Histogram Research Articles

Determination of Geochemical Background and Baseline and Research on Geochemical Zoning in the Desert and Sandy Areas of China

Resources in deserts and sandy landscapes have potential for development, but existing surveys and sampling have not collected desert soil samples. As such, the geochemical background of these spaces remains unexplored due to the vastness and desolation of deserts. Therefore, researching the geochemical background values and geochemical baseline values of deserts is of long-term significance. Our research indicates that in addition to macrostructural environmental divisions, microelement geochemistry can also be used for geological unit zoning. In this paper, geochemical background and geochemical baseline values of 61 desert elements were calculated using the iterative method, frequency histograms method, and multifractal concentration-area method. It also analyzes the distribution characteristics of major, trace, and rare earth elements, and divides the 12 desert sand regions into different geochemical zones. This paper determines, for the first time, the geochemical background values of elements in Chinese deserts, filling the gap in the study of desert background values. By combining machine learning methods, different deserts have been divided into three geochemical zones. This research will greatly enhance our ability to interpret the geochemical distribution and evolutionary patterns of desert elements in China, and it has important scientific significance and practical value for desert research.

Evaluating the potential of a wake steering co-design for wind farm layout optimization through a tailored genetic algorithm

Abstract. Wake steering represents a viable solution to mitigate the wake effect within a wind farm. New research that considers the effect of the control strategy within the layout optimization is emerging, adopting a co-design approach. This study estimates the potential of this technique within the layout optimization for a wide range of realistic conditions. To capture the benefits of such methods, a genetic algorithm tailored to the layout optimization problem has been developed in this work; hence this is referred to as a layout optimization genetic algorithm (LO-GA). The crossover phase is designed to recognize and exploit the differences and the similarities between parent layouts, whereas the randomness of the mutation is limited to improve the exploration of the design space. New relations have been introduced to calculate the geometric yaw angles based on the reciprocal positions between the turbines. For a base case of 16 turbines located at the Hollandse Kust Noord site, a gain in the annual energy production (AEP) between 0.3 % and 0.4 % is obtained when the co-design approach is adopted. This increases up to 0.6 % for larger farms, saturating after 25 turbines. However, the benefit of the co-design decreases in the case of low power densities or if the wind resource is highly unidirectional. On the other hand, in the case that wake steering is not applied during the operation of the farm, a decrease in the AEP up to 0.6 % is registered for a layout optimized with the co-design method. To prevent the risk related to future decisions on the control strategy, a multi-objective co-design approach is proposed. This is based on the simultaneous optimization of the layout for a scenario in which wake steering is applied versus a case where wake steering is not adopted during the operation of the farm. We have concluded that the solutions obtained with this method ensure an AEP gain higher than 0.3 % for a 16-turbine farm while limiting the loss to below 0.1 % in the case that wake steering is not applied. However, these AEP gains are affected by the size of the wind direction bins adopted in the simulations, enhancing the necessity of taking into account the wind direction errors and the yaw actuation constraints for a realistic evaluation of the co-design approach.

Clinical and epidemiological characteristics of patients with pheochromocytoma and retroperitoneal paraganglioma

