Regression models, where the response variable is circular, are common in areas such as biology, geology and meteorology. A typical model assumes that the conditional distribution of the response follows a von-Mises distribution. However, this assumption is inadequate when the response variable is multimodal. For this reason, in this paper, a finite mixture of regressions model is proposed for the case of a circular response variable and a set of circular and/or linear covariates. Mixture models are very useful when the underlying population is multimodal. Despite the prevalence of multimodality in regression modelling of circular data, the use of mixtures of regressions has received no attention in the literature. This paper aims to close this knowledge gap. To estimate the proposed model, we develop a maximum likelihood estimation procedure via the Expectation-Maximization algorithm. An extensive simulation study is used to demonstrate the practical use and performance of the proposed model and estimation procedure. In addition, the model is shown to be useful as a model-based clustering tool. Lastly, the model is applied to a real dataset from a wind farm in South Africa.
Group-occurring loess falls are common catastrophic geological hazards on the Loess Plateau, typically triggered by heavy rainfall, excessive irrigation, or earthquakes. However, group-occurring loess falls induced by sustained sewage discharge are exceedingly rare. To better understand the failure mechanism of group-occurring loess falls caused by persistent domestic sewage scouring in Liudian Village, Qingyang City, Gansu Province, multi-temporal remote-sensing images and UAV-derived DEMs, together with field investigations, were used to track slope evolution. ERT and drilling were used to characterize subsurface moisture and stratigraphy. A flume test was set up using analogous materials, and water infiltrated from the constant-level tank at the slope toe. Displacement and inclination were monitored. The results show that basal loess saturation increased progressively and caused collapsible settlement. Once the basal layer became fully saturated, the slope toe gradually softened, and capillary action promoted the upward migration of moisture, saturating the upper slope. This hydrological process induced plastic deformation and slow creep, eventually resulting in an overall slope fall and sliding. The entire failure process can be divided into three stages: steady-state deformation, accelerated deformation, and final failure, each displaying distinct characteristics. These observations indicate an erosion-controlled, time-dependent cumulative failure pattern with recurrent collapses under sustained toe water supply. In the future, mitigation can focus on sewage diversion and toe flow interception, together with localized toe protection, to reduce long-term scouring and infiltration.
Robotic telepresence enables users to navigate and experience remote environments. However, effective navigation and situational awareness depend on users' prior knowledge of the environment, limiting the usefulness of these systems for exploring unfamiliar places. We explore how integrating location-aware LLM-based narrative capabilities into a mobile robot can support remote exploration. We developed a prototype system, called NarraGuide, that provides narrative guidance for users to explore and learn about a remote place through a dialogue-based interface. We deployed our prototype in a geology museum, where remote participants (n=20) used the robot to tour the museum. Our findings reveal how users perceived the robot's role, engaged in dialogue in the tour, and expressed preferences for bystander encountering. Our work demonstrates the potential of LLM-enabled robotic capabilities to deliver location-aware narrative guidance and enrich the experience of exploring remote environments.
Takahiro Hiraki, Takahiko Masuda, Sayuri Takatori
et al.
Mössbauer spectroscopy is widely used in biochemistry, geology, and solid-state physics to obtain structural information on materials. Here, we extend this technique into the optical range using a vacuum ultraviolet laser to probe the low-energy nuclear transitions of thorium-229, doped in calcium fluoride crystals. We discover four distinct doping sites for the thorium ions, determine the characteristic electric field gradients emerging in the interaction with the host crystal, and identify the microscopic structure of the two dominant configurations. Site-selective laser excitation allows to study the isomeric state lifetime and laser-induced quenching for all sites. This laser-based Mössbauer spectroscopy provides a powerful probe of the nuclear environment, yielding foundational data for designing future solid-state nuclear clocks.
Simone Marchi, David Vokrouhlický, David Nesvorný
et al.
The NASA Lucy mission is scheduled to fly-by the main belt asteroid (52246) Donaldjohanson on April 20, 2025. Donaldjohanson (DJ hereafter) is a member of the primitive (C-type class) Erigone collisional asteroid family located in the inner main belt in proximity of the source regions of asteroid (101955)~Bennu and (162173)~Ryugu, visited respectively by OSIRIS-REx and Hayabusa2 missions. In this paper we provide an updated model for the Erigone family age, and discuss DJ evolution resulting from non-gravitational forces (namely Yarkovsky and YORP), as well as its collisional evolution. We conclude the best-fit family age to be $\sim 155$~Myr, and that, on such timescales, both Yarkovsky and YORP effects may have affected the orbit and spin properties of DJ. Furthermore, we discuss how the NASA Lucy mission could provide independent insights on such processes, namely by constraining DJ shape, surface geology and cratering history.
Plamen Georgiev, Marina Nicolova, Irena Spasova
et al.
