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Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters, making the slope reliability assessment closer to the engineering reality. However, multi-source information (including test data, monitored data, field observation and slope survival records) is rarely used in current probabilistic back-analysis. Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved. In this paper, a framework by integrating a modified Bayesian Updating with Subset simulation (mBUS) method with adaptive Conditional Sampling (aCS) algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction. Within this framework, the high-dimensional probabilistic back-analysis problem can be easily tackled, and the multi-source information (e.g. monitored pressure heads and slope survival records) can be fully used in the back-analysis. A real Taoyuan landslide case in Taiwan, China is investigated to illustrate the effectiveness and performance of the established framework. The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations. Furthermore, the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12, 2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City, Taiwan, China.

Abstract Objective To find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD). Methods Differential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated. Results Differential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed. Conclusion Among all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.

Abstract This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its eastern region (Ganzhou East), and Ruijin County in Ganzhou East were chosen. Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m, as well as slope units that were extracted by multi-scale segmentation method. The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs. Then, landslide susceptibility maps (LSMs) of Ganzhou City, Ganzhou East and Ruijin County are produced using a support vector machine (SVM) and random forest (RF), respectively. The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City, along with the LSMs of Ruijin County from Ganzhou East. Additionally, LSMs of Ruijin at various mapping unit scales are generated in accordance. Accuracy and landslide susceptibility indexes (LSIs) distribution are used to express LSP uncertainties. The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City, Ganzhou East to Ruijin County, whereas those under slope units are less affected by study area scales. Of course, attentions should also be paid to the broader representativeness of large study areas. The LSP accuracy of slope units increases by about 6%–10% compared with those under grid units with 30 m and 60 m resolution in the same study area's scale. The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large. The importance of environmental factors varies greatly with the 60 m grid unit, but it tends to be consistent to some extent in the 30 m grid unit and the slope unit. Graphic abstract

Introduction Mild cognitive impairment (MCI) is an intermediate phase between normal cognitive ageing and dementia and poses a serious threat to public health worldwide; however, it might be reversible, representing the best opportunity for secondary prevention against serious cognitive impairment. As a non-pharmacological intervention for those patients, interventions that combine physical exercise and cognitive training, whether delivered simultaneously or sequentially, may have superior effects on various cognitive domains, including global cognition, memory, executive function and attention. The supportive evidence remains incomplete. This study aims to assess the effectiveness of a combined exercise and cognitive intervention in Chinese older adults with mild cognitive impairment (COGITO), empowered by digital therapy and guided by the Health Action Process Model and the Theory of Planned Behaviour (HAPA-TPB theory) in a home-based setting.Methods and analysis This study is a randomised controlled, assessor-blinded multi-centre study. Four parallel groups will include a total of 160 patients, receiving either a combined exercise and cognitive intervention, an isolated exercise intervention, an isolated cognitive intervention or only health education. These interventions will be conducted at least twice a week for 50 min each session, over 3 months. All interventions will be delivered at home and remotely monitored through RehabApp and Mini-programme, along with an arm-worn heart rate telemetry device. Specifically, supervisors will receive participants’ real-time training diaries, heart rates or other online monitoring data and then provide weekly telephone calls and monthly home visits to encourage participants to complete their tasks and address any difficulties based on their training information. Eligible participants are community-dwelling patients with no regular exercise habit and diagnosed with MCI. The primary outcome is cognitive function assessed by the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-Cog) and Community Screening Instrument for Dementia (CSI-D), with baseline and three follow-up assessments. Secondary outcomes include quality of life, physical fitness, sleep quality, intrinsic capacity, frailty, social support, adherence, cost-effectiveness and cost-benefit.Ethics and dissemination The study was approved by the Institutional Review Board of Peking University. Research findings will be presented to stakeholders and published in peer-reviewed journals and at provincial, national and international conferences.Trial registration number ChiCTR2300073900.

Probabilistic inverse-analysis is an essential approach to infer statistical characteristics of uncertain soil parameters, making the slope reliability assessment closer to engineering reality. However, current probabilistic inverse analysis rarely integrates multi-source information, including monitored data, field observation information, and slope survival records. Conducting the probabilistic inverse-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating the multi-source information is a challenging issue due to the involvement of thousands of random variables and the evaluation of high-dimensional likelihood functions. In this paper, a modified Bayesian updating with subset simulation (mBUS) method is combined with adaptive conditional sampling (aCS) algorithm to establish a framework for probabilistic inverse analysis of spatially variable soil parameters and reliability prediction of slopes. The effectiveness of this framework is validated using a highway slope as a case study. The research results show that the posterior statistical characteristics of soil parameters obtained by integrating multi-source information are in good agreement with field observation results. Additionally, the probability of slope failure under heavy rainfall on September 12, 2004 with the updated soil parameters is 23.1 %, which is in line with the actual slope instability. Within this framework, multi-source information can be fully utilized to address high-dimensional probabilistic inverse analysis problems.

