Output Factors Research Articles

Modelling and commissioning validation of eclipse conical cone collimator for stereotactic radiosurgery using Monte Carlo simulation

Abstract Purpose: The miniaturized conical cones for stereotactic radiosurgery (SRS) make it challenging in measurement of dosimetric data needed for commissioning of treatment planning system. This study aims at validating dosimetric characteristics of conical cone collimator manufactured by Varian using Monte Carlo (MC) simulation technique. Methods & Material: Percentage depth dose (PDD), tissue maximum ratio (TMR), lateral dose profile (LDP) and output factor (OF) were measured for cones with diameters of 5mm, 7·5mm, 10mm, 12·5 mm, 15 mm and 17·5 mm using EDGE detector for 6MV flattening filter-free (FFF) beam from Truebeam linac. Similarly, MC modelling of linac for 6MVFFF beam and simulation of conical cones were performed in PRIMO. Subsequently, measured beam data were validated by comparing them with results obtained from MC simulation. Results: The measured and MC-simulated PDDs or TMRs showed close agreement within 3% except for cone of 5mm diameter. Deviations between measured and simulated PDDs or TMRs were substantially higher for 5mm cone. The maximum deviations at depth of 10cm, 20cm and at range of 50% dose were found 4·05%, 7·52%, 5·52% for PDD and 4·04%, 7·03%, 5·23% for TMR with 5mm cone, respectively. The measured LDPs acquired for all the cones showed close agreement with MC LDPs except in penumbra region around 80% and 20% dose profile. Measured and MC full-width half maxima of dose profiles agreed with nominal cone size within ± 0·2 mm. Measured and MC OFs showed excellent agreement for cone sizes ≥10 mm. However, deviation consistently increases as the size of the cone gets smaller. Findings: MC model of conical cones for SRS has been presented and validated. Very good agreement was found between experimentally measured and MC-simulated data. The dosimetry dataset obtained in this study validated using MC model may be used to benchmark beam data measured for commissioning of SRS for cone planning.

Dosimetric Evaluation of Semiflex Three-dimensional Chamber under Unflatten Beam in Comparison among Different Detectors.

The goal of this study is to investigate the dosimetric properties of a Semiflex three-dimensional (3D) chamber in an unflatten beam and compare its data from a small to a large field flattening filter-free (FFF) beam with different radiation detectors. The sensitivity, linearity, reproducibility, dose rate dependency, and energy dependence of a Semiflex 3D detector in flattening filter and filter-free beam were fully investigated. The minimum radiation observed field widths for all detectors were calculated using lateral electronic charged particle equilibrium to investigate dosimetric characteristics such as percentage depth doses (PDDs), profiles, and output factors (OPFs) for Semiflex 3D detector under 6FFF Beam. The Semiflex 3D measured data were compared to that of other detectors employed in this study. The ion chamber has a dosage linearity deviation of +1.2% for <10 MU, a dose-rate dependency deviation of +0.5%, and significantly poorer sensitivity due to its small volume. There is a difference in field sizes between manufacturer specs and derived field sizes. The measured PDD, profiles, and OPFs of the Semiflex 3D chamber were within 1% of each other for all square field sizes set under linac for the 6FFF beam. It was discovered to be an appropriate detector for relative dose measurements for 6 FFF beams with higher dose rates for field sizes more than or equal to 3 cm × 3 cm.

Investigation of emission plane control in GaInN/GaN multiple-quantum shells for efficient nanowire-based LEDs.

