Search Results (72)

M-ary frequency-shift keying (MFSK) is employed for several applications, including Internet-of-Things (IoT) and sensor-based communications. Previous studies have demonstrated that coherent detection of well-designed nonorthogonal MFSK signals outperforms orthogonal MFSK. This paper optimizes the error performance of nonorthogonal MFSK signals when the receiver uses a simple coherent detector on a symbol-by-symbol basis. First, we derive the theoretical conditions on the frequency separations to produce M symbol waveforms with negative crosscorrelation. Second, assuming equispaced frequencies, we analytically determine the optimum modulation index that maximizes the minimum distance among the symbol waveforms. Third, assuming non-equispaced frequencies, we optimize both nonorthogonal 4FSK and 8FSK signal sets. The optimized signal waveforms reduce the symbol error probability with respect to the current-best MFSK schemes existing in the literature, at the price of a bandwidth increase. For additive white Gaussian noise (AWGN) channels, an accurate expression for the symbol error probability of nonorthogonal 4FSK is also proposed. Full article

Community detection is a key task in the analysis of complex networks, particularly in social network analysis, where uncovering cohesive and well-separated groups is essential for understanding structural organization and interaction patterns. Many existing centroid-based community detection methods rely primarily on node degree for centroid selection, which often leads to centroid crowding and insufficient spatial separation among communities. To address these limitations, this paper proposes Degree–Distance Centroid–Community Detection with Influential Nodes (DDC-CDIN), a distance-driven and influence-aware community detection framework. In the proposed approach, nodes are first ranked according to an Enhanced Degree Centrality measure that incorporates degree information, neighbourhood structure, and local clustering characteristics to identify structurally influential nodes. Centroids are then selected iteratively from the top-ranked influential nodes by maximizing shortest-path distances, ensuring that the chosen centroids are both representative and well dispersed within the network. Once the centroids are determined, the remaining nodes are assigned to communities based on the minimum geodesic distance, yielding compact, clearly separated clusters. Extensive experiments across multiple real-world networks show that DDC-CDIN achieves competitive performance compared to traditional centroid-based and modularity-driven methods in terms of modularity, community cohesion, and boundary clarity. The results indicate that jointly incorporating influence-aware node ranking with distance-based centroid dispersion effectively mitigates centroid crowding and enhances overall community detection quality. These findings demonstrate the effectiveness and robustness of DDC-CDIN for detecting well-structured and topologically coherent communities in complex networks. Full article

Background/Objectives: Reproducible evaluation of aerosol dispersibility remains a key challenge in the development of dry powder inhalers (DPIs), where small variations in particle cohesion, morphology, or device resistance can lead to large differences in aerodynamic performance. In passive DPIs, the forces required for powder fluidization and aerosolization arise from the interaction of patient inspiratory airflow with device geometry and must overcome strong interparticle cohesive forces to enable effective lung delivery. Cascade impaction is the gold standard for determining aerodynamic particle size distribution (APSD), but its low throughput and experimental burden limit its utility for systematic formulation and device screening. Prior studies have explored laser diffraction-based particle sizing under varying dispersion energies as indirect metrics of powder dispersibility. Here, we extend this approach by introducing a mathematically rigorous, distribution-based framework that applies the first-order Wasserstein distance (Earth Mover’s Distance) to quantify relative dispersibility with respect to a material-specific maximally dispersed reference state. Methods: Mannitol, trehalose, and inulin were spray-dried under matched conditions to generate model dry powders. Particle size distributions were measured by laser diffraction (Sympatec HELOS/R) using both a RODOS dry dispersion module to define a maximally dispersed reference state and an INHALER module to generate aerosols under clinically relevant dispersion conditions spanning multiple device resistances and pressure drops. For each condition, the Wasserstein-1 distance (W₁) was computed between cumulative volume-based size distributions obtained under reference and inhaler-based dispersion. Cascade impaction was used as an orthogonal method to characterize aerodynamic performance under a representative dispersion condition. Results: W₁ captured formulation-, device-, and flow-dependent differences in dispersibility that were not readily separable by visual inspection of particle size distributions alone. Crystalline mannitol exhibited the largest and most flow-rate-dependent W₁ values, whereas amorphous trehalose and polymeric inulin showed smaller W₁ values with distinct, non-monotonic pressure responses that depended on device resistance. W₁ qualitatively aligned with cascade impaction metrics, exhibiting a positive association with mass median aerodynamic diameter and an inverse association with fine particle fraction, while also demonstrating that efficient dose emission can occur despite incomplete deagglomeration. Conclusions: This study establishes the Wasserstein distance as a physically interpretable, formulation-agnostic metric for quantifying aerosol dispersibility relative to a material-specific reference state. This framework enables systematic comparison of dispersion efficiency across devices and operating conditions using standard laser diffraction data and provides a reproducible basis for mechanistic optimization of DPI formulations and inhaler designs. Full article

