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1
Denuit, Michel. "SIZE-BIASED TRANSFORM AND CONDITIONAL MEAN RISK SHARING, WITH APPLICATION TO P2P INSURANCE AND TONTINES." ASTIN Bulletin 49, no. 03 (July 17, 2019): 591–617. http://dx.doi.org/10.1017/asb.2019.24.
Анотація:
AbstractUsing risk-reducing properties of conditional expectations with respect to convex order, Denuit and Dhaene [Denuit, M. and Dhaene, J. (2012). Insurance: Mathematics and Economics 51, 265–270] proposed the conditional mean risk sharing rule to allocate the total risk among participants to an insurance pool. This paper relates the conditional mean risk sharing rule to the size-biased transform when pooled risks are independent. A representation formula is first derived for the conditional expectation of an individual risk given the aggregate loss. This formula is then exploited to obtain explicit expressions for the contributions to the pool when losses are modeled by compound Poisson sums, compound Negative Binomial sums, and compound Binomial sums, to which Panjer recursion applies. Simple formulas are obtained when claim severities are homogeneous. A couple of applications are considered: first, to a peer-to-peer insurance scheme where participants share the first layer of their respective risks while the higher layer is ceded to a (re)insurer; second, to survivor credits to be shared among surviving participants in tontine schemes.
2
Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. "Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation." Journal of Computational and Graphical Statistics 24, no. 1 (January 2, 2015): 44–65. http://dx.doi.org/10.1080/10618600.2014.907095.
3
Fernández-Centeno, Miguel Á., Patricia Alocén, and Miguel Á. Toledo. "Identification of Trends in Dam Monitoring Data Series Based on Machine Learning and Individual Conditional Expectation Curves." Water 16, no. 9 (April 26, 2024): 1239. http://dx.doi.org/10.3390/w16091239.
Анотація:
Dams are complex systems that involve both the structure itself and its foundation. Rheological phenomena, expansive reactions, or alterations in the geotechnical parameters of the foundation, among others, result in non-reversible and cumulative modifications in the dam response, leading to trends in the monitoring data series. The accurate identification and definition of these trends to study their evolution are key aspects of dam safety. This manuscript proposes a methodology to identify trends in dam behavioural data series by identifying the influence of the time variable on the predictions provided by the ML models. Initially, ICE curves and SHAP values are employed to extract temporal dependence, and the ICE curves are found to be more precise and efficient in terms of computational cost. The temporal dependencies found are adjusted using a GWO algorithm to different function characteristics of irreversible processes in dams. The function that provides the best fit is selected as the most plausible. The results obtained allow us to conclude that the proposed methodology is capable of obtaining estimates of the most common trends that affect movements in concrete dams with greater precision than the statistical models most commonly used to predict the behaviour of these types of variables. These results are promising for its general application to other types of dam monitoring data series, given the versatility demonstrated for the unsupervised identification of temporal dependencies.
4
Akushevich, I., M. Kovtun, K. G. Manton, and A. I. Yashin. "Linear Latent Structure Analysis and Modelling of Multiple Categorical Variables." Computational and Mathematical Methods in Medicine 10, no. 3 (2009): 203–18. http://dx.doi.org/10.1080/17486700802259798.
Анотація:
Linear latent structure analysis is a new approach for investigation of population heterogeneity using high-dimensional categorical data. In this approach, the population is represented by a distribution of latent vectors, which play the role of heterogeneity variables, and individual characteristics are represented by the expectation of this vector conditional on individual response patterns. Results of the computer experiments demonstrating a good quality of reconstruction of model parameters are described. The heterogeneity distribution estimated from 1999 National Long Term Care Survey (NLTCS) is discussed. A predictive power of the heterogeneity scores on mortality is analysed using vital statistics data linked to NLTCS.
5
Xu, Shizhong. "Computation of the Full Likelihood Function for Estimating Variance at a Quantitative Trait Locus." Genetics 144, no. 4 (December 1, 1996): 1951–60. http://dx.doi.org/10.1093/genetics/144.4.1951.
Анотація:
The proportion of alleles identical by descent (IBD) determines the genetic covariance between relatives, and thus is crucial in estimating genetic variances of quantitative trait loci (QTL). However, IBD proportions at QTL are unobservable and must be inferred from marker information. The conventional method of QTL variance analysis maximizes the likelihood function by replacing the missing IBDs by their conditional expectations (the expectation method), while in fact the full likelihood function should take into account the conditional distribution of IBDs (the distribution method). The distribution method for families of more than two sibs has not been obvious because there are n(n – 1)/2 IBD variables in a family of size n, forming an n × n symmetrical matrix. In this paper, I use four binary variables, where each indicates the event that an allele from one of the four grandparents has passed to the individual. The IBD proportion between any two sibs is then expressed as a function of the indicators. Subsequently, the joint distribution of the IBD matrix is derived from the distribution of the indicator variables. Given the joint distribution of the unknown IBDs, a method to compute the full likelihood function is developed for families of arbitrary sizes.
6
Li, Langping, and Hengxing Lan. "Bivariate Landslide Susceptibility Analysis: Clarification, Optimization, Open Software, and Preliminary Comparison." Remote Sensing 15, no. 5 (March 2, 2023): 1418. http://dx.doi.org/10.3390/rs15051418.
Анотація:
Bivariate data-driven methods have been widely used in landslide susceptibility analysis. However, the names, principles, and correlations of bivariate methods are still confused. In this paper, the names, principles, and correlations of bivariate methods are first clarified based on a comprehensive and in-depth survey. A total of eleven prevalent bivariate methods are identified, nominated, and elaborated in a general framework, constituting a well-structured bivariate method family. We show that all prevalent bivariate methods depend on empirical conditional probabilities of landslide occurrence to calculate landslide susceptibilities, either exclusively or inclusively. It is clarified that those eight “conditional-probability-based” bivariate methods, which exclusively depend on empirical conditional probabilities, are particularly strongly correlated in principle, and therefore are expected to have a very close or even the same performance. It is also suggested that conditional-probability-based bivariate methods apply to a “classification-free” modification, in which factor classifications are avoided and the result is dominated by a single parameter, “bin width”. Then, a general optimization framework for conditional-probability-based bivariate methods, based on the classification-free modification and obtaining optimum results by optimizing the dominant parameter bin width, is proposed. The open software Automatic Landslide Susceptibility Analysis (ALSA) is updated to implement the eight conditional-probability-based bivariate methods and the general optimization framework. Finally, a case study is presented, which confirms the theoretical expectation that different conditional-probability-based bivariate methods have a very close or even the same performance, and shows that optimal bivariate methods perform better than conventional bivariate methods regarding both the prediction rate and the ability to reveal the quasi-continuous varying pattern of sensibilities to landslides for individual predisposing factors. The principles and open software presented in this study provide both theoretical and practical foundations for applications and explorations of bivariate methods in landslide susceptibility analysis.
7
Jagers, Peter, and Sergei Zuyev. "Populations in environments with a soft carrying capacity are eventually extinct." Journal of Mathematical Biology 81, no. 3 (August 20, 2020): 845–51. http://dx.doi.org/10.1007/s00285-020-01527-5.
Анотація:
Abstract Consider a population whose size changes stepwise by its members reproducing or dying (disappearing), but is otherwise quite general. Denote the initial (non-random) size by $$Z_0$$ Z 0 and the size of the nth change by $$C_n$$ C n , $$n= 1, 2, \ldots $$ n = 1 , 2 , … . Population sizes hence develop successively as $$Z_1=Z_0+C_1,\ Z_2=Z_1+C_2$$ Z 1 = Z 0 + C 1 , Z 2 = Z 1 + C 2 and so on, indefinitely or until there are no further size changes, due to extinction. Extinction is thus assumed final, so that $$Z_n=0$$ Z n = 0 implies that $$Z_{n+1}=0$$ Z n + 1 = 0 , without there being any other finite absorbing class of population sizes. We make no assumptions about the time durations between the successive changes. In the real world, or more specific models, those may be of varying length, depending upon individual life span distributions and their interdependencies, the age-distribution at hand and intervening circumstances. We could consider toy models of Galton–Watson type generation counting or of the birth-and-death type, with one individual acting per change, until extinction, or the most general multitype CMJ branching processes with, say, population size dependence of reproduction. Changes may have quite varying distributions. The basic assumption is that there is a carrying capacity, i.e. a non-negative number K such that the conditional expectation of the change, given the complete past history, is non-positive whenever the population exceeds the carrying capacity. Further, to avoid unnecessary technicalities, we assume that the change $$C_n$$ C n equals -1 (one individual dying) with a conditional (given the past) probability uniformly bounded away from 0. It is a simple and not very restrictive way to avoid parity phenomena, it is related to irreducibility in Markov settings. The straightforward, but in contents and implications far-reaching, consequence is that all such populations must die out. Mathematically, it follows by a supermartingale convergence property and positive probability of reaching the absorbing extinction state.
8
Mirzaei Aliabadi, Mostafa, Hamed Aghaei, Omid kalatpuor, Ali Reza Soltanian, and Asghar Nikravesh. "Analysis of the severity of occupational injuries in the mining industry using a Bayesian network." Epidemiology and Health 41 (May 11, 2019): e2019017. http://dx.doi.org/10.4178/epih.e2019017.
