Miracle-dz/UniFAR-FTUs huggingface dataset:
The solar cycle variation of total ozone: Dynamical forcing in the lower stratosphere

Multiple regression methods are applied to estimate the solar cycle variation of (1) zonal mean ozone as a function of altitude and latitude using a combination of Nimbus 7 solar backscattered ultraviolet (SBUV) and National Oceanic and Atmospheric Administration (NOAA) 11 SBUV/2 ozone profile data for a 15-year period; (2) total ozone as a function of latitude, longitude, and season using Nimbus 7 total ozone mapping spectrometer (TOMS) data for a 13.3-year period; (3) lower stratospheric temperature as a function of latitude, longitude, and season using microwave sounding unit (MSU) Channel 4 data for a 16-year period; and (4) lower stratospheric geopotential height as a function of latitude, longitude, and season in the northern hemisphere using Berlin height data for a 30-year period. According to the SBUV-SBUV/2 data, most (about 85%) of the 1.5–2% solar cycle variation of global mean total column ozone occurs in the lower stratosphere (altitudes < 28 km). Evidence is obtained for a related solar cycle variation of lower stratospheric temperature (50–150 mbar) and geopotential height (30, 50, and 100 mbar) with geographic dependences similar to that of the solar cycle variation of total ozone. Specifically, total ozone, lower stratospheric temperature, and lower stratospheric geopotential height have annual mean solar regression coefficients in the northern hemisphere that reach a maximum near 30°N latitude within a longitude sector extending from approximately 160°E to 250°E. Maximum variations from solar minimum to maximum in this sector are approximately 11 Dobson units, 0.8 K near 100 mbar, and 60 m at 50 mbar, respectively. Seasonal solar regression coefficients tend to be statistically significant over larger areas in summer but have larger amplitudes within limited regions in winter. These geographic similarities between total ozone, lower stratospheric temperature, and geopotential height solar coefficients suggest that changes in lower stratospheