Search Results (1,007)

During machining, most of the mechanical energy is converted into heat. A substantial part of this heat is transferred to the cutting tool, causing a rapid rise in tool temperature. Excessive thermal loads accelerate tool wear and lead to displacement of the tool center point, reducing machining accuracy and workpiece quality. This challenge is particularly pronounced when machining titanium alloys. Due to their low thermal conductivity, titanium alloys impose significantly higher thermal loads on the cutting tool compared to conventional carbon steels, making the process more difficult. To reduce temperatures in the cutting zone, cutting fluids are widely employed in titanium machining. They have been shown to significantly extend tool life. Cutting fluids are broadly categorized into cutting oils and water-based cutting fluids. Owing to their distinct thermophysical properties, these fluids exhibit notably different cooling and lubrication performance. However, current research lacks comprehensive cross-comparative studies of different cutting fluid types, which hinders the selection of optimal cutting fluids for process optimization. This study examines the influence of three cutting fluids—emulsion, cutting oil, and synthetic oil-free fluid—on tool wear, temperature, surface quality, and energy consumption during flood-cooled end milling of Ti-6Al-4V. A novel experimental setup incorporating embedded thermocouples enabled real-time temperature measurement near the cutting edge. Tool wear, torque, and surface roughness were recorded over defined feed lengths. Among the tested fluids, emulsion achieved the best balance of cooling and lubrication, resulting in the longest tool life with a feed travel path of 12.21 m. This corresponds to an increase of approximately 200 % compared to cutting oil and oil-free fluid. Cutting oil offered superior lubrication but limited cooling capacity, resulting in localized thermal damage and edge chipping. Water-based cutting fluids reduced tool temperatures by over 300 ${}^{\circ }$C compared to dry cutting but, in some cases, increased notch wear due to higher mechanical stress at the entry point. Power consumption analysis revealed that the cutting fluid supply system accounted for 60–70 % of total energy use, particularly with high-viscosity fluids like cutting oil. Complementary thermal and CFD simulations were used to quantify heat partitioning and convective cooling efficiency. The results showed that water-based fluids achieved heat transfer coefficients up to 175 kW/m²· K, more than ten times higher than those of cutting oil. These findings emphasize the importance of selecting suitable cutting fluids and optimizing their supply to enhance tool performance and energy efficiency in Ti-6Al-4V machining. Full article

Primary or recurrent infection of cytomegalovirus (CMV) in pregnant women may cause transplacental transmission to fetuses. We aimed to investigate the rate of transplacental CMV transmission in women with positive anti-CMV IgG and negative anti-CMV IgM and its impact on newborns. Pregnant women with positive anti-CMV IgG and negative anti-CMV IgM during the first or second trimester who delivered by Cesarean section were included. Amniotic fluid collected during the Cesarean section was tested for CMV DNA with quantitative real-time polymerase chain reaction. CMV IgG and IgM were measured with enzyme-linked immunosorbent assay. A total of 695 pregnant women were enrolled between April 2019 and February 2023. Of them, 567 (81.6%) were single pregnancies and 128 (18.4%) were twin pregnancies, and 594 (85.5%) were full-term pregnancies and 101 (14.5%) were premature pregnancies. Of the 823 newborns, 7 (0.9%) were CMV DNA positive in amniotic fluid, demonstrating the transplacental CMV transmission. One of these seven neonates was diagnosed with intrauterine growth restriction at gestation week 25⁺¹ and at birth at a gestational age of 30⁺² weeks. However, all seven children had normal hearing, vision, and neurodevelopment at the age of 18–56 months. Transplacental CMV transmission may occur in offspring of pregnant women with positive anti-CMV IgG and negative anti-CMV IgM, but the long-term sequelae appear to be minimal. Full article

