Miracle-dz/UniFAR-FTUs huggingface dataset:
MMA‐EoS: A Computational Framework for Mineralogical Thermodynamics

We present a newly developed software framework, MMA‐EoS, that evaluates phase equilibria and thermodynamic properties of multicomponent systems by Gibbs energy minimization, with application to mantle petrology. The code is versatile in terms of the equation‐of‐state and mixing properties and allows for the computation of properties of single phases, solution phases, and multiphase aggregates. Currently, the open program distribution contains equation‐of‐state formulations widely used, that is, Caloric‐Murnaghan, Caloric–Modified‐Tait, and Birch‐Murnaghan–Mie‐Grüneisen‐Debye models, with published databases included. Through its modular design and easily scripted database, MMA‐EoS can readily be extended with new formulations of equations‐of‐state and changes or extensions to thermodynamic data sets. We demonstrate the application of the program by reproducing and comparing physical properties of mantle phases and assemblages with previously published work and experimental data, successively increasing complexity, up to computing phase equilibria of six‐component compositions. Chemically complex systems allow us to trace the budget of minor chemical components in order to explore whether they lead to the formation of new phases or extend stability fields of existing ones. Self‐consistently computed thermophysical properties for a homogeneous mantle and a mechanical mixture of slab lithologies show no discernible differences that require a heterogeneous mantle structure as has been suggested previously. Such examples illustrate how thermodynamics of mantle mineralogy can advance the study of Earth's interior.
The name is the message: eagle-stones and materia medica in South America

Abstract This chapter presents one case history – the transfer of the name and virtues of ‘eagle-stones’ to Andean minerals and terebratulid brachiopods such as Clarkeia antisiensis. Eagle-stones, an ancient remedy of Asian origin, were used in early modern Europe to prevent abortion and as a charm to assist obstetric delivery. In the eighteenth century eagle-stones were the subject of what G. Baronti (Tra bambini e acque sporche Immersioni nella collezione di amuleti di Giuseppe Bellucci, Morlacchi, Perugia, 2008) calls the process of folklorization of European learned medicine, becoming a ‘superstition’ and a popular remedy of medical lore. Based on secondary bibliography and documents from the Archivo de Indias in Seville, the paper discusses the uses of eagle-stones in Spain and Spanish America in connection to the texts published, written or translated in the Iberian Peninsula (lapidaries, early modern medical books). The last section proposes clues to analyse the expansion of the trade in eagle-stones to Spanish America, to finally survey the references to ‘eagle-stones’ in Latin American popular medicine of the late nineteenth and early twentieth centuries. Eagle-stones are inscribed not only in the longue-durée but also in the intricate networks of commerce.
Changing NPP consumption patterns in the Holocene: From megafauna-‘liberated’ NPP to ‘ecological bankruptcy’

There have been vast changes in how net primary production (NPP) has been consumed by humans and animals through the Holocene. Here we ask: how much NPP energy may have become available following the megafauna extinctions? When did humans, through agriculture and livestock, consume more NPP than wild mammals? When did humans and wild mammals use more energy than was available in total NPP in each country? The megafauna extinctions potentially liberated ~2.2–5.3% of global NPP that early humans eventually consumed. By 1850, humans began to consume more than wild mammals (globally averaged). Currently, >82% of people live in ‘ecologically bankrupt’ countries where all plant production could not satisfy our energy demands. To summarize, we began the Holocene with an NPP energy surplus, became the dominant consumers of NPP over the natural world by the start of the Industrial Revolution, but now consume more total energy (including fossil fuels) than is available in NPP in most countries.
Optimal Feature Identification and Major Depressive Disorder Prediction through Correlation-Based Machine Learning Approach

Major Depressive Disorder (MDD) is a mental health disorder characterized by continuous feelings of sadness, diminished interest in activities, and a range of physical and emotional symptoms that profoundly affect a person’s daily life and functioning. Early detection of MDD is crucial for improved treatment outcomes. The detection of Major Depressive Disorder (MDD) relies on extracting several features from raw EEG signals. This study presents a machine learning-based approach to identify MDD using minimal EEG signal features, employing a correlation analysis with various correlation coefficient values. The proposed method achieved an impressive accuracy of 84% with SVM, showing 91.5% sensitivity, 75.5% specificity, and 85.7% f1-score with best correlation coefficient value. To evaluate performance, 5-fold cross-validation with inter-subject variability is utilized. The study extracts several feature sets using different coefficient values and assesses their performance with diverse coefficient values, aiming to identify the most effective features for early diagnosis of MDD.
Multi-Object Tracking with Object Candidate Fusion for Camera and LiDAR Data

