Distribution Of Variables Research Articles

Prevalence and influencing factors of medication-related burden among patients with late-life depression in typical city of eastern China: a cross-sectional study

AimTo evaluate the medication-related burden (MRB) of patients with late-life depression (LLD) and its influencing factors in China using the Living with Medicines Questionnaire-3 (LMQ-3), providing reference for reducing the MRB of those patients.MethodA cross-sectional study was conducted between September 2023 and January 2024 on 588 patients with LLD. LMQ-3 and MRB factors questionnaire were used for data collection. The distribution of variables was assessed using descriptive analysis, while analyses of Mann-Whitney and Kruskal-Wallis were performed to evaluate inter-group differences. To explore the MRB among patients with LLD and influencing factors, multiple linear regression analysis was performed.ResultsThe median (IQR) LMQ-3 score of 588 participants was 102 (18), indicating a moderate MRB level. Regression analysis revealed a significant trend toward higher perceived burden among patients aged 70–79 years old, living in rural areas, receiving more medical insurance settlements, using all cash, taking more than 5 drugs each time, and taking medicine more than 3 times a day (p < 0.05), which were risk factors for higher MRB. Conversely, patients who lived with their children, had an annual household income (including adult children) more than 50,000 Chinese Yuan, and no adverse drug reactions had lower LMQ-3 scores (p < 0.05), which were protective factors. Patients’ concerns about medicine, their lack of autonomy in medicine regimens, and the lack of communication between patients and doctors on treatment regimens were the main causes of the burden.ConclusionsResults of this study provided preliminary evidence of the MRB among patients with LLD. Age, residence, living status, annual household income, type of drug payment, quantity and frequency of medication, and adverse reactions significantly affected the perceived medication burden. It is advisable for health policy makers and health care providers to implement appropriate intervention strategies and burden reduction programs for this vulnerable group.

Shear capacity model of RC beams based on MCMC approach and reliability analysis

The research question revolves around accurately predicting the shear capacity of RC beams and enhancing the reliability of such prediction models. This study aims to refine the accuracy of the RC beam shear capacity model. To augment the safety margin, the enhanced model underwent further calibration through a reliability analysis. The shear strength models, rooted in Bayesian theory’s Markov Chain Monte Carlo (MCMC) method and the Modified Compressive Field Theory (MCFT), were introduced. A research database was curated from test data of 782 RC beams sets under shear stress. From this database, six mechanism models were juxtaposed with the MCMC models for comparison. To meet the desired reliability indexes ([Formula: see text] = 3.2, 3.7, 4.0) in these models, resistance factors ([Formula: see text]) were incorporated. Both the Monte Carlo and First Order Second Moment (FOSM) methods were employed for the reliability analysis, considering inherent model errors and the probability distribution of primary variables. The effect of different elements on the target reliability index was assessed using a tornado diagram. Findings showed that the refined shear model displayed minimal variance when benchmarked against models from existing literature and national codes, yielding a Vtest/ Vcal ratio close to 1.0. The shear components aligned with established mechanical mechanism. Depending on various operational conditions and load combinations, resistance factors fluctuated from 0.377 to 0.516 in the RC beams without stirrup, while resistance factors fluctuated from 0.479 to 0.621 in the RC beams with stirrup. Key determinants, such as the inherent uncertainty in computing model, the live loads, and the cylinder compression strength, significantly influenced the reliability index.

Socioeconomic, health, and social connectedness factors associated with self-rated health of octogenarians and nonagenarians in South Korea: urban and rural comparison

