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The rich feature information contained in the diverse remote sensing data has also exhibited growing potential in the field of image classification. However, the processing of multi-feature data still grapples with the challenges posed by the “curse of dimensionality” and the high computational costs. This paper proposes a remote sensing feature parameter selection method. This method is based on the Gini index of a Random Forest (RF) classifier and employs a 10% threshold decision to identify the optimal combination of remote sensing feature parameters. First, spectral features, texture features, temperature features, elevation features, and principal component features are selected to create a stack of remote sensing images. Second, multiple sets of decision trees are established to cross-validate the contributions of various feature parameters. The feature rankings are determined based on the normalized mean of feature importance. Subsequently, a threshold isset to filter out remote sensing feature parameters that meet the criteria. Finally, an iterative parameter optimization process is carried out to obtain the optimal combination of remote sensing feature parameters. The Landsat 8 image covering Hangzhou Bay in eastern China and the Sentinel-2 remote sensing image of Yancheng Nature Reserve in Jiangsu, China were selected for experiments. The results show that the feature parameters of the remote sensing image screened by the method in this paper are representative, and the proposed method demonstrates strong adaptability and generalization performance in complex environments. The study has important guiding significance for regional spatial planning and sustainable development.

Abstract Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in the monitoring of global carbon. Environmental variables play a significant role in improving the accuracy of the SOC content estimation model. This study focuses on modeling methodologies and environmental variables, which significantly influence the SOC content estimation model. The modeling methods used in this research comprise multiple linear regression (MLR), partial least squares regression (PLSR), random forest, and support vector machines (SVM). The analyzed environmental variables include terrain, climate, soil, and vegetation cover factors. The original spectral reflectance (OSR) of Landsat 5 TM images and the spectral reflectivity after the derivative processing were combined with the above environmental variables to estimate SOC content. The results showed that: (1) The SOC content can be efficiently estimated using the OSR of Landsat 5 TM, however, the derived processing method cannot significantly improve the estimation accuracy. (2) Environmental variables can effectively improve the accuracy of SOC content estimation, with climate and soil factors producing the most significant improvements. (3) Machine learning modeling methods provide better estimation accuracy than MLR and PLSR, especially the SVM model which has the highest accuracy. According to our observations, the best estimation model in the study area was the “OSR + SVM” model (R2 = 0.9590, RMSE = 13.9887, MAE = 10.8075), which considered four environmental factors. This study highlights the significance of environmental variables in monitoring SOC content, offering insights for more precise future SOC assessments. It also provides crucial data support for soil health monitoring and sustainable agricultural development in the study area.

Abstract Visceral adiposity index (VAI) is a reliable indicator of visceral adiposity. However, no stu-dies have evaluated the association between VAI and DKD in US adults with diabetes. Theref-ore, this study aimed to explore the relationship between them and whether VAI is a good pr-edictor of DKD in US adults with diabetes. Our cross-sectional study included 2508 participan-ts with diabetes who were eligible for the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. Univariate and multivariate logistic regression were used to an-alyze the association between VAI level and DKD. Three models were used to control for pot-ential confounding factors, and subgroup analysis was performed for further verification. A tot-al of 2508 diabetic patients were enrolled, of whom 945 (37.68%) were diagnosed with DKD. Overall, the VAI was 3.36 ± 0.18 in the DKD group and 2.76 ± 0.11 in the control group. VAI was positively correlated with DKD (OR = 1.050, 95% CI 1.049, 1.050) after fully adjusting for co-nfounding factors. Compared with participants in the lowest tertile of VAI, participants in the highest tertile of VAI had a significantly increased risk of DKD by 35.9% (OR = 1.359, 95% CI 1.355, 1.362). Through subgroup analysis, we found that VAI was positively correlated with the occurrence of DKD in all age subgroups, male(OR = 1.043, 95% CI 1.010, 1.080), participants wit-hout cardiovascular disease(OR = 1.038, 95% CI 1.011, 1.069), hypertension (OR = 1.054, 95% CI 1.021, 1.090), unmarried participants (OR = 1.153, 95% CI 1.036, 1.294), PIR

