Search Results (14,172)

Hyperventilation (HV) is an activation technique performed during clinical practices to trigger epileptiform activities, supporting the neurophysiological evaluation of patients with epilepsy. Although the role of HV has often been questioned, especially in the case of focal epilepsy, no studies have ever assessed how cortical structures respond to such a maneuver via intracranial EEG recordings. This work aims to fill this gap by evaluating the HV effects on the Stereo-EEG (SEEG) signals from a cohort of 10 patients with drug-resistant focal epilepsy. We extracted multiple quantitative metrics from the SEEG signals and compared the results obtained during HV, awake status, non-REM sleep, and seizure onset. Our findings show that the cortical connectivity, estimated via the phase transfer entropy (PTE) algorithm, strongly increases during the HV maneuver, similar to non-REM sleep. The opposite effect is observed during seizure onset, as ictal transitions involve the desynchronization of the brain structures within the epileptogenic zone. We conclude that HV promotes a conductive environment that may facilitate the propagation of epileptiform activities but is not sufficient to trigger seizures in focal epilepsy. Full article

Ship wake detection stands as a pivotal task in marine environment monitoring. The main challenge in ship wake detection is to improve detection accuracy and mitigate false alarms. To address this challenge, a novel procedure for ship wake detection in a single SAR image is proposed in this study. Initially, an entropy distance similarity criterion is designed to measure nonlocal image patch similarity. Based on the proposed criterion, a low-rank and sparse decomposition method is modified using nonlocal similar patch matrix construction to separate the sparse wake. Subsequently, a field-of-experts (FOE) model is introduced to generate a series of multi-view wake feature maps, which are fused to construct an enhanced feature map. The sparse wake is further enhanced in the Radon domain with the enhanced feature map. The experimental results demonstrate the effectiveness of the proposed method on real SAR ship wake images. Full article

In this paper, a method for augmenting samples of side-scan sonar seafloor sediment images based on CBAM-BCEL1-INGAN is proposed, aiming to address the difficulties in acquiring and labeling datasets, as well as the insufficient diversity and quantity of data samples. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the residual blocks of the INGAN generator to enhance the learning of specific attributes and improve the quality of the generated images. Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. Finally, augmented samples are input into an AlexNet classifier to verify their authenticity. Experimental results demonstrate the excellent performance of the method in generating images of coarse sand, gravel, and bedrock, as evidenced by significant improvements in the Frechet Inception Distance (FID) and Inception Score (IS). The introduction of the CBAM and BCEL1 loss function notably enhances the quality and details of the generated images. Moreover, classification experiments using the AlexNet classifier show an increase in the recognition rate from 90.5% using only INGAN-generated images of bedrock to 97.3% using images augmented using our method, marking a 6.8% improvement. Additionally, the classification accuracy of bedrock-type matrices is improved by 5.2% when images enhanced using the method presented in this paper are added to the training set, which is 2.7% higher than that of the simple method amplification. This validates the effectiveness of our method in the task of generating seafloor sediment images, partially alleviating the scarcity of side-scan sonar seafloor sediment image data. Full article

The Remote Sensing Ecological Index (RSEI) model is widely used for large-scale, rapid Ecological Environment Quality (EEQ) assessment. However, both the RSEI and its improved models have limitations in explaining the EEQ with only two-dimensional (2D) factors, resulting in inaccurate evaluation results. Incorporating more comprehensive, three-dimensional (3D) ecological information poses challenges for maintaining stability in large-scale monitoring, using traditional weighting methods like the Principal Component Analysis (PCA). This study introduces an Improved Remote Sensing Ecological Index (IRSEI) model that integrates 2D (normalized difference vegetation factor, normalized difference built-up and soil factor, heat factor, wetness, difference factor for air quality) and 3D (comprehensive vegetation factor) ecological factors for enhanced EEQ monitoring. The model employs a combined subjective–objective weighting approach, utilizing principal components and hierarchical analysis under minimum entropy theory. A comparative analysis of IRSEI and RSEI in Miyun, a representative study area, reveals a strong correlation and consistent monitoring trends. By incorporating air quality and 3D ecological factors, IRSEI provides a more accurate and detailed EEQ assessment, better aligning with ground truth observations from Google Earth satellite imagery. Full article

