Search Results (3,545)

Thermal management is an area of study in electronics focused on managing temperature to improve reliability and efficiency. When temperatures are too high, cooling systems are activated to prevent overheating, which can lead to reliability issues. To monitor the temperatures, sensors are often placed on-chip near hotspot locations. These sensors should be very small to allow them to be placed among compact, high-activity circuits. Often, they are connected to a central control circuit located far away from the hot spot locations where more area is available. This paper proposes sensing units for a novel temperature sensing architecture in the TSMC 180 nm process. This architecture functions by approximating the current through the sensing unit at a reference voltage, which is used to approximate the temperature in the digital back end using fitting functions. Sensing units are selected based on how well its temperature–current relationship can be modeled, sensing unit area, and power consumption. Many sensing units will be experimented with at different reference voltages. These temperature–current curves will be modeled with various fitting functions. The sensing unit selected is a diode-connected p-type MOSFET (Metal Oxide Semiconductor Field Effect Transistor) with a size of W = 400 nm, L = 180 nm. This sensing unit is exceptionally small compared to existing work because it does not rely on multiple devices at the sensing unit location to generate a PTAT or IPTAT signal like most work in this area. The temperature–current relationship of this device can also be modeled using a 2nd order polynomial, requiring a minimal number of trim temperatures. Its temperature error is small, and the power consumption is low. The range of currents for this sensing unit could be reasonably made on an IDAC. Full article

The polytetrafluoroethylene/aluminum (PTFE/Al) granular composite, a common formulation in impact-initiated energetic materials, undergoes mechanochemical coupling reactions under sufficiently strong dynamic loading. This investigation discusses the dynamic properties and the constitutive relationship of the PTFE/Al granular composite to provide a preliminary guide for the research on mechanical properties of a series of composite materials based on PTFE/Al as the matrix. Firstly, the 26.5Al-73.5PTFE (wt.%) composite specimens are prepared by preprocessing, mixing, molding, high-temperature sintering, and cooling. Then, the quasi-static compression and Hopkinson bar tests are performed to explore the mechanical properties of the PTFE/Al composite. Influences of the strain rate of loading on the yield stress, the ultimate strength, and the limited strain are also analyzed. Lastly, based on the experimental results, the material parameters in the Johnson–Cook constitutive model are obtained by the method of piecewise fitting to describe the stress–strain relation of the PTFE/Al composite. Combining the experimental details and the obtained material parameters, the numerical simulation of the dynamic compression of the PTFE/Al composite specimen is carried out by using the ANSYS/LS-DYNA platform. The results show that the computed stress–strain curves present a reasonable agreement with the experimental data. It should be declared that this research does not involve the energy release behavior of the 26.5Al-73.5PTFE (wt.%) reactive material because the material is not initiated within the strain rate range of the dynamic test in this paper. Full article

Titanium alloys are frequently subjected to surface treatments to enhance their biocompatibility and corrosion resistance in biological environments. Plasma electrolytic oxidation (PEO) is an environmentally friendly electrochemical technique capable of forming oxide layers characterized by high corrosion resistance, biocompatibility, and strong adhesion to the substrate. In this study, the PEO process was performed using a low-melting-point ternary eutectic electrolyte composed of Ca(NO₃)₂–NaNO₃–KNO₃ (41–17–42 wt.%) with the addition of ammonium dihydrogen phosphate (ADP). The use of this electrolyte system enables a reduction in the operating temperature from 280 to 160 °C. The effects of applied voltage from 200 to 400V, current frequency from 50 to 1000 Hz, and ADP concentrations of 0.1, 0.5, 1, 2, and 5 wt.% on the growth of titanium oxide composite coatings on a Ti-6Al-4V substrate were investigated. The incorporation of Ca and P was confirmed by phase and chemical composition analysis, while scanning electron microscopy (SEM) revealed a porous surface morphology typical of PEO coatings. Corrosion resistance in Hank’s solution, evaluated via Tafel plot fitting of potentiodynamic polarization curves, demonstrated a substantial improvement in electrochemical performance of the PEO-treated samples. The corrosion current decreased from 552 to 219 nA/cm², and the corrosion potential shifted from −102 to 793 mV vs. the Reference Hydrogen Electrode (RHE) compared to the uncoated alloy. These findings indicate optimal PEO processing parameters for producing composite oxide coatings on Ti-6Al-4V alloy surfaces with enhanced corrosion resistance and potential bioactivity, which are attributed to the incorporation of Ca and P into the coating structure. Full article

