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Numerous engineering case studies suggest that the complexity and oscillatory of landslide displacement types, influenced by diverse deformation patterns, monitoring errors, and varied observation time frames, present challenges for the application of traditional warning criteria in assessing landslide movement in practice. As such, this study attempts to propose a data-dependent early warning criterion through the dimensionless analysis of displacement for two typical landslide displacement patterns: exponential pattern and step-like pattern. The displacement trend in exponential pattern landslides is extracted using the simple moving average (SMA) method, with the onset of acceleration (OOA) determined through the application of the Pettitt test. The Deformation Standardized Anomaly Index (DSAI) is defined to quantitatively evaluate the alert levels corresponding to each monitored displacement data of both patterns. After validating the proposed criterion in ten landslide cases, results indicate that the landslides have a high probability of losing stability when DSAI exceeds four, and the landslide tends to stabilize as DSAI decreases. Compared to displacement speed ratio (DSR), the proposed DSAI is more applicable, aligning with the actual conditions of both landslides with exponential pattern and step-like pattern and exhibiting a relatively low false alarm rate, making it a valuable criterion for landslide early warning. [ABSTRACT FROM AUTHOR]

Timely evaluation of landslide risk based on displacement data is crucial for the effective warning of landslide events. However, the complexity and diversity of displacement curves obtained in the field make the selection of evaluation criteria and thresholds challenging and subjective. Thus, this study proposes a new early warning criterion called the deformation probability index (DPI) to overcome this limitation. By analyzing features of landslide displacement curves obtained from the field, four linetypes were identified, and differences in velocity data between pre-failure and post-failure landslides were summarized from a probabilistic perspective. Subsequently, the DPI is defined to assess the early warning level of a landslide by converting observed velocity values into standardized velocity values using cumulative distribution function standardization. The resulting DPI values are divided into five early warning intervals with three alerting levels to indicate the relative stability of the landslide. Applying the proposed method to 16 landslides from around the world demonstrates the effectiveness of the DPI criterion in evaluating landslide risk, with a high correlation between alerting levels and observed landslide occurrences. The DPI criterion demonstrates notable sensitivity exclusively to displacement velocity and monitoring frequency, exhibiting remarkable generality and accuracy, thus offering a new method for landslide early warning. [ABSTRACT FROM AUTHOR]

Analyzing and quantifying the deformation process of landslides is of paramount importance in facilitating landslide early warning. As such, this study is committed to proposing a universal phenomenological model for deformation stages division and early warning of landslides based on the kinematic features. First, five landslide deformation patterns were classified based on the creep theory, and suggestions for stage division of each deformation pattern are presented. Then, the statistical characteristics of landslide velocity were analyzed, and a probability-based deformation stage division method was proposed. Finally, the Comprehensive Standardized Deformation Index (CSDI) model, which includes the calculation of the CSDI M - M (Min-Max normalization) and CSDI M (Mean normalization) was proposed and verified in 24 landslides worldwide. The results show that, except for the oscillating pattern, the CSDI M - M is feasible in the stages division of all deformation patterns with a strong correspondence with the actual state of the landslides. The CSDI M is a reliable landslide warning criterion and threshold determination method, as it is effective in the early warning of imminent landslides with a low false alarm rate. The CSDI model provides new insight into the division of landslide deformation stages and landslide risk assessment. [ABSTRACT FROM AUTHOR]

Mid‐infrared (MIR) semiconductor laser sources are essential for applications in gas detection, infrared imaging, and high‐speed free space optical communications. At present, the dynamic wavelength tunability of MIR lasers based on lead salt, antimonide, and quantum cascade lasers is limited and their compact size as well as heterogeneous integration are still challenging. Two‐dimensional (2D) materials, used as optical gain materials, have the flexible tunability and compatible van der Waals integrations—providing many new possibilities for constructing MIR laser sources of large tunability and high integration. Here, wide‐wavelength tunable MIR vertical cavity surface emitting lasers (VCSELs) integrated on silicon substrates are realized based on 2D black arsenic phosphorus (b‐AsxP1−x). The emission wavelength of the optically pumped laser device is demonstrated to be tuned from 3.42 to 4.65 µm at room temperature, which could be controlled by adjusting the alloy composition and thickness of the gain media b‐AsxP1−x. The tunable MIR VCSEL device would pave the way for further exploration of 2D materials‐based IR lasers working as on‐chip light sources. [ABSTRACT FROM AUTHOR]

Abstract Maximum Entropy Method (MEM) is one of powerful tools for material science. We developed the program Z-MEM for evaluation of electron and nuclear density distribution by MEM analysis and Fourier synthesis. This code is designed by the multi-threading library OpenMP, so that the calculation performance is significantly improvements for MEM analysis. Z-MEM is bundled in Z-Rietveld , which is Rietveld software for X-ray and neutron powder diffraction, via the application programming interface (API). Since Z-MEM have equipped with the graphical user interface operations, users can perform from Rietveld refinement to MEM analysis seamlessly. [ABSTRACT FROM AUTHOR]

The scratch performance of semi-crystalline polymers is highly dependent on the crystalline morphology. To study the effect of thermal annealing on the scratch behavior of Polylactide (PLA), a series of scratch tests were conducted on multilayer assembled PLAs, which had been annealed for different time. It was found that greatly delayed critical damage including groove formation and ploughing was achieved, accompanied with obviously suppressed surface cracking and deformation following the increase of annealing time. The structural evolution during the annealing process was further investigated for better construction of structure-property relationship, which implied that the higher crystallinity and tensile strength originating from the perfection of crystalline morphology, played a significant role in restraining the surface deformation and cracking during the scratch process. [ABSTRACT FROM AUTHOR]

This paper employs input-output structural decomposition analysis (IO-SDA) to explore the driving factors of Beijing's energy intensity, including energy input coefficient, technology coefficient (Leon- tief inverse coefficient), final demands structure by product, final demands by category, and final energy consumption coefficient. Based on the constant price energy-input-output tables from 1997 to 2012, the analysis results show that 1) in general, Beijing's energy intensity declined gradually; 2) the major driving factors were energy input coefficient and final demands by category; 3) the impacts of energy input coefficient and technology coefficient on energy-intensity decrease varied positively or negatively with time; 4) the impact of final energy consumption coefficient was somewhat weak. [ABSTRACT FROM AUTHOR]

With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors. [ABSTRACT FROM AUTHOR]

