Search Results (14,328)

Cellular senescence can occur with similar phenotypes in normal cells, during aging, and in tumor cells, spontaneously or after cytostasis. The fall or increase in proliferative activity are key aspects of the respective conditions, in which the levels of reactive oxygen species can vary, affecting the cellular redox homeostasis. This work aimed to study the relationships between senescence and transformation by comparing cells with different proliferative activities and phenotypes attributable to transformation (NIHs cultures) or senescence (NIHv cultures), before and after incubation with hydrogen peroxide. Both cultures were derived from the NIH/3T3 cell line, which was used here as a reference (NIHb), after the serum starvation. Our experimental model can be representative of the heterogeneity of cell subpopulations, with different degrees of transformation and senescence, found in some tumors. The characterization of the functional properties of NIHb, NIHs, and NIHv cells was performed by a morphocytometric analysis of the cell cycle progression, mitochondrial and lysosomal content/activity, and superoxide anion production. The efficiency of the lysosomal compartment was also assessed by estimating the autophagic activity and measuring lipofuscin autofluorescence. Comparisons of nuclear and cytoplasmic parameters before and after the incubation with hydrogen peroxide revealed differences in the expression and modulation of cellular senescence patterns. The treatment effects were very limited in the NIHb culture; the senescence condition was essentially maintained in the NIHv cells, while the most relevant changes were found in the NIHs cells. In the latter, the acquisition of the senescent phenotype, also demonstrated by the positivity of SA-β-galactosidase, was correlated with a decrease in proliferative activity and a change in the content/activity of the mitochondria and lysosomes, which showed similarities with the basal senescence conditions of NIHv cells. In NIHs cells, increased autophagy events and lipofuscin accumulation also indicate the establishment of cytoplasmic dynamics typical of senescence. The variable responses to hydrogen peroxide, besides depending on the different basal cytokinetic activity of the cultures examined, appeared to be related to the specific cell redox state resulting from the balance between endogenous ROS and those produced after treatment. Especially in NIHs cells, the slowing down of the cell cycle was linked to dynamic interconnections between the mitochondrial and lysosomal compartments. This would indicate that transformed cells, such as NIHs, may express morpho-functional aspects and markers typical of cellular senescence, as a consequence of the modulation of their redox state. Full article

Rotor equipment material samples with varying degrees of degradation during long-term operation are characterized by lower (up to 17%) corrosion and hydrogen resistance compared to the initial state. The scheme of redistribution of carbides in structural components in the initial state and after long-term operation is presented. The schemes of the turning rotor shaft are visualized, while taking the microstructure features into account. During long-term service, the properties of steels are affected by changes in the parameters of structural components caused by the action of a hydrogen-containing environment. Based on the experimental data, the regression equation and approximation probability R² value describing the change in the electrochemical parameters of 38KhN3MFA rotor steel samples after 200, 225, 250, and 350 thousand hours of operation were obtained. During machining, an increase in hydrogen content was recorded in the chips, especially from degraded areas of the rotor shaft (up to 7.94 ppm), while in undegraded zones, it ranged from 2.1 to 4.4 ppm. A higher hydrogen concentration was correlated with increased surface roughness. The use of LCLs improved surface quality by 1.5 times compared to LCL_p. Dispersion caused by degradation contributed to hydrogen accumulation and changed the nature of material destruction. After repair, the rotors demonstrated stable operation for over 25 thousand hours, with no reappearance of critical defects observed during scheduled inspections. Full article

