Miracle-dz/UniFAR-FTUs huggingface dataset:
Seismic Probabilities and Multi-point Geostatistics - A Successful Solution to Model a Complex Depositional Environment

This paper describes the application of an advanced workflow for seismic driven reservoir characterization to an offshore field of West Africa in ultra-deep water environment. Probabilistic Petrophysical Seismic Inversion and Multiple-Point Simulation are the core of the workflow. These are successfully applied to model - at different scales - facies of complex architectural depositional elements, and the related petrophysical properties, moreover integrating data from different sources and relevant uncertainties. Geostatistical simulation of facies is based on a petro-elastic model of four classes defined at well scale, the same used in seismic inversion. Three geological models are created to mimic a thick Miocene turbidite sequence, one for each geological interval (Upper, Middle and Lower). The application achieved a satisfactory optimization and standardization of seismic integration workflow to generate geological/geophysical scenarios and provide a “tuned” reservoir description. The probabilistic approach also drove the technique used to select the reference cases (Low, Medium and High) for the dynamic model and the risk analysis. Finally the availability of multiple scenarios was useful to assess the impact of different geo-bodies occurrence on the reference development scheme. As result the geological uncertainty and the dynamic reservoir behaviour were fully addressed.
Relating Extractable Soil Phosphorus to Phosphorus Losses in Runoff

Phosphorus in agricultural runoff can cause accelerated lake and stream eutrophication. Where producers have applied P at rates exceeding crop uptake, soil P has sometimes become the main source of P in runoff. We hypothesized that soil test P (STP) correlation to dissolved reactive P (DRP) and bioavailable P (BAP) in runoff varies, depending on the extraction method. To investigate which STP extraction method would be best for predicting DRP and BAP concentration and load in runoff, soil samples were taken from the 0- to 2-cm depth of 54 grass plots (5% slopes) on Captina silt loam (fine-silty, siliceous, mesic Typic Fragiudult). The STP was extracted by six methods and the ranges of results (mg kg -1 ) were: 54-490 (Mehlich III), 27-592 (Bray-Kurtz P1), 25-169 (Olsen), 14-110 (distilled water), 23-170 (Fe oxide paper), and 105-1131 (acidified ammonium oxalate). The soil P saturation ranged from 16 to 80%. Simulated rain was applied at 100 mm h -1 and runoff was collected for 30 min. The concentration of DRP in total runoff ranged from 0.31 to 1.81 mg L -1 , and BAP from 0.37 to 2.18 mg L -1 The r 2 values for STP by each extraction method correlated with runoff DRP and BAP, respectively, were: 0.72 and 0.72 (Mehlich III), 0.75 and 0.73 (Bray-Kurtz P1), 0.72 and 0.72 (Olsen), 0.82 and 0.82 (distilled water), 0.82 and 0.82 (iron oxide paper), 0.85 and 0.82 (acidified ammonium oxalate), and 0.77 and 0.76 (P-saturation). All correlations were significant (P < 0.001), but the high r 2 values of those obtained from distilled water, iron oxide paper, and acidified ammonium oxalate extractants indicate better precision for predicting DRP and BAP concentrations in runoff. Correlations of STP with DRP load (range: 43.4 to 472.8 g ha -1 ) and BAP load (54.2 to 542.0 g ha -1 ) were not useful (r 2 < 0.18), possibly because runoff volumes were highly variable.
Artificial intelligence-driven approach for patient-focused drug development

Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven to be one of the most useful data sources when it comes to understanding a company's potential audience to drive more targeted impact. Navigating through an ocean of information is a tedious task where techniques such as artificial intelligence and text analytics have proven effective in identifying relevant posts for healthcare business questions. Here, we present an enterprise-ready, scalable solution demonstrating the feasibility and utility of social media-based patient experience data for use in research and development through capturing and assessing patient experiences and expectations on disease, treatment options, and unmet needs while creating a playbook for roll-out to other indications and therapeutic areas.
What Matters for Life Satisfaction of University Students: the Role of Social Capital and Self-efficacy?