BackgroundThe aging population, including octogenarians (aged 80–89) and nonagenarians (aged 90–99), is rapidly increasing. Understanding their self-rated health in urban and rural settings is vital for public health policy development. This study examined factors associated with self-rated health among octogenarians and nonagenarians across urban and rural areas of South Korea.MethodsWe analyzed data from 21,896 older adults (aged 80–99) from the 2023 Korea Community Health Survey. Self-rated health was assessed on a 5-point scale and categorized as “good” or “poor.” Variables included age, residence area (urban/rural), sociodemographic characteristics, health behaviors, healthcare utilization, morbidity, and social interactions. Descriptive statistics were used to examine variable distributions, and logistic regression models identified factors associated with self-rated health in each age group and residential area.ResultsAmong octogenarians, 18.7% in urban areas reported good self-rated health, compared to 15.0% in rural areas. Conversely, a smaller proportion of nonagenarians in urban areas (13.6%) reported good self-rated health than those in rural areas (14.7%). Among octogenarians, having a high school education or higher was associated with better self-rated health compared to those with no formal education (urban OR = 1.67, 95% CI = 1.39–2.00; rural OR = 1.67, 95% CI = 1.30–2.14). Furthermore, a monthly household income of 3 million Korean Won (KRW) or more, compared to an income of less than 1 million KRW, was associated with better self-rated health among octogenarians (urban OR = 1.35, 95% CI = 1.09–1.66; rural OR = 1.54, 95% CI = 1.19–2.00). The associations between educational level and self-rated health were less pronounced among nonagenarians. Consistently across both age groups and settings, regular walking (OR range: 1.39 [95% CI = 1.21–1.60] for rural octogenarians to 2.17 [95% CI = 1.78–2.64] for urban nonagenarians), good self-rated oral health (OR range: 2.51 [95% CI = 1.96–3.22] for urban nonagenarians to 3.51 [95% CI = 3.01–4.09] for rural octogenarians), and participation in social activities (OR range: 1.23 [95% CI = 1.09–1.41] for urban octogenarians to 1.58 [95% CI = 1.20–2.07] for rural octogenarians) were positively associated with good self-rated health.ConclusionsCharacteristics associated with self-rated health differed significantly between octogenarians and nonagenarians in rural and urban areas. Public health strategies must support health-promoting behaviors such as regular walking, enhance oral health services, improve healthcare accessibility, reduce environmental stressors, and strengthen social support networks to promote health among octogenarians and nonagenarians.

Study on the Application Method of Aquifer Depth Distribution Patterns as Model Input on the Performance of a Physically Based Distributed Hydrologic Model

Groundwater discharge is critical for maintaining river flow during dry seasons, especially in lowland areas. Despite its significance, groundwater resources have often been overlooked highlighting the need for comprehensive studies amidst growing pressure to develop new water resources. This study focuses on the Soyang River Basin, South Korea, including its ungauged northern regions, the nearby DMZ (Demilitarized Zone), using the physically based Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. A three-year simulation was conducted to examine variable aquifer depth distribution patterns by assuming an inverse relationship between surface elevation and aquifer bottom depth. Three case studies (i.e., equal distribution, linear regression, and logarithmic regression) were evaluated and compared. The method to identity optimal aquifer depth distributions to enhance groundwater simulation accuracy in regions with significant topographical variation was incorporated. Groundwater levels at six monitoring sites showed that altitude-based variable aquifer depths outperformed the equal distribution case. The results showed strong agreement between simulated and observed values, particularly in the linear regression case with an R-squared statistic of 0.858 and Nash–Sutcliffe Efficiency index of 0.789, indicating that linear regression-based aquifer depth estimation can significantly improves long-term runoff modeling and groundwater simulation accuracy. The logarithmic regression case had the lowest relative peak error in peak flow. These findings highlight the importance of adjusting aquifer depth distributions in physically based hydrologic models to better reflect real-world conditions. Overall, this study contributes to advance groundwater modeling by integrating variable aquifer depth distributions into a physically based hydrologic model for large scale watersheds.

The finite-size effect on continuous-variable quantum key distribution in the Terahertz band

We conducted a study to evaluate the performance of Terahertz Continuous Variable Quantum Key Distribution (THz CVQKD) systems in atmospheric conditions with the finite-size effect. Our research delved into the secure key rates and transmission distances of THz CVQKD for four protocols, all based on Gaussian coherent states, under the threat of collective Gaussian attacks. These protocols include reverse reconciliation with homodyne detection, direct reconciliation with homodyne detection, reverse reconciliation with heterodyne detection, and direct reconciliation with heterodyne detection. Our findings revealed distinctive key rates across various THz frequencies and protocols. However, the finite-size effect had a diminishing impact on the key rates of CVQKD in all scenarios. Fortunately, by harnessing a substantial number of keys exceeding 1012 and optimizing modulation variance, both the key rates and transmission distances of THz CVQKD can be significantly extended. This research contributes to advancing the practicality of THz CVQKD technology.