Wind power output sequences exhibit strong randomness and intermittency characteristics; traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise and predictive accuracy is still notably poor at the peaks where the power curve undergoes abrupt changes. To address the poor fitting at peaks, a short-term wind power forecasting method based on the improved Informer model is proposed. First, the temporal convolutional network (TCN) is introduced to enhance the model’s ability to capture regional segment features along the temporal dimension, enhancing the model’s receptive field to address wind power fluctuation under varying environmental conditions. Next, a discrete cosine transform (DCT) is employed for adaptive modeling of frequency dependencies between channels, converting the time series data into frequency domain representations to extract its frequency features. These frequency domain features are then weighted using a channel attention mechanism to improve the model’s ability to capture peak features and resist noise interference. Finally, the Informer generative decoder is used to output the power prediction results, this enables the model to simultaneously leverage neighboring temporal segment features and long-range inter-temporal dependencies for future wind-power prediction, thereby substantially improving the fitting accuracy at power-curve peaks. Experimental results validate the effectiveness and practicality of the proposed model; compared with other models, the proposed approach reduces MAE by 9.14–42.31% and RMSE by 12.57–47.59%. Full article

Background and Objectives: Handgrip strength represents a critical indicator of physical fitness and nutritional status in adolescents, yet population-specific reference values remain limited in developing countries. Understanding age- and sex-specific variations is crucial for accurate clinical assessment and effective health monitoring. The objective of this study was to establish comprehensive reference values for handgrip strength in healthy Tunisian adolescents aged 13–19 years and examine sex and age group differences in these measures. Materials and Methods: This cross-sectional study was conducted between September 2024 and June 2025, involving a sample of 950 participants (482 males, 468 females) aged 13–19 years from northwest Tunisia. Handgrip strength was measured using standardized dynamometry protocols for both hands. Anthropometric measurements included height, weight, and body mass index. Percentile curves were generated using the LMS method, and correlations between handgrip strength and anthropometric variables were analyzed using Pearson correlation coefficients. Results: Males demonstrated significantly higher handgrip strength than females from age 13 onward (13 years: p = 0.021; 14–19 years: p ≤ 0.001). Effect sizes for sex differences were consistently large across age groups (Cohen’s d range: 0.53–2.09 for the dominant hand). Mean dominant handgrip strength ranged from 25.60 ± 7.73 kg to 47.60 ± 12.45 kg in males and 21.90 ± 6.13 kg to 28.40 ± 4.74 kg in females across age groups. After adjusting for body mass, sex differences remained significant between groups (13 years: p = 0.014; d= 1.5; 14–19 years: p ≤ 0.001; d: 1.71–3.12). Strong positive correlations emerged between handgrip strength and height (males: r = 0.748, females: r = 0.601), body mass (males: r = 0.659, females: r = 0.601), and body mass index (BMI) (males: r = 0.391, females: r = 0.461). Body mass and height emerged as the strongest predictors of handgrip strength in both sexes, while BMI showed a smaller but still significant contribution. Conclusions: This study provides the first comprehensive age- and sex-specific reference values for handgrip strength in Tunisian adolescents. Healthcare providers can utilize these percentile charts for the clinical assessment and identification of musculoskeletal fitness deficits. The results suggest its use in educational and clinical contexts. Full article

Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle avoidance, and smooth motion through innovative strategies. A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. First, the Chaotic Elite Pool strategy is employed to generate an elite population, enhancing population diversity and improving the quality of initial solutions, thereby boosting the algorithm’s global search capability. Second, adaptive weights are introduced, and the traditional simulated annealing algorithm is improved to obtain the Rapid Annealing Method. The improved simulated annealing algorithm is then combined with the White Shark algorithm to avoid getting stuck in local optima and accelerate convergence speed. Finally, third-order Bézier curves are used to smooth the path. Path length and path smoothness are used as fitness evaluation metrics, and an evaluation function is established in conjunction with a non-complete model that reflects actual motion to assess the effectiveness of path planning. Simulation results show that on the simple 20 × 20 grid map, the fusion of the Fused Multi-strategy White Shark Optimisation algorithm (FMWSO) outperforms WSO, D*, A*, and GWO by 8.43%, 7.37%, 2.08%, and 2.65%, respectively, in terms of path length. On the more complex 40 × 40 grid map, it improved by 6.48%, 26.76%, 0.95%, and 2.05%, respectively. The number of turning points was the lowest in both maps, and the path smoothness was lower. The algorithm’s runtime is optimal on the 20 × 20 map, outperforming other algorithms by 40.11%, 25.93%, 31.16%, and 9.51%, respectively. On the 40 × 40 map, it is on par with A*, and outperforms WSO, D*, and GWO by 14.01%, 157.38%, and 3.48%, respectively. The path planning performance is significantly better than other algorithms. Full article

Histamine sensing that uses enzymatic reactions is the most common form of testing due to its selectivity for histamine. However, enzymes are difficult to store for long periods of time, and the inactivation of enzymes decreases the reliability of the results. In this study, we developed a novel, quick, and easily operated histamine sensing technique that takes advantage of the histamine redox reaction and does not require enzyme-based processes. Because the redox potential of histamine is relatively high, we used a boron-doped diamond (BDD) electrode that has a wide potential window. At pH 8.4, which is between the acidity constant of histamine and the isoelectric point of histidine, it was found that an oxygen-terminated BDD surface successfully detected histamine, both selectively and exclusively. Measurements of the sensor’s responses to extracts from fish meat samples that contained histamine at various concentrations revealed that the sensor responds linearly to the histamine concentration, thus allowing it to be used as a calibration curve. The sensor was used to measure histamine in another fish meat sample treated as an unknown sample, and the response was fitted to the calibration curve to perform an inverse estimation. When estimated in this way, the histamine concentration matched the certified value within the range of error. A more detailed examination showed that the sensor response was little affected by the histidine concentration in the sample. The detection limit was 20.9 ppm, and the linear response range was 0–150 ppm. This confirms that this sensing method can be used to measure standard histamine concentrations. Full article

To tackle the inefficiencies in 3D mine tunnel modeling and the tedious task of drawing centerlines, this study introduces a faster method for generating centerlines using CAD secondary development. Starting with the tunnel centerline, the research then dives into techniques for creating detailed 3D tunnel models. The team first broke down the steps and logic behind tunnel modeling, designing a 3D tunnel framework and its data structure—complete with key geometric components like traverse points, junctions, nodes, and centerlines. By refining older centerline drawing techniques, they built a CAD-powered tool that slashes time and effort. The study also harnessed advanced algorithms, such as surface fitting and curve lofting, to swiftly model tricky tunnel sections like curves and crossings. This method fixes common problems like warped or incomplete surfaces in linked tunnel models, delivering precise and lifelike 3D scenes for VR-based mining safety drills and simulations. Full article

Polyurethane foam is widely used as a primary filling material in car seats. While it provides good damping and energy absorption, the mechanical properties are complex but play a vital role in vibration attenuation and vehicle ride comfort. This study proposes a comprehensive experimental and analytical method to characterize the visco-hyperelastic properties of seat-grade polyurethane foam. Quasi-static and dynamic compression tests were conducted on foam blocks to obtain load–deflection curves and dynamic stiffness. A visco-hyperelastic material model was developed, where the hyperelastic response was derived via the hereditary integral and difference-stress method, and viscoelastic behavior was captured using a Prony series fitted to dynamic stiffness data. The model was validated using finite element simulations, showing good agreement with experimental results in both static and dynamic conditions. The proposed method enables accurate characterization of the visco-hyperelastic material properties of seat-grade polyurethane foam. Full article