A large number of laboratory investigations related to the permeability have been conducted on the sliding zones. Yet little attention has been paid to the particular sliding zones of the slide-prone Badong Formation. Here, we experimentally investigate the permeability nature and the mechanism of seepage in the viscous sliding zone of the Huangtupo Landslide. Saturated seepage tests have been performed first with consideration of six dry densities and thirteen hydraulic gradients, in conjunction with the mercury intrusion porosimetry test and scanning electron microscopy test for the microstructure analysis after seepage. The results show that seepage in the sliding zone soil does not follow Darcy's Law, since there is a threshold hydraulic gradient (i0) below which no flow is observed and a critical hydraulic gradient (icr) over which the hydraulic conductivity (K) tends to be stable. The percentage of bound water could be responsible for the occurrence of i0 and icr. Furthermore, pore size distributions (PSD) less than 0.6 µm and between 10 and 90 µm exhibit positive and negative correlations with the i0, respectively, indicating that the i0 is related to the PSD. The mechanism accounting for this result is that pore water pressure forces fine clay particles into the surrounding large pores and converts arranged particles to discretely distributed ones, thereby weakening the connectivity of pores. The seepages in the sliding zones behave differently from that in the sliding mass and sliding bed in response to the permeability. [ABSTRACT FROM AUTHOR]

At the second stage of China Spallation Neutron Source (CSNS-II), it is predicted that 2 PB raw experimental data will be produced annually from twenty instruments. Scientific computing puts forward higher requirements for data sharing, utilization, retrieval, analysis efficiency, and security. However, the existing data management system (DMS) based on ICAT has several limitations including poor scalability of metadata database, imperfect data-management lifecycle and inflexible API. To ensure the accuracy, usability, scalability and efficiency of CSNS-II experimental data, a new scientific data management system is therefore designed based on the DOMAS framework developed by the Computing Center of IHEP. The data acquisition, transmission, storage and service systems are re-designed and tailored specifically for CSNS-II. Upon its completion, the new DMS will overcome the existing challenges and offer functions such as online display, search functionality and rapid download capabilities for metadata, raw data and analyzed data; flexible and user-friendly authorization; and data lifecycle management. Ultimately, the implementation of the new Data Management System (DMS) is expected to enhance the efficiency of experimental data analysis, propelling CSNS-II to achieve international advanced standards. Furthermore, it aims to reinforce self-reliance and technological strength in the field of science and technology at a high level in China. The development and deployment of the new DMS begin at the end of 2023. [ABSTRACT FROM AUTHOR]

Hyperbolic phonon polaritons in optically uniaxial hexagonal boron nitride (hBN) have shown considerable potential for manipulating light on the deep‐subwavelength scale, but the inherent in‐plane isotropy of hBN hinders their further application in planar polariton meta‐optics. Engineering these polaritons to achieve ultraconfined in‐plane anisotropic propagation is of scientific significance for facilitating light–matter interactions toward an extreme scale. Here, the polariton features are systematically investigated in a vertically stacked van der Waals heterostructure consisting of hBN and tellurium (Te), a highly birefringent van der Waals crystal in the mid‐infrared region. Polaritons in this heterostructure exhibit Te‐thickness‐dependent anisotropic dispersion, and the configurable in‐plane anisotropy and enhanced field confinement are experimentally demonstrated using scanning near‐field optical microscopy. It is concluded that these findings contribute to the fundamental understanding of anisotropic polaritons in hBN van der Waals heterostructures, providing a practical avenue for effectively engineering anisotropic PhPs in an intrinsically isotropic van der Waals material. [ABSTRACT FROM AUTHOR]

In the Three Gorges Reservoir area (TGRA), landslides typically exhibit slow-moving characteristic deformations in response to seasonal changes in precipitation and reservoir level. This study proposed an optimal combination of methods in which the frequency components of decomposed inducing factors are considered for predicting landslide displacement. In preprocessing, the monitored surface displacements are decomposed into trend displacement and periodic displacement components via ensemble empirical mode decomposition (EEMD); the time series of the inducing factors (rainfall and reservoir level) were reconstructed and decomposed into high- and low-frequency time series via complete ensemble empirical mode decomposition (CEEMD) and t -test methods, among which two dominant inducing factors are identified by analyzing the longest common subsequence (LCSS) distance of the normalized data. In the prediction of the periodic displacement component, three optimal methods that use the support vector regression (SVR) model have been considered for comparison: the artificial bee colony (ABC) algorithm, the genetic algorithm (GA) and particle swarm optimization (PSO). The results demonstrate that the combination of CEEMD-LCSS reconstruction and ABC-SVR yields a robust model with remarkable prediction performance, which can increase the prediction accuracy of landslide displacements by 54% at most in the evaluation of RMSE. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel nanopeptide is self-assembled from maize tetrapeptide anchored AuNPs. • Nanopeptide has a neuroprotective effect on l -glutamate-induced extracellular model. • Nanopeptides have therapeutic effects on murine Alzheimer's disease models. • Nanopeptides may reduce model injury through the Nrf2/Keap1 signaling pathway. Nanopeptide assembled from peptide-anchored nanoparticles possess an enormous research potential in the field of cellular medicine and disease treatment. The aim of this study was to explore the neuroprotective effects of maize tetrapeptide anchored gold nanoparticles against l -glutamic acid-induced PC12 cell apoptosis and a murine Alzheimer's disease model induced by aluminum chloride and d -galactose. The results revealed that the nanopeptide antioxidant inhibited intracellular ROS accumulation and promoted cell differentiation than that of maize bioactive tetrapeptide. Compared with untreated Alzheimer's disease model mice, nanopeptide administration shortened the escape latency time in a water maze test and improved the movements in the autonomic activity test. After 16 days of nanopeptide administration, the central cholinergic system function of acetylcholine and cholineacetyltransferase were enhanced, and the level of acetylcholinesterase was reduced. It also increased superoxide dismutase and glutathione peroxidase activity in sera and hypothalami. Moreover, nanopeptide treatment upregulated cerebral nuclear factor erythroid 2-related factor 2 and heme-oxygenase-1 and downregulated kelch-like ECH-associated protein 1 relative to untreated Alzheimer's disease model mice. Thus, the novel nanopeptide is expected to be used as the neuroprotective agent to prevent Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR. [ABSTRACT FROM AUTHOR]

