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Recent Progress of Polymer-Based Biosensors for Cancer Diagnostic Applications: Natural versus Synthetic Polymers
No full textSubject-independent multi-channel voting for EEG-based emotion recognition using wavelet scattering deep network and advanced signal metrics
No full textElectroencephalography (EEG) signals, reflecting human brain activity, hold potential beyond medical diagnosis, particularly in emotion recognition. Despite the development of machine learning models utilizing EEG data for this purpose, achieving good enough accuracy remains a challenge due to signals complexity and non-stationary nature, especially in extracting effective features that encapsulate temporal and frequency information. This paper introduces a novel hand-crafted feature extraction technique that avoids conventional signal segmentation and analyzes the entire length of EEG signals. This method builds a convolutional network utilizing Wavelet Scattering Transform (WST) blocks, followed by deriving a comprehensive 17-feature set from the raw EEG data and WST scattering coefficients. This integrative set takes advantage of the WST’s ability to produce a signal representation that is stable against noise, invariant to time shifts, and captures both temporal and frequency components while also leveraging the intrinsic properties of the raw data, offering an alternative to the computational deep models. The integration of Linear Discriminant Analysis for dimensionality reduction and the K-Nearest Neighbors algorithm for classification, further refined by a majority voting mechanism across all channels, results in a robust classification framework. The proposed method is evaluated across GAMEEMO and DEAP datasets with two and four emotional classes, using Leave-One-Subject-Out validation, achieving classification accuracy exceeding 97%. The findings support the effectiveness of this approach in EEG-based emotion recognition. Furthermore, an ablation study on the two datasets is implemented to assess each component’s impact, revealing insights into the model’s effectiveness and improvement area
Enhancing topical delivery of N-acetylcysteine and collagen via a novel electrospun collagen/PMMA nanofibrous mats as facial mask development: Nanofibers optimization and In vitro experiments
No full textFuzzy Hypothesis Testing for Radar Detection: A Statistical Approach for Reducing False Alarm and Miss Probabilities
No full textThis paper addresses a fundamental challenge in statistical radar detection systems: optimizing the trade-off between the probability of a false alarm () and the probability of a miss (). These two metrics are inversely related and critical for performance evaluation. Traditional detection approaches often enhance one aspect at the expense of the other, limiting their practical applicability. To overcome this limitation, a fuzzy hypothesis testing framework is introduced that improves decision making under uncertainty by incorporating both crisp and fuzzy data representations. The methodology is divided into three phases. In the first phase, we reduce the probability of false alarm while maintaining a constant probability of miss using crisp data characterized by deterministic values and classical statistical thresholds. In the second phase, the inverse scenario is considered: minimizing while keeping fixed. This is achieved through parameter tuning and refined threshold calibration. In the third phase, a strategy is developed to simultaneously enhance both and , despite their inverse correlation, by adopting adaptive decision rules. To further strengthen system adaptability, fuzzy data are introduced, which effectively model imprecision and ambiguity. This enhances robustness, particularly in scenarios where rapid and accurate classification is essential. The proposed methods are validated through both real and synthetic simulations of radar measurements, demonstrating their ability to enhance detection reliability across diverse conditions. The findings confirm the applicability of fuzzy hypothesis testing for modern radar systems in both civilian and military contexts, providing a statistically sound and operationally applicable approach for reducing detection errors and optimizing system performance
Spherically symmetric solution in quadratic non-metricity gravity
No full textWe explicitly find an exact spherically symmetric solution in quadratic non-metricity gravity. We show that the quadratic term acts as a cosmological constant. This solution contradicts all the claims in the literature that there is no spherically symmetric solution for higher-order non-metricity gravity. Moreover, we demonstrate that for the charged field equations, the solution can be identical to the non-charged case. This is because the off-diagonal components of the field equation do not feel the effect of the charge
Bovine Teeth as Substitutes for Human Teeth in Dental Research Ultrastructural and Radiographic Analysis
No full textObjectives Obtaining an alternative for human teeth in research remains challenging. The current study aimed to determine the validity of utilizing bovine teeth as a substitute for human teeth. Materials and Methods Sound human maxillary premolars and bovine permanent lower central incisors were obtained. The human and bovine teeth were divided into groups (n¼35) for scanning electron microscope (SEM) analysis alongside energy dispersive X-ray spectroscopy (EDX) and optical radiographic density. Statistical Analysis The data was statistically analyzed using the one-way analysis of variance along with a two-sample t-test, comparing the means of each two groups. The results were expressed as means standard deviations, and statistical significance was determined at an alpha of 0.05. Results SEM analysis of human and bovine samples in different hard tissues showed minor changes, mainly the human enamel had a smoother surface with distinct prism profiles, whereas the bovine dentin had larger and more widely separated dentinal tubules. EDX analysis revealed that the compositions of Ca and P, along with their Ca/P ratios in terms of enamel, dentin, and cementum, were comparable. For radiographic density, the findings revealed minor differences between human and bovine samples. No statistically significant differences among the studied groups were detected. Conclusion This study revealed minor variations in structure, chemical composition, and radiographic density between human and bovine hard tissues, but without statistical significance, supporting the utilization of bovine teeth as a substitute for that of humans in dental research
Performance analysis of PEM fuel cells via Puma optimizer with the aid of practical verifications
