Search Results (37,437)

Environments with sparse or repetitive geometric structures, such as long corridors and narrow stairwells, remain challenging for LiDAR–inertial simultaneous localization and mapping (LiDAR–IMU SLAM) due to insufficient geometric observability and unreliable data associations. To address these issues, we propose VINA-SLAM, a novel LiDAR–IMU SLAM framework that constructs a unified global voxel map to explicitly exploit structural consistency. VINA-SLAM continuously tracks surface normals stored in the global voxel map using a normal-guided correspondence strategy, enabling stable scan-to-map alignment in degenerate scenes. Furthermore, a tangent-space metric is introduced to supplement missing rotational constraints around planar regions, providing reliable initial pose estimates for local optimization. A tightly coupled sliding-window bundle adjustment is then formulated by jointly incorporating IMU factors, voxel normal consistency factors, and planar regularization terms. In particular, the minimum eigenvalue of each voxel’s covariance is used as a statistically principled planar constraint, improving the Hessian conditioning and cross-view geometric consistency. The proposed system directly aligns raw LiDAR scans to the voxelized map without explicit feature extraction or loop closure. Experiments on 25 sequences from the HILTI and MARS-LVIG datasets show that VINA-SLAM reduces ATE by 25–40% on average while maintaining real-time performance at 10 Hz in the evaluated geometrically degenerate environments. Full article

Tight sandstone reservoirs are characterized by highly heterogeneous pore structures, in which multiscale pore–throat systems jointly control the shapes of capillary pressure curves and relative permeability, thereby exerting a fundamental influence on water production behavior and the overall development performance of gas reservoirs. The Ordos Basin is generally characterized by the development of tight sandstone. The tight sandstones exhibit porosities of 2–13% and permeabilities of 0.01–10 × 10⁻³ μm². To quantitatively elucidate the controlling mechanisms of multiscale pore structure on capillary pressure curve morphology and relative permeability, this study systematically investigates the fractal and multifractal characteristics of pore structures in tight sandstones based on high-pressure mercury intrusion (MICP) and nuclear magnetic resonance (NMR) experimental data, and establishes a quantitative relationship between fractal parameters and the capillary pressure curve shape parameter λ. First, capillary pressure curves were fitted using the Brooks–Corey model within the effective saturation interval to extract the shape parameter λ, which characterizes the concentration degree of pore-size distribution and the drainage behavior. Subsequently, based on NMR T₂ spectra, the small-pore fractal dimension D₁, large-pore fractal dimension D₂, and the multifractal singularity spectrum width Δα were extracted to quantitatively describe the geometric complexity of pore structures at different scales. On this basis, the correlations between λ and D₁, D₂, and Δα were systematically analyzed, and the predictive performance of λ under different parameter combinations was compared. The results indicate that: (1) the pore structures of tight sandstones exhibit pronounced fractal and multifractal characteristics at the NMR T₂ scale, with significant differences among samples; (2) λ shows an overall negative correlation with fractal parameters, among which the correlations with the large-pore fractal dimension D₂ and the multifractal spectrum width Δα are the most significant; (3) compared with models using a single fractal dimension, the multiparameter model incorporating Δα provides a more comprehensive characterization of multiscale pore heterogeneity, leading to a substantial improvement in the accuracy and stability of λ prediction; and (4) λ exerts a clear control on the shape of relative permeability curves, where a larger λ corresponds to earlier initiation and forward-shifted rising segments of water-phase flow, while a smaller λ results in overall flatter relative permeability curves. From the perspectives of fractal and multifractal theory, this study establishes an intrinsic linkage among pore structure, capillary pressure curve shape parameters, and relative permeability, providing a novel quantitative framework for constraining relative permeability curve morphology in tight sandstones under conditions where systematic relative permeability experiments are unavailable. Full article

Accurate prediction of shipping freight indices, represented by the Baltic Dry Index (BDI), is crucial for operational decision-making and risk management in the shipping industry. Existing models mainly rely on historical time-series data and often overlook the influence of unstructured information such as market sentiment. To address this limitation, this study proposes a dynamic freight rate prediction framework integrating a shipping text sentiment index. First, a shipping news sentiment index is constructed using a RoBERTa-based pre-trained model to quantify the impact of market sentiment on freight rate fluctuations. Second, the BDI series is decomposed and reconstructed through Variational Mode Decomposition (VMD) and Fuzzy C-Means (FCM) clustering to extract multiscale features. Finally, a deep learning based multi-step prediction model is developed by incorporating the sentiment index into the forecasting process. Empirical results show that the proposed model significantly outperforms benchmark models without sentiment information in terms of MAE, RMSE, and R², and demonstrates greater robustness under extreme market conditions. These findings provide a novel methodological framework for improving freight rate forecasting accuracy and offer practical decision support for shipping enterprises. Full article