Stability of Differential Equations with Random Impulses and Caputo-Type Fractional Derivatives

In this paper, we study nonlinear differential equations with Caputo fractional derivatives with respect to other functions and impulses. The main characteristic of the impulses is that the time between two consecutive impulsive moments is defined by random variables. These random variables are independent. As the distribution of these random variables is very important, we consider the Erlang distribution. It generalizes the exponential distribution, which is very appropriate for describing the time between the appearance of two consecutive events. We describe a detailed solution to the studied problem, which is a stochastic process. We define the p-exponential stability of the solutions and obtain sufficient conditions. The study is based on the application of appropriate Lyapunov functions. The obtained sufficient conditions depend not only on the nonlinear function and impulsive functions, but also on the function used in the fractional derivative. The obtained results are illustrated using some examples.

Influence of Genetic Ancestry on Gene-Environment Interactions of Polygenic Risk and Sociocultural Factors: Results from the Hispanic Community Health Study/Study of Latinos.

Many present analyses of Hispanic/Latino populations in epidemiologic research aggregate all members of this ethnic group, despite immense diversity in genetic backgrounds, environment, and culture between and across Hispanic/Latino background groups. Using the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we examined the role of self-identified background group and genetic ancestry proportions in gene-environment interactions influencing the relationship between body mass index (BMI) and a polygenic score for BMI (PGS BMI ). Weighted univariate and multivariable generalized linear models were executed to compare the effects of environmental variables identified a priori by McArdle et al. 2021. Both Amerindigenous (AME) ancestry proportion and background group identity were statistically modeled as confounders both through stratified and joint analyses to understand their influence on the relationship between BMI and PGS BMI , while incorporating gene-environment interactions of PGS x diet and PGS x age-at-immigration. After complex survey weighting, 7,075 participants remained in the analytic sample, representing individuals of six background groups: Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American. The distributions of key environmental and sociocultural variables were heterogeneous between Hispanic/Latino background groups. Associations of these variables with AME ancestry were similarly heterogeneous upon stratification, indicating confounding by background group. In a predictive model for BMI incorporating health, immigration, and environmental variables, PGS BMI performance decreased with increasing AME ancestry proportion. In this model, most statistically significant GxE interactions lost significance after ancestry and background stratification, except for PGS x age-at-immigration interactions in some strata: Mexican background individuals born in the US compared to those >=21 years old at migration (β=1.33, p<0.01), Dominican background individuals 6-12 years old at migration compared to those >=21 years old at migration (β=4.38, p<0.001), and Cuban background individuals 0-5 years old at migration compared to those >=21 years old at migration (β=2.20, p=0.015), where US-born includes individuals born in the US 50 states/DC. Controlling for self-identified background group identity and genetic ancestry did not eliminate statistically significant differences in interactions between AME ancestry and environmental variables in certain strata of AME ancestry among some Hispanic/Latino background groups in HCHS/SOL.

Soluble P-selectin as an inflammatory mediator potentially influencing endothelial activation in people living with HIV in sub-rural areas of Limpopo, South Africa.

There is a growing need to understand the potential role of soluble platelet selectin (sP-selectin) in sustained endothelial activation through increased levels of soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular adhesion-1 (sVCAM-1) in people living with HIV (PLWH) on highly active antiretroviral therapy (HAART). This was a cross-sectional study involving PLWH on HAART (n = 55), in comparison to PLWH not on treatment (HAART-naïve) (n = 29), and (iii) HIV negative controls (n = 48) from the Mankweng area in the Limpopo province, South Africa. We quantified serum levels of sP-selectin, together with sICAM-1 and sVCAM-1. Most of the HAART-exposed group were on treatment for <5 years. We further performed frequency distribution and descriptive statistics for categorical variables. Soluble P-selectin was positively correlated with sVCAM-1 (r = 0.469; p<0.001) in PLWH on HAART, even after adjusting for confounding factor such as age, BMI, and total cholesterol (r = 0.467; p<0.001). Moreover, in PLWH on HAART sP-selecting was independently associated with the release of sVCAM-1 (β = 0.445; p<0.001), even after adjusting for confounders (β = 0.475; p = 0.001). Serum levels of low-density lipoprotein cholesterol (LDL-C) (p = 0.004) and total cholesterol (p<0.001) were significantly higher in PLWH on HAART as compared to the HAART-naïve group. There is a need for more studies to investigate the role of sP-selectin in promoting endothelial activation and CVD-risk in PLWH on HAART, especially within the sub-Saharan Africa region.