In recent years, research has focused on biomarkers as key tools to predict clinical outcomes and guide therapeutic decisions in Multiple Sclerosis (MS). MicroRNAs (miRs)—small non-coding RNA molecules that regulate gene expression at the post-transcriptional level—have emerged as promising biomarkers in MS due to their accessibility in biological fluids. This study investigates the role of specific serum miRs mainly involved in immune response regulation as potential prognostic biomarkers in MS, focusing on young patients with recent diagnosis. The study had a prospective design, involving a cohort of patients followed in the Hub and Spoke MS network of Verona province. Fifty-one patients (33F) aged 18–40 years with recent MS diagnosis (≤2 years; 45 relapsing-remitting, 6 primary progressive) were consecutively enrolled. At baseline, serum samples were collected for miR analysis alongside clinical-demographic and MRI data, including T2 lesion volume, normalized brain volume (NBV), gray matter volume, white matter volume (WMV) calculated at baseline and annual percentage brain volume change (PBVC) and occurrence of new T2 or gadolinium-enhancing (Gd+) lesions on follow-up scans. Candidate miRs were chosen based on their potential biological role in MS pathogenesis reported in the literature. miRs assays were done using real-time PCR and expressed as a ratio relative to a normalizer (i.e., miR-425-5p). Levels of miR-34a-5p were significantly higher in patients with Gd+ lesions (p < 0.001) and correlated to lower NBV (rho = −0.454, p = 0.001) and WMV (rho = −0.494, p < 0.001). Conversely, miR-140-5p exhibited a protective effect against occurrence of new T2 or Gd+ lesions over time (HR 0.43; IC 95% 0.19–0.99; p = 0.048). Additionally, miR-30b-5p correlated directly with PBVC (adjusted rho = −0.646; p < 0.001). These findings support the potential of serum miR-34a-5p, miR-140-5p, and miR-30b-5p as markers of disease activity and progression in patients with recently diagnosed MS. Full article

Large-scale particle-based fluid simulations present significant computational challenges, particularly in achieving interactive frame rates while maintaining visual quality. Unity3D’s widespread adoption in game development, VR/AR applications, and scientific visualization creates a unique need for efficient fluid simulation within its ecosystem. This paper presents a GPU-accelerated Smoothed Particle Hydrodynamics (SPH) framework implemented in Unity3D that effectively addresses these challenges through several key innovations. Unlike previous GPU-accelerated SPH implementations that typically struggle with scaling beyond 100,000 particles while maintaining real-time performance, we introduce a novel fusion of Count Sort with Parallel Prefix Scan for spatial hashing that transforms the traditionally expensive O(n²) neighborhood search into an efficient O(n) operation, significantly outperforming traditional GPU sorting algorithms in particle-based simulations. Our implementation leverages a Structure of Arrays (SoA) memory layout, optimized for GPU compute shaders, achieving 30–45% improved computation throughput over traditional Array of Structures approaches. Performance evaluations demonstrate that our method achieves throughput rates up to 168,600 particles/ms while maintaining consistent 5.7–6.0 ms frame times across varying particle counts from 10,000 to 1,000,000. The framework maintains interactive frame rates (>30 FPS) with up to 500,000 particles and remains responsive even at 1 million particles. Collision rates approaching 1.0 indicate near-optimal hash distribution, while the adaptive time stepping mechanism adds minimal computational overhead (2–5%) while significantly improving simulation stability. These innovations enable real-time, large-scale fluid simulations with applications spanning visual effects, game development, and scientific visualization. Full article

Proper estimation of flowing bottomhole pressure at coiled tubing depth (BHP-CTD) is crucial in optimization of nitrogen lifting operations in oil wells. Conventional estimation techniques such as empirical correlations and mechanistic models may be characterized by poor generalizability, low accuracy, and inapplicability in real time. This study overcomes these shortcomings by developing and comparing sixteen machine learning (ML) regression models, such as neural networks and genetic programming-based symbolic regression, in order to predict BHP-CTD with field data collected on 518 oil wells. Operational parameters that were used to train the models included fluid flow rate, gas–oil ratio, coiled tubing depth, and nitrogen rate. The best performance was obtained with the neural network with the L-BFGS optimizer (R² = 0.987) and the low error metrics (RMSE = 0.014, MAE = 0.011). An interpretable equation with R² = 0.94 was also obtained through a symbolic regression model. The robustness of the model was confirmed by both k-fold and random sampling validation, and generalizability was also confirmed using blind validation on data collected on 29 wells not included in the training set. The ML models proved to be more accurate, adaptable, and real-time applicable as compared to empirical correlations such as Hagedorn and Brown, Beggs and Brill, and Orkiszewski. This study does not only provide a cost-efficient alternative to downhole pressure gauges but also adds an interpretable, data-driven framework to increase the efficiency of nitrogen lifting in various operational conditions. Full article

