Relative Importance Of Factors Research Articles

Spatial-temporal characteristics and the importance of environmental factors in relation to algal blooms in coastal seas

In the prediction of algal blooms with the artificial intelligence (AI) technology, the proper selection of the indices is crucial to the quality of the results. Although eutrophication and climate change have been identified as the main contributors to algal blooms, limited knowledge of the relationships among environmental factors hinders the accurate and correct AI prediction. To explore the importance of environmental factors in the occurrence of algal blooms in coastal seas and facilitate the selection of inputs for the prediction model, this study examines observed water quality data and analyzes their relationship using machine learning methods. It was found that nutrients, water temperature, and salinity play important roles in controlling algal blooms in coastal seas, and their influence varies depending on locations. The high-risk cases of algal bloom mainly occur in environments with medium salinity (22–28 psu), particularly in open estuaries with high nutrient levels. According to the feature importance analysis, water temperature presented the most significant impact on algal bloom risk compared to other environmental factors. The variations of water temperature and Chl-a concentration are synchronous when the water temperature is below 32 °C. Additionally, the relationships between inorganic nutrients (NO3−-NO2− and Ortho-P) and Chl-a exhibit asynchronous across multi-temporal scales. The uptake of inorganic nutrients occurs in large quantities within the 24 h prior to the boom in Chl-a concentration. This demonstrates that variations in concentration of inorganic nutrients can be considered as crucial factors in algal bloom predictions, and their significant decrease can be served as an important signal of an impending algal bloom. This study enhances our understanding of the mechanisms behind algal blooms and facilitates their management and prediction strategies in coastal seas.

Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach.

Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM). Patients with CRLM who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values. Among 1410 patients undergoing curative-intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4months (interquartile range [IQR] 29.9-46.7) versus 120.5months (IQR 97.2-134.0), respectively (p<0.001). The ML predictive model had c-index values of 0.77 (95% CI, 0.72-0.81) and 0.77 (95% CI, 0.73-0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy-to-use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/. An easy-to-use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative-intent resection for CRLM. This tool may help clinicians in decision-making related to treatment strategies for patients with CRLM.

An Empirical Analysis of Factors Affecting China's Comprehensive Grain Production Capacity

China, with the largest population in the world, is also the largest grain consumer. Grain security is fundamental to national welfare and the economy, playing a crucial role in socioeconomic development. Since the reform and opening-up, China's grain output has doubled, successfully overcoming the global grain crisis in 2006 and the global food crisis caused by the COVID-19 pandemic in 2020. However, we cannot be overly optimistic; we must realize that China's grain output growth has relied on soil fertility depletion and excessive pesticide and fertilizer usage, putting significant pressure on the environment. This paper analyzes the factors influencing China's grain output based on data from 1980 to 2020. Through this, it reflects the comprehensive grain production capacity and offers feasible suggestions based on the findings. The analysis is carried out from both theoretical and empirical perspectives. On the theoretical side, the paper considers the impact of variables such as the area of irrigated arable land (X1, in thousand hectares), total agricultural machinery power (X2, in ten thousand kilowatts), grain crop sowing area (X3, in thousand hectares), fertilizer use (X4, in ten thousand tons), and natural population growth rate (X5, in percentage) on grain output. On the empirical side, five grain-related indicators are selected for principal component analysis, and the model is tested using Eviews. The relative importance of factors affecting comprehensive grain production capacity varies with different stages of grain production and development. Finally, the paper offers recommendations for improving China's comprehensive grain production capacity based on the research findings.

Urbanization alters the relative importance of local and landscape factors affecting plant communities in the Tokyo megacity.

Plant communities are impacted by local factors (related to environmental filtering) and landscape factors (related to dispersal limitation). While many studies have shown that the relative importance of these factors in understanding plant community dynamics due to urbanization, little is known about how they are altered by urbanization-a significant threat to biodiversity. This study evaluates the relative importance of local environmental (local factors), landscape, and spatial (landscape factors) variables that influence plant communities in 34 urban green spaces comprising two different habitats (forests and grasslands) along the urban-rural gradients in the Tokyo megacity, Japan. To continuously assess the relative importance of each factor along the urban-rural gradients, we extracted 1000 landscapes within a certain range that contained several sites. Subsequently, the relative importance of each factor and urbanization rate (proportion of artificial built-up area) were estimated for each landscape. Our study found that the relative importance of both local and landscape factors decreased, while that of local factor for native species in forest habitats and that of landscape factors for native species in grassland habitats increased. Collectively, these findings suggest that city size and habitat characteristics must be considered when predicting changes in plant communities caused by urbanization.

