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Key message: Two significant studies have unveiled the pivotal role of BR regulation in shaping distinct features: the clustered-spikelet architecture in rice and the superior semi-dwarf stature in wheat. [ABSTRACT FROM AUTHOR]

Aqueous Zn‐ion batteries (ZIBs) are promising next‐generation energy storage devices, yet suffer from the issues of hydrogen evolution reaction (HER) and intricate side reactions on the Zn anode surface. The hydrogen (H)‐bond networks play a critical role in interfacial proton transport that may closely relate to HER but are rarely investigated. Herein, we report a self‐assembled monolayer (SAM) strategy which is constructed by anchoring ionic liquid cations on Ti3C2Tx substrate for HER‐free Zn anode. Molecule dynamics simulations reveal that the rationally designed SAM with a high coordination number of water molecules (25–27, 4–6 for Zn2+) largely reduces the interfacial densities of H2O molecules, therefore breaking the connectivity of H‐bond networks and blocking proton transport on the interface, by which the HER is suppressed. Then, a series of in situ characterizations demonstrate that negligible amounts of H2 gas are collected from the Zn@SAM‐MXene anode. Consequently, the symmetric cell enables a long‐cycling life of 3000 h at 1 mA cm−2 and 1000 h at 5 mA cm−2. More significantly, the stable Zn@SAM‐MXene films are successfully used for coin full cells showing high‐capacity retention of over 94 % after 1000 cycles and large‐area (10×5 cm2) pouch cells with desired performance. [ABSTRACT FROM AUTHOR]

Aqueous Zn‐ion batteries (ZIBs) are promising next‐generation energy storage devices, yet suffer from the issues of hydrogen evolution reaction (HER) and intricate side reactions on the Zn anode surface. The hydrogen (H)‐bond networks play a critical role in interfacial proton transport that may closely relate to HER but are rarely investigated. Herein, we report a self‐assembled monolayer (SAM) strategy which is constructed by anchoring ionic liquid cations on Ti3C2Tx substrate for HER‐free Zn anode. Molecule dynamics simulations reveal that the rationally designed SAM with a high coordination number of water molecules (25–27, 4–6 for Zn2+) largely reduces the interfacial densities of H2O molecules, therefore breaking the connectivity of H‐bond networks and blocking proton transport on the interface, by which the HER is suppressed. Then, a series of in situ characterizations demonstrate that negligible amounts of H2 gas are collected from the Zn@SAM‐MXene anode. Consequently, the symmetric cell enables a long‐cycling life of 3000 h at 1 mA cm−2 and 1000 h at 5 mA cm−2. More significantly, the stable Zn@SAM‐MXene films are successfully used for coin full cells showing high‐capacity retention of over 94 % after 1000 cycles and large‐area (10×5 cm2) pouch cells with desired performance. [ABSTRACT FROM AUTHOR]

Practical Application This study aimed to evaluate the anti‐cervical cancer activity of chondroitin sulfate–functionalized selenium nanoparticles (SeCS) and to elucidate their action mechanism. Cytotoxic effect of SeCS on HeLa cells was assessed by MTT assay. Further molecular mechanism of SeCS was analyzed by flow cytometric assay and western blotting. The results showed that treatment with SeCS resulted in a dose‐ and time‐dependent inhibition in the proliferation of HeLa cells. The data obtained from flow cytometry demonstrated that SeCS inhibited HeLa cell growth via the induction of S‐phase arrest and cell apoptosis. Further mechanism analysis found that SeCS down‐regulated expression levels of cyclin A and CDK2 and up‐regulated p21 expression, which contributed to S arrest. Moreover, SeCS increased the level of Bax and decreased the expression of Bcl‐2, resulting in the release of cytochrome C from mitochondria and activating caspase‐3/8/9 for caspase‐dependent apoptosis. Meanwhile, intracellular reactive oxygen species (ROS) levels were elevated after SeCS treatment, suggesting that ROS might be upstream of SeCS‐induced S‐phase arrest and cell apoptosis. These data show that SeCS has anti‐tumor effects and possesses the potential to become a new therapeutic agent or adjuvant therapy for cancer patients.In our previous study, we used chondroitin sulfate to stabilize nano‐selenium to obtain SeCS to improve the bioactivity and stability of nano‐selenium. We found that it possessed an inhibitory effect on HeLa cells. However, the molecular mechanism remains unclear. This study elucidated the mechanism of SeCS damage to HeLa cells. SeCS has the potential to become a new therapeutic agent or adjuvant therapy for cancer patients. [ABSTRACT FROM AUTHOR]

The chemical distribution on the lunar surface results from the combined effects of both endogenic and exogenic geological processes. Exploring global maps of chemical composition helps to gain insights into the compositional variation among three major geological units, unraveling the geological evolution of the Moon. The existing oxide abundance maps were obtained from spectral images of remote sensing and geochemical data from samples returned by Apollo and Luna, missing the chemical characteristics of the Moon's late critical period. In this study, by adding geochemical data from Chang'e (CE)-5 lunar samples, we construct inversion models between the Christiansen feature (CF) and oxide abundance of lunar samples using the particle swarm optimization–extreme gradient boosting (PSO-XGBoost) algorithm. Then, new global oxide maps (Al2O3, CaO, FeO, and MgO) and Mg# with the resolution of 32 pixels/degree (ppd) were produced, which reduced the space weathering effect to some extent. The PSO-XGBoost models were compared with partial least square regression (PLSR) models and four previous results, indicating that PSO-XGBoost models possess the capability to effectively describe nonlinear relationships between CF and oxide abundance. Furthermore, the average contents of our results and the Diviner results for 21 major maria demonstrate high correlations, with R2 of 0.95, 0.82, 0.95, and 0.86, respectively. In addition, a new Mg# map was generated, which reveals different magmatic evolutionary processes in the three geologic units. [ABSTRACT FROM AUTHOR]

For high-precision positioning applications, various GNSS errors need to be mitigated, including the tropospheric error, which remains a significant error source as it can reach up to a few meters. Although some commercial GNSS correction data providers, such as the Quasi-Zenith Satellite System (QZSS) Centimeter Level Augmentation Service (CLAS), have developed real-time precise regional troposphere products, the service is available only in limited regional areas. The International GNSS Service (IGS) has provided precise troposphere correction data in TRO format post-mission, but its long latency of 1 to 2 weeks makes it unable to support real-time applications. In this work, a real-time troposphere prediction method based on the IGS post-processing products was developed using machine learning techniques to eliminate the long latency problem. The test results from tropospheric predictions over a year using the proposed method indicate that the new method can achieve a prediction accuracy (RMSE) of 2 cm, making it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Mare basalts on the lunar surface are tangible expressions of the complex thermal evolution and geological processes that have occurred within the lunar interior. These basaltic manifestations are highly important because they provide invaluable insights into lunar geological evolution. Notably, the Oceanus Procellarum region, which is renowned for its extensive and long-lasting basaltic volcanism, is a premier location to investigate late-stage lunar thermal evolution. The primary aim of this research is to elucidate the geomorphological, compositional, and temporal attributes that define the mare basalts within the Oceanus Procellarum region. To achieve this aim, we comprehensively analyzed the geomorphological features present within the region, leveraging Kaguya/SELENE TC images and digital elevation models. Specifically, these geomorphological features encompass impact craters, wrinkle ridges, sinuous rilles, and volcanic domes. Subsequently, we thoroughly examined the mineralogical attributes of basalts in the Oceanus Procellarum region, leveraging Kaguya/SELENE MI data and compositional map products. To more accurately reflect the actual ages of the mare basalts in the Oceanus Procellarum region, we carefully delineated the geological units within the area and employed the latest crater size-frequency distribution (CSFD) technique to precisely determine their ages. This refined approach allowed for a more comprehensive and accurate understanding of the basaltic rocks in the study area. Overall, our comprehensive study included an in-depth analysis of the volcanic activity and evolution of the Oceanus Procellarum region, along with an examination of the correlation between the mineralogical composition and ages of mare basalts. The findings from this exhaustive investigation reveal a definitive age range for basalt units within the Oceanus Procellarum region from approximately 3.69 Ga to 1.17 Ga. Moreover, the latest mare basalts that formed were pinpointed north of the Aristarchus crater. Significantly, the region has experienced at least five distinct volcanic events, occurring approximately 3.40 Ga, 2.92 Ga, 2.39 Ga, 2.07 Ga, and 1.43 Ga, leading to the formation of multiple basalt units characterized by their unique mineral compositions and elemental abundances. Through the application of remote sensing mineralogical analysis, three primary basalt types were identified: low-titanium, very-low-titanium, and intermediate-titanium basalt. Notably, the younger basalt units exhibit an elevated titanium proportion, indicative of progressive olivine enrichment. Consequently, these younger basalt units exhibit more intricate and complex mineral compositions, offering valuable insights into the dynamic geological processes shaping the lunar surface. [ABSTRACT FROM AUTHOR]

