Search Results (7,116)

To address issues of residual stress concentration and deformation in large-scale multi-seam laser welding of tube-to-tubesheet, we established a 12 mm thick Q235 steel simulation model. The model considers the material’s high-temperature performance and mechanical properties. We designed three welding paths: sequential welding, block-by-block symmetrical welding, and inward–outward symmetrical radial welding. The welding simulation software InteWeld 4.0 was used to study the effects of these paths on deformation. Results showed that the inside-out symmetric radiation welding path disperses heat input effectively. It prevents stiffness reduction from local heat accumulation. By using symmetrically distributed shrinkage forces that offset each other, this path greatly inhibits deformation accumulation. The maximum deformation was only 1.6 mm—5.9% and 33% lower than with block-by-block symmetric welding (1.7 mm) and sequential welding (2.4 mm). This path also resulted in a uniform residual stress distribution, with a maximum stress of only 250 MPa, making it the best option for suppressing deformation. Full article

Reducing fuel costs, maximizing waste utilization, and improving energy efficiency are critical challenges in agricultural thermal processes. This study addresses these issues by developing and evaluating a mixed-fuel burner and furnace system for steaming mushroom substrate cubes using crude glycerol and recycled vegetable oil as low-cost alternative energy sources. The experimental investigation assessed boiler thermal efficiency, combustion efficiency, exhaust-gas composition, temperature distribution, steam generation, and combustion-gas dispersion within the furnace. In parallel, analytical modeling of pressure, temperature, and gas-flow behavior was performed to validate the experimental observations. Five fuel compositions were examined, including 100% used vegetable oil, 100% crude glycerol, and blended ratios of 50/50, 25/75, and 10/90 (glycerol/vegetable oil), with all tests conducted in accordance with DIN EN 203-1 standards. The results demonstrate that blending used vegetable oil with glycerol significantly improves flame stability, increases peak combustion temperatures, and suppresses incomplete-combustion byproducts compared with pure glycerol operation. Combustion efficiencies of 90–99% and boiler thermal efficiencies of 72–73% were achieved. Among the tested fuels, the optimal balance between combustion stability, efficiency, and cost was achieved with a 25% glycerol and 75% used vegetable oil mixture. Economic analysis revealed that the proposed mixed-fuel system offers superior viability compared with LPG, reducing annual fuel costs by approximately 50%, shortening steaming time by 2 h per batch, and achieving a payback period of only 3.26 months. These findings confirm the feasibility of the proposed waste-to-energy system for small- and medium-scale agricultural applications. To further enhance sustainability and renewable fuel utilization, future work should focus on improving air–fuel mixing for higher glycerol fractions, scaling the system for larger farms, and extending its application to other agricultural thermal processes. Full article

Owing to their high strength characteristics, chemical stability, and piezoelectric activity, vinylidene fluoride (VDF) copolymers have become promising materials for creating implants to replace bone tissue defects. However, a significant drawback of these materials is the biological inertness of their surface, which leads to unsatisfactory integration with the patient’s bone tissue. In this study, we propose a single-step approach for immobilizing hydroxyapatite (HAp) on the surface of porous implants made of vinylidene fluoride and tetrafluoroethylene copolymer (P(VDF-TeFE)). This method consists of treating the surface of the product with a mixture of solvents while simultaneously capturing HAp microparticles. Using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS), it was shown that the proposed method preserves the morphology of model implants (pore diameter and printed line thickness) and allows HAp to cover up to 63 ± 14% of their surface, reaching concentrations of calcium and phosphorus up to 6.0 ± 1.3 and 3.6 ± 0.7 at. %, respectively, imparting superhydrophilic properties to them. Optical profilometry revealed that the surface roughness of samples increased by more than seven times as a result of HAp immobilization. X-ray diffraction analysis (XRD) confirmed that the piezoelectric phase of P(VDF-TeFE) is preserved after treatment, as are the compressive strength characteristics of the samples. Hydroxyapatite immobilization significantly improved the adhesion and osteogenic differentiation of multipotent stem cells cultured with P(VDF-TeFE)-based samples. Thus, the proposed method can significantly enhance the biological activity of implants based on the piezoelectric VDF copolymer. Full article

