Search Results (314)

Poly (lactic-co-glycolic acid) (PLGA) is a widely used copolymer with applications across medical, pharmaceutical, and other industrial fields. Its biodegradability and biocompatibility make it one of the most versatile polymers for nanoscale drug delivery. The present review addresses current knowledge and recent advances in PLGA-based co-delivery nanoformulations with a special reference to design strategies, functional mechanisms, and translational potential. Conventional and advanced fabrication methods, the structural design of PLGA-based nanocarriers, approaches to scale-up and reproducibility, classification of co-delivery types, mechanisms governing drug release, surface modification and functionalization are all discussed. Special attention is given to PLGA-based co-delivery systems, encompassing drug–drug, drug–gene, gene–gene and multi-modal combinations, supported by recent studies demonstrating synergistic therapeutic outcomes. The review also examines clinical translation efforts and the regulatory landscape for PLGA-based nanocarriers. Unlike most existing reviews that typically focus either on PLGA fundamentals or on co-delivery approaches in isolation, this article bridges these domains by providing an integrated, comparative analysis of PLGA-based co-delivery systems and elucidating a critical gap in linking design strategies with translational requirements. In addition, by emphasising the relevance of PLGA-based co-delivery for combination therapies, particularly in cancer and other complex diseases, the review highlights the strong clinical and translational potential of these platforms. Key challenges, such as reproducibility, large-scale manufacturing, and complex regulatory pathways, are discussed alongside emerging trends and future perspectives. Taken together, this review positions PLGA-based co-delivery strategies as a critical driver for advancing precision therapeutics and shaping the future landscape of nanomedicine. Full article

High-valent metal species (iron, manganese, cobalt, copper, and ruthenium) based advanced oxidation processes (AOPs) have emerged as sustainable technologies for water remediation. These processes offer high selectivity, electron transfer efficiency, and compatibility with circular chemistry principles compared to conventional systems. This comprehensive review discusses recent advances in the synthesis, stabilization, and catalytic applications of high-valent metals in aqueous environments. This study highlights their dual functionality, not only as conventional oxidants but also as mechanistic mediators within redox cycles that underpin next-generation AOPs. In this review, the formation mechanisms of these species in various oxidant systems are critically evaluated, highlighting the significance of ligand design, supramolecular confinement, and single-atom engineering in enhancing their stability. The integration of high-valent metal-based AOPs into photocatalysis, sonocatalysis, and electrochemical regeneration is explored through a newly proposed classification framework, highlighting their potential in the development of energy efficient hybrid systems. In addition, this work addresses the critical yet underexplored area of environmental fate, elucidating the post-oxidation transformation pathways of high-valent species, with particular attention to their implications for metal recovery and nutrient valorization. This review highlights the potential of high-valent metal-based AOPs as a promising approach for zero wastewater treatment within circular economies. Future frontiers, including bioinspired catalyst design, machine learning-guided optimization, and closed loop reactor engineering, will bridge the gap between laboratory research and real-world applications. Full article

Strip steel surface defect detection remains a challenging task due to the diverse scales and uneven spatial distribution of defects, which often lead to incomplete feature representation and missed detections in sparsely distributed regions. To address these challenges, we propose a novel cross-scale spatial–semantic feature aggregation network (CSSFAN) that achieves fine-grained and semantically consistent feature fusion across multiple scales. Specifically, CSSFAN adopts a bottom-up feature aggregation strategy equipped with a series of cross-scale spatial–semantic aggregation modules (CSSAMs). Each CSSAM first establishes a mapping relationship between high-level feature points and low-level feature regions and then introduces a cross-scale attention mechanism that adaptively injects spatial details from low-level features into high-level semantic representations. This aggregation strategy bridges the gap between spatial precision and semantic abstraction, enabling the network to capture subtle and irregular defect patterns. Furthermore, we introduce an adaptive region proposal network (ARPN) to cope with the uneven spatial distribution of defects. ARPN dynamically adjusts the number of region proposals according to the local feature complexity, ensuring that regions with dense or subtle defects receive more proposal attention, while sparse or background regions are adaptively suppressed, thereby enhancing the model’s sensitivity to defect-prone areas. Extensive experiments on two strip steel surface defect datasets demonstrate that our method significantly improves detection performance, validating its effectiveness and robustness. Full article

