Schedule Components Research Articles

Work Schedule Characteristics, Missed Nursing Care, and Organizational Commitment Among Hospital Nurses in Korea

This study describes Korean nurses' work schedule characteristics and identifies their components to investigate associations of work schedule components with missed nursing care and organizational commitment. This cross-sectional secondary analysis used survey data of 1,057 nurses in 111 units at six hospitals in South Korea. Data were collected between April 2017 and March 2018. A self-administered survey, including seven work schedule characteristic items, the Korean version of the MISSCARE Survey, and the Korean version of the Organizational Commitment Questionnaire, was employed. To construct independent components of work schedule characteristics, a principal component analysis was performed. The associations of work schedule components with missed nursing care and organizational commitment were analyzed using multiple linear regression models with generalized estimating equation methods. The average number of daily work hours was 9.7. Nearly half of the study population worked while sick once or more per month. The two components of nurses' work schedule characteristics were "long work hours" and "lack of rest," and these components showed variations between units. Unhealthy work schedule components were linked to frequently missed nursing care and decreased organizational commitment. This study showed that proper work hours and adequate rest are important to reduce missed nursing care tasks and enhance organizational commitment, both of which are critical for better patient care and organizational outcomes. Healthcare organizations should provide adequate nursing staff and assign reasonable workloads. Furthermore, hospitals should periodically monitor the work schedule characteristics of nurses and actively intervene in cases of scheduling issues to resolve them.

On the optimal planning of a hydrogen refuelling station participating in the electricity and balancing markets

This paper presents an optimisation model to assess the techno-economic feasibility of a hydrogen refuelling station, which purchases power from the electricity market, supplies the mobility sector with hydrogen, and participates in the ancillary service market. The problem is formed as a mixed-integer nonlinear programming model to investigate the optimal operational plans considering the nonlinear behaviour of an electrolyser and grid costs calculation model. Obtained results from various scenarios in 2020 and 2030 show that participation in the reserve market considering optimal sizing and dispatch of components increase revenues up to 16%, and as a result, decrease the hydrogen break-even price by up to 4.7% and 6.4% in 2020 and 2030, respectively. Exemption from tax and levies for connection to the grid reduces the hydrogen break-even price by up to 13%. Plant operators could benefit from the proposed approach to schedule components reliably while meeting the hydrogen demand and maximising the annual profits.

Evaluating skills correlated with discriminated responding in multiple schedule arrangements.

One potential solution for excessively high-rate functional communication responses (FCR) is to establish stimulus control of the FCR through a multiple schedule. However, several studies have demonstrated difficulty with establishing discriminated responding across multiple schedule components. The primary aim of the current study was to evaluate how participants' skills related to color discrimination may be related to the emergence of discriminated responding in a multiple schedule with colors as the schedule-correlated stimuli. Three secondary aims of the current study were to evaluate: a) varied multiple schedule arrangements, b) if topographically dissimilar stimuli facilitated the emergence of discriminated responding, and c) if employing different colored stimuli across multiple schedule arrangements reduced the likelihood that discriminated responding emerged simultaneously across varied arrangements. Nine participants' ability to match, select, tact, and respond intraverbally to colors was assessed, and 1 of 2 evaluations of multiple schedule arrangements were conducted. Results indicated that participants' ability to select and tact colors was strongly correlated with the efficacy of standard multiple schedule arrangements. Additionally, multiple schedule arrangements employing topographically dissimilar stimuli were observed to be equally as effective as standard arrangements and the inclusion of different colored stimuli across arrangements did not reduce the likelihood that discriminated responding emerged simultaneously across all conditions, when it was observed to emerge at all.

A Decision-Support Model for Additive Manufacturing Scheduling Using an Integrative Analytic Hierarchy Process and Multi-Objective Optimization

Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners’ applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.