Анотація:
OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents.RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers’ experience had the strongest influence on the severity of accidents.CONCLUSIONS: Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
9
Gill, Navdeep, Patrick Hall, Kim Montgomery, and Nicholas Schmidt. "A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing." Information 11, no. 3 (February 29, 2020): 137. http://dx.doi.org/10.3390/info11030137.
Анотація:
This manuscript outlines a viable approach for training and evaluating machine learning systems for high-stakes, human-centered, or regulated applications using common Python programming tools. The accuracy and intrinsic interpretability of two types of constrained models, monotonic gradient boosting machines and explainable neural networks, a deep learning architecture well-suited for structured data, are assessed on simulated data and publicly available mortgage data. For maximum transparency and the potential generation of personalized adverse action notices, the constrained models are analyzed using post-hoc explanation techniques including plots of partial dependence and individual conditional expectation and with global and local Shapley feature importance. The constrained model predictions are also tested for disparate impact and other types of discrimination using measures with long-standing legal precedents, adverse impact ratio, marginal effect, and standardized mean difference, along with straightforward group fairness measures. By combining interpretable models, post-hoc explanations, and discrimination testing with accessible software tools, this text aims to provide a template workflow for machine learning applications that require high accuracy and interpretability and that mitigate risks of discrimination.
10
Jongsomjit, Tita, and Rattana Lerdsuwansri. "Estimation of Population Size Based on One-Inflated, Zero-Truncated Count Distribution with Covariate Information." Sains Malaysiana 52, no. 12 (December 31, 2023): 3577–87. http://dx.doi.org/10.17576/jsm-2023-5212-18.
Анотація:
In order to estimate the unknown size of the population that is difficult or hidden to enumerate, the capture-recapture method is widely used for this purpose. We propose the one-inflated, zero-truncated geometric (OIZTG) model to deal with three important characteristics of some capture–recapture data: zero-truncation, one-inflation, and observed heterogeneity. The OIZTG model is generated by two distinct processes, one from a zero-truncated geometric (ZTG) process, and the other one-count producing process. To explain heterogeneity at an individual level, the OIZTG model provides a simple way to link the covariate information. The new estimator was proposed based on the OIZTG distributions through the modified Horvitz-Thomson approach, and the parameters of the OIZTG distributions are estimated by using a maximum likelihood estimator (MLE). With regard to making inferences about the unknown size of the population, confidence interval estimations are proposed where variance estimate of population size estimator is achieved by using conditional expectation technique. All of these are assessed through simulation studies. The real data sets are provided for understanding the methodologies.
11
Cai, Jun, and Ken Seng Tan. "Optimal Retention for a Stop-loss Reinsurance Under the VaR and CTE Risk Measures." ASTIN Bulletin 37, no. 01 (May 2007): 93–112. http://dx.doi.org/10.2143/ast.37.1.2020800.
Анотація:
We propose practical solutions for the determination of optimal retentions in a stop-loss reinsurance. We develop two new optimization criteria for deriving the optimal retentions by, respectively, minimizing the value-at-risk (VaR) and the conditional tail expectation (CTE) of the total risks of an insurer. We establish necessary and sufficient conditions for the existence of the optimal retentions for two risk models: individual risk model and collective risk model. The resulting optimal solution of our optimization criterion has several important characteristics: (i) the optimal retention has a very simple analytic form; (ii) the optimal retention depends only on the assumed loss distribution and the reinsurer’s safety loading factor; (iii) the CTE criterion is more applicable than the VaR criterion in the sense that the optimal condition for the former is less restrictive than the latter; (iv) if optimal solutions exist, then both VaR- and CTE-based optimization criteria yield the same optimal retentions. In terms of applications, we extend the results to the individual risk models with dependent risks and use multivariate phase type distribution, multivariate Pareto distribution and multivariate Bernoulli distribution to illustrate the effect of dependence on optimal retentions. We also use the compound Poisson distribution and the compound negative binomial distribution to illustrate the optimal retentions in a collective risk model.
12
Cai, Jun, and Ken Seng Tan. "Optimal Retention for a Stop-loss Reinsurance Under the VaR and CTE Risk Measures." ASTIN Bulletin 37, no. 1 (May 2007): 93–112. http://dx.doi.org/10.1017/s0515036100014756.
Анотація:
We propose practical solutions for the determination of optimal retentions in a stop-loss reinsurance. We develop two new optimization criteria for deriving the optimal retentions by, respectively, minimizing the value-at-risk (VaR) and the conditional tail expectation (CTE) of the total risks of an insurer. We establish necessary and sufficient conditions for the existence of the optimal retentions for two risk models: individual risk model and collective risk model. The resulting optimal solution of our optimization criterion has several important characteristics: (i) the optimal retention has a very simple analytic form; (ii) the optimal retention depends only on the assumed loss distribution and the reinsurer’s safety loading factor; (iii) the CTE criterion is more applicable than the VaR criterion in the sense that the optimal condition for the former is less restrictive than the latter; (iv) if optimal solutions exist, then both VaR- and CTE-based optimization criteria yield the same optimal retentions. In terms of applications, we extend the results to the individual risk models with dependent risks and use multivariate phase type distribution, multivariate Pareto distribution and multivariate Bernoulli distribution to illustrate the effect of dependence on optimal retentions. We also use the compound Poisson distribution and the compound negative binomial distribution to illustrate the optimal retentions in a collective risk model.
13
Kim, Kipyo, Hyeonsik Yang, Jinyeong Yi, Hyung-Eun Son, Ji-Young Ryu, Yong Chul Kim, Jong Cheol Jeong, et al. "Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation." Journal of Medical Internet Research 23, no. 4 (April 16, 2021): e24120. http://dx.doi.org/10.2196/24120.
Анотація:
Background Acute kidney injury (AKI) is commonly encountered in clinical practice and is associated with poor patient outcomes and increased health care costs. Despite it posing significant challenges for clinicians, effective measures for AKI prediction and prevention are lacking. Previously published AKI prediction models mostly have a simple design without external validation. Furthermore, little is known about the process of linking model output and clinical decisions due to the black-box nature of neural network models. Objective We aimed to present an externally validated recurrent neural network (RNN)–based continuous prediction model for in-hospital AKI and show applicable model interpretations in relation to clinical decision support. Methods Study populations were all patients aged 18 years or older who were hospitalized for more than 48 hours between 2013 and 2017 in 2 tertiary hospitals in Korea (Seoul National University Bundang Hospital and Seoul National University Hospital). All demographic data, laboratory values, vital signs, and clinical conditions of patients were obtained from electronic health records of each hospital. We developed 2-stage hierarchical prediction models (model 1 and model 2) using RNN algorithms. The outcome variable for model 1 was the occurrence of AKI within 7 days from the present. Model 2 predicted the future trajectory of creatinine values up to 72 hours. The performance of each developed model was evaluated using the internal and external validation data sets. For the explainability of our models, different model-agnostic interpretation methods were used, including Shapley Additive Explanations, partial dependence plots, individual conditional expectation, and accumulated local effects plots. Results We included 69,081 patients in the training, 7675 in the internal validation, and 72,352 in the external validation cohorts for model development after excluding cases with missing data and those with an estimated glomerular filtration rate less than 15 mL/min/1.73 m2 or end-stage kidney disease. Model 1 predicted any AKI development with an area under the receiver operating characteristic curve (AUC) of 0.88 (internal validation) and 0.84 (external validation), and stage 2 or higher AKI development with an AUC of 0.93 (internal validation) and 0.90 (external validation). Model 2 predicted the future creatinine values within 3 days with mean-squared errors of 0.04-0.09 for patients with higher risks of AKI and 0.03-0.08 for those with lower risks. Based on the developed models, we showed AKI probability according to feature values in total patients and each individual with partial dependence, accumulated local effects, and individual conditional expectation plots. We also estimated the effects of feature modifications such as nephrotoxic drug discontinuation on future creatinine levels. Conclusions We developed and externally validated a continuous AKI prediction model using RNN algorithms. Our model could provide real-time assessment of future AKI occurrences and individualized risk factors for AKI in general inpatient cohorts; thus, we suggest approaches to support clinical decisions based on prediction models for in-hospital AKI.
14
Martínez, Sergio. "Mediciones ideales en la mecánica cuántica." Crítica (México D. F. En línea) 20, no. 60 (December 10, 1988): 13–30. http://dx.doi.org/10.22201/iifs.18704905e.1988.680.
Анотація:
As a series of investigations have shown, the interpretation of the change upon measurement in quantum mechanics described by the "projection pastulate", as a purely statistical formula, is clear. This formula, here denoted by VNL, is a version of the conditional expectation in Hilbert spaces, and this mathematical result can be given a salid physical interpretation in terlDl of quantum statistics. The problem of interpretation arises when the formula comes in for interpretation, as a description of what happens to (states of) individual systems in measurement. Usual interpretations see the formula VNL as a description of a class of measurement transformations which are "minimally disturbing". However, a series of arguments show that such an interpretation is seriouslf flawed. (See Teller (1983), Martínez (1988) and references therein.] In Martínez (1987) I have derived the formula VNL from simple physical assumptions in a lattice theoretical framework. The formula so derived describes individual state transformations of a certain type. The states involved are states that describe the properties a system has relative to magnitudes (measuring situations). In this paper I propase that the change of state on measurement deseribed by the formula VNL, as derived in (1987), can be understood as a change on individual states along the lines of Von Neumann's initial proposal for interpreting non-maximal measurements. I discuss the objections raised by Lüders and others to Von Neumann's idea and show that they do not apply to the interpretation proposed here. This proposal has the advantage, among others, that it does not need to reify a controversial class of "minimally disturbing measurement transformations".
