Search Results (95)

CO₂ circulation between subsurface wells is a promising approach for geothermal energy recovery from deep saline formations originally developed for Carbon Capture and Storage (CCS). This study evaluates the feasibility, operability, and performance of sustained CO₂ flow between an injector and a producer at the Canadian Aquistore site, a location with active CO₂ injection and an established geological model. A high-resolution sector model, derived from a history-matched parent simulation, was used to conduct a comprehensive uncertainty analysis targeting key operational and subsurface variables, including injection and production rates, downhole pressures, completion configurations and near-wellbore effects. All simulation scenarios retained identical initial and boundary conditions to isolate the impact of each variable on system behavior. Performance metrics, including flow rates, pressure gradients, brine inflow, and CO₂ retention, were analyzed to evaluate CO₂ circulation efficiency. Simulation results reveal several critical findings. Elevated injection rates expanded the CO₂ plume, while bottomhole pressure at the producer controlled brine ingress from the regional aquifer. Once the CO₂ plume was fully developed, producer parameters emerged as dominant control factors. Completion designs at both wells proved vital in maximizing CO₂ recovery and suppressing liquid loading. Permeability variations showed limited influence, likely due to sand-dominated continuity and established plume connectivity at Aquistore. Visualizations of water saturation and CO₂ plume geometry underscore the need for constraint optimization to reduce fluid mixing and stabilize CO₂-rich zones. The study suggests that CO₂ trapped during circulation contributes meaningfully to permanent storage, offering dual environmental and energy benefits. The results emphasize the importance of not underestimating subsurface complexity when CO₂ circulation is expected to occur under realistic operating conditions. This understanding paves the way to guide future pilot tests, operational planning, and risk mitigation strategies in CCS-enabled geothermal systems. Full article

The precise reconstruction of target scattering centers (TSCs) using sensors plays a crucial role in feature extraction and identification of non-cooperative targets. Radar sensor networks (RSNs) are well suited for this task, as they are capable of illuminating targets from multiple aspect angles and rapidly capturing reflected signals. However, the complex geometry and diverse material composition of real-world targets result in significant variations in the radar cross-section (RCS) observed at different angles. Although these RCS responses are interrelated, they exhibit considerable angular diversity. Furthermore, achieving precise spatiotemporal registration and fully coherent processing is infeasible for RSNs composed of small mobile sensor platforms, such as drone swarms. Therefore, an intelligent algorithm is required to extract and accumulate correlated and meaningful information from the target echoes received by the RSN. In this work, a novel collaborative TSC reconstruction framework for RSNs is proposed. The framework performs similarity evaluation on wide-angle high-resolution range profiles (HRRPs) to achieve adaptive angular segmentation of TSC models. It combines the expectation–maximization (EM) algorithm with an enhanced Arctic puffin optimization (EAPO) algorithm to effectively integrate echo information from the RSN in a non-coherent manner, thereby enabling accurate TSC estimation. The proposed method outperforms existing mainstream approaches in terms of spatiotemporal registration requirements, estimation accuracy, and stability. Comparative experiments on measured datasets demonstrate the robustness of the framework and its adaptability to complex target scattering characteristics, confirming its practical value. Full article

Carrier frequency offset (CFO) estimation is crucial for underwater acoustic (UWA) multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. By employing pilot symbols, a CFO estimation scheme utilizing least squares (LS)-based tentative channel estimation and equalization can achieve an improved CFO estimation performance. However, it suffers from performance degradation due to inaccurate tentative channel estimation in scenarios with relatively long channels or a relatively large number of transmitting transducers. To address this problem, we propose a sparse Bayesian learning (SBL)-based CFO estimation scheme, which employs the expectation-maximization SBL (EM-SBL) algorithm as the tentative channel estimator. In addition, to reduce computational complexity caused by matrix inversion, a refined scheme employing variational Bayesian inference (VBI) technology is proposed, which achieves comparable performance to the original scheme with lower complexity. Finally, numerical simulations demonstrate that our proposed schemes can achieve a remarkably low root mean square error (below ${10}^{-2}$) and outperform existing methods across diverse system configurations and simulated channels. Full article

