Miracle-dz/UniFAR-FTUs huggingface dataset:
A parametric level-set approach for seismic full-waveform inversion

In seismic exploration, the delineation of large bodies with hard exterior contrasts but nearly constant interior properties is a challenge. Examples include salt diapirs, salt slabs, anhydrite or basalt layers. Salt geometries are of particular interest because they often have hydrocarbon reservoirs on their sides or underneath. This papers introduces a parametric level-set method for the reconstruction of such geometries in seismic full-waveform inversion (FWI). The level-set determines the outline of the salt geometry and evolves during the inversion in terms of its underlying parameters. For the latter, we employ Gaussian radial basis functions that can represent a large class of shapes with a small number of parameters. This keeps the dimensionality of the inverse problem small, which makes it easier to solve. First tests on a simple 2-D square box model show dramatic improvements over classic FWI.
Dynamics of sustainable fisheries: a mathematical approach using Lotka-Volterra equations

This study delves into the intricate dynamics of sustainable fisheries through the lens of mathematical modeling, specifically employing the Lotka-Volterra equa-tions. The Lotka-Volterra equations, originally conceived to understand predator-prey interactions, offer a potent framework to model and predict fish population dynamics within aquatic ecosystems. The aim of this research is to demonstrate the applicability and efficacy of the Lotka-Volterra equations in understanding the delicate balance of fish populations and aiding sustainable fisheries man-agement. Through this mathematical approach, we seek to provide a structured methodology and present results that showcase the potential of this model in pre-dicting and managing fish populations. In our analysis, we adjust the parameters of the Lotka-Volterra equations to simulate various scenarios, enabling a compre-hensive understanding of sustainable fishing practices. Our findings underscore the significance of mathematical models, particularly the Lotka-Volterra equa-tions, in informing strategies to maintain fish populations in a sustainable and ecologically balanced manner, thereby ensuring the longevity of marine ecosys-tems and the fisheries that rely upon them.Keywords: Runge-Kutta, Two-step, Exponentially-fitted, Periodic; Oscillatory;
Organic Contaminant Release from Melting Snow

The release of organic contaminants from melting snow poses risks to aquatic and terrestrial organisms and to humans that rely on drinking water and food production from regions that are seasonally snow-covered. Measured and model-predicted spring peak concentrations in waters receiving snowmelt motivate a thorough investigation of organic contaminant behaviour during melting. On the basis of the current understanding of snow metamorphosis, snowmelt hydrology and chemical partitioning in snow, this critical review aims to provide a qualitative picture of the processes involved in the release of organic contaminants from a melting snow pack. The elution sequence of organic substances during snowmelt is strongly dependent on their environmental partitioning properties and the physical properties of the snow pack. Water soluble organic contaminants can be discharged in greatly elevated concentrations at an early stage of melting, while the bulk of the hydrophobic chemicals attached to particles is often released at the end of the melt period. Melting of a highly metamorphosed and deep snow pack promotes such shock load releases, whereas a shallow snow cover over a relatively warm ground experiencing irregular melting over the winter season is unlikely to generate notable peak releases of organic substances. Melt water runoff over frozen ground directly transfers contaminant shock loads into receiving water bodies while permeable soils buffer and dilute the contaminants. A more quantitative understanding of the behaviour of organic contaminants in varying snowmelt scenarios will depend on controlled laboratory studies combined with field investigations. Reliable numerical process descriptions will need to be developed to integrate water quality and contaminant fate models.
Linking Biogeochemical and Hydrodynamic Processes to Model Methane Fluxes in Shallow, Tropical Floodplain Lakes

Floodplains lakes are abundant in the Amazon basin and are important methane sources to the atmosphere. Existing biogeochemical models require modifications and inclusion of hydrodynamic processes operative in shallow, warm waters to be applied to these aquatic ecosystems. We modified a 1‐dimensional process‐based, lake biogeochemical model and combined a 3‐dimensional hydrodynamic model to suit Amazon floodplains. We evaluated the combined model's performance simulating methane concentrations and fluxes and several related processes in the open lake and an embayment of a well‐studied Amazon lake. Parameters for calibration were selected through sensitivity tests using a machine learning‐based algorithm, classification, and regression trees. Comparison between simulated and measured fluxes indicate generally good agreement in seasonal patterns and magnitudes. Comparisons of near‐surface concentrations varied with no clear patterns. Simulations of methane concentrations at near‐surface and near‐bottom, and diffusive emissions are most sensitive to carbon mineralization rate, Q10 factors for methanogenesis and oxidation, and methane oxidation potential. Modeled rates of planktonic photosynthesis were generally lower than measurements, though simulated planktonic respiration was often similar to measurements. Simulated rates of methane oxidation were considerably lower, with a few exceptions, than measurements of methane oxidation in oxic water of the lake. Improvements of results of the linked hydrodynamic‐biogeochemical model will result from inclusion of advective transport, use of parameter values appropriate for tropical waters, especially for methane oxidation and photosynthesis, and addition of changes in hydrostatic pressure to model of ebullition.
Supply Chain Disruption Risk and Market Performance in Nigeria: A Case of Oil and Gas Marketing Companies

