Systematic Model Research Articles

A systematic review and BMD modeling approach to develop an AOP for humidifier disinfectant-induced pulmonary fibrosis and cell death

Dosimetry modeling and point of departure (POD) estimation using in vitro data are essential for mechanism-based hazard identification and risk assessment. This study aimed to develop a putative adverse outcome pathway (AOP) for humidifier disinfectant (HD) substances used in South Korea through a systematic review and benchmark dose (BMD) modeling. We collected in vitro toxicological studies on HD substances, including polyhexamethylene guanidine hydrochloride (PHMG-HCl), PHMG phosphate (PHMG-p), a mixture of 5-chloro-2-methyl-4-isothiazolin-3-one and 2-methyl-4-isothiazolin-3-one (CMIT/MIT), CMIT, and MIT from scientific databases. A total of 193 sets of dose-response data were extracted from 34 articles reporting in vitro experimental results of HD toxicity. The risk of bias (RoB) in each study was assessed following the office of health assessment and translation (OHAT) guideline. The BMD of each HD substance at different toxicity endpoints was estimated using the US Environmental Protection Agency (EPA) BMD software (BMDS). Interspecies- or interorgan differences or most critical effects in the toxicity of the HD substances were analyzed using a 95% lower confidence limit of the BMD (BMDL). We found a critical molecular event and cells susceptible to each HD substance and constructed an AOP of PHMG-p- or CMIT/MIT-induced damage. Notably, PHMG-p induced ATP depletion at the lowest in vitro concentration, endoplasmic reticulum (ER) stress, epithelial-to-mesenchymal transition (EMT), inflammation, leading to fibrosis. CMIT/MIT enhanced mitochondrial reactive oxygen species (ROS) production, oxidative stress, mitochondrial dysfunction, resulting in cell death. Our approach will increase the current understanding of the effects of HD substances on human health and contribute to evidence-based risk assessment of these compounds.

Developing a community-based model of care for venipuncture in children and young adults with an intellectual disability: a retrospective study

BackgroundRegular blood tests for monitoring metabolic side effects are often unable to be collected for people with an intellectual/developmental disability (ID/DD) and challenging behaviours (CBs) using usual pathways. We aimed...

Patient Blood Management Program Implementation: Comprehensive Recommendations and Practical Strategies.

Blood transfusion is one of the most common medical practices worldwide. However, current scientific literature has shown that the immunomodulatory effects of blood transfusion are associated with an increased likelihood of infection, prolonged hospitalization, and morbimortality. Also, it means high costs for healthcare systems. In this context, acknowledging that blood transfusions are essentially heterologous cell transplantations, the use of therapeutic options has gained strength and is collectively known as the patient blood management (PBM) program. PBM is an approach based on three main pillars: (1) treating anemias and coagulopathies in an optimized manner, especially in the preoperative period; (2) optimizing perioperative hemostasis and the use of blood recovery systems to avoid the loss of the patient's blood; (3) anemia tolerance, with improved oxygen delivery and reduced oxygen demand, particularly in the postoperative period. Current scientific evidence supports the effectiveness of PBM by reducing the need for blood transfusions, decreasing associated complications, and promoting more efficient and safer blood management. Thus, PBM not only improves clinical outcomes for patients but also contributes to the economic sustainability of healthcare systems. The aim of this review was to summarize PBM strategies in a comprehensive, evidence-based approach through a systematic and structured model for PBM implementation in tertiary hospitals. The recommendations proposed herein are from researchers and experts of a high-complexity university hospital in the network of the Sistema Único de Saúde, presenting itself as a strategy that can be followed as a guideline for PBM implementation in other settings.