Co_0.95R_0.05Fe₂O₄ nanoparticles were synthesized using a sol-gel approach incorporating bio-based agents and were found to be single phases adopting a cubic Fd-3m structure. XPS shows the presence of Gd³⁺ and Ho³⁺ ions. The spin–orbit splitting of about 15.4 eV observed in Co 2p core-level spectra is an indication that Co is predominantly present as Co³⁺ state, while the satellite structures located at about 6 eV higher energies than the main lines confirm the existence of divalent Co in Co_0.95R_0.05Fe₂O₄. The positions of the Co 3s and Fe 3s main peaks obtained by curve fitting and the exchange splitting obtained values for Co 3s and Fe 3s levels point to the high Co³⁺/Co²⁺ and Fe³⁺/Fe²⁺ ratios in both samples. The saturation magnetizations are smaller for the doped samples compared to the pristine ones. For theoretical magnetization calculation, we have considered that the heavy rare earths are in octahedral sites and their magnetic moments are aligned antiparallelly with 3d transition magnetic moments. ZFC-FC curves shows that some nanoparticles remain superparamagnetic, while the rest are ferrimagnetic, ordered at room temperature, and showing interparticle interactions. The M_S/Ms ratio at room temperature is below 0.5, indicating the predominance of magnetostatic interactions. Full article

The development of alginate/polyacrylamide hydrogels for various biomedical applications has attracted significant interest, particularly due to their potential use in wound healing and tissue engineering. This study explores the fabrication of these hydrogels via 3D bioprinting with ultraviolet light curing, focusing on how the alginate concentration and curing speed impact their mechanical properties. Rheological testing was employed to examine the viscoelastic behavior of alginate/polyacrylamide hydrogels manufactured using a 3D bioprinting technique. The relaxation behavior and dynamic response of these hydrogels were analyzed under torsional stress, with relaxation curves fitted using a two-term Prony series. Fourier Transform Infrared (FTIR) spectroscopy was also employed to assess biocompatibility and the conversion of acrylamide. This study successfully demonstrated the printability of alginate/polyacrylamide hydrogels with varying alginate contents. The rheological results indicated that 3D bioprinted hydrogels exhibited significantly high stiffness, viscoelasticity, and long relaxation times. The curing speed had a minimal impact on these properties. Additionally, the FTIR analysis confirmed the complete conversion of polyacrylamide, ensuring no harmful effects in biological applications. The study concludes that 3D bioprinting significantly enhances the mechanical properties of alginate/polyacrylamide hydrogels, with the alginate concentration playing a key role in the shear modulus. These hydrogels show promising potential for biocompatible applications such as wound healing dressings. Full article

Allergy is recognized as a public health problem with pandemic consequences and is estimated to affect more than 50% of Europeans in 2025. Prebiotic and probiotic food implementation has recently emerged as an alternative strategy to promote immunomodulatory beneficial effects in allergic patients. Among prebiotics, Phaeophyceae algae represent a niche of research with enormous possibilities. The present study aims to evaluate the in vitro prebiotic potential of fucoidan from Fucus vesiculosus, Macrocystis pyrifera, and Undaria pinnatifida algae, to promote the growth of Limosilactobacillus reuteri and Lacticaseibacillus rhamnosus GG as probiotic bacteria added to the formulation of a novel yogurt. Concentrations of fucoidan of 100 and 2000 µg/mL were added to reference growth media and kinetic growth curves for both microorganisms were fitted to the Gompertz equation. Optimized prebiotic conditions for fucoidan were selected to validate in vitro results by means of the formulation of a novel fermented prebiotic yogurt. Conventional yogurts (including Streptococcus thermophilus and Lactobacillus delbrueckii subs. bulgaricus) were formulated with the different fucoidans, and production batches were prepared for L. rhamnosus and L. reuteri. Increased L. reuteri and L. rhamnosus populations in 1.7–2.2 log₁₀ cycles just after 48 h of in vitro exposure were detected in fucoidan supplemented yogurt. M. pyrifera and U. pinnatifida fucoidans were the most effective ones (500 µg/mL) promoting probiotic growth in new formulated yogurts (during the complete shelf life of products, 28 days). Diet supplementation with fucoidan can be proposed as a strategy to modulate beneficial microbiota against allergy. Full article