Impact of Phone-Call and Access-Enhancing Intervention on Mammography Uptake among Primary Care Patients at an Urban Safety-Net Hospital: A Randomized Controlled Study

Our urban safety-net hospital (SNH) has very low screening mammogram rates within its primary care clinics. Despite Commission on Cancer (CoC) accreditation, we see ~ 3 × more late-stage breast cancer diagnoses than other CoC sites across the country, and recently showed this to be strongly associated with lack of screening (Ahmadiyeh et al. in J Health Care Poor Underserved, in press, 2020). Here we study whether a two-step intervention (phone calls and assistance scheduling mammograms) increases uptake over usual care. Randomized controlled study of 890 women aged 50-65years who were due for biennial screening mammograms and who were established within one of five primary care clinics at an urban SNH. Each patient in the intervention group was called with overdue status (up to three times, voicemail left if needed) and offered assistance scheduling mammogram appointment. Mammography uptake at 3 and 6months was analyzed. Intervention significantly increased uptake compared with usual care at both timepoints (18% versus 6% at 3months; χ2 = 27.597, p < 0.0001; 23% versus 12% at 6months; χ2 = 18.0, p < 0.0001), with scheduling component driving effectiveness. Of those who were successfully contacted, uptake was significantly greater among those who scheduled appointments versus those who did not (47% versus 9%, χ2 = 95, p < 0.0001), and uptake was no different between contacted but not scheduled patients and usual care controls. Phone call with access-enhancing intervention (facilitating mammogram appointments) increased screening mammogram uptake among primary care patients in an urban safety-net setting and may be applicable to other urban SNHs around the country.

The appearance of matching

A computerized version of Houston and McNamara’s (1981) model of concurrent variable time schedules was used to generate large data sets consisting of behavior inputs and the corresponding reinforcement outputs. The data was analyzed to assess the hypothesis that matching behavior observed with CONCVTVT (or CONCVIVI) schedules is an inevitable product of the operation of the schedule. The results suggest that the generalized matching law does not provide the best description of the results obtained with CONCVTVT schedules; that the parameter value of schedule components determine the framework of the reinforcement efficiency maps of behavior choices; and that by random chance alone matching-like behavior is likely to appear if the reinforcement generation efficiency of the behavior is high.

Assessment of Regional Inequality in Female Work Participation: Measurement of Disparity and Determinants

The regional difference of complex Indian social structure and customs have a different impact on the nature of women’s work participation. The present study aims at unravelling the influence of social, cultural and economic forces in differentiating the level of women work participation in different eco-regions of West Bengal. The study is based purely on secondary sources and data have been collected from the Census of India. It is in the rural areas that the female work participation is directly linked to agriculture and allied activities and the study confines itself to an understanding of work participation of women only in the rural areas. The modern technological implication as a result of green revolution has a worse impact on women work participation particularly in the South Bengal plain and some parts of East Rarh Plain region. The high gender gap is noticed in Nadia district located in the middle part of South Bengal Plain causes very high withdrawn of female from there. Effect of socioeconomic variables, work participation of Scheduled component in main economic activity is also varied from the non-scheduled component. Non-scheduled worker participated more in non-agricultural sector rather than the scheduled counter parts. Subsequently, the low growth rate of female work participation represents a distress picture in work force structure which is a cause of worried also.

Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

A vibrant on demand service of today’s era is cloud computing where one can utilize computer resources without indirect active management by user where one can use computing resources to achieve coherence in economic scale. Since cloud computing feel like Everything as a service so there should be highly scalable and reliable mechanisms to distribute the load evenly across the VMs evenly. Innumerable cloudlet mapping policies are presented in various research articles to achieve the high performance, better QOS and minimized task execution time but maximum are conventional approaches. No unconventional realistic scheduling algorithms is available which can schedule the tasks in heterogeneous manner. Since cloudlet scheduling is crucial metrics of cloud computing that has to be heightened by combining the different parameters. This paper tried to provide effectiveness and improvement in task scheduling using nature inspired Particle Swarm optimization (PSO) strategy. A powerful nature inspired load balancing mechanism is proposed in this paper which optimized makespan and throughput in environment of varying cloudlets and virtual machines results as compared to other conventional approaches. Proposed (EPSO) algorithm is with four scheduling policies namely FCFS, Round Robin (RR) and Shortest Job First (SJF) and get near twice good throughput percentage and minimized makespan in two different environments. Author used Cloud sim toolkit and some Open Source cloud packages to simulate the results of various scheduling components. Experimental results of various components are tested and simulated on java based CloudSim toolkit framework.