Sandstone-hosted uranium deposits are indeed significant sources of uranium resources globally. They are typically found in sedimentary basins and have been extensively explored and exploited in various countries. They play a significant role in meeting global uranium demand and are considered important resources for nuclear energy production. Erlian Basin, as one of the sedimentary basins in northern China, is known for its uranium mineralization hosted within sandstone formations. In this research, machine learning (ML) methodology was applied to mineral prospectivity mapping (MPM) of the metallogenic zone in the Manite depression of the Erlian Basin. An ML model of 92% accuracy was implemented with the random forest algorithm. Additionally, the confusion matrix and receiver operating characteristic curve were used as model evaluation indicators. Furthermore, the model explainability research with post hoc interpretability algorithms bridged the gap between complex opaque (black-box) models and geological cognition, enabling the effective and responsible use of AI technologies. The MPM results shown in QGIS provided vivid geological insights for ML-based metallogenic prediction. With the favorable prospective targets delineated, geologists can make decisions for further uranium exploration. [ABSTRACT FROM AUTHOR]

Objective: To establish and validate an individualized nomogram to predict mortality risk within 30 days in patients with sepsis from the emergency department. Methods: Data of 1205 sepsis patients who were admitted to the emergency department in a tertiary hospital between Jun 2013 and Sep 2021 were collected and divided into a training group and a validation group at a ratio of 7:3. The independent risk factors related to 30-day mortality were identified by univariate and multivariate analysis in the training group and used to construct the nomogram. The model was evaluated by receiver operating characteristic (ROC) curve, calibration chart and decision curve analysis. The model was validated in patients of the validation group and its performance was confirmed by comparing to other models based on SOFA score and machine learning methods. Results: The independent risk factors of 30-day mortality of sepsis patients included pro-brain natriuretic peptide, lactic acid, oxygenation index (PaO2/FiO2), mean arterial pressure, and hematocrit. The AUCs of the nomogram in the training and verification groups were 0.820 (95% CI: 0.780–0.860) and 0.849 (95% CI: 0.783–0.915), respectively, and the respective P-values of the calibration chart were 0.996 and 0.955. The DCA curves of both groups were above the two extreme curves, indicating high clinical efficacy. The AUC values were 0.847 for the model established by the random forest method and 0.835 for the model established by the stacking method. The AUCs of SOFA model in the model and validation groups were 0.761 and 0.753, respectively. Conclusion: The sepsis nomogram can predict the risk of death within 30 days in sepsis patients with high accuracy, which will be helpful for clinical decision-making. [ABSTRACT FROM AUTHOR]

Aqueous zinc-ion batteries (ZIBs) have significant potential for large energy storage systems because of their high energy density, cost-effectiveness and environmental friendliness. However, the limited voltage window, poor reaction kinetics and structural instability of cathode materials are current bottlenecks which contain the further development of ZIBs. In this work, we rationally design a Ni-doped V2O5@3D Ni core/shell composite on a carbon cloth electrode (Ni-V2O5@3D Ni@CC) by growing Ni-V2O5 on free-standing 3D Ni metal nanonets for high-voltage and high-capacity ZIBs. Impressively, embedded Ni doping increases the interlayer spacing of V2O5, extending the working voltage and improving the zinc-ion (Zn302+) reaction kinetics of the cathode materials; at the same time, the 3D structure, with its high specific surface area and superior electronic conductivity, aids in fast Zn302+ transport. Consequently, the as-designed Ni-V2O5@3D Ni@CC cathodes can operate within a wide voltage window from 0.3 to 1.8 V vs. Zn30/Zn302+ and deliver a high capacity of 270 mAh g−1 (~1050 mAh cm−3) at a high current density of 0.8 A g−1. In addition, reversible Zn2+ (de)incorporation reaction mechanisms in the Ni-V2O5@3D Ni@CC cathodes are investigated through multiple characterization methods (SEM, TEM, XRD, XPS, etc.). As a result, we achieved significant progress toward practical applications of ZIBs. [ABSTRACT FROM AUTHOR]

Significant interest has been focused on recovery rates, recovery options, and recovery utilization when tailings impoundments are re-mined. However, the stability of the tailings dams during the recovery process is also a severe issue. Based on engineering geological surveys and laboratory tests, the evolution of the Tongling tailings impoundment's instability characteristics under different recovery heights and diverse working conditions was analyzed by numerical simulation. Firstly, with the help of 2D software, the position of the tailings dam infiltration line and the alteration of the dam safety factor during the stoping process were calculated. Secondly, 3Dmine (2017) software was used to create the 3D surface structure of the tailings impoundment, and then a 3D numerical analysis model was established by means of Midas GTS NX software. The numerical simulation of seepage and stress analyses were conducted based on the model. Consequently, the evolution of the stability characteristics of tailings dam under different operating conditions was calculated. The research demonstrates that the dry beach length of the tailings pond gradually reduces with a decrease in the extraction height, resulting in a lower infiltration line. Under flood conditions, the saturation line has partial overflow due to the poor seepage discharge capacity of the dam. The total displacement of the dam body is inversely proportional to the retrieval height. The more extreme the analyzed working conditions, the more the safety factor will be reduced. Additionally, the plastic variation area of the dam body will be more comprehensive, which will increase the risk of a dam collapse. [ABSTRACT FROM AUTHOR]

Background: Leishmaniasis is one of the most neglected tropical diseases and is spread mainly in impoverished regions of the world. Although many studies have focused on the host's response to Leishmania invasion, relatively less is known about the complex processes at the metabolic level, especially the metabolic alterations in the infected hosts. Methods: In this study, we conducted metabolomics analysis on the urine of golden hamsters in the presence or absence of visceral leishmaniasis (VL) using the ultra-performance liquid chromatography (UPLC) system tandem high-resolution mass spectrometer (HRMS). The metabolic characteristics of urine samples, along with the histopathological change and the parasite burden of liver and spleen tissues, were detected at 4 and 12 weeks post infection (WPI), respectively. Results: Amino acid metabolism was extensively affected at both stages of VL progression. Meanwhile, there were also distinct metabolic features at different stages. At 4 WPI, the significantly affected metabolic pathways involved alanine, aspartate and glutamate metabolism, the pentose phosphate pathway (PPP), histidine metabolism, tryptophan metabolism and tyrosine metabolism. At 12 WPI, the markedly enriched metabolic pathways were almost concentrated on amino acid metabolism, including tyrosine metabolism, taurine and hypotaurine metabolism and tryptophan metabolism. The dysregulated metabolites and metabolic pathways at 12 WPI were obviously less than those at 4 WPI. In addition, seven metabolites that were dysregulated at both stages through partial least squares-discriminant analysis (PLS-DA) and receiver-operating characteristic (ROC) tests were screened to be of diagnostic potential. The combination of these metabolites as a potential biomarker panel showed satisfactory performance in distinguishing infection groups from control groups as well as among different stages of infection. Conclusion: Our findings could provide valuable information for further understanding of the host response to Leishmania infection from the aspect of the urine metabolome. The proposed urine biomarker panel could help in the development of a novel approach for the diagnosis and prognosis of VL. [ABSTRACT FROM AUTHOR]