How foodstagramming posts influence restaurant visit intention: the mediating role of goal relevance and mimicking desire

PurposeThis study aims to explore the role of social media visual posts (known as foodstagramming) on restaurant visit intention. Drawing on the heuristic–systematic model and normative focus theory, this research introduces a framework that assesses the effects of key foodstagramming attributes – vicarious expression, aesthetic appeal and post popularity – and the mediating roles of goal relevance and mimicking desire, in the process.Design/methodology/approachStructural equation modelling was performed to test the proposed model using a sample of tourists (n = 377) and residents (n = 341). Multi-group analysis was performed to compare the differences between these groups.FindingsResults reveal that mimicking desire and goal relevance influence restaurant visit intention; however, mimicking desire has a stronger influence than goal relevance. Little difference was found between the tourist and the resident groups in the proposed relationships, except that vicarious expression positively influences mimicking desire in the tourist group but not in the resident group.Practical implicationsThis study guides restauranteurs and social media influencers (foodstagrammers). It shows that consumers value the textual content and aesthetic appeal of photos over the popularity of a post. It also indicates that vicarious expression is more important for tourists than for residents.Originality/valueThis research advances social media marketing literature by proposing a new information processing framework. To the best of the authors’ knowledge, this study is one of the first studies to explore the impact of visual post attributes on individual decision-making behaviours through socially acceptable norms.

Toward a theory of team resource mobilization: A systematic review and model of sustained agile team effectiveness

The notion of resources is central to many theories in HRM and applied psychology. Prominent resource-based theories in HRM tend to focus on issues related to accessing resources at the firm-level (e.g., resource-based view of the firm) or the employee-level (e.g., job demands - resources theory). However, at the team-level, the critical issue is often a matter of resource mobilization rather than resource access. Previous research has discovered that a team's ability to use resources effectively is indicative of collective intelligence. Instead of explaining this ability with a latent collective intelligence factor, we argue that teams can develop this ability by using agile work practices (AWPs). Through a systematic review of the agile team literature, we describe how agile teams mobilize resources embedded in the internal and external environment to achieve sustained team effectiveness. Generalizing beyond the agile team context, we propose a model that introduces team-internal and team-external resource mobilization as unique predictors of sustained team effectiveness. We further propose that resource mobilization is strengthened by challenge demands (e.g., work complexity) and weakened by hindrance demands (e.g., role conflict). We hope our model of sustained team effectiveness inspires future research into how teams can perform effectively across multiple episodes, without this going at the cost of members' health and well-being.

Multiscale Models to Evaluate the Impact of Chemical Compositions and Test Conditions on the Mechanical Properties of Cement Mortar for Tile Adhesive Applications.

This study aims to develop systematic multiscale models to accurately predict the compressive strength of cement mortar for tile adhesive applications, specifically tailored for applications in the construction industry. Drawing on data from 200 cement mortar tests conducted in previous studies, various factors such as cement/water ratios, curing times, cement/sand ratios, and chemical compositions were analyzed through static modeling techniques. The model selection involved utilizing various approaches, including linear regression, pure quadratic, interaction, M5P tree, and artificial neural network models to identify the most influential parameters affecting mortar strength. The analysis considered the water/cement ratio, testing ages, cement/sand ratio, and chemical compositions, such as silicon dioxide, calcium dioxide, iron (III) oxide, aluminum oxide, and the pH value. Evaluation metrics, such as the determination coefficient, mean absolute error, root-mean-square error, objective function, scatter index, and a-20 index, were employed to ensure the accuracy of the compressive strength estimates. Additionally, empirical equations were utilized to predict flexural and tensile strengths based on the compressive strength of the cement mortar for tile adhesive applications.

Unveiling the controversies of brand identity management: A holistic framework for global B2B companies through a hybrid systematic literature review and interpretive structural modelling

Corporations that present a distinctive and different brand identity are preferable for customers and the market, create value, and lead in pricing. It helps customers feel the corporation is trustworthy and reliable. Despite the remarkable advantages, the definition of brand identity for global B2B companies needs to be clearer and more complex than the definition of other close concepts. It deals with some problems and causes some challenges for managers. The present study aims to identify the main controversies and challenges in brand identity management for global B2B companies. Accordingly, a hybrid systematic literature review (SLR) and thematic analysis (TA) have been employed to extract the initial drawbacks. Afterwards, the relationships among the extracted themes are identified using an Interpretive Structural Modelling (ISM) to recognise the network of the problems surrounding the brand identity of global B2B companies. Ultimately, the identified themes are also categorised via the MICMAC method (Cross-Impact Matrix Multiplication Applied to Classification). After reviewing 142 articles, 12, 11, and 9 challenges were extracted as the main themes for corporate, brand, and stakeholder levels. These themes were analysed and categorised into drivers, linkages, dependents, and autonomous challenges. Moreover, three subsystems were presented, covering the causal relationship among the challenges.

