Frontline hotel employees are expected to notice changing guest needs and initiate service improvements before formal procedures can be adjusted, yet less is known about whether occupation-specific stigma constrains such proactive behavior. Using cognitive-affective personality system (CAPS) theory, this study examines how perceived occupational stigma undermines proactive customer service performance (PCSP) among frontline service employees. We propose two psychological routes—a cognitive route through organization-based self-esteem (OBSE) and an affective route through harmonious work passion (HWP)—and examine core self-evaluations (CSE) as a boundary condition. Across a scenario experiment, a 2 × 2 experiment, and a three-wave field survey, perceived occupational stigma consistently reduced PCSP. OBSE and HWP transmitted this effect, and CSE weakened the negative indirect pathways. These findings clarify how occupational stigma is processed in frontline hospitality work and offer guidance for sustaining proactive service behavior in stigmatized roles.
Tissue stiffness assessment plays a critical role in the clinical diagnosis of liver fibrosis and the differentiation of benign from malignant tumors, among other conditions. Conventional magnetic resonance elastography (MRE) enables noninvasive evaluation of tissue stiffness but relies on dedicated external vibration devices, which limits its widespread clinical adoption. Virtual magnetic resonance elastography (vMRE) has emerged as a promising branch of MRE and has attracted increasing attention in recent years. vMRE is primarily based on microstructural analysis using diffusion-weighted imaging (DWI), from which virtual shear modulus is derived by modeling water molecule diffusion, thereby eliminating the need for additional mechanical excitation hardware. Tissue elastic parameters can also be estimated from conventional MRI sequences, offering advantages in convenience and compatibility. However, the diagnostic performance of vMRE relative to conventional MRE remains controversial. Existing studies are mostly small-sample, single-center investigations, and both the models and scanning protocols have yet to be standardized. Systematic reviews and standardized guidelines are still lacking. This article reviews the technical characteristics, application progress, influencing factors, and optimization strategies of vMRE, aiming to provide a reference for its clinical translation and future research.
Motivated by the classification of one-to-one rational functions of low degree in terms of their equivalence classes, we determine all many-to-one (including one-to-one) rational functions of degree two or three on the projective line explicitly in terms of their coefficients. Furthermore, we study the linear-fractional equivalence classes and the value sets of these rational functions. As an application, we characterize two classes of many-to-one quadranomials using their coefficients. These one-to-one quadranomials unify and generalize many results in the literature.
Despite being deemed the state-of-the-art catalyst for acidic oxygen evolution, RuO2 confronts a critical durability constraint when deployed under the high anodic potentials and acidic conditions of proton exchange membrane water electrolysis. The involvement of lattice oxygen in the oxidative pathway tends to over-oxidize Ru4+ into soluble RuO4, which ultimately triggers the permanent dissolution of active sites and structural collapse. To circumvent this inherent drawback, we have developed a triphasic RuO2/Mn2O3/Mn3O4 heterointerface enriched with oxygen vacancies, synthesized via a hydrothermal route followed by a controlled calcination step. Oxygen vacancies originate from the electron redistribution from RuO2 to the Mn2O3/Mn3O4 interface. This coupling effect weakens the Mn-O bonds and lowers the desorption energy barrier of lattice oxygen, thereby spontaneously inducing the formation of oxygen vacancies. Electrochemical evaluation reveals that Ru0.10MnO2 catalyst delivers 10 mA/cm2 at an overpotential of merely 234 mV in 0.5 M H2SO4, and after 500 h of continuous operation at 100 mA/cm2, its catalytic activity remains virtually unchanged. Moreover, when employed as the anodic material in a practical PEMWE cell, the catalyst enables a current density of 1000 mA/cm2 at a cell voltage of only 1.64 V, and the assembled electrolyser sustains steady operation for no less than 1200 h.
An adaptive optimal neural network algorithm with fast finite-time convergence is proposed for stochastic multiagent systems (SMASs) under deception attacks, time-varying asymmetric output constraints and dead zones. An additional attack signal corrupts the state information of nonlinear systems, resulting in the unavailability of real state information for controller development. To overcome this obstacle, a reinforcement learning (RL)-based identifier-actor-critic-disturbance architecture is used to develop a fast finite-time adaptive optimal tracking algorithm for each subsystem in SMASs, which alleviates the negative effects of cyberattacks that intentionally tamper with sensor signals. Herein, a barrier function is designed to transform the constrained system into an unconstrained equivalent. Furthermore, time-varying dead zones in SMASs pose considerable challenges for controller design, while enhancing the applicability of the system in practical scenarios. The proposed resilient adaptive optimal tracking algorithm guarantees the boundedness of all signals in the overall system in probability. Eventually, two simulation results are conducted to prove the effectiveness of the proposed method.