Multi-Object Tracking (MOT) is one of the core tasks in perception for autonomous driving. It helps vehicles understand the dynamic environment and is useful for subsequent trajectory prediction, motion planning, and other tasks. Most current methods based on LiDAR or camera are not robust enough. LiDAR provides accurate spatial information but suffers when objects are too far away to be detected reliably. Camera-based methods have rich semantic information, but struggle with positioning accuracy. Taking advantage of the rapid development of deep learning in object detection, we propose a novel camera-LiDAR fusion MOT method based on the spatial localization and confidence scores of candidates obtained from both 2D and 3D detectors. Each modality can operate independently in case of sensor failure, and improve its accuracy through the fusion module. Our method also makes the first attempt to associate the cross-modality candidates in only a one-stage association process for better real-time performance. Extensive experiments are conducted on the KITTI tracking benchmark, and our method achieves a competitive performance of 74.04 HOTA on the KITTI test dataset.
Studies on the existing in-plane shear equations of partially grouted reinforced masonry

Abstract This paper provides critical analyses of the presence of the reinforced grout cores within the masonry walls with particular reference to the existing in-plane shear design expressions in several national standards; these expressions comprise terms which define the shear strength of masonry, the vertical load and the area of reinforcement. These expressions treat the wall height to length ratio (termed as wall aspect ratio) as a prime factor in their respective masonry strength terms. The consideration of the wall aspect ratio in these design expressions implies existence of a compression strut along the wall diagonal, although such a phenomenon is not established in the literature. In this paper, through an extensive analyses of partially grouted reinforced masonry shear walls using a validated nonlinear finite element model, it is shown that the aspect ratio of the unreinforced masonry panels inscribed within the vertical and the horizontal reinforced grouted cores has profound effect on the in-plane shear capacity of the walls. It is also shown that the horizontal and the vertical reinforcements do never yield and hence their inclusion in the in-plane shear capacity expressions is not justified. A recently reported expression by the authors is also reviewed and compared with the other international design expressions.
TUMTraf Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception

Intelligent Transportation Systems (ITS) allow a drastic expansion of the visibility range and decrease occlusions for autonomous driving. To obtain accurate detections, detailed labeled sensor data for training is required. Unfortunately, high-quality 3D labels of LiDAR point clouds from the infrastructure perspective of an intersection are still rare. Therefore, we provide the TUM Traffic (TUMTraf) Intersection Dataset, which consists of labeled LiDAR point clouds and synchronized camera images. Here, we recorded the sensor output from two roadside cameras and LiDARs mounted on intersection gantry bridges. The data was labeled in 3D by experienced annotators. Furthermore, we provide calibration data between all sensors, which allow the projection of the 3D labels into the camera images and an accurate data fusion. Our dataset consists of 4.8k images and point clouds with more than 57.4k manually labeled 3D boxes. With ten classes, it has a high diversity of road users in complex driving maneuvers, e.g. left/right turns, overtaking, and U-turns. In experiments, we provided baselines for the perception tasks. Overall, our dataset is a valuable contribution to the scientific community to perform complex 3D camera-LiDAR roadside perception tasks. Find data and code at https://innovation-mobility.com/tumtraf-dataset.
Sentiment Analysis of Google Classroom Application Using Machine Learning Techniques

This research analyzes the social sentiment of the academic community towards the digital education platform Google Classroom. Artificial intelligence algorithms such as Naïve Bayes, K-Nearest Neighbor (KNN), and Random Forest are implemented to perform sentiment analysis based on Play Store comment data. The dataset is 7000 comments from the academic community on the Google Classroom application, and the best model of the three algorithms is selected based on the accuracy, precision, recall, and f1 values. The results showed that the Random Forest algorithm produced the highest accuracy of 84.05%. Naïve Bayes algorithm with a multinomial model produces the highest accuracy of 83.96%. Meanwhile, KNN produces an accuracy of 77.96%. This research illustrates how artificial intelligence algorithms can be applied to analyze sentiment in observing the social behavior of the communities towards digital education applications. This can be the basis for effective improvement of the app user experience.
Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series