This study was to examine the protective effects of curcumin/cyclodextrin polymer inclusion complex (CUR/CDP) on ethanol‐induced liver injury in mice and to explore its potential mechanisms. In the ethanol‐induced acute injury mouse model, the effects of pretreatment with silymarin, cyclodextrin polymer (CDP), curcumin (CUR) and CUR/CDP at low, middle, and high doses were evaluated by biochemical and histopathological examination. The liver index, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) levels in serum of the mice were measured. The superoxide dismutase (SOD), glutathione peroxidase (GSH‐PX) activities, and malondialdehyde (MDA) level in liver tissue were assessed by assay kits. Moreover, hematoxylin–eosin (HE) staining was carried out to observe pathological changes of liver. Western blotting was performed for determining the changes in the expressions of DNA damage‐associated proteins. The results showed that compared with the control group, the liver index and the levels of ALT, AST, LDH, and MDA in the ethanol treatment group were significantly increased and the activities of GSH‐Px and SOD were obviously decreased. However, pretreatment with silymarin, CUR, and CUR/CDP reversed the change of above indicators except CDP. Moreover, CUR/CDP at high dose further weakened the liver index, inhibited the biochemical indexes, and enhanced the activities of antioxidant enzymes to a greater extent than silymarin and CUR. Western blot analysis indicated that CUR/CDP significantly down‐regulated the expressions of DNA damage‐related proteins including p‐ATM, γ‐H2AX, p‐p53, and p‐p38MAPK, which inhibited ethanol‐induced the G2/M arrest and ultimately prevented liver function from oxidative stress injury. These results indicated that CUR/CDP possessed good protective effect on mice liver damage in vivo by increasing the activities of GSH‐Px and SOD to suppress DNA damage. [ABSTRACT FROM AUTHOR]

The rock slopes along the Sichuan–Tibet railway are characterized by noncontactless, inaccessible, dramatically fluctuating topography and large-scale areas. Numerous high-position, concealed dangerous rock blocks (HPCDRBs) are located on such slopes, potentially threatening the construction and safety operation of the Sichuan–Tibet railway. To this end, a new framework is proposed for geometrical investigation and stability analysis of HPCDRBs. The main steps of the framework are: (1) establishment of a three-dimensional high-resolution rock surface model using a new unmanned aerial vehicle-based photography method; (2) identification of possible HPCDRBs based on the theory of structure-controlled rock mass; (3) geometrical characterization of possible HPCDRBs utilizing polyhedral models; and (4) application of block theory for the stability analysis of possible HPCDRBs. In this framework, the actual positions and geometric information of these blocks are well-reflected, and the obtained parameters can directly serve the stability analysis. The proposed method is applied to an ultrahigh-steep rock slope located on the left bank of the Kang Yu Qu (KYQ) Bridge of the Sichuan–Tibet railway. A total of 40 HPCDRBs with volumes between 2.1 and 148.2 m3 are determined, and the geometric shapes are mainly hexahedron and heptahedron. The mean sliding orientation of these blocks is 124.6°∠51.4°. A seismic stability analysis is finally conducted to further discuss the stability of the HPCDRBs under a real earthquake. In summary, the collapse of the unstable HPCDRBs may cause single block falls which pose a potential threat to the KYQ Bridge. [ABSTRACT FROM AUTHOR]

Old deposits are typical products of the rapid topographical changes on the southeastern margin of the Qinghai-Tibet Plateau and its surroundings. Catastrophic landslides often occur in unstable areas of old deposits, causing great damage to life, infrastructure, and the environment. This study takes the Sangushui old deposit as the research object. Considering the large number of people and buildings on the deposit surface, a potential failure analysis of the deposit is important for disaster prevention and control. The field investigation revealed the existence of local deformation and failure. Small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology was used to analyze the surface displacement characteristics of the deposit and to determine the potential instability zone (Zones A and B). Following this, a finite element model (FEM) was used to analyze the stability of the deposit in natural and seismic conditions. The results reveal the existence of two potential failure zones at the front of the deposit. The volumes of Part I and Part II are determined as 974.7 × 104 m3 and 587.5 × 104 m3, respectively. The location of the failure zone is essentially consistent with the results of the SBAS-InSAR analysis. Affected by the topography, material composition and flow velocity of the Jinsha River, although the potential landslides will not block the river for a long period of time, they may induce damage to the cultivated land and infrastructure. Therefore, it is necessary to define and adopt preventive measures and establish an early warning system. [ABSTRACT FROM AUTHOR]

As the need to discovers new mineral deposits and occurrences has intensified in recent years, it has become increasingly apparent that we need to map potentials via integrated information on the basis of metallogeny. Occurrences of mineralization such as tungsten (W), tin (Sn), columbium (Nb), tantalum (Ta), gold (Au), copper (Cu), lead (Pb), zinc (Zn), manganese (Mn) and monazite (Mnz) have been discovered in Rwanda. The objective of this study was to present a regional quantitative mineral prospectivity mapping (MPM) of W, Sn and Nb-Ta mineralization in Rwanda using the random forest (RF) method on the basis of open source data, such as geological maps, Bouguer gravity anomalies, magnetic anomalies, Landsat 8 images, ASTER GDEM, Globeland30, and OpenStreetMap. In addition, a newly introduced interpolation–density–delineation (IDD) process was applied to deal with the blank (masked) areas in remotely sensed mineral alteration extraction. Additionally, a k2-fold cross-validation method was also proposed to obtain more reasonable test errors. Firstly, the metallogenic regularity of W, Sn and Nb-Ta in Rwanda was summarized with the help of articles online. Secondly, original geological, geophysical, and remote sensing data were utilized to generate secondary data. Specifically, the IDD process was applied subsequent to the directed principal component analysis method (DPCA) to reconstruct the alteration anomaly map, and a relevant dataset was formed by the combination of original and secondary data. Thirdly, specific predictor layers for W, Sn and Nb-Ta were selected from relevant data via spatial correlation with known deposits, respectively, and the predictive models were established. Finally, near 26,000 squares were zoned in Rwanda, and RF was optimized and applied, the k2-fold cross-validation method was utilized to assess test errors, metallogenic belts and prospective areas for W, Sn, and Nb-Ta were delineated on the basis of total mineralization potential map and likelihoods map. Results proved that the open source data online were valid for drawing a preliminary mineralization potential map. Furthermore, it was also shown that the IDD method is suitable for the postprocessing of masked alteration anomaly maps. Belt IV-4 in the northwest and belt IV-2, IV-1 in the middle-east of Rwanda, containing a number of prospective areas, possess considerable likelihoods of deposits, and mining in Rwanda is at its dawn, with potential worth expecting. [ABSTRACT FROM AUTHOR]

Purpose: The TMEM196 protein, which comprises four membrane-spanning domains, belongs to the TMEM protein family. TMEM196 was identified as a candidate tumor suppressor gene in lung cancer. However, its role and mechanism in lung cancer metastasis remain unclear. Here, we study the role of TMEM196 in tumor metastasis to further verify the function in lung cancer. Methods: In this study, we used qRT-PCR, western blot analysis and immunohistochemistry to examine the expression levels of TMEM196 and related proteins in lung cancer tissues and tumor cells. We utilized Transwell assays, xenograft nude mouse models, and TMEM196−/− mouse models to evaluate the effects of TMEM196 on tumor invasion and metastasis. Finally, we used bioinformatics analysis and dual-luciferase reporter gene assays to explore the molecular mechanism of TMEM196 as a tumor suppressor. Results: We found that TMEM196 mRNA and protein expression levels were significantly decreased in lung cancer tissues and cells. Low expression of TMEM196 in clinical patients was associated with poor prognosis. TMEM196 strongly inhibited tumor metastasis and progression in vitro and in vivo. The primary lung tumors induced by tail vein-inoculated B16 cells in TMEM196−/− mice were significantly larger than those in TMEM196+/+ mice. Mechanistically, TMEM196 inhibited the Wnt signaling pathway and repressed β-catenin promoter transcription. TMEM196 silencing in lung cancer cells and mice resulted in significant upregulation of the expression of β-catenin and Wnt signaling pathway downstream target genes (MMP2 and MMP7). Decreasing β-catenin expression in TMEM196-silenced cancer cells attenuated the antimetastatic effect of TMEM196. Conclusions: Our results revealed that TMEM196 acts as a novel lung cancer metastasis suppressor via the Wnt/β-catenin signaling pathway. [ABSTRACT FROM AUTHOR]

