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A wide variety of higher-order symmetry-protected topological phases (HOSPT) with gapless corners or hinges have been proposed as descendants of topological crystalline insulators protected by spatial symmetry. In this work, we address a new class of higher-order topological states that do not require crystalline symmetries but instead rely on subsystem symmetry for protection. We propose several strongly interacting models with gapless hinges or corners based on a decorated hinge-wall condensate picture. The hinge-wall, which appears as the defect configuration of a Z 2 paramagnet, is decorated with a lower-dimensional SPT state. Such a unique hinge-wall decoration structure leads to gapped surfaces separated by gapless hinges. The non-trivial nature of the hinge modes can be captured by a 1 + 1 D conformal field theory with a Wess–Zumino–Witten term. Moreover, we establish a no-go theorem to demonstrate the ungappable nature of the hinges by making a connection between the generalized Lieb–Schultz–Mattis theorem and the boundary anomaly of the HOSPT state. This universal correspondence engenders a comprehensive criterion to determine the existence of HOSPT under certain symmetries, regardless of the microscopic Hamiltonian.

This analysis revisits the decades-old relative deprivation theory of migration. In contrast to the traditional view that migration is driven by absolute income maximization, we test whether relative deprivation induces migration in the context of sub-Saharan Africa. Taking advantage of the internationally comparable longitudinal data from integrated household and agriculture surveys from Tanzania, Ethiopia, Malawi, Nigeria, and Uganda, we use panel fixed effects to estimate the effects of relative deprivation on migration decisions. Using per capita consumption expenditure and multidimensional wealth index as well-being measures, we find that a household’s migration decision is based not only on its absolute well-being level but also on the relative position of the household in the well-being distribution of the community in which it resides. We also discover that the effect of relative deprivation on migration is amplified in rural, agricultural, and male-headed households. Results are robust to alternative specifications including the use of Hausman Taylor Instrumental Variable (HTIV) estimator and pooled data across the five countries. Results confirm that the “migration-relative deprivation” relationship also holds in the context of sub-Saharan Africa. We argue that policies designed to check rural–urban migration through rural transformation and poverty reduction programs should use caution because such programs can increase economic inequality, which further increases migration flow.

Recently, the log-structured merge-tree (LSM-tree) has been widely adopted for use in the storage layer of modern NoSQL systems. Because of this, there have been a large number of research efforts, from both the database community and the operating systems community, that try to improve various aspects of LSM-trees. In this paper, we provide a survey of recent research efforts on LSM-trees so that readers can learn the state of the art in LSM-based storage techniques. We provide a general taxonomy to classify the literature of LSM-trees, survey the efforts in detail, and discuss their strengths and trade-offs. We further survey several representative LSM-based open-source NoSQL systems and discuss some potential future research directions resulting from the survey.

Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference datasets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2-12% and 57-75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

This work evaluates the performance of three machine learning (ML) techniques, namely logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), for mapping landslide susceptibility in the Chitral district, northern Pakistan. Moreover, we create landslide inventory maps from LANDSAT-8 satellite images through the change vector analysis (CVA) change detection method. The change detection yields more than 500 landslide spots. After some manual post-processing correction, the landslide inventory spots are randomly split into two sets with a 70/30 ratio for training and validating the performance of the ML techniques. Sixteen topographical, hydrological, and geological landslide-related factors of the study area are prepared as GIS layers. They are used to produce landslide susceptibility maps (LSMs) with weighted overlay techniques using different weights of landslide-related factors. The accuracy assessment shows that the ML techniques outperform the MCDM methods, while SVM yields the highest accuracy of 88% for the resulting LSM.

Members of the Lsm protein family are found in all three domains of life: bacteria, archaea, and eukarya. They are involved in numerous processes associated with RNA processing and gene expression regulation. A common structural feature of all Lsm family proteins is the presence of the Sm fold consisting of a five-stranded β-sheet and an α-helix at the N-terminus. Heteroheptameric eukaryotic Sm and Lsm proteins participate in the formation of spliceosomes and mRNA decapping. Homohexameric bacterial Lsm protein, Hfq, is involved in the regulation of transcription of different mRNAs by facilitating their interactions with small regulatory RNAs. Furthermore, recently obtained data indicate a new role of Hfq as a ribosome biogenesis factor, as it mediates formation of the productive structure of the 17S rRNA 3′- and 5′-sequences, facilitating their further processing by RNases. Lsm archaeal proteins (SmAPs) form homoheptamers and likely interact with single-stranded uridine-rich RNA elements, although the role of these proteins in archaea is still poorly understood. In this review, we discuss the structural features of the Lsm family proteins from different life domains and their structure–function relationships.

