Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative, nondestructive estimates of coarse root biomass in a temperate pine forest using 3‐D ground‐penetrating radar (GPR)

Coarse root biomass was estimated in a temperate pine forest using high‐resolution (1 GHz) 3‐D ground‐penetrating radar (GPR). GPR survey grids were acquired across a 400 m2 area with varying line spacing (12.5 and 25 cm). Root volume and biomass were estimated directly from the 3‐D radar volume by using isometric surfaces calculated with the marching cubes algorithm. Empirical relations between GPR reflection amplitude and root diameter were determined for 14 root segments (0.1–10 cm diameter) reburied in a 6 m2 experimental test plot and surveyed at 5–25 cm line spacing under dry and wet soil conditions. Reburied roots >1.4 cm diameter were detectable as continuous root structures with 5 cm line separation. Reflection amplitudes were strongly controlled by soil moisture and decreased by ~40% with a twofold increase in soil moisture. GPR line intervals of 12.5 and 25 cm produced discontinuous mapping of roots, and GPR coarse root biomass estimates (0.92 kgC m−2) were lower than those obtained previously with a site‐specific allometric equation due to nondetection of vertical roots and roots <1.5 cm diameter. The results show that coarse root volume and biomass can be estimated directly from interpolated 3‐D GPR volumes by using a marching cubes approach, but mapping of roots as continuous structures requires high inline sampling and line density (<5 cm). The results demonstrate that 3‐D GPR is viable approach for estimating belowground carbon and for mapping tree root architecture. This methodology can be applied more broadly in other disciplines (e.g., archaeology and civil engineering) for imaging buried structures.
Range Specification Bug Detection in Flight Control System Through Fuzzing

Developers and manufacturers provide configurable control parameters for flight control programs to support various environments and missions, along with suggested ranges for these parameters to ensure flight safety. However, this flexible mechanism can also introduce a vulnerability known as range specification bugs. The vulnerability originates from the evidence that certain combinations of parameter values may affect the drone's physical stability even though its parameters are within the suggested range. The paper introduces a novel system called <sc>icsearcher</sc>, designed to identify incorrect configurations or unreasonable combinations of parameters and suggest more reasonable ranges for these parameters. <sc>icsearcher</sc> applies a metaheuristic search algorithm to find configurations with a high probability of driving the drone into unstable states. In particular, <sc>icsearcher</sc> adopts a machine learning-based predictor to assist the searcher in evaluating the fitness of configuration. Finally, leveraging searched incorrect configurations, <sc>icsearcher</sc> can summarize the feasible ranges through multi-objective optimization. <sc>icsearcher</sc> applies a predictor to guide the search, which eliminates the need for realistic/simulation executions when evaluating configurations and further promotes search efficiency. We have carried out experimental evaluations of <sc>icsearcher</sc> in different control programs. The evaluation results show that the system successfully reports potentially incorrect configurations, of which over <inline-formula><tex-math notation="LaTeX">$94\%$</tex-math><alternatives><mml:math><mml:mn>94</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math><inline-graphic xlink:href="han-ieq1-3354739.gif"/></alternatives></inline-formula> leads to unstable states.
Applications of distributed temperature sensing in subsurface hydrology