Nowadays, millions of patients suffer from physical disabilities, including lower-limb disabilities. Researchers have adopted a variety of physical therapies based on the lower-limb exoskeleton, in which it is difficult to adjust equipment parameters in a timely fashion. Therefore, intelligent control methods, for example, deep reinforcement learning (DRL), have been used to control the medical equipment used in human gait adjustment. In this study, based on the key-value attention mechanism, we reconstructed the agent's observations by capturing the self-dependent feature information for decision-making in regard to each state sampled from the replay buffer. Moreover, based on Softmax Deep Double Deterministic policy gradients (SD3), a novel DRL-based framework, key-value attention-based SD3 (AT_SD3), has been proposed for gait adjustment. We demonstrated the effectiveness of our proposed framework in gait adjustment by comparing different gait trajectories, including the desired trajectory and the adjusted trajectory. The results showed that the simulated trajectories were closer to the desired trajectory, both in their shapes and values. Furthermore, by comparing the results of our experiments with those of other state-of-the-art methods, the results proved that our proposed framework exhibited better performance. [ABSTRACT FROM AUTHOR]

A large amount of lacustrine sediments was found in the Suwalong reach, which is located upstream of the Jinsha River at the southeastern margin of the Tibetan Plateau. The study area belongs to the alpine valleys, with arid and semiarid climate. The appearance of lacustrine sediments indicates that river-blocking events had occurred in this reach and formed stable dammed lakes. By optically stimulated luminescence (OSL) dating, the burial time of lacustrine sediments was analysed, and the time sequence of river blocking and dam breaking in this reach was obtained. According to the burial time and thickness of lacustrine sediments, the deposition rate of sediments was obtained, and the maximum deposition rate is 170.5 mm/a, indicating that the provenance was abundant during the formation of lacustrine sediments. The results of grain size and SEM analysis show that the lacustrine sediments were mainly transported into lakes by wind, and the results of major element analysis also indicate that lacustrine sediments did not come from local areas. Combined with the above experimental results, we come to the conclusion that during the river-blocking period, the tectonic activities in the Suwalong reach and its vicinity were very active, triggered numerous landslides and rockfalls, and produced abundant silt particles. These silt particles were transported into the dammed lake of the Suwalong reach by wind. [ABSTRACT FROM AUTHOR]

One of the most critical processes for the long-term management of groundwater resources is Groundwater Potential Zonation (GWPZ). Despite their importance, traditional groundwater studies are costly, difficult, complex, and time-consuming. This study aims to investigate GWPZ mapping for the Al-Qalamoun region, in the Western part of Syria. We combined the Multi-Influence Factor (MIF) and Analytic Hierarchy Process (AHP) methods with the Geographic Information Systems (GIS) to estimate the GWPZ. The weight and score factors of eight factors were used to develop the GWPZ including drainage density, lithology, slope, lineament density, geomorphology, land use/land cover, rainfall, and soil. According to the findings, about 46% and 50.6% of the total area of the Al-Qalamoun region was classified as suitable for groundwater recharge by the AHP and MIF methods, respectively. However, 54% and 49.4% of the area was classified as having poor suitability for groundwater recharge by the AHP and MIF methods, respectively. These areas with poor suitability can be utilized for gathering surface water. The validation of the results showed that the AHP and MIF methods have similar accuracy for the GWPZ; however, the accuracy and results depend on influencing factors and their weights assigned by experts. [ABSTRACT FROM AUTHOR]

Phytohormones play important roles in plant immunity. Recently, Chen et al. discovered that the tomato spotted wilt virus attacks the plant hormone receptor to promote infection. Plants evolved an immune receptor to mimic the attacked hormone receptors to recognize the virus, thereby activating a robust immune response. [ABSTRACT FROM AUTHOR]

In situ resource utilization (ISRU) is required for the operation of both medium and long-term exploration missions to provide metallic materials for the construction of lunar base infrastructure and H2O and O2 for life support. The study of the distribution of the lunar surface elements (Fe, Ti, Al, and Si) is the basis for the in situ utilization of mineral resources. With the arrival of the era of big data, the application of big data concepts and technical methods to lunar surface chemistry inversion has become an inevitable trend. This paper is guided by big data theory, and the Apollo 17 region and the area near the Copernicus crater are selected for analysis. The dimensionality of the first-order differential spectral features of lunar soil samples is reduced based on Pearson correlation analysis and the successive projections algorithm (SPA), and the extremely randomized trees (Extra-Trees) algorithm is applied to Chang'E-1 Interference Imaging Spectrometer (IIM) data to establish a prediction model for the lunar surface chemistry and generate FeO, TiO2, Al2O3, and SiO2 distribution maps. The results show that the optimum number of variables for FeO, TiO2, Al2O3, and SiO2 is 17, 5, 8, and 30, respectively. The accuracy of the Extra-Trees model using the best variables was improved over that of the original band model, with determination coefficients (R2) of 0.962, 0.944, 0.964, and 0.860 for FeO, TiO2, Al2O3, and SiO2, and root mean square errors (RMSEs) of 1.028, 0.672, 0.942, and 0.897, respectively. The modeling feature variables and model preference methods in this study can improve the inversion accuracy of chemical abundance to some extent, demonstrating the potential of IIM data in predicting chemical abundance and providing a good data basis for lunar geological evolution studies and ISRU. [ABSTRACT FROM AUTHOR]

Sol‐gel‐derived ZnO is one of the most widely used electron‐transport layers in inverted organic solar cells. The sol‐gel ZnO precursor consists of zinc acetate dehydrate (ZAH) and ethanolamine dissolved in 2‐methoxyethanol, where ethanolamine chelates with ZAH, which helps ZAH dissolve in the 2‐methoxyethanol. However, an annealing temperature above 120 °C is required to convert the complexes into ZnO. High temperatures are incompatible with flexible plastic substrates such as polyethylene terephthalate. In this work, we report an amine‐free recipe consisting of ZAH in methanol to prepare ZnO films. The complex formed in the amine‐free precursor solution is methanol‐solvated ZAH, which is simpler than that of the amine‐containing precursor solution. The temperature required for converting the precursor complex into ZnO was reduced to 90 °C for the amine‐free recipe. Low‐temperature‐processed ZnO can function efficiently as an electron‐transport layer in both rigid and flexible inverted nonfullerene solar cells. [ABSTRACT FROM AUTHOR]

Sol‐gel‐derived ZnO is one of the most widely used electron‐transport layers in inverted organic solar cells. The sol‐gel ZnO precursor consists of zinc acetate dehydrate (ZAH) and ethanolamine dissolved in 2‐methoxyethanol, where ethanolamine chelates with ZAH, which helps ZAH dissolve in the 2‐methoxyethanol. However, an annealing temperature above 120 °C is required to convert the complexes into ZnO. High temperatures are incompatible with flexible plastic substrates such as polyethylene terephthalate. In this work, we report an amine‐free recipe consisting of ZAH in methanol to prepare ZnO films. The complex formed in the amine‐free precursor solution is methanol‐solvated ZAH, which is simpler than that of the amine‐containing precursor solution. The temperature required for converting the precursor complex into ZnO was reduced to 90 °C for the amine‐free recipe. Low‐temperature‐processed ZnO can function efficiently as an electron‐transport layer in both rigid and flexible inverted nonfullerene solar cells. [ABSTRACT FROM AUTHOR]

The geological conditions in the upper reaches of the Jinsha River on the southeastern margin of the Qinghai–Tibet Plateau are extremely complex. Many paleo-landslides and lacustrine sediments were formed in this area under the synergistic effect of tectonic activity and climate. The study of old deposits is helpful for understanding paleoenvironment, paleoclimate, and river geomorphology evolution. This study addressed the Wangdalong landslide damming event as the object and implemented a detailed on-site investigation. The geological characteristics of the landslide body, sliding surface, source area, and lacustrine deposits are determined. The 3D topographic restoration and the SFLOW software were used to simulate landslide dynamics, and the geomorphological evolution sequence of the Wangdalong landslide is proposed. Based on the landslide dating, the mineral analysis of lacustrine sediments, and the numerical simulation, the landslide was determined to be the product of the synergistic effect of climate and structure, and an earthquake or a heavy rainfall was the most probable triggering factor of the landslide. This study established a systematic analysis of the landslide damming event, which has reference significance for similar research. [ABSTRACT FROM AUTHOR]

