Search Results (21,374)

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, while ultra-high-energy cosmic rays (UHECRs; $E\gtrsim {10}^{18}$ eV) remain among the most important unresolved problems in astroparticle physics. This review examines the viability of FRBs and their central engines as sources of UHECRs within a comprehensive multi-messenger framework. We summarize the observational constraints on UHECR source populations imposed by the energy spectrum, nuclear composition, anisotropy measurements, diffuse $\gamma$-ray background, and high-energy neutrino observations, which, together, favor source classes capable of accelerating heavy nuclei with hard injection spectra, modest cosmological evolution, and sufficiently high source densities. We then review the current landscape of FRB progenitor and engine models, including magnetars, supramassive neutron stars, compact-object mergers, and accretion-powered systems, emphasizing their energetics, environments, and particle-acceleration capabilities through relativistic shocks, magnetic reconnection, magnetar wind nebulae, and direct electromagnetic acceleration by ultra-relativistic FRB pulses. We discuss how these scenarios are constrained by neutrino and $\gamma$-ray observations from IceCube, KM3NeT, and Fermi-LAT, as well as by large-scale UHECR anisotropy measurements from the Pierre Auger Observatory and Telescope Array. Finally, we examine the observational tests that will become possible in the coming decade through large samples of localized FRBs, composition-resolved UHECR measurements, next-generation neutrino observatories, and wide-field $\gamma$-ray facilities. We emphasize that FRB dispersion and rotation measures provide unique probes of the baryonic and magnetic environments relevant for UHECR acceleration and propagation, enabling a new form of multi-messenger tomography of cosmic-ray source environments and allowing the FRB–UHECR connection to become a quantitatively testable astrophysical framework. Full article

Consistent laser cutting quality is one of the problems associated with the nonlinearity of relationships between process parameters and output responses. This problem acquires particular importance when it comes to cutting advanced nanocomposites, which requires precise tuning. Despite the wide adoption of intelligent modelling, few studies have investigated the comparative efficiency of various approaches based on the use of the same dataset. In this research, the effectiveness of three models—Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic System (FLS)—was tested on experimental data related to the CO₂ laser cutting of ABS/CNT nanocomposites. Input parameters included laser power and cutting speed, whereas HAZ width, kerf width, and surface electrical resistivity were used as output data. Data was split into training, testing, and validation datasets; models were created using supervised machine learning. Model performance was evaluated using Root Mean Square Error (RMSE). Analysis of results showed that ANN demonstrated acceptable predictive capabilities, yielding correlation coefficients (R) close to 1 (≈0.99) and RMSE values of 0.2956 for HAZ, 0.2061 for kerf width, and 2.3655 for surface electrical resistivity. Prediction by means of FLS was able to identify general tendencies; however, it produced RMSE values of 0.4741 for HAZ, 0.6297 for kerf width, and 1.9258 for surface electrical resistivity. Finally, the ANFIS model proved to be the most reliable model, yielding the lowest RMSE values for HAZ (0.2784), kerf width (0.0450), and surface electrical resistivity (0.0905). In conclusion, this research shows that ANFIS can be used effectively for building models predicting laser cutting processes; therefore, it represents an approach worth using in future investigations in this field. Full article

Artificial-source frequency-domain electromagnetic methods are important tools for deep mineral exploration and concealed geological structure detection. Apparent resistivity is a key parameter linking measured electromagnetic fields to the interpretation of subsurface electrical structures, and its calculation method directly affects geological interpretation and engineering applicability. Although substantial efforts have been devoted to the theoretical development, data processing, and practical application of different apparent resistivity formulations, most previous studies have focused on the analysis and improvement of a single method. Systematic comparisons of the main apparent resistivity formulations under unified conditions remain limited, particularly in terms of deep basement characterization, anti-interference performance, and engineering applicability. To fill this gap, this study systematically compares the $E-E_x$ wide-field apparent resistivity, the $E-E_x$ far-zone apparent resistivity, and the $E-Z_{xy}$ Cagniard apparent resistivity. Through theoretical derivation, forward modeling of typical one-dimensional models, and field verification, the differences among these three formulations in geological characterization, anti-interference capability, and engineering applicability are analyzed, with the aim of clarifying their applicable boundaries and selection principles for artificial-source frequency-domain electromagnetic exploration. Full article