In modern industry, the spring full-lift safety valve is a key device for safe pressure relief of pressure-bearing systems. Its valve seat sealing surface is easily damaged after long-term use, causing internal leakage, resulting in safety hazards and economic losses. Therefore, it is of great significance to quickly and accurately diagnose its internal leakage state. Among the current methods for identifying fluid machinery faults, model-based methods have difficulties in parameter determination. Although the data-driven convolutional neural network (CNN) has great potential in the field of fault diagnosis, it has problems such as hyperparameter selection relying on experience, insufficient capture of time series and multi-scale features, and lack of research on valve internal leakage type identification. To this end, this study proposes a safety valve internal leakage identification method based on high-frequency FPGA data acquisition and improved CNN. The acoustic emission signals of different internal leakage states are obtained through the high-frequency FPGA acquisition system, and the two-dimensional time–frequency diagram is obtained by short-time Fourier transform and input into the improved model. The model uses the leaky rectified linear unit (LReLU) activation function to enhance nonlinear expression, introduces random pooling to prevent overfitting, optimizes hyperparameters with the help of horned lizard optimization algorithm (HLOA), and integrates the bidirectional gated recurrent unit (BiGRU) and selective kernel attention module (SKAM) to enhance temporal feature extraction and multi-scale feature capture. Experiments show that the average recognition accuracy of the model for the internal leakage state of the safety valve is 99.7%, which is better than the comparison model such as ResNet-18. This method provides an effective solution for the diagnosis of internal leakage of safety valves, and the signal conversion method can be extended to the fault diagnosis of other mechanical equipment. In the future, we will explore the fusion of lightweight networks and multi-source data to improve real-time and robustness. Full article

Hydrocarbon fluids have a widespread presence in modern society due to their role in the global energy and fuel supply. The ability to distinguish between hydrocarbon fluids from different manufacturing processes is essential in industrial and government settings. Currently, performing such analyses is expensive and time-consuming, as standard practice involves sending samples to a laboratory for gas chromatography-mass spectrometry (GC-MS) analysis. The inherent limitations of traditional separation techniques often make them unsuitable for the demands of real-time process monitoring and control. This work proposes the use of handheld Raman spectroscopy for rapid classification of petroleum- and synthetic-based hydrocarbon fluids. A total of 600 Raman spectra were collected from six different hydraulic fluids and analyzed. Preliminary visual observations revealed reproducible spectral differences between various types of hydraulic fluids. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate the data further. The findings indicate that handheld Raman spectrometers are capable of detecting chemical features of hydrocarbon fluids, supporting the classification of their formulations. Full article

This paper addresses the urgent need to redefine education strategies for the Net Generation in the context of rapid technological and societal changes. First, the educational challenge is placed within a broader philosophical and cultural framework, focusing on the fluid and evolving nature of knowledge and human experience. Building on the paradigm shift from Web 2.0 to Web 4.0 and the emergence of Education 5.0, this paper investigates the pedagogical implications of these developments. Through conceptual analysis supported by contemporary educational theory, this paper proposes a model of education that integrates personalized learning, real-time feedback, and collaborative, interdisciplinary environments. A special focus is placed on the role of educators as mentors, rather than mere transmitters of information, and on the ethical, social, and emotional dimensions of digital learning. This article highlights the importance of adjusting educational practices to real-life contexts and future challenges of young learners while ensuring that the humanistic essence of education is not lost. Full article

Under physiological and pathological conditions, all cells release extracellular vesicles named exosomes, which act as transporters of lipidic, protein, and genetic material from parent to recipient cells. Neoplastic cells can secrete higher number of exosomes to exert pro-tumoral effects such as microenvironmental changes, disease progression, immunosuppression and drug-resistance. This holds true for both organ-specific cancers and hematologic malignancies. One of the most important components of exosomal cargo are microRNAs which can mediate all the abovementioned effects. More specifically, microRNAs are small non-coding RNAs, routinely detected through quantitative real-time PCR, which act as translational suppressors by regulating protein-coding genes. Considering their high stability in all body fluids and viability in circulation, research is currently focusing on this type of RNAs for the so called “liquid biopsy”, a non-invasive tool for disease diagnosis and longitudinal monitoring. However, several issues remain to be solved including the lack of standardized protocols for exosome isolation and miRNA detection. Starting with this premise, our review aims to provide a wide description of the known microRNA panels employed in the prominent hematological malignancies, which will hopefully redefine the approach to these very challenging diseases in the near future. Full article