Soil erosion is a major threat to the sustainable productivity of arable lands, making the accurate prediction of soil erodibility essential for effective soil conservation planning. Soil erodibility is strongly controlled by intrinsic soil properties that regulate aggregate resistance and detachment processes under erosive forces. In this study, machine learning (ML) models, including the Multi-layer Perceptron Regressor (MLP), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost), were applied to predict the soil erodibility factor (K-factor). A comprehensive set of soil properties, including soil texture, clay ratio (CR), organic matter (OM), aggregate stability (AS), mean weight diameter (MWD), dispersion ratio (DR), modified clay ratio (MCR), and critical level of organic matter (CLOM), was analyzed using 110 soil samples collected from the northern part of Adana Province, Türkiye. The observed K-factor was calculated using the RUSLE equation, and ML-based predictions were spatially mapped using Geographic Information Systems (GISs). The mean K-factor values for arable, forest, and horticultural land uses were 0.065, 0.071, and 0.109 t h MJ⁻¹ mm⁻¹, respectively. Among the tested models, XGBoost showed the best predictive performance, with the lowest MAE (0.0051) and RMSE (0.0110) and the highest R² (0.9458). Furthermore, the XGBoost algorithm identified the CR as the most influential variable, closely followed by clay and MCR content. These results highlight the potential of ML-based approaches to support erosion risk assessment and soil management strategies at the regional scale. Full article

This study investigated the effects of different types and ratios of plasticizers on the fabrication and properties of hot-melt-extruded filaments and fused deposition modeling (FDM) three-dimensional printed tablets containing theophylline (THEO). Polyethylene glycol (PEG) 1500 and stearic acid (SA) were used as plasticizers to prepare THEO-loaded filaments in a hydroxypropyl cellulose matrix via hot melt extrusion (HME), which were subsequently fabricated into tablets using an FDM 3D printer. The physicochemical properties of the filaments and printed tablets were evaluated using scanning electron microscopy, X-ray powder diffraction, and Fourier transform infrared spectroscopy. Drug release behavior was assessed using four tablet formulations (T1–T4) with different plasticizer types and ratios. All fabricated filaments exhibited sufficient hardness and flexibility for reliable 3D printing, and solid-state analyses confirmed partial molecular dispersion of THEO within the polymer matrix. In dissolution studies, PEG-containing formulations showed faster drug release than SA-based formulations, while all 3D-printed tablets achieved approximately 80% drug release within 6 h. Overall, this study demonstrates that the combined use of HME and FDM-based 3D printing, together with rational plasticizer selection, enables the development of personalized pharmaceutical tablets with tunable immediate and sustained drug release profiles. Full article

Recent developments in discrete probability models play a crucial role in reliability and survival analysis when lifetimes are recorded as counts. Motivated by this need, we introduce the discrete ZLindley (DZL) distribution, a novel discretization of the continuous ZL law. Constructed using a survival-function approach, the DZL retains the analytical tractability of its continuous parent while simultaneously exhibiting a monotonically decreasing probability mass function and a strictly increasing hazard rate—properties that are rarely achieved together in existing discrete models. We derive key statistical properties of the proposed distribution, including moments, quantiles, order statistics, and reliability indices such as stress–strength reliability and the mean residual life. These results demonstrate the DZL’s flexibility in modeling skewness, over-dispersion, and heavy-tailed behavior. For statistical inference, we develop maximum likelihood and symmetric Bayesian estimation procedures under censored sampling schemes, supported by asymptotic approximations, bootstrap methods, and Markov chain Monte Carlo techniques. Monte Carlo simulation studies confirm the robustness and efficiency of the Bayesian estimators, particularly under informative prior specifications. The practical applicability of the DZL is illustrated using two real datasets: failure times (in hours) of 18 electronic systems and remission durations (in weeks) of 20 leukemia patients. In both cases, the DZL provides substantially better fits than nine established discrete distributions. By combining structural simplicity, inferential flexibility, and strong empirical performance, the DZL distribution advances discrete reliability theory and offers a versatile tool for contemporary statistical modeling. Full article