Significant attention has been directed toward core-shell GaInN/GaN multiple-quantum shell (MQS) nanowires (NWs) in the context of high-efficiency micro light-emitting diodes (micro-LEDs). These independent three-dimensional NWs offer the advantage of reducing the impact of sidewall etching regions. Furthermore, the emitting plane on the sidewalls demonstrates either nonpolar or semipolar orientation, while the dislocation density is exceptionally low. In this study, we assessed how changes in the NW morphology are affected by GaInN/GaN superlattice (SL) structures grown at varying growth temperatures, as well as control of the emission plane via the p-GaN shell and emission sizes. The SL growth rate was enhanced at elevated growth temperatures, accompanied by the shrinkage of the (0001)-plane and expansion of the (11̄01)-plane on the NWs. The samples exhibited a higher light output when the SLs were grown at elevated temperatures compared to those grown with lower temperatures. A similar trend was observed for the samples with a gradual temperature transition during the growth. These findings indicate that the dimensions of the (0001)-plane can be controlled through SL growth, which in turn influences the emission properties of NW-LEDs. In addition, the emission properties of NW-LEDs with different growth time p-GaN shells and different emission sizes were investigated. Based on the NW-LED characteristics, it was revealed that the weak emission of the (0001)-plane was the dominant factor for the limited light output, and the most effective way to realize high efficiency devices is to suppress current injection into the apex or minimize the grown (0001)-plane region. Overall, it is one promising way to control the emission planes of NWs, which holds significant relevance for the potential application of NW-LEDs in the realm of micro-LEDs.

Research on the Operation Optimization of Aggregate Resources under the Dual Factors of Power Grid and Economy

Under the influence of power grid and economy, the operation optimization of aggregate resources has high practical value and significance. For the problems of the existing aggregate resource operation optimization methods, this paper presents a completely new optimization method, considering the economic factors of power output, grid line transmission, grid stability, transformer capacity, energy storage charging and discharge loss, grid transmission loss, transformer loss and production arrangement of adjustable resource aggregate, and combined with the idea of electric power translation in the operation of the load device. By adopting a mixed-integer linear programming model (Mixed-Integer Linear Programming, MILP), the operation optimization problem of the aggregate resource is transformed into a mathematical planning problem to be solved, which effectively avoids the start-stop delay of the load device, and shortens the time taken to adjust power supply and demand, reduces the construction cost and operation and maintenance cost of the aggregate, and realizes the optimal allocation of electric power resources, improves the sustainable energy utilization rate and the stability of the power system, and provides a new direction for the continuous development and progress of the power system.

경기도 지자체 공간 활동의 기술적 효율성 비교연구

According to the analysis results for 2020, the efficiency of household activities was high in areas such as Yeoju, Gwangmyeong City, Yeoncheon and Yangpyeong County, which is a result of changes in economic activities in these areas. However, over time, some areas showed an increase in technical efficiency, while others showed a decline.&#x0D; In 2020, the efficiency of commercial activities significantly improved in areas such as Pyeongtaek, Icheon, Anseong, Hwaseong, Gwangju, Yeoju and Pocheon City compared to 2008, but Hanam and Guri saw a decrease in technical efficiency. These results demonstrate that local economic conditions, industrial structure, infrastructure, and policies have a significant impact on the efficiency of spatial activities.&#x0D; Yangpyeong County, which had the lowest efficiency in commercial activities in 2008, showed the highest efficiency in household and commercial activities in 2020. This reflects the county's active economic activities through efficient resource utilization, proximity to Suwon, development of transportation and logistics infrastructure, and the formation of various industrial parks and commercial facilities played a major role in revitalizing Yangpyeong County.&#x0D; This study confirmed the need for regional and real estate development to increase output effects through the quantitative measurement of spatial activities and the rational designation of land areas as input factors. Accordingly, it suggested the need for efficient management of input and output factors in each local government.