Abstract Interstitial iodides are the most critical type of defects in perovskite solar cells that limits efficiency and stability. They can be generated during solution, film, and device processing, further accelerating degradation. Herein, we find that introducing a small amount of a zinc salt- zinc trifluoromethane sulfonate (Zn(OOSCF3)2) in the perovskite solution can control the iodide defects in resultant perovskites ink and films. CF3SOO̶ vigorously suppresses molecular iodine formation in the perovskites by reducing it to iodide. At the same time, zinc cations can precipitate excess iodide by forming a Zn-Amine complex so that the iodide interstitials in the resultant perovskite films can be suppressed. The perovskite films using these additives show improved photoluminescence quantum efficiency and reduce deep trap density, despite zinc cations reducing the perovskite grain size and iodide interstitials. The zinc additives facilitate the formation of more uniform perovskite films on large-area substrates (78-108 cm2) in the blade-coating process. Fabricated minimodules show power conversion efficiencies of 19.60% and 19.21% with aperture areas of 84 and 108 cm2, respectively, as certified by National Renewable Energy Laboratory (NREL), the highest efficiency certified for minimodules of these sizes.

Abstract Spintronics in halide perovskites has drawn significant attention in recent years, due to their highly tunable spin-orbit fields and intriguing interplay with lattice symmetry. Here, we perform first-principles calculations to determine the spin relaxation time (T 1) and ensemble spin dephasing time ( $${T}_{2}^{*}$$ T 2 * ) in a prototype halide perovskite, CsPbBr3. To accurately capture spin dephasing in external magnetic fields we determine the Landé g-factor from first principles and take it into account in our calculations. These allow us to predict intrinsic spin lifetimes as an upper bound for experiments, identify the dominant spin relaxation pathways, and evaluate the dependence on temperature, external fields, carrier density, and impurities. We find that the Fröhlich interaction that dominates carrier relaxation contributes negligibly to spin relaxation, consistent with the spin-conserving nature of this interaction. Our theoretical approach may lead to new strategies to optimize spin and carrier transport properties.

Abstract Perovskite photovoltaics have been shown to recover, or heal, after radiation damage. Here, we deconvolve the effects of radiation based on different energy loss mechanisms from incident protons which induce defects or can promote efficiency recovery. We design a dual dose experiment first exposing devices to low-energy protons efficient in creating atomic displacements. Devices are then irradiated with high-energy protons that interact differently. Correlated with modeling, high-energy protons (with increased ionizing energy loss component) effectively anneal the initial radiation damage, and recover the device efficiency, thus directly detailing the different interactions of irradiation. We relate these differences to the energy loss (ionization or non-ionization) using simulation. Dual dose experiments provide insight into understanding the radiation response of perovskite solar cells and highlight that radiation-matter interactions in soft lattice materials are distinct from conventional semiconductors. These results present electronic ionization as a unique handle to remedying defects and trap states in perovskites.

In the existing landslide susceptibility prediction (LSP) models, the influences of random errors in landslide conditioning factors on LSP are not considered, instead the original conditioning factors are directly taken as the model inputs, which brings uncertainties to LSP results. This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP uncertainties, and further explore a method which can effectively reduce the random errors in conditioning factors. The original conditioning factors are firstly used to construct original factors-based LSP models, and then different random errors of 5%, 10%, 15% and 20% are added to these original factors for constructing relevant errors-based LSP models. Secondly, low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method. Thirdly, the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case. Three typical machine learning models, i.e. multilayer perceptron (MLP), support vector machine (SVM) and random forest (RF), are selected as LSP models. Finally, the LSP uncertainties are discussed and results show that: (1) The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties. (2) With the proportions of random errors increasing from 5% to 20%, the LSP uncertainty increases continuously. (3) The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors. (4) The influence degrees of two uncertainty issues, machine learning models and different proportions of random errors, on the LSP modeling are large and basically the same. (5) The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide susceptibility. In conclusion, greater proportion of random errors in conditioning factors results in higher LSP uncertainty, and low-pass filter can effectively reduce these random errors.

Landslide inventory is an indispensable output variable of landslide susceptibility prediction (LSP) modelling. However, the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored. Adopting Xunwu County, China, as an example, the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions, after which different landslide inventory sample missing conditions are simulated by random sampling. It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%, 20%, 30%, 40% and 50%, as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation. Then, five machine learning models, namely, Random Forest (RF), and Support Vector Machine (SVM), are used to perform LSP. Finally, the LSP results are evaluated to analyze the LSP uncertainties under various conditions. In addition, this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions. Results show that (1) randomly missing landslide inventory samples at certain proportions (10%–50%) may affect the LSP results for local areas. (2) Aggregation of missing landslide inventory samples may cause significant biases in LSP, particularly in areas where samples are missing. (3) When 50% of landslide samples are missing (either randomly or aggregated), the changes in the decision basis of the RF model are mainly manifested in two aspects: first, the importance ranking of environmental factors slightly differs; second, in regard to LSP modelling in the same test grid unit, the weights of individual model factors may drastically vary.

Most literature related to landslide susceptibility prediction only considers a single type of landslide, such as colluvial landslide, rock fall or debris flow, rather than different landslide types, which greatly affects susceptibility prediction performance. To construct efficient susceptibility prediction considering different landslide types, Huichang County in China is taken as example. Firstly, 105 rock falls, 350 colluvial landslides and 11 related environmental factors are identified. Then four machine learning models, namely logistic regression, multi-layer perception, support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide. Thirdly, three different landslide susceptibility prediction (LSP) models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility: (i) united method, which combines all landslide types directly; (ii) probability statistical method, which couples analyses of susceptibility indices under different landslide types based on probability formula; and (iii) maximum comparison method, which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides. Finally, uncertainties of landslide susceptibility are assessed by prediction accuracy, mean value and standard deviation. It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County. The united method has the best susceptibility prediction performance, followed by the probability method and maximum susceptibility method. More cases are needed to verify this result in-depth. LSP considering different landslide types is superior to that taking only a single type of landslide into account.