The purpose of this article is to look at the life satisfaction of university students in the Chinese city of Ji'nan. Because of the vital importance of university students, their life satisfaction is valuable and should be investigated. The literature on students' life satisfaction leaves the three factors in the study unexplored. As a result, this research attempts to explain the relationship between university students' life satisfaction and individual social capital through the mediating influence of self-efficacy, which may be a novel conceptual model rarely used in prior studies, particularly in Ji'nan, China. The results indicates that the significant positive association of individual social capital with life satisfaction for university students with the mediating effect of self-efficacy. As a non-experimental descriptive study approach, a quantitative (deductive reasoning) technique was utilized to quantify the issue, in which a survey questionnaire was used to collect numerical data, which was then converted into a table.
Drivers’ Comprehensive Emotion Recognition Based on HAM

Negative emotions of drivers may lead to some dangerous driving behaviors, which in turn lead to serious traffic accidents. However, most of the current studies on driver emotions use a single modality, such as EEG, eye trackers, and driving data. In complex situations, a single modality may not be able to fully consider a driver’s complete emotional characteristics and provides poor robustness. In recent years, some studies have used multimodal thinking to monitor single emotions such as driver fatigue and anger, but in actual driving environments, negative emotions such as sadness, anger, fear, and fatigue all have a significant impact on driving safety. However, there are very few research cases using multimodal data to accurately predict drivers’ comprehensive emotions. Therefore, based on the multi-modal idea, this paper aims to improve drivers’ comprehensive emotion recognition. By combining the three modalities of a driver’s voice, facial image, and video sequence, the six classification tasks of drivers’ emotions are performed as follows: sadness, anger, fear, fatigue, happiness, and emotional neutrality. In order to accurately identify drivers’ negative emotions to improve driving safety, this paper proposes a multi-modal fusion framework based on the CNN + Bi-LSTM + HAM to identify driver emotions. The framework fuses feature vectors of driver audio, facial expressions, and video sequences for comprehensive driver emotion recognition. Experiments have proved the effectiveness of the multi-modal data proposed in this paper for driver emotion recognition, and its recognition accuracy has reached 85.52%. At the same time, the validity of this method is verified by comparing experiments and evaluation indicators such as accuracy and F1 score.
The Gangotri granite (Garhwal Himalaya) : laccolithic emplacement in an extending collisional belt

The Gangotri Miocene leucogranite is composed of several laccoliths (6–7 km long, 1.5–2 km thick), which can be divided into two sets of lenses: the southern lenses, intruded in the lower part of the High Himalaya sedimentary cover, and the northern lenses, intruded in an older porphyritic biotite-bearing granite. In both cases, the magmatic fabric is commonly weak with no dominant stretching direction, although a rough E-W trend is present in the northern lenses. The magnetic fabric is characterized by the fact that drastic changes in the direction, in the magnitude of the magnetic parameters, or in the fabric type (planar or linear) may take place over very short distances. Measurements of preferred orientations in thin sections indicate that the strain regime was largely dominated by a coaxial component for both sets of lenses, in agreement with the large dispersion observed in both the field and magnetic lineations. The comparison of the granite and host rock structures shows that the leucogranite emplacement dates the onset of the extensional tectonism in the High Himalaya range and is not related to a southward directed thrust event associated with the Main Central Thrust. This is exemplified by the vertical attitude of the feeder dikes that intrude the metasedimentary rocks beneath die southern lenses. The presence of these dikes indicates in turn that magma ascent occurred by fracture propagation. The spatial disposition of the southern lenses could have resulted either from the disruption of a single laccolithic intrusion by crustal scale boudinage due to a northern gravity backslide of the top of the Tibetan Slab or from the intrusion of independent laccoliths. The use of the elastic bending theory of Pollard and Johnson (1973) shows that in both hypotheses, the current laccolith sizes are compatible with a laccolithic mode of magma emplacement. However, neither the density contrast between the magma and its enclosing rocks nor the lithological boundary between the Tibetan Slab and the overlying Tibetan metasedimentary series controlled the level of magma emplacement. Rather, flat-lying collapse structures, which intersected the upward propagating magma dikes, are the most likely causes of magma arrest. Such a mechanism was favored by the schist-rich lithology of the metasedimentary host rocks. In addition, field relationships indicate that the melt supply through the dike system was a continuous, rather than pulsed, process. Existing numerical treatments on the rates of magma transport through fractures show that in such a case, the laccoliths could have been built in less than 100 years. This short time of emplacement, the small size of the laccoliths, and their peripheral disposition relative to the Badrinath granite suggest that the Gangotri lenses may represent the initial stage of pluton accretion in the High Himalaya which ultimately gave rise to a much larger massif such as the Manaslu granite.
Hydrogeologic structure underlying a recharge pond delineated with shear-wave seismic reflection and cone penetrometer data

