Search Results (497)

This study aimed to investigate the efficacy of a composite coating composed of 150 mg/L ε-Poly-L-lysine (ε-PL) and 5 g/L chitosan (CTS) in extending the shelf life and maintaining the postharvest quality of fresh Tremella fuciformis. Freshly harvested T. fuciformis were treated by surface spraying, with distilled water serving as the control. The effects of the coating on storage quality, physicochemical properties, reactive oxygen species (ROS) metabolism, and membrane lipid metabolism were evaluated during storage at (25 ± 1) °C. The results showed that the ε-PL/CTS composite coating significantly retarded quality deterioration, as evidenced by reduced weight loss, maintained whiteness and color, and higher retention of soluble sugars, soluble solids, and soluble proteins. The coating also effectively limited water migration and loss. Mechanistically, the coated T. fuciformis exhibited enhanced antioxidant capacity, characterized by increased superoxide anion (O₂⁻) resistance capacity, higher activities of antioxidant enzymes (SOD, CAT, APX), and elevated levels of non-enzymatic antioxidants (AsA, GSH). This led to a significant reduction in malondialdehyde (MDA) accumulation, alongside improved DPPH radical scavenging activity and reducing power. Furthermore, the ε-PL/CTS coating preserved cell membrane integrity by inhibiting the activities of lipid-degrading enzymes (lipase, LOX, PLD), maintaining higher levels of key phospholipids (phosphatidylinositol and phosphatidylcholine), delaying phosphatidic acid accumulation, and consequently reducing cell membrane permeability. In conclusion, the ε-PL/CTS composite coating effectively extends the shelf life and maintains the quality of postharvest T. fuciformis by modulating ROS metabolism and preserving membrane lipid homeostasis. This study provides a theoretical basis and a practical approach for the quality control of fresh T. fuciformis. Full article

The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article

As large language models (LLMs) and vision–language models (VLMs) become increasingly used in robotics area, a crucial question arises: to what extent do these models replicate human-like cognitive processes, particularly within socially interactive contexts? Whilst these models demonstrate impressive multimodal reasoning and perception capabilities, their cognitive plausibility remains underexplored. In this study, we address this gap by using human visual attention as a behavioural proxy for cognition in a naturalistic human-robot interaction (HRI) scenario. Eye-tracking data were previously collected from participants engaging in social human-human interactions, providing frame-level gaze fixations as a human attentional ground truth. We then prompted a state-of-the-art VLM (LLaVA) to generate scene descriptions, which were processed by four LLMs (DeepSeek-R1-Distill-Qwen-7B, Qwen1.5-7B-Chat, LLaMA-3.1-8b-instruct, and Gemma-7b-it) to infer saliency points. Critically, we evaluated each model in both stateless and memory-augmented (short-term memory, STM) modes to assess the influence of temporal context on saliency prediction. Our results presented that whilst stateless LLaVA most closely replicates human gaze patterns, STM confers measurable benefits only for DeepSeek, whose lexical anchoring mirrors human rehearsal mechanisms. Other models exhibited degraded performance with memory due to prompt interference or limited contextual integration. This work introduces a novel, empirically grounded framework for assessing cognitive plausibility in generative models and underscores the role of short-term memory in shaping human-like visual attention in robotic systems. Full article

Secure autonomous secret key distillation (SKD) systems traditionally depend on external common randomness (CR) sources, which often suffer from instability and limited reliability over long-term operation. In this work, we propose a novel SKD architecture that synthesizes CR by combining a keystream of a shared-key keystream generator $KSG\left(K_G\right)$ with locally generated binary Bernoulli noise. This construction emulates the statistical properties of the classical Maurer satellite scenario while enabling deterministic control over key parameters such as bit error rate, entropy, and leakage rate (LR). We derive a closed-form lower bound on the equivocation of the shared-secret key $K_G$ from the viewpoint of an adversary with access to public reconciliation data. This allows us to define an admissible operational region in which the system guarantees long-term secrecy through periodic key refreshes, without relying on advantage distillation. We integrate the Winnow protocol as the information reconciliation mechanism, optimized for short block lengths ($N=8$), and analyze its performance in terms of efficiency, LR, and final key disagreement rate (KDR). The proposed system operates in two modes: ideal secrecy, achieving secret key rates up to 22% under stringent constraints (KDR < 10⁻⁵, LR < 10⁻¹⁰), and perfect secrecy mode, which approximately halves the key rate. Notably, these security guarantees are achieved autonomously, without reliance on advantage distillation or external CR sources. Theoretical findings are further supported by experimental verification demonstrating the practical viability of the proposed system under realistic conditions. This study introduces, for the first time, an autonomous CR-based SKD system with provable security performance independent of communication channels or external randomness, thus enhancing the practical viability of secure key distribution schemes. Full article

The advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. To assess its effectiveness, we conducted experiments on the widely used LIAR dataset utilizing Term Frequency–Inverse Document Frequency (TF-IDF) features, as well as transformer-based DistilBERT embeddings. The experimental results demonstrate that the HQDNN achieves a superior recall performance—92.58% with TF-IDF and 94.40% with DistilBERT—surpassing traditional machine learning models such as Logistic Regression, Linear SVM, and Multilayer Perceptron. Additionally, we compare the HQDNN with SetFit, a recent CPU-efficient few-shot transformer model, and show that while SetFit achieves higher precision, the HQDNN significantly outperforms it in recall. Furthermore, an ablation experiment confirms the critical contribution of the quantum component, revealing a substantial drop in performance when the quantum layer is removed. These findings highlight the potential of hybrid quantum–classical models as effective and compact alternatives for high-sensitivity classification tasks, particularly in domains such as fake news detection. Full article

Accurate time-series forecasting plays a vital role in sensor-driven applications such as energy monitoring, traffic flow prediction, and environmental sensing. While most existing approaches focus on extracting local patterns from historical observations, they often overlook the global temporal information embedded in timestamps. However, this information represents a valuable yet underutilized aspect of sensor-based data that can significantly enhance forecasting performance. In this paper, we propose a novel timestamp-guided knowledge distillation framework (TKDF), which integrates both historical and timestamp information through mutual learning between heterogeneous prediction branches to improve forecasting robustness. The framework comprises two complementary branches: a Backbone Model that captures local dependencies from historical sequences, and a Timestamp Mapper that learns global temporal patterns encoded in timestamp features. To enhance information transfer and reduce representational redundancy, a self-distillation mechanism is introduced within the Timestamp Mapper. Extensive experiments on multiple real-world sensor datasets—covering electricity consumption, traffic flow, and meteorological measurements—demonstrate that the TKDF consistently improves the performance of mainstream forecasting models. Full article

This study proposes an application framework based on Large Language Models (LLMs) to analyze multimodal heterogeneous data in the power sector and introduces the CLB-BER model for classifying user electricity consumption behavior. We first employ the Euclidean–Cosine Dynamic Windowing (ECDW) method to optimize the adjustment phase of the CLUBS clustering algorithm, improving the classification accuracy of electricity consumption patterns and establishing a mapping between unlabeled behavioral features and user types. To overcome the limitations of traditional clustering algorithms in recognizing emerging consumption patterns, we fine-tune a pre-trained DistilBERT model and integrate it with a Softmax layer to enhance classification performance. The experimental results on real-world power grid data demonstrate that the CLB-BER model significantly outperforms conventional algorithms in terms of classification efficiency and accuracy, achieving 94.21% accuracy and an F1 score of 94.34%, compared to 92.13% accuracy for Transformer and lower accuracy for baselines like KNN (81.45%) and SVM (86.73%); additionally, the Improved-C clustering achieves a silhouette index of 0.63, surpassing CLUBS (0.62) and K-means (0.55), underscoring its potential for power grid analysis and user behavior understanding. Our framework inherently preserves temporal symmetry in consumption patterns through dynamic sequence alignment, enhancing its robustness for real-world applications. Full article