In the study of dynamic subsurface processes there is a need to monitor temperature and groundwater fluxes efficiently in both time and space. Distributed Temperature Sensing has recently become more accessible to researchers in Earth Sciences, and allows temperatures to be measured simultaneously, at small intervals, and over large distances along fibre optic cables. The capability of DTS in conjunction with heat injection to detect groundwater fluxes, is assessed in this thesis using a combination of numerical modelling, laboratory tests, and field trials at the Ploemeur research site in Brittany, France. In particular, three methodological approaches are developed: thermal dilution tests, point heating, and the hybrid cable method. A numerical model was developed to assess the sensitivity range of thermal dilution tests to groundwater flow. Thermal dilution tests undertaken at Ploemeur showed lithological contrasts, and allowed the apparent thermal conductivity to be estimated in-situ, but failed to detect previously identified transmissive fractures. The use of DTS to monitor in-well vertical flow is then investigated. This is first using a simple experiment deploying point heating (T-POT), which tracks a parcel of heated water vertically through the borehole. The method allowed for the relatively quick estimation of velocities in the well. The use of heated fibre optics is then trialled, and through a field test was shown to be sensitive to in-well vertical flow. However, the data suffered from a number of artefacts related to the cable installation. To address this, a hybrid cable system was deployed in a flume to determine the sensitivity relationship with flow angle and electrical power input. Additionally, a numerical model was developed, which suggested a lower limit for velocity estimation due to thermal buoyancy. With the emergence of Distributed Acoustic Sensing, fibre optics may become an increasingly practicable and complete solution for monitoring subsurface processes.
Maternal Exposure to Endocrine-Disrupting Chemicals: Analysis of Their Impact on Infant Gut Microbiota Composition

Endocrine disruptors (EDCs) are chemicals that interfere with the endocrine system. EDC exposure may contribute to the development of obesity, type 2 diabetes, and cardiovascular diseases by impacting the composition of an infant’s gut microbiota during the first 1000 days of life. To explore the relationship between maternal urinary levels of Bisphenol-A and phthalates (UHPLC-MS/MS), and the composition of the infant gut microbiota (16S rDNA) at age 12 months (T3) and, retrospectively, at birth (T0), 1 month (T1), and 6 months (T2), stool samples from 20 infants breastfed at least once a day were analyzed. Metataxonomic bacteria relative abundances were correlated with EDC values. Based on median Bisphenol-A levels, infants were assigned to the over-exposed group (O, n = 8) and the low-exposed group (B, n = 12). The B-group exhibited higher gut colonization of the Ruminococcus torques group genus and the O-group showed higher abundances of Erysipelatoclostridium and Bifidobacterium breve. Additionally, infants were stratified as high-risk (HR, n = 12) or low-risk (LR, n = 8) exposure to phthalates, based on the presence of at least three phthalates with concentrations exceeding the cohort median values; no differences were observed in gut microbiota composition. A retrospective analysis of gut microbiota (T0–T2) revealed a disparity in β-diversity between the O-group and the B-group. Considering T0–T3, the Linear Discriminant Effect Size indicated differences in certain microbes between the O-group vs. the B-group and the HR-group vs. the LR-group. Our findings support the potential role of microbial communities as biomarkers for high EDC exposure levels. Nevertheless, further investigations are required to deeply investigate this issue.
The improvement of antioxidant contents and fruit quality of Sukkary date cultivar using various Potassium levels and Irrigation interval

Sukkary date palms (Phoenix dactylifera L.) were treated with three levels of potassium fertilizer combined with three irrigation intervals regime, five replicates were allocated per treatment, to enhance better date peel color; decrease loose crust, and subsequently, improve high market ability. Potassium sulfate (49% K2O) was applied annually at a rate of 490 g (as common fertilization applied in the farm in which this experiment was carried out, therefore, it was considered as a control treatment), 735 g, or 980 g K2O per palm tree, each level was divided into three equal doses. The first dose was added one month after pollination; the other two doses were added at two month interval, whereas the irrigation treatments were as follows: 24 hrs (as common irrigation application in the farm used for this study, and it was considered as a control treatment), 48 hrs, or 72 hrs. The results showed a great decrease in the loose crust dates at harvesting time. An inverse proportional relationship between irrigation intervals and loose crust of dates was observed. The High potassium fertilizer treatment accompanied with 72 hrs irrigation interval treatment produced a significantly higher date quality regarding with bright yellow color, less darkening, and loose crust. High potassium fertilizer treatments (735, or 980 g K2O per palm) increased total yield, improved date weight, size, and color. Moreover, increasing potassium level affected significantly the date total antioxidant. This study showed that, a potential means to control peel darkening and loose date crust and improve quality of cv. Sukkary dates, which ultimately improves its market ability, through implementing appropriate potassium and irrigation regimes.
SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature selection methods (SSFS) have been proposed to select a subset of features, but they still have some drawbacks limiting their performance, for e.g., many SSFS methods underutilize the structural distribution information available within labeled and unlabeled data. To address this issue, we proposed a semi-supervised feature selection method based on an adaptive graph with global and local constraints (SFS-AGGL) in this paper. Specifically, we first designed an adaptive graph learning mechanism that can consider both the global and local information of samples to effectively learn and retain the geometric structural information of the original dataset. Secondly, we constructed a label propagation technique integrated with the adaptive graph learning in SFS-AGGL to fully utilize the structural distribution information of both labeled and unlabeled data. The proposed SFS-AGGL method is validated through classification and clustering tasks across various datasets. The experimental results demonstrate its superiority over existing benchmark methods, particularly in terms of clustering performance.
A Case of a Father and Son With Complex Regional Pain Syndrome Type 1 Exhibiting Different Resting-State Functional Connectivity on Functional MRI

