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One significant characteristic of Multiple Sclerosis (MS), a chronic inflammatory demyelinating disease of the central nervous system, is the evolution of highly variable patterns of white matter lesions. Based on geostatistical metrics, the MS-Lesion Pattern Discrimination Plot reduces complex three- and four-dimensional configurations of MS-White Matter Lesions to a well-arranged and standardized two-dimensional plot that facilitates follow-up, cross-sectional and medication impact analysis. Here, we present a script that generates the MS-Lesion Pattern Discrimination Plot, using the widespread statistical computing environment R. Input data to the script are Nifti-1 or Analyze-7.5 files with individual MS-White Matter Lesion masks in Montreal Normal Brain geometry. The MS-Lesion Pattern Discrimination Plot, variogram plots and associated fitting statistics are output to the R console and exported to standard graphics and text files. Besides reviewing relevant geostatistical basics and commenting on implementation details for smooth customization and extension, the paper guides through generating MS-Lesion Pattern Discrimination Plots using publicly available synthetic MS-Lesion patterns. The paper is accompanied by the R script LDPgenerator.r , a small sample data set and associated graphics for comparison.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system with presumed autoimmune origin. The development of lesions within the gray matter and white matter, which are highly variable with respect to number, total volume, morphology and spatial evolution and which only show a limited correlation with clinical disability, is a hallmark of the disease. Population-based studies indicate a distinct outcome depending on gender. Here, we studied gender-related differences in the evolution of white matter MS-lesions (MS-WML) in early MS by using geostatistical methods. Within a 3 years observation period, a female and a male MS patient group received disease modifying drugs and underwent standardized annual brain magnetic resonance imaging, accompanied by neurological examination. MS-WML were automatically extracted and the derived binary lesion masks were subject to geostatistical analysis, yielding quantitative spatial-statistics metrics on MS-WML pattern morphology and total lesion volume (TLV). Through the MS-lesion pattern discrimination plot, the following differences were disclosed: corresponding to gender and MS-WML pattern morphology at baseline, two female subgroups (F1, F2) and two male subgroups (M1, M2) are discerned that follow a distinct MS-WML pattern evolution in space and time. F1 and M1 start with medium-level MS-WML pattern smoothness and TLV, both behave longitudinally quasi-static. By contrast, F2 and M2 start with high-level MS-WML pattern smoothness and medium-level TLV. F2 and M2 longitudinal development is characterized by strongly diminishing MS-WML pattern smoothness and TLV, i.e., continued shrinking and break-up of MS-WML. As compared to the male subgroup M2, the female subgroup F2 shows continued, increased MS-WML pattern smoothness and TLV. Data from neurological examination suggest a correlation of MS-WML pattern morphology metrics and EDSS. Our results justify detailed studies on gender-related differences.

This paper reports on the development of an open-source image analysis software 'pipeline' dedicated to petrographic microscopy. Using conventional rock thin sections and images from a standard polarising microscope, the pipeline can classify minerals and subgrains into objects and obtain information about optic-axis orientation. Five metamorphic rocks were chosen to test and illustrate the method. Thin sections were imaged using reflected and cross- and plane-polarised transmitted light. Images were taken at different angles of the polariser and analyser (360° with 10° steps), both with and without the full-lambda plate. The resulting image stacks were analysed with a modular pipeline for optic-axis mapping (POAM). POAM consists of external and internal software packages that register, segment, classify, and interpret the visible light spectra using object-based image analysis (OBIAS). The mapped fields-of-view and grain orientation stereonets of interest are presented in the context of whole-slide images. Two innovations are reported. First, we used hierarchical tree region merging on blended multimodal images to classify individual grains of rock-forming minerals into objects. Second, we assembled a new optical mineralogy algorithm chain that identifies the mineral slow axis orientation. The c-axis orientation results were verified with scanning electron microscopy electron backscattered diffraction (SEM-EBSD) data. For quartz (uniaxial) in a granite mylonite the test yielded excellent correspondence of c-axis azimuth and good agreement for inclination. For orthorhombic orthopyroxene in a deformed garnet harzburgite, POAM produced acceptable results for slow axis azimuth. In addition, the method identified slight anisotropy in garnet that would not be appreciated by traditional microscopy. We propose that our method is ideally suited for two commonly performed tasks in mineralogy. First, for mineral grain classification of entire thin sections scans on blended images to provide automated modal abundance estimates and grain size distribution. Second, for prospective fields of view of interest, POAM can rapidly generate slow axis crystal orientation maps from multiangle image stacks on conventionally prepared thin sections for targeting detailed SEM-EBSD studies., (© 2024 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.)