Line feature matching is a fundamental and extensively studied subject in the fields of photogrammetry and computer vision. Traditional methods, which rely on handcrafted descriptors and distance-based filtering outliers, frequently encounter challenges related to robustness and a high incidence of outliers. While some approaches leverage point features to assist line feature matching by establishing the invariant geometric constraints between points and lines, this typically results in a considerable computational load. In order to overcome these limitations, we introduce a novel Bayesian posterior probability framework for line matching that incorporates three geometric constraints: the distance between line feature endpoints, midpoint distance, and angular consistency. Our approach initially characterizes inter-image geometric relationships using Fourier representation. Subsequently, we formulate the posterior probability distributions for the distance constraint and the uniform distribution based on the constraint of angular consistency. By calculating the joint probability distribution under three geometric constraints, robust line feature matches are iteratively optimized through the Expectation–Maximization (EM) algorithm. Comprehensive experiments confirm the effectiveness of our approach: (i) it outperforms state-of-the-art (including deep learning-based) algorithms in match count and accuracy across common scenarios; (ii) it exhibits superior robustness to rotation, illumination variation, and motion blur compared to descriptor-based methods; and (iii) it notably reduces computational overhead in comparison to algorithms that involve point-assisted line matching. Full article

Data-driven empirical models, including those based on reaction kinetics, are well-regarded for their ability to make accurate predictions and uncover underlying relationships. While such models have been extensively employed for microbial communities, their use in agricultural populations remains comparatively limited. In this study, researchers analyzed data from hydroponic lettuce cultivation experiments observing nitrogen-, phosphorus-, and potassium-limited growth. Dynamic μ models, which incorporated nutrient-fueled growth and maturity-based rate decay, were adapted to accommodate a variable nutrient supply, as would be expected for nutrient recovery efforts using domestic wastewater. To test these models, researchers analyzed multiple approaches, differing variations in analyses, and other agricultural models against observed biomass measurements. The resulting Dynamic μ biomass models showed significantly less error than all other tested models, were validated against three variable nutrient treatments, and were evaluated against expected wastewater concentrations. Wastewater-cultivated lettuce was predicted to grow between 20 and 72% of fresh mass compared to lettuce grown under ideal nutrient concentrations, and models identified 41.7 days to maximize dry biomass, with a final harvest time of 44.0 days to maximize fresh biomass. Finally, this research demonstrates the application of agricultural modeling for profit estimation and informing decisions on supplemental nutrient use, providing guidance for nutrient recovery from wastewater. Full article

The identification of lithium (Li) geochemical anomalies is crucial for the exploration of Li mineral resources. However, variations in lithological backgrounds in lithologically complex regions often hinder the accurate identification of these anomalies. In this study, we employ an unsupervised Expectation-Maximization (EM) clustering algorithm to tackle this issue. Using 1:200,000 scale geochemical data from 2559 stream sediment samples in Chenzhou, Hunan Province, China, we selected seven major elements—SiO₂, Al₂O₃, Fe₂O₃, MgO, CaO, Na₂O, and K₂O—as clustering indicators. This approach allowed us to classify the samples into six distinct groups, significantly reducing the influence of lithological background on the detection of Li anomalies. After applying the 3σ technique to eliminate 122 outliers and conducting Z-score normalization on Li concentration data within each group, Li anomalies were identified using a uniform threshold of the mean + two standard deviations. The results indicate that the EM clustering method effectively suppresses pronounced yet spurious anomalies in high-background areas where granitic intrusions are present, accounting for approximately 0.6% of the total study area, while simultaneously uncovering subtle but significant anomalies in low-background regions characterized by slightly metamorphic and siliceous rocks, accounting for approximately 1.7% of the total study area. This approach substantially improves the reliability of anomalies, offering a robust tool for Li exploration in lithologically complex regions. Full article

Active inference offers a unified framework in which agents can exhibit both goal-directed and epistemic behaviors. However, implementing policy search in high-dimensional continuous action spaces presents challenges in terms of scalability and stability. Our previously proposed model, T-GLean, addressed this issue by enabling efficient goal-directed planning through low-dimensional latent space search, further reduced by conditioning on prior habituated behavior. However, the lack of an epistemic term in minimizing expected free energy limited the agent’s ability to engage in information-seeking behavior that can be critical for attaining preferred outcomes. In this study, we present EFE-GLean, an extended version of T-GLean that overcomes this limitation by integrating epistemic value into the planning process. EFE-GLean generates goal-directed policies by inferring low-dimensional future posterior trajectories while maximizing expected information gain. Simulation experiments using an extended T-maze task—implemented in both discrete and continuous domains—demonstrate that the agent can successfully achieve its goals by exploiting hidden environmental information. Furthermore, we show that the agent is capable of adapting to abrupt environmental changes by dynamically revising plans through simultaneous minimization of past variational free energy and future expected free energy. Finally, analytical evaluations detail the underlying mechanisms and computational properties of the model. Full article