Introduction. Pheochromocytoma/paraganglioma (PPGL) is a rare hormonally active tumor of chromaffin tissue. Delayed diagnosis of this disease can lead to severe, even fatal, complications. Surgical treatment of PPGL without the necessary preoperative preparation can cause intraoperative uncontrolled hemodynamic impairment. In addition, since 2017, PPGL has been recognized as a malignant tumor due to the presence of metastatic potential. All these factors determine the need for early diagnosis of PPGL, which is based, first of all, on knowledge of the clinical picture by specialists in various fields.Aim. To check and confirm the epidemiological characteristics of PPGL as well as the most common clinical symptoms in the whole group of PPGL and in subgroups of clinical forms; to form a risk group for screening for PPGL.Materials and methods. There were enrolled 353 patients in the study. These patients were operated on at the St. Petersburg State University Hospital in the period from 2010 to 2022 inclusive. All the patients were verified PPGL diagnosis histologically. Clinical symptoms were assessed: a) retrospectively, by analyzing complaints and anamnesis from the initial examination in medical histories upon admission to the hospital for surgical treatment b) both in the entire group as a whole and in subgroups of clinical forms (paroxysmal, constant, mixed, asymptomatic). Statistical analysis was performed using the Python 3.11 programming language (Python Software Foundation, USA). descriptive statistics of quantitative characteristics are presented in the form of medians, first and third quartiles in me format [q1; q3], categorical features – in the form of absolute and relative frequencies in the format n (%). the frequency histogram was constructed using the Matplotlib 3.8.2 library.Results. PPGL is most common in women over 40 years of age. The most common symptoms of PPGL are the following: a feeling of rapid heartbeat (55.95%), headache (41.07%), arterial hypertension (AH) in the form of hypertensive crises (55.65%), constant increase in blood pressure (38.39%), episodes of moderate increases in blood pressure (24.11%), sweating (30.65%), general weakness and fatigue (26.19%), hot flashes or chills (24.11%). These symptoms are formed into the criteria for screening for PPGL. In the asymptomatic clinical form – lower back pain is more common (14%); with paroxysmal – panic attacks (10%); with mixed – thirst and dry mouth (10%), and with constant form – the actual constant nature of hypertension (100%).Conclusion. In a large sample of patients with a rare pathology, the most characteristic symptom complex was identified, characteristic of both PPGL and individual clinical forms. A risk group has been formed for screening for PPGL.

Optimization of Parameter Estimation for Weibull-Distributed Interval-censored Data

Abstract The proposed method deals with a common type of data in reliability engineering with the number of failures checked at predetermined intervals. Different from the numerical algorithms, approximate analytical expressions for the Parameter Estimation of Weibull Distributions are obtained in this paper by fitting the probability density with the frequency histogram directly. Theoretical proof of the analytical solutions is given in terms of the dual least squares method, and the effectiveness of the analytical solutions is also verified through Monte Carlo simulations. Moreover, the method’s superiority is demonstrated by comparing it with the related techniques in the literature. Finally, the proposed method is applied to the Hong Kong casualty data from the World Health Organization, and the distribution of population in each age group among the unknown casualties is effectively predicted.

Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data’s storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to 99.58%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event.

Machine Learning in Agriculture: KNN-Based Classification of Fruits and Vegetables

In modern agriculture and the food industry, it is essential to classify fruits and vegetables accurately and efficiently to meet growing consumer demand and reduce post-harvest losses. Traditional manual methods are often labor-intensive and error prone, highlighting the need for automated solutions. This paper discusses the application of the k Nearest Neighbor (KNN) algorithm to fruit and vegetable recognition using image processing technology. In this study, image data sets are used for feature extraction using Directional Gradient Histogram (HOG) and dimensionality reduction using Principal Component Analysis (PCA). The accuracy of KNN model on verification set and test set reaches 97%, which proves its validity. Confusion matrix analysis and F1 score evaluation further revealed the strengths and areas of improvement of the model, particularly in distinguishing visually similar categories. The results show that the integration of artificial intelligence (especially KNN) offers great potential for the automation of agricultural classification tasks. Future studies could combine more advanced models, such as CNNS, with larger datasets to improve accuracy and robustness.

Exploring pharmaceutical powder behavior in commercial-scale bin blending: A DEM simulation study

Bin blending is one of the main steps in pharmaceutical production processes. Commercial-scale production of expensive products typically does not allow to perform a large number of experiments in order to optimize the process. Alternatively, Discrete Element Method (DEM) simulations can be used to evaluate the powder behavior (flow and blending pattern) during blending, identify the risks (e.g., segregation), and provide solutions to mitigate them. In this work, DEM simulations are used to investigate the blending of two granulated powders in commercial-scale cone and cylindrical (hoop) blenders. The DEM contact model parameters were calibrated based on the experimental compression and ring shear tests for both granulated powders to mimic the bulk powder behavior in the simulations. The model's output was compared to the experiments in one of the blending cases. The blending efficiency in the cone blenders was evaluated considering the fill levels, the presence of baffles, the rotating directions, the filling order, and the bin sizes. Furthermore, for the hoop blenders, the effects of blender's angle, rotation speed, and filling order were addressed. The main findings of the work were that, in cone blenders, the blending can be improved by introducing baffles and changing in the rotational direction frequently. In hoop blenders, blending can be improved by increasing the inclination angle from the horizontal plane and the rotational speed.

