Search Results (666)

The current development of ultra-wide band localization systems focuses on reducing the number of infrastructure nodes (anchors). In certain areas and applications the full three-dimensional position is not necessary; therefore, constraining the solution brings an opportunity to use fewer anchors. In this work, soft constraining of lateral and vertical position components for Time Difference of Arrival positioning in a corridor-like scenario is presented. Implementation in extended and unscented Kalman filter solvers is described. Tests in a real environment suggests that the constraints enable reliable along-track position estimation even with two or three anchors in sight, and the accuracy is better than 30 $\mathrm{c}$$\mathrm{m}$ (RMS). Moreover, the soft nature of constraints allows for uncertainty in the constraint definition. Full article

Balancing structural safety and economic efficiency in super-tall building design remains a formidable challenge. To address this issue, this study proposes a genetic-algorithm-based multi-variable, multi-objective optimization method. The design variables include the member sizes and vertical layout positions of outrigger and belt trusses, as well as the cross-sectional dimensions of mega-columns. Total structural weight and maximum inter-story drift ratio are adopted as objective functions, while code-specified constraints, such as shear-weight ratio, stiffness-weight ratio, and axial compression ratio, are incorporated to formulate the fitness evaluation for optimization. Taking a 300 m baseline structure designed for 6-degree seismic intensity and equipped with two outrigger trusses and three belt trusses as an example, single-variable sensitivity analyses are first performed. The results show that optimizing any single parameter can yield certain local improvements, yet it cannot overcome the weight–deformation trade-off induced by strong variable coupling. By selecting representative feasible solutions from the multi-variable solution set that match the “optimal” values identified by single-variable optimization as benchmarks, the multi-variable optimum reduces the total structural weight by approximately 6.5–18.4% relative to these representative designs. Moreover, optimal layout strategies of outrigger and belt trusses are investigated for two typical building heights (200 m and 300 m) and two seismic intensity levels associated with design ground motions having a 10% exceedance probability in 50 years, namely 6-degree (0.05 g) and 8-degree (0.20 g). Finally, the proposed method is validated through a case study of a super-tall financial center in Chongqing, where the total structural weight is reduced by 12.3% after optimization while the inter-story drift ratio still satisfies relevant code requirements. The results demonstrate that the proposed framework can generate competitive feasible solutions and provide a systematic means to achieve a balanced trade-off between structural safety and economic efficiency for outrigger–belt-truss super-tall buildings. Full article

This paper proposes an end-effector (EE) driven whole-body trajectory tracking control algorithm for wheeled mobile manipulators based on linear and angular motion decomposition. Instead of solving a high-dimensional optimization problem across all degrees of freedom, the proposed method formulates the control objective directly in the EE space and decomposes the required motion into planar linear, vertical, and angular components. To address redundancy between the mobile base and the manipulator under non-holonomic constraints, a control authority switching strategy with a radial blending function is introduced. This approach eliminates ambiguity in control allocation while preventing abrupt switching near workspace boundaries. The kinematic controller guarantees exponential convergence of position and orientation errors without requiring a full dynamic model. Numerical simulations demonstrate stable tracking performance in three-dimensional space. Compared with a quadratic programming-based whole-body controller, the proposed method achieves comparable or faster error convergence while reducing computational burden by more than 13 times on average. These results indicate that the proposed EE-driven framework provides a computationally efficient and practically deployable solution for real-time mobile manipulator control. Full article

The crystal structure of proteins is generally considered static due to the constraints imposed by crystal packing. We determined the crystal structure of rice NADP-malic enzyme 2 (OsNADP-ME2), an oxidative decarboxylase that converts malic acid to pyruvate and provides NADPH to generate reactive oxygen species. The OsNADP-ME2 is crystallized as a tetramer in the space group of P2₁. In the crystal, all the crystal packing interactions are made through the NADP-binding domain of the enzyme. Interestingly, a protomer shows a conformational change, with a 7.4° tilt in the NADP-binding domain. Basically, the crystal packing consists of a horizontal arrangement of vertically parallel P2₁ screw axes. In the vertical direction, a protomer (Mol A) is tightly sandwiched by two protomers (Mol C) of nearby tetramers and vice versa. In the horizontal direction, two protomers (Mol B and D) of a tetramer are parallelly bound to nearby tetramers, of which one protomer (Mol B) has tighter interactions than the other protomer (Mol D). The protomer Mol D, with the least interaction surface in the crystal packing, adopts an open conformation of the NADP-binding domain, which may be the flexible part of the enzyme for NADP+ cofactor binding. Crystallization can provide valuable information for protein structure. Full article