High manganese steel can improve its microstructure after aging treatment, which is beneficial for enhancing strength, toughness, and wear resistance. This study aims to explore the effect of aging treatment on mechanical properties and wear resistance of high manganese steel (containing 25% Mn, called Mn25 steel) by designing different aging temperatures (450 °C, 500 °C, and 550 °C) with the same aging time (1 h). The results indicated that with the increase in aging treatment temperature, the surface hardness of Mn25 steel first increased and then decreased, but was still higher than that of untreated Mn25 steel. In addition, the impact toughness of steel decreased first and then increased with the increase in aging temperature, with the optimal hardness and impact toughness exhibited at 550 °C. The impact abrasive wear test results showed that the weight loss of Mn25 steel decreased with the increase in aging treatment temperature. After aging treatment at 550 °C, the weight loss is the lowest, which shows the optimal wear resistance performance. Under a high-impact load of 5.0 J, the hardness increased by nearly 49.96% after impact abrasive wear, and the effective hardening layer of the steel was the thickest, about 3800 μm. This is mainly related to the best match between the hardness and impact toughness of high manganese steel after aging treatment. The wear morphology is often caused by various wear mechanisms working together to cause the wear loss of Mn25 steel during the impact wear process. The wear morphologies of the Mn25 steel were mainly characterized by press-in particles, furrow, spalling, and strain fatigue. Through experimental analysis, a suitable aging treatment process has been determined, providing a theoretical basis for the practical application of high manganese steel. Full article

In hyperspectral image (HSI) classification, feature learning and label accuracy play a crucial role. In actual hyperspectral scenes, however, noisy labels are unavoidable and seriously impact the performance of methods. While deep learning has achieved remarkable results in HSI classification tasks, its noise-resistant performance usually comes at the cost of feature representation capabilities. High-dimensional and deep convolution can capture rich deep semantic features, but with high complexity and resource consumption. To deal with these problems, we propose a CViT Weakly Supervised Network (CWSN) for HSI classification. Specifically, a lightweight 1D-2D two-branch network is used for local generalization and enhancement of spatial–spectral features. Then, the fusion and characterization of local and global features are achieved through the CNN-Vision Transformer (CViT) cascade strategy. The experimental results on four benchmark HSI datasets show that CWSN has good anti-noise ability and ensures the robustness and versatility of the network facing both clean and noisy training sets. Compared to other methods, the CWSN has better classification accuracy. Full article

The characteristic parameters of fixed joint surfaces, such as stiffness and damping, play a dominant role in determining the overall performance of various mechanical structures, especially in high-performance machine tools. However, accurate characterization of these joint parameters remains challenging, due to a lack of direct validation methods. In this study, we consider the deformation of the base caused by the interaction of micro-convex bodies based on the 3D Kogut and Etsion (KE) model, and modify the characteristic parameter model of joint surfaces. Interfacial fractal parameters were determined using the structural function method, which enabled direct experimental validation of the characteristic parameter model. Finally, a comprehensive dynamic performance analysis of the GP300 grinding machine was conducted, and the results revealed that the joint surface changed the various modes of vibration and the corresponding natural frequencies of the machine tool. These findings deepen our understanding of the characteristic parameters of the binding surface and their effects, and have important guiding significance for the performance analysis and design of machine tools. Full article

Adipose tissue exhibits dynamic metabolic and structural changes in response to environmental stimuli, including temperature fluctuations. While continuous cold exposure has been extensively studied, the molecular effects of prolonged intermittent cold exposure (ICE) remain poorly characterized. Here, we present a proteomic analysis of inguinal white adipose tissue (IWAT) from mice subjected to a 16-week regimen of short-term daily ICE (6 °C for 6 h, 5 days per week) without compensatory caloric intake. Mass spectrometry identified 1108 proteins, with 140 differentially expressed between experimental and control groups. ICE significantly upregulated mitochondrial proteins associated with lipid and carbohydrate catabolism, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and lipogenesis, including LETM1, AIFM1, PHB, PHB2, ACOT2, NDUA9, and ATP5J. These changes reflect enhanced metabolic activity and mitochondrial remodeling. In contrast, proteins linked to oxidative stress, insulin resistance, inflammation, and extracellular matrix remodeling were downregulated, such as HMGB1, FETUA, SERPH1, RPN1, and AOC3. Notably, gamma-synuclein (SYUG), which inhibits lipolysis, was undetectable in ICE-treated samples. Our findings support the hypothesis that ICE promotes thermogenic reprogramming and metabolic rejuvenation in subcutaneous fat through activation of futile cycles and mitochondrial restructuring. This study offers molecular insights into adaptive thermogenesis and presents intermittent cold exposure as a potential strategy to mitigate adipose tissue aging. Full article

