Search Results (133)

Motor Imagery (MI) based Brain Computer Interfaces (BCIs) have promising applications in neurorehabilitation for individuals who have lost mobility and control over parts of their body due to brain injuries, such as stroke patients. Accurately classifying MI tasks is essential for effective BCI performance, but this task remains challenging due to the complex and non-stationary nature of EEG signals. This study aims to improve the classification of left and right-hand MI tasks by utilizing high-resolution time-frequency features extracted from EEG signals, enhanced with deep learning-based data augmentation techniques. We propose a novel deep learning framework named the Generalized Wavelet Transform-based Deep Convolutional Network (GDC-Net), which integrates multiple components. First, EEG signals recorded from the C3, C4, and Cz channels are transformed into detailed time-frequency representations using the Generalized Morse Wavelet Transform (GMWT). The selected features are then expanded using a Deep Convolutional Generative Adversarial Network (DCGAN) to generate additional synthetic data and address data scarcity. Finally, the augmented feature maps data are subsequently fed into a hybrid CNN-LSTM architecture, enabling both spatial and temporal feature learning for improved classification. The proposed approach is evaluated on the BCI Competition IV dataset 2b. Experimental results showed that the mean classification accuracy and Kappa value are 89.24% and 0.784, respectively, making them the highest compared to the state-of-the-art algorithms. The integration of GMWT and DCGAN significantly enhances feature quality and model generalization, thereby improving classification performance. These findings demonstrate that GDC-Net delivers superior MI classification performance by effectively capturing high-resolution time-frequency dynamics and enhancing data diversity. This approach holds strong potential for advancing MI-based BCI applications, especially in assistive and rehabilitation technologies. Full article

This paper investigates the application of non-calcined metal tartrate as a novel alternative pore former to prepare functional ceramic composites to fabricate solid oxide fuel cells (SOFCs). Compared to carbonaceous pore formers, non-calcined pore formers offer high compatibility with various ceramic composites, providing better control over porosity and pore size distribution, which allows for enhanced gas diffusion, reactant transport and gaseous product release within the fuel cells’ functional layers. In this work, nanocrystalline gadolinium-doped ceria (GDC) and Ni-Gd-Ce-tartrate anode powders were prepared using a single-step co-precipitation synthesis method, based on the carboxylate route, utilising ammonium tartrate as a low-cost, environmentally friendly precipitant. The non-calcined Ni-Gd-Ce-tartrate was used to fabricate dense GDC electrolyte pellets (5–20 μm thick) integrated with a thin film of Ni-GDC anode with controlled porosity at 1300 °C. The dilatometry analysis showed the shrinkage anisotropy factor for the anode substrates prepared using 20 wt. The percentages of Ni-Gd-Ce-tartrate were 30 wt.% and 40 wt.%, with values of 0.98 and 1.01, respectively, showing a significant improvement in microstructural properties and pore size compared to those fabricated using a carbonaceous pore former. The results showed that the non-calcined pore formers can also lower the sintering temperature for GDC to below 1300 °C, saving energy and reducing thermal stresses on the materials. They can also help maintain optimal material properties during sintering, minimising the risk of unwanted chemical reactions or contamination. This flexibility enables the versatile designing and manufacturing of ceramic fuel cells with tailored compositions at a lower cost for large-scale applications. Full article

Internal Dry Reforming of Methane (IDRM) in biogas fed Solid Oxide Fuel Cells (SOFCs) was investigated on Fe-Au modified Ni/GDC electrolyte-supported cells at 900 and 850 °C. The aim was to clarify the synergistic interaction between Fe and Au, focusing on the effect of X wt.% of Au loading (where X = 1 or 3 wt.%) in binary Au-Ni/GDC and ternary 0.5 wt.% Fe-Au-Ni/GDC fuel electrodes. The investigation combined i-V, Impedance Spectroscopy and Gas Chromatography electrocatalytic measurements. It was found that modification with 0.5Fe-Au enhanced significantly the electrocatalytic activity of Ni/GDC for the IDRM reaction, whereas the low wt.% Au content had the most promoting effect. The positive interaction of 0.5 wt.% Fe with 1 wt.% Au increased the conductivity of Ni/GDC and enhanced the corresponding IDRM charge transfer electrochemical processes, especially those in the intermediate frequency region. Comparative long-term measurements, between cells comprising Ni/GDC and 0.5Fe-1Au-Ni/GDC, highlighted the significantly higher IDRM electrocatalytic activity of the modified electrode. The latter operated for almost twice the time (280 h instead of 160 h for Ni/GDC) with a lower degradation rate (0.44 mV/h instead of 0.51 mV/h). Ni/GDC degradation was ascribed to inhibited charge transfer processes in the intermediate frequencies region and to deteriorated ohmic resistance. Stoichiometric analysis on the (post-mortem) surface state of each fuel electrode showed that the wt.% content of reduced nickel on Ni/GDC was lower, compared to 0.5Fe-1Au-Ni/GDC, verifying the lower re-oxidation degree of the latter. This was further correlated to the hindered H₂O production during IDRM operation, due to the lower selectivity of the modified electrode for the non-desired RWGS reaction. Full article

