Modelling a Nurse Shift Schedule with Multiple Preference Ranks for Shifts and Days-Off

When it comes to nurse shift schedules, it is found that the nursing staff have diverse preferences about shift rotations and days-off. The previous studies only focused on the most preferred work shift and the number of satisfactory days-off of the schedule at the current schedule period but had few discussions on the previous schedule periods and other preference levels for shifts and days-off, which may affect fairness of shift schedules. As a result, this paper proposes a nurse scheduling model based upon integer programming that takes into account constraints of the schedule, different preference ranks towards each shift, and the historical data of previous schedule periods to maximize the satisfaction of all the nursing staff's preferences about the shift schedule. The main contribution of the proposed model is that we consider that the nursing staff’s satisfaction level is affected by multiple preference ranks and their priority ordering to be scheduled, so that the quality of the generated shift schedule is more reasonable. Numerical results show that the planned shifts and days-off are fair and successfully meet the preferences of all the nursing staff.

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Improving highway accident management through patrol beat scheduling

Policing An International Journal of Police Strategies and Management ◽

10.1108/pijpsm-12-2012-0116 ◽

2014 ◽

Vol 37 (1) ◽

pp. 108-125 ◽

Cited By ~ 2

Author(s):

Jiann-Sheng Wu ◽

Tze-chiang Lou

Keyword(s):

Historical Data ◽

Response Times ◽

Work Hours ◽

Simulation Program ◽

Personnel Cost ◽

Content Type ◽

Daily Work ◽

Scheduling Model ◽

Proposed Model ◽

Accident Management

Purpose – The purpose of this paper is to improve the efficiency of accident management from the angle of reducing highway accident response times while taking into account total daily work hours. Design/methodology/approach – The authors developed a patrol beat scheduling model, which is formulated as a chance-constrained optimization model, with the objective of minimizing the sum of officer work hours. Along with the model, a simulation program was also developed to help evaluate the effectiveness of the model-generated beat schedule in terms of response times. Findings – This study concluded that, first, the current manually designed beat schedule could be improved should the National Highway Police Bureau adopt the proposed model. Second, the total daily work hours of the model-generated schedule at the confidence level of 100 percent were 64 hours, 21 hours less than the average work hours recorded in the 2006 data, or about an improvement of 24 percent. Should the model be adopted, not only response times will be improved, the 24 percent reduction in work hours could be translated into a cut in personnel cost. Research limitations/implications – The scheduling model and simulation program are both built upon one-year historical data whose accuracy and completeness is prerequisite. Practical implications – The proposed model can be adopted by other public service agencies such as fire departments, or emergency service centers. By replacing the historical data used in the study with their own data, agencies can evaluate the efficiency of their existing schedule or generate various schedules based on institutional needs. Originality/value – This model utilizes historical accident data to generate optimal beat schedule and evaluate the efficiency of such schedule. Similar models have not been found in other studies.

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Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

Information and Control Systems ◽

10.31799/1684-8853-2018-4-45-51 ◽

2018 ◽

pp. 45-51

Author(s):

A. V. Ponomarev

Keyword(s):

Large Scale ◽

Wide Spectrum ◽

Preference Relation ◽

Pairwise Comparison ◽

Ground Truth ◽

Comparison Method ◽

Characteristic Matrix ◽

Image Tagging ◽

Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems).

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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Applied Sciences ◽

10.3390/app11062838 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2838

Author(s):

Nikitha Johnsirani Venkatesan ◽

Dong Ryeol Shin ◽

Choon Sung Nam

Keyword(s):

Neural Network ◽

Radiation Dose ◽

Convolutional Neural Network ◽

Model Performance ◽

Performance Comparison ◽

Apache Spark ◽

Training Time ◽

Learning Framework ◽

Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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Operating Rooms efficiency: leveraging data to improve management of elective surgery

European Journal of Public Health ◽

10.1093/eurpub/ckaa166.528 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

C Quercioli ◽

G A Carta ◽

G Cevenini ◽

G Messina ◽

N Nante ◽

...

Keyword(s):

Historical Data ◽

Elective Surgery ◽

District General Hospital ◽

Operating Rooms ◽

Accurate Estimation ◽

Surgical Time ◽

Accurate Analysis ◽

Management Efficiency ◽

Scheduling Model ◽

The Mean

Abstract Background Careful scheduling of elective surgery Operating Rooms (ORs) is crucial for their efficient use, to avoid low/over utilization and staff overtime. Accurate estimation of procedures duration is essential to improve ORs scheduling. Therefore analysis of historical data about surgical times is fundamental to ORs management. We analyzed the effect, in a real setting, of an ORs scheduling model based on estimated optimum surgical time in improving ORs efficiency and decreasing the risk of overtime. Methods We studied all the 2014-2019 elective surgery sessions (3,758 sessions, 12,449 interventions) of a district general hospital in Siena's Province, Italy. The hospital had3 ORs open 5 days/week 08:00-14:00. Surgery specialties were general surgery, orthopedics, gynecology and urology. Based on a pilot study conducted in 2016, which estimated a 5 times greater risk of having an OR overtime for sessions with a surgical time (incision-suture)>200 minutes, from 2017 all the ORs were scheduled using a maximum surgical time of 200 minutes calculated summing the mean surgical times for intervention and surgeon (obtained from 2014-2016 data). We carried out multivariate logistic regression to calculate the probability of ORs overtime (of 15 and 30 minutes) for the periods 2014-2016 and 2017-2019adjusting for raw ORs utilization. Results The 2017-2019 risk of an OR overtime of 15 minutes decreased by 25% compared to the 2014-2016 period (OR = 0.75, 95%CI=0.618-0.902, p = 0.003); the risk of a OR overtime of 30 minutes decreased by 33% (OR = 0.67, 95%CI= 0.543-0.831, p < 0.001). Mean raw OR utilization increase from 62% to 66% (p < 0.001). Mean number of interventions per surgery sessions increased from 3.1 to 3.5 (p < 0.001). Conclusions This study has shown that an analysis of historical data and an estimate of the optimal surgical time per surgical session could be helpful to avoid both a low and excessive use of the ORs and therefore to increase the efficiency of the ORs. Key messages An accurate analysis of surgical procedures duration is crucial to optimize operating room utilization. A data-based approach can improve OR management efficiency without extra resources.

