Search Results (2,894)

Background: Using natural substances for cancer therapy has attracted considerable interest due to their safety and reduced systemic toxicity. Nigella sativa (NS) oil, a traditional natural oil rich in bioactive compounds, possesses significant therapeutic potential. Brucine (BR), an alkaloid, exhibits potent cytotoxicity against various cancer cell lines; however, its poor selectivity and high systemic toxicity limit its clinical application. Objective: To overcome these challenges, this study aimed to enhance drug delivery and improve therapeutic efficacy. Method: A PEGylated nanoemulsion (NE) incorporating NS and BR was developed and characterized for particle size, size distribution, zeta potential, viscosity, and drug content. The in vitro release of BR was evaluated both with and without serum incubation. A quantitative amount of serum protein associated with the surface of the NE was estimated, and a hemolytic safety assay was carried out. Finally, an in vitro cytotoxicity study was conducted, and the in vivo anti-tumor effect of the developed PEGylated BR-loaded NE was evaluated and compared with its naked counterpart. Result: The developed PEGylated BR-loaded NE possessed favorable characteristics as a nanocarrier for parenteral administration, with a particle size of 188.5 nm, a zeta potential of −1.61, a viscosity of 3.4 cP, and 99% drug content uniformity. It released up to 60.4% of BR over 12 h, while only 18.4 µg/µmol of the total lipids were adsorbed on the surface of the formulation, compared with 54.5 µg/µmol for the naked counterpart. The PEGylated NE was safe, inducing less than 5% of hemolysis, and displayed substantial inhibition of MDA cell growth. Conclusions: The PEGylated NE achieved a significant reduction in tumor volume, suggesting that PEGylated NE may serve as a promising platform for enhancing anti-tumor activity. Full article

Al-SiC composite coatings were successfully fabricated through the process of electrodeposition utilizing an AlCl₃-LiAlH₄-benzene-THF system. This method allows for the incorporation of silicon carbide (SiC) particles into the aluminum matrix, enhancing the coating’s properties. The study examined various factors that influence the coating characteristics, including current density, temperature, and the quantity of SiC particles added to the formula. The findings revealed that these parameters significantly affect the resulting surface morphology, corrosion resistance, and hardness of the Al-SiC composite coatings. Specifically, the analysis demonstrated that the Al-SiC composite coating produced optimal surface morphology, which is crucial for its performance and durability in various applications. when the current density is 50 mA/cm², the bath temperature is at 30 °C, and the addition amount of SiC particles is optimized to 40 g/L. Combined with electrochemical experimental data, the corrosion resistance of the composite coating prepared under this condition was significantly improved. The results of scanning electron microscopy showed that the surface of the composite coating prepared under this process parameter was uniform and dense, without obvious holes and cracks, and the SiC particles were uniformly distributed in the coating with high density. Through the hardness test of composite coatings with different SiC particle contents, it was found that in the research interval, when the SiC particle content was less than 3 wt%, the hardness of the coating changed relatively slowly. As the amount of SiC particles surpassed 4 wt%, there was a notable increase in hardness. At a SiC concentration of 5%, the coating exhibited a hardness level of 152.1 HV. Full article

Background/Objectives: Pharmaceutical preparation technologies can enhance the bioavailability of poorly water-soluble drugs. Ursolic acid (UA) has been found to possess anti-cancer and hepatoprotective properties, demonstrating its potential as a therapeutic agent; however, its hydrophobicity and low solubility present challenges in the development of drug formulations. This study investigates the preparation of a nano-UA suspension by wet grinding, researches the influence of process parameters on particle size, and explores the rules of particle breakage and agglomeration by combining model fitting. Methods: Wet grinding experiments were conducted using a laboratory-scale grinding machine. The particle size distributions (PSDs) of UA suspensions under different grinding conditions were measured using a laser particle size analyzer. A single-factor experimental design was employed to optimize operational conditions. Model parameters for a population balance model considering both breakage and agglomeration were determined by an evolutionary algorithm optimization method. By measuring the degree to which UA inhibits the colorimetric reaction between salicylic acid and hydroxyl radicals, its antioxidant capacity in scavenging hydroxyl radicals was indirectly evaluated. Results: Wet grinding process conditions for nano-UA particles were established, yielding a UA suspension with a D50 particle size of 122 nm. The scavenging rate of the final grinding product was improved to three times higher than that of the UA raw material (D50 = 14.2 μm). Conclusions: Preparing nano-UA suspensions via wet grinding technology can significantly enhance their antioxidant properties. Model regression analysis of PSD data reveals that increasing the grinding mill’s stirring speed leads to more uniform particle size distribution, indicating that grinding speed (power) is a critical factor in producing nanosuspensions. Full article

Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article

This study investigates the structural and magnetic properties of CoFe₂O₄ nanoparticles prepared by five different synthesis methods: coprecipitation, ultrasound-assisted coprecipitation, coprecipitation coupled with mechanochemical treatment, microemulsion and microwave-assisted hydrothermal synthesis. The produced powders were additionally functionalized with starch to improve biocompatibility and colloidal stability. The starch-coating procedure itself by sonication in starch solution, as well as its result, affects the structural and magnetic properties of functionalized nanoparticles. The resulting changes of properties in the process of ligand addition depend significantly on the starting nanoparticles, or rather, on the method of their synthesis. The structural, magnetic and spectroscopic properties of the resulting materials were systematically investigated using X-ray diffraction (XRD), Raman spectroscopy, Mössbauer spectroscopy and magnetic measurements. Taken together, XRD, Raman and Mössbauer spectroscopy show that starch deposition reduces structural disorder and internal stress, resulting in nanoparticles with a more uniform size distribution. These changes, in turn, affect all magnetic properties—magnetization, coercivity and magnetic anisotropy. Magnetic responses are preserved what is desirable for future biomedical applications. This work emphasizes the importance of surface modification for tailoring the properties of magnetic nanoparticles while maintaining their desired functionality. Full article

Finite element (FE) models are widely used in structural health monitoring to represent real structures and assess their condition, but discrepancies often arise between numerical and actual structural behavior due to simplifying assumptions, uncertain parameters, and environmental influences. Temperature variation, in particular, significantly affects structural stiffness and modal properties, yet it is often treated as noise in traditional model updating methods. This study treats temperature changes as valuable information for model updating and structural damage quantification. The Bayesian model updating approach (BMUA) is a probabilistic approach that updates uncertain model parameters by combining prior knowledge with measured data to estimate their posterior probability distributions. However, traditional BMUA methods assume mass is known and only update stiffness. A novel BMUA framework is proposed that incorporates thermal buckling and temperature-dependent stiffness estimation and introduces an algorithm to eliminate the coupling effect between mass and stiffness by using temperature-induced stiffness changes. This enables the simultaneous updating of both parameters. The framework is validated through numerical simulations on a three-story aluminum shear frame under uniform and non-uniform temperature distributions. Under healthy and uniform temperature conditions, stiffness parameters were estimated with high accuracy, with errors below 0.5% and within uncertainty bounds, while mass parameters exhibited errors up to 13.8% that exceeded their extremely low standard deviations, indicating potential model bias. Under non-uniform temperature distributions, accuracy declined, particularly for localized damage cases, with significant deviations in both parameters. Full article

Hand rehabilitation exoskeletons play a critical role in restoring motor function in patients with stroke or hand injuries. However, most existing designs rely on fixed-axis assumptions, neglecting the rolling–sliding coupling of finger joints that causes instantaneous center of rotation (ICOR) drift, leading to kinematic misalignment and localized pressure concentrations. This study proposes the Instant Radius Method (IRM) based on medical imaging to continuously model ICOR trajectories of the MCP, PIP, and DIP joints, followed by the construction of an equivalent ICOR through curve fitting. Crossing-type biomimetic kinematic pairs were designed according to the equivalent ICOR and integrated into a three-loop ten-linkage exoskeleton capable of dual DOFs per finger (flexion–extension and abduction–adduction, 10 DOFs in total). Kinematic validation was performed using IMU sensors (Delsys) to capture joint angles, and interface pressure distribution at MCP and PIP was measured using thin-film pressure sensors. Experimental results demonstrated that with biomimetic kinematic pairs, the exoskeleton’s fingertip trajectories matched physiological trajectories more closely, with significantly reduced RMSE. Pressure measurements showed a reduction of approximately 15–25% in mean pressure and 20–30% in peak pressure at MCP and PIP, with more uniform distributions. The integrated framework of IRM-based modeling–equivalent ICOR–biomimetic kinematic pairs–multi-DOF exoskeleton design effectively enhanced kinematic alignment and human–machine compatibility. This work highlights the importance and feasibility of ICOR alignment in rehabilitation robotics and provides a promising pathway toward personalized rehabilitation and clinical translation. Full article