Complex regional pain syndrome (CRPS) type 1 is a chronic pain condition whose pathogenesis involves changes in the central and peripheral nervous systems, with potential genetic contributions. Functional magnetic resonance imaging (fMRI) studies report that alterations in resting-state functional connectivity (rsFC) may reflect central nervous system anomalies in CRPS type 1. Herein, we describe the case of a father and son with CRPS type 1 who exhibited different rsFC patterns in fMRI analyses correlating with their individual CRPS phenotypes. A 39-year-old male and his 61-year-old father presented with severe pain and mobility limitations in their right upper limbs following a vehicle accident and a fall, respectively, and were diagnosed with CRPS type 1. Despite receiving treatment, they experienced severe pain and limited mobility. The son exhibited dystonia and musculoskeletal atrophy while the father experienced extensive sensory disturbances. Bone scintigraphy revealed increased uptake in affected regions. The patients' resting-state fMRI data were compared with those of 48 healthy adults using the CONN software, with the false discovery rate set at p<0.05. Distinct brain regions for the father and son exhibited decreased rsFC (between the rostral prefrontal cortex and orbitofrontal cortex in the father and between the supplementary motor area and pallidum in the son; all in the right hemisphere). These changes corresponded to pain sensation and cognitive-emotional alterations in the father and limb movement disorders (dystonia) in the son. Our findings strongly support the idea that abnormalities in rsFC are closely linked to CRPS type 1 phenotypes.
Structure-from-Motion photogrammetry for high-resolution coastal and fluvial geomorphic surveys

Photogrammetry is an old remote sensing technique that has undergone significant new developments related to the emergence of new computer vision algorithms, notably the workflow technique called Structure-from-Motion (SfM photogrammetry). These innovations facilitate the utilization of this technique by non-specialists. SfM photogrammetry now enables the production of high-resolution morphometric 3D models and derived products such as digital surface models (DSMs) and orthophotographs. We conducted three SfM-photogrammetric experiments, in French Guiana on an embayed beach in Cayenne and on a small mudflat sector of a coastal mud bank near the Sinnamary estuary, and finally in the French southern Alps on a section of the braided Buech River near Sisteron. We developed a simple field protocol based on subvertical aerial photography from a microlight aircraft at low altitude (1000 feet). We generated DSMs and the other products with user-friendly Agisoft Photoscan Professional © software. These SfM implementations were aimed at highlighting the subtle morphological variations characterising these features and some of the processes involved in morphological change. The DSMs were produced at a resolution of 5 to 10 cm per pixel and had a mean vertical accuracy less than 10 cm compared to the Real-Time Kinematic Differential Global Positioning System (RTK-DGPS) control points, with a maximum of 20 cm in marginal sectors near high vegetation such as trees and shrubs and on wet surfaces close to water-filled channels or in the lower intertidal zone. Low-cost, user-friendly SfM photogrammetry offers interesting new perspectives in the fields of coastal and fluvial geomorphology requiring high-resolution topographic data. The technique combines the advantages of the reproducibility of GPS topographic surveys and the high density and accuracy of airborne LIDAR, but at very advantageous cost compared to the latter.
Geographical Differences in Wound Complication Rates Following the Sinus Tarsi Approach in Displaced Intra-articular Calcaneal Fractures: A Systematic Review of the Literature