ФОРМАЛІЗАЦІЯ ТА ІЄРАРХІЧНА КЛАСИФІКАЦІЯ ПАРАМЕТРІВ ВПЛИВУ НА ЕНЕРГОЕФЕКТИВНІСТЬ ОГОРОДЖУВАЛЬНИХ КОНСТРУКЦІЙ БУДІВЕЛЬ

Based on the results of the analysis of research on the development of scientific and methodological foundations and practical experience of organizing and managing the technological process to increase the energy efficiency of buildings, the main factors influencing the amount of energy costs during the life cycle to ensure the standard sanitary and hygienic parameters of the premises were identified. The necessity of identifying the regularities of the methodological foundations of optimization of factors influencing the energy efficiency of the enclosing structures of buildings is emphasized. It is pointed out the expediency of taking into account the total energy consumption during all stages of the life cycle of buildings when determining the energy efficiency of the enclosing structures of buildings.&#x0D; Mathematical modeling of multi-parameter assessment of energy efficiency of building enclosing structures is expedient to be performed using the method of fuzzy logic and linguistic variables. The proposed hierarchical classification and formalization of the parameters influencing the energy efficiency of the enclosing structures of buildings, which are a fuzzy knowledge base combining linguistic assessments of input and output factors that affect the target function, are given.&#x0D; The total energy expenditure for the entire life cycle to ensure the energy efficiency of the enclosing structures of buildings at the system level as a linguistic variable is characterized by functional dependence on the linguistic variables that characterize the energy expenditure for capital construction, operation and recycling of materials. The generalized model of the relationship between the parameters of influence at the system level on ensuring the energy efficiency of the enclosing structures of buildings as their hierarchical classification is presented in the form of a logical conclusion tree. The root of the logical conclusion tree determines the total energy consumption to ensure the energy efficiency of the enclosing structures of buildings, and the hanging vertices are the determining parameters of influence, the variation of which allows finding the optimal value of the objective function according to the specified criteria.

An FCM based weather type classification method considering photovoltaic output and meteorological characteristics and its application in power interval forecasting

AbstractWith the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop reliable PV power interval forecasting technology. However, the complex relationship between PV output and meteorological factors makes it challenging for a single forecasting model to meet the forecasting demand. To solve this problem, this paper proposes a weather classification method that takes into account both PV output and meteorological characteristics. Initially, the relationship between PV output and meteorological factors is analyzed, and weather types are classified using fuzzy c‐means algorithm (FCM). Then, an extreme learning machine (ELM) is employed to establish point forecasting model. By combining kernel density estimation, a PV power generation interval forecasting model is derived. The results demonstrate that the FCM‐ELM model achieves higher forecasting accuracy and narrower interval width compared to traditional ELM models, with accuracy improvement of more than 2%. Additionally, the proposed method outperforms seasonal models with an accuracy improvement of more than 1%. The contribution of this paper includes identifying the limitations of traditional weather classification methods, proposing a novel multi‐model approach for PV interval forecasting.

Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks

This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions.

Magnetic field influence on the light yield from fiber-coupled BCF-60 plastic scintillators of relevance for output factor dosimetry in MR-linacs

Organic plastic scintillators are of interest for ionizing radiation dosimetry in megavoltage photon beams because plastic scintillators have a mass density very similar to that of water. This leads to insignificant perturbation of the electron fluence at the point of measurement in a water phantom. This feature is a benefit for dosimetry in strong magnetic fields (e.g., 1.5 T) as found in linacs with magnetic resonance imaging. The objective of this work was to quantify if the light yield per dose for the scintillating fiber BCF-60 material from Saint-Gobain Ceramics and Plastics Inc. is constant regardless of the magnetic flux density. This question is of importance for establishing traceable measurement in MR linacs using this detector type. Experiments were carried out using an accelerator combined with an electromagnet (max 0.7 T). Scintillator probes were read out using chromatic stem-removal techniques based on two optical channels or full spectral information. Reference dosimetry was carried out with PTW31010 and PTW31021 ionization chambers. TOPAS/GEANT4 was used for modelling. The light yield per dose for the BCF-60 was found to be strongly influenced by the magnitude of the magnetic field from about 1 mT to 0.7 T. The light yield per dose increased (1.3 ± 0.2)% (k = 1) from 1 mT to 10 mT and it increased (4.5 ± 0.9)% (k = 1) from 0 T to 0.7 T. Previous studies of the influence of magnetic fields on medical scintillator dosimetry have been unable to clearly identify if observed changes in scintillator response with magnetic field strength were related to changes in dose, stem signal removal, or scintillator light yield. In the current study of BCF-60, we see a clear change in light yield with magnetic field, and none of the other effects.

Effects of input enhancement and genre on L2 learners’ performance in the continuation writing task

Despite much research on xu-argument based continuation writing tasks, little has been done to explore its role with multiple factors in language output. This study, recruiting 80 Chinese high school students, attempted to investigate the effects of input enhancement and genre on Chinese EFL learners’ linguistic performance measured by complexity, accuracy, and fluency (CAF) in continuation writing tasks. A two-factor mixed experimental design was adopted, and simple effect tests were conducted to analyze the interaction effect. It was found that: (a) Input enhancement did not significantly impact complexity and fluency, but significantly impacted accuracy; genre had significant effects on complexity, accuracy, and fluency. (b) There was interaction between input enhancement and genre in accuracy, but not in complexity and fluency. Pedagogical implications are discussed.

