Search Results (282)

Identification of recharge sources in the Mekelle area is controversial, as some studies indicate that precipitation is the only source of recharge, while others indicate the existence of inter-catchment/inter-basin groundwater transfers with the possibility of intermediate-to-regional flow system recharge sources. The objectives of this study were, therefore, to identify the sources of recharge, quantify the groundwater recharge and develop a local meteoric waterline (LMWL) for the Mekelle area using the stable isotopic compositions (δ¹⁸O and δ²H) of rainwater. To that end, 13 precipitation, 34 groundwater, and two surface water samples were analyzed for δ¹⁸O and δ²H signatures. Accordingly, the local meteoric water line (LMWL: δ²H = 6.78 δ¹⁸O + 9.83) was developed from ten monthly accumulated precipitation samples and three rainfall event samples. Moreover, the spatial variations in the isotopic compositions of the groundwater with respect to the LMWL showed the existence of three zones/groups with different groundwater flow conditions, namely, shallow, intermediate, and deep flow systems. Two groundwater recharge sources were also identified from the dual-isotope slope of the groundwater isotope lines with respect to the LMWL. Furthermore, the groundwater recharge was estimated to be 49 mm/year using the chloride mass balance (CMB) method. Full article

This review comprehensively summarizes the recent research progress of multi-source sensing technologies and intelligent modeling methods in vegetation monitoring. It elucidates the physical mechanisms and complementary natures of core active and passive technologies, including optical hyperspectral remote sensing, LiDAR structural detection, and microwave/millimeter-wave radar. Furthermore, it systematically analyzes advanced data processing methods, covering physics-based radiative transfer models, data-driven machine learning, Physics-Data Dual-Driven Modeling transfer learning, and multi-source data fusion strategies. The paper highlights successful applications across diverse typical scenarios, such as crop precision nitrogen diagnosis, forest biomass estimation, crop lodging risk prediction, and vegetation stress assessment. Finally, it discusses critical challenges like data heterogeneity and model generalization, while presenting a future outlook focused on building collaborative “space-air-ground” integrated monitoring networks and advanced AI fusion methods. Full article

China’s pursuit of its dual-carbon targets amid rapid artificial intelligence (AI) development raises an important question: do the environmental implications of AI innovation change as regional innovation advances? Using a balanced panel of 30 Chinese provinces from 2011 to 2024, this study measured patent-based AI innovation intensity using applications identified through a strict AI patent classification. Linear and quadratic models with province and year fixed effects were estimated, and the Lind–Mehlum test was used to assess the shape of the relationship within the observed range. The preferred specification indicates an inverted-U-shaped association: carbon emission intensity initially increases with patent-based AI innovation but declines beyond an interior turning point. The negative quadratic coefficient remains stable when the emissions data source, patent classification, sample period, treatment of outliers, and timing of the AI terms are varied. Supplementary Bartik and copula-control analyses preserve the negative curvature, although their identification limitations preclude a definitive causal interpretation. A Kaya-based exact decomposition shows that the estimated curvature is concentrated in energy intensity rather than the carbonization factor. Human capital strengthens the estimated concavity, while the clearest regional contrast is observed between central and eastern China, with the strongest curvature in the central provinces. These findings suggest that greater AI patenting does not automatically reduce emissions. Its environmental implications depend on the stage of regional innovation and its interaction with energy efficiency and absorptive capacity. Policies promoting AI innovation should therefore be coordinated with cleaner energy supply, efficiency improvements, and human capital investment. More broadly, the study provides a stage-sensitive basis for evaluating the sustainability implications of patent-based AI innovation through measurable changes in carbon emission intensity. Full article

With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread application of the Internet of Things and novel sensing technologies has given rise to structural health monitoring data increasingly characterized by massive volume, high dimensionality, multi-source heterogeneity, and strong spatiotemporal coupling. Traditional data processing methods based on mechanical analysis, empirical formulas, or numerical simulation have increasingly exposed limitations of insufficient accuracy, lengthy computation times, and weak generalization capability when confronted with such engineering big data. Machine learning and deep learning technologies, by virtue of their superior nonlinear mapping capability, advantages in feature extraction from massive data, and flexible architectural design, provide solutions for efficient knowledge extraction and intelligent assessment of underground engineering monitoring data. This paper reviews the current application status and frontier advances of machine learning technologies in the field of underground engineering monitoring data processing in recent years. First, the development trajectory of analytical algorithms evolving from classical shallow machine learning, through temporal and spatial deep learning, to physics-data dual-driven approaches is delineated. Second, targeting the critical challenges of missing field data and sparse sensor deployment, spatiotemporal fusion imputation techniques and spatial reconstruction methods incorporating mechanical prior knowledge are thoroughly evaluated, elucidating the paradigm shift in monitoring philosophy from discrete point-based alarming to inference-augmented sparse sensing that approximates full-field state awareness through model-dependent estimation rather than direct measurement. Third, the applications of machine learning in underground structural deformation mechanism interpretation, key influencing factor identification based on explainable artificial intelligence (AI), and rapid back-analysis of geomechanical parameters are summarized. Finally, composite network architectures and physics-constrained guidance strategies for non-stationary deformation time series prediction under complex and variable working conditions are discussed. A methodological audit of the 73 included studies—of which 33 enter the quantitative comparison tables—reveals that 26 of the 33 audited studies (78.8%) validate exclusively on single-project data, only 1 study conducts rigorous out-of-distribution generalization testing, and none of the 33 studies (0%) provides uncertainty quantification. These findings highlight cross-project generalization and probabilistic prediction as important methodological challenges. This paper aims to provide theoretical references and methodological guidance for safety early warning, intelligent construction, and full life-cycle health management of underground engineering. Full article

Urban road transport policies must simultaneously address climate mitigation, local air quality, and the infrastructure requirements associated with vehicle electrification. However, these dimensions are rarely evaluated within a unified city-level framework. This study develops an integrated assessment framework that combines a bottom-up co-source inventory of CO₂ and seven air pollutants, Long-range Energy Alternatives Planning (LEAP)-based scenario modeling, policy contribution analysis, elasticity-based co-benefit assessment, and electric vehicle charging demand estimation for Zhengzhou, China. In 2022, the road transport sector consumed 10,178 ktce of energy and emitted 27.8 Mt of CO₂. Private cars contributed 66.7% of CO₂ emissions, whereas heavy- and medium-duty trucks and light-duty trucks contributed 48.1% and 27.9% of NOx emissions, respectively, collectively accounting for 76.0% of the total. Under the existing policy scenario (EPS), CO₂ emissions increase to 45 Mt in 2030 and 55 Mt in 2040. Under the dual carbon scenario (DCS), emissions peak at approximately 36 Mt in 2030 and decline to 32 Mt by 2040, representing reductions of 20% and 42% relative to the EPS, respectively. Electric vehicle promotion and green transport development contribute 42% and 32% of peak-year CO₂ mitigation. Policy effectiveness differs across emission types. Electric vehicle promotion and green public transport are relatively more effective for CO₂ mitigation, whereas old vehicle retirement, motorcycle phase-out, light-truck electrification, and tighter emission standards provide greater air pollutant reduction benefits. Supporting an electric vehicle stock of approximately 1.22 million in 2030 would require about 610,000 charging piles at a vehicle-to-charger ratio of 2:1. The principal contribution of this study is to demonstrate how complementary vehicle technology, transport structure, emission control, power sector, and infrastructure policies can be combined to support city-level carbon peaking and air pollution co-control. Full article

Passive localization of broadband impulsive sources in shallow water is particularly challenging when using a single hydrophone, especially over a sloping seabed. This study presents a novel single-hydrophone localization method for broadband impulsive sources in sloping shallow water. The approach utilizes warping-transformed eigenfrequencies derived from the energy density function of received signals. A key innovation is the method’s capability for simultaneous range and depth estimation in sloping seabed environments, achieved through eigenfrequency pattern matching for range estimation and normalized amplitude matching for depth estimation. The method depends primarily on waveguide depth and average sound speed, enabling robust estimation while exhibiting inherent insensitivity to variations in other environmental parameters. Both simulated and experimental data from a winter sea trial in the East China Sea validate the performance of this dual-parameter estimation approach. The results demonstrate practical applicability for underwater acoustic monitoring and remote sensing in challenging shallow water environments with complex seabed topography. Full article

Single-tree diameter at breast height (DBH) and tree height (H) are fundamental parameters for forest inventory, forest structure characterization, and forest carbon stock estimation. However, single-source LiDAR data cannot simultaneously capture complete trunk and canopy structural information, limiting the accuracy of single-tree structural parameter estimation. To address this issue, a Bi-Directional Cross-Attention Fusion Network (BCAF-Net) is proposed to estimate DBH and H separately by integrating ground-based Simultaneous Localization and Mapping LiDAR (SLAM LiDAR) and Unmanned Aerial Vehicle LiDAR (UAV LiDAR) data. The framework employs a dual-branch encoder and a bidirectional cross-attention mechanism to establish cross-view structural relationships between trunk and canopy observations, enabling effective multi-source feature fusion. Experiments conducted at two urban forest sites demonstrated that BCAF-Net achieved the highest estimation accuracy, with RMSE of 0.82 cm for DBH and 0.91 m for H and corresponding R² values of 0.97 and 0.96, respectively. Furthermore, the model maintained stable performance under varying forest structural complexities, cross-site conditions, and tree species. These results demonstrate that cross-view structural interaction effectively exploits complementary information from SLAM LiDAR and UAV LiDAR data, thereby improving single-tree structural parameter estimation in complex forest environments. Full article

