Search Results (464)

This paper investigates the channel performance through a high-gain, circularly polarized microstrip patch antenna that is developed for contemporary wireless communication systems. The proposed antenna creates two orthogonal modes for circular propagation with slightly varying resonance frequencies by using a cross line and truncations to circulate surface currents. Compactness, reduced surface wave losses, and enhanced impedance bandwidth are made possible by the coaxial probe feed, periodic electromagnetic gap (EBG) slots, and fractal patch geometry. For in-phase reflection and beam focusing, a specially designed single-layer metasurface (MTS) reflector with an 11 × 11 circular aperture array is placed 20 mm behind the antenna. A log-normal shadowing model was used to test the antenna in real-world scenarios, and the results showed a strong correlation between the model predictions and actual data. At up to 250 m, the polarization-agile, high-gain antenna demonstrated reliable performance across a variety of channel conditions, enabling accurate characterization of the Channel Quality Indicator (CQI), Signal-to-Noise Ratio (SNR), and Reference Signal Received Power (RSRP). By combining cutting-edge antenna architecture with an empirical channel performance study, this research presents a compact, affordable, and fabrication-friendly solution for increased wireless coverage and efficiency. Full article

Marine heatwaves (MHWs) pose a serious threat to the marine ecosystems and fishery resources in the East China Sea (ECS). Based on National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature High Resolution version 2 data, this study investigated the regional divergence in long-term trends of MHWs in the ECS from 1982 to 2023. The principal findings were as follows. Concerning MHWs, the coastal waters of China from northern Jiangsu coast to northeast of Taiwan Island experienced a relatively high annual average frequency, the longest duration, largest number of total days, strongest intensity, and the most pronounced seasonal signals. Additionally, the areas along the Kuroshio path showed significant levels of frequency, duration, and total days, but with comparatively weak intensity. In the empirical orthogonal function (EOF) analysis, EOF1 of the total days and cumulative intensity exhibited notable variation along the path of the Kuroshio and its offshoots, and in Chinese coastal areas. EOF2 showed significantly more conspicuous variation in areas extending from the Yangtze River Estuary to the northern Jiangsu coast. Furthermore, the MHW indices generally showed a positive trend in the ECS from 1982 to 2023. Importantly, the regions with high annual average MHW indices were also characterized by a significantly positive increasing trend. Moderate (79.10%) and strong (19.94%) events were most prevalent, whereas severe (0.82%) and extreme (0.14%) events occurred infrequently. The enhanced solar radiation and the reduced latent heat loss were the main contributing factors of MHWs in the ECS. These findings provide valuable insights into the ecological environment and resources of the ECS as a marine pastoral area. Full article

The diesel particulate filter (DPF) is among the most effective measures for controlling particulate emissions from diesel vehicles. Therefore, resource-efficient DPF design and operation are critical to sustainable deployment. In practical engineering, the pursuit of high filtration efficiency inevitably leads to excessively high pressure drop, which in turn impairs the fuel economy and operational reliability of the engine. To address this pair of conflicting objectives, this study introduces a hybrid GRA-MLR-WOA approach, with the initial filtration efficiency and pressure drop at an 80 g soot capture amount as the optimization objectives, to optimize the structural parameters of the DPF. Firstly, based on a computational fluid dynamics (CFD) model and orthogonal experimental design, combined with grey relational analysis (GRA), the effects of key structural parameters on filtration efficiency and pressure drop were evaluated. Secondly, Box–Behnken Design (BBD) was integrated with multiple linear regression (MLR) to establish mathematical regression models describing the relationships between structural parameters, filtration efficiency, and pressure drop. Finally, the whale optimization algorithm (WOA) was employed to obtain the Pareto frontier of the regression models. Through screening with the goal of maximizing initial filtration efficiency, the optimized DPF achieved a 46.85% increase in initial filtration efficiency and a 34.88% reduction in pressure drop compared to the original model. This study targets sustainable filtration design and proposes an optimization framework that jointly optimizes pressure drop and the initial filtration efficiency. The results provide a robust empirical basis for engineering practice and demonstrate strong reproducibility. Full article

To meet the demand for simulative materials exhibiting suitable hydraulic characteristics in geomechanical model tests, this research developed a type of simulative material using iron powder, quartz sand, and barite powder as aggregates, white cement as binder, and silicone oil as additive. An orthogonal experimental design L₁₆(4⁴) was employed to prepare 16 distinct mix proportions. Advanced statistical methods, including range analysis, residual analysis, Pearson correlation analysis, and multiple regression performed with SPSS 27.0.1, were applied to analyze the influence of four factors—aggregate-to-cement ratio (A), water–cement ratio (B), silicone oil content (C), and moisture content (D)—on physical and mechanical parameters such as density, uniaxial compressive strength, elastic modulus, angle of internal friction, and permeability coefficient. Range analysis results indicate that the aggregate-to-cement ratio serves as the primary controlling factor for density and elastic modulus; moisture content exerts the most significant effect on compressive strength and permeability; while the water–cement ratio is the dominant factor influencing the internal friction angle. Empirical formulas were established through multiple regression to quantitatively correlate mix proportions with material properties. The resulting similitude materials cover a wide range of mechanical and hydraulic parameters, satisfying the requirements of large-scale physical modeling with high similitude ratios. The proposed equations allow efficient inverse design of mixture ratios based on target properties, thereby supporting the rapid preparation of simulative materials for advanced model testing. Full article

Droplet-based microlens fabrication using Ultra Violet (UV) curable polymers demands the precise measurement of three-dimensional geometries, especially for non-axisymmetric shapes influenced by electric field deformation. In this work, we present a polar coordinate-based contour fitting method combined with Hermite interpolation to reconstruct 3D droplet geometries from two orthogonal shadowgraphy images. The image segmentation process integrates superpixel clustering with active contours to extract the droplet boundary, which is then approximated using a spline-based polar fitting approach. The two resulting contours are merged using a polar Hermite interpolation algorithm, enabling the reconstruction of freeform droplet shapes. We validate the method against both synthetic Computer-Aided Design (CAD) data and precision-machined reference objects, achieving volume deviations below 1% for axisymmetric shapes and approximately 3.5% for non-axisymmetric cases. The influence of focus, calibration, and alignment errors is quantitatively assessed through Monte Carlo simulations and empirical tests. Finally, the method is applied to real electrically deformed droplets, with volume deviations remaining within the experimental uncertainty range. This demonstrates the method’s robustness and suitability for metrology tasks involving complex droplet geometries. Full article

Pervious concrete is challenged by the inherent trade-off between permeability and mechanical strength. This study presents a systematic optimization of its mix design to achieve a balance between these properties. Single-factor experiments and an L₉(3³) orthogonal array test were employed to evaluate the effects of target porosity (14–26%), water–cement ratio (0.26–0.34), sand rate (0–10%), and VMA dosage (0–0.02%). Additionally, Spearman rank correlation analysis and nonlinear regression fitting were utilized to develop quantitative relationships correlating the measured porosity to material performance. The results revealed that increasing target porosity enhances permeability but reduces compressive and splitting tensile strengths. The optimal water-to-cement ratio (w/c) was found to be 0.32, balancing both permeability and strength. An appropriate sand content of 6% improved mechanical properties, while a VMA dosage of 0.01% effectively enhanced bonding strength and workability. The orthogonal experiment identified the optimal mix ratio as a w/c ratio of 0.3, VMA dosage of 0.12%, target porosity of 14%, and sand content of 7%, achieving a compressive strength at 28-days of 43.5 MPa and a permeability coefficient of 2.57 mm·s⁻¹. Empirical relationships for the permeability coefficient and mechanical properties as functions of the measured porosity were derived, demonstrating a positive exponential correlation between the measured porosity and the permeability coefficient, and a negative correlation with compressive and splitting tensile strengths. This research provides a systematic framework for designing high-performance pervious concrete with balanced permeability and mechanical properties, offering valuable insights for its development and application in green infrastructure projects. Full article

The deep-sea convergence zone (CZ) is a critical phenomenon for long-range underwater acoustic propagation. Accurate prediction of its distance, width, and gain is essential for enhancing sonar detection performance. However, conventional ray-tracing models, which assume vertically stratified sound speed profiles (SSPs), fail to account for horizontal sound speed gradients in the mixed layer, leading to significant prediction errors. To address this, we propose a novel ray-tracing model that incorporates horizontally inhomogeneous SSPs in the mixed layer. Our approach combines empirical orthogonal function (EOF) decomposition with the Del Grosso sound speed formula to construct a continuous 3D sound speed field. We further derive a modified ray equation including horizontal gradient terms and solve it using a fourth-order Runge–Kutta method. Simulation and experimental validation in the South China Sea demonstrate that our model reduces the prediction error for the first CZ distance by 2.26%, width by 2.66%, and gain deviation by 5.85% compared to the Bellhop model. These results confirm the effectiveness of our method in improving CZ parameter prediction accuracy. Full article

In this paper, we develop a deep learning neural network characterized by feature fusion and physical guidance (denoted as FFPG-net) for reconstructing subsurface sea temperature (T) and salinity (S) from sea surface data. Designed with the idea of feature fusion, FFPG-net combines the deep learning algorithms of residual and channel attention with the physical constraints of vertical modes of T/S profiles decomposed by empirical orthogonal functions (EOFs). The results from a series of single point experiments show that FFPG-net outperforms the CNN or CNN-PG (without physical guidance or feature fusion) in the reconstruction of subsurface T/S in a region of the South China Sea (SCS), with monthly mean RMSEs of 0.31 °C (0.35 °C) and 0.06 psu (0.07 psu) for the reconstructed T/S profiles in winter (summer), averaged over the water depth of 1200 m and the study area. In addition, the performance of the FFPG-net can be improved significantly by incorporating full surface currents or geostrophic currents derived from SSH into the input variables for training the neural network. The preliminary application of FFPG-net in the SCS using satellite-derived sea surface observations indicates that FFPG-net is reliable and feasible for reconstructing subsurface ocean thermal fields in real situations. Our study highlights the advantages and necessity of combining deep learning algorithms with physical constraints in reconstructing subsurface T/S profiles. It provides an effective tool for reconstructing the subsurface global ocean from remote-sensing sea surface observations in the future. Full article

This study investigates the synergistic modification of cement–soil using waste brick powder (WBP) and polyvinyl alcohol (PVA) fibers to address the growing demand for sustainable construction materials and recycling of demolition waste. An orthogonal experimental design was employed with 5% WBP (by mass) and PVA fiber content (0–1%), evaluating mechanical properties based on unconfined compressive strength (UCS) and splitting tensile strength (STS) and microstructure via scanning electron microscopy (SEM) across 3–28 days of curing. The results demonstrate that 0.75% PVA optimizes performance, enhancing UCS by 28.3% (6.87 MPa) and STS by 34.6% (0.93 MPa) at 28 days compared to unmodified cement–soil. SEM analysis revealed that PVA fibers bridged microcracks, suppressing propagation, while WBP triggered pozzolanic reactions to densify the matrix. This dual mechanism concurrently improves mechanical durability and valorizes construction waste, offering a pathway to reduce reliance on virgin materials. This study establishes empirically validated mix ratios for eco-efficient cement–soil composites, advancing scalable solutions for low-carbon geotechnical applications. By aligning material innovation with circular economy principles, this work directly supports global de-carbonization targets in the construction sector. Full article

Aiming at the problem of accuracy degradation caused by the mixing of spatiotemporal disturbance patterns in sound speed profile (SSP) inversion using the traditional geographic grid division method, this study proposes an acoustic stability pre-clustering framework that integrates principal component analysis and machine learning clustering. Disturbance mode principal component analysis is first used to extract characteristic parameters, and then a machine learning clustering algorithm is adopted to pre-classify SSP samples according to acoustic stability. The SSP inversion experimental results show that: (1) the SSP samples of the South China Sea can be divided into three clusters of disturbance modes with statistically significant differences. (2) The regression inversion method based on cluster attribution reduces the average error of SSP inversion for data from 2018 to 1.24 m/s, which is more than 50% lower than what can be achieved with the traditional method without pre-clustering. (3) Transmission loss prediction verification shows that the proposed method can produce highly accurate sound field calculations in environmental assessment tasks. The acoustic stability pre-clustering technology proposed in this study provides an innovative solution for the statistical modeling of marine environment parameters by effectively decoupling the mixed effect of SSP spatiotemporal disturbance patterns. Its error control level (<1.5 m/s) is 37% higher than that of the single empirical orthogonal function regression method, showing important potential in underwater acoustic applications in complex marine dynamic environments. Full article

By utilizing daily precipitation data from 400 meteorological stations in the Yellow River Basin (YRB) of China, atmospheric and oceanic reanalysis data, this study investigates the climatological characteristics, leading modes, and relationships with atmospheric circulation and sea surface temperature (SST) of summer extreme precipitation in the YRB from 1981 to 2020 through the extreme precipitation metrics and Empirical Orthogonal Function (EOF) analysis. The results indicate that both the frequency and intensity of extreme precipitation exhibit an eastward and southward increasing pattern in terms of climate state, with regions of higher precipitation showing greater interannual variability. When precipitation in the YRB exhibits a spatially coherent enhancement pattern, high latitudes exhibits an Eurasian teleconnection wave train that facilitates the southward movement of cold air. Concurrently, the northward extension of the Western Pacific subtropical high (WPSH) enhances moisture transport from low latitudes to the YRB, against the backdrop of a transitioning SST pattern from El Niño to La Niña. When precipitation in the YRB shows a “south-increase, north-decrease” dipole pattern, the southward-shifted Ural high and westward-extended WPSH converge cold air and moist in the southern YRB region, with no dominant SST drivers identified. Full article

Sound speed profile (SSP) inversion based on remote sensing parameters allows for the acquisition of global quasi-real-time SSPs without the need for on-site measurements, thereby fulfilling the requirements of many acoustic applications. This study makes two enhancements to the single empirical orthogonal function regression (SEOF-R) method. First, the k-means clustering algorithm is utilized to cluster SSP samples, ensuring the consistency of perturbation modes in the physical regression. Second, baroclinic modes are employed to derive a novel SSP basis function, named the ocean mode basis, which accurately characterizes the inversion relationship. Validation experiments using data from the South China Sea yield promising results. Compared with the SEOF-R method, the reconstruction error of the improved approach is reduced by 27%, with an average reconstruction error of 1.73 m/s. The average prediction transmission loss error decreases by 70%, reaching 1.29 dB within 50 km. The grid-free processing and low sample dependence of the proposed method further enhance the applicability and accuracy of remote sensing-based SSP inversion. Full article

The recent economic turmoil and the increasing volatility of bitcoins have necessitated the need for exploring safe-haven assets for bitcoins. In this quest, the present study aims to investigate the safe haven for bitcoins by examining the dynamic relationship between bitcoins, gold, foreign exchange, and stablecoins. This is achieved by calculating hedge ratios and portfolio weight ratios for various asset classes, by employing adaptive-based techniques such as generalized orthogonal generalized autoregressive conditional heteroscedasticity, corrected dynamic conditional correlation, corrected asymmetric dynamic conditional correlation, and asymmetric dynamic conditional correlation under various market and time-varying conditions. The empirical estimate reveals that all the selected asset classes are effective risk diversifiers for bitcoins. However, among all the asset classes, as per the hedge and portfolio weight ratio, Japanese yen, stablecoin for Japanese yen and Great Britain Pound, and Crypto Holding Frank Token (lowest-cost hedging strategies) are the most effective risk diversifiers when compared with bitcoins. Moreover, while considering external economic shocks, the empirical estimate posits that stablecoins are more stable risk diversifiers compared to the asset class they represent. Furthermore, in terms of the bivariate portfolio analysis formed with bitcoin, this study concludes that the weight of bitcoin is more stable when combined with gold, tether gold, Euro, Great Britain Pound, Swiss franc, and Japanese Yen. Thus, these assets are attractive for long-term investment strategies. This study provides investors and policymakers with significant insight into understanding safe-haven assets for bitcoin’s volatility and constructing a flexible portfolio that is dependent on the investment timeline and the prevailing market conditions. Full article

The sound speed profile (SSP) is an important feature in the field of ocean acoustics. The accurate estimation of SSP is significant for the development of underwater position, communication, and associated fundamental marine research. The Res-SACNN model is proposed for SSP inversion based on the convolutional neural network (CNN) embedded with the residual network and self-attention mechanism. It combines the spatiotemporal characteristics of sea level anomaly (SLA) and sea surface temperature anomaly (SSTA) data and establishes a nonlinear relationship between satellite remote sensing data and sound speed field by deep learning. The single empirical orthogonal function regression (sEOF-r) method is used in a comparative experiment to confirm the model’s performance in both the time domain and the region. Experimental results demonstrate that the proposed model outperforms sEOF-r regarding both spatiotemporal generalization ability and inversion accuracy. The average root mean square error (RMSE) is decreased by 0.92 m/s in the time-domain experiment in the South China Sea, and the inversion results for each month are more consistent. The optimization ratio hits 71.8% and the average RMSE decreases by 7.39 m/s in the six-region experiment. The Res-SACNN model not only shows more superior inversion ability in the comparison with other deep-learning models, but also achieves strong generalization and real-time performance while maintaining low complexity, providing an improved technical tool for SSP estimation and sound field perception. Full article

Traditional methods, such as full-factorial, orthogonal, and empirical experiments, show limited accuracy and efficiency in selecting cleaning agents for decommissioned oil and gas pipelines. They also lack the ability to quantitatively analyze the impact of multiple variables. This study proposes a data-driven optimization approach to address these limitations. Residue samples from six regions, including Dalian and Shenyang, were analyzed for inorganic components using XRD and for organic components using GC. Citric acid was used as a model cleaning agent, and cleaning efficiency was tested under varying temperature, agitation, and contact time. Key variables showed significant correlations with cleaning performance. To further quantify the combined effects of multiple factors, multivariate regression methods such as multiple linear regression and ridge regression were employed to establish predictive models. A weighted evaluation approach was used to identify the optimal model, and a method for inverse prediction was proposed. This study shows that, compared with traditional methods, the data-driven approach improves accuracy by 3.67% and efficiency by 82.5%. By efficiently integrating and analyzing multidimensional data, this method not only enables rapid identification of optimal formulations but also uncovers the underlying relationships and combined effects among variables. It offers a novel strategy for the efficient selection and optimization of cleaning agents for decommissioned oil and gas pipelines, as well as broader chemical systems. Full article