Modeling Of Effects Research Articles

Towards a better understanding of anticipatory postural adjustments in people with Parkinson's disease.

Previous studies have shown that anticipatory postural adjustments (APAs) are altered in people with Parkinson's disease but its meaning for locomotion is less understood. This study aims to investigate the association between APAs and gait initiation, gait and freezing of gait and how a dynamic postural control challenging training may induce changes in these features. Gait initiation was quantified using wearable sensors and subsequent straight walking was assessed via marker-based motion capture. Additionally, turning and FOG-related outcomes were measured with wearable sensors. Assessments were conducted one week before (Pre), one week after (Post) and 4 weeks after (Follow-up) completion of a training intervention (split-belt treadmill training or regular treadmill training), under single task and dual task (DT) conditions. Statistical analysis included a linear mixed model for training effects and correlation analysis between APAs and the other outcomes for Pre and Post-Pre delta. 52 participants with Parkinson's disease (22 freezers) were assessed. We found that APA size in the medio-lateral direction during DT was positively associated with gait speed (p<0.001) and stride length (p<0.001) under DT conditions at Pre. The training effect was largest for first step range of motion and was similar for both training modes. For the associations between changes after the training (pooled sample) medio-lateral APA size showed a significant positive correlation with first step range of motion (p = 0.033) only in the DT condition and for the non-freezers only. The findings of this work revealed new insights into how APAs were not associated with first step characteristics and freezing and only baseline APAs during DT were related with DT gait characteristics. Training-induced changes in the size of APAs were related to training benefits in the first step ROM only in non-freezers. Based on the presented results increasing APA size through interventions might not be the ideal target for overall improvement of locomotion.

Modeling the impact of urban and hospital eco-exposomes on antibiotic-resistance dynamics in wastewaters

The emergence and selection of antibiotic resistance is a major public health problem worldwide. The presence of antibiotic-resistant bacteria (ARBs) in natural and anthropogenic environments threatens the sustainability of efforts to reduce resistance in human and animal populations. Here, we use mathematical modeling of the selective effect of antibiotics and contaminants on the dynamics of bacterial resistance in water to analyze longitudinal spatio-temporal data collected in hospital and urban wastewater between 2012 and 2015. Samples were collected monthly during the study period at four different sites in Haute-Savoie, France: hospital and urban wastewater, before and after water treatment plants. Three different categories of exposure variables were collected simultaneously: 1) heavy metals, 2) antibiotics and 3) surfactants for a total of 13 drugs/molecules; in parallel to the normalized abundance of 88 individual genes and mobile genetic elements, mostly conferring resistance to antibiotics. A simple hypothesis-driven model describing weekly antibiotic resistance gene (ARG) dynamics was proposed to fit the available data, assuming that normalized gene abundance is proportional to antibiotic resistant bacteria (ARB) populations in water. The detected compounds were found to influence the dynamics of 17 genes found at multiple sites. While mercury and vancomycin were associated with increased ARG and affected the dynamics of 10 and 12 identified genes respectively, surfactants antagonistically affected the dynamics of three genes. The models proposed here make it possible to analyze the relationship between the persistence of resistance genes in the aquatic environment and specific compounds associated with human activities from longitudinal data. Our analysis of French data over 2012–2015 identified mercury and vancomycin as co-selectors for some ARGs.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML)models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Modeling of plasmonic and polaritonic effects in photocurrent nanoscopy

We present a basic framework for modeling collective mode effects in photocurrent measurements performed on two-dimensional materials using nano-optical scanned probes. We consider photothermal, photovoltaic, and bolometric contributions to the photocurrent. We show that any one of these can dominate depending on frequency, temperature, applied bias, and sample geometry. Our model is able to account for periodic spatial oscillations (fringes) of the photocurrent observed near sample edges or inhomogeneities. For the case of a non-absorbing substrate, we find a direct relation between the spectra measured by the photocurrent nanoscopy and its parental scanning technique near-field optical microscopy.