In this article, a new self-supervised strategy for learning meaningful representations of complex optical satellite image time series (SITS) is presented. The methodology proposed, named Unet-BERT spAtio-temporal Representation eNcoder (U-BARN), exploits irregularly sampled SITS. The designed architecture allows learning rich and discriminative features from unlabeled data, enhancing the synergy between the spatio-spectral and the temporal dimensions. To train on unlabeled data, a time-series reconstruction pretext task inspired by the BERT strategy but adapted to SITS is proposed. A Sentinel-2 large-scale unlabeled dataset is used to pretrain U-BARN. During the pretraining, U-BARN processes annual time series composed of a maximum of 100 dates. To demonstrate its feature learning capability, representations of SITS encoded by U-BARN are then fed into a shallow classifier to generate semantic segmentation maps. Experimental results are conducted on a labeled crop dataset (PASTIS) as well as a dense land cover dataset (MultiSenGE). Two ways of exploiting U-BARN pretraining are considered: either U-BARN weights are frozen or fine-tuned. The obtained results demonstrate that representations of SITS given by the frozen U-BARN are more efficient for land cover and crop classification than those of a supervised-trained linear layer. Then, we observe that fine-tuning boosts U-BARN performances on MultiSenGE dataset. In addition, we observe on PASTIS, in scenarios with scarce reference data that the fine-tuning brings a significative performance gain compared to fully supervised approaches. We also investigate the influence of the percentage of elements masked during pretraining on the quality of the SITS representation. Eventually, semantic segmentation performances show that the fully supervised U-BARN architecture reaches better performances than the spatio-temporal baseline (U-TAE) on both downstream tasks: crop and dense land cover segmentation.
Relict landscape resistance to dissection by upstream migrating knickpoints

Expanses of subdued topographies are common at high elevation in mountain ranges. They are often interpreted as relict landscapes and are expected to be replaced by steeper topography as erosion proceeds. Preservation of such relict fragments can merely reflect the fact that it takes time to remove any preexisting topography. However, relict fragments could also possess intrinsic characteristics that make them resilient to dissection. We document here the propagation of a wave of dissection across an uplifted relict landscape in Puerto Rico. Using 10Be‐26Al burial dating on cave sediments, we show that uplift started 4 Ma and that river knickpoints have since migrated very slowly across the landscape. Modern detrital 10Be erosion rates are consistent with these long‐term rates of knickpoint retreat. Analysis of knickpoint distribution, combined with visual observations along the streambeds, indicates that incision by abrasion and plucking is so slow that bedrock weathering becomes a competing process of knickpoint retreat. The studied rivers flow over a massive stock of quartz diorite surrounded by an aureole of metavolcanic rocks. Earlier studies have shown that vegetation over the relict topography efficiently limits erosion, allowing for the formation of a thick saprolite underneath. Such slow erosion reduces streambed load fluxes delivered to the knickpoints, as well as bed load grain size. Both processes limit abrasion. Compounding the effect of slow abrasion, wide joint spacing in the bedrock makes plucking infrequent. Thus, the characteristics of the relict upstream landscape have a direct effect on stream incision farther downstream, reducing the celerity at which the relict, subdued landscape is dissected. We conclude that similar top‐down controls on river incision rate may help many relict landscapes to persist amidst highly dissected topographies.
Behavioral Intention to Use Mobile Banking using UTAUT 2 Model

Today’s non-cash transaction solution is mobile banking, which is quick and to the point. only possible with a smartphone. Nowadays, nearly everyone owns a smartphone. But not all of them favor using mobile banking for their transactions. This is what sparked our interest in gathering data on the variables affecting consumers of mobile banking. The purpose of this study was to identify the variables that may affect bank customers’ behavioral intentions when utilizing the mobile banking application to complete routine banking operations using the UTAUT 2 Model Approach. This study collected 200 samples from DKI Jakarta-based users of mobile banking applications utilizing a quantitative research method and the probability sampling methodology called convenience sampling. SmartPLS 4 software was used to process the data once it had been obtained and subjected to SEM-PLS analysis. The hedonic motivation, habit, and price value variables each had favorable and significant effect on behavioral intention of mobile banking users, according to the analysis and processing of the data results. In the meanwhile, expectations for performance, effort, social impact, and facilitation each had no significance effect on behavioral intention
Taxonomy Mining from a Smart City CMS using the Multidimensional Knowledge Representation Approach

Taxonomy mining plays an important role for organizing and structuring of data in Content Management Systems (CMS). In this paper, we propose a novel approach that leverages multidimensional knowledge representation (MKR) for taxonomy mining from text documents and enriching the extracted information via Large Language Model (LLM). The data originates from a Smart City project in Germany, which addresses housing, care and health for elderly people. The applied method involves the extraction of relevant keywords from text and the utilization of the MKR framework to analyze and represent the information. Results are provided for a context builder that utilizes GPT-4 to enrich the taxonomy. The enriched taxonomy is then used in a WordPress CMS for information search, structuring and tagging of the blog entries accordingly.
Bat Algorithm: A Review on Theory, Modifications and Applications

