Understanding Older Vehicle Users: An Interpretative Approach

Future adaptations in vehicle design should be linked in some parts to the age-related changes often faced by the older users. The aim of this research is to investigate the multiple age-related changes of Chinese older vehicle users in order to assist designers to better understand current and future older users’ needs. Although qualitative interpretative approaches have rarely been applied in the field of traffic gerontology research, they are widely used in current design research to explore persons’ lived experiences, behaviours and emotions. Therefore, this study employed qualitative research methods consisting of observation, interview, travel logbook and co-discovery to explore older vehicle users’ travel needs. The interpretative analysis confirmed that multiple methods such as interview, travel logbook, and co-discovery are useful to gain a holistic understanding of older drivers’ travel needs. However, the one journey driving observation cannot provide valuable categories to explore older users’ multiple travel needs due to daily living context absence in the one trip experiment. It is clear that the useful methods for determining research for older users will depend on the product. The findings demonstrate that Chinese future older generations are more concerned about their age-related differences from social and cultural perspective rather than physiological perspective. Social and cultural context play important role to shape older vehicle users’ future travel needs. From design a point of view, understanding the social activity and cultural context surrounds older vehicle users should make it possible to predict older drivers’ needs related to vehicle property. Keywords: Older Vehicle Users; Cultural Context; Social Context; Vehicle Design; Qualitative Research</p

Data-driven Piecewise Affine Decision Rules for Stochastic Programming with Covariate Information

Focusing on stochastic programming (SP) with covariate information, this paper proposes an empirical risk minimization (ERM) method embedded within a nonconvex piecewise affine decision rule (PADR), which aims to learn the direct mapping from features to optimal decisions. We establish the nonasymptotic consistency result of our PADR-based ERM model for unconstrained problems and asymptotic consistency result for constrained ones. To solve the nonconvex and nondifferentiable ERM problem, we develop an enhanced stochastic majorization-minimization algorithm and establish the asymptotic convergence to (composite strong) directional stationarity along with complexity analysis. We show that the proposed PADR-based ERM method applies to a broad class of nonconvex SP problems with theoretical consistency guarantees and computational tractability. Our numerical study demonstrates the superior performance of PADR-based ERM methods compared to state-of-the-art approaches under various settings, with significantly lower costs, less computation time, and robustness to feature dimensions and nonlinearity of the underlying dependency

Modeling Bounded Rationality in Capacity Allocation Games with the Quantal Response Equilibrium

We consider a supply chain with a single supplier and two retailers. The retailers choose their orders strategically, and if their orders exceed the supplier\u27s capacity, quantities are allocated proportionally to the orders. We experimentally study the capacity allocation game using subjects motivated by financial incentives. We find that the Nash equilibrium, which assumes that players are perfectly rational, substantially exaggerates retailers\u27 tendency to strategically order more than they need. We propose a model of bounded rationality based on the quantal response equilibrium, in which players are not perfect optimizers and they face uncertainty in their opponents\u27 actions. We structurally estimate model parameters using the maximum-likelihood method. Our results confirm that retailers exhibit bounded rationality, become more rational through repeated game play, but may not converge to perfect rationality as assumed by the Nash equilibrium. Finally, we consider several alternative behavioral theories and show that they do not explain our experimental data as well as our bounded rationality model

Effects of Cations and PH on Antimicrobial Activity of Thanatin and s-Thanatin against _Escherichia coli_ ATCC25922 and _B. subtilis_ ATCC 21332

Thanatin and s-thanatin were insect antimicrobial peptides which have shown potent antimicrobial activities on a variety of microbes. In order to investigate the effect of cations and pH on the activity of these peptides against Gram-negative bacteria and Gram-positive bacteria, the antimicrobial activities of both peptides were studied in increasing concentrations of monovalent cations (K^+^ and Na^+^), divalent cations (Ca^2+^ and Mg^2+^) and H^+^. The NCCLS broth microdilution method showed that both peptides were sensitive to the presence of cations. The divalent cations showed more antagonized effect on the activity against Gram-negative bacteria than the monovalent cations, since the two peptides lost the ability to inhibit bacterial growth at a very low concentration. In addition, the activities of both peptides tested were not significantly affected by pH. Comparing to studies of other antibacterial peptide activities, our data support a hypothesis that positive ions affect the sensitivity to cation peptides

The Role of Long Noncoding RNAs in Gene Expression Regulation

Accumulating evidence highlights that noncoding RNAs, especially the long noncoding RNAs (lncRNAs), are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. The regulatory mechanisms of lncRNAs have been categorized into four major archetypes: signals, decoys, scaffolds, and guides. Increasing evidence points that lncRNAs are able to regulate almost every cellular process by their binding to proteins, mRNAs, miRNA, and/or DNAs. In this review, we present the recent research advances about the regulatory mechanisms of lncRNA in gene expression at various levels, including pretranscription, transcription regulation, and posttranscription regulation. We also introduce the interaction between lncRNA and DNA, RNA and protein, and the bioinformatics applications on lncRNA research