During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods suffer from suboptimal segmentation quality. To improve mural image segmentation, this study proposes an efficient mural image segmentation method termed Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) by integrating an adaptive learning strategy, a nonlinear factor, and a third-order Bernstein-guided strategy into the Parrot Optimizer (PO). In ANBPO, First, to address PO’s limited global exploration capability, the adaptive learning strategy is introduced. By considering individual information disparities and learning behaviors, this strategy effectively enhances the algorithm’s global exploration, enabling a thorough search of the solution space. Second, to mitigate the imbalance between PO’s global exploration and local exploitation phases, the nonlinear factor is proposed. Leveraging its adaptability and nonlinear curve characteristics, this factor improves the algorithm’s ability to escape local optimal segmentation thresholds. Finally, to overcome PO’s inadequate local exploitation capability, the third-order Bernstein-guided strategy is introduced. By incorporating the weighted properties of third-order Bernstein polynomials, this strategy comprehensively evaluates individuals with diverse characteristics, thereby enhancing the precision of mural image segmentation. ANBPO was applied to segment twelve mural images. The results demonstrate that, compared to competing algorithms, ANBPO achieves a 91.6% win rate in fitness function values while outperforming them by 67.6%, 69.4%, and 69.7% in PSNR, SSIM, and FSIM metrics, respectively. These results confirm that the ANBPO algorithm can effectively segment mural images while preserving the original feature information. Thus, it can be regarded as an efficient mural image segmentation algorithm. Full article

Accurate estimation of erupted lava volumes is essential for understanding volcanic processes, interpreting eruptive cycles, and assessing volcanic hazards. Traditional methods based on Mid-Infrared (MIR) satellite imagery require clear-sky conditions during eruptions and are prone to sensor saturation, limiting data availability. Here, we present an alternative approach based on the post-eruptive Thermal InfraRed (TIR) signal, using the recently proposed VRP_TIR method to quantify radiative energy loss during lava flow cooling. We identify thermally anomalous pixels in VIIRS I5 scenes (11.45 µm, 375 m resolution) using the TIRVolcH algorithm, this allowing the detection of subtle thermal anomalies throughout the cooling phase, and retrieve lava flow area by fitting theoretical cooling curves to observed VRP_TIR time series. Collating a dataset of 191 mafic eruptions that occurred between 2010 and 2025 at (i) Etna and Stromboli (Italy); (ii) Piton de la Fournaise (France); (iii) Bárðarbunga, Fagradalsfjall, and Sundhnúkagígar (Iceland); (iv) Kīlauea and Mauna Loa (United States); (v) Wolf, Fernandina, and Sierra Negra (Ecuador); (vi) Nyamuragira and Nyiragongo (DRC); (vii) Fogo (Cape Verde); and (viii) La Palma (Spain), we derive a new power-law equation describing mafic lava flow thickening as a function of time across five orders of magnitude (from 0.02 Mm³ to 5.5 km³). Finally, from knowledge of areas and episode durations, we estimate erupted volumes. The method is validated against 68 eruptions with known volumes, yielding high agreement (R² = 0.947; ρ = 0.96; MAPE = 28.60%), a negligible bias (MPE = −0.85%), and uncertainties within ±50%. Application to the February-March 2025 Etna eruption further corroborates the robustness of our workflow, from which we estimate a bulk erupted volume of 4.23 ± 2.12 × 10⁶ m³, in close agreement with preliminary estimates from independent data. Beyond volume estimation, we show that VRP_TIR cooling curves follow a consistent decay pattern that aligns with established theoretical thermal models, indicating a stable conductive regime during the cooling stage. This scale-invariant pattern suggests that crustal insulation and heat transfer across a solidifying boundary govern the thermal evolution of cooling basaltic flows. Full article

Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely studied in the intelligent transportation system. In this study, a neural network model based on the data-driven technology is established for the prediction of passenger flow in multiple urban rail transit stations to enable smart perception for optimizing urban railway transportation. The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. Experiments based on the actual passenger flow data of Beijing Metro Line 13 are conducted to compare the prediction performance of the proposed data-driven model with the other baseline models. The experimental results demonstrate that the proposed prediction model achieves lower MAE and RMSE in passenger flow prediction, and its fitted curve more closely aligns with the actual passenger flow data. This demonstrates the model’s practical potential to enhance intelligent transportation system management through more accurate passenger flow forecasting. Full article