Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of sea-level change in deep water deposition along a carbonate shelf margin, Early and Middle Permian, Delaware Basin: implications for reservoir characterization

Abstract The architecture and sedimentary characteristics of deep water deposition can reflect influences of sea-level change on depositional processes on the shelf edge, slope, and basin floor. Outcrops of the northern slope and basin floor of the Delaware Basin in west Texas are progressively exposed due to canyon incision and road cutting. The outcrops in the Delaware Basin were measured to characterize gravity flow deposits in deep water of the basin. Subsurface data from the East Ford and Red Tank fields in the central and northeastern Delaware Basin were used to study reservoir architectures and properties. Depositional models of deep water gravity flows at different stages of sea-level change were constructed on the basis of outcrop and subsurface data. In the falling-stage system tracts, sandy debris with collapses of reef carbonates are deposited on the slope, and high-density turbidites on the slope toe and basin floor. In the low-stand system tracts, deep water fans that consist of mixed sand/mud facies on the basin floor are comprised of high- to low-density turbidites. In the transgression and high-stand system tracts, channel-levee systems and elongate lobes of mud-rich calciturbidite deposits formed as a result of sea level rise and scarcity of sandy sediment supply. For the reservoir architecture, the fan-like debris and high-density turbidites show high net-to-gross ratio of 62 %, which indicates the sandiest reservoirs for hydrocarbon accumulation. Lobe-like deep water fans with net-to-gross ratio of 57 % facilitate the formation of high quality sandy reservoirs. The channel-levee systems with muddy calciturbidites have low net-to-gross ratio of 30 %.
An Annotated Corpus of Argumentative Microtexts

We present a freely available corpus of argumentative "microtexts", featuring short and dense authentic arguments, annotated according to a scheme for representing text-level argumentation structure. The corpus consists of 112 German texts plus professional English translations that preserve linearization and argumentative structure. We provide statistics of the variety and the linguistic realization of argumentation structure in the corpus. We hope the data release serves the needs of data-driven approaches to argument mining and qualitative analysis alike.
Design for An Active Ankle-Foot Orthosis

Active ankle-foot orthoses are used to assist patients suffering from stroke, cerebral palsy etc. through providing them an external force supply to track normal gait cycles. In this paper, we propose a novel backstepping control design for active ankle-foot orthoses to track desired gait trajectories and to reduce the effects of unknown disturbances such as ground reaction force and weight of the foot. The actual system is modeled as two-degree-of-freedom mass-spring system. Disturbances are modeled as a finite sum of sinusoidal signals with unknown amplitudes, frequencies and phases, and an unknown constant. An adaptive backstepping control law is designed for force input to the system. It is proved that the equilibrium of the closed loop system is stable, and vertical position of the patient’s heel tracks the desired trajectory. Finally, simulations are performed to show superiority of the proposed control design over a PID control.
Title Skill Assessment of Water Supply Forecasts for Western Sierra Nevada Watersheds Permalink

The western slope of the Sierra Nevada contains 13 major river basins with sufficient long-term seasonal forecast and runoff data to assess forecast skill. These seasonal forecasts are issued by the California Department of Water Resources on February 1, March 1, April 1, and May 1 of each year for the major watersheds in California. Annual average precipitation in these river basins goes from 1,500 mm in the Yuba in the north, to 600 mm in the Kern to the south. Average runoff fraction (April to July) for the various watersheds ranged from 0.1 to 0.5 with the lower-elevation watersheds, and the Kern in the south having lowest values. The difference between precipitation and runoff, an index of evapotranspiration, was highest in the lower-elevation Cosumnes and Mokelumne Basins. Approximately half of the April 1 annual forecasts had a percent bias of 15%. Skill scores for the 13 watersheds showed low scores (0.3) for forecasts in February, increasing through the forecast season to 0.8 for forecasts issued May 1, with 1.0 being a perfect forecast. Correlation skill measures, such as the Nash Sutcliffe scores, also exhibited increases in skill through the season from 0.45 in February to 0.95 in May. A linear regression between Nash Sutcliff scores and watershed elevation yielded a strong relationship with a coefficient of determination of 0.77. This relationship between higher elevation basins and greater forecast skill reflects the stronger statistical relationship between snow accumulations at index sites and seasonal runoff, versus more rainfall dominance in lower elevation watersheds. April through July runoff for each year was classified as the lower 30%, the mid 40%, and upper 30%; categorical skill measures were computed on the three runoff categories. Increases in forecast skill during the forecast season were visible in the low-flow and high-flow years versus the midflow years. Over forecasting of flow in the middle category was especially apparent early in the season, illustrated by high early season false-alarm rate and over forecasting bias. Difficulty in making accurate forecasts for midflow runoff along with the under forecast of high-runoff years and the over forecast of low-runoff years are shown to be common difficulties in runoff forecasting, especially early in the forecast season. Forecast skill is shown to be elevation dependent and can be expected to decrease with increasing temperatures. DOI: 10.1061/(ASCE)HE.1943-5584.0001327. © 2016 American Society of Civil
Geochemical Assessment of Sediment Quality Using Multivariate Statistical Analysis of Ennore Creek, North of Chennai, SE Coast of India.

