Search Results (3,215)

During the COVID-19 pandemic, researchers have made efforts to detect COVID-19 through various methods. In the dataset used for this study, COVID-19 patients were identified using chest computed tomography (CT) images. High dimensionality is frequently an issue in machine learning image classification. Accordingly, this study implemented three dimensionality reduction methods in combination with various machine learning algorithms for improved classification. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and diffusion maps were applied to the dataset to extract the most important features of the chest CT images. The extracted features were given as input either to logistic regression or the extreme gradient boosting (XGBoost) algorithm to perform classification. The strongest model identified from this study was diffusion maps in combination with logistic regression. This model, evaluated against existing models from similar studies in recent years, yielded strong performance for detecting COVID-19 cases using chest CT images. Our proposed model achieved 97.35% accuracy, 92.16% sensitivity, and 98.59% specificity on the held-out test set in differentiating between COVID-19-positive cases and healthy, non-COVID-19 cases. This study aimed to detect COVID-19 without the use of viral testing. Importantly, this method could assist clinicians in making an initial diagnosis, especially when viral testing is not available or timely enough for the patient’s case. This study also provides deeper insight into various dimensionality reduction methods and how compatible they are with biomedical imaging data. Models were trained using stratified cross-validation on the training set, with final performance evaluated on a held-out test set at the patient level to prevent data leakage. Additional imbalance-aware metrics were used to assess robustness given class distribution differences. Full article

An inhomogeneous thin internal stratum sometimes exists between two dissimilar materials, which is usually caused by non-uniform thermal distribution, interaction of different media, diffusion impurity or material degeneration and damage. In this paper, it is considered as a functional graded (FG) piezoelectric material in surface acoustic wave devices, and we investigate its effect on Love wave propagation within the framework of the linear piezoelectric theory. Correspondingly, the power series technique is presented and applied to solve the dynamic governing equations, i.e., two-dimensional partial differential equations with variable coefficients, with the convergence and correctness being proved. In this method, the material coefficients can change in random functions along the thickness direction, which reveals the generality of this method to some extent. As the numerical case, the elastic coefficient, piezoelectric coefficient, dielectric permittivity, and mass density change in the linear form but with different graded parameters, and the influence of material inhomogeneity on the Love wave propagation is systematically investigated, including the phase velocity, electromechanical coupling factor, and displacement distribution. In addition, the FG piezoelectric material caused by piezoelectric damage and material bonding is discussed. Numerical results demonstrated that both piezoelectric damaged and material bonding can make the higher modes appear earlier for the electrically open case, decrease the initial phase velocity, and limit the existing region of the fundamental Love mode for the electrically shorted case. The qualitative conclusions and quantitative results can provide a theoretical guide for the structural design of surface wave devices and sensors. Full article

GH3536 superalloy is widely used in the high-temperature components of aerospace applications for its excellent high-temperature strength and corrosion resistance. However, under such a harsh environment, surface defects can make the superalloy prone to corrosion and fatigue fractures. Therefore, it is important to eliminate surface defects through polishing. However, the existing polishing methods usually suffer from some issues such as surface integrity damage, low efficiency, and poor environmental sustainability. More importantly, these methods fail to account for the requirement of surface roughness below 0.05 μm in some high-precision aerospace components. Herein, the plasma electrolytic polishing (PEP) of GH3536 superalloy is systematically investigated and optimized through single-factor experiments and response surface methodology (RSM). A minimum surface roughness Ra of 0.044 μm with a mirror-like surface was achieved at a voltage of 303.8 V, electrolyte temperature of 66.2 °C, polishing time of 5 min, and submersion depth of 7.5 cm. At the same optimized condition, the material removal rate was 59.12 mg·min⁻¹. After polishing, the surface composition of GH3536 superalloy varied negligibly, while its corrosion resistance improved markedly, with a 53.72% increase in polarization resistance and a 43.46% decrease in corrosion current density. Meanwhile, the microhardness slightly decreased due to the removal of the work-hardened layer and the compressive residual stress exhibited a more uniform distribution across the surface, contributing to improved near-surface mechanical stability. This study establishes an optimized PEP parameter for improving the surface quality of GH3536 superalloy, offering a practical method for the precision finishing of aerospace-grade superalloy components. Full article