[Display omitted] • The exploration criteria were constructed through text big data mining. • Text-mining-based exploration criteria are more comprehensive and scientific. • Quantitative analyses of ore-controlling factors were completed. • Completed the deep 3D prospecting prediction by random forests model. The advent of the big data era has gradually brought about a new demand for the integration of geological text big data in three-dimensional (3D) mineral prospectivity mapping (3DMPM). Here, we report a novel workflow suitable for the application of natural language processing in 3DMPM. Our study area is the Xiyu kimberlite-type diamond deposit in Shandong, China, which has significant prospecting potential in its deep portion. In this study, exploration criteria served as a bridge connecting geological text big data and 3DMPM, and exploration criteria for the Xiyu diamond deposit were constructed employing text mining technologies. A comparative evaluation of conventional and text-mining-based exploration criteria was also conducted. Considering the text-mining-based deposit exploration criteria, we reconstructed 3D spatial anomalies and quantitatively analyzed ore-controlling factors. A random forests classification model, created using the deposit exploration criteria obtained through text mining, was applied to prospecting predictions in the Xiyu deposit, with superior results being obtained. The prediction results confirmed the applicability of the workflow and demonstrated its capacity for effective dual coupling of geological data and knowledge. [ABSTRACT FROM AUTHOR]

In this study, the effect of the fracturing degree of the source rock on rock avalanche river-blocking behavior was investigated. The study included the analysis of mass movement behavior, impulse wave behavior, and the formation of landslide dams. The study included a series of simulations of rock avalanche river-blocking based on the coupled Eulerian-Lagrangian (CEL) technique. Prior to the simulation, a water column collapse model was applied to validate the use of the CEL technique on fluid-structure interaction, and to calibrate the material parameters. The source rock in the rock avalanche simulation was cut by different groups of structural planes, with the number of 0 × 0 × 0, 1 × 1 × 1, 4 × 4 × 4, 9 × 9 × 9, 14 × 14 × 14, 19 × 19 × 19 in each dimension, respectively, to represent different fracturing degrees, on the premise of the same volume and shape of the source rock. The simulation results showed that the sliding mass exhibited structure stabilization, such that the structure of the sliding mass gradually stabilized to a steady status over time, in the mass movement process. The structure stabilization made the center of the sliding mass constantly decrease, and provided a higher speed of movement for the rock avalanches with higher fracturing degrees of the source rock. As for the impulse wave behavior, with the increase in the fracturing degree of the source rock, the maximum kinetic energy of the water decreased, and the maximum height and propagation speed of the impulse waves decreased, which indicated that the maximum height and the propagation speed of the impulse waves were positively correlated with the maximum kinetic energy of the water. In regard to the formation of the landslide dams, when the fracturing degree of the source rock was low, the shape of the landslide dam was very different. With the increase of the fracturing degree of the source rock, the shapes of the landslide dams stabilized, and varied slightly after the fracturing degree of the source rock reached a threshold value. [ABSTRACT FROM AUTHOR]

Identification of rock discontinuity sets is essential for each studied jointed rock slope, and is also an initial step in many existing methods for rock slope stability analysis. This paper presents a new hierarchical agglomerative clustering method using modified agglomerative nesting (MAGNES) algorithm for automatically partitioning discontinuity sets. It is an orientation-based clustering method, and different linkage criteria (single, complete, and average) are incorporated for merging two closest clusters. The performance of MAGNES is tested using a complicate artificial data set, Shanley and Mahtab’s data set, and a real data set from unmanned aerial vehicle (UAV) survey. In addition, the clustering results of four other well-recognized clustering methods are also chosen as comparisons. It shows that the single linkage criterion is inapposite for partitioning orientations and the complete linkage criterion is not robust. Only MAGNES using average linkage criterion (MAGNES_AVG) shows good performance for detecting discontinuity sets. Generally, the main discrepancies among the clustering results lie mainly in the poles at the boundary of two adjacent joint sets. Considering the real data sets are characterized by “ground truth,” the artificial data set with known classification labels is used to further test which method performs better. The number of misclassification points is adopted as an evaluation index, and MAGNES_AVG performs best in partitioning the poles at the boundary of adjacent joint sets. Another advantage of the proposed algorithm is that it is independent of initial parameters, which is user-friendly. [ABSTRACT FROM AUTHOR]

With the era of big data, the prediction and evaluation of geological mineral resources have gradually entered into a new stage from digital prospecting to intelligent prospecting. The theoretical method of big data mining can contribute to deep mineral resource prediction and evaluation. This paper extracts ore-causing and ore-caused anomaly information based on text intelligent mining technology, and constructs a regional conceptual prospecting model based on geological prospecting big data. First, we set up a corpus based on text big data discovery and preprocessing technology. Second, we used CNN multiple scale text classification technology to analyze geological text data from the two main aspects: ore-causing anomalies and ore-caused anomalies. Third, we used a statistical method to analyze the semantic links between content-words, and we constructed chord diagrams and ternary diagrams to visualize the content-words and their links. Finally, we constructed a regional conceptual prospecting model based on the knowledge graphs. [ABSTRACT FROM AUTHOR]

Numerous paleolandslide dams are distributed along the upper reaches of the Jinsha River under the special geological setting of the Tibetan Plateau. A field investigation revealed that the Rongcharong paleoriver-blocking event formed one such dam. Optically stimulated luminescence results show that the paleolandslide occurred in 2.2 ± 0.1 Ka BP and blocked the Jinsha River for approximately 800 years. The failure mechanism of the paleolandslide is the steeply inclined antidip rock topple under self-gravity. A distinct element method-based numerical landslide model is constructed using the topography restoration method. The parameter sensitivity results show that microparameters have important effects on the peak particle velocity and morphology of landslide dams. The optimal microparameters are determined by adopting back analysis based on the geomorphic parameters of the landslide dam. The numerical simulation results show that the sliding process lasts for 65 s; the corresponding peak velocity and runout distance are 28.2 m/s and 742.5 m, respectively; and the landslide forms a dam 1129 m long, 967 m wide, and 105 m high. The genetic mechanism of the paleolandslide is reflected in the calibration process of the microparameters and load type of the numerical simulation. Finally, the implications of microparameters for the longevity of the landslide dam are presented. [ABSTRACT FROM AUTHOR]

In the age of big data, the prediction and evaluation of geological mineral resources have gradually entered a new stage, intelligent prospecting. This review briefly summarizes the research development of textual data mining and spatial data mining. It is considered that the current research on mineral resource prediction has integrated logical reasoning, theoretical models, computational simulations, and other scientific research models, and has gradually advanced toward a new model. This type of new model has tried to mine unknown and effective knowledge from big data by intelligent analysis methods. However, many challenges have come forward, including four aspects: (i) discovery of prospecting big data based on geological knowledge system; (ii) construction of the conceptual prospecting model by intelligent text mining; (iii) mineral prediction by intelligent spatial big data mining; (iv) sharing and visualization of the mineral prediction data. By extending the geological analysis in the process of prospecting prediction to the logical rules associated with expert knowledge points, the theory and methods of intelligent mineral prediction were preliminarily established based on geological big data. The core of the theory is to promote the flow, invocation, circulation, and optimization of the three key factors of "knowledge", "model", and "data", and to preliminarily constitute the prototype of intelligent linkage mechanisms. It could be divided into four parts: intelligent datamation, intelligent informatization, intelligent knowledgeization, and intelligent servitization. [ABSTRACT FROM AUTHOR]

Lithology identification is an essential fact for delineating uranium-bearing sandstone bodies. A new method is provided to delineate sandstone bodies by a lithological automatic classification model using machine learning techniques, which could also improve the efficiency of borehole core logging. In this contribution, the BP neural network model for automatic lithology identification was established using an optimized gradient descent algorithm based on the neural network training of 4578 sets of well logging data (including lithology, density, resistivity, natural gamma, well-diameter, natural potential, etc.) from 8 boreholes of the Tarangaole uranium deposit in Inner Mongolia. The softmax activation function and the cross-entropy loss function are used for lithology classification and weight adjustment. The lithology identification prediction was carried out for 599 samples, with a prediction accuracy of 88.31%. The prediction results suggest that the model is efficient and effective, and that it could be directly applied for automatic lithology identification in sandstone bodies for uranium exploration. [ABSTRACT FROM AUTHOR]