Utilizing both borehole and Cone Penetration Testing (CPT) data in soil stratification helps to get more convincing soil stratification results. However, the soil classification results revealed by borehole (Unified Soil Classification System, USCS) and CPT tests (soil behavior type, SBT) are commonly not consistent. This study proposes a feasible solution to integrate the borehole and CPT data with the tree-based method. The tree-based method is naturally suitable for soil stratification tasks as it aims to divide the subsurface space into several clusters based on the similarities of the soil types. A novel boundary dictionary method is proposed to enhance the model performance on complex soil layer conditions. A probabilistic mapping matrix between the USCS-SBT system is built based on a collected municipal database with collocated borehole and CPT data. The optimal soil stratification results can be selected based on considering multiple borehole information and pruning the structure of trees. The structure of the trees can be optimized in a back analysis perspective with the Sequential Model-Based Global Optimization (SMBO) algorithm which aims to maximize the possibility of observing the borehole information based on the USCS-SBT probabilistic mapping matrix. The uncertainties of the optimal soil stratification results can be estimated based on a weighted Gini index method. The performance of the proposed method is validated based on a real case in New Zealand with a cross-validation method. The results indicate that the proposed method is robust and effective.

The development of perovskite solar cells (PSCs) has ushered in a new era of solar technology, characterized by its exceptional efficiency and cost-effective production. However, the soft and fragile nature of perovskites makes module encapsulation challenging. Polyolefin elastomers (POEs) have been reported to be promising encapsulants for perovskite modules. However, little research exists on identifying criteria among different types of POEs as encapsulants. Here, two POEs with different morphologies were compared as encapsulants. The first POE crystallizes during encapsulation (crystal content ∼40%), and the resulting shrinkage or warpage leads to delamination, causing minimodule failure. In contrast, perovskite minimodules encapsulated with a mostly amorphous POE exhibited better reliability and reproducibility. The best perovskite minimodules passed the thermal cycling test for 240 cycles between −40 and 85 °C and the damp heat test for 1419 h, according to the IEC 61215 standard. This study highlights the importance of the morphology of encapsulants in achieving high-quality encapsulation.

Abstract Background Major depressive disorder (MDD) with atypical features, namely depression with atypical features (AFD), is one of the most common clinical specifiers of MDD, closely associated with bipolar disorder (BD). However, there is still a lack of clinical guidelines for the diagnosis, treatment, and prognosis of AFD. Our study mainly focuses on three issues about how to identify AFD, what is the appropriate individualized treatment for AFD, and what are the predictive biomarkers of conversion to BD. Methods The Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD) is a multicenter, prospective, open-label study consisting of a 12-week randomized controlled trial (RCT) and a continued follow-up until 4 years or reaching the study endpoint. It is enrolling 480 patients with AFD (120 per treatment arm), 100 patients with BD, and 100 healthy controls (HC). Multivariate dimension information is collected including clinical features, cognitive function, kynurenine pathway metabolomics, and multimodal magnetic resonance imaging (MRI) data. Firstly, multivariate informatics analyses are performed to recognize patients with AFD from participants including the first-episode and recurrent atypical depression, patients with BD, and patients with HC. Secondly, patients with atypical depression are randomly allocated to one of the four treatment groups including “single application of selective serotonin reuptake inhibitor (SSRI) or serotonin-noradrenaline reuptake inhibitor (SNRI)”, “SSRI/SNRI combined with mood stabilizer,” “SSRI/SNRI combined with quetiapine (≥ 150 mg/day),” or “treatment as usual (TAU)” and then followed up 12 weeks to find out the optimized treatment strategies. Thirdly, patients with atypical depression are followed up until 4 years or switching to BD, to explore the risk factors of conversion from atypical depression to BD and eventually build the risk warning model of conversion to BD. Discussion The first enrolment was in August 2019. The iDoT-AFD study explores the clinical and biological markers for the diagnosis, treatment, and prognosis of AFD and further provides evidence for clinical guidelines of AFD. Trial registration ClinicalTrials.gov NCT04209166. Registered on December 19, 2019.

To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient and automatic multi-scale segmentation (MSS) method proposed by the authors promotes the application of slope units. However, LSP modeling based on these slope units has not been performed. Moreover, the heterogeneity of conditioning factors in slope units is neglected, leading to incomplete input variables of LSP modeling. In this study, the slope units extracted by the MSS method are used to construct LSP modeling, and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean, standard deviation and range. Thus, slope units-based machine learning models considering internal variations of conditioning factors (variant slope-machine learning) are proposed. The Chongyi County is selected as the case study and is divided into 53,055 slope units. Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations. Random forest (RF) and multi-layer perceptron (MLP) machine learning models are used to construct variant Slope-RF and Slope-MLP models. Meanwhile, the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors, and conventional grid units-based machine learning (Grid-RF and MLP) models are built for comparisons through the LSP performance assessments. Results show that the variant Slope-machine learning models have higher LSP performances than Slope-machine learning models; LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models. It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling, and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides. The research results have important reference significance for land use and landslide prevention.

