We develop and analyse a stochastic host-vector model for chikungunya virus (CHIKV) transmission that explicitly incorporates the extrinsic incubation period in mosquitoes. The model couples a human SIR structure with a mosquito SEI structure and is driven by both multiplicative Brownian perturbations and compensated jump noise, capturing gradual environmental variability and abrupt shocks. Under minimal regularity and integrability assumptions on the diffusion coefficients and jump kernels, we derive a deterministic basic reproduction number mo0 and a noise-corrected effective threshold (R) over tilde (& diam;)(0), showing that stochastic perturbations always decrease the transmission potential in the sense that 0 < <(R)over tilde>(& diam;)(0) <= (R) over tilde (2)(0). On this basis, we obtain explicit sufficient conditions for three contrasting regimes: almost sure exponential extinction of the infection, persistence in the mean, and the existence of an endemic stationary distribution for the coupled human and mosquito infectious classes. Our results reveal that the classical condition mo0> 1 is no longer sufficient to guarantee persistence in the stochastic setting; it must be complemented by a moderate-noise requirement (R) over tilde (& diam; )(0)> 1, whereas strong noise and jump activity can enforce extinction even when R-0> 1. The analytical findings are illustrated by numerical simulations implemented in Python. An extinction scenario is calibrated to the 2025 Foshan outbreak data, while a persistence scenario is constructed from theoretically motivated parameters. In both cases, sample paths and joint stationary densities exhibit qualitative behaviour in excellent agreement with the thresholds mo0 and mo0 & diam;, providing a coherent picture of how environmental noise can either suppress or sustain CHIKV transmission.
Reflux after primary one anastomosis gastric bypass (OAGB) remains controversial. Despite this, no review has holistically examined and narrated the post-OAGB reflux construct, problematizing its interlacing parameters to contribute better understandings of the possible sources/reasons behind the controversies. Using three electronic databases, a scoping review of the literature explored seven interlaced questions addressing the post-OAGB reflux construct. A total of 113 items were included. Our main findings include: (1) Terminology – multiple terms describing piece-meal features of the reflux notion; (2) Timing – needed to distinguish new-onset reflux vs. post-operative changes in pre-operative reflux; (3) Diagnostics – wide variety used to different extents alone or in combinations, exhibiting various levels of certainty, and with needed clarity about, rationale, choice, and diagnostic accuracy/ies; (4) Frequency of surveillance – many propositions, wide variations, and unclear rationale regarding the pre-emptive screening; (5) Length of follow up – web of possible time durations of monitoring patients, with ambiguity about how these were determined or guided; (6) Potential confounders – lack of clarity on how H. pylori, proton pump inhibitors, and hiatal hernia could in/directly influence reflux appraisals and how to consider such effects in analyses; and, (7) Relevance/utility of clinical symptoms as indicators – deficient, with occasional ad hoc descriptions but no systematic appraisal of the dynamic relationships between the reflux notion/lack thereof and symptoms/lack thereof. Implications of the findings and potential avenues for a stronger evidence base are discussed.
Federated learning (FL) is a privacy-preserving method for short-term load forecasting in energy networks. However, current defense mechanisms against adversarial attacks often depend on supplementary machine learning frameworks, such as anomaly detection models or Byzantine-robust aggregators. These frameworks add significant computational overhead, straining edge devices such as smart meters and IoT systems with limited processing power. To solve this issue, we propose a new defense-free framework called federated random layer aggregation (FedRLA). By aggregating only one randomly chosen neural network layer per communication round, FedRLA limits adversarial influence to isolated layers. This reduces attack surfaces by 66% compared to full-model aggregation (FedAvg). Using 8-bit quantization, FedRLA cuts data transmission by 92.97% without accuracy loss (MAE: 0.08 kWh vs. FedAvg's 0.076 kWh). Under four model poisoning attacks, it reduces forecasting errors by 19%-35% compared to FedAvg. FedRLA also uses 24% less CPU and 13% less memory than frameworks such as FedProx, while training 58% faster. It combines communication efficiency (0.195 MB/round), adversarial robustness (MAE <= 0.11 kWh under & varepsilon; = 0.2 DP), and low resource consumption, offering a scalable solution for secure FL in resource-constrained energy networks.
The factors influencing customer citizenship behavior (CCB) are critical in the hospitality literature. This area is worth investigating, considering the potential value CCB generates for the company. This study aims to examine brand authenticity's direct and indirect influence on CCB and identifies critical antecedents of CCB. Also, this study investigates the mediating role of brand self-connection and brand prominence in the relationship between brand authenticity and CCB. Data was collected through a self-administered questionnaire, which surveyed 415 hotel guests. Structural equation modeling (SEM) is used to analyze the data. The findings supported the role of brand authenticity, self-brand connection, and brand prominence in predicting the CCB in hospitality services. This study enhances the theoretical underpinning of brand authenticity and CCB in consumer behavior research by acknowledging the significance of self-brand connection and brand prominence and applying them in the hospitality sector.
This study investigates the relationship between financial constraints and a firm's sustainability performance. Our empirical analysis utilises a panel of 40,445 observations from 9466 listed non-financial firms across 44 countries, spanning the period from 2002 to 2019. We provide strong evidence that financial constraints significantly hinder a firm's corporate sustainability performance. Further analyses show that a firm's climate exposure affects the negative impact of financial constraints on sustainability performance. In other words, firms exposed to climate change tend to show greater commitment to sustainability practices regardless of their financial constraints. However, we find no evidence that external and public attention to climate change issues persuades financially constrained firms to enhance their sustainability performance. Our study offers new insights into the link between financial constraints and corporate sustainability, as well as the implications of climate exposure and public attention to climate change.