15
McClure, Foster D., and Jung K. Lee. "Use of Prediction Methods to Assess Laboratory Bias and Mean Values Associated with an Interlaboratory Study for Method Validation and/or Proficiency Testing." Journal of AOAC INTERNATIONAL 97, no. 2 (March 1, 2014): 624–29. http://dx.doi.org/10.5740/jaoacint.12-457.
Анотація:
Abstract Two methods of prediction of random variables, best predictor (BP) and best linear unbiased predictor (BLUP), are discussed as potential statistical methods to predict laboratory true mean and bias values using the sample laboratory mean (yi) from interlaboratory studies. The predictions developed here require that the interlaboratory and/or proficiency study be designed and conducted in a manner consistent with the assumptions of a one-way completely randomized model (CRM). Under the CRM the individual laboratory true mean and bias are not parameters but are defined to be random variables that are unobservable and considered as realized values that cannot be estimated but can be predicted using methods of “prediction.” The BP method is applicable when all salient parameters are known, e.g., the consensus true overall mean (μ) and repeatability and reproducibility components (σr2 and σR2), while the BLUP method is useful when σ2r and σR2 are known, but μ is estimated by the generalized least square estimator. Although the derivations of predictors are obtained by minimizing the mean-square error under the CRM assumptions, the predictors are the expected laboratory true mean and bias given the sample laboratory mean, i.e., conditional expectation.
16
Alejandro Fernández Fernández, José. "United States banking stability: An explanation through machine learning." Banks and Bank Systems 15, no. 4 (December 16, 2020): 137–49. http://dx.doi.org/10.21511/bbs.15(4).2020.12.
Анотація:
In this paper, an analysis of the prediction of bank stability in the United States from 1990 to 2017 is carried out, using bank solvency, delinquency and an ad hoc bank stability indicator as variables to measure said stability. Different machine learning assembly models have been used in the study, a random forest is developed because it is the most accurate of all those tested. Another novel element of the work is the use of partial dependency graphs (PDP) and individual conditional expectation curves (ICES) to interpret the results that allow observing for specific values how the banking variables vary, when the macro-financial variables vary.It is concluded that the most determining variables to predict bank solvency in the United States are interest rates, specifically the mortgage rate and the 5 and 10-year interest rates of treasury bonds, reducing solvency as these rates increase. For delinquency, the most important variable is the unemployment rate in the forecast. The financial stability index is made up of the normalized difference between the two factors obtained, one for solvency and the other for delinquency. The index prediction concludes that stability worsens as BBB corporate yield increases.
17
Cakiroglu, Celal, Yaren Aydın, Gebrail Bekdaş, and Zong Woo Geem. "Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach." Materials 16, no. 13 (June 25, 2023): 4578. http://dx.doi.org/10.3390/ma16134578.
Анотація:
Basalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength.
18
Shuhua, Xia, and Zhao Qianqian. "Development and Test of Social Director of Sports Psychological Contract Scale." Tobacco Regulatory Science 7, no. 6 (November 3, 2021): 5859–74. http://dx.doi.org/10.18001/trs.7.6.67.
Анотація:
In order to provide important realistic basis for standardizing, guiding and governing the guiding behavior of social sports instructors, the measuring tools of psychological expectation were compiled. According to the survey data of 285 social sports instructors and 365 social sports instructors, project analysis, exploratory factor analysis and confirmatory factor analysis were carried out, and reliability and validity tests were conducted. The results are as follows: (1) the social sports instructors psychological contract scale by the growth and development, belonging and identity and norms and guidelines of three dimensions, 36 projects, divided into organizational responsibility subscales (growth liability, ownership, standard responsibility) and personal responsibility subscales (development rule of liability, recognition and responsibility) two subscales. (2) The Cronbach's Alpha value of organizational responsibility scale was 0.927 and KMO was 0.904 (P<0.5); the Cronbach's Alpha value of personal responsibility scale was 0.932 and KMO was 0.921 (P<0.5). The subscales of organizational responsibility and personal responsibility χ2/ DF < 3, GFI > 0.8, CFI < 0.9, RMSEA < 0.8; the structure and data fit well and meet the requirements of psychological test. (3) It is consistent with the realistic, conditional exchange and development-oriented dimensions of the authoritative psychological contract research contents at home and abroad, and reflects the group characteristics of social sports instructors in the expressions and specific items. Individual responsibility is greater than organizational responsibility; Organizational norms and personal norms have the highest expectation of responsibility; the pursuit of growth and development is greater than belonging identification. The psychological contract scale for social sports instructors has a reasonable structure, good reliability and validity, and reflects the value pursuit of the group. It is an ideal evaluation tool for studying the psychological contract of social sports instructors.
19
Chang, Shih-Chieh, Chan-Lin Chu, Chih-Kuang Chen, Hsiang-Ning Chang, Alice M. K. Wong, Yueh-Peng Chen, and Yu-Cheng Pei. "The Comparison and Interpretation of Machine-Learning Models in Post-Stroke Functional Outcome Prediction." Diagnostics 11, no. 10 (September 28, 2021): 1784. http://dx.doi.org/10.3390/diagnostics11101784.
Анотація:
Prediction of post-stroke functional outcomes is crucial for allocating medical resources. In this study, a total of 577 patients were enrolled in the Post-Acute Care-Cerebrovascular Disease (PAC-CVD) program, and 77 predictors were collected at admission. The outcome was whether a patient could achieve a Barthel Index (BI) score of >60 upon discharge. Eight machine-learning (ML) methods were applied, and their results were integrated by stacking method. The area under the curve (AUC) of the eight ML models ranged from 0.83 to 0.887, with random forest, stacking, logistic regression, and support vector machine demonstrating superior performance. The feature importance analysis indicated that the initial Berg Balance Test (BBS-I), initial BI (BI-I), and initial Concise Chinese Aphasia Test (CCAT-I) were the top three predictors of BI scores at discharge. The partial dependence plot (PDP) and individual conditional expectation (ICE) plot indicated that the predictors’ ability to predict outcomes was the most pronounced within a specific value range (e.g., BBS-I < 40 and BI-I < 60). BI at discharge could be predicted by information collected at admission with the aid of various ML models, and the PDP and ICE plots indicated that the predictors could predict outcomes at a certain value range.
20
Liu, Guiwen, Jie Liu, Neng Wang, Xuanyi Xue, and Youjia Tan. "Machine Learning-Aided Prediction of Post-Fire Shear Resistance Reduction of Q690 HSS Plate Girders." Buildings 12, no. 9 (September 17, 2022): 1481. http://dx.doi.org/10.3390/buildings12091481.
Анотація:
Fire has significant effects on the residual resistance of steel structures. It is necessary to accurately clarify its effects on Q690 HSS plate girders, which have been widely used. In this paper, the ultimate resistance and effective service resistance of Q690 HSS plate girders after a fire are obtained using material tests and finite element (FE) analysis including parametric studies, where the data of 210 models were collected. The effects of four key parameters (hw/tw ratio, a/hw ratio, exposure temperature and cooling method) on post-fire shear resistance reduction of Q690 HSS plate girders are roughly investigated by individual conditional expectation (ICE), showing exposure temperature is the most important factor. The popular algorithms of machine learning (ML), namely artificial neural network (ANN) and support vector regression (SVR) algorithms, are utilized in model training to predict the reduction factors of both ultimate resistance and effective service resistance. Finally, the results indicate that the prediction using ML shows much better performance than that with traditional ordinary least squares (OLS) regression, and SVR with genetic algorithm (GA) provides the highest prediction accuracy. The results of this paper show the superiority of machine learning for solving prediction problems of steel structures, compared with conventional methods such as linear regression.
21
Zhang, Feng, Chenxin Wang, Xingxing Zou, Yang Wei, Dongdong Chen, Qiudong Wang, and Libin Wang. "Prediction of the Shear Resistance of Headed Studs Embedded in Precast Steel–Concrete Structures Based on an Interpretable Machine Learning Method." Buildings 13, no. 2 (February 11, 2023): 496. http://dx.doi.org/10.3390/buildings13020496.
Анотація:
Headed shear studs are an essential interfacial connection for precast steel–concrete structures to ensure composite action; hence, the accurate prediction of the shear capacity of headed studs is of pivotal significance. This study first established a worldwide dataset with 428 push-out tests of headed shear studs embedded in concrete with varied strengths from 26 MPa to 200 MPa. Five advanced machine learning (ML) models and three widely used equations from design codes were comparatively employed to predict the shear resistance of the headed studs. Considering the inevitable data variation caused by material properties and load testing, the isolated forest algorithm was first used to detect the anomaly of data in the dataset. Then, the five ML models were established and trained, which exhibited higher prediction accuracy than three existing design codes that were widely used in the world. Compared with the equations from AASHTO (the one that has the best prediction accuracy among design specifications), the gradient boosting decision tree (GBDT) model showed an 80% lower root mean square error, 308% higher coefficient of determination, and 86% lower mean absolute percent error. Lastly, individual conditional expectation plots and partial dependence plots showed the relationship between the individual parameters and the predicted target based on the GBDT model. The results showed that the elastic modulus of concrete, the tensile strength of the studs, and the length–diameter ratio of the studs influenced most of the shear capacity of shear studs. Additionally, the effect of the length–diameter ratio has an upper limit which depends on the strength of the studs and concrete.