The desire for intrinsically low lattice thermal conductivity (κL) in thermoelectrics motivates numerous efforts on understanding the microscopic mechanisms of heat transport in solids. Here, based on theoretical calculations, we demonstrate that α-MgAgSb hosts low-energy localized phonon bands and avoided crossing of the rattler modes, which coincides with the inelastic neutron scattering result. Using the two-channel lattice dynamical approach, we find, besides the conventional contribution (∼70% at 300 K) from particlelike phonons propagating, the coherence contribution dominated by the wavelike tunneling of phonons accounts for ∼30% of the total κL at 300 K. By considering dual contributions, our calculated room-temperature κL of 0.64 W m−1 K−1 well agrees with the experimental value of 0.63 W m−1 K−1. More importantly, our computations give a nonstandard κL ∝ T−0.61 dependence, perfectly explaining the abnormal temperature-trend of ∼T−0.57 in experiment for α-MgAgSb. By molecular dynamics simulation, we reveal that the structure simultaneously has soft crystalline sublattices with the metavalent bonding and fluctuating liquid-like sublattices with thermally induced large amplitude vibrations. These diverse forms of chemical bonding arouse mixed part-crystal part-liquid state, scatter strongly heat-carrying phonons, and finally produce extremely low κL. The fundamental research from this study will accelerate the design of ultralow-κL materials for energy-conversion applications. [ABSTRACT FROM AUTHOR]

Background: The location of gastrointestinal perforation is essential for severity evaluation and optimizing the treatment approach. We aimed to retrospectively analyze the clinical characteristics, laboratory parameters, and imaging features of patients with gastrointestinal perforation and construct a predictive model to distinguish the location of upper and lower gastrointestinal perforation. Methods: A total of 367 patients with gastrointestinal perforation admitted to the department of emergency surgery in Fujian Medical University Union Hospital between March 2014 and December 2020 were collected. Patients were randomly divided into training set and test set in a ratio of 7:3 to establish and verify the prediction model by logistic regression. The receiver operating characteristic curve, calibration map, and clinical decision curve were used to evaluate the discrimination, calibration, and clinical applicability of the prediction model, respectively. The multiomics model was validated by stratification analysis in the prediction of severity and prognosis of patients with gastrointestinal perforation. Results: The following variables were identified as independent predictors in lower gastrointestinal perforation: monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, free air in peritoneal cavity by univariate logistic regression analysis and stepwise regression analysis. The area under the receiver operating characteristic curve of the prediction model was 0.886 (95% confidence interval, 0.840–0.933). The calibration curve shows that the prediction accuracy and the calibration ability of the prediction model are effective. Meanwhile, the decision curve results show that the net benefits of the training and test sets are greater than those of the two extreme models as the threshold probability is 20–100%. The multiomics model score can be calculated via nomogram. The higher the stratification of risk score array, the higher the number of transferred patients who were admitted to the intensive care unit (P < 0.001). Conclusion: The developed multiomics model including monocyte absolute value, mean platelet volume, albumin, fibrinogen, pain duration, rebound tenderness, and free air in the peritoneal cavity has good discrimination and calibration. This model can assist surgeons in distinguishing between upper and lower gastrointestinal perforation and to assess the severity of the condition. [ABSTRACT FROM AUTHOR]

Saturable absorber is a key component in ultrafast pulsed laser systems, which are pivotal in ultrafast physics, high‐speed communication, etc. 2D materials appear to be superior candidates as saturable absorbers for compact pulsed fiber lasers in recent years due to their wide band response and ease of integration. However, the integration of mode‐locked devices inevitably involves contamination or polymer in the interface between the layered saturable absorber and the fiber, which contributes to a high start‐up threshold for the ultrafast pulsed lasers. This study develops an on‐fiber femtosecond laser with 2D narrow‐gap semiconducting BiCuSeO as a saturable absorber by a fully dry integration process thanks to the controllable growth of freestanding quaternary BiCuSeO nanoflakes. The corresponding thermodynamic growth mechanism is systemically revealed by electron microscopy and bind‐energy calculation. An electrostatic‐force‐assisted transfer method is developed to avoid damage or chemical contamination. The BiCuSeO‐based femtosecond laser shows decent output performance (pulse width, signal‐noise ratio and repetition rate are 395 fs, 61 dB, 17.1 MHz) with an ultra‐low threshold of 30 mW. The strategy of controllable growth of free‐standing 2D material facilitates a clean interface between the saturable absorber and fibers, which is favorable for high‐performance ultrafast photonic devices. [ABSTRACT FROM AUTHOR]

The soil parameters for large, complex landslides are typically derived from incomplete information based on a small sample set due to budgetary constraints. This informational incompleteness results in large statistical uncertainty in landslide reliability analyses. In this article, the bootstrap technique is proposed to quantify the statistical uncertainties associated with a small sample set, and a practice-oriented reliability analysis is performed. The results suggest that the obtained reliability indices are characterized by a long tail, in which the worst-case scenario has a local extreme value and a small population. The statistical uncertainties are quantified and characterized by a confidence interval at a specified confidence level. The confidence interval of the reliability index and identification of the worst-case scenario enable engineers to make more informed decisions. [ABSTRACT FROM AUTHOR]

A spoil dump slope is a kind of artificial slope formed by the accumulation of engineering waste residue from multiple sources, which results in high variability of its mechanical properties. Large amounts of groundwater inside such a structure can make it prone to slope instability. Although an air-injection drainage method has been developed in recent years to reinforce common soil slopes by facilitating drainage, research involving the underlying mechanisms still needs to be further strengthened. In this study, a gas–water two-phase flow theory was applied for unsaturated soil seepage in a spoil dump slope, and the uncertainty of the spoil’s physical and mechanical properties was considered in a reliability analysis to evaluate the reinforcement effect of the air-injection drainage method. Based on a case study, the changes in reliability index and failure probability were determined before and after injection under different air pressures. The results show that air-injection drainage methods can provide effective reinforcement for spoil dump slopes by significantly reducing porewater pressures in a potential slip zone, thus reducing the possibility of slope failure. Moreover, a spoil slope reinforced by this method can better resist the adverse impact of rainfall. [ABSTRACT FROM AUTHOR]

Background: Leishmaniasis caused by protozoan parasite Leishmania is a neglected disease which is endemic in the northwest of China. Reptiles were considered to be the potential reservoir hosts for mammalian Leishmaniasis, and Leishmania had been detected in lizards from the epidemic area in the northwest of China. To date, few studies are focused on the natural infection of snakes with Leishmania. Methods: In this study, 15 snakes captured from 10 endemic foci in the northwest of China were detected Leishmania spp. on the base of mitochondrial cytochrome b, heat shock protein 70 gene and ribosomal internal transcribed spacer 1 regions, and identified with phylogenetic and network analyses. Result: In total, Leishmania gene was found in 7 snakes. The phylogenetic inference trees and network analysis suggests that the species identification was confirmed as Leishmania donovani, L. turanica and L. (Sauroleishmania) sp. Conclusion: Our work is the first time to investigate the natural Leishmania spp. infection of snakes in the northwest of China. Mammalian Leishmania (L. donovani and L. turanica) was discovered in snakes and the reptilian Leishmania (Sauroleishmania sp.) was closely related to the clinical strains both prompt the importance of snakes in the disease cycle. To indicate the epidemiological involvement of snakes, a wide sample size in epidemic area and the pathogenic features of reptilian Leishmania promastigotes are recommended in the future research. [ABSTRACT FROM AUTHOR]