Optimizing porous asphalt mix design for permeability and air voids using response surface methodology and artificial neural networks

Easy infiltration of grouting material in Porous Asphalt Mixture (PAM) is one of the crucial phases in the construction of semi-flexible pavement (SFP). The gradation and compaction primarily influence the pores and their arrangement in PAM, which affects the air voids and permeability of porous mixes and effectiveness of slurry infiltration. Complexity of gradation and compaction makes it very difficult to predict air voids and permeability in PAM. Statistical analysis of these parameters based on experimental data requires very high volume of samples which is missing in literature. Compaction effort has been kept constant in most of them which otherwise is important for stability. Also, machine learning models to predict mix design of PAM has not been explored. This study investigates the PAM using systematic experimentation and modelling approaches such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) to establish new and low trial-based guidelines for establishing a relationship between air voids and permeability greater than 0.12 cm/sec, while ensuring the stability of the mix. Using a Simplex Lattice design approach, five distinct types of aggregates were utilized as input variables and twenty-six PAM mixes were generated, which were subjected to 5 compaction levels. A total of 260 mixes were tested and then models were prepared based on RSM and ANN, which considered two output variables: air voids and permeability. RSM and ANN models demonstrated strong agreement between predicted values and actual experiment outcomes. It is observed that optimal permeability in PAM is achieved by adjusting aggregate proportions based on compaction levels. Proportion limits for low compaction suggested 60–70 % coarse aggregates and up to 20 % fines. For higher compaction (25–75 blows), it is recommended to increase the coarse aggregates up to70–90 % and limit fines up to 10 %. All proposed limits satisfied minimum permeability criteria (> 0.12 cm/sec) while ensuring the stability of the mixes. The proposed modelling approach offers greater efficiency and accuracy and reduces trial batch time, minimizing the need for destructive experiments and material wastage.

Radiofrequency Induction Heating for Green Chemicals Manufacture: A Systematic Model of Energy Losses and a Scale-Up Case-Study.

Radiofrequency (RF) induction heating has generated much interest for the abatement of carbon emissions from the chemicals sector as a direct electrification technology. Three challenges have held back its deployment at scale: reactors must be built from nonconductive materials which eliminates steel as a design choice; the viability of scale-up is uncertain; and to date the reported energy efficiency has been too low. This paper presents a model that for the first time makes a comprehensive analysis of energy losses that arise from RF induction heating. The maximum energy efficiency for radio frequency induction heating was previously reported to be 23% with a typical frequency range of 200-400 kHz. The results from the model show that an energy efficiency of 65-82% is achieved at a much lower frequency of 10 kHz and a reactor diameter of 0.2 m. Energy efficiency above 90% with reactor diameters above 1 m in diameter are predicted if higher voltage radio frequency sources can be developed. A new location of the work coil inside of the reactor wall is shown to be highly effective. Losses arising from heating a steel reactor wall in this configuration are shown to be insignificant, even when the wall is immediately adjacent to the work coil. This analysis demonstrates that RF induction heating can be a highly efficient and effective industrial technology for coupling high energy demand chemicals manufacture electricity from zero carbon renewables.

Genome-Wide Identification and Interaction Analysis of Turbot Heat Shock Protein 40 and 70 Families Suggest the Mechanism of Chaperone Proteins Involved in Immune Response after Bacterial Infection.

Hsp40-Hsp70 typically function in concert as molecular chaperones, and their roles in post-infection immune responses are increasingly recognized. However, in the economically important fish species Scophthalmus maximus (turbot), there is still a lack in the systematic identification, interaction models, and binding site analysis of these proteins. Herein, 62 Hsp40 genes and 16 Hsp70 genes were identified in the turbot at a genome-wide level and were unevenly distributed on 22 chromosomes through chromosomal distribution analysis. Phylogenetic and syntenic analysis provided strong evidence in supporting the orthologies and paralogies of these HSPs. Protein-protein interaction and expression analysis was conducted to predict the expression profile after challenging with Aeromonas salmonicida. dnajb1b and hspa1a were found to have a co-expression trend under infection stresses. Molecular docking was performed using Auto-Dock Tool and PyMOL for this pair of chaperone proteins. It was discovered that in addition to the interaction sites in the J domain, the carboxyl-terminal domain of Hsp40 also plays a crucial role in its interaction with Hsp70. This is important for the mechanistic understanding of the Hsp40-Hsp70 chaperone system, providing a theoretical basis for turbot disease resistance breeding, and effective value for the prevention of certain diseases in turbot.