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Getnet, Fentabil, Meaza Demissie, Alemayehu Worku, Tesfaye Gobena, Berhanu Seyoum, Rea Tschop, and Chris Anderson. "Determinants of Patient Delay in Diagnosis of Pulmonary Tuberculosis in Somali Pastoralist Setting of Ethiopia: A Matched Case-Control Study." International Journal of Environmental Research and Public Health 16, no. 18 (September 12, 2019): 3391. http://dx.doi.org/10.3390/ijerph16183391.
Анотація:
Background: Healthcare-seeking behavior is the basis to ensure early diagnosis and treatment of tuberculosis (TB) in settings where most cases are diagnosed upon self-presentation to health facilities. Yet, many patients seek delayed healthcare. Thus, we aimed to identify the determinants of patient delay in diagnosis of pulmonary TB in Somali pastoralist area, Ethiopia. Methods: A matched case-control study was conducted between December 2017 and October 2018. Cases were self-presented and newly diagnosed pulmonary TB patients aged ≥ 15 years who delayed > 30 days without healthcare provider consultation, and controls were patients with similar inclusion criteria but who consulted a healthcare provider within 30 days of illness; 216 cases sex-matched with 226 controls were interviewed using a pre-tested questionnaire. Hierarchical analysis was done using conditional logistic regression. Results: After multilevel analysis, pastoralism, rural residence, poor knowledge of TB symptoms and expectation of self-healing were individual-related determinants. Mild-disease and manifesting a single symptom were disease-related, and >1 h walking distance to nearest facility and care-seeking from traditional/religious healers were health system-related determinants of patient delay > 30 days [p < 0.05]. Conclusion: Expansion of TB services, mobile screening services, and arming community figures to identify and link presumptive cases can be effective strategies to improve case detection in pastoral settings.
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Noviyanti, Lienda, Achmad Zanbar Soleh, Anna Chadidjah, and Hasna Afifah Rusyda. "Optimal Retention for a Quota-Share Reinsurance." Jurnal Teknik Industri 20, no. 1 (June 17, 2018): 25–32. http://dx.doi.org/10.9744/jti.20.1.25-32.
Анотація:
The Indonesian Financial Services Authority (OJK) has instructed all insurance providers in Indonesia to apply a mandatory tariff for property insurance. The tariff has to be uniformly applied and the rule of set the maximum and minimum premium rates for protection against losses. Furthermore, the OJK issued the new rule regarding self-retention and domestic reinsurance. Insurance companies are obliged to have and implement self-retention for each risk in accordance with the self-retention limits. Fluctuations of total premium income and claims may lead the insurance company cannot fulfil the obligation to the insured, thus the company needs to conduct reinsurance. Reinsurance helps protect insurers against unforeseen or extraordinary losses by allowing them to spread their risks. Because reinsurer chargers premium to the insurance company, a properly calculated optimal retention would be nearly as high as the insurer financial ability. This paper is aimed at determining optimal retentions indicated by the risk measure Value at Risk (VaR), Expected Shortfall (ES) and Minimum Variance (MV). Here we use the expectation premium principle which minimizes individual risks based on their quota share reinsurance. Regarding to the data in an insurance property, we use a bivariate lognormal distribution to obtain VaR, ES and MV, and a bivariate exponential distribution to obtain MV. The bivariate distributions are required to derive the conditional probability of the amount of claim occurs given the benefit has occurred.
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Liu, Chengcheng, Xuandong Wang, Weidong Cai, Yazhou He, and Hang Su. "Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass." Processes 11, no. 9 (September 21, 2023): 2806. http://dx.doi.org/10.3390/pr11092806.
Анотація:
The prediction of the glass-forming ability (GFA) of metallic glasses (MGs) can accelerate the efficiency of their development. In this paper, a dataset was constructed using experimental data collected from the literature and books, and a machine learning-based predictive model was established to predict the GFA. Firstly, a classification model based on the size of the critical diameter (Dmax) was established to determine whether an alloy system could form a glass state, with an accuracy rating of 0.98. Then, regression models were established to predict the crystallization temperature (Tx), glass transition temperature (Tg), and liquidus temperature (Tl) of MGs. The R2 of the prediction model obtained in the test set was greater than 0.89, which showed that the model had good prediction accuracy. The key features used by the regression models were analyzed using variance, correlation, embedding, recursive, and exhaustive methods to select the most important features. Furthermore, to improve the interpretability of the prediction model, feature importance, partial dependence plot (PDP), and individual conditional expectation (ICE) methods were used for visualization analysis, demonstrating how features affect the target variables. Finally, taking Zr-Cu-Ni-Al system MGs as an example, a prediction model was established using a genetic algorithm to optimize the alloy composition for high GFA in the compositional space, achieving the optimal design of alloy composition.
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Frees, Edward W., Peng Shi, and Emiliano A. Valdez. "Actuarial Applications of a Hierarchical Insurance Claims Model." ASTIN Bulletin 39, no. 1 (May 2009): 165–97. http://dx.doi.org/10.2143/ast.39.1.2038061.
Анотація:
AbstractThis paper demonstrates actuarial applications of modern statistical methods that are applied to detailed, micro-level automobile insurance records. We consider 1993-2001 data consisting of policy and claims files from a major Singaporean insurance company. A hierarchical statistical model, developed in prior work (Frees and Valdez (2008)), is fit using the micro-level data. This model allows us to study the accident frequency, loss type and severity jointly and to incorporate individual characteristics such as age, gender and driving history that explain heterogeneity among policyholders.Based on this hierarchical model, one can analyze the risk profile of either a single policy (micro-level) or a portfolio of business (macro-level). This paper investigates three types of actuarial applications. First, we demonstrate the calculation of the predictive mean of losses for individual risk rating. This allows the actuary to differentiate prices based on policyholder characteristics. The nonlinear effects of coverage modifications such as deductibles, policy limits and coinsurance are quantified. Moreover, our flexible structure allows us to “unbundle” contracts and price more primitive elements of the contract, such as coverage type. The second application concerns the predictive distribution of a portfolio of business. We demonstrate the calculation of various risk measures, including value at risk and conditional tail expectation, that are useful in determining economic capital for insurance companies. Third, we examine the effects of several reinsurance treaties. Specifically, we show the predictive loss distributions for both the insurer and reinsurer under quota share and excess-of-loss reinsurance agreements. In addition, we present an example of portfolio reinsurance, in which the combined effect of reinsurance agreements on the risk characteristics of ceding and reinsuring company are described.
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Cakiroglu, Celal, Gebrail Bekdaş, Sanghun Kim, and Zong Woo Geem. "Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete." Sustainability 14, no. 21 (November 7, 2022): 14640. http://dx.doi.org/10.3390/su142114640.
Анотація:
Self-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable’s effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity.
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Astutik, Lilis Hidayati Yuli, Iffatin Nur та Mashuri Mashuri. "Family Expectation and Poverty Alleviation Program: Approaches to Family Development Laws, Sustainable Development Goals, and Maqāṣid Sharīa". Justicia Islamica 19, № 1 (26 червня 2022): 38–56. http://dx.doi.org/10.21154/justicia.v19i1.3227.
Анотація:
This research intends to evaluate Family Expectation Program to alleviate poverty through the lens of Population and Family Development Laws, Sustainable Development Goals, and Maqāṣid Sharīa. Poverty becomes a problem in human life and brings implications for individual and social lives. This condition has made the United Nations initiate the Sustainable Development Goals (SDGs) program, which among others, aims to alleviate poverty worldwide. As a member country of the United Nations, Indonesia welcomes this initiative by preparing several national poverty alleviation programs, including Program Keluarga Harapan (PKH)/ Conditional Cash Transfer Program. This study used a qualitative research approach with a case study and multi-site design to evaluate this program. Through the study locus in Tulungagung and Trenggalek Regencies, East Java, the results of this study indicate that the PKH program in the two locations is following the objectives of Islamic law, including the protection of religion (hifẓ al- dīn), the protection of human soul and body (hifẓ al-nafs), the protection of wealth (hifẓ al-māl), the protection of mind (intelligence) (hifẓ al-'aql), the protection of lineage (hifẓ al-nasl), and the protection of honor (hifẓ al-'irḍ). The results of this research hopefully contribute to setting up the poverty alleviation-based -government policies through family development programs to pursue Sustainable Development Goals (SDGs) based on the values of maqāṣid sharīa.Kajian ini bertujuan untuk melakukan evaluasi terhadap Program Keluarga Harapan (PKH) dalam mengentaskan kemiskinan melalui pendekatan Undang-Undang Perkembangan Kependudukan dan Pembangunan Keluarga, Sustainable Development Goals (SDGs), dan maqāṣid sharīa. Kondisi kemiskinan telah menjadi masalah dalam kehidupan manusia dan berimplikasi terhadap kehidupan individu maupun sosial. Kondisi ini membuat PBB menginisiasi program Sustainable Development Goals (SDGs) yang antara lain bertujuan untuk mengentaskan kemiskinan di seluruh dunia. Sebagai salah satu negara anggota PBB, Indonesia menyambut positif inisiasi ini dengan menyiapkan beberapa program pengentasan kemiskinan yang dijalankan secara nasional, antara lain Program Keluarga Harapan (PKH). Sebagai upaya evaluasi Program Keluarga Harapan (PKH) dalam pengentasan kemiskinan, kajian ini merupakan kajian dengan pendekatan penelitian kualitatif dengan jenis studi kasus dan rancangan multisitus. Melalui lokus kajian di Kabupaten Tulungagung dan Trenggalek Provinsi Jawa Timur, hasil kajian ini menunjukkan bahwa program PKH di kedua lokasi tersebut sesuai dengan tujuan syariat Islam antara lain perlindungan terhadap agama (hifẓ al- dīn), jiwa dan raga (hifẓ al-nafs), harta (hifẓ al-māl) , kecerdasan (hifẓ al-'aql), keturunan (hifẓ al-nasl), dan kehormatan (hifẓ al-'irḍ). Melalui kajian ini, berkontribusi membantu pemerintah dalam perumusan kebijakan yang berorientasi pada pengentasan kemiskinan melalui program pembangunan keluarga sebagai upaya mewujudkan Sustainable Development Goals (SDGs) berbasis nilai-nilai maqāṣid sharīa.