The 7075-T7451 aluminum alloy, widely used in aerospace, aviation, and automotive fields for critical load-bearing components due to its excellent mechanical properties, suffers from residual stresses induced by thermo-mechanical coupling during milling, which deteriorate workpiece performance. This study explores how key milling parameters—spindle speed *n_c*, feed per tooth *f_z*, cutting depth *a_p*, and cutting width *a_e*—affect surface residual stress and cutting force via orthogonal experiments and finite element analysis (FEA). Results show *a_e* is critical for X-direction residual stresses, while *f_z* dominates Y-direction ones. Cutting force increases with *f_z*, *a_p*, and *a_e* but decreases with higher *n_c*. Multivariate regression-based prediction models for residual stress and cutting force were established, which effectively characterize parameter–response relationships with maximum prediction errors of 18.69% (residual stress) and 12.27% (cutting force), showing good engineering applicability. The findings provide theoretical and experimental foundations for multi-parameter optimization in aluminum alloy milling and residual stress/cutting force control, with satisfactory practical effectiveness. Full article

The rapid proliferation of heterogeneous Internet of Things (IoT) devices has introduced a wide range of operational and security challenges, particularly in the domains of device identification and behavior profiling. Traditional fingerprinting methods, which rely primarily on time domain features, often fail to capture the complex, periodic, and often bursty nature of IoT communication—especially in environments characterized by sparse, irregular, or noisy traffic patterns. To address these limitations, two novel frequency-based fingerprinting techniques have been proposed: Spectral-Only Frequency Fingerprint (SFF) and Spectro-Correlative Frequency Fingerprint (SCFF). These approaches shift the analysis from the time domain to the frequency domain, enabling the extraction of richer and more robust behavioral signatures from network traffic. While SFF focuses on capturing the core spectral features of device traffic, SCFF extends this by incorporating inter-feature correlations, offering a more nuanced and comprehensive representation of device behavior. The effectiveness of SFF and SCFF is evaluated across multiple publicly available IoT datasets using a range of machine learning classifiers. Experimental results demonstrate that both fingerprinting methods significantly outperform traditional time domain approaches in terms of accuracy, precision, recall, and F1-score—across all tested classifiers and datasets. Full article

In multi-criteria decision-making (MCDM) environments characterized by uncertainty and data irregularities, the reliability of weighting methods becomes critical for ensuring robust and accurate decisions. This study introduces a novel hybrid objective weighting method—IQRBOW-E (Interquartile Range-Based Objective Weighting with Entropy)—which dynamically combines the statistical robustness of the IQRBOW method with the information sensitivity of Entropy through a tunable parameter $\beta$. The method allows decision-makers to flexibly control the trade-off between robustness and information contribution, enhancing the adaptability of decision support systems. A comprehensive experimental design involving ten simulation scenarios was implemented, in which the number of criteria, alternatives, and outlier ratios were varied. The IQRBOW-E method was integrated into the VIKOR framework and evaluated through average $Q$ values, stability ratios, SRD scores, and the Friedman test. The results indicate that the proposed hybrid approach achieves superior decision stability and performance, particularly in data environments with increasing outlier contamination. Optimal $\beta$ values were shown to shift systematically depending on data conditions, highlighting the model’s sensitivity and adaptability. This study not only advances the methodological landscape of MCDM by introducing a parameterized hybrid weighting model but also contributes a robust and generalizable weighting infrastructure for modern decision-making under uncertainty. Full article

