Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining Absolute Plate Motions Since the Triassic

The absolute motion of tectonic plates since Pangea can be derived from observations of hotspot trails, paleomagnetism, or seismic tomography. However, fitting observations is typically carried out in isolation without consideration for the fit to unused data or whether the resulting plate motions are geodynamically plausible. Through the joint evaluation of global hotspot track observations (for times <80 Ma), first‐order estimates of net lithospheric rotation (NLR), and parameter estimation for paleo–trench migration (TM), we present a suite of geodynamically consistent, data‐optimized global absolute reference frames from 220 Ma to the present. Each absolute plate motion (APM) model was evaluated against six published APM models, together incorporating the full range of primary data constraints. Model performance for published and new models was quantified through a standard statistical analyses using three key diagnostic global metrics: root‐mean square plate velocities, NLR characteristics, and TM behavior. Additionally, models were assessed for consistency with published global paleomagnetic data and for ages <80 Ma for predicted relative hotspot motion, track geometry, and time dependence. Optimized APM models demonstrated significantly improved global fit with geological and geophysical observations while performing consistently with geodynamic constraints. Critically, APM models derived by limiting average rates of NLR to ~0.05°/Myr and absolute TM velocities to ~27‐mm/year fit geological observations including hotspot tracks. This suggests that this range of NLR and TM estimates may be appropriate for Earth over the last 220 Myr, providing a key step toward the practical integration of numerical geodynamics into plate tectonic reconstructions.
A Constructive Neural Network Learning Method Based on Quotient Space and Its Application in Coal Mine Gas Prediction

Under the framework of quotient space granular computing model,using constructive neural network learning approach to predict gas concentrations. Using quotient space granular computing theory,the problem can be macro -level analysis-examining different particle size between the quotient space conversion,movement,interdependent relations,and the original features of the database information to build grain size,using a variety of granularity,from different levels of analysis of complex gas data makes the learning characteristics of the sample is more obvious,in order to better meet the requirements of machine learning. Constructive neural network learning method can be different from the micro-size structures on the quotient space data mining. At last,the method is applied to predict gas concentration,and the satisfying results are achieved. It is expected that Constructive Neural Network Learning Method will have wide applications.
Metamorphic and crustal evolution of Australian-Antarctic Proterozoic margins

In situ LA–ICP–MS U–Pb monazite geochronology from the eastern Musgrave Inlier, central Australia, suggests that high-T, suprasolidus conditions were maintained for >70 Myr during the Grenvillian (ca. 1220–1140 Ma). Calculated phase equilibria modelling constrain peak metamorphic conditions to ~900 °C at 6.0–6.5 kbar. Collectively, the P–T–t conditions reflect that high-T, low-P metamorphism was contemporaneous with similarly thermally extreme metamorphism in the west and central Musgrave Inlier, and therefore that regionally high geothermal gradients (140–150 °C/kbar) prevailed across the entire terrane. However, despite an apparently long-lived history of monazite growth, the results of this study also find evidence for arguably shorter-lived, high-T to ultrahigh temperature metamorphism. Conceivably, the preservation of high-T major element compositions and relict Y + REE growth profiles in small (<500 μm) garnet grains suggest that the rocks cooled relatively rapidly to sub-diffusive temperatures. At least initially the peak to retrograde P–T path was also isobaric, or experienced a slight increase in pressure. Grenvillian-aged metamorphism in the eastern Musgrave Inlier accordingly appears to also record the effects of a transient thermal anomaly. This likely developed in response to magmatic-loading and the advection of heat from syn-metamorphic magmatic rocks of the Pitjantjatjara Supersuite, which were emplaced at mid–lower crustal depths, within a longer-lived system characterised by deep-seated, mantle-driven regional metamorphism.
Vision Assisted Web structure and Data Extraction Methods

Web page segmentation has been done to address the problems in different fields including mobile web, archiving, phishing, etc. In this paper, different algorithms are summarized that web page segmentation addresses in different fields . Web page segmentation has myriad applications like information retrieval, page type classification etc. . This paper presents a survey of web page segmentation algorithms including DOM Tree, VIPS and SD Tree algorithms. VIPS approach is independent of underlying HTML representation and works well even when layout structure is different from the HTML structure. As there is difficulty in finding the meaningful blocks existing approaches presented can extract informative parts from web pages by creating meaningful blocks and segmenting noisy WebPages.
Spatial Variation in the Responses of the Surface External and Induced Magnetic Field to the Solar Wind

