Hung Yen University of Technology and Education

In the past decade, numerous deep learning-based object detection approaches have been released and obtained desirable performance when executing in favorable weather conditions. Nevertheless, these detectors attain incomplete results of detecting objects in the rain, a frequently occurring weather condition, due to a drop in visibility and lack of crucial features for depicting objects. In this work, to bridge this gap, we publish a novel approach for elevating the efficiency of object classification and localization impaired by rain, termed UFA-Net. Our proposed approach achieves attractive object detection results via absorption sharp features at diverse scales generated from rainy images. To this end, the UFA-Net consists of three subnetworks, namely, a feature restitution (FR) subnetwork, an unsupervised learning (UL) subnetwork, and an object detection (OD) subnetwork, where, the FR subnetwork provides robust information to the OD subnetwork through the UL subnetwork. In our architecture, the FR and UL subnetworks are solely activated during training progress, and the OD subnetwork is accountable for estimating the object localization and its label in rainy weather conditions. The exhaustive experimental results substantiate that our UFA-Net achieves the highest mean average precision (mAP) scores on published light, medium, heavy, synthetic, and natural rainy image sets, respectively, surpassing compared object detection models and combinations of detectors and rain removal methods.

This paper investigates the finite-time dissipativity problem for uncertain tempered fractional-order neural networks with constant time delays and external disturbances. By employing a novel analytical framework combining the inf-sup technique, Laplace transform for tempered Caputo derivatives, generalized Grönwall inequality, and linear matrix inequality (LMI) conditions, new tractable and less conservative finite-time dissipativity criteria are derived. The proposed approach ensures both transient state boundedness and input-output energy dissipation performance over finite time horizons, offering stronger performance guarantees than classical finite-time stability. Numerical simulations are presented to validate the theoretical results.

Vietnam’s wooden forest products industry is an important export sector, contributing to industrial growth and employment. However, it is facing increasing pressures related to challenges such as forest and export sustainability. Despite its potential, Vietnam’s export performance remains uneven across destination markets, related to the presence of significant unrealized trade potential. This study examines the determinants of export efficiency in Vietnam’s wooden forest products sector by moving beyond traditional gravity variables to incorporate institutional and cultural dimensions. Using a panel of 70 trading partners between 2004 and 2023, covering more than 93% of Vietnam’s total wood exports, this study employs an instrumental-variable single-stage stochastic frontier gravity model (IV-SFGM) to estimate trade potential. The results show that economic size, favorable exchange rates, and shared borders significantly enhance export performance. Furthermore, geographical distance and land enclosure remain persistent structural barriers, particularly relevant for bulky and logistics-intensive wood products. Institutional and cultural distance constitute substantial non-tariff barriers, significantly reducing export efficiency across markets. Conversely, regional trade agreements, trade freedom, and foreign direct investment play a critical role in mitigating inefficiencies and facilitating market penetration. Export efficiency in Vietnam’s wooden forest products sector indicates considerable improvement, rising from approximately 25% in the mid-2000s to over 55% in recent years, indicating notable progress in the market and highlighting considerable untapped potential. So, integrating institutional and cultural factors into a frontier-based gravity framework, this study offers novel empirical evidence from an emerging, biodiversity-rich economy with evolving governance institutions. The findings provide important policy implications for aligning export growth with institutional reform and trade liberalization, thereby contributing to the achievement of SDGs such as Decent Work and Economic Growth.

Uncompliant behavior regarding the use of Personal Protective Equipment (PPE) is one of the major factors contributing to workplace injuries within the construction field. Meanwhile, current static camera surveillance systems are limited by a fixed field of view, rendering them unable to continuously track mobile targets. To address this challenge, this research proposes an active surveillance system that integrates the YOLOv11 object detection algorithm with a Nonlinear Model Predictive Controller (NMPC) designed for Pan-Tilt camera mechanism. The approach automates PPE violation recognition and target tracking, factoring in the physical motion limitations of the hardware. Numerical simulation results indicate that the NMPC significantly surpasses the conventional PID controller. Notably, NMPC achieves fast and stable system response, while the PID controller fails to stabilize the system, resulting in persistent oscillations. This study validates the feasibility and effectiveness of a unified framework, paving the way for automated, intelligent, and continuous safety monitoring systems in dynamic construction environments.

This study employs molecular dynamics (MD) simulations to systematically investigate the effect of tailoring the atomic fractions (at.%) of the constituent elements (Cr, Cu, Co, Ni, and Fe) on the mechanical response and protective performance of non-equiatomic CrCuCoNiFe high-entropy alloy (HEA) coatings, in comparison with the equiatomic configuration, on a crystalline Cu substrate during nanoindentation. Mechanical behavior is characterized through the evolution of crystal structure, stacking fault, dislocation density, stress distribution, elastic recovery, and surface morphology. The results show that the hardness of the HEA coatings ranges from 11.13 GPa for Cr30(CuCoNiFe)70 (Cr-rich) to 12.85 GPa for Fe30(CrCuCoNi)70 (Fe-rich). Notably, all samples exhibit pronounced anisotropic elastic recovery, with the recovery ratio along the indentation depth being 2.43-10.8 times higher than that in the lateral direction. Furthermore, Cr-rich and Co30(CrCuNiFe)70 (Co-rich) coatings demonstrate superior protective performance by effectively suppressing defect propagation into the Cu substrate.