Spin-exchange relaxation-free (SERF) atomic magnetometers have emerged as highly promising candidates for ultra-weak magnetic field detection, particularly in biomagnetic imaging, owing to their exceptional sensitivity, amenability to miniaturization, and near-room-temperature operation. While current miniaturized magnetometers typically employ laser chips with fixed optical power, the quantitative impact of laser power on critical performance metrics remains to be fully elucidated. This study systematically investigates the influence of laser power on sensitivity, bandwidth, and dynamic range by incorporating considerations of power broadening, saturation absorption, and noise constraints. A miniaturized probe, integrated with an actively controlled vertical-cavity surface-emitting laser (VCSEL), was developed for experimental validation. Theoretical and experimental results consistently demonstrate that as optical power increases, sensitivity exhibits a non-monotonic dependence, whereas both bandwidth and dynamic range manifest a monotonic upward trend, aligning well with theoretical simulations. The optimized sensor achieved a peak sensitivity of 16 fT/√Hz at 300 μW, while the bandwidth and dynamic range reached 230 Hz and ±5.4 nT at 500 μW, respectively. This work establishes a robust theoretical and experimental framework for the comprehensive performance optimization of laser-integrated miniaturized atomic magnetometers. Full article

Secondary succession in tropical forests is traditionally described as a linear process driven by time since disturbance. However, growing evidence suggests that recovery pathways depend strongly on historical and environmental contexts. We evaluated how disturbance legacies and edaphic constraints interact to shape successional trajectories in a tropical karst landscape of the Maya Forest, Mexico. We sampled 100 plots along a chronosequence, quantifying vegetation structure, floristic diversity, biomass (NDVI), disturbance legacies, and soil properties. Using unsupervised clustering (K-means) and multivariate ordination, we identified four contrasting ecological typologies that represent distinct successional states rather than transient stages. Our results show a pronounced dichotomy in vegetation dynamics following the abandonment of land-use practices: while some sites are experiencing diverse development due to positive forest legacies (Typology B), others remain stalled (Typology C), dominated by lianas, where biotic barriers inhibit tree regeneration despite decades of abandonment. Additionally, we documented an asynchronous recovery between floristic recovery and vertical development; in sites with edaphic constraints, forests reach high diversity and biomass but exhibit stunted growth (Typology D). This suggests that severe abiotic constraints—specifically high rockiness and shallow soils—limit the dominance of highly competitive species, thereby acting as a filter that maintains high levels of diversity despite structural limitations. Edaphic analysis confirmed that chemical fertility and physical constraints (rockiness and shallow depth) act as orthogonal filters. This explains the persistence of structurally constrained yet functionally mature forests as stable, edaphically determined outcomes. Overall, secondary succession in tropical karst is nonlinear and path-dependent, governed by a hierarchical filtering model where historical land use dictates community identity and physical substrate limits structural architecture. These findings highlight the need for trajectory-specific management and the abandonment of uniform expectations of forest recovery in karst landscapes. Full article

Geothermal field characteristics fundamentally control hydrocarbon generation, phase evolution, and preservation, and are particularly critical in deep to ultra-deep hydrocarbon exploration. The Tazhong Uplift is a key area for deep to ultra-deep hydrocarbon exploration in the Tarim Basin; however, its deep thermal regime and controlling factors remain inadequately characterized. This study aims to accurately characterize the geothermal field and crustal thermal structure of the Tazhong Uplift to provide thermal constraints for ultra-deep exploration. We systematically compiled system steady-state temperature data from 24 wells, bottom-hole temperature (BHT) data from 51 wells, and rock thermal property measurements. Using the one-dimensional steady-state heat conduction equation, present-day geothermal gradients at 0–5000 m depths and terrestrial heat flow were calculated, and formation temperatures were predicted at deep horizons (6000–10,000 m). Results show geothermal gradients at 0–5000 m of 18.5–26.7 °C/km (average 23.06 °C/km) and heat flow of 39.3–59.8 mW/m² (average 48.1 mW/m²), both significantly higher than basin averages. The distribution of the geothermal field is jointly controlled by basement structure and rock thermophysical properties. Basement highs typically exhibit elevated geothermal gradients and high heat flow. The dual-layer structure of “upper clastic rocks (low thermal conductivity, high heat production) + lower carbonate rocks (high thermal conductivity, low heat production)” results in a vertical differentiation characterized by a “high-upper, low-lower” geothermal gradient. Notably, the thick Upper Ordovician mudstone acts as a regional “thermal blanket”, significantly reducing geothermal parameters in the northern slope area. Crustal thermal structure analysis indicates a “cold mantle” signature of cratonic basins, with a thermal lithosphere thickness of ~134–145 km and a Moho temperature of ~581 °C. These findings reveal that despite the ultra-deep burial (>8000 m), the “cold” thermal background and the thermal regulation of the overlying diverse lithologies maintain formation temperatures within a range favorable for liquid hydrocarbon preservation, significantly expanding the depth limit for oil exploration in the Tarim Basin. Full article

Potholes are typical negative road obstacles that can significantly compromise vehicle safety and ride comfort when traversed at inappropriate speeds. To address this issue, this paper proposes a pothole-detection-based, comfort-oriented pothole traversal algorithm that integrates multi-sensor fusion perception, comfort-constrained speed planning, and fuzzy control. A camera and a single-point ranging LiDAR are first fused to extract key geometric features of potholes, including contour, area, and depth. Based on these features, a vehicle–pothole dynamic model is developed in ADAMS to quantify the influence of pothole area and depth on vehicle vertical vibration. The vertical frequency-weighted root-mean-square (RMS) acceleration is adopted as the ride comfort indicator, based on which the maximum allowable traversal speed under different pothole geometries is determined. Furthermore, a longitudinal pothole traversal control strategy based on fuzzy theory is designed to regulate vehicle acceleration, enabling the vehicle to reach the comfort-constrained limiting speed within a finite preview distance while ensuring braking safety. The proposed method is validated through multi-scenario co-simulations using MATLAB/Simulink and CarSim, as well as real-vehicle experiments. Results demonstrate that the proposed strategy can effectively adjust vehicle speed before pothole traversal, satisfying comfort constraints and improving ride comfort without sacrificing driving safety. Full article

The deterministic simplification of parking maneuvers in traditional traffic models presents a critical challenge for the safe integration of Autonomous Vehicles (AVs). This study establishes a stochastic human baseline to provide a naturalistic ground truth dataset essential for calibrating perception and prediction sensors in mixed traffic scenarios. Through the analysis of 1038 maneuvers observed in a university shared space in Catania, Generalized Linear Models and Kaplan–Meier estimators were applied to quantify the impact of geometric constraints on 0°, 45°, and 90° configurations. Results identify 45° angled parking as the Pareto-optimal solution regarding stability and speed, achieving an average maneuver time of 7.54 s. Furthermore, a vertical parking paradox emerges: in the presence of narrow aisles, entry times increase drastically, generating bottlenecks with an 85th percentile exceeding 50 s. Finally, a structural functional asymmetry reveals that exit maneuvers require approximately 54% of the time needed for entry. These findings provide empirical metrics essential for validating human behavior models and fine-tuning decision-making and timeout logic in autonomous driving systems. Full article

In China, a majority of the proven crude oil reserves are found in clastic rock reservoirs, which typically exhibit low natural energy levels. Water injection has become the most widely adopted technique for maintaining reservoir pressure and enhancing oil recovery in such formations. However, conventional water injection strategies heavily rely on empirical knowledge, often failing to accurately characterize the dynamic inter-well connectivity between injection and production wells. This limitation hinders the effective management of fluid injection and production processes. To address this challenge, we propose an intelligent optimization method for water allocation in high-water cut, low-permeability reservoirs. Our approach employs a Bidirectional Long Short-Term Memory (BiLSTM) neural network to learn the complex patterns from historical injection data in a data-driven manner. Furthermore, we design a well distance and time joint attention mechanism, which is integrated after the dual BiLSTM layers to enhance the model’s ability to capture the critical dynamic relationships among wells. This mechanism decouples temporal pattern recognition and the spatial physical constraints, laying the foundation for interpretable injection strategy optimization. We name this architecture “AttBiLSTM”, which is designed for optimizing injection strategies for individual layers in separate-layer water injection wells (The layer refers to the basic geological unit or flow unit within a vertically heterogeneous reservoir that is delineated and requires independent water injection regulation). Using field data from the Xinjiang Oilfield, we validate the proposed method and compare its performance against traditional water injection schemes and mainstream data-driven models. The experimental results demonstrate that the AttBiLSTM model effectively establishes a nonlinear mapping between the injection volumes and oil production rates, showing strong performance in both production prediction and injection optimization. An independent numerical reservoir simulation verification confirms that the optimized scheme increases well group oil production by over 3.6%, with no premature water breakthrough risk in a 5-year development cycle. This study provides a novel and practical technical framework for efficiently developing low-porosity, low-permeability, and highly heterogeneous reservoirs. Full article

Traditional concert-hall evaluations primarily rely on ISO 3382-1 scalar parameters (e.g., C₅₀ and C₈₀), which summarize temporal energy behavior but provide limited insight into the directional composition of early reflections, particularly in geometrically shadowed seating zones. This paper presents a first-order Ambisonics (FOA)-based 3D acoustic sensing framework to diagnose under-balcony directional imbalance, with emphasis on early vertical-reflection deficiency. Scene-based FOA impulse responses (WXYZ) were measured at 11 audience positions (P1–P11) in the National Concert Hall (Taipei) and analyzed using intensity-based direction-of-arrival (DoA) proxies, axis-resolved directional energy build-up, and a distributional descriptor based on directional spatial entropy. Results are presented at three representative frequencies (125 Hz, 1 kHz, and 4 kHz) and analyzed within full (0–200 ms), early (0–80 ms), and late (80–200 ms) windows. While the magnitude proxy $\mid p^{meas}\left(f\right)\mid$ exhibits strong seat-to-seat variability and does not support a uniform attenuation assumption under the balcony, direction-resolved metrics reveal a consistent under-balcony signature. Specifically, the early–late vertical energy discrepancy $\Delta R_z={\displaystyle R_z^{early}}-{\displaystyle R_z^{late}}$ is persistently negative at under-balcony positions (P7–P11) across all three frequencies, indicating a selective reduction in early vertical contribution relative to the late field. Directional entropy analysis further shows predominantly negative $\Delta H_n={\displaystyle H_n^{early}}-{\displaystyle H_n^{late}}$, with more negative values in the under-balcony group, consistent with stronger early directional constraint in shadowed seats. Spatial trend maps are provided via Gaussian RBF interpolation within the audience domain for visualization only. The proposed FOA-based diagnostic framework provides a practical and physically interpretable approach to identify direction-specific early-reflection deficits that remain masked in conventional scalar evaluations, supporting mechanism-oriented assessment and targeted intervention in geometrically constrained listening areas. Full article

Efficient UAV logistics in complex urban airspaces requires a synergistic approach to task allocation and path planning. However, traditional methods often decouple these two phases, leading to physically infeasible or sub-optimal delivery schedules. This paper proposes a Dual-Layer Optimization Framework (D-LOF) to address the Multi-UAV delivery problem in 3D urban environments. The upper layer utilizes an improved Genetic Algorithm (GA) with a specialized constraint repair operator to optimize task sequences for a heterogeneous UAV fleet. The lower layer employs an altitude-aware A* algorithm that dynamically balances vertical energy costs and horizontal cruise efficiency across multiple altitude layers. Unlike conventional models, our framework iteratively feeds precise 3D flight costs from the lower layer back to the upper layer to guide evolutionary search. Simulation results demonstrate that the D-LOF consistently achieves global convergence within 20 generations. Compared to single-altitude planning and rule-based strategies, the proposed method can reduce total operational costs and maintains zero time-window violations in high-density obstacle scenarios. This study provides a robust decision-making tool for “last-mile” urban logistics by navigating the trade-offs between 3D spatial constraints and delivery punctuality. Full article

Document-level Event Extraction (DEE) requires identifying complex event records and arguments dispersed across unstructured texts. However, applying general Large Language Models (LLMs) to DEE is intrinsically hindered by their lack of inductive bias for rigid structural constraints, often leading to schema violations and suboptimal performance in complex structural prediction tasks. To address this, we propose the S tructure-Aware Lightweight DEE, termed SALE, which leverages the structural reasoning potential of Code-Based LLMs (Code-LLMs) as a favorable inductive preference. We leverage the natural isomorphism between event schemas and programming object definitions, formulating event extraction as a Python 3.9 class instantiation task to bridge the gap between semantic understanding and structural adherence. Specifically, SALE employs a novel two-stage training paradigm: First, a Structure-Aware Fine-tuning stage injects general structural knowledge via diverse code-style instruction tasks derived from broad Information Extraction (IE) datasets; second, an Event Extraction Alignment stage utilizes a reward-based alignment loss—optimized via policy gradient—to adapt this capability to document-level intricacies. The effectiveness of SALE stems from the synergy between its structure-aware prompting and the specialized alignment stage built on a code-oriented backbone. Extensive experiments on established news-domain benchmarks (RAMS and WikiEvents) demonstrate that our approach significantly outperforms representative supervised and general LLM baselines in cross-task zero-shot and few-shot transfer settings (e.g., surpassing supervised baselines by over 7% in F1 score). Furthermore, SALE maintains a highly efficient inference profile and parameter-efficient footprint, offering a practical and scalable solution for vertical domain applications. Full article

Against the macro-background of balancing development and food security strategies, China has implemented a land-use regulation system centered on farmland protection. However, the economic impacts of such regulation lack sufficient quantitative evaluation. Using farmland retention targets at the county-level in the administrative region and combining them with relevant data, this study employs an Intensity Difference-in-Differences (Intensity DID) approach to examine how land-use regulation affects county-level economic growth and convergence. The findings reveal a U-shaped relationship between land-use regulation and county-level economic growth, suggesting that, at the current stage, the intensity of land-use regulation generally promotes economic growth. Heterogeneity analysis further indicates that county economies in major grain production areas (MGPAs) and main grain-producing counties (MGPCs) experience stronger negative constraints related to the policy, while MGPCs in non-major grain production areas (non-MGPAs) are most sensitive to land-use regulation. China’s county economies exhibit convergence; however, land-use regulation may reduce the growth rate of counties that were underdeveloped in the base period, thereby widening inter-county development disparities. This divergence is manifested in the lack of convergence between the clubs of MGPCs and non-MGPCs. Mechanism analysis suggests that differences in industrial structure, capital investment, and fiscal expenditure constitute the key focal points for addressing the issue. Policy implications indicate that China should strengthen land-use regulation on the premise of rationally determining the functions and scale of various land types, continue to advance market-oriented reforms of land factors, improve the vertical and horizontal interest compensation mechanism for MGPAs, and stimulate the endogenous development momentum of these regions. Full article

Tunnel lighting systems serving pedestrian and vehicular traffic must simultaneously satisfy visual performance requirements and energy efficiency constraints. This study investigates the optimization of tunnel lighting design using a sustainable engineering approach based on Response Surface Methodology (RSM) and Specific Lighting Energy Consumption (SLEC). Software-assisted lighting simulations were performed for two tunnel geometries—straight and double-curved—and horizontal (Eh) and vertical (Ev) illuminance levels were evaluated at five representative locations. The resulting data were used to construct RSM-based predictive models and to assess energy performance through SLEC. The effects of mounting height, luminaire spacing, luminous flux, number of luminaires, and tunnel type were systematically analyzed. The results demonstrate that luminaire spacing is the dominant parameter influencing illuminance levels and energy consumption. An optimal configuration consisting of a 12 m luminaire spacing, 5 m mounting height, and 10,000–12,000 lm luminous flux achieved a favorable balance between lighting quality and energy efficiency. Additionally, straight tunnels exhibited higher illuminance uniformity at shorter spacings, whereas curved tunnels showed improved performance under wider spacing conditions. The proposed RSM–SLEC framework provides a robust, data-driven methodology for sustainable tunnel lighting design without compromising safety or visual comfort. Full article