Lecture Scheduling System Using Welch Powell Graph Coloring Algorithm in Informatics Engineering Departement of Universitas Malikussaleh

Scheduling lectures is a routine work in the academic system in higher education every time to facing a new semester. In the implementation, often the schedule that has been issued has not been fixed so it requires to rescheduling. For this reason, a new lecture scheduling system is needed using the Welch Powell Graph Coloring Algorithm. This study was done in the Informatics Engineering Departement of Universitas Malikussaleh using the subjects schedule during 2013, 2014 and 2015. Based on the results of this study it can be concluded that the Welch Powell Graph Coloring Algorithm can solve properly the problem of scheduling lectures, in this case there are no clashes between scheduled components.

User Preferences With Urgent-Time Intervals in a Wireless-Network

This paper focuses on a scheduler component in a wireless network, with constraints based on user preferences. These constraints reflect the fact that users wish to privatize networks for fixed periods (intervals) in order to send urgent data. The user sets in advance certain data constraints and time periods. Each time period is presented as a frozen interval for the router and other users. Only the administrator can send data during these specifics periods and all others users will be frozen. During this interval, the intelligencer can send confidential and emergency data without any concurrency with other data through this interval, which is called an “emergency and secret interval”. Each time, a scheduling problem due to unavailable machines and jobs will be solved. This problem is NP-hard. In this research, we develop two algorithms for transmitting data from the sender to the receiver.

Analyzing Task Scheduling Algorithms and Load Balancing Approach to Enhance the Performance of Cloud Computing

As a key and outskirts field of the present local and global PC innovation, distributed computing is a processing worldview that can give dynamic also adaptable virtual assets concluded Internet administration to clients on interest, and furthermore, it is further improvement of disseminated registering, parallel figuring also matrix figuring. As indicated by new highlights of distributed computing, for example, adaptability, virtualization and so forth, this paper talks about a three levels task Scheduling component dependent on load balancing in distributed computing. Effective task scheduling system may see clients' necessities, and improve the asset usage, along these lines upgrading general execution of the distributed computing condition. This paper defines various scheduling algorithms which use in cloud computing on various parameters, especially on makespan. Also, a layered model for cloud computing and important features of clouds computing is described.Task Scheduling issues are critical in distributed computing, also become particularly testing once considering load balance.

Adjustments of public transit operations planning process for the use of electric buses

This work investigates and discusses how the introduction of electric buses (EB), both battery and plug-in hybrid EB, will and should change the operations planning of a public transit system. It is shown that some changes are required in the design of a transit route network, and in the timetabling and vehicle scheduling processes. Other changes are not required, but are advisable, using this opportunity upon the introduction of EB. The work covers the main characteristics of different types of EB with a short description, including the most popular charging technologies, and it presents the generally accepted transit operations planning process. Likewise, it describes and analytically formulates new challenges that arise when introducing EB. The outcome of the analyses shows that multiple new considerations must take place. It is also shown that the different charging techniques will influence the operations planning process in different ways and to a varying extent. With overnight, quick and continuous charging, the main challenges are in the network route design step, given the possibility of altering the existing network of routes, with efficient and optimal changes of the timetabling and vehicle scheduling components. An illustrative example, based on four bus lines in Norrköping, Sweden, is formulized and introduced using three problem instances of 48, 82, and 116 bus trips. The main results exhibit the minimum number of vehicles required using different scenarios of charging stations.

