Search Results (1,190)

National-scale long-term forest ecosystem surveys based on systematic sampling offer a robust framework for detecting temporal growth trends of specific tree species across regions. The National Forest Inventory (NFI) of the Republic of Korea serves as a vital source for analyzing long-term forest dynamics on a national scale by providing regularly collected large-scale forest data. However, various limitations, such as the lack of individual-level and spatial interaction data, restrict the development of reliable individual tree growth models. To overcome this, distance-independent models, compatible with the structure and data resolution of the NFI, provide a practical alternative for simulating individual tree and stand-level growth by utilizing straightforward attributes, such as diameter at breast height (DBH). This study aimed to analyze the growth patterns and construct species-specific models for two major plantation species in South Korea, Pinus koraiensis and Larix kaempferi, using data from the 5th (2006–2010), 6th (2011–2015), and 7th (2016–2020) NFI survey cycles. The sampling points included 117 and 171 plots for P. koraiensis and L. kaempferi, respectively. An additional matching process was implemented to improve species identification and tracking across multiple survey years. The final models were parameterized using a distance-independent model, integrating the estimation of potential diameter growth (PG) and a modifier (MOD) function to adjust for species- and site-specific variabilities. Consequently, the models for each species demonstrated strong performance, with P. koraiensis showing an R² of 0.98 and RMSE of 1.15 (cm), and L. kaempferi showing an R² of 0.98 and RMSE of 1.14 (cm). This study provides empirical evidence for the development of generalized and scalable growth models using NFI data. As the NFI increases in volume, the framework can be expanded to underrepresented species to improve the accuracy of underperforming models. Ultimately, this study lays a scientific foundation for the future development of tree-level simulation algorithms for forest dynamics, encompassing mortality, harvesting, and regeneration. Full article

Sustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels—including sentence length, punctuation density, word length, and type–token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT’s communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation—though often subtle—may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability—key pillars of sustainable development. Full article

The concentration characteristics of SO₂, NO₂, O₃, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous observational data remain scarce. To address this gap, this study proposes a Transfer Learning–Convolutional Neural Network (TL-CNN) model that integrates ERA5 meteorological data, EAC4 atmospheric composition reanalysis data, and ground-based observations through multi-source data fusion. During data preprocessing, the Data Interpolating Empirical Orthogonal Function (DINEOF), inverse distance weighting (IDW) spatial interpolation, and Gaussian filtering methods were employed to improve data continuity and consistency. Using ERA5 meteorological variables as inputs and EAC4 pollutant concentrations as training targets, a CNN-based inversion framework was constructed. Results show that the CNN model achieved an average coefficient of determination (R²) exceeding 0.80 on the pretraining test set, significantly outperforming random forest and deep neural networks, particularly in reproducing nearshore gradients and regional spatial distributions. After incorporating transfer learning and fine-tuning with station observations, the model inversion results reached an average R² of 0.72 against site measurements, effectively correcting systematic biases in the reanalysis data. Among the pollutants, the inversion of SO₂ performed relatively poorly, mainly because emission reduction trends from anthropogenic sources were not sufficiently represented in the reanalysis dataset. Overall, the TL-CNN model provides more accurate pollutant concentration fields for offshore regions with limited observations, offering strong support for marine atmospheric environment studies and assessments of marine ecological effects. It also demonstrates the potential of combining deep learning and transfer learning in atmospheric chemistry research. Full article

Hybrid cascaded multi-terminal HVDC systems represent a significant advancement in HVDC transmission technology. A notable real-world implementation of this concept is the bipolar hybrid cascaded multi-terminal high voltage direct current (MTDC) project in China, which successfully transmits hydropower from Baihetan to Jiangsu. This system combines MMCs for system support with LCCs for high-power transmission, offering both flexibility and efficiency in long-distance power delivery. This research explores the characteristics of main DC fault types in such systems, classifying faults based on sections and modes while analyzing their unique outcomes depending on DC fault locations. By focusing on the DC-side terminal behavior of the MMCs and LCCs, the main response processes to asymmetrical DC faults are investigated in detail. This study offers a detailed analysis of asymmetrical DC faults in bipolar HVDC systems, proposing a new classification based on fault characteristics such as current, voltage, active power, and reactive power. A supporting theoretical analysis is also presented. It identifies specific control demands needed for effective fault mitigation. PSCAD/EMTDC simulation results demonstrate that DC faults with similar characteristics can be consistently grouped into distinct categories by this new classification method. Each category is further linked to specific control demands, providing a strong basis for developing advanced protection strategies and practical solutions that enhance the stability and reliability of hybrid cascaded HVDC systems. Full article

