Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate of the Past A 70-yr record of oxygen-18 variability in an ice core from the Tanggula Mountains , central Tibetan Plateau

A 33 m ice core was retrieved from the Tanggula Mts, central Tibetan Plateau at 5743 m a.s. l. in August 2005. Annual averageδ18O values were determined for the upper 17 m depth (14.6 m w.eq.), representing the time series since the mid-1930s. Data are compared to previous results of an ice core from Mt. Geladaindong, 100 km to the northwest, for the period 1935–2003. During the time 1935–1960, δ18O values differed by 2–3‰ between the two ice cores, with generally lower ratios preserved in the Tanggula 2005 core. Differences in interannual variability and overall average ratios between the two study locations highlight the spatially variable climate controls on ice core isotope ratios within the boundary of monsoonand westerly-impacted regions of the central Tibetan Plateau. Average annual net accumulation was 261 mm w.eq. for the period 1935–2004. The overall averageδ18O value was−13.2‰ and exhibited a statistically significant increase from the 1935–1969 average ( −13.7‰) to the 1970–2004 average ( −12.6‰). Despite the observed increase in isotope ratios, isotopic temperature dependence was not evident, based on comparison with long-term data from meteorological stations to the north and southwest of the study location. Lack of correlation between average δ18O values and temperature is likely due to monsoon influence, which results in relatively greater isotopic depletion of moisture during the warm season. Evidence of monsoon impacts on precipitation in the central Tibetan Plateau has been previously documented, and statistically significant negative correlation (r=−0.37,p <0.01) between the annual average ice coreδ18O values and North India monsoon rainfall was observed for the period 1935–2004. Although the δ18O data Correspondence to: D. R. Joswiak (daniel@itpcas.ac.cn) agree well with the monsoon rainfall amount, no significant correlation was observed between the core accumulation and the monsoon rainfall amount. Previous model and observational results suggest monsoon impact on δ18O in precipitation may extend beyond the immediate extent of heavy monsoon rainfall, reaching the central Tibetan Plateau. These results provide evidence that the δ18O variability at this study location may be sensitive to southern monsoon intensity.
Living Cases: Authentic Learning in Action

The paper outlines the merit of using case studies in teaching, such as providing real life examples to contextualise theoretical concepts and shifting the emphasis from teacher-centred to more studentcentred activities. While there are a variety of case types the material they provide is traditionally static. However, in real life, solutions are affected by changed circumstances reflecting a volatile environment. Hence, the paper reports on the approach of using a ̳living‘ case where students are confronted with teacher-initiated interventions while solving the case. The specific case was that of a university deciding to make, purchase or outsource an Information Technology (IT) project for which students applied techniques taught in the unit. Interventions occurred in three stages and reflected the authentic challenges confronted by an IT professional. The paper reports on students‘ reactions to the interventions they experienced at short notice, their performance in developing the project case and conclusions that can be drawn on the more sustained learning that resulted from the approach.
Thermal and mechanical properties of polyethylene/doped-TiO 2 nanocomposites synthesized using in situ polymerization

Ethylene polymerization was carried out using highly active metallocene catalysts (Cp2ZrCl2 and Cp2TiCl2) in combination with methylaluminoxane. Titanium (IV) oxide containing 1% Mn as dopant was used as nanofillers. The effects of filler concentration, reaction temperature, and pressure on the thermal and mechanical properties of polymer were analyzed. The improvement of nanoparticles dispersion in the polyethylene matrix was checked byWAXD. The thermal properties were analyzed using differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The filler impact on the melting temperature of polyethylene synthesized using Cp2ZrCl2 was very minimal which is due to the degree of short-chain branching. The ash content was also analyzed for each nanocomposite and found to be in line with the activity of the catalyst. There was a significant increase in the mechanical properties of the polyethylene by addition of filler.
Speech Based Biometric System Using GFCC Features