Abstract The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues can affect the accuracy of slope stability prediction. Therefore, a deep learning algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict slope stability. Taking the Ganzhou City in China as the study area, the landslide inventory and their characteristics of geotechnical parameters, slope height and slope angle are analyzed. Based on these characteristics, typical soil slopes are constructed using the Geo-Studio software. Five control factors affecting slope stability, including slope height, slope angle, internal friction angle, cohesion and volumetric weight, are selected to form different slope and construct model input variables. Then, the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors. Each slope stability coefficient and its corresponding control factors is a slope sample. As a result, a total of 2160 training samples and 450 testing samples are constructed. These sample sets are imported into LSTM for modelling and compared with the support vector machine (SVM), random forest (RF) and convolutional neural network (CNN). The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features. Furthermore, LSTM has a better prediction performance for slope stability compared to SVM, RF and CNN models.

Abstract Background The ongoing benefits of coronavirus disease 2019 (COVID-19) nonpharmaceutical interventions (NPIs) for respiratory infectious diseases in China are still unclear. We aimed to explore the changes in seven respiratory infectious diseases before, during, and after COVID-19 in China from 2010 to 2021. Methods The monthly case numbers of seven respiratory infectious diseases were extracted to construct autoregressive integrated moving average (ARIMA) models. Eight indicators of NPIs were chosen from the COVID-19 Government Response Tracker system. The monthly case numbers of the respiratory diseases and the eight indicators were used to establish the Multivariable generalized linear model (GLM) to calculate the incidence rate ratios (IRRs). Results Compared with the year 2019, the percentage changes in 2020 and 2021 were all below 100% ranging from 3.81 to 84.71%. Pertussis and Scarlet fever started to increase in 2021 compared with 2020, with a percentage change of 183.46 and 171.49%. The ARIMA model showed a good fit, and the predicted data fitted well with the actual data from 2010 to 2019, but the predicted data was bigger than the actual number in 2020 and 2021. All eight indicators could negatively affect the incidence of respiratory diseases. The seven respiratory diseases were significantly reduced during the COVID-19 pandemic in 2020 and 2021 compared with 2019, with significant estimated IRRs ranging from 0.06 to 0.85. In the GLM using data for the year 2020 and 2021, the IRRs were not significant after adjusting for the eight indicators in multivariate analysis. Conclusion Our study demonstrated the incidence of the seven respiratory diseases decreased rapidly during the COVID-19 pandemic in 2020 and 2021. At the end of 2021, we did see a rising trend for the seven respiratory diseases compared to the year 2020 when the NPIs relaxed in China, but the rising trend was not significant after adjusting for the NPIs indicators. Our study showed that NPIs have an effect on respiratory diseases, but Relaxation of NPIs might lead to the resurgence of respiratory diseases.

Metal-halide perovskites (MHPs) are attracting considerable interest for optoelectronic applications, with Cs_{2}AgBiBr_{6} one of the main contenders among lead-free systems. Cs_{2}AgBiBr_{6} crystallizes in a nominally double-perovskite structure, but exhibits a soft lattice with large atomic fluctuations characteristic of MHPs. While crucial to understand electron-phonon and phonon-phonon couplings, the spatiotemporal correlations of these fluctuations remain largely unknown. Here, we reveal these correlations using comprehensive neutron and x-ray scattering measurements on Cs_{2}AgBiBr_{6} single crystals, complemented with first-principles simulations augmented with machine-learned neural-network potentials. We report the discovery of an unexpected complex modulated ground-state structure containing several hundred atoms, arising from a soft-phonon instability of the low-temperature tetragonal phase. Further, our experiments and simulations both reveal extensive correlated two-dimensional fluctuations of Br octahedra at finite temperature, arising from soft optic phonons that are strongly broadened by anhamonicity, reflecting very shallow potential wells. These results provide new insights into the atomic structure and fluctuations in MHPs, critical to understand and control their thermal and optoelectronic properties.

The current limitations of user–product interaction with smart speakers have spurred the proposal of a model to circumvent these challenges. We used the ViP design principle to redefine the user’s approach to interacting with the product. Throughout the deconstruction and design stages, we explored the structure and function of the conventional product across three layers: the product layer, interaction layer, and context layer using three models. We used the hierarchical analysis method to effectively quantify the design factors affecting user experience and identify the key design factors. This approach enabled us to contextualize the smart audio system, explore the interaction dynamics between the product and the user, and provide valuable insights on designing new products. A questionnaire method was used to survey 67 users, and a reliability test was conducted to ensure the validity of the questionnaire v (Cronbach’s coefficient α = 0.868). A pairwise comparison of factors was conducted on a 1–9 scale, with weights determined through the analytic hierarchy process (AHP). The combination of the ViP design principle and hierarchical analysis presents a novel and objective paradigm to guide designers to customize product characteristics (design attributes) to enhance user human–computer interaction experience. We validated the feasibility of the innovative design approach using the smart speaker model, offering insights for research on designing similar products.