First Things First: Adopting a Holistic, Needs-Driven Approach to Improving the Quality of Routinely Collected Data

Background and context: Routine collection of quality oncology data remains underprioritized in resource limited settings. For breast cancer (BC) care at Princess Marina Hospital (PMH) in Botswana, this hampers its use for oncology program evaluation and clinical research. The Peo Data Acquisition Core (DAC), part of a consortium for planning of a noncommunicable diseases center for research excellence in Southern Africa, engaged relevant stakeholders and departments at PMH to establish health care worker driven processes that support sustained improvements in the quality of routinely collected breast cancer data. Aim: Assess PMH BC care needs to support the improvement of routinely collected data. Strategy/Tactics: Conventional routine data quality assessments guide iterative identification of data quality gaps for improvement, but tend to consume additional human resources to implement and prioritize data quality over existing provider concerns. We undertook a holistic approach to identifying needs challenging the improvement of data routinely collected about PMH BC patients. Program/Policy process: A need assessment began with mapping of patient care process, capturing process steps, including subprocesses, actors, and an inventory of related data capture systems. The assessment also gathered care providers' perceptions of challenges to providing care as well as perceptions of the up-time of an existing electronic health record (EHR). Outcomes: BC patient management involves multiple care providers who attend to patients in different locations within the hospital. Except for EHR captured laboratory data, nearly all other documentation of patient care occurred through paper-based registers, diaries and general clinical forms. Providers indicated the overbooking of patient appointments and use of different formats to manage patient data as key challenges for them. EHR appointment scheduling components appear underutilized by providers, and a brief monitoring of provider perceptions of the EHR's speed and reliability suggests it is poorest in the mornings - at a time when providers are the busiest attending to patients. What was learned: Needs assessment findings suggest several opportunities to respond to provider recognized challenges through greater adoption of EHR usage. Establishment of a Quality Improvement (QI) group that champions improvements in routine BC data quality should integrate standardization of patient data formats and EHR centralized appointment booking. QI group composition should include both care providers and an EHR IT technician.

Partially-Decoupled Service Agent - Transport Agent Task Allocation and Scheduling

We present an approach to performing efficient schedule generation of a heterogeneous team of autonomous agents in a service agent - transport agent scenario where the task allocation and scheduling components are partially-decoupled. In the scenario, service agents must perform tasks at a number of locations. The agents are free to move between locations, however the agents may also be transported throughout the region by a limited number of faster-moving transport agents. The goal of the problem is to plan a schedule of service agent and transport agent actions such that all locations are serviced in the shortest amount of time. While in previous work we formulated the problem as a holistic mixed-integer linear program, we present a novel method to solve the problem in a hierarchical and partially-decoupled manner for faster optimization and to require less information to be processed and communicated in a centralized manner to perform the schedule planning. The original solution method required up to 20 minutes to obtain an efficient solution. The new methodology, using hierarchical task allocation and a bidding-based scheduling algorithm, can create an efficient solution in seconds.

Design and implementation of an android and web-based university timetable customization system

Timetabling is an essential component of scheduling in all sectors of education. University timetabling entails the scheduling of times and venues for lectures and examinations. These activities are logically structured in a time-wise manner and presented on papers and placed on notice boards in order to avoid conflicts of event. However, timetable schedule forgetfulness, misinterpretation, miscopying, cost of rescheduling and slow time to reach for the rescheduled timetables are the challenges posed by the use of paper and notice board-based timetables; hence, the need for automated timetables. In this paper, we discuss the implementation of Android and web-based timetable customization systems for the Faculty of Computer Science and Information Technology, Bayero University, Kano-Nigeria. The web-based system provides an administrative module that facilitates scheduling and rescheduling of lecture, examination and invigilation timetables. In addition, the Android timetable customization system permits the user to view, customize, set reminders and provide rescheduled timetable updates. Keywords : Android, Customization, Timetable and Web based

Framework for Scalable File System Metadata Crawling and Differencing

Identifying file systems metadata changes such as which files have been added, modified or removed from the file system has many usages. In this paper we present a framework we developed for identifying those changes in increasing speeds. Our framework which is composed of crawling, hashing, and scheduling components, allows to scale the crawl to multiple client workstations that operate in parallel on the same file system in a non-overlapping fashion. Experiments carried using real-world data indicate performance improvement (speedup) of up to 36X using our framework compared to legacy crawling utilities such as Linux’s ‘find’.