With the goal of improving the understanding of the subsurface structure beneath the Harkins Slough recharge pond in Pajaro Valley, California, USA, we have undertaken a multimodal approach to develop a robust velocity model to yield an accurate seismic reflection section. Our shear-wave reflection section helps us identify and map an important and previously unknown flow barrier at depth; it also helps us map other relevant structure within the surficial aquifer. Development of an accurate velocity model is essential for depth conversion and interpretation of the reflection section. We incorporate information provided by shear-wave seismic methods along with cone penetrometer testing and seismic cone penetrometer testing measurements. One velocity model is based on reflected and refracted arrivals and provides reliable velocity estimates for the full depth range of interest when anchored on interface depths determined from cone data and borehole drillers’ logs. A second velocity model is based on seismic cone penetrometer testing data that provide higher-resolution 1D velocity columns with error estimates within the depth range of the cone penetrometer testing. Comparison of the reflection/refraction model with the seismic cone penetrometer testing model also suggests that the mass of the cone truck can influence velocity with the equivalent effect of approximately one metre of extra overburden stress. Together, these velocity models and the depth-converted reflection section result in a better constrained hydrologic model of the subsurface and illustrate the pivotal role that cone data can provide in the reflection processing workflow.
Not Nature Exposure but Awe Promotes Cooperation in Social Dilemmas

Purpose of the study: The purpose of this study was to investigate the relationship between the experience of awe and cooperative behavior, with a particular focus on the mediating role of the small-self concept. Theoretical framework: Drawing on social-psychological and emotion theories, this study proposed an integrative model that includes two key variables: the experience of awe, elicited by various stimuli, and the small-self concept. It hypothesized that individuals who experience awe would exhibit increased cooperative behavior and that the small-self concept would mediate this relationship. Method: Across three studies, diverse methodologies and measures were employed, including Common-Pool Resource Dilemmas and Public Goods Games, to assess levels of cooperation. The Awe Scale and Perceived Self-Size Scale were also utilized to measure awe and the small-self concept, respectively. In total, 320 participants were engaged in the study, being assigned to different experimental and control conditions. Results and conclusion: The results consistently demonstrated that the experience of awe led to an increased willingness to cooperate across all studies. It was also discovered that the small-self concept played a complete mediating role in the relationship between the experience of awe and cooperative behavior, confirming the hypotheses. These findings remained robust even when different awe-inducing stimuli were employed, such as pictures of nature, images of architecture, or videos of an infant. Search implications: Our findings underscore the importance of the awe experience as a potential driver of cooperative behavior. The results contribute to a better understanding of the emotional and cognitive processes underlying cooperation, suggesting that fostering awe experiences in individuals may be a promising avenue for promoting cooperative behavior. Future research should explore the practical applications of our findings in fields such as conflict resolution, environmental conservation, and social cooperation. Originality value: This research uniquely contributes to the literature by integrating the concept of awe and small-self within the context of cooperative behavior. It adds to the existing body of knowledge by providing a deeper understanding of the underlying mechanisms that influence cooperative behavior. This study presents a novel approach by examining the mediating role of the small-self concept, offering new insights into how awe experiences can impact cooperation.
Tectono‐sedimentary evolution of active extensional basins