Engression: extrapolation through the lens of distributional regression

Abstract Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates. Popular methods include linear and tree ensemble based quantile regression. We propose a neural network-based distributional regression methodology called ‘engression’. An engression model is generative in the sense that we can sample from the fitted conditional distribution and is also suitable for high-dimensional outcomes. Furthermore, we find that modelling the conditional distribution on training data can constrain the fitted function outside of the training support, which offers a new perspective to the challenging extrapolation problem in nonlinear regression. In particular, for ‘preadditive noise’ models, where noise is added to the covariates before applying a nonlinear transformation, we show that engression can successfully perform extrapolation under some assumptions such as monotonicity, whereas traditional regression approaches such as least-squares or quantile regression fall short under the same assumptions. Our empirical results, from both simulated and real data, validate the effectiveness of the engression method. The software implementations of engression are available in both R and Python.

Heavy metals in core sediments from the western Bay of Bengal: implications on historical pollution, eco-environmental risks, and potential sources

The western Bay of Bengal is particularly susceptible to the harmful impacts of increasing coastal pollution, as coastal population and urban development are occurring at unprecedented rates. The rapid urbanization and industrialization along the east coast of India coupled with the transfer of contaminants into the Bay of Bengal through riverine systems, causing a direct impact on marine ecosystems. In the present study, an attempt has been made to understand the heavy metal distribution on shelf sediments in the western part of the Bay of Bengal to infer their source, processes, and historical changes in marine pollution. Three short sediment core samples were collected and analysed for sediment texture, organic matter, and heavy metals. Cores 1 and 2 contain a high amount of sand content, representing shallow marine with moderate to high-energy conditions, and were deposited at a recent time. Core 3 was dominated by muddy sand to sandy mud, which denotes calm sedimentation without any turbulence, and these sediments were deposited under relatively low-energy conditions. The higher concentration of organic matter in the top layers of the core indicates both marine and terrestrial input. The heavy metal contents show quite heterogeneous and variable distributions from one element to another and from one core to another. The calculated pollution indices such as Contamination Factor (CF), and Geoaccumulation Index (Igeo) and Pollution Load Index (PLI) values indicate that all the core sub-samples are severely enriched and highly polluted by Cd and moderately polluted by Pb. The increased heavy metal concentration in the upper portion of the sediment cores and moderate to considerable ecological risks indicate that these metals have been deposited in the study area for the past few decades.

Research on Oil and Gas-Bearing Zone Prediction and Identification Based on the SVD–K-Means Algorithm—A Case Study of the WZ6-1 Oil-Bearing Structure in the Beibu Gulf Basin, South China Sea

The WZ6-1 oil-bearing structure in the Beibu Gulf Basin of the South China Sea has well-developed faults with significant variations in fault sealing capacity, resulting in a complex and highly variable distribution of oil, gas, and water, and limited understanding of hydrocarbon accumulation patterns. Traditional methods, such as single seismic attributes and linear fusion of multiple seismic attributes, have proven ineffective in identifying and predicting oil and gas-bearing areas in this region, leading to five unsuccessful wells. Through comprehensive analysis of drilled wells and seismic data, six types of horizon seismic attributes were selected. A novel approach for predicting oil-bearing zones was proposed, employing SVD–K-means nonlinear clustering for multiple seismic attribute fusion. The application results indicate: ① Singular value decomposition (SVD) technology not only reduces the correlation redundancy among seismic attribute data variables, but enables data dimensionality reduction and noise suppression, decreasing ambiguity in prediction results and enhancing reliability. ② The K-means nonlinear clustering method facilitates the nonlinear fusion of multiple seismic attribute parameters, effectively uncovering the nonlinear features of the underlying relationship between seismic attributes and reservoir oil-bearing characteristics, thereby amplifying the hydrocarbon information within the seismic attribute variables. ③ Compared to K-means, SVD–K-means demonstrates superior performance across all metrics, with an 18.4% increase in the SC coefficient, a 57.8% increase in the CH index, and a 24.7% improvement in the DB index. ④ The results of oil-bearing zone prediction using the SVD–K-means algorithm align well with the drilling outcomes in the study area and correspond to the geological patterns of hydrocarbon enrichment in this region. This has been confirmed by the high-yield industrial oil flow obtained from the newly drilled WZ6-1-A3 well. The SVD–K-means algorithm for predicting oil and gas-bearing zones provides a new approach for predicting hydrocarbon-rich areas in complex fault block structures with limited drilling and poor-quality seismic data.

