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Large visual language models like Contrastive Language-Image Pre-training (CLIP), despite their excellent performance, are highly vulnerable to the influence of adversarial examples. This work investigates the accuracy and robustness of visual language models (VLMs) from a novel multi-modal perspective. We propose a multi-modal fine-tuning method called Multi-modal Depth Adversarial Prompt Tuning (MDAPT), which guides the generation of visual prompts through text prompts to improve the accuracy and performance of visual language models. We conducted extensive experiments and significantly improved performance on three datasets (ϵ=4/255). Compared with traditional manual design prompts, the accuracy and robustness increased by an average of 17.84% and 10.85%, respectively. Not only that, our method still has a very good performance improvement under different attack methods. With our efficient settings, compared with traditional manual prompts, our average accuracy and robustness are improved by 32.16% and 21.00%, respectively, under three different attacks.

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals.

The efficiency of remanufacturing systems is closely related to the modular design. This paper proposes a sustainable design concept aiming at active remanufacturing. This paper adopts the modular method related to parameter flow to decompose and construct the product’s function. The author applies the clustering method to the parameter flow correlation determination to realize the quantitative division of modules. This paper combines sustainable design principles with the life cycle characteristics of products. In this paper, the sustainable design is divided into modules based on the degree of component association. Finally, this paper uses an example to demonstrate that the model in this paper can divide products into modules in a sustainable way.

This paper aims at improving and utilizing the ontology information in ontology design of FOAF and vCard in real time, and the application of open relational data technology, SPARQL query information results and sending RDF/JSON data format. In addition, improve the effectiveness and efficiency of patient information extraction from the medical information website. This article includes two web search engines that are used to inform patients about medical care information. The experiment uses Drupal as the main software tool, and the Drupal RDF extension module provides some meaningful mapping. In the evaluation part, the structure of the experimental test platform is established and the system function test is carried out. The evaluation results include consumers or patients retrieving the latest doctor information and comparing search capabilities and techniques, between our system and existing systems.

Aiming at the problems and shortcomings of genetic algorithm, a hybrid genetic algorithm based on chaos genetic algorithm is designed in this paper. Based on the actual situation of universities, a mathematical model of timetabling problem is proposed. In view of the deficiency of genetic algorithm, chaos is introduced into the genetic algorithm by using the inherent regularity of chaotic sequence, effectively guiding crossover and mutation operation, and avoiding the defect that standard genetic algorithm is easy to fall into local minimum. The simulation of the course scheduling problem under the same conditions is conducted at the end of the paper, with the standard genetic algorithm and hybrid genetic algorithm. By comparing the calculation results, the results proved that the hybrid genetic algorithm is fully applicable to the scheduling problem, and has a high efficiency. At last, it can be concluded that the chaos genetic algorithm provides new ideas for timetabling problem.

Underwater images frequently suffer from chromatic distortion, blurred details, and low contrast, posing significant challenges for enhancement. This paper introduces AquaTree, a novel underwater image enhancement (UIE) method that reformulates the task as a Markov Decision Process (MDP) through the integration of Monte Carlo Tree Search (MCTS) and deep reinforcement learning (DRL). The framework employs an action space of 25 enhancement operators, strategically grouped for basic attribute adjustment, color component balance, correction, and deblurring. Exploration within MCTS is guided by a dual-branch convolutional network, enabling intelligent sequential operator selection. Our core contributions include: (1) a multimodal state representation combining CIELab color histograms with deep perceptual features, (2) a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency, and (3) an alternating training strategy co-optimizing enhancement sequences and network parameters. We further propose two inference schemes: an MCTS-based approach prioritizing accuracy at higher computational cost, and an efficient network policy enabling real-time processing with minimal quality loss. Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority, significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.

The increasing adoption of unmanned aerial vehicles (UAVs) in urban low-altitude logistics systems, particularly for time-sensitive applications like parcel delivery and supply distribution, necessitates sophisticated coordination mechanisms to optimize operational efficiency. However, the limited capability of UAVs to extract state-action information in complex environments poses significant challenges to achieving effective cooperation in dynamic and uncertain scenarios. To address this, we presents an Improved Multi-Agent Hybrid Attention Critic (IMAHAC) framework that advances multi-agent deep reinforcement learning (MADRL) through two key innovations. Firstly, a Temporal Difference Error and Time-based Prioritized Experience Replay (TT-PER) mechanism that dynamically adjusts sample weights based on temporal relevance and prediction error magnitude, effectively reducing the interference from obsolete collaborative experiences while maintaining training stability. Secondly, a hybrid attention mechanism is developed, integrating a sensor fusion layer—which aggregates features from multi-sensor data to enhance decision-making—and a dissimilarity layer that evaluates the similarity between key-value pairs and query values. By combining this hybrid attention mechanism with the Multi-Actor Attention Critic (MAAC) framework, our approach strengthens UAVs’ capability to extract critical state-action features in diverse environments. Comprehensive simulations in urban air mobility scenarios demonstrate IMAHAC’s superiority over conventional MADRL baselines and MAAC, achieving higher cumulative rewards, fewer collisions, and enhanced cooperative capabilities. This work provides both algorithmic advancements and empirical validation for developing robust autonomous aerial systems in smart city infrastructures.

Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers. However, CNNs exhibit constrained performance in high-resolution image transmission, while Transformers incur high computational cost due to quadratic complexity. Recently, VMamba, a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities, has shown great potential in computer vision tasks. Inspired by this, we propose MNTSCC, an efficient VMamba-based nonlinear joint source-channel coding (JSCC) model for wireless image transmission. Specifically, MNTSCC comprises a VMamba-based nonlinear transform module, an MCAM entropy model, and a JSCC module. In the encoding stage, the input image is first encoded into a latent representation via the nonlinear transformation module, which is then processed by the MCAM for source distribution modeling. The JSCC module then optimizes transmission efficiency by adaptively assigning transmission rate to the latent representation according to the estimated entropy values. The proposed MCAM enhances the channel-wise autoregressive entropy model with attention mechanisms, which enables the entropy model to effectively capture both global and local information within latent features, thereby enabling more accurate entropy estimation and improved rate-distortion performance. Additionally, to further enhance the robustness of the system under varying signal-to-noise ratio (SNR) conditions, we incorporate SNR adaptive net (SAnet) into the JSCC module, which dynamically adjusts the encoding strategy by integrating SNR information with latent features, thereby improving SNR adaptability. Experimental results across diverse resolution datasets demonstrate that the proposed method achieves superior image transmission performance compared to existing CNN- and Transformer-based semantic communication models, while maintaining competitive computational efficiency. In particular, under an Additive White Gaussian Noise (AWGN) channel with SNR = 10 dB and a channel bandwidth ratio (CBR) of 1/16, MNTSCC consistently outperforms NTSCC, achieving a 1.72 dB Peak Signal-to-Noise Ratio (PSNR) gain on the Kodak24 dataset, 0.79 dB on CLIC2022, and 2.54 dB on CIFAR-10, while reducing computational cost by 32.23%. The code is available at (accessed on 09 July 2025).