Extreme rainfall events characterized by small catchments with high-velocity flows pose critical challenges to infrastructure resilience, particularly the rail infrastructure, due to its partial location near rivers and in mountainous regions, and the limited availability of alternative routes. This can lead to severe damages, often resulting in long-term route closures. To mitigate flash flood damage, detailed information about affected structures and damage processes is necessary. Therefore, this study presents a newly developed multi-criteria flash flood damage assessment framework for the rail infrastructure and a QGIS-based analysis of the most frequent damages. Applying the framework to Eifel route damages in Western Germany after the July 2021 flood disaster shows that nearly 45% of the damages affected the track superstructure, especially tracks and bedding. Additionally, power supply systems, sealing and drainage systems, as well as railway overpasses or bridges, were impacted. Approximately 30% of the railway section showed washout of ballast, gravel and soil. In addition, deposit of wood or stones occurred. Most damages were classified as minor (47%) or moderate (34%). Furthermore, damaged track sections were predominantly located within a 50 m distance to the Urft river, whereas undamaged track sections are often located at a greater distance to the Urft river. These findings indicate that the proposed framework is highly applicable to assess and classify damages. Critical elements and relations could be identified and can help to adapt standards and regulations, as well as to develop preventive measures in the next step. Full article

Silicon carbide (SiC) is a promising semiconductor material for electronics and photonics. Ultrafast laser processing of SiC enables three-dimensional nanostructuring, enriching and expanding the functionalities of SiC devices. However, challenges arise in delivering uniform, high-aspect-ratio (length-to-width) nanostructures due to difficulties in confining light energy at the nanoscale while simultaneously regulating intense photo modifications. In this study, we report the controllable growth of long-distance, high-straightness, and high-parallelism multifilament structures in SiC using ultrafast laser processing. The mechanism is the formation of femtosecond multifilaments through the nonlinear effects of clamping equilibrium, which allow highly confined light to propagate without diffraction in parallel channels, further inducing high-aspect-ratio nanostripe-like photomodifications. By employing an elliptical Gaussian beam—rather than a circular one—and optimizing pulse durations to stabilize multifilaments with regular positional distributions, the induced multifilament structures can reach a length of approximately 90 μm with a minimum linewidth of only 28 nm, resulting in an aspect ratio of over 3200:1. Raman tests indicate that the photomodified regions consist of amorphous SiC, amorphous silicon, and amorphous carbon, and photoluminescence tests reveal that silicon vacancy color centers could be induced in areas with lower light power density. By leveraging femtosecond multifilaments for diffraction-less light confinement, this work proposes an effective method for manufacturing deep-subwavelength, high-aspect-ratio nanostructures in SiC. Full article

This study addresses autonomous underwater vehicle (UUV) recovery onto dynamic docking stations by proposing a fork-column recovery control system with a segmented docking strategy (long-distance approach + guided descent). To enhance model fidelity, transmission lag of actuators is captured by a specified transfer function, and nonlinear dynamics are characterized as an improved quasi-linear parameter-varying (QLPV) model. An adaptive variable–prediction–step mechanism was designed to accommodate different phases of acoustic–optical guided recovery. A model predictive controller (MPC) was developed based on an improved dynamic model to effectively handle complex constraints during the recovery process. Simulation and physical experiments demonstrated that the proposed system significantly reduces errors, among which the control accuracy (tracking error under disturbance < 0.3 m) and docking success rate (>95%) are notably superior to traditional methods, providing a reliable solution for the dynamic recovery of unmanned underwater vehicles (UUVs). Full article

Due to the increase in global energy demand, as well as environmental concerns, hydrogen presents itself as a promising energy source. Liquid hydrogen is more suited for long-distance transportation, but hydrogen liquefaction is an energy-intensive process, and many studies have been published proposing more efficient liquefaction cycles. In this study, simple hydrogen liquefaction cycles like Claude, pre-cooled Linde–Hampson (PLH), single mixed refrigerant (SMR), and dual mixed refrigerant (DMR) were assessed regarding the influence of the cycle’s high pressure on energy efficiency, exergy destruction, and its distribution along the equipment. Among the main results, Claude presented the best specific energy consumption (SEC) of 16.47 kWh/kg_LH, followed by DMR with an SEC of 17.30 kWh/kg_LH, SMR with 17.58 kWh/kg_LH, and finally PLH, with an SEC of 45.07 kWh/kg_LH. The exergy efficiency followed the same pattern as the SEC, with Claude having the lowest exergy destruction, followed by DMR and SMR with close exergy destruction, and finally PLH. Nonetheless, although cycles were not optimized in evaluating the effect of increasing the high pressure, which constrains the direct applicability of the result found, especially in the pre-cooled cycles, the analysis provides valuable insights into the sensitivity of cycle performance. The method and its findings provide the basis for further studies, including optimization steps. Full article

While backpropagation (BP) has long served as the cornerstone of training deep neural networks, it relies heavily on strict differentiation logic and global gradient information, lacking biological plausibility. In this paper, we systematically present a novel neural network training paradigm that depends solely on signal propagation, which we term Backward Signal Propagation (BSP). The core idea of this framework is to reinterpret network training as a symmetry-driven process of discovering inverse causal relationships. Starting from symmetry principles, we define symmetric differential equations and leverage their inherent properties to implement a learning mechanism analogous to differentiation. Furthermore, we introduce the concept of causal distance, a core invariant that bridges the forward propagation and inverse learning processes. It quantifies the influence strength between any two elements in the network, leading to a generalized form of the chain rule. With these innovations, we achieve precise, pointwise adjustment of model parameters. Unlike traditional BP, the BSP method enables parameter updates based solely on local signal features. This work offers a new direction toward efficient and biologically plausible learning algorithms. Full article

Pre-extraction gas technology is commonly used in coal mines to extract gas from single coal seams, initial protective layers, and both unprotected and protected coal seams. With the development of drilling equipment, directional long drilling, pre-extraction, coal seam gas technology has been widely applied, and negative pressure extraction is one of the key factors affecting the effectiveness of directional long drilling gas extraction. In order to determine the reasonable length of directional long boreholes, studying the negative pressure distribution and time-varying rules within such boreholes is of great significance for guiding later borehole layout and gas extraction. The COMSOL Multiphysics software v.5.3. was used to couple and solve the dynamic model of temperature, stress, and seepage in coal-containing gas, as well as the mathematical model of negative pressure attenuation in directional long boreholes. The gas pressure distribution in the coal surrounding the directional long borehole and the distribution and time-varying law of negative pressure in the borehole were studied. Then, the distribution and time-varying law of negative pressure in directional long borehole extraction were tested on site. Research has shown that the negative pressure attenuation during directional long drilling has a relatively small impact on the effectiveness of coal gas extraction, while the negative pressure at the hole opening is the key factor affecting the effectiveness of gas extraction. In the early stage of extraction, as the drilling depth increases, the pressure loss inside the hole increases and the negative pressure inside the hole decreases. As the extraction time becomes longer, the pressure loss inside the borehole decreases and the negative pressure inside the borehole gradually returns to the negative pressure value at the orifice. The gas flow velocity inside the extraction borehole gradually increases from the bottom of the hole to the hole opening, and the flow velocity at the bottom of the hole remains basically constant. The gas flow velocity inside the hole gradually decreases with the extension of extraction time, and the smaller the distance from the extraction hole opening, the greater the flow attenuation. The collapse of drilling holes during extraction affects the attenuation of negative pressure inside the hole in the short term. As the extraction time increases, the impact of the collapse on the negative pressure inside the hole is limited. The temperature of coal can significantly affect the negative pressure and gas flow distribution inside the pores. Considering the temperature effect, the gas flow velocity inside the pores is higher and the pressure loss is lower in the short term. On-site tests have determined that the depth of ultra-long directional drilling holes is shallower than 327 m, and the negative pressure changes inside the borehole are not significantly different from the negative pressure at the hole opening. The negative pressure stabilization speed near the hole opening and bottom is fast, usually reaching its peak within 3–10 min. The negative pressure stabilization process from the borehole opening to the hole bottom shows a “fast slow fast” trend. When using double-sided extraction, the time for negative pressure to reach stability is significantly shortened compared to single-sided extraction, and double-sided extraction is beneficial for improving the effectiveness of coalbed methane extraction. Full article

A damping spacer rod is a key protective device in ultrahigh voltage transmission lines, which not only keeps the distance of split wires and limits the whipping and collision caused by the relative motion between sub-wires, but also inhibits the vibration of wires. This study aims to solve the problem of typical faults, such as loose wire clamps, that are prone to occur in damping isolation rods during long-term operation in ultra-high voltage transmission lines. Taking the spacer rod FGZ-450/34B as the object, a new high-pressure casting process for spacer rod frames is explored. The spacer rods were simulated by using the ABAQUS finite element software to predict the stress distribution and identify the dangerous sections. Based on this, the mold process was optimized to avoid die-casting defects. Meanwhile, mechanical property tests were carried out on the products produced by the two types of molds. The research finds that by optimizing the mold process, the die-casting quality of the dangerous section of the spacer rod can be effectively improved, and the best high-pressure die-casting scheme has been obtained through comparison. This research achievement provides technical support for enhancing the anti-vibration performance, anti-loosening reliability, short-circuit current thermal shock resistance, and anti-ultraviolet aging performance of damping isolation rods. It is of great significance for ensuring the stable operation of ultra-high voltage transmission lines and improving the production process level of damping isolation rods. Full article