Feasibility of Induced Magnetic Gradient Surveying for Seepage Detection in Earth-filled Dams: Insights from Synthetic and Field Studies

Seepage is a common hydrogeological hazard in engineering. Determining the seepage paths is vital for de-risking the instability of embankment structures. With the improvement of the acquisition accuracy of magnetic sensors, the magnetometric resistivity method has become an emerging technology for detecting seepage paths through earth-filled dams. This technique is non-destructive and gives prominent signals. However, the resulting magnetic data have seen ambiguity in fully determining the targets. We propose an induced magnetic gradient surveying approach to monitor seepage paths in earth-filled dams. First, we briefly review the electromagnetic theory for the magnetic gradient tensor based on Maxwell’s equations. To match against the measurements, we present an accurate modeling framework using the third-order finite element method and a novel compact difference scheme. We verify our approach on both semi-analytical 1D and 3D models. Systematic modeling studies are then carried out to investigate the spatial distribution characteristics and sensitivities of the induced magnetic gradient to the seepage in typical dam scenarios. In addition, we conducted two field experiments in the Zhongmou experimental base and Xixiayuan Reservoir in Henan Province, China,respectively. The induced magnetic field vector and its gradient components were both acquired. Cross-validation with a-priori geological information shows that the seepage path can be spatially identified by the induced magnetic gradient components Byy, Byz, Bzy, and Bzz while the field components failed to locate the seepage pathways. This successful application indicates that the proposed approach could be a promising solution for seepage path discrimination in earth-filled dams with high resolution.

Origin of the up/down poloidal asymmetric dependence of ELM control in ITER-like RMP configuration in KSTAR

Recent edge-localized modes (ELM) control experiments via resonant magnetic perturbation (RMP) in KSTAR have shown a strong up/down poloidal asymmetric coupling dependence. Specifically, in lower single null (LSN) plasmas at q95 ≳ 5, the lower two-row (middle/bottom) RMPs among ITER-like three-row (top/middle/bottom) in-vessel control coils (IVCC) in KSTAR were more effective in suppressing ELM-crashes than the upper two-row (top/middle) RMPs. In contrast, at q 95 ∼ 4, the upper two-row RMPs turned out to be more effective than the lower counterpart. Since the ITER baseline scenario is planned to operate at q 95 ∼ 3, the understanding of the origin of such up/down poloidal asymmetric coupling dependence, as well as the prediction about ITER-relevant conditions at a lower q 95, would be quite important and potentially impactful to the RMP ELM control in ITER. A linear, resistive, single-fluid MHD code MARS-F has been utilized to address and model the up/down poloidal asymmetric RMP coupling dependence. Specifically, based on two contrasting exemplary discharges with up/down poloidal (i) asymmetric at q 95 ∼ 4 and (ii) symmetric behavior at q 95 ∼ 5, among tens of otherwise similar discharges, a systematic MARS-F modeling has been thoroughly conducted. As a result, the plasma response investigation suggests that the X-point displacement (ξ X), rather than any other figures of merit, would be a directly relevant metric for the up/down poloidal asymmetric coupling in RMP-driven, ELM-crash suppression in KSTAR. Based on a sensitivity study of the edge safety factor in MARS-F modeling, the ξ X variation follows the same quantitative trend as observed in experiments. However, no or little plasma pressure dependence has been found, though ξ X increases with plasma pressure. At the ITER-relevant low q 95 ∼ 3 in a scaled KSTAR equilibrium, such modeling predicts the upper two-row RMPs would be more favorable in suppressing the ELM-crashes than the lower counterpart.

