Special Reference to Tasks Done by Nurses

Utilization of a Voluntary Reporting System in Quantitative Risk Assessment for Medical Tasks in a Hospital Setting‐with Special Reference to Tasks Done by Nurses: Kayoko Inoue, et al. Department of Health and Environmental Sciences, Kyoto University Graduate School of Medicine—Adverse events in hospitals are a world‐ wide concern. To develop a tool for quantitative risk assessment of medical error, human reliability analysis was applied to incident reports from a prospective cohort study in a hospital. 374 nurses in a tertiary‐ care hospital of 670 beds joined this study. Numbers of tasks, classified into 170 items, were actually measured by nurses during three weekdays and one weekend. The total number of tasks per year was found to be about 13.2 million. 1,030 incidents were reported over a year. The gross failure rate for tasks was estimated to be ‐10‐4. The relative risk for failure rates in midnight and night shifts was 2‐4 times greater than in dayshifts. When the failure rate for a patient category of ages 15 and 64 yr old was taken as a standard, the relative risk of failure was about 200 times greater in a patient category of problematic behavior and 90 times greater in patient categories of clouding of consciousness and postsurgical conditions. Failure rates for individual tasks were in the range of 10‐5 to 10‐3: the tasks with the highest failure rate were prevention of problematic behavior (2.32 times 10∼3), safeguards against falls (1.47 times 10‐3), intramuscular injection (1.30 times 10‐3) and subcutaneous injection of insulin (9.82 times 10‐4). The present study revealed two potential risks: occasional tasks with high failure rates and patient categories with small numbers of patients with high failure rates. The present protocol enabled us to conduct quantitative risk assessment on occupational health for medical workers.

An Efficient Algorithm for a Class of Two-Resource Allocation Problems

We propose an efficient algorithm for the two resource allocation problem defined on a series-parallel graph, where nodes represent tasks, and arcs represent precedence relationships. The completion time of each task is inversely proportional to the amount of resource allocated, and the objective is to minimize the length of the longest path on the graph. Our algorithm is derived based on a formal analysis of the primal and Lagrangian dual optimal solutions. The resulting search process always ends up with one of three exclusive cases. For the first two cases, a closed form solution exists. For the third case, a bisectional search is used. The computational complexity of the algorithm is thus bounded fromabove by O(Nlog(1/ɛ)), where N is the total number of tasks, and ∈ is any given accuracy.

Use of computers in hospitals: an analysis of intensive and nonintensive users.

Differences between "Intensive" and non-intensive users of computers among hospitals are analyzed using step wise discriminant analysis. Hospitals are classified into "Intensive" and "Nonintensive" users of computers based on the total number of tasks computerized by each hospital. Results show that size, location, and competition from other hospitals in the area, are the significant differences between the two groups. Differences in the computer usage patterns are also examined. Possible reasons for the differences between the two groups are discussed.

Increasing processor utilization in hard-real-time systems with checkpoints

Often hard real-time systems require results that are produced on time despite the occurrence of processor failures. This paper considers a distributed system where tasks are periodic and each task occurs in multiple copies which are periodically synchronized in order to handle failures. The problem of preemptively scheduling a set of such tasks is discussed where every occurrence of a task has to be completely executed before the next occurrence of the same task. First, a static scheduling algorithm is proposed which uses periodic checkpoints to tolerate processor failures. Then, the performance of the algorithm is substancially improved employing a mixed strategy which constructs a schedule where high frequency tasks are duplicated, and low frequency tasks are periodically checkpointed. The performance of the solution proposed is evaluated in terms of the minimum achievable processor utilization due to the useful computation of the tasks. Moreover, analytical and simulation studies are used to reveal interesting trade-offs associated with the scheduling algorithm. In particular, if high frequency tasks are less than 70 percent of the total number of tasks then the mixed strategy yields a higher processor utilization than the task duplication scheme.

Skill-related task structures, explicitness, and accountability: relationships with student achievement.

