Search Results (138)

Compared with conventional symmetric aircraft, the oblique-wing aircraft offers significant advantages across a wide speed range due to the variable oblique angle. However, the asymmetric aerodynamic characteristics will arise from the differential leading-edge sweep between the forward and aft wings during the rotation process. This study investigates the aerodynamic effects of a conceptual oblique-wing configuration at transonic (Mach 0.85) and supersonic (Mach 1.40) flight conditions. For the baseline design, peak lift-to-drag ratio occurs at oblique angles of 30° and 60°, respectively. Analysis at Mach 0.85 reveals that the forward wing dominates the aerodynamic performance of the whole configuration. The parameter study of the leading-edge sweep confirms that the configuration combining a smaller forward-wing sweep with a larger aft-wing sweep is an effective design for achieving the balanced aerodynamic performance, namely, the forward wing with a 24° leading-edge sweepback angle and the after wing with 33° yield a high lift-to-drag ratio, achieving an optimal trade-off with rolling moment minimization. This drag reduction is achieved through the simultaneous decrease in both wave drag and induced drag. Furthermore, downwash analysis reveals that the inherent rolling moment originates from asymmetric tail loads induced by uneven downwash distribution. These findings provide guidance for the aerodynamic design of future oblique-wing aircraft. Full article

When offshore wind power is connected to a grid via Modular multilevel converter-based High Voltage Direct Current (MMC-HVDC), the sending-end alternating current (AC) system is susceptible to asymmetrical faults. These faults lead to overcurrent surges, voltage drops, and second harmonic circulating currents, which seriously threaten the safe operation of the system. To quickly suppress fault current surges, achieve precise control of system variables, and improve fault ride-through capability, this study proposes a collaborative control strategy. This strategy integrates generalized virtual impedance current limiting, positive- and negative-sequence collaborative feedforward control, and model-predictive control-based suppression of arm energy and circulating currents. The positive- and negative-sequence components of the voltage and current are quickly separated by extending and decoupling the decoupled double synchronous reference frame phase-locked loop (DDSRF-PLL). A generalized virtual impedance with low positive-sequence impedance and high negative-sequence impedance was designed to achieve rapid current limiting. Simultaneously, negative-sequence current feedforward compensation and positive-sequence voltage adaptive support are introduced to suppress dynamic fluctuations. Finally, an arm energy and circulating current prediction model based on model predictive control (MPC) is established, and the second harmonic circulating currents are precisely suppressed through rolling optimization. Simulation results based on PSCAD/EMTDC show that the proposed control strategy can effectively suppress the negative-sequence current, significantly improve voltage stability, and greatly reduce the peak fault current. It significantly enhances the fault ride-through capability and operational reliability of offshore wind power MMC-HVDC-connected systems and holds significant potential for engineering applications. Full article

This study investigates how U.S. Federal Reserve interest rate cuts during the 2019–2020 easing cycle influenced the performance of equity mutual funds, with a particular emphasis on contrast between growth and value investment styles. Using an event study framework, we examine abnormal returns, cumulative abnormal returns, and risk-adjusted performance metrics, including those based on both 30 days static and rolling Jensen’s alpha and Sharpe ratios, across short-term (30-day) and long-term (6-month and 1-year) windows surrounding three major rate cut events. Our empirical results show that growth funds significantly outperform value funds following rate reductions, especially over longer horizons. This performance advantage is more pronounced in risk-adjusted measures and strengthens when incorporating rolling dynamics, indicating that and asymmetric sensitivity of fund styles to interest rate changes, shaped by differences in duration exposure and investor sentiment. Overall, this study offers novel insights into how monetary policy influences fund-level dynamics beyond broad market movements and deepens the understanding of monetary transmission in asset management by incorporating time-varying performance metrics. Full article

Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between natural processes and human activities often limit the effectiveness of conventional prediction methods. To address this, a hybrid CNN-LSTM deep learning model is constructed. This model is designed to extract multivariate coupled features and capture temporal dependencies from multi-variable time series data, while simultaneously simulating the nonlinear and delayed responses of aquifers to groundwater abstraction. Specifically, the convolutional neural network (CNN) component extracts the multivariate coupled features of hydro-meteorological driving factors, and the long short-term memory (LSTM) network component models the temporal dependencies in groundwater level fluctuations. This integrated architecture comprehensively represents the combined effects of natural recharge–discharge processes and anthropogenic pumping on the groundwater system. Utilizing monitoring data from 2021 to 2024, the model was trained and tested using a rolling time-series validation strategy. Its performance was benchmarked against traditional models, including the autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN), and standalone LSTM. The results show that the CNN-LSTM model delivers superior performance across diverse hydrogeological conditions: at the upstream well AJC-7, which is dominated by natural recharge and discharge, the Nash–Sutcliffe efficiency (NSE) coefficient reached 0.922; at the downstream well AJC-21, which is subject to intensive pumping, the model maintained a robust NSE of 0.787, significantly outperforming the benchmark models. Further sensitivity analysis reveals an asymmetric response of the model’s predictions to uncertainties in pumping data, highlighting the role of key hydrogeological processes such as delayed drainage from the vadose zone. This study not only confirms the strong applicability of the hybrid deep learning model for groundwater level prediction in data-scarce arid regions but also provides a novel analytical pathway and mechanistic insight into the nonlinear behavior of aquifer systems under significant human influence. Full article

In this study, the asymmetrical rolling technique was employed to fabricate 75 μm-thick Ti-6Al-4V ultra-thin strips from the initial 0.45 mm sheet without intermediate annealing, aiming for applications in fuel cell bipolar plates. The rolled strips exhibited good surface quality without cracking. In order to enhance both the mechanical response and the shaping capability of Ti-6Al-4V strips produced by asymmetric rolling, the material was subjected to annealing at various temperatures, and the resulting changes in microstructural features and mechanical performance were systematically examined. The findings indicated that the cold-rolled Ti-6Al-4V exhibited a microstructure primarily composed of subgrains with an average size of approximately 0.41 μm, a feature that contributed to improved corrosion resistance and enhanced ductility after annealing. When the alloy was subjected to heat treatment within the range of 650–800 °C, it was observed that annealing temperatures below 700 °C favored microstructural changes governed predominantly by recovery processes and the onset of recrystallization. At 700 °C, the grains became equiaxed and uniformly distributed, and the dislocation density significantly decreased. The tensile strength reached 887 MPa, while the elongation increased to 13.7%, achieving an excellent strength-ductility balance. Once the annealing temperature rose above 700 °C, noticeable grain growth took place, accompanied by a more pronounced grain-size gradient and a renewed increase in dislocation density. Meanwhile, the dimples observed on the fracture surface became finer, collectively contributing to a decline in tensile elongation. The Ti-6Al-4V ultra-thin strip annealed at 700 °C was used for bipolar plate stamping, producing fine micro-channels with an aspect ratio of 0.43. Finally, TiN coating was applied to the surface, which significantly improved the corrosion resistance and reduced the interfacial contact resistance (ICR), meeting the performance requirements for bipolar plates. Full article

This paper proposes a hybrid integer optimization method based on the Whale Optimization Algorithm (WOA) for the asymmetric central air conditioning chiller system of a 530-m super high-rise building in Guangzhou. Firstly, a three-hidden-layer multilayer perceptron (MLP) chiller model based on 16,276 sets of measured data and a gradient boosting regression cooling tower model based on 21,369 sets of operating condition data were constructed, achieving high-precision modeling of the energy consumption of all equipment in the chiller system. Secondly, a hybrid encoding strategy of “threshold truncation + continuous relaxation” was proposed to integrate discrete on-off states and continuous operating parameters into WOA, and a three-layer constraint repair mechanism was designed to ensure the physical feasibility of the optimization process and the safe operation of equipment. Verification across three load scenarios—low, medium, and high—showed that the optimized system’s energy efficiency ratio (EER) increased by 15.01%, 12.61%, and 11.86%, respectively, with energy savings of 12.91%, 11.18%, and 10.58%. The annual rolling optimization results showed that the average EER increased from 5.07 to 5.88 (16.1%), with energy savings ranging from 8.59% to 18.92%. Sensitivity analysis indicated that pump quantity is the most influential parameter affecting system energy consumption, with an additional pump reducing it by 1.1%. The optimization method proposed in this paper meets the minute-level real-time scheduling requirements of building automation systems and provides an implementable solution for energy-saving optimization of central air conditioning chiller systems in super high-rise buildings. Full article

This paper proposes a fuzzy copula-based optimization framework for modeling dependence structures and financial risk under parameter uncertainty. The parameters of selected copula families are represented as trapezoidal fuzzy numbers, and their α-cut intervals capture both the support and core ranges of plausible dependence values. This fuzzification transforms the estimation of copula parameters into a fuzzy optimization problem, enhancing robustness against sampling variability. The methodology is empirically applied to gold and oil futures (1 January 2015–1 January 2025), comparing symmetric copulas, i.e., Gaussian and Frank and asymmetric copulas, i.e., Clayton, Gumbel and Student-t. The results prove that the fuzzy copula framework provides richer insights than classical point estimation by explicitly expressing uncertainty in dependence measures (Kendall’s τ, Spearman’s ρ) and risk indicators (Value-at-Risk, Conditional Value-at-Risk). Rolling-window analyses reveal that fuzzy VaR and fuzzy CVaR effectively capture temporal dependence shifts and tail severity, with fuzzy CVaR consistently producing more conservative risk estimates. This study highlights the potential of fuzzy optimization and fuzzy dependence modeling as powerful tools for quantifying uncertainty and managing extreme co-movements in financial markets. Full article

Cryosat-2 SARin altimetric FBR data facilitates an opportunity to investigate phase differences between inland water radar reflections at the two antennae. With the antennae positioned cross-track, SARin was designed for the recovery of slope over ice margins, but here, it was used to recover off-pointing over inland waters. The ability to measure non-nadir off-pointing is verified using ocean data near the Amazon estuary to determine the satellite roll angle. Over inland waters, off-pointing requires correction to the nadir range and the geographic location of the reflectance. By using an SRTM-based water mask, the number of inland water reflectance increases significantly when off-pointing is considered. Comparisons between altimetric and river heights utilise gauge data at Tabatinga on the Solimões–Amazon. A least-squares adjustment yielded a river slope of −0.03506 ± 0.00003 m/km and a mean velocity of 1.803 ± 0.014 m/s over a river stretch of nearly 290 km. RMSE differences between the gauge and altimetry improve from 0.423 m to 0.404 m when off-pointing is taken into account for nadir inland water returns, showing the asymmetric effect of off-pointing. If all potential off-pointings are considered, the number of measurements increases by 66%, but the RMSE of 0.524 m is higher due to additional errors in the off-pointing corrections. Full article

Three-wheeled vehicles are increasingly adopted as sustainable transport solutions, but their asymmetric design and lightweight structure make them vulnerable to ride discomfort and rollover instability. This study develops a high-fidelity 12-degrees-of-freedom (DOF) dynamic model in MATLAB/Simulink and MSC ADAMS to analyze and improve ride comfort, handling, and roll stability. The model captures longitudinal, lateral, vertical, roll, pitch, and yaw motions, along with tire dynamics represented through the Magic Formula, and is validated using real-world data from an instrumented test vehicle. In this research, both active and passive control strategies were separately implemented and studied. The active strategy involves an Active Vehicle Roll Dynamics Control (VRDC) system with an active rear suspension to suppress roll and yaw during aggressive maneuvers. The passive strategy focuses on improving rollover resistance by modulating throttle input based on sensor data from gyroscopes, accelerometers, and compasses. Simulation and experimental results show that each strategy, when applied independently, enhances roll stability, reduces yaw rate deviations, and improves handling performance. These findings demonstrate the effectiveness of both approaches in improving the safety and dynamic behavior of electric three-wheeled vehicles under real-world conditions. Full article

Compaction quality is a critical factor in ensuring the long-term performance of subgrade structures; however, traditional testing methods are limited by their destructive nature and delayed feedback. To address these shortcomings, this study proposes a dynamic identification method for subgrade compaction based on embedded attitude sensors. A customized sensor unit integrated with an inertial measurement module was embedded in soil samples to record triaxial acceleration and attitude angles during the compaction process. Signal processing techniques, including an improved wavelet-based denoising strategy, were employed to separate long-term compaction trends from transient impact disturbances. Attitude features such as cumulative angular change, angular velocity, root mean square values, and a comprehensive inclination index were extracted as predictive variables. Ridge regression, random forest, and XGBoost models were constructed to establish the mapping relationship between attitude features and compaction degree. Experimental results on clay, loam, and sand samples indicate that the yaw angle is most sensitive to vertical settlement, while pitch and roll angles provide complementary information on lateral and rotational behaviors. Comparative analysis of filtering methods shows that the transient masking interpolation (TMI) approach outperforms the traditional asymmetric wavelet thresholding (AWT) method in effectively preserving baseline trends. Among the regression models, XGBoost demonstrated the best predictive performance, achieving an R² exceeding 0.995 at high compaction levels. The proposed method has been experimentally demonstrated as a laboratory-scale proof of concept, showing strong potential for future real-time field application, offering a novel technological pathway for intelligent quality control in road construction. Full article

Positive percentage time series are present in many empirical applications; they take values in the continuous interval (0,1) and are often modeled with linear dynamic models. Risks of biased predictions (outside the admissible range) and problems of heteroskedasticity in the presence of asymmetric distributions are ignored by practitioners. Alternative models are proposed in the statistical literature; the most suitable is the dynamic beta regression which belongs to generalized linear models (GLM) and uses the logit transformation as a link function. However, owing to the Jensen inequality, this approach may also not be optimal in prediction; thus, the aim of the present paper is the in-depth forecasting comparison of linear and beta autoregressions. Simulation experiments and applications to nonstationary time series (the US unemployment rate and BR hydroelectric energy) are carried out. Rolling regression for time-varying parameters is applied to both linear and beta models, and a prediction criterion for the joint selection of model order and sample size is defined. Full article

ASYMMETRIC LEAVES1 (AS1) and AS2 play essential roles in regulating leaf development in plants. However, their functional roles in soybean remain poorly understood. Here, we identified two members of the soybean AS1 gene family, GmAS1a and GmAS1c, which exhibit high expression levels in stem and leaf tissues. Using the CRISPR/Cas9 system, we targeted four GmAS1 and three GmAS2 genes, generating mutant lines with distinct leaf development phenotypes, including wrinkling (refers to fine lines and creases on the leaf surface, like aged skin texture), curling (describes the inward or outward rolling of leaf edges, deviating from the typical flat shape), and narrow. We found that functional redundancy exists among the four GmAS1 genes in soybean. GmAS1 and GmAS2 cooperatively regulate leaf curling, leaf crinkling phenotypes, and leaf width in soybean, with functional redundancy also observed between these two genes. Transcriptome sequencing analysis of w3 mutant (as1b as1c as1d as2a as2b as2c) identified 1801 differentially expressed genes (DEGs), including 192 transcription factors (TFs). Gene ontology enrichment analysis revealed significant enrichment of DEGs in pathways associated with plant hormone biosynthesis and signal transduction. A detailed examination of the DEGs showed several genes involved in the development of leaf lateral organs, such as KNOX (SHOOT MERISTEMLESS (STM), KNAT1, KNAT2, and KNAT6), LOB (LBD25, LBD30), and ARP5, were down-regulated in w3/WT (wild-type) comparison. CRISPR/Cas9-mediated targeted mutagenesis of the GmAS1/2 genes significantly impairs leaf development and polarity establishment in soybean, providing valuable germplasm resources and a theoretical framework for future studies on leaf morphogenesis. Full article

In this study, we propose a novel asymmetric high-performance MEMS pendulum accelerometer comprising a sensitive structure and an interface circuit. The sensitive structure, designed with asymmetric mass blocks, significantly improves both sensitivity and structural stability. The sensor is fabricated using a double-side polished (100) N-type silicon wafer and its structure is ultimately realized through ICP (Inductively Coupled Plasma) etching. We also develop and fabricate the corresponding interface circuit. The accelerometer is evaluated through a static field roll-over test, demonstrating excellent performance with a sensitivity of 1.247 V/g and a nonlinearity of 0.8% within the measurement range of −2 g to 2 g. Full article

Tire contact imprint characteristics and pressure distribution directly affect their lateral mechanical characteristics under rolling conditions, which are the key influencing factors for vehicle handling stability. Based on the nonlinear finite element method, an explicit dynamic model of radial tires is established using Abaqus, and its contact process is simulated through phased load transfer and kinematic inversion. The modified mathematical model of contact pressure distribution is introduced from the geometric evolution law of contact imprint and the nonlinear characteristics of contact pressure distribution. The corrected lateral force and aligning torque and contact imprint behavior are analyzed. The results show that in the low roll-angle range, with the increase in the roll angle, the contact imprint shrinks asymmetrically, the pressure center shifts to the outer shoulder of the roll direction, and the lateral force and aligning torque show linear growth characteristics. At the critical value ±8°, the growth rate is significantly slowed down due to the stress saturation effect of the shoulder area. The research analyzes the evolution mechanism of the lateral mechanical characteristics of the contact imprint geometry and pressure distribution drive tires under roll conditions, providing theoretical support for vehicle handling stability optimization and tire structure design. Full article

Ensuring the smooth production and distribution of agricultural products is a crucial pathway to achieving a balance between supply and demand. However, the information within the agricultural product supply chain is characterized by its dynamic and asymmetric nature, compounded by frequent outbreaks of infectious diseases that lead to supply interruptions and allocation difficulties. These factors collectively undermine the operational efficiency and resilience of the agricultural product supply chain. This study develops an integrated allocation-location optimization model for emergency agricultural product supply chains based on a rolling horizon approach. The model accounts for both supply shortage and sufficient scenarios, with objectives to maximize the comprehensive material satisfaction rate, minimize the activation cost of distribution centers, and minimize allocation time. The proposed model is solved using the Benders decomposition algorithm. Finally, a case study based on the Shanghai pandemic outbreak is conducted for numerical simulation. The results demonstrate the effectiveness of the model: the comprehensive material satisfaction rate increases progressively over the rolling periods, rising from approximately 84% in period 1 to 100% by period 3. Furthermore, fairness analysis confirms that the model also effectively ensures equitable distribution of supplies. Full article