Search Results (3,841)

Magnetoliposomes are hybrid nanostructures that integrate superparamagnetic ultrasmall carbon-coated ferrite nanodots (MNCDs) within liposomes (Lipo) composed of egg yolk-derived phospholipids and stabilized with an environmentally benign potato peel extract (PPE), enabling enhanced magnetic resonance imaging (MRI) and optical imaging. The hydrothermally synthesized MNCDs were entrapped in liposomes prepared by thin-film hydration, and physicochemical properties were established at each stage of engineering. These magnetoresponsive vesicles (MNCDs+Lipo@PPE) serve as a triple-mode medical imaging contrast for T1 & T2-weighted MRI, while simultaneously enabling optical tracking of liposome degradation under an external magnetic field. They exhibited long-term enhanced fluorescence intensity and colloidal stability over 30 days, with hydrodynamic diameters ranging from 190 to 331 nm and an improved surface charge following PPE coating. In vitro cytotoxicity assays (MTT and Live/Dead staining) demonstrated over 87% cell viability for MNCDs+Lipo@PPE up to 2.7 mM concentration in A549 cells, indicating considerable toxicity. This multimodality engineering facilitates precise image-guided anticancer doxorubicin delivery and magnetic-responsive controlled release. The theoretical model shows that the release profile follows the Korsmeyer-Peppas profile. The externally applied magnetic field enhances the release by 1.4-fold. To demonstrate the anticancer efficiency in vitro with minimum off-target cytotoxicity, MTT and live/dead cell assay were performed against A549 cells. The reported study is a validated demonstration of magnetic-responsive nanocarrier systems for anticancer therapy and multimodal MRI and optical imaging-based diagnosis. Full article

This study addresses deviations between observed nonstationary tides and physical-model results caused by multiple indirectly observed factors. The S_TIDE framework performs well in estuaries by introducing time-varying nonequilibrium physical factors to represent tidal characteristics and is applicable to diverse nonstationary regimes. However, S_TIDE remains limited: even combined with the Enhanced Harmonic Analysis (EHA) scheme, which improves extraction of characteristic tidal levels, it still fails to capture differences between observed and harmonically analyzed tides driven by regional nonlinear processes, so tidal errors remain large. We develop a hybrid scheme coupling S_TIDE with an ensemble-learning model. The physically computed tide provides a constrained backbone; the observed–physical difference is formulated as a residual series, and the PELM ensemble learns regional tidal characteristics encoded in these residuals to provide targeted corrections. Using research-grade records from 528 tide-gauge stations of the University of Hawaii Sea Level Center (UHSLC), PELM increases tidal-simulation accuracy, yielding an average error-reduction of 45.63% across all stations; 66.10% of sites improve by more than 40%, and stations with large initial physical-tide errors improve on average by more than 65%. These results demonstrate that the Physics-Constrained Ensemble-Learning Method (PELM) scheme is highly effective and generalizable for extracting characteristic tidal levels and reducing tidal-simulation errors at the global scale. Full article

Extracting road information from remote sensing imagery is essential for numerous applications. Traditional methods have heavily depended on manual feature extraction, but recent progress has shifted the focus towards deep learning-based approaches. However, many existing methods primarily focus on the continuity of local roads, neglecting challenges associated with road shape extraction. These challenges include interference from shadows, buildings, vehicles, and complex backgrounds, often resulting in discontinuous results. To tackle these issues, this paper introduces a novel U-shaped deep learning network that integrates a Hybrid Convolution Module and Block Loop Attention to achieve robust road extraction, which called HLU-Net. The Hybrid Convolution Module, with its two parallel branches featuring convolution kernels of varying scales, not only broadens the receptive field to capture comprehensive contextual information but also concentrates on local details for accurate feature extraction. Furthermore, each branch employs unique convolution techniques to reduce computational redundancy and enhance operational efficiency. In addition, the proposed Block Loop Attention mechanism effectively models the relationships between elements within feature vectors, capturing intrinsic dependencies, and thus facilitating more efficient computations without merely reducing complexity. Experiments conducted on the DeepGlobe and CITY-OSM datasets demonstrate that our approach generally surpasses several state-of-the-art road extraction methods. Full article

