Search Results (792)

With the rapid expansion of multi-platform remote sensing applications, cloud contamination significantly impedes cross-platform data utilization. Current cloud detection methods face critical technical challenges in cross-platform settings, including neglect of atmospheric radiative transfer mechanisms, inadequate multi-scale structural decoupling, high intra-class variability coupled with inter-class similarity, cloud boundary ambiguity, cross-modal feature inconsistency, and noise propagation in pseudo-labels within semi-supervised frameworks. To address these issues, we introduce a Physics-Guided Multi-Representation Network (PGMRN) that adopts a student–teacher architecture and fuses tri-modal representations—Pseudo-NDVI, structural, and textural features—via atmospheric priors and intrinsic image decomposition. Specifically, PGMRN first incorporates an InfoNCE contrastive loss to enhance intra-class compactness and inter-class discrimination while preserving physical consistency; subsequently, a boundary-aware regional adaptive weighted cross-entropy loss integrates PA-CAM confidence with distance transforms to refine edge accuracy; furthermore, an Uncertainty-Aware Quadruple Consistency Propagation (UAQCP) enforces alignment across structural, textural, RGB, and physical modalities; and finally, a dynamic confidence-screening mechanism that couples PA-CAM with information entropy and percentile-based thresholding robustly refines pseudo-labels. Extensive experiments on four benchmark datasets demonstrate that PGMRN achieves state-of-the-art performance, with Mean IoU values of 70.8% on TCDD, 79.0% on HRC_WHU, and 83.8% on SWIMSEG, outperforming existing methods. Full article

The acquisition of nighttime remote-sensing visible-light images is often accompanied by low-illumination effects and haze interference, resulting in significant image quality degradation and greatly affecting subsequent applications. Existing low-light enhancement and dehazing algorithms can handle each problem individually, but their simple cascade cannot effectively address unknown real-world degradations. Therefore, we design a joint processing framework, WFDiff, which fully exploits the advantages of Fourier–wavelet dual-domain features and innovatively integrates the inverse diffusion process through differentiable operators to construct a multi-scale degradation collaborative correction system. Specifically, in the reverse diffusion process, a dual-domain feature interaction module is designed, and the joint probability distribution of the generated image and real data is constrained through differentiable operators: on the one hand, a global frequency-domain prior is established by jointly constraining Fourier amplitude and phase, effectively maintaining the radiometric consistency of the image; on the other hand, wavelets are used to capture high-frequency details and edge structures in the spatial domain to improve the prediction process. On this basis, a cross-overlapping-block adaptive smoothing estimation algorithm is proposed, which achieves dynamic fusion of multi-scale features through a differentiable weighting strategy, effectively solving the problem of restoring images of different sizes and avoiding local inconsistencies. In view of the current lack of remote-sensing data for low-light haze scenarios, we constructed the Hazy-Dark dataset. Physical experiments and ablation experiments show that the proposed method outperforms existing single-task or simple cascade methods in terms of image fidelity, detail recovery capability, and visual naturalness, providing a new paradigm for remote-sensing image processing under coupled degradations. Full article

The architecture, engineering, and construction (AEC) industry is highly fragmented, yet decisions made during the design phase critically shape downstream sustainability performance. Unlike prior research that primarily weighted interactions by frequency, this study introduces an Interaction-Quality Index that evaluates the quality of design team interactions. This represents a novel approach, as it combines Social Network Analysis with Monte Carlo simulation to quantify how collaboration, coordination, and trust influence sustainable outcomes in construction projects. Through a structured literature review, three systemic interaction drivers; collaboration, coordination, and trust were identified. An interaction-quality index was then formulated, weighting each driver according to its relative impact on sustainable outcomes. Social Network Analysis coupled with Monte Carlo simulation validated the index in a real-world building project, demonstrating its usefulness in identifying critical interaction nodes and highlighting how improvements in collaboration, coordination, and trust can strengthen network cohesion and enhance sustainability-oriented decision-making. The proposed index offers construction managers a quantitative tool to integrate social dynamics into holistic sustainability strategies, advancing practice in line with systems-thinking approaches to sustainable construction management. Full article