In this study, Sr_0.95Bi_0.05TiO₃ was synthesized via solid state reaction, characterized, and applied as a visible-light-active photocatalyst for the degradation of oxytetracycline, imidacloprid, and their mixture. To evaluate the influence of the aqueous matrix on pollutant degradation, photocatalytic experiments were carried out in both distilled water and a real environmental sample (surface water). The Sr_0.95Bi_0.05TiO₃ perovskite showed high photocatalytic performance under visible light, achieving nearly complete degradation of oxytetracycline after 2 h, and significant removal of imidacloprid in river water (60% after 3 h). Enhanced degradation in surface water was attributed to favorable ionic composition and pH. The perovskite oxide maintained its photocatalytic performance over five consecutive cycles, with no significant loss in photocatalytic activity or structural and morphological stability. Ecotoxicological assessment using Daphnia magna confirmed that the treated water was non-toxic, indicating that no harmful byproducts were formed. Complementary Density Functional Theory calculations were conducted to complement experimental findings, providing insights into the structural, electronic, and optical properties of the photocatalyst, enhancing the understanding of the degradation mechanisms involved. This integrated approach, combining experimental photocatalytic performance evaluation in different matrices, ecotoxicity testing, and theoretical modeling, highlights Sr_0.95Bi_0.05TiO₃ as a promising, stable, and environmentally safe photocatalyst for practical wastewater treatment applications. Full article

Detecting biased language in large-scale corpora, such as the Wiki Neutrality Corpus, is essential for promoting neutrality in digital content. This study systematically evaluates a range of machine learning (ML) and deep learning (DL) models for the detection of biased and pre-conditioned phrases. Conventional classifiers, including Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), are compared with advanced neural architectures such as Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM) networks, and Generative Adversarial Networks (GANs). A novel hybrid architecture is proposed, integrating DistilBERT, LSTM, and GANs within a unified framework. Extensive experimentation with intermediate variants DistilBERT + LSTM (without GAN) and DistilBERT + GAN (without LSTM) demonstrates that the fully integrated model consistently outperforms all alternatives. The proposed hybrid model achieves a cross-validation accuracy of 99.00%, significantly surpassing traditional baselines such as XGBoost (96.73%) and LightGBM (96.83%). It also exhibits superior stability, statistical significance (paired t-tests), and favorable trade-offs between performance and computational efficiency. The results underscore the potential of hybrid deep learning models for capturing subtle linguistic bias and advancing more objective and reliable automated content moderation systems. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

This study provides a comprehensive overview of vertical-axis wind turbines (VAWTs) for emerging energy applications by combining a bibliometric analysis and a thematic mini-review. Scopus-indexed publications from 1979 to 2025 were analyzed using PRISMA guidelines and bibliometric tools (Bibliometrix, CiteSpace, and VOSviewer) to map global research trends, and a parallel mini-review distilled recent advances into five thematic areas: aerodynamic strategies, advanced materials, urban integration, hybrid systems, and floating offshore platforms. The results reveal that VAWT research output has surged since 2006, led by China with strong contributions from Europe and North America, and is concentrated in leading renewable energy journals. Dominant topics include computational fluid dynamics (CFD) simulations, performance optimization, wind–solar hybrid integration, and adaptation to turbulent urban environments. Technologically, active and passive aerodynamic innovations have boosted performance albeit with added complexity, remaining mostly at moderate technology readiness (TRL 3–5), while advanced composite materials are improving durability and fatigue life. Emerging applications in microgrids, building-integrated systems, and offshore floating platforms leverage VAWTs’ omnidirectional, low-noise operation, although challenges persist in scaling up, control integration, and long-term field validation. Overall, VAWTs are gaining relevance as a complement to conventional turbines in the sustainable energy transition, and this study’s integrated approach identifies critical gaps and high-priority research directions to accelerate VAWT development and help transition these turbines from niche prototypes to mainstream renewable solutions. Full article