Due to the similarity of conditioning factors, the aggregation feature of landslides and the multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be considered in landslide susceptibility prediction (LSP). The ignorance of this issue will result in some biases and time-invariance in landslide susceptibility. Hence, a novel framework has been proposed to update landslide susceptibility by simultaneously considering the spatial and temporal effects of landslides at the regional scale. In this framework, the landslide inventory of Chongyi County has been divided into pre- and fresh-landslide inventories. According to the LSP results predicted by the support vector machine (SVM) model using the slope unit-based conditioning factors and pre-landslide inventory, a normalized spatial distance index (NSDI) is calculated to quantitatively represent the spatial correlation between landslides and surrounding slope units to develop the SVM-NSDI model. Furthermore, the SVM-Updating model, which incorporates the LSP results of the SVM-NSDI model and fresh-landslide inventory, could be developed to update the LSP results. Subsequently, the confusion matrix, the area under the receiver operating characteristic curve (AUC) and frequency ratio (FR) accuracy are used to evaluate the prediction performance of the above LSP models. The F1-score values of the SVM, SVM-NSDI and SVM-Updating models are 0.776, 0.816 and 0.831, respectively. The AUC values are 0.869, 0.903 and 0.914 and the FR accuracies are 0.795, 0.853 and 0.873. It can be concluded that landslide susceptibility is a time-variant variable, which can be updated by considering the spatial correlation between landslides and surrounding slope units as well as the temporal effects of multi-temporal landslide inventory. This study provides a new framework to update landslide susceptibility over time and also provides more accurate LSP results for decision-makers.

This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning (ML) methods. Instead of using XY coordinate fields directly as model input, a series of autocorrelated geotechnical distance fields (GDFs) is designed to enable the ML models to infer the spatial relationship between the sampled locations and unknown locations. The whole framework using GDF with ML methods is named GDF-ML. This framework is purely data-driven which avoids the tedious work in the scale of fluctuations (SOFs) estimating and data detrending in the conventional spatial interpolation methods. Six local mapping ML methods (extra trees (ETs), gradient boosting (GB), extreme gradient boosting (XGBoost), random forest (RF), general regression neural network (GRNN) and k-nearest neighbors (KNN)) are compared in the GDF-ML framework. The results show that the GDFs are better than the conventional XY coordinate fields based ML methods in both accuracy and spatial continuity. GDF-ML is flexible which can be applied to high-dimensional, multi-variable and incomplete datasets. Among these six methods, GDF with ET method (GDF-ET) clearly shows the best accuracy and best spatial continuity. The proposed GDF-ET method can provide a fast and accurate interpretation of the soil property profile. Sensitivity analysis shows that this method is applicable to very small training dataset size. The associated statistical uncertainty can also be quantified so that the reliability of the subsurface modeling results can be estimated objectively and explicitly. The uncertainty results clearly show that the prediction becomes more accurate when more sampled data are available.

Background: Atherosclerosis (AS) is a chronic inflammatory disease involving various cell types, cytokines, and adhesion molecules. Herein, we aimed to uncover its key molecular mechanisms by single-cell RNA-seq (scRNA-seq) analysis.Methods: ScRNA-seq data of cells from atherosclerotic human coronary arteries were analyzed using the Seurat package. Cell types were clustered, and differentially expressed genes (DEGs) were screened. GSVA (Gene Set Variation Analysis) scores of hub pathways were compared among different cell clusters. DEGs in endothelial cells between apolipoprotein-E (ApoE)−/− mice and specific TGFbR1/2 KO ApoE−/− mice fed with high-fat diet were overlapped with those from human AS coronary arteries. In fluid shear stress and AS, hub genes were determined based on the protein–protein interaction (PPI) network, which were verified in ApoE−/− mice. Finally, hub genes were validated in three pairs of AS coronary arteries and normal tissues by histopathological examination.Results: ScRNA-seq identified nine cell clusters in human coronary arteries, namely, fibroblasts, endothelial cells, macrophages, B cells, adipocytes, HSCs, NK cells, CD8+ T cells, and monocytes. Among them, endothelial cells had the lowest fluid shear stress and AS and TGF-beta signaling pathway scores. Compared to ApoE−/− mice fed with normal diet, fluid shear stress and AS and TGF-beta scores were both significantly lower in endothelial cells from TGFbR1/2 KO ApoE−/− mice fed with normal or high-fat diet. Furthermore, the two hub pathways had a positive correlation. Three hub genes (ICAM1, KLF2, and VCAM1) were identified, and their expression was distinctly downregulated in endothelial cells from TGFbR1/2 KO ApoE−/− mice fed with normal or high-fat diet than in those from ApoE−/− mice fed with a normal diet, which were confirmed in human AS coronary artery.Conclusion: Our findings clarified the pivotal impacts of pathways (fluid shear stress and AS and TGF-beta) and genes (ICAM1, KLF2, and VCAM1) in endothelial cells on AS progression.