Abstract In the realm of multiferroicity in 2D magnets, whether magnetic and polar skyrmions can coexist within a single topological entity has emerged as an important question. Here, we study Janus 2D magnets CrInX 3 (X=Se, Te) for a comprehensive investigation of the magnetic ground state, magnetic excited state, and corresponding ferroelectric polarization by first-principles electronic structure calculations and Monte Carlo simulations. Specifically, we have thoroughly elucidated the magnetic exchange mechanisms, and have fully exemplified the magnetic field dependence of the magnon spectrum. More importantly, our study reveals a previously unrecognized, remarkably large spin-spiral-induced ferroelectric polarization (up to 194.9 μC/m2) in both compounds. We propose an approach to identify polar skyrmions within magnetic skyrmions, based on the observed direct correlation between spin texture and polarization density. Elucidating this correlation not only deepens our understanding of magnetic skyrmions but also paves the way for innovative research in the realm of multiferroic skyrmions.

Highlights The interfacing between SnO2 and MXene alters electronic structures, shifting the d-band center in transition metals, enhancing catalytic efficiency by reducing electron filling in antibonding orbitals. A binder-free, ultrathin, laminar heterostructured interlayer on polypropylene separator is demonstrated. The ionic sieving mechanism and efficient adsorption–catalysis process enable deeper charge/discharge cycle and improved stability. The improved catalytic conversion and suppressed lithium dendrites formation enable a high loading of 7.5 mg cm−2 and an initial area capacity of 7.6 mAh cm−2.

Abstract Objective Evaluate efficacy, safety, and tolerability of adjunctive brivaracetam (BRV) in adult Asian patients with focal‐onset seizures (FOS). Methods Phase III, randomized, double‐blind, placebo‐controlled study (EP0083; NCT03083665) evaluating BRV 50 mg/day and 200 mg/day in patients (≥16–80 years) with FOS with/without secondary generalization (focal to bilateral tonic–clonic seizures) despite current treatment with 1 or 2 concomitant antiseizure medications. Following an 8‐week baseline, patients were randomized 1:1:1 to placebo, BRV 50 mg/day, or BRV 200 mg/day, and entered a 12‐week treatment period. Efficacy outcomes: percent reduction over placebo in 28‐day FOS frequency (primary); 50% responder rate in FOS frequency; median percent reduction in FOS frequency from baseline; seizure freedom during treatment period (secondary). Primary safety endpoints: incidences of treatment‐emergent adverse events (TEAEs); TEAEs leading to discontinuation; serious TEAEs. Results In this study, 448/449 randomized patients (mean age, 34.5 years; 53.8% female) received ≥1 dose of study medication (placebo/BRV 50 mg/BRV 200 mg/day: n = 149/151/148). Percent reduction over placebo in 28‐day adjusted FOS frequency was 24.5% (p = 0.0005) and 33.4% (p

Mangroves are one of the most important marine ecosystems globally, their spatial distribution is crucial for promoting mangrove ecosystems conservation, restoration, and sustainable managements. This study proposed a novel Unet-Multi-Scale High-Resolution Vision Transformer (UHRViT) model for classifying mangrove species using unmanned aerial vehicle (UAV-RGB), UAV-LiDAR, and Gaofen-3 Synthetic Aperture Radar (GF-3 SAR) images. The UHRViT utilized a multi-scale high-resolution visual Transformer as its backbone network and was designed to a multi-branch U-shaped network structure to extract features of different scales layer by layer, and to facilitate the interaction of high and low-level semantic information. We further verified the classification performance superiority of UHRViT model by comparing to HRViT and HRNetV2 algorithms. We also systematically investigated the effects of active–passive image combination ratios on mangrove communities mapping. The results revealed that: UAV-RGB images exhibited the better classification accuracy (mean F1-score＞95 %) for mangrove species than UAV-LiDAR and GF-3 SAR images; The classification performances and stability of UHRViT algorithm in the fifteen datasets outperformed the HRViT and HRNetV2 algorithms; Combining UAV-RGB with GF-3 SAR or UAV-LiDAR images respectively, both achieved better classifications than the single data source. Based on the UHRViT algorithm, the combination of UAV-RGB and UAV-LiDAR achieved the highest classification accuracy (Iou = 0.944, MIou = 50.2 %) for Avicennia corniculatum (AC). When the combination ratio of UAV-RGB with GF-3 SAR or UAV-LiDAR was 3:1, Avicennia marina and AC both obtained the optimal classification accuracy with average F1-scores of 98.19 % and 97.3 %, respectively. Our works revealed that the changes in the classification accuracies of mangrove communities under multi-sensor image combination ratios, and demonstrated that our model could effectively improve the classification accuracy of mangrove communities.