This study investigates the feasibility of treating acidic gases produced in oilfields using a novel method that combines cavitation-jet reactor (CJR) technology with electric arc discharge (EAD). The integration of these two approaches enhances the ionization process by converting neutral gas molecules into chemically reactive ion-radical and radical fragments. These highly reactive species eventually recombine, creating new chemical compounds and simpler molecules from incoming acid gas and water vapor. Theoretical validation and experimental demonstration have revealed possible mechanisms and pathways of low-temperature plasma-chemical processes resulting from the synergistic effects of cavitating-jet flow and arc discharge on the molecular degradation of neutral gaseous molecules, such as hydrogen sulfide and carbon dioxide in water vapor, which lead to the generation of new compounds. Research indicates that the most effective method for processing associated petroleum gas (APG) involves minimizing the sequential nature of chemical reactions in low-temperature non-equilibrium plasma environments, thus eliminating the need for costly and complex catalysts. Additionally, studies have shown that the cavitation-jet flow of a gas–vapor–liquid mixture, when combined with an electric arc discharge in the truncated region of the low-temperature plasma of CJR, results in the synthesis of hydrogen, two forms of S₈ (S₈^I and S₈^II), crystalline carbon, and its organic derivatives containing oxygen and nitrogen, specifically methanol, ethanol, acetone, and acetonitrile. The data obtained suggest that the generation of low-temperature plasma in the cavitation-jet chamber, induced by an electric discharge, is essential for the production of reaction products, such as hydrogen, sulfur, and oxygen- and nitrogen-containing derivatives of organic carbon, when water vapor and acid gas molecules traverse the reactor. Full article

Lipoprotein(a) [Lp(a)] is a low-density lipoprotein-like particle that contains a unique apolipoprotein(a) [apo(a)] component covalently bound to apolipoprotein B-100. Elevated levels of Lp(a) have been identified as a well-established and genetically determined risk factor for atherosclerotic cardiovascular disease, including coronary artery disease, stroke, and calcific aortic valve stenosis. In contrast to other lipids, Lp(a) concentrations are minimally influenced by lifestyle or traditional lipid-lowering therapies, emphasizing the necessity for novel treatment approaches. This narrative review summarizes current and emerging therapeutic strategies for reducing Lp(a) levels. Such strategies include traditional agents such as niacin and PCSK9 inhibitors, as well as innovative therapies such as antisense oligonucleotides, RNA interference-based molecules, and small-molecule inhibitors. The mechanisms of action of these agents, in addition to clinical trial data and their capacity to modify cardiovascular outcomes, are explored in further detail. Furthermore, the current status of clinical guidelines and the evolving role of Lp(a)-targeted therapies in cardiovascular risk stratification are reviewed. A particular emphasis is placed on therapies that are in the advanced stages of clinical development. These include late-phase outcome trials and orally administered agents, which have the potential to significantly impact future clinical practice. The integration of mechanistic data with ongoing and completed clinical studies has been undertaken in order to provide a comprehensive framework for understanding the therapeutic potential of Lp(a) in the context of cardiovascular prevention. Full article

Accurate prediction of contact temperature in disconnectors is critical for early fault detection. However, purely physics-based models face difficulties in parameter identification, while purely data-driven models often suffer from error accumulation in long-term forecasting. To address these challenges, this paper proposes a novel framework named Hybrid Physics-Informed Long Short-Term Memory (Hybrid-PI-LSTM). Firstly, this paper mathematically formulates the transient heat transfer process as a constrained optimization problem governed by a nonlinear ordinary differential equation (ODE), embedding physical laws into the loss function as a regularization term to promote dynamic consistency. Secondly, to address the inverse problem of parameter drift caused by environmental changes, an Adaptive Parameter Updating (APU) mechanism is introduced. This algorithm utilizes a gradient-based iterative approach to dynamically estimate equivalent physical coefficients (e.g., heat capacity) from observational residuals during inference. Finally, numerical experiments on a real-world dataset demonstrate that the proposed framework significantly outperforms baseline models. Specifically, it achieves a Root Mean Squared Error (RMSE) of 0.283 at a 720-step forecasting horizon, reducing the prediction error by over 35% compared to static-parameter physical models. The results indicate that the proposed adaptive constraint mechanism contributes to enhanced long-term numerical stability and physics-guided parameter tracking. Full article