Introduction: A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented., Methods: A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill., Results: Parameters Range and Sill correlate with MS-lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS-lesion patterns based on geometry: the so-called MS-Lesion Pattern Discrimination Plot., Conclusions: The geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis.

Unlike traditional base materials such as concrete or masonry, there are no guidelines for rock as a base material for post-installed anchors. The varying rock properties (e.g., rock type, discontinuities) and numerous installation parameters (e.g., embedment depth, anchor diameter) leave engineers with limited information on design resistances, leading to an uncertain basis for anchor applications in rock. To identify the key parameters that determine rock as a base material, an evaluation of rock characteristics was conducted, combined with in situ pull-out tests in different key geologies (granite, limestone, mica schist, dolomite, granulite) and discrete element modeling, which has been found to be suitable for investigating the load-bearing behavior of post-installed anchors in rock. Discontinuities were identified as the main factor influencing the load-bearing capacity of post-installed anchors in rock mass. Based on the in situ investigations, assessment methods for rock as a base material were proposed, along with corresponding resistance partial safety factors for design of 2.5, 2.0, and 1.7 for high, medium, and low levels of uncertainty regarding possible inhomogeneities. A limit value R ≥ 36, associated with rebound hammer assessments, was defined for the low degree of uncertainty, showing limitations for schistose rock. This is concluded by a design approach for determining design resistances of shallow fasteners in rock mass. [ABSTRACT FROM AUTHOR]

In geology and mineralogy, optical microscopic images have become a primary research focus for intelligent mineral recognition due to their low equipment cost, ease of use, and distinct mineral characteristics in imaging. However, due to their close reflectivity or transparency, some minerals are not easily distinguished from other minerals or background. Secondly, the number of background pixels often vastly exceeds the number of pixels for individual mineral particles, and the number of pixels of different mineral particles in the image also varies significantly. These have led to the issue of data imbalance. This imbalance results in lower recognition accuracy for categories with fewer samples. To address these issues, a flexible ensemble learning for semantic segmentation based on multiple optimized Res-UNet models is proposed, introducing dice loss and focal loss functions and incorporating a pre-positioned spatial transformer networks block. Twelve optimized Res-UNet models were used to construct multiple Res-UNet ensemble learnings using heterogeneous ensemble strategies. The results demonstrate that the system integrated with five learners using the weighted voting fusion method (RUEL-5-WV) achieved the best performance with a mean Intersection over Union (mIOU) of 91.65 across all nine categories and an IOU of 84.33 for the transparent mineral (gangue). The results indicate that this ensemble learning scheme outperforms individual optimized Res-UNet models. Compared to the classical Deeplabv3 and PSPNet, this scheme also exhibits significant advantages. [ABSTRACT FROM AUTHOR]

Background and Purpose: In multiple sclerosis (MS) the individual disease courses are very heterogeneous among patients and biomarkers for setting the diagnosis and the estimation of the prognosis for individual patients would be very helpful. For this purpose, we are developing a multidisciplinary method and workflow for the quantitative, spatial, and spatiotemporal analysis and characterization of MS lesion patterns from MRI with geostatistics., Methods: We worked on a small data set involving three synthetic and three real-world MS lesion patterns, covering a wide range of possible MS lesion configurations. After brain normalization, MS lesions were extracted and the resulting binary 3-dimensional models of MS lesion patterns were subject to geostatistical indicator variography in three orthogonal directions., Results: By applying geostatistical indicator variography, we were able to describe the 3-dimensional spatial structure of MS lesion patterns in a standardized manner. Fitting a model function to the empirical variograms, spatial characteristics of the MS lesion patterns could be expressed and quantified by two parameters. An orthogonal plot of these parameters enabled a well-arranged comparison of the involved MS lesion patterns., Conclusions: This method in development is a promising candidate to complement standard image-based statistics by incorporating spatial quantification. The work flow is generic and not limited to analyzing MS lesion patterns. It can be completely automated for the screening of radiological archives., (Copyright © 2013 by the American Society of Neuroimaging.)

The analysis of the influence of human activities and climate change on groundwater is an important basis for formulating groundwater management policies. However, the relationship between climate change, human activities and groundwater system is complex, and the research on the response of groundwater to changing environment is in the initial stage. In this paper, the interactions between groundwater water cycle and climate change and human activities are analyzed, based on climate change data and hydrogeological information from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The MODFLOW model was used to develop a numerical model of shallow groundwater movement in Zhongmu County, Henan province, and to predict the response of groundwater levels to climate change and human activities in three cases from 2016 to 2050. The results show that under the current scenario, the groundwater level will decrease at an average annual rate of 4.24 cm/A from 2016 to 2050. Under the climate change scenario, the precipitation increased by an average of 5.01%, the annual evaporation increased by an average of 17.84% and the annual temperature increased by an average of 1.29 °C from 2016 to 2050 under the three emission cases of RCP2.6, RCP4.5 and RCP8.5, under the climate change–autonomous human activities scenario, when water conservation and South–North Water Transfer Project water supply are implemented simultaneously, the water table will decrease by an average of 5.58 CMA per year under the direct impact scenario and by an average of 4.44 CMA per year under the indirect impact scenario, the water table dropped by 3.21 cm/A. The changing environment will have an important effect on groundwater circulation, and appropriate measures must be taken to deal with the continuous decline of groundwater level. [ABSTRACT FROM AUTHOR]

To further enhance the intelligent technology, platformisation, and systematisation of coalbed methane extraction sealing technology, this paper analyses the research progress of theories, technologies, and sealing materials related to coalbed methane extraction sealing and systematically summarises the latest achievements of the basic theories, key technologies, and sealing materials of coalbed methane extraction. Considering the increasing mining depth, advancements in intelligent technology, and the evolving landscape of coalbed methane development, it is particularly important to establish a more comprehensive coalbed methane extraction borehole sealing system. Based on this, future development trends and research prospects are proposed: In terms of coalbed-methane-extraction-related theories, there should be a stronger focus on fundamental research such as on gas flow within the coal matrix. For coalbed methane extraction borehole sealing technologies and devices, efforts should be made to enhance research on intelligent, platform-based, and systematic approaches, while adapting to the application of directional long borehole sealing processes. In terms of coalbed methane extraction borehole leakage detection, non-contact measurement and non-destructive monitoring methods should be employed to achieve dynamic monitoring and early warning of methane leaks, integrating these technologies into coalbed methane extraction system platforms. For coalbed methane extraction borehole sealing materials, further development is needed for liquid sealing materials that address borehole creep and the development of fractures in surrounding rock, as well as solid sealing materials with Poisson's ratios similar to that of the surrounding rock mass. [ABSTRACT FROM AUTHOR]

The study area is about the evaporitic-dominated Middle Miocene sequence situated in the southwest of the Kağızman Basin in Eastern Anatolia. The aim of this study is to investigate the formation conditions and diagenetic development of pseudomorph gypsum formed after halite. Pseudomorph gypsum formations are intriguing geological features found in terrestrial deposits. These formations are replaced by primary halite crystals during the very early phases of diagenesis, giving the appearance of halite crystals but being composed of gypsum. The development of these pseudomorphs is indicative of specific paleoenvironmental conditions. The fact that these gypsum pseudomorphs are found in shallow depths of the lake and are well-preserved, smooth-surfaced, and varying in size suggests that they were the result of intense evaporation and rapid fluctuations in the water and pH level. This evaporation likely led to a decrease in the lake level and changes in the concentration of saltwater over time. The correlation coefficient relationships and element concentration values of these gypsums show that these elements are both continental in origin and subject to microbial influence. These pseudomorph gypsum and the clastic materials (transported by fluvial activity) that were interbedded gave important insights into the hot, long-drought, and lowhumidity climate of the era and adapted to the Middle Miocene global warming conditions. [ABSTRACT FROM AUTHOR]

Lake Urmia in the northwest of Iran is one of the largest vanishing lakes in the world. Several water-saving strategies have been implemented in the lake basin over the last decade, but they are not producing efficient results. This study employed a modified version of the Soil and Water Assessment Tool (SWAT), an agrohydrological model, to investigate the basin-scale effectiveness of some water-saving plans implemented at the farm scale. Data collected from 301 monitoring sites in the Lake Urmia basin were applied to the modified SWAT model. The modified SWAT model was used to estimate water balance components and was coupled with the Water Accounting Plus framework (WA+). Six discharge stations, crop yields, evapotranspiration, and groundwater level tables were calibrated and validated from 1987 to 2015 within the Zarrineh Rud Basin (ZRB), the most crucial subbasin in the Lake Urmia basin. Next, four individual water-saving plans—changes in irrigation management and developments in irrigation systems, changes in fertilizer type and regime, changes in the type and method of cultivation, and farm size and shape adjustments—as well as seven combinations of the water-saving plans, were applied to the agrohydrological model. The results reveal that assessing restoration plans for Lake Urmia without considering both farm and basin scales provides no reliable results. With changes in management and the development of irrigation systems, considerable differences in water withdrawal were observed. Developing irrigation systems leads to enhanced water consumption and evapotranspiration, which is expected to improve water yield and crop productivity. However, individual plans such as developments in irrigation systems cannot increase the inflow to Urmia Lake, and combined water-saving strategies can help restore the lake only to a limited extent, because the changes in inflow are not substantial. [ABSTRACT FROM AUTHOR]

Sex and sex hormones are thought to influence multiple sclerosis (MS) through effects on inflammation, myelination and neurodegeneration, and exogenous hormones have been explored for their therapeutic potential. However, our understanding of how sex hormones influence MS disease processes and outcomes remains incomplete. Furthermore, our current knowledge is derived primarily from studies that focus exclusively on cisgender populations with exclusion of gender-diverse people. Gender-affirming hormone therapy comprising exogenous sex hormones or sex hormone blocking agents are commonly used by transgender and gender-diverse individuals, and it could influence MS risk and outcomes at various stages of disease. A better understanding of the impact and potential therapeutic effects of both endogenous and exogenous sex hormones in MS is needed to improve care and outcomes for cisgender individuals and, moreover, for gender-diverse populations wherein an evidence base does not exist. In this Perspective, we discuss the effects of endogenous and exogenous sex hormones in MS, including their potential therapeutic benefits, and examine both established sex-based dimorphisms and the potential for gender-diverse dimorphisms. We advocate for future research that includes gender-diverse people to enhance our knowledge of the interplay of sex and sex hormones in MS, leading to the development of more effective and inclusive treatment strategies and improvement of care for all individuals with MS., Competing Interests: Competing interests: A.S.C. has received product from Besins Healthcare for investigator-initiated clinical studies using oestradiol and progesterone. No monetary support from Besins Healthcare has been received for any studies, and Besins Healthcare have had no input into study design, data analysis or writing of any manuscripts., (© 2024. Springer Nature Limited.)

Glacierized alpine catchments are rapidly evolving due to glacier retreat and consequent geomorphological and ecological changes. As more terrain becomes ice-free, reworking of exposed terrain by the river as well as thawing of the top layer may lead to an increase in surface and subsurface water exchanges, leading to potential changes in water storage and release, which in turn may impact ecological, geomorphological and hydrological processes. In this study, we aim to understand the current and future hydrological functioning of a typical outwash plain in a Swiss Alpine catchment. As with many other fluvial aquifers in alpine environments, this outwash plain is located at the valley bottom, where catchment-wide water and sediment fluxes tend to gather from multiple sources, may store water and provide specific habitats for alpine ecosystems. Their dynamics are however rarely studied in post Little Ice Age proglacial zones. Based on geophysical investigations as well as year-round stream and groundwater observations, we developed a simplified physically based 3D MODFLOW model and performed an optimized automatic calibration using PEST HP. We highlight the strong interactions between the upstream river and the aquifer, with stream infiltration being the dominant process of recharge. Groundwater exfiltration occurs in the lower half of the outwash plain, balancing out the amount of river infiltration at a daily timescale. We show that hillslope contributions from rain and snowmelt have little impact on groundwater levels. We also show that the outwash plain acts as a bedrock-dammed aquifer and can maintain groundwater levels close to the surface during dry periods lasting months, even in the absence of glacier meltwater, but may in turn provide only limited baseflow to the stream. Finally, we explore how new outwash plains may form in the future in this catchment due to glacier recession and discuss from a hydrological perspective which cascading impacts the presence of multiple outwash plains may have. For this case study, we estimate the total dynamic storage of future outwash plains to be about 20 mm, and we demonstrate their limited capacity to provide more stream water than that which they infiltrate upstream, except for very low river flows (<150 to 200 L s -1). Below this limit, they can provide limited baseflow on timescales of weeks, thus maintaining moisture conditions that may be beneficial for proglacial ecosystems. Their role in attenuating floods also appears limited, as less than 0.5 m 3 s -1 of river water can be infiltrated. The studied outwash plain appears therefore to play an important role for alpine ecosystems but has a marginal hydrological effect on downstream river discharge. [ABSTRACT FROM AUTHOR]

The beach sand minerals are in great demand since they are the source of titanium and are widely used in atomic energy and many other industries. Despite that, the identification and classification of beach sand minerals are challenging because of the width of the deposit, grain size, mineral composition, and locality. The proposed work develops a beach sand identification and mineral classification methodology using a deep computer vision technique with the support of neural networks. This work develops the deep residual neural network (D-Resnet) model, which extracts the mineral image features with the support of a convolutional feature selection process and filters the extracted mineral features. The D-Resnet model minimises the reliance on high-resolution mineral images and provides a more improved and efficient model. Further, the residual neural networks with the depth of 18, 34, 50, and VGG with a depth of 16 models are built by embedding various pooling methods for classifying beach sand minerals. The performance of the proposed D-Resnet model has been systematically experimented with and evaluated with the confusion matrix, classification accuracy, sensitivity, and specificity scores. Indeed, the D-Resnet model resulted in 89% of accuracy for classifying six types of beach sand minerals. [ABSTRACT FROM AUTHOR]

In this Tutorial, we combine the different scientific fields of information theory, thermodynamics, regularization theory, and non-destructive imaging, especially for photoacoustic and photothermal imaging. The goal is to get a better understanding of how information gaining for subsurface imaging works and how the spatial resolution limit can be overcome by using additional information. Here, the resolution limit in photoacoustic and photothermal imaging is derived from the irreversibility of attenuation of the pressure wave and of heat diffusion during the propagation of the signals from the imaged subsurface structures to the sample surface, respectively. The acoustic or temperature signals are converted into so-called virtual waves, which are their reversible counterparts and which can be used for image reconstruction by well-known ultrasound reconstruction methods. The conversion into virtual waves is an ill-posed inverse problem, which needs regularization. The reason for that is the information loss during signal propagation to the sample surface, which turns out to be equal to the entropy production. As the entropy production from acoustic attenuation is usually small compared to the entropy production from heat diffusion, the spatial resolution in acoustic imaging is higher than in thermal imaging. Therefore, it is especially necessary to overcome this resolution limit for thermographic imaging by using additional information. Incorporating sparsity and non-negativity in iterative regularization methods gives a significant resolution enhancement, which was experimentally demonstrated by one-dimensional imaging of thin layers with varying depth or by three-dimensional imaging, either from a single detection plane or from three perpendicular detection planes on the surface of a sample cube. [ABSTRACT FROM AUTHOR]

In this case study, rubble masonry walls from the Sierra Nevada de Santa Marta in Northern Colombia were investigated. We applied a combined object‐based image analysis (OBIA), photogrammetry, and petrography method to characterize and determine the provenance of the materials used in three targeted terraces of the archaeological site of La Palma. In situ data acquisition included detailed photographic records and petrographic descriptions of a selected section of masonry walls, as well as mapping of local geological units. Photographs were processed using OBIA. The data obtained allowed characterization and systematization of the constructive elements, via parameters that include architectural function within the construction (headers, stretchers, and wedges), roundness, and lithotype. This methodology enabled us to identify the source of the lithotypes from local rock outcrops (metagranodiorite and schist) as well as rock boulders and cobbles from the nearby riverbeds. The results suggest that pre‐Hispanic communities had knowledge of the properties of the geological resources available in the region, which they used to build masonry that has endured more than five centuries. Vertical differences identified in segments of different terrace walls suggest changes in the constructive process and therefore may serve to determine a construction chronology. [ABSTRACT FROM AUTHOR]

Debris flow is a devastating phenomenon that happens in hilly and mountainous regions and has a serious impact on affected areas. It causes casualties and serious damage to the environment and society. Therefore, a susceptible assessment is necessary to prevent, mitigate, and raise awareness of the impact of debris flows. This paper focuses on evaluating the deposition area along the deposition board. The methodology involved an experiment on a physical model by demonstrating the debris flow based on the steepness of the flume slope at 15°, 20°, and 25° angles. The limestone particles with a total volume of 2.5 × 106 mm3 acted as debris and were released with water from the tank to the deposition board with an area of 10 × 105 mm2. The volume, area, and length of particle distribution carried from the flume to the deposition board were then determined. Based on the experimental results, the deposition board is covered with particles of about 696.19 × 103 mm3, 748.29 × 103 mm3, and 505.19 × 103 mm3 volume for each 15°, 20°, and 25° angle, respectively. In actual situations, debris flow is capable of causing significant risk to the affected area. This study can be deemed useful for a risk assessment approach, to help develop guidelines, and to mitigate the regions where debris flows are most probable to occur. [ABSTRACT FROM AUTHOR]

The Ludoialm landslide, which is located in the municipality of Münster in Tyrol, Austria, represents a large-scale translational landslide in glacial soil sediments characterised by an exceptionally low inclined basal shear zone of only 12 ° . Although a temporal coincidence between meteorological events and slope displacement is obvious, the hydromechanical coupled processes responsible for the initial landslide formation and the ongoing movement characteristics have not yet been identified. This article provides a comprehensive analysis of the predisposition factors and the initial failure mechanism of this landslide from geological and geotechnical perspectives. We use a prefailure geometry of the cross section to simulate the initial slope failure process by a limit equilibrium analysis (LEA), a strength-reduction finite element method (SRFEM), and a finite element limit analysis (FELA). The shape and location of the computationally obtained basal sliding zone compare well with the geologically assumed one. Based on the computational study, it turns out that a high groundwater table probably caused by snow melting in combination with different permeabilities for the different layers is needed for the formation of the exceptionally low inclined basal shear zone. This paper presents the failure mechanism of the Ludoialm landslide and discusses the role of the shear band propagation in the process of slope destabilization. [ABSTRACT FROM AUTHOR]