Mangroves face decline and degradation due to human activities and natural forces, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices that rely on multispectral image spectral characteristics suffer from limitations in terms of recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring.

China’s coastal mudflats, threatened by artificial reclamation and climate change, are undergoing drastic changes and their accurate mapping is important for their conservation and restoration. Traditional classification methods, which require large samples and complex classifiers, tend to have low computational efficiency and poor generalization ability; thus, they are unsuitable for the rapid and accurate extraction of coastal mudflats. This study proposes a Mudflat Index (MFI) based on hyperspectral images. MFI amplifies the difference in spectral characteristics between mudflats and other land cover types in intertidal environments, effectively improving the discrimination between coastal mudflats, salt marshes, mangroves, and muddy waters. Four typical coastal mudflat areas (i.e., the Yellow River Delta in Shandong, the Radial Sand Ridges of the South Yellow Sea in Jiangsu, Hangzhou Bay in Zhejiang, and the Qinzhou Bay-Nanliu River Estuary in Guangxi) based on ZY1-02D were selected as the study areas. The extraction accuracies in the four study areas are 97.60%, 96.88%, 97.16% and 96.97%, respectively. The further extraction experiments were calculated based on hyperspectral data from GF-5, PRISMA, and Hyperion. Sample datasets were produced using field surveys and Google Earth high-resolution imagery. Compared to the Hyperspectral Bare Soil Index (HBSI), Normalized Difference Bare Soil Index (NDBSI) and Microphytobenthos Index (MPBI), MFI demonstrates superior performance with average SDI value improvements of 0.82, 0.71 and 1.17, respectively, in distinguishing mudflats from other typical land cover types in the intertidal zone. The extraction results were also compared with those derived from Support Vector Machine (SVM) and Random Forest (RF) classifications, showing that MFI outperformed SVM and RF by an average of 1.52% and 0.58%. The results show that MFI can be applied to different hyperspectral remote sensing images and different areas of mudflat extraction. The MFI-based method is simple, fast and accurate at extracting the mudflat in the intertidal environment.

While the current research has achieved satisfactory results for the registration of single mode data, there has always been a significant challenge in the registration of multi-modal images due to the obvious nonlinear radiation differences caused by different imaging mechanisms and imaging time. For example, multi-temporal visible, visible-synthetic aperture radar, visible-near infrared, near infrared-short wave infrared, visible-MAP, etc. To address this problem, we propose a Robust and Accurate Feature Matching Method for Multi-modal Geographic Images Spatial Registration (RAMMR) to fully extract common key points between images, weaken the radiation difference between data, and finally accurately match more inliers to realize multi-modal image registration. Considering the influence of noise and edge information on key point extraction, RAMMR first constructs a new scale space by introducing the Side Window Filter (SWF); Then, we improve Harris algorithm to extract key points based on the SWF scale space; After that, we propose an enhanced log-polar descriptor based on the gradient angles and gradient amplitudes of the scale space, which effectively improves the quality of the descriptor and avoids the mismatch of key points; Based on the standard Euclidean distance, we design a re-match strategy to obtain the initial matching results, and Random Sample Consensus (RANSAC) is used to eliminate outliers. Finally, the affine transformation parameters are calculated based on inliers, and multi-modal image registration is realized. RAMMR is evaluated on different multi-modal datasets and compared with some state-of-art methods. The experimental results show that RAMMR accurately registers multi-modal geographic images and obtains comparative results compared with benchmark methods. Our source datasets are publicly available at https://github.com/RSmfmr/multimodal-dataset.