To mitigate the adverse effects of fine-grained lithium mica tailings and other solid wastes generated from the extraction of lithium ore mining, as well as the limitations of traditional cement-based binders for lithium mica fine tailings, this study explores the feasibility of using a binder composed of ordinary Portland cement, lithium slag, fly ash, and desulfurization gypsum to stabilize lithium fine tailings into cemented lithium tailings backfill. Compared with traditional cementitious binders, an extensive array of experiments and analyses were conducted on binders formed by various material proportion combinations, employing uniaxial compressive strength tests, microstructural morphology, grayscale analyses, and flowability tests. The results show the following: (1) In this study, an LSB binder exhibiting superior mechanical properties compared to traditional cementitious binders was identified, with an optimal OPC:LS:FA:DG ratio of 2:1:1:1. (2) In the context of cemented lithium mica fine tailings, the LSB-CLTB material exhibits higher unconfined compressive strength and lower self-weight compared to OPC-CLTB materials. At a binder content of 10 wt%, the UCS values achieved by the LSB-CLTB material at curing periods of 7 days, 14 days, and 28 days are 0.97 MPa, 1.52 MPa, and 2.1 MPa, respectively, representing increases of 40.6%, 34.5%, and 44.8% over the compressive strength of OPC-based materials under the same conditions. (3) The LSB binder not only exhibits enhanced pozzolanic reactivity but also facilitates the infilling of detrimental pores through its inherent particle size and the formation of AFt and C-(A)-S-H gels via hydration reactions, thereby effectively improving the compressive strength performance of fine-grained tailings backfill.

Abstract Background Acute kidney injury (AKI) stage 3, one of the most severe complications in patients with heart transplantation (HT), is associated with substantial morbidity and mortality. We aimed to develop a machine learning (ML) model to predict post-transplant AKI stage 3 based on preoperative and perioperative features. Methods Data from 107 consecutive HT recipients in the provincial center between 2018 and 2020 were included for analysis. Logistic regression with L2 regularization was used for the ML model building. The predictive performance of the ML model was assessed using the area under the curve (AUC) in tenfold stratified cross-validation and was compared with that of the Cleveland-clinical model. Results Post-transplant AKI occurred in 76 (71.0%) patients including 15 (14.0%) stage 1, 18 (16.8%) stage 2, and 43 (40.2%) stage 3 cases. The top six features selected for the ML model to predicate AKI stage 3 were serum cystatin C, estimated glomerular filtration rate (eGFR), right atrial long-axis dimension, left atrial anteroposterior dimension, serum creatinine (SCr) and FVII. The predictive performance of the ML model (AUC: 0.821; 95% confidence interval [CI]: 0.740–0.901) was significantly higher compared with that of the Cleveland-clinical model (AUC: 0.654; 95% [CI]: 0.545–0.763, p

Understanding photo-physics giving rise to quantum beating oscillations in hybrid organic-inorganic perovskites aids their applications in spintronics and quantum information science. Here, authors demonstrate that quantum beatings observed in single crystal perovskite at cryogenic temperatures are originating from positive and negative trions.

Perovskite photovoltaics has become more competitive against silicon counterpart in reducing cost of solar energy, yet the management of toxic lead hampers it application. Here, the authors propose a cost-effective environmental-friendly approach to recycle lead and transparent conductors.

Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions. HIGHLIGHTS The Lévy flight was improved bat algorithm to overcome the shortcoming about speed and effectiveness of bat algorithm.; We integrated the advantage of Gaussian and polynomial functions as the kernel function of KELM (PGKELM).; The LBA-PGKELM at the top of LBA-SVM and BPNN which based on gradient descent method.;

Abstract Background The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. Methods 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. Results Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values

The performance of a metal halide perovskite single crystal is governed by the defect density. Here, the authors report a high quality single crystal perovskite grown by a ligand-assisted solution process with DPSI achieving 23-fold smaller trap density than that without DPSI.

Ruddlesden popper layered perovskites can be used in optoelectronic devices, but the understanding of their lattice strain as well as ferroelasticity is still lacking. Here, the authors find ferroelasticity in layered perovskites with layer number more than one and reveal its mechanism.

Intentional doping is important in semiconductors in order to tune the material property, yet the mechanism in metal halide perovskite is not well-understood. Here, the authors use silver, strontium, and cerium ions to showcase n-type doping on perovskite surface even up to metallic state.

Fabrication of efficient large-area perovskite light emitting diodes catering towards mass-production is hampered by the difficulty in getting homogenous uniform films and the high cost. Here, the authors demonstrate sol-gel engineering of low-temperature blade-coated perovskite films which can overcome these limitations.

In some studies on landslide susceptibility mapping (LSM), landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate polygon form. Different expressions of landslide boundaries and spatial shapes may lead to substantial differences in the distribution of predicted landslide susceptibility indexes (LSIs); moreover, the presence of irregular landslide boundaries and spatial shapes introduces uncertainties into the LSM. To address this issue by accurately drawing polygonal boundaries based on LSM, the uncertainty patterns of LSM modelling under two different landslide boundaries and spatial shapes, such as landslide points and circles, are compared. Within the research area of Ruijin City in China, a total of 370 landslides with accurate boundary information are obtained, and 10 environmental factors, such as slope and lithology, are selected. Then, correlation analyses between the landslide boundary shapes and selected environmental factors are performed via the frequency ratio (FR) method. Next, a support vector machine (SVM) and random forest (RF) based on landslide points, circles and accurate landslide polygons are constructed as point-, circle- and polygon-based SVM and RF models, respectively, to address LSM. Finally, the prediction capabilities of the above models are compared by computing their statistical accuracy using receiver operating characteristic analysis, and the uncertainties of the predicted LSIs under the above models are discussed. The results show that using polygonal surfaces with a higher reliability and accuracy to express the landslide boundary and spatial shape can provide a markedly improved LSM accuracy, compared to those based on the points and circles. Moreover, a higher degree of uncertainty of LSM modelling is present in the expression of points because there are too few grid units acting as model input variables. Additionally, the expression of the landslide boundary as circles introduces errors in measurement and is not as accurate as the polygonal boundary in most LSM modelling cases. In addition, the results under different conditions show that the polygon-based models have a higher LSM accuracy, with lower mean values and larger standard deviations compared with the point- and circle-based models. Finally, the overall LSM accuracy of the RF is superior to that of the SVM, and similar patterns of landslide boundary and spatial shape affecting the LSM modelling are reflected in the SVM and RF models.

