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An integrated thermodynamic model was developed to determine the equilibrium between the slag and the steel at high temperatures, employing the Unified Interaction Parameter Formalism (UIPF) and the Associate Model (AM) for a twelve-element molten steel system (Fe–Al–C–Ca–Cr–Mg–Mn–N–O–S–Si–Ti). A model for slag activity was based on the Ion and Molecular Coexistence Theory (IMCT) and applied to a refining slag system (CaO–MgO–FeO–MnO–CaF2–SiO2–Al2O3–Fe2O3). Comparative analyses were conducted using FactSage software and experimental data, validating the accuracy of the established model. The impact of slag modifiers, specifically aluminum granules, on slag oxidability, and the role of CaO/Al2O3 ratio in slag desulfurization were investigated. The effect of slag modifier on the oxidability and the influence of CaO/Al2O3 on the desulfurization of the slag were determined using the current model. Aluminum in the slag modifier reacted with MnO and FeO in the slag, thereby reducing the oxidability of the slag. With a 30 wt% slag modifier, the MnO content in the slag (initially 15 wt%) stabilized and showed minimal change, dropping below 1 wt%. An increase in CaO content above 80 wt% in the slag led to a reduction of equilibrium sulfur in the steel to below 10 ppm. Little amount of calcium transferred from the slag into the steel with the increase in CaO content, but the [Ca] content remained below 0.5 ppm even though the content of CaO in the slag was greater than 80 wt%. The developed model offered a versatile tool for designing slag compositions tailored to various purposes.

Convolutional neural network (CNN) models are widely used in landslide susceptibility assessment due to their powerful feature extraction capabilities, and traditional CNN is no longer able to meet the requirements. Therefore, this paper proposes a multi-scale convolutional neural networks (MSCNN) model that can take into account deep and shallow features. By increasing the depth of the model and expanding the receptive field of samples, the MSCNN can tap deeper and more stable features to improve the reliability of landslide susceptibility assessment in complex scenarios. In this study, Shenzhen City is selected as the research area, and 12 landslide conditioning factors of landslides in Shenzhen City were selected based on systematic and representative principles. A multi-scale convolutional neural network landslide susceptibility assessment model is constructed and compared with methods such as multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF). The results show that the AUC value (0.99) of the MSCNN model constructed in this paper is higher than that of MLP (0.97), SVM (0.91), and RF (0.85), which proves that the proposed MSCNN model has excellent prediction ability. The area of extremely high susceptibility in Shenzhen City is approximately 105.3 km², accounting for 4.98% of the total area of the study area, mainly distributed in Longgang District with steep slopes, sparse vegetation cover, and frequent human engineering activities. Slope, surface roughness, and surface relief are identified as the main conditioning factors affecting landslides in Shenzhen City. The landslide susceptibility mapping implemented in this paper reflects the current distribution of landslide disasters in Shenzhen City, providing data support and key technical support for future landslide disaster prevention and control in Shenzhen City.

Abstract Background An increasing number of clinical trials for new therapeutic strategies are underway or being considered for dystrophinopathy. Having detailed data on the natural progression of this condition is crucial for assessing the effectiveness of new drugs. However, there’s a lack of data regarding the long-term data on the natural course and how it’s managed in China. In this study, we offer a comprehensive overview of clinical and molecular findings, as well as treatment outcomes in the Chinese population. Methods Institutional data on all patients with dystrophinopathy from August 2011 to August 2021 were retrospectively reviewed. The data included geographic distribution, age at diagnosis, molecular findings, and treatment options, such as corticosteroids, cardiac interventions, and clinical outcomes. Results In total, 2097 patients with dystrophinopathy, including 1703 cases of Duchenne muscular dystrophy (DMD), 311 cases of Becker muscular dystrophy (BMD), 46 cases of intermediate muscular dystrophy (IMD), and 37 cases categorized as “pending” (individuals with an undetermined phenotype), were registered in the Children’s Hospital of Fudan University database for dystrophinopathy from August 2011 to August 2021. The spectrum of identified variants included exonic deletions (66.6%), exonic duplications (10.7%), nonsense variants (10.3%), splice-site variants (4.5%), small deletions (3.5%), small insertions/duplications (1.8%), and missense variants (0.9%). Four deep intronic variants and two inversion variants were identified. Regarding treatment, glucocorticoids were administered to 54.4% of DMD patients and 39.1% of IMD patients. The median age at loss of ambulation was 2.5 years later in DMD patients who received glucocorticoid treatment. Overall, one cardiac medicine at least was prescribed to 7.4% of DMD patients, 8.3% of IMD patients, and 2.6% of BMD patients. Additionally, ventilator support was required by four DMD patients. Eligibility for exon skipping therapy was found in 55.3% of DMD patients, with 12.9%, 10%, and 9.6% of these patients being eligible for skipping exons 51, 53, and 45, respectively. Conclusions This is one of the largest studies to have evaluated the natural history of dystrophinopathy in China, which is particularly conducive to the recruitment of eligible patients for clinical trials and the provision of real-world data to support drug development.

CrMnFeCoNi high-entropy alloy (HEA) manufactured by laser powder bed fusion has been demonstrated to possess a superior trade-off of tensile strength and ductility due to unique microstructures during the printed process. However, the improvement of mechanical properties of 3D printed-CrMnFeCoNi HEA through the addition of nano-scaled ceramic particles instead of in-situ precipitated particles is poorly studied. In this work, the effects of WC additions on the microstructure evolution and strengthening mechanism of additive-manufactured HEA samples at both room temperature (298 K) and cryogenic temperature (77 K) were systematically investigated. Except for the representative hierarchical microstructure characteristics of 3D printed- HEA samples, WC particles are dissolved completely in the CrMnFeCoNi matrix without obvious element segregation. The mechanical tests at room temperature (RT) revealed an excellent comprehensive mechanical property (tensile strength of 925 MPa and ductility of 38%) of the HEA sample with 5 wt% WC addition, which is mainly attributed to the solid solution strengthening induced by WC dissolution. In addition, the strength and ductility of the HEA sample synchronously increase when the testing temperature decreases from 298 K to 77 K, due to the reaction between dislocation and high-density deformation twins (DTs). The tensile strength (∼1420 MPa) of HEA-5 wt% WC sample at 77 K is higher than that of traditional CrMnFeCoNi HEA, because of contribution to strengthening from dislocation pile-up, entanglement and wavy dislocation slide.

Abstract A climatology of stratospheric gravity waves (SGWs) induced by tropical cyclones (TC-SGWs) is necessary for stratospheric aviation safety and accurate numerical weather prediction. Few previous works have characterized the climatology of TC-SGWs, and the relative importance of the source and background wind for TC-SGWs has not been clearly established. Here we present the distribution of TC-SGWs formed over the northwest Pacific Ocean based on 30 years of ERA5 reanalysis data. Using explainable artificial intelligence, we found the background wind in the lower stratosphere to be more important than TCs in controlling the distribution pattern of TC-SGWs. The background wind shear can influence the direction of TC-SGW propagation by refraction and filtering, which influences the distribution pattern of the TC-SGWs. The TC intensity only influences the TC-SGW intensity. Our results provide information on TC-SGW hotspots and suggest the importance of the background wind shear.

