Search Results (10,102)

A method for super-resolution reconstruction of sonograms based on Residual Dense Conditional Generative Adversarial Network (RDC-GAN) is proposed in this paper. It is well known that the resolution of medical ultrasound images is limited, and the single-frame image super-resolution algorithms based on a convolutional neural network are prone to losing texture details, extracting much fewer features, and then blurring the reconstructed images. Therefore, it is very important to reconstruct high-resolution medical images in terms of retaining textured details. A Generative Adversarial Network could learn the mapping relationship between low-resolution and high-resolution images. Based on GAN, a new network is designed, where the generation network is composed of dense residual modules. On the one hand, low-resolution (LR) images are input into the dense residual network, then the multi-level features of images are learned, and then are fused into the global residual features. On the other hand, conditional variables are introduced into a discriminator network to guide the process of super-resolution image reconstruction. The proposed method could realize four times magnification reconstruction of medical ultrasound images. Compared with classical algorithms including Bicubic, SRGAN, and SRCNN, experimental results show that the super-resolution effect of medical ultrasound images based on RDC-GAN could be effectively improved, both in objective numerical evaluation and subjective visual assessment. Moreover, the application of super-resolution reconstructed images to stage the diagnosis of cirrhosis is discussed and the accuracy rates prove the practicality in contrast to the original images. Full article

Asbestos tailings represent a historical liability in many countries. Canada aims at transforming this industrial legacy into an opportunity to both mitigate the environmental footprint and recover critical (such as magnesium, nickel, chromium, and cobalt) and strategic metals, which represent significant economic development potential. This study aimed to investigate the recovery of critical and strategic metals (CSMs) from asbestos tailings using hydrochloric (HCl) acid leaching, with acid concentration (2–12 mol/L), leaching temperature (20–90 °C), and solid–liquid ratio (10–40%) as key process parameters. The tailing samples studied is composed mostly of chrysotile and lizardite. It contains about 40% magnesium (as its oxide MgO) and nickel and chromium showing contents 52 and 60 times higher than their respective average crustal abundances (Clarke values). Iron content is 8.7% (expressed as its ferric oxide Fe₂O₃). To optimize key factors influencing the leaching process, a statistical experimental design was employed. The designed leaching experiments were subsequently performed, and results were used to define leaching conditions aiming at maximizing Mg and Ni recoveries while minimizing iron contamination using response surface methodology (RSM) based on the central composite design (CCD). A quadratic polynomial model was developed to describe the relationship between the process parameters and metal recoveries. Among the tested effects of acid concentration, temperature, and pulp density on magnesium recovery, the modeling indicated that both hydrochloric acid concentration and leaching temperature significantly enhanced metal recovery, whereas increasing pulp density had a negative effect at low temperature. The empirical mathematical model derived from the experimental data, accounting for the uncertainties on chemical data, indicated that high magnesium recovery was achieved at 90 °C, with 10–12 N hydrochloric acid and a solid-to-liquid ratio of 33.6–40%. These findings reveal the potential for the recovery of critical and strategic metals, both in terms of efficiency and economic viability. Full article

This study aims to enhance fundamental research on the reaction behavior between ferric oxide and H₂–CO gas mixtures and to provide theoretical support for optimizing the injection of hydrogen-containing materials in the ironmaking process. In this study, the ultrafine ferric oxide powder was isothermally reduced with H₂–CO gas mixture at 1023 K–1373 K. The results indicated that when H₂ content is less than 30% at 1023 K, the ferric oxide sample reduced by the H₂–CO gas mixture exhibits a pronounced carbon deposition phenomenon during the reduction stage. The gas reactant composition had a relatively large influence on the reaction rate at the third stage of the reduction reaction (FeO → Fe). Assuming the single-step nucleation assumption theory together with kinetic experimental data, the relationship between the average reaction rate and the gas composition of the H₂–CO gas mixture was established for the FeO reduction stage. In addition, the apparent activation energy of the reduction reaction was generally in the range of 20–45 kJ/mol, indicating that the possible rate-controlling step was combined gas diffusion and interfacial gas–solid chemical reaction. Full article