The design of mega-constellations poses a formidable challenge, as the selection of an optimal configuration directly governs system-level performance, while the computational efficiency of the design methodology remains a critical concern. To address this, this paper presents a high-efficiency, versatile optimization framework predicated on a genetic algorithm. The framework is architected to design diverse configurations, including Walker-$\delta$ and Rose constellations, and supports two distinct optimization objectives: the minimization of satellite count for prescribed performance requirements, or the maximization of coverage performance for a fixed number of satellites. To ensure computational tractability, the GA is holistically integrated with a rapid and accurate coverage analysis engine based on an area-adaptive uniform point distribution. The framework’s efficacy and validity are rigorously demonstrated through extensive simulations. The results exhibit strong consistency with the industry-standard Systems Tool Kit 11 software, with average deviations for key performance indicators—namely, coverage time ratio, average coverage multiplicity, and revisit time—controlled within 1%, 0.1, and 35 s, respectively. Moreover, when applied to a specific optimization task, the algorithm successfully identified a 181-satellite constellation that satisfied a given revisit requirement. The proposed method therefore constitutes an efficient, reliable, and automated tool for the design of complex mega-constellation architectures, promoting the diversified development of constellation configurations and enhancing the performance and resource optimization of satellite systems. Full article

Silicon carbide (SiC) is a promising semiconductor material for electronics and photonics. Ultrafast laser processing of SiC enables three-dimensional nanostructuring, enriching and expanding the functionalities of SiC devices. However, challenges arise in delivering uniform, high-aspect-ratio (length-to-width) nanostructures due to difficulties in confining light energy at the nanoscale while simultaneously regulating intense photo modifications. In this study, we report the controllable growth of long-distance, high-straightness, and high-parallelism multifilament structures in SiC using ultrafast laser processing. The mechanism is the formation of femtosecond multifilaments through the nonlinear effects of clamping equilibrium, which allow highly confined light to propagate without diffraction in parallel channels, further inducing high-aspect-ratio nanostripe-like photomodifications. By employing an elliptical Gaussian beam—rather than a circular one—and optimizing pulse durations to stabilize multifilaments with regular positional distributions, the induced multifilament structures can reach a length of approximately 90 μm with a minimum linewidth of only 28 nm, resulting in an aspect ratio of over 3200:1. Raman tests indicate that the photomodified regions consist of amorphous SiC, amorphous silicon, and amorphous carbon, and photoluminescence tests reveal that silicon vacancy color centers could be induced in areas with lower light power density. By leveraging femtosecond multifilaments for diffraction-less light confinement, this work proposes an effective method for manufacturing deep-subwavelength, high-aspect-ratio nanostructures in SiC. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

This study demonstrates the successful preparation of pristine and modified PVC polymer films with (0.7, 1.0, 2.0, and 3.0 wt%) Cr_1.4Ca_0.6O₄ by the solution casting method. These films were characterized using XRD, FTIR, XPS, SEM, TGA, and a UV–vis spectrophotometer. The XRD confirmed the amorphous nature of PVC films and a tetragonal zircon-type structure of Cr_1.4Ca_0.6O₄ as a dopant in the PVC polymer. The XPS survey spectra of pristine Cr_1.4Ca_0.6O₄ and its composites with PVC reveal essential insights into the materials’ surface composition and chemical states. The spectra clearly show peaks corresponding to O1s, Ca2p, and Cr2p, with the Cr2p signals being notably weaker than the other peaks. SEM images showed a uniform distribution of Cr_1.4Ca_0.6O₄ within the PVC polymer films despite the presence of some minor agglomerations. The TGA analysis revealed that incorporating Cr_1.4Ca_0.6O₄ enhanced the thermal stability of PVC films, particularly at a 0.7 wt% concentration of Cr_1.4Ca_0.6O₄. Moreover, incorporation of Cr_1.4Ca_0.6O₄ improved the optical parameters of PVC films, i.e., linear refractive index, nonlinear refractive index, and optical susceptibility. These findings proposed the modified PVC with Cr_1.4Ca_0.6O₄ for optoelectronic applications. Full article