Most humanitarian logistics research focuses on immediate response efforts, leaving a gap regarding the long-term socio-economic impacts of post-tragedy financial aid. Our research investigates the Brumadinho landslide tragedy in Minas Gerais, Brazil, analyzing the effectiveness of financial aid in fostering sustainable recovery and resilience in affected communities. We employ a hybrid multi-criteria decision-making (MCDM) and machine learning model to quantitatively assess the socio-economic impact on affected municipalities. Using social responsibility indices from official state government datasets and data from the PTR transparency initiative—a financial aid program determined by the Judicial Agreement for Full Reparation and operationalized by FGV Projetos, which allocates USD 840 million for the reparation of damages, negative impacts, and socio-environmental and socio-economic losses—our analysis covers all municipalities in Minas Gerais over 14 years (10 years before and 4 years after the tragedy). We determine a final socio-economic performance score using the max entropy hierarchical index (MEHI). Additionally, we assess the efficiency of the PTR financial aid in affected municipalities through examining MEHI changes before and after the transfers using a difference-in-differences (DiD) approach. Our findings reveal both direct and indirect impacts of the tragedy, the efficacy of financial aid distribution, and the interplay of various socio-economic factors influencing each municipality’s financial health. We propose policy recommendations for targeted and sustainable support for regions still coping with the long-term repercussions of the Brumadinho landslide. Full article

To improve the water resources carrying capacity of the Xiangjiang River Basin and achieve sustainable development, this article evaluates and predicts the Xiangjiang River Basin’s water resources carrying capacity level based on county-level units. This article takes 44 county-level units in the Xiangjiang River Basin as the evaluation target, selects TOPSIS and the entropy weight method to determine weights, calculates the water resources carrying capacity level of the evaluation sample, uses a BP neural network model to calculate the predicted water resources carrying capacity level for the next 5 years, and adds the GIS method for spatiotemporal analysis.(1) The water resources carrying capacity of the Xiangjiang River Basin has remained relatively stable for a long period, with overloaded areas being the majority. (2) There are relatively significant spatial differences in the carrying capacity of water resources: Zixing City, located upstream of the tributary, is far ahead due to its possession of the Dongjiang Reservoir; the water resources carrying capacity in the middle and lower reaches (northern region) is generally higher than that in the upper reaches (southern region). (3) According to the BP neural network model prediction, the water resources carrying capacity of the Xiangjiang River Basin will maintain a stable development trend in 2022, while areas such as Changsha and Zixing City will be in a critical state, and other counties and cities will be in an overloaded state.This study has important references value for the evaluation and early warning work of the Xiangjiang River Basin and related research, providing a scientific and systematic evaluation method and providing strong support for water resource management and planning in Hunan Province and other regions. Full article

The characteristics and influencing factors of gas production in CBM wells are analyzed based on the field geological data and the productivity data of coalbed methane (CBM) wells in the Zhengbei block, and then the favorable areas are divided. The results show that the average gas production of No. 3 coal seam CBM wells in the study area is in the range of 0~1793 m³/d, with an average of 250.97 m³/d; 80% of the wells are less than 500 m³/d, and there are fewer wells above 1000 m³/d. The average gas production is positively correlated with gas content, critical desorption pressure, permeability, Young’s modulus, and Schlumberger ratio, and negatively correlated with fracture index, fault fractal dimension, Poisson’s ratio, and horizontal stress difference coefficient. The relationship between coal seam thickness and the minimum horizontal principal stress is not strong. The low-yield wells have the characteristics of multiple pump-stopping disturbances, unstable casing pressure control, overly rapid pressure reduction in the single-phase flow stage, sand and pulverized coal production, and high-yield water in the later stage during the drainage process. It may be caused by the small difference in compressive strength between the roof and floor and the coal seam, and the small difference in the Young’s modulus of the floor. The difference between the two high-yield wells is large, and the fracturing cracks are easily controlled in the coal seam and extend along the level. The production control factors from strong to weak are as follows: critical desorption pressure, permeability, Schlumberger ratio, fault fractal dimension, Young’s modulus, horizontal stress difference coefficient, minimum horizontal principal stress, gas content, Poisson’s ratio, fracture index, coal seam thickness. The type I development unit (development of favorable areas) of the Zhengbei block is interspersed with the north and south of the block on the plane, and the III development unit is mainly located in the east of the block and near the Z-56 well. The comprehensive index has a significant positive correlation with the gas production, and the prediction results are accurate. Full article

