Search

Low resolution of input data and equifinality in model calibration can lead to inaccuracy and insufficient reflection of spatial differences, thereby increasing model errors. However, the impact of input data accuracy, catchment threshold area, and calibration algorithm on model uncertainty reduction has not yet been well understood. The sequential uncertainty fitting version 2 (SUFI‐2) that is linked with the Soil and Water Assessment Tool (SWAT) in the package called SWAT Calibration Uncertainty Programs (SWAT‐CUP) was introduced to quantify the effects of different input data resolutions on parameter sensitivity and model uncertainty in the Jinghe River watershed, and the effects of different sub‐basin delineations and other two calibration algorithms on model uncertainty were also comparatively analysed. (i) USLE_C, EPCO, ALPHA_BNK, and CN2 are the most sensitive parameters among all SWAT projects. When the change of digital elevation model (DEM) resolution is small, the sensitivity of parameters does not change obviously. When the DEM resolution changes significantly, BIOMIX, LAT_SED, USLE_K, and CH_N1 become highly sensitive parameters by replacing OV_N, SMTMP, SURLAG, and USLE_P. However, the change in land use resolution has little impact on parameter sensitivity, with only a slight change in the sensitivity ranking of specific parameters. (ii) Model uncertainty responded to changes in the resolution of DEM more than land use. Most of the runoff simulations had smaller uncertainties (P factor, R factor, percentage of bias [PBIAS]) than sediment. High resolution DEM data reduced model uncertainty, but the models with 2000 m DEM resolution also achieved small uncertainty. Small catchment threshold area leads to high uncertainty of the model, and large catchment threshold areas decrease the model uncertainty. The model has relatively good simulation effects in runoff and sediment when the catchment threshold area was 2000 km2. (iii) The SWAT model has different simulation deviations and uncertainties in different calibration algorithms, the SUFI‐2 and generalized likelihood uncertainty estimation (GLUE) algorithms show better applicability than particle swarm optimization (PSO). The NSE indicators of the three algorithms are in the following order: SUFI‐2 > GLUE > PSO for runoff, and GLUE > SUFI‐2 > PSO for sediment. This study helps us understand the cause, knowledge of which moves from the particular to the general by the comprehension of essence, power, and nature in reducing model uncertainty. [ABSTRACT FROM AUTHOR]

Summary: Aiming at the problems of time‐varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second‐order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particle filter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage. [ABSTRACT FROM AUTHOR]

Summary: Power performance of pneumatic motor (PM) limits its application in compressed air vehicle (CAV) as an auxiliary power. This study presents an investigation of the power system with parallel operation mode based on experiments to improve the power performance of PM. First, the test bench of PM in parallel operation mode is built. Then, a series of experiments are conducted to investigate the effect of key parameters on the power performance, economy and energy conversion efficiency of CAV. The interaction among the key parameters of the PM and the influence of them on the power performance are discussed. Subsequently, the power performance, economy and energy conversion efficiency of the PM working in single and parallel operation modes are compared. Experimental results show that the maximum power output of the PM is 979 W, and the energy conversion efficiency of PMs 1 and 2 are 6.54% and 6.80% when the PM works in parallel operation mode. The parallel operation mode of PM can improve the power performance, economy and energy conversion efficiency of CAV. [ABSTRACT FROM AUTHOR]

Summary: Supercapacitors are characterized by a long service lifetime and high power density, which can meet the instantaneous high‐power demand during the acceleration of electric vehicles. In this study, a fractional‐order model is developed to simulate the polarization effect and charging/discharging characteristics of supercapacitors, considering the precision of the electrochemical model and the amount of calculation of the equivalent circuit model and using the adaptive genetic algorithm to identify the parameters. The accurate prediction of the state of charge (SOC) can improve efficiency, prolong the service lifetime, and ensure the safety of supercapacitors. This study proposes a multi‐innovation unscented Kalman filter algorithm based on the fractional‐order model to improve the SOC estimation accuracy. The proposed algorithm is compared with other algorithms and analyzed under different temperatures and operating conditions to verify the accuracy and effectiveness of the proposed algorithm in estimating the SOC and tracking the terminal voltage. Experimental results show that the root mean squared error and mean absolute error of the proposed algorithm are less than those of the other algorithms. The proposed algorithm accurately estimates the SOC and tracks the terminal voltage. The maximum root mean squared error and mean absolute error of SOC estimation error are 1.8% and 1.78%, respectively. [ABSTRACT FROM AUTHOR]

Microscale compressed air energy storage is not dependent on geographical environment, exhibits a flexible layout, and is especially suitable for distributed energy systems. Herein, a test bench of microscale compressed air energy storage is established. The effects of key parameters, such as regulated pressure, current, voltage, and rotating speed, on the output performance of a pneumatic motor (PM) and generator are examined. The power performance, economic performance, and energy efficiency of the PM in forward and reverse rotations are also investigated. The differences in the output performances of the PM and generator under the forward and reverse rotations of the PM are analyzed. Experimental results show that the PM's power performance, economy, and efficiency in forward rotation are greater than those in reverse rotation. The power output of the generator with the PM in forward rotation is greater than that in reverse rotation. The maximum values of the power output and efficiency and the minimum compressed air consumption rate of the PM are 650 W, 6.92%, and 0.165 g J−1, respectively. The maximum efficiency of the generator is 94.35%. [ABSTRACT FROM AUTHOR]