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Bekdaş, Gebrail, Celal Cakiroglu, Sanghun Kim, and Zong Woo Geem. "Optimal Dimensions of Post-Tensioned Concrete Cylindrical Walls Using Harmony Search and Ensemble Learning with SHAP." Sustainability 15, no. 10 (May 11, 2023): 7890. http://dx.doi.org/10.3390/su15107890.
Анотація:
The optimal design of prestressed concrete cylindrical walls is greatly beneficial for economic and environmental impact. However, the lack of the available big enough datasets for the training of robust machine learning models is one of the factors that prevents wide adoption of machine learning techniques in structural design. The current study demonstrates the application of the well-established harmony search methodology to create a large database of optimal design configurations. The unit costs of concrete and steel used in the construction, the specific weight of the stored fluid, and the height of the cylindrical wall are the input variables whereas the optimum thicknesses of the wall with and without post-tensioning are the output variables. Based on this database, some of the most efficient ensemble learning techniques like the Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Gradient Boosting (CatBoost) and Random Forest algorithms have been trained. An R2 score greater than 0.98 could be achieved by all of the ensemble learning models. Furthermore, the impacts of different input features on the predictions of different machine learning models have been analyzed using the SHapley Additive exPlanations (SHAP) methodology. The height of the cylindrical wall was found to have the greatest impact on the optimal wall thickness, followed by the specific weight of the stored fluid. Also, with the help of individual conditional expectation (ICE) plots the variations of predictive model outputs with respect to each input feature have been visualized. By using the genetic programming methodology, predictive equations have been obtained for the optimal wall thickness.
29
Cakiroglu, Celal. "Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks." Applied Sciences 13, no. 21 (November 2, 2023): 11991. http://dx.doi.org/10.3390/app132111991.
Анотація:
The current study offers a data-driven methodology to predict the ultimate strain and compressive strength of concrete reinforced by aramid FRP wraps. An experimental database was collected from the literature, on which seven different machine learning (ML) models were trained. The diameter and length of the cylindrical specimens, the compressive strength of unconfined concrete, the thickness, elasticity modulus and ultimate tensile strength of the FRP wrap were used as the input features of the machine learning models, to predict the ultimate strength and strain of the specimens. The experimental dataset was further enhanced with synthetic data using the tabular generative adversarial network (TGAN) approach. The machine learning models’ performances were compared to the predictions of the existing strain capacity and compressive strength prediction equations for aramid FRP-confined concrete. The accuracy of the predictive models was measured using state-of-the-art statistical metrics such as the coefficient of determination, mean absolute error and root mean squared error. On average, the machine learning models were found to perform better than the available equations in the literature. In particular, the extra trees regressor, XGBoost and K-nearest neighbors algorithms performed significantly better than the remaining algorithms, with R2 scores greater than 0.98. Furthermore, the SHapley Additive exPlanations (SHAP) method and individual conditional expectation (ICE) plots were used to visualize the effects of various input parameters on the predicted ultimate strain and strength values. The unconfined compressive strength of concrete and the ultimate tensile strength of the FRP wrap were found to have the greatest impact on the machine learning model outputs.
30
Rajczakowska, Magdalena, Maciej Szeląg, Karin Habermehl-Cwirzen, Hans Hedlund, and Andrzej Cwirzen. "Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete." Materials 16, no. 3 (February 2, 2023): 1273. http://dx.doi.org/10.3390/ma16031273.
Анотація:
Developing accurate and interpretable models to forecast concrete’s self-healing behavior is of interest to material engineers, scientists, and civil engineering contractors. Machine learning (ML) and artificial intelligence are powerful tools that allow constructing high-precision predictions, yet often considered “black box” methods due to their complexity. Those approaches are commonly used for the modeling of mechanical properties of concrete with exceptional accuracy; however, there are few studies dealing with the application of ML for the self-healing of cementitious materials. This paper proposes a pioneering study on the utilization of ML for predicting post-fire self-healing of concrete. A large database is constructed based on the literature studies. Twelve input variables are analyzed: w/c, age of concrete, amount of cement, fine aggregate, coarse aggregate, peak loading temperature, duration of peak loading temperature, cooling regime, duration of cooling, curing regime, duration of curing, and specimen volume. The output of the model is the compressive strength recovery, being one of the self-healing efficiency indicators. Four ML methods are optimized and compared based on their performance error: Support Vector Machines (SVM), Regression Trees (RT), Artificial Neural Networks (ANN), and Ensemble of Regression Trees (ET). Monte Carlo analysis is conducted to verify the stability of the selected model. All ML approaches demonstrate satisfying precision, twice as good as linear regression. The ET model is found to be the most optimal with the highest prediction accuracy and sufficient robustness. Model interpretation is performed using Partial Dependence Plots and Individual Conditional Expectation Plots. Temperature, curing regime, and amounts of aggregates are identified as the most significant predictors.
31
Cakiroglu, Celal, and Gebrail Bekdaş. "Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods." Sustainability 15, no. 6 (March 10, 2023): 4957. http://dx.doi.org/10.3390/su15064957.
Анотація:
Construction and demolition waste (CDW) together with the pollution caused by the production of new concrete are increasingly becoming a burden on the environment. An appealing strategy from both an ecological and a financial point of view is to use construction and demolition waste in the production of recycled aggregate concrete (RAC). However, past studies have shown that the currently available code provisions can be unconservative in their predictions of the shear strength of RAC beams. The current study develops accurate predictive models for the shear strength of RAC beams based on a dataset of experimental results collected from the literature. The experimental database used in this study consists of full-scale four-point flexural tests. The recycled coarse aggregate (RCA) percentage, compressive strength (fc′), effective depth (d), width of the cross-section (b), ratio of shear span to effective depth (a/d), and ratio of longitudinal reinforcement (ρw) are the input features used in the model training. It is demonstrated that the proposed machine learning models outperform the existing code equations in the prediction of shear strength. State-of-the-art metrics of accuracy, such as the coefficient of determination (R2), mean absolute error, and root mean squared error, have been utilized to quantify the performances of the ensemble machine learning models. The most accurate predictions could be obtained from the XGBoost model, with an R2 score of 0.94 on the test set. Moreover, the impact of different input features on the machine learning model predictions is explained using the SHAP algorithm. Using individual conditional expectation plots, the variation of the model predictions with respect to different input features has been visualized.
32
Safaei, Nima, Babak Safaei, Seyedhouman Seyedekrami, Mojtaba Talafidaryani, Arezoo Masoud, Shaodong Wang, Qing Li, and Mahdi Moqri. "E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database." PLOS ONE 17, no. 5 (May 5, 2022): e0262895. http://dx.doi.org/10.1371/journal.pone.0262895.
Анотація:
Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients’ survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients’ discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models’ predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.
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Welchowski, Thomas, Kelly O. Maloney, Richard Mitchell, and Matthias Schmid. "Techniques to Improve Ecological Interpretability of Black-Box Machine Learning Models." Journal of Agricultural, Biological and Environmental Statistics 27, no. 1 (October 28, 2021): 175–97. http://dx.doi.org/10.1007/s13253-021-00479-7.
Анотація:
AbstractStatistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological health of streams within the contiguous USA, as measured by a benthic macroinvertebrate biotic index. Based on these data and a simulation study, we demonstrate the advantages and limitations of partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) in their ability to identify covariate–response relationships. Additionally, interaction effects were quantified according to interaction strength (IAS) and Friedman’s $$H^2$$ H 2 statistic. Interpretable machine learning techniques are useful tools to open the black-box of gradient boosted trees in the environmental sciences. This finding is supported by our case study on the effect of impervious surface on the benthic condition, which agrees with previous results in the literature. Overall, the most important variables were ecoregion, bed stability, watershed area, riparian vegetation and catchment slope. These variables were also present in most identified interaction effects. In conclusion, graphical tools (PDP, ICE, ALE) enable visualization and easier interpretation of GBT but should be supported by analytical statistical measures. Future methodological research is needed to investigate the properties of interaction tests. Supplementary materials accompanying this paper appear on-line.
34
Ferreño, Diego, Marta Serrano, Mark Kirk, and José A. Sainz-Aja. "Prediction of the Transition-Temperature Shift Using Machine Learning Algorithms and the Plotter Database." Metals 12, no. 2 (January 19, 2022): 186. http://dx.doi.org/10.3390/met12020186.