Combustion instability poses a significant challenge in aerospace propulsion systems, particularly in afterburners that employ bluff-body flame stabilizers. The flame transfer function (FTF) is essential for characterizing the dynamic response of flames to perturbations, which is critical for predicting and controlling these instabilities. This study experimentally investigates the effect of varying the number of fuel injection holes (N = 3, 4, 5, 6) on the FTF and flame dynamics in a model afterburner combustor. Using acoustic excitations, the FTF was measured across a range of frequencies, with flame behavior analyzed via high-speed imaging and chemiluminescence techniques. Results reveal that the FTF gain exhibits dual-peak characteristics, initially decreasing and then increasing with higher N values. The frequencies of these gain peaks shift to higher values as N increases, while the time delay between velocity and heat release rate fluctuations decreases, indicating a faster flame response. Flame morphology analysis shows that higher N leads to shorter, taller flames due to enhanced fuel distribution and mixing. Detailed examination of flame dynamics indicates that different pulsation modes dominate at various frequencies, elucidating the observed FTF behavior. This research provides novel insights into the optimization of fuel injection configurations to enhance combustion stability in afterburners, advancing the development of more reliable and efficient aerospace propulsion systems. Full article

Endotracheal prosthesis placement is employed as a therapeutic intervention for tracheal lesions in cases where conventional surgical approaches are not feasible. The learning curve for endotracheal stent placement can vary depending on the type of stent, the training environment, and the clinician’s prior experience; however, it is generally considered moderately complex. Inadequate practice can have serious consequences, as the procedure involves a critical area such as the airway. The main risks and complications associated with inadequate technique or improper execution can include stent migration, formation of granulation tissue or hyperplasia, tracheal or pulmonary infection, obstruction or fracture of the stent, hemorrhage and tracheal perforation, among others. The purpose of the present study is to summarize important information and evaluate the role of different material features in the 3D printing manufacturing of an appropriate tracheobronchial medical device, which should be as appropriate as possible to facilitate placement during surgical practice. A complex stent design was fabricated using three different biodegradable materials, polycaprolactone (PCL), polydioxanone (PDO), and polymer blend of polylactic acid/polycaprolactone (PLA/PCL), through additive manufacturing, specifically fused filament fabrication (FFF)3D printing. Parameter optimization of the 3D printing process was required for each material to achieve an adequate geometric quality of the stent. Experimental analyses were conducted to characterize the mechanical properties of the printed stents. Flexural strength and radial compression resistance were evaluated, with particular emphasis on radial force due to its clinical relevance in preventing collapse after implantation in the trachea. The results provide valuable insights into how material selection could influence device behavior during placement to support surgical requirements. Full article

Conductive composites are a flexible class of engineered materials that combine conductive fillers with an insulating matrix—usually made of ceramic, polymeric, or a hybrid material—to customize a system’s electrical performance. By providing tunable electrical properties in addition to benefits like low density, mechanical flexibility, and processability, these materials are intended to fill the gap between conventional insulators and conductors. The increasing need for advanced technologies, such as energy storage devices, sensors, flexible electronics, and biomedical interfaces, has significantly accelerated their development. The electrical characteristics of composite materials, including metallic, ceramic, polymeric, and nanostructured systems, are thoroughly examined in this review. The impact of various reinforcement phases—such as ceramic fillers, carbon-based nanomaterials, and metallic nanoparticles—on the electrical conductivity and dielectric behavior of composites is highlighted. In addition to conduction models like correlated barrier hopping and Debye relaxation, the study investigates mechanisms like percolation thresholds, interfacial polarization, and electron/hole mobility. Because of the creation of conductive pathways and improved charge transport, developments in nanocomposite engineering, especially with regard to graphene derivatives and silver nanoparticles, have shown notable improvements in electrical performance. This work covers the theoretical underpinnings and physical principles of conductivity and permittivity in composites, as well as experimental approaches, characterization methods (such as SEM, AFM, and impedance spectroscopy), and real-world applications in fields like biomedical devices, sensors, energy storage, and electronics. This review provides important insights for researchers who want to create and modify multifunctional composite materials with improved electrical properties by bridging basic theory with technological applications. Full article