With the increase in energy density of power batteries, the risk of thermal runaway significantly increases under extreme working conditions. Therefore, this article proposes a biomimetic liquid cooling plate design based on the fractal structure of fir needle leaf veins, combined with Murray’s mass transfer law, which has significantly improved the heat dissipation performance under extreme working conditions. A multi-field coupling model of electrochemistry fluid heat transfer was established using ANSYS 2022 Fluent, and the synergistic mechanism of environmental temperature, coolant parameters, and heating power was systematically analyzed. Research has found that compared to traditional serpentine channels, leaf vein biomimetic structures can reduce the maximum temperature of batteries by 11.78 °C at a flow rate of 4 m/s and 5000 W/m³. Further analysis reveals that there is a critical flow rate threshold of 2.5 m/s for cooling efficiency (beyond which the effectiveness of temperature reduction decreases by 86%), as well as a thermal saturation temperature of 28 °C (with a sudden increase in temperature rise slope by 284%). Under low-load conditions of 2600 W/m ³, the system exhibits a thermal hysteresis plateau of 40.29 °C. To predict the battery temperature in advance and actively intervene in cooling the battery pack, based on the experimental data and thermodynamic laws of the biomimetic liquid cooling system mentioned above, this study further constructed a support vector machine (SVM) prediction model to achieve real-time and accurate prediction of the highest temperature of the battery pack (validation set average relative error 1.57%), providing new ideas for intelligent optimization of biomimetic liquid cooling systems. Full article

Currently, flexible sensors based on electrochemical principles are predominantly limited to single-parameter detection, making it challenging to meet the demand for synchronous monitoring of multiple analytes in complex physiological environments. This study presents a 3D-printed flexible sensor for synchronous glucose/pH detection. Glucose was quantified via H₂O₂ oxidation current (GOD-catalyzed reaction), while pH was measured through polyaniline (PANI) resistance changes. The ionogel-based microneedle electrode ensures mechanical robustness. At 0.2 V, optimal signal decoupling was achieved: glucose oxidation current dominates, while PANI’s polarization effect is minimized. Neutral pH minimally affected glucose oxidase (GOD) activity, and low glucose concentrations induced negligible pH interference, ensuring orthogonality. In artificial interstitial fluid, the sensor showed glucose: linear response (0.5–2.5 g·L⁻¹, 0.288 μA·mM⁻¹·cm⁻²); pH: piecewise-linear sensitivity (0.155 Ω/pH·cm² for pH > 7; 0.135 Ω/pH·cm² for pH < 7). The design enables real-time multiparameter monitoring with high selectivity, addressing current limitations in flexible electrochemical sensors. Full article

Background/Objectives: Aneurysmal subarachnoid hemorrhage (aSAH) is frequently complicated by cerebral vasospasm, a major contributor to delayed cerebral ischemia and poor neurological outcomes. Early prediction remains challenging, and there is a critical need for reliable biomarkers. MicroRNAs (miRNAs) in cerebrospinal fluid (CSF) have emerged as promising indicators of acute neuropathological changes. This study aimed to evaluate CSF miRNA expression profiles in patients with aSAH to identify early predictors of vasospasm and improve clinical risk stratification. Methods: We conducted a prospective observational study involving 48 patients (40 patients with aSAH (20 who developed vasospasm, 20 who did not) and 8 healthy controls). A panel of 20 candidate miRNAs was analyzed in CSF samples collected on days 1 and 5 post−hemorrhage using quantitative real−time PCR. Expression differences between groups were assessed, and receiver operating characteristic (ROC) curves were used to evaluate diagnostic performance. Results: Several miRNAs were differentially expressed in patients who developed vasospasm. On day 1, miR−221−3p and miR−183−5p were significantly upregulated (p = 0.014, p = 0.009), while miR−126, miR−29a, and miR−27b−3p were downregulated (p = 0.006, 0.021, 0.028) compared with controls. MiR−126 remained suppressed on day 5 (p = 0.002). These early changes showed high predictive accuracy (e.g., day 1 AUC for miR−221−3p ≈ 0.98, 95% CI 0.83–1.00). Compared with non−vasospasm patients, miR−221−3p was lower (0.12−fold), while miR−9−3p and miR−183−5p were higher (13.4−fold and 2.7−fold, respectively; all p < 0.01). MiR−24 and miR−21−5p correlated with more severe grades and poorer outcomes (p < 0.05). Conclusions: Specific CSF miRNAs—particularly miR−221−3p, miR−9−3p, and miR−183−5p—may serve as early biomarkers for vasospasm, warranting further validation in larger cohorts. Full article