Purpose: Studies across developed and emerging economies alluded to the persistent decline in the performance of oil and gas marketing firms, which had been attributed to both micro-economic and macro-economic factors. The objective of the study was to determine the effect of supply chain disruptions on market performance of selected oil and gas marketing companies in Lagos State, Nigeria. Materials and Methods: The study adopted a survey research design. The total population of the study was 1,044 full-time employees of five selected oil and gas marketing companies in the downstream sector of the petroleum industry in Nigeria. The study sampled three hundred and sixty-two (362) respondents, who participated in the study. Stratified random sampling method was adopted, because it ensures less homogeneity, and a true representative of the sample, thereby the accuracy of statistical results can be higher than that of simple random sampling. Structured questionnaire was used to collect the primary data from the sample size. The research targeted 362 respondents in the survey, while 322 copies of the questionnaire administered were filled and returned. Therefore, the response rate was 91.7%. Findings: The three independent variables that were studied, explained 4.8% of the market performance as represented by adjusted R-square. This, therefore, meant that other variables not studied in this research contributed 95.2% of the market performance. Implications to Theory, Practice and Policy: The study recommended that oil and gas marketing companies should increase their commitment to adopting scenario planning and supply chain mapping strategies, in order to mitigate risks threatening their supply chains, in order to accomplish better performance.
Governance and Citizen Satisfaction in Intermediate Cities of Ancash (Peru)

The study analyzes the relationship between governance and citizen satisfaction in three intermediate cities of Aija, Casma and Yungay (Ancash, Peru). In recent decades, the trend in these cities has been population growth in urban areas with signs of corruption. Context where governance is in charge of municipal or municipal authorities. The main purpose of the research was to determine the relationship between governance and citizen satisfaction, taking into account citizen participation, development in the educational field and citizens' expectations of governance with a view to the future. For this purpose, a correlational descriptive research was carried out with a quantitative approach. The sample consisted of 320 male and female villagers aged between 18 and 70 years. For data collection, the survey technique was used and the questionnaire was used as an instrument, which included 20 questions. The main findings are that the relationship between governance and citizen satisfaction is low. Similarly, citizen participation in public works is low, as well as attention to educational services is low. For this reason, the population has no greater expectations for the development of their peoples. Consequently, governance in the intermediate cities of Ancash needs to be modernized, reinvented and enable citizen participation in the planning and execution of public works in order to respond with relevance according to each specific reality.
Delayed Reconstruction of the Perforator Pedicle Propeller Flap after the Induced Membrane Technique for Gustilo IIIB Open Distal Tibial Fracture Verzögerte Rekonstruktion des Perforator-Pedikel-Propellerlappens nach der induzierten Membrantechnik bei o ﬀ ener distaler Tibiafraktur von Gustilo IIIB

Objective This study aimed to evaluate the safety and e ﬃ cacy of delayed reconstruction of the perforator pedicle propeller flap after the induced membrane technique in the treatment of Gustilo IIIB open distal tibial fracture, and to evaluate the clinical outcome and complications of two di ﬀ erent perforator pedicle propeller flaps. Methods Thirty-four patients with Gustilo IIIB open distal tibial fractures treated by the induced membrane technique and delayed reconstruction of two di ﬀ erent perforator pedicle pro-peller flaps from May 2017 to March 2022 were retrospectively analyzed. Patients were divided into two groups according to the di ﬀ erent kinds of perforator pedicle propeller flaps covered. The operation required two stages. The Radiographic Union Score for Tibial fractures (RUST) was used to evaluate the healing of the tibial bone defect. The American Orthopaedic Foot and Ankle Society (AOFAS) score was used to evaluate ankle function. The complications associated with the technique were recorded.
Stress Beats: A Continuum of Learning Methods for Personalized Stress Detection