Three-dimensional spiral model of the wicking effect for continuous polyester filaments

The wicking effect constitutes a pivotal determinant in facilitating the ingress and transference of liquid water within yarns and fabrics. Its significance looms prominently in the context of subsequent product processing, particularly concerning the immersion and interface bonding of textile matrix composites. The twist exerts profound influence over the fiber disposition and density within yarns, as well as the yarn and the wicking pathways for liquid water. We use a mathematical model grounded in the three-dimensional helical capillary permeation mechanism, inherently linked to the twist factor. This model operates under the assumption that the yarn's fibers exhibit uniform diameters and arrangements. Leveraging the macroscopic force equilibrium method, a function of liquid capillary rise with wicking time was deduced. and the dynamic progression of liquid water ascent within the yarn was simulated using the COMSOL platform. Subsequently, a series of wicking experiments were executed on polyester filament yarns, each characterized by varying twist levels. The results revealed that the experimental data coincided well with the theoretical prediction, thus affirming the model's accuracy.

Critical pulse in multi-shot femtosecond laser ablation on metallic surfaces

Thermal effect remains a thorny issue for femtosecond-laser surface engineering and nanostructuring on metallic targets with high pulse energies or high repetition rates, which needs to be paid adequate attentions. Herein, we have experimentally investigated the heat diffusion and accumulations during single-shot and multi-shot femtosecond laser ablation on metallic surfaces. We have for the first time observed a novel phenomenon that the thermal effect was intensified abruptly when the laser-pulse number goes over a threshold (approximately between 10 and 20 for aluminum alloy with laser fluence of 6 J cm−2), accompanied with a dramatic reduction of ablated depth and complicated plasma dynamics. Based on both optical and thermodynamic analysis, we introduced a defocusing-dominated plasma-assistant model for this abnormal thermal effect. This work explored the critical experimental parameters for femtosecond-laser surface modification and processing in micro-scale engineering applications.

Experimental Monitoring of Dynamic Parameters of the Sub-Ballast Layers as a Prerequisite for a High-Quality and Sustainable Railway Line

Monitoring dynamic load transfer from train traffic to sub-ballast layers is crucial for verifying the reliability and safety of railway lines, assessing the design cost-effectiveness and achieving minimum environmental impact. For this purpose, measurements in labs, in situ or modeling the influence of dynamic loads on the immediate and long-term roadway quality are often performed using suitable software. The available test sections enabled monitoring of the dynamic loads and optimizing the critical spots where increased dynamic effects from railway traffic may occur. The subject of this paper is the calibration of the sensors installed in the different test sections of the trans-European corridor number V. As a result, the necessary input parameters for the subsequent numerical modeling of the dynamic effects on the track substructure and vibration propagation on the available sections of the upgraded railway line were obtained. The sensor calibration was carried out on the experimental field, part of the Experimental Basis of the Department of Railway Engineering and Track Management. As part of the calibration, the sensitivity of the sensors embedded in the track bed to the applied dynamic loads resulting from the impact effects of the lightweight deflectometer was assessed. The result of the calibration was the demonstration of sufficient sensitivity of the sensors and their suitability for implementation in an actual railway track structure, with the aim of obtaining relevant values of the response of the sub-ballast layers to dynamic loads and assessing the operational impacts on the sustainable environment. Also, the main result of the research was the possibility of using the theoretical–experimental route to optimize the layers of the railway body.

Analysis and modeling of the aerodynamic ceiling effect on small-scale propellers with tilted angles

Micro air vehicles with propellers have been widely used in military and civilian applications due to their high maneuverability and low cost. As the mission scenarios and requirements of rotorcrafts become more complex, proximity effects caused by confined environments, such as the ceiling effect cause significant interference to the performance and stability of rotors. Most researchers have analyzed this phenomenon by considering the ceiling effect in hover conditions. However, few scholars considered the tilt angle between the rotor and the ceiling, which is more likely to occur in real missions, such as the close inspection of bridges or large equipment, flight in narrow indoor environments, and flight near the ceiling for fully actuated multirotors. In this study, a CFD-based study is conducted to evaluate the ceiling effect for small-scale tilted propellers. The aerodynamic analysis was experimentally evaluated through a series of ground tests in a test bench designed for this research. The CFD results were validated by a mesh-independent study and experimental data comparison. The results show that in contrast to the phenomenon of reduced thrust when tilt angles exist between the rotors and ground, the existence of tilt angles between the propeller and the ceiling reduces the thrust-increasing effect of the ceiling when the dimensionless distance is greater than 0.6. When the distance gets smaller, the thrust-increasing effect increases when the tilt angle is greater than 10∘. In particular, when the distance is less than 0.3, the existence of tilt angles will increase the thrust-increasing effect of the ceiling. Additionally, a data-driven model has been proposed to predict the thrust with the distance to the ceiling and the angle inclination between the rotor and the ceiling. This model enables future rotorcrafts that require accurate models to operate in narrow environments.