An essential component of modern musculoskeletal rehabilitation systems is treadmills of various sizes, the control of which may rely either on manual adjustment of treadmill speed, fixed for the entire training session, or on automatic regulation based on analysis of the user’s movements and velocity. The aim of this study was to experimentally compare the control functions of an adaptive treadmill designed for musculoskeletal rehabilitation and to assess the influence of the hardware configuration and tracking systems on user stability and the smoothness of transient processes. Two running platforms (of different lengths, one equipped with handrails and one without), two tracking systems (virtual reality trackers and a computer vision system using the MediaPipe Pose model), and three control functions—linear, nonlinear, and proportional-integral-derivative (PID)—were investigated. A set of metrics with both metrical and physiological interpretability was proposed (including positional stability, duration and amplitude of transient processes in position and velocity, subjective assessment, and others), all integrated into a single quality control criterion. This study presents extensive experimental research comparing various designs of adaptive running platforms and tracking systems, exploring the relationships between the available working area length and user comfort, and determining the optimal parameters for the selected control functions. The optimal control function was identified as the linear law for the tracking system based on virtual reality trackers and the PID function for the computer-vision-based tracking system. The conducted experiments made it possible to formulate recommendations regarding the minimum permissible working area length of treadmill platforms and the selection of tracking systems and control functions for musculoskeletal rehabilitation systems. The obtained results are of practical relevance for developing adaptive rehabilitation simulators and creating control algorithms that ensure smooth and stable treadmill motion, thereby enhancing user comfort, efficiency, and safety during musculoskeletal rehabilitation exercises. Full article

Family dogs stand out with regard to their special (human-like) attachment behavior towards their owners. This dog–owner attachment bond, analogous to the human infant–mother relationship, has been extensively documented at the behavioral level. Capitalizing on the fully non-invasive polysomnography protocol, the current study compares family dogs’ sleep structure when sleeping in the company of their owners versus an experimenter (a friendly stranger human). Subjects (N = 9) participated in three recording sessions, each lasting 3 h. The first session served as an adaptation to the recording environment, while the second and third were the test sessions analyzed for the present paper. On these two occasions, dogs slept, in a counterbalanced order, once in the company of their owner, while on the other occasion they slept in the company of an experimenter, while the owner was outside the room. Polysomnography recordings were used to extract high-resolution information (in 20 sec epochs) on the time dogs spend awake and in each of the sleep stages (drowsiness, non-REM, and REM). Our results show a robust difference between dogs’ sleep structure with and without the owner. In addition to an increased sleep latency and worsened sleep efficiency, dogs spent considerably less time in deep sleep (non-REM) when their owner was absent. These findings add to the increasing body of literature dealing with dog-to-owner attachment and provide unique physiological evidence for the phenomenon, complementing the widely reproduced behavioral data. Full article

In order to improve the fluidity, pumpability, and strength of separation-layer grouting fire-protecting filling material and reliability with multiple parameters and factors in traditional orthogonal tests, the coupling theory of the response surface-satisfaction function is applied to optimize the ratio of separation-layer grouting fire-protecting filling material. Cement content, the ash–gangue ratio, slurry concentration, and admixture were selected as the influencing factors for the ratio optimization of separation-layer grouting fire-protecting filling material and slump, with the bleeding rate and compressive strength selected as the evaluation indexes of material properties. The Box–Behnken experimental design method was applied to conduct 25 groups of experiments with different material ratios, and the response surface functions of various material performance evaluation indexes were constructed. The relationship between the influencing factors of fire protecting and filling material ratios and the target responsiveness was studied, as well as the optimal ratio of separation-layer grouting fire-protecting filling materials under multi-objective conditions. The results show that the influence of the slurry concentration and cement content on the degree of collapse is significant. The cement content and slurry concentration had significant influence on the compressive strength. The ash–gangue ratio has a significant impact on bleeding rate. Meanwhile, the interaction of the ash–gangue ratio, slurry concentration, and cement content also has a significant impact on the bleeding rate. For waste rock cementation abscission-layer grouting fire protecting and filling material, the optimal ratio is an ash and gangue ratio of 1:2, the cement content is 12.12%, the admixture is 1.49%, and the slurry concentration is 52%. The ratio of the corresponding response under the condition of prediction result is a slurry slump of 28.5 cm, bleeding rate of 2.36%, and filling body strength of 4.62 MPa, which basically coincide with the experimental results and verification and provide evidence for the abscission layer grouting field industrial test. Full article