This study provides current evidence for efficacy and safety of treating COVID‐19 with combined traditional Chinese medicine (TCM) and conventional western medicine (CWM). Six databases were searched from January 1 to December 31, 2020. Randomized controlled trials (RCTs), case–control studies (CCTs), and cohort studies on TCM or TCM combined with CWM treatment for COVID‐19 were included. The quality of included RCTs was assessed by Cochrane risk of bias tool, and the Newcastle‐Ottawa Scale (NOS) was used to assess the quality of cohort studies and CCTs. Review Manager 5.4 software was used to perform meta‐analysis. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. A total of 35 studies (3,808 patients) composing 19 RCTs and 16 observational studies were included. The results of meta‐analysis revealed that comparing with CWM alone, integrated TCM and CWM had significant improvement in total effective rate, improvement rate of chest CT, the rate of disease progression, as well as improvement of fever, fatigue and cough. The overall quality of evidence was very low to moderate. In conclusion, TCM combined with CWM was a potential treatment option for increasing clinical effective rate, improving the clinical symptoms, and preventing disease progression in COVID‐19 patients. High‐quality clinical trials are required in the further. [ABSTRACT FROM AUTHOR]

For many complex diseases, gene-environment (G-E) interactions have independent contributions beyond the main G and E effects. Despite extensive effort, it still remains challenging to identify G-E interactions. With the long accumulation of experiments and data, for many biomedical problems of common interest, there are existing studies that can be relevant and informative for the identification of G-E interactions and/or main effects. In this study, our goal is to identify G-E interactions (as well as their corresponding main G effects) under a joint statistical modeling framework. Significantly advancing from the existing studies, a quasi-likelihood-based approach is developed to incorporate information mined from the existing literature. A penalization approach is adopted for identification and selection and respects the "main effects, interactions" hierarchical structure. Simulation shows that, when the existing information is of high quality, significant improvement can be observed. On the other hand, when the existing information is less informative, the proposed method still performs reasonably (and hence demonstrates a certain degree of "robustness"). The analysis of The Cancer Genome Atlas (TCGA) data on cutaneous melanoma and glioblastoma multiforme demonstrates the practical applicability of the proposed approach and also leads to sensible findings. [ABSTRACT FROM AUTHOR]

Malware mutation is pervasive among networks. Modeling and understanding its propagation characteristics have been of great importance. In this study, a new compartmental model that extends the present model by incorporating mutated malware into the modeling process as a separate dynamic variable is proposed and theoretically analyzed to deepen the understanding of the spreading mechanisms of mutated malware. The model involves two equilibria, namely, malware‐free equilibrium and malware equilibrium, wherein both have proven to be locally and globally asymptotically stable through the Routh‐Hurwitz criterion and Lyapunov functional approach, respectively. An epidemic threshold is obtained that clearly forms the boundary among the comprehensive dynamics of the model between two distinct ramifications: one with mutation infection prevalence and the other without any mutation infection. Both are incarnated via the existence and stability of the equilibria admitted by the model. Further analyses show that the mutation is related not only to the epidemic threshold, but also to the malware prevalence level. The numerical simulations based on the analytic results demonstrate that the diffusion of mutated malware can fall away or can be maintained at a suitable level. [ABSTRACT FROM AUTHOR]

The family can exert an important influence on career decision making. This study investigated the impact of adaptability and cohesion in family‐of‐origin relationships on dysfunctional career thoughts in 269 college students (221 women, 48 men). The Career Thoughts Inventory and the Family Adaptability and Cohesion Scales–IV were used to assess family dynamics and dysfunctional career thoughts. The study addressed the following research question: What is the impact of family cohesion and adaptability in the participant's family of origin on dysfunctional career thoughts, specifically decision‐making confusion, commitment anxiety, and external conflict? Results revealed that higher levels of family cohesion were associated with lower levels of decision‐making confusion, commitment anxiety, and external conflict and that higher levels of family adaptability were associated with lower levels of external conflict. [ABSTRACT FROM AUTHOR]

A better understanding of the professional life of women faculty is gained by comparing the work experiences of two subgroups: those working in disciplines considered 'traditional' for women and those working in 'nontraditional' disciplines. [ABSTRACT FROM AUTHOR]

Four themes that have emerged from the previous chapters are discussed; changes and improvements are suggested for future data collection and institutional research on faculty diversity. [ABSTRACT FROM AUTHOR]

Bayesian network is introduced and illustrated as a flexible and reliable data analytical technique for extracting useful information from large-scale institutional databases. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses an article on the postsecondary faculty population in the U.S., international faculty members, and non-tenure-track faculty (NTTF) in higher education system of the U.S.