The low-cost Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) is widely used in autonomous vehicles for positioning and navigation. Initial alignment is a critical stage for SINS operations, and the alignment time and accuracy directly affect the SINS navigation performance. To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. A new observation vector based on the magnitude matching is then constructed to effectively reduce the effect of outliers on the alignment process. An adaptive multiple backtracking method is designed in which the window size can be dynamically adjusted based on the innovation gradient; thus, the alignment time can be significantly shortened. Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. Simulation and experimental results demonstrate that the proposed method significantly reduces alignment time and improves alignment accuracy. Taking heading error as the critical evaluation indicator, the proposed method achieves rapid alignment within 120 s and maintains a stable error below 1.2° after 80 s, yielding an improvement of over 63% compared to the backtracking-based Kalman filter (BKF) method and over 57% compared to the fuzzy adaptive KF (FAKF) method. Full article

Most image forgery localization methods rely on supervised learning, requiring large labeled datasets for training. Recently, several unsupervised approaches based on the variational autoencoder (VAE) framework have been proposed for forged pixel detection. In these approaches, the latent space is built by a simple Gaussian distribution or a Gaussian Mixture Model. Despite their success, there are still some limitations: (1) A simple Gaussian distribution assumption in the latent space constrains performance due to the diverse distribution of forged images. (2) Gaussian Mixture Models (GMMs) introduce non-convex log-sum-exp functions in the Kullback–Leibler (KL) divergence term, leading to gradient instability and convergence issues during training. (3) Estimating GMM mixing coefficients typically involves either the expectation-maximization (EM) algorithm before VAE training or a multilayer perceptron (MLP), both of which increase computational complexity. To address these limitations, we propose the Deep ViT-VAE-GMM (DVVG) framework. First, we employ Jensen’s inequality to simplify the KL divergence computation, reducing gradient instability and improving training stability. Second, we introduce convolutional neural networks (CNNs) to adaptively estimate the mixing coefficients, enabling an end-to-end architecture while significantly lowering computational costs. Experimental results on benchmark datasets demonstrate that DVVG not only enhances VAE performance but also improves efficiency in modeling complex latent distributions. Our method effectively balances performance and computational feasibility, making it a practical solution for real-world image forgery localization. Full article

Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). Full article

Pastoral activities are an essential part of the cultural and ecological landscape of Central Italy. This traditional practice supports local economies, maintains biodiversity, and contributes to the sustainable use of natural resources. Understanding livestock behavior in response to environmental variability is essential for improving grazing management and animal welfare and ensuring the sustainability of these systems. This study evaluated the movement patterns of sheep grazing on pastures with differing vegetation indices in the Sibillini Mountains. Twenty lactating ewes foraging on two different pastures were monitored from June to October 2023 using GPS collars and accelerometers. GPS tracks were segmented using the Expectation Maximization Binary Clustering (EmBC) method to characterize movement behaviors, such as foraging, traveling, and resting. The NDVI was used to characterize vegetation dynamics, showing notable differences between the two pastures and across the grazing season. Additive mixed models were used to analyze data, accounting for individual variability and temporal autocorrelation in the sample. The results suggest that variations in the NDVI influence grazing behavior, with sheep in areas of lower vegetation density exhibiting increased movement during foraging. These findings provide valuable insights for optimizing grazing practices and promoting sustainable land use. Full article

This study aimed to evaluate the characteristics of short acquisition times using the Clear adaptive Low-noise Method (CaLM) and Advanced intelligent clear-IQ engine (AiCE) reconstructions in a semiconductor-based positron emission tomography (PET)/computed tomography system. PET data were acquired for 30 min in list mode and resampled into time frames ranging from 15 to 120 s. Images were reconstructed using three-dimensional ordinary Poisson ordered-subset expectation maximization (OSEM) with time of flight (TOF) and OSEM with TOF and point spread function modeling (PSF) algorithms, with OSEM iterations adjusted from 1 to 20 and CaLM applied under Mild, Standard, and Strong settings. AiCE reconstruction allows for the modification of only the acquisition time. The images were evaluated based on the coefficient of variation, recovery coefficient, % background variability (N_10mm), % contrast-to-% background variability ratio (Q_H10mm/N_10mm), and contrast-to-noise ratio. While OSEM with TOF reconstruction did not significantly reduce the acquisition time, the addition of PSF correction suggested the potential for further reduction. Given that the AiCE characteristics may vary depending on the equipment used, further investigation is required. Full article

Human Leukocyte Antigen (HLA) genes are remarkable for their structural complexity and polymorphism. Located on chromosome 6 within the Major Histocompatibility Complex (MHC), these genes exhibit significant frequency variations across human populations and play a crucial role in immune responses, disease susceptibility, and transplant compatibility. This study aimed to assess the genetic profiles and HLA-A/HLA-B/HLA-C/HLA-DRB1 haplotype frequencies in a Romanian cohort. Whole venous blood samples were collected from 405 healthy, unrelated Romanian volunteers. Using next-generation sequencing (NGS), the study population was genotyped for HLA class I (HLA-A, HLA-B, and HLA-C) and class II (HLA-DRB1) loci. Haplotype frequencies were estimated using the expectation-maximization algorithm, addressing phase and allelic ambiguity. The Romanian cohort was compared with multiple populations sourced from the Allele Frequencies Net Database. The study identified 635 different HLA-A/HLA-B/HLA-C/HLA-DRB1 haplotypes. Among them, two haplotypes had frequencies close to 3%: HLA-A*01:01:01/HLA-B*08:01:01/HLA-C*07:01:01/HLA-DRB1*03:01:01, with a frequency of 3.33%, and HLA-A*02:01:01/HLA-B*18:01:01/HLA-C*17:01:01/HLA-DRB1*11:04:01, with a frequency of 2.84%. All other 633 haplotypes (approximately 99.7% of the total) had frequencies below 1%. The results of the current study underscore the extremely high diversity of HLA haplotypes in this population and the fact that even the most frequent haplotypes are relatively low in prevalence (each under 5% in this cohort). These findings and the great haplotypical diversity detected highlight the importance of NGS and high-resolution HLA typing in hematopoietic stem cell and solid organ transplantation, while also contributing to the better understanding of the area-specific population genetics resulting from historical regional dynamics. Further research with larger cohorts is necessary to validate these findings and expand upon their clinical implications. Full article

The presence of multiplex edges and sparse links often hampers the efficacy of link prediction (LP) tasks. By harnessing the expressive power of Markov logic network (MLN) formulations, multiplex edges can be unified to enhance LP effectiveness. However, scaling up inferences for effective LP remains challenging due to the inefficiency of traditional MLN inference methods. To tackle this issue, we redefine LP tasks within heterogeneous networks using MLN inferences and introduce a tailored inference framework to handle unobserved nodes and complex MLN structures. We propose a method to partition the MLN structure into discrete substructures and compute node label distributions using the variational expectation maximization (VEM) algorithm. Additionally, we establish a termination condition to streamline inference search space and present the MLN-based LP algorithm. Experimental findings demonstrate the efficacy of our VEM-driven MLN inference framework for LP tasks in heterogeneous networks, showcasing superior accuracy compared to existing approaches. Full article

Autonomous ride-hailing services, as an innovative solution in the shared mobility sector, have sparked intense competition with traditional ride-hailing platforms. This study examines a traditional large-scale ride-hailing platform and an autonomous ride-hailing platform, constructing profit models for both platforms under competitive and cooperative scenarios. The impact of these scenarios on the platforms’ optimal profits is analyzed using a game-theoretic framework. The study identifies passenger trust in the autonomous platform and the commission rate as critical factors influencing the strategic choices of the two platforms. Surprisingly, irrespective of variations in passenger valuation coefficients and commission rates, there is no scenario where both platforms simultaneously prefer cooperation, which contradicts intuitive expectations. Furthermore, the findings suggest that when passenger trust and valuation differences are relatively low, the autonomous platform can maximize profits by adopting a high-pricing strategy. However, as passenger trust and valuation differences increase, the autonomous platform must adjust its strategy, shifting toward cost optimization and price competition. The study also explores the role of transfer payments as an incentive mechanism for traditional platforms to encourage cooperation from autonomous platforms, providing a robust theoretical foundation for fostering collaboration between traditional and autonomous ride-hailing platforms. Full article