A general virtual experiment for the time-of-flight neutron reflectometer is introduced to simulate the experimental data collection and processing, including instrument simulation, sample modeling, detector simulation, and data reduction. The process mimics the experimental data processing and reduction routines. The reduced data agree well with the theoretical calculation, confirming the reliability of the data reduction algorithms. The methodology and algorithms developed in this study pave the way for the future development of data processing and data reduction software for the time-of-flight neutron reflectometer of China Spallation Neutron Source. [ABSTRACT FROM AUTHOR]

Objectives: Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1–T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC. Methods: We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). Results: In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features. Conclusion: The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model. [ABSTRACT FROM AUTHOR]

Despite the interest toward the terahertz (THz) rapidly increasing, the high‐efficient detection of THz photon is not widely available due to the low photoelectric conversion efficiency at this low‐energy photon regime. Excitonic insulator (EI) states in emerging materials with anomalous optical transitions and renormalized valence band dispersions render their nontrivial photoresponse, which offers the prospect of harnessing the novel EI properties for the THz detection. Here, an EI‐based photodetector is developed for efficient photoelectric conversion in the THz band. High‐quality EI material Ta2NiSe5 is synthesized and the existence of the EI state at room temperature is confirmed. The THz scanning near‐field optical microscopy experimentally reveals the strong light–matter interaction in the THz band of EI state in the Ta2NiSe5. Benefiting from the strong light–matter interaction, the Ta2NiSe5‐based photodetectors exhibit superior THz detection performances with a detection sensitivity of ≈42 pW Hz−1/2 and a response time of ≈1.1 µs at 0.1 THz at room temperature. This study provides a new avenue for realizing novel high‐performance THz photodetectors by exploiting the emerging EI materials. [ABSTRACT FROM AUTHOR]

Owing to the high incidence and mortality rates of colorectal cancer (CRC), novel biomarkers for CRC diagnosis are critically needed. Therefore, this study is aimed at exploring the clinical utility of serum C-X-C motif chemokine 8 (CXCL-8) for CRC diagnosis and progression compared to the routinely used biomarkers, carcinoembryonic antigen (CEA), and carbohydrate antigen-19-9 (CA19-9). This study included 227 patients with CRC, 110 patients with colorectal adenoma (CA), and 123 healthy participants, who were recruited from the Fujian Medical University Union Hospital from July 1, 2019 to October 31, 2020. Serum concentrations of CXCL-8, CEA, and CA19-9 were detected using enzyme-linked immunosorbent assay and chemiluminescent microparticle immunoassay. Clinicopathological features of patients with CRC were collected and analyzed. The diagnostic efficacy of CXCL-8, CEA, and CA19-9 for CRC was evaluated using receiver operating characteristic (ROC) curves. We found that the serum concentrations of CXCL-8, CEA, and CA19-9 were significantly higher in patients with CRC than those in patients with CA and healthy controls. The diagnostic sensitivity of CXCL-8 alone was higher than those of CEA and CA19-9 both and when combined; thus, CXCL-8 may be better at discriminating patients with CRC from healthy controls and patients with CA. Moreover, combining CXCL-8 with CEA or CA19-9 improved their respective diagnostic performances in distinguishing patients with CRC from CA patients and healthy participants. Notably, we also found that serum concentrations of CXCL-8 were positively correlated with metastases and tumor size. Therefore, our study suggests that serum CXCL-8 may serve as an improved biomarker for CRC diagnosis compared to the traditional tumor markers CEA and CA19-9. Moreover, our findings indicate the potential efficacy of serum CXCL-8 levels as a CRC prognostic biomarker. [ABSTRACT FROM AUTHOR]

Determination of the shear failure zones of rock joints leads to a better understanding of their shear mechanical behavior. In this paper, we present a low-cost approach to determining the shear damage zones of rock joints after direct shear tests. Experimental direct shear tests were performed on one pair of man-made and six pairs of natural samples to collect digital high-resolution images using a digital camera before and after the tests. For each sample, the images acquired before and after tests were brought into registration, and a disparity image was generated. Then, a binary image with the shear damage zones marked as true was generated, and the noise was filtered out with postprocessing methods. The traditional RISS and MN methods were employed to examine the performance of the proposed approach by comparing their accuracy, time consumption, adaptability and robustness. The results demonstrated that the proposed approach IR is comparable to the MN approach in terms of accuracy but saves 82.92% of the time, and has better accuracy, adaptability and robustness compared to the RISS approach. Highlights: A new approach based on the image registration technique was proposed to determine the shear damage zones of rock joints. The proposed approach showed better efficiency, accuracy, and robustness than the two traditional methods in the comparative study. The proposed approach has been integrated into the open-sourced SDZM toolbox, which facilitates engineering applications and comparative studies. [ABSTRACT FROM AUTHOR]

Purpose. The clinical utility of plasma methylated septin 9 (mSEPT9) DNA in screening and recurrence monitoring for colorectal cancer (CRC) is highly promising. The present study was performed to determine the diagnostic value of mSEPT9 in CRC detection and recurrence monitoring in Chinese patients. Methods. Overall, 616 patients newly diagnosed with CRC and 122 individuals with no evidence of disease were recruited from October 1, 2019, to May 31, 2021, at Fujian Medical University Union Hospital. Plasma and serum samples were collected for analyzing mSEPT9, carcinoembryonic antigen (CEA), and carbohydrate antigen-19-9 (CA19-9). Data on clinicopathological characteristics were collected and analyzed. Sensitivity and specificity were calculated to evaluate the diagnostic potential of each marker; the receiver operating characteristic (ROC) curve was applied for the assessment of diagnostic value, and comparisons among mSEPT9, CEA, CA19-9, and their combination were assessed via ROC curves. Results. mSEPT9 achieved an overall sensitivity and specificity of 72.94% and 81.97%, respectively, with an area under the curve (AUC) value of 0.826, which were higher than those of CEA (sensitivity: 43.96%; specificity: 96.72%; AUC: 0.789) and CA19-9 (sensitivity: 14.99%; specificity: 96.61%; AUC: 0.590). The combination of mSEPT9, CEA, and CA19-9 further improved sensitivity, specificity, and AUC value (sensitivity: 78.43%; specificity: 86.07%; AUC: 0.878), respectively. Notably, the mSEPT9 positivity rate was significantly associated with TNM stage, T stage, N stage, tumor size, vascular invasion, and nerve invasion among patients with CRC. A 100% correlation was observed between the positive results of the mSEPT9 test and recurrence or metastasis in patients after therapeutic intervention. Conclusion. Our findings suggest that mSEPT9 may represent a potential biomarker for the diagnosis and prognosis of CRC compared with CEA and CA19-9. Postoperative mSEPT9 status may represent the first noninvasive marker of CRC recurrence or metastasis. [ABSTRACT FROM AUTHOR]