This study presents a novel data-driven framework for developing airport-specific landing policies and procedures from historical successful-landing data. The proposed process, termed the Airport-Dependent Landing Procedure (ADLP), is motivated by the fact that airports rely on uniquely tailored approach charts reflecting local operational constraints and environmental conditions. While existing approach charts and landing procedures are primarily designed based on expert knowledge, safety margins, and regulatory conventions, the authors argue that data science and data mining techniques offer a complementary and empirically grounded methodology for extracting operationally meaningful structures directly from historical landing data. In this work, we construct a probabilistic three-dimensional environment from real-world aircraft approach trajectories, capturing spatiotemporal relationships under varying atmospheric conditions during approach. The proposed methodology integrates Adversarial Inverse Reinforcement Learning (AIRL) with Recurrent Proximal Policy Optimization (R-PPO) to establish a foundation for automated landing without pilot intervention. AIRL infers reward functions that are consistent with behaviors exhibited in prior successful landings. Subsequently, R-PPO is employed to learn control policies that satisfy safety constraints related to airspeed, sink rate, and runway alignment. Application of the proposed framework to real approach trajectories at Guam International Airport demonstrates the efficiency and effectiveness of the methodology. Full article

Underwater acoustic coatings are widely used to suppress low-frequency noise radiation and sonar reflection in underwater vehicles. In this study, an underwater acoustic coating model consisting of viscoelastic rubber layers and micro-perforated panel (MPP) structures is investigated, with particular emphasis on the low-frequency sound absorption mechanism and predictive modeling. Based on an improved transfer function method, a novel Micro-Perforated Panel Acoustic Coating Layer (MPPACL) model is developed to describe the coupled acoustic behavior of multilayer coatings under underwater conditions. The low-frequency sound absorption performance is primarily governed by the viscoelastic characteristics of the rubber layer, including material damping and complex modulus, while the incorporation of the MPP further enhances absorption through resonance effects. To efficiently explore the relationship between structural parameters and acoustic response, an ensemble learning-based deep neural network (ELDNN) is constructed using analytically generated data, enabling both forward prediction of sound absorption performance and inverse prediction of structural design parameters. The results show that the frequency prediction accuracy of the IDNN model is 3.7 times that of the DNN model. Furthermore, the proposed MPPACL model has achieved a significantly enhanced sound absorption effect within the frequency range of 50 to 2000 hertz. This effect has also been further verified through underwater experiments. The proposed framework provides an efficient and reliable approach for the design and optimization of underwater acoustic coatings. Full article

Bolted joints are extensively used in mechanical and civil engineering structures because of their low cost, standardized design, and ease of installation and maintenance. The preload in a bolted connection is critical for ensuring joint stability and service reliability; however, preload degradation commonly occurs under complex operating conditions, particularly in environments involving sustained or cyclic vibration. To tackle this problem, this study proposes a portable, force-controllable percussion system for bolt looseness detection. The system integrates a solenoid-driven automatic percussion device, acoustic signal acquisition, onboard data-processing, and real-time visualization of diagnostic results. By adjusting the driving current of the solenoid, the percussion force can be accurately controlled, ensuring stable and repeatable excitation. Benefiting from its compact structure and low cost, the proposed system is suitable for real-time, on-site inspection of bolt looseness. Furthermore, a novel audio-processing approach based on a Siamese Capsule Network is developed to identify bolt looseness conditions. Compared with existing percussion-based techniques, the proposed method exhibits improved classification performance, especially in recognizing bolt states that are unseen during training. Exploratory experimental results validate the effectiveness of the proposed system and demonstrate its strong potential for practical engineering applications. Full article