Laser surface texturing (LST) structures or laser-induced periodic surface structures (LIPSS) are typically created using laser pulses with durations ranging from femtoseconds to nanoseconds. However, nanosecond pulsed lasers, as cost-effective and more productive alternatives, can also be used to generate LST structures on stainless steel (SS) surfaces, making these structures more suitable for industrial applications. In this study, pulsed laser processing is employed to create LST structures on SS (AISI 304), with varying pulse and accumulated fluences, effective pulse counts, and scan parameters, such as pulse-to-pulse distance (pitch) and hatch spacing between scanning lines. A methodology for calculating oxidation density on processed AISI 304 surfaces is presented. Oxidation density, defined as the ratio of the oxidized area to the total processed area, is determined as a function of accumulated fluence, laser power, pulse-to-pulse distance, and hatch spacing. Optical images of the surfaces are analyzed, and oxidation regions are identified using machine learning techniques. The images are converted to grayscale, and machine learning algorithms are applied to classify the images into oxidation and non-oxidation regions based on pixel intensity values. This approach identifies the optimal threshold for separating the two regions by maximizing inter-class variance. Experimental modeling using response surface methodology is applied to experimentally generated data. Optimization algorithms are then employed to determine the process parameters that maximize pulsed laser irradiation performance while minimizing surface oxidation and processing time. This paper also presents a novel method for characterizing oxidation density using image segmentation and machine learning. The results provide a comprehensive understanding of the process and offer optimized models, contributing valuable insights for practical applications. Full article

In recent years, the design priorities of modern marine propellers have shifted from maximizing efficiency to minimizing vibration-induced noise emissions and improving structural durability. However, an optimized design does not necessarily ensure optimal performance across the full operational range of a vessel. Due to operational constraints such as reduced docking times and regional speed regulations, propellers frequently operate off-design. This deviation from the design point leads to periodic turbulent boundary layer separation on the propeller blades, resulting in increased unsteady pressure fluctuations and, consequently, elevated hydroacoustic noise emissions. To mitigate these effects, bio-inspired modifications have been investigated as a means of improving flow characteristics and reducing pressure fluctuations. Tubercles, characteristic protrusions along the leading edge of humpback whale fins, have been shown to enhance lift characteristics beyond the stall angle by modifying the flow separation pattern. However, their influence on transient pressure fluctuations and the associated hydroacoustic behavior of marine propellers remains insufficiently explored. In this study, we apply the concept of tubercles to the blades of a hubless propeller, also referred to as a rim-drive propeller. We analyze the pressure fluctuations on the blades and in the wake by comparing conventional propeller blades with those featuring tubercles. The flow fields of both reference and tubercle-modified blades were simulated using the Stress Blended Eddy Simulation (SBES) turbulence model to highlight differences in the flow field. In both configurations, multiple helix-shaped vortex systems form in the propeller wake, but their decay characteristics vary, with the vortex structures collapsing at different distances from the propeller center. Additionally, Proper Orthogonal Decomposition (POD) analysis was employed to isolate and analyze the periodic, coherent flow structures in each case. Previous studies on the flow field of hubless propellers have demonstrated a direct correlation between transient pressure fluctuations in the flow field and the resulting noise emissions. It was demonstrated that the tubercle modification significantly reduces pressure fluctuations both on the propeller blades and in the wake flow. In the analyzed case, a reduction in pressure fluctuations by a factor of three to ten for the different BPF orders was observed within the wake flow. Full article