We analyze the spatial variation in the response of the surface geomagnetic field (or the equivalent ionospheric current) to variations in the solar wind. Specifically, we regress a reanalysis of surface external and induced magnetic field (SEIMF) variations onto measurements of the solar wind. The regression is performed in monthly sets, independently for 559 regularly spaced locations covering the entire northern polar region above 50° magnetic latitude. At each location, we find the lag applied to the solar wind data that maximizes the correlation with the SEIMF. The resulting spatial maps of these independent lags and regression coefficients provide a model of the localized SEIMF response to variations in the solar wind, which we call “Spatial Information from Distributed Exogenous Regression. ” We find that the lag and regression coefficients vary systematically with ionospheric region, season, and solar wind driver. In the polar cap region the SEIMF is best described by the By component of the interplanetary magnetic field (50–75% of total variance explained) at a lag ∼20–25 min. Conversely, in the auroral zone the SEIMF is best described by the solar wind ϵ function (60–80% of total variance explained), with a lag that varies with season and magnetic local time (MLT), from ∼15–20 min for dayside and afternoon MLT (except in Oct–Dec) to typically 30–40 min for nightside and morning MLT and even longer (60–65 min) around midnight MLT.
Identifying Relationships between Scientific Datasets

Scientific datasets associated with a research project can proliferate over time as a result of activities such as sharing datasets among collaborators, extending existing datasets with new measurements, and extracting subsets of data for analysis. As such datasets begin to accumulate, it becomes increasingly difficult for a scientist to keep track of their derivation history, which complicates data sharing, provenance tracking, and scientific reproducibility. Understanding what relationships exist between datasets can help scientists recall their original derivation history. For instance, if dataset A is contained in dataset B, then the connection between A and B could be that A was extended to create B. We present a relationship-identification methodology as a solution to this problem. To examine the feasibility of our approach, we articulated a set of relevant relationships, developed algorithms for efficient discovery of these relationships, and organized these algorithms into a new system called ReConnect to assist scientists in relationship discovery. We also evaluated existing alternative approaches that rely on flagging differences between two spreadsheets and found that they were impractical for many relationship-discovery tasks. Additionally, we conducted a user study, which showed that relationships do occur in real-world spreadsheets, and that ReConnect can improve scientists’ ability to detect such relationships between datasets. The promising results of ReConnect’s evaluation encouraged us to explore a
Towards an Automatic Early Warning System of Flood 
Hazards based on Precipitation Forecast: The case of the 
Miño River (NW Spain)

Abstract. An Early Warning System for flood prediction based on precipitation forecast is presented. The system uses rainfall forecast provided MeteoGalicia in combination with a hydrologic (HEC-HMS) and a hydraulic (Iber+) models. The upper reach of the Miño River and the city of Lugo (NW Spain) are used as a study area. Starting from rainfall forecast, HEC-HMS calculates the streamflow and Iber+ is automatically executed when a certain threshold is exceeded for some previously defined risk areas. The analysis based on historical extreme events shows that the system can provide accurate results in less than one hour for a forecast horizon of 3 days and report an alert situation to decision-makers.
A new convective model of the Weddell Polyny: Deep convection in the Southern Ocean

The Weddell Polynya, a large hole in the Antarctic sea ice, reappeared in 2017. The polynya forms due to deep convection, which is caused by static instability of the water column. Observations and model studies show periodic heat accumulation in the subsurface layer prior to a polynya. This heat accumulation could be caused by internal ocean dynamics: the Southern Ocean Mode. Periodic subsurface heat and salt accumulation could be the major driver in causing periodic deep convection, which is in contrast with earlier studies. These studies focus on surface processes, and see the polynya as an irregular event. In this study a simple convective model is used to look into this contrast. Model simulations excluding and including periodic subsurface heat and salt fluxes have been performed. Multiple polynya events were only simulated in the model set up including subsurface fluxes. The dominant frequency for polynya events in these simulations equals the frequency of the subsurface heat and salt accumulation. This frequency is still visible in runs with white noise added to the freshwater flux, showing the importance and dominance of the subsurface forcing. In combination with earlier studies, this study suggests that periodic subsurface processes are most dominant and govern the initial formation and periodicity of the Weddell Polynya.
Extraction and Recovery of Polycyclic Aromatic Hydrocarbons in Petroleum Contaminated Soils Using Supercritical Water by Response Surface Methodology