Detecting Patient Position Using Bed-Reaction Forces for Pressure Injury Prevention and Management

A key best practice to prevent and treat pressure injuries (PIs) is to ensure at-risk individuals are repositioned regularly. Our team designed a non-contact position detection system that predicts an individual’s position in bed using data from load cells under the bed legs. The system was originally designed to predict the individual’s position as left-side lying, right-side lying, or supine. Our previous work suggested that a higher precision for detecting position (classifying more than three positions) may be needed to determine whether key bony prominences on the pelvis at high risk of PIs have been off-loaded. The objective of this study was to determine the impact of categorizing participant position with higher precision using the system prediction F1 score. Data from 18 participants was collected from four load cells placed under the bed legs and a pelvis-mounted inertial measurement unit while the participants assumed 21 positions. The data was used to train classifiers to predict the participants’ transverse pelvic angle using three different position bin sizes (45°, ~30°, and 15°). A leave-one-participant-out cross validation approach was used to evaluate classifier performance for each bin size. Results indicated that our prediction F1 score dropped as the position category precision was increased.

Bias in O-Information Estimation.

Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information-theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of n=3 variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller-Maddow method is derived for the O-information. We find that for systems of n=3 variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of the O-information.

Intestinal Motility Dysfunction in Goto-Kakizaki Rats: Role of the Myenteric Plexus.

The morpho-quantitative analysis of myenteric plexus neurons in the small and large intestines of 120-day-old male GK rats was investigated. The diabetes was confirmed by high fasting blood glucose levels. The myenteric plexus was evaluated through wholemount immunofluorescence. The morpho-quantitative analyses included evaluating neuronal density (neurons per ganglion) of the total neuronal population, the cholinergic and nitrergic subpopulations, and enteric glial cells per ganglion. The cell body area of 100 neurons per segment per animal was measured. The total neurons and nitrergic subpopulation were unaltered in the GK rats' small and large intestines. The cholinergic subpopulation exhibited decreased density in the three segments of the small intestine and an increased number in the proximal colon of the GK rats. The number of enteric glial cells increased in the ileum of the GK rats, which could indicate enteric gliosis caused by the intestinal inflammatory state. The area of the cell body was increased in the total neuronal population of the jejunum and ileum of the GK rats. Frequency histograms of the cell body area distribution revealed the contribution of cholinergic neurons to larger areas in the jejunum and nitrergic neurons in the ileum. The constipation previously reported in GK rats might be explained by the decrease in the density of cholinergic neurons in the small intestine of this animal model.

New model for evaluating the impact response of steel fiber reinforced concrete subjected to the repeated drop-weight

The investigation of available experimental results obtained in the non-instrumented drop weight test recommended by American Concrete Institute on the steel-fiber-reinforced concrete reveals that the data distribution does not match a normal distribution. For this reason, logarithmic scales of the first-crack (number of blows to cause the first visible crack) and failure strengths (number of blows to spread the cracks sufficiently) are adopted to increase the fitness of the normal curve to the frequency histogram. The authors propose a novel model to predict the failure strength and evaluate the steel fiber effect on the post-cracking behavior of steel-fiber-reinforced concrete. The new model improves the prediction of the experimental results obtained in literature review for the various ranges of volume fraction of steel fiber analyzed. The proposed model is compared with other models using statistical parameters and the comparison shows a significant improvement considering different failure states. To verify the performance of the proposed model, in addition to results from another research, an experimental impact test was conducted. Besides, a reliable regression model is developed to validate experimental results. The findings underscore the potential of the proposed model to significantly enhance predictive accuracy, thereby proposing valuable insights for optimizing steel-fiber-reinforced concrete.