Titanium and Ti-based alloys are widely used metallic biomaterials, however, the surgical infections related to bacteria always occur on the implants. Developing the antibacterial titanium alloys has been studied as one of the most effective methods to lower the inflammatory response. The addition of Cu element to Ti matrix can produce strong antibacterial properties. Here, the antibacterial Ti-xCu alloys (x = 3, 5, and 7 wt%) were fabricated by powder metallurgy (PM) technique using spark plasma sintering (SPS) route. The effects of Cu addition, SPS temperature, and post-heat treatment on microstructure and tensile properties of Ti–Cu alloys were investigated. The strengthening and deformation mechanisms were also discussed. The microstructures of PM Ti–Cu alloy consisting of α-Ti and Ti2Cu phases are fine and homogenous as well as free of porosity. With the increasing Cu addition, the yield and tensile strengths of Ti–Cu alloys increase, but the elongation decreases. The precipitation of Ti2Cu phases contributes the most increments of yield strength, and can also act as the barriers to pile up the dislocations during the plastic deformation, improving the work hardening rate and tensile strength. PM Ti–Cu alloys possess a good tensile strength of 575–810 MPa, and an excellent elongation of 11%–29%. The optimized tensile properties of PM Ti–5Cu alloys are comparable and even superior to those of Ti and other Ti-based alloys produced by traditional methods. Further, insights for the design and processing of PM Ti–Cu alloys with ample shaping freedom fabricated by SPS method are generalized and discussed.

Heavy rainfall is an important weather that causes flood disasters, and it is also one of the most important natural disasters in our country. With development of the high-resolution numerical models, numerical weather prediction has been the main method for heavy rainfall forecasting. However, the accuracy of the numerical prediction depends on the completeness of the atmospheric motion equations, the accuracy of the initial state, the reasonability of the physical process, and the robustness of the calculation method. As the atmosphere is a nonlinear chaotic system, the small errors in these aspects will cause significant uncertainty in the forecast results. Therefore, the improvement of the rainstorm numerical prediction is closely related to the development of data assimilation, parameterization of physical processes, and ensemble prediction, especially for the role of parameterization schemes of cloud microphysical processes that produce precipitation in numerical models. In addition, in order to improve and perfect the numerical model, the investigation of the evaluation method of the forecast results can also not be ignored and is a crucial part of the numerical prediction. This review describes the development of several key numerical weather prediction techniques. The four-dimensional ensemble variational assimilation method, the microphysics parameterization scheme, and the stochastic kinetic energy backscatter method of ensemble prediction model perturbation are highlighted. An evaluation method of model results based on kinetic energy spectrum analysis is also proposed. Finally, the future research directions in these aspects are summarized.

Abstract Background Some previous observational studies have linked deep venous thrombosis (DVT) to thyroid diseases; however, the findings were contradictory. This study aimed to investigate whether some common thyroid diseases can cause DVT using a two-sample Mendelian randomization (MR) approach. Methods This two-sample MR study used single nucleotide polymorphisms (SNPs) identified by the FinnGen genome-wide association studies (GWAS) to be highly associated with some common thyroid diseases, including autoimmune hyperthyroidism (962 cases and 172,976 controls), subacute thyroiditis (418 cases and 187,684 controls), hypothyroidism (26,342 cases and 59,827 controls), and malignant neoplasm of the thyroid gland (989 cases and 217,803 controls. These SNPs were used as instruments. Outcome datasets for the GWAS on DVT (6,767 cases and 330,392 controls) were selected from the UK Biobank data, which was obtained from the Integrative Epidemiology Unit (IEU) open GWAS project. The inverse variance weighted (IVW), MR-Egger and weighted median methods were used to estimate the causal association between DVT and thyroid diseases. The Cochran’s Q test was used to quantify the heterogeneity of the instrumental variables (IVs). MR Pleiotropy RESidual Sum and Outlier test (MR-PRESSO) was used to detect horizontal pleiotropy. When the causal relationship was significant, bidirectional MR analysis was performed to determine any reverse causal relationships between exposures and outcomes. Results This MR study illustrated that autoimmune hyperthyroidism slightly increased the risk of DVT according to the IVW [odds ratio (OR) = 1.0009; p = 0.024] and weighted median methods [OR = 1.001; p = 0.028]. According to Cochran’s Q test, there was no evidence of heterogeneity in IVs. Additionally, MR-PRESSO did not detect horizontal pleiotropy (p = 0.972). However, no association was observed between other thyroid diseases and DVT using the IVW, weighted median, and MR-Egger regression methods. Conclusions This study revealed that autoimmune hyperthyroidism may cause DVT; however, more evidence and larger sample sizes are required to draw more precise conclusions.

Abstract Essential amino acids (EAA) and microRNAs (miRs) control biological activity of a cell. Whether EAA regulates the activity of miR has never been demonstrated. Here, as proof‐of‐concept, a tryptophan (Trp, an EAA) complex containing Argonaute 2 (Ago2) and miRs including miR‐193a (Trp/Ago2/miR‐193a) is identified. Trp binds miR‐193a‐3p and interacts with Ago2. Trp/Ago2/miR‐193a increases miR‐193a‐3p activity via enhancing Argonaute 2 (Ago2) RNase activity. Other miRs including miR‐103 and miR‐107 in the Trp complex enhance miR‐193a activity by targeting the same genes. Mechanistically, the Trp/Ago2/miR‐193a complex interacts with Trp‐binding pockets of the PIWI domain of Ago2 to enhance Ago2 mediated miR activity. This newly formed Ago2/Trp/miR‐193a‐3p complex is more efficient than miR‐193a‐3p alone in inhibiting the expression of targeted genes and inhibiting colon cancer liver metastasis. The findings show that Trp regulates miR activity through communication with the RNA‐induced silencing complexes (RISC), which provides the basis for tryptophan based miR therapy.

Abstract Mitochondria play a multifaceted role in supporting bladder cancer progression. Translocase of inner mitochondrial membrane 44 (TIMM44) is essential for maintaining function and integrity of mitochondria. We here tested the potential effect of MB-10 (MitoBloCK-10), a first-in-class TIMM44 blocker, against bladder cancer cells. TIMM44 mRNA and protein expression is significantly elevated in both human bladder cancer tissues and cells. In both patient-derived primary bladder cancer cells and immortalized (T24) cell line, MB-10 exerted potent anti-cancer activity and inhibited cell viability, proliferation and motility. The TIMM44 blocker induced apoptosis and cell cycle arrest in bladder cancer cells, but failed to provoke cytotoxicity in primary bladder epithelial cells. MB-10 disrupted mitochondrial functions in bladder cancer cells, causing mitochondrial depolarization, oxidative stress and ATP reduction. Whereas exogenously-added ATP and the antioxidant N-Acetyl Cysteine mitigated MB-10-induced cytotoxicity of bladder cancer cells. Genetic depletion of TIMM44 through CRISPR-Cas9 method also induced robust anti-bladder cancer cell activity and MB-10 had no effect in TIMM44-depleted cancer cells. Contrarily, ectopic overexpression of TIMM44 using a lentiviral construct augmented proliferation and motility of primary bladder cancer cells. TIMM44 is important for Akt-mammalian target of rapamycin (mTOR) activation. In primary bladder cancer cells, Akt-S6K1 phosphorylation was decreased by MB-10 treatment or TIMM44 depletion, but enhanced after ectopic TIMM44 overexpression. In vivo, intraperitoneal injection of MB-10 impeded bladder cancer xenograft growth in nude mice. Oxidative stress, ATP reduction, Akt-S6K1 inhibition and apoptosis were detected in MB-10-treated xenograft tissues. Moreover, genetic depletion of TIMM44 also arrested bladder cancer xenograft growth in nude mice, leading to oxidative stress, ATP reduction and Akt-S6K1 inhibition in xenograft tissues. Together, targeting overexpressed TIMM44 by MB-10 significantly inhibits bladder cancer cell growth in vitro and in vivo.