Анотація:
The long-term operating strategy of nuclear plants must ensure the integrity of the vessel, which is subjected to neutron irradiation, causing its embrittlement over time. Embrittlement trend curves used to predict the dependence of the Charpy transition-temperature shift, ΔT41J, with neutron fluence, such as the one adopted in ASTM E900-15, are empirical or semi-empirical formulas based on parameters that characterize irradiation conditions (neutron fluence, flux and temperature), the chemical composition of the steel (copper, nickel, phosphorus and manganese), and the product type (plates, forgings, welds, or so-called standard reference materials (SRMs)). The ASTM (American Society for Testing and Materials) E900-15 trend curve was obtained as a combination of physical and phenomenological models with free parameters fitted using the available surveillance data from nuclear power plants. These data, collected to support ASTM’s E900 effort, open the way to an alternative, purely data-driven approach using machine learning algorithms. In this study, the ASTM PLOTTER database that was used to inform the ASTM E900-15 fit has been employed to train and validate a number of machine learning regression models (multilinear, k-nearest neighbors, decision trees, support vector machines, random forest, AdaBoost, gradient boosting, XGB, and multi-layer perceptron). Optimal results were obtained with gradient boosting, which provided a value of R2 = 0.91 and a root mean squared error ≈10.5 °C for the test dataset. These results outperform the prediction ability of existing trend curves, including ASTM E900-15, reducing the prediction uncertainty by ≈20%. In addition, impurity-based and permutation-based feature importance algorithms were used to identify the variables that most influence ΔT41J (copper, fluence, nickel and temperature, in this order), and individual conditional expectation and interaction plots were used to estimate the specific influence of each of the features.
35
Tran, Van Quan. "Using Artificial Intelligence Approach for Investigating and Predicting Yield Stress of Cemented Paste Backfill." Sustainability 15, no. 4 (February 6, 2023): 2892. http://dx.doi.org/10.3390/su15042892.
Анотація:
The technology known as cemented paste backfill (CPB) has gained considerable popularity worldwide. Yield stress (YS) is a significant factor considered in the assessment of CPB’s flowability or transportability. The minimal shear stress necessary to start the flow is known as Yield stress (YS), and it serves as an excellent measure of the strength of the particle-particle interaction. The traditional evaluation and measurement of YS performed by experimental tests are time-consuming and costly, which induces delays in construction projects. Moreover, the YS of CPB depends on numerous factors such as cement/tailing ratio, solid content and oxide content of tailing. Therefore, in order to simplify YS estimation and evaluation, the Artificial Intelligence (AI) approaches including eight Machine Learning techniques such as the Extreme Gradient Boosting algorithm, Gradient Boosting algorithm, Random Forest algorithm, Decision Trees, K-Nearest Neighbor, Support Vector Machine, Multivariate Adaptive Regression Splines and Gaussian Process are used to build the soft-computing model in predicting the YS of CPB. The performance of these models is evaluated by three metrics coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The 3 best models were found to predict the Yield Stress of CPB (Gradient Boosting (GB), Extreme Gradient Boosting (XGB) and Random Forest (RF), respectively) with the 3 metrics of the three models, respectively, GB {R2 = 0.9811, RMSE = 0.1327 MPa, MAE = 0.0896 MPa}, XGB {R2 = 0.9034, RMSE = 0.3004 MPa, MAE = 0.1696 MPa} and RF {R2 = 0.8534, RMSE = 0.3700 MPa, MAE = 0.1786 MPa}, for the testing dataset. Based on the best performance model including GB, XG and RF, the other AI techniques such as SHapley Additive exPlanations (SHAP), Permutation Importance, and Individual Conditional Expectation (ICE) are also used for evaluating the factor effect on the YS of CPB. The results of this investigation can help the engineers to accelerate the mixed design of CPB.
36
Ozdin, Deniz, Naveen Sharma, Jorge Lujan-Zilbermann, Philippe Colucci, Isadore Kanfer, and Murray P. Ducharme. "Revisiting FDA’s 1995 Guidance on Bioequivalence Establishment of Topical Dermatologic Corticosteroids: New Research Based Recommendations." Journal of Pharmacy & Pharmaceutical Sciences 21 (November 8, 2018): 413–28. http://dx.doi.org/10.18433/jpps30021.
Анотація:
Purpose: As per the US FDA guidance issued on June 2, 1995, the establishment of bioequivalence for topical dermatologic corticosteroids is based on comparing the pharmacodynamic (PD) effects of Test and Reference products at the dose duration corresponding to the population ED50, determined either by naïve pooled data or nonlinear mixed effect modeling (NLME). The guidance was introduced using a study case example where the expectation maximization (EM) NLME algorithm, as implemented in P-PHARM®, was used. Although EM methods are relatively common, other methods such as the First-Order Conditional Estimation (FOCE) as implemented in the NONMEM® software are even more common. The objective of this study was to investigate the impact of using different parametric population modeling/analysis methods and distribution assumptions on population analysis results. Methods: The dose duration-response data from 11 distinct skin blanching blinded pilot studies were fitted using FOCE (NONMEM®) and an EM algorithm (ADAPT5® (MLEM)). Three different Emax models were tested for each method. Population PD estimates and associated CV%, and the agreement between model predicted values and observed data were compared between the two methods. The impact of assuming different distributions of PD parameters was also investigated. Results: The simple Emax model, as proposed in the FDA guidance, appeared to best characterize the data compared to more complex alternatives. The MLEM method in general appeared to provide better results than FOCE; lower population PD estimates with less inter-individual variability, and no variance shrinkage issues. The results also favored ln-normal versus normal distribution assumptions. Conclusions: The population ED50 estimates were influenced by both the type of population modeling methods and the distribution assumptions. We recommend updating the FDA guidance with more specific instructions related to the population approach to be used (EM-like versus FOCE-like methods) and to the normality assumptions that need to be set (ln-normal versus normal distribution).
37
Abrahamsen, Petter, Ragnar Hauge, Knut Heggland, and Petter Mostad. "Estimation of Gross Rock Volume of Filled Geological Structures With Uncertainty Measures." SPE Reservoir Evaluation & Engineering 3, no. 04 (August 1, 2000): 304–9. http://dx.doi.org/10.2118/65419-pa.
Анотація:
Summary The gross rock volume of a filled structure is uncertain because of uncertainty in the determination of caprock depth and the uncertainty in depth to the hydrocarbon contact determined by the spill point of the caprock. Ignoring this uncertainty might lead to biased volume estimates. This paper reports two procedures to assist with assessing this uncertainty to obtain better estimates. The first is to use conditional simulation techniques to generate realizations of the depth to the caprock. The second procedure is a new fast algorithm that determines the location of the spill point and trapped area of each caprock realization. Taken together, the two procedures determine the thickness and lateral extension of each reservoir realization. Finally, gross rock volume for each realization can be calculated and the volumetric uncertainty can be quantified in terms of expectation, histograms, percentiles, etc., for the whole set of realizations. A synthetic example and an example from the North Sea illustrate the use of these procedures. A method for including knowledge of the spill-point depth for improving depth maps is also presented. Introduction The uncertainty in gross rock volume can be large and even dominate the uncertainty in STOOIP and recoverable reserves.1 To assess this uncertainty Monte Carlo approaches are widely used. These range from simple spreadsheet methods2 to elaborate approaches including stochastic simulation of surface geometry and two-dimensional (2D) or three-dimensional (3D) stochastic simulation of reservoir properties.1 The purpose of this paper is to establish a general method for estimating gross rock volume of filled structures where the uncertainty in the caprock depth is believed to be significant. The key parts are the conditional simulation of caprock depth and the new spill-point detection algorithm. Both are explained in some detail in the following sections. The importance of having a procedure that takes account of the uncertainty in caprock depth is illustrated by two examples. A synthetic example is used to show that the expected volume decreases with higher depth uncertainty for a simplistic anticline. The second example is from real data and shows that the expected volume is larger when considering the uncertainty in caprock depth. This shows that uncertainty in caprock depth influences volumetric estimates, and that every caprock structure needs individual consideration. Construction of Caprock Depth There are two principally different approaches to generating the depth maps for the caprock. Either we use some prediction method for obtaining "the best map," or we can use some Monte Carlo method to obtain a set of realizations of depth maps. The traditional approach is to consider a prediction and use this as the basis for further calculations and decisions. In the presence of uncertainties, calculations based on the prediction can be biased so decisions are made on false assumptions. Considering a set of realizations spanning the uncertainty partly solves this problem, but at the expense of more calculations and a more complicated situation for decision making. In the authors opinion, kriging methods are the best methods for making the depth map prediction. Kriging methods can include flexible trends from seismic data, and they can be calibrated to available well data. Alternative gridding algorithms such as splines and triangulation may give very good results but they lack the possibility of efficient estimation of parameters in the algorithms. Another appealing property of kriging methods is that they are closely linked to conditional simulation (Monte Carlo) methods. There is a conditional simulation method corresponding to every choice of kriging method. The kriging and the corresponding simulation method share the same set of model assumptions, input data, and modal parameters. Thus, we can prepare a single set of assumptions, data, and parameters and choose either kriging or conditional simulation for investigating different properties of the phenomenon under study. This ensures that results from different investigations are consistent in the sense that they rely on the same assumptions and data. Moreover, the average of a large set of conditional simulated depth map realizations will coincide with the predicted map given by kriging. Thus, taking the average of a large set of conditional simulations is an inefficient way of calculating a prediction. Kriging. The most flexible (standard) kriging method is universal kriging. The basic assumption is that we can write, e.g., the depth to the caprock as depth (x, y)=trend(x, y)+residual(x, y). The trend must be linear in unknown coefficients (a, b, c, . . .) and must have the form trend(x,y)=af1(x,y)+bf2(x,y)+cf3(x,y)+⋯ where fi(x, y)are known functions. The residual is assumed to have zero mean and a variogram which must be specified, or if possible estimated. Universal kriging can be formulated as a two-step procedure. First, the trend is fitted to data by generalleast-squares estimation of the coefficients. Second, the residual is predicted using simple kriging so that the sum of the fitted trend and predicted residual interpolates the observations. The result is a depth surface with large-scale features given by the trend and with small modifications near wells given by the predicted residual. An error map showing the uncertainty in the depth prediction can also be calculated.