Background The sinus tarsi approach (STA) has gained popularity for the treatment of displaced intra-articular calcaneal fractures. No large studies comparing wound complications worldwide after STA surgery are available. The aim of this systematic review was to compare postoperative wound complication (POWC) and postoperative wound infection (POWI) rates following STA surgery between continents and countries and their differences in climate. Methods A literature search was performed using the databases of PubMed, Embase, and the Cochrane Library. Studies published before January 1, 2000, including < 10 patients and written in a language other than English were excluded. Results In total, 86 studies containing 4,392 surgeries via STA from 20 different countries were included. The mean POWC was 5.9% and the mean POWI was 4.4%. The highest median POWC rate was in North America (8.5%) and the lowest in South America (2.0%). No significant differences were found in the POWC and POWI rates between countries (p = 0.178 and p = 0.570, respectively), but significant differences were found between the POWC and POWI rates between continents (p = 0.011 and p = 0.036, respectively). The number of surgeries per year and climate differences, as represented by mean local temperature, were not correlated with both the POWC/POWI rates and functional outcome scores. Conclusions Significant differences between the POWC and POWI rates were found between continents but not between individual countries. With a mean POWC of 5.9% and a mean POWI rate of 4.4%, STA has an intrinsic low risk for complications given the minimally invasive nature of the approach and is inevitably becoming the gold standard for calcaneal surgery.
Influence of Humans and Climatic Variability on Historic Wildfire Dynamics in Jasper National Park, Alberta, Canada

INFLUENCE OF HUMANS AND CLIMATIC VARIABILITY ON HISTORIC WILDFIRE DYNAMICS IN JASPER NATIONAL PARK, ALBERTA, CANADA Theresa Dinh University of Guelph, 2014 Advisor: Dr. Ze'ev Gedalof A fire-scar based dendroecological reconstruction of wildfire history was used to detect the influence of climatic variability and land-use change on wildfire dynamics in the lodgepole pine (Pinus contorta var. latifolia Engelm.) forests of Jasper National Park in the Canadian Rocky Mountains. New original analyses were performed on 170 cross-section samples collected in the 1970s from 52 fire history sites (approximately 435 km 2 study area) from around Jasper, Alberta. The updated fire record reveals that fire occurrence was highly variable (range of mean fire return intervals: 4.9 to 52.5 years); although fire activity (i.e. fire size and frequency) decreased through time with fire exclusion becoming widespread during the 1950s. Along with less fire activity on the landscape, there were also shifts in fire seasonality from early human-ignited "spring burns" to more natural lightning-ignited "summer" fires (p < 0.05). Most fires were locally occurring and seemingly driven by topographic controls whereas, the 18 widespread fires (events occurring across at least 10 % of sites) were primarily driven by dry and warm climate conditions (e.g. El Niño years). Large regional fires burned across almost all sites, although some remnant patches (i.e. sites) remained unburned thus providing evidence for a historically mixedseverity fire regime. To be consistent with historic patterns, large-scale restoration via prescribed burning should be scheduled to promote a frequent, patchy, and mixed-severity wildfire regime.
An enhanced Bayesian fingerprinting framework for studying sediment source dynamics in intensively managed landscapes