The composite nature of Ennore Creek, receiving polluted waters from Buckingham canal, River Kortalaiyar, and the presence of numerous industries rapidly degrade the coastal environment. In this study, multivariate statistical technique was used to assess the nature of pollution and to identify the factors responsible for the enrichment of trace metals in the creek sediments. Forty samples were collected during pre- and post-monsoon periods to evaluate the seasonal variations on the concentration of trace metals in the sediments. The results indicate that not much seasonal variation exists in the concentration of trace metals in the sediments of Ennore creek. Results of the cluster analysis illustrate that the enrichment of trace metals was mainly from anthropogenic sources. Meanwhile, correlation coefficient among the metals reveals that some of the metals (Fe and Al) derived were of natural origin. The complex data matrix of the sediment were interpreted after reduction to three factors and the results illustrate the extent of the influence of anthropogenic activities. The spatial distribution diagrams demonstrate and demarcate the region of enrichment of metals in the sediments of Ennore creek.
Robust Event-Triggered Energy-to-Peak Filtering for Polytopic Uncertain Systems over Lossy Network with Quantized Measurements

The event-triggered energy-to-peak filtering for polytopic discrete-time linear systems is studied with the consideration of lossy network and quantization error. Because of the communication imperfections from the packet dropout of lossy link, the event-triggered condition used to determine the data release instant at the event generator (EG) can not be directly applied to update the filter input at the zero order holder (ZOH) when performing filter performance analysis and synthesis. In order to balance such nonuniform time series between the triggered instant of EG and the updated instant of ZOH, two event-triggered conditions are defined, respectively, whereafter a worst-case bound on the number of consecutive packet losses of the transmitted data from EG is given, which marginally guarantees the effectiveness of the filter that will be designed based on the event-triggered updating condition of ZOH. Then, the filter performance analysis conditions are obtained under the assumption that the maximum number of packet losses is allowable for the worst-case bound. In what follows, a two-stage LMI-based alternative optimization approach is proposed to separately design the filter, which reduces the conservatism of the traditional linearization method of filter analysis conditions. Subsequently a codesign algorithm is developed to determine the communication and filter parameters simultaneously. Finally, an illustrative example is provided to verify the validity of the obtained results.
An Authenticated Encryption Scheme — Schnorr signature + RSA encryption

This paper proposes a new scheme of authenticated encryption that is either publicly verifiable or not publicly verifiable depending on the quantity of information the recipient released. This property would give recipient much flexibility in many applications. This scheme combines the RSA encryption with Schnorr signature. Considering the security goal of signature, the resultant scheme is at least as secure as that of the combined signature scheme. As for the security goal of encryption, it is proven directly related to the security of signature under the chosen ciphertext attack. Furthermore, this new scheme is also secure against one-more-decryption attack. This novel security goal may be valuable in the applications of private information retrieval.
Application of SeDDaRA Blind Deconvolution for Efficient Improvement of Confocal Microscopy Images

This paper presents the application of the SeDDaRA blind deconvolution method to image sequences produced by confocal microscopy. In contrast to more common methods, such as Lucy-Richardson and maximum entropy, SeDDaRA is non-iterative. As such, this approach requires significantly less computation while still producing significant blur reduction. Based on scene-statistics, successful application of this method results from finding a suitable representation of the scene, as opposed to deriving a reasonable model of the point spread function. The method is described and applied to two distinct confocal image stacks that are available from the National Institute of Health web site. A resolutionbased image metric, derived from the full-width half-maximum values of image features, is applied to assess image improvement. Based on visual inspection and the image metric, the processed image sets show significant improvements in image resolution and contrast.
1 SIMULATION OF SHIELDING GAS FLOW INSIDE THE TORCH AND IN THE PROCESS REGION OF GMA WELDING