Design and Implementation of Task Reprocessing on Medium-large Multi-core Architecture

EDF-Based real-time scheduling approach is one of the efficient way of scheduling the recurrent real-time tasks in both soft real-time and hard real-time systems. EDF algorithms entile heavy overhead due to lower scheduling and huge migration. In this paper, an exploratory procedure has been presented for reprocessing of neglected task sets and also examine, whether an improved heuristic set attributed as Enhanced shared CAche Performance (ENCAP) can improve the shared cache performance which processes the task set based on Earliest-Deadline-First (EDF). An Object-Oriented real-time code reusable scheduling components which is designed from the scratch referred as LITMUSRT has been used to test the efficiency and average-case accomplishments where per task utilizations are shortened and overall utilization are not restricted. We also discuss the implementation and experimental evaluation of Task-Repo procedure for co-scheduling task sets, under the utilization of 50-60% (per-task) on small to medium-large multicore LINUX SYSTEM.

Hybrid scheduling algorithm in early warning systems

The development of an efficient Early Warning System (EWS) is essential for the prediction and prevention of imminent natural hazards. In addition to providing a computationally intensive infrastructure with extensive data transfer, high-execution reliability and hard-deadline satisfaction are important requirements of EWS scenario processing. This is due to the fact that EWS has a limited window of opportunity to discern if a scene shows signs of an impending natural disaster. In this paper, the scheduling component of the EWS scenario is investigated and an efficient hybrid algorithm for the urgent workflows scheduling is proposed. The developed algorithm is based on traditional heuristic and meta-heuristic approaches along with state-of-the-art cloud computing principles.

Value-based optimisation of replacement intervals for critical spare components

For competitive industries, such as mining, value-adding demands enriched methods to efficiently manage critical equipment and spare components. These components are related to lengthy shutdowns with significant financial impact. In this context, cost optimisation is a widely used principle to schedule component replacements. However, this practice traditionally does not consider external factors of interest, such as business-market conditions, which can radically change decisions. To overcome this limitation, we have proposed a criterion based on the estimation of revenues–under several commodity price scenarios–at both the component intervention period and major shutdown time-windows. The paper presents a model to guide the decision about the best epoch to replace critical components, considering the maximisation of value-adding rather than simply minimisation of costs. Such optimal interval is optimised by comparing net benefits with reliability constraints from a condition-based maintenance strategy. As result, the decision-making process is enriched by quantifying the real value of postponing or accelerating the most favourable epoch to perform a replacement. The proposed value-based approach increases business competitiveness when included as a systemic part of an asset management perspective, thus obtaining higher profitability.

Locality-Load-Prediction Aware Multi-Objective Task Scheduling in the Heterogeneous Cloud Environment

Objectives: Current state-of-the-art task scheduling algorithms were mainly focused on deadline, load and energy factors in centralized cloud context. So, the proposed research objective focuses on dynamic and decentralized context. Methods/Statistical Analysis: Multi-objective task scheduling has become an important criterion for the dynamic and decentralized nature of cloud environment. Moreover, existing research works assumes that the resource load, energy and task execution time are known due its homogeneous nature. In order to improve the cloud consumer’s satisfaction, a novel Locality-Load-Prediction Aware Multi-objective Task Scheduling (LLPAMTS) algorithm is proposed to eventually distribute the tasks according to dynamic nature of cloud virtual machines. Findings: Proposed LLPAMTS algorithm will effectively schedule the tasks in an optimized manner by VM Scheduler component. This scheduling algorithm exploits the various monitoring parameters like locality, load and prediction parameters. It outperforms the existing deadline, load and energy aware scheduling algorithms in terms of task transfer time, task waiting time, task execution time, and task completion time. Applications/Improvements: The proposed LLPAMTS algorithm provides an average of 5 to 10% less task completion time compared to the existing deadline, load and energy aware scheduling algorithms.