Job Satisfaction of Female Workers in Different Garments Factories in Dhaka City: An Intertemporal Comparison

Job satisfaction is an attitude that reflects the extent to which an individual is gratified by or fulfilled in his/her job. It is an affective or emotional response toward various facets of one’s job. This meaning of job satisfaction implies that job satisfaction is not a unitary concept. Rather a person can be relatively satisfied with one aspect of his or her job and dissatisfied with one or more other aspects. Thus, we can differentiate at least two aspects of job satisfaction ⎯ facet satisfaction and overall satisfaction. Facet satisfaction is the tendency for an employee to be more or less satisfied with various facets of his or her job. A large number of related research has been conducted in different countries including Bangladesh. Alam (1986) conducted a research on the job satisfaction of female workers in different garment factories in Dhaka City in 1983. He measured the level of overall satisfaction and analyzed the nature of facet satisfaction of 200 female workers. He concluded that a minor percentage of female workers in different garment factories are satisfied with their jobs, and there is a positive relationship between their level of job satisfaction and the level of wages/salaries and other identified factors. The objective of the present study is to measure the overall level of job satisfaction of female workers in different garment factories and to identify factors contributing to job satisfaction and factors contributing to job dissatisfaction. The present study also ranks all identified factors according to their relative importance by using a formula developed by Alam in 1983 (Alam, 1986). Finally, the present study makes intertemporal comparisons of overall job satisfaction level and of the nature of facet satisfaction over the twenty two-year period (between 1983 and 2005). Such intertemporal comparisons are necessary because of the fact that over the last twenty two years, as the garment industries of the country has expanded significantly, the nature of human resource management in this sector has changed in many aspects. As a result, job satisfaction status and the absolute as well as relative importance of factors contributing to satisfaction level are expected to change.

Distribution, relationship, and environmental driving factors of chlorophyll-a and algal cell density: A national view of China

Chlorophyll-a (Chl-a) and algal cell density (ACD) are vital for assessing algal proliferation and eutrophication in aquatic ecosystems. Although Chl-a is often used as a proxy for ACD, its accuracy requires validation, and studies on their linear correlation are scarce. Additionally, Chl-a and ACD are influenced by various factors, including nutritional, economic, biochemical, physicochemical, and meteorological factors. However, the relative importance of these factors on Chl-a and ACD remains insufficiently studied. This study analyzed data from 57 lakes and reservoirs across China from March 2021 to February 2023, investigating the distribution patterns of Chl-a and ACD across different regions and seasons. The study employed a linear regression model to explore the correlation between Chl-a and ACD in various regions and seasons throughout China. Furthermore, Mantel test and generalized linear model were used to evaluated the relative importance of nutritional factors (total nitrogen (TN), total phosphorus (TP), TN/TP ratio (TN/TP), and ammonia nitrogen (NH3-N)), economic factors (gross domestic product), meteorological factors (surface pressure, net solar radiation, air temperature, wind speed (WS), and rainfall (RF)), as well as biochemical and physicochemical factors (turbidity (TUR), pH value, water temperature (WT), permanganate index, and dissolved oxygen) on Chl-a and ACD. Results showed that the average concentrations of Chl-a and ACD in South China were highest, at 12 μg/L and 19.5 × 106 cells/L, respectively. Seasonally, Chl-a peaked in spring and was lowest in winter, while ACD peaked in summer and was lowest in winter. Significant seasonal and regional variations in the Chl-a and ACD correlation were observed, with spring showing the strongest relationship. In Central China, Chl-a and ACD were significantly correlated throughout the four seasons, whereas correlations were less distinct in Eastern and Western regions. Therefore, Chl-a as a proxy for ACD requires caution. Nutrient factors (TN, TP, TN/TP, NH3-N) were identified as the primary drivers of Chl-a and ACD. Meteorological factors (WS, RF), along with biochemical and physicochemical factors (WT, TUR) also emerged as critical predictors of Chl-a and ACD, with spatial variations. This study validates Chl-a as a proxy for ACD, analyzes their spatiotemporal distribution, and assesses the influence of various factors on Chl-a and ACD, enhancing our understanding of algal dynamics in Chinese lakes and reservoirs.