Industrial effluents often contain azeotropic mixtures that are difficult to separate by conventional distillation. An illustrative case is the methanol/methyl acetate/ethyl acetate (MA/ME/EA) mixture. To address these challenges, this work studies the conceptual design and optimization of the reactive distillation-based hybrid processes for separating the MA/ME/EA mixture with an EA-rich feed composition (0.25/0.20/0.55 mol fraction). An improved triple-column extractive–reactive distillation with a side-draw product (TCERD-SP) and its heat-integrated variant (TCERD-SP-HI) have been developed. In the TCERD-SP process, EA is strategically withdrawn as a side product, reconfiguring the extractive column into integrated pre-separation and entrainer-recovery sections, thereby reducing entrainer and energy demands. A four-step process design methodology is applied, including thermodynamics analysis, conceptual design, rigorous optimization via Aspen Plus integrated with the genetic algorithm to minimize total annual cost (TAC), and comparative evaluation of economic and environmental performance. The results show that the basic double-column pre-separation-reactive distillation (DCPSRD) process, optimal for a previous feed composition, exhibits unsatisfactory TAC performance for this EA-rich feed composition. Among the configurations studied, the TCERD-SP process exhibits superior performance, saving TAC by 8.4% and 14.4% compared to the TCERD and DCPSRD processes, respectively. In addition, based on the advantage of convenient heat integration between the side reboiler and the reactive distillation column condenser, the heat-integrated TCERD-SP-HI process achieves a further 10.7% TAC reduction. Thus, for this EA-rich feed examined in this work, the TCERD-SP and TCERD-SP-HI processes are demonstrated as effective solutions for recovering these valuable chemicals. Full article

This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to map citation networks, keyword co-occurrence patterns, and thematic evolution. The results identify nine major clusters spanning machine learning, natural language processing, semantic modeling, expert systems, knowledge-based decision support, and emerging hybrid techniques. Collectively, these findings indicate a field-wide shift from manual codification toward scalable, context-aware, and semantically enriched approaches that better support tacit knowing in organizational practice. Building on these insights, the paper introduces the AI–Tacit Knowledge Co-Evolution Model, which situates AI as an epistemic partner—augmenting human interpretive processes rather than merely codifying experience. The framework integrates Polanyi’s concept of tacit knowing, Nonaka’s SECI model, and sociotechnical learning theories to elucidate how human–AI interaction transforms the dynamics of knowledge creation. The review consolidates fragmented research streams and provides a conceptual foundation for guiding future methodological development in AI-enabled tacit knowledge management. Full article

The large scale and shape variation in breast lesions make their segmentation extremely challenging. A breast ultrasound image segmentation model integrating Mamba-CNN and feature interaction is proposed for breast ultrasound images with a large amount of speckle noise and multiple artifacts. The model first uses the visual state space model (VSS) as an encoder for feature extraction to better capture its long-range dependencies. Second, a hybrid attention enhancement mechanism (HAEM) is designed at the bottleneck between the encoder and the decoder to provide fine-grained control of the feature map in both the channel and spatial dimensions, so that the network captures key features and regions more comprehensively. The decoder uses transposed convolution to upsample the feature map, gradually increasing the resolution and recovering its spatial information. Finally, the cross-fusion module (CFM) is constructed to simultaneously focus on the spatial information of the shallow feature map as well as the deep semantic information, which effectively reduces the interference of noise and artifacts. Experiments are carried out on BUSI and UDIAT datasets, and the Dice similarity coefficient and HD₉₅ indexes reach 76.04% and 20.28 mm, respectively, which show that the algorithm can effectively solve the problems of noise and artifacts in ultrasound image segmentation, and the segmentation performance is improved compared with the existing algorithms. Full article

The physical layer security of Internet of Things (IoT) networks has become increasingly important but also introduces major security vulnerabilities due to the open and shared nature of wireless channels. Therefore, authentication and authorization remain critical challenges. To address these issues, this paper proposes a lightweight hybrid authentication framework that integrates Extended Kalman Filter (EKF)-based signal refinement with machine learning (ML) classification to strengthen device trust verification at the physical layer. The framework operates across device, edge, and cloud tiers, utilizing real-time received signal strength indicator (RSSI), link quality indicator (LQI), temperature, and battery level to generate unique device fingerprints. The EKF minimizes environmental noise and extracts stable signal characteristics, while the XGBoost classifier provides adaptive and efficient authentication. Experimental results show that the proposed hybrid model achieves 99.56% accuracy, a 99.71% F1-score, and a very low false acceptance rate. These findings confirm that the EKF–ML integration enhances signal stability and resistance to spoofing, offering a secure and scalable authentication solution for IoT networks. Full article

The high-precision prediction of near-space atmospheric temperature holds significant importance for aerospace, national defense security, and climate change research. To address the deficiencies of extracting features in conventional convolutional neural networks, this paper designs a ConvLSTM hybrid model that combines the spatiotemporal feature extraction capability of 3D convolution with a residual attention mechanism, effectively capturing the dynamic evolution patterns of the near-space temperature field. The comparative analysis with various models, including GRU, shows that the proposed model demonstrates superior performance, achieving an RMSE of 2.433 K, a correlation coefficient R of 0.993, and an MRE of 0.76% on the test set. Seasonal error analysis reveals that the prediction stability is better in winter than in summer, with errors in the mesosphere primarily stemming from the complexity of atmospheric processes and limitations in data resolution. Compared to traditional CNNs and single time-series models, the proposed method significantly enhances prediction accuracy, providing a new technical approach for near-space environmental modeling. Full article