Excessive bait wastage is a major issue in aquaculture, leading to higher farming costs, economic losses, and water pollution caused by bacterial growth from unremoved residual bait. To address this problem, we propose a bait residue detection and counting model named YOLOv8-BaitScan, based on an improved YOLO architecture. The key innovations are as follows: (1) By incorporating the channel prior convolutional attention (CPCA) into the final layer of the backbone, the model efficiently extracts spatial relationships and dynamically allocates weights across the channel and spatial dimensions. (2) The minimum points distance intersection over union (MPDIoU) loss function improves the model’s localization accuracy for bait bounding boxes. (3) The structure of the Neck network is optimized by adding a tiny-target detection layer, which improves the recall rate for small, distant bait targets and significantly reduces the miss rate. (4) We design the lightweight detection head named Detect-Efficient, incorporating the GhostConv and C2f-GDC module into the network to effectively reduce the overall number of parameters and computational cost of the model. The experimental results show that YOLOv8-BaitScan achieves strong performance across key metrics: The recall rate increased from 60.8% to 94.4%, mAP@50 rose from 80.1% to 97.1%, and the model’s number of parameters and computational load were reduced by 55.7% and 54.3%, respectively. The model significantly improves the accuracy and real-time detection capabilities for underwater bait and is more suitable for real-world aquaculture applications, providing technical support to achieve both economic and ecological benefits. Full article

Green data centers (GDCs) are increasingly deployed worldwide to power digital infrastructure sustainably. These centers integrate renewable energy sources, such as solar and wind, to reduce dependence on grid electricity and lower operational costs. When distributed geographically, GDCs face considerable challenges due to spatial variations in renewable energy availability, electricity pricing, and bandwidth costs. This paper addresses the joint optimization of operational cost and service quality for delay-sensitive applications scheduled across distributed green data centers (GDDCs). We formulate a multi-objective optimization problem that minimizes total operational costs while reducing the Average Task Loss Probability (ATLP), a key Quality of Service (QoS) metric. To solve this, we propose an Adaptive Firefly-Based Bi-Objective Optimization (AFBO) algorithm that introduces multiple adaptive mechanisms to improve convergence and diversity. The minimum Manhattan distance method is adopted to select a representative knee solution from each algorithm’s Pareto front, determining optimal task service rates and ISP task splits into each time slot. AFBO is evaluated using real-world trace-driven simulations and compared against benchmark multi-objective algorithms, including multi-objective particle swarm optimization (MOPSO), simulated annealing-based bi-objective differential evolution (SBDE), and the baseline Multi-Objective Firefly Algorithm (MOFA). The results show that AFBO achieves up to 64-fold reductions in operational cost and produces an extremely low ATLP value ($1.875\times {10}^{-7}$) that is nearly two orders of magnitude lower than SBDE and MOFA and several orders better than MOPSO. These findings confirm AFBO’s superior capability to balance energy cost savings and Quality of Service (QoS), outperforming existing methods in both solution quality and convergence speed. Full article

In computer vision applications, the primary task of object detection is to answer the following question: “What object is present and where is it located?”. However, underwater environments introduce challenges, such as poor lighting, high complexity, and diverse marine organism shapes, leading to missed detections or false positives in deep learning-based algorithms. To improve detection accuracy and robustness, this paper proposes an enhanced YOLOv11-based algorithm for underwater object detection that strengthens the ability to capture both local and global details and global contextual information in complex underwater environments. To better capture local and global features while integrating contextual information, the proposed method introduces several enhancements. The backbone incorporates a DualBottleneck module to enhance feature extraction, replacing the standard bottleneck structure in C3k, thus enhancing the feature extraction and the channel aggregation. The detection head adopts DyHead-GDC, integrating ghost depthwise separable convolution with DyHead for greater efficiency. Furthermore, the ADown module replaces conventional feature extraction and downsampling convolutions, reducing parameters and FLOPs by 14%. The C2PSF module, combining focal modulation and C2, strengthens local feature extraction and global context processing. Additionally, a SCSA module is inserted before the detection head to fully utilize multi-semantic information, improving the detection performance in complex underwater scenes. Experimental results confirm the effectiveness of these improvements. The model achieves 84.2% mAP50 on UTDAC2020, 84.4% on DUO and 86.7% on RUOD, surpassing the baseline by 2.5%, 1.6% and 1.2%, respectively. It remains lightweight, with 6.5 M parameters and a computational cost of 7.1 GFLOPs. Full article

To analyze the dynamic characteristics of single-tooth rock-cutting behavior, this paper proposes that the height of the extrusion zone (h_c) is not constant and does not equal the cutting depth in real rock-cutting behavior, and introduces a new dynamic cutting model (NDCM). By analyzing the single-tooth rock-cutting process, the concepts of the single cutting process and cutting frequency (f_c) are defined. A method for determining f_c based on the tangential cutting force (F_c^t) is also proposed. A series of single-tooth rock-cutting tests were conducted using numerical simulation, and the influence of rake angle (a), cutting speed (v), and cutting depth (h) on f_c was analyzed. The geometric difference coefficient (GDC) is introduced in the new dynamic cutting model, defined as the ratio of h_c to h. The determination method of GDC and its relationship with cutting parameters are explored from both theoretical and experimental perspectives. The results show that the cutting frequency corresponds to the main frequency of the tangential cutting force. f_c is linearly proportional to v and decreases nonlinearly with increasing h, while the rake angle has little effect on cutting frequency within the range of 10–20°. h_c exhibits a nonlinear relationship with h: when the cutting depth is small, GDC is close to 1.0; as h increases, GDC gradually decreases and eventually stabilizes, which aligns with experimental findings. The results of this study provide valuable insights for engineers to better understand the dynamic characteristics of tool–rock interaction in single-tooth rock cutting and offer new perspectives for applying cutting force and optimizing rock-cutting models. Full article

BUB1B, a member of the spindle assembly checkpoint family known as BUB1 mitotic checkpoint serine/threonine kinase B, has been associated with the promotion of tumor progression. Nevertheless, its specific contributions to tumorigenesis remain largely unexplored. This study seeks to offer a systematic and comprehensive analysis of the role of BUB1B in the progression of various cancers, with a particular focus on lung adenocarcinoma, utilizing a range of databases. We investigated BUB1B’s role in pan-cancer using TCGA data, analyzing it with platforms like HPA, TIMER, TISIDB, GEPIA, cBioPortal, GDC, LinkedOmics, and CancerSEA. Additionally, we assessed BUB1B’s impact on lung adenocarcinoma proliferation and migration through CCK-8, wound healing, transwell assays and Western blot analysis. This study found that BUB1B was upregulated in most cancers and was significantly linked to patient prognosis. Its expression correlated with immune cell infiltration and genetic markers of immunomodulators across different cancers. BUB1B was involved in the acute inflammatory response and IgA production pathways but negatively correlated with inflammation in lung adenocarcinoma. Moreover, the siRNA-mediated knockdown of BUB1B resulted in the inhibition of proliferation and migration of lung cancer cells in vitro. This study underscores the potential of BUB1B as a biomarker and a promising therapeutic target for patients with lung adenocarcinoma. Full article

Thin films of gadolinium-doped ceria (GDC) nanoparticles were fabricated as electrolytes for low-temperature solid oxide fuel cells (SOFCs) by combining electrophoretic deposition (EPD) and the successive ionic layer adsorption and reaction-air spray plus (SILAR-A+) method. The Ce_1−xGd_xO_2−x/2 solid solution was synthesized using hydrothermal (HY) and solid-state (SS) procedures to produce high-quality GDC nanoparticles suitable for EPD fabrication. The crystalline structure, cell parameters, and phases of the GDC products were analyzed using X-ray diffraction. Variations in oxygen vacancy concentrations in the GDC samples were achieved through the two synthetic methods. The ionic conductivities of pressed pellets from the HY, SS, and commercial G_0.2DC samples, measured at 150 °C, were 0.6 × 10⁻⁶, 2.6 × 10⁻⁶, and 2.9 × 10⁻⁶ S/cm, respectively. These values were determined using electrochemical impedance spectroscopy (EIS) with a simplified equivalent circuit method. The morphologies of G_0.2DC thin films prepared via EPD and SILAR-A+ processes were characterized, with particular attention to surface cracking. Crack-free GDC thin films, approximately 730–1200 nm thick, were successfully fabricated on conductive substrates through the hybridization of EPD and SILAR-A+, followed by hydrothermal annealing. EIS and ionic conductivity (1.39 × 10⁻⁹ S/cm) measurements of the G_0.2DC thin films with thicknesses of 733 nm were performed at 300 °C. Full article