Dual-band photodetectors have attracted intensive attention because of the requirement of multiband information [such as visible (VIS) and near-infrared (NIR)] in multicolor imaging technology, in which additional information beyond human vision could assist object identification and navigations. The use of 2D materials can break the limitation of high cost of conventional epitaxial semiconductors and a complex cryogenic cooling system for multi-band detection, but there is still much room to improve the performance, especially in responsivity and signal noise ratio. Herein, we have fabricated a VIS-NIR dual-band photodetector based on a multilayer Ta2NiSe5/GaSe heterojunction. Benefiting from the type-II heterojunction, the separation of photo-induced carriers is naturally enhanced, which promotes the responsivity of this dual-band photodetector to 4.8 A W−1 (VIS) and 0.15 A W−1 (NIR) at room temperature with a suppressed dark current at ∼4 pA. Our work suggests that the Ta2NiSe5/GaSe heterostructure is a promising candidate for ultrasensitive VIS-NIR dual-band photodetection. [ABSTRACT FROM AUTHOR]

[Display omitted] The timely suppression of inflammatory mediator production and mitigation of their effects on pancreatic acinar cells are crucial for the successful management of acute pancreatitis. To achieve effective treatment, we present a novel approach utilizing cysteine modified PEG nanoparticles for both precise accumulation at the site of pancreatitis and specific targeting of acinar cells. Methylprednisolone, a nonsteroidal anti-inflammatory drug, was tailored to enhance its circulation time in the bloodstream, preferentially accumulate in the pancreas and enhance cell uptake efficiency by acinar cells through specifically targeting L-Type amino acid transporter 1. The nanosystem significantly downregulated pro-inflammatory cytokines in plasma, resulting in the effective suppression of inflammation in acinar cells within an acute pancreatitis rat model. The utilization of the dual targeted therapy strategy holds considerable potential for the clinical management of pancreatitis. [ABSTRACT FROM AUTHOR]

Aberrant expression of long noncoding RNA cyclin-dependent kinase inhibitor 2B antisense RNA 1 (CDKN2B-AS1) has been detected in human colorectal cancer (CRC). This study aimed to investigate the role of CDKN2B-AS1 and the underlying mechanism in human CRC. Gain- and loss-of-function assays were performed to explore the role of CDKN2B-AS1 in the malignant behavior of HCT116 and SW480 CRC cells in vitro and in vivo. RNA pull-down assay was conducted to identify the target of CDKN2B-AS1 in CRC cells. The physical and functional interactions between CDKN2B-AS1 and the target were examined. CDKN2B-AS1 inhibited CRC cell proliferation and migration while promoting apoptosis in vitro via activation of mitogen-activated protein kinase kinases (MEK)/extracellular signal-regulated kinase (ERK)/p38 signaling. CDKN2B-AS1 bound to mitogen-activated protein kinase (MAPK) inactivator dual-specificity phosphatase 1 (DUSP1) in CRC cells. In contrast to CDKN2B-AS1, DUSP1 promoted CRC cell proliferation, suppressed apoptosis and inactivated MEK/ERK/p38 signaling in CRC cells. Furthermore, CDKN2B-AS1 overexpression attenuated DUSP1 expression in normal colonic myofibroblasts and CRC cells. Overexpression of DUSP1 effectively countered the activation of MEK/ERK/p38 signaling induced by CDKN2B-AS1 overexpression or further blocked MEK/ERK/p38 signaling suppressed by CDKN2B-AS1 silencing. In the mouse xenograft model, CDKN2B-AS1 suppressed CRC growth, whereas DUSP1 promoted CRC growth. CDKN2B-AS1 induced cell apoptosis while suppressing EMT (epithelial–mesenchymal transition), whereas DUSP1 suppressed cell apoptosis while inducing EMT in CRC, as evidenced by the alterations in the protein levels of apoptosis and EMT markers in tumor tissue samples. CDKN2B-AS1 regulates CRC cell growth and survival by targeting MAPK inactivator DUSP1. [ABSTRACT FROM AUTHOR]

A total scattering neutron instrument is simulated using a ray-tracing package to evaluate the technique and data reduction algorithms. The neutron guide and chopper system were first optimized to obtain the highest neutron flux with satisfactory angular divergence. Next, a series of virtual experiments were conducted using heavy water and calibration vanadium samples. Data analysis algorithms were also developed. The resultant simulated sample static structure factor was in excellent agreement with the experimental measurements. Additionally, analytical and Monte Carlo methods are applied to account for multiple scattering in a variety of sample sizes. The effectiveness of this method and possible improvements are discussed. [ABSTRACT FROM AUTHOR]

A coupled structural decomposition analysis (SDA) and sensitivity analysis approach is developed to explore the drivers of China's CO2 emission intensity at both general and sectoral levels and from both ex-post and ex-ante perspectives. Two steps are involved—structural decomposition and sensitivity analysis. First, the popular factor decomposition method, SDA, is implemented to identify which drivers "have" made the largest contribution to emission intensity changes. Second, an emerging ex-ante approach, sensitivity analysis, is introduced to answer how and to what extent such drivers "will" influence future emission intensity at a sectoral level. Based on China's input-output tables for 1997–2012, the empirical study provides a hotspot map of China's energy system. (1) Direct-emission coefficient and technology coefficient are observed as the top two overall drivers. (2) For the former, reducing direct-emission coefficient in an emission-intensity sector (e.g., electricity and heat sectors) by 1% will mitigate China's total emission intensity by at least 0.05%. (3) For the latter, future emission intensity is super-sensitive to direct transactions in emission-intensity sectors (particularly the chemical industry with elasticities up to 0.82%). [ABSTRACT FROM AUTHOR]

