Search Results (52)

This work presents the structural analysis and validation of a sub-250 g FPV drone chassis, emphasizing both theoretical rigor and practical applicability. The novelty of this contribution lies in four complementary aspects. First, the structural philosophy introduces a screwless frame with interchangeable arms, joined through interlocking mechanisms inspired by traditional Japanese joinery. This approach mitigates stress concentrations, reduces weight by eliminating fasteners, and enables rapid arm replacement in the field. Second, validation relies on nonlinear static and transient FEM simulations, explicitly including crash scenarios at 5 m/s, systematically cross-checked with bench tests and instrumented flight trials. Third, unlike most structural studies, the framework integrates firmware (Betaflight), GPS, telemetry, and real flight performance, linking structural reliability with operational robustness. Finally, a practical materials pathway was implemented through a dual-track strategy: PETG for rapid, low-cost prototyping, and carbon fiber composites as the benchmark for production-level performance. Nonlinear transient FEM analyses were carried out using Inventor Nastran under multiple load cases, including maximum motor acceleration, pitch maneuvers, and lateral impact at 40 km/h, and were validated against simplified analytical models. Experimental validation included bench and in-flight trials with integrated telemetry and autonomous features such as Return-to-Home, demonstrating functional robustness. The results show that the prototype flies correctly and that the chassis withstands the loads experienced during flight, including accelerations up to 4.2 G (41.19 m/${\mathrm{s}}^2$), abrupt changes in direction, and high-speed maneuvers reaching approximately 116 km/h. Quantitatively, safety factors of approximately 5.3 under maximum thrust and 1.35 during impact confirm sufficient structural integrity for operational conditions. In comparison with prior works reviewed in this study, the key contribution of this work lies in unifying advanced, crash-resilient FEM simulations with firmware-linked flight validation and a scalable material strategy, establishing a distinctive and comprehensive workflow for the development of sub-250 g UAVs. Full article

A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities. Full article

This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture. Full article

In this paper, we investigate the generalized coupled Zakharov system (GCZS), a fundamental model in plasma physics that describes the nonlinear interaction between high-frequency Langmuir waves and low-frequency ion-acoustic waves, including the influence of magnetic fields on weak ion-acoustic wave propagation. This research aims to achieve three main objectives. First, we uncover soliton solutions of the coupled system in hyperbolic, trigonometric, and rational forms, both in single and combined expressions. These results are obtained using the extended rational sinh-Gordon expansion method and the $\left({\displaystyle \frac{G^{\prime }}{G}},{\displaystyle \frac{1}{G}}\right)$-expansion method. Second, we analyze the dynamic characteristics of the model by performing bifurcation and sensitivity analyses and identifying the corresponding Hamiltonian function. To understand the mechanisms of intricate physical phenomena and dynamical processes, we plot 2D, 3D, and contour diagrams for appropriate parameter values. We also analyze the bifurcation of phase portraits of the ordinary differential equations corresponding to the investigated partial differential equation. The novelty of this study lies in the fact that the proposed model has not been previously explored using these advanced methods and comprehensive dynamical analyses. Full article

This study develops a computational framework for the simultaneous quantification of seismic resilience and economic losses in corrosion-affected coastal continuous girder bridges. The proposed model integrates adjustment factors to reflect delays in post-earthquake repairs and cost increments caused by progressive material degradation. Finite element methods and nonlinear dynamic time-history simulations were conducted on an existing coastal continuous girder bridge to validate the proposed model. The key innovation lies in a probability-weighted resilience index incorporating damage state occurrence probabilities, which overcomes the computational inefficiency of traditional recovery function approaches. Key findings demonstrate that chloride exposure duration exhibits a statistically significant positive association with earthquake-induced structural failure probabilities. Sensitivity analysis reveals two critical patterns: (1) a 0.3 g PGA increase causes a 11.4–18.2% reduction in the resilience index (RI), and (2) every ten-year extension of corrosion exposure decreases RI by 2.7–6.2%, confirming seismic intensity’s predominant role compared to material deterioration. The refined assessment approach reduces computational deviation to ±2.4%, relative to conventional recovery function methods. Economic analysis indicates that chloride-induced aging generates incremental indirect losses ranging from $58,000 to $108,000 per decade, illustrating compounding post-disaster socioeconomic consequences. This work systematically bridges corrosion-dependent structural vulnerabilities with long-term fiscal implications, providing decision-support tools for coastal continuous girder bridges’ maintenance planning. Full article

In this paper, we investigate the Malliavin differentiability and density smoothness of solutions to stochastic differential equations (SDEs) with non-Lipschitz coefficients. Specifically, we consider equations of the form $d{X}_t= b\left(X_t\right)dt + \sigma \left(X_t\right)d{W}_t, X_0= x_0$ where the drift b(·) and diffusion σ(·) may violate the global Lipschitz condition but satisfy weaker assumptions such as Hölder continuity, linear growth, and non-degeneracy. By employing Malliavin calculus theory, large deviation principles, and Fokker–Planck equations, we establish comprehensive results concerning the existence and uniqueness of solutions, their Malliavin differentiability, and the smoothness properties of density functions. Our main contributions include (1) proving the Malliavin differentiability of solutions under the standard linear growth condition combined with Hölder continuity; (2) establishing the existence and smoothness of density functions using Norris lemma and the Bismut–Elworthy–Li formula; and (3) providing optimal estimates for density functions through large deviation theory. These results have significant applications in financial mathematics (e.g., CIR, CEV, and Heston models), biological system modeling (e.g., stochastic population dynamics and neuronal and epidemiological models), and other scientific domains. Full article

Nonlinear photocurrents (NPs) are electrical currents expected to be measured at the electrodes of a device consisting of an active area, sensitive to light, with a higher-order in-electric field where light-impinging geometry (LIG) is the determining factor in the experimental observation. Although the phenomenology of this light–matter interaction is clear for light directed on a lateral device plane with well-defined azimuthal and incidence angles, as well as light polarization angle, it can be quite complicated for a vertical device structure and reconsideration of the expected NP contributions is necessary in the latter case. In this study, we used a visual approach to describe the LIG for vertical device structures using a specific example of a photodiode, and showed that these angles must be redefined, namely, the interchangeability of azimuthal and incidence angles. The influence of device geometry-dependent optical illumination is reflected on the behavior of NP; therefore, the NPs that are known to be forbidden in certain LIGs can be allowed and vice versa. These results pave the way for the utilization of NPs in flexible optoelectronic applications. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

We are reporting the development of a high-performance, non-enzymatic electrochemical biosensor for selective lactate detection, integrating laser-induced graphene (LIG), gold nanoparticles (AuNPs), and a molecularly imprinted polymer (MIP) synthesized from poly(3,4-ethylenedioxythiophene) (PEDOT). The LIG electrode offers a highly porous, conductive scaffold, while electrodeposited AuNPs enhance catalytic activity and signal amplification. The PEDOT-based MIP layer, electropolymerized via cyclic voltammetry, imparts molecular specificity by creating lactate-specific binding sites. Cyclic voltammetry confirmed successful molecular imprinting and enhanced interfacial electron transfer. The resulting LIG/AuNPs/MIP biosensor demonstrated a wide linear detection range from 0.1 µM to 2500 µM, with a sensitivity of 22.42 µA/log(µM) and a low limit of detection (0.035 µM). The sensor showed excellent selectivity against common electroactive interferents such as glucose and uric acid, long-term stability, and accurate recovery in artificial saliva (>95.7%), indicating strong potential for practical application. This enzyme-free platform offers a robust and scalable strategy for continuous lactate monitoring, particularly suited for wearable devices in sports performance monitoring and critical care diagnostics. Full article

We propose a novel dynamic gradient descent (DGD) framework integrated with reinforcement learning (RL) for AI-enhanced indoor environmental simulation, addressing the limitations of static optimization in dynamic settings. The proposed method combines a hybrid optimizer—stochastic gradient descent with momentum and adaptive learning rates—with an RL-driven meta-controller to dynamically adjust hyperparameters in response to real-time environmental fluctuations. The core innovation lies in the time-varying optimization landscape, where a Transformer-based policy network modulates the learning process based on a reward signal that balances prediction accuracy and parameter stability. Furthermore, the system employs a multilayer perceptron predictor trained on computational fluid dynamics-augmented data to model nonlinear thermal–airflow interactions, replacing conventional lumped-parameter models. The integration of these components enables autonomous adaptation to short-term disturbances (e.g., occupancy changes) and long-term drifts (e.g., seasonal variations) without manual recalibration. Experiments demonstrate that the framework significantly improves simulation accuracy and control efficiency compared to existing methods. The contributions include a unified adaptive optimization-RL architecture, a closed-loop hyperparameter control mechanism, and scalable implementation on GPU-accelerated hardware. This work advances the state-of-the-art in intelligent building systems by enabling self-tuning simulations for real-world dynamic environments. Full article

Selecting input data points in the context of high-dimensional, nonlinear, and complex data in Riemannian space is challenging. While optimal experimental design theory is well-established in Euclidean space, its extension to Riemannian manifolds remains underexplored. Li and Del Castillo recently obtained new theoretical results on D-optimal and G-optimal designs on Riemannian manifolds. This paper follows their framework to investigate A-optimal designs on such manifolds. We prove an equivalence theorem for A-optimality under the manifold regularization model. Based on this result, a sequential algorithm for identifying A-optimal designs on manifold data is developed. Numerical studies using both synthetic and real datasets show the validity of the proposed method. Full article

Nonlinear correction was performed on the mechanical motion of ultra-thin cantilever beams, and strain effects were calculated on ultra-thin multi-layer heterogeneous material stacked cantilever beams. The atomic structure and piezoelectric properties of ZnO were studied using first-principles calculations. In this study, generalized gradient approximations of Perdew–Burke–Erzerhof (GGA-PBE) functionals and Plain Wave Basis Sets were used to calculate the electronic structure, density of states, energy bands, charge density, and piezoelectric coefficient of intrinsic ZnO. Research and calculations were conducted on Li-doped ZnO with different ratios. According to our calculations, as the Li doping ratio increases from 0 to 10%, the bandgap width of ZnO material increases from 0.74 to 1.21 eV. The results for the density of states and partial density of states indicate that the increase in band gap is due to the movement of Zn-3d states towards the high-energy end, and the piezoelectric coefficient of the material increases from 2.07 to 3.3 C/m². Meanwhile, based on the optimized Li-doped ZnO cantilever beam accelerometer, an ultra-thin cantilever beam accelerometer with a sensitivity of 7.04 mV/g was fabricated. Full article

We demonstrate a flexible electrochemical biosensor for non-enzymatic glucose detection under different bending conditions. The novel flexible glucose sensor consists of a Cu nanoparticle (NP)/laser-induced graphene fiber (LIGF)/porous laser-induced graphene (LIG) network structure on a polyimide film. The bare LIGF/LIG electrode fabricated using an 8.9 W laser power shows a measured sheet resistance and thickness of 6.8 Ω/□ and ~420 μm, respectively. In addition, a conventional Cu NP electroplating method is used to fabricate a Cu/LIGF/LIG electrode-based glucose sensor that shows excellent glucose detection characteristics, including a sensitivity of 1438.8 µA/mM∙cm², a limit of detection (LOD) of 124 nM, and a broad linear range at an applied potential of +600 mV. Significantly, the Cu/LIGF/LIG electrode-based glucose sensor exhibits a relatively high sensitivity, low LOD, good linear detection range, and long-term stability at bending angles of 0°, 45°, 90°, 135°, and 180°. Full article

This study proposes a novel modelling approach that integrates mutual information (MI)-based parameter screening with random forest (RF) modelling to achieve an accurate quantitative prediction of surface hardness and impact energy in two martensitic stainless steels (1Cr13 and 2Cr13). Preliminary analyses indicated that the magnetic parameters derived from Barkhausen noise (MBN), and the incremental permeability (IP) measurements showed limited linear correlations with the target properties (surface hardness and impact energy). To address this challenge, an MI feature screening method has been developed to identify both the linear and non-linear parameter dependencies that are critical for predicting target mechanical properties. The selected features were then fed into an RF model, which outperformed traditional multiple linear regression in handling the complex, non-monotonic relationships between magnetic signatures and mechanical performance. A key advantage of the proposed MI-RF framework lies in its robustness to small sample sizes, where it achieved high prediction accuracy (e.g., R² > 0.97 for hardness, and R² > 0.86 for impact energy) using limited experimental data. By leveraging MI’s ability to capture multivariate dependencies and RF’s ensemble learning power, it effectively mitigates overfitting and improves generalisation. In addition to demonstrating a promising tool for the non-destructive evaluation of martensitic steels, this study also provides a transferable paradigm for the quantitative assessment of other mechanical properties by magnetic feature fusion. Full article

The impetus to study and develop polymer electrolytes for metal-ion batteries is due to their enhanced safety compared to flammable organic liquid electrolytes, promising ionic conductivity, and broad electrochemical stability window, making them to viable candidates for battery application. In the current work, we present a simple fabrication procedure and a comprehensive physico–chemical study of various PVDF-HFP-based electrolyte formulations with a sufficient addition of PEO polymer, LiTFSI conducting salt, and EMIMTFSI ionic liquid. The ionic conductivity, activation energy for ionic movement and thickness of the resulting polymer electrolyte show a non-linear dependency on the PVDF-HFP/PEO ratio. The electrolyte composition with a 0.35PEO-0.65PVDF-HFP/1LiTFSI/1EMIMTFSI mass fraction exhibits the highest ionic conductivity among the compositions, revealing $7.7\times {10}^{-5} \mathrm{S} {\mathrm{c}\mathrm{m}}^{-1}$ at 30 °C. Electrochemical tests in half full and full Li-ion batteries with a LiFePO₄ cathode and Li₄Ti₅O₁₂ anode also emphasized this composition as the most promising one, providing an initial capacity in full cells of 120 mAh g⁻¹ and a capacity retention of about 75% after 50 charge/discharge cycles at a 0.1 C current rate. In the PEO/PVDF-HFP polymer blend with EMIMTFSI as a plasticizer, the amount of crystalline parts, which are detrimental to a fast ionic diffusion, is significantly reduced. Full article