This study presents a nonlinear regression expansion model tailored to the characteristics of fissured highly expansive soils. Through in-depth investigations, fissure ratio (K_r), dry density (ρ_d), initial water content (w₀), and overburden stress (ln(1 + σ)) were identified as critical factors influencing expansion behavior. Experimental results revealed linear relationships between ultimate expansion (δ_ep) and w₀, ρ_d, and ln(1 + σ), and an exponential relationship with K_r. A multivariate nonlinear regression model was developed and validated, demonstrating high predictive accuracy. The model highlights the significant role of fissure infill materials, particularly gray-green clay, on soil expansiveness. It provides a reliable tool for predicting the expansion characteristics of fissured expansive soils under various conditions, offering theoretical and practical support for engineering applications in expansive soil regions. This study uses a single highly expansive clay from the Nanyang section. The soil is a transported Middle Pleistocene alluvial–proluvial clay (al-plQ2) in which fissures are predominantly filled by 2–5 mm gray-green clay. Accordingly, the proposed regression is most applicable to fissure systems that are largely infilled; extrapolation to open or partially infilled fissures should be made with caution. Full article

The application of efficient optical-electrical sorting technology for the automatic separation of copper mine waste rocks not only enables the recovery of valuable copper metals and promotes the resource utilization of non-ferrous mine waste, but also conserves large areas of land otherwise used for waste disposal and alleviates associated environmental issues. However, the process is challenged by the low copper content, fine dissemination of copper-bearing minerals, and complex mineral composition and associated relationships. To address these challenges, this study leverages dual-energy X-ray imaging and multi-modal learning, proposing a lightweight twin-tower convolutional neural network (CNN) designed to fuse high- and low-energy spectral information for the automated sorting of copper mine waste rocks. Additionally, the study integrates an emerging Kolmogorov-Arnold network as a classifier to enhance the sorting performance. To validate the efficacy of our approach, a dataset comprising 31,057 pairs of copper mine waste rock images with corresponding high- and low-energy spectra was meticulously compiled. The experimental results demonstrate that the proposed lightweight method achieves competitive, if not superior, performance compared to contemporary mainstream deep learning networks, yet it requires merely 1.32 million parameters (only 6.2% of ResNet-34), thereby indicating extensive potential for practical deployment. Full article

Residual magnetism in the core of a power transformer can lead to an increased inrush current during closing, which may trigger relay protection malfunctions and cause equipment aging. Accurate detection of residual magnetism is crucial for grid safety. Traditional offline detection requires interrupting operation, while online methods are susceptible to interference and have limited accuracy. This paper proposes a method for detecting residual magnetism in power transformers based on the hysteresis area of magnetization. First, the magnetic flux distribution of the transformer is analyzed through finite element simulation, revealing that low-frequency excitation can make the core’s magnetic flux density distribution more uniform, and that the leakage flux and the flux inside the core have similar characteristics, which helps to determine the optimal position for flux detection. Next, the relationship between the small hysteresis loop area and residual magnetism is studied, revealing a monotonic mapping relationship between the normalized area of the negative hysteresis loop under current pulse excitation and the residual magnetism. Finally, experimental verification shows that this method effectively detects residual magnetism under different levels and operational conditions. The method is non-invasive, real-time, and highly resistant to interference, offering a new approach for residual magnetism detection in power transformers. Full article