Finding an environment-friendly and affordable method to remove contaminated soils from Polycyclic Aromatic Hydrocarbons (PAHs) has now become an attractive field for researchers, with super-critical fluid extraction being an innovative process in the field of contaminated soil treatment. Extraction with super-critical fluid is a simple and rapid extraction process that uses super-critical fluids as solvents. The present study has investigated the extraction of contaminated soil with Polycyclic Aromatic Hydrocarbons (PAHs) by means of batch supercritical water reactor, employing variables like pressure (100–300 bar), temperature (60–140 ◦C), residence time (0.5–3 hours), and base, acidic, and neutral pH values. In order optimize the process parameters, Response Surface Methodology (RSM) has been used. Results show that removal efficiency of PAHs is between 82%-100%, where the highest PAHs removal efficiency (100%) has been observed in Test No. 22, with a pressure of 300 bars, temperature of 500°C, acidic pH equal to 5, and duration of 3 hours. In addition, the lowest removal efficiency of these compounds (82%) has been obtained in Test No. 26, with a pressure of 300 bars, temperature of 350°C, base pH of 9, and duration of half an hour. According to the results from this study, it has become clear that residence time is the most important and most effective parameter for removing PAHs from contaminated soil. Afterwards, temperature and pH are most influential with pressure showing the least effect. Using supercritical water method in appropriate conditions can eliminate more than 99% of aromatic contamination.
Integrating Biomass Conversion Technologies with Recovery Operations In-Woods: Modeling Supply Chain

Economic potential of feedstock generated low-valued forest residue can be enhanced by emerging biomass conversion technologies (BCT), such as torrefaction, briquetting, and gasification. However, for implementing these emerging processes within the woods, several hurdles are to be overcome, among which a balanced supply chain is pivotal. Centralized biomass recovery operation (CBRO) could be an economically viable solution in accessing harvesting sites and allows integration of BCT into forest management. The goal of this study was to examine the logistic effects of integrating a BCT into a CBRO, under different in-wood scenarios based on variations in travel time between the facility locations, amount of raw materials handled, intermediate storage capacity, and duration (number of days) of annual operations. Specific objectives included analyzing the effects of forest residue recoverability (BDMT, bone dry metric ton/ha), total transportation time from the harvest unit to the market, and the annual number of in-woods production sites on the overall efficiency of the BCT operations. Concurrently, this study examined the forest managerial impacts due to such an integration. Location-allocation tool (maximize market share problem type) within the ArcGIS Network Analyst platform was utilized to model the scenarios and generate one-way travel times from the harvest site to final markets. Results from geospatial analysis showed that there were 89–159 and 64–136 suitable locations for the BCT for logistics model (LM) I and II, respectively. Total one-way travel time for all the models ranged between 1.0–1.7 h. Additionally, the annual numbers of BCT sites was inversely proportional to the total one-way travel time (i.e., harvest unit to market). Arranging CBRO and BCT operations to occur at the same in-woods site returned shorter total and average travel times than arranging the two activities at separate in-woods sites. The model developed for this study can be used by forest managers and entrepreneurs to identify sites for placing BCTs in the forest that minimizes transportation times.
Benthic Foraminiferal Assemblages from Mexican Continental Shelves

The recent benthic foraminifera 1 fauna of the continental margins of Mexico (Gulf of Campeche and Gulf of Tehuantepec) was studied. Surficial sediment samples (lOcc) were collected using a Shipeck dredge and were stained with Sudan Black "B" in order to differentiate the living fauna from the death assemblage. In the 41 samples from the Gulf of Campeche, 279 species were found in the total assemblage in water depths ranging from 9 to 586 m. In the 22 samples in the Gulf of Tehuantepec, 132 species were identified from the total assemblage in water depths of 20 to 180 m. A total of 175 species were found alive in the Gulf of Campeche compared to the 70 found in the Gulf of Tehuantepec. Shannon-Wiener values indicated that the Gulf of Campeche species diversity was higher than that of the Gulf of Tehuantepec. However, living and total faunas in both areas follow the same diversity trends in relation to water depth. Even though the samples from Tehuantepec show lower diversity, the fauna shows a more uniform distribution in terms of evenness (E). The observed diversity trends could be explained by the Time Stability Theory. Based on water depth, the following three assemblages were found in both the Gulf of Campeche and the Gulf of Tehuantepec: Inner neritic (less than 50 m), central and outer neritic (50-150 m), and upper bathyal. Even though assemblages of the two areas are different at the species level, there are similarities at the generic level. The cluster analysis provided a better correlation in both areas
Visitor responses to environmental interpretation in protected areas in Vietnam: a motivation-based segmentation analysis

ABSTRACT Environmental interpretation is regarded as an effective soft management strategy for educating visitors and managing their impacts on protected areas. Only limited research has been conducted on visitors’ views on environmental interpretation in protected areas in the rapidly developing destinations of South-East Asia, with particular gaps in understanding different visitor groups. This article seeks to fill this gap in the context of Vietnam by examining visitor responses to services for environmental interpretation in one of the country’s largest national parks. The research employed importance-performance analysis and subsequent motivation-based visitor segmentation based on 237 sets of pre- and post-visit questionnaires distributed by the authors as self-complete questionnaires at the entry and exit gateway to the national park. The findings highlight that site interpreters were considered the most important service providers, while displays at the museum and videos were identified as important but low performing. A number of differences between motivation-based visitor groups as well as some culturally anchored response patterns emerged which highlighted the need for park management to consider different visitor groups; not only in terms of their motivations to visit but also their cultural backgrounds when designing, investing maintenance funding, and evaluating interpretive services.
RF Frontend for Spectrum Analysis in Cognitive Radio