As unconventional hydrocarbon resources gain increasing importance, the risk of lost circulation during drilling operations has also grown significantly. Accurate and reliable risk diagnosis methods are essential to ensure safety and operational efficiency in complex drilling environments. This study proposes a novel lost circulation risk monitoring framework based on time-series shapelet transformation, integrated with Generative Adversarial Network (GAN)-based data augmentation and real-time model updating strategies. GANs are employed to synthesize diverse, high-quality samples, enriching the training dataset and improving the model’s ability to capture rare or latent lost circulation signals. Shapelets are then extracted from the time series using a supervised shapelet transform that searches for discriminative subsequences maximizing the separation between normal and lost-circulation samples. Each time series is subsequently represented by its minimum distances to the learned shapelets, so that critical local temporal patterns indicative of early lost circulation can be explicitly captured. To further enhance adaptability during field applications, a real-time model updating mechanism is incorporated. The system incrementally refines the classifier using newly incoming data, where high-confidence predictions are selectively added for online updating. This strategy enables the model to adjust to evolving operating conditions, improves robustness, and provides earlier and more reliable risk warnings. We implemented and evaluated Support Vector Machine (SVM), k-Nearest Neighbors (kNNs), Logistic Regression, and Artificial Neural Networks (ANNs) on the transformed datasets. Experimental results demonstrate that the proposed method improves prediction accuracy by 6.5%, measured as the accuracy gain of the SVM classifier after applying the shapelet transformation (from 84.7% to 91.2%) compared with using raw, untransformed time-series features. Among all models, SVM achieves the best performance, with an accuracy of 91.2%, recall of 90.5%, and precision of 92.3%. Moreover, the integration of real-time updating further boosts accuracy and responsiveness, confirming the effectiveness of the proposed monitoring framework in dynamic drilling environments. The proposed method offers a practical and scalable solution for intelligent lost circulation monitoring in drilling operations, providing a solid theoretical foundation and technical reference for data-driven safety systems in dynamic environments. Full article

This study investigated the compatibility of lead with distinct crystal planes of Fe with a body-centered cubic (bcc) crystal structure and Ni with a face-centered cubic (fcc) crystal structure using molecular dynamics (MD) simulation. It was found that corrosion anisotropy depends mainly on the role of different crystal planes in regulating the spatial distribution of liquid lead. The essence of this regulation can be attributed to the interaction between the crystal plane and the liquid lead atoms. In consequence of the periodic arrangement of the crystal planes, the close-packed plane exhibits the highest atomic density and the widest interplanar distance. This configuration minimizes the interaction of the liquid lead atoms with the other crystal planes, thereby maximizing the regulatory effect on the distribution of the liquid lead atoms. The regulatory effect results in the formation of a regular layer-like distribution of the lead atoms, with a spacing between layers that is analogous to the crystal planes. This distribution mechanism effectively prevents the dissolution of atoms on the crystal surface into the liquid lead side by separating the atoms of the solid–liquid system from each other. Accordingly, for pure metals with a bcc crystal structure, corrosion resistance anisotropy indicates that the (111) plane is the most susceptible to corrosion, followed by the (001) plane, and the close-packed plane of (110) exhibits the most corrosion-resistant properties. As for fcc crystals, the corrosion resistance of the distinct planes is ordered as follows: (111) > (001) > (110). Full article

Fractal dimension (Frac) and lacunarity (Lac) are frequently proposed as biomarkers of multiscale image complexity, but their incremental value over standardized radiomics remains uncertain. We position both measures within the Image Biomarker Standardisation Initiative (IBSI) feature space by running a fully reproducible comparison in two settings. In a baseline experiment, we analyze $N=1000$ simulated $64\times 64$ textured ROIs discretized to $N_g=64$, computing 92 IBSI descriptors together with Frac (box counting) and Lac (gliding box), for 94 features per ROI. In a wavelet-augmented experiment, we analyze $N=1000$ ROIs and add level-1 wavelet descriptors by recomputing first-order and GLCM features in each sub-band (LL, LH, HL, and HH), contributing $4\times (19+19)=152$ additional features and yielding 246 features per ROI. Feature similarity is summarized by a consensus score that averages z-scored absolute Pearson and Spearman correlations, distance correlation, maximal information coefficient, and cosine similarity, and is visualized with clustered heatmaps, dendrograms, sparse networks, PCA loadings, and UMAP and t-SNE embeddings. Across both settings a stable two-block organization emerges. Frac co-locates with contrast, difference, and short-run statistics that capture high-frequency variation; when wavelets are included, detail-band terms from LH, HL, and HH join this group. Lac co-locates with measures of large, coherent structure—GLSZM zone size, GLRLM long-run, and high-gray-level emphases—and with GLCM homogeneity and correlation; LL (approximation) wavelet features align with this block. Pairwise associations are modest in the baseline but become very strong with wavelets (for example, Frac versus GLCM difference entropy, which summarizes the randomness of gray-level differences, with $\left|r\right|\approx 0.98$; and Lac versus GLCM inverse difference normalized (IDN), a homogeneity measure that weights small intensity differences more heavily, with $\left|r\right|\approx 0.96$). The multimetric consensus and geometric embeddings consistently place Frac and Lac in overlapping yet separable neighborhoods, indicating related but non-duplicative information. Practically, Frac and Lac are most useful when multiscale heterogeneity is central and they add a measurable signal beyond strong IBSI baselines (with or without wavelets); otherwise, closely related variance can be absorbed by standard texture families. Full article