Solvent effects in anion recognition.

Anion recognition is pertinent to a range of environmental, medicinal and industrial applications. Recent progress in the field has relied on advances in synthetic host design to afford a broad range of potent recognition motifs and novel supramolecular structures capable of effective binding both in solution and at derived molecular films. However, performance in aqueous media remains a critical challenge. Understanding the effects of bulk and local solvent on anion recognition by host scaffolds is imperative if effective and selective detection in real-world media is to be viable. This Review seeks to provide a framework within which these effects can be considered both experimentally and theoretically. We highlight proposed models for solvation effects on anion binding and discuss approaches to retain strong anion binding in highly competitive (polar) solvents. The synthetic design principles for exploiting the aforementioned solvent effects are explored.

Finite element modeling of the free boundary effect on gyroid additively manufactured samples

There is a significant need for models that can capture the mechanical behavior of complex porous lattice architectures produced by 3D printing. The free boundary effect is an experimentally observed behavior of lattice architectures including the gyroid triply periodic minimal surface where the number of unit cell repeats has been shown to influence the mechanical performance of the lattice. The purpose of this study is to use finite element modeling to investigate how architecture porosity, unit cell size, and sample size dictate mechanical behavior. Samples with varying porosity and increasing number of unit cells (relative to sample size) were modeled under an axial compressive load to determine the effective modulus. The finite element model captured the free boundary effect and captured experimental trends in the structure’s modulus. The findings of this study show that samples with higher porosity are more susceptible to the impact of the free boundary effect and in some samples, the modulus can be 20% smaller in samples with smaller numbers of unit cell repeats within a given sample boundary. The outcomes from this study provide a deeper understanding of the gyroid structure and the implications of design choices including porosity, unit cell size, and overall sample size.

Network modeling of aesthetic effect for Chinese Yue Opera costume simulation images

Abstract In the digital age, virtual simulation images have become an important communication form of Chinese traditional Yue Opera costumes and their culture. Focusing on the lay viewers’ aesthetic experience, we collected 16 subjective evaluations, including favorites of basic design elements, semantic impressions, and high-level aesthetic experience, of 257 participants on 5 virtual simulation pictures of Yue Opera costumes for different characters and used network analysis to explore the possible relationship of the variables. The inferred networks showed that the archetypal aesthetic was more relevant to the costume design elements, emotions, interests, and the sense of high level. The conspicuous clothing, understanding of creative intent, and the desire for cultural knowledge were key “bridges” in the aesthetic process. Although the aesthetic network of each costume varied partially based on character differences, it showed more similarities overall. On the basis of the findings, we discuss the possible theoretical basis of the networks and make practical suggestions for the virtual dissemination of Yue Opera costumes.

FEM-BEM Modeling of Nonlinear Magnetoelectric Effects in Heterogeneous Composite Structures

FEM-BEM Modeling of Nonlinear Magnetoelectric Effects in Heterogeneous Composite Structures

Transforming nursing assessment in acute hospitals: A cluster randomised controlled trial of an evidence-based nursing core assessment (the ENCORE trial)

BackgroundPatient safety is threatened when early signs of clinical deterioration are missed or not acted upon. This research began as a clinical-academic partnership established around a shared concern of nursing physical assessment practices on general wards and delayed recognition of clinical deterioration. The outcome was the development of a complex intervention facilitated at the ward level for proactive nursing surveillance. MethodsThe evidence-based nursing core assessment (ENCORE) trial was a pragmatic cluster-randomised controlled trial. We hypothesised that ward intervention would reduce the incidence of patient rescue events (medical emergency team activations) and serious adverse events. We randomised 29 general wards in a 1:2 allocation, across 5 Australian hospitals to intervention (n = 10) and usual care wards (n = 19). Skilled facilitation over 12 months enabled practitioner-led, ward-level practice change for proactive nursing surveillance. The primary outcome was the rate of medical emergency team activations and secondary outcomes were unplanned intensive care unit admissions, on-ward resuscitations, and unexpected deaths. Outcomes were prospectively collected for 6 months following the initial 6 months of implementation. Analysis was at the patient level using generalised linear mixed models to account for clustering by ward. ResultsWe analysed 29,385 patient admissions to intervention (n = 11,792) and control (n = 17,593) wards. Adjusted models for overall effects suggested the intervention increased the rate of medical emergency team activations (adjusted incidence rate ratio 1.314; 95 % confidence interval 0.975, 1.773), although the confidence interval was compatible with a marginal decrease to a substantial increase in rate. Confidence intervals for secondary outcomes included a range of plausible effects from benefit to harm. However, considerable heterogeneity was observed in intervention effects by patient comorbidity. Among patients with few comorbid conditions in the intervention arm there was a lower medical emergency team activation rate and decreased odds of unexpected death. Among patients with multimorbidity in the intervention arm there were higher rates of medical emergency team activation and intensive care unit admissions. ConclusionTrial outcomes have refined our assumptions about the impact of the ENCORE intervention. The intervention appears to have protective effects for patients with low complexity where frontline teams can respond locally. It also appears to have redistributed medical emergency team activations and unplanned intensive care unit admissions, mobilising higher rates of rescue for patients with multimorbidity. Trial registration numberACTRN12618001903279 (Date of registration: 22/11/2018; First participant recruited: 01/02/2019).