Accurate measurement of spinal metrics is critical for diagnosing and treating spinal disorders. However, discrepancies between X-ray and ultrasound imaging data pose a challenge in standardizing clinical assessments. This study introduces a novel methodology that combines geometric scaling factors and extrapolation techniques to align spinal metrics from X-ray and ultrasound modalities. Data were collected from fifteen healthy adult volunteers (8 males, 7 females) aged from early adulthood to middle age, all without a history of low back pain, who underwent a standardized chiropractic traction protocol. X-ray imaging was performed pre-procedure, and ultrasound imaging was conducted both pre-procedure and during the procedure at the L3–L4, L4–L5, and L5–S1 levels under graded traction forces (1.8 kg, 3.6 kg, 5.4 kg, and 11.3 kg). Extrapolation methods were applied to standardize measurements across pre- and during-procedure conditions. Significant findings include consistent increases in spinal metrics, such as height and area, indicating positive elongation and flexibility under progressive weights. The integration of these methods bridges the gap between static and real-time imaging data, potentially enhancing diagnostic accuracy and leads to clinical relevance. This proof-of-concept study lays the groundwork for developing standardized spinal imaging protocols and adapting the methodology to broader imaging applications for improved patient outcomes. Full article

Accurate short-range precipitation prediction plays a crucial role in daily life and disaster mitigation. However, the existing methods often suffer from inefficient large-scale feature extraction, severe redundant information interference, and insufficient attention to the problem of imbalanced data distributions, leading to unsatisfactory performance. To address these issues, in this paper, we first propose a novel spatiotemporal module called Group Large Kernel Convolution (GLKC) and develop a short-range precipitation forecasting model based on it, GLKC-Net, using multiple meteorological variables. Specifically, we use decomposed large-kernel convolution to enhance the ability to understand large-scale atmospheric processes. Meanwhile, we introduce the group convolution and channel shuffle operator to control the fusion of channel-wise information, enabling efficient information exchange and reducing redundancy in the channel dimension with multiple variables. Furthermore, we treat the causes of poor model performance for extreme precipitation events with an imbalanced data distribution perspective and design a Multi-threshold Adaptive Loss function (MTA Loss). This function strengthens the model’s focus on high-threshold precipitation events that are inherently difficult to forecast, aiming to improve model performance for extreme events. Finally, forecasting experiments for validation were conducted over southwestern China using ERA5-Land and CMPAS datasets. The results demonstrate that our proposed method outperforms several existing approaches in terms of forecasting accuracy. Full article

Groundwater management increasingly relies on participatory governance, yet most existing participatory frameworks lack mechanisms for iterative learning and continuous improvement and further lack structured operational indicators, systematic monitoring–feedback integration, and institutionalized mechanisms that embed participation within measurable governance cycles rather than treating it as a one-time procedural input. Conversely, Lean thinking, particularly the Plan–Do–Check–Act (PDCA)-based continuous improvement principles, offers systematic methods for feedback and adaptation, but remains underexplored in environmental governance contexts. This paper bridges these traditions by conceptualizing participatory groundwater governance as a continuous improvement system, thus aligning community participation with PDCA logic in order to enhance adaptive management and sustainability outcomes. This study introduces a novel conceptual synthesis that integrates Lean management principles into participatory groundwater governance. In the current research, a methodological framework is proposed for integrating Lean thinking, particularly the Plan–Do–Check–Act cycle, with participatory groundwater governance, thus producing a Lean–participatory groundwater governance (Lean–PGG) framework. To conceptualize the framework, a set of eight rubric-based indicators was developed from a literature matrix of 54 peer-reviewed case studies selected through predefined inclusion criteria and multi-stage screening procedures, in order to evaluate participation, governance readiness, tool application, data use, monitoring, learning, and institutionalization. Each variable indicator was then scored on a three-point scale and categorized into the PDCA maturity levels The findings suggest a consistent heuristic trend across cases, characterized by comparatively stronger performance in the planning and implementation stages. A clear majority of studies scored in the moderate-to-high range (≥2.5/3) for the Plan and Do dimensions, whereas only a limited proportion demonstrated structured Check mechanisms and fewer still exhibited institutionalized Act processes. This asymmetry indicates persistent gaps in the consolidation of evaluation and feedback within participatory groundwater governance systems. This Lean–PGG framework thus demonstrates how continuous improvement mechanisms, i.e., feedback loops, reflection, and adaptive standardization, can strengthen participatory groundwater governance. The proposed framework offers a replicable and practical model for integrating continuous improvement into environmental and groundwater governance, fostering adaptive management, resource efficiency, and sustainability outcomes. Full article