Summary: Visceral leishmaniasis is a tropical and neglected disease with an estimated 200 000–400 000 cases and 60 000 deaths worldwide each year. Currently, no clinically valid vaccine is available for this disease. In this study, we formulated DNA and protein vaccines encoding HLA‐A2, HLA‐A24 and HLA‐DR1 restricted epitopes of CaNA2 against visceral leishmaniasis. We predicted the secondary and tertiary structures, surface properties, subcellular localization, potential binding sites and HLA‐A2, HLA‐A24 and HLA‐DR1 restricted epitopes of CaNA2. The best candidate CpG ODN (2395, M362, D‐SL03 or 685) was screened out as a DNA vaccine adjuvant. We also prepared Kmp‐11 and Kmp‐11/CaNA2 DNA and protein vaccines, respectively, for comparison. BALB/c mice were immunized with a DNA prime‐protein boost immunization strategy and challenged with a newly isolated Leishmania strain from an individual with visceral leishmaniasis. The IgG antibody titers showed that our vaccine had strong immunogenicity with a long duration, especially cellular immunity. The spleen parasite burden of each group demonstrated that the CaNA2 vaccine had a certain immune protective effect on visceral leishmaniasis in BALB/c mice, and the amastigote reduction rate reached 76%. Preliminary safety tests confirmed the safety of the vaccine. Our work demonstrates that the HLA‐A2, HLA‐A24 and HLA‐DR1 restricted epitope CaNA2 DNA prime‐protein boost vaccine may be a safe and effective epitope vaccine candidate against visceral leishmaniasis. The eukaryotic recombinant plasmids encoding CaNA2 HLA‐A2, HLA‐A24 and HLA‐DR1 restricted epitopes, KMP‐11 and KMP‐11/CaNA2 were successfully constructed, and their transient and stable expression were verified in NIH3T3 cells. The prokaryotic recombinant plasmids of the corresponding target genes were also successfully constructed, expressed and purified. In BALB/c mice, the DNA prime‐protein boost vaccine of HLA‐restricted CaNA2 induced strong cellular immunity and immune protection against visceral leishmaniasis. [ABSTRACT FROM AUTHOR]

The rice heading stage is an essential phase of rice production as it directly affects the rice yield. This paper transforms the issue of rice heading stage automatic observation into the problem of rice spike detection and proposes a new method for automatic observation of the rice heading stage. Rice spike detection is achieved using a new multi-classifier cascade method comprised of the following steps: First, SVM with color feature as input is utilized to distinguish the rice spike image patches from the background patches (leaf, soil, water, etc.); Second, a gradient histogram method is applied to remove the yellow leaf patches from consideration; Third, a convolutional neural network (CNN) is utilized to further reduce the false positive rate. The arrival of the rice heading stage is determined by the number of the detected spike patches. To evaluate the proposed method, it was applied to the automatic rice heading stage observation of six image sequences collected by the designed observation device between 2011 and 2013. In the experiment, the proposed method produced similar results to the conventional manual observation method in determining the arrival of the rice heading stage. The differences between the proposed method and manual way were within two days. Experiments demonstrated that the proposed method is an effective approach of automatic observation of the rice heading stage in paddy fields and can be utilized to replace the manual observation. [ABSTRACT FROM AUTHOR]

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

The general purpose powder diffractometer (GPPD) is the first powder diffractometer at the China Spallation Neutron Source (CSNS). Its aim is to provide a high-resolution, good-intensity instrument for users to study crystallographic and magnetic structures. The best resolution will be better than 0.2% at high-angle detector bank. The neutron flux at the sample position will be at the order of 107 n/s/cm2. The joint beam commissioning among accelerators, target station and instruments started in the CSNS on November 1, 2017. The neutron beam shutter for the general purpose powder diffractometer (GPPD) was opened for the first time. Initial measurements and standard sample diffraction experiments are reported, which are used to characterize the instrument performance. It is shown that the results agree well with the design goal. During the commissioning, a series of user experiments have been conducted. Two user experimental results have already been published. [ABSTRACT FROM AUTHOR]

The physical model test of landslides is important for studying landslide structural damage, and parameter measurement is key in this process. To meet the measurement requirements for deep displacement in landslide physical models, an automatic flexible inclinometer probe with good coupling and large deformation capacity was designed. The flexible inclinometer probe consists of several gravity acceleration sensing units that are protected and positioned by silicon encapsulation, all the units are connected to a 485-comunication bus. By sensing the two-axis tilt angle, the direction and magnitude of the displacement for a measurement unit can be calculated, then the overall displacement is accumulated according to all units, integrated from bottom to top in turn. In the conversion from angle to displacement, two spline interpolation methods are introduced to correct and resample the data; one is to interpolate the displacement after conversion, and the other is to interpolate the angle before conversion; compared with the result read from checkered paper, the latter is proved to have a better effect, with an additional condition that the displacement curve move up half the length of the unit. The flexible inclinometer is verified with respect to its principle and arrangement by a laboratory physical model test, and the test results are highly consistent with the actual deformation of the landslide model. [ABSTRACT FROM AUTHOR]

Small-angle neutron scattering (SANS) is used to characterize material structures in the nanometer to micrometer range. The solution of the structural model constructed from SANS strongly depends on the accuracy of the reduced data. The time-of-flight (TOF) SANS measurements are dependent on the wavelength of the pulsed neutron source. Therefore, data reduction must be handled carefully to transform measured neutron events into neutron scattering intensities. In this study, reduction algorithms for TOF SANS data were developed and optimized using simulated data from virtual neutrons. Each possible effect on the measured data was studied systematically, and suitable corrections were performed to obtain high-quality data. This work will facilitate scientific research and the instrument design at the China Spallation Neutron Source. [ABSTRACT FROM PUBLISHER]

Orientation statistics are prone to bias when surveyed with the scanline mapping technique in which the observed probabilities differ, depending on the intersection angle between the fracture and the scanline. This bias leads to 1D frequency statistical data that are poorly representative of the 3D distribution. A widely accessible estimator named after Terzaghi was developed to estimate 3D frequencies from 1D biased observations, but the estimation accuracy is limited for fractures at narrow intersection angles to scanlines (termed the blind zone). Although numerous works have concentrated on accuracy with respect to the blind zone, accuracy outside the blind zone has rarely been studied. This work contributes to the limited investigations of accuracy outside the blind zone through a qualitative assessment that deploys a mathematical derivation of the Terzaghi equation in conjunction with a quantitative evaluation that uses fractures simulation and verification of natural fractures. The results show that the estimator does not provide a precise estimate of 3D distributions and that the estimation accuracy is correlated with the grid size adopted by the estimator. To explore the potential for improving accuracy, the particular grid size producing maximum accuracy is identified from 168 combinations of grid sizes and two other parameters. The results demonstrate that the 2° × 2° grid size provides maximum accuracy for the estimator in most cases when applied outside the blind zone. However, if the global sample density exceeds 0.5°, then maximum accuracy occurs at a grid size of 1° × 1°. [ABSTRACT FROM AUTHOR]