Abstract Purpose To evaluate the efficacy and safety of selective aneurysmal sac neck-targeted embolization in endovascular aneurysm repair (EVAR) in patients with a hostile neck anatomy (HNA). Materials and methods Between October 2020 and June 2022, patients with an abdominal aortic aneurysm (AAA) and HNA who underwent EVAR with a low-profile stent graft and a selective aneurysmal sac neck-targeted embolization technique were analysed. An HNA was defined by the presence of any of the following parameters: infrarenal neck angulation > 60°; neck length

Non-landslide samples influence the outcomes of landslide susceptibility assessment. Existing studies did not fully consider the equilibrium between landslide and non-landslide samples in similar environments, resulting in poor reliability of landslide susceptibility assessment. This study proposed a non-landslide samples optimization method with a constraint of disaster-pregnant environment similarity to construct the balanced landslide and non-landslide samples. We employed the heterogeneous stacking and blending ensemble learning models to generate the landslide susceptibility assessment. This study focused on the Bailong River Basin with complex environment and frequent landslides as the study area. First, we extracted 12 landslide influencing factors based on multiple sources and analyzed the spatial distribution patterns of landslides. Second, we constructed similar environments based on the assessment units obtained from the curvature watershed method and selected an equal amount of both landslide and non-landslide samples in every different environment. Finally, three classic neural network models, namely multilayer perceptron, convolutional neural network, and gated recurrent unit were used as base models for stacking and blending ensemble learning models to assess landslide susceptibility. The findings suggested that the landslide susceptibility assessment results with optimized non-landslide samples were more reliable, especially the proposed method improved the prediction results in sample-sparse regions. The stacking ensemble constructed in this study demonstrated the highest area under the curve of 0.88 for the testing dataset, outperforming the blending ensemble and three base models. The issue of unreliable landslide susceptibility assessment results in sample-sparse regions within complex environments can be effectively addressed by considering the balanced sampling of non-landslide samples under the constraint of similar environments.

ABSTRACTWe develop an integrated neural network landslide susceptibility assessment (LSA) method that integrates temporal dynamic features of interferometry synthetic aperture radar (InSAR) deformation data and the spatial features of landslide influencing factors. We construct a time-distributed convolutional neural network (TD-CNN) and bidirectional gated recurrent unit (Bi-GRU) to better understand the temporal dynamic features of InSAR cumulative deformation, and construct a multi-scale convolutional neural network (MSCNN) to determine the spatial features of landslide influencing factors, and construct a parallel unified deep learning network model to fuse these temporal and spatial features for LSA. Compared with the traditional MSCNN method, the accuracy of the proposed model is improved by 1.20%. The performance of the proposed model is preferable to MSCNN. The area under the receiver operating characteristic curve (AUC) of the testing set reaches 0.91. Our LSA results show that the proposed model clearly depicts areas with very high susceptibility landslides. Further, only 10.18% of the study area accurately covers 84.79% of historical landslide areas. Subjective consequences and objective indicators show that the proposed model that is integrated time-series InSAR deformation dynamic features can make full use of landslide characteristics and effectively improve the reliability of LSA.

Abstract Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research.

IntroductionSalt stress is a major environmental factor that constrains soybean growth, development, and productivity. Flavonoids are key secondary metabolites that play a crucial role in enhancing plant resistance to both biotic and abiotic stress. However, a comprehensive understanding of the regulatory mechanisms underlying flavonoid biosynthesis under salt stress in soybean is lacking.MethodsIn this study, an integrative analysis of soybean metabolome and transcriptome was conducted using two soybean lines, FQ03 (salt-sensitive, SS) and FQ07 (salt-tolerant, ST).ResultsA total of 650 significantly changed metabolites were identified in SS and ST after salt stress treatment. Among them, 151 flavonoids were categorized into nine classes, with flavones and flavonols being the predominant flavonoid types in soybean. Heatmap analysis showed higher contents of most flavonoid metabolites in ST than in SS under salt stress, and the total flavonoid content in ST was significantly higher than that in SS. In addition, transcriptome analysis revealed a higher number of differentially expressed genes (DEGs) in ST than in SS under salt stress. KEGG enrichment analysis revealed that DEGs were mainly enriched in pathways related to phenylpropanoid biosynthesis, isoflavonoid biosynthesis, flavonoid biosynthesis, as well as flavone and flavonol biosynthesis. Notably, 55 DEGs that were mapped to the flavonoid biosynthetic pathway were identified, with most showing higher expression levels in ST than in SS. Weighted gene correlation network analysis identified eight structural genes and six transcription factor genes as key regulators of flavonoid biosynthesis within the blue module. Furthermore, qRT-PCR results confirmed the accuracy of the transcriptomic data and reliability of the identified candidate genes.DiscussionThis study provides insights into the regulatory mechanisms underlying salt stress responses in soybean and highlights hub genes as potential targets for developing salt-tolerant soybean varieties.

While macrophage is one of the major type I interferon (IFN-I) producers in multiple tissues during viral infections, it also serves as an important target cell for many RNA viruses. However, the regulatory mechanism for the IFN-I response of macrophages to respond to a viral challenge is not fully understood. Here we report ADAP, an immune adaptor protein, is indispensable for the induction of the IFN-I response of macrophages to RNA virus infections via an inhibition of the conjugation of ubiquitin-like ISG15 (ISGylation) to RIG-I. Loss of ADAP increases RNA virus replication in macrophages, accompanied with a decrease in LPS-induced IFN-β and ISG15 mRNA expression and an impairment in the RNA virus-induced phosphorylation of IRF3 and TBK1. Moreover, using Adap-/- mice, we show ADAP deficiency strongly increases the susceptibility of macrophages to RNA-virus infection in vivo. Mechanically, ADAP selectively interacts and functionally cooperates with RIG-I but not MDA5 in the activation of IFN-β transcription. Loss of ADAP results in an enhancement of ISGylation of RIG-I, whereas overexpression of ADAP exhibits the opposite effect in vitro, indicating ADAP is detrimental to the RNA virus-induced ISGylation of RIG-I. Together, our data demonstrate a novel antagonistic activity of ADAP in the cell-intrinsic control of RIG-I ISGylation, which is indispensable for initiating and sustaining the IFN-I response of macrophages to RNA virus infections and replication.

Abstract The physical behavior of a falling raindrop is governed by delicate fluid dynamics and thermodynamics, and oscillates with time. Despite this time‐variant nature, past observational and simulation studies have aimed to generalize parameterizations for describing rain microphysics bearing the assumption that raindrops fall at terminal speeds with an equilibrium shape. However, the applicability of this hypothesis in a realistic atmosphere that is inherently turbulent remains an open question. Here, we employ novel retrieval techniques to quantify the impact of turbulence on raindrop microphysics using long‐term in situ observations with careful assessment of the wind effect. We find that raindrop microphysics increasingly deviate from the equilibrium state as the turbulence dissipation rate increases, and this effect is more pronounced for large raindrops. We present turbulence‐invoked rain microphysical parameterizations which shed light on the complex interactions between turbulence dynamics and raindrop microphysics.

The Yellow River basin (YRB) is a major ecological functional area in China, and its ecological safety development and change have extremely significant impacts on the natural environment and human society. However, existing studies on the YRB lack spatiotemporal characteristics analysis and prediction of ecological safety with vegetation as the core. Therefore, this study proposes to construct an ecological safety index (ESI) of the YRB based on the comprehensive multidimensional ecological safety evaluation system “vigor-pressure-state-response,” using the normalized difference vegetation index, vegetation carbon sink indicator parameters, temperature, precipitation, the digital elevation model, population density, and the per capita gross domestic product from 2000 to 2020. The spatiotemporal characteristics of the ESI were then analyzed for the YRB, and a long-term and short-term memory network model was constructed to predict the ESI trend of the YRB over the next 10 years. According to the results, from 2000 to 2020, the ESI of the YRB showed a fluctuating upward trend, and the annual average of the ESI changed abruptly in 2015 due to drastic changes in hazardous areas. The ESI in most areas of the YRB showed a significant upward trend, the stability of ESI changes was weak in some areas, and the overall spatial distribution showed significant positive spatial agglomeration characteristics. Further, the response of ESI to landscape complexity in different reaches of the YRB varied. Most of the middle reaches were positively correlated with landscape complexity, while most of the upper and lower reaches were not significantly or negatively correlated. Notably, over the next 10 years, YRB's ESI growth will slow down, with areas with degradation increasing, areas with significant growth decreasing, and areas currently showing stability improvement.