Aeolian sand bodies unrelated either to coastal barrier systems of Holocene or earlier age or to modern beaches have been identified along the central New South Wales coast of southeast Australia. Some of these deposits cap headlands or are located above high sea-cliffs. Others lie below modern sea-levels, whilst one substantial dune field extends 12 km inland. Many of these have previously been interpreted as Early Holocene cliff-top dunes, the product of the migration of beach sands up aeolian sand ramps at the foot of the sea-cliffs of the region and onto the cliff tops. The rising sea-levels of the Middle Holocene eroded the ramps and cut off the supply of sand to the dunes, allowing them to stabilise. But re-investigation shows that these dune fields accumulated at times of low Quaternary sea-levels, with a particle-size distribution suggestive of an inland rather than a coastal origin. We therefore propose an alternative model for the accumulation of these features. At times of low sea-level, sediments exposed on the inner shelf were reworked onto the adjacent coast by onshore winds, where they accumulated in locations unconnected to the modern or the earlier Holocene coastal aeolian sedimentary regime. This model challenges the conventional story that the dominant glacial maximum winds across southeastern Australia were from the west (and thus offshore). This pattern of sediment accumulation and its associated wind regime may have been more stable (continuing for over 30 000 years) and more long-lived (repeated through at least the last two glacial cycles) than has previously been believed. Although the cliff-top dune model has been widely applied, we question its suitability in its type location and suggest a more cautious approach to its application elsewhere. We argue that the products of the landward aeolian reworking of sediment exposed on the continental shelf have been overlooked, despite their potential for the preservation of long-term environmental records. Full article

In light of the rapid development of the electric vehicle market, permanent magnet synchronous motors (PMSMs) are becoming essential components of propulsion systems. This is due to their high efficiency, remarkable power density, and ability to deliver high torque over a wide speed range. However, the optimal control of these motors under dynamic conditions remains a major challenge due to system nonlinearities, parameter variations, and external disturbances. Conventional strategies such as field-oriented control (FOC), direct torque control (DTC), and fuzzy logic control (FLC) show variable performance in terms of current quality, robustness, and energy efficiency. To overcome these limitations, this study proposes an intelligent control strategy based on artificial neural networks (ANNs), which ensures efficient operation and high control performance under various operating conditions. This approach leverages the learning capabilities of deep neural networks to improve control accuracy, system stability, and overall energy performance. The results obtained show a significant reduction in the current’s total harmonic distortion (THD) as well as an improvement in the stator’s current quality and the electromagnetic torque’s dynamic behavior compared to conventional methods. This improvement reduces overall losses in the electric drive system, thereby contributing to increased vehicle energy efficiency. As a result, the electric vehicle’s range is extended, and the dynamic performance of the PMSM is optimized. These results confirm the potential of artificial intelligence techniques for developing intelligent, robust, and adaptive control systems designed for modern electric propulsion applications. Full article

3D magnetotelluric (MT) inversion based on the Gauss–Newton (GN) framework plays an important role in deep mineral exploration by imaging subsurface electrical conductivity structures. However, large-scale 3D MT inversion remains computationally expensive due to the high cost of sensitivity-matrix construction. To address this challenge, we develop a stochastic Gauss–Newton (SGN) framework that reduces computational cost through random data subsampling while preserving the practical convergence behavior of GN inversion. In the proposed framework, only a randomly selected subset of data is used to approximate the GN search direction. By exploiting a key property of MT forward modelling, namely that responses at all receivers are obtained simultaneously for each frequency, the line search is performed using the full dataset, ensuring stable convergence of the inversion process. The SGN framework is validated using both a synthetic multiblock model and a field dataset from the Akebasitao area in Xinjiang, China. The recovered models remain highly consistent with those obtained using conventional full-data Gauss–Newton inversion across a wide range of sampling ratios. For the synthetic example, reducing the sampling ratio from 100% to 10% decreases peak memory consumption from approximately 433 GB to 242 GB and reduces runtime from 86.8 h to 23.9 h while maintaining comparable inversion quality. Similar computational savings are achieved for the field-data inversion. The field application successfully recovers the major conductive structures along the margins of the intrusion that are associated with hydrothermal alteration and fluid activity, highlighting the capability of SGN to delineate geologically meaningful targets relevant to deep mineral exploration. These results demonstrate that SGN provides an efficient and scalable approach for large-scale 3D MT inversion. Full article

Background: Panoramic radiography is one of the most widely used imaging examinations in dental practice, providing a broad view of the jaws and adjacent head and neck structures. Although primarily prescribed for odontogenic assessment, its field of view may reveal non-odontogenic findings with potential clinical significance. Methods: A structured narrative review was conducted according to SANRA criteria. A literature search was performed in PubMed/MEDLINE, Embase, Scopus, and Web of Science for English-language publications from January 2010 to May 2026. Backward and forward citation tracking of relevant articles, key reviews, and reference textbooks was also performed. Eligible studies and authoritative sources addressing non-odontogenic findings detectable on panoramic radiographs were qualitatively synthesized. Results: The review focuses on carotid artery calcifications, maxillary sinus abnormalities, mandibular radiomorphometric signs related to low skeletal bone mineral density, elongation or calcification of the stylohyoid complex, sialoliths, tonsilloliths, calcified lymph nodes, phleboliths, and laryngeal cartilage calcifications. These findings range from benign anatomical variants to radiographic indicators that may require medical or specialist evaluation. Conclusions: Panoramic radiography should be regarded as a tool for recognition and clinical suspicion rather than definitive diagnosis of extraoral or systemic disease. Dentists play a central role in systematically assessing the entire image, documenting relevant abnormalities, correlating them with patient history and risk factors, and initiating appropriate referral when indicated. Full article