Conventional petroleum-based protective coatings release high levels of volatile organic compounds (VOCs) and contribute to resource depletion, urging the development of environmentally responsible alternatives. Among the bio-based candidates, microalgae and Cyanobacteriophyta have recently gained attention for their ability to produce diverse biopolymers and pigments with intrinsic protective functionalities. However, existing literature has focused mainly on algal biofuels and general biopolymers, leaving a major gap in understanding their application as sustainable coating materials. This review addresses that gap by providing the first integrated assessment of algae-based protective coatings. It begins by defining abiotic and biotic surface degradation mechanisms, including microbiologically influenced corrosion, to establish performance benchmarks. The review then synthesizes recent findings on key algal components, including alginate, extracellular polymeric substances (EPS), and phycocyanin, linking biochemical composition to functional performance, techno-economic feasibility, and industrial scalability. It evaluates their roles in adhesion strength, UV stability, corrosion resistance, and antifouling activity. Reported performance metrics include adhesion strengths of 2.5–3.8 MPa, UV retention above 85% after 2000 h, and corrosion rate reductions of up to 40% compared with polyurethane systems. Furthermore, this study introduces the concept of carbon-negative, multifunctional coatings that simultaneously protect infrastructure and mitigate environmental impacts through CO₂ sequestration and pollutant degradation. Challenges involving biomass variability, processing costs (>USD 500/ton), and regulatory barriers are critically discussed, with proposed solutions through hybrid cultivation and biorefinery integration. By bridging materials science, environmental engineering, and sustainability frameworks, this review establishes a foundation for transforming algae-based coatings from laboratory research to scalable, industrially viable technologies. Full article

With the rapid increase in plastic consumption, waste polypropylene (WPP) has become one of the major components of municipal solid waste, posing significant environmental and resource challenges. According to statistics, polypropylene accounts for approximately 19.1% of the total global plastic waste, posing significant environmental challenges. In recent years, the recycling and reuse of WPP in asphalt pavement materials have received increasing attention due to its excellent mechanical properties, thermal stability, and low cost. This review systematically summarizes the physicochemical properties and recycling technologies of WPP, including mechanical, chemical, and energy recovery routes. Furthermore, the modification mechanisms, preparation methods, and performance characteristics of WPP-modified asphalt binders and mixtures are comprehensively discussed, focusing on their high-temperature stability, compatibility, low-temperature cracking resistance, and anti-moisture damage. Research indicates that WPP modification significantly enhances high-temperature rutting resistance, and thermo-chemical modifiers have successfully enabled the application of WPP in warm-mix asphalt. This review uniquely integrates recent advances in thermo-mechanochemical upcycling with mixture-level performance, bridging molecular design and field application. However, critical challenges, including poor compatibility, insufficient storage stability, and the lack of a unified assessment for the high variability of WPP raw materials, still need to be addressed. Finally, this review primarily focuses on the recycling technologies of WPP, its modification mechanisms in asphalt binders, and the resulting impact on the pavement performance of WPP-modified mixtures. Full article

Intelligent Fault Diagnosis (IFD) systems are integral to predictive maintenance and real-time monitoring but often encounter challenges such as data scarcity, non-linearity, and changing operational conditions. To address these challenges, we propose an enhanced transfer learning framework that integrates the Universal Adaptation Network (UAN) with Spectral-normalized Neural Gaussian Process (SNGP), WideResNet, and attention mechanisms, including self-attention and an outlier attention layer. UAN’s flexibility bridges diverse fault conditions, while SNGP’s robustness enables uncertainty quantification for more reliable diagnostics. WideResNet’s architectural depth captures complex fault patterns, and the attention mechanisms focus the diagnostic process. Additionally, we employ Optuna for hyperparameter optimization, using a structured study to fine-tune model parameters and ensure optimal performance. The proposed approach is evaluated on benchmark datasets, demonstrating superior fault identification accuracy, adaptability to varying operational conditions, and resilience against data anomalies compared to existing models. Our findings highlight the potential of advanced machine learning techniques in IFD, setting a new standard for applying these methods in complex diagnostic environments. Full article

Falls among the elderly represent a leading cause of injury and mortality worldwide, necessitating reliable and real-time monitoring solutions. This study aims to develop a lightweight, accurate, and efficient fall detection framework based on an improved YOLOv5s model. The proposed architecture incorporates a Convolutional Block Attention Module (CBAM) to enhance salient feature extraction, optimizes multi-scale feature fusion in the Neck for better small-object detection, and re-clusters anchor boxes tailored to the horizontal morphology of elderly falls. A multi-scene dataset comprising 11,314 images was constructed to evaluate performance under diverse lighting, occlusion, and spatial conditions. Experimental results demonstrate that the improved YOLOv5s achieves a mean average precision (mAP@0.5) of 94.2%, a recall of 92.5%, and a false alarm rate of 4.2%, outperforming baseline YOLOv5s and YOLOv4 models while maintaining real-time detection speed at 32 FPS. These findings confirm that integrating attention mechanisms, adaptive fusion, and anchor optimization significantly enhances robustness and generalization. Although performance slightly declines under extreme lighting or heavy occlusion, this limitation highlights future opportunities for multimodal fusion and illumination-invariant modeling. Overall, the study contributes a scalable and deployable AI framework that bridges the gap between algorithmic innovation and real-world elderly care applications, advancing intelligent and non-intrusive safety monitoring in aging societies. Full article