Mythos and catharsis in Aristotle’s Poetics

Abstract The paper argues that catharsis in Aristotle’s Poetics is a two-pronged process, which applies both to the construction of the mythos by the poet and its reception by the audience. Catharsis may plausibly be taken as the poet’s distillation of events that constitute a mythos so as to clear away anything that might undermine its coherence or disturb its unity. The sequencing of fearful and pitiable events in the plot according to probability or necessity allows the audience to evaluate or reevaluate properly the relative importance of factors that determine or influence the behavior of the tragic characters and the fulfilment of their fate. In several cases at least, the plot also leads the audience to realize that other features such as the heroes’ social and intellectual advantages, which they and/or the characters may have considered important in principle, because of their cultural or social background, and/or in the early stages of the plot, were not involved in the downfall (or salvation) of the heroes. This gradual and plot-induced shift in the audience’s appreciation of the relative importance of various factors constitutes the catharsis experienced by the audience. They gain a clear(er) understanding of the reason(s) why the heroes suffer and how they bring about (or escape) disaster. Sophocles’ OT and Euripides’ IT, which Aristotle praises highly in the Poetics, are discussed as examples that illustrate how their plots lead the audience to experience catharsis.

The Shifting Reasons for Beveridge Curve Shifts

We discuss how the relative importance of factors that contribute to movements of the US Beveridge curve has changed from 1959 to 2023. We review these factors in the context of a simple flow analogy used to capture the main insights of search and matching theories of the labor market. Changes in inflow rates, related to demographics, accounted for Beveridge curve shifts between 1959 and 2000. A reduction in matching efficiency, that depressed unemployment outflows, shifted the curve outwards in the wake of the Great Recession. In contrast, the most recent shifts in the Beveridge curve appear driven by changes in the eagerness of workers to switch jobs. Finally, we argue that, while the Beveridge curve is a useful tool for relating unemployment and job openings to inflation, the link between these labor market indicators and inflation depends on whether and why the Beveridge curve shifted. Therefore, a careful examination of the factors underlying movements in the Beveridge curve is essential for drawing policy conclusions from the joint behavior of unemployment and job openings.

Understanding the reasons why career changers pursue or pass on a STEM teaching career: a Best–Worst Scaling (BWS) approach

AbstractIntractable shortages of science, technology, engineering, and mathematics (STEM) teachers have prompted international policy efforts to recruit career changers to the profession. This research determines the significant influences on career changers’ decisions to pursue or pass on STEM teaching careers. Surveys completed by 91 career changers from Queensland, Australia, were analysed with Best–Worst Scaling (BWS) methods and Margaret Archer’s theories of reflexivity to establish the relative importance of factors influencing career changers’ deliberations on a STEM teaching career. The social impact of teaching was the most influential factor for career changers considering STEM teaching. Career changers who pursued the profession were also influenced by past teaching experiences and feelings towards STEM subjects. Conversely, career changers who decided against STEM teaching indicated their personal traits and life circumstances might not suit a teaching career. These findings offer implications for research and policy aimed at recruiting career changers into STEM teaching careers.

Well-being of mental health workers during the Russian-Ukrainian War

Introduction: While the detrimental impact of the Russian-Ukrainian War on mental health of Ukrainian population has been well-documented, less attention has been given to mental health workers (MHWs) providing mental health support services amid the war.&#x0D; Purpose: To examine levels of domain-specific well-being in Ukrainian MHWs; identify and quantify the relative importance of factors associated with well-being; and examine interactions of risk and protective factors in relation to each well-being domain.&#x0D; Methodology: A convenience sample of 178 MHWs completed a survey that assessed their well-being, and sociodemographic, war-related, mental health, and psychosocial characteristics.&#x0D; Results: Lower levels of mental health difficulties such as anxiety and posttraumatic stress symptoms, and higher levels of psychosocial factors such as higher optimism, gratitude, and presence and search for meaning were associated with higher well-being. MHWs with high levels of protective psychosocial factors were more likely to report higher well-being even in the presence of mental health difficulties.&#x0D; Conclusions: Results of this study provide insight into the levels and correlates of multiple well-being domains among Ukrainian MHWs amid the ongoing war. They further suggest that interventions to mitigate mental health difficulties and bolster protective psychosocial factors may help promote well-being in this population