Geolocating Twitter users from social media data holds significant value in applications such as targeted advertising, disaster response, and social network analysis. However, existing social network-based geolocation methods tend to focus primarily on mention relations while neglecting other critical interactions like retweet relationships. Moreover, effectively integrating diverse social features remains a key challenge, which limits the overall performance of geolocation models. To address these issues, this paper proposes a novel Twitter user geolocation method based on multi-graph feature fusion with a gating mechanism, termed MGFGCN, which fully leverages heterogeneous social network information. Specifically, MGFGCN first constructs separate mention and retweet graphs to capture multi-dimensional user relationships. It then incorporates the Information Gain Ratio (IGR) to select discriminative keywords and generates Term Frequency–Inverse Document Frequency (TF-IDF) features, thereby enhancing the semantic representation of user nodes. Furthermore, to exploit complementary information across different graph structures, we propose a Structure-aware Gated Fusion Mechanism (SGFM) that dynamically captures differences and interactions between nodes from each graph, enabling the effective fusion of node representations into a unified representation for subsequent location inference. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art baselines in the Twitter user geolocation task across two public datasets. Full article

Reliable forecasting of tropical cyclone (TC) intensity—particularly rapid intensification (RI) events—remains a major challenge in meteorology, largely due to the inherent difficulty of accurately quantifying predictive uncertainty. Traditional numerical approaches are computationally expensive, while statistical models often fail to capture the highly nonlinear relationships involved. Mainstream machine learning models typically provide only deterministic point forecasts and lack the ability to represent uncertainty. To address this limitation, we propose Tropical Cyclone Diffusion Model (TCDM), the first conditional diffusion-based probabilistic forecasting framework for TC intensity. TCDM integrates multimodal meteorological data, including satellite imagery, re-analysis fields, and environmental predictors, to directly generate the full probability distribution of future intensities. Experimental results show that TCDM not only achieves highly competitive deterministic accuracy (low MAE and RMSE; high $R^2$), but also delivers high-quality probabilistic forecasts (low CRPS; high PICP). Moreover, it substantially improves RI detection by achieving higher hit rates with fewer false alarms. Compared with traditional ensemble-based methods, TCDM provides a more efficient and flexible approach to probabilistic forecasting, offering valuable support for TC risk assessment and disaster preparedness. Full article

This study introduces Internal Induction Heating (In-IH) as an efficient method for local mold temperature control in thin-walled polypropylene (PP) injection molding. Unlike conventional systems that are slow and energy-intensive, the insert is integrated directly into the induction circuit in the In-IH system, generating eddy currents for rapid and localized heating. Numerical and experimental analyses were performed to examine the effects of insert geometry and heating parameters; it was found that thinner inserts achieved higher surface temperatures—the 0.5 mm insert reached ~550 °C, while the 2.0 mm insert reached only ~80 °C—confirming an inverse relationship between thickness and temperature. Narrower inserts (25 mm) concentrated heat more effectively, whereas wider ones yielded better temperature uniformity. The cooling conditions strongly affected the temperature gradients. Mold-filling experiments demonstrated that In-IH significantly improved the flowability of PP: at 180 °C, the 0.4 mm specimen achieved a flow length of 85.33 mm, compared with 43.66 mm for the 0.2 mm specimen. At 250–300 °C, all samples approached full filling (~100 mm). The simulation and experimental results agreed, with a maximum deviation of 10%, confirming that In-IH provides rapid, energy-efficient, and precise temperature control, thus enhancing melt flow and product quality for thin-walled PP components. Full article