CO2 emission prediction based on carbon verification data of 17 thermal power enterprises in Gansu Province.

The energy and power industry is an important field for CO2 emission reduction. The CO2 emitted by thermal power enterprises is a major cause of global climate change, and also a key challenge for China to achieve the goals of "carbon peaking and carbon neutrality." Therefore, it is essential to scientifically and accurately predict the CO2 emissions of key thermal power enterprises in the region. This will guide carbon reduction strategies and policy recommendations for leaders, and also provide a valuable reference for similar regions globally. This study utilizes the factor analysis method to extract the common factors influencing CO2 emissions based on the carbon verification data of 17 thermal power enterprises in Gansu Province. Additionally, the DISO (distance between indices of simulation and observation) index is employed to comprehensively evaluate three prediction models, namely multiple linear regression, support vector regression, and GA-BP neural network. Ultimately, this study provides a reasonable prediction of CO2 emissions for the aforementioned enterprises in Gansu Province. The results show that the three common factors obtained by factor analysis, namely energy consumption and output factor, energy quality factor, and energy efficiency factor, can effectively predict the CO2 emissions from thermal power enterprises. In the three prediction models, GA-BP neural network has the best overall performance with DISO value of 0.95, RMSE value of 11848.236, and MAE value of 7880.543. Over the period 2022-2030, CO2 emissions from 17 thermal power enterprises in Gansu Province are predicted to increase. Under the low-carbon, scenario baseline, and high-carbon scenarios, the CO2 emissions will reach 71.58 Mt, 79.25 Mt, and 87.97 Mt, respectively, by 2030.

Effects of tissue heterogeneity and comparisons of collapsed cone and Monte Carlo fast neutron patient dosimetry using the University of Washington clinical neutron therapy system (CNTS)

Fast neutron therapy is a high linear energy transfer (LET) radiation treatment modality offering advantages over low LET radiations. Multileaf collimator technology reduces normal-tissue dose (toxicity) and makes neutron therapy more comparable to MV x-ray treatments. Published clinical-trial and other experiences with fast neutron therapy are reported. Early comparative studies failed to consider differences in target-dose spatial conformality between x-ray and neutron treatments, which is especially important for organs-at-risk close to tumor targets. Treatments planning systems (TPS) for high-energy neutrons lag behind TPS tools for MV x-rays, creating challenges for comparative studies of clinical outcomes. A previously published Monte Carlo model of the University of Washington (UW) Clinical Neutron Therapy System (CNTS) is refined and integrated with the RayStation TPS as an external dose planning/verification tool. The collapsed cone (CC) dose calculations in the TPS are based on measured dose profiles and output factors in water, with the absolute dose determined using a tissue-equivalent ionization chamber. For comparison, independent (external) Monte Carlo simulation computes dose on a voxel-by-voxel basis using an atlas that maps Hounsfield Unit (HU) numbers to elemental composition and density. Although the CC algorithm in the TPS accurately computes neutron dose to water compared to Monte Carlo calculations, calculated dose to water differs from bone or tissue depending largely on hydrogen content. Therefore, the elemental composition of tissue and bone, rather than the material or electron density, affects fast neutron dose. While the CC algorithm suffices for reproducible patient dosimetry in fast neutron therapy, adopting methods that consider tissue heterogeneity would enhance patient-specific neutron dose accuracy relative to national standards for other types of ionizing radiation. Corrections for tissue composition have a significant impact on absolute dose and the relative biological effectiveness (RBE) of neutron treatments compared to other radiation types (MV x-rays, protons, and carbon ions).