Gas metal arc (GMA) welding of aluminium, high allo yed steel or titanium requires a cover of shielding gas in order to provide a low PPM concentration of oxyg en at the joint. However, it is very difficult to o bserve the flow of shielding gas during arc welding proces ses. Responsible for this are the covered areas ins ide the blowpipe, temperatures up to 20,000 K, the high rad iation of the arc and the electromagnetic field. Although recent simulation models of GMA welding pr ocesses include the arc as well as the drip transit io , they assume a perfect cover of the shielding gas wi thin an atmosphere of 100% argon. This paper introduces a model that describes the shielding gas flow and visualizes the causes and effects, which lead to atmosphere contaminations. The simulation model con tains the effects of the arc apart from turbulence models and diffusion effects. It displays the flow conditions within the blowpipe as well as the resul ting cover of shielding gas at the process part. The geo metrical influences of various blowpipe components are simulated. The model was validated by the lambda se nsor principle. The gas, which has to be analysed i taken directly from the arc.
Risk and Reward: A Neurofinance Perspective

When dealing with finance, there is an underlying assumption that the markets are rational. Historically, however, it can be observed that the markets sometimes behave in what seems like an externally irrational manner. This seeming irrationality prompts the question of what forces then govern the financial markets. Literature has tried to establish a few links between emotional responses by investors as the governing forces of financial and economic theories. Since emotional responses are hard to quantify, and the mechanistic approaches of emotions are not well understood, there seems to be a deficiency in understanding the processes of finance. A paradigm shift needs to take place that explains finance through the mechanistic approaches of cognitive science and neuroscience. This will lead to an increased accuracy in decision making models, as well as a facilitation of the overall understanding of finances and other similar fields.
N Timed Refusals Graph for Non-Deterministic Timed Systems

In this paper we are interested in refusals based model for validating timed systems. We propose a new refusals graph named timed refusals regions graphs (TRRGs). In this case specifications are modeled by durational actions timed automata (DATA*) based on maximality semantics which claim that actions have durations. This latter model is in one hand useful for modeling and validating reel aspects of systems. In the other hand, it is determinizable. In TRRG, refusals could be temporary or permanent. Permanent refusals are provoked by the nondeterminism in the specifications. However, temporary refusals are the result of the fact that actions elapse in time. We propose a framework for generating timed refusals regions graph. This framework is implemented by a combination of Meta-modelling and Graph Grammars, to transform a DATA* structure into a TRRG. This permits the automatic generation of a visual modeling tool. Finally, we argue the use of TRRG in formal test of timed systems.
Research Plan For Ph . D . Programme 2009-10 Robust Automatic Speaker Recognition System

This research work aims at designing both text-dependent and text-independent speaker recognition system based on mel frequency cepstral coefficients (MFCCs) and voice activity detector (VAD). VAD has been employed to suppress the background noise and distinguish between silence and voice activity. MFCCs will be extracted from the detected voice sample and will be compared with the database for recognition of the speaker. A new criterion for detection is proposed which is expected to show very good performance in noisy environment. The system will be implemented on MATLAB platform and a new approach for designing a voice activity detector (VAD) has been proposed. The effectiveness of the proposed system can be proved by comparative analysis of the proposed design approach with the artificial neural networks technique. In past few years there has been lot of work that has proved artificial neural networks (ANN's) as a powerful tool for speaker recognition. The performance of both the systems will be evaluated under different noisy environments and in different languages and emotions. The overall efficiency of the proposed speaker recognition system depends mainly on the detection criteria used for recognizing a particular speaker. Global optimization techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc. can prove very useful in this context and hence for setting up of the detection criteria Genetic Algorithm will be employed.
Leaf Classification based on GLCM Texture and SVM

This paper involves classification of leaves using GLCM (Gray Level Co-occurrence matrix) texture and SVM (Support Vector Machines). GLCM is used for extracting texture feature of leaves. Creating a plant database for quick and efficient classification and recognition is an important step for their conservation. This approach would help to extract useful features of leaf and improve the accuracy of leaf classification. The standard leaf images are subjected to pre-processing. Feature values are extracted from pre-processed image and they are trained and classified. Standard data sets are used for enhancing the properties of the image.
A Location-Based Business Information Recommendation Algorithm

Recently, many researches on information (e.g., POI, ADs) recommendation based on location have been done in both research and industry. In this paper, we firstly construct a region-based location graph (RLG), in which region node respectively connects with user node and business information node, and then we propose a location-based recommendation algorithm based on RLG, which can combine with user short-ranged mobility formed by daily activity and long-distance mobility formed by social network ties and sequentially can recommend local business information and long-distance business information to users. Moreover, it can combine user-based collaborative filtering with item-based collaborative filtering, and it can alleviate cold start problem which traditional recommender systems often suffer from. Empirical studies from large-scale real-world data from Yelp demonstrate that our method outperforms other methods on the aspect of recommendation accuracy.
Urban Form and Density in Sustainable Development