Travel time prediction (TTP) is a fundamental pillar of intelligent transportation systems (ITS). However, deploying highly parameterized deep learning models in data-scarce environments—referred to as the “cold-start” problem—remains a critical bottleneck, frequently leading to overfitting and severe error accumulation on ultra-long trajectories. To surmount these limitations, this study proposes the Dual-Attentional Gated Residual Network (DAGRN), a data-efficient forecasting framework driven by a novel topology-temporal coordination mechanism. Specifically, the framework introduces three integrated innovations: (1) transforming the primal network into a physics-aware Line Graph to explicitly filter out illegal movements and dynamically modulating topological propagation via Feature-wise Linear Modulation (FiLM); (2) coupling a Bidirectional GRU backbone with a Multi-Head Attention module to simultaneously capture global trends and localized intersection delays; (3) employing a Gated Residual Fusion mechanism that preserves dimensional consistency and facilitates gradient flow in extensive sequences. To rigorously validate the model’s robustness, we conduct evaluations on a highly constrained, stratified dataset comprising merely 2000 trajectories. Experimental results demonstrate that DAGRN achieves state-of-the-art predictive precision with an RMSE of 415.485 s and an R² of 0.848, significantly outperforming 12 advanced baseline models and reducing error by up to 13.8% against the strongest graph baseline. Comprehensive ablation studies confirm the absolute necessity of the Multi-Head Attention module, whose removal causes the most severe performance degradation (RMSE surging to 521.495 s). Ultimately, DAGRN presents a readily deployable solution for sparse-data ITS regimes, actively paving the way for future hybrid integrations with microscopic traffic simulations and evolutionary road network optimization algorithms. Full article

Background and Objectives: While associations between attachment styles and certain dermatologic conditions have been documented, their role in chronic pruritus remains unexplored. Given the significant psychosomatic component in the etiology of chronic pruritus, this study aimed to assess attachment styles in patients with chronic pruritus in the absence of organic or psychiatric disorders and to examine their potential contribution to its development. Methods: Sixty patients with chronic pruritus were compared with a healthy control group (n = 60). Socio-demographic data, the duration of the disease, and the itch severity were noted. Additionally, assessments performed via the Questionnaire of Relation Scale, Questionnaire of Relation, Hospital Anxiety and Depression Scale, General Health Questionnaire, and Dermatology Life Quality Index (DLQI). Results: Statistically higher scores of fearful, dismissive, and preoccupied attachment styles were observed in the patient group compared to the control group. Among patients, those with moderate to high itch severity had higher mean scores of anxiety and preoccupied attachment than those with low itch severity. In contrast, secure attachment scores were significantly higher in the control group than in the patient group. Limitations: Attachment styles were examined with a self-report instrument without stimulated recall procedures. Conclusions: Our findings clearly demonstrate that patients with chronic pruritus exhibit significantly higher levels of insecure attachment styles alongside elevated anxiety, depression, and psychosocial burden. Notably, the association between preoccupied attachment and greater itch severity highlights how emotional dysregulation may intensify pruritus symptoms. Due to limited research directly examining attachment in chronic pruritus, our study provides novel insight and supports a biopsychosocial approach to care. Full article

Based on monolithic microwave integrated circuit (MMIC) technology, this paper presents the design and implementation of a low-cost, highly integrated Ka-band sixteen-channel transmit/receive (T/R) module, specifically tailored to meet the application requirements of phased array antennas in airborne and spaceborne radar systems, satellite communications, and 5G/6G millimeter-wave networks. The proposed module employs an MMIC-based single-channel dual-chip discrete architecture, optimally integrating amplitude-phase multifunction chips and transmit-receive multifunction chips in terms of both fabrication process and performance characteristics, achieving a favorable balance between high performance and high-integration density. Using low-cost, low-temperature co-fired ceramic (LTCC) substrates, full-silver conductive paste, and a nickel–palladium–gold plating process, a novel “back-to-back” thin-slice packaging technique is presented to improve integration, lower manufacturing costs, and boost long-term reliability. Furthermore, the design incorporates glass insulators and a direct array interconnection scheme, which significantly minimizes transmission losses and reduces interface dimensions. The final module measures 70.3 mm × 26.2 mm × 10.9 mm and weighs only 34 g. Experimental results demonstrate a transmit output power of at least 23 dBm, a receive gain exceeding 26 dB, and a noise figure below 3.5 dB, achieving a 22.5–58% reduction in volume per channel while maintaining competitive RF performance. To improve testing effectiveness and guarantee data consistency, an automated radio frequency (RF) test system based on Python 3.11.5 was also developed. This work provides a practical technical approach for the engineering realization of Ka-band phased array systems. Full article