The rapid growth of the green bond market has intensified the need for transparent and reliable monitoring systems, particularly in circular-economy environments characterized by complex, multi-stakeholder, and dynamic interactions. However, existing monitoring approaches still rely heavily on static, issuer-driven disclosures, which sustain information asymmetry and increase the risk of greenwashing. This study systematically reviews the role of digital technologies in enhancing green bond monitoring within circular economy systems. A systematic literature review (SLR) was conducted using the Scopus database, covering publications from 2022 to 2026 and yielding 56 eligible studies. A bibliometric analysis using VOSviewer identified major research trends, thematic clusters, and collaboration patterns within the field. The findings reveal four dominant technological pillars—blockchain, artificial intelligence (AI), Internet of Things (IoT), and digital twin—that support data verification, automated analytics, real-time environmental monitoring, and system-wide integration. Although these technologies show significant potential, the literature remains fragmented and lacks comprehensive monitoring architectures that integrate technological, governance, and regulatory dimensions. This study contributes to the literature by synthesizing these technologies through a business informatics perspective and highlighting digital twin architectures as a promising foundation for integrated green bond monitoring. The findings provide practical insights for regulators, issuers, and investors seeking interoperable, transparent, and trustworthy monitoring ecosystems that strengthen accountability and credibility in sustainable finance. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Self-compacting concrete (SCC) is widely adopted in complex structural engineering due to its excellent flowability and filling capacity. However, in harsh corrosive environments, its complex internal pore structure can easily serve as a preferential pathway for the transport of aggressive media, leading to durability deterioration. This study systematically investigates the effects of chemical attack inhibitor (CAI) on the workability, mechanical properties, sulfate attack resistance, and chloride ion penetration resistance of SCC. The micro-mechanisms governing pore structure evolution are elucidated using low-field nuclear magnetic resonance (LF-NMR) and X-ray computed tomography (X-CT). At a CAI dosage of 2%, the fresh SCC exhibits a slump of 260 mm and slump flow of 720 mm, indicating excellent filling and gap-passing abilities. Meanwhile, the compressive strengths at 3 d, 7 d, and 28 d remain at a high level. After 120 sulfate wet-dry cycles, the strength loss rate is only 8.4%, with an erosion resistance coefficient exceeding 90%. In addition, the resistance to chloride ion penetration is significantly improved, with an electric flux of only 1331 C, which is considerably lower than that of the control group (1637 C). At the optimal dosage of CAI, the concrete exhibits a dense and uniform internal structure devoid of macroscopic defects or cracks, with minimized porosity, thus synergistically enhancing the resistance to sulfate attack and chloride attack. On the contrary, further increasing the CAI dosage markedly intensifies the inhibitory effect of organic components on cement hydration, leading to increased early-age defects and enhanced pore connectivity. Thus, an appropriate amount of CAI can effectively improve the overall performance of SCC, providing a solid experimental basis and theoretical support for its engineering application in harsh corrosive environments. Full article

Non-contact magnetic sensing technology is widely adopted in transmission line online monitoring scenarios including current measurement and fault location for its non-contact measurement capability, strong environmental robustness and low deployment cost. However, existing magnetic-sensing-based galloping monitoring methods suffer from two critical limitations: no theoretical guidance is provided for sensor placement, and a high false detection rate is observed under current fluctuation conditions. To address these issues, a novel transmission line galloping monitoring method based on spatial magnetic field distribution features is proposed in this paper. A conductor galloping-power frequency magnetic field coupling model is first established to derive the optimal magnetic sensor array arrangement strategy. Subsequently, a galloping detection algorithm fusing multi-node frequency-domain features and phase difference information is proposed to eliminate current fluctuation induced false detection. Simulations conducted based on actual 500 kV transmission line parameters and verification tests carried out on a scaled-down laboratory platform confirm that reliable galloping detection can be realized by the proposed method under both current low-frequency oscillation and random fluctuation scenarios. With advantages of non-contact deployment, high anti-interference performance and detection accuracy, the proposed method has promising application potential in engineering-oriented high-voltage transmission line monitoring. Full article