Harnessing Liquid Metals with In Situ Polymerized Electrolyte for Anode‐Free Lithium Metal Batteries

AbstractTo enhance safety and energy density in conventional Li‐ion batteries, anode‐free or zero‐lithium configurations using only a current collector (CC) in the anode have emerged. However, challenges including rapid Li dendrite growth, low Coulombic efficiency, safety concerns, and thickness issues hinder the practical use of anode‐free batteries (AFBs) with liquid electrolytes (LEs) or solid electrolytes (SEs). Herein, potential AFBs using an anode current collector coated with a liquid metal (LM)@C nanocomposite with an in situ polymerized electrolyte (PE) are reported. Interestingly, LM nanoparticles added to the composite layer on CC play a crucial role in promoting uniform Li plating/stripping behavior through a self‐healing mechanism, along with reversible liquid–solid–liquid phase transitions caused by alloying and de‐alloying of Li and LMs. Furthermore, incorporating in situ polymerized electrolytes stabilizes LMs by preventing the agglomeration of LM nanoparticles, resulting in significantly improved cell performance compared to other conventional LEs. A systematical model study with ex situ analysis unveils the synergetic effects between LMs and PE, along with elucidating the mechanism of in situ polymerization and Li‐LMs reactions. The investigation contributes valuable insights for future studies on practical applications of AFBs using polymer electrolytes and composite interlayers.

SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification

A common and natural physiological response of the human body is cough, which tries to push air and other wastage thoroughly from the airways. Due to environmental factors, allergic responses, pollution or some diseases, cough occurs. A cough can be either dry or wet depending on the amount of mucus produced. A characteristic feature of the cough is the sound, which is a quacking sound mostly. Human cough sounds can be monitored continuously, and so, cough sound classification has attracted a lot of interest in the research community in the last decade. In this research, three systematic conglomerated models (SCMs) are proposed for audio cough signal classification. The first conglomerated technique utilizes the concept of robust models like the Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) model, Least Absolute Shrinkage and Selection Operator (LASSO) model, elastic net regularization model with Gabor dictionary analysis and efficient ensemble machine learning techniques, the second technique utilizes the concept of stacked conditional autoencoders (SAEs) and the third technique utilizes the concept of using some efficient feature extraction schemes like Tunable Q Wavelet Transform (TQWT), sparse TQWT, Maximal Information Coefficient (MIC), Distance Correlation Coefficient (DCC) and some feature selection techniques like the Binary Tunicate Swarm Algorithm (BTSA), aggregation functions (AFs), factor analysis (FA), explanatory factor analysis (EFA) classified with machine learning classifiers, kernel extreme learning machine (KELM), arc-cosine ELM, Rat Swarm Optimization (RSO)-based KELM, etc. The techniques are utilized on publicly available datasets, and the results show that the highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with an arc-cosine ELM classifier.

Tuning negative stiffness mechanical metamaterial's snap-through behavior with a series-connected spring

Negative stiffness mechanical metamaterials have shown great promise for various applications and are progressing toward programmability. The key challenge in designing programmable metamaterials lies in achieving efficient tunability. However, due to the limitations of current tuning strategies, many existing programmable metamaterials fall short of expectations. Here, we introduce a strategy that facilitates efficient response transformation and performance tuning across scales for negative stiffness mechanical metamaterials. The implementation of this novel tuning approach requires only the adjustment of a serial spring's stiffness which is much easier and more practical than controlling the non-linear contributions of the buckling parts. A systematic theoretical model has been developed to illustrate this innovative approach, and the results have been validated through simulations and experiments. This research can serve as a reference for designing programmable negative stiffness mechanical metamaterials (NSMMs).

Model selection for long-term load forecasting under uncertainty

Purpose The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors. Design/methodology/approach The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases. Findings From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment. Research limitations/implications These findings can help utilities to align model selection strategies with their risk tolerance. Originality/value To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

Effect of temperature and pre-stretch on the dynamic performance of dielectric elastomer minimum energy structure