Abstract Rheumatoid arthritis (RA) is a chronic autoimmune disease with an unknown etiology. RA cannot be fully cured and requires lengthy treatment, imposing a significant burden on both individuals and society. Due to the lack of specific drugs available for treating RA, exploring a key new therapeutic target for RA is currently an important task. Activated fibroblast-like synoviocytes (FLSs) play a crucial role in the progression of RA, which release interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α resulting in abnormal inflammatory reaction in the synovium. A previous study has highlighted the correlation of m6A reader insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) with inflammation-related diseases in human. However, the role of IGF2BP2 in the inflammatory reaction of FLSs during RA progression has not been assessed. In this study, IGF2BP2 expression was decreased in the synovial tissues of RA patients and collagen-induced arthritis (CIA) rats. Intra-articular injection of an adeno-associated virus (AAV) vector overexpressing IGF2BP2 relieved paw swelling, synovial hyperplasia and cartilage destruction in CIA rats. IGF2BP2 overexpression also inhibited lipopolysaccharide (LPS)-mediated RA fibroblast-like synoviocytes (RA-FLSs) migration and invasion accompanied by a decreased level of inflammatory factors in vitro. Conversely, IGF2BP2 suppression promoted RA-FLSs migration and invasion with an elevated level of inflammatory factors in vitro. The sequencing result showed that glutathione S-transferase Mu 5 (GSTM5), a key antioxidant gene, was the target mRNA of IGF2BP2. Further experiments demonstrated that IGF2BP2 strengthened the stability of GSTM5 mRNA, leading to weakened inflammatory reaction and reduced expression of matrix metalloproteinase 9 and 13 (MMP9, MMP13). Therefore, IGF2BP2-GSTM5 axis may represent a potential therapeutic target for RA treatment.

ABSTRACTIn this study, foamed ceramics were successfully prepared using cyanide tailings (CTs) with a high sulfur content as the main raw material. The effects of sintering temperature, holding time, and heating rate on the bulk density, water absorption, porosity, pore structure, and compressive strength of foamed ceramics were systematically investigated. The results showed that the elevated sintering temperature promoted the generation of liquid phases, resulting in an obvious enlargement of the pore size and a decrease in bulk density. In addition, a similar evolution trend could be found in the pore size and bulk density with the extension of the holding time, which was attributed to the degrees of silicon carbide reaction. Foamed ceramics with small pore sizes could be prepared at a heating rate that did not exceed 4°C/min. Then, the higher heating rates could bring abnormally large pores and be detrimental to the mechanical properties of the foamed ceramics, which were proven to be related to the decomposition of FeS2 introduced by CTs during the heating process. In conclusion, the foamed ceramics prepared with CTs in this study provide effective solid waste utilization and have good application prospects in wall composite one-piece insulation panels.

Mangroves are vital coastal ecosystems that provide crucial links between land and sea. Tree height is a key indicator for assessing mangroves’ health status. Currently, there are still numerous challenges in estimating mangrove tree height. In this study, multiple deep learning and shallow machine learning regression models were developed to accurately estimate mangrove tree height using multi-dimensional Light Detection and Ranging (LiDAR) point clouds and their derivatives. We constructed a novel CNN_RepMLP model for mangrove tree height mapping. We also further verified the applicability of different types of regression models in estimating mangrove tree heights, and explored the influence of different LiDAR-derived features on the inversion accuracy for tree heights. The results indicated the following: (1) The CNN_RepMLP displayed satisfactory performance in the inversion of mangrove tree heights, and exhibited better robustness and generalization ability than the convolutional neural network (CNN) model. (2) Among the different LiDAR-derived feature combinations, combining height variables with intensity variables can not only mitigate the negative impact of height variables on inversion models, but also enhance the mangrove tree height inversion accuracy. (3) The ensemble learning framework with ExtraTrees as the meta-model can make better use of the differences and complementarities between different single base models, and exhibited better accuracy in estimating the height of mangrove trees compared with other ensemble learning models. (4) Multiple machine learning models based on multi-dimensional UAV-LiDAR point-cloud-derived features are suitable for the inversion of mangrove tree height. The CNN_RepMLP model outperformed the CNN and stacking ensemble learning models and had more detailed differentiation in terms of mangrove tree height. Its prediction results can more realistically reflect the spatial differentiation characteristics of mangrove tree height.