In this study, a novel casting–forging hybrid forming technique, introducing torsional shear during squeeze casting, was investigated. This approach enhances the forming efficiency and refines the grain size. Using a finite element method coupled with a viscoplastic self-consistent model, a macro-microscopic simulation model of the squeeze casting torsional forming process was established. The introduction of torsional shear in SQ results in a more uniform distribution and lower equivalent stress, thereby improving the forming efficiency. Additionally, the shear force is increased during the forming process, the shear force is greater with the distance from the torsional axis increasing, and the great shear force could be maintained for a long time. Ultimately, this leads to a thinner wall thickness, finer secondary dendrites, and eutectic Si in the workpiece. During the SQT process, for introducing $(1\overline{1}1)[10\overline{1}]$ slip during the late stage of deformation, a significant shift in grain rotation directions happens and the grain rotation angles increase, finally attributed to the development of the $(1\overline{1}\overline{1})[0\overline{1}1]$ texture. Full article

Phase molybdenum disulfide (1T-MoS₂) holds significant promise as an anode material for sodium-ion batteries (SIBs) due to its metallic conductivity and expanded interlayer distance. However, the practical application of 1T-MoS₂ is hindered by its inherent thermodynamic metastability, which poses substantial challenges for the synthesis of high-purity, long-term stable 1T phase MoS₂. Herein, a synergetic ethanol molecule intercalation and electron injection engineering is adopted to induce the formation and stabilization of 1T-MoS₂ (E-1T MoS₂). The obtained E-1T MoS₂ consists of regularly arranged sphere-like ultrasmall few-layered 1T-MoS₂ nanosheets with expanded interlayer spacing. The high intrinsic conductivity and enlarged interlayer spacing are greatly favorable for rapid Na⁺ or e⁻ transport. The elaborated nanosheets structure can effectively relieve volume variation during Na⁺ intercalating/deintercalating processes, shorten transport path of Na⁺, and enhance diffusion kinetics. Furthermore, a novel sodium reaction mechanism involving the formation of MoS₂ nanoclusters during cycling is revealed to produce the higher surface pseudocapacitive contribution to Na⁺ storage capacity, accelerating Na⁺ reaction kinetics, as confirmed by the kinetics analysis and ex-situ structural characterizations. Consequently, the E-1T MoS₂ electrode exhibits an excellent sodium storage performance. This work provides an important reference for synthesis and reaction mechanism analysis of metastable metal sulfides for advanced SIBs. Full article

Cooperative breeding, or helping behavior, has long been recognized in birds. An ignored dichotomy, however, separates the helping found in many individuals of some long-lived, sedentary species from the helping occasionally found in the territories of isolated breeding pairs of some short-lived, long-distance migrant species. Both types of helping are called “cooperative breeding” in the literature. However, recognizing a dichotomy of “frequent” versus “infrequent” cooperative breeding would help justify the study of infrequent helping as a distinct discipline. Cooperative breeding in Wilson’s Warblers is infrequent, and among the unique behaviors found during this study were (1) solicitations by helper males, which aborted host male attacks and apparently initiated territorial acceptance, (2) an absence of sexual aggression between helper males and fertile host females, (3) attacks by helper males on intruding males during host female nest building, (4) helper males singing with impunity when host males were absent from territories, but being attacked when host males were present, and (5) a single male simultaneously serving as a helper in four adjacent host territories. Infrequent helping has essentially been ignored in studies and summaries of cooperative breeding. However, recognizing and studying infrequent helping as a distinct behavioral process could reveal interactions between helping and population ecology. Thus, infrequent cooperative breeding detected in a breeding population could reveal territorial saturation and could indicate that the population is likely ecologically healthy. Full article

Appropriate accelerated deterioration methods are crucial for studying the deterioration behavior of reinforced concrete linings in subsea tunnels. To investigate the deterioration mechanisms of reinforced concrete (RC) structures in marine environments, this study employed the electromigration method to simulate accelerated chloride-induced corrosion of steel reinforcement. The results demonstrate that under a direct current (DC) electric field, chloride ions migrate directionally and accumulate on the side of the steel facing the chloride source, successfully inducing non-uniform corrosion features that closely resemble those in natural environments. The side facing chloride ingress exhibited severe corrosion and significant cross-sectional loss, while the shielded side remained largely intact. The experimental process clearly reveals that the applied electric field does not directly initiate corrosion of the steel reinforcement before chloride ions migrate to its surface. Furthermore, analysis of experimental parameters showed that symmetrical perforations on electrode plates are crucial for a uniform electric field, while perforation ratio and electrode–specimen distance have a minor influence. The average chloride penetration depths corresponding to electrode plate perforation areas of 5.5%, 15%, 25.5%, and 38.1% were measured as 1.63 cm, 1.67 cm, 1.57 cm, and 1.57 cm, respectively. This research confirms electromigration as an efficient and reliable technique for accelerated corrosion testing, providing a significant theoretical basis for assessing and predicting the long-term durability of marine engineering structures. Full article