Medium-entropy alloys (MEAs) exhibit exceptional mechanical properties, thermal properties, and irradiation resistance, making them promising candidates for aerospace and nuclear applications. This study utilized molecular dynamics simulations to examine the defect behavior in PdTi alloys under various irradiation conditions. Simulations were performed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) with the modified embedded-atom method (MEAM) potential to describe interatomic interactions. Various temperatures, primary knock-on atom (PKA) energies, and elemental ratios were tested to understand the formation and evolution of defects. The results show that compared to pure Pd, PdTi alloys with increased entropy exhibit significantly enhanced irradiation resistance at higher temperatures and PKA energies. This study explored the impact of different elemental ratios, including Pd, PdTi_1.5, PdTi, and Pd_1.5Ti. Findings indicate that increasing the Pd concentration enhances the alloy’s irradiation resistance, improving mobility and recombination rates of defect clusters. A one-to-one Pd-to-Ti ratio demonstrated optimal performance. Temperature analysis revealed that at 300 K and 600 K, PdTi alloys exhibit excellent irradiation resistance at a PKA energy of 30 keV. However, as the temperature rises to 900 K, the irradiation resistance decreases slightly, and at 1200 K, the performance is likely to decline further. This study offers some useful insights into the irradiation evolution and radiation resistance of PdTi medium-entropy alloys, which may help inform their potential applications in the nuclear field and contribute to the further development of MEAs in this area. Full article

In order to improve the magnetocaloric properties of MnNiSi-based alloys, a new type of high-entropy magnetocaloric alloy was constructed. In this work, Mn_0.6Ni_1−xSi_0.62Fe_0.4Co_xGe_0.38 (x = 0.4, 0.45, and 0.5) are found to exhibit magnetostructural first-order phase transitions from high-temperature Ni₂In-type phases to low-temperature TiNiSi-type phases so that the alloys can achieve giant magnetocaloric effects. We investigate why c_hexagonal/a_hexagonal (c_hexa/a_hexa) gradually increases upon Co substitution, while phase transition temperature (T_tr) and isothermal magnetic entropy change (ΔS_M) tend to gradually decrease. In particular, the x = 0.4 alloy with remarkable magnetocaloric properties is obtained by tuning Co/Ni, which shows a giant entropy change of 48.5 J∙kg⁻¹K⁻¹ at 309 K for 5 T and an adiabatic temperature change (ΔT_ad) of 8.6 K at 306.5 K. Moreover, the x = 0.55 HEA shows great hardness and compressive strength with values of 552 HV2 and 267 MPa, respectively, indicating that the mechanical properties undergo an effective enhancement. The large ΔS_M and ΔT_ad may enable the MnNiSi-based HEAs to become a potential commercialized magnetocaloric material. Full article

This study evaluates the impact of green finance on agricultural non-point source pollution control and emission reduction in 30 Chinese provinces from 2005 to 2022. Utilizing the entropy value method and the unit survey inventory method, the research measures the levels of green finance development and agricultural non-point source pollution. It employs a mediation effect model to empirically assess the pollution control efficacy of green finance and to elucidate the mechanisms underlying its influence. The findings indicate that green finance development significantly curtails agricultural non-point source pollution emissions. This conclusion is still valid after a series of robustness tests. The results of mechanism analysis show that environmental regulation and land transfer are important channels for green finance to reduce agricultural non-point source pollution. However, the slowing effect of green finance is stronger in provinces where the economic development level is still in the catch-up zone. Consequently, this study suggests strengthening green finance infrastructure in rural areas, coordinating green finance and environmental regulation policies, optimizing land transfer systems to promote scale management, and developing differentiated green finance policies based on regional economic development levels. These measures aim to augment the role of green finance in pollution treatment and emission reduction, thereby optimizing the green financial system, advancing environmental protection, and fostering sustainable development in China’s agricultural sector. Full article

Deep learning models excel in interpreting the exponentially growing amounts of remote sensing data; however, they are susceptible to deception and spoofing by adversarial samples, posing catastrophic threats. The existing methods to combat adversarial samples have limited performance in robustness and efficiency, particularly in complex remote sensing scenarios. To tackle these challenges, an unsupervised domain adaptation algorithm is proposed for the accurate identification of clean images and adversarial samples by exploring a robust generative adversarial classification network that can harmonize the features between clean images and adversarial samples to minimize distribution discrepancies. Furthermore, linear polynomial loss as a replacement for cross-entropy loss is integrated to guide robust representation learning. Additionally, we leverage the fast gradient sign method (FGSM) and projected gradient descent (PGD) algorithms to generate adversarial samples with varying perturbation amplitudes to assess model robustness. A series of experiments was performed on the RSSCN7 dataset and SIRI-WHU dataset. Our experimental results illustrate that the proposed algorithm performs exceptionally well in classifying clean images while demonstrating robustness against adversarial perturbations. Full article

The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCH–copula causality methods for daily Bitcoin, Fintech, energy consumption, and CO₂ emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, Rényi, and Tsallis entropy measures; Kolmogorov–Sinai complexity; Hurst–Mandelbrot and Lo’s R/S tests; and Phillips’ and Geweke and Porter-Hudak’s fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCH–copula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO₂ emissions as well as causality from energy consumption to CO₂ emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech. Full article