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Key Determinants of Airline Loyalty Modeling in Thailand

Sustainability ◽

10.3390/su12104165 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4165 ◽

Cited By ~ 1

Author(s):

Dissakoon Chonsalasin ◽

Sajjakaj Jomnonkwao ◽

Vatanavongs Ratanavaraha

Keyword(s):

Airline Industry ◽

Structural Equation ◽

Statistical Significance ◽

Perceived Quality ◽

Equation Modeling ◽

Structural Factors ◽

Proposed Model ◽

Airline Passengers ◽

Domestic Airline

The airline industry in Thailand has grown enormously over the past decade. Competition among airline companies to reach market share and profit has been intense, requiring strong strategic abilities. To increase the service quality of such companies, identifying factors related to the context of airlines is important for policymakers. Thus, this study aims to present empirical data on structural factors related to the loyalty of domestic airline passengers. Structural equation modeling was used to confirm the proposed model. The questionnaire was used to survey and collect data from 1600 airline passengers. The results indicate that satisfaction, trust, perceived quality, relationship, and image of airlines positively influenced loyalty with a statistical significance of α = 0.05. Moreover, the study found that expectation and perceived quality indirectly influenced loyalty. The findings provide a reference for airline operators to clearly understand the factors that motivate passenger loyalty, which can be used to develop the sustainability of marketing strategies and support competitiveness.

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A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

Journal of Data and Information Quality ◽

10.1145/3418035 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1-19

Author(s):

Sreelakshmy I. J. ◽

Binsu C. Kovoor

Keyword(s):

Patch Size ◽

Image Inpainting ◽

Qualitative Evaluation ◽

Natural Images ◽

External Information ◽

Image Editing ◽

Model Combining ◽

Proposed Model ◽

The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

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Healthy aging at work – Development of a preventive group intervention to promote quality of life of nursing staff aged 45 years and older

Mental Health & Prevention ◽

10.1016/j.mhp.2016.01.002 ◽

2016 ◽

Vol 4 (1) ◽

pp. 42-48 ◽

Cited By ~ 4

Author(s):

I. Maatouk ◽

A. Mueller ◽

R. Schmook ◽

P. Angerer ◽

K. Herbst ◽

...

Keyword(s):

Quality Of Life ◽

Nursing Staff ◽

Group Intervention ◽

Healthy Aging

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Adaptive Reservation of Network Resources According to Video Classification Scenes

Sensors ◽

10.3390/s21061949 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1949

Author(s):

Lukas Sevcik ◽

Miroslav Voznak

Keyword(s):

User Satisfaction ◽

Quality Evaluation ◽

Spatial Information ◽

Service Providers ◽

Video Quality ◽

Subjective Assessment ◽

Video Sequences ◽

Network Resources ◽

Proposed Model

Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

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Auriculotherapy to reduce anxiety and pain in nursing professionals: a randomized clinical trial

Revista Latino-Americana de Enfermagem ◽

10.1590/1518-8345.1761.2843 ◽

2017 ◽

Vol 25 (0) ◽

Cited By ~ 4

Author(s):

Leonice Fumiko Sato Kurebayashi ◽

Ruth Natalia Teresa Turrini ◽

Talita Pavarini Borges de Souza ◽

Carolina Felicio Marques ◽

Renata Tavares Franco Rodrigues ◽

...

Keyword(s):

Quality Of Life ◽

Clinical Trial ◽

Randomized Clinical Trial ◽

Nursing Staff ◽

Repeated Measures ◽

Statistical Significance ◽

Cohen’S D ◽

Pain Visual Analog Scale ◽

Cohen's D

RESUMEN Objectives: to evaluate the effectiveness of the auricular protocol (APPA) in reducing pain and anxiety and improving the quality of life of the nursing staff of a hospital. Method: randomized clinical trial with an initial sample of 180 professionals divided into 4 groups Control (G1), Seed (G2), Needle (G3) and Tape (G4). The evaluation instruments were the State-Trait Anxiety Inventory, Pain Visual Analog Scale and Quality of Life instrument, applied at the start and after five and 10 sessions (five weeks). Descriptive statistics, analysis of variance (ANOVA) and Cohen's d Index were used in the analysis. Results: there was a statistical difference (p < 0.05) for anxiety according to the repeated measures ANOVA, with better results for the G3 in the final assessment (Cohen's d index 1.08/17% reduction). There was a reduction of pain of 36% in G3 and 24% in G2 and a 13% increase in the mental aspect of quality of life for the G3, although without statistical significance. Conclusion: the APPA protocol reduced the anxiety levels of nursing staff after 10 sessions. Further studies are, however, suggested with new populations and in different contexts so that the results can be confirmed. RBR-5pc43m.

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Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

Radiation Oncology ◽

10.1186/s13014-021-01783-9 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Mingli Wang ◽

Huikuan Gu ◽

Jiang Hu ◽

Jian Liang ◽

Sisi Xu ◽

...

Keyword(s):

Cervical Cancer ◽

Prediction Model ◽

Volumetric Modulated Arc Therapy ◽

Model Evolution ◽

Prediction Ability ◽

Knowledge Based ◽

Proposed Model ◽

Arc Therapy ◽

Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

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