The Relationship between Service Quality, Customer, Satisfaction, Trust, and Commitment: A Case Study of Fudian Bank's Customers in Kunming, Yunnan, China

This research aims to investigate the relationships among four variables (service quality, customer satisfaction, trust and commitment) to enhance the understanding of the factors that affect the commitment of customers to a bank, in this case, the Fudian Bank in Kunming, China. The data was collected from 399 respondents by using the non-probability sampling procedure. The research outcomes reveal that technical quality (in term of employees’ technical ability, employees’ knowledge, technical solutions, computerized systems, and machine quality), functional quality (in terms of employees behavior, attitude, accessibility, appearance, customer conduct, internal relationship, and service- mindedness) have a statistically significant influence on customer satisfaction and trust. Moreover, both customer satisfaction and trust toward the bank is proven to be positively associated with commitment

BBR-induced Stark shifts and level broadening in helium atom

The precise calculations of blackbody radiation (BBR)-induced Stark shifts and depopulation rates for low-lying states of helium atom with the use of variational approach are presented. An effect of the BBR-induced induced Stark-mixing of energy levels is considered. It is shown that this effect leads to a significant reduction of lifetimes of helium excited states. As a consequence the influence of Stark-mixing effect on the decay rates of metastable states in helium is discussed in context of formation processes of the cosmic microwave background

SAARC: Achievements and Challenges

[Despite progress made by SAARC in recent years, a number of challenges continue to confront the organization. To play an effective role as a regional grouping, challenges such as poverty alleviation, the energy crisis, combating terrorism, and effects of globalization, among others, should be tackled jointly. SAARC countries, in general, need assistance in all these fields owing mainly to their weaker economies. Observers, having closer relationship with SAARC members, may be instrumental in meeting these challenges.

Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network

The bailout strategy is crucial to cushion the massive loss caused by systemic risk in the financial system. There is no closed-form formulation of the optimal bailout problem, making solving it difficult. In this paper, we regard the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that follows the E-N framework for measuring the systemic risk of the financial system. We propose the so-called ``Prediction-Gradient-Optimization'' (PGO) framework to solve it, where the ``Prediction'' means that the objective function without a closed-form is approximated and predicted by a neural network, the ``Gradient'' is calculated based on the former approximation, and the ``Optimization'' procedure is further implemented within a gradient projection algorithm to solve the problem. Comprehensive numerical simulations demonstrate that the proposed approach is promising for systemic risk management

Elucidating the Synergic Effect in Nanoscale MoS\u3csub\u3e2\u3c/sub\u3e/TiO\u3csub\u3e2\u3c/sub\u3e Heterointerface for Na-Ion Storage

Interface engineering in electrode materials is an attractive strategy for enhancing charge storage, enabling fast kinetics, and improving cycling stability for energy storage systems. Nevertheless, the performance improvement is usually ambiguously ascribed to the “synergetic effect”, the fundamental understanding toward the effect of the interface at molecular level in composite materials remains elusive. In this work, a well-defined nanoscale MoS2/TiO2 interface is rationally designed by immobilizing TiO2 nanocrystals on MoS2 nanosheets. The role of heterostructure interface between TiO2 and MoS2 by operando synchrotron X-ray diffraction (sXRD), solid-state nuclear magnetic resonance, and density functional theory calculations is investigated. It is found that the existence of a hetero-interfacial electric field can promote charge transfer kinetics. Based on operando sXRD, it is revealed that the heterostructure follows a solid-solution reaction mechanism with small volume changes during cycling. As such, the electrode demonstrates ultrafast Na+ ions storage of 300 mAh g−1 at 10 A g−1 and excellent reversible capacity of 540 mAh g−1 at 0.2 A g−1. This work provides significant insights into understanding of heterostructure interface at molecular level, which suggests new strategies for creating unconventional nanocomposite electrode materials for energy storage systems

Invariant Feature Regularization for Fair Face Recognition

Fair face recognition is all about learning invariant feature that generalizes to unseen faces in any demographic group. Unfortunately, face datasets inevitably capture the imbalanced demographic attributes that are ubiquitous in real-world observations, and the model learns biased feature that generalizes poorly in the minority group. We point out that the bias arises due to the confounding demographic attributes, which mislead the model to capture the spurious demographic-specific feature. The confounding effect can only be removed by causal intervention, which requires the confounder annotations. However, such annotations can be prohibitively expensive due to the diversity of the demographic attributes. To tackle this, we propose to generate diverse data partitions iteratively in an unsupervised fashion. Each data partition acts as a self-annotated confounder, enabling our Invariant Feature Regularization (INV-REG) to deconfound. INV-REG is orthogonal to existing methods, and combining INV-REG with two strong baselines (Arcface and CIFP) leads to new state-of-the-art that improves face recognition on a variety of demographic groups. Code is available at https://github.com/PanasonicConnect/InvReg.Comment: Accepted by International Conference on Computer Vision (ICCV) 202

A Rapid and Sensitive Europium Nanoparticle-Based Lateral Flow Immunoassay Combined with Recombinase Polymerase Amplification for Simultaneous Detection of Three Food-Borne Pathogens

Food-borne pathogens have become an important public threat to human health. There are many kinds of pathogenic bacteria in food consumed daily. A rapid and sensitive testing method for multiple food-borne pathogens is essential. Europium nanoparticles (EuNPs) are used as fluorescent probes in lateral flow immunoassays (LFIAs) to improve sensitivity. Here, recombinase polymerase amplification (RPA) combined with fluorescent LFIA was established for the simultaneous and quantitative detection of Listeria monocytogenes, Vibrio parahaemolyticus, and Escherichia coli O157:H7. In this work, the entire experimental process could be completed in 20 min at 37 degrees C. The limits of detection (LODs) of EuNP-based LFIA-RPA were 9.0 colony-forming units (CFU)/mL for Listeria monocytogenes, 7.0 CFU/mL for Vibrio parahaemolyticus, and 4.0 CFU/mL for Escherichia coli O157:H7. No cross-reaction could be observed in 22 bacterial strains. The fluorescent LFIA-RPA assay exhibits high sensitivity and good specificity. Moreover, the average recovery of the three food-borne pathogens spiked in food samples was 90.9-114.2%. The experiments indicate the accuracy and reliability of the multiple fluorescent test strips. Our developed EuNP-based LFIA-RPA assay is a promising analytical tool for the rapid and simultaneous detection of multiple low concentrations of food-borne pathogens