Speech is one of the earliest-affected modalities in Parkinson’s disease (PD). For more reliable PD evaluation, speech-based telediagnosis studies often use multiple samples from the same subject to capture variability in speech recordings. However, many existing studies split samples—rather than subjects—between training and testing, creating a biased experimental setup that allows data (samples) from the same subject to appear in both sets. This raises concerns for reliable PD evaluation due to data leakage, which results in over-optimistic performance (often close to 100%). In addition, detecting subtle vocal impairments from speech recordings using multiple feature extraction techniques often increases data dimensionality, although only some features are discriminative while others are redundant or non-informative. To address this and build a reliable speech-based PD telediagnosis system, the key contributions of this work are two-fold: (1) a uniform (fair) experimental setup employing subject-wise symmetric (stratified) splitting in 5-fold cross-validation to ensure better generalization in PD prediction, and (2) a novel hybrid data complexity-aware (HDC) feature selection method that improves class separability. This work further contributes to the research community by releasing a publicly accessible five-fold benchmark version of the Parkinson’s speech dataset for consistent and reproducible evaluation. The proposed HDC method analyzes multiple aspects of class separability to select discriminative features, resulting in reduced data complexity in the feature space. In particular, it uses data complexity measures (F4, F1, F3) to assess minimal feature overlap and ReliefF to evaluate the separation of borderline points. Experimental results show that the top-50 discriminative features selected by the proposed HDC outperform existing feature selection algorithms on five out of six classifiers, achieving the highest performance with 0.86 accuracy, 0.70 G-mean, 0.91 F1-score, and 0.58 MCC using an SVM (RBF) classifier. Full article

Impressed current cathodic protection (ICCP) systems can experience acidification, which deteriorates the interface between the anode and the anode backfill mortar. This deterioration may necessitate premature intervention to remove and reinstate the backfill and, in some cases, replace the anode. If left unaddressed, acidification ultimately leads to debonding between the anode and the backfill mortar, resulting in the failure of the ICCP system. This paper presents the development of a specialised acid-resistant hybrid mortar designed for ICCP systems used to protect reinforced concrete bridges in marine environments. It also investigates the effects of acidification on the physical and mechanical properties of the proposed anode backfill mortars. Additionally, the study characterises acidification products from both field-extracted ICCP systems and laboratory-based accelerated testing, providing deeper insights into the acidification mechanisms. Novel mortar samples were subjected to varying concentrations of hydrochloric acid (HCl) under accelerated testing conditions. The incorporation of supplementary cementitious materials (SCMs), calcium sulfoaluminate (CSA) cement and zeolite significantly enhanced the strength and durability of the backfill mortars in acidic environments, while maintaining compliance with the electrical resistivity requirements (20–100 kΩ·cm) for ICCP systems. The lowest compressive strength loss observed in the developed hybrid mortar was 54% after 28 days of immersion in 5% HCl and 83% in 15% HCl. Microstructural analyses revealed that gypsum formation and chloride–sulphate competitive binding interactions are key mechanisms contributing to the improved acid resistance, particularly in CSA cement-containing formulations. Full article

Wireless Sensor Networks (WSNs) are increasingly being used in mission-critical infrastructures. In such applications, they are evaluated on the risk of cyber intrusions that can target the already constrained resources. Traditionally, Intrusion Detection Systems (IDS) in WSNs have been based on machine learning techniques; however, these models fail to capture the nonlinear, temporal, and topological dependencies across the network nodes. As a result, they often suffer degradation in detection accuracy and exhibit poor adaptability against evolving threats. To overcome these limitations, this study introduces a hybrid deep learning-based IDS that integrates multi-scale convolutional feature extraction, dual-stage attention fusion, and graph convolutional reasoning. Moreover, bidirectional long short-term memory components are embedded into the unified framework. Through this combination, the proposed architecture effectively captures the hierarchical spatial–temporal correlations in the traffic patterns, thereby enabling precise discrimination between normal and attack behaviors across several intrusion classes. The model has been evaluated on a publicly available benchmarking dataset, and it has been found to attain higher classification capability in multiclass scenarios. Furthermore, the model outperforms conventional IDS-focused approaches. In addition, the proposed design aims to retain suitable computational efficiency, making it appropriate for edge and distributed deployments. Consequently, this makes it an effective solution for next-generation WSN cybersecurity. Overall, the findings emphasize that combining topology-aware learning with multi-branch attention mechanisms offers a balanced trade-off between interpretability, accuracy, and deployment efficiency for resource-constrained WSN environments. Full article