Grain boundaries are known to be generally detrimental to the operation of metal halide perovskite solar cells. Here Xiao et al. show with scanning photocurrent microscopy that ferroelastic twin boundaries are benign to the electrical properties in crystalline samples, in contrast to grain boundaries.

The orientation of layered perovskites plays a crucial role in their charge transport behavior and hence, the efficiency of related solar cells. Here, the authors find that preformed 3D-like perovskites can efficiently template the growth of layered perovskites and determine their orientation.

Despite the rapid progress on perovskite light emitting diodes (PeLEDs), the efficiency of blue PeLEDs is lagging behind. Here Wang et al. employ yttrium (III) chloride additive to yield enhanced photoluminescence in the perovskite materials and thus record high device efficiencies for sky-blue and blue PeLEDs.

Tin-based perovskites possess the suitable narrow-bandgap for tandem solar cells but their short carrier diffusion lengths limit device efficiency. Here Yang et al. add cadmium ions to increase diffusion length to above 2 µm by reducing the background free hole concentration and electron trap density.

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation. This study presents a machine learning approach based on the C5.0 decision tree (DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data (70% landslide pixels) and validation data (30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model. Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC (area under the receiver operating characteristic (ROC) curve) of the proposed model was the highest, reaching 0.88, compared with traditional models (support vector machine (SVM) = 0.85, Bayesian network (BN) = 0.81, frequency ratio (FR) = 0.75, weight of evidence (WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km2 and 0.88/km2, respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area. Our results indicate that the distribution of high susceptibility zones was more focused without containing more “stable” pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

Purpose: The aim of this study is i) to establish a strategy to estimate the area under the curve of the dosing interval (AUC0–12h) of mycophenolic acid (MPA) in the heart transplant recipients and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure.Methods: This single-center, prospective, open-label, observational study was conducted in 91 adult heart transplant recipients orally taking mycophenolate mofetil dispersible tablets. Samples collected intensively and sparsely were analyzed by the enzyme-multiplied immunoassay technique, and all the data were used in PPK modeling. Potential covariates were tested stepwise. The goodness-of-fit plots, the normalized prediction distribution error, and prediction-corrected visual predictive check were used for model evaluation. Optimal sampling times by ED-optimal strategy and multilinear regression (MLR) were analyzed based on the simulated data by the final PPK model. Moreover, using intensive data from 14 patients, the accuracy of AUC0–12h estimation was evaluated by Passing–Bablok regression analysis and Bland–Alman plots for both the PPK model and MLR equation.Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model. Co-medication of proton pump inhibitors (PPIs), estimated glomerular filtration rate (eGFR), and albumin (ALB) were found to significantly affect bioavailability (F), clearance of central compartment (CL/F), and the distribution volume of the central compartment (V2/F), respectively. Co-medication of PPIs decreased F by 27.6%. When eGFR decreased by 30 ml/min/1.73 m2, CL/F decreased by 23.7%. However, the impact of ALB on V2/F was limited to MPA exposure. The final model showed an adequate fitness of the data. The optimal sampling design was pre-dose and 1 and 4 h post-dose for pharmacokinetic estimation. The best-fit linear equation was finally established as follows: AUC0–12h = 3.539 × C0 + 0.288 × C0.5 + 1.349 × C1 + 6.773 × C4.5.Conclusion: A PPK model was established with three covariates in heart transplant patients. Co-medication of PPIs and eGFR had a remarkable impact on AUC0–12h of MPA. A linear equation was also concluded with four time points as an alternative way to estimate AUC0–12h for MPA.

Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood characteristics of landslide spatial datasets for reducing the LSP uncertainty. Neighborhood environmental factors were acquired and managed by remote sensing (RS) and the geographic information system (GIS), then used to represent the influence of landslide neighborhood environmental factors. The landslide aggregation index (LAI) was proposed to represent the landslide clustering effect in GIS. Taking Chongyi County, China, as example, and using the hydrological slope unit as the mapping unit, 12 environmental factors including elevation, slope, aspect, profile curvature, plan curvature, topographic relief, lithology, gully density, annual average rainfall, NDVI, NDBI, and road density were selected. Next, the support vector machine (SVM) and random forest (RF) were selected to perform LSP considering the neighborhood characteristics of landslide spatial datasets based on hydrologic slope units. Meanwhile, a grid-based model was also established for comparison. Finally, the LSP uncertainties were analyzed from the prediction accuracy and the distribution patterns of landslide susceptibility indexes (LSIs). Results showed that the improved frequency ratio method using LAI and neighborhood environmental factors can effectively ensure the LSP accuracy, and it was significantly higher than the LSP results without considering the neighborhood conditions. Furthermore, the Wilcoxon rank test in nonparametric test indicates that the neighborhood characteristics of spatial datasets had a great positive influence on the LSP performance.

Halide lead perovskites have emerged recently as possible candidates for high performance radiation detectors besides efficient solar cells. Here Wei et al. review the recent progress on perovskite based radiation detectors and suggest that they may compete with the conventional counterparts.

Two dimensional halide perovskites solar cells have attracted research interest due to their higher stability compared to three dimensional counterparts. Here Hu et al. show that fine tuning of the chemical structure of the spacer cations leads to different packing arrangements and device efficiency.

It is well-accepted that the two dimensional layered halide perovskite can improve the device stability of perovskite solar cells but the operation mechanism remains unclear. Here Lin et al. reveal the real morphology of the hot-cast layered perovskite solar cells and understand the working mechanism.

With the rapidly improving efficiency and stability of perovskite solar cells, the transition of small area device fabrication innovations into modules is becoming increasingly important for the commercialization of this technology. The record efficiencies of small perovskite cells are already approaching that of the best silicon crystal solar cells, but the module efficiencies are still far behind. Understanding the factors that cause the cell-to-module (CTM) efficiency loss is critical for large area perovskite module development. Here, we experimentally validate a comprehensive model that analyzes the CTM efficiency loss with a precision better than 97%. Using the model, we decipher the impact of the critical module components and fabrication variables, including perovskite band gap, transparent electrodes, scribing lines, and film uniformity, on module aperture efficiency. Our analysis provides pathways toward the aperture efficiency ceiling of 25.8% for single-junction perovskite solar modules with a band gap of 1.49 eV. Enlightened by the model, we find that tandem structures have intrinsic merit to achieve high efficiency perovskite modules of 28.4% with a much lower CTM derate due to the smaller photocurrent but larger photovoltage.

Optoelectronic devices based on organic–inorganic halide perovskites show promising performance, but their poor stability impedes the commercialization. Here Lin et al. show that excess free charges are detrimental and efficient charge-carrier extraction is necessary for improved device stability.

Metabolic associated fatty liver disease (MAFLD) is a multifactorial systemic disorder that occurs in the absence of excessive alcohol consumption. The disease is characterized by fatty degeneration and fat accumulation in liver parenchymal cells, the incidence of which is increasing annually, particularly in younger adults. MAFLD is caused by genetic and metabolism related disorders, of which mitochondrial dysfunction is the major contributor. Natural products can relieve MAFLD through restoring mitochondrial function. In this article, we describe the relationship between mitochondria and MAFLD and discuss the beneficial effects of natural products as a future anti-MAFLD strategy. Significance Statement. We herein propose that the development of mitochondrial regulators/nutrients from natural products can remedy mitochondrial dysfunction which represents an attractive strategy for the treatment of MAFLD. Furthermore, the mitochondrial regulation of natural products can provide new insight into the underlying mechanisms of action of natural products used for future MAFLD therapeutics.

This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. Within the framework of the proposed approach, a complex Bayesian back analysis problem is transformed into an equivalent structural reliability problem based on subset simulation. Hermite polynomial chaos expansion-based surrogate models are constructed to improve the computational efficiency of probabilistic back analysis. The reliability of tunneling-induced ground settlements is updated in the process of sequential back analyses. A real shield tunnel project of No. 1 Nanchang Metro Line in China is investigated to assess the effectiveness of the approach. The proposed approach is able to infer the posterior distributions of uncertain geomechanical parameters (i.e., Young’s moduli of surrounding soil layers and ground vehicle load). The reliability of tunneling-induced ground settlements can be updated in a real-time manner by fully utilizing the time-series monitoring data. The results show good agreement with the variation trend of field monitoring data of ground settlement and the post-event investigations.

Background Pulmonary hypertension occurs in approximately 1% of the global population, and the prognosis for such patients may be poor. However, the mechanisms underlying the development of this disease remain unclear. Thus, understanding the development of pulmonary hypertension and finding new therapeutic targets and approaches are important for improved clinical outcomes. Methods Lung tissue specimens were collected from six patients with atrial septal defect and pulmonary hypertension (all women, with a mean age of 46.5 ± 4.7 years, and their condition could not be corrected with an internal medical occlusion device) and from nine control patients with lung cancer who underwent lobectomy (six men and three women, with a mean age of 56.7 ± 1.7 years). Isobaric tags for relative and absolute quantitation and liquid chromatography tandem mass spectrometry analyses were used to detect protein expression levels. Results We found 74 significantly upregulated and 88 significantly downregulated differentially expressed proteins between control and pulmonary hypertensive lung tissue specimens. Gene ontology analyses identified the top 20 terms in all three categories, that is, biological process, cellular component, and molecular function. Kyoto Encyclopedia of Genes and Genomes and protein–protein interaction analyses determined the top 10 signaling pathways and found that the six hub proteins associated with the differentially expressed upregulated proteins (PRKAA1, DHPR, ACTB, desmin, ACTG1, and ITGA1) were all involved in hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy, and dilated cardiomyopathy. Conclusion Our results identified protein expression profile changes in lung tissue derived from patients with pulmonary hypertension, providing potential new biomarkers for clinical diagnosis and prognosis for patients with pulmonary hypertension and offering candidate protein targets for future therapeutic drug development.