In terminal flight trajectory, significant dispersion poses a challenge for accurate localization, as the velocity vector of a supersonic flying object increasingly deviates from the normal vector of the measurement plane under gravitational and aerodynamic effects. Therefore, in this study, an impact point localization method, utilizing a dual-rectangular-ring linear optical microphone array based on apparent shock-wave velocity, was developed. A shock-wave measurement array was developed using a dual rectangular ring composed of linear optical microphone arrays. A time-equivalent model, derived from shock-wave propagation, was introduced to analyze the apparent velocity of the shock-wave within the measurement plane. The time difference in the shock-wave arrivals at the dual rectangular ring, combined with the distances between the inner and outer rectangular rings, was used to calculate the non-uniform apparent shock-wave velocity, thereby enabling the localization of supersonic flying objects. The method’s constraints were examined, and its measurement errors were evaluated. The simulation and experimental results showed that the error was less than 0.5 mm. The proposed novel and cost-effective method for impact point localization aids in the effective dispersion assessment of flying objects. Full article

Bell–CHSH is an inequality about unconditional expectations: under measurement independence, Bell locality, and bounded outcomes, the CHSH value satisfies $S\le 2$. Experimental correlators, however, are often computed on an accepted subset of trials defined by detection logic, coincidence matching, quality cuts, and analysis windows. We model this by an acceptance probability $\gamma (a,b,\lambda )\in [0,1]$ and the resulting accepted hidden-variable law ${\nu }_{ab}$ obtained by weighting the measurement-independent prior $\rho$ by $\gamma$ and renormalizing. If ${\nu }_{ab}$ depends on the setting pair then the four correlators entering CHSH are expectations under four different measures, and a Bell-local measurement-independent model can yield $S_{\mathrm{obs}}>2$ by selection alone. We quantify the required setting dependence in total variation (TV) distance. For any reference law $\mu$ we prove the sharp bound $S_{\mathrm{obs}}\le 2+2{\sum }_{q\in Q}\mathrm{TV}({\nu }_q,\mu )$ for a CHSH quartet Q. Optimizing over $\mu$ yields the intrinsic dispersion bound $S_{\mathrm{obs}}\le 2+2{\Delta }_Q$, and, in particular, $S_{\mathrm{obs}}\le min\{4,2+6D_Q\}$, where $D_Q$ is the quartet TV diameter. The constants are optimal. Consequently, reproducing Tsirelson’s value $2\sqrt{2}$ within Bell-local measurement-independent models via setting-dependent acceptance requires ${\Delta }_Q\ge \sqrt{2}-1$ (hence, $D_Q\ge (\sqrt{2}-1)/3$). We then propose a two-lane experimental audit protocol: (i) prior-relative fair-sampling diagnostics using tags recorded on all trials, and (ii) prior-free dispersion diagnostics using accepted-tag distributions across settings, with ${\Delta }_{Q,X}$ computable by linear programming on finite tag alphabets. Full article

Fibrin clots with strain-hardening characteristics exhibit pronounced material nonlinearity and acoustic dispersion under ultrasound, leading to waveform distortion and shock formation during finite-amplitude wave propagation. However, peak-shock stress is limited by viscoelastic dissipation and dispersion, constraining the efficiency of ultrasound in applications such as thrombus ablation. To overcome this limitation, a shock wave amplification method using designed multi-wave-packet sequences is proposed. Based on a power-law model from quasi-static compression tests, shock generation under a single sinusoidal pulse was first simulated. The dual-wave-packet chasing strategy was then developed, in which the amplitude, frequency, and time delay of the second packet were tuned to achieve effective superposition with the precursor. The waveform superposition factor (WSF) was introduced for quantitative evaluation. Numerical results demonstrate that this strategy can significantly increase the peak-shock-wave stress, with a maximum gain of 22.7%. Parametric analysis further identified amplitude as the dominant factor influencing wavefront steepness and amplification effectiveness. This study provides a novel method and theoretical support for developing efficient and controllable ultrasonic sequences for thrombolysis. Full article

In the context of fifth-generation (5G) communications and the dawn of sixth-generation (6G) networks, a surged societal demand on bandwidth and data rate and more stringent commercial requirements on transmission efficiency, cost, and reliability are increasingly evident and, hence, driving the maturity of reconfigurable millimeter-wave (mmW) and terahertz (THz) devices and systems, in particular, liquid crystal (LC)-based tunable solutions for delay line phase shifters (DLPSs). However, the field of LC-combined electronics has witnessed only incremental developments in the past decade. First, the tuning principle has largely been unchanged (leveraging the shape anisotropy of LC molecules in microscale and continuum mechanics in macroscale for variable polarizability). Second, LC-enabled devices’ performance has yet to be standardized (suboptimal case by case at different frequency domains). In this context, this work points out three underestimated knowledge gaps as drawn from our theoretical designs, computational simulations, and experimental prototypes, respectively. The first gap reports previously overlooked physical constraints from the analytical model of an LC-embedded coaxial DLPS. A new geometry-dielectric bound is identified. The second gap deals with the lack of consideration in the suboptimal dispersion behavior in differential delay time (DDT) and differential delay length (DDL) for LC phase-shifting devices. A new figure of merit (FoM) is proposed and defined at the V-band (60 GHz) to comprehensively evaluate the ratios of the DDT and DDL over their standard deviations across the 54 to 66 GHz spectrum. The third identified gap deals with the in-depth explanation of our recent experimental results and outlook for partial leakage attack analysis of LC phase shifters in modern eavesdropping. Full article

Accurate information regarding species boundaries is essential for ecological research and conservation planning. This information is particularly difficult to obtain but essential for cryptic amphibian species. The distribution and potential dispersal corridors of two cryptic salamander species, the Korean clawed (Onychodactylus koreanus) and the Yangsan clawed (O. sillanus) salamanders, were investigated using integrated approaches for high-resolution species distribution modeling (SDM), genetic species identification, and habitat connectivity analysis. The SDM results showed high habitat suitability in mid- and high-mountainous areas, but very low suitability in riverine areas for both species. Genetic species identification of the 25 populations delimited the distribution boundary between the two species along the Nakdong and Geumho rivers. Dispersal corridors of the two species commonly involved a detour around the major rivers and produced only one possible dispersal route, where both species moved into the opposite species’ habitat along the east side of the mountainous areas of the Geumho River. The findings not only clarify the distribution range of two cryptic Onychodactylus species in the Republic of Korea but also highlight the importance of the unique dispersal route for studying species interactions and maintaining ecological connectivity. Full article

Soil organic matter (SOM) is a key indicator for evaluating soil fertility and ecological functions, and hyperspectral technology provides an effective means for its rapid and non-destructive estimation. However, in practical soil systems, the spectral response of SOM is often highly covariant with mineral composition, moisture conditions, and soil structural characteristics. Under small-sample conditions, hyperspectral SOM modeling results are usually highly sensitive to spectral preprocessing methods, sample perturbations, and model architecture and parameter configurations, leading to fluctuations in predictive performance across independent runs and thereby limiting model stability and practical applicability. To address these issues, this study proposes a multi-strategy collaborative deep learning modeling framework for small-sample conditions (SE-EDCNN-DA-LWGPSO). Under unified data partitioning and evaluation settings, the framework integrates spectral preprocessing, data augmentation based on sensor perturbation simulation, multi-scale dilated convolution feature extraction, an SE channel attention mechanism, and a linearly weighted generalized particle swarm optimization algorithm. Subtropical red soil samples from Guangxi were used as the study object. Samples were partitioned using the SPXY method, and multiple independent repeated experiments were conducted to evaluate the predictive performance and training consistency of the model under fixed validation conditions. The results indicate that the combination of Savitzky–Golay filtering and first-derivative transformation (SG–1DR) exhibits superior overall stability among various preprocessing schemes. In model structure comparison and ablation analysis, as dilated convolution, data augmentation, and channel attention mechanisms were progressively introduced, the fluctuations of prediction errors on the validation set gradually converged, and the performance dispersion among different independent runs was significantly reduced. Under ten independent repeated experiments, the final model achieved R² = 0.938 ± 0.010, RMSE = 2.256 ± 0.176 g·kg⁻¹, and RPD = 4.050 ± 0.305 on the validation set, demonstrating that the proposed framework has good modeling consistency and numerical stability under small-sample conditions. Full article

Acoustic propagation in the ice cover of the Polar Ocean is of increasing interest from both scientific and engineering perspectives. The low-frequency elastic waves propagating in floating ice are primarily governed by waveguides stemming from the layered structure of the medium. For shallow water areas, experimental observation indicates that two Scholte-like waves are observed at low frequencies, i.e., the quasi-Scholte (QS) and Scholte–Stoneley (SS) waves, which are different from deep-sea cases. Due to the finite depths of ice, water, and sediment layers, both waves are dispersive. By modeling the waveguide of an ice-covered shallow-water (ICSW) system, the dispersion characteristics of both waves are derived, validated through numerical simulation, and analyzed with respect to layer structure for both soft and hard sediment. Results indicate a consistent conclusion; the QS wave exhibits a unique sensitivity to ice thickness, whereas the SS wave shows marginal sensitivity to ice thickness, and is controlled by the ratio of water depth to sediment depth, regardless of their absolute values. Based on these dispersion characteristics, a two-step inversion procedure is developed and applied to the synthetic signals from a numerical simulation. The conditional observability of the SS wave at the ice surface is also investigated and discussed. Full article

The unique physicochemical properties of gold nanoparticles (GNPs) have made them versatile tools for biomedical applications, such as imaging, therapy, and drug delivery. The surface modification of GNPs with polymers or biomolecules can enhance their colloidal stability and facilitate internalization into cells. However, the efficient and biocompatible delivery to the central nervous system remains a major challenge, as many existing nanocarriers show poor capacity to cross the blood-brain barrier. We developed a method to coat GNPs with linear polyethyleneimine (GNP@PEI) through a chemical reduction bottom-up approach, in which linear PEI hydrochloride acts simultaneously as a reducing and stabilizing agent of colloidal dispersion. This strategy yielded monodisperse spherical GNP@PEI nanoparticles with an average diameter of 50 nm. The physicochemical profile, biocompatibility, and capacity for neural uptake of this potentially brain-targeted nanoplatform were then evaluated. GNP@PEI nanoparticles exhibited high biocompatibility in several primary neural cultures and cell lines, with cellular uptake showing clear cell-type-dependent differences. In vivo studies carried out in a murine model demonstrated that after the intranasal or intraperitoneal administrations of GNP@PEI nanoparticles, detectable levels of gold were found in several organs, including the brain. Collectively, these findings highlight the potential of GNP@PEI as a promising nanoplatform for brain-targeted delivery and for advancing the development of therapeutic strategies for neurological disorders. Full article

Accurate quantification of methane (CH₄) emissions from individual point sources is essential for understanding localized greenhouse gas dynamics and supporting mitigation strategies. This study employs satellite-based point-source emission rate data from the Carbon Mapper initiative, combined with ERA5 meteorological reanalysis, to simulate near-surface CH₄ dispersion using a Gaussian plume model coupled with Monte Carlo simulations. This approach captures local dispersion characteristics around each emission source. Simulations driven by these emission inputs reveal a highly skewed, heavy-tailed concentration distribution (consistent with log-normal characteristics), where the 95th percentile (1292.1 ppm) significantly exceeds the mean (475.9 ppm), indicating the dominant influence of a small number of super-emitters. Sectoral analysis shows that coal mining contributes the most high-emission sites, while the solid waste and oil & gas sectors present higher per-source intensities, averaging 1931.1 ppm and 1647.6 ppm, respectively. Spatially, emissions are concentrated in North and Northwest China, particularly Shanxi Province, which hosts 62 high-emission sites with an average maximum of 1583.9 ppm. Sensitivity analysis reveals that emission rate perturbations produce nearly linear responses in concentration, whereas wind speed variations induce an inverse and asymmetric nonlinear response, with sensitivity amplified under low wind speed conditions (a ±30% change in wind speed results in more than ±25% variation in concentration). Under stable atmospheric conditions (Class E), concentrations are approximately 1.3 times higher than those under weakly unstable conditions (Class C). Monte Carlo simulations further indicate that output uncertainty peaks within 150–300 m downwind of emission sources. These results provide a quantitative basis for improving uncertainty characterization in satellite-based methane inversion and for prioritizing risk-based monitoring strategies. Full article