Numerous Biometric modalities have emerged recently as researchers are continuously striving to achieve higher security standards for applications that use biometrics to provide access to users in critical environment. Gait of a person, Thermo gram, Near Infrared Images, Smile Recognition, Lip Movement recognition, Thermal Palm, Hand Finger knuckle, Finger veins, Nail ID, Skin Spectroscopy, Electrocardiogram, Dental Biometrics and DNA are some of the biometric techniques being used recently.Speech is simplest, nonintrusive, unimodal biometric parameter. However acoustic degradation, background noise, channel noise, health and ageing of an individual affect the quality of voice which makes Speaker Recognition System not very efficient. Hence, pre-processing and voice enhancing techniques and strategies need to be considered. The vulnerability lies when the testing and enrolment conditions are not perfectly matched which is often the case in real time environment. Degraded speech signals hence limit the effectiveness of Speaker identification and verification. In the proposed work, speaker identification has been carried out using gammatone frequency cepstral coefficients (GFCC) as these found robust to noise than other popular speech features i.e. Mel-frequency cepstral coefficients (MFCC). To divide the speech features in small space, clustering has been carried out using Gaussian mixtures which provide combined feature-set properties of a speech sample. Further these features are fed to pattern recognition based neural network which classifies the data into particular speaker identification. Experimental results found 92% of accuracy for the collected dataset.
Determination of basalt zones using basalt extraction index (BEI) and ASTER image classification

Abstract One of the most important applications of remote sensing is presented in mining and exploration of mineral deposits and evaluation of prospective targets. This project discusses how to use remote sensing knowledge in order to make classification and separation of surface rocks in the Dir-o-Morreh mine. The main purpose of this research is to identify the areas containing high-quality basalt. In this regard, we utilize ASTER multi-spectral satellite imagery, which has relatively good spectral and spatial resolution. At the first step, in order to achieve the correct spectral composition of the basalt spectrum, the spectral signature of basalt stone, defined by Johns Hopkins University, was used. Afterward, the basalt extraction index (BEI) was defined regarding the behavior of the ASTER satellite image bands as well as the initial data provided by the owners of the intended study area. Then, the Convolution and Morphology filter was applied over the images to extract high-quality basalt using an appropriate color composition of the images. At the next step, in order to have better visualization, different maps containing different classes were created using the Maximum Likelihood algorithm. Finally, two indices were developed regarding all research data and field investigations in order to extract basalt zones. The first index discovers basalt zones in the study area, and the second one classifies high-quality basalt and altered basalt zones.
PASI area and erythema scoring using YIQ color space

Psoriasis is an autoimmune inflammatory skin disease, afflicting skin with red plaques that are usually covered with silvery, white scales. Many treatments exist to control psoriasis; however, unfortunately there is no definite cure. PASI (Psoriasis Area and Severity Index) measures psoriasis severity by evaluating the area, erythema, scaliness, and thickness of the plaques, and is the gold standard method used to assess treatment performance. PASI parameters are calculated by dermatologists manually, a time-consuming process that can lead to inconsistent and diverse results. This paper presents a computer-based automatic method to measure the area and erythema parameters of PASI. In the proposed method YCbCr and YIQ color spaces are used, and clustering is done using the K-means algorithm. Seventeen patients have been assessed in this study, and the results of ten patients with different erythema scores are presented in this paper. Results of this study showed that the proposed method is highly accurate. Other advantages of the proposed method include: differentiating moles and scars from psoriasis patches, omitting light reflections, and reducing the calculation time by using color spaces which have linear transformation from the RGB color space.
Geological engineering criteria for deep solids injection

Slurried solids injection is a procedure for placement of granular solid waste deep into porous and permeable geological strata. This technology is currently used by the petroleum industry in several countries (United States, Canada, North Sea, and Indonesia) to dispose of nonhazardous oil-field waste solids. Granular or ground solids of grain size less than 5 mm (0.19 in.) are mixed with waste liquids to form a slurry that is pumped down a deep well under conditions of continuous hydraulic fracturing pressure (pinjv). This article discusses rock mechanics aspects and geological engineering criteria for deep slurry injection. Specifically, the geometrical, lithostratigraphical, and physical parameters that characterize a stratum as a suitable target reservoir for slurried waste placement are addressed. The most important criteria are permeability, porosity, reservoir thickness, depth, and structural geology (of the region). A geological assessment model was developed to serve as a screening process to select suitable reservoirs for slurried waste placement. The screening process to select suitable reservoirs is composed of two steps: a decision tree and a semiquantitative ranking system that provides a numerical score for the stratum. The apparently robust assessment model was tested on several sites representing diverse sedimentary geology in the United States, Canada, the North Sea, and Indonesia.
SMART METERS ADOPTION : RECENT ADVANCES AND FUTURE

