Search Results (781)

Porous materials are characterized by the pore surface area (S) and volume (V) accessible to a confined fluid. For mesoporous materials NMR measurements of diffusion are used to assess the S/V ratio, because at short times, only the diffusivity of molecules in the adsorbed layer is affected by confinement and the fractional population of these molecules is proportional to the S/V ratio. For materials with sub-nanometer pores, this might not be true, as the adsorbed layer can encompass the entire pore volume. Here, using molecular simulations, we explore the role played by S and S/V in determining the dynamical behavior of two carbon-bearing fluids—CO₂ and ethane—confined in sub-nanometer pores of silica. S and V in a silicalite model representing a sub-nanometer porous material are varied by selectively blocking a part of the pore network by immobile methane molecules. Three classes of adsorbents were thus obtained with either all of the straight (labeled ‘S-major’) or zigzag channels (‘Z-major’) remaining open or a mix of a fraction of both types of channel blocked, resulting in half of the total pore volume being blocked (‘Half’). While the adsorption layers from opposite surfaces overlap, encompassing the entire pore volume for all pores except the intersections, the diffusion coefficient is still found to be reduced at high S/V, especially for CO₂, albeit not so strongly as would be expected in the case of wider pores. This is because of the presence of channel intersections that provide a wider pore space with non-overlapping adsorption layers. Full article

Wind power output sequences exhibit strong randomness and intermittency characteristics; traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise and predictive accuracy is still notably poor at the peaks where the power curve undergoes abrupt changes. To address the poor fitting at peaks, a short-term wind power forecasting method based on the improved Informer model is proposed. First, the temporal convolutional network (TCN) is introduced to enhance the model’s ability to capture regional segment features along the temporal dimension, enhancing the model’s receptive field to address wind power fluctuation under varying environmental conditions. Next, a discrete cosine transform (DCT) is employed for adaptive modeling of frequency dependencies between channels, converting the time series data into frequency domain representations to extract its frequency features. These frequency domain features are then weighted using a channel attention mechanism to improve the model’s ability to capture peak features and resist noise interference. Finally, the Informer generative decoder is used to output the power prediction results, this enables the model to simultaneously leverage neighboring temporal segment features and long-range inter-temporal dependencies for future wind-power prediction, thereby substantially improving the fitting accuracy at power-curve peaks. Experimental results validate the effectiveness and practicality of the proposed model; compared with other models, the proposed approach reduces MAE by 9.14–42.31% and RMSE by 12.57–47.59%. Full article

This study addresses the challenges of manual measurement bias and low robustness in detecting small, occluded targets in complex marine environments during real-time onboard sorting of Portunus trituberculatus. We propose YOLO-DFAM, an enhanced YOLOv11n-based model that replaces the global average pooling in the Focal Modulation module with a spatial–channel dual-attention mechanism and incorporates the ASF-YOLO cross-scale fusion strategy to improve feature representation across varying target sizes. These enhancements significantly boost detection, achieving an mAP@50 of 98.0% and precision of 94.6%, outperforming RetinaNet-CSL and Rotated Faster R-CNN by up to 6.3% while maintaining real-time inference at 180.3 FPS with only 7.2 GFLOPs. Unlike prior static-scene approaches, our unified framework integrates attention-guided detection, scale-adaptive tracking, and lightweight weight estimation for dynamic marine conditions. A ByteTrack-based tracking module with dynamic scale calibration, EMA filtering, and optical flow compensation ensures stable multi-frame tracking. Additionally, a region-specific allometric weight estimation model (R² = 0.9856) reduces dimensional errors by 85.7% and maintains prediction errors below 4.7% using only 12 spline-interpolated calibration sets. YOLO-DFAM provides an accurate, efficient solution for intelligent onboard fishery monitoring. Full article

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance. Full article

During the last decade, the blockchain landscape has rapidly evolved, fostering the development of decentralized applications (DApps) that utilize cross-chain interactions. Although existing technologies have enhanced transaction processing and introduced interoperability solutions, scalability challenges persist, undermining their effectiveness. In particular, traditional cross-chain DApp interaction protocols experience performance bottlenecks due to their dependence on on-chain validation mechanisms, resulting in increased latency and computational costs. To address these issues, this paper presents the ScaL2Chain protocol, which is designed to facilitate efficient and secure cross-chain transactions for DApps. ScaL2Chain leverages off-chain technologies, such as payment channels, to enable participants to conduct transactions with a minimal on-chain footprint. By implementing an innovative state verification mechanism, ScaL2Chain guarantees high performance, confidentiality, and transaction integrity. Our empirical evaluations indicate that ScaL2Chain significantly outperforms existing solutions in terms of transaction throughput. Specifically, compared to baseline systems, ScaL2Chain achieves a 7.9-times to 8.4-times improvement in permissionless environments and a 1.9-times to 35.8-times improvement in permissioned environments under workloads with 4-64 DApps and varying cross-chain transaction ratios (0–100%). Full article