Abstract 2388: Artificial neural network based ensemble models for single-arm glioblastoma trials: A new historical control adjustment method

Abstract Background: Despite a long history of poor decision making leading to repeated failed phase 3 trials, phase 2 single-arm studies continue to be common in glioblastoma (GBM). With the drawback of inflated false positive rate in current adjustment method for historical control data as concurrent controls, the new statistical methods and validation approach will be needed for adequate covariate adjustment in the GBM single-arm trial data. Methods: The systematic review was performed to extract the summary-level external data of survival outcomes (mean progression-free survival, mPFS, and PFS at 6 months, PFS6) and prognostic factors (age, gender, extent of resection, KPS, methylation status, and treatment history) from published randomized clinical trials for the standard of care (SOC) treatment in GBM. Four models of the standard, bootstrap aggregating, random subspaces, and random patches models were developed to construct a powerful predictive tool by comprising a series of connections producing survival outputs as a function of prognostic inputs using (1) artificial neural network (ANN) method and (2) linear method (Fig1). The root mean square errors (RMSEs) were calculated to evaluate the model performance using the five-fold cross-validation approach with the effects of ensemble size. The predictive survival outcomes among prognostic factors were calculated to evaluate the relative importance of factors. Results: We identified 19 Phase 2 and 3 clinical trials in glioblastoma and two survival outcomes and seven prognostic factors (Table 1). ANN models have less prediction error and therefore better performance on the test set than linear models. Presumably, the relationships in the data are highly nonlinear, and thus linear models cannot capture these relationships (Table 2). Bagging models typically outperform their standard counterparts for both ANNs and linear models. Ensemble methods can capture many trends within the data by manipulation of the training set and are therefore less prone to biases of individual models exacerbated by the small sample size. The previous treatment history and methylation status were the top two influential prognostic factors for both mPFS and PFS6, followed by the extent of resection and mean age (Fig2). With the examination of the ANN parameters of sample size, learning rate, training hyperparameters, it could be observed that there is no clear change in error when increasing sample size beyond 10 (Fig3). Conclusions: High failure rate of phase 3 glioblastoma trials emphasizes the need for greater reliability of earlier studies in phase 2 single-arm trials. The proposed statistical and epidemiologic approach based on the ensemble-based ANN models will be an effective tool to lead to better decision with phase 2 single-arm trials, which may give better chance to appropriately advance to phase 3 trials with higher predictive power. Citation Format: Wonsuk Yoo, Tortsen Ullrich, Nader Sanai. Artificial neural network based ensemble models for single-arm glioblastoma trials: A new historical control adjustment method [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2388.

Yield gap analysis and the relative importance of factors explaining yield variability in paddy fields

Closing the yield gap of strategic crops can facilitate achieving sustainable solutions to alleviate hunger, ensure food security, and enhance nutrition. This study aimed to quantify factors affecting rice yield gap in northern Iran using experimental and survey data on yields and management practices. The CERES-Rice model, calibrated with data (2011, 2016, 2019) from two regions, was used to estimate potential yield (Yp) of different rice cultivars. Yield gaps were calculated as the difference between economic yield potential and the average yield attained by farmers. The calibration process showed that cultivar coefficients varied across rice varieties. The simulated yields (2012–2021) showed that the mean potential yields were 13362 kg ha−1 and 8818 kg ha−1 for the late and early-maturing cultivars, respectively. High temperature was the most important yield defining factor across the rice growing areas. The yield gap was 3751 kg ha−1 and 2830 kg ha−1 in the late- and early-maturing cultivars, respectively. Nitrogen fertilizer rate, number of fertilizer applications, phosphorus fertilizer rate, transplanting date, potassium fertilizer rate, and seedling age were the top factors influencing the yield gap. Among the management factors, rice fertilizer management explained about 65% of the yield variability. Together, transplanting date and seedling age explained 15.3% of the variability in rice production, while pest, disease, and weed damage accounted for 9.6%. Breaking down the yield gap into affecting factors allows growers to improve the production system by providing the best crop management according to their facilities and capabilities. This approach is essential for optimizing agricultural productivity and achieving sustainable food production goals.