Accurate path-loss characterization in the upper mid-band is critical for 5G/6G outdoor planning, yet classical deterministic expressions lose fidelity at 18 GHz, and purely data-driven regressors offer limited physical insight. We present a physics-guided residual learner that couples a calibrated two-ray model with an XGBoost regressor trained on the deterministic residuals. To enlarge the feature space without promoting overfitting, synthetic samples obtained by perturbing antenna height and ground permittivity within realistic bounds are introduced with a weight of $w=0.3$. The methodology is validated with narrowband measurements collected along two straight 25 m corridors. Under cross-corridor transfer, the hybrid predictor attains $0.59$–$0.62$ dB RMSE and $R^2\ge 0.996$, reducing the error of a pure-ML baseline by half and surpassing deterministic formulas by a factor of four. Small-scale analysis yields decorrelation lengths of 0.23 m and 0.41 m; a cross-correlation peak of unity at $\Delta =0.10$ m confirms the physical coherence of both corridors. We achieve <1 dB error using a small set of field measurements plus simple synthetic data. The method keeps a clear mathematical core and can be extended to other priors, NLOS cases, and semi-open hotspots. Full article

Background: Neoadjuvant chemotherapy (NAC) is the important and effective approach to treat locally advanced breast cancer (LABC). The prediction of response to NAC prior to start is an efficient approach to obtaining perspective about the effectiveness of treatment. The objective of this study is to design a machine learning pipeline to predict tumor response to NAC treatment for patients with LABC using the combination of clinical features and radiomics computed tomography (CT) features. Method: A total of 858 clinical and radiomics CT features were determined for 117 patients with LABC to predict the tumor response to NAC treatment. Since the number of features is greater than the number of samples, dimensionality reduction is an indispensable step. To this end, we proposed a novel hybrid feature selection to not only select top features but also optimize the classifier hyperparameters. This hybrid feature selection has two phases. In the first phase, we applied a filter-based strategy feature selection technique using matrix rank theorem to remove all dependent and redundant features. In the second phase, we applied a genetic algorithm which coupled with the SVM classifier. The genetic algorithm determined the optimum number of features and top features. Performance of the proposed technique was assessed by balanced accuracy, accuracy, area under curve (AUC), and F1-score. This is the binary classification task to predict response to NAC. We consider three models for this study including clinical features, radiomics CT features, and a combination of clinical and radiomics CT features. Results: A total of 117 patients with LABC with a mean age of 52 ± 11 were studied in this study. Of these, 82 patients with LABC were the responder group (response to NAC) and 35 were the non-response group to chemotherapy. The best performance was obtained by the combination of clinical and CT radiomics features with Accuracy = 0.88. Conclusion: The results indicate that the combination of clinical features and CT radiomic features is an effective approach to predict response to NAC treatment for patients with LABC. Full article

In the context of chlorate’s application as a cathodic reagent of power sources, the mechanism of its electroreduction has been studied in electrochemical cells under diffusion-limited current conditions with operando spectrophotometric analysis. Prior to electrolysis, the electrolyte is represented as an aqueous mixed NaClO₃ + H₂SO₄ solution (both components being non-electroactive within the potential range under study), without addition of any external electroactive catalyst. In the course of potentiostatic electrolysis, both the cathodic current and the ClO₂ concentration demonstrate a temporal evolution clearly pointing to an autocatalytic mechanism of the process (regions of quasi-exponential growth and of rapid diminution, separated by a narrow maximum). It has been substantiated that its kinetic mechanism includes only one electrochemical step (chlorine dioxide reduction), coupled with two chemical steps inside the solution phase: comproportionation of chlorate anion and chlorous acid, as well as chlorous acid disproportionation via two parallel routes. The corresponding set of kinetic equations for the concentrations of Cl-containing solute components (ClO₃⁻, ClO₂, HClO₂, and Cl⁻) has been solved numerically in a dimensionless form. Optimal values of the kinetic parameters have been determined via a fitting procedure with the use of non-stationary experimental data for the ClO₂ concentration and for the current, taking into account the available information from the literature on the parameters of the chlorous acid disproportionation process. Predictions of the proposed kinetic mechanism agree quantitatively with these experimental data for both quantities within the whole time range, including the three characteristic regions: rapid increase, vicinity of the maximum, and rapid decrease. Full article