The purpose of this study was to determine relationships of task structures, explicitness, and accountability with student achievement in physical education. The subjects were teachers of 10 physical education classes and their 202 students. Each class participated in instruction that was videotaped, and the students were pre- and posttested. Videotapes were coded to collect data on each skill-related task including the task presentation time, skill being taught, explicitness of the task presentation, primary and secondary accountability systems used, level of student participation, and total task time. Among the many results, for the volleyball forearm pass, significant relationships were found for the total number of tasks and time spent in tasks when expectations included outcome, situation, and criteria-product. For the volleyball underhand serve, significant relationships were found for the total number of tasks and time spent in tasks when accountability included teacher feedback with follow-up and monitoring of off-task behavior.

User-controlled optimization of task scheduling for imprecise computer systems

The performance of imprecise computer systems with optimized task scheduling is studied. An imprecise computer system trades off computational accuracy for faster response time of tasks (when necessary) and is therefore potentially useful in real-time applications. In the authors' system, tasks arrive randomly during runtime. Each task has two levels of computation time requirements: the full-level computation requirement and the reduced-level computation requirement. The reduced-level computation of a task takes less time to accomplish than the full-level one and produces results that are acceptable as, though less precise than, those of the full-level computation. The tasks are scheduled in the following way: if the total number of tasks in the system is no more than M — a system parameter — the tasks are executed at the full level; otherwise, the tasks are executed at the reduced level. An optimization procedure that is driven by user requirements is studied. It is proposed to select the system parameter M such that an objective function is optimized and a set of user-specified requirements is met. The optimization procedure aims to give the user the control in determining the parameter M according to performance requirements. The performance of the system under the control of the optimization procedure is examined.

Performance analysis of dynamic multitasking imprecise computation system

The scheduling of tasks in dynamic multitasking computer systems using imprecise (partial) computations is studied. In our system, tasks arrive randomly during run-time, and they are to be processed as quickly as possible. We model the task in such a way that each task has two parts of computation: a mandatory part, and an optional part. The tasks are scheduled such that: if the total number of tasks in the system is no more than M (a system parameter), the tasks are executed fully to produce precise results, i.e. both the mandatory and the optional parts are executed. When the total number of tasks exceeds M, then only the mandatory parts of the tasks are executed. As less computation resourse is used on each task under this imprecise computation condi- tion, the response time of tasks should improve significantly. To study this dynamic imprecise computation system, we propose three per- formance metrics to measure responsiveness and quality of computation of tasks. Queueing methods are used to analyse the scheduling problem, and performance metrics are computed numerically. The performance of the scheme is assessed, and found to be effective in keeping the mean task waiting time at a low value when the system is heavily loaded or overloaded. The effect of variations in parameter values on the system performance is also examined.

Optimally Balancing Large Assembly Lines with “Fable”

A new algorithm for optimally balancing assembly lines is formulated and tested. Named “FABLE,” it obtains proven optimal solutions for ten 1000 task lines, which each possess the computationally favorable conditions of an average of at least 6 tasks per work station and a small number of between-task precedence requirements, in less than 20 seconds of IBM 3033U CPU time for each problem. FABLE also performs very favorably on a benchmark group of 64 test problems drawn from the literature, which are of up to 111 tasks each. FABLE finds and proves an optimal solution to the 64 problems in a total of 3.16 seconds of IBM 3090 CPU time. FABLE is a ‘laser’ type, depth-first, branch-and-bound algorithm, with logic designed for very fast achievement of feasibility, ensuring a feasible solution to any line of 1000 or even more tasks. It utilizes new and existing dominance rules and bound arguments. A total of 549 problems of various characteristics are solved to determine conditions under which FABLE performs most and least favorably. Performance is sensitive to average number of tasks per work station, number of between-task precedence requirements (measured by ‘order strength’), and the total number of tasks per problem. A heuristic variant of FABLE is also described.