Accurate surface deformation (SD) predictions are critical for early warning and timely remediation of infrastructure damage. However, the current SD prediction models do not integrate the multiscale features of InSAR SD and environmental factors (EFs), which make their prediction results inaccurate. To address these limitations, we proposed a bidirectional gated recurrent unit (BiGRU) multioutput SD prediction network (GLER-BiGRUnet), which mainly included global–local feature extraction (GLFE), multifactor cross-attention residual (MCAR), and local residual module embedded in self-attention mechanism (RCSA) modules. Specifically, dense and one-dimensional convolutional layers were concatenated in the GLFE module to extract global–local SD features. The long time-series dependence between EFs and SD was learned in the MCAR module using the multihead cross-attention mechanism to obtain the corresponding attention weight feature matrix. The residual connection and self-attention mechanisms were used in the RCSA module to merge the multiscale features and enhance the model fitting ability. We chose four typical regions in the permafrost area of Qinghai–Tibet Railway as the scene for the experiment. The spatial distribution and local profile exhibited relatively small discrepancies between the prediction results of the GLER-BiGRUnet model and the InSAR SD. Meanwhile, the average root-mean-square error of the GLER-BiGRUnet model in the four typical regions was 0.19 mm, and the proposed model had the best evaluation index compared with other SD prediction models. Additionally, the prediction trend of SD of the proposed GLER-BiGRUnet model was consistent with the original InSAR SD, and the prediction results were more stable than those of the other prediction models. The SD prediction model proposed in this article contributes to early warning of SD.

Landslide susceptibility mapping (LSM) is of great significance for regional land resource planning and disaster prevention and reduction. The machine learning (ML) method has been widely used in the field of LSM. However, the existing LSM model fails to consider the correlation between landslide and disaster-prone environment (DPE) and lacks global information, resulting in a high false alarm rate of LSM. Therefore, we propose an LSM method with graph–transformer that considers the DPE characteristics and global information. First, correlation analysis and importance analysis are employed on nine landslide contributing factors, and the landslide dataset is generated by combining remote sensing image interpretation and field verification. Second, a graph constrained by environment similarity relationship is constructed to realize the correlation between landslide and DPE. Then, the transformer module is introduced to construct a graph–transformer model that considers the global information. Finally, the LSM is generated and analyzed, and the accuracy of the proposed model is compared and evaluated. The experimental results show that the environment similarity relationship graph effectively improves the accuracy of the models and weakens the influence of environmental differences on the models. Compared with graph convolutional network, graph sample and aggregate, and graph attention network models, the area under the curve (AUC) value of the proposed model is more than 2.05% higher under the environment similarity relationship. In addition, the AUC value of the proposed model is more than 8.8% higher than that of traditional ML models. In conclusion, our proposed model framework can get better evaluation results than most existing methods, and its results can provide effective ways and key technical support for landslide disaster investigation and control.

Landslide susceptibility mapping (LSM) is of great significance in geohazard early warning and prevention. The existing LSM methods mostly used traditional static landslide conditioning factors (LCFs), which only considered the spatial features of single-pixel neighborhoods and could not extract the time-series dynamic features of developing landslides, resulting in low accuracy and insufficient reliability of LSM. To solve this problem, this study proposes to introduce time-series rainfall factors based on the traditional static factors and construct an integrated time-series dynamic neural network (TSDNN) model for LSM. A convolutional neural network (CNN) adding time-distributed convolution and a bidirectional long and short-term memory neural network is utilized to extract time-series rainfall features, and a multiscale convolutional neural network (MSCNN) is utilized to extract static features of the static LCFs. In this study, multicollinearity analysis and geodetector are utilized to analyze the LCFs. Multiple evaluation metrics are utilized to analyze the proposed model performance. The results indicate that the overall accuracy has improved by introducing time-series rainfall factors, and the susceptibility area of actual predicted is more refined. The study indicates that significant advantages of the proposed TSDNN model are over models [CNN, MSCNN, random forest (RF)] when processing combined static and rainfall data. This is notably evident that the accuracy is enhanced by 12.9%, 10.7%, and 11.4% compared to CNN, MSCNN, and RF models in the receiver operating characteristic curve analysis, respectively. Moreover, two typical areas containing three recent landslide events validate the reliability of the proposed TSDNN model. The proposed network model framework for LSM considering time-series rainfall factors can provide new ideas and key technical support for landslide disaster prevention.

Surface deformation of alpine peatland in China has an important effect on runoff and is of great significance for wetland ecosystem protection. However, spatio-temporal characteristics of alpine peatland surface deformation in China lack systematic studies, and the driving mechanism is not yet clear. In this study, we selected the alpine peatland of Maduo County in China as the research object, surface deformation of peatland based on the small baseline subset radar interferometry technique was obtained, we analyzed spatio-temporal deformation characteristics and patterns of peatland, explored the driving mechanism of the peatland surface deformation with single-factor and multifactor combinations of Geo-detector, respectively. The results showed that the overall subsidence rates of peatlands in Maduo County, China slowed down year by year from 2018 to 2020, but there was seasonal freezing and thawing, subsidence rates of peatlands at high elevation and high slopes were stable, peatlands at low elevation and low slope were vulnerable to disturbance, subsidence rates are largest. Maliecuo, Bailongqu, and Gaerlawangzang regions were serious subsidence, the maximum subsidence rate was 159 mm/year. Meteorological factors and geological conditions were the main reasons for the surface deformation of alpine peatland in Maduo County, China. This study provides a theoretical basis for the conservation and restoration of peatland ecosystems in the alpine regions of China.

The ancient Suoertou landslide seriously threatens the surrounding population's lives and property. Monitoring and predicting this landslide is crucial to ensure the affected areas’ safety. The previous research on the landslide's displacement characteristics and mechanisms has lacked detailed analyses. In addition, its future development trends must be understood. Therefore, we conducted a detailed analysis of the ancient Suoertou landslide's displacement characteristics and mechanisms using small baseline subset interferometric synthetic aperture radar monitoring results from 2018 to 2023. Furthermore, by applying a gated recurrent unit prediction mode that incorporates the refined displacement characteristics and mechanisms, we forecasted the landslide's displacement trends. The results show that this landslide is currently undergoing overall slow displacement with violent fluctuations in localized areas. Tectonic movement, precipitation, human activities, river erosion, and other factors interact, forming a vicious development displacement mechanism. According to our prediction, the displacement of this landslide will be in a trend of fluctuating increase from June 2023 to June 2024. In particular, the local area will undergo abnormal displacement acceleration. The results of the current research provide a scientific basis upon which to monitor landslides, promote their management, and reduce the risk of losses due to landslide disasters.