The operational state of high-voltage disconnectors plays a critical role in ensuring the safety, stability, and power supply reliability of electrical systems. To enable accurate identification of the operational status of high-voltage disconnectors, this paper proposes a fault diagnosis method based on a Multi-Scale Residual Depthwise Separable Convolutional Neural Network (MSRDSN). First, wavelet transform is applied to vibration signals to perform multi-scale analysis and enhance detail resolution. Then, a novel network architecture, referred to as RDSN, is constructed to extract discriminative high-level features from vibration signals by integrating residual learning blocks and depthwise separable convolution blocks. Furthermore, a combined loss function is introduced to optimize the RDSN, which simultaneously maximizes inter-class distance, minimizes intra-class distance, and reduces feature redundancy. Experimental results show that the proposed method achieves a top accuracy of 99.44% on a balanced dataset, outperforming the sub-optimal approach by 1.11%. This study offers a novel and effective solution for fault diagnosis in high-voltage disconnectors. Full article

This study introduces a novel and versatile application of neural networks (NNs) to enhance two distinct aspects of Wireless Power Transfer (WPT) systems. First, a compact NN architecture is presented for accurate distance estimation and automated impedance matching in a WPT system. Trained on either impedance measurements or scattering parameters acquired from the transmitter side, this NN effectively predicts the inter-coil distance and identifies optimal capacitance values for maximizing power transfer. Validation using both simulated and experimental data demonstrates consistently low prediction error rates. Second, a separate NN is employed to predict the optimal transmission frequency for minimizing the phase angle between voltage and current, thereby maximizing the power factor. This NN, validated on experimental data spanning various load conditions and inter-coil distances, achieves performance comparable to traditional PI control, but with significantly faster prediction speeds. This speed advantage is crucial for real-time applications and directly contributes to improved power efficiency. The results presented in this study, including the high accuracy of distance and capacitance prediction and the rapid determination of optimal frequencies for power factor maximization, showcase the significant potential of NNs for optimizing WPT systems. These findings open the way for more efficient, adaptable, and intelligent wireless energy transfer solutions, with potential applications ranging from dynamic charging of electric vehicles to real-time optimization of implantable medical devices. Full article

This study introduces two novel data reduction approaches for efficient sentiment analysis: High-Distance Sentiment Vectors (HDSV) and Centroid Sentiment Embedding Vectors (CSEV). By leveraging embedding space characteristics from DistilBERT, HDSV selects maximally separated sample pairs, while CSEV computes representative centroids for each sentiment class. We evaluate these methods on three benchmark datasets: SST-2, Yelp, and Sentiment140. Our results demonstrate remarkable data efficiency, reducing training samples to just 100 with HDSV and two with CSEV while maintaining comparable performance to full dataset training. Notable findings include CSEV achieving 88.93% accuracy on SST-2 (compared to 90.14% with full data) and both methods showing improved cross-dataset generalization, with less than 2% accuracy drop in domain transfer tasks versus 11.94% for full dataset training. The proposed methods enable significant storage savings, with datasets compressed to less than 1% of their original size, making them particularly valuable for resource-constrained environments. Our findings advance the understanding of data requirements in sentiment analysis, demonstrating that strategically selected minimal training data can achieve robust and generalizable classification while promoting more sustainable machine learning practices. Full article

Brain–computer interfaces (BCIs) provide a direct communication pathway between the central nervous system and external environments, enabling human–machine interaction control. Among them, event-related potential (ERP)-based BCIs are among the most accurate and reliable BCI systems. However, current mainstream classification algorithms struggle to eliminate calibration requirements and rely heavily on costly labeled data, limiting the practical usability of ERP-based BCIs. To address this, the development of unsupervised algorithms is critical for advancing real-world BCI applications. In this study, we propose the spatio-temporal equalization sliding-window distribution distance maximization (STE-sDDM) algorithm, which introduces spatio-temporal equalization (STE) to unsupervised ERP classification for the first time and integrates it with a novel unsupervised classification method, sliding-window distribution distance maximization (sDDM). STE estimates and removes colored noise interference in background noise to enhance the signal-to-noise ratio of inputs for sDDM. Meanwhile, sDDM leverages an enhanced inter-class divergence metric based on the ergodic hypothesis theory, utilizing sliding windows to emphasize temporally discriminative features, thereby improving unsupervised classification accuracy. The experimental results demonstrate that the integration of STE and sDDM significantly enhances ERP feature separability, outperforming state-of-the-art unsupervised online classification algorithms in spelling accuracy and the information transfer rate (ITR), facilitating more accurate and faster plug-and-play real-time control for BCI systems. Additionally, static spatio-temporal equalizer architectures were found to outperform dynamic architectures when combined with this framework. Full article