Exploring the coordination of agricultural water resources (W), cultivated land (L), and the ecoenvironment (E) system is crucial for sustainable agriculture in the North China Plain (NCP). However, the synergistic effects of this composite system remain unclear. Coupling coordination degrees (CCDs) of 53 cities in the NCP for the years 2011, 2015, and 2020 were evaluated using the TOPSIS model, and the coupling coordination model, combined with the analytic hierarchy process and entropy weight method. The evaluation results were further analyzed to identify obstacle factors. The findings reveal the following: (1) The comprehensive development level showed a fluctuating upward trend, with closeness values ranging from 0.418 to 0.574 in 2020, indicating an improvement of 14.6–52.3% compared to 2011. The coefficient of variation (CV) for each province rose from 12.65% in 2011 to 13.64% and subsequently declined to 9.12% by 2020. (2) Between 2011 and 2020, CCDs of the W–L–E composite system exhibited a consistent upward trend. In 2020, regions with intermediate or better coordination accounted for 34.0%, and were primarily located in Jiangsu Province, the southern part of Anhui Province, the northwestern part of Shandong Province, and the municipalities of Beijing and Tianjin. (3) In 2011 and 2015, significant obstacle factors included the water quality compliance rate and the per capita disposable income of rural residents, although these were not primary obstacles in 2020. The water supply modulus and multiple cropping index were major obstacle factors in 2011, 2015, and 2020. Developing water-appropriate cropping patterns based on regional water resource endowment is the essential path for the sustainable and coordinated development of water, land, and ecology in the NCP. Full article

The oxidation of six NbTi-i refractory medium- and high-entropy alloys (NbTi + Ta, NbTi + CrTa, NbTi + AlTa, NbTi + AlMo, NbTi + AlMoTa and NbTi + AlCrMo) were investigated at 1000 °C for 20 h. According to our observation, increased Cr content promoted the formation of protective CrNbO₄ and Cr₂O₃ oxides in NbTi + CrTa and NbTi + AlCrMo, enhancing oxidation resistance. The addition of Al resulted in the formation of AlTi-rich oxide in NbTi + AlTa. Ta addition resulted in the formation of complex oxides (MoTiTa₈O₂₅ and TiTaO₄) and decreased oxidation resistance. Meanwhile, Mo’s low oxygen solubility could be beneficial for oxidation resistance while protective Cr₂O₃/CrNbO₄ layers were formed. In NbTi + Ta, NbTi + AlTa and NbTi + CrTa, a considerable quantity of Ti-rich oxide was observed at the interdendritic region. In NbTi + AlCrMo, the enrichment of Cr and Ti at the interdendritic region could fasten the rate of oxidation. Compared to the recent research, NbTi + AlCrMo alloy is a light-weight oxidation-resistant alloy (weight gain of 1.29 mg/cm² at 1000 °C for 20 h and low density (7.2 g/cm³)). Full article

The Han River Ecological Economic Belt (HREEB) has a substantial amount of water resources; however, its distribution is uneven, and issues such as seasonal and engineering water shortages are prevalent. This necessitates a thorough assessment of the current water resource situation and trends in water resource carrying capacity (WRCC) to provide scientific support for the rational allocation of water resources. This study employed the RAGA-PP model to establish a WRCC evaluation index system composed of four subsystems: water resources, economy, society, and the ecological environment. The WRCC of the 17 major cities in the HREEB was evaluated from 2008 to 2022. The differentiation method was introduced to compare the reliability of the RAGA-PP model with three evaluation methods: the entropy weight TOPSIS method, the rank sum ratio method, and the principal component analysis method. In addition, an obstacle degree model was introduced to analyze the factors influencing WRCC enhancement. The results indicated the following. (1) In the differentiation test of the four models, the RAGA-PP model was found to have the highest differentiation value, and the results showed that it was more reliable in the WRCC evaluation of HREEB. (2) WRCC in the HREEB underwent significant changes between 2008 and 2022. (3) The WRCC in Shiyan and Wuhan, which are located in the eastern part of the HREEB, were high in Hubei, low in four cities in Henan, and satisfactory in three cities in Shaanxi. (4) The carrying capacity of the subsystems of the cities in the HREEB exhibited fluctuating changes with obvious internal variations. (5) The problems in the WRCC guideline layer were consistent across all cities in the HREEB, with limited per capita water resources being the primary issue in the indicator layer. Assessing WRCC is essential for achieving sustainable water resource use and high-quality regional development. Full article