The pervasive use of wearable technology widens opportunities for personalized Machine Learning (ML) for mental health and well-being, providing non-intrusive means of tracking physiological stress indicators (e.g., heart rate, skin conductance) in real-time. Stress detection is a critical task for mitigating its adverse effects on physical and mental health. However, prior work primarily focused on “one-size-fits-all” ML approaches, neglecting individual differences and often suffering from “cold-start” problems and overfitting due to limited data availability. To overcome these challenges, this work aims to accurately identify an individual's stress levels using data from wearables. To achieve this, we employ a “continuum” of ML techniques tailored to capture the unique patterns of each person's stress response. For leveraging these personalized “stress beats”, we emphasize multi-task learning and personalization on a user, group, and population level, capturing the interplay between inter-and intra-personal stress indicators. Our models are validated using real-world datasets, which are benchmarked under a spectrum of learning paradigms. The experimental results demonstrate the superiority of personalized models over “one-size-fits-all” approaches, underscoring the importance of personalization. Notably, group-based personalized models achieved up to 99% Fl-score in lab settings and up to 66% Fl-score in-the-wild, outperforming “one-size-fits-all” approaches by 15 % and illustrating the potential of personalized ML techniques in healthcare applications beyond stress detection.
Origin and Evolution of High-Titanium Mare Basalts

by Patrick H. Donohue The Moon is the sole known locality of exposed high-titanium (high-Ti) basalts in the solar system, but their occurrence has implications for the early evolution of the terrestrial planets. High-Ti basalts derive from partial melts of cumulates in the lunar upper mantle. The ilmenite, clinopyroxene, and olivine cumulates from which these basalts form are late-stage products of crystallization of the lunar magma ocean, a planetary-scale melting event that also likely occurred early on in the evolution of Venus, Mercury, the Earth and Mars. Fortunately, despite the ancient nature of mare volcanism, pristine high-Ti basalts are preserved on the relatively inert lunar surface, and the lunar sample collection contains abundant high-Ti basaltic material. Crystals are quantifiable components of the basaltic system, and are a record of the compositional and temporal history of magmatic evolution. Complementary techniques of textural and in-situ trace element geochemical analyses comprise the crystal stratigraphy method used to investigate this history. I use crystal size distributions and spatial distribution profiles to identify crystal populations and quantitatively evaluate rock textures. I use EPMA and LA-ICP-MS analysis of major crystallizing phases to identify processes affecting
Control of microwave effective properties of 3D printed materials and applications to microwave absorption

3D printing of composite materials appears to be an innovative way to control microwave properties of materials. Indeed, intrinsic properties are linked to the composition of composites but extrinsic properties (effective permittivity) can be managed by the shape and the control of porosity. Moreover, multi-materials 3D printing makes it possible to combine different materials so that to enlarge the available range of microwave properties. In this paper, it will be shown that the control of 3D printed materials can be complex but that design possibilities are almost endless. The spatial evolution and wide attainable range of microwave properties by using multi-materials honeycomb cells will be discussed and illustrated by the design, fabrication and characterization of a thin X-Ku band microwave absorber.
A Novel Approach for Digital Image Resemblance Using Deep Learning with Image Augmentation

Getting information on a picture is still a disorganized and time-consuming procedure in a society that seeks instant fixes for every issue. An individual must first determine the sort of image it is before doing a manual search for its data. In any online store, manually looking for a product and browsing through the entire website or application may take a lot of time, and there's a chance the user won't receive the outcome they were hoping for. Additionally, it might be challenging to grasp a product simply by reading it. In this study, we examine how well the models do in matching the photos to a large number of photographs in the dataset. Here, we look at the traits that the Convolutional Neural Network (CNN) model has, majorly VGGNet16. The dataset for the study was created entirely by us. The most significant part of picture resemblance was discovered to be feature extraction, data augmentation, and similarity measures. Furthermore, the results show that increasing the dataset increases accuracy even further.
The Relationship between General Movements and Risk Factors in Moderate-Late Preterm Infants: A Prospective Cohort Study

Introduction: Moderate-late preterm infants constitute the largest segment of preterm births globally. While previously considered to have a low neurological risk, recent research has uncovered an elevated incidence of neurodevelopmental conditions in this group. This study aimed to assess the relationship between the general movement assessment and birth-related risk factor-based tools in moderate-late preterm infants. Methods: A prospective cohort study of 65 moderate-late preterm infants in a neonatal intensive care unit involved the evaluation of general movements, the Nursery Neurobiologic Risk Score, and the Perinatal Risk Inventory. Associations were analyzed using Fisher’s exact test, Spearman’s correlation was used for ordinal variables, and backward stepwise logistic regression was used to identify predictor variables for the assessments. Results: The findings indicated a high prevalence of normal (41%) and poor (52%) repertoire patterns during the writhing period. While no significant associations were found between the three assessments, a slight approximation emerged between dysmorphic traits and patterns (p = 0.053). Furthermore, an extended period of ventilation correlated with a higher likelihood of developing a cramped synchronized pattern and there was a correlation between both risk factor-based tools (p < 0.001). Conclusions: This research enhances our understanding of the early impact on general movement assessments in moderate-late preterm infants. While no clear relationship emerged between general movement assessment and risk factor-based tools, there was a subtle connection noted with dysmorphic traits. A longer ventilation duration was linked to a higher risk of developing cramped synchronized patterns.
GAN and CNN Model for Generation of Synthetic Medical Images and Classification