In some ports, there are separate very narrow places between the quays and other navigational obstacles, where the distance between the quays or between the quays and navigational obstacles is very small. Narrow gaps or channels in the water area, where quays are built and ships are berthing, make it difficult for ships to berth at such quays. Accurate knowledge of a ship’s manoeuvrability characteristics, combined with the application of these characteristics in berthing operations and the optimal use of tugboat capabilities, allows for better utilization of restricted port spaces. The article presents a developed ship berthing methodology designed for ship berthing in extremely limited conditions, utilizing the ship’s manoeuvrability capabilities and maximizing the capabilities of tugboats when mooring ships in extremely limited conditions. The developed methodology was tested with real ships and tugboats in specific port conditions and using calibrated simulators, and the results of the experimental research and theoretical calculations are presented in the article as a case study. The research results (methodology) obtained and presented in the article can be applied to any ships and ports, precisely adapting them to specific port situations. The article studies ship manoeuvrability and tugboat capabilities under various hydrometeorological and hydrological conditions, assesses the impact of shallow depths (shallowness), and determines the boundary conditions for ship berthing. Full article

Background/Objectives: Although the importance of magnesium for overall health and physiological function is well established, its influence on exercise performance is less clear. The primary study objective was to determine the influence of short-term magnesium supplementation on cycle ergometer exercise performance. The hypothesis was that magnesium would elicit an ergogenic effect. Methods: A randomized, double-blind, placebo-controlled, two-period crossover design was used to study men and women who were regular exercisers. Fifteen participants ingested either a placebo or magnesium chloride (MgCl₂ 300 mg) twice per day, for 9 days, separated by a 3-week washout. During days 8 and 9, participants completed a battery of cycle ergometer exercise tests, and whole blood, vastus lateralis, and stools were sampled. The primary outcomes were the maximal oxygen uptake (VO_2max), a simulated 10 km time trial, and the sprint exercise performance. Additional outcomes included skeletal muscle mitochondrial respiration, and, on account of the known laxative effects of magnesium, the gut microbiota diversity. Results: Compared with a placebo, MgCl₂ supplementation increased the circulating ionized Mg concentration (p < 0.03), decreased the VO_2max (44.4 ± 7.7 vs. 41.3 ± 8.0 mL/kg/min; p = 0.005), and decreased the mean power output during a 30 s sprint (439 ± 88 vs. 415 ± 88 W; p = 0.03). The 10 km time trial was unaffected (1282 ± 126 vs. 1281 ± 97 s; p = 0.89). In skeletal muscle, MgCl₂ decreased mitochondrial respiration in the presence of fatty acids at complex II (p = 0.04). There were no significant impacts on the gut microbiota richness (CHAO1; p = 0.68), Shannon’s Diversity (p = 0.23), or the beta-diversity (Bray–Curtis distances; p = 0.74). Conclusions: In summary, magnesium supplementation had modest ergolytic effects on cycle ergometer exercise performance and mitochondrial respiration. We recommend that regular exercisers, free from hypomagnesemia, should not supplement their diet with magnesium. Full article

In the context of Industry 5.0, human–robot collaboration (HRC) is increasingly crucial for enabling safe and efficient operations in shared industrial workspaces. This study aims to implement a hybrid robotic architecture based on the Speed and Separation Monitoring (SSM) collaborative scenario defined in ISO/TS 15066. The system calculates the minimum protective separation distance between the robot and the operators and slows down or stops the robot according to the risk assessment computed in real time. Compared to existing solutions, the approach prevents collisions and maximizes workcell production by reducing the robot speed only when the calculated safety index indicates an imminent risk of collision. The proposed distributed software architecture utilizes the ROS2 framework, integrating three modules: (1) a fast and reliable human tracking module based on the OptiTrack system that considerably reduces latency times or false positives, (2) an intention estimation (IE) module, employing a linear Kalman filter (LKF) to predict the operator’s next position and velocity, thus considering the current scenario and not the worst case, and (3) a robot control module that computes the protective separation distance and assesses the safety index by measuring the Euclidean distance between operators and the robot. This module dynamically adjusts robot speed to maintain safety while minimizing unnecessary slowdowns, ensuring the efficiency of collaborative tasks. Experimental results demonstrate that the proposed system effectively balances safety and speed, optimizing overall performance in human–robot collaborative industrial environments, with significant improvements in productivity and reduced risk of accidents. Full article