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

The growing demand for reliable and secure vehicle-to-vehicle (V2V) communication in next-generation intelligent transportation systems has accelerated the adoption of reconfigurable intelligent surfaces (RIS) as a means of enhancing link quality, spectral efficiency, and physical layer security. In this paper, we investigate the problem of secrecy rate maximization in a cooperative dual-RIS-aided V2V communication network, where two cascaded RISs are deployed to collaboratively assist with secure data transmission between mobile vehicular nodes in the presence of eavesdroppers. To address the inherent complexity of time-varying wireless channels, we propose a novel evolutionary transformer-gated recurrent unit (Evo-Transformer-GRU) framework that jointly learns temporal channel patterns and optimizes the RIS reflection coefficients, beam-forming vectors, and cooperative communication strategies. Our model integrates the sequence modeling strength of GRUs with the global attention mechanism of transformer encoders, enabling the efficient representation of time-series channel behavior and long-range dependencies. To further enhance convergence and secrecy performance, we incorporate an improved gray wolf optimizer (IGWO) to adaptively regulate the model’s hyper-parameters and fine-tune the RIS phase shifts, resulting in a more stable and optimized learning process. Extensive simulations demonstrate the superiority of the proposed framework compared to existing baselines, such as transformer, bidirectional encoder representations from transformers (BERT), deep reinforcement learning (DRL), long short-term memory (LSTM), and GRU models. Specifically, our method achieves an up to 32.6% improvement in average secrecy rate and a 28.4% lower convergence time under varying channel conditions and eavesdropper locations. In addition to secrecy rate improvements, the proposed model achieved a root mean square error (RMSE) of 0.05, coefficient of determination ($R^2$) score of 0.96, and mean absolute percentage error (MAPE) of just 0.73%, outperforming all baseline methods in prediction accuracy and robustness. Furthermore, Evo-Transformer-GRU demonstrated rapid convergence within 100 epochs, the lowest variance across multiple runs. Full article

Understanding the rheological properties of fresh cement slurries is essential to maintain optimal pumpability, achieve dependable zonal isolation, and preserve long-term well integrity in oil and gas cementing operations and the 3D printing cement and concrete industry. However, accurately and efficiently modeling the rheological behavior of cement slurries remains challenging due to the complex fluid properties of fresh cement slurries, which exhibit non-Newtonian and thixotropic behavior. Traditional numerical solvers typically require mesh generation and intensive computation, making them less practical for data-scarce, high-dimensional problems. In this study, a physics-informed neural network (PINN)-based framework is developed to solve the governing equations of steady-state cement slurry flow in a tilted channel. The slurry is modeled as a non-Newtonian fluid with viscosity dependent on both the shear rate and particle volume fraction. The PINN-based approach incorporates physical laws into the loss function, offering mesh-free solutions with strong generalization ability. The results show that PINNs accurately capture the trend of velocity and volume fraction profiles under varying material and flow parameters. Compared to conventional solvers, the PINN solution offers a more efficient and flexible alternative for modeling complex rheological behavior in data-limited scenarios. These findings demonstrate the potential of PINNs as a robust tool for cement slurry rheological modeling, particularly in scenarios where traditional solvers are impractical. Future work will focus on enhancing model precision through hybrid learning strategies that incorporate labeled data, potentially enabling real-time predictive modeling for field applications. Full article

Nanostructured multilayer anticorrosion coatings offer an effective strategy to mitigate the poor corrosion resistance of aluminum alloys and extend their service life. In this study, four types of Ti/Cr multilayer coatings with varied modulation periods along the growth direction were deposited on 7050 aluminum alloy substrates using direct current magnetron sputtering. The cross-sectional microstructure of the coatings was characterized by scanning electron microscopy (SEM), while their mechanical and corrosion properties were systematically evaluated through nanoindentation and electrochemical measurements. The influence of modulation period distribution on the corrosion resistance of Ti/Cr multilayers was thoroughly investigated. The results show that the average thickness of the Ti/Cr multilayer coatings is 680 nm, the structure is dense, and the coarse columnar crystals are not seen. All Ti/Cr multilayer coatings significantly reduced the corrosion current density of 7050 aluminum alloy by about 10 times compared with that of the substrate, showing good protective effect. Modulation period along the coating growth direction decreases the Ti/Cr multilayer coating surface heterogeneous interface density increases, inhibits the formation of corrosion channels, hindering the penetration of corrosive media, and the other three coatings and aluminum alloy compared to its corrosion surface did not see obvious pore corrosion, showing the most excellent corrosion resistance. Full article

