Search Results (6,178)

The sustainable use of freshwater resources includes balancing between human demand for water and the long-term health of river systems. Although dams and reservoirs are essential for water supply, flood control and energy generation, they can induce significant hydrological alterations, affecting water quality, sediment transport, downstream water availability, and aquatic and riparian ecosystems. In this study, we employed multifractal analysis to investigate hydrological changes in the São Francisco River basin, Brazil, resulting from the construction of a cascade of dams and reservoirs. We applied multifractal detrended fluctuation analysis (MFDFA) to daily streamflow time-series spanning the period from 1929 to 2016, at locations both upstream and downstream of cascade dams, and for periods before and after dam construction. We calculated multifractal spectra f(α) and analyzed key complexity parameters: the position of the spectrum maximum α_0, representing the overall Hurst exponent H; the spectrum width W indicating the degree of multifractality; and the asymmetry parameter r, which reflects the dominance of small (r > 1) and large (r < 1) fluctuations. We found that after the construction of Sobradinho dam, located in the Sub-Middle São Francisco region, streamflow dynamics shifted towards a regime characterized by uncorrelated increments (H~0.5) and stronger multifractality (larger W), with the dominance of small fluctuations (r > 1). In contrast, the cumulative effect of all cascade dams downstream, in the Lower São Francisco region, led to streamflow regime with similarly uncorrelated increments (H~0.5), but with weaker multifractality (smaller W) and a dominance of large fluctuations (r < 1). The novelty of this work is the use of a sliding-window MFDFA approach to explore the temporal evolution of streamflow multifractality. This method uncovered otherwise hidden aspects of hydrological alterations, such as increasing tendency in spectrum width, indicating stronger multifractality and higher complexity of streamflow dynamics after the dam construction. These results demonstrate that multifractal analysis is a powerful tool for assessing the complexity of hydrological changes induced by human activities. Full article

Sidewalk high-definition (HD) maps require centimetre-level representation of pedestrian barriers to support mobility assistance and barrier-free infrastructure management. This study evaluates six mobile light detection and ranging (LiDAR) platforms for sidewalk HD mapping: terrestrial laser scanning (TLS), a push-cart mobile mapping system (MMS), two backpack systems (GNSS/INS (Global Navigation Satellite System/Inertial Navigation System)-aided and SLAM (simultaneous localization and mapping)-based), and two handheld systems (GNSS/INS-aided and SLAM-based). Surveys were conducted at two sites with contrasting occlusion and GNSS conditions (park and dense downtown corridors). Point clouds were transformed to a common control network, with independent checkpoints for absolute accuracy. The reference dataset achieved a planimetric root mean square error (RMSE) of 0.017–0.049 m and vertical RMSE of 0.009–0.014 m across sites. Platforms were compared for positional accuracy, point density, and extractability of key accessibility attributes (effective width, step height, and longitudinal slope). Cart-mounted MMS provided stable geometry under occlusion, while SLAM-based handheld mapping improved robustness in GNSS-degraded areas; backpack SLAM performance depended on loop-closure opportunities and scene dynamics. We provide guidance on selecting pedestrian-scale LiDAR platforms for sidewalk HD mapping under different survey conditions. Full article

Cryptocurrency trading presents significant challenges due to extreme market volatility, rapid regime transitions, and non-stationary dynamics that render traditional trading strategies ineffective. Existing reinforcement learning approaches for cryptocurrency trading typically employ simplistic profit-based reward functions that fail to adequately capture risk management considerations, market microstructure costs, temporal dependencies, and regime-specific optimal behaviors. This limitation often results in strategies that perform well during favorable market conditions but suffer catastrophic losses during downturns. This paper introduces five novel reward functions grounded in economic utility theory, market microstructure, behavioral finance, adaptive risk management, and regime-conditional optimization. We systematically evaluate these reward functions across three reinforcement learning algorithms (Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic) and four distinct market regimes (bull, bear, high volatility, and recovery), using Bitcoin hourly data from 2018–2022. Our comprehensive experimental evaluation demonstrates that the Adaptive Risk Control reward function achieves exceptional performance, with a Sharpe ratio of 2.47, cumulative return of 26.4%, and maximum drawdown of only 16.8% during the predominantly bearish 2022 test period. Critically, regime-specific analysis reveals substantial performance heterogeneity: Adaptive Risk Control excels during high volatility (Sharpe ratio 3.21), while Temporal Coherence and Asymmetric Market-Conditional rewards dominate in trending and bear markets, respectively. These findings establish that sophisticated, theory-grounded reward engineering—rather than algorithmic innovations alone—constitutes the primary lever for improving RL trading systems, enabling positive risk-adjusted returns even during severe market downturns. Full article