Land development is an important component in sustainable development because it is where our food and raw materials come from and it is also the habitat for wildlife and fauna. Similar to other resources, it is a scarce commodity. Some of the destruction of this valuable natural resource is irreversible. The reduction of agricultural land will reduce our food supply capacity, and the destruction of environmental sensitive areas, such as wetlands, will affect the habitat and ecology of wildlife. The use of land unsuitable for development may bring harm to both the natural environment and human life. With increasing population and rapid economic development, there is a concomitant increase in land consumption. The encroachment of urban land use on agricultural land and environmental sensitive areas is a problem in many rapidly growing regions in Asia. The diminishing of agricultural land and environmental sensitive areas is affecting sustainable development of the region. There is a need to examine the amount of land consumption and their distribution in Asia to see how it is affecting sustainable development. Through better planned compact development and increase in density (Yeh, 2000; Yeh and Li, 2000),, a more sustainable land development can be achieved without limiting economic development.
McAlister, Clare (2010) Cooperative Learning in Scotland. Perspectives on the role of cooperative learning in supporting curricular policy and innovation. In: The European Conference on Educational research

The stated aim of the new Curriculum for Excellence is to deliver an education system in Scotland that meets the demands of the 21st Century. The new curriculum has been the subject of controversy relating to its capacity to support learning and the approaches to learning and teaching it advocates. The changes in curriculum require developments, for some practitioners, in how learning and teaching takes place with a focus on active learning. This paper explores whether one active learning strategy, cooperative learning, can assist teachers in delivering the new curriculum. Cooperative learning is a pedagogy that has been the focus of significant research in the United States and Canada with developing interest in a variety of countries (Gillies 2000; Gillies & Boyle 2005; Johnson 1993; Johnson 1985; Kagan & Kagan 2009; Slavin 1984; Weigmann 1992) but to date the research in the UK is limited. This paper explores findings on cooperative learning in a global context and through a case study in Scotland. The case study reported in this paper reflects on the responses of pupils to the introduction of cooperative learning in a secondary school in Scotland and the ways in which this approach appeared to support them in developing the four capacities of the new curriculum.
ESTIMATING CONSERVATION VALUE OF “DASHTENAZE SARI” WILDLIFE REFUGE, AND FACTORS AFFECTING THE WILLINGNESS TO PAY FOR SITE CONSERVATION

Forests and rangelands are the most important natural ecosystems which provide many economic benefits to the society due to their biodiversity and ecological functionalities. Dashtenaz wildlife refuge, Sari, is a sylvan region, covering 55 ha of rangeland with abundant diversity, is refuge of an endangered species of deer called Dama mesopotamica. In this study, Contingent Valuation Method (CVM) and dichotomous choice were used to estimate the conservation value of this region in 2014. Moreover, the Logit regression model has been used to calculate individual’s Willingness to Pay (WTP), and parameters have been estimated based on maximum likelihood method. Firstly 35 questionnaires were distributed among the sample members. Validity and reliability of questionnaire were examined by experts and Cronbach`s Alpha test (0.84), respectively. Cochrane formula and random sampling method were used to estimate the sample size. Then, 429 questionnaires were selected in the cities surrounding rangeland (Sari, Behshahr, Miandorood, and Neka). Results showed that 77% of respondents were willing to pay for conserving the rangeland. On the other hand, variables such as bid (the amount of money proposed to respondents to pay for conserving rangeland), monthly income, level of responsibility, financial capability, environmental orientations, the number of visits and educations, had significant impact on individual’s WTP for area conservation at 1 to 10 percent level. In addition, per capita WTP for conserving Dashtenaze Sari has been calculated about 0.3 $ monthly and the annual value of Dashtenaze Sari per hectare was 18125 $.
A microfluidic chip for measurement of biomolecules using a microbead-based quantum dot fluorescence assay

This paper describes a custom-designed microfluidic chip for sensitive detection of antibody using quantum dot fluorescence in a microbead-based assay. The microfluidic chip is designed to isolate a single microbead where the binding reaction of antibodies happens on the surface. The microfluidic chip is fabricated on a glass substrate using transparent silicone elastomer, PDMS, for easy access to monitoring and flexible gate operations to capture the single microbead. For antibody detection, a sequence of functionalized assay has been performed in the fabricated chip including: capturing of microbeads; antibody injection into a micro-chamber; quantum dot injection; and fluorescence detection. Various concentrations of antibody have been tested and have shown good agreement with correlated fluorescent intensities.