This study examines the effect of smooth and humped flow channels on the recovery of industrial magnetic seeds in a drum magnetic separator. The results demonstrate that under varying feeding slurry quantities and drum rotational speeds, the humped channel consistently achieves higher recovery rates compared with the smooth channel, with an improvement of up to 3%. Scanning electron microscopy and vibrating sample magnetometry analyses of the samples reveal the presence of a small amount of impurities (predominantly consisting of elements, such as Al, Si, and Ti) in the industrial magnetite magnetic particles. These impurities exhibit lower magnetization, leading to reduced capture efficiency in the conventional smooth-channel drum magnetic separator. Simulations of the magnetic field, flow field, and particle trajectory indicate that the magnetic field force at the bottom of the smooth channel is only 0.6 kg²/(m·s⁴·A²), i.e., approximately 18 times lower than that at the roller surface. The incorporation of a humped channel shifts the impure magnetic seeds from a region with low magnetic field force to a region with higher magnetic field force, significantly enhancing the capture efficiency of the impure magnetic seeds. Full article

The deep integration of smart grid and Internet of Things technologies has made high-speed power line carrier communication a key communication technology in energy management, industrial monitoring, and smart home applications, owing to its advantages of requiring no additional wiring and offering wide coverage. However, the inherent characteristics of power line channels, such as strong noise, multipath fading, and time-varying properties, pose challenges to traditional encryption algorithms, including low key distribution efficiency and weak anti-interference capabilities. These issues become particularly pronounced in high-speed transmission scenarios, where the conflict between data security and communication reliability is more acute. To address this problem, a secure transmission method for high-speed power line carrier communication based on generalized two-dimensional polynomial chaotic mapping is proposed. A high-speed power line carrier communication network is established using a power line carrier routing algorithm based on the minimal connected dominating set. The autoregressive moving average model is employed to determine the degree of transmission fluctuation deviation in the high-speed power line carrier communication network. Leveraging the complex dynamic behavior and anti-decoding capability of generalized two-dimensional polynomial chaotic mapping, combined with the deviation, the communication key is generated. This process yields encrypted high-speed power line carrier communication ciphertext that can resist power line noise interference and signal attenuation, thereby enhancing communication confidentiality and stability. By applying reference modulation differential chaotic shift keying and integrating the ciphertext of high-speed power line carrier communication, a secure transmission scheme is designed to achieve secure transmission in high-speed power line carrier communication. The experimental results demonstrate that this method can effectively establish a high-speed power line carrier communication network and encrypt information. The maximum error rate obtained by this method is 0.051, and the minimum error rate is 0.010, confirming its ability to ensure secure transmission in high-speed power line carrier communication while improving communication confidentiality. Full article

Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. A 128-channel LiDAR sensor captures signal intensity images, which are processed by a ResNet-18 convolutional neural network to classify floor types as wood, smooth, or rough. Simultaneously, pitch angles from an onboard IMU detect terrain inclination. These inputs are transformed into fuzzy sets and evaluated using a Mamdani-type fuzzy inference system. The controller adjusts brush height, brush speed, and robot velocity through 81 rules derived from 48 structured cleaning experiments across varying terrain and slopes. Validation was conducted in low-light (night-time) conditions, leveraging LiDAR’s lighting-invariant capabilities. Field trials confirm that the robot responds effectively to environmental conditions, such as reducing speed on slopes or increasing brush pressure on rough surfaces. The integration of deep learning and fuzzy control enables safe, energy-efficient, and adaptive cleaning in complex outdoor environments. This work demonstrates the feasibility and real-world applicability for combining perception and inference-based control in terrain-adaptive robotic systems. Full article