We present conceptual models for the tectono‐sedimentary evolution of rift basins. Basin architecture depends upon a complex interaction between the three‐dimensional evolution of basin linkage through fault propagation, the evolution of drainage and drainage catchments and the effects of changes in climate and sea/lake level. In particular, the processes of fault propagation, growth, linkage and death are major tectonic controls on basin architecture. Current theoretical and experimental models of fault linkage and the direction of fault growth can be tested using observational evidence from the earliest stages of rift development. Basin linkage by burial or breaching of crossover basement ridges is the dominant process whereby hydrologically closed rifts evolve into open ones. Nontectonic effects arising from climate, sea or lake level change are responsible for major changes in basin‐scale sedimentation patterns. Major gaps in our understanding of rift basins remain because of current inadequacies in sediment, fault and landscape dating.
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision

Accurate lung nodules segmentation from Computed Tomography (CT) images is crucial to the analysis and diagnosis of lung diseases such as COVID-19 and lung cancer. However, due to the smallness and variety of lung nodules and the lack of high-quality labeling, accurate lung nodule segmentation is still a challenging problem. To address these issues, we propose a complete paradigm for accurate lung nodules segmentation. First, we introduce a new segmentation mask named Soft Mask which has richer and more accurate edge details description and better visualization. Correspondingly, we develop a universal semi-automatic Soft Mask annotation pipeline to deal with different datasets. Second, a novel Network with Detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. In our DSNet, we design a novel Selective Detailed Representation Fusion Module to reconstruct the detailed representation to alleviate the small size of lung nodules images. In addition, the adversarial training framework with Soft Mask is proposed to further improve the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms the state-of-the-art methods for accurate lung nodules segmentation. And our method also demonstrates competitive results in other accurate medical segmentation tasks. Besides, we provide a new challenging lung nodules segmentation dataset for further studies.
Biomass estimation in a tropical wet forest using Fourier transforms of profiles from lidar or interferometric SAR

Tropical forest biomass estimation based on the structure of the canopy is a burgeoning and crucial remote sensing capability for balancing terrestrial carbon budgets. This paper introduces a new approach to structural biomass estimation based on the Fourier transform of vertical profiles from lidar or interferometric SAR (InSAR). Airborne and field data were used from 28 tropical wet forest stands at La Selva Biological Station, Costa Rica, with average biomass of 229 Mg‐ha−1. RMS scatters of remote sensing biomass estimates about field measurements were 58.3 Mg‐ha−1, 21%, and 76.1 Mg‐ha−1, 26%, for lidar and InSAR, respectively. Using mean forest height, the RMS scatter was 97 Mg‐ha−1, ≈34% for both lidar and InSAR. The confidence that Fourier transforms are a significant improvement over height was >99% for lidar and ≈90% for InSAR. Lidar Fourier transforms determined the useful range of vertical wavelengths to be 14 m to 100 m.
Multi-Attention Bottleneck for Gated Convolutional Encoder-Decoder-Based Speech Enhancement