The experience with Hugo™ robot-assisted surgery on complex gynecological patients in Panama.

The Hugo™ robotic assisted surgery system is a relatively new robotic platform developed by Medtronic. The study objective was to describe the experience of using Hugo™ robotic assisted surgery in gynecological surgeries and compare robotic assisted surgery-related outcomes between complex and non-complex gynecological patients at the Pacifica Salud Hospital. We performed secondary data retrospective analysis of 144 consecutive patients who underwent gynecological surgery with Hugo™ robotic assisted surgery system (Medtronic) at the Pacifica Salud hospital in Panama City from July 19, 2021, to August 3, 2023. Complex patients were defined as those with one or more risk factors for surgery complications. Descriptive analysis of participants' sociodemographic and robotic assisted surgery-related characteristics. Due to the non-normal distribution of the RAS-related numeric variables, we compared these variables between complex and non-complex cases of gynecological patients using Kruskal-Wallis's test. The study found that Hugo™ robotic assisted surgery system was safe for gynecological surgery in patients with and without risk factors for developing major surgery complications. None of the patients experienced any complications, and they had short hospital stays with low blood loss without requiring a blood transfusion. The Hugo™ robotic assisted surgery system was technically sound and did not present technical failures. The results could be a reference for adopting this technology and developing best practices in the Latin American region.

Kernel density estimation of a sensitive variable in the presence of auxiliary information

. Auxiliary information is widely used in various studies to improve the precision of estimation. In this article, we extend the application of auxiliary information within the context of randomized response techniques (RRT), building upon the prior research on kernel density estimation (KDE) under additive RRT models. Inspired by Mostafa and Ahmad (2019), we proposed a kernel density estimator that incorporates an auxiliary variable to enhance the accuracy of estimating the distribution of a sensitive variable. Extensive simulations are conducted to evaluate the performance of this proposed methodology, highlighting the advantages of utilizing auxiliary information and the impact of factors such as noise levels, sample size, and the correlation between the auxiliary variable and the sensitive variable.

Semantic and distributional patterns of Spanish negation with nouns and adjectives: A Lexical-Realizational Functional Grammar approach

This study examines the semantic and distributional characteristics of the Spanish negation no when it occurs before nouns and adjectives, specifically, whether these instances diverge from sentential negation, but also from negation via prefixes such as des- ‘dis-’ or in- ‘in-, un-’. Using data from the Spanish Web 2011 corpus, it is shown that the use of no before adjectives aligns with other forms of constituent negation, often resulting in a contradictory interpretation, that is similar to that of sentential negation. However, the interpretation of no preceding nouns exhibits a broader range of interpretations, depending on whether the noun refers to an eventuality, quality, or entity. In such instances, no shows parallels to negative prefixes, frequently indicating privation rather than contradiction. Consequently, no can be analyzed as a building block of phrases, expressing syntactic negation, behaving similarly to other adverbs, as well as a building block of words, expressing lexical negation, resembling Spanish prefix-like elements like cuasi ‘almost’ or ex ‘ex-’. The analysis is couched within the framework of Lexical-Realizational Functional Grammar (LRFG), integrating aspects of Lexical Functional Grammar and Distributed Morphology. This approach does not treat morphology and syntax as separate modules, thus allowing us to account for the variable distribution and interpretation of no by means of a single representation in vocabulary structure, drawing on constituent structure representations which are needed on independent grounds for adverbs and for prefix-like elements.