Purpose: Treatment of total brachial plexus avulsion (TBPA) is a challenge in the clinic, especially the restoration of hand function. The current main surgical order is from proximal to distal joints. The purpose of this study was to demonstrate the outcomes of "distal to proximal" surgical method. Methods: Thirty-nine patients underwent contralateral C7 (CC7) nerve transfer to directly repair the lower trunk (CC7-LT) and phrenic nerve transfer to the suprascapular nerve (PN-SSN) during the first stage, followed by free functional gracilis transplantation (FFGT) for elbow flexion and finger extension. Muscle strength of upper limb, degree of shoulder abduction and elbow flexion, and Semmes–Weinstein monofilament test and static two-point discrimination of the hand were examined according to the modified British Medical Research Council (mBMRC) scoring system. Results: The results showed that motor recovery reached a level of M3 + or greater in 66.7% of patients for shoulder abduction, 87.2% of patients for elbow flexion, 48.7% of patients for finger extension, and 25.6% of patients for finger flexion. The mean shoulder abduction angle was 45.5° (range 0–90°), and the average elbow flexion angle was 107.2° (range 0–142°), with 2.5 kg average flexion strength (range 0.5–5 kg). In addition, protective sensibility (≥ S2) was found to be achieved in 71.8% of patients. Conclusion: In reconstruction of TBPA, CC7 transfer combined with free functional gracilis transplantation is an available treatment method. It could help patients regain shoulder joint stability and the function of elbow flexion and finger extension and, more importantly, provide finger sensation and partial finger flexion function. However, the pick-up function was unsatisfied, which needed additional surgery. [ABSTRACT FROM AUTHOR]

The objective of the present study was to explore the protective effects of the curcumin/cyclodextrin polymer (CUR/CDP) inclusion complex on hydrogen peroxide (H2O2)‐induced LO2 cells damage. In this study, a H2O2‐induced cells oxidative injury model was established to test the protective effects of the CUR/CDP inclusion complex. The cell viability of cells was detected by the thiazolyl blue tetrazolium bromide (MTT) assay. The extracellular lactate dehydrogenase (LDH) activity, catalase (CAT) activity, and malondialdehyde (MDA) level were detected by assay kits. The cellular reactive oxygen species (ROS) level was detected using the dichlorodihydrofluorescein (DCF) fluorescence assay. Western blotting analysis was conducted to assess the changes of phosphorylated‐p53 and caspase‐3. The results showed that 700 μM H2O2‐treated LO2 cells for 3 h resulted in a significant decrease of cell viability to 53.00 ± 1.68%, which established the cell oxidative injury model. Cells treated with H2O2 led to a significant increase of extracellular LDH activity, MDA content, and ROS level, and decreased CAT activity. Treatment with CUR/CDP significantly reversed the changes of the above indicators. Moreover, CUR/CDP treatment at 20 and 40 μg/ml inhibited H2O2‐induced increase in phosphorylated‐p53 and caspase‐3 expression, indicating that CUR/CDP suppressed cell apoptosis to alleviate liver injury. The results of those studies demonstrated that CUR/CDP had a protective effect on the oxidative damage of LO2 cells, and it could be developed as a new type of natural liver protection product to apply in the prevention of liver injury. [ABSTRACT FROM AUTHOR]

As a result of the influence of plate movement, the upper reaches of Jinsha River have strong geological tectonic activities, large topographic fluctuations, and complex climate characteristics, which result in the frequent occurrence of landslide disasters. Hence, there is the need to carry out landslide susceptibility mapping in the upper reaches of Jinsha River to ensure the safety of local people's property and the safe exploitation of hydraulic resources. In this study, InSAR technology and a field geological survey were used to map the landslides. Then, the curvature watershed method was used to divide the slope units. A conditioning factor system was established, which can reflect the characteristics of the rapid uplift and vertical distribution of rainfall in the special geological environment of the study area. Finally, logistic regression, random forest, and artificial neural network models were used to establish the landslide susceptibility model. The results show that the random forest model is optimal for the landslide susceptibility mapping in this area. Additionally, the area percentages of the very low, low, moderate, high, and very high susceptibility classes were 40.13%, 20.06%, 13.39%, 12.55%, and 13.87%, respectively. Based on the analysis of the landslide susceptibility map, we suggest that the landslide geological hazards resulting from the rapid uplift of the Tibetan Plateau and the significant decrease in sea level during a glacial period in the upper reaches of Jinsha River are controlled by the double disaster effect of the geodynamic system. Consequently, this study can guide local prevention and mitigation. [ABSTRACT FROM AUTHOR]

Distribution characteristics and strength of 3D discrete fractures have a decisive effect on the rock slope stability. How to use the fractures obtained on site to establish a 3D fractured rock slope for stability analysis is a difficult problem in the field of rock mass engineering. This study selected the rock slope on the right bank of Yigong Zangbu Bridge for a detailed field investigation. Exposed joints and fractures were measured and counted. Occurrence, size, and position distribution models of the fractures were used to obtain the parameters of the spatial distribution using probability and statistic theory. Then, a complex 3D discrete fracture network (DFN) was established and simplified. After that, a 3D rock slope model was built and the DFN was used to cut the slope model. Finally, the strength reduction method was used to analyze the slope stability. The results reveal that the overall slope stability is good (safety factor is 2.0), but the local block deformations are large and are mainly concentrated in the lower part of the slope. Due to the existence of structural planes dipping outside the slope on the right bank, local unstable blocks are easily formed. In this study, the whole process from field fracture acquisition to the DFN generation is applied to the 3D fracture rock slope stability analysis, which is of great importance for complex rock mass engineering assessment and disaster prevention. [ABSTRACT FROM AUTHOR]

A large paleolandslide occurred opposite the Gangda village in the upper Jinsha River, SE Tibetan Plateau. Field geological investigations and remote sensing indicated that the Gangda paleolandslide once blocked the Jinsha River. Evidence of river blocking, including landslide dam relics, upstream lacustrine sediments, and downstream outburst sediments, has been well preserved. To understand the river-blocking event including landslide, dam breach, and associated outburst flooding, optically stimulated luminescence (OSL) dating and numerical simulations were performed in this study. OSL dating results showed that the paleolandslide dam was formed at 5.4 ± 0.5 ka BP and breached at 3.4 ± 0.3 ka BP, indicating that the dam lasted approximately 2000 years. The discrete element method was used to simulate the dynamics of the Gangda rock landslide based on the restored topography, while a fluid–solid coupling model was performed to simulate the landslide dam breaching and flooding. The fluid–solid coupling model can simultaneously reflect the process of landslide-dam collapse and the propagation of outburst flood. The simulated results indicate that the whole landslide process lasted about 60 s with a peak velocity of 38 m/s. It is significant that the simulated morphology of the residual landslide dam and downstream outburst sediments is consistent with the field observations. The combined numerical investigation in this paper provided new insights into the research of landscape evolution and helped to understand the chain disaster of landslide, dam breach, and flooding. [ABSTRACT FROM AUTHOR]

• A proxy based on mass movements for assessing climate change was proposed. • The periodicity of mass movements conforms to the changing patterns of traditional climate proxies. • Climate change determines the spatial and temporal distribution of mass movements. The spatial and temporal distribution, as well as the formation and evolution of mass movements in the Qinghai-Tibet Plateau orogenic belt, have been extensively discussed. We gathered research papers focusing on the ages of Quaternary residual deposits associated with mass movements in the surrounding orogenic belts of the Qinghai-Tibet Plateau. A total of 172 large-scale typical deposits and their ages were analyzed to explore the primary driving mechanisms behind mass movements in the Qinghai-Tibet Plateau. As these deposits were collected from diverse geological environments and the ages primarily concentrated in the Late Pleistocene and Holocene periods, we categorized mass movements with ages younger than 140 ka into three distinct regional climate systems: the Indian Summer Monsoon, the East Asian Summer Monsoon, and the Westerlies. The relationship between the emergence of mass movements and climate change was thoroughly investigated. Our research indicates that mass movements can serve as a reliable proxy for indicating climate change. The cycles of mass movements are closely synchronized with the warm and cold alternations of regional climates, holding significant importance in reshaping the environmental evolutionary history. We propose that the extensive mass movements in the Qinghai-Tibet Plateau are primarily driven by periodic climate variations, although they may not exhibit a perfect one-to-one correspondence. [ABSTRACT FROM AUTHOR]

