Ministry of Communications

Federated learning (FL) over wireless networks has become a key enabler for privacy-preserving distributed artificial intelligence (AI). However, high learning latency remains a critical bottleneck due to the presence of stragglers, limited wireless resources, and frequent model uploads. While model quantization can mitigate this issue by reducing communication overhead, its effectiveness is sensitive to device heterogeneity and time-varying channel conditions. To address this issue, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedDamQu</i>, a communication-efficient FL framework with doubly-adaptive model quantization, which dynamically adjusts quantization bit-widths across devices and communication rounds to balance latency and accuracy. Our objective is to maximize the model performance under learning latency constraints. The main contributions are summarized as follows. 1) Convergence Analysis under Unreliable Channels: We derive a novel convergence error upper bound for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedDamQu</i>, which explicitly quantifies the impact of device selection, unreliable transmission, and quantization error on the global model performance, under both fixed and dynamic quantization gain settings. 2) Joint Optimization Framework: Based on the knowledge from the proposed theoretical bound, we formulate a joint mixed integer nonlinear programming (MINLP) problem that integrates device selection, quantization bit-width configuration, and bandwidth allocation to minimize the convergence error under latency constraints. 3) Efficient Solution Design: The MINLP problem is decomposed into three subproblems, where closed-form solutions for quantization bit-width configuration and bandwidth allocation subproblems are derived, and a lightweight yet effective iterative algorithm is developed to obtain a suboptimal solution for the device selection subproblem. Extensive experiment results validate the theoretical analysis and demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedDamQu</i> consistently outperforms existing methods in terms of convergence rate and model accuracy, while significantly reducing the overall learning latency.

The immersed tunnels of Hong Kong–Zhuhai–Macao Bridge and the Shenzhen–Zhongshan Channel have lengths up to 6–7 km along the longitudinal direction and cross several soil regions. The traveling wave effect is observed in these long immersed tunnels when subjected to seismic waves. Based on the improved response displacement method, a theoretical model of a long immersed tunnel containing 33 tube elements and 32 flexible joints and considering the continuity conditions of flexible joints was developed in this study. The incorporation of the traveling wave effect was achieved by taking into account both the longitudinal propagation distance of the seismic wave within the immersed tunnel and the dynamic variations in seismic wave parameters as the wave traversed through diverse soil regions. An evaluation was developed to assess the traveling wave effect on the response of the long immersed tunnel, and the influences of incidence angles and initial apparent wave velocities of seismic wave were investigated. The results showed that ignoring traveling wave effect for the long immersed tunnel through various soil regions would result in significant underestimations of relative deformations of flexible joints by approximately one order of magnitude, leading to unsafe seismic design.

Atopic dermatitis (AD) is a difficult-to-treat and recurrent skin condition that often imposes a heavy burden on patients and healthcare systems due to the high costs associated with its treatment and management. Astragalus membranaceus (AM), as a botanical drug, has been shown to alleviate skin diseases through multiple mechanisms. However, its systematic mechanism of action against AD remains unclear. This research summarizes the molecular mechanisms through which AM and its active components (polysaccharides, saponins, flavonoids) mitigate AD. The study proposes, for the first time, that AM may alleviate the onset and progression of AD by inhibiting the translocation of gut-derived inflammatory factors to the skin through the Gut-Skin Axis (GSA). Through comprehensive analysis of network pharmacology, molecular docking, and molecular dynamics simulations, compounds with potentially high activity of AM were preliminarily screened. The potential interaction mechanism between this compound molecule and the target protein in AD treatment was further explored. A total of 89 common targets were identified between AM and AD. Enrichment analysis suggests that signaling pathways such as IL-6, TNF-α, NF-κB, and IL-17 may serve as key regulatory hubs in the progression of AD. At conventional doses, AM exhibits a good safety profile. However, the risk of interactions when combined with traditional AD treatments (such as tacrolimus) warrants attention, necessitating enhanced safety evaluations before clinical application. Overall, AM holds potential as an adjunctive therapy for mitigating side effects and improving symptoms, offering a safer alternative to existing treatments. It contributes to shifting AD treatment strategies from purely symptom control toward addressing both symptoms and underlying causes.

Based on the fundamental theory of vehicle-bridge coupling vibration, this study proposes a novel method for identifying the bridge damping ratio from the vehicle-bridge interaction by using the bridge’s mode shape. This study conducts theoretical derivation via indirect measurement techniques and performs numerical simulation analysis using a finite element model built in MATLAB. First, acceleration sensors are used to measure the bridge response caused by a single-axle vehicle crossing; subsequently, band-pass filtering is employed to extract the vibration component corresponding to the bridge’s first-order frequency, and the Hilbert transform is used to identify the first-order mode shape curve. Ultimately, the bridge damping ratio is rapidly identified by leveraging the analytical relationship between the damping and the location of the mode shape’s peak. The effectiveness of the method is verified by theoretical derivations and numerical simulations, with a relative error of only 2% under the baseline condition. More importantly, parametric analysis shows that the method has strong robustness, with the relative identification error remaining within 5% even at a higher vehicle speed of 8 m/s and in a strong noise environment of 20 dB. Compared with traditional indirect methods that rely on iterative calculations, the method proposed in this study provides a more efficient, direct analytical identification approach, offering a novel idea for structural health monitoring based on indirect measurement.

This study aims to explore the impact of the economic and political environment on the effectiveness of capital increases in reducing credit risk, and to analyze the influence of capital growth on banking stability and the extent to which it contributes to credit risk mitigation. The study sample consists of a group of banks listed on the Iraq Stock Exchange during the period from 2018 to 2023. The deductive analytical method was employed to analyze financial statements and assess the relationship between capital increase and credit risk. The study reached several important findings. Notably, some banks failed to manage credit risk effectively. Data revealed that in certain cases, an increase in capital was accompanied by a rise in risk levels, indicating an inverse relationship between the two variables. Although a few banks demonstrated slight improvement in risk management, it remained below the required standard—highlighting weaknesses in internal oversight, poor supervision, and insufficient adherence to corporate governance guidelines.

We report the recent progress of waveguide-coupled O-band GeSi quantum confined stark effect electroabsorption modulators, monolithically integrated in a Si photonics platform on 300 mm Silicon-on-insulator wafers with 220 nm thick Si top layer. A wafer-scale analysis of static insertion loss (IL) and extinction ratio (ER) is presented, showing IL down to 7.5 dB with ER of 5 dB for a 36.8 µm long device, at drive voltages of 2 V peak-to-peak. Modulation bandwidths beyond 50 GHz are demonstrated, with an extracted junction capacitance of 57 fF and series resistance of 8.3 Ω. Finally, open eye diagrams are demonstrated for non-return-to-zero on-off keying (NRZ-OOK) modulation for data rates from 40 Gb/s up to 64 Gb/s, with dynamic extinction ratio of 2.5 dB, at 1320 nm wavelength.