Abstract Background As a key regulatory enzyme in the glycolysis pathway, pyruvate kinase (PK) plays crucial roles in multiple physiological processes during plant growth and is also involved in the abiotic stress response. However, little information is known about PKs in soybean. Results In this study, we identified 27 PK family genes against the genome of soybean cultivar Zhonghuang13. They were classified into 2 subfamilies including PKc and PKp. 22 segmental duplicated gene pairs and 1 tandem duplicated gene pair were identified and all of them experienced a strong purifying selective pressure during evolution. Furthermore, the abiotic stresses (especially salt stress) and hormone responsive cis-elements were present in the promoters of GmPK genes, suggesting their potential roles in abiotic stress tolerance. By performing the qRT-PCR, 6 GmPK genes that continuously respond to both NaCl and ABA were identified. Subsequently, GmPK21, which represented the most significant change under NaCl treatment was chosen for further study. Its encoded protein GmPK21 was localized in the cytoplasm and plasma membrane. The transgenic Arabidopsis overexpressing GmPK21 exhibited weakened salinity tolerance. Conclusions This study provides genomic information of soybean PK genes and a molecular basis for mining salt tolerance function of PKs in the future.

The radiometric calibration technique of spaceborne synthetic aperture radar (SAR) aims to establish a constant relationship between the SAR target pulse response energy and the actual radar cross-section (RCS) of the ground. It is an essential technology for achieving quantitative observations of the Earth using radar. In this article, the process of in-orbit radiometric calibration campaign for the SuperView Neo-2 SAR satellite is introduced, which is the distributed X-band SAR satellite in the new generation of Chinese SiWei commercial remote sensing satellite system. More specifically, the initial step involves deriving the total transfer function of the SAR system using the radar equation and establishing a stable calibration constant model for the SAR system. Further, by using the obtained calibration constant to invert the RCS value of the corner reflectors in the Otog Banner calibration field, while validating and analyzing the absolute radiometric accuracy. Considering that the elevation antenna pattern (EAP) predominantly affects the consistency of image radiometric correction, the method of extracting EAP and beam center bias angle from the rainforest is presented. Finally, the quality validation of impulse response function is performed over the California calibration field, which can effectively guarantee the quality of SuperView Neo-2 SAR satellite products.

In the realm of speech emotion recognition, researchers strive to refine representation methods for improved emotional information capture. Traditional one-dimensional time series classification falls short in expressing intricate emotional patterns present in speech signals, posing challenges in accuracy and robustness. This study introduces an innovative algorithm leveraging Hilbert curves to transform one-dimensional speech data into two-dimensional form, enhancing feature extraction accuracy. A tiling module based on Hilbert curve maximizes Hilbert curve arrangements for improved emotional information capture. Results reveal spatial efficiency gains up to 23,195 times pixel units, enhancing data storage. With an exceptional 98.73% accuracy, the proposed approach traditional methods, affirming its superior emotion classification performance on the same dataset. These empirical findings underscore the effectiveness of our proposed method in advancing speech emotion recognition.

Accurate and efficient landslide identification is an important basis for landslide disaster prevention and control. Due to the diversity of landslide features, vegetation occlusion, and the complexity of the surrounding surface environment in remote sensing images, deep learning models (such as U-Net) for landslide detection based only on optical remote sensing images will lead to false and missed detection. The detection accuracy is not high, and it is difficult to satisfy the demand. Synthetic aperture radar (SAR) has penetrability, and SAR images are highly sensitive to changes in surface morphology and structure. In this study, a multi-input channel U-Net landslide detection method fusing SAR, optical, and topographic multisource remote sensing data is proposed. First, a multi-input channel U-Net model fusing SAR multisource remote sensing data is constructed, then an attention mechanism is introduced into the multi-input channel U-Net to adjust the spatial weights of the feature maps of the multisource data to emphasize the landslide-related features, and finally, the proposed model is applied to the experimental scene for validation. The experimental results demonstrate that the proposed model combined with SAR multisource remote sensing data improves the perception ability of landslide features, focuses on learning landslide-related features, improves the accuracy of landslide detection, and reduces the rate of false detections and missed detections. Compared with the traditional U-Net landslide detection method based on SAR multisource remote sensing data and the traditional U-Net method that disregards SAR multisource remote sensing data, the proposed method has the best quantitative evaluation indicators. Among them, the proposed method obtained the highest F1 value (66.18%), indicating that fused SAR remote sensing data can provide rich and complementary landslide feature information, simultaneously setting up a multichannel U-Net model to input multisource remote sensing data can effectively process landslide feature information. The proposed method can provide theoretical and technical support for landslide disaster prevention and control.

Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing images (such as U-Net) have received a lot of attention. However, because of the variable shape and texture features of landslides in remote sensing images, the rich spectral features, and the complexity of their surrounding features, landslide extraction using U-Net can lead to problems such as false detection and missed detection. Therefore, this study introduces the channel attention mechanism called the squeeze-and-excitation network (SENet) in the feature fusion part of U-Net; the study also constructs an attention U-Net landside extraction model combining SENet and U-Net, and uses Sentinel-2A remote sensing images for model training and validation. The extraction results are evaluated through different evaluation metrics and compared with those of two models: U-Net and U-Net Backbone (U-Net Without Skip Connection). The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%, which is about 2% and 3% higher than U-Net and U-Net Backbone, respectively, with less false detection and more accurate extraction results.

The anomalous movements of glaciers cause disasters, such as debris flows and landslides. It is very important to assess the glacier movements and their future trends. Glacier velocity refers to movement process. The current research aims to analyse past and current spatiotemporal changes in glacier velocity. No study has used neural network model to conduct a spatiotemporal prediction for glacier velocity. Therefore, this paper selected typical mountain glaciers G2 and G5 along the Sichuan-Tibet Railway as research objects and constructed the Convolutional Gate Recurrent Unit (ConvGRU) spatiotemporal prediction model based on 1988–2018 Landsat data to predict velocities in 2019–2028, and analysed the future trends of G2 and G5. The evaluation indexes met the model requirements to a large extent, quantitatively showing that the model has high accuracy and can successfully capture the fluctuation changes in time series data of glacier velocity. The mean deviations of G2 and G5 were 0.09 and −0.47 m/yr, respectively, reflecting the high reliability of the model applied to extraction of glacier velocity. The velocities of G2 and G5 showed a slow downtrend with fluctuations; that is, they will not cause damage to the construction and operation of the Sichuan-Tibet Railway in the short term.

With the development of automation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor shortages. However, conventional industrial mobile robots’ route-tracking algorithms typically rely on passive markers, leading to issues such as inflexibility in changing routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks—battery-powered luminous landmarks that enable robots to recognize and adapt to flexible navigation requirements. However, the reliance on batteries necessitates frequent recharging, prompting the development of an automatic power supply system. This system integrates omnidirectional contact electrodes on mobile robots, allowing to recharge active landmarks without precise positional alignment. Despite these advancements, challenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues, this research focuses on miniaturizing active landmarks and optimizing power distribution among landmarks. The experimental results of this study demonstrated the effectiveness of our automatic power supply method and the high accuracy of landmark detection. Our power distribution calculation method can adaptively manage energy distribution, improving the system’s persistence by nearly three times. This study aims to enhance the practicality and efficiency of mobile robot remote control systems utilizing active landmarks by simplifying installation processes and extending operational durations with adaptive and automatic power supply distribution.