Food preferences in schoolchildren

Introduction. Understanding children’s eating behaviour is crucial for promoting healthy nutrition. Objective. To assess the food preferences in schoolchildren. Materials and methods. A mixed-mode survey was conducted in one hundred twelve 6 to 17 years schoolchildren, including 72 (70.5%) boys and 81 (72.3%) girl from Moscow. The average age was 12.23 ± 3.69 years (median = 13.0 years), with no significant age differences between genders (p = 0.855) or between Moscow and other regions (p = 0.690). The survey (α Cronbach = 0.891) included demographic and eating behaviour questions. Data were analyzed with SPSS using Spearman’s rank correlation, Mann–Whitney U test, Kruskal–Wallis test, and χ2 test, given the non-normal distribution of variables (p ≤ 0.001). Results. Daily, 11 (9.8%) of the respondents eat vegetables, 14 (12.5%) eat fruits or berries, 18 (16.1%) have breakfast, and 87 (77.7%) consume sweets. Additionally, 62 (55.4%) often skip main meals due to sweets; 81 (72.3%) always have sweets at home; and 51 (45.5%) favour of fast food. Children in families where sweets are always available are more likely to skip main meals and breakfast, consume sweets, and prefer fast food, but eat vegetables, fruits, and healthy breakfasts less frequently (p ≤ 0.001). Daily sweet consumption is linked to lower fruit intake (p ≤ 0.001). Demographic characteristics did not impact food preferences (0.068 ≤ p ≤ 0.946). The highest index of correct eating habits, equal to 0.86, was in 2 (1.8%) children. There is no relationship between the number of correct eating habits and age (p = 0.379) and gender (p = 0.172). The criterion of “correct eating habits” is (p = 0.046) higher in Moscow schoolchildren than in children from the regions. Conclusion. Unhealthy food preferences in children reflect parents’ non-compliance with healthy eating recommendations, which underscores the need for family initiatives in the field of healthy nutrition.

A New Centralized Statistical Monitoring Method for Detecting Atypical Distribution of Qualitative Variables in Multicenter Randomized Controlled Trials

Centralized statistical monitoring (CSM) detects clinical trial centers in which the distribution of a variable is atypical compared to its distribution in other centers. Most proposed CSM methods concern quantitative variables. Here we propose a new hierarchical Bayesian beta-binomial (HBBB) method for categorical variables and report the results of a simulation study assessing the performance of the method and of an application study using a real database to assess its usefulness. In the simulation study, sensitivity exceeded 90% when the sample size in the atypical center (N a ) was ≥ 20 and the difference in the proportion of events between the atypical center and the other centers ( δ ) was ≥ 0.4; when N a was ≥ 40 and δ ≥ 0.3; and when N a was ≥ 150 and δ ≥ 0.2. Specificity exceeded 90% when N a was ≥ 150 in all scenarios, and remained between 75% and 90% when N a was lower. In the application study, the method detected two centers in which N a was 50 and 200, and δ was 0.12 and 0.04, respectively. The performance of the HBBB method was similar to that proposed by competing approach. The modeling is easy and specificity is good in many scenarios with a limited sample size.

Predicting Electric Vehicle Charging Demand in Residential Areas Using POI Data and Decision‐Making Model

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Analysis of the degree of correlation of spatial distribution of electricity theft and exogenous variables: case study of Florianopolis, Brazil

Abstract This article presents a geospatial study case on electricity theft. The main objective is to identify the degree of correlation between exogenous variables and areas with a high density of irregular cases. Firstly, the geospatial study is carried out to asses the null hypothesis and check whether the data pattern presents clustering, for this the ANN method is applied, which ruled out the null hypothesis for the data set. Once the clustering pattern is confirmed, the spatial weight matrix is created to study spatial autocorrelation by applying Global Moran’s I and Local Moran’s I. Moran scatterplot is used to evaluate the degree of fitness, identify outliers, and local pockets of stationarity. The Local Moran index is used to determine the location of the clusters and the relationship between the points. In the data pre-processing step, spatial interpolation is implemented to the exogenous variables as a tool to better association of consumer units points and socioeconomic variables, the method utilized is IDW interpolation. The R-squared value of the spatial lag model after model tuning by feature selection was 87 % indicating that the model fit the observed data well.