Copper slag, a by-product of copper ore and concentrate smelting, is rich in non-ferrous metals; therefore, it has been considered a valuable raw material in recent years. This study aimed to compare the extraction of zinc, copper, and cobalt from two types of copper slag from a dump located near the village of Eliseyna, Bulgaria, which differ in mineralogical composition and chemical content, using indirect bioleaching with a spent medium of <i>Aspergillus niger</i> and <i>Penicillium ochrochloron</i>. Chemical leaching with sulphuric acid revealed that zinc and cobalt existed mainly as an acidic-soluble phase in both types of copper slag. In contrast, it contained 50–75% of the total copper content. Each fungal species was cultivated for one week, and the biomass and the spent medium were separated a week later. Owing to the production of a higher concentration of citric acid, <i>A. niger</i> facilitated more efficient base metal recovery. However, their effective recovery from the acidic-soluble phase required leaching at a 5% pulp density and supplementing the spent medium with sulphuric acid. The temperature played a secondary role. Conclusions: Non-ferrous metal extraction from copper slag exposed to weathering using a spent medium supplemented with sulphuric acid was achieved under milder leaching conditions and with better selectivity. In contrast, slag unaffected by weathering behaved as a refractory due to the worsened results of base metal extraction under similar experimental conditions.
This study presents a facial emotion recognition network based on UniRepLKNet to address the difficulty in effectively capturing feature information and preventing key facial information from occupying a more prominent position in the facial emotion recognition process. Moreover, to extract facial emotional features more accurately, the study designs a masked polarized self-attention module that combines U-Net and a polarized self-attention mechanism. This module can deeply mine the dependency between channels and spaces. It can also strengthen the influence of local key information of the face on emotion recognition through a multi-scale feature fusion strategy. The study optimizes UniRepLKNet, a universal large kernel Convolutional Neural Network (CNN), and proposes the EmoRepLKNet neural network structure. In EmoRepLKNet, the mask-polarized self-attention module enables the network to extract key information for facial emotion recognition. Combined with the wide receptive field of large kernel CNN, facial emotions can be recognized effectively. Experimental results show that on the facial emotion recognition dataset FER2013, EmoRepLKNet achieves an accuracy of 76.20%, outperforming existing comparison models and significantly improving facial emotion recognition accuracy compared to that of UniRepLKNet. Additionally, on the single-label portion of the RAF-DB dataset, the proposed method achieves an accuracy of 89.67%.
The Rietveld method refines a theoretical line profile until it fits the measured profile using a least squares methodology. In Rietveld method, complete qualitative mineral determination, mineralchemistry, appropriate XRD analytical conditions and sample preparation steps should be applied correctly. This study aims to determine the grain size-dependent variations in the quantitativecompositions of minerals with various chemical compositions and crystallographic characteristics. Eight pure minerals (quartz, calcite, halite, colemanite, barite, polyhalite, gypsum, thenardite) are been selected, powdered, pulverized and separated into 3 different grain sizes as 228μm (Dv90), 29μm (Dv90) and 8μm (Dv90). These minerals are mixed in certain proportions to prepare three mixture samples. Mixture samples are evaluated using the Rietveld Method on XRD whole rock patterns and then compared to the actual reference mixture sample whose mixing ratio is known for each grain size. Sample with a grain size of 8μm (Dv90) with random orientation gave the closest result compared to the reference content. If the structural and physical refinement stages are processed properly, accuracy of percentage mineralogical composition increases when the grain size decreases.
Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensional time-series data, however, posing significant challenges for analyzing latent structures over extended temporal scales. Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. While previous surveys have focused on specific methodological categories, we bridge the gap between traditional clustering methods and emerging deep learning-based algorithms, presenting a comprehensive, unified taxonomy for this research area. This survey highlights key developments and provides insights to guide future research in time-series clustering.
Malia L. Kao, Keith Hawkins, Laura K. Rogers
et al.
White dwarfs (WDs) polluted by exoplanetary material provide the unprecedented opportunity to directly observe the interiors of exoplanets. However, spectroscopic surveys are often limited by brightness constraints, and WDs tend to be very faint, making detections of large populations of polluted WDs difficult. In this paper, we aim to increase considerably the number of WDs with multiple metals in their atmospheres. Using 96,134 WDs with Gaia DR3 BP/RP (XP) spectra, we constructed a 2D map using an unsupervised machine learning technique called Uniform Manifold Approximation and Projection (UMAP) to organize the WDs into identifiable spectral regions. The polluted WDs are among the distinct spectral groups identified in our map. We have shown that this selection method could potentially increase the number of known WDs with 5 or more metal species in their atmospheres by an order of magnitude. Such systems are essential for characterizing exoplanet diversity and geology.
Stalyn Paucar, Christian Mejía-Escobar y Víctor Collaguazo
The identification and characterization of various rock types is one of the fundamental activities for geology and related areas such as mining, petroleum, environment, industry and construction. Traditionally, a human specialist is responsible for analyzing and explaining details about the type, composition, texture, shape and other properties using rock samples collected in-situ or prepared in a laboratory. The results become subjective based on experience, in addition to consuming a large investment of time and effort. The present proposal uses artificial intelligence techniques combining computer vision and natural language processing to generate a textual and verbal description from a thin section image of rock. We build a dataset of images and their respective textual descriptions for the training of a model that associates the relevant features of the image extracted by EfficientNetB7 with the textual description generated by a Transformer network, reaching an accuracy value of 0.892 and a BLEU value of 0.71. This model can be a useful resource for research, professional and academic work, so it has been deployed through a Web application for public use.
Chunyi Zhang, Marcos Calegari Andrade, Zachary K. Goldsmith
et al.
The electrical double layer (EDL) at aqueous solution-metal oxide interfaces critically affects many fundamental processes in electrochemistry, geology and biology, yet understanding its microscopic structure is challenging for both theory and experiments. Here we employ ab initio-based machine learning potentials including long-range electrostatics in large-scale atomistic simulations of the EDL at the TiO2-electrolyte interface. Our simulations provide a molecular-scale picture of the EDL that demonstrates the limitations of standard mean-field models. We further develop a method to accurately calculate the electrostatic potential drop at the interface. The computed capacitance originating from the adsorbed charges and the potential drop agrees with experiments, supporting the reliability of our description of the EDL. The larger interfacial capacitance of basic relative to acidic solutions originates from the higher affinity of the cations for the oxide surface and gives rise to distinct charging mechanisms on negative and positive surfaces.
ABSTRACT: China established Xiong'an New Area in Hebei Province in 2017, which is planned to accommodate about 5 million people, aiming to relieve Beijing City of the functions non-essential to its role as China's capital and to expedite the coordinated development of the Beijing-Tianjin-Hebei region. From 2017 to 2021, the China Geological Survey (CGS) took the lead in multi-factor urban geological surveys involving space, resources, environments, and disasters according to the general requirements of “global vision, international standards, distinctive Chinese features, and future-oriented goals” in Xiong'an New Area, identifying the engineering geologic conditions and geologic environmental challenges of this area. The achievements also include a 3D engineering geological structure model for the whole area, along with “one city proper and five clusters”, insights into the ecology and the background endowment of natural resources like land, geothermal resources, groundwater, and wetland of the area before engineering construction, a comprehensive monitoring network of resources and environments in the area, and the “Transparent Xiong'an” geological information platform that is open, shared, dynamically updated, and three-dimensionally visualized. China's geologists and urban geology have played a significant role in the urban planning and construction of Xiong'an New Area, providing whole-process geological solutions for urban planning, construction, operation and management. The future urban construction of Xiong'an New Area will necessitate the theoretical and technical support of earth system science (ESS) from various aspects, and the purpose is to enhance the resilience of the new type of city and to provide support for the green, low-carbon, and sustainable development of this area.
Abstract Metasomatized mantle xenoliths containing hydrous minerals, such as amphiboles, serpentine, and phlogopite, likely represent the potential mineralogical compositions of the metasomatized upper mantle, where low seismic velocities are commonly observed. This study presents the first experimentally determined single‐crystal elasticity model of an Fe‐free near Ca, Mg‐endmember amphibole tremolite at high pressure and/or temperature conditions (maximum pressure 7.3(1) GPa, maximum temperature 700 K) using Brillouin spectroscopy. We found that sound velocities of amphiboles strongly depend on the Fe content. We then calculated the sound velocities of 441 hydrous‐mineral‐bearing mantle xenoliths collected around the globe, and quantitatively evaluated the roles that amphiboles, phlogopite and serpentine played in producing the low velocity anomalies in the metasomatized upper mantle.
Jinlong Liu, Shubhangi Gupta, Jonny Rutqvist
et al.
Abstract We conducted two‐dimensional numerical simulations to investigate the mechanisms underlying the strong spatiotemporal correlation observed between submarine landslides and gas hydrate dissociation due to glacial sea‐level drops. Our results suggest that potential plastic deformation or slip could occur at localized and small scales in the shallow‐water portion of the gas hydrate stability zone (GHSZ). This shallow‐water portion of the GHSZ typically lies within the area enclosed by three points: the BGHSZ–seafloor intersection, the seafloor at ∼600 m below sea level (mbsl), and the base of the GHSZ (BGHSZ) at ∼1,050 mbsl in low‐latitude regions. The deep BGHSZ (>1,050 mbsl) could not slip; therefore, the entire BGHSZ was not a complete slip surface. Glacial hydrate dissociation alone is unlikely to cause large‐scale submarine landslides. Observed deep‐water (much greater than 600 mbsl) turbidites containing geochemical evidence of glacial hydrate dissociation potentially formed from erosion or detachment in the GHSZ pinch‐out zone.