The shear stirrups and bend-up reinforcement in ultra-high-performance concrete (UHPC) beams could potentially be excluded due to the superior mechanical properties of UHPC. This paper reports the new findings of an experimental research into the factors that influence the shear behavior of non-stirrup UHPC beams. Fourteen beams were tested in shear, comprising twelve non-stirrup UHPC beams and two normal concrete (NC) beams reinforced with stirrups. The test variables included the steel fiber volume content (2.0%, 1.5%, and 0%), the shear span-to-effective-depth ratio (1.2, 1.8, 2.0, and 3.1), beam width (150 mm and 200 mm), and beam height (300 mm, 350 mm, and 400 mm). The results demonstrated that the steel fiber volume content had a significant influence on the shear behavior of the non-stirrup UHPC beams. The failure modes of the beams without steel fibers were typically brittle, whereas those reinforced with steel fibers exhibited ductile failure. The shear resistance of the beams could be significantly enhanced by the addition of steel fibers in the concrete mix. Furthermore, the post-cracking load-bearing performance of the beams could also be markedly improved by the addition of steel fibers. In addition, the shear span-to-effective-depth ratio had a considerable impact on the failure mode and the ultimate shear strength of the tested beams. The contribution of steel fibers to the shear capacity of the UHPC beams was observed to increase as the shear span-to-effective-depth ratio increased. The French standard formulae tended to overestimate the contribution of steel fibers, and the calculation results were found to be more accurate for UHPC beams with a moderate shear span-to-effective-depth ratio (around 2.0). Moreover, the French standard formulae demonstrated greater accuracy at a larger beam height for calculating the contribution of UHPC matrix.

In response to water scarcity in the Bashang area of northwest Hebei Province, a cold and arid region in north China, and to address the diminishing groundwater levels caused by pumping irrigation, this study investigated the impact of rainwater tank size and water supply on kidney beans production in greenhouses under various precipitation scenarios to determine the production potential and development strategies for regional precipitation resources. Under the background of average annual precipitation, kidney bean yield increased with increasing reservoir volume and shorter irrigation cycles. Under a 4-day irrigation cycle, the water demand satisfaction rate of kidney beans reached 100% water demand when the rainwater tank size was 15.7 m3. Against the wide variation in multi-year regional precipitation from 1992 to 2023, the annual effect of rainwater harvest was simulated using precipitation data collected 20 years with an 80% precipitation guarantee rate. The average minimum yield reduction rate obtained was 9.4%, and the corresponding minimum rainwater tank size was 29.5 m3. By superimposing the rainwater harvested in the shed and nonshed areas, the volume of the reservoir without yield reduction could be reduced to 20.0 m3. The sum of discharged and inventory water was much greater than the water scarcity in each water supply situation. Simulating and analyzing the effect of the relationship between rainwater tank size and water supply on rainwater harvesting in regional farmland by year provides important data affecting the construction of regional rainwater storage facilities and water supply efficiency. To achieve a high, stable yield of kidney beans grown in a greenhouse with shed film and shed area rainwater harvesting in north China, 2.6 m3 supplementary groundwater irrigation is still needed during the annual growing season.

The swine industry generates millions of gallons (thousands of cubic meters) of wastewater every day, posing significant environmental risk due to high concentrations of organics and nutrients. This study aims to investigate the effectiveness of attached-growth anaerobic bioreactors for treating swine wastewater by utilizing sulfate-reducing bacteria, focusing on the impact of chemical oxygen demand (COD)/sulfate mass ratios on organics degradation. A series of lab-scale anaerobic bioreactors were employed to treat swine wastewater for a 14-day period. The study evaluated changes in pH, acidity, alkalinity, COD, sulfate, and various nutrients along with total suspended solids (TSS) and volatile suspended solids (VSS) before and after treatment. At a COD/sulfate mass ratio of 2:1, the bioreactors achieved optimum removal efficiencies of 80% for TSS, 83% for VSS, 86–88% for COD, 82–87% for sulfate, 73% for sulfide, and 73% for sulfite. The nutrient removal efficiency was 67% for nitrate and 72% for nitrite. The acidity and alkalinity were effectively controlled, with alkalinity values reaching up to 2161 ± 92.5 mg/L and pH within the range of 7–7.24. The findings demonstrated that anaerobic bioreactor at a COD/sulfate mass ratio of 2:1 significantly enhanced the degradation of organic matter coupling with sulfate reduction in swine wastewater, providing an efficient and sustainable treatment method.

We report a transformative epoxy system with a microalgae-derived bio-binder from hydrothermal liquefaction processing (HTL). The obtained bio-binder not only served as a curing agent for conventional epoxy resin (e.g., EPON 862), but also acted as a modifying agent to enhance the thermal and mechanical properties of the conventional epoxy resin. This game-changing epoxy/bio-binder system outperformed the conventional epoxy/hardener system in thermal stability and mechanical properties. Compared to the commercial EPON 862/EPIKURE W epoxy product, our epoxy/bio-binder system (35 wt.% bio-binder addition with respect to the epoxy) increased the temperature of 60% weight loss from 394 °C to 428 °C and the temperature of maximum decomposition rate from 382 °C to 413 °C, while the tensile, flexural, and impact performance of the cured epoxy improved in all cases by up to 64%. Our research could significantly impact the USD 38.2 billion global market of the epoxy-related industry by not only providing better thermal and mechanical performance of epoxy-based composite materials, but also simultaneously reducing the carbon footprint from the epoxy industry and relieving waste epoxy pollution.

This study addresses the challenge of simulating fluid flow around flexible net panels, which are critical components in aquaculture systems. Traditional grid-based Eulerian methods struggle with the complex deformations and interactions of such panels. To fill this gap, a numerical approach leveraging the Smoothed Particle Hydrodynamics (SPH) method is introduced. The proposed approach employs a knot-spring model to represent the flexible net panels. The nets are idealized as a structure of physical net-knots linked by virtual elastic springs. The SPH method is used to simulate the fluid flow, capturing the movement of the knots and the resulting deformation of the nets. The model's parameters and numerical schemes are detailed, along with the implementation process. The accuracy of the model is validated through comparisons with existing experimental and simulation data. The results indicate that the SPH-based approach accurately simulates the flow past flexible net panels, providing a detailed velocity distribution and deformation analysis. The study's findings demonstrate the potential of the SPH method for aquaculture engineering applications. The proposed approach offers a promising alternative to traditional methods, particularly in scenarios involving significant net deformation or interaction with other objects. The implications for the design and operation of aquaculture facilities are discussed, along with suggestions for future research directions.

Every spring, a large part of China is confronted with sand and dust storms (SDS) – mainly originating in the Gobi (including Chinese and Mongolian Gobi) and Taklamakan deserts. In March-April 2023, most of northern, northwestern and northeastern China was struck by three sandstorms that affected an area with more than 500 million people. In this study, aerosol optical, microphysical and radiative properties were studied during these SDS events using an integrated approach that combines satellite, terrestrial and re-analysis data. The results showed that dusty conditions were observed in most areas north of the Yangtze River (Chang Jiang) with daily average PM10 concentrations exceeding 1000 µg/m3 in many cities. VIIRS aerosol optical depth (AOD) at 550 nm during three SDS events exceeded a value of 1 throughout nearly the entire northern part of the country. The AERONET data obtained from the AOE_Baotou site showed a significant increase in total AOD and a corresponding decrease in AE during the SDS. The single scattering albedo (SSA), asymmetry parameter (ASY), real refractive index (RRI) and imaginary refractive index (IRI) values indicate an abundance of scattering coarse-mode particles. Aerosol radiative forcing (ARF) at top of the atmosphere and at the earth's surface was nearly always negative during the period and ranged from −48.5 to +2.7 Wm−2 and from −180.8 to −66.6 Wm−2, resulting in high positive ARF values at ATM (from +63.8 to +132.3 Wm−2). Each of these affects the heating of the atmosphere and cooling on the earth's surface. The atmospheric heating rates ranged from 1.8 to 3.7 K day−1. The formation of these SDS mainly resulted from the passage of cold fronts associated with low pressure systems in the Gobi and Taklamakan deserts, creating conditions for dust to rise into the atmosphere and move further downwind.

Leaf senescence is essential for the growth and development of deciduous trees in the next season. Larix gmelinii, a deciduous coniferous tree, exhibits its most distinctive feature by turning yellow in the autumn and eventually shedding its leaves, resulting in significant changes in its appearance during the fall. Lysine acetylation plays an important role in diverse cellular processes; however, limited knowledge is available regarding acetylations in the needle senescence of L. gmelinii. In this study, the proteomics and acetylated modification omics of two phenotypic leaves, yellow and green (senescent and non-senescent) needles, were analyzed before autumn defoliation. In total, 5022 proteins and 4469 unique acetylation sites in 2414 lysine acylated proteins were identified, and this resulted in the discovery of 1335 differentially expressed proteins (DEPs) and 605 differentially expressed acetylated proteins (DAPs) in yellow versus green needles. There are significant differences between the proteome and acetylome; only 269 proteins were found to be DEP and DAP, of which 136 proteins were consistently expressed in both the DEP and DAP, 91 proteins were upregulated, and 45 proteins were down-regulated. The DEPs participate in the metabolism of starch and sucrose, while the DAPs are involved in glycolysis and the tricarboxylic acid cycle. Among them, DEPs underwent significant changes in glycolysis and citric acid cycling. Most of the enzymes involved in glycolysis and the citrate cycle were acetylated. DAPs were down-regulated in glycolysis and up-regulated in the citrate cycle. In all, the results of this study reveal the important role of lysine acetylation in the senescence of L. gmelinii needles and provide a new perspective for understanding the molecular mechanism of leaf senescence and tree seasonal growth.

The automatic cultivation of corn has become a significant research focus, with precision equipment operation being a key aspect of smart agriculture’s advancement. This work explores the tracking process of corn, simulating the detection and approach phases while addressing three major challenges in multiple object tracking: severe occlusion, dense object presence, and varying viewing angles. To effectively simulate these challenging conditions, a multiple object tracking dataset using simulated corn was created. To enhance accuracy and stability in corn tracking, an optimization algorithm, YOLOv8MS, is proposed based on YOLOv8. Multi-layer Fusion Diffusion Network (MFDN) is proposed for improved detection of objects of varying sizes, and the Separated and Enhancement Attention Module (SEAM) is introduced to tackle occlusion issues. Experimental results show that YOLOv8MS significantly enhances the detection accuracy, tracking accuracy and tracking stability, achieving a mean average precision (mAP) of 89.6% and a multiple object tracking accuracy (MOTA) of 92.5%, which are 1% and 6.1% improvements over the original YOLOv8, respectively. Furthermore, there was an average improvement of 4% in the identity stability indicator of tracking. This work provides essential technical support for precision agriculture in detecting and tracking corn.

With the worldwide awareness of sustainability, biomass-derived carbon electrode materials for supercapacitors have attracted growing attention. In this research, for the first time, we explored the feasibility of making use of the carbon byproduct from hydrothermal liquefaction (HTL) of microalgae, termed herein as algae-derived carbon (ADC), to prepare sustainable carbon electrode materials for high-performance supercapacitor development. Specifically, we investigated carbon activation with a variety of activating reagents as well as N- and Fe-doping of the obtained ADC with the intention to enhance its electrochemical performance. We characterized the structure of the activated and doped ADCs using scanning electron microscope (SEM), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and BET surface area and pore analysis, and correlated the ADCs’ structure with their electrochemical performance as evaluated using cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), impedance, and cycle stability through an assembled symmetric two-electrode cell with 1 M H2SO4 as electrolyte. It was found that the ADC that is activated using KOH (KOH-ADC) showed the best electrochemical performance, and its specific capacitance was 14.1-fold larger with respect to that of the raw ADC and reached 234.5 F/g in the GCD test at a current density of 0.5 A/g. The KOH-ADC also demonstrated excellent capacitance retention (97% after 10,000 cycles at a high current density of 10 A/g) for stable long-term operations. This research pointed out a promising direction to develop sustainable electrode materials for supercapacitors from the carbon byproduct produced after HTL processing of algae.

Corn is a global crop that requires the breeding of superior varieties. A crucial aspect of the breeding process is the accurate extraction of phenotypic parameters from corn plants. The existing challenges in phenotypic parameter extraction include low precision, excessive manual involvement, prolonged processing time, and equipment complexity. This study addresses these challenges by opting for binocular cameras as the data acquisition equipment. The proposed stereo corn phenotype extraction algorithm (SCPE) leverages binocular images for phenotypic parameter extraction. The SCPE consists of two modules: the YOLOv7-SlimPose model and the phenotypic parameter extraction module. The YOLOv7-SlimPose model was developed by optimizing the neck component, refining the loss function, and pruning the model based on YOLOv7-Pose. This model can better detect bounding boxes and keypoints with fewer parameters. The phenotypic parameter extraction module can construct the skeleton of the corn plant and extract phenotypic parameters based on the coordinates of the keypoints detected. The results showed the effectiveness of the approach, with the YOLOv7-SlimPose model achieving a keypoint mean average precision (mAP) of 96.8% with 65.1 million parameters and a speed of 0.09 s/item. The phenotypic parameter extraction module processed one corn plant in approximately 0.2 s, resulting in a total time cost of 0.38 s for the entire SCPE algorithm to construct the skeleton and extract the phenotypic parameters. The SCPE algorithm is economical and effective for extracting phenotypic parameters from corn plants, and the skeleton of corn plants can be constructed to evaluate the growth of corn as a reference. This proposal can also serve as a valuable reference for similar functions in other crops such as sorghum, rice, and wheat.

Due to the extraordinary mechanical properties of ultra-high-performance concrete (UHPC), the shear stirrups in UHPC beams could potentially be eliminated. This study aimed to determine the effect of beam height and steel fiber volume content on the shear behavior of non-stirrup UHPC beams under a larger shear span–depth ratio (up to 2.8). Eight beams were designed and fabricated including six non-stirrup UHPC beams and two comparing stirrup-reinforced normal concrete (NC) beams. The experimental results demonstrated that the steel fiber volume content could be a crucial factor affecting the ductility, cracking strength, and shear capacity of non-stirrup UHPC beams and altering their failure modes. Additionally, the height of the beam had a considerable effect on its shear resistance. French standard formulae were more accurate for the UHPC beams with larger shear span–depth ratios, PCI-2021 formulae greatly overestimated the shear capacity of UHPC beams with larger shear span–depth ratios, and Xu’s formulae were more accurate for the steel fiber-reinforced UHPC beams with larger shear span–depth ratios. In summary, French standard formulae were the most suitable formulae for predicting the shear capacity of UHPC beams in this paper.

The study provides an in-depth study on the gas adsorption and transport behaviors of MOF glass membranes for the first time. Temperature dependance of gas permeability, adsorption coefficients and diffusion coefficients between 298 ​K and 313 ​K for TIF-4 and ZIF-62 glass membrane were evaluated. The CO2 permeability was dominated by the adsorption process, while CH4 transport was mainly driven by the activated diffusion. Further, the MOF glass membranes exhibited significant entropic selectivity in adsorption, along with notable enthalpic selectivity in diffusion.

Single crystals of the intermetallic phase with composition Ti4Ni2C were serendipitously obtained by high-pressure sintering of a mixture with initial chemical composition Ti2Ni. The Ti4Ni2C phase crystallizes in the Fd\overline{3}m space group and can be considered as a partially filled Ti2Ni structure with the C atom occupying an octahedral void. Ti4Ni2C is isotypic with Ti4Ni2O, Nb4Ni2C and Ta4Ni2C, all of which were studied previously by means of powder diffraction.