Legionella pneumophila is the main causative agent of Legionnaires' disease, which is a severe multi-system disease with pneumonia as the primary manifestation. We designed a recombinant Mip-PilE-FlaA dominant epitopes vaccine against Legionella pneumophila to prevent the disease and evaluated its immunogenicity and protective immunity. The protein structures of Mip, PilE and FlaA were analyzed using a computer, and the gene sequences of the dominant epitopes of the three proteins were selected to construct and optimize the vaccine. The optimized mip , pilE , flaA and recombinant mip-pilE-flaA gene sequences were cloned, expressed and purified. The purified proteins were used as dominant epitopes vaccines to immunize BALB/c mice and determine the protective immunity and immunogenicity of these purified proteins. The identification confirmed that the recombinant mip-pilE-flaA was successfully cloned and expressed. ELISA revealed that the Mip-PilE-FlaA group produced the highest IgG response, and this protein may considerably improve the production of some cytokines in BALB/c mice. Histopathology analyses of lungs from mice immunized with Mip-PilE-FlaA revealed a certain protective effect. Our work demonstrated that the recombinant dominant epitopes of Mip-PilE-FlaA exhibited strong immunogenicity and immune protection, and this protein may be an efficient epitopes vaccine candidate against Legionella pneumophila. [ABSTRACT FROM AUTHOR]

Background The sensitivity and specificity of noninvasive prenatal testing (NIPT) for detection of sex chromosome aneuploidies (SCAs) compared to common autosomal trisomies are significantly lower. We speculated that in addition to altered maternal X chromosome karyotype, maternal X chromosome copy number variations (CNVs) may also contribute to discordant NIPT SCA results. Methods Clinical NIPT was performed for pregnant women at a single hospital. Copy number variation sequencing (CNV-Seq) was used to identify and quantitate the copy number of maternal X chromosome CNVs for each positive SCA pregnancy. Results Two out of 25 SCA positive NIPT samples had slightly abnormal ChrX/ChrY z-scores and were referred for invasive test confirmation. However, fetal karyotypes were found to be normal. CNV-Seq analysis of the maternal white blood cell DNA archived from the original two NIPT blood samples identified small CNVs spanning the STS gene, which is associated with X-linked ichthyosis. Correcting for the altered plasma levels of X chromosome DNA caused by the two CNVs and, taking into consideration the phenotypic consequences for X-linked disease, both fetuses were diagnosed as normal. Conclusions Maternal DNA sequencing is recommended for all positive NIPT SCA results to avoid unnecessary referral for invasive testing and also to evaluate the risk to the fetus of X-linked disease. [ABSTRACT FROM AUTHOR]

This paper compares the performance of five popular machine learning methods, namely, particle swarm optimization–extreme learning machine (PSO–ELM), particle swarm optimization–kernel extreme learning machine (PSO–KELM), particle swarm optimization–support vector machine (PSO–SVM), particle swarm optimization–least squares support vector machine (PSO–LSSVM), and long short-term memory neural network (LSTM), in the prediction of reservoir landslide displacement. The Baishuihe, Shuping, and Baijiabao landslides in the Three Gorges reservoir area of China were used for case studies. Cumulative displacement was decomposed into trend displacement and periodic displacement by the Hodrick–Prescott filter. The double exponential smoothing method and the five machine learning methods were used to predict the trend and periodic displacement, respectively. The five machine learning methods are compared in three aspects: highest single prediction accuracy, mean prediction accuracy, and prediction stability. The results show that no method performed the best for all three aspects in the three landslide cases. LSTM and PSO–ELM achieved better single prediction accuracy, but worse mean prediction accuracy and stability. PSO–KELM, PSO–LSSVM, and PSO–SVM always yielded consistent predictions with slight variations. On the whole, PSO–KELM and PSO–LSSVM are recommended for their superior mean prediction accuracy and prediction stability. • The performances of machine learning methods depend on the selected evaluation index and reservoir landslide case. • Using single prediction accuracy for evaluating the superiority of machine learning methods may be unreliable. • PSO-KELM and PSO-LSSVM are recommended for their superior mean prediction accuracy and prediction stability. [ABSTRACT FROM AUTHOR]

Abstract: Oxide ion conduction and phase transition phenomena have been investigated on La2−xPbxMo2O9−x/2 solid solutions (x≤0.6). X-ray and neutron diffraction experiments as well as thermal analyses indicate that a single phase of high-temperature polymorph (β-phase) is preserved down to room temperature for the composition of x≥0.06. In the solid solution range, ionic conductivity gradually decreases with Pb-substitution. Such phase transition and ionic conduction properties of La2−xPbxMo2O9−x/2 are quite similar to the Bi-substituted system, La2−xBixMo2O9, although ionic conductivity at lower temperatures is slightly higher in the former system than the latter with the same-amount of substitution. Room temperature neutron diffraction experiment suggests that oxide-ion deficiency is introduced mainly into O2 site by the charge compensation with Pb-substitution, which would contribute to a little enhancement in conductivity compared with Bi-substituted samples along with the variation of nearest O2 O3 bond length. [Copyright &y& Elsevier]

Abstract: The phase relationship of the TbO1.81–Mn3O4–Fe2O3 pseudo-ternary system synthesized at 1200°C was determined using X-ray and neutron diffraction method. Four solid solutions, Mn3−3 x Fe3 x O4 (0.00⩽ x ⩽0.278), Mn3−3 x Fe3 x O4 (0.291⩽ x ⩽0.667), Mn2−2 x Fe2 x O3 (0.89⩽ x ⩽1.00), and (Tb1− y Mn z Fe x )(Fe w Mn1− w )O3 (1−y + x + z ⩽1, 0.00⩽ w ⩽1.00) were found along with six two-phase regions and three three-phase regions. The boundaries were determined by several different methods such as phase disappearing, the Vigard’s law, and the lever rule. [Copyright &y& Elsevier]

Suggests a satellite system with sensors of visible and infrared spectrums for impending earthquake forecasts. Uses of infrared sensors on meteosat probes; Specifications of radiometers of the system; Conclusion.