An enhanced revision of the Fox and Papanicolaou (hereafter referred to as “F‐P”) (2008a) Bayesian, Markov Chain Monte Carlo fingerprinting framework for estimating sediment source contributions and their associated uncertainties is presented. The F‐P framework included two key deterministic parameters, α and β, that, respectively, reflected the spatial origin attributes of sources and the time history of eroded material delivered to and collected at the watershed outlet. However, the deterministic treatment of α and β is limited to cases with well‐defined spatial partitioning of sources, high sediment delivery, and relatively short travel times with little variability in transport within the watershed. For event‐based studies in intensively managed landscapes, this may be inadequate since landscape heterogeneity results in variabilities in source contributions, their pathways, delivery times, and storage within the watershed. Thus, probabilistic treatments of α and β are implemented in the enhanced framework to account for these variabilities. To evaluate the effects of the treatments of α and β on source partitioning, both frameworks are applied to the South Amana Subwatershed (SASW) in the U.S. midwest. The enhanced framework is found to estimate mean source contributions that are in good agreement with estimates from other studies in SASW. The enhanced framework is also able to produce expected trends in uncertainty during the study period, unlike the F‐P framework, which does not perform as expected. Overall, the enhanced framework is found to be less sensitive to changes in α and β than the F‐P framework, and, therefore, is more robust and desirable from a management standpoint.
Prediction of Forex Prices on USD/NGN Using Deep Learning (LSTM and GRU) Techniques

The goal of the project is to develop a model to forecast the Foreign Exchange (FOREX) prices of United State Dollar to Nigerian Naira (USD/NGN), utilizing two machine learning algorithms, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). These were chosen for this study because they have been found to be effective in previous studies that have been examined. The principles of machine learning and its applications, as well as the many machine learning techniques and algorithms will be covered in this study. Additionally, various extraction methods that will be used in the study will be presented. Data from the Investing.com dataset would be retrieved for this study's purpose and divided into training and test sets. Using the two machine learning techniques previously mentioned, the model would be trained and tested. Then, to measure the model's performance in terms of accuracy and precision, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error would be utilized. The results obtained showed that, GRU performed better than LSTM with a 0.950 Test R2 score and an adjusted R2 score of 0.122. The RMSE is way lower than LSTMs at 0.105 and MAE is even lower at 0.950.
ASSESSMENT OF A COMMUNITY MANAGEMENT PROGRAM OF RIVER TURTLES (PODOCNEMIDIDAE: Podocnemis) IN ZÁBALO, ECUADORIAN AMAZON

In Zábalo community within the Cuyabeno Fauna Production Reserve, a 26-year local initiative aimed at river turtle management was initiated following a significant decline in Podocnemis unifilis and P. expansa populations. Despite the program's lengthy history, weak monitoring and validation procedures prompted a comprehensive evaluation of management efficacy to strengthen the communitarian program. This assessment encompasses population estimates derived from nesting data (density, abundance, hatching success, hatchling production) for P. unifilis between 1994 and 2019. Some available data were also compiled for P. expansa which has not been the main focus of the communitarian program. Results highlight a positive temporal trend in P. unifilis population estimators, and a consistent high hatching success (81%), underscoring the program's contribution to the conservation of the species. Nonetheless, further research is imperative to assess age-specific population trends and enhance monitoring and management strategies for the relatively rare P. expansa.
Effects of pit plantings on tree growth in semi-arid environments

ABSTRACT This study developed a pit planting technique in which only planting spots were dug to reduce earthwork costs and to enhance tree growth. This technique, in which pits of different depths were dug, was actually applied to a semi-arid experiment site to analyze its effects on soil moisture and tree growth. The soil moisture content in the pit planting 30 cm in depth (30 cm pit planting) and 50 cm in depth (50 cm pit planting) was 6.5 ± 0.1% and 6.7 ± 0.1%, respectively, which was at least 1.3 times higher (P < 0.01) than that of the control plot (surface planting). The crown growth rate of Populus sibirica trees for 2 years following the planting was 733.5 ± 43.3% in the 30 cm pit planting and 889.4 ± 58.5% in the 50 cm pit planting which, at their maximum, were 1.7 times higher than in the control plot. The stem diameter growth and the new shoot length were maximally 1.5 times better in the pit planting than in the control plot. The pit planting technique developed will be widely useful to promote the rooting and growth of trees in planting areas with problems of strong winds and dryness.