Microglia surveillance manifests itself as dynamic changes in cell morphology and functional remodeling. Whether and how microglia surveillance is coupled to brain state switches during natural sleep-wake cycles remains unclear. To address this question, we used miniature two-photon microscopy (mTPM) to acquire time-lapse high-resolution microglia images of the somatosensory cortex, along with EEG/EMG recordings and behavioral video, in freely-behaving mice. We uncovered fast and robust brain state-dependent changes in microglia surveillance, occurring in parallel with sleep dynamics and early-onset phagocytic microglial contraction during sleep deprivation stress. We also detected local norepinephrine fluctuation occurring in a sleep state-dependent manner. We showed that the locus coeruleus-norepinephrine system, which is crucial to sleep homeostasis, is required for both sleep state-dependent and stress-induced microglial responses and β2-adrenergic receptor signaling plays a significant role in this process. These results provide direct evidence that microglial surveillance is exquisitely tuned to signals and stressors that regulate sleep dynamics and homeostasis so as to adjust its varied roles to complement those of neurons in the brain. In vivo imaging with mTPM in freely behaving animals, as demonstrated here, opens a new avenue for future investigation of microglia dynamics and sleep biology in freely behaving animals.

Three B2-type intermetallic AlFe1 – δ phases (0.18 < δ < 0.05) in the Al–Fe binary system were synthesized by smelting and high temperature sintering methods. The exact crystal structure for δ = 0.05 was refined by single-crystal X-ray diffraction. The amount of vacancy defects at the Fe atom sites was obtained by refining the corresponding site occupancy factor, converging to the chemical formula AlFe0.95, with a structure identical to that of ideal AlFe models inferred from powder X-ray or neutron diffraction patterns.

Abstract The responses of atmospheric kinetic energy (KE) spectra to three convective parameterizations (CPs) in global high‐resolution simulations are revealed. The results show that the KE spectra exhibit high sensitivity to the CPs, mainly at mesoscales in the middle and upper troposphere. The New Tiedtke scheme produces the steepest mesoscale slope, followed by the Kain‐Fritsch scheme and then the Grell‐Freitas scheme. In general, there is a compensating relationship between latent heat released by the CP and microphysics parameterization (MP). The less latent heat released by the CP is compensated by the more latent heat released by the MP. The shallowest mesoscale spectra for the Grell‐Freitas scheme are related to the strongest downscale cascade dominated by the rotational component of the flow, and this is attributed to more latent heat released from MP enhancing the intensity of vorticity in the troposphere and producing more gravity wave activities in the lower stratosphere.

IntroductionMethylenetetrahydrofolate dehydrogenase 2 (MTHFD2), whose aberrant expression is common in cancers, has recently been identified as a potential regulator of immune response. However, its immune-related role in bladder cancer (BLCA) and its association with immunotherapy efficacy remain unclear.MethodsRNA sequencing data from The Cancer Genome Atlas (TCGA) was applied to analyze the immunological roles and prognostic value of MTHFD2 in pan-cancers. The association of MTHFD2 with several immunological features of tumor microenvironment (TME), including cancer-immunity cycle, immune cells infiltration, immune checkpoints expression, and T cell inflamed score was analyzed in TCGA-BLCA cohort. The predictors of cancer treatments effectiveness, including the expression and mutation of certain genes, molecular subtypes, and several signatures were evaluated as well. These results were validated by another independent cohort (GSE48075). Finally, the predictive value of MTHFD2 for TME and immunotherapy efficacy were validated using immunohistochemistry assay and RNA sequencing data from IMvigor210 cohort, respectively. ResultsMTHFD2 was found to be positively associated with several immunological features of an inflamed tumor microenvironment (TME) in various cancers and could predict BLCA patients’ prognosis. In BLCA, high expression of MTHFD2 was observed to be positively related with the cancer–immunity cycle, the infiltration of several immune cells, and the expression of immunoregulators and T-cell inflamed scores, indicating a positive correlation with the inflamed TME. Moreover, patients with high MTHFD2 expression were more likely to be basal-like subtypes and respond to BLCA treatments, including immunotherapy, chemotherapy, and target therapy. The clinical data of the IMvigor210 cohort confirmed the higher response rates and better survival benefits of immunotherapy in high-MTHFD2-expression patients.ConclusionCollectively, high MTHFD2 predicts an inflamed TME, a basal-like subtype, and a better response to various therapeutic strategies, especially the ICB therapy, in bladder cancer.

Background: Cytotoxic T-lymphocyte associated antigen-4 (CTLA-4) is an immunosuppressive checkpoint that is involved in the development and metastasis of cancers. Several studies revealed that CTLA-4 rs231775A/G polymorphism may be associated with the risk of cancer in some populations, but the conclusions of these studies are not consistent. Methods: We conducted a pooled analysis with eligible studies to explore the association between the CTLA-4 rs231775 variant and cancer risk. Additionally, we used in silico tools to evaluated the expression of CTLA-4 on urinary system cancer. Moreover, we adopted the enzyme-linked immunosorbent assay (ELISA), and Gene Set Enrichment Analysis (GSEA) to investigate the effects of CTLA-4 on bladder cancer (BLCA). Results: In total, 92 case-control studies involving 29,987 patients with cancer and 36,484 healthy individuals (controls) were included in the pooled analysis. In the stratified analysis based on cancer type, the rs231775 A/G polymorphism was associated with increased bladder cancer risk in the heterozygote contrast model (OR = 1.23, 95% CI = 1.01–1.51, P = 0.040). The race-stratified analysis revealed that East Asians with the GG genotype had a 12% lower risk of developing cancer than those with the GA + AA genotype (95% CI = 0.81–0.95, P = 0.001). The in silico analysis showed that CTLA-4 expression was augmented in patients with BLCA. The ELISA results revealed that CTLA-4 expression was reduced in patients with BLCA carrying the AA genotype. Several signaling pathways, including cytokine-cytokine receptor interactions and T-cell receptor signaling, were associated with CTLA-4 expression. Conclusion: The CTLA-4 rs231775 A/G polymorphism is associated with cancer risk in East Asian population. This polymorphism is especially associated with BLCA.

Net ecosystem productivity (NEP) quantifies magnitude of the terrestrial vegetation carbon sinks. Drought is one of the most important stressors affecting vegetation NEP. At present, the spatiotemporal dynamics of vegetation NEP in drought-prone of Northwest China (NWC) lack discussion under different climatic zones and land cover types, and the response of vegetation NEP to drought remains unclear. Hence, we estimated the vegetation NEP in NWC using ground and remote sensing data and quantified the spatiotemporal differentiation of NEP under different climatic zones and land cover types. The drought fluorescence monitoring index (DFMI) was developed to examine the relationship between vegetation NEP and drought response based on the solar-induced chlorophyll fluorescence (SIF) data. Our results suggested that vegetation carbon sinks increased significantly at 7.09 g C m−2 yr−1 in NWC during 2000–2019, mainly in northern Shaanxi, eastern and southern Gansu, and southern Ningxia. NEP showed increasing trends under different climatic zones and land cover types, but there were differences in carbon sink capacity. The strongest carbon sink capacity was in humid regions and forests, while the weakest was in arid regions and grasslands. The vegetation carbon sinks showed a non-linear relationship with the drought degree reflecting multiple trend differences, especially in forests and grasslands. The response to drought was faster and more significant in semi-arid and semi-humid transition zones and extreme humid regions when vegetation carbon sinks decreased. DFMI was a good indicator to monitor drought conditions in NWC. NEP and DFMI were an 8–20-month periodic positive correlation and showed a high correlation with high–high and low–low clustering spatially. Drought significantly weakened vegetation carbon sinks in NWC. This study emphasizes the demand to rapidly identify climatic conditions that lead to decrease significantly in vegetation carbon sinks and to formulate adaptation strategies aimed at reducing drought risk under global warming.