A system of ODEs for representing trends of CGM signals

Diabetes Mellitus is a metabolic disorder which may result in severe and potentially fatal complications if not well-treated and monitored. In this study, a quantitative analysis of the data collected using CGM (Continuous Glucose Monitoring) devices from eight subjects with type 2 diabetes in good metabolic control at the University Polyclinic Agostino Gemelli, Catholic University of the Sacred Heart, was carried out. In particular, a system of ordinary differential equations whose state variables are affected by a sequence of stochastic perturbations was proposed and used to extract more informative inferences from the patients’ data. For this work, Matlab and R programs were used to find the most appropriate values of the parameters (according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)) for each patient. Fitting was carried out by Particle Swarm Optimization to minimize the ordinary least squares error between the observed CGM data and the data from the ODE model. Goodness of fit tests were made in order to assess which probability distribution was best suitable for representing the waiting times computed from the model parameters. Finally, both parametric and non-parametric density estimation of the frequency histograms associated with the variability of the glucose elimination rate from blood were conducted and their representative parameters assessed from the data. The results show that the chosen models succeed in capturing most of the glucose fluctuations for almost every patient.

Evaluating and identifying changes in the rate of unplanned discharge among Department of Veterans Affairs (VA) facilities providing substance use disorder residential care

IntroductionQuality improvement (QI) methods play a critical role in ensuring that patients receive high-quality and timely care. Healthcare systems should use valid and reliable measures to inform QI efforts. Mental health settings including substance use disorder (SUD) residential programs have been slow to develop and incorporate quality measurement into routine practice. Unplanned discharge is of particular concern because this event is associated with harm including suicide. Healthcare systems require criteria that they can use to operationalize unplanned discharge as a quality measure in SUD residential programs. MethodsThe study included all discharges from the Department of Veterans Affairs (VA) residential SUD programs between 2018 and 2022. The study calculated crude and adjusted rates of irregular discharge. The study used the first two years of observation (2018–2019) in a logistic regression model to determine the parameter estimates for three important covariates, age, risk for homelessness, and principal diagnosis. The study tested permutations of bin size (N) and days (D) per bin to identify a single set of parameters to enable small and large facilities to have sufficient power to detect out-of-control processes (i.e., significant worsening or improvement in rates). Aligned with standard nomenclature, the study calculated the control limits based on three standard deviations (SD). Values that fell above or below three SD were statistically significant. ResultsThe cohort included 56 facilities (26,361 discharges). Irregular discharge was associated with younger age (18–40 years) and a principal diagnosis of a drug use disorder. Testing parameter values of 100 discharges (N) over 120 days (D) would yield enough power to detect modest relative changes to the irregular discharge rate for small and large facilities while testing frequently enough to make the evaluations temporally relevant. Because secular trends such as staff changes over time will impact results, the quality control method should allow for real-time feedback to those most proximal to the event. ConclusionsThe study created a set of parameters and a methodology that residential SUD programs can use to operationalize unplanned discharge locally. These data could assist programs in conducting QI work to address unplanned discharge and related harms.

Analysis of the Role of Garbage Bank in Improving the Community Economy in Medan Belawan District

Waste is a serious problem in Indonesia, including in Medan Belawan Regency. The Waste Bank Program is presented as a solution for waste management and community economic empowerment. This study aims to determine the role of the Waste Bank in community income. The research method used is descriptive statistics presented in the form of frequency distribution tables, histograms, steam and leaf (stem and leaf diagrams) or box plots (box line diagrams). Data collection was carried out through interviews, questionnaires and observations. The data analysis technique used is statistical analysis. The Waste Bank Program in Medan Belawan Regency has provided economic, social and environmental benefits. Economic benefits include increasing community income, creating jobs, and saving waste transportation costs. Social benefits include increasing public awareness of the importance of good waste management, and increasing public concern for the environment. Environmental benefits include reducing rubber production, reducing environmental pollution, and preserving natural resources. The Waste Bank Program in Medan Belawan Regency is one example of the application of an effective and sustainable green economy concept. This program can be a model for other waste management programs in Indonesia.