Bubbling Inception Temperature (BIT) is a critical metric that indicates the temperature at which gas bubbles form due to cellulose decomposition in a paper–oil insulation system. It serves as a key indicator of the thermal stability of transformer insulation, offering valuable insights into its performance under elevated temperatures. Building on findings from a companion study that examined the BIT of Kraft paper (KP), thermally upgraded Kraft paper (TUK), and aramid paper in mineral oil, this research expands the analysis to assess the impact of moisture, aging, and alternative dielectric fluids. Using the same customized experimental setup featuring precise dynamic load control, real-time bubble detection, and continuous monitoring of moisture and temperature, this study evaluates BIT across four distinct oil–paper aging stages: new (0 h) and 2 weeks, 4 weeks, and 6 weeks of accelerated thermal aging. This approach enables a comparative analysis of BIT in various paper–oil systems, focusing on both mineral oil and synthetic esters, as well as the influence of different moisture levels in the paper insulation. The results show that BIT decreases with aging, indicating reduced thermal stability. Furthermore, KP impregnated with synthetic ester exhibits a higher BIT than when impregnated with mineral oil, suggesting that synthetic esters may offer better resistance to bubble formation under thermal stress. Based on these results, empirical BIT models were developed as a function of degree of polymerization (DP) and water content in paper (WCP). This study further demonstrates how these models can be applied to quantify safety margins under emergency overloading conditions, providing a practical tool for operational decision-making in transformer thermal risk management. Full article

Achieving the full potential of wind energy in the global renewable transition depends critically on enhancing the performance and reliability of polymer composite components. This review synthesizes recent advances from 2022 to 2025, including the development of next-generation hybrid composites and the application of high-fidelity computational methods—finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI)—to optimize structural integrity and aerodynamic performance. It also explores the transformative role of artificial intelligence (AI) in structural health monitoring (SHM) and the integration of Internet of Things (IoT) systems, which are becoming essential for predictive maintenance and lifecycle management. Special focus is given to harsh offshore environments, where polymer composites must withstand extreme wind and wave conditions. This review further addresses the growing importance of circular economy strategies for managing end-of-life composite blades. While innovations such as the geometric redesign of floating platforms and the aerodynamic refinement of blade components have yielded substantial gains—achieving up to a 30% mass reduction in PLA prototypes—more conservative optimizations of internal geometry configurations in GFRP blades provide only around 7% mass reduction. Nevertheless, persistent challenges related to polymer composite degradation and fatigue under severe weather conditions are driving the adoption of real-time hybrid predictive models. A bibliometric analysis of over 1000 publications confirms more than 25 percent annual growth in research across these interconnected areas. This review serves as a comprehensive reference for engineers and researchers, identifying three strategic frontiers that will shape the future of wind turbine blade technology: advanced composite materials, integrated computational modeling, and scalable recycling solutions. Full article

Background and Objectives: Rheumatoid arthritis (RA) is a systemic autoimmune inflammatory disease, and progressive arthritis is its primary clinical manifestation. The role of human parvovirus B19 (B19V) infection in the progression of RA remains unclear. This study aims to investigate the association between B19V infection and viral genetic distribution in Vietnamese RA patients. Materials and Methods: 115 Vietnamese RA patients and 86 healthy controls (HCs) were enrolled in this observational study at the Thai Nguyen National Hospital from January 2019 to December 2021. B19V DNA was examined in serum and synovial fluid samples from RA patients using nested PCR and real-time PCR. B19V antibodies were detected in serum samples using ELISA. Results: B19V DNA was detected in the serum of 2 out of 115 (1.74%) RA patients but not in any HCs. Interestingly, B19V DNA was present in 12 out of 68 (17.65%) RA patients with knee effusion in their synovial fluid. Anti-B19V-IgG and anti-B19V-IgM were detected in the serum of 42.61% and 2.61% of RA patients, respectively, and in 24.42% and 12.79% of HCs, respectively. Anti-B19V-IgG levels were significantly higher in the serum of RA patients than in the serum of HCs (p = 0.007). However, anti-B19V-IgM was more commonly detected in HC serum than in RA patient serum (p = 0.006). Phylogenetic analysis showed that all B19V strains belonged to genotype 1 and subgenotype 1A. Conclusions: B19V infection is frequent in RA patients and suggests a contribution of B19V to the progression of RA, particularly in a B19V genotype-1- and subgenotype-1A-dependent manner and emphasises the need for early detection and management of B19V infection in RA patients. Full article