Due to its many applications in a variety of domains, including clustering, cloud computing, and power systems, metaheuristic algorithms have attracted a lot of interest from the scholarly community. By using their self-explorative and adaptive processes, these algorithms provide certain benefits over conventional methods. One well-known metaheuristic approach is effective in resolving optimization problems with constraints and without constraints. This technique was inspired by nature, notably the echolocation ability of bat species. The Bat Algorithm (BA) makes use of a powerful search mechanism to provide the best results when compared to other algorithms of the same class. The theoretical underpinnings of the BA are explored in depth in this research, along with several changes and advancements that have improved its functionality. These enhancements include adjustments to search tactics as well as other developments. The report also thoroughly examines how the BA may be used across a variety of fields, demonstrating its competence and adaptability. The BA has been effectively used in several real-world applications, from cloud computing to clustering, and each one is carefully investigated. The study also offers a thoughtful examination of the BA's possible advancements and future possibilities. It also entails investigating novel modifications and tactics that might improve its effectiveness in handling challenging optimization issues. This study seeks to direct practitioners and academics toward more effective and efficient metaheuristic algorithms, thereby stimulating improvements in optimization approaches. It does this by summarising the present state of the BA and imagining its future development.
Comprehensive Analysis of Microbiological and Physicochemical Characteristics of Solar-Dried Allium Sativum L in the Adrar Region: A Study of Quality and Safety Parameters

Garlic (Allium sativum L) is a plant that is widely used in cooking in different types, fresh, pickled, and dried, additionally in medicinal effects, however, little research studied the properties of physicochemical and microbiological of garlic after treatment with solar drying. In the present work, dried garlic was brought to determine the physicochemical properties including Moisture content, ash content, the dosage of fat, the dosage of protein, and the total dosage of sugars.in addition, microbiological analysis such as Mesophilic aerobic bacteria, total and fecal coliforms, Enterobacteriaceae, Yeast, and molds. The results showed that the solar dried garlic saved the same properties physicochemical compared to the literature on fresh garlic, then, for the microbiological analysis, we note some colonies of Mesophilic aerobic bacteria and yeasts and molds that didn't have any risk to human health a. In conclusion, solar drying is a method that promotes for conservation of post-harvest products without any degradation of nutritive value and is apt for human consumption
An Event-based Stereo 3D Mapping and Tracking Pipeline for Autonomous Vehicles

Event cameras are bio-inspired, motion-activated sensors which generate asynchronous events instead of intensity images at a fixed rate. These sensors have been shown to outperform traditional frame-based cameras by large margins, in case of high-speed motions and scenes with high dynamic range. Next-generation intelligent vehicles are expected to greatly benefit from these novel cameras, especially in adverse lighting conditions, and their potential is still largely untapped. In the last decade, the continuous stream of events produced by an event camera has been exploited in numerous 3D perception tasks (depth estimation, 6-DoF tracking, visual-inertial odometry, etc.). In this paper, we propose an event-based stereo pipeline for simultaneous 3D mapping and tracking. The mapping module relies on DSI (Disparity Space Image) fusion, and the tracking module makes use of time surfaces as anisotropic distance fields, to estimate the pose of the stereo camera. Numerical experiments with a publicly-available event dataset recorded by a car in different urban environments, show the effectiveness of the proposed architecture.
A multi-scale feature pyramid fusion network for predicting the driver's visual attention

Traffic driving scenarios are sophisticated and varied. Experienced drivers routinely allocate visual attention to critical targets and areas in advance and judge their intentions to ensure driving safety. Consequently, the research on the driver's visual attention allocation behavior is essential for the development of assisted driving systems and self-driving. Data-driven visual attention research has largely profited from the development of Convolutional Neural Networks in recent years. In this paper, we present a novel model that predicts accurate visual attention maps through the construction of a deep neural network, the integration of multiscale pyramid features, and the incorporation of neural attention mechanisms. Finally, extensive experimental evaluations indicate that the proposed model outperforms classical models and several excellent deep learning-based approaches on the TDV dataset.
Correlation and path analysis in fingermillet under mid altitude conditions

(.) The path analysis indicated that the tillers/plant and the ears/plant were the direct contributors to yield. These two characters also affected the grain yield indirectly through each other in a positive direction. Though the leaves/plant, branches/plant and fingers/plant had the positive and significant correlations with the grain yield yet they exerted the direct and indirect negative effects. The tillers/plant and the ears/plant could be taken as an index for improving the grain yield in fingermillet
Effect of Fin-width on the DC Performance of AlGaN/GaN Fin-HEMTs

The effects of fin-width on the DC performance of AlGaN/GaN Fin-HEMTs is investigated in this work, demonstrating significant improvement in gate control as the sub-threshold swing is suppressed from 802.15 to 72.53 mV/decade and the maximum trans-conductance has increased from 205.728 to 464.247 mS/mm by reducing the fin-width. The threshold voltage of Fin-HEMTs also shifted towards the positive side with decreasing fin-width. In addition, it has been found that the on current obtained is high compared to planar devices and the high on-off current ratio indicates superior DC performance for the Fin-HEMT structure.