Use of Rock Mass Rating (RMR) values for support designs of tunnels excavated in soft rocks without squeezing problem

Effect of the rock material strength on the RMR value and tunnel support designs were investigated within this study including site works, analytical and numerical analyses. It was found that rock material strength effect is quite limited in the RMR method to determine an accurate rock mass class to design tunnel support. Since the limitation, rock mass classes are evaluated to be usually misleading and supports designed in accordance with the RMR value are insufficient for tunnels excavated in rock masses with low strength values of rock materials. Totally, five different tunnels in Turkey have been supported using a new strength adjustment factor calculated in consideration of the in-situ stress and the uniaxial compressive strength values of rock materials. As confirmed by the field applications, analytical and numerical analyses, a newly modified RMR value (RMRus) was suggested to be used in tunnel support design works

A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

A simulation-based decision support tool for informing the management of patients in retinal services

Retinal vascular diseases are a leading cause of blindness in the Western world. Advancement in the clinical management of these diseases has been fast-paced, with new treatments becoming available. Eye care services account for nearly one in ten hospital outpatient appointments in England. This paper discusses the development of a decision support toolkit (DST) that facilitates the improvement of retinal services by identifying cost savings and efficiencies within the pathway of care. The paper describes the development of the DST with the help of NHS and commercial experts in the retinal pathway. The DST enables users to model their own services by working with the DST interface allowing them to specify local services. Users can input local estimates or data of service demands and capacities thus creating a baseline discrete event simulation model. Users can then compare the baseline with potential changes in the patient pathway in the safety of a virtual environment. The tool enables key decision makers to estimate the likely impact of changes, such as increased use of new treatment vs. existing treatment regime. By making such changes the impact on activity, cost, staffing levels, skill-mix and utilisation of resources can be easily understood. Such previously unobtainable quantitative information can be used to support business cases for change in retinal servicesFinal Accepted Versio

Enabling better management of patients: discrete event simulation combined with the STAR approach

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of the Operational Research Society, on 1 May 2017, available online at: https://www.tandfonline.com/doi/full/10.1057/s41274-016-0029-y.Squeezed budgets and funding cuts are expected to become a feature of the healthcare landscape in the future, forcing decision makers such as service managers, clinicians and commissioners to find effective ways of allocating scarce resources. This paper discusses the development of a decision support toolkit (DST) that facilitates the improvement of services by identifying cost savings and efficiencies within the pathway of care. With the help of National Health Service and commercial experts, we developed a discrete event simulation model for Deep Vein Thrombosis (DVT) patients and adapted the socio technical allocation of resources (STAR) approach to answer crucial questions like: what sort of interventions should we spend our money on? Where will we get the most value for our investment? How will we explain the choices we have made? The DST enables users to model their own services by working with the DST interface allowing users to specify local DVT services. They can input local estimates, or data of service demands and capacities, thus creating a baseline discrete event simulation model. The user can then compare the baseline with potential changes in the patient pathway in the safety of a virtual environment. By making such changes key decision makers can easily understand the impact on activity, cost, staffing levels, skill-mix, utilisation of resources and, more importantly, it allows them to find the interventions that have the highest benefit to patients and provide best value for money.Peer reviewe

The Effect of Priming Treatments on Germination and Seedling Performance of Purslane (Portulaca oleracea) Seed Lots

This study was conducted to test the effect of a priming combination on the seed germination percentage and seedling emergence performance of purslane under climate chamber and field conditions. Four purslane seed lots were treated according five different methods, which were T1: Seeds kept at a hundred percent relative humidity for four hours at 20 °C; T2: Seeds kept at a hundred percent relative humidity for four hours at 20 °C, and then soaked in distilled water for 8 hours at 5 °C; T3: Seeds kept at a hundred percent relative humidity for four hours at 20 °C, and then soaked in distilled water for 8 hours at 20 °C; T4: Seeds soaked in distilled water for 8 hours at 5 °C; T5: Seeds soaked in distilled water for 8 hours at 20 °C; and C: Control (untreated). Seed germination was calculated for 14 days at 20 °C, seedling emergence percentages were calculated in the climatically-controlled chamber for 21 days at 22 °C, and in the field for 35 days at 15-25 °C. The highest seed germination (94%) and seedling emergence in the climatically-controlled chamber (87%) and field (82%) were obtained from seeds that had been kept at a hundred percent relative humidity for four hours at 20 °C, then soaked in distilled water for eight hours at 5 °C. Results indicated that farm-priming, can be an efficient priming method in purslane seeds

Length Effect on Load Bearing Capacities of Friction Rock Bolts

Change in the load bearing capacity of the split set type friction rock bolts with variations of bolt lengths was investigated within this study. To determine a relation between the load bearing capacity and bolt length parameters, different friction bolt models with various lengths were analyzed with a numerical modelling study. In addition, a series of pull-out tests was carried out to evaluate the load bearing capacities of the split set type friction rock bolts with different lengths. The load bearing capacity of the bolts was found to decreasingly increase with the increase in the bolt length. As an outcome of this study, a relation between the load bearing capacity and rock bolt length parameters is suggested in accordance with the results obtained from both numerical and experimental studies