Abstract MP02: Sociodemographic and Behavioral Factors Associated With Diet Quality Among Low-Income Community Health Center Patients With Hypertension

Objective: Identify sociodemographic and behavioral factors related to the diet of low-income adults with hypertension to inform the development of a community health worker (CHW) nutrition security intervention to improve blood pressure. Design: In this cross-sectional analysis, dietary recalls were used to assess Healthy Eating Index-2020 (HEI-2020) total (range: 0 to 100 [best diet quality]) and component scores and sodium intake. Self-reported sociodemographic and behavioral data were entered into a Least Absolute Shrinkage and Selection Operator (LASSO) regression model to determine the relative importance of factors related to diet quality. Setting: Five community health centers in Boston, Massachusetts. Participants: Adults (&gt;20 years old) with a hypertension diagnosis. Results: Participants (N=291) were mostly female (65.0%), on Medicaid (82.8%), food insecure (59.5%), and Hispanic (52.2%). The mean (95% CI) HEI-2020 score was 63.0 (62.3, 65.7) Component scores were low for sodium and whole grains; mean (SE) sodium intake was 2676.9 (45.5) mg/day. The most important factors associated with lower HEI-2020 scores were: not having own housing, male gender, tobacco use, marijuana use, and skipping meals; the most important factors associated with higher HEI-2020 scores were Hispanic ethnicity and receipt of community food resources (5-fold cross-validated R 2 =0.17). Conclusions: In this population of low-income adults with hypertension, diet quality would be improved by reducing sodium and increasing whole grain intake. In the future, a CHW intervention could help provide tailored dietary guidance while addressing social (e.g., community food resources) and behavioral (e.g., meal skipping) factors associated with poor diet in patients with hypertension.

Problems and prospects of development of public-private partnership in the Republic of Kazakhstan: Political analysis

Relevance. Public-private partnership (PPP) is considered as a priority in Kazakhstan's state policy for achieving strategic plans, fostering political stability, and boosting the economy by attracting private investment in key sectors. Thus, studying the PPP issues and prospects is vital, necessitating a comprehensive economic perspective and integration with other societal spheres of public life. Purpose. The study aims to explore the political aspect of PPP development in Kazakhstan and seeks to assess the extent of influence exerted by various factors on the PPP mechanism. Additionally, the study intends to identify measures for enhancing the PPP framework, with particular attention to the relative importance of factors impacting its progress. Methodology. The study's empirical basis involved a PEST analysis of PPP issues, utilizing expert questionnaires from 5 political scientists, 5 economists, 5 sociologists, and 5 technology specialists. An expert survey with scaling estimates was conducted, followed by an analysis of these assessments. Results. The results of the study reveal ways to enhance the PPP mechanism by examining the specific impacts of political, economic, social, and technological factors. The study is theoretically grounded in neocorporatism, which advocates for the systematic involvement of interest groups in shaping state policies that connect government and business entities. The hypothesis was tested by evaluating the influence of these factors on PPP development, identifying key challenges, and outlining future prospects for PPPs in Kazakhstan. Conclusions. Based on the findings of the PEST analysis, it is evident that updating the legislative framework to align with current requirements is crucial for enhancing the efficiency of PPP initiatives. Improving PPP project quality necessitates prioritizing projects addressing fundamental societal needs and critical issues. Keywords: strategic partnership; business; state; neo-corporatism; information technology; democratic society

Exploring the Factors that Drive Clinical Negligence Claims: Stated Preferences of Those Who Have Experienced Unintended Harm.

Better understanding of the factors that influence patients to make a financial claim for compensation is required to inform policy decisions. This study aimed to assess the relative importance of factors that influence those who have experienced a patient safety incident (PSI) to make a claim for compensation. Participants completed an online discrete choice experiment (DCE) involving 10 single profile tasks where they chose whether or not to file a claim. DCE data were modelled using logistic, mixed logit and latent class regressions; scenario analyses, external validity, and willingness to accept were also conducted. A total of 1029 participants in the United Kingdom responded to the survey. An appropriate apology and a satisfactory investigation reduced the likelihood of claiming. Respondents were more likely to claim if they could hold those responsible accountable, if the process was simple and straightforward, if the compensation amount was higher, if the likelihood of compensation was high or uncertain, if the time to receive a decision was quicker, and if they used the government compensation scheme. Men are more likely to claim for low impact PSIs. The actions taken by the health service after a PSI, and people's perceptions about the probability of success and the size of potential reward, can influence whether a claim is made. Results show the importance of giving an appropriate apology and conducting a satisfactory investigation. This stresses the importance around how patients are treated after a PSI in influencing the clinical negligence claims that are made.