Hyperspectral imagery (HSI) has demonstrated significant potential in remote sensing applications because of its abundant spectral and spatial information. However, current mainstream hyperspectral image classification models are generally characterized by high computational complexity, structural intricacy, and a strong reliance on training samples, which poses challenges in meeting application demands under resource-constrained conditions. To this end, a lightweight hyperspectral image classification model inspired by bionic design, named BioLiteNet, is proposed, aimed at enhancing the model’s overall performance in terms of both accuracy and computational efficiency. The model is composed of two key modules: BeeSenseSelector (Channel Attention Screening) and AffScaleConv (Scale-Adaptive Convolutional Fusion). The former mimics the selective attention mechanism observed in honeybee vision for dynamically selecting critical spectral channels, while the latter enables efficient fusion of spatial and spectral features through multi-scale depthwise separable convolution. On multiple hyperspectral benchmark datasets, BioLiteNet is shown to demonstrate outstanding classification performance while maintaining exceptionally low computational costs. Experimental results show that BioLiteNet can maintain high classification accuracy across different datasets, even when using only a small amount of labeled samples. Specifically, it achieves overall accuracies (OA) of 90.02% ± 0.97%, 88.20% ± 5.26%, and 78.64% ± 7.13% on the Indian Pines, Pavia University, and WHU-Hi-LongKou datasets using just 5% of samples, 10% of samples, and 25 samples per class, respectively. Moreover, BioLiteNet consistently requires fewer computational resources than other comparative models. The results indicate that the lightweight hyperspectral image classification model proposed in this study significantly reduces the requirements for computational resources and storage while ensuring classification accuracy, making it well-suited for remote sensing applications under resource constraints. The experimental results further support these findings by demonstrating its robustness and practicality, thereby offering a novel solution for hyperspectral image classification tasks. Full article

Generally, most inversion approaches model the seabed as a stack of range-independent homogeneous layers with unknown geoacoustic parameters and layer numbers. In our previous study, we established a layered geoacoustic seabed model based on sub-bottom profiler data to characterize low-frequency (100–500 Hz) airgun signal propagation at short ranges (0–20 km). However, when applying the same model to simulate high-frequency (500–1000 Hz) explosive sound signal propagation, it failed to adequately reproduce the observed significant transmission loss phenomenon. Through systematic analysis of transmission loss (including water column sound speed profiles, seabed topography, and sediment properties), this study proposes a range-dependent layered geoacoustic model using the Range-dependent Acoustic Model–Parabolic Equation (RAM-PE). Stepwise inversion implementation has successfully explained the observed experimental phenomena. To generalize the proposed model, this study further introduces a trans-dimensional inversion framework that automatically resolves sediment property interfaces along propagation paths. The method effectively combines prior information with trans-dimensional inversion techniques, providing improved characterization of range-dependent seabed environments. Full article

Accurately characterizing the energy evolution during rock failure is crucial in understanding instability mechanisms and enabling the real-time monitoring and early warning of geological hazards in mining and geotechnical engineering. However, the energy evolution characteristics and correlations of multi-physics signals like acoustic emission (AE) and infrared radiation (IR) require further investigation. This study specifically investigated the energy evolution of AE and IR and their correlation during the uniaxial compression failure process of phosphate rock. Tests were performed on specimens under different loading rates to analyze energy dissipation and damage progression. Based on damage mechanics theory, damage evolution models were developed to describe the relationship between the cumulative AE energy, IR radiation variations (specifically the change in the average infrared radiation temperature, ΔAIRT), and strain under varying loading conditions. The results indicate that the loading rate significantly influences the energy release mechanism, with higher rates intensifying rock damage. The peak AE energy rate coincides with the inflection point of the cumulative energy curve, marking substantial internal energy release at failure. Additionally, as the loading rate increases, high-temperature regions in IR thermograms appear earlier, while the variation in ΔAIRT follows a decreasing trend. From an energy perspective, the correlation between AE ringing counts and the average IR temperature was analyzed at both the precursor and failure stages, revealing a strong relationship between AE activity and thermal energy dissipation. Furthermore, mathematical expressions for rock damage variables and coupled relationship equations were derived and validated using experimental data, yielding correlation coefficients (R²) exceeding 0.92. These findings provide a theoretical and methodological foundation for the development of enhanced real-time rock monitoring and early warning systems, contributing to improved safety in geological and mining engineering. Full article