High growth in electricity demand and peaks in the load curve, caused mainly by households, require big investments in infrastructure that are used for short periods. Because of this, it is necessary to look for new developments that allow meeting the needs of users as well as using the electricity system resources efficiently. This is possible through the Smart Grid (SG), which additionally allows users to have autonomy in the electricity supply chain. Our focus of investigation is with households because they can monitor their demand and help to reduce the peaks of the load curve. To do this, users must use Smart Meters, because these devices allow consumers to obtain the information necessary to control their demand. This paper presents a systematic analysis of published literature related to the study of the SG from the demand side, analyses the current situation of this topic and the impact of Smart Meter penetration in households.
Does Spatial Structure Persist Despite Resource and Population Changes? Effects of Experimental Manipulations on Coyotes

Abstract We tested the influence of a change in food resource distribution on space use and diet of coyotes (Canis latrans). We focused on 2 facets of space use: maintenance of home ranges by residents, and establishment of home ranges by immigrants after a coyote removal program. The study was conducted on 2 populations of coyotes in southern Texas. In both populations, a clumped, high-quality food source was added to randomly selected feeding stations to measure the influence of food distribution and abundance on home-range patterns, trespassing rates, and consumption of native prey. In established home ranges, coyotes visited and foraged at stations regularly and were found closer to stations during the treatment period. Although there was no overall treatment effect on home-range size (F = 1.66, d.f. = 5, P = 0.15), home ranges without supplemental food remained stable in size, whereas home ranges that had received supplemental food increased during the posttreatment period (t = 2.09, d.f. = 1, P = 0.04). Core areas showed a similar trend; there was no overall treatment effect (F = 1.51, d.f. = 2, P = 0.24); however, core areas of home ranges that received supplemental food were smaller than those of controls during the treatment period (t = 2.71, d.f. = 1, P < 0.01). There were no statistical differences in occurrence of any species, such as small mammals or white-tailed deer (Odocoileus virginianus), in scats of treatment versus control coyotes. Coyotes within the study site after removals were located closer to feeding stations during treatment than posttreatment (F = 8.83, d.f. = 1, P < 0.02, n = 897) periods, yet home-range size with supplemental food was larger than home-range size during the posttreatment period. Our findings suggest that a resource other than food influences coyote spatial patterns.
ORION: clearing near-Earth space debris in two years using a 30-kW repetitively-pulsed laser

Nearly 200,000 pieces of debris in the 1 - 20 cm range in low- Earth orbit (LEO), a legacy of 35 years of spaceflight now threaten long-term space missions. An economical solution to the problem is to use a ground-based laser to create a photoablation jet on the objects and cause them to re-enter the atmosphere and burn up. A sensitive optical detector is required to locate objects as small as 1 cm at 1500 km range. Applied when the object is rising and between about 45 and 15- degree zenith angle, the necessary (Delta) v is of order 100 m/s. A laser of 30 kW average power at 5-ns pulsewidth and a 4 - 6 m mirror with adaptive optics can clear near-Earth space of the 1-20-cm debris in 2 years of operation. A high altitude site minimizes turbulence correction, interference from nonlinear optical effects, and absorption. We discuss the effect of nonlinear optical processes in the atmosphere as boundaries on propagation, and how to choose system parameters to guarantee optimum conversion of laser energy to target momentum. The laser might be Nd:glass (1.06 micrometer/530 nm), or iodine (1.3 micrometer).
A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

Many researchers focus on developing protein-named entity recognition (Protein-NER) or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM) and parsing tree. PPIMiner consists of three main models: natural language processing (NLP) model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.
Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference

In this paper, we use augmented the hierarchical latent variable model to model multi-period time series, where the dynamics of time series are governed by factors or trends in multiple periods. Previous methods based on stacked recurrent neural network (RNN) and deep belief network (DBN) models cannot model the tendencies in multiple periods, and no models for sequential data pay special attention to redundant input variables which have no or even negative impact on prediction and modeling. Applying hierarchical latent variable model with multiple transition periods, our proposed algorithm can capture dependencies in different temporal resolutions. Introducing Bayesian neural network with Horseshoe prior as input network, we can discard the redundant input variables in the optimization process, concurrently with the learning of other parts of the model. Based on experiments with both synthetic and real-world data, we show that the proposed method significantly improves the modeling and prediction performance on multi-period time series.
DELEGATION OF CROSSING OPERATIONS TO THE FLIGHT CREW FIRST QUANTITATIVE RESULTS

In the scope of assessing the impact of the delegation of sequencing operations to the flight crew, an experiment with twelve controllers from different European countries has been carried out. The airspace simulated was a part of the Paris terminal area, and consisted of two measured sectors with arrival flights from cruise level to initial approach fix. The feeling from controllers is positive: the delegation is perceived as satisfying, and should enable a workload reduction. The acceptance is also revealed by the significant rate of use. The delegation allows for a significant reduction in the number of instructions given, specifically those given near the exit point. This suggests a positive impact on controller activity. Typically, while building sequences still remain to the controllers, maintaining sequences were delegated to flight crews. It was observed however that the sector configuration has an impact on the use of delegation. In terms of efficiency, time, distance and fuel consumption are also reduced, and it appears also that trajectories become more stable. For one of the two sessions, workload issues were investigated through selfassessment and physiological measurements. However, due to problems encountered, they did not provide a clear view. In terms of safety, no significant trend emerges although errors were observed.
Design and Implementation of a Simulator for Photovoltaic Modules

Proposed in this paper is the development of a photovoltaic module simulator, one capable of running an output characteristic simulation under normal operation according to various electrical parameters specified and exhibiting multiple advantages of being low cost, small sized, and easy to implement. In comparison with commercial simulation tools, Pspice and Solar Pro, the simulator developed demonstrates a comparable I-V as well as a P-V output characteristic curve. In addition, a series-parallel configuration of individual modules constitutes a photovoltaic module array, which turns into a photovoltaic power generation system with an integrated power conditioner.
UNEMPLOYMENT AND PRODUCTIVITY GROWTH: AN EMPIRICAL ANALYSIS OF CAUSALITY

In the literature of the past decade, some innovative contributions identified a relation between productivity growth and unemployment. In most unemployment models, productivity growth is considered as an exogenous independent variable. On the other side, the reverse causality from unemployment to productivity growth has been explored in some recent endogenous growth models. In fact, it is likely that growth and employment go hand in hand. In this paper, I look at panal data from 8 OECD countries over the period 19661995, to investigate causality between unemployment and productivity growth. I find some empirical evidence suggesting that there is two-way causation, i. e. a feedback relationship between unemployment and productivity growth. Then I estimate unemployment and growth models where both unemployment and growth are considered as endogenous variables. On balance, the estimated coefficients show a significantly negative relationship between unemployment and productivity growth.
A Novel Approach to Build Reliable and Efficient Query Services in Cloud with RASP Data Perturbation

Inspired and motivated by the widespread development and deployment of public cloud computing infrastructures, using clouds to host data query services has become an fascinating solution for the advantages on scalability and cost-saving. However, Data owners show reluctance to share the sensitive and confidential data unless the Data integrity is guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security
The Effects of Income inequality on Economic Growth Evidence from MENA Countries

The main focus of this research paper is to investigate the impacts of income inequality and other economic growth determinants on economic growth of some selected oil-importing MENA countries using a panel data for the time span 1980-2007. This paper contributes to the literature on income inequality, economic development indicators, and economic development in novel ways. Our findings indicate that income inequality decelerate the rate of change of economic development. However, the results of this research might conflict with others’ results if their research combines oil-exporting and oil-importing MENA countries in their sample or use different methodologies. Therefore, the policy recommendations should be used cautiously.