Mine water inrush poses a severe threat to coal mine safety, making rapid and accurate identification of water sources essential. Existing methods, including conventional hydrochemical diagrams and machine learning, struggle with high-dimensional, nonlinear hydrogeochemical data characterized by implicit temporal dynamics. This study proposes an intelligent identification model integrating convolutional neural networks (CNNs), long short-term memory (LSTM), and an attention mechanism (CNN–LSTM–Attention). The model employs a CNN to extract local fingerprint features from hydrochemical indicators (K⁺+Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and HCO₃⁻), uses LSTM to model evolutionary patterns, and leverages an attention mechanism to adaptively focus on critical discriminative features. Based on 76 water samples from the Tangjiahui Coal Mine, the model achieved 91% accuracy on the test set, outperforming standalone CNN, LSTM, and CNN–LSTM models. Visualization of attention weights further revealed key diagnostic indicators, enhancing interpretability and bridging data-driven methods with hydrogeochemical mechanisms. This study provides a powerful and interpretable tool for water inrush source identification, supporting the transition toward intelligent and transparent coal mine water hazard prevention. Full article

In the era of Artificial Intelligence-Generated Content (AIGC) transforming advertising production, existing research lacks comprehensive exploration of how AIGC advertisements shape consumer responses. This study integrates attention allocation theory and the Elaboration Likelihood Model (ELM) to investigate dual cognitive processing mechanisms of relevant and divergent AI advertisements via eye-tracking experiments and questionnaires. Findings reveal that relevant AI advertisements enhance perceived usefulness (PU) through product area attention allocation, improving purchase intention; Divergent AI advertisements boost perceived entertainment (PE) via non-product creative cues, positively influencing ad attitudes; and product involvement (PI) moderates these paths as high PI strengthens PU’s role in central processing, while low PI amplifies PE’s effect in peripheral processing. By constructing a dual-path cognitive model, this research bridges gaps in understanding AI advertising’s implicit attention mechanisms and explicit perceptual outcomes. The findings provide theoretical guidance for advertisers to optimize AIGC strategies, balancing technological utility and creative appeal to achieve precise attention guidance and enhance smart marketing effectiveness. Full article

The hierarchical structure and multifunctional properties of bio-based cellular materials, particularly cellulose, hemicellulose, and lignin, have attracted increasing attention and interest due to their sustainability and versatility. Recent advances in computational modeling and machine learning strategies have provided transformative insights into the molecular, mechanical, thermal, and electronic behaviors of these biopolymers. This review categorizes the conducted studies based on key material properties and discusses the computational methods utilized, including quantum mechanical approaches, atomistic and coarse-grained molecular dynamics, finite element modeling, and machine learning techniques. For each property, such as structural, mechanical, thermal, and electronic, we have analyzed the progress made in understanding inter- and intra-molecular interactions, deformation mechanisms, phase behavior, and functional performance. For instance, atomistic simulations have shown that cellulose nanocrystals exhibit a highly anisotropic elastic response, with axial elastic moduli ranging from approximately 100 to 200 GPa. Similarly, thermal transport studies have shown that the thermal conductivity along the chain axis (≈5.7 W m⁻¹ K⁻¹) is nearly an order of magnitude higher than that in the transverse direction (≈0.7 W m⁻¹ K⁻¹). In recent years, this research area has also experienced rapid advancement in data-driven methodologies, with the number of machine learning applications for biopolymer systems increasing more than fourfold over the past five years. By bridging multiscale modeling and data-driven approaches, this review aims to illustrate how these techniques can be integrated into a unified framework to accelerate the design and discovery of high-performance bioinspired materials. Eventually, we have discussed emerging opportunities in multiscale modeling and data-driven discovery to outline future directions for the design and application of high-performance bioinspired materials. This review aims to bridge the gap between molecular-level understanding and macroscopic functionality, thereby supporting the rational design of next-generation sustainable materials. Full article

Effective safety management in air traffic is essential for operational reliability and risk reduction. We propose a multi-channel fusion framework to predict intervals between consecutive air traffic incidents by combining structural, semantic, and temporal information. Inter-incident time series are transformed into complex networks via visibility graphs to learn node embeddings capturing structural recurrence. Semantic features are derived through latent Dirichlet allocation (LDA) and bidirectional encoder representations from Transformers (BERT) embeddings to reveal latent risk-related topics, and an adaptive spectral filter enhances temporal features. These are processed through three modules: a gravity-inspired visibility graph model (GVG), a semantic-aware LSTM (Sem-LSTM), and a spectral-enhanced temporal convolutional network (Spec-TCN). An attention mechanism fuses all modules to predict incident intervals. Using 1298 real-world incidents from China’s Central and Southern Region for validation, the model achieves a mean absolute error of 1.42 h and sMAPE of 17.5%. SHAP analysis indicates that structural similarity and incident topics jointly drive prediction. By integrating interval predictions with topic cues, we construct a safety management framework enabling proactive decision-making. This framework delivers a practical bridge from interval predictions to proactive air traffic control (ATC) decisions. Full article