Extensive models used in debris flow runout simulations are two-dimensional with many limitations. Considering these limitations, a new three-dimensional computational fluid dynamics (CFD) code based on the finite difference method (FDM) was introduced to simulate debris flow runouts. The unique fractional area-volume obstacle representation (FAVOR), true-volume of fluid (Tru-VOF), and the renormalized group (RNG) model in the 3D CFD code were used to tackle mesh processing, free surface tracking, and turbulence, respectively. The RNG model has great performance in describing low-intensity flows and flows with strong shear regions. In addition, the 3D CFD modeling considers the vertical mobility of debris flow, which offers higher simulation accuracy compared to 2D approaches. Through simulating a case and a mesh size study, the accuracy of the model was validated and the optimal mesh size of Xiaojia Gully debris flow model was obtained, respectively. The affected areas, runout distances, deposition depths, and velocities of potential debris flows in Xiaojia Gully were acquired by adopting the present model. The simulation results show that debris flows with return periods of 50 years, 100 years, and 200 years will threaten the lives and safety of residents and their property in Xiaojia Gully. A sensitivity analysis was used to evaluate the influences of rheological parameters on this model, further verifying the rationality of the selected parameters. In general, the present model can scientifically and accurately simulate debris flows on irregular terrain and can be employed for similar risk management and engineering designs. [ABSTRACT FROM AUTHOR]

Background: The common use of singleton fetal growth standard to access twin growth might lead to over-monitoring and treatment. We aimed to develop fetal growth standards for Chinese twins based on ultrasound measurements, and compare it with Zhang's and other twin fetal growth charts.Methods: A cohort of uncomplicated twin pregnancies were prospectively followed in 2014-2017. Smoothed estimates of fetal growth percentiles for both monochorionic (MC) and dichorionic (DC) twins were obtained using a linear mixed model. We also created growth charts for twins using a model-based approach proposed by Zhang et al. Our twin standards were compared with Hadlock's (singleton) in predicting adverse perinatal outcomes.Results: A total of 398 twin pregnancies were included, with 214 MC and 582 DC live-born twins. The MC twins were slightly lighter than the DC twins, with small differences throughout the gestation. Our ultrasound-based fetal weight standards were comparable to that using Zhang's method. Compared with previous references/standards from the US, Brazil, Italy and UK, our twins had very similar 50th percentiles, but narrower ranges between the 5th and 95th or 10th and 90th percentiles. Compared with the Hadlock's standard, the risks of neonatal death and adverse perinatal outcomes for small for gestational age (SGA) versus non-SGA were substantially elevated using our standards.Conclusions: A normal fetal growth standard for Chinese twins was created. The differences between MC and DC twins were clinically insignificant. The 50th weight percentiles of the Chinese twins were identical to those in other races/ethnicities but the ranges were markedly narrower. Our standard performed much better than the Hadlock's in predicting low birth weight infants associated with adverse perinatal outcomes in twin pregnancies. The present study also indicated that Zhang's method is applicable to Chinese twins, and other areas may use Zhang's method to generate their own curves for twins if deemed necessary. [ABSTRACT FROM AUTHOR]

Objectives: To establish and validate an individualized nomogram to predict the probability of death within 30 days in patients with sepsis-induced blood pressure drop would help clinical physicians to pay attention to those with higher risk of death after admission to wards. Methods: A total of 1023 patients who were admitted to the Dongyang People's Hospital, China, enrolled in this study. They were divided into model group (717 patients) and validation group (306 patients). The study included 13 variables. The independent risk factors leading to death within 30 days were screened by univariate analyses and multivariate logistic regression analyses and used for Nomogram. The discrimination and correction of the prediction model were assessed by the area under the Receiver Operating Characteristic (ROC) curve and the calibration chart. The clinical effectiveness of the prediction model was assessed by the Decision Curve Analysis (DCA). Results: Seven variables were independent risk factors, included peritonitis, respiratory failure, cardiac insufficiency, consciousness disturbance, tumor history, albumin level, and creatinine level at the time of admission. The area under the ROC curve of the model group and validation group was 0.834 and 0.836. The P value of the two sets of calibration charts was 0.702 and 0.866. The DCA curves of the model group and validation group were above the two extreme (insignificant) curves. Conclusions: The model described in this study could effectively predict the death of patients with sepsis-induced blood pressure drop. [ABSTRACT FROM AUTHOR]

Today's era of big data is witnessing a gradual increase in the amount of data, more correlations between data, as well as growth in their spatial dimension. Conventional linear statistical models applied to mineral prospectivity mapping (MPM) perform poorly because of the random and nonlinear nature of metallogenic processes. To overcome this performance degradation, deep learning models have been introduced in 3D MPM. In this study, taking the Huayuan sedimentary Mn deposit in Hunan Province as an example, we construct a 3D digital model of this deposit based on the prospectivity model of the study area. In this approach, 3D predictor layers are converted from the conceptual model and employed in a 3D convolutional neural network (3D CNN). The characteristics of the spatial distribution are extracted by the 3D CNN. Subsequently, we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3D CNN model and weight of evidence (WofE) method on each group. The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies, and the correlation between different ore-controlling factors. The analysis of 12 factors indicates that the 3D CNN model performs well in the 3D MPM, achieving a promising accuracy of up to 100% and a loss value below 0.001. A comparison shows that the 3D CNN model outperforms the WofE model in terms of predictive evaluation indexes, namely the success rate and ore-controlling rate. In particular, the 1–12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors. Consequently, we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults. The experimental results confirm that the proposed 3D CNN is promising for 3D MPM as it eliminates the interference factors. [ABSTRACT FROM AUTHOR]

The identification of the origin and source of natural gas is always a difficult and hot issue. Hereinto, the maturity identification is one of the most important scientific problems. Many empirical equations have been established to decipher the relationship between the maturity of gas source rocks and the carbon isotopic composition of natural gas. However, these equations proposed often fail to identify the maturity of the source rocks correctly, which in turn prevents the identification of genetic types and source rocks of the natural gas because the petroliferous sedimentary basins in China are complex and diverse, with multiple sets of source rocks and different thermal history. In this paper, the oil-associated gas from the Permian lacustrine source rocks and the coal-derived gas from the Jurassic source rocks in Junggar and Turpan-Hami basins have been investigated to decipher the relationship between the maturity (vitrinite reflectance) of gas source rocks and the carbon isotopic composition of methane. The equations established are δ13C1=25lgRo−42.5 for oil-associated gas, and δ13C1=25lgRo−37.5 for coal-derived gas. These new equations are suitable for the maturity identification of source rocks in most petroliferous basins, and favorable for the identification of the genetic type and source of natural gas, which is very important to improve the geological theory of natural gas. [ABSTRACT FROM AUTHOR]

• Consequent, diagonal, transverse and obsequent slope structures and soil stability are incorporated into the DFS assessment. • Unsupervised learning is used to optimize the sampling strategy of negatives. • A state-of-the-art meta-modeling technique is used to optimize supervised learning. • A progressive framework is proposed to improve the reliability of DFS assessment. This research aims to improve the reliability of debris flow susceptibility (DFS) assessment, which is crucial for disaster prevention and mitigation in mountainous regions. A progressive framework was proposed and applied to Pinggu District, Beijing, China. First, 16 debris flow predisposing factors (DFPFs) were selected, and the slope structure and soil stability were incorporated to account for material sources. Watershed units instead of grid units were used to extract data. Then, the multi-collinearity among the factors was reduced by using variance inflation factors (VIF) and information gain (IG), and 13 DFPFs were retained. Three unsupervised learning algorithms (i.e., affinity propagation (AP), Gaussian mixture model (GMM) and self-organizing maps (SOM)) were used to optimize the sampling strategy of non-debris flow units. Subsequently, the occurrence probability of debris flows in each unit was predicted by using four supervised learning algorithms (i.e., logistic regression (LR), random forest (RF), adaptive boosting (ADAB) and extreme gradient boosting (XGB)). They were optimized by a state-of-the-art meta-modeling approach. Finally, the DFPF's importance was ranked. The main contributions of our framework are establishing a high-quality data set and optimizing the prediction algorithms. The results show that the tree-based models perform well, and the boosting-based algorithms outperform the bagging-based algorithms. Supervised learning is more suitable for DFS assessment than unsupervised learning. Debris flows are most likely to occur on a dolomite consequent or diagonal slope with a relief amplitude above 540 m. [ABSTRACT FROM AUTHOR]