Demand and Capacity Modelling for Acute Services using Discrete Event Simulation

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Health Systems following peer review. The final publication [Demir, E., Gunal, M & Southern, D., Health Syst (2016), first published online March 11, 2016, is available at Springer via http://dx.doi.org/doi:10.1057/hs.2016.1 © 2016 Operational Research Society Ltd 2016Increasing demand for services in England with limited healthcare budget has put hospitals under immense pressure. Given that almost all National Health Service (NHS) hospitals have severe capacity constraints (beds and staff shortages) a decision support tool (DST) is developed for the management of a major NHS Trust in England. Acute activities are forecasted over a 5 year period broken down by age groups for 10 specialty areas. Our statistical models have produced forecast accuracies in the region of 90%. We then developed a discrete event simulation model capturing individual patient pathways until discharge (in A&E, inpatient and outpatients), where arrivals are based on the forecasted activity outputting key performance metrics over a period of time, e.g., future activity, bed occupancy rates, required bed capacity, theatre utilisations for electives and non-electives, clinic utilisations, and diagnostic/treatment procedures. The DST allows Trusts to compare key performance metrics for 1,000’s of different scenarios against their existing service (baseline). The power of DST is that hospital decision makers can make better decisions using the simulation model with plausible assumptions which are supported by statistically validated data.Peer reviewedFinal Accepted Versio

A statistical assessment of association between meteorological parameters and COVID-19 pandemic in 10 countries

Background Eleven out of 13 published articles reported temperature and humidity as factors that could reduce the daily confirmed COVID-19 cases among many other findings. However, there are significant caveats, related to statistical assumptions and the spatial-temporal nature of the data. Methods Associative and causative analyses of data was conducted for 10 countries representing 6 continents of the world, with data obtained between January 22, 2020 to April 30, 2020. Daily confirmed cases, number of deaths, recovered cases, lockdown stringency index, and several meteorological factors are considered. Also, a Granger-Causality test was performed to check if any COVID-19 outcomes are influenced by itself and not by any or combination of maximum temperature, humidity, wind speed and stringency index. Results Most of the associations reported in the literature, between meteorological parameters and COVID-19 pandemic are weak evidence, need to be interpreted with caution, as most of these articles neglected the temporal spatial nature of the data. Based on our findings, most of the correlations no matter which coefficient is used are mostly and strictly between -0.5 and 0.5, and these are weak correlations. An interesting finding is the correlation between stringency and each of the COVID-19 outcomes, the strongest being between stringency and confirmed cases, 0.80 (0.78, 0.82) P<.0001. Similarly, wind speed is weakly associated with recovery rate, 0.22 (0.16, 0.28) P<.0001. Lastly, the Granger-Causality test of no dependencies was accepted at P=0.1593, suggesting independence among the parameters. Conclusions Although many articles reported association between meteorological parameters and COVID-19, they mainly lack strong evidence and clear interpretation of the statistical results (e.g. underlying assumption, confidence intervals, a clear hypothesis). Our findings showed that, without effective control measures, strong outbreaks are likely in more windy climates and summer weather, humidity or warmer temperature will not substantially limit pandemic growth.Peer reviewedFinal Published versio

Radicle Emergence Test Estimates Predictions of Percentage Normal Seedlings in Standard Germination Tests of Aubergine (Solanum melongena L.) Seed Lots

An experiment was made to test the potential for radicle emergence (RE), and predict the germination percentage of normal seedlings both at constant (25 °C) and fluctuating (20/30 °C, 16 h/8 h) temperatures for 23 commercially available Thiram treated and untreated aubergine (Solanum melongena L.) seed lots. Frequent counts of RE at 96, 104, 112, 120, 128 and 136 hours in two different temperature regimes (constant and alternating) consistently predicted final normal germination after 14 days. The R2 values at fluctuating temperatures (R2=0.69 and 0.88, p &lt; 0.001) were generally higher than those at a constant (R2=0.60-0.63, p &lt; 0.01) temperature. Among the 23 seed lots, nine were Thiram threated. The R2 relationship in both temperature regimes were reduced (for Thiram-threated seed lots ranging between R2=0.60-0.79 at 25°C and 20/30°C respectively, and for untreated lots ranging between R2=0.68-0.91 at 25 °C and 20/30°C, respectively). Cumulative germination was slightly higher in the lots kept at fluctuating temperatures than in those kept at a constant temperature. The results showed that the RE test (i.e. 104 h count) can be used to make quick and repeatable predictions of the percentage of normal seedlings in aubergine lots. Moreover it was also significantly related to mean germination time (MGT) values at constant (R2= 0.769, p &lt; 0.001) and alternating temperatures (R2= 0.861, p &lt; 0.001)

Using simulation modelling to transform hospital planning and management to address health inequalities

© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.Peer reviewe