This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records. A total of 249 landslide sites have been identified to date and were used in this study to model and validate the LSMs of the study region. Of the 249 landslide locations, 70% were used as training data and 30% for the validation of the resulting LSMs. Sixteen factors related to topographical, hydrological, soil type, geological and environmental conditions were used and a multi-collinearity test of the landslide conditioning factors (LCFs) was performed. Using the natural break method (NBM) in a geographic information system (GIS), the LSMs generated by the RF, FLDA, and ADTree models were categorized into five classes, namely very low, low, medium, high and very high landslide susceptibility (LS) zones. The very high susceptibility zones cover 15.37% (ADTree), 16.10% (FLDA) and 11.36% (RF) of the total catchment area. The results of the different models (FLDA, RF, and ADTree) were explained and compared using the area under receiver operating characteristics (AUROC) curve, seed cell area index (SCAI), efficiency and true skill statistic (TSS). The accuracy of models was calculated considering both the training and validation data. The results revealed that the AUROC success rates are 0.89 (ADTree), 0.92 (FLDA) and 0.97 (RF) and predication rates are 0.82 (ADTree), 0.79 (FLDA) and 0.98 (RF), which justifies the approach and indicates a reasonably good landslide prediction. The results of the SCAI, efficiency and TSS methods showed that all models have an excellent modeling capability. In a comparison of the models, the RF model outperforms the boosted regression tree (BRT) and ADTree models. The results of the landslide susceptibility modeling could be useful for land-use planning and decision-makers, for managing and controlling the current and future landslides, as well as for the protection of society and the ecosystem.

The prevention of forgery and alternation of important data of blockchain technology is contributing widely to the expanding usage of this technology to areas and industries such as real estate and agriculture. Despite the high utilization of the blockchain, its write-intensive feature causes a large amount of disk I/Os when trying to index and process queries over the data. Among previous studies, the hierarchical quadrant spatial LSM tree (i.e., HQ-sLSM tree) was proposed as an effective structure to index large amounts of geospatial point data from the blockchain and process queries while triggering a low number of disk I/Os. However, geospatial data exist in forms such as lines and polygons inside cadastral maps and survey information. In this paper, we propose an extended version of the HQ-sLSM tree which indexes geospatial line and polygon data. The extended tree, named the HQ-sLSM + tree, inherits and adapts some common features and the low disk I/O algorithms of the original HQ-sLSM tree, fitting them to the line and polygon data types. Furthermore, an algorithm to process the spatial join query over two HQ-sLSM + trees is proposed. A concept of a spatial join filter is introduced to access disk components efficiently. Experiments confirmed that the number of disk I/Os triggered when spatially joining two HQ-sLSM + trees was much less compared to existing baseline index trees such as the R-tree and the LSM R-tree.

Soil moisture (SM) in the profile is the main reservoir of water available for vegetation. Therefore, monitoring SM during dry-down periods is crucial for understanding vegetation water status, among other applications. Datasets derived from Land Surface Models, such as the ERA5-Land dataset, provide SM estimates at different depths. The aim of this study was to evaluate the ability of ERA5-Land SM data to detect dry-down periods and to test whether its drying time scale aligns with field measurements at three sites in the Argentine Pampas. The analysis was carried out across the three standard soil layers used by ERA5-Land: layer 1 (L1, 0-7 cm), layer 2 (L2, 7-28 cm), and layer 3 (L3, 28-100 cm). First, the evaluation of the ERA5-Land SM data showed a moderate agreement with field data, although it exhibited a high overestimation (bias > |0.09| m3/m3) in the SM estimates. On the other hand, dry-down periods analysis indicated that ERA5-Land SM data was able to detect a similar number of dry-down periods and drying time scales as observed in the field for L1 and L2. In contrast, at L3, both the number of detected periods and the estimated drying time scale were lower. ERA5-Land SM data showed a consistent and expected pattern of faster drying in the shallower layers, demonstrating its potential for monitoring SM dynamics within the profile.

Life-space mobility (LSM) is a concept for assessing patterns of functional mobility over time. LSM is gaining traction in the research of geriatric population health. Several instruments have been developed to measure LSM, such as the University of Alabama at Birmingham Life-Space Assessment (LSA) or the Nursing Home Life-Space Diameter instrument. There has been exponential growth in the use of instruments measuring LSM in studies of older adults since the concept was introduced in 1985. In response to the increased volume of publications with clinical applicability to those working in geriatric health or conducting population-based research in older adults, we conducted a narrative review: a) to provide a summary of the articles that have assessed validation of the University of Alabama at Birmingham LSA instrument, the most widely used instrument to assess LSM in older adults; and b) to provide a summary of the research articles that have examined LSM as independent or outcome variable. Studies for this review were obtained with an organized search format and were included if they were published in the past 20 years, written in English, published in peer-reviewed literature, and included LSM as an independent or outcome variable. Seventy-nine articles were identified: 36 that employed a cross-sectional design and 22 that employed a longitudinal/prospective design to examine LSM as outcome variable; 17 longitudinal/prospective design articles that examined LSM as primary independent variable; 3 review articles; and 1 systematic review. Areas of research included physical function, cognitive function, sensory impairment, mental health, falls, frailty, comorbidities, healthcare use, mortality, and social/environmental factors. These studies showed that LSM instruments can accurately predict morbidity, mortality, and healthcare use.