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Schaffer, Lena, and Gabriele Spilker. "Adding Another Level Individual Responses to Globalization and Government Welfare Policies." Political Science Research and Methods 4, no. 2 (May 8, 2015): 399–426. http://dx.doi.org/10.1017/psrm.2015.10.
Анотація:
Literature on the compensation hypothesis overwhelmingly concentrates on either the macro or micro level of the relationship between globalization and welfare spending. This paper explicitly addresses this shortcoming by using individual citizens and country-specific characteristics in a hierarchical model framework. We start by examining individual’s context-conditional reactions to actual economic globalization and welfare generosity; after which, we make the effect of actual economic globalization (welfare generosity) conditional on whether the individual is a globalization winner or loser. In contrast to theoretical expectations, our results indicate that actual economic globalization does not affect people’s perception in the manner expected by the compensation hypothesis. However, individuals display more positive attitudes toward globalization if welfare state generosity is proxied using government spending on active labor market programs.
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Livingston, Beth A., Pauline Schilpzand, and Amir Erez. "Not What You Expected to Hear." Journal of Management 43, no. 3 (July 9, 2016): 804–33. http://dx.doi.org/10.1177/0149206314541151.
Анотація:
In this article we address the increasingly important yet understudied phenomenon of nonnative accentedness on decision making. In three experimental studies, we investigated whether messages about a company delivered in nonstandard-American-accented speech influenced choice. In Study 1, we found that individuals were more likely to choose a company or a product when a message was read in a standard American English accent than when the message was delivered with a Mandarin Chinese or a French accent. In Study 2, we found that expectations regarding company messages are violated when speakers have accents and that, in turn, expectation violations mediated the relationship between accent and choice. In Study 3, we replicated the findings of the effect of accent on choice using Indian and British accents. We also hypothesized and found support for a conditional indirect effects model such that implicit pro-American bias moderated the indirect relationship between accent and choice as mediated by expectation violations. Theoretical and practical implications of this topic of study are discussed.
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Kulinkina, Alexandra V., Andrea Farnham, Nana-Kwadwo Biritwum, Jürg Utzinger, and Yvonne Walz. "How do disease control measures impact spatial predictions of schistosomiasis and hookworm? The example of predicting school-based prevalence before and after preventive chemotherapy in Ghana." PLOS Neglected Tropical Diseases 17, no. 6 (June 16, 2023): e0011424. http://dx.doi.org/10.1371/journal.pntd.0011424.
Анотація:
Background Schistosomiasis and soil-transmitted helminth infections are among the neglected tropical diseases (NTDs) affecting primarily marginalized communities in low- and middle-income countries. Surveillance data for NTDs are typically sparse, and hence, geospatial predictive modeling based on remotely sensed (RS) environmental data is widely used to characterize disease transmission and treatment needs. However, as large-scale preventive chemotherapy has become a widespread practice, resulting in reduced prevalence and intensity of infection, the validity and relevance of these models should be re-assessed. Methodology We employed two nationally representative school-based prevalence surveys of Schistosoma haematobium and hookworm infections from Ghana conducted before (2008) and after (2015) the introduction of large-scale preventive chemotherapy. We derived environmental variables from fine-resolution RS data (Landsat 8) and examined a variable distance radius (1–5 km) for aggregating these variables around point-prevalence locations in a non-parametric random forest modeling approach. We used partial dependence and individual conditional expectation plots to improve interpretability. Principal findings The average school-level S. haematobium prevalence decreased from 23.8% to 3.6% and that of hookworm from 8.6% to 3.1% between 2008 and 2015. However, hotspots of high-prevalence locations persisted for both diseases. The models with environmental data extracted from a buffer radius of 2–3 km around the school location where prevalence was measured had the best performance. Model performance (according to the R2 value) was already low and declined further from approximately 0.4 in 2008 to 0.1 in 2015 for S. haematobium and from approximately 0.3 to 0.2 for hookworm. According to the 2008 models, land surface temperature (LST), modified normalized difference water index (MNDWI), elevation, slope, and streams variables were associated with S. haematobium prevalence. LST, slope, and improved water coverage were associated with hookworm prevalence. Associations with the environment in 2015 could not be evaluated due to low model performance. Conclusions/significance Our study showed that in the era of preventive chemotherapy, associations between S. haematobium and hookworm infections and the environment weakened, and thus predictive power of environmental models declined. In light of these observations, it is timely to develop new cost-effective passive surveillance methods for NTDs as an alternative to costly surveys, and to focus on persisting hotspots of infection with additional interventions to reduce reinfection. We further question the broad application of RS-based modeling for environmental diseases for which large-scale pharmaceutical interventions are in place.
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Zeyad Amin Al-Absi, Mohd Isa Mohd Hafizal, Noor Faisal Abas, and Faizal Baharum. "A Comparison in Perception of Local and Foreign Residents to Thermal Comfort in Naturally Conditioned Residential Buildings." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 100, no. 3 (December 31, 2022): 78–91. http://dx.doi.org/10.37934/arfmts.100.3.7891.
Анотація:
Thermal comfort is the individual satisfaction with the surrounding thermal environment. It is mainly affected by environmental factors (i.e., air temperature, relative humidity, air movement, and mean radiant temperature) and individual factors (i.e., activity and clothing). However, other factors such as acclimatization, experiences and expectations, food and drink, body shape and subcutaneous fat, age and gender, and state of health might play a significant role in the individual sensation and satisfaction of the thermal environment. This study investigates the possible differences in thermal perception between local and foreign residents, which might occur due to the influence of the individual and contributing factors that are linked to their thermal, cultural, and behavioural backgrounds. High-rise residential buildings that accommodate local and foreign residents were selected, and a questionnaire survey was distributed to assess their thermal comfort perception. The results showed differences between local and foreign residents in thermal comfort perception. The foreign residents were more satisfied and comfortable with the thermal environment compared to the local residents. However, this difference was found to be statistically insignificant; therefore, it might be linked to factors linked to the current study, including acclimatization, expectation, clothing insulation and activity levels. Therefore, a further large-scale investigation might be required with more analysis on the role and influence of the contributing factors on the thermal sensation of different gropes.
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Bicchieri, Cristina, Enrique Fatas, Abraham Aldama, Andrés Casas, Ishwari Deshpande, Mariagiulia Lauro, Cristina Parilli, Max Spohn, Paula Pereira, and Ruiling Wen. "In science we (should) trust: Expectations and compliance across nine countries during the COVID-19 pandemic." PLOS ONE 16, no. 6 (June 4, 2021): e0252892. http://dx.doi.org/10.1371/journal.pone.0252892.
Анотація:
The magnitude and nature of the COVID-19 pandemic prevents public health policies from relying on coercive enforcement. Practicing social distancing, wearing masks and staying at home becomes voluntary and conditional on the behavior of others. We present the results of a large-scale survey experiment in nine countries with representative samples of the population. We find that both empirical expectations (what others do) and normative expectations (what others approve of) play a significant role in compliance, beyond the effect of any other individual or group characteristic. In our vignette experiment, respondents evaluate the likelihood of compliance with social distancing and staying at home of someone similar to them in a hypothetical scenario. When empirical and normative expectations of individuals are high, respondents’ evaluation of the vignette’s character’s compliance likelihood goes up by 55% (relative to the low expectations condition). Similar results are obtained when looking at self-reported compliance among those with high expectations. Our results are moderated by individuals’ trust in government and trust in science. Holding expectations high, the effect of trusting science is substantial and significant in our vignette experiment (22% increase in compliance likelihood), and even larger in self-reported compliance (76% and 127% increase before and after the lockdown). By contrast, trusting the government only generates modest effects. At the aggregate level, the country-level trust in science, and not in government, becomes a strong predictor of compliance.
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Brito, Giovani Antonio Silva, Antonio Carlos Coelho, and Alexsandro Broedel Lopes. "Loss recognition timeliness in Brazilian banks: The influence of state ownership." Journal of Governance and Regulation 2, no. 1 (2013): 71–88. http://dx.doi.org/10.22495/jgr_v2_i1_p6.