ABSTRACTMangroves are one of the important components of Earth's carbon sinks. The current problems of base-model composition strategy of ensemble learning and image features combination are still major challenges in mangrove species classification. This paper constructed two novel adaptive ensemble learning frameworks (AME-EL and AOS-EL) to explored the effect of combing different spatial-resolution optical and SAR images on classification performance, and evaluated the ability in mangrove species classification between dual-polarization and full-polarization SAR images. Finally, we used the SHAP method to explore the effects of different feature interactions on mangrove species classification. The results indicated that: (1) AME-EL and AOS-EL achieve the fine classification of mangrove species with overall accuracies between 77.50% and 94.77%. (2) Combination of Gaofen-7 multispectral and Gaofen-3 SAR improved the classification accuracy for Kandelia candel, with the F1 score increasing from 26.4% to 40.2%. (3) The VV/VH polarization performed better in the classification, with the F1 scores for Aegiceras corniculatum and Kandelia candel were higher than those of HH/HV and AHV polarization by 7%−16.1% and 5.9%−16.1%, respectively. (4) SAR features interacted well with other spectral features, which made a strong contribution to the classification accuracy of mangrove species, and effectively affect the prediction results.

ABSTRACTThe extraction of coastal aquaculture ponds from remote sensing images is affected by the effect of ‘same object with different spectrums’ and ‘different objects with same spectrums’. The U2-Net deep learning network is able to capture more contextual information from different scales to solve this problem and is suitable for salient object detection. This study proposed a remote sensing information extraction model of coastal ponds based on U2-Net deep learning network, completed the remote sensing information extraction of coastal aquaculture ponds in Zhoushan Archipelago from 1984 to 2022, analyzed the spatiotemporal evolution of coastal aquaculture ponds in Zhoushan Archipelago. The results showed that the developed model was more accurate with a precision rate of 96.12%, a recall rate of 95.43%, and an F1-measure of 0.96. During the study period, the area of the aquaculture ponds in the Zhoushan Archipelago demonstrated an increasing trend, expanding from 471.21 hm2 in 1984–3668.55 hm2 in 2022. In addition, over half of the aquaculture ponds had been active for more than 10 years. The method developed in this study is capable of rapidly and accurately mapping coastal aquaculture ponds, and thus is significant for the management of marine resources and promoting sustainable development.

ABSTRACTStudies on the retrieval of ocean surface current by Synthetic Aperture Radar (SAR) mainly concentrate on coastal regions with land coverage, allowing for continuous land to estimate non-geophysical Doppler shifts. As a strong western boundary in the Western Pacific, the Kuroshio flows through mostly islands or open sea, and lacks land coverage for range bias correction. We propose a non-geophysical Doppler shift correction algorithm suitable for Kuroshio observation. Three solutions matching different land coverage were adopted in the range direction, and the scalloping was removed by using average filtering in the azimuth direction. Comparison between SAR radial velocity and Global Surface Lagrangian Drifter (GLD) reveals that the correlation coefficient (R2) increase to 0.686, and root mean square error (RMSE) of 0.215 m/s after non-geophysical corrections, while the wind-wave bias corrections make R2 increase to 0.806 and RMSE decrease to 0.13 m/s. There is also a good consistency between SAR inversion and Regional Ocean Circulation Model (ROMS). Sensitivity analysis illustrates that ascending pass is more suitable for monitoring Kuroshio, and high incidence angles are less affected by ocean conditions and non-geophysical factors. The algorithm can be used in regions with discontinuous or absent land.

ABSTRACTAccurately mapping large-scale areas of aquaculture ponds along China’s coastal areas is challenging. This study proposes a multi-featured decision tree method that incorporates multi-spectral indices, water inundation frequency (WIF), and object-oriented rules to extract aquaculture ponds in coastal zones using 65332 Sentinel-2 images on the GEE platform. The proposed method was validated, with the average overall accuracy of the extraction results from 2017 to 2021 reaching 90.80%, and an average F1 Score of 0.91. The results of this study indicate that the overall area of aquaculture ponds in China's coast in 2021 was 7633.83 km2, mainly located in coastal bay estuaries in Shandong Province, Jiangsu Province and Guangdong Province. In recent years, the area of coastal ponds for aquaculture in China has shown a decreasing trend, with an average annual decrease of 235.65 km2. The analysis combined with land use data shows that the ponds that have been reduced in the past five years have mainly been converted to abandoned aquaculture ponds, wetlands, and other land use types.