Background/Objectives: Crystallization is a key process in the manufacturing of active pharmaceutical ingredients (APIs), as it significantly affects the physical and chemical properties of the final product. Nicergoline, a clinically relevant ergot derivative, was chosen as a model compound to investigate how different crystallization strategies affect particle attributes. The objective of this study was to compare controlled and uncontrolled crystallization techniques and evaluate their impact on the physicochemical properties of nicergoline. Methods: Nicergoline was crystallized using controlled methods, including sonication-induced and seeding-induced crystallization, and uncontrolled methods, namely cubic and linear cooling, as well as acetone evaporation. The resulting powders were characterized by using a range of physicochemical techniques to assess particle morphology, size distribution, agglomeration behavior, and surface properties. Results: Uncontrolled crystallization methods produced particles prone to agglomeration, resulting in a broader particle size distribution ranging from 8 to 720 µm and heterogeneous surface characteristics. In contrast, controlled crystallization generated more uniform particles with reduced agglomeration and narrower particle size distributions. Among the evaluated methods, sonocrystallization provided the most effective control over particle size and morphology, demonstrated by a narrow size distribution ranging from 16 to 39 µm which correlated with improved flowability and surface energy. Conclusions: The study demonstrates that the choice of crystallization method significantly influences the structural and physicochemical properties of nicergoline. These findings highlight the importance of method selection for tailoring API properties to enhance downstream processing and product quality. Full article

This study examines the mechanical, thermal, and electrical properties of copper tool electrodes processed via Equal Channel Angular Pressing (ECAP), with a specific focus on their performance in Electrical Discharge Machining (EDM) applications. A novel Crystal Plasticity Finite Element Method (CPFEM) framework is employed to model anisotropic slip behavior and microscale deformation mechanisms. The primary objective is to elucidate how initial crystallographic orientation influences hardness, thermal conductivity, and electrical conductivity. Simulations are performed on single-crystal copper for three representative Face Centered Cubic (FCC) orientations. Using an explicit CPFEM model, the study examines texture evolution and deformation heterogeneity during the ECAP process of single-crystal copper. The results indicate that the <100> single-crystal orientation exhibits the highest Taylor factor and the most homogeneous distribution of plastic equivalent strain (PEEQ), suggesting enhanced resistance to plastic flow. In contrast, the <111> single-crystal orientation displays localized deformation and reduced hardening. A decreasing Taylor factor correlates with more uniform slip, which improves both electrical and thermal conductivity, as well as machinability, by minimizing dislocation-related resistance. These findings make a novel contribution to the field by highlighting the critical role of crystallographic orientation in governing slip activity and deformation pathways, which directly impact thermal wear resistance and the fabrication efficiency of ECAP-processed copper electrodes in EDM. Full article

Carbon-fiber-reinforced-polymer/aluminum (CFRP/Al) double-sided countersunk riveted joint is a key joining technology for lightweight and high-performance aircraft structures. Advanced numerical simulation techniques are helpful in predicting riveting damage evolution and the optimization of the joining process. In this study, a discrete crack modeling (DCM) method based on the floating node method (FNM) was employed to investigate the initial riveting damage behavior and interference characteristics during the electromagnetic riveting (EMR) process with five cases of rivet-hole clearances. The results were compared with those obtained from the conventional smeared crack method (SCM). The findings show that the interference distribution along the axial direction of the joint is non-uniform, and increasing the rivet-hole clearance helps alleviate the initial riveting damage. The FNM accurately modeled the initiation and propagation of matrix cracks and delamination, albeit at the cost of some computational efficiency. Full article

Soil erosion is a key factor in land degradation across Mediterranean mountain regions, yet comprehensive assessments at the national scale are still uncommon. In this study, the Erosion Potential Method (EPM, Gavrilović method) was applied to 1127 mountainous watersheds of Greece in order to classify their erosion severity through the erosion coefficient (Z). Information on relief, geology and vegetation was combined so that each watershed could be assigned to one of five erosion severity classes. The classification revealed that 53.2% of the watersheds fall into the slight category, while 26.0% are moderate and 16.3% are very slight. Severe cases account for 3.9%, and only 0.5% are classified as excessive, though these few basins are locally very important. The distribution is far from uniform: severe watersheds occur more often in North Peloponnese (EL02), Thessaly (EL08), and the Western Sterea Ellada (EL04). By contrast, Crete (EL13) and the Aegean Islands (EL14) include a relatively greater proportion of watersheds in the moderate category. This variation indicates that erosion risk should not be considered a uniform condition across the country. Even watersheds with low overall Z may contain steep or degraded slopes that act as local hotspots. Consequently, effective management should move beyond country-wide averages and instead focus on the sub-areas that are most exposed and susceptible to erosion. Full article