Background/Objectives: There is a sudden and noticeably increasing focus on naturally found antiplatelet inhibitors that humans can use habitually. Given that athletes receive annual training with periods of recovery that are not always suitably adapted to the workload, this study aimed to explore the association between dietary fat intakes and the indices of blood profiles, concentrating on platelet variables in a sample of high-performance athletes. Methods: The sample encompassed 19.8 ± 2.2-year-old Lithuanian high-performance athletes (n = 82). The assessment of the nutritional profile of study participants was performed using a 3-day food record approach. In laboratory settings, the hematology profile of athletes was assessed via the Nihon Khoden automated hematology analyzer. Results: The recorded mean consumption of energy, carbohydrates, protein, and fat in elite athletes was 49 kcal/kg/day, 5.4 g/kg/day, 1.6 g/kg/day, and 40.3% of energy intake (EI), respectively. The study highlighted the excessive consumption of saturated fatty acids (FA) (13.4–14.3% of EI) and dietary cholesterol (698–982 mg/day). Also, considering that the ideal human omega-6 to omega-3 FA ratio is commonly deemed to be between 1:1 and 4:1, an athlete’s ‘Western diet’ was heavily skewed with a ratio fluctuating from 18.9:1 to 19:4 in favor of omega-6 FA. Furthermore, the study found that the outcomes related to slightly higher levels of blood platelet counts and plateletcrit, however, being within normal limits, were associated with a higher intake of omega-6 FA (adjusted odds ratio (AOR) 9.5, 95% confidence interval (CI) 1.2; 9.9, p = 0.029). A higher platelet-to-hemoglobin ratio as a novel indirect blood-based biomarker pronouncing the potential inflammatory processes in the body revealed the reverse relationship of higher intake levels of dietary omega-3 FA (AOR 6.7, 95% CI 1.3; 12.2, p = 0.029), omega-6 FA (AOR 6.2, 95% CI 2.7; 11.5, p = 0.009), and saturated FA (AOR 8.5, 95% CI 1.5; 9.1, p = 0.020) among elite athletes. Conclusions: The prospect of personalized nutrition targeted at the professional athletes’ segment may provide an innovative opportunity to increase athletes’ capacity to manage the platelet function via diet while stressing the importance of further empirical experimental research in this dynamic and vital biomedical field. Full article

Binary code similarity detection serves as a critical front-line defense mechanism in cybersecurity, playing an indispensable role in identifying known vulnerabilities, detecting emergent malware families, and preventing intellectual property theft via code plagiarism. However, existing methods based on Control Flow Graphs (CFGs) often suffer from two major limitations: the inadequate capture of deep semantic information within CFG nodes, and the neglect of structural relationships across different functions. To address these issues, we propose Breg, a novel framework that synergistically integrates pre-trained semantic features with cross-graph structural features. Breg employs a BERT model pre-trained on a large-scale binary corpus to capture nuanced semantic relationships, and introduces a Cross-Graph Neural Network (CGNN) to explicitly model topological correlations between two CFGs, thereby generating highly discriminative embeddings. Extensive experimental validation demonstrates that Breg achieves leading F1-scores of 0.8682 and 0.8970 on Dataset3. In real-world vulnerability search tasks on Dataset4, Breg achieves an MRR@10 of 0.9333 in the challenging MIPS32-to-x64 search task, a clear improvement over the 0.8533 scored by the strongest baseline. This underscores its superior effectiveness and robustness across diverse compilation environments and architectures. To the best of our knowledge, this is the first work to integrate a pre-trained language model with cross-graph structural learning for binary code similarity detection, offering enhanced effectiveness, generalization, and practical applicability in real-world security scenarios. Full article

Accurate fault localization in rail transit train cables is hindered by impedance mismatch, which induces overshoot interference and attenuates reflected signals, causing traditional peak-detection methods to fail. This study proposes a novel traveling wave dual-peak identification method to address this challenge. The approach employs signal polarity normalization to eliminate phase inversion, Gaussian-weighted filtering to suppress noise and distortion, and local extrema screening to robustly isolate incident and reflected wave peaks amidst complex backgrounds including overshoot oscillations and electromagnetic crosstalk. A dual-Gaussian model is optimized via nonlinear fitting to precisely quantify peak arrival times while compensating for waveform broadening. Fault distance is derived from the optimized time difference and wave velocity. Experimental validation across single-core coaxial, twin-core coaxial, and harness cables with open/short-circuit faults at multiple distances confirms the method’s effectiveness. Results demonstrate strong linear relationships between time differences and fault distances for all cable types, with successful peak identification achieved even under severe signal attenuation or strong coupling interference. This method significantly enhances localization accuracy for rail transit cable systems under impedance mismatch conditions. Full article