47 Physical activity, sedentary behavior, and sleep across lifespan in adults across European countries: Background and Design

Abstract Introduction Regular physical activity (PA) has benefits for health throughout the lifespan. In adults, PA benefits musculoskeletal, cardiometabolic health, and overall well-being (Dale et al., 2019). To promote health and well-being, the World Health Organization (WHO) recommends at least 150 minutes of moderate to vigorous (MV)PA or 75 min of vigorous-intensity PA (Bull et al., 2020). However, about 40 – 60% of the EU adults population is leading a predominantly sedentary lifestyle (Nikitara et al., 2021). Sedentary behavior (SB) includes activities such as watching television, playing computer games, and browsing the Internet (Wang et al., 2019). High levels of SB have been associated with decline of PA which, in turn, is associated with negative health outcomes, including obesity. Furthermore, there has been a growing focus on exploring the relationship between SB and sleep duration. This relation between SB and sleep duration is noteworthy, especially when considering that sleep duration guidelines recommend 7-9 hours of sleep for adults in the EU. In 2018, a systematic review synthesized findings from 56 studies that employed iso-temporal substitution models in PA, SB and sleep. Research has shown that changes in the time spent on these movement-related behaviors that make up a 24-h day can interact to influence health outcomes (Trembley et al., 2016). However, to -date, there are no specific recommendations on SB, and sleep is often not included. Therefore, this project will present the background, methods and design of a 24 hour movement summary of behaviors (PA, SB, sleep) for Europe. Methods Utilizing a cross-sectional design we will analyze existing data for PA, SB, and sleep from the Eurostat Organization on European adults 18 to 65+. Descriptive analysis using mean, median, and 95% Confidence Intervals, will be complemented by frequency distributions and histograms. Results The findings from this research have the potential to inform surveillance efforts, public health strategies, evidence-based guidelines, and possibly improve overall well-being. Conclusion Implications of this research may inform researchers on further questions to pursue, policy makers in resource allocation, and practitioners on where to focus intervention efforts.

Analysis of very small-small theropod footprints data from Imilchil tracksite (Hig Central Atlas. Morocco). A concordance model for a global scale

In the ensemble of theropod footprints from the Imilchil macrosite, a group of small size separated from the rest is distinguished. We had detected this grouping in other sites studied by our team in La Rioja and in the work of drawing up scatter plots and frequency histograms of the measurements, on a global scale, of the length and width of the theropod footprints (these data are available on request from Farlow [farlow@pfw.edu]). We analyze the particular grouping of small footprints, providing the study of the theropod footprint measurements of 14 new sites with dinosaur footprints in Imilchil and the graphical representation (frequency histograms and scatter plots) of the global data. The work has been done by projecting and comparing the length and width measurements of the Imilchil footprints and the global ones of what we call circumscribed geographical environments (TGC or Temporal Geographical Circumscriptions [cf. Mínguez Ceniceros et al., 2022]). The ichnotaxonomic study of the footprints referred to in this work can be found in Masrour et al., (2023). Finally we interpret the position of maxima and minima in the frequency diagrams, both of Imilchil, and of the other areas in which Farlow has found records (up to the year 2022).

A Protocol to Extract a Specific Genomic Region from a Public Whole-Genome Database and Modify Analytical Bin Length for Population Genetic Studies.

With the advent of "next-generation" sequencing and the continuous reduction in sequencing costs, an increasing amount of genomic data has emerged, such as whole-genome, whole-exome, and targeted sequencing data. These applications are popular not only in mega sequencing projects, such as the 1000 Genomes Project and UK BioBank, but also among individual researchers. Evolutionary genetic analyses, such as the dN/dS ratio and Tajima's D, are demanded more and more for whole-genome-level population data. These analyses are often carried out under a uniform custom bin size across the genome. However, these analyses require subdivision of a genomic region into functional units, such as protein-coding regions, introns, and untranslated regions, and computing these genetic measures for large-scale data remains challenging. In a recent investigation, we successfully devised a method to address this issue. This method requires a multi-sample VCF file containing population data, a reference genome, target regions in the BED file, and a list of samples to be included in the analysis. Given that the targeted regions are extracted in a new VCF file, targeted population genetic analysis can be performed. We conducted Tajima's D analysis using this approach on intact and pseudogenes, as well as non-coding regions.