Анотація:
This paper investigates the effect of state ownership on the conditional conservatism of financial reports of Brazilian banks. State controlled banks in Brazil face additional monitoring from government authorities and managers risk litigation as individuals with potential effects on their personal wealth. Thus we hypothesize that state ownership would have a positive marginal effect on conditional conservatism in this institutional environment. Using a times series conditional conservatism model our results confirm our expectations and show that state ownership has a positive effect on the conditional conservatism of earnings in Brazil. Using a logit model we also corroborate this effect after controlling for the effect of unconditional conservatism and earnings smoothing.
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Mahoney, Shawn, Jay Hosler, and Brian H. Smith. "Reinforcement expectation in the honeybee (Apis mellifera): Can downshifts in reinforcement show conditioned inhibition?" Learning & Memory 31, no. 5 (May 2024): a053915. http://dx.doi.org/10.1101/lm.053915.124.
Анотація:
When animals learn the association of a conditioned stimulus (CS) with an unconditioned stimulus (US), later presentation of the CS invokes a representation of the US. When the expected US fails to occur, theoretical accounts predict that conditioned inhibition can accrue to any other stimuli that are associated with this change in the US. Empirical work with mammals has confirmed the existence of conditioned inhibition. But the way it is manifested, the conditions that produce it, and determining whether it is the opposite of excitatory conditioning are important considerations. Invertebrates can make valuable contributions to this literature because of the well-established conditioning protocols and access to the central nervous system (CNS) for studying neural underpinnings of behavior. Nevertheless, although conditioned inhibition has been reported, it has yet to be thoroughly investigated in invertebrates. Here, we evaluate the role of the US in producing conditioned inhibition by using proboscis extension response conditioning of the honeybee (Apis mellifera). Specifically, using variations of a “feature-negative” experimental design, we use downshifts in US intensity relative to US intensity used during initial excitatory conditioning to show that an odorant in an odor–odor mixture can become a conditioned inhibitor. We argue that some alternative interpretations to conditioned inhibition are unlikely. However, we show variation across individuals in how strongly they show conditioned inhibition, with some individuals possibly revealing a different means of learning about changes in reinforcement. We discuss how the resolution of these differences is needed to fully understand whether and how conditioned inhibition is manifested in the honeybee, and whether it can be extended to investigate how it is encoded in the CNS. It is also important for extension to other insect models. In particular, work like this will be important as more is revealed of the complexity of the insect brain from connectome projects.
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Novara, Caroline, Cindy Lebrun, Alexandra Macgregor, Bruno Vivet, Pierre Thérouanne, Delphine Capdevielle, and Stephane Raffard. "Acquisition and maintenance of disgust reactions in an OCD analogue sample: Efficiency of extinction strategies through a counter-conditioning procedure." PLOS ONE 16, no. 7 (July 14, 2021): e0254592. http://dx.doi.org/10.1371/journal.pone.0254592.
Анотація:
Background Obsessive-compulsive disorder (OCD) has long been considered as an anxiety disorder, disgust is the dominant emotion in contamination-based OCD. However, disgust seems resistant to exposure with response prevention partly due to the fact that disgust is acquired through evaluative conditioning. Aims The present research investigates a counter-conditioning intervention in treating disgust-related emotional responses in two groups of individuals with high (High contamination concerns, HCC, n = 24) and low (Low contamination concerns LCC, n = 23) contamination concerns. Methods The two groups completed a differential associative learning task in which neutral images were followed by disgusting images (conditioned stimulus; CS+), or not (CS-). Following this acquisition phase, there was a counter-conditioning procedure in which CS+ was followed by a very pleasant unconditional stimulus while CS- remained unreinforced. Results Following counter-conditioning, both groups reported significant reduction in their expectancy of US occurrence and reported less disgust with CS+. For both expectancy and disgust, reduction was lower in the HCC group than in the LCC group. Disgust sensitivity was highly correlated with both acquisition and maintenance of the response acquired, while US expectation was predicted by anxiety. Conclusion Counter-conditioning procedure reduces both expectations and conditioned disgust.
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Cristina State, Ion Tudor, and Valentina Nicolae. "Empirical Research on the Latent Factors that Facilitate the Individuals’ Interaction with the Community." European Journal of Sustainable Development 8, no. 5 (October 1, 2019): 278. http://dx.doi.org/10.14207/ejsd.2019.v8n5p278.
Анотація:
The communion of interests and the open, voluntary membership which characterise social economy enterprises are a challenge we will be trying to deal with in this paper. Our dilemma regarding the existence of some conditionality between the individuals’ expectations from their community and their availability to get involved in solving community problems has become the main objective of the study. Solving this dilemma came as a natural consequence of the initiation of a questionnaire-based study, including separate sets of questions concerning the perception of the participants about how the community meets their expectations, combined with questions about their availability to act to the benefit of the community. The work hypotheses were tested with the IBM Statistics and Microsoft Excel applications. The results obtained after testing the hypotheses signal two important aspects: on the one hand, the availability of the participants in the study to act for the benefit of their communities is not conditional on the expectations they have from the community and, on the other hand, at the time of the survey, the preference of the study participants to act for the benefit of the community is not sufficiently well defined.Keywords: communities, social economic enterprise, solidarity, social implication
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Doherty, Kathleen M., David E. Lewis, and Scott Limbocker. "Presidential Control and Turnover in Regulatory Personnel." Administration & Society 51, no. 10 (September 18, 2019): 1606–30. http://dx.doi.org/10.1177/0095399719875458.
Анотація:
Career executives often occupy administrative positions that determine the pace and content of policy, such as those responsible for developing regulations. Yet, presidential administrations need control over these positions to achieve policy aims. This article considers the extent to which new presidential administrations marginalize career executives in key regulatory positions by transferring responsibilities to another individual and whether the mere expectation of political conflict with a new administration drives career regulators from their positions. Using unique new data on 866 career regulators that led major rulemaking efforts between 1995 and 2013, we demonstrate that turnover among career executives in key regulatory positions increases following a party change in the White House. Turnover also increases during a presidential election year, but this effect is conditioned by bureaucrats’ expectations of the election outcome. Finally, career executives are more likely to depart in response to favorable labor market conditions. Given our findings that turnover in regulatory responsibilities is driven both by presidential marginalization and strategic exit by bureaucrats, we conclude with implications for presidential efforts to control the administrative state.
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Schwartz, Germano, and Matteo Finco. "Introduction." Oñati Socio-Legal Series 14, no. 3 (June 3, 2024): 640–47. http://dx.doi.org/10.35295/osls.iisl.2015.
Анотація:
Today claims relating to health are so common that distinguishing between rights and interests is harder and harder: definitions of health as the one provided by the World Health Organization legitimise the expectation of a continuous improvement in standards of care, access to treatment and measures of prevention. Moreover, with COVID-19 pandemic, health seems to gain the status of a supreme and unquestionable value. Here the hypotheses of a “Healthization of Law” is presented: it indicates both a kind of supremacy of Law on other spheres of society when health (public or individual) is at stake, and also the fact the Law is strongly conditioned by the emergence of health as the highest value to which all spheres of society must orient themselves. Then analysing the relationship between the structural conditions of world society and the semantics of health it is urgent than ever, in order to understand which kind of expectations and claims pandemic legitimised, and how the semantics of health (and wellbeing) describes social structure and its changes.
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Bechtel, Michael M., and Kenneth F. Scheve. "Who Cooperates? Reciprocity and the Causal Effect of Expected Cooperation in Representative Samples." Journal of Experimental Political Science 4, no. 3 (2017): 206–28. http://dx.doi.org/10.1017/xps.2017.16.
Анотація:
AbstractWhen do societies succeed in providing public goods? Previous research suggests that public goods contributions correlate with expectations about cooperation by others among students and other demographic subgroups. However, we lack knowledge about whether the effect of expected cooperation is causal and a general feature of populations. We fielded representative surveys (N = 8,500) in France, Germany, the United Kingdom, and the United States that included a public goods game and a novel between-subjects experiment. The experiment varied expectations about cooperation by others. We find that higher expected cooperation by others causes a significant increase in individual contributions. When classifying contribution schedules, we find that almost 50% of the population employs a conditionally cooperative strategy. These individuals are on average richer, younger, and more educated. Our results help explain the varying success of societal groups in overcoming cooperation problems and assist policymakers in the design of institutions meant to solve social dilemmas.
50
Wang, Yoy-Gan, and Mervyn R. Thomas. "Accounting for individual variability in the von Bertalanffy growth model." Canadian Journal of Fisheries and Aquatic Sciences 52, no. 7 (July 1, 1995): 1368–75. http://dx.doi.org/10.1139/f95-132.
Анотація:
Estimation of von Bertalanffy growth parameters has received considerable attention in fisheries research. Since Sainsbury (1980, Can. J. Fish. Aquat. Sci. 37: 241–247) much of this research effort has centered on accounting for individual variability in the growth parameters. In this paper we demonstrate that, in analysis of tagging data, Sainsbury's method and its derivatives do not, in general, satisfactorily account for individual variability in growth, leading to inconsistent parameter estimates (the bias does not tend to zero as sample size increases to infinity). The bias arises because these methods do not use appropriate conditional expectations as a basis for estimation. This bias is found to be similar to that of the Fabens method. Such methods would be appropriate only under the assumption that the individual growth parameters that generate the growth increment were independent of the growth parameters that generated the initial length. However, such an assumption would be unrealistic. The results are derived analytically, and illustrated with a simulation study. Until techniques that take full account of the appropriate conditioning have been developed, the effect of individual variability on growth has yet to be fully understood.