Matheus Rufino, Arnaldo Luis Lixandrão Filho, Sandro Guedes
The main feature of the Fission-Track Thermochronology is its ability to infer the thermal histories of mineral samples in regions of interest for geological studies. The ingredients that make the thermal history inference possible are the annealing models, which capture the annealing kinetics of fission tracks for isothermal heating experiments, and the Principle of Equivalent Time (PET), which allows the application of the annealing models to variable temperatures. It turns out that the PET only applies to specific types of annealing models describing single activation energy annealing mechanisms (parallel models). However, the PET has been extensively applied to models related to multiple activation energy mechanisms (fanning models). This procedure is an approximation that has been overlooked due to the lack of a suitable alternative. To deal with this difficult, a formalism, based on physicochemical techniques, that allows to quantify the effects of annealing on the fission tracks for variable temperatures, is developed. It is independent of the annealing mechanism and, therefore, is applicable to any annealing model. In the cases in which the PET is valid, parallel models, the proposed method and the PET predict the same degrees of annealing. However, deviations appear when the methods are applied to the fanning models, with the PET underestimating annealing effects. The consequences for the inference of thermal histories are discussed.
We are of the opinion that several anomalies in the atmosphere of Venus provide evidence of yet-unknown processes and systems that are out of equilibrium. The investigation of these anomalies on Venus should be open to the wide range of explanations, including unknown biological activity. We provide an overview of two anomalies, the tentative detection of ammonia and phosphine in Venus's atmosphere. These anomalies fly in the face of the tacit assumption that the atmosphere of Venus must be in chemical redox equilibrium, an assumption connected to the belief that Venus is lifeless. We then discuss several major past discoveries in astronomy, biology and geology, which lead to the abandonment of certain assumptions held by many scientists as though they were well-established principles. The anomalies of ammonia and phosphine in the atmosphere of Venus are placed in the context of these historical discoveries. This context supports our opinion that persistence by the community in the exploration of these anomalies with a skeptical eye towards tacit assumptions will increase the chances of making profound discoveries about the atmosphere of Venus and the diverse and often strange nature of planetary environments.
BACKGROUND Water quality security and human health are important to ensure rural revitalization of old liberated areas. The continuous development and utilization of ion-adsorption type REE deposits in northern Ganzhou aggravate trace elements from ores and tailings to the water cycle, thereby endangering the sanitation and safety of drinking water. OBJECTIVES To investigate and evaluate health risk and water quality on the watershed scale of rare earth ore concentration area in the Huangpi River Basin. METHODS The contents of manganese, cadmium and other elements were determined by inductively coupled plasma-optical emission spectrometry/mass spectrometry(ICP-OES/MS). By choosing "standards for drinking water quality" (GB 5749—2006) as the evaluation basis, the water quality index (WQI), hazard quotient (HQ), and cancer risk (CR)were adopted to evaluate water quality and human health risks through analyzing 9 indices including Pb and Mn. RESULTS NH3-N and Mn were anomaly indices whether in surface water or groundwater. The average values of NH3-N were 750μg/L and 4533μg/L in surface water and groundwater, respectively. Index values of Mn were 207μg/L and 4009μg/L in surface water and groundwater, respectively. Arsenic, class I carcinogen published by the World Health Organization, had no abnormality in surface water and groundwater. Moreover, 85.7% of surface water and groundwater was found to be suitable for drinking upon analyzing the WQI values. The HQ average value of NH3-N was less than 1 in surface water and groundwater so it had no harmful effects on human health. However, the HQ average value of Mn was more than 1, which may be harmful to human health. CR values of As varying from 10-6 to 10-4 were also calculated, and the risk of cancer was acceptable. CONCLUSIONS It is suggested that relevant departments should pay attention to the status of NH3-N and heavy metal elements in water during the reclamation evaluation of rare earth mines. The research detailed in this paper confirms that the groundwater monitoring system of manganese should be improved.
The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time (SFT). The observational and model uncertainties could lead the predicted SFT calculated from the phenomenological models to deviate from the actual SFT. Currently, very limited study has been conducted on how to evaluate the effect of such uncertainties on SFT prediction. In this paper, a comprehensive slope failure database was compiled. A Bayesian machine learning (BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction, through which the probabilistic distribution of the SFT can be obtained. This method was illustrated in detail with an example. Verification studies show that the BML-based method is superior to the traditional inverse velocity method (INVM) and the maximum likelihood method for predicting SFT. The proposed method in this study provides an effective tool for SFT prediction.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction