Search Results (868)

This review provides a comparative analysis of the structure and physical properties of low-dimensional aperiodic crystalline solids, aiming to elucidate the origin and nature of aperiodicity in reduced-dimensional lattices. The breakdown of periodicity in low-dimensional systems arises from several mechanisms, including the suppression of specific force constants, thermodynamic instabilities, and topological constraints associated with imperfect space filling. At the nanoscale, certain cubic crystalline materials can form finite, zero-dimensional multiply twinned particles (MTPs) with decahedral or icosahedral symmetry. These clusters lack translational invariance and experience intrinsic structural strain due to solid-angle mismatch at twin junctions, which limits their characteristic size and renders them finite aperiodic solids. Particular attention is devoted to the electronic and spin properties of pentagonally symmetric MTPs: icosahedral particles exhibit symmetry-protected spin degeneracy consistent with centrosymmetric lattices, whereas non-centrosymmetric decahedral particles may display spin polarization and emergent low-dimensional magnetism. Collectively, these systems illustrate the diverse physical origins, manifestations, and consequences of aperiodicity in low-dimensional crystalline matter. Full article

Perpetual futures account for approximately 93% of cryptocurrency futures trading volume, yet funding rate dynamics across fragmented markets remain understudied. We construct a high-frequency panel dataset comprising 35.7 million one-minute observations across 26 cryptocurrency exchanges (11 centralized, 15 decentralized) spanning 749 symbols over eight consecutive days. Using time-series econometrics, correlation analysis, and Granger causality tests, we characterize funding rate dynamics, market integration, and information flow. We find evidence of a two-tiered market structure: centralized exchanges (CEX) dominate price discovery with 61% higher integration than decentralized exchanges (DEX), and all significant information flow runs CEX-to-DEX with zero reverse causality. While 17% of observations exhibit economically significant arbitrage spreads (≥20 basis points), only 40% of top opportunities generate positive returns after transaction costs and spread reversals. Delta-neutral portfolio simulations reveal that successful arbitrage requires both high spreads and sufficient duration before inevitable reversals, with forced exits occurring in 95% of opportunities. The findings show that cryptocurrency derivatives markets exhibit a persistent two-tiered structure in which centralized platforms dominate price discovery while transaction costs and spread reversal risks prevent arbitrage from eliminating large mispricings between platforms, resolving the apparent paradox of substantial price fragmentation coexisting with market efficiency. Full article

Achieving dynamic stability in bipedal locomotion against sustained external disturbances remains a significant challenge in humanoid robotics. Traditional methods, such as zero moment point (ZMP) preview control, often lack the reactive compliance and predictive planning necessary for robust performance on uneven terrain or under continuous force. This paper proposes a novel control framework that synergistically integrates a resistance torso compliance controller with a nonlinear model predictive control (NMPC)-based walking pattern generator. The compliance controller actively modulates the torso’s dynamics via impedance control, creating a virtual mass–spring–damper system that absorbs impacts and generates counterforces to resist sustained pushes. Concurrently, the NMPC module reformulates gait generation as a real-time optimization problem, simultaneously determining optimal footstep positions and orientations while respecting nonlinear constraints derived from centroidal momentum dynamics. Simulation results demonstrate that this integrated approach reduces the maximum ZMP error by 34.1% and the RMS ZMP error by 37.3% compared to traditional ZMP preview control, with a 38.9% improvement in settling time after a disturbance. This work establishes that the tight coupling of reactive impedance control and predictive optimization provides a foundational framework for enhancing the robustness and adaptability of bipedal locomotion. Full article

To address the significant vibration and noise problems caused by the zero-order radial electromagnetic force (REF) in integer-slot permanent magnet synchronous motors (PMSMs), while simultaneously improving the motor’s overall electromagnetic performance, this paper proposes a hierarchical iterative optimization strategy integrating Taguchi methods and genetic algorithms. The optimization objectives include minimizing the zero-order REF amplitude, cogging torque, and torque ripple, while maximizing the average torque, with efficiency and back electromotive force total harmonic distortion (back-EMF THD) treated as constraints. First, an 8-pole 48-slot double-layer embedded PMSM model is constructed. An innovative parameter selection strategy, combining theoretical analysis with finite-element analysis, is employed to investigate the spatial order and frequency characteristics of the electromagnetic force. Subsequently, a sensitivity analysis is performed to stratify parameters: highly sensitive parameters undergo first-round optimization via the Taguchi method, followed by second-round optimization using a multi-objective genetic algorithm. The results demonstrate significant reductions in both the zero-order REF amplitude and cogging torque. Specifically, the motor’s peak vibration acceleration is reduced by 32.96%, and the peak sound pressure level (SPL) drops by 9.036 dB. Vibration acceleration and sound pressure across all frequency bands are significantly reduced to varying extents, validating the effectiveness of the proposed optimization approach. Full article

Triply Periodic Minimal Surface (TPMS) structures, with their zero average curvature, excellent energy absorption properties, high specific strength and high surface-to-volume ratio, could be used in a wide range of applications, such as the creation of lightweight and durable structures, grafts and implants. In this study, an internal TPMS structure inspiring trabecular bone and an external TPMS structure inspiring cortical bone were combined with infill density and topologically functionally graded to obtain hybrid structures. The aim of the study was to investigate the effects of functional grading on mechanical properties, energy absorption capacity and surface/volume (S/V) ratio and to determine the best combination. The novelty of the study is to obtain hybrid structures close to bone structures with a functional grading approach. The experimental design of the study was performed using the Design of Experiment (DoE) approach and the Taguchi method. Specimens were created according to the established experimental design and fabricated using a Masked Stereolithography (mSLA)-type 3D printer with bio-resin. The fabricated structures were subjected to compression tests; the results were examined in terms of deformation behavior, first peak, maximum force, energy absorption, specific energy absorption and S/V ratio. The optimal structures for defined input parameters were determined using signal-to-noise (S/N) ratios and ANOVA results. Deformations for diamond and primitive specimens began as shear band formation. Deformations for Neovius structures were mostly as brittle fracture. The highest first peak of 18.96 kN was obtained with the DN specimens, while the highest maximum force of 23.77 kN was obtained with the ND specimens. The best energy absorption property was also obtained with ND. The highest S/V ratio was 5.65 in the GP specimens. The statistical analyses were in accordance with the experimental results. Infill density increases decreased the S/V ratio while increasing all other parameters. This demonstrated the importance of mechanical-strength/porosity optimization for bone scaffold surrogate applications in this study. Full article

Rocker–bogie suspension systems have been extensively employed in planetary exploration rovers due to their ability to traverse highly irregular terrains while maintaining ground contact. Traditionally, their mechanical behavior has been analyzed using quasi-static models, given the low operational speeds typical of space missions. However, similar configurations are now being proposed for terrestrial applications in agriculture, defense, and logistics, where higher traversal speeds and more varied terrain conditions require a deeper understanding of the system’s dynamic response. This study analyzes some aspects of the kinematic and dynamic behavior of a rover with rocker–bogie suspension while traversing an obstacle with a harmonic profile. Both quasi-static and dynamic simulations are conducted, focusing on the time-varying contact forces at the wheels. Key findings include identifying the rate at which load reduction at which the load on one wheel becomes zero and the wheel tends to lift off the ground. These threshold speeds are mapped as a function of height and wavelength of the bump, providing design insights for applications requiring higher traversal speeds on uneven terrain. The analysis may also prove valuable for rovers equipped with visual sensor systems capable of mapping their surroundings and identifying obstacles, to determine whether they can be traversed and, if so, at what maximum speed. An experimental investigation was conducted with a small-scale rover to verify the theoretical results, for which the threshold speed was found to be 0.3 m/s, calculated for h = 16 mm and λ = 80 mm. Full article

This paper addresses the signal detection problem in orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems using deep learning (DL) techniques. In particular, a DL-based detector termed FullTrans-IM is proposed, which integrates the Transformer architecture with long short-term memory (LSTM) networks. Unlike conventional methods that treat signal detection as a classification task, the proposed approach reformulates it as a sequence prediction problem by exploiting the sequence modeling capability of the Transformer’s decoder rather than relying solely on the encoder. This formulation enables the detector to effectively learn channel characteristics and modulation patterns, thereby improving detection accuracy and robustness. Simulation results demonstrate that the proposed FullTrans-IM detector achieves superior bit error rate (BER) performance compared with conventional methods such as zero-forcing (ZF) and existing DL-based detectors under Rayleigh fading channels. Full article

Nowadays, the automotive industry is primarily driven by the CO₂ policy that targets net zero carbon emissions by 2035 from passenger cars and commercial vehicles. The main path to achieve this goal is the implementation of electric powertrains with the energy stored in batteries, as the case for battery electric vehicles (BEV). However, this technology still faces some difficulties in terms of energy density, overall weight, charging time, and vehicle autonomy. From the other point of view, fuel cell electric vehicles (FCEV) offer the same advantages as BEV in terms of CO₂ reduction, providing better autonomy and lower refueling time. The energy demand by the electric powertrain strongly depends on the vehicle driving conditions as it directly affects energy consumption. In that context, the article aims to study the electrical energy demand of an ultra-energy efficient vehicle intended for a Shell eco-marathon competition in order to minimize hydrogen consumption. The study was carried out over a single lap on the racing track in Nogaro, France while applying the race rules from the competition in 2023. It includes a numerical evaluation of the vehicle resistance forces in different driving strategies and experimental validation on the propulsion test bench. Full article

In this study, we consider multi-pair user frequency division duplexing massive MIMO relay systems and design a two-stage combining and beamforming (TSCB) scheme based on statistical channel state information (S-CSI). By leveraging S-CSI to co-design the pre-combining matrix and the pre-beamforming matrix, the scheme reduces the equivalent channel matrix dimensions, thereby cutting the pilot overhead. In the first stage, the two matrices are constructed through a selection of beams from a discrete Fourier transform codebook and mathematically cast as a multivariate optimization problem. An alternative optimization algorithm is proposed by splitting it into three sub-problems. The first two are 0–1 integer programming problems solved by iterative beam selection, while the third is a convex problem that is solved using a convex optimization algorithm. In the second stage, the reduced-dimension equivalent matrices are then estimated with low overhead, and a digital precoding matrix is then designed using zero-forcing algorithms. Simulations confirm the TSCB scheme’s superior ESE performance over that of existing methods. Full article

A rise in seawater salinity results in an increase in its viscosity, which presents a coupled influence on the distribution of fluid pressure, temperature and deformation at the sealing face, leading to fluctuations in sealing performance and forming the salinity effect in seawater thermoelastohydrodynamic lubrication (TEHL). Here, for a double spiral groove face seal, a TEHL model is established and numerical analysis is carried out, taking account of the salinity effect and cavitation effect, with the aim to ensure that the seal maintains stable performance under varying conditions of sea depth and speed. It is found that the effect of salinity on the opening force and leakage rate exhibits obvious nonlinear variations. As salinity rises from 0 to the standard 35 g/kg, the opening force changes by about 5%, and there is a transition between forward and reverse leakage, with variations of approximately ±100%. More importantly, the double spiral grooves offer the potential for a zero-leakage design in seawater face seals, even under pressures exceeding 4 MPa, through precise design. Additionally, the double spiral groove face seal shows excellent adaptability under multipoint conditions and can facilitate a zero-leakage design in varying pressure, speed and temperature conditions. This provides theoretical support for deep-sea equipment and applications in other extreme environments. Full article

With the rapid development of low-Earth-orbit (LEO) satellite communications, multi-beam precoding has emerged as a key technology for improving spectrum efficiency. However, the long propagation delay and large Doppler frequency offset pose significant challenges to existing precoding techniques. To address this issue, this paper investigates downlink precoding design for multi-beam LEO satellite communications. First, the downlink channel and signal models are established. Then, we reveal that traditional zero-forcing (ZF), regularized zero-forcing (RZF), and minimum mean square error (MMSE) precoding schemes all require the satellite transmitter to acquire the instantaneous channel state information (iCSI) of all users, which is challenging to obtain in satellite communication systems. Subsequently, we propose a downlink precoding design based on statistical channel state information (sCSI) and derive closed-form solutions for statistical-ZF, statistical-RZF, and statistical-MMSE precoding. Furthermore, we propose that sCSI can be computed using the positions of the satellite and users, which reduces the system overhead and complexity of sCSI acquisition. Monte Carlo simulations under the 3GPP non-terrestrial network (NTN) channel model are employed to verify the performance of the proposed method. The simulation results show that the proposed method achieves sum-rate performance comparable to that of iCSI-based schemes and the optimal transmission performance based on sum-rate maximization. In addition, the proposed method significantly reduces the computational complexity of the satellite payload and the system feedback overhead. Full article

Beamforming is highly significant for the physical layer of wireless communication systems, for multi-antenna systems such as multiple input multiple output (MIMO) and massive MIMO, since it improves spectral efficiency and reduces interference. Traditional linear beamforming methods such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming provide closed-form solutions. Yet, their performance drops when they face non-ideal conditions such as imperfect channel state information (CSI), dynamic propagation environment, or high-dimensional system configurations, primarily due to static assumptions and computational limitations. These limitations have led to the rise of deep learning-based beamforming, where data-driven models derive beamforming solutions directly from CSI. By leveraging the representational capabilities of cutting-edge deep learning architectures, along with the increasing availability of data and computational resources, deep learning presents an adaptive and potentially scalable alternative to traditional methodologies. In this work, we unify and systematically compare our two unsupervised learning architectures for uplink receive beamforming: a simple neural network beamforming (NNBF) model, composed of convolutional and fully connected layers, and a transformer-based NNBF model that integrates grouped convolutions for feature extraction and transformer blocks to capture long-range channel dependencies. They are evaluated in a common multi-user single input multiple output (MU-SIMO) system model to maximize sum-rate across single-antenna user equipments (UEs) under 3GPP-compliant channel models, namely TDL-A and UMa. Furthermore, we present a FLOPs-based asymptotic computational complexity analysis for the NNBF architectures alongside baseline methods, namely ZFBF and MMSE beamforming, explicitly characterizing inference-time scaling behavior. Experiments for the simple NNBF are performed under simplified assumptions such as stationary UEs and perfect CSI across varying antenna configurations in the TDL-A channel. On the other hand, transformer-based NNBF is evaluated in more realistic conditions, including urban macro environments with imperfect CSI, diverse UE mobilities, coding rates, and modulation schemes. Results show that the transformer-based NNBF achieves superior performance under realistic conditions at the cost of increased computational complexity, while the simple NNBF presents comparable or better performance than baseline methods with significantly lower complexity under simplified assumptions. Full article

This paper presents a joint design approach for intelligent reflecting surface (IRS)-assisted multi-user millimeter-wave (mmWave) systems. Our goal is to maximize the sum-rate of all users by optimizing the hybrid beamforming at the base station and the low-resolution phase shifters (e.g., 1 bit) at the IRS. To address this, we first adopt a zero-force (ZF) technique to design fully-digital (FD) beamforming and develop a cross-entropy optimization (CEO) framework-based iterative algorithm to calculate IRS phase shifts. Specifically, in this framework, the probability distributions of IRS elements are updated by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Then, based on the obtained FD beamforming, an alternating minimization method is applied to acquire hybrid beamforming. Simulation results show that our proposed joint design scheme can achieve enhanced performance compared to the existing schemes while maintaining a lower computational complexity. Full article

The thermal forcing of the Tibetan Plateau (TP) significantly influences the Asian summer monsoon. However, its interaction with cloud feedbacks remains unclear due to the limitations of synchronous analysis and traditional cloud classification over the TP. By applying an improved cloud-classification algorithm—which integrates cloud microphysical properties to improve low-cloud detection—to CERES data (2001–2023), we generated a long-term cloud-type dataset. Combined with ERA5 reanalysis data, we systematically analyzed the trends and lead–lag relationships among cloud vertical structure, surface radiation, cloud radiative forcing (CRF), heat fluxes, snowfall, and the TP Monsoon Index (TPMI). Results indicate a vertical cloud redistribution over the TP, with high cloud cover (HCC) decreasing and low cloud cover (LCC) increasing. HCC is strongly synchronized with snowfall and significantly affects surface radiation, while net CRF and sensible heat flux show delayed responses, peaking when HCC leads by about one month. A composite analysis of winter low-HCC events reveals that reduced HCC suppresses snowfall, weakens net CRF, and reduces sensible heat flux after approximately 1–2 months, while the TPMI shows a significant response around month zero. These findings highlight the key role of cloud–radiation–snowfall interactions in modulating TP thermal forcing. Full article

In order to effectively reveal the nonlinear characteristics of a dish concentrating solar thermal power system (DCSTPS) under pulsating wind-induced loads, a fluid simulation model of the DCSTPS was established, and the simulated pulsating winds were developed via the user-defined function (UDF) combined with the autoregressive (AR) model using MATLAB (R2015b). And based on the fluid simulation calculations of the DCSTPS, the time-range data of the relevant wind vibration coefficients under different working conditions were obtained. The research results show the following: (1) When the altitude angle α is 0° or 180° due to the azimuth angle β = 0°, the maximum values of their drag coefficient C_x, lateral force coefficient C_y, and lift coefficient C_z are similar, and the maximum of rolling moment coefficient C_Mx is significantly smaller than the values at the other two angles; the maximum of the pitch moment coefficient C_My and maximum of the azimuth moment coefficient C_Mz are significantly larger than the values of the other two angles. (2) The increase in altitude angle α leads to a reduction in the drag coefficient C_x, an increase in the lift force coefficient C_z, and an increase of the pitch moment C_Mx. Moreover, an improved phase space delay reconstruction method was developed to calculate the delay time, Lyapunov exponent, and Kolmogorov entropy of the DCSTPS, and the research results show that (1) the maximum Lyapunov exponent and Kolmogorov entropy of the DCSTPS are greater than zero under the action of pulsating wind; (2) the action of pulsating wind will cause increases in the maximum Lyapunov exponent and Kolmogorov entropy of the DCSTPS and will accelerate the divergence speed of the DCSTPS trajectory; and (3) the time for the DCSTPS to enter the chaotic state will be shortened, while the time of entering a chaotic state and degree of subsequent chaotic states will be significantly affected by relevant wind vibration coefficients but without regularity. Full article