Comparison of the Relative Importance of Factors Affecting the Conveyance of Bulk and Liquid Cargo

The transportation of bulk or liquid cargo is crucial for ensuring the delivery of such cargo to areas lacking well-established infrastructure or favorable natural conditions. These areas may lack sufficient rail or air transport accessibility, or may not have suitable embankments along rivers, lakes or seas with appropriate loading and unloading facilities. Regarding distinctive characteristics, we would like to emphasize the following: (1) Efficient freight accessibility without the need for more infrastructure that is tailored to the existing natural circumstances; (2) An efficient and cost-effective mode of transportation for the movement of small quantities of goods; (3) The cargo philosophy is designed to meet customer expectations by providing a convenient and personalised service that is focused on delivering goods directly to their door. This research employed theoretical methodologies to characterize the primary force exerted by vehicles in the transportation of bulk and liquid commodities (the relationship between centrifugal force and speed and the relationship between the center of gravity and speed). Furthermore, by using expert assessment, the significance indicators pertaining to the individual criteria impacting the transportation of bulk and liquid cargoes were ascertained, along with the identification of their interrelationships (comparison of the different forces in the transportation process of bulk and liquid cargoes, comparison of the criteria of centrifugal force in the transportation process of bulk and liquid cargoes, comparison of the dangerous factors in the transportation process of bulk and liquid cargoes, comparison of the critical level of tank filling in the transportation process of bulk and liquid cargoes, and comparison of the factors that can affect the dynamics of a vehicle in the transportation process of bulk and liquid cargoes).

Soil, climate, time and site factors as drivers of soil structure evolution in agricultural soils from a temperate-boreal region

The evolution of soil structure in agricultural soils is driven by natural and anthropogenic factors including inherent soil properties, climate and soil management interventions, all acting at different spatial and temporal scales. Although the causal relationships between soil structure and these individual factors are increasingly understood, their relative importance and complex interactive effects on soil structure have so far not been investigated across a geo-climatic region.Here we present the first attempt to identify the relative importance of factors that drive the evolution of soil structure in agricultural soils as well as their direction of effect with a focus on the temperate-boreal zone. This was done using a random forest (RF) approach including soil, climate, time, and site factors as covariates. Relative entropy, as quantified by the Kullback-Leibler (KL) divergence, was used as a quantitative index of soil structure, which is derived from the particle-size distribution and soil water retention data, and integrates the effects of soil structure on pores from the micrometre-scale to large macropores. Our dataset includes 431 intact topsoil and subsoil samples from 89 agricultural sites across Sweden and Norway, which were sampled between 1953 and 2017. The relative importance of covariates for the evolution of soil structure was identified and their non-linear and non-monotonic effects on the KL divergence were investigated through partial dependence analysis. To reveal any differences between topsoils (0–30 cm; n = 174) and subsoils (30–100 cm; n = 257), the same analysis was repeated separately on these two subsets.The covariates were able to explain on average more than 50% of the variation in KL divergence for all soil samples and when only subsoil samples were included. However, the predictions were poorer for topsoil samples (≈ 35%), underlining the complex dynamics of soil structure in agricultural topsoils. Parent material was the most important predictor for the KL divergence, followed by clay content for all soil samples and sampling year for only subsoil samples. Mean annual air temperature ranked third and annual precipitation ranked fourth for subsoil samples. However, it remains unclear whether the effects of climate factors are direct (e.g., freezing and thawing, wetting and drying, rainfall impact) or indirectly expressed through interactions with soil management. The partial dependence analysis revealed a soil organic carbon threshold of around 3% below which soil structure starts to deteriorate. Besides this, our results suggest that subsoil structure in the agricultural land of Sweden deteriorated steadily during the 1950′s to 1970′s, which we attribute to traffic compaction as a consequence of agricultural intensification. We discuss our findings in the light of data bias, laboratory methods and multicollinearity and conclude that the approach followed here gave valuable insights into the drivers of soil structure evolution in agricultural soils of the temperate-boreal zone. Theses insights will be of use to inform soil management interventions that address soil structure or soil properties and functions related to it.