Dielectric Elastomer Minimum Energy Structures (DEMES) have the ability of actively adjusting their shape to accommodate complex scenarios, understanding the actuation mechanism of DEMES is essential for their effective design and control, which has rendered them a focus of research in the field of soft robotics. The actuation ability of DEMES is usually influenced by external conditions, among which the electromechanical properties of DE materials are highly sensitive to temperature changes, and the pre-stretch ratio of DE materials has a significant impact on the dynamic performance of DEMES. Therefore, it is necessary to study the effects of temperature and pre-stretch ratio on the nonlinear dynamic behavior of DEMES. In this paper, in response to the lack of research on the influence of DE pre-stretch ratio on the actuation characteristics of DEMES, this paper proposes a systematic modeling and analysis framework that comprehensively considers pre-stretch factors, temperature factors, and viscoelastic factors, and establishes the motion control equation of DEMES affected by the coupling effect of DE pre-stretch ratio and temperature. The proposed analytical framework is used to analyze the evolution of the electromechanical response of DEMES under voltage excitation under the coupling of DE pre-stretch ratio and temperature. The results indicate that the bending angle, inelastic deformation, resonant frequency, and dynamic stability of DEMES can be jointly adjusted by the DE pre-stretch ratio and ambient temperature. A low pre-stretch ratio of DE can lead to dynamic instability of DEMES, while appropriate temperature conditions and higher pre-stretch ratios can significantly improve the actuation ability of DEMES. This can provide theoretical guidance for the design and deformation control of DEMES.

Surface plasma wave excitation using laser beat-wave and terahertz generation

In this paper, a study of surface plasma wave excitation from a magnetized metallic surface using two co-propagating lasers is conducted. Two lasers propagating in the same direction with frequency difference of plasmon frequency of the metal excite plasma oscillations on the metallic surface, which generate surface plasma waves. The transverse components of the field associated with these surface plasma wave lead to excite transverse current at terahertz frequency. We report a systematic theoretical model for this study and subsequent numerical results are presented for THz field estimation.

Associations of 10-year atherosclerotic cardiovascular disease risk scores with cerebral small vessel disease: the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study.

10-year atherosclerotic cardiovascular disease (ASCVD) risk scores were useful for predicting large vessel disease, but the relationships between them and cerebral small vessel disease (CSVD) were unclear. Our study aimed to evaluate associations of 10-year ASCVD risk scores with CSVD and its magnetic resonance imaging (MRI) markers. Community-dwelling residents from the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events study were included in this cross-sectional study. At baseline, we collected data related to the Framingham Risk Score (FRS), pooled cohort equation (PCE), prediction for ASCVD risk in China (China-PAR) and Systematic COronary Risk Evaluation model 2 (SCORE2), and classified participants into low, moderate and high groups. Participants underwent brain MRI scans. We evaluated white matter hyperintensity (WMH), lacunes, cerebral microbleeds (CMBs) and enlarged perivascular spaces in basal ganglia (BG-EPVS) according to criteria of Wardlaw and Rothwell, and calculated total CSVD score and modified total CSVD score. A total of 3063 participants were included, and 53.5% of them were female. A higher FRS was associated with higher total CSVD score (moderate vs. low: cOR 1.89, 95% CI 1.53-2.34; high vs. low: cOR 3.23, 95%CI 2.62-3.97), and the PCE, China-PAR or SCORE2 score was positively related to total CSVD score (P < 0.05). Moreover, higher 10-year ASCVD scores were associated with higher odds of WMH (P < 0.05), lacunes (P < 0.05), CMBs (P < 0.05) and BG-EPVS (P < 0.05). The 10-year ASCVD scores were positively associated with CSVD and its MRI markers. These scores provided a method of risk stratification in the population with CSVD.

Freedom, Creativity, the Self, and God: Between Rabbi Kook and Bergson’s Lebensphilosophie

AbstractIn this essay, I examine the intersection between the concepts of freedom, the self, God, and creativity in the works of one of the most prominent twentieth-century Jewish thinkers, Rabbi Abraham Isaac HaCohen Kook (1865–1935), exploring his use of these concepts through the lens of the Lebensphilosophie of the French philosopher Henri Bergson (1859–1941). I first draw a historical and thematic parallel between Bergson’s and Kook’s philosophies that to date has not been considered extensively. I then argue that five different interpretative puzzles related to the topic of freedom in Kook’s teachings can be explained against the background of Bergson’s thought. This Bergsonian interpretation enables the reader to appreciate in what way different aspects of Kook’s thought—the metaphysical, ethical, epistemological, and theological—are interconnected and can be understood as an organic whole. I thereby show that the Bergsonian philosophical and systematic models are an important, and yet unexplored, interpretative tool for the study of Kook’s theological and philosophical thought.