Short-term load forecasting is essential for optimizing power system operations and supporting renewable energy integration. However, accurately capturing the complex nonlinear features in load data remains challenging. To improve forecasting accuracy, this paper proposes a hybrid CNN-BiGRU-Attention model optimized by the Beluga Whale Optimization (BWO) algorithm. The proposed method integrates deep learning with metaheuristic optimization in four steps: First, a Convolutional Neural Network (CNN) is used to extract spatial features from input data, including historical load and weather variables. Second, a Bidirectional Gated Recurrent Unit (BiGRU) network is employed to learn temporal dependencies from both forward and backward directions. Third, an Attention mechanism is introduced to focus on key features and reduce the influence of redundant information. Finally, the BWO algorithm is applied to automatically optimize the model’s hyperparameters, avoiding the problem of falling into local optima. Comparative experiments against five baseline models (BP, GRU, BiGRU, BiGRU-Attention, and CNN-BiGRU-Attention) demonstrate the effectiveness of the proposed model. The experimental results indicate that the optimized model achieves superior predictive performance with significantly reduced error rates in terms of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), along with a higher Coefficient of Determination ($R^2$) compared to the benchmarks, confirming its high accuracy and reliability for power load forecasting. Full article

The Transformer architecture has achieved remarkable success across numerous computer vision tasks due to its superior capability for global dependency modeling. However, the high computational complexity and hardware-unfriendly operations such as Layer Normalization (LN), Softmax, and GELU severely hinder its deployment on resource-constrained platforms. To address these challenges, this paper proposes a hardware-friendly CNN-Transformer hybrid pyramid architecture that effectively balances accuracy, efficiency, and deployability. The proposed model integrates convolutional bottlenecks with Transformer encoders to capture both local and global contextual information while maintaining low computational cost. A pyramid feature extraction structure is further introduced to enhance multi-scale semantic representation. To improve hardware efficiency, we redesign key nonlinear components by introducing hardware-friendly activation, normalization, and Softmax approximations. Specifically, GELU and LN are replaced by ReLU and Batch Normalization (BN), and a simplified logarithmic-exponential formulation termed Softmax2 is proposed, which eliminates complex exponential and division operations, significantly reducing hardware implementation cost. Extensive experiments demonstrate the effectiveness of the proposed framework. The experimental results validate that the proposed architecture offers a promising and practical solution for real-time and embedded vision applications. Full article

Background: Pancreatic and periampullary cancers are common tumors of the digestive tract. As a radical surgical approach, laparoscopic pancreaticoduodenectomy requires crucial preoperative assessment of its surgical difficulty. Materials and methods: A retrospective cohort of 150 patients who underwent LPD between June 2019 and June 2023 was enrolled. The criteria for defining the difficult group were identified as unplanned conversion to open procedure, intraoperative blood loss, and operative time. Participants were randomly allocated to a training set (n = 105) or a testing set (n = 45) in a 7:3 ratio. Hand-crafted radiomics (HCR) features and deep learning-derived radiomics (DLR) features were extracted from portal venous phase CT images, focusing on gross tumor volume and gross peri-tumor volume. A hybrid prediction model was developed using a support vector machine algorithm, with performance evaluated through receiver operating characteristic analysis, calibration curves, and decision curve analysis (DCA). Results: The combined model demonstrated significantly superior discriminative ability, achieving an area under the curve (AUC) of 0.942 (95% CI: 0.893–0.992) in the training set and 0.848 (95% CI: 0.738–0.958) in the testing set. This performance exceeded both the standalone HCR model (testing AUC = 0.754) and the DLR model (testing AUC = 0.816). DCA further confirmed the clinical utility of the combined model, showing the highest net benefit across threshold probabilities exceeding 20%. Conclusions: The novel integrated model combining hand-crafted and deep learning-derived radiomics features enables effective prediction of surgical difficulty in laparoscopic pancreaticoduodenectomy. Full article

Addressing the limitations of traditional acoustic turbidity inversion models in complex marine environments—specifically their reliance on empirical parameters and lack of vertical resolution—this study presents a novel CNN-ResNet-RF hybrid model based on the simultaneous ADCP and turbidity observations in the Chengshantou sea area. Unlike conventional approaches, the proposed framework integrates deep spatio-temporal features automatically extracted by a ResNet-enhanced CNN, utilizing a Random Forest (RF) regressor for final prediction, thereby avoiding the limitations of artificial feature engineering. To ensure rigorous evaluation and mitigate stochastic bias, the model was validated using a 5-fold cross-validation strategy with dynamic Z-score normalization. Experimental results demonstrate that the proposed model significantly outperforms benchmark methods (CNN, RF, and CNN-RF), achieving an average R² of 0.782, an MAE of 4.454, and a MAPE of 15.42% on the test sets. This study confirms that the hybrid framework successfully combines the feature extraction power of deep learning with the robustness of ensemble learning, providing a robust and high-precision tool for the vertical structural analysis of ocean turbidity. Full article