Identifying the microstructural characteristics of volcanic ash particles is key in developing our understanding of their production, transport and impact. Volcanic glass in ash clouds can damage aircraft engines, surfaces and coatings, and if inhaled cause harm to humans and animals on which they depend. We present results of 3D imaging of volcanic glass particles from two Icelandic volcanoes together with two samples of experimentally vesiculated volcanic glass. True colour 3D images were obtained using an Olympus LEXT 3D laser scanning confocal microscope. The images enabled examination of bubble structure, fracture patterns, and shape morphology characteristics of grains. For the two simulated fragments, the bubbles had a median long axis lengths of 13.2 and 37.0 μm. For the material from Grimsvötn and Eyjafjallajökul volcanic eruptions, the median long axis length was 25.2 μm and 23.1 μm, respectively. Laser confocal microscopy-based 3D imaging of volcanic glass fragments provides a rapid, non-destructive way to assess 3D particle geometry and quantify internal artefacts responsible for their formation. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis of neurodegenerative diseases. One field of extensive clinical use of MRI is the accurate and automated classification of degenerative disorders. Most of current classification studies either do not mirror medical practice where patients may exhibit early stages of the disease, comorbidities, or atypical variants, or they are not able to produce probabilistic predictions nor account for uncertainty. Also, the spatial heterogeneity of the brain alterations caused by neurodegenerative processes is not usually considered, despite the spatial configuration of the neuronal loss is a characteristic hallmark for each disorder. In this article, we propose a classification technique that incorporates uncertainty and spatial information for distinguishing between healthy subjects and patients from four distinct neurodegenerative diseases: Alzheimer's disease, mild cognitive impairment, Parkinson's disease, and Multiple Sclerosis. We introduce a spatially informed Bayesian neural network (SBNN) that combines a three‐dimensional neural network to extract neurodegeneration features from MRI, Bayesian inference to account for uncertainty in diagnosis, and a spatially informed MRI image using hidden Markov random fields to encode cerebral spatial information. The SBNN model demonstrates that classification accuracy increases up to 25% by including a spatially informed MRI scan. Furthermore, the SBNN provides a robust probabilistic diagnosis that resembles clinical decision‐making and can account for the heterogeneous medical presentations of neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

This article presents a methodological proposal for the three-dimensional reconstruction of rock samples via structure-from-motion. The presented methodological steps aimed to provide a reproducible workflow to create virtual rock samples to be applied in virtual applications. The proposed methodology works as a how-to guide as well as a preemptive troubleshooting guide for the complete process. Four geologists with different scholar levels volunteered to test this methodological proposal, applying it to three rock samples as the methodology steps were provided in an inverse-proportional manner to the graduate level. When analyzing the results of the performed reconstructions, all analyzed elements presented a proportional reduction due to the lack of information provided. An initial questionnaire was applied to verify the difficulties encountered, and subsequently, all volunteers received the complete methodology. In the second reconstruction, the results were equivalent to those obtained initially with the complete methodology. A technology acceptance model questionnaire was applied to determine the perception of utility and ease of use of the presented methodology. In both cases the results presented themselves in a positive way, indicating that the methodology was able to solve the problems found simply and objectively through a repeatable workflow. [ABSTRACT FROM AUTHOR]

We propose the neural network-based method for segmentation of minerals in images of geological polished sections. We use a specific image set where the images are taken in plane-polarized (PPL) and cross-polarized (XPL) light, with different rotation angles relative to the optical axis of the camera. The data set, formed in that way, allows to obtain additional information that improves the quality of the anisotropic mineral segmentation, as that type of minerals changes its color ("blinks") depending on the rotation angle when imaging under the XPL light. The peculiarity of our method is the registration of the XPL images with the PPL images of the same polished section which is further fed to the neural network. Additionally, a data balancing algorithm was used to compensate for the non-uniform occurrence of different minerals in the image set. Five segmentation models have been trained both with using additional images and without using them. The results have demonstrated that using XPL images, registered in advance with the corresponding PPL image, improves the quality of segmentation by 3–12 percent for anisotropic minerals and by 1–8 percent for isotropic minerals. [ABSTRACT FROM AUTHOR]