Electric vehicle (EV) charging infrastructures raise significant concerns about data security and user privacy because traditional centralized authorization and billing frameworks expose sensitive information to breaches and profiling. To address these vulnerabilities, we propose a novel decentralized framework that couples a permissioned blockchain with fully homomorphic encryption (FHE). Unlike prior blockchain-only or blockchain-and-machine-learning solutions, our architecture performs all authorization and billing computations on encrypted data and records transactions immutably via smart contracts. We implemented the system on Hyperledger Fabric using the CKKS-based TenSEAL library, chosen for its efficient arithmetic on real-valued vectors, and show that homomorphic operations are executed off-chain within a secure computation layer while smart contracts handle only encrypted records. In a simulation involving 20 charging stations and up to 100 concurrent users, the proposed system achieved an average authorization latency of 610 ms, a billing computation latency of 310 ms, and transaction throughput of 102 Tx min while maintaining energy overhead below 0.14 kWh day per station. When compared to state-of-the-art blockchain-only approaches, our method reduces data exposure by 100%, increases privacy from “moderate” to “very high,” and achieves similar throughput with acceptable computational overhead. These results demonstrate that privacy-preserving EV charging is practical using present-day cryptography, paving the way for secure, scalable EV charging and billing services. Full article

Localizing ground-penetrating radar (LGPR) benefits from deep subsurface coupling, ensuring robustness against surface variations and adverse weather. While LGPR is widely recognized as the complement of existing vehicle localization methods, its reliance on prior maps introduces significant challenges. Channel misalignment during traversal positioning and time-dimension distortion caused by non-uniform platform motion degrade matching accuracy. Furthermore, rain and snow conditions induce subsurface water-content variations that distort ground-penetrating radar (GPR) echoes, further complicating the localization process. To address these issues, we propose a weather-resilient adaptive spatio-temporal mask alignment algorithm for LGPR. The method employs adaptive alignment and dynamic time warping (DTW) strategies to sequentially resolve channel and time-dimension misalignments in GPR sequences, followed by calibration of GPR query sequences. Moreover, a multi-level discrete wavelet transform (MDWT) module enhances low-frequency GPR features while adaptive alignment along the channel dimension refines the signals and significantly improves localization accuracy under rain or snow. Additionally, a local matching DTW algorithm is introduced to perform robust temporal image-sequence alignment. Extensive experiments were conducted on both public LGPR datasets: GROUNDED and self-collected data covering five challenging scenarios. The results demonstrate superior localization accuracy and robustness compared to existing methods. Full article

Aryl diazonium salt chemistry offers a robust and versatile approach for the modification of material surfaces via the covalent immobilization of reactive functional groups under mild conditions. In this study, this strategy was successfully applied to graft the chelating agent 4-(2-pyridylazo)resorcinol (PAR) onto Amberlite XAD-4 resin. Initially, 4-nitrobenzenediazonium tetrafluoroborate (NBDT) was covalently anchored onto the resin surface using hypophosphorous acid as a reducing catalyst to introduce aryl nitro groups. These nitro groups were subsequently reduced to aniline functionalities, enabling diazo coupling with PAR. The successful modification of the resin was confirmed by ATR-FTIR spectroscopy, thermogravimetric analysis (TGA), and X-ray photoelectron spectroscopy (XPS). The synthesized chelating resin exhibited sorption capacities of 0.152, 0.167, and 0.172 mM g⁻¹ for Co(II), Ni(II), and Cu(II), respectively. The functionalized resin was packed into standard SPE cartridges and employed as a selective sorbent for the extraction and preconcentration of trace metals from groundwater samples collected from Dhalamah Valley, Al-Madinah Al-Munawwarah, prior to quantification by inductively coupled plasma mass spectrometry (ICP-MS). These results demonstrate the effectiveness of rapid diazonium-based surface functionalization for the preparation of selective polymeric metal chelators suitable for the extraction of trace metals from complex groundwater matrices. Full article

Accurate and scalable estimation of forest production is essential for quantifying carbon sequestration, forecasting timber yields, and guiding climate change mitigation strategies. While prior studies established a positive linkage between net primary production (NPP) and canopy structural complexity (CSC) metrics derived from terrestrial LiDAR, the spatial coverage of ground-based surveys is limited. Airborne laser scanning (ALS) could offer a rapid and spatially extensive alternative to terrestrial scanning, but the predictive capacity of ALS-derived CSC metrics for estimating forest production remains insufficiently explored. To address this gap, we derived a suite of three-dimensional (3D) CSC metrics from small-footprint, high-density ALS data collected by the National Ecological Observatory Network’s Airborne Observation Platform. We evaluated relationships between CSC metrics and the NPP of plots nested within seven deciduous and evergreen temperate forests. Optimal metric combinations for predicting NPP within and across forest types were identified using partial least squares regression coupled with recursive feature elimination. ALS-derived CSC metrics explained 77% (RMSE = 11%) and 76% (RMSE = 13%) of the variance in deciduous and evergreen forest plot NPP, respectively. Our findings demonstrate that 3D CSC metrics derived from high-density ALS are robust predictors of plot-level NPP, offering performance comparable to terrestrial scanners while enabling greater scalability and more efficient data acquisition. Full article

Network trace is a comprehensive record of data packets traversing a computer network, serving as a critical resource for analyzing network behavior. However, in practice, the limited availability of high-quality network traces, coupled with the presence of sensitive information such as IP addresses and MAC addresses, poses significant challenges to advancing network trace analysis. To address these issues, this paper focuses on network trace synthesis in two practical scenarios: (1) data expansion, where users create synthetic traces internally to diversify and enhance existing network trace utility; (2) data release, where synthesized network traces are shared externally. Inspired by the powerful generative capabilities of latent diffusion models (LDMs), this paper introduces NetSynDM, which leverages LDM to address the challenges of network trace synthesis in data expansion scenarios. To address the challenges in the data release scenario, we integrate differential privacy (DP) mechanisms into NetSynDM, introducing DPNetSynDM, which leverages DP Stochastic Gradient Descent (DP-SGD) to update NetSynDM, incorporating privacy-preserving noise throughout the training process. Experiments on five widely used network trace datasets show that our methods outperform prior works. NetSynDM achieves an average 166.1% better performance in fidelity compared to baselines. DPNetSynDM strikes an improved balance between privacy and fidelity, surpassing previous state-of-the-art network trace synthesis method fidelity scores of 18.4% on UGR16 while reducing privacy risk scores by approximately 9.79%. Full article

Collaborative carbon regulation in Karst mountains critically reconciles socio-ecological conflicts. While intercity linkages drive spatial carbon heterogeneity, prior studies have focused on administrative-scale accounting, neglecting systematic spatial association network (SAN) analysis. Integrating SAN and geospatial detector models, we reveal county-level carbon balance dynamics in Guizhou, China (2000–2020). The key findings show the following: provincial carbon emissions rose 53% (0.96 to 1.47 × 108 t) against a 15% sequestration decline (0.67 to 0.57 × 108 t); emission networks shifted from single-core clustering to the axial Liupanshui–Guiyang–Tongren corridor, while sequestration networks retained peripheral ecological dominance; carbon balance capacity (CBC) exhibited an inverted C-shaped pattern (higher in the southeast, lower in the central–west) with westward centroid migration; and electricity consumption dominated spatial heterogeneity, with synergistic nighttime light–PM2.5 interactions showing strongest nonlinear enhancement. Notably, Jianhe County maintained peak CBC (16.5) via forest carbon sinks, whereas Shiqian County suffered the steepest decline due to industrial encroachment. This work pioneers dynamic carbon coupling analysis in fragile ecosystems, offering transdisciplinary tools for global “dual-carbon” governance. Full article

This paper proposes a cascaded state estimation framework based on proprioception for robots. A generalized-momentum-based Kalman filter (GMKF) estimates the ground reaction forces at the feet through joint torques, which are then input into an error-state Kalman filter (ESKF) to obtain the robot’s prior state estimate. The system’s dynamic equations on the Lie group are parameterized using canonical coordinates of the first kind, and variations in the tangent space are mapped to the Lie algebra via the inverse of the right trivialization. The resulting parameterized system state equations, combined with the prior estimates and a sliding window, are formulated as a moving horizon estimation (MHE) problem, which is ultimately solved using a parallel real-time iteration (Para-RTI) technique. The proposed framework operates on manifolds, providing a tightly coupled estimation with higher accuracy and real-time performance, and is better suited to handle the impact noise during foot–ground contact in legged robots. Experiments were conducted on the BQR3 robot, and comparisons with measurements from a Vicon motion capture system validate the superiority and effectiveness of the proposed method. Full article

This article examines the evolution of the most extensively researched subjects in e-mobility during the previous two decades. The objective of this analysis is to identify the lessons that the State of Kuwait, which is falling behind other nations in terms of e-mobility, can learn from in its efforts to adopt electric vehicles (EVs). To strengthen the body of knowledge and determine the most effective and efficient route to an “EV-ready” nation, the authors compiled data on the latest developments in the EV industry. A bibliometric analysis was performed on 3962 articles using VOSviewer software, which identified six noteworthy clusters that warranted further discussion. Additionally, we examined the sequential progression of these clusters as follows: (1) the environmental ramifications of electric mobility; (2) advancements in EV technology, including range extension and soundless engines, as well as the capital expenditure (CAPEX) and operating expenditure (OPEX) of purchasing and operating EVs; (3) concerns regarding the effectiveness and durability of EV batteries; (4) the availability of EV charging stations and grid integration; (5) charging time; and, finally, (6) the origin and source of the energy used in the development of e-mobility. Delineating critical aspects in the development of e-mobility can help to equip policymakers and decision makers in Kuwait in formulating timely and economical choices pertaining to sustainable transportation. This study contributes by cross-walking six global bibliometric clusters to Kuwait’s ten EV adoption barriers and mapping each to actionable policy levers, linking evidence to deployment guidance for an emerging market grid. Unlike prior bibliometric overviews, our analysis is Kuwait-specific and heat-contextual, and it reports each cluster’s size and recency to show where the field is moving. Using Kuwait driving logs, we found that summer (avg 43.2 °C) reduced the effective full-charge range by 24% versus pre-winter (approximately 244 km vs. 321 km), underscoring the need for shaded PV-coupled hyper-hubs and active thermal management. Full article

Using multiple reanalysis datasets, this study investigates the decadal variability in the relationship between Northeast Pacific Sea surface temperature (SST) and Arctic stratospheric ozone (ASO), with a focus on the role of atmospheric dynamics in mediating this connection. A significant decadal shift is identified around the year 2000, characterized by a weakening of the previously strong negative correlation between January–February SST anomalies and February–March ASO. Prior to 2000 (1980–2000), warm SST in the northeastern Pacific suppressed upward planetary wave propagation, resulting in decreased stratospheric wave activity and a weakened Brewer–Dobson circulation. The weakened BD circulation reduced poleward transport of tropical ozone and heat, yielding a colder, ozone-poor polar vortex. The strong relationship enabled skillful seasonal predictability of ASO using SST precursors in a linear regression model. However, post-2000 (2001–2022), the weakened planetary wave response to SST anomalies resulted in a breakdown of this relationship, yielding non-significant predictive skill. The findings highlight the non-stationary nature of ocean-stratosphere coupling and underscore the importance of accounting for such decadal shifts in climate models to improve projections of Arctic ozone recovery and its surface climate impacts. Full article