Direction-of-arrival (DOA) estimation is a core research topic in array signal processing, and two-dimensional (2-D) DOA estimation can jointly acquire the azimuth and elevation of incoming signals, which bears great practical value. Traditional subspace and sparse reconstruction algorithms are plagued by heavy computation and deteriorated accuracy under imperfect array manifolds, low signal-to-noise ratios (SNRs) and insufficient snapshots. To enhance estimation robustness and inference speed simultaneously, this paper presents a dual-branch convolutional neural network (CNN) for 2-D DOA estimation based on uniform rectangular arrays. The network takes the sample covariance matrix of array received data as input. A shared feature encoder with residual blocks and channel-attention modules extracts common spatial features, followed by two prediction heads with independent parameters for elevation and azimuth estimation. Because each branch has a 61-dimensional output while two sources may be simultaneously present, the angle estimation is formulated as multi-label classification using sigmoid outputs and weighted binary cross-entropy. Simulations covering diverse SNRs, snapshot counts, angular intervals and off-grid cases verify that the proposed network obtains smaller root mean square errors than methods with multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT) and ordinary CNN methods, with millisecond-level inference latency. This framework offers an efficient, high-precision real-time 2-D DOA estimation scheme for complicated electromagnetic scenes. Full article

High-precision estimation of forest aboveground biomass (AGB) is crucial for global carbon cycle monitoring and sustainable forest management. However, existing machine learning-based approaches often suffer from high-dimensional feature redundancy, uneven spatial distribution of training samples, and inefficient hyperparameter optimization, which collectively limit estimation accuracy and computational efficiency. To address these issues, this study proposes a synergistic feature-sample optimization framework (DFS) for high-precision forest AGB estimation. First, with the involvement of forestry experts, we constructed the Hunan and Hubei datasets covering typical subtropical forest types through multi-source remote sensing and ground plot sampling. Second, we propose the Dual-Criteria Adaptive Feature Selection (DCAFS) method, integrating ReliefF and mutual information criteria to adaptively select key features highly correlated with AGB, eliminating spectral redundancy while preserving biomass-sensitive information. Next, we introduce a Bidirectional Active Learning Sample Optimization mechanism, called BALSO, and in its forward step, plots with high uncertainty and representativeness are given priority, so samples with high AGB variability can be captured effectively; in the backward step, spatially redundant samples and feature-redundant samples are removed through density peak clustering, and by doing this, sample selection and spatial distribution are optimized at the same time, so plot balance gets improved. Finally, the framework brings in a parameter tuning structure based on Dream Optimization Algorithm, namely DOA, and through staged exploration together with local fine-tuning, DOA makes model hyperparameters and AGB data distribution characteristics align in an adaptive manner, which helps improve convergence efficiency and estimation stability. Input variables comprise Landsat 8 OLI spectral bands, GLCM texture features, vegetation indices, and Sentinel-1/2 data. On the Hunan dataset, the framework achieved an $R^2$ of 0.83 and an RMSE of 25.6 Mg·ha⁻¹; on the Hubei dataset, it achieved an $R^2$ of 0.86 and an RMSE of 26.8 Mg·ha⁻¹. The framework was further validated on an independent public dataset from Inner Mongolia. These results demonstrate that the DFS framework provides an effective and feasible approach for regional-scale forest AGB estimation and carbon monitoring. Full article

With the widespread integration of renewable energy sources and distributed energy storage systems, intraday dispatch of distribution networks (DNs) has gradually evolved into a multistage sequential decision-making problem with intertemporal state coupling and progressively revealed uncertainty. Stochastic optimization under the stagewise independence assumption and limited horizon prediction cannot adequately capture temporal transition characteristics, which may lead to biased future cost estimation and myopic decisions. To address this obstacle, this paper proposes a conditional-cut-enhanced Markov chain stochastic dual dynamic programming strategy (MC-SDDP-CC) for multistage intraday dispatch of DNs. Feature encoding and scenario-lattice-driven trajectory sampling with Markovian path dependence are introduced to accurately characterize the temporal dependence in DNs. To improve computational efficiency, a node-wise conditional cut management scheme is developed to accelerate the recursion and tighten the conditional value function approximation. Numerical studies on modified IEEE 33-bus and 123-bus networks, as well as a practical system in a southwestern province of China, verify the effectiveness of the proposed method in terms of optimality, scalability and ablation performance. Full article

With the gradual decline in conventional oil and gas production growth, unconventional natural gas has become a strategic alternative for hydrocarbon supply. Coal-rock gas (CRG) represents a deep unconventional gas resource with huge potential. Major exploration breakthroughs of CRG have been achieved in China, while systematic research targeting CRG as an independent gas reservoir is still lacking internationally. After effective commercial development, CRG serves as an important supplementary energy source for the domestic natural gas supply. Existing logging evaluation methods exhibit notable deficiencies, as porosity is typically estimated by fitting well logging data or proximate analysis data, resulting in limited accuracy. To address the lack of a dedicated logging volumetric model, ambiguous coal-matrix framework parameters, and substantial porosity calculation errors in deep CRG reservoirs, this study investigates the medium–high rank No. 8 coal seam of the Benxi Formation in the central-eastern Ordos Basin. From an oil and gas reservoir logging evaluation perspective, multi-scale experiments were conducted to systematically characterize the material composition and microscopic characteristics of the coal rock. From the perspective of oil and gas reservoir logging evaluation, a three-component logging volumetric model, consisting of a coal matrix, inorganic minerals, and pore fluids, was constructed, and the corresponding coal-matrix framework parameters were calibrated. The results demonstrate that coal rock is an organic–inorganic composite system, with organic macerals dominated by vitrinite (averaging 59.1%) and inertinite (27.1%). The sum of fixed carbon and volatiles exhibits strong correlations with total organic carbon (TOC) and micro-CT-derived coal-matrix content, yielding determination coefficients of 0.99 and 0.95, respectively, which validates the reliability of the multi-scale quantitative composition characterization. The coal-matrix framework parameters are non-constant: density ranges from 1.08 to 1.56 g·cm⁻³, acoustic slowness from 281 to 425 μs·m⁻¹, and compensated neutron from 39% to 79%. Borehole enlargement severely affects compensated density and neutron logs but has negligible interference with acoustic slowness. Notably, inertinite content shows a significant negative correlation with the acoustic-slowness framework response (R² = 0.80), indicating that structurally dense inertinite is a key intrinsic factor controlling the elastic response of the coal matrix. For porosity evaluation, a dual-variable framework model is proposed. The core novelty of this method is that it simultaneously incorporates variations in inorganic mineral content and differences in inertinite proportion within organic components as dynamic framework constraints, breaking through the limitation of the conventional constant-matrix assumption. The acoustic-slowness-based model achieves an average relative error of merely 7.1%, effectively resolving the large errors inherent in conventional fitting methods. The dedicated coal-rock logging evaluation system established in this study overcomes the limitations of fixed framework models, offers a scientific basis for fine-scale interpretation and resource assessment of deep CRG reservoirs, and provides a valuable reference for evaluating analogous reservoirs. Full article

Urban employment restructuring generates job–housing mismatches whose planning consequences depend on spatial scale and temporal trajectory, yet existing data sources rarely combine fine spatial resolution with long temporal coverage. This study applies multiscale geographically weighted regression (MGWR) to reconstruct annual employment surfaces at 1 km² resolution for Tianjin, China, from 2010 to 2023, using a single anchor year of mobile signaling data. Spatial relationships estimated from 2020 records were transferred across time using annual updates of Points of Interest (POI) density, nighttime light intensity, and population density, with aggregate consistency enforced against official employment totals. Validation confirmed the spatial ordering of employment concentration in the calibration year (Spearman ρ = 0.509, RMSE = 2856 jobs/km²), temporal stability in an independent year (ρ = 0.522, RMSE = 2640 jobs/km² for 2023), and consistency with district statistics (R² = 0.718). Directional robustness to anchor-year selection was further supported by reverse-transfer validation (ρ = 0.652) and by a 2023-anchored reconstruction that replicated the principal directional findings. Three findings emerged that remain invisible at conventional administrative resolution: employment decentralization was spatially bounded at the intermediate ring by an economic activity intensity gradient; the dual-core structure remained morphologically stable while the two poles diverged functionally due to structurally different employment bases; and employment agglomeration change preceded spatial job–housing co-location adjustment by approximately three years. This exploratory lag is consistent with a theoretical assumption of sequential adjustment—a city-specific exploratory signal whose planning implications warrant further investigation in other restructuring cities. Full article

To address the problems that existing UAV RF signal azimuth estimation methods rely on idealized simulation data and lack accuracy and robustness in complex environments, a high-precision azimuth estimation method based on an improved ResNet, namely the Dual-Branch Circular-Regression Network, is proposed. Firstly, UCA-8 array data is generated from measured single-channel RF signals, and non-ideal factors such as channel mismatch and mutual coupling among array elements are incorporated to simulate the real RF receiving environment. Secondly, ResNet is improved from three aspects: input normalization, dynamic dual-branch (DDB) learning features and periodic angle regression. The input normalization strategy based on Per-Sample Complex Root Mean Square (PSCRMS) is adopted to improve the adaptability of the model to signal scale changes. The DDB structure is adopted to adaptively fuse I/Q spatiotemporal features with a spatial covariance statistical prior to enhance the spatial feature expression ability in complex scenes. A periodic angle regression method based on Unit Circular Vector Representation (UCVR) and the Huber Loss (GAH Loss) of geodesic angle distance is adopted to realize periodic angle continuous modeling and suppress abnormal angle errors. Finally, comparative and ablation experiments are conducted on the constructed UCA-8 dataset. The experimental results show that compared with the baseline ResNet, the Dual-Branch Circular-Regression Network achieves 85.8%, 95.3%, and 81.5% reductions in MAE, RMSE and P₉₅, respectively, and maintains higher estimation accuracy and good robustness under low signal-to-noise ratio, hardware mismatch and co-frequency dual-source interference. Full article

Smartphones incorporate acoustic components, including a speaker and multiple microphones, which can be used as a low-cost, contactless platform for vital signs monitoring. However, extracting breathing rate (BR) and heart rate (HR) from smartphone acoustic reflections remains challenging in indoor environments because thoracic reflections are weak and are often mixed with static clutter, hand motion, environmental multipath, and other dynamic sources. In this work, we present a smartphone-based frequency-modulated continuous wave (FMCW) acoustic sensing system that enables simultaneous BR and HR estimation using the integrated speaker and two physical microphones. Instead of processing the received signal as a single, mixed signal, the proposed method leverages distance information from the FMCW beat frequency and an angular phase index (AoA information), derived from dual-microphone and virtual aperture processing, to organize moving reflectors into a joint distance–angle–time representation. A 3D-DBSCAN clustering module is then applied to this representation to separate candidate dynamic sources from static and multipath components, without presupposing the number of sources. To further handle ambiguous cases where multiple candidate dynamic sources are detected, a Siamese similarity network is introduced as a conditional second-stage source-association module. The Siamese model compares candidate thoracic waveforms and estimates whether multiple detected components are likely to originate from the same physical source or different sources, thus improving source selection without resorting to classical blind source separation. The system was evaluated on 20 participants in two indoor environments, a laboratory and a bedroom, using three consumer smartphones and an electrocardiogram (ECG) reference device. In the smartphone-only blind configuration, the proposed pipeline achieved MAEs of 2.312 bpm for HR and 1.394 bpm for BR. In the ECG-assisted calibrated configuration, which is used to evaluate physiological coherence rather than deployable smartphone-only performance, the errors decreased to 0.462 bpm for HR and 0.091 bpm for BR. These results demonstrate that spatial clustering and conditional Siamese source pairing improve the robustness of acoustic vital sign detection using smartphones in indoor environments. Full article

The transition towards decarbonised energy systems, often characterised by high photovoltaic penetration, imposes crucial challenges for operational security, flexibility and grid resilience. In this landscape, meteorological-data reliability has emerged as a strategic pillar to mitigate systemic risks arising from forecasting uncertainty, including grid imbalances, electricity-market volatility, and structural asset safety during extreme weather. This study provides a comparative analysis of four forecasting providers, evaluating their accuracy across atmospheric and solar irradiance parameters through heterogeneous datasets spanning diverse climatic zones and seasons. The analysis is performed by employing a dual-source validation framework that benchmarks every forecast against independent, real-world references rather than the providers’ own model-derived observations: first, the atmospheric variables are compared with real surface-station measurements; second, plane-of-array irradiance is benchmarked directly against on-site sensors at operational photovoltaic plants. This dual-source approach isolates systematic model biases relative to real-world environmental conditions, yielding a provider-independent estimate of accuracy against the true atmospheric and irradiance state. This work therefore proposes not merely a comparative analysis but a validation methodology that addresses a specific limitation of existing forecast-comparison approaches, offering actionable insights to minimise financial and operational risks while fostering a secure, resilient, and sustainable energy infrastructure. Full article