RF Frontend for Spectrum Analysis in Cognitive Radio Karthik Tripurari Jayaraman Advances in wireless technology have sparked a plethora of mobile communication standards to support a variety of applications. FCC predicts a looming crisis due to the exponentially growing demand for spectrum and it recommends to increase the efficiency of spectrum utilization. Cognitive Radio (CR) is envisioned as a radio technology which detects and exploits empty spectrum to improve the quality of communication. Spectrum analyzer for detecting spectrum holes is a key component required for implementing cognitive radio. Mitola’s vision of using an RF Analog-to-Digital (ADC) to digitize the entire spectrum is not yet a reality. The traditional spectrum analysis technique based on a RF Front end using an LO Sweep is too slow, making it unsuitable to track fast hopping signals. In this work, we demonstrate an RF Frontend that can simplify the ADC’s requirement by splitting the input spectrum into multiple channels. It avoids the problem of PLL settling by incorporating LO synthesis within the signal path using a concept called Iterative Down Converter. An example 0.75GHz-11.25GHz RF Channelizer is designed in 65nm Standard CMOS Process. The channelizer splits the input spectrum (10.5GHz bandwidth) into seven channels (each of bandwidth 1.5GHz). The channelizer shows the ability to rapidly switch from one channel to another (within a few ns) as well as down-converting multiple channels simultaneously (concurrency). The channelizer achieves a dynamic range of 54dB for a bandwidth of 10.5GHz, while consuming 540mW of power. Harmonic rejection mixer plays a key role in a broadband receiver. A novel orderscalable harmonic rejection mixer architecture is described in this research. A proof-ofprinciple prototype has been designed and fabricated in a 45nm SOI technology. Experimental results demonstrate an operation range of 0.5GHz to 1.5GHz for the LO frequency while offering harmonic rejection better than 55dB for the 3rd harmonic and 58dB for the 5th harmonic across LO frequencies. While cognitive radio solves the spectrum efficiency problem in frequency domain, the electronic beam steering provides a spatial domain solution. Electronic beam forming using phased arrays have been claimed to improve spectrum efficiency by serving more number of users for a given bandwidth. A LO path phase-shifter with frequency-doubling is demonstrated for WiMAX applications.
Multi‐century trends to wetter winters and drier summers in the England and Wales precipitation series explained by observational and sampling bias in early records

Globally, few precipitation records extend to the 18th century. The England Wales Precipitation (EWP) series is a notable exception with continuous monthly records from 1766. EWP has found widespread use across diverse fields of research including trend detection, evaluation of climate model simulations, as a proxy for mid‐latitude atmospheric circulation, a predictor in long‐term European gridded precipitation data sets, the assessment of drought and extremes, tree‐ring reconstructions and as a benchmark for other regional series. A key finding from EWP has been the multi‐centennial trends towards wetter winters and drier summers. We statistically reconstruct seasonal EWP using independent, quality‐assured temperature, pressure and circulation indices. Using a sleet and snow series for the UK derived by Profs. Gordon Manley and Elizabeth Shaw to examine winter reconstructions, we show that precipitation totals for pre‐1870 winters are likely biased low due to gauge under‐catch of snowfall and a higher incidence of snowfall during this period. When these factors are accounted for in our reconstructions, the observed trend to wetter winters in EWP is no longer evident. For summer, we find that pre‐1820 precipitation totals are too high, likely due to decreasing network density and less certain data at key stations. A significant trend to drier summers is not robustly present in our reconstructions of the EWP series. While our findings are more certain for winter than summer, we highlight (a) that extreme caution should be exercised when using EWP to make inferences about multi‐centennial trends, and; (b) that assessments of 18th and 19th Century winter precipitation should be aware of potential snow biases in early records. Our findings underline the importance of continual re‐appraisal of established long‐term climate data sets as new evidence becomes available. It is also likely that the identified biases in winter EWP have distorted many other long‐term European precipitation series.
The Impact of Online Training on Facebook Privacy

ABSTRACT Facebook is a major part of the lives of many consumers who share a considerable amount of information with friends, acquaintances, and commercial interests via the platform, leading to greater exposure to privacy risks. Training has been shown to be effective in reducing computer risk in a variety of contexts. This study investigates the effectiveness of training on consumer attitudes and behavioral intentions toward Facebook privacy risk. The study highlights the importance of training consumers on how and why they need to protect their privacy. Findings suggest that training can reduce consumer risk, but effectiveness can vary across types of training. For example, Facebook’s Privacy Tour was less effective than third-party training videos in improving consumer vigilance. Implications of the findings for consumers and privacy advocates are discussed.