Electrochemiluminescence immunoassay (ECLIA) is widely used in clinical diagnostics owing to its high sensitivity, broad dynamic range, and excellent analytical stability. However, the influence of magnetic bead deposition behavior on electrochemiluminescence (ECL) signal performance remains insufficiently characterized. In this study, a quantitative evaluation method for magnetic bead distribution uniformity on the electrode surface was established and applied to optimize fluidic parameters in an ECLIA measurement system. By combining microscopic imaging with image analysis, magnetic bead spreading behavior under different flow conditions was systematically characterized and correlated with luminescence signal intensity. Optimization of the flow rate (18.46 µL·s⁻¹) improved bead distribution uniformity and resulted in a 26.32% increase in luminescence intensity without altering bead coverage or assay chemistry. The optimized system was further validated using thyroid-stimulating hormone (TSH) detection, showing a linear response over 0.016–120 µIU·mL⁻¹ (R² > 0.996) and high consistency with a commercial analyzer (R² = 0.998) from Roche. These results demonstrate that quantitative control of magnetic bead distribution provides an effective strategy for improving ECLIA performance and offers a general optimization framework for bead-based electrochemiluminescence systems. Full article

This paper investigates the problem of parameter estimation and reliability analysis for the two-parameter Maxwell distribution under a random censoring mechanism. To address the limitation of the traditional single-parameter Maxwell distribution in practical applications, which lacks the threshold parameter, this paper proposes a two-parameter Maxwell distribution model. By introducing a threshold parameter, this model can more accurately characterize survival data with a minimum life or guaranteed operating time. Specifically, we construct a random censoring data model wherein both the failure time and censoring time are assumed to follow a two-parameter Maxwell distribution. The main research contents include: establishing a randomly censored data model, deriving classical inference methods based on maximum likelihood estimation. Under the general entropy loss function, Bayesian estimation is conducted using conjugate inverse Gamma priors for scale parameters and a uniform prior for the threshold parameter. A hybrid MCMC algorithm is implemented to generate posterior samples and construct highest posterior density credible intervals. We compare their performance through Monte Carlo simulations, evaluating finite-sample behavior in terms of bias, mean squared error, and interval estimation, and finally validating the practicality and superiority of the two-parameter model using real medical datasets from a colon cancer clinical trial. The results demonstrate that the two-parameter Maxwell distribution can more accurately describe survival data with threshold characteristics and outperforms the single-parameter model in terms of model fit and reliability estimation. Full article

Lightweight aggregate concrete (LWAC) is a practical alternative to conventional concrete in civil engineering, offering advantages such as reduced density, enhanced insulation properties, and improved seismic performance. However, segregation during compaction remains a limitation, as it can lead to non-uniform material distribution and reduced compressive strength. This study addresses this issue by combining non-destructive techniques with deep learning methods to predict the compressive strength of LWAC. We propose an explainable approach based on a convolutional recurrent neural network architecture, enhanced by unsupervised clustering and SHapley Additive exPlanations (SHAP), to improve interpretability. To optimize predictive performance, several aggregation strategies are evaluated at the recurrent layer before the dense layers, including full-sequence flattening, max pooling, average pooling, and an attention mechanism over the full sequence. Experimental results show that the proposed model outperforms conventional machine learning methods such as multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR), as well as ensemble methods such as gradient boosting (GBR), XGBoost, and weighted average ensemble (WAE). Furthermore, when combined with unsupervised clustering, the model identifies latent behavioral patterns that are not observable through traditional evaluation techniques. This demonstrates the potential of integrating non-destructive testing with interpretable deep learning as a reliable approach for the structural assessment of LWAC. Full article

Neural regeneration requires an optimal environment, including structural, chemical, mechanical, and electrical properties. Alginate (Alg) and graphene oxide (GO) are promising biomaterials for nerve tissue engineering, as Alg provides biocompatibility and hydrogel formation, while GO enhances mechanical strength and conductivity. For this study, GO was synthesized using the modified Hummer’s method, and Alg–GO scaffolds with varying GO concentrations were developed. FTIR spectroscopy confirmed the successful incorporation of GO into the Alg matrix, while UV–Vis and photoluminescence analyses demonstrated GO-induced modifications of the optical properties. Thermal analysis revealed improved stability with increasing GO content, whereas swelling tests showed enhanced water uptake and retention. Conductivity measurements indicated a clear improvement in electrical conductivity, particularly at moderate GO concentrations. SEM imaging confirmed a homogeneous distribution of GO within the Alg matrix, with structural uniformity across all samples. Cytocompatibility was assessed using SH–SY5Y neuroblastoma cells through MTT, LDH, and LIVE/DEAD assays. All composites supported cell attachment, viability, and proliferation, with GO concentrations up to 6% promoting optimal cell growth without inducing cytotoxicity. In contrast, excessive GO content (9%) resulted in reduced proliferation, although biocompatibility was maintained. These results highlight the potential of Alg–GO scaffolds as promising candidates for neural tissue engineering. The findings demonstrate the potential of Alg–GO scaffolds as advanced biomaterials for regenerative medicine. Future research should focus on in vivo evaluations to confirm their therapeutic applicability. Full article

The rapid integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace has introduced complex security challenges, particularly regarding the protection of critical infrastructure and personal privacy. While conventional detection mechanisms such as radar and optical sensors are widely deployed, they are frequently limited by line-of-sight obstructions and the small radar cross-section of modern commercial drones. Acoustic analysis presents a viable passive alternative; however, accurate three-dimensional localization remains a computationally demanding task, further complicated by the use of directional sensors with non-uniform sensitivity patterns. In this paper, a deep learning framework is proposed to address these ambiguities. The method involves the fusion of raw acoustic data with explicit sensor geometry metadata within a neural network architecture. To enhance localization precision, a composite loss function is introduced, which independently optimizes planar and altitude coordinates while penalizing outlier predictions. Experimental validation was conducted using a custom dataset of real-world drone flights, utilizing a distributed array of directional microphones. The results demonstrate that the proposed system effectively mitigates the spatial irregularities of ad hoc sensor deployment, achieving robust localization performance in complex acoustic environments. Full article

Background/Objectives: Dementia remains one of the major global health challenges of the modern era. Researchers worldwide continue to seek effective therapeutic strategies to combat this neurodegenerative condition. Silymarin is a natural compound with strong neuroprotective and antioxidant properties that holds great potential for dementia management; however, its poor aqueous solubility and limited ability to cross the blood–brain barrier (BBB) have restricted its clinical application. This study focused on the formulation and evaluation of a heparin–pullulan silymarin liposomal (HPSL) nano-gel to enhance the neuroprotective efficacy of silymarin, with potential for improved brain targeting effects. Methods: The HPSL nano-gel was synthesized using the thin-film hydration technique and optimized based on entrapment efficiency, particle size distribution, zeta potential, and in vitro release kinetics. The neuroprotective efficacy of the HPSL nano-gel was evaluated in mice using behavioral evaluations, biochemical quantification of oxidative stress markers, evaluation of cholinergic enzyme activity and detailed histopathological examination of brain tissues. Results: Morphological characterization using scanning electron microscopy (SEM) confirmed a uniform nano-scale structure. The optimized formulation (HPSL-3) exhibited a particle size of 406.07 ± 19.33 nm, zeta potential of −23.72 ± 7.64 mV and an entrapment efficiency of 73.53 ± 12.05%, indicating good colloidal stability and efficient drug loading. The in vitro release profile followed non-Fickian diffusion kinetics, suggesting sustained drug release behavior. Behavioral studies in scopolamine-induced amnesic mice (elevated plus maze, hole board, and light/dark paradigms) demonstrated significant (p ≤ 0.001) improvements in learning and memory retention. Biochemical analyses showed increased levels of ChAT, SOD, CAT, and GSH, along with decreased AChE and MDA levels, supporting the neuroprotective potential of the formulation. Histopathological evaluation revealed marked attenuation of neuronal degeneration, inflammation, and edema (HAI = 4) compared to the scopolamine-treated group (HAI = 11). Conclusions: Overall, the HPSL-2 formulation effectively enhanced silymarin delivery across the BBB, demonstrating potent antioxidant, neuroprotective, and cholinergic modulatory effects. These findings suggest that HPSL-2 represents a promising nano-carrier system for the management of dementia and other oxidative-stress-related neurological disorders. Full article