Observation and modeling of irradiance near water surface of a photovoltaic pond

Accurate evaluation of near ground solar radiation in photovoltaic (PV) covered areas is essential for controlling adverse environmental effects and comprehensively utilizing the earth’s surface resources. The irradiance near the water surface of a photovoltaic fishpond was measured with eighteen distributed pyranometers. A simplified penetration model is proposed to reveal the shielding effect of PV panels on direct and diffuse irradiance. The model can well capture the spatial–temporal variation of the global irradiance due to the shielding effect of PV arrays. Numerical results show that proper modeling of the shading effect on diffuse irradiance is essential for the calculation accuracy of the cumulative global irradiance loss for various weather types and sky-view restriction levels. The spatial variation range of global radiation penetration ratio under the PV panels is 10%–90%. The spatially-averaged monthly shielding effects show that the maximum (87%) and minimum (46%) relative irradiance loss ratios in the studied PV area occur in December and June, respectively.

Импульс мобилизации и электоральные результаты «партии власти»

The article examines the connection between protests and elections in an authoritarian context. Using the concept of mobilization momentum, the authors attempt to find out whether intensive and massive collective actions on the eve of elections can affect electoral mobilization and support for the “party in power”, and if so, in what direction. Based on the modern literature on this topic, the authors identify two alternative models of the possible electoral effect of the mobilization momentum. According to the first model, by breaking through the information blockade and signaling to citizens about the widespread dissatisfaction with the authorities’ policies, intense mass protests contribute to an increase in voters’ turnout and a decrease in support for pro-government forces. According to the second model, such protests may easily increase the electoral performance of the “party in power,” since they often lead to the mobilization of an electorate that is loyal to the ruling circles. The authors test these hypotheses against the evidence from the 2011— 2016 parliamentary electoral cycle in Russia using methods of standard multiple linear regression, difference-in-differences and synthetic control. The results are mixed. It appears that the mobilization momentum has a weak electoral effect or even a positive effect on the performance of the “party in power,” but its influence may depend on threshold values: in the most protesting cities, support for the United Russia turns out to be lower than it would be in the absence of the mobilization momentum. At the same time, the findings convincingly demonstrate that the evidence and data from the Russian context, combined with modern methods of analysis, opens up an excellent opportunity for research at the intersection of the spheres of political participation and electoral politics.

The Impact of Overall Intervention Model on Price of Wheat

The analysis of observed data at different time points is prominent to single problems when the scientist studied random sampling data over time restricts the applicability of many traditional statistical methods that require random sampling. The analysis of this data is usually mentioned to the stochastic process, in a special case, if it relates to time, then it is called time series [1]. The aim of our study is to focus on the case of observations that are made at certain times of the year, specifically in the autumn season, in equal periods of time, and we estimate the effect of interference on wheat prices n order to obtain the best predictive model and the best order of this model for the overall effect on the price of wheat for the coming years.

