Modeling and adaptive tracking for stochastic nonholonomic constrained mechanical systems

This paper is devoted to the problem of modeling and trajectory tracking for stochastic nonholonomic dynamic systems in the presence of unknown parameters. Prior to tracking controller design, the rigorous derivation of stochastic nonholonomic dynamic model is given. By reasonably introducing so-called internal state vector, a reduced dynamic model, which is suitable for control design, is proposed. Based on the backstepping technique in vector form, an adaptive tracking controller is then derived, guaranteeing that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters. The efficiency of the controller is demonstrated by a mechanics system: a vertical mobile wheel in random vibration environment

Small Business Lending: Barriers and Trends

In recent years, small-business lending has been discussed widely among the commercial banking community, a reflection of both the growing relative importance of small businesses to the U.S. economy and the fact that large companies have developed the ability to access capital markets directly for debt and equity financing. Although their market share in small-business lending has been eroded in recent years by competition from depository and non-depository financial institutions, commercial banks remain the most frequently used source of financing for small businesses. Accordingly, this paper will focus on commercial banks. At some point during their operating history, most small businesses experience difficulties in obtaining bank financing. Some of these difficulties are due to the inherent financial fragility of small businesses, while others result from banks\u27 traditional underwriting and risk management practices.Beyond these standard impediments to financing, small businesses in environmentally-sensitive industries face additional credit barriers, which suggests that the issues surrounding financing for environmental purposes are sufficiently problematic to merit special consideration. Recognizing this situation, the Great Lakes Environmental Finance Center (GLEFC) has undertaken a project to identify barriers to environmentally-related financing (with an emphasis on pollution prevention), and to devise strategies for overcoming such impediments. First, the paper presents a brief picture of small business\u27 current role in the national economy. Second, it summarizes the most important barriers they face in obtaining financing. Finally, the paper discusses recent changes in lending practices that promise to increase the overall availability of credit for small businesses, and explores the implications of these changes for small businesses that seek financing for environmentally-related purposes

Small Business Lending: Barriers and Trends

In recent years, small-business lending has been discussed widely among the commercial banking community, a reflection of both the growing relative importance of small businesses to the U.S. economy and the fact that large companies have developed the ability to access capital markets directly for debt and equity financing. Although their market share in small-business lending has been eroded in recent years by competition from depository and non-depository financial institutions, commercial banks remain the most frequently used source of financing for small businesses. Accordingly, this paper will focus on commercial banks. At some point during their operating history, most small businesses experience difficulties in obtaining bank financing. Some of these difficulties are due to the inherent financial fragility of small businesses, while others result from banks\u27 traditional underwriting and risk management practices.Beyond these standard impediments to financing, small businesses in environmentally-sensitive industries face additional credit barriers, which suggests that the issues surrounding financing for environmental purposes are sufficiently problematic to merit special consideration. Recognizing this situation, the Great Lakes Environmental Finance Center (GLEFC) has undertaken a project to identify barriers to environmentally-related financing (with an emphasis on pollution prevention), and to devise strategies for overcoming such impediments. First, the paper presents a brief picture of small business\u27 current role in the national economy. Second, it summarizes the most important barriers they face in obtaining financing. Finally, the paper discusses recent changes in lending practices that promise to increase the overall availability of credit for small businesses, and explores the implications of these changes for small businesses that seek financing for environmentally-related purposes

NENet: Monocular Depth Estimation via Neural Ensembles

Depth estimation is getting a widespread popularity in the computer vision community, and it is still quite difficult to recover an accurate depth map using only one single RGB image. In this work, we observe a phenomenon that existing methods tend to exhibit asymmetric errors, which might open up a new direction for accurate and robust depth estimation. We carefully investigate into the phenomenon, and construct a two-level ensemble scheme, NENet, to integrate multiple predictions from diverse base predictors. The NENet forms a more reliable depth estimator, which substantially boosts the performance over base predictors. Notably, this is the first attempt to introduce ensemble learning and evaluate its utility for monocular depth estimation to the best of our knowledge. Extensive experiments demonstrate that the proposed NENet achieves better results than previous state-of-the-art approaches on the NYU-Depth-v2 and KITTI datasets. In particular, our method improves previous state-of-the-art methods from 0.365 to 0.349 on the metric RMSE on the NYU dataset. To validate the generalizability across cameras, we directly apply the models trained on the NYU dataset to the SUN RGB-D dataset without any fine-tuning, and achieve the superior results, which indicate its strong generalizability. The source code and trained models will be publicly available upon the acceptance

Genome-wide identification, characterization, evolution and expression analysis of the DIR gene family in potato (Solanum tuberosum)

The dirigent (DIR) gene is a key player in environmental stress response and has been identified in many multidimensional tube plant species. However, there are few studies on the StDIR gene in potato. In this study, we used genome-wide identification to identify 31 StDIR genes in potato. Among the 12 potato chromosomes, the StDIR gene was distributed on 11 chromosomes, among which the third chromosome did not have a family member, while the tenth chromosome had the most members with 11 members. 22 of the 31 StDIRs had a classical DIR gene structure, with one exon and no intron. The conserved DIR domain accounts for most of the proteins in the 27 StDIRs. The structure of the StDIR gene was analyzed and ten different motifs were detected. The StDIR gene was divided into three groups according to its phylogenetic relationship, and 22 duplicate genes were identified. In addition, four kinds of cis-acting elements were detected in all 31 StDIR promoter regions, most of which were associated with biotic and abiotic stress. The findings demonstrated that the StDIR gene exhibited specific responses to cold stress, salt stress, ABA, and drought stress. This study provides new candidate genes for improving potato’s resistance to stress

Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening

An Economic Development Strategy for the Community of Waynesville in a Dynamic Regional Setting

The Waynesville community is noted for its antique shops, good schools and high quality living environment. The Community is located in Warren County, Ohio. This report explores the opportunities and challenges of developing and implementing a strategic plan for coordinated economic development of the Community. The study examines economic trends and community locational attributes to help detect economic opportunities the community could exploit A focus group session with business leaders helped to pinpoint economic development opportunities. Citizens input into shaping the focus of a development strategy was obtained by a telephone survey of 140 randomly selected local residents. The sample size is sufficient to provide statistical validity at the 90 percent level of confidence