Discussing the limitations of unconditional stability indicators in evaluating thermoacoustic quality of burners with flames

Assessing the thermoacoustic performance of designed combustors, with a focus on the stability quality factor, is crucial. Thermoacoustic instability in combustion appliances arises from intricate interactions among unsteady combustion, heat transfer, and (maybe) acoustic modes within the system. Accurate prediction of system stability requires modeling all components, including the burner with flame. Traditionally, the burner in the presence of combustion is represented as an acoustically (active) two-port block with passive upstream and downstream acoustic terminations. The dispersion relation of the thermoacoustic system is commonly used for anticipating eigen-frequencies and assessing stability. However, practical scenarios often lack specific information about upstream and downstream terminations during development. This raises a critical question: How can the thermoacoustic performance of burners and their associated flames be evaluated without specified acoustics? This article addresses this question by exploring the concept of unconditional stability in a generic two-port thermoacoustic system. The unconditional stability criteria have been used as quality indicators in designing electrical devices. This rich toolbox has been introduced in thermoacoustics. We first scrutinize assumptions underlying two most known unconditional stability-based criteria called [Formula: see text] and [Formula: see text] factors, connecting them to the general thermoacoustic problems. Then, the application of these criteria in assessing the thermoacoustic quality of burners with flames are discussed. This investigation revealed that while they are able to accurately predict the histogram of unstable frequencies and critical frequency bands, their use as reliable indicators to assess thermoacoustic quality in burners are not recommended due to their mathematical limitations and high level of conservatism of these factors.

Shellogy: reading abalone shells to empower reef-scale management

ABSTRACT Developing spatially explicit assessment and management techniques that engage stakeholder behaviour has been an enduring priority for fisheries targeting benthic marine invertebrates like abalone. This study describes the basis of ‘Shellogy’; a concept with the potential to empower reef-scaled management by using shell morphology to qualitatively evaluate abalone populations and their variability. The premise of shellogy is that a range of shell characteristics, previously established as distinguishing between populations with differing life history parameters, are also indicative of relative age and stage of maturation. Using the analysis of existing data, literature synthesis and illustrations, together with simulation of length frequency histograms, this study establishes from first principles that the morphometric ratios of abalone shells, along with superficial internal and external shell characteristics, change predictably with age and maturation. This study explains how recognising the connection between shell shape, and relative age and maturity, enables the appearance of shells in populations and catches, to be used to inform qualitative reef-scale assessment and facilitate stakeholder engagement with reef-scale management initiatives.

Validation of the Elekta Synergy Platform Linac at 6 MV Photon Beam using PRIMO Monte Carlo Software.

The objective was to validate the initial beam parameters of the Davao Doctors Hospital's 6 MV Elekta Synergy Platform linac, which performs to the specification of the commissioning data per our records using the gamma-index analysis toolkit integrated inside PRIMO software. In PRIMO, a sequence of optimization processes is performed, in which the measured and simulated percent depth dose (PDD) and lateral beam profiles at various depths are compared, using the stringent gamma-index passing rate at 1%/1 mm criteria (GPR11). Using four fields of sizes 3 cm × 3 cm, 4 cm × 4 cm, 5 cm × 5 cm, and 10 cm × 10 cm, the dose is calculated on a water phantom measuring 16.2 cm × 16.2 cm × 30.0 cm. In addition, one field of size 20 cm × 20 cm is used on a 50.0 cm × 50.0 cm × 30.0 cm water phantom with a bin size of 0.2 cm × 0.2 cm × 0.2 cm at a source-surface distance of 100.0 cm. For PDD and beam profiles comparison at the largest field size, the 6.5 MeV initial electron beam energy, 0.25 MeV full-width-half-maximum energy, 0.20 cm focal spot size, and 3° beam divergence tuned configuration yield GPR11 values of 94.0% and 97.7% (PRIMO PDD and lateral beam profile at 200 mm scan depth, respectively) with a statistical uncertainty of 2.9%. For lower field sizes, the GPR11 values are consistent at more than 90% for the PDD, whereas GPR11 values of 80.3% and 70.6% for the lateral beam profiles (at 15 mm and 200 mm scan depths) at 10 cm × 10 cm and 5 cm × 5 cm, respectively. The percentage difference between the measured and simulated PDD 20,10 ratios of not more than 2.45% is observed in all field settings. These tuned beam parameters are remarkably in agreement with the suggested beam parameters listed on the PRIMO website for the 6 MV Elekta linac which was optimized with a different set of measurements.