Ultrahigh-porosity Ranunculus-like MgO adsorbent coupled with predictive deep belief networks: A transformative method for phosphorus treatment

Phosphorus is a nonrenewable material with a finite supply on Earth; however, due to the rapid growth of the manufacturing industry, phosphorus contamination has become a global concern. Therefore, this study highlights the remarkable potential of ranunculus-like MgO (MO4–MO6) as superior adsorbents for phosphate removal and recovery. Furthermore, MO6 stands out with an impressive adsorption capacity of 596.88 mg/g and a high efficacy across a wide pH range (2–10) under varying coexisting ion concentrations. MO6 outperforms the top current adsorbents for phosphate removal. The process follows Pseudo-second-order and Langmuir models, indicating chemical interactions between the phosphate species and homogeneous MO6 monolayer. MO6 maintains 80 % removal and 96 % recovery after five cycles and adheres to the WHO and EUWFD regulations for residual elements in water. FT-IR and XPS analyses further reveal the underlying mechanisms, including ion exchange, electrostatic, and acid-base interactions. Ten machine learning (ML) models were applied to simultaneously predict multi-criteria (sorption capacity, removal efficiency, final pH, and Mg leakage) affected by 15 diverse environmental conditions. Traditional ML models and deep neural networks have poor accuracy, particularly for removal efficiency. However, a breakthrough was achieved by the developed deep belief network (DBN) with unparalleled performance (MAE = 1.3289, RMSE = 5.2552, R2 = 0.9926) across all output features, surpassing all current studies using thousands of data points for only one output factor. These captivating MO6 and DBN models also have immense potential for effectively applying in the real water test with error < 5 %, opening immense horizons for transformative methods, particularly in phosphate removal and recovery.

A Monte Carlo model of an agility head for a 6 MV Elekta photon beam

IntroductionWith the advance in radiotherapy delivery, the need for a method allowing for a precise dose calculation increased. Monte Carlo modelling of radiotherapy dosages has become an important methodology for the evaluation of dose and beam performance, especially given recent advances in radiotherapy. In this study, an Elekta equipped with an Agility multileaf collimator will be modelled and validated in order to be used in further studies. MethodologyA 6 MV photon beam for the Elekta Synergy linear accelerator with an Agility head was simulated, adjusted, and confirmed using the BEAMnrc/EGSnrc algorithm. Tuning involved comparing percentage depth dose with measured data using different incident electron energies, as analyzed using the gamma index. The best energy so calculated was then used with different full half width maximums (FWHMs) and different angular divergences in order to tune the lateral profiles. Output factors for different field sizes were validated with measurements. Then, the validated model was checked for different depths. ResultsFor PDD comparison, the best electron energy was found to be 6.6 MeV, where the gamma analysis is 100%. A 0.15 cm circle FWHM was found to have the best FWHM. with an angular diverge of 0.03°. The output factors match measured values, proving that the model can achieve a suitably high accuracy. ConclusionThe validated model can be used in advanced radiotherapy dose calculation such as in VMAT, IMRT, and Stereotactic radiotherapy. The model needs to be validated in future studies for smaller field sizes, surface dose, and out-of-field dosimetry.

SMART MINING: JOINT MODEL FOR PARAMETRIZATION OF COAL EXCAVATION PROCESS BASED ON ARTIFICIAL NEURAL NETWORKS

&amp;lt;p&amp;gt;In the present paper we propose a new artificial neural network model for the estimation of coal cutting resistance and excavator performance as a nonlinear relationship between the examined input (excavator movement angle in the left and right direction, slice height and thickness, coal unit weight, compressive and shear strength) and output factors (excavator effective capacity, maximum current/power/force/energy consumption, linear and areal cutting resistance). We analyze the dataset collected from three open-pit coal mines in Serbia: Field D, Tamnava Eastern Field and Tamnava Western Field (all part of the Kolubara coal basin). The model is developed using a multilayer feed-forward neural network, with a Levenberg-Marquardt learning algorithm. Results of the preformed analysis indicate satisfying statistical accuracyof the developed model (R&amp;amp;gt;0.9). Additionally, we analyze the individual effects of input factors on the properties of coal cutting resistance and performance of the excavator, by invokling the multiple linear regression. As a result, we single out the statististically significant and physically possible interactions between the individual controlling factors&amp;lt;/p&amp;gt;

Influence of magnetic field on a novel scintillation dosimeter in a 1.5 T MR-linac.

For commissioning and quality assurance for adaptive workflows on the MR-linac, a dosimeter which can measure time-resolved dose during MR image acquisition is desired. The Blue Physics model 10 scintillation dosimeter is potentially an ideal detector for such measurements. However, some detectors can be influenced by the magnetic field of the MR-linac. To assess the calibration methods and magnetic field dependency of the Blue Physics scintillator in the 1.5 T MR-linac. Several calibration methods were assessed for robustness. Detector characteristics and the influence of the calibration methods were assessed based on dose reproducibility, dose linearity, dose rate dependency, relative output factor (ROF), percentage depth dose profile, axial rotation and the radial detector orientation with respect to the magnetic field. The potential application of time-resolved dynamic dose measurements during MRI acquisition was assessed. A variation of calibration factors was observed for different calibration methods. Dose reproducibility, dose linearity and dose rate stability were all found to be within tolerance and were not significantly affected by different calibration methods. Measurements with the detector showed good correspondence with reference chambers. The ROF and radial orientation dependence measurements were influenced by the calibration method used. Axial detector dependence was assessed and relative readout differences of up to 2.5% were observed. A maximum readout difference of 10.8% was obtained when rotating the detector with respect to the magnetic field. Importantly, measurements with and without MR image acquisition were consistent for both static and dynamic situations. The Blue Physics scintillation detector is suitable for relative dosimetry in the 1.5 T MR-linac when measurements are within or close to calibration conditions.

Emergency Department Overcrowding in Canada

What Is the Issue?&#x0D; &#x0D; Emergency departments (EDs) across Canada are under strain and experiencing overcrowding, a situation that arises when the demand for health services in the ED exceeds the capacity of the health system — which includes the ED, hospital, and community — to provide quality care in a reasonable amount of time.&#x0D; ED overcrowding is contributing to a deteriorating standard of care as health care providers and staff become overworked and burned out, is putting health and lives at risk, and is placing additional strain on an already overwhelmed health care system.&#x0D; &#x0D; What Did We Do?&#x0D; &#x0D; The CADTH Health Technology Expert Review Panel (HTERP) convened to develop objective, impartial, trusted pan-Canadian guidance to inform decisions about which evidence-informed solutions should be considered to help alleviate ED overcrowding in Canada.&#x0D; &#x0D; What Is HTERP’s Position on ED Overcrowding?&#x0D; &#x0D; ED overcrowding is a complex health system issue. EDs operate within hospitals and broader health and social systems, which means that accountability for causes, impacts, and solutions do not lie solely within the ED and its operations.&#x0D; Output factors (e.g., misalignment between acute care bed capacity within the hospital and population needs) and input factors (e.g., misalignment between care available in the community and population needs, including care outside of regular business hours) respectively, are the main contributors to ED overcrowding in Canada. Health system capacity is not aligned with, nor has kept pace with, the growing and changing health care needs of the population, which results in overcrowded EDs.&#x0D; Health systems will observe better results by implementing strategies that improve patient flow and focus on output and input factors relative to throughput factors.&#x0D; &#x0D; What Is HTERP’s Guidance to Help Alleviate ED Overcrowding?&#x0D; &#x0D; Interventions to alleviate overcrowding need to align with the main contributing factors to ED overcrowding in the particular context in which they will be implemented.&#x0D; Understanding the context in which ED overcrowding is occurring, with attention to bottlenecks to patient flow, should be the first step to identify evidence-informed solutions.&#x0D; Transparency and accountability should be key principles in ED, hospital, and health system operations. HTERP recommends identifying and ensuring clear roles, responsibilities, and reporting relationships embedded within an accountability framework for ED overcrowding across health system partners, including a commitment to act on data.&#x0D; Each province and territory should mandate consistent and comprehensive reporting by all hospitals to the Canadian Institute of Health Information’s National Ambulatory Care Reporting System (NACRS) database.&#x0D; HTERP’s guidance includes an Evidence Navigation Guide to support identification of evidence-informed interventions to help alleviate ED overcrowding.&#x0D;