Accurate prediction of the energy yield of bifacial photovoltaic (PV) modules requires a proper evaluation of albedo irradiance and the associated mismatch losses. In this work, an advanced tool for the assessment of the power production of bifacial modules is presented. The tool benefits from a refined numerical evaluation of ground-reflected irradiance performed through a view-factor-based cell-level approach within a realistic three-dimensional (3D) Sun-module-shadow geometry. This allows capturing both vertical and lateral nonuniformities in the irradiance distributions over the module surfaces, which are neglected in conventional module-level models. The irradiances incident on the cells are subsequently supplied to a circuit-based block, operating with a cell-level granularity as well, which computes the I–V characteristics and the maximum power point (MPP) at selected time instants. Simulations performed on a simplified tool variant assuming uniform albedo irradiance show that this approximation leads to a non-negligible overestimation of power output. An extensive comparison against state-of-the-art tools, including the previous version of our framework, allows us to conclude that the proposed method is especially advantageous for standalone modules or short-row configurations under medium-to-high albedo conditions. Moreover—like its previous version—the tool can handle a large variety of detrimental effects, namely, partial architectural shading, localized snow coverage, bird droppings, and faulty cells. Additionally, a non-zero elevation from the ground can be effectively described. It is also found that south-oriented 30°-tilted bifacial modules suffer from appreciable albedo-induced mismatch losses on the rear surface during summer under medium-albedo conditions, whereas vertically-mounted West- and East-oriented configurations are less affected by such losses. Experimental validation confirms the accuracy of the proposed framework. Full article

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy is highly sensitive to panel discretization strategies, which are often determined heuristically. This study proposes a bio-inspired optimization framework for VLM panel discretization and evaluates it through a systematic case study on a representative wing geometry. A grid-convergence analysis was initially carried out to ensure solution independence across various spanwise-to-chordwise panel ratios. Subsequently, a novel Hybrid Response Surface Methodology (HRSM), integrating Box–Behnken and Central Composite experimental designs, was employed to enable a more comprehensive exploration of the factor space while quantifying the effects of clustering parameters at the leading-edge, trailing-edge, root, and tip regions of the wing. The HRSM dataset was further utilized to train Ensemble Machine-Learning surrogate models, which were coupled with bio-inspired Crayfish and Aquila optimization algorithms, alongside a classical Genetic Algorithm (GA) as a performance benchmark, to identify the optimal discretization strategy and to enable a comparative assessment of their convergence behavior and robustness against the numerical noise of the ensemble-based landscape. Compared to base (i.e., uniform) panel distribution, the optimally clustered discretization enhanced overall aerodynamic prediction accuracy by approximately 33%, particularly at low angles of attack, while maintaining robust performance at higher angles. Both algorithms converged to similar minima; however, the Aquila algorithm achieved higher solution consistency, whereas the Crayfish algorithm exhibited greater dispersion despite faster convergence, revealing a multimodal optimization landscape. The variance decomposition revealed that trailing-edge clustering dominated aerodynamic accuracy at low angles of attack, contributing up to 90% of the total variance, whereas tip clustering became increasingly influential at higher angles, exceeding 30%, highlighting the need for adaptive discretization strategies to ensure reliable VLM-based aerodynamic analyses. Full article

With the deepening implementation of the dual-carbon strategy, the penetration rates of distributed power sources and flexible loads in new distribution grids continue to rise, posing significant challenges to system security and stability due to output fluctuations and randomness. To enhance voltage quality and achieve low-carbon economic operation in distribution grids, this paper proposes a multi-objective optimization model for Distributed Energy Storage System allocation. The model integrates power quality, economic benefits, and net carbon emissions. To efficiently solve this high-dimensional nonlinear problem, an improved Multi-Objective Gray Wolf Optimization algorithm is proposed. It employs a chaotic map to initialize the population, enhancing global distribution uniformity. A nonlinear convergence factor is introduced to dynamically balance global exploration and local exploitation. A dynamic grouping collaboration strategy is designed, combining Lévy flight and the elite crossover strategy to enhance search capability and convergence accuracy. Simulations on an IEEE 33-node system show that the improved MOGWO-optimized energy storage scheme reduces average voltage deviation by 37.0%, total operating costs by 7.0%, and net carbon emissions by 4.1%, compared to a no-storage scenario. Compared to the standard MOGWO algorithm, the proposed method achieves further optimization across all objectives, validating its effectiveness and superiority in realizing coordinated energy storage planning that balances safety, economy, and low-carbon goals. Full article