Under complex operating conditions, vibration signals from rotating machinery often exhibit non-stationary characteristics with non-proportional and closely spaced instantaneous frequency (IF) components. Traditional time–frequency analysis (TFA) methods struggle to accurately extract such features due to energy leakage and component mixing. In response to these issues, an enhanced time–frequency analysis approach, termed Local Entropy Selection Scaling–Reassigning Chirplet Transform (LESSRCT), has been developed to improve the representation accuracy for complex non-stationary signals. This approach constructs multi-channel time–frequency representations (TFRs) by introducing multiple scales of chirp rates (CRs) and utilizes a Rényi entropy-based criterion to adaptively select multiple optimal CRs at the same time center, enabling accurate characterization of multiple fundamental components. In addition, a frequency reassignment mechanism is incorporated to enhance energy concentration and suppress spectral diffusion. Extensive validation was conducted on a representative synthetic signal and three categories of real-world data—bat echolocation, inner race bearing faults, and wind turbine gearbox vibrations. In each case, the proposed LESSRCT method was compared against SBCT, GLCT, CWT, SET, EMCT, and STFT. On the synthetic signal, LESSRCT achieved the lowest Rényi entropy of 13.53, which was 19.5% lower than that of SET (16.87) and 35% lower than GLCT (18.36). In the bat signal analysis, LESSRCT reached an entropy of 11.53, substantially outperforming CWT (19.91) and SBCT (15.64). For bearing fault diagnosis signals, LESSRCT consistently achieved lower entropy across varying SNR levels compared to all baseline methods, demonstrating strong noise resilience and robustness. The final case on wind turbine signals demonstrated its robustness and computational efficiency, with a runtime of 1.31 s and excellent resolution. These results confirm that LESSRCT delivers robust, high-resolution TFRs with strong noise resilience and broad applicability. It holds strong potential for precise fault detection and condition monitoring in domains such as aerospace and renewable energy systems. Full article

Anthracyclines have been clinically well established in cancer chemotherapy for decades. The main limitations of this drug class are the development of resistance and severe side effects. In the present investigation, we analyzed 30 anthracyclines in a panel of 59 cell lines of the National Cancer Institute, USA. The log₁₀IC₅₀ values varied from −10.49 M (3′-deamino-3′-(4″-(3″-cyano)morpholinyl)-doxorubicin, 1) to −4.93 M (N,N-dibenzyldaunorubicin hydrochloride, 30). Multidrug-resistant NCI-ADR-Res ovarian cancer cells revealed a high degree of resistance to established anthracyclines (between 18-fold to idarubicin (4) and 166-fold to doxorubicin (13) compared to parental, drug-sensitive OVCAR8 cells). The resistant cells displayed only low degrees of resistance (1- to 5-fold) to four other anthracyclines (7, 18, 28, 30) and were even hypersensitive (collaterally sensitive) to two compounds (1, 26). Live cell time-lapse microscopy proved the cross-resistance of the three chosen anthracyclines (4, 7, 9) on sensitive CCRF/CEM and multidrug-resistant CEM/ADR5000 cells. Structure–activity relationships showed that the presence of tertiary amino functions is helpful in avoiding resistance, while primary amines rather increased resistance development. An α-aminonitrile function as in compound 1 was favorable. Investigating the mRNA expression of 49 ATP-binding cassette (ABC) transporter genes showed that ABCB1/MDR1 encoding P-glycoprotein was the most important one for acquired and inherent resistance to anthracyclines. Molecular docking demonstrated that all anthracyclines bound to the same binding domain at the inner efflux channel side of P-glycoprotein with high binding affinities. Kaplan–Meier statistics of RNA sequencing data of more than 8000 tumor biopsies of TCGA database revealed that out of 23 tumor entities high ABCB1 expression was significantly correlated with worse survival times for acute myeloid leukemia, multiple myeloma, and hepatocellular carcinoma patients. This indicates that ABCB1 may serve as a prognostic marker in anthracycline-based chemotherapy regimens in these tumor types and a target for the development of novel anthracycline derivatives. Full article

Low-latency and robust beamforming are vital for sustaining signal quality and spectral efficiency in emerging high-mobility 5G and future 6G wireless networks. Conventional beam management approaches, which rely on periodic Channel State Information feedback and static codebooks, as outlined in 3GPP standards, are insufficient in rapidly varying propagation environments. In this work, we propose a Dominance-Enforced Adaptive Clustered Sliding Window Regression (DE-ACSW-R) framework for predictive beamforming in O-RAN Split 7-2x architectures. DE-ACSW-R leverages a sliding window of recent angle of arrival (AoA) estimates, applying in-window change-point detection to segment user trajectories and performing both Linear Regression (LR) and curvature-adaptive Support Vector Regression (SVR) for short-term and non-linear prediction. A confidence-weighted fusion mechanism adaptively blends LR and SVR outputs, incorporating robust outlier detection and a dominance-enforced selection regime to address strong disagreements. The Open Radio Unit (O-RU) autonomously triggers localised MUSIC scans when prediction confidence degrades, minimising unnecessary full-spectrum searches and saving delay. Simulation results demonstrate that the proposed DE-ACSW-R approach significantly enhances AoA tracking accuracy, beamforming gain, and adaptability under realistic high-mobility conditions, surpassing conventional LR/SVR baselines. This AI-native modular pipeline aligns with O-RAN architectural principles, enabling scalable and real-time beam management for next-generation wireless deployments. Full article