for any machine learning or deep learning model, data is the most essential ingredient. Data augmentation has become the need of the hour as the required amount of data is not available in many fields and real world scenarios. In such situations, synthetic data comes into the picture. Synthetic data generation techniques have made it possible to create a huge amount of data from really small data. It has been possible to make realistic artificial data that resembles real data. Finding a particular dataset that has required features is a difficult task. Medical data is rare and expensive. It is nearly impossible to get real data with clinical trials in the required amount. In this paper, we will be discussing, synthetic medical image generation with Generative Adversarial Networks (GANs). We used Deep Convolutional GAN for the generation of medical images. We were able to generate 400 synthetic images of retinal fundus from a tiny dataset of 20 images. We calculated the FID score to evaluate the generated synthetic images and we were able to keep it as low as 38.31. We applied the generated synthetic images and real images on CNN model for a comparative study on accuracies achieved with both the images. We were able to get the accuracy of 60% with synthetic images out of dataset consisting of only 20 images.
Dynamics of phosphorus transfers from heavily manured Coastal Plain soils to drainage ditches

Abstract: Understanding the dynamics of phosphorus (P) transport in agricultural drainage ditches is essential to their improved management for water quality protection. Seven ditches draining soils with a 20+ year history of receiving poultry litter were monitored: two for five years and five for one year. Ditches receiving runoff from point sources (e.g., barns) exported 4.3 to 25.3 kg total P ha−1 (3.8 to 22.6 lb total P ac−1) from 2005 to 2006, while ditches draining areas with only nonpoint source contributions exported 2.6 to 4.8 kg total P ha−1 (2.3 to 4.3 lb total P ac−1) during that period. High concentrations of P in field soils (Mehlich-3 P averaged 441 mg kg−1, or parts per million) and ditch soils (Mehlich-3 P averaged 171 mg kg−1) suggest that desorption is the key nonpoint source process controlling P in ditch flow. Over five years, annual total P losses from two ditches with only nonpoint source P contributions were 1.4 to 26.2 kg ha−1 (1.3 to 23.4 lb ac−1). Overland flow from the fields to these two ditches accounted for ⩽ 8% of annual ditch P export, pointing to groundwater as a key pathway for P transport to ditches. Because P export from ditches was primarily in storm flow and groundwater sampling was primarily during base flow, this study does not provide compelling insight into the role of groundwater in ditch P transport. Only occasionally did dissolved P concentrations in groundwater and ditch flow correspond, and P export from the ditches occurred primarily in storm flow. Sampling of algal mats formed on the bottom of ditches suggests that floating algae may exacerbate sediment-related P transport. Results point to the need for new ditch management practices that can sequester dissolved forms of P and trap floating sources of P, in combination with traditional methods that primarily address sediment-bound P.
The Role of Federated Learning and CNNs in the Detection and Classification of Zucchini Leaf Diseases: A Severity-Based Analysis

This study investigates using FL_CNN to categorize zucchini leaves into five severity levels. In this investigation, five customers representing various severity levels were used. The global model was iteratively trained and updated using CNNs deployed on client devices as part of The experimental technique. The analysis results demonstrated the model’s effectiveness and robustness, managing the impacts of increasing severity levels on complexity while retaining good accuracy to values across all clients. With macro, weighted, and micro averages for 86.04% to 94.29%, 86.69% to 94.42%, and 86.33% to 94.35%, respectively, while maintaining an overall high accuracy of 95-98%, the local data client’s performance notably illustrated the value of the Federated Learning model. The investigation also looks at global results, where the performance of the worldwide model, which integrates all local models, is evaluated. The global model performed well, with macro, weighted, and micro averages between 86.35% and 94.40%, despite the inherent complexity associated with varying sickness severities. The model’s ability to provide a high level of precision, recall, and overall accuracy across all severity levels was highlighted by this result. Subsequent analysis confirmed the global model’s ability to effectively learn from various data sources, making it a crucial tool in precision agriculture.