A single-bed and dual-bed catalyst system was studied to maximize H₂ production from the combination of partial oxidation of CH₄ and water gas shift reaction. In addition, the different types of catalysts, including Ni, Cu, Ni-Re, and Cu-Re supported on gadolinium-doped ceria (GDC) were investigated under different operating conditions of temperature (400–650 °C). Over Ni-based catalysts, methane can easily dissociate on a Ni surface to give hydrogen and carbon species. Then, carbon species react with lattice oxygen of ceria-based material to form CO. The addition of Re to Ni/GDC enhances CH₄ dissociation on the Ni surface and increases oxygen storage capacity in the catalyst, thus promoting carbon elimination. In addition, the results showed that a dual-bed catalyst system exhibited catalytic activity better than a single-bed catalyst system. The dual-bed catalyst system, by the combination of 1%Re4%Ni/GDC as a partial oxidation catalyst and 1%Re4%Cu/GDC as a water gas shift catalyst, provided the highest CH₄ conversion and H₂ yield. An addition of Re onto Ni/GDC and Cu/GDC caused an increase in catalytic performance because Re addition could improve the catalyst reducibility and increase metal surface area, as more of their surface active sites are exposed to reactants. Full article

Gastric duplication cysts (GDCs) are rare congenital anomalies, often identified during infancy or childhood. Although typically benign, there have been sporadic reports of malignant transformations, including adenocarcinoma and rare mixed tumors. Herein, we describe a rare case of mixed pancreatobiliary ductal adenocarcinoma and squamous cell carcinoma occurring within a GDC in a 54-year-old Korean woman with a history of melena and hematemesis. Initial gastroscopy and positron emission tomography–computed tomography (PET-CT) revealed a protruding stomach mass. A laparoscopic total gastrectomy was performed, and histological examination confirmed a mixed carcinoma originating from an ectopic pancreas within the duplication cyst. This case is unique as it is the first reported instance in the world of mixed pancreatobiliary ductal adenocarcinoma and squamous cell carcinoma arising from an ectopic pancreas within a GDC. This highlights the importance of considering pancreatobiliary-type adenocarcinoma in the differential diagnosis of malignancies originating from GDCs, which has implications for treatment strategies. Full article

The water–gas shift (WGS) reaction is one of the most significant reactions in hydrogen technology since it can be used directly to produce hydrogen from the reaction of CO and water; it is also a side reaction taking place in the hydrocarbon reforming processes, determining their selectivity towards H₂ production. The development of highly active WGS catalysts, especially at temperatures below ~450 °C, where the reaction is thermodynamically favored but kinetically limited, remains a challenge. From a fundamental point of view, the reaction mechanism is still unclear. Since specific nanoshapes of CeO₂-based supports have recently been shown to play an important role in the performance of metal nanoparticles dispersed on their surface, in this study, a comparative study of the WGS is conducted on Pt nanoparticles dispersed (with low loading, 0.5 wt.% Pt) on CeO₂ and gadolinium-doped ceria (GDC) supports of different nano-morphologies, i.e., nanorods (NRs) and irregularly faceted particle (IRFP) CeO₂ and GDC, produced by employing hydrothermal and (co-)precipitation synthesis methods, respectively. The results showed that the support’s shape strongly affected its physicochemical properties and in turn the WGS performance of the dispersed Pt nanoparticles. Nanorod-shaped CeO_2,NRs and GDC_NRs supports presented a higher specific surface area, lower primary crystallite size and enhanced reducibility at lower temperatures compared to the corresponding irregular faceted CeO_2,IRFP and GDC_IRFP supports, leading to up to 5-fold higher WGS activity of the Pt particles supported on them. The Pt/GDC_NRs catalyst outperformed all other catalysts and exhibited excellent time-on-stream (TOS) stability. A variety of techniques, namely N₂ physical adsorption–desorption (the BET method), scanning and transmission electron microscopies (SEM and TEM), powder X-ray diffraction (PXRD) and hydrogen temperature programmed reduction (H₂-TPR), were used to identify the texture, structure, morphology and other physical properties of the materials, which together with the in situ diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) and detailed kinetic studies helped to decipher their catalytic behavior. The enhanced metal–support interactions of Pt nanoparticles with the nanorod-shaped CeO_2,NRs and GDC_NRs supports due to the creation of more active sites at the metal–support interface, leading to significantly improved reducibility of these catalysts, were concluded to be the critical factor for their superior WGS activity. Both the redox and associative reaction mechanisms proposed for WGS in the literature were found to contribute to the reaction pathway. Full article