• A series of river terraces are developed in the upper Jinsha River. • The short-term incision rate of the upper Jinsha River is 0.18–5.47 mm/yr. • The two depth intervals of the incision are 1200 ± 220 m and 340 ± 250 m respectively. • The main driving force of river incision is tectonic uplift. • Climate change is the switch button of river incision and accumulation behavior. The region occupied by three parallel rivers on the southeastern edge of the Tibetan Plateau is a zone with dramatically fluctuating topography and a complex tectonic setting. Deeply incised river valleys have become an ideal place for scholars to study the coupling relationships among river erosion, climate change and regional uplift. The fluvial terraces preserved in the upper Jinsha River provide the basis for exploring the history of river erosion. Through field investigation and dating results of terraces, the short-term incision rates of the upper Jinsha River are determined to be 0.18–5.47 mm/yr. In addition, the palaeo-profiles, which were reconstructed using two analytical methods, show that there are two depth intervals for the incision in the Jinsha River, namely, 1200 ± 220 m and 340 ± 250 m. They correspond to two early planation surfaces of the river, and the long-term average incision rates of the upper Jinsha River are estimated to be 0.23–0.34 mm/yr. By analysing the relationship between fluvial terrace formation and climate change, combined with the formation history of the early planation surface, it is recognized that the main force driving incision in the upper reaches of the Jinsha River comes from regional uplift. Climate change causes a continuous alteration between river incision and sediment accumulation, and this transition is also perturbed by mass flow events. [ABSTRACT FROM AUTHOR]

• Genetic algorithm boosts the synthesis of high-quality MOFs via a microdroplet route. • ZIF-8 with highest surface area (1748 m2/g) is achieved with least trial and error. • General guidance is derived and demonstrated with the synthesis of ZIF-67. As an emerging strategy for the synthesis of metal-organic frameworks (MOFs), a microdroplet-based spray method holds merits of improved heat and mass transfer rates, which allows the formation of MOF crystals in a much faster manner. To optimize the spray route for the MOF synthesis, further exploration is needed to understand the dominant variables controlling the quality of the products. With a series of experiments and advanced computational analysis, we present here general guidance for the synthesis of representative zeolitic imidazolate frameworks (i.e., ZIF-8 and ZIF-67) using the spray route. [ABSTRACT FROM AUTHOR]

The mapping unit is the smallest indivisible unit of landslide susceptibility mapping. The mapping unit considerably affects the effect of landslide susceptibility mapping. Among many mapping units, the slope unit is one of the most suitable units for landslide susceptibility mapping. The methods available for classifying slope unit are not uniform, and the dividing effects differ. In this study, the hydrological method and curvature watershed method were used to classify the slope units in the study area on the upper Jinsha River, south-western China. Lithology, slope angle, slope aspect, normalized difference vegetation index (NDVI), land cover, rainfall, curvature, distance-to-river, distance-to-fault, were selected as the landslide conditioning factors. Support vector machine were applied to the landslide susceptibility modeling. Statistical indexes, 5-flod cross validation, Kappa coefficient, and AUC values were introduced to validate the prediction accuracy of the landslide susceptibility model. By comparing the shape characteristics of slope units classified by the two methods, we found that the slope unit classified based on curvature watershed method has a uniform size, shape between circle and equilateral triangle, and small internal terrain variation. As for the mean prediction accuracy, Kappa coefficient and AUC values of landslide susceptibility model, in the training stage, were 81.62%, 63.23%, and 89.72%, respectively, for the hydrological method, while 84.26%, 68.51%, and 90.80%, respectively, for the curvature watershed method; in the testing stage, were 80.70%, 61.40%, and 88.08%, respectively, for the hydrological method, while 83.24%, 66.49%, and 88.96%, respectively, for the curvature watershed method, which means that the curvature watershed method is more effective to produce landslide susceptibility map of the study area. [ABSTRACT FROM AUTHOR]

We propose an optimization method, named as the Multistep-Actor Critic (MAC) algorithm, which uses the value-network and the action-network, where the action-network is based on the deep Q-network (DQN). The proposed method is intended to solve the problem of energy conservation optimization of heating, ventilating, and air-conditioning (HVAC) system in a large action space, principally for the cases with high computation and convergence time. The method employs the multistep action-network and search tree to generate the original state and then selects the optimal state based on the value-network for the original and the adjacent states. The results from the application of the MAC algorithm to a simulation problem on the TRNSYS system, where the simulation problem is referring to a real supertall building in Hong Kong, have shown that the proposed MAC algorithm balances control actions between different HVAC subsystems. Further, it substantially saves the computational time while maintaining a good energy conservation performance. [ABSTRACT FROM AUTHOR]

Introduction: Preeclampsia affects about 10% of twin pregnancies and significantly increases the risk of adverse pregnancy outcomes. However, screening models for preeclampsia in twin pregnancies remain elusive. The present study aimed to evaluate the performance of a multi-marker first trimester preeclampsia screening model in low-risk twin pregnancies.Material and Methods: Between 2014 and 2017, we prospectively assessed first trimester biomarkers for preeclampsia in a 'low-risk' twin pregnancy cohort at a single center. Multiple logistic regression was used to determine significant predictors for early preeclampsia (occurring prior to 34 weeks) and late preeclampsia (occurring after 34 weeks). The performance of the screening models fitted using the significant predictors was calculated using receiver operating characteristics curves, and internal validation was performed using bootstrapping.Results: A total of 769 twin pregnancies were included in the study. Early preeclampsia and late preeclampsia developed in 27 (3.5%) and 59 (7.7%) cases, respectively. Logistic regression analyses showed that maternal age, body mass index, mean artery pressure and placental growth factor were significant predictors for early preeclampsia. Maternal age, body mass index, mean artery pressure and pregnancy-associated plasma protein A were significant for late preeclampsia. Uterine artery pulsatility index was not predictive of either early or late preeclampsia. For the fitted screening model of early and late preeclampsia, the areas under receiver operating characteristics curves were 0.82 (95% confidence interval [CI] 0.76-0.88) and 0.66 (95% CI 0.59-0.73), which were expected to decrease to 0.77 and 0.60, respectively, based on bootstrapping; the positive predictive values were 10.2% and 12.5%; and the estimated detection rates were 40.7% and 22.0%, respectively, at a false-positive rate of 10%.Conclusions: A multi-marker screening model for preeclampsia in low-risk twin pregnancies, using a modified version of Fetal Medicine Foundation predictors in singletons, does not perform well. Uterine artery pulsatility index is of little value in screening for preeclampsia in low-risk twin pregnancies. [ABSTRACT FROM AUTHOR]

There are many challenges in the task of predicting ore deposits from big data repositories. The data are inherently complex and of great significance to the intervenient spatial relevance of deposits. The characteristics of the data make it difficult to use machine learning algorithms for the quantitative prediction of mineral resources. There are considerable interest and value in extracting spatial distribution characteristics from two-dimensional (2-d) ore-controlling factor layers under different metallogenic conditions. In this paper we undertake such analysis using a deep convolutional neural network algorithm named AlexNet. Training on the 2-d mineral prediction and classification model is performed using data from the Songtao–Huayuan sedimentary manganese deposit. It mines the coupling correlation between the spatial distribution of chemical elements, sedimentary facies, the outcrop of Datangpo Formation, faults, water system, and the areas where manganese ore bodies are present, as well as the correlation among different ore-controlling factors by employing the AlexNet networks. By comparing the training loss, training accuracy, verification accuracy, and recall of models trained by different scales of grids and different combinations of ore-controlling factor layers, we further discuss the most appropriate scale division and the optimal combination of ore-controlling factors to make the model achieve its strongest robustness. It is found that the prediction performance of AlexNet networks reaches its peak when selecting a grid division of 200 pixels × 200 pixels (the actual distance is 10 km × 10 km) and inputting the distribution layers of 21 chemical elements maps, lithofacies–paleogeographic map, formation and tectonic map, outcrop map of Datangpo Formation, and water system map. The training loss, training accuracy, verification accuracy, and recall of the optimal model are 0.0000001, 100.00%, 86.21%, and 91.67%, respectively. The proposed method is successfully applied to the 2-d metallogenic prediction in Songtao–Huayuan study area. And five metallogenic prospective areas from A to E are delineated with large probability for potential ore bodies. [ABSTRACT FROM AUTHOR]