Comprehensive data-driven methods for estimating the thermal conductivity of biodiesels and their blends with alcohols and fossil diesels

Biodiesels offer viable alternatives to fossil diesels as renewable and clean energy sources. Understanding the thermal conductivity of biodiesels is crucial for engine design and heat transfer rate calculations in thermal systems. However, existing literature lacks validated predictive tools for this property. To address this, a study utilized 2552 data points from 18 published studies with uncertainties less than 2 %, covering 26 fatty acid alkyl ester biodiesels, 12 biodiesel-alcohol mixtures, and 9 biodiesel-fossil diesel blends across various pressure and temperature conditions. Three machine learning algorithms were employed, using input variables of reduced temperature, reduced pressure, and average molecular weight. Models were trained on 80 % of the data and evaluated on the remaining 20 %. Statistical error metrics and graphical analysis confirmed high precision in predicting biodiesel thermal conductivity. The Gaussian Process Regression model showed the best consistency, with an average absolute relative error (AARE) of 0.36 % and determination coefficient (R2) of 99.69 % on the testing dataset. These models were versatile, applicable to different biodiesel mixtures, accurately capturing thermal conductivity behavior under diverse operating conditions. In contrast, existing literature correlations were limited to specific conditions and required complex calculations. Additionally, a sensitivity analysis determined the relative importance of factors influencing thermal conductivity.

The Relative Importance of Factors That Applicants Weigh When Ranking Adult Reconstruction Fellowships as Well as Their Perspectives on Robotic-Assisted Arthroplasty

BackgroundOrthopedic Surgery Fellowship programs offer highly specialized training that varies based on the training environment and surgical experience. Additionally, for Adult Reconstruction programs, robotic-assisted surgery exposure has been a widely discussed topic. The purpose of this study was to determine the relative value of various factors to Adult Hip and Knee Fellowship applicants, and their perceptions of robotic-assisted arthroplasty. MethodsWe surveyed 780 applicants who applied to our fellowship to matriculate in 2020 to 2024. We received 158 responses (20.3% response rate). We assessed factors concerning people and perceptions, logistics, salary and benefits, program reputation and curriculum, and surgical experience. Additionally, we surveyed fellows’ attitudes toward using robotic surgery and its impact on patient outcomes. ResultsThe highest-rated factors were Level of Hands-On Operative Experience (4.83), Revision Hip Volume (4.72), Revision Knee Volume (4.71), Multiple Surgical Exposures to the Hip (4.59), and Clinical Case Variety (4.59). Respondents who were postfellowship matriculation placed significantly more value on Exposure to Multiple Attendings with Surgical Diversity (P = .01), and Anterior Hip Volume (P = .04), and less value on Geographic Location (P = .04) and Patient-Specific Instrumentation (P = .02) than prematriculates. Overall, 65% of applicants plan to or currently use robotics, 7.6% do not, and 27.2% said “Maybe”. Those who plan to or currently use robotics most cited procedure fidelity, patient-preference, and marketability as reasons to use robotics. ConclusionsHands-on surgical experience and revision volume were the most important factors for fellowship applicants. Applicants placed lower importance on robotics exposure and their perspectives on robotics in their future practice were highly variable. Our results will inform fellowship programs and future applicants what previous applicants have valued in their training to help guide fellowship program structure, resource management, as well as recruitment.

MCDM-Based Wildfire Risk Assessment: A Case Study on the State of Arizona

The increasing frequency of wildfires has posed significant challenges to communities worldwide. The effectiveness of all aspects of disaster management depends on a credible estimation of the prevailing risk. Risk, the product of a hazard’s likelihood and its potential consequences, encompasses the probability of hazard occurrence, the exposure of assets to these hazards, existing vulnerabilities that amplify the consequences, and the capacity to manage, mitigate, and recover from their consequences. This paper employs the multiple criteria decision-making (MCDM) framework, which produces reliable results and allows for the customization of the relative importance of factors based on expert opinions. Utilizing the AROMAN algorithm, the study ranks counties in the state of Arizona according to their wildfire risk, drawing upon 25 factors categorized into expected annual loss, community resilience, and social vulnerability. A sensitivity analysis demonstrates the stability of the results when model parameters are altered, reinforcing the robustness of this approach in disaster risk assessment. While the paper primarily focuses on enhancing the safety of human communities in the context of wildfires, it highlights the versatility of the methodology, which can be applied to other natural hazards and accommodate more subjective risk and safety assessments.