As critical end-effectors enabling the practical deployment of marine robotic systems, soft hands face persistent challenges including multi-finger asynchronization, unbalanced force distribution, and insufficient anti-disturbance robustness, compounded by constraints from soft material nonlinearity and harsh marine environmental disturbances. To address these limitations, this paper proposes a dexterous grasping method integrating coupled dynamical systems and adaptive force planning control, designed to enhance operational reliability in complex marine environments. An intermediate dynamic layer is embedded to ensure precise multi-finger synchronization, a hybrid force planning algorithm balances force uniformity and constraint satisfaction, and an adaptive controller synergizes with a Neo-Hookean model to compensate for nonlinear deviations. Simulations and physical experiments demonstrate that the method delivers excellent grasping stability and accuracy for uneven mass distribution targets such as cylinders and spheres, while balancing synchronization precision, constraint compliance, and anti-disturbance capability. Compared with the traditional coupled dynamical systems (DSs), the constraint violation is reduced by up to 18.2%, the friction force is increased by 4.0%, and the force distribution uniformity is improved by approximately 5.1%.Compared with the particle swarm optimization (PSO) strategy, the constraint violation is reduced by up to 50.5%, the friction force is increased by 40.9%, and the force distribution uniformity is also improved by about 5.1%. This work fills a key gap in balancing multiple performance metrics for marine soft hands, providing a reliable technical solution to accelerate the real-world deployment of marine robotic systems. Full article

CoNi layered double hydroxides (CoNiLDHs) were successfully synthesized on nickel foam (NF) using a hydrothermal method. X-ray diffraction (XRD) analysis confirmed the formation of a well-defined hydrotalcite-like phase, including a strong (003) peak, indicating layered stacking. Scanning electron microscopy (SEM) revealed a 3D hierarchical nanosheet structure resembling flower-like arrays, which was further supported by EDS mapping showing a uniform distribution of Co, Ni, and O. Electrochemical studies demonstrated excellent OER activity, with a low overpotential of 188 mV at 10 mA/cm² and a Tafel slope of 97.48 mV/dec, inferring rapid reaction kinetics. Furthermore, the material exhibited a significant electrochemical surface area (ECSA) compared to bare NF. Chronoamperometry over 24 h confirmed the operational durability catalyst, stabilizing around 7–8 mA/cm², validating its potential as a cost-effective and efficient OER electrocatalyst in alkaline media. Full article

Background/Objectives: Current clinical evidence suggests that cannabidiol (CBD) demonstrates therapeutic potential in the management of chronic pain, particularly in conditions involving inflammation. However, its therapeutic potential is severely limited by poor oral bioavailability, extensive first-pass metabolism, and the need for frequent high-dose administration, which compromises patient adherence and tolerability. Long-acting injectable (LAI) delivery systems offer a strategy to overcome these limitations by providing sustained plasma concentrations and reducing dosing frequency. This study aimed to develop and optimise CBD-loaded poly (lactic-co-glycolic acid) (PLGA) microspheres for LAI delivery and to evaluate poly(2-ethyl-2-oxazoline) (POx) as a functional and biocompatible alternative to the conventionally used poly (ethylene glycol) (PEG). Methods: CBD-loaded microspheres were prepared using emulsion–solvent evaporation technique. The formulations were optimised based on entrapment efficiency (EE), drug loading (DL), particle size distribution, surface morphology, thermal behaviour, in vitro release kinetics, and cytocompatibility using NIH 3T3 fibroblasts. Multiple in vitro release methodologies, including dialysis bag, shaking-flask, and USP Apparatus IV, were evaluated to identify the most discriminative and practical approach for long-term release assessment. Results: The optimised POx-based microspheres demonstrated superior control over particle size, yielding significantly smaller and more uniform particles compared with PEG-based microspheres (124 ± 1.47 µm vs. 218 ± 13.5 µm, respectively). Differential scanning calorimetry (DSC) confirmed molecular dispersion of CBD within the polymer matrix. In vitro release studies demonstrated sustained drug release over 20 days. Conclusions: POx represents a promising alternative to PEG for the formulation of CBD-loaded PLGA microspheres, offering enhanced physicochemical stability and biological compatibility. This platform supports the development of safe and effective long-acting injectable CBD therapies and consideration of POx as an alternative to PEG. Full article