With the advancement of UAV (Unmanned Aerial Vehicle) technology, flying ad-hoc networks (FANETs), composed of multiple coordinating UAVs, demonstrate tremendous application potential in disaster relief, environmental monitoring and intelligent logistics. However, inherent resource constraints and unpredictable operating environments make UAV failures a frequent and critical challenge. Particularly in mission-critical applications, simultaneous or consecutive failures of multiple UAVs can severely disrupt network topology, leading to catastrophic consequences such as network fragmentation and service interruptions. Furthermore, traditional topology reconstruction algorithms suffer from high computational overhead and significant communication delays. Primarily designed for single-node failure recovery, they are ill-equipped to address the challenge of concurrent multi-node failures. To address these challenges, this paper proposes a topology reconstruction algorithm tailored for multi-node failure scenarios in FANETs. The core objective of this algorithm is to minimize communication overhead and secondary damage to the network during the reconstruction process while ensuring basic reconstruction results, thereby improving the system’s energy efficiency and robustness. The proposed framework integrates three key phases: First, overlapping communication coverage areas among neighbors of failed nodes are leveraged to define first and second regions, enabling rapid identification of connection restoration candidate positions and avoiding computationally intensive global calculations. Second, a comprehensive importance evaluation mechanism is constructed based on the topological and functional attributes of node, categorizing nodes into different importance types. For failed nodes of varying importance, differentiated search ranges and retry strategies are employed to ensure the most suitable nodes are selected for reconstruction tasks. Third, the inflexibility of repulsion ranges in traditional artificial potential field (APF) method is addressed by introducing dynamic repulsion influence zones and a composite repulsion model. The improved APF algorithm enhances safety in high-speed scenarios and reduces the probability of UAVs becoming trapped in local minima. Finally, extensive simulations validate that the proposed algorithm accurately identifies critical network nodes and promptly implements effective reconstruction measures to minimize network damage. Full article

The dynamic approach integrates Global Positioning System and K-band range-rate (KRR) observations to enable precise orbit determination (POD) and gravity field recovery. However, background model uncertainties and temporal aliasing introduce frequency-dependent noise into the post-fit KRR residuals, thereby degrading overall solution accuracy. To mitigate these effects, empirical signals are typically modeled using either dynamic (DYN) or kinematic (KIN) parameterization strategies. Nevertheless, the combined use of DYN and KIN parameterizations remains largely unassessed, and their potential synergistic impact on POD and gravity field recovery merits systematic evaluation. This study evaluates the individual and joint impacts of DYN and KIN (DYN+KIN) on The Gravity Recovery and Climate Experiment (GRACE) Follow-On orbit accuracy and monthly gravity field recovery using nearly one year of 2019 data (excluding February due to severe data gaps). The refined solutions act as empirical temporal filters, effectively suppressing low-frequency components in KRR residuals, particularly below 1-cycle-per-revolution. Relative to nominal ambiguity-fixed reduced-dynamic orbits, the refined solutions mainly enhance the cross-track component, with DYN+KIN showing the largest improvement, while along-track precision experiences only minor (sub-millimeter) degradation. Overall three-dimensional orbit accuracy improves from 3.8 cm to 3.0 cm (DYN), 2.8 cm (KIN), and 2.8 cm (DYN+KIN). In terms of gravity field recovery, the DYN+KIN solution begins to exhibit more pronounced deviations from the other solutions beyond degree and order 30. Over oceanic regions, residual mass anomaly analysis shows that the DYN+KIN solution is associated with an approximately 16% higher noise level compared to the individual DYN and KIN strategies, which exhibit modest noise reductions relative to the nominal solution. The DYN+KIN also exhibits a dampened ~160-day periodicity in the temporal evolution of low-degree coefficients (e.g., C_2,0), likely due to spectral overlap between empirical parameter frequencies and low-degree gravity signal components. These results indicate that over-parameterization introduces spectral redundancy and absorbs geophysical signals, underscoring the need to balance parameter flexibility and signal fidelity in gravity recovery strategies. Full article

Accurate identification of hydrodynamic parameters is essential for high-fidelity modeling and control of unmanned underwater vehicles (UUVs). Compared with towing tank experiments and computational fluid dynamics simulations, system identification based on free-running trial data offers a cost-effective and scalable alternative. However, in real ocean environments, unmodeled lumped disturbances—such as shear currents, stratification-induced buoyancy variations, and wave-induced drift forces—strongly couple with the vehicle’s intrinsic dynamics. Conventional least-squares estimators and physics-informed neural networks tend to absorb environmental effects into the physical parameters, leading to physically inconsistent estimates. To address this challenge, this paper proposes a physics-guided hybrid network (PG-HyNet) with input-domain structural decoupling. The architecture explicitly separates the intrinsic rigid-body dynamics from spatially varying environmental disturbances by assigning dynamics-related states to a physics-constrained branch and position-dependent variables to a residual disturbance branch. A staged training strategy is introduced to stabilize identification and suppress parameter drift during optimization. The framework is validated using high-fidelity simulations incorporating shear currents, density stratification, and wave drift effects, as well as real-world lake trial data. The results demonstrate that PG-HyNet significantly improves robustness against disturbance-induced parameter compensation, enabling physically consistent hydrodynamic parameter recovery while accurately capturing spatially varying environmental disturbance effects. Full article

Low-intensity pulsed ultrasound (LIPUS) has emerged as a versatile, non-invasive physical modality with growing potential in regenerative medicine and neural repair. Advances in ultrasound physics and biomedical engineering have enabled precise spatiotemporal control of acoustic stimulation, positioning therapeutic ultrasound as an alternative to conventional pharmacological and surgical interventions that often suffer from limited targeting and substantial side effects. Unlike high-intensity focused ultrasound, which relies primarily on thermal ablation, LIPUS operates within a low-energy, non-thermal regime and modulates cellular behavior through mechanical cues, mechano-transduction, and downstream biological responses. Accumulating evidence demonstrates that LIPUS regulates calcium dynamics, cytoskeletal remodeling, neurotrophic factor expression, inflammation, myelination, and local vascular remodeling, thereby promoting functional recovery in both peripheral and central nerve injury models. Moreover, the integration of LIPUS with biomaterials, including piezoelectric scaffolds and acoustically responsive drug delivery systems, has expanded its functionality from direct stimulation to on-demand electrical signaling and controlled therapeutic release. Despite these advances, challenges remain regarding parameter standardization, mechanistic consistency, and clinical translation. In this review, we summarize the systems, parameters, and biological mechanisms underlying LIPUS, discuss its applications in peripheral and central nerve injury repair, and highlight emerging strategies and translational barriers toward intelligent, multimodal, and personalized ultrasound-based therapies. Full article

Underwater wireless power transfer (UWPT) technology can improve the endurance of unmanned underwater vehicles (UUVs). The stability and efficiency of UWPT depend on the success rate of UUV docking. A novel detection model, TFDF-YOLO, is proposed for dynamic position identification of UUV docking. First, a spatial–frequency decoupling (SFD) module is proposed by using Fourier-based degradation cues to guide Top-K proxy attention to boost blurred edge extraction capability. A relevance-difference fusion (RD-Fusion) strategy is improved by a global channel attention mechanism to realize multi-scale feature recognition. Furthermore, a new adaptive loss function (U-CIoU) is developed to suppress illumination bias and anchor inflation. Results on a reliable multi-source dataset demonstrate that the proposed model achieves 91.5% accuracy and 92.7% mAP@0.5. This work could enhance the success rate and reliability of UWPT. It shows potential for broader underwater applications, including deep-sea docking and multi-AUV cooperative systems. Full article

Accurate indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GPS-denied environments, especially for small-object detection and under low-light conditions. We propose Robust Wavelet-Aware YOLO (RWA-YOLO), a vision-based detection framework that integrates a wavelet-aware attention fusion module with a dual multi-path aggregation mechanism to enhance small-object detection and multi-scale feature representation. UAV-mounted LEDs are utilized to ensure robust visual perception in low-light indoor scenarios. The UAV’s three-dimensional position is estimated through multi-view geometric triangulation without relying on external beacons or artificial markers. Beyond static localization, the system is validated under dynamic flight conditions, demonstrating smooth and temporally coherent trajectory reconstruction suitable for real-time control loops (update rate $\approx 25\mathrm{FPS}$). Extensive experiments in real indoor environments achieve centimeter-level localization accuracy (root mean square error: 9.9 mm, 95th percentile error: 13.5 mm), outperforming state-of-the-art vision-based methods and achieving accuracy comparable to or better than representative hybrid ultra-wideband–vision systems reported in the literature. These results confirm the effectiveness, robustness, and real-time capability of RWA-YOLO for indoor UAV navigation in constrained environments. Full article

Phase 6 of the Sophimatics framework represents the culmination of a comprehensive research program integrating philosophical wisdom with computational sophistication to address fundamental challenges in artificial intelligence systems. Building upon the Complex-Time Recursive Model established in Phase 5, this phase introduces a human-in-the-loop iterative refinement methodology specifically designed for security-critical applications. Through systematic validation across real-world cybersecurity datasets, including NSL-KDD and CICIDS2017, alongside healthcare privacy scenarios using MIMIC-III derived data, we demonstrate that collaborative human–AI co-creation significantly enhances system performance across multiple dimensions, including interpretive accuracy, contextual fidelity, and ethical consistency. The proposed architecture implements three complementary feedback mechanisms: symbolic knowledge base refinement through expert-provided ontological corrections, neural parameter optimization guided by human evaluation of ethical alignment, and dynamic weight adjustment for value-system integration. Experimental results show substantial improvements over baseline approaches, with intrusion detection accuracy reaching 98.7% on NSL-KDD while maintaining 94.3% privacy preservation scores as measured by differential privacy guarantees. The healthcare privacy experiments demonstrate 97.2% sensitive attribute protection with only 2.1% utility loss compared to non-private baselines. Critical analysis reveals that human oversight mechanisms reduce false positive rates in ethical constraint violations by 67% compared to purely automated systems, while convergence analysis indicates stable performance after approximately 12–15 iterations across diverse application domains. These findings establish Phase 6 as an essential bridge between theoretical Sophimatics foundations and practical deployment in privacy-sensitive contexts, demonstrating that philosophically grounded AI architectures can achieve superior performance when augmented with structured human feedback loops. The work contributes both methodological innovations in human–AI collaboration and empirical validation, demonstrating the viability of Sophimatics principles for addressing contemporary challenges in data protection and cybersecurity. Full article

Circulating tumor DNA (ctDNA) has emerged as a promising and versatile biomarker in colorectal cancer (CRC), providing real-time insights into the tumor burden, minimal residual disease (MRD), and treatment response across both early and metastatic stages. In patients with resected stage II–III CRC, post-operative ctDNA positivity is a robust predictor of recurrence and may outperform traditional clinicopathologic risk factors. It can facilitate adjuvant therapy discussions; however, treatment escalation or de-escalation based solely on ctDNA results is not yet supported by available interventional data. In the metastatic setting, ctDNA-based techniques could provide non-invasive molecular profiling and a monitoring response to systemic therapies. Peripheral blood-based techniques could also help detect emerging resistance to systemic therapy. Emerging evidence highlights that quantitative assessment of ctDNA dynamics, including the baseline burden and post-treatment clearance, could further refine risk stratification and inform treatment personalization. Collectively, ctDNA represents a promising and evolving biomarker with well-established prognostic and emerging predictive potential and is poised to support precision oncology across the continuum of CRC. Full article

Water scarcity poses a critical challenge to sustainable agricultural development, particularly in arid regions such as Saudi Arabia. This study examines whether AI-compatible smart irrigation, digital water monitoring, and integrated water resource management (IWRM) are associated with improvements in agricultural water sustainability. Using a regional–crop panel dataset covering 13 Saudi administrative regions and six major crops over the period 2010–2024, the analysis evaluates their relationships with water-use efficiency, crop water productivity, and crop yield. To address persistence, endogeneity, and unobserved heterogeneity, the study employs a comprehensive multi-method empirical strategy combining dynamic panel models (System GMM), difference-in-differences, and event-study designs. The results provide internally consistent and empirically robust evidence in support of the proposed hypotheses. AI-compatible smart irrigation is positively and significantly associated with improvements in agricultural water efficiency and productivity, with effects that strengthen over time, reflecting gradual technology assimilation and learning processes. These findings capture the performance gains from irrigation modernization that enables data-driven and algorithm-supported decision-making, rather than the direct causal impact of autonomous artificial intelligence deployment. Integrated water resource management independently exhibits a positive association with higher agricultural performance, underscoring the importance of coordinated governance alongside technological adoption. Digital water monitoring shows a positive and statistically significant relationship with all outcome measures and appears to reinforce the effectiveness of both AI-compatible irrigation and integrated water governance. Robustness analyses excluding extreme drought years confirm that these relationships reflect persistent efficiency patterns rather than transitory climatic shocks. Overall, the findings provide context-specific and methodologically rigorous evidence that AI-compatible irrigation, digital monitoring, and integrated water governance operate as complementary components of agricultural water sustainability in a highly water-scarce economy, offering evidence-informed and policy-relevant insights, aligned with Saudi Vision 2030. Full article

As satellite navigation systems show vulnerabilities in specific circumstances such as urban canyons or jamming and spoofing situations, additional sensors such as cameras may be incorporated on the platform. Despite advancements in the robotics and computer vision community, which have led to increasingly accurate Simultaneous Localization and Mapping (SLAM) positioning solutions, visual navigation has its own vulnerabilities. It therefore remains of critical importance for many applications to study the integrity of fused navigation algorithms and their components, which is done less for SLAM than for satellite navigation. In this paper, a framework for integrity monitoring (IM) of a visual SLAM algorithm is proposed. A sensor-level IM scheme analyses feature reprojection errors. It is demonstrated that, in dynamic environments, multiple hypotheses can be generated from different subsets of extracted features. Additionally, the factor graph-based framework employs a fusion-level IM scheme which deals with these multiple hypotheses and selects the most probable one by calculating the sum of weighted measurement residuals. These concepts are applied to scenarios from real and simulated experiments in order to demonstrate applicability. Full article

This paper addresses the position control problem for a Lagrangian pendulum. Using a strict Lyapunov function, a rigorous analysis is presented to prove that the closed-loop system equilibrium point composed of the pendulum dynamics and a classical linear PID control is globally asymptotically stable. Motivated by these results, the theoretical proposal is extended to analyze a novel hyperbolic PID-type control scheme; reformulating the Lyapunov function, global asymptotic stability of the equilibrium point for the corresponding closed-loop equation is demonstrated. The proposed hyperbolic scheme is a rational function with bounded control action composed of a suitable combination of hyperbolic sine and cosine functions. The hyperbolic structure is used in the proportional, integral, and derivative terms of the control algorithm to drive the position error and joint velocity to zero. Experimental results of both a linear PID and a novel hyperbolic PID-type controller on a direct-drive pendulum are presented to illustrate the effectiveness and performance of the proposed control algorithm. Full article

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position $(9.97\times {10}^{-5}$ vs. $7.39\times {10}^{-5}$) and velocity $(2.52\times {10}^{-5}$ vs. $3.76\times {10}^{-5})$, outperforming periodic communication. Full article