Synthetic aperture radar (SAR) ship detection faces three persistent challenges: coherent speckle noise that obscures target boundaries, heterogeneous background clutter in coastal and harbor scenes, and ship targets whose spatial extent varies by more than an order of magnitude within the same image. To address these issues jointly, this paper proposes MSA-DET, an improved SAR ship detection network built upon YOLOv11. In the backbone, a Multi-Scale Cross-axis Attention module (MSCAttention) runs horizontal and vertical axial attention branches in parallel across multiple receptive-field scales, sharpening feature representations for ship targets that vary widely in size and orientation. In the neck, the standard C3k2 block is redesigned as C3k2_SSA by embedding sparse self-attention, which selectively focuses on the most discriminative spatial tokens while suppressing speckle interference and reducing computational overhead. An Adaptive Spatial Feature Fusion detection head (ASFF) replaces fixed pyramid-level aggregation with learned per-pixel blending weights, resolving gradient conflicts across scales and improving localization consistency for both small and large ships. On the HRSID dataset, MSA-DET achieves an mAP@0.5:0.95 of 63.6% and mAP@0.5 of 88.1%, representing gains of 4.0% and 1.6% over the YOLOv11n baseline; on SSDD, it reaches 69.6% and 97.7%, surpassing the baseline by 7.2% and 2.1%, respectively. These results demonstrate that coordinated multi-stage redesign—rather than isolated module substitution—is an effective strategy for SAR-oriented ship detection. The accuracy gains are accompanied by a moderate increase in model size (8.9 M parameters versus 2.6 M for YOLOv11n) and computational cost (9.6 G FLOPs versus 6.3 G), a trade-off that is justified by the substantial improvement in detection quality. Full article

Low-speed permanent magnet synchronous motors (LS-PMSM) have been widely adopted in fields such as mining and oil extraction due to their excellent stability and high efficiency. In practical applications, current harmonics cause a decline in motor control performance and, in severe cases, can damage the motor. This paper analyzes the mechanism of harmonic generation in LS-PMSM and derives a mathematical model for these harmonics. To address the 5th and 7th harmonics, which are particularly prominent, an improved harmonic voltage compensation module was added to the efficiency-optimized control system. The current averaging method was employed to extract harmonic currents in real time, and a fuzzy PI controller was used to perform closed-loop control of the harmonic currents, thereby obtaining accurate harmonic voltage compensation values. Finally, the control effectiveness of the harmonic voltage compensation was analyzed through simulation. The results indicate that this method can effectively suppress 5th and 7th harmonics and reduce torque ripple. Furthermore, the improved algorithm enhances the accuracy of harmonic current extraction and improves the dynamic performance of the controller. Full article

Short-term captivity is widely used in experimental studies but may unintentionally alter host-associated microbiomes, potentially confounding biological interpretation of experimental outcomes. Here, we evaluated the effects of 35 days of captivity on the gut microbiome of Fundulus heteroclitus collected from Long Island Sound (Milford, CT, USA) using 16S rRNA gene sequencing. Comparisons between Field Control (FC) and short-term Captive Treatment (CT) groups revealed a marked reduction in microbial diversity under captive conditions. Observed richness decreased approximately five-fold (Field Control: 1026 features; Captive Treatment: 221 features), and Shannon diversity declined from 8.89 to 5.93. Beta diversity analyses based on UniFrac distances demonstrated clear separation between groups, indicating substantial shifts in community composition. Taxonomic profiling revealed reduced community complexity in captive fish, with increased dominance of Proteobacteria and loss of diverse environmental taxa. Predicted enrichment of pathways associated with stress response, altered respiration, and metabolic flexibility in captivity reflects inferred functional potential rather than direct functional activity. Given the use of pooled samples with limited biological replication, these findings should be interpreted as strong community-level patterns rather than population-level inference. Collectively, these results indicate that short-term captivity alters the F. heteroclitus gut microbiome. Full article

Pose estimation is an important task in the field of machine vision, being widely used in robot grasping, augmented reality, and other applications. Weakly textured objects pose severe challenges due to scarce texture and low-density features, becoming a bottleneck in robot grasping. This paper systematically reviews recent progress in weakly textured object pose estimation, classifying methods into traditional and deep learning categories, and further dividing deep learning methods into instance-level, category-level, and unseen object-level. This review further summarizes the core issues of generalization limitations, real-time contradictions, and data bottlenecks in existing research. Combined with the practical needs of weakly textured scenes, the review points out that multimodal fusion optimization, lightweight model design, and low-cost annotation technology development are the future core research directions. The research results can provide a reference for algorithm design, experimental verification, and engineering applications in the field of weakly textured object pose estimation. Full article