The Effect of Winding Yarn (Just In Time) on Winding Machine Productivity

An appropriate production system (just in time) is a concept that supports cost management to anticipate changes that occur in the industrial environment due to advances in technology and automation. In the just-in-time concept, production is carried out through a certain amount of inventory (inventory equals 0). This elimination of inventory amounts automatically eliminates carrying and other costs, leading to a decrease in tolerance levels for product errors.&#x0D; The aim of this research is to find out and examine the influencing factors of input and output, just in time simultaneously which have a significant influence on production machines effectively and efficiently at PT. Asindo, and to find out and research the dominant influencing factors of just in time on production machines effectively and efficiently at PT. Asindo.&#x0D; The results of the analysis show that the first hypothesis proposed in this research is that just-in-time production factors simultaneously have a significant influence on effective and efficient production machines at PT Asindo. This is supported by the results of the t-test, where the results show thatof (19.75) &gt; t table of (2.03693). By looking at the value of R square = R=0.96135, meaning that the up-down variation of variable Y (effective and efficient production machines at PT. Asindo) is 92.419%, influenced by the independent variables in the model and the remaining 7.581% is due to other factors not included in this analysis. This means that the contribution of machine productivity in this model is good because it is more than 50%.&#x0D; From a series of design activities to achieve high production volumes with minimum inventory or minimum defective products for production and finished products, where the winding machine in production only produces output according to demand requirements.

Field output correction factors of small static field for IBA razor nanochamber

Purpose. The goal of this work is present results of field output factors (OF) using an IBA CC003 (Razor NanoChamber) and compare these results with PTW 60019 (MicroDiamond) and IBA Razor Diode. The experimental results for IBA CC003 were also compared with Monte Carlo (MC) Simulation, using Penelope and Ulysses programs. In addition, field output correction factors () for IBA CC003 were derived with three different methods: (1) using PTW 60019 and IBA Razor as reference detectors; (2) comparison between MC and experimental measurements; and (3) using only MC.Material and Methods. The beam collimation included in this study were (1) square field size between 10 × 10 and 0.5 × 0.5 cm2 defined by the MLC and jaws and (2) cones of different diameters. For IBA CC003 it was determined the polarity and ion collection efficiency correction factors in parallel and perpendicular orientation.Results. The results indicate (1) the variation of polarity effect with the field size is relevant for the determination of OF using IBA CC003, especially for parallel orientation; (2) there is no significant variation of the ion collection efficiency with the field size using IBA CC003 in parallel orientation; (3) OF differences between IBA CC003 and PTW 60019/IBA Razor, and experimental and MC results, increase with decreasing field size;The results indicate (1) using the first and second method, increase with decreasing field size, which can be related with the influence of the volume effect and (2) using the third method, decrease with decreasing field size, which can be explained by the perturbation effect.Conclusions. Our results demonstrate the need of applying for IBA CC003 for 1 cm, to compensate for volume averaging and perturbations effects.

Characteristics of a plastic scintillation detector in photon beam dosimetry.

Plastic scintillating detectors (PSD) have gained popularity due to small size and are ideally suited in small-field dosimetry due to no correction needed and hence detector reading can be compared to dose. Likewise, these detectors are active and water equivalent. A new PSD from Blue Physics is characterized in photon beam. Innovation in small-field dosimetry detector has led us to examine Blue Physics PSD (BP-PSD) for use in photon beams from linear accelerator. BP-PSD was acquired and its characteristics were evaluated in photon beams from a Varian TrueBeam. Data were collected in a 3D water tank. Standard parameters; dose, dose rate, energy, angular dependence and temperature dependence were studied. Depth dose, profiles and output in a reference condition as well as small fields were measured. BP-PSD is versatile and provides data very similar to an ion chamber when Cerenkov radiation is properly accounted. This device measures data pulse by pulse which very few detectors can perform. The differences between ion chamber data and PSD are<2% in most cases. The angular dependence is a bit pronounces to 1.5% which is due to PSD housing. Depth dose and profiles are comparable within<1% to an ion chamber. For small fields this detector provides suitable field output factor compared to other detectors and Monte Carlo (MC) simulated data without any added correction factor. The characteristics of Blue Physics PSD is uniquely suitable in photon beam and more so in small fields. The data are reproducible compared to ion chamber for most parameters and ideally suitable for small-field dosimetry without any correction factor.