Introduction: Fertile soil has a balanced pH and nutrient profile (potassium, phosphorus, and nitrogen), water retention capability, and organic substances. Fertile soil allows for better plant growth, leading to better production. The soil fertility requirements vary from crop to crop. So, it is essential to identify the soil fertility level according to the crop type. Objective: The objective of this paper is to develop a robust model that is capable of predicting the soil fertility. The model is integrated with IoT-generated data and federated learning-based feature selection techniques to improve the accuracy of the dataset. Materials/Methods: Different feature selection techniques were applied to the dataset. Then, we applied machine learning algorithms such as logistic regression, decision tree, and naïve Bayes, as well as their combinations to analyze and improve the performance. The federated learning approach was implemented to train the local models using the individual partitioned datasets. Each local model of the client shared the cryptic output weight and bias without sharing the raw data. There was a centralized model at the server end that collected these weights and biases, preserving data privacy. These collected data were aggregated and applied to find the least square error (LSE). Then, a gradient descent curve (GDC) was applied to identify the optimized weight and bias, which were fed back again to improve the accuracy of the predictions. Result: From our experimental observations, we analyzed the performance metrics of different ML classifiers, and it was revealed that the ensemble of logistic regression and decision tree had a better performance than the other models. One of our client models generates weight and bias with a precision of 87%, an accuracy of 87%, a recall of 87%, and an F1-score of 86%. Further, we collected two of our client system model outcomes from a server model and applied the LSE to identify the optimal W and B. In future work, we wll improve the performance of our model with a recursive approach by verifying the W and B at the client model in a feedback process. Full article

Catalytic activity, mechanisms, and active sites were determined for methane steam reforming (MSR) over gadolinium-doped ceria (GDC) supported iridium (0.1 wt%) prepared by impregnation of GDC with iridium acetylacetonate. Isothermal steady-state rate measurements followed by micro-gas chromatography analysis were performed at 660 and 760 °C over Ir/GDC samples pretreated in N₂ or H₂ at 900 °C. Transient responses to CH₄ or H₂O step changes in isothermal conditions were carried out at 750 °C over Ir/GDC pretreated in He or H₂ using online quadrupole mass spectrometry. In the proposed mechanism, Ir/GDC proceeds through a dual-type active site associating, as follows: (i) Ir metallic particles surface as active sites for the cracking of CH₄ into reactive C species, and (ii) reducible (Ce⁴⁺) sites at GDC surface responsible for a redox mechanism involving Ce⁴⁺/Ce³⁺ sites, being reduced by reaction with reactive C into CO (or CO₂) depending on the oxidation state of GDC and re-oxidized by H₂O. Full reduction of reducible oxygen species is possible with CH₄ after He treatment, whereas only 80% is reached in CH₄ after H₂ treatment. Full article

Background: Lung and colon cancers are among the most prevalent and lethal malignancies worldwide, underscoring the urgent need for advanced diagnostic methodologies. This study aims to develop a hybrid deep learning and machine learning framework for the classification of Colon Adenocarcinoma, Colon Benign Tissue, Lung Adenocarcinoma, Lung Benign Tissue, and Lung Squamous Cell Carcinoma from histopathological images. Methods: Current approaches primarily rely on the LC25000 dataset, which, due to image augmentation, lacks the generalizability required for real-time clinical applications. To address this, Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to enhance image quality, and 1000 new images from the National Cancer Institute GDC Data Portal were introduced into the Colon Adenocarcinoma, Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma classes, replacing augmented images to increase dataset diversity. A hybrid feature extraction model combining MobileNetV2 and EfficientNetB3 was optimized using the Grey Wolf Optimizer (GWO), resulting in the Lung and Colon histopathological classification technique (MEGWO-LCCHC). Cross-validation and hyperparameter tuning with Optuna were performed on various machine learning models, including XGBoost, LightGBM, and CatBoost. Results: The MEGWO-LCCHC technique achieved high classification accuracy, with the lightweight DNN model reaching 94.8%, LightGBM at 93.9%, XGBoost at 93.5%, and CatBoost at 93.3% on the test set. Conclusions: The findings suggest that our approach enhances classification performance and offers improved generalizability for real-world clinical applications. The proposed MEGWO-LCCHC framework shows promise as a robust tool in cancer diagnostics, advancing the application of AI in oncology. Full article