Landslide displacement monitoring plays a fundamental role in the study of landslide evolution mechanisms, forecasting, risk assessment, prevention, and control. To fill the deficiencies of traditional instrumentation for measuring landslide displacement distributed along lateral direction, a landslide displacement measurement method based on deformation-coupled pipeline trajectory measurement is proposed, and a pipeline trajectory inertial measurement instrument is developed. The developed instrument, primarily comprised of a single shaft gyro, two axis accelerometers, and an external roller encoder, is designed as an axial half strapdown-radial half platform structure combined with a mechanical gravity platform. This structure avoids the singularity of pitch angle and roll angle and can expediently calculate a pipeline trajectory with an Eulerian transformation when obtaining several basic physical variables, e.g., the axial linear velocity, pitch angle, roll angle, and azimuth angle. Additionally, the pipeline trajectory, measured at different times, possesses the ability to reflect the displacement evolution feature of landslides. The results of prototype simulation tests imply a single measurement accuracy of a 12 cm/100 m span and a singly periodic multiple (more than five times) measurement accuracy of a 3 cm/100 m span, which meets medium-precision displacement measurement requirements for a landslide. Additionally, the finished instrument has been successfully applied to the deformation monitoring of the Majiagou I# landslide, which further verifies its feasibility and offers a reference for similar landslides. [ABSTRACT FROM AUTHOR]

The search for new materials that could improve the energy density of Li-ion batteries (LIB) is one of today's most challenging issues. Recently, cation-disordered lithium-excess metal oxides have emerged as a promising new class of cathode materials for LIB, due to their high reversible capacities and nice structural stability. However, a full structural model of the Li-transition metal (TM) sharing sublattice and the origin of short range ordering (SRO) of cations requires further investigation. In this work, we put forward a Monte Carlo strategy of building cation-disordered rocksalt material supercell models. The cations of Li 1.0 Ti 0.5 Ni 0.5 O 2 (LTNO) are placed at the FCC sublattice sites with the constraint of Pauling's electroneutrality rule, instead of a random way. This constraint causes the Li-Ti and Ni-Ni clustering. Based on this model, we discussed the relationship between the SRO, the local distorting, the theoretical capacity and the order-disorder strengths. A unified understanding of these factors in cation-disordered materials may enable a better design of disordered-electrode materials with high capacity and high energy density. • In this work, we described a Monte Carlo strategy to build the model of cation-disordered material Li 1.0 Ti 0.5 Ni 0.5 O 2. The cations are distributed at the FCC sublattice with a constraint of Pauling's electroneutrality rule, instead of a random way. The model is relaxed by an atomistic force field to get into the thermal equilibrium. • We conclude that the electroneutrality causes the short-range ordering (SRO) in the cation sublattice and results the Li-Ti and Ni-Ni clustering. • The SRO is favoured by the lattice completeness and lower the local distortion of the cations. But it also decreases the content of 0-TM channels and the capacities of the materials. So we can expect for a more 'disordered' material with larger capacity. The energy calculation shows that this can be done by sintering the materials at higher temperatures which has been proved by several experiments. [ABSTRACT FROM AUTHOR]

Assessing the influence of the impact pressure exerted on surrounding rock mass by potential debris flows is one of the challenges in a mountain tunnel construction project. This paper proposes an effective mathematical model for the assessment of impact pressure exerted by debris flows on obstacles. The characteristics of the new model are analyzed, and the parameters determination method for the new model are deduced. Also, a new method for dynamic reliability analysis of the rock mass surrounding tunnels is proposed and is constituted into three key steps. First, by inputting impact pressure of debris flows generated by the aforementioned new model, the FLAC3D is used as the dynamic analysis engine to obtain the time-varying stress distribution of surrounding rock mass during the impact of debris flow. Second, the factor of safety (FOS) contours of surrounding rock mass are generated based on element states using the maximum tensile stress criterion and the Mohr-Coulomb yield criterion. Third, by using the time-varying contours of the FOS, a failure probability and a reliability index are obtained from a reliability perspective. A case study shows the complete analysis process and confirms the effectiveness of the above methodology. In sum, this paper provides new ideas, new methods, and a reference example for evaluating the influence of dynamic impact exerted by debris flows on the rock mass surrounding tunnels. • A novel mathematical model is proposed for the assessment of impact pressure exerted by debris flows on obstacles. • A new method is proposed for dynamic reliability analysis of the rock mass surrounding tunnels. • The instantaneous factor of safety (FOS) of each element of the rock mass surrounding tunnels is applied based on the maximum tensile stress criterion and the Mohr-Coulomb yield criterion. • The failure probability and the reliability index of the rock mass surrounding tunnels are obtained based on space-time-varied stress fields. [ABSTRACT FROM AUTHOR]

Here, we propose a new two-dimensional (2D) monolayer YS 2 and investigate its properties as electrode material for Li-ion and Na-ion batteries (LIBs and NIBs), by combining the density functional theory (DFT) and the crystal structure prediction techniques. The results of phonon dispersion and ab-initio molecular dynamics (AIMD) simulations confirm that it is dynamically and thermally stable. The adsorption energies of Li and Na on YS 2 are −2.038 and −1.939 eV, respectively. Besides, the superior electrical conductivity is maintained during the whole lithiation/sodiumation process. The calculated diffusion energy barriers for Li and Na on this YS 2 monolayer are 0.33 and 0.24 eV, respectively, demonstrating a fast ion transfer rate. The theoretical specific capacities for Li and Na adatoms on YS 2 are both 350 mA h g−1, comparable to the graphite electrode. These features indicate that 2D YS 2 monolayer could be considered as a promising electrode material for both LIBs and NIBs. Unlabelled Image • We predicted a new 2D transition metal disulfide (TMD), namely, YS 2 monolayer. • The YS 2 keeps metallic during the whole lithiation/sodiumation process. • The diffusion barrier of Li-ion (Na-ion) for YS 2 is 0.33 (0.24) eV. • The theoretical specific capacities for Li and Na atoms on YS 2 are both 350 mA h g−1. [ABSTRACT FROM AUTHOR]

A laboratory model test is an effective method for studying landslide risk mitigation. In this study, thermal infrared (TIR) imagery, a modern no-contact technique, was introduced and integrated with terrestrial laser scanning (TLS) and particle tracking velocimetry (PTV) to characterize the failure of a landslide model. The characteristics of the failure initiation, motion, and region of interest, including landslide volume, deformation, velocity, surface temperature changes, and anomalies, were detected using the integrated monitoring system. The laboratory test results indicate that the integrated monitoring system is expected to be useful for characterizing the failure of landslide models. The preliminary results of this study suggest that a change in the relative TIR signal (ΔTIR) can be a useful index for landslide detection, and a decrease in the average value of the temperature change ( Δ T I R ¯ ) can be selected as a precursor to landslide failure. [ABSTRACT FROM AUTHOR]