No full textThis manuscript presents a novel application of the recently developed Puma Optimizer (PO) for identifying the unknown parameters in Mann’s model, which is widely used for characterizing the behavior of Polymer Electrolyte Membrane Fuel Cells (PEMFCs). The proposed PO-based methodology is rigorously evaluated using three test cases. One test case involves experimental I–V measurements under various operating conditions from a commercial PEMFC stack, the Horizon H-100 (100 W), which was assembled and tested in the laboratory. The other two cases are established benchmark PEMFC systems, the Ballard Mark V 5 kW and BCS 500 W units. Comprehensive statistical analyses over multiple independent runs are performed to confirm the consistency and reliability of the PO-based approach. To evaluate the optimizer’s accuracy and robustness, comparisons are made with three other metaheuristic algorithms: two recently developed methods, the Propagation Search Algorithm (PSA) and the Walrus Optimization Algorithm (WOA), and a widely recognized one, the Slime Mold Optimizer (SMO). The comparisons show that the PO optimizer consistently achieved the lowest total square error (TSE) across all test cases, outperforming the other algorithms. Specifically, it develops the lowest values of 0.835811 for the Ballard Mark V 5 kW, 0.011281 for the BCS 0.5 kW, 0.711452 at 30 °C, 0.886144 at 35 °C, and 2.057015 at 40 °C for the Horizon H-100 fuel cell. These findings confirm that the PO is a reliable and effective tool for parameter estimation in PEMFC modeling under both simulated and real-world experimental conditions
Introducing a Novel Figure of Merit for Evaluating Stability of Perovskite Solar Cells: Utilizing Long Short-Term Memory Neural Networks
No full textThis study introduces a novel figure of merit for evaluating the stability of perovskite solar cells (PSCs) by employing advanced Long Short-Term Memory (LSTM) neural networks to investigate degradation mechanisms. By harnessing the power of artificial intelligence and data analytics, we analyzed extensive datasets encompassing PSC parameters, experimental results, and environmental conditions, revealing critical insights into the degradation patterns affecting cell performance over time. Our findings indicate that the LSTM model effectively captures and predicts the complex relationships between key design parameters—efficiency, fill factor, and open-circuit voltage—and degradation-induced changes in PSCs. Specifically, we identified three degradation coefficients associated with the electron transport layer, hole transport layer, and perovskite active layer. These coefficients serve as a new figure of merit, facilitating numerical studies on degradation and stability in PSCs, mainly focusing on cesium lead halides. Furthermore, the enhanced LSTM architecture, featuring deeper layers, dropout for regularization, and batch normalization, demonstrated improved stability and training speed, leading to a test Mean Absolute Error (MAE) of 0.0354 and an R2 value of 0.9991, indicating near-perfect predictive accuracy. The comparative analysis of model complexity confirmed that increasing the sophistication of the LSTM model significantly enhances predictive accuracy and generalization capabilities. Identifying crucial design parameters offers actionable insights for optimizing PSC designs, materials selection, and operational conditions, ultimately contributing to enhanced long-term stability and efficiency of PSCs. Future research should prioritize using experimental datasets to achieve more realistic predictions, thereby driving innovation and unlocking the full potential of machine learning and deep learning in optimizing PSC design and performance
Radiographic Assessment Following Maxillary Bone Augmentation Using Patient Specific Titanium Meshes Loaded with Bone Marrow Aspirate Mixed with Xenograft versus Xenograft Mixed with Autografts Only: A Randomized Clinical Trial
No full textAim: The aim of the study is to evaluate the efficacy of three-dimensional bone augmentation in maxillary atrophied alveolar ridges by comparing the utilization of bone marrow aspirate concentrate along with a mix of autograft and xenograft versus autograft and xenograft alone. utilizing patient-specific titanium meshes. Subjects and methods: Ten patients experiencing severe vertical and horizontal deficiencies in the entire maxillary arch underwent guided bone regeneration. Utilizing specialized software, virtual bone augmentation were performed for the deficient ridge across the entire maxillary alveolus. This process involved generating virtually augmented models to guide the preoperative prebending of titanium meshes. In the study group, prebent meshes were filled with a combination of xenograft and bone marrow aspirate concentrate (BMAC). Conversely, in the control group, the meshes were loaded with xenograft mixed solely with autograft in a 1:1 ratio. Results: All patients experienced uneventful wound healing. Six months postoperatively, Cone Beam Computed Tomography (CBCT) scans were conducted for each patient, revealing a higher Mean vertical bone gain in the Study group (3.46 ± 0.89 mm) while the Control group showed (2.57±0.96 mm). The Mean horizontal bone gain in the Study group (3.80±1.20 mm) while the Control group was (3.91±0.89 mm). Conclusion: The difference between the study and control group in both vertical and horizontal bone gain was not statistically significant. Three-dimensional bone augmentation using prebent titanium meshes loaded with xenograft with bone marrow aspirate concentrate could be a reliable less morbid technique
Flexural behavior of cold-formed steel Z-purlin overlapped connections
No full textThis research presents a novel investigation into the structural performance of cold-formed steel (CFS) overlapped purlin connections under monotonic loading, addressing a gap in understanding their moment capacity and optimizing connection configurations. A combined experimental and numerical approach was employed to evaluate the influence of overlap length and bolt placement on connection strength. Three experimental programs were conducted, each examining a specimen setup with high-strength bolts (grade 8.8, 12 mm diameter) connecting Z-section purlins. The tests were performed under continuous support conditions spanning 1500 mm. Finite element modeling was performed using shell elements and mesh-independent, point-based fasteners to validate experimental results, achieving a discrepancy of less than 5 %. Results indicated that increasing the overlap length-to-span ratio (Lp/S) from 0.2 to 0.67 significantly improved the negative moment capacity—by 36.3 % using web bolts, 35.8 % using flange bolts, and 35 % with both. Notably, configurations with bolts in both the web and flange exhibited the highest strength. These findings provide valuable insights for optimizing purlin connections in lightweight steel construction, enhancing structural efficiency and design reliability in industrial and commercial applications