The integration of advanced computer vision and artificial intelligence (AI) techniques into collaborative robotic systems holds the potential to revolutionize human–robot interaction, productivity, and safety. Despite substantial research activity, a systematic synthesis of how vision and AI are jointly enabling context-aware, adaptive cobot capabilities across perception, planning, and decision-making remains lacking (especially in recent years). Addressing this gap, our review unifies the latest advances in visual recognition, deep learning, and semantic mapping within a structured taxonomy tailored to collaborative robotics. We examine foundational technologies such as object detection, human pose estimation, and environmental modeling, as well as emerging trends including multimodal sensor fusion, explainable AI, and ethically guided autonomy. Unlike prior surveys that focus narrowly on either vision or AI, this review uniquely analyzes their integrated use for real-world human–robot collaboration. Highlighting industrial and service applications, we distill the best practices, identify critical challenges, and present key performance metrics to guide future research. We conclude by proposing strategic directions—from scalable training methods to interoperability standards—to foster safe, robust, and proactive human–robot partnerships in the years ahead. Full article

Plant diseases significantly undermine agricultural productivity. This study introduces an improved YOLOv10n model named WD-YOLO (Weighted and Double-scale YOLO), an advanced architecture for efficient plant disease detection. The PlantDoc dataset was initially enhanced using data augmentation techniques. Subsequently, we developed the DSConv module—a novel convolutional structure employing double-scale weighted convolutions that dynamically adjust to different scale perceptions and optimize attention allocation. This module replaces the conventional Conv module in YOLOv10. Furthermore, the WTConcat module was introduced, dynamically merging weighted concatenation with a channel attention mechanism to replace the Concat module in YOLOv10. The training of WD-YOLO incorporated knowledge distillation techniques using YOLOv10l as a teacher model to refine and compress the architectural learning. Empirical results reveal that WD-YOLO achieved an mAP50 of 65.4%, outperforming YOLOv10n by 9.1% without data augmentation and YOLOv10l by 2.3%, despite having significantly fewer parameters (9.3 times less than YOLOv10l), demonstrating substantial gains in detection efficiency and model compactness. Full article

Digital holography can capture and reconstruct 3D object information, making it valuable for biomedical imaging and materials science. However, traditional holographic reconstruction methods require the use of phase shift operation in the time or space domain combined with complex computational processes, which, to some extent, limits the range of application areas. The integration of deep learning (DL) advancements with physics-informed methodologies has opened new avenues for tackling this challenge. However, most of the existing DL-based holographic reconstruction methods have high model complexity. In this study, we first design a lightweight model with fewer parameters through the synergy of deep separable convolution and Swish activation function and then employ it as a teacher to distill a smaller student model. By reducing the number of network layers and utilizing knowledge distillation to improve the performance of a simple model, high-quality holographic reconstruction is achieved with only one hologram, greatly reducing the number of parameters in the network model. This distilled lightweight method cuts computational expenses dramatically, with its parameter count representing just 5.4% of the conventional Unet-based method, thereby facilitating efficient holographic reconstruction in settings with limited resources. Full article

This research presents a novel, sustainable, and eco-friendly method for the rapid green synthesis of nanoparticles with antibacterial properties. This method employs steam distillation to extract reducing and stabilizing agents from orange peel waste, followed by ultrasound-assisted synthesis. To the best of our knowledge, this is the first reported integration of these two techniques for nanoparticle production. The extracted materials were then subjected to rigorous characterization through a combination of analytical techniques, including FTIR, HPLC, and TEM. These analytical approaches enabled a comprehensive analysis of the synthesized NPs, revealing their size distribution within the range of 1.5 to 14 nm. Among the synthesized nanomaterials, AgNPs exhibited the most potent antibacterial activity, with statistically significant minimum inhibitory concentrations (MICs) of 16 ppm for E. coli ATCC and 32 ppm for resistant E. coli and E. faecalis strains. This study underscored the promise of valorizing citrus waste for nanomaterial synthesis and introduced a novel, scalable methodology for producing bioactive nanoparticles, promoting a more sustainable technology for this purpose. Notably, this research aligns with United Nations Sustainable Development Goal 12, which promotes responsible consumption and production by transforming organic waste into high-value functional nanomaterials for biomedical and environmental applications. Full article