Beyond a constant proton relative biological effectiveness: A survey of clinical and research perspectives among proton institutions in Europe and the United States.

Although proton relative biological effectiveness (RBE) depends on factors like linear energy transfer (LET), tissue properties, dose, and biological endpoint, a constant RBE of 1.1 is recommended in clinical practice. This study surveys proton institutions to explore activities using functionalities beyond a constant proton RBE. Research versions of RayStation integrate functionalities considering variable proton RBE, LET, proton track-ends, and dirty dose. A survey of 19 institutions in Europe and the United States, with these functionalities available, investigated clinical adoption and research prospects using a 25-question online questionnaire. Of the 16 institutions that responded (84% response rate), 13 were clinically active. These clinical institutions prescribe RBE=1.1 but also employ planning strategies centered around special beam arrangements to address potentially enhanced RBE effects in serially structured organs at risk (OARs). Clinical plan evaluation encompassed beam angles/spot position (69%), dose-averaged LET (LETd) (46%), and variable RBE distributions (38%). High ratings (discrete scale: 1-5) were reported for the research functionalities using linear LETd-RBE models, LETd, track-end frequency and dirty dose (averages: 4.0-4.8), while LQ-based phenomenological RBE models dependent on LETd scored lower for optimization (average: 2.2) but congruent for evaluation (average: 4.1). The institutions preferred LET reported as LETd (94%), computed in unit-density water (56%), for all protons (63%), and lean toward LETd-based phenomenological RBE models for clinical use (>50%). Proton institutions recognize RBE variability but adhere to a constant RBE while actively mitigating potential enhancements, particularly in serially structured OARs. Research efforts focus on planning techniques that utilize functionalities beyond a constant RBE, emphasizing standardized LET and RBE calculations to facilitate their adoption in clinical practice and improve clinical data collection. LETd calculated in unit-density water for all protons as input to adaptable phenomenological RBE models was the most suggested approach, aligning with predominant clinical LET and variable RBE reporting.

Accuracy estimation of a CFD multiphysics approach to study a mixed parallel and serpentine flow channels PEM fuel cell

Abstract This study presents a comprehensive analysis of a 50 cm2 fuel cell with mixed parallel and serpentine flow channels using Computational Fluid Dynamics modelling. Through rigorous validation against experimental measurements, the numerical model ensures accuracy in capturing convective and diffusive fluid flows, thermodynamic, and electrochemical phenomena and their mutual interaction. The study discusses a systematic procedure for achieving model convergence and identifies key parameters influencing the fitting of numerical polarization curves with experimental data. Operating within a temperature of 333 K, at atmospheric pressure, with a relative humidity of 100% for both the anode and cathode, the model offers crucial insights for advancing our understanding of fuel cell operation and guiding the development of more efficient and reliable technologies. In the activation region, reference exchange current densities and charge transfer coefficients were proved to play a significant role in reproducing the PEM fuel cell behaviour. Increasing the exchange current densities raises the voltage output in the activation polarization region while charge transfer coefficients affect the slope of the curve. In the ohmic region, a proton conduction coefficient of 1.84 and a contact resistance between gas diffusion layers and bipolar plates equal to 4.03e-7 Ω·m2 were used to fit the experimental polarization curve. After the tuning process, the numerical curve closely matches the experimental one with a maximum relative error of 2.40% and an R2 value of 0.9848. Moreover, a detailed analysis of the internal distribution of key variables was conducted for a specific point on the polarization curve (0.25 A/cm2). This analysis provided significant insights into the complex interplay of fluid dynamics, heat transfer, and electrochemical processes. Specifically, it was observed how the flow distribution, pressure drop, and reactant concentrations within the fuel cell channels and gas diffusion layers affect overall performance, highlighting the crucial role of convection and diffusion in reactant transport and the importance of managing heat for optimal cell operation.