As a pivotal global commodity, crude oil price volatility directly impacts economic stability and strategic security. Being the most widely traded asset worldwide, it also serves as a key financial barometer and a critical transition fuel in the shift towards renewable energy. Nevertheless, accurate forecasting of crude oil prices remains challenging due to three persistent challenges: (1) the lack of a systematic method to filter out redundant and noisy features for deep learning models; (2) the limited ability of existing models to simultaneously capture both local bidirectional dependencies and global periodic patterns; and (3) the non-adaptive nature of conventional attention mechanisms, which restricts their capacity to dynamically focus on the most informative historical periods. To bridge these gaps, this study introduces a novel forecasting framework with three key contributions. First, we introduce a hierarchical feature selection paradigm based on LightGBM to systematically eliminate data redundancy and noise, thereby constructing an optimal feature subset for subsequent deep modeling. Second, an improved Autoformer encoder, integrated with Bidirectional GRUs, is designed to simultaneously capture local bidirectional dependencies and global periodic patterns, enabling a more comprehensive multi-scale temporal representation. Third, a dynamic fusion mechanism is incorporated to adaptively recalibrate the significance of historical timesteps. This enables the model to focus on periods rich in information, enhancing contextual awareness in predictions. Future research aims to enhance forecasting capabilities by achieving a deeper integration of local and global temporal representations, potentially through exploring advanced gating or sparse attention mechanisms. Full article

Succinoglycan (SG), a rhizobial exopolysaccharide produced by Sinorhizobium meliloti, has attracted increasing attention as a sustainable biomaterial due to its unique molecular structure and versatile physicochemical properties. Over the past decade, an expanding number of studies have explored SG in biomedical, pharmaceutical, and materials-science contexts; however, a comprehensive understanding linking its biosynthetic mechanisms, structural features, chemical modifications, and functional performances has not yet been systematically summarized. This review therefore aims to bridge this gap by providing an integrated overview of recent advances in SG research from biosynthesis and molecular design to emerging multifunctional applications, while highlighting the structure, property, and function correlations that underpin its material performance. This review summarizes recent advances in SG biosynthesis, structural characterization, chemical modification, and multifunctional applications. Progress in oxidation, succinylation, and phenolic grafting has yielded derivatives with remarkably enhanced rheological stability, antioxidant capacity, antibacterial activity, and multi-stimuli responsiveness. These developments have supported the creation of biodegradable and bioactive smart films possessing superior barrier, mechanical, and optical properties, thereby extending their potential use in bio-medical and biotechnological applications such as food packaging and wound dressings. In parallel, SG-based hydrogels exhibit self-healing, adhesive, and injectable characteristics with tunable multi-stimuli responsiveness, offering innovative platforms for con-trolled drug delivery and tissue engineering. Despite these advances, industrial translation remains hindered by challenges including the need for scalable fermentation, reproducible quality control, and standardized modification protocols to ensure batch-to-batch consistency. Overall, the structural tunability and multifunctionality of SG highlight its promise as a next-generation platform for polysaccharide-based biomaterials. Full article

For humanoid robots to interact naturally with humans and seamlessly integrate into daily life, natural language serves as an essential communication medium. While recent advances in imitation learning have enabled robots to acquire complex motions through expert demonstration, traditional approaches often rely on rigid task specifications or single-modal inputs, limiting their ability to interpret high-level semantic instructions (e.g., natural language commands) or dynamically switch between actions. Directly translating natural language into executable control commands remains a significant challenge. To address this, we propose ToggleMimic, an end-to-end imitation learning framework that generates robotic motions from textual instructions, enabling language-driven multi-task control. In contrast to end-to-end methods that struggle with generalization or single-action models that lack flexibility, our ToggleMimic framework uniquely combines the following: (1) a two-stage policy distillation that efficiently bridges the sim-to-real gap, (2) a lightweight cross-attention mechanism for interpretable text-to-action mapping, and (3) a gating network that enhances robustness to linguistic variations. Extensive simulation and real-world experiments demonstrate the framework’s effectiveness, generalization capability, and robust text-guided control performance. This work establishes an efficient, interpretable, and scalable learning paradigm for cross-modal semantic-driven autonomous robot control. Full article

Acute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today’s data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models—particularly ensemble algorithms and biomarker-integrated approaches—have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI’s growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics—enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia—showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling—potentially through the development of digital twins—will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA. Full article