Experimental Investigation of Characteristics and Development of Nonlinearity Models for FR1-Range Radio-Frequency Amplifiers

Experimental studies of nonlinear properties of low-power radio-frequency amplifiers similar to those used in 4G/5G mobile communication equipment of the FR1 frequency range are carried out. The measurements of the characteristics of the amplifiers are performed by using the double-frequency testing technique at frequencies of the n7 band (2500–2570 / 2620–2690 MHz), which is allocated in Belarus for 4G mobile communication systems, and the n78 band (3300–3800 MHz), which is planned to be used in Belarus for 5G mobile communications. Based on the results of the measurements of double-frequency characteristics of the amplifiers, their single-tone amplitude characteristics, as well as two-tone characteristics and values of the dynamic range of 3-, 5-, 7-, and 9th order intermodulation in the first harmonic zone, high-order polynomial models of transfer characteristics of the investigated amplifiers are synthesized. The synthesized models are suitable for use in a wide dynamic range of input signals in case of simultaneous modeling of nonlinear effects of all kinds that pose a danger to radio reception in a complex electromagnetic environment created in the frequency bands of mobile (cellular) communications: both “subtle” effects (intermodulation) and “rough” effects (desensitization, cross-modulation). When using the technology of discrete nonlinear analysis of the behavior of radio equipment in a complex electromagnetic environment, the obtained models provide high efficiency of simulation and quantitative analysis of nonlinear processes and radio interference occurring in 4G/5G/6G radio equipment and networks in a complex electromagnetic environment.

High-resolution land surface modeling of the irrigation effects on evapotranspiration over the Yellow River basin

Accurate estimation of irrigation-induced evapotranspiration (ET) is critical for understanding the terrestrial water cycle. However, current estimation has large uncertainties due to the model limitations and the lack of accurate water use data. In this study, a water-energy balanced irrigation scheme is incorporated into the Conjunctive Surface-Subsurface Process version 2 (CSSPv2) model to improve the hydrological simulations over the Yellow River basin (YRB) where irrigation is extensive. The coupled model (CSSPv2-IRR) is calibrated against long-term water use data from provincial survey, and it is used to perform a 6-km resolution simulation over the YRB during 1991–2017. The results show that the coupled model can well reproduce hydrological processes within the basin, and the simulated irrigation amount, ET and surface soil moisture largely agree with the validation datasets. Specifically, the annual irrigation amount and irrigation-induced ET changes from the CSSPv2-IRR are more reasonable than ISIMIP2a and reanalysis products, with a reduction in bias by 79.5–97.3 % and 81.2–98.8 % respectively. The CSSPv2-IRR model simulation suggests that irrigation-induced annual ET change is 26.4 mm/year during 1991–2017, contributing to an 8.9 % increase in annual ET over the YRB, and the increase can reach 44.3 % over major irrigation districts. In addition, the contribution of irrigation to long-term increasing trend in ET over the irrigation districts in the upper reach is greater than the contribution of climate change. This study suggests a growing control of irrigation on the ET over the YRB, which has implications for regional water cycle and water resources management.

Thermal conductivity of nanocrystalline alumina films fabricated by aerosol deposition

Films fabricated by aerosol deposition (AD) are expected to be used as electrical insulating coatings. High thermal conductivity is required in addition to electrical insulation properties for electrical insulation substrate applications, but the thermal conductivity of AD films has not been reported. AD films are nanocrystalline films consisting of crystals several tens of nanometers in size, and it is unclear what effect the small particle size has on thermal conductivity. In this study, the thermal conductivity of an AD alumina film was measured by the light flash method as 15–31 W m−1 K−1 at room temperature and was large despite the particle size of several tens of nanometers. Analysis was performed using a model of grain size effects on thermal conductivity.

Torsional actuation and vari-stiffness characteristics of SMA/basalt hybrid braided composite tubes

Shape memory alloy hybrid composite (SMAHC) tubes have the potential to actively control the torsional stiffness of structures. In this work, shape memory alloy (SMA) wires orthogonally braided with basalt fiber were heated by electricity to generate the torsional torque of the SMAHC tube, and then the torsional stiffness of the tube was measured. The macroscopic finite element model of the SMAHC tube was established to assist in the analysis of the torsional stiffness variation before and after heating. According to the experimental results, the actuating torsional ability was affected by the phase transformation recovery force of SMA. The active control of torsional stiffness depends on the change of SMA modulus, the magnitude and direction of SMA wires’ recovery force, and the thermodynamic properties of matrix at different temperatures. Then an evaluation model for the torsional stiffness control effect was proposed. These research results can be used for the adaptive control of the stiffness and vibration characteristics of rotating composite structures, and provide a novel and effective method for the control of structural torsional characteristics with lightweight effects.