The Study on the Preferences of Customer Personal Values with Chinese Culture Background in Services

AbstractCustomer personal values are the important factors which affect customer behaviors, and they guide and decide the customer's attitudes and behaviors on the products or the services. The paper thinks there are only several important customer personal values to guide customer's decisions, and these values will have -strong cultural differences. This study focuses on discussing the preferences of customer personal values with Chinese culture background when customers consume service and analyzes on the customer preferences of customer personal values with the deep interview method. After interviewing 16 responders with the semi-structured questionnaires, the study finds out some interesting results: (1) Some customers have recognized the existent of customer personal values, even though customer perceived values still have the strong influences on customer behaviors. (2) As they pursue to high quality lives, customers enjoy the lives in easy and pleasure way and care about the safe of the family. Quick response, simple and professional services contribute to enhance the experiences of easy and pleasure lives. (3) Non-rational consumers need the respect from the staff and the companies seriously. In comparison, the rational customers care less about the respect. (4) The sociable requirements have become a common consuming psychology of the customers. More and more customers try to gain the friends by consuming some services. (5) The preferences of customer personal values have a close relationship with the Chinese culture, such as collective values, family conception and “face” culture. The results benefit for service companies improving service brands and service quality

Modified Glucose-Insulin-Potassium Regimen Provides Cardioprotection With Improved Tissue Perfusion in Patients Undergoing Cardiopulmonary Bypass Surgery

Background Laboratory studies demonstrate glucose-insulin-potassium (GIK) as a potent cardioprotective intervention, but clinical trials have yielded mixed results, likely because of varying formulas and timing of GIK treatment and different clinical settings. This study sought to evaluate the effects of modified GIK regimen given perioperatively with an insulin-glucose ratio of 1:3 in patients undergoing cardiopulmonary bypass surgery. Methods and Results In this prospective, randomized, double-blinded trial with 930 patients referred for cardiac surgery with cardiopulmonary bypass, GIK (200 g/L glucose, 66.7 U/L insulin, and 80 mmol/L KCl) or placebo treatment was administered intravenously at 1 mL/kg per hour 10 minutes before anesthesia and continuously for 12.5 hours. The primary outcome was the incidence of in-hospital major adverse cardiac events including all-cause death, low cardiac output syndrome, acute myocardial infarction, cardiac arrest with successful resuscitation, congestive heart failure, and arrhythmia. GIK therapy reduced the incidence of major adverse cardiac events and enhanced cardiac function recovery without increasing perioperative blood glucose compared with the control group. Mechanistically, this treatment resulted in increased glucose uptake and less lactate excretion calculated by the differences between arterial and coronary sinus, and increased phosphorylation of insulin receptor substrate-1 and protein kinase B in the hearts of GIK-treated patients. Systemic blood lactate was also reduced in GIK-treated patients during cardiopulmonary bypass surgery. Conclusions A modified GIK regimen administered perioperatively reduces the incidence of in-hospital major adverse cardiac events in patients undergoing cardiopulmonary bypass surgery. These benefits are likely a result of enhanced systemic tissue perfusion and improved myocardial metabolism via activation of insulin signaling by GIK. Clinical Trial Registration URL: clinicaltrials.gov. Identifier: NCT01516138

On the null space property of lq -minimization for 0<q≤1 in compressed sensing

The paper discusses the relationship between the null space property (NSP) and the lq-minimization in compressed sensing. Several versions of the null space property, that is, the lq stable NSP, the lq robust NSP, and the lq,p robust NSP for 0<p≤q<1 based on the standard lq NSP, are proposed, and their equivalent forms are derived. Consequently, reconstruction results for the lq-minimization can be derived easily under the NSP condition and its equivalent form. Finally, the lq NSP is extended to the lq-synthesis modeling and the mixed l2/lq-minimization, which deals with the dictionary-based sparse signals and the block sparse signals, respectively. © 2015 Yi Gao et a

A differential ML combiner for differential amplify-and-forward system in time-selective fading channels

We propose a new differential maximum-likelihood (DML) combiner for noncoherent detection of the differential amplify-and-forward (D-AF) relaying system in the time-selective channel. The weights are computed based on both the average channel quality and the correlation coefficient of the direct and relay channels. Moreover, we derive a closed-form approximate expression for the average bit error rate (BER), which is applicable to any single-relay D-AF system with fixed weights. Both theoretical and simulated results are presented to show that the time-selective nature of the underlying channels tends to reduce the diversity gains at the low-signal-to-noise-ratio (SNR) region, resulting in an asymptotic BER floor at the high-SNR region. Moreover, the proposed DML combiner is capable of providing significant BER improvements compared with the conventional differential detection (CDD) and selection-combining (SC) schemes

OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments

Environment representations endowed with sophisticated semantics are pivotal for facilitating seamless interaction between robots and humans, enabling them to effectively carry out various tasks. Open-vocabulary maps, powered by Visual-Language models (VLMs), possess inherent advantages, including zero-shot learning and support for open-set classes. However, existing open-vocabulary maps are primarily designed for small-scale environments, such as desktops or rooms, and are typically geared towards limited-area tasks involving robotic indoor navigation or in-place manipulation. They face challenges in direct generalization to outdoor environments characterized by numerous objects and complex tasks, owing to limitations in both understanding level and map structure. In this work, we propose OpenGraph, the first open-vocabulary hierarchical graph representation designed for large-scale outdoor environments. OpenGraph initially extracts instances and their captions from visual images, enhancing textual reasoning by encoding them. Subsequently, it achieves 3D incremental object-centric mapping with feature embedding by projecting images onto LiDAR point clouds. Finally, the environment is segmented based on lane graph connectivity to construct a hierarchical graph. Validation results from public dataset SemanticKITTI demonstrate that OpenGraph achieves the highest segmentation and query accuracy. The source code of OpenGraph is publicly available at https://github.com/BIT-DYN/OpenGraph