Convolutional encoder-decoder (CED) has emerged as a powerful architecture, particularly in speech enhancement (SE), which aims to improve the intelligibility and quality and intelligibility of noise-contaminated speech. This architecture leverages the strength of the convolutional neural networks (CNNs) in capturing high-level features. Usually, the CED architectures use the gated recurrent unit (GRU) or long-short-term memory (LSTM) as a bottleneck to capture temporal dependencies, enabling a SE model to effectively learn the dynamics and long-term temporal dependencies in the speech signal. However, Transformers neural networks with self-attention effectively capture long-term temporal dependencies. This study proposes a multi-attention bottleneck (MAB) comprised of a self-attention Transformer powered by a time-frequency attention (TFA) module followed by a channel attention module (CAM) to focus on the important features. The proposed bottleneck (MAB) is integrated into a CED architecture and named MAB-CED. The MAB-CED uses an encoder-decoder structure including a shared encoder and two decoders, where one decoder is dedicated to spectral masking and the other is used for spectral mapping. Convolutional Gated Linear Units (ConvGLU) and Deconvolutional Gated Linear Units (DeconvGLU) are used to construct the encoder-decoder framework. The outputs of two decoders are coupled by applying coherent averaging to synthesize the enhanced speech signal. The proposed speech enhancement is examined using two databases, VoiceBank+DEMAND and LibriSpeech. The results show that the proposed speech enhancement outperforms the benchmarks in terms of intelligibility and quality at various input SNRs. This indicates the performance of the proposed MAB-CED at improving the average PESQ by 0.55 (22.85%) with VoiceBank+DEMAND and by 0.58 (23.79%) with LibriSpeech. The average STOI is improved by 9.63% (VoiceBank+DEMAND) and 9.78% (LibriSpeech) over the noisy mixtures.
A technique for improving webcam precision in biological plant measurement

In an attempt to harness webcam technology for the biological monitoring of grassland plants, a Microsoft LifeCam‐based photogrammetric system was developed for the remote capture of 3D measurements of plants. Research was necessary to determine the metric quality of this webcam imagery for photogrammetric 3D measurement. This paper presents the details of a qualitative evaluation of a LifeCam VX‐3000 for indoor and outdoor photogrammetric measurement together with the results of a case study involving two common small grassland plants. In the evaluation, laboratory and in‐situ principal distance (PD) determination techniques were performed, thus evaluating the significance of the latter for improving measurement accuracy in an uncontrolled environment despite the ineffective focusing of this imaging sensor. The effect of outdoor ambient temperature on the PD was investigated. Results show that sharp focusing is a problem with this webcam, as shown by the disparity of the laboratory‐determined PD of various object distances. However, the determination of the PD using field images improves the measurement accuracy. Using this approach, 3D measurement accuracy of between 0·3 ± 0·2 and 1·7 ± 0·4 mm was achieved, depending on the type of grass observed and the digitising technique used.
Sea Turtle Research

1.1 Definition Telemetry includes an array of techniques that allow remote monitoring, measurement and recording or reporting of information. It was first used in weather research and has expanded quickly to other disciplines. Relatively accurate measurements without direct observer participation allow some important research that was impossible to conduct in the past. This has an important implication for study of life history traits of species that migrates long-distances, such as sea turtles. The ocean habitat, wide distribution ranges and movement across political boundaries all create difficulties for direct study of sea turtle behavior. Telemetry can overcome these obstacles and is a cost-effect tool for behavioral ecology.
Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: a case study

A method for producing soil moisture maps in mountainous areas by using Environment Satellite Advanced Synthetic Aperture Radar (ENVISAT/ASAR) images at C-band is described in this paper. For this purpose, experimental campaigns were carried out in 2004 in the Cordevole watershed in Italy during ENVISAT passes. Ground truth measurements of soil and vegetation parameters were obtained simultaneously using satellite surveys. A preliminary classification of the area was carried out to mask those zones in which soil moisture measurement was unobtainable. The performance of an inversion algorithm, based on artificial neural networks (ANNs), in retrieving soil moisture content (SMC) from the collected images was then tested and compared with ground measurements. The results obtained on a restricted portion of the watershed show reasonable agreement of backscattering (σ0) with ground truth data and meteorological conditions, thus making it possible to extend the algorithm to the entire test area. The contribution of vegetation cover was then simulated by using a discrete elements model based on radiative transfer theory. Three pixel-by-pixel soil moisture maps of the test site, with four levels of soil moisture, were generated from the available images by using a new ANN that took into account the effects of vegetation.