As mining and tunnelling projects advance to deeper areas, strainbursts occur more frequently. This failure mode is extremely dangerous, as the rock mass fails abruptly, releasing high amounts of energy. This poses a high risk to the life of workers and equipment used. For a robust strainburst risk assessment many factors have to be taken into account. Besides geological features, overburden, excavation method, etc., rocks' intrinsic proneness to strainburst plays a major role. Whether a rock tends to this failure behaviour depends strongly on the rocks' mechanical and structural characteristics at the grain-level, especially on its heterogeneity. The authors demonstrate this based on different rock-like sample sets, consisting of a very fine-grained fibreless ultra-high-performance concrete (UHPC) and a constant volumetric fraction of coarse aggregates. Thereby, the heterogeneity aspect was introduced by the different properties of the aggregates. A laboratory program was performed, taking into account uniaxial compression tests (including post-failure tests to evaluate the failure energy) and Acoustic Emission Testing (to monitor the cracking activity). The study underlines the high suitability of using Acoustic Emission Testing (AET) in strainburst risk assessment. In addition, the authors analyse empirical parameters commonly used to evaluate rocks' intrinsic proneness to strainburst, and give recommendations regarding their application. Overall, the study substantiates former research and emphasises the usefulness of petrographic information within strainburst risk analysis. It also sets the base for future research on real rock, which will hopefully lead to more specific recommendations for practitioners on how to include rocks' grain-scale characteristics in strainburst risk analysis. Highlights: Laboratory test program demonstrating the potential of including rocks structure at grain-scale in estimating rocks intrinsic proneness regarding strainburst. Stiffness and shape aspects at grain-scale have a great influence on the intrinsic proneness to strainburst of rocks. Acoustic Emission Testing in laboratory is highly suited for capturing the materials intrinsic proneness to strainbursting. Commonly used empirical parameters based on laboratory tests, which evaluate rocks intrinsic proneness regarding strainburst, have to be applied with caution. [ABSTRACT FROM AUTHOR]

Neuroimaging literature in healthy humans has shown that there are sex-related differences in healthy brain's anatomical structure, associated function and susceptibility to neurological diseases. This mini-review summarizes findings derived fromthe current neuroimaging studies focused on sex-related brain structural and functional damage in women with multiple sclerosis (MS). MS is a chronic, multifactorial, immune-mediated disorder of the central nervous system that affects mostly women. Even if recent neuroimaging studies have shed light on distinctive features of sex-related MS differences in brain structural and functional damage, more research is needed to better elucidate sex-related MS pathological changes and susceptibility and to implement sex-tailored treatment strategies in MS. [ABSTRACT FROM AUTHOR]

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In the past few decades, digital technologies have played a more and more important role in landslide disaster risk management. To identify the progress and future directions with regard to the use of digital technologies in landslide disaster risk management, a systematic review of journal papers in the ISI Web of Science is conducted in this study. Findings indicate that in the early phase, landslide risk management research mainly focused on hazard evaluation and zonation. Then, studies about the spatial predictions of landslides and landslide susceptibility appeared. The research scale of landslides is developing from large scale to fine scale. The use of digital technologies in landslides has been widely discussed since 2009. The use of digital technologies has been developing in the directions of deep learning and artificial intelligence. The monitoring means has been gradually developing from high altitude to low altitude and to ground sensors. Processing technologies are the most widely used in landslide disaster risk research, followed by sensing technologies. Different types of digital technologies play different roles in landslide disaster management. Digital technologies account for a low proportion in the mitigation phase, but contribute the most in the disaster preparation phase. In the future, digital technologies can further strengthen mitigation for and responses to landslide disasters. The application of digital technologies in landslide disaster management should gradually adapt to the needs of the vulnerable group. The government should implement differentiated landslide disaster management according to the regional level of economic development and digital technology development. This study not only reviews the state of the latest technology, but also addresses the future trend of research and provides support for scientists and decision-makers involved in landslide disaster management. [ABSTRACT FROM AUTHOR]

In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks. [ABSTRACT FROM AUTHOR]

This literature review focuses on land quality evaluation (LQE) and its effects on sustainable development through quantitative analysis and value-added information. In contrast to the traditional perspective, a structured review based on bibliometric indicators and social network analysis allows identifying hidden evidence for answering the following research questions: (i) What: What is the application of LQE? (ii) Which: Which sustainable development goals does the application contribute to? (iii) Why and how: What are the main applications and methods of each topic? (iv) Where: Where is the hotspot of the problem? What is the future research orientation of the topic? (v) How and when: How has the topic grown since 2000? Data investigation explores 4029 articles in 2000–2019 from four publishers. With the support of VOSviewer software, six clusters corresponding to six main applications of LQE are classified. Overlapping keywords in several clusters are resolved by the binary term frequency counter for the cluster preference determination. After conducting the data verification and editing process, a structured review is performed again with systematic research questions. This research offers a synthesis of traditional and novel quantitative analysis for literature review, which is comprehensive, accurate, and reliable. [ABSTRACT FROM AUTHOR]