During the service of asphalt pavement, it is affected by natural environmental factors for a long time, and the pavement performance declined. In order to simulate the cumulative effects of multiple aging components, the indoor ''ultraviolet radiation, high temperature and water'' multi-factor cyclic coupling aging test was designed. The properties of asphalt during cyclic coupled aging were measured by conventional physical properties tests, photoelectric colorimetric tests, and Fourier Transform Infrared Spectroscopy test. Then the surface morphology of asphalt was observed by camera and optical microscope. After asphalt aged, a large number of oxidation functional groups were formed, causing asphalt to become hard and brittle, adhesion to diminish, and surface roughness to increase. Ultraviolet light and high temperatures led to the exudation and dissolution of the liquid constituents within the asphalt, which were subsequently washed away by water. The thermal stress induced the shrinkage and wrinkle of the asphalt film, resulting in the formation of cracks. Under high temperatures, a limited number of cracks could potentially self-repair during the early stages of aging. Nevertheless, as the aging process progressed, the self-healing capacity of the asphalt diminished, leading to the widening and deepening of cracks. Concurrently, water infiltrated along the cracks into the inner layers of the asphalt, exacerbating the degradation of its properties.

Mangroves play a pivotal role in the attainment of United Nations Sustainable Development Goals. Leaf equivalent water thickness (EWT) and leaf mass per area (LMA) are vital functional trait parameters for monitoring heath status of mangroves. However, their inherent spectral response characteristics remain unclear, which presents a challenge for high-precision estimating EWT and LMA using remote sensing images. In this study, we attempted to synergistic retrieval of mangrove EWT and LMA using field hyperspectral and satellite data. Specifically, we proposed a novel Fractional-order assisted Spectral angle Analysis (FSA) approach to accurately identify sensitive spectral domains of EWT and LMA for three mangrove species based on in situ full-spectrum hyperspectral (350–2500 nm) data. We constructed a new spectral matching index (SMI) to screen the satellite-based sensitive spectral bands based on the field-based spectral domains, and further verified their feasibility of estimating EWT and LMA using three optical satellite sensors (Sentinel-2A, SDGSAT-1, and OHS-3D). Finally, we explored the effectiveness of combining SAR and thermal infrared (TIR) images with sensitive spectral bands on improving retrieval performance in estimating EWT and LMA, respectively. Results indicated that FSA approach could capture the sensitive spectral domains of EWT (1125–1868 nm) and LMA (1419–1999 nm) for three mangrove species. The SMI could effectively realize the spectral matching between field-based sensitive domains and satellite-based bands, and perform the synergistic retrieval of mangrove EWT (R2 = 0.65) and LMA (R2 = 0.70). Intriguing finding is that the additional TIR bands significantly enhanced the EWT and LMA inversion accuracy (R2 increased by 9.8 %–68.3 %) when compared to SAR images. We confirmed that EWT displayed an inverse trend of LMA with the flooding frequency increasing. Our works provide a scientific basis for protection and sustainable management of mangroves.

The magnetism-ductility contradictory relationship presents a significant challenge in the development of magnetic alloys. The impact of the BCC-B2 transition, along with Mn site occupancy, on magnetism and ductility have been investigated by using first-principles calculations. The calculations involved the evaluation of magnetic moments, density of states (DOS), phase stability and ductility of FeCoMn alloys. The results of binary alloys confirm the enhancement of magnetism due to the BCC-B2 transition. Furthermore, the ordering phase transition can strengthen the magnetic interaction between Fe and Mn atoms, which is associated with minimal variations in the density of states of Fe and Mn in the B2 structure. Regarding the ductility of FeCoMn alloys, two factors contribute to increased brittleness. Firstly, the increased covalent component in bonding, as a result of the strong hybridization between different elements, leads to an increased brittleness. Secondly, the increased Peierls stress provides a larger resistance to dislocation motion, which also contributes to the increased brittleness. Finally, the Pearson correlation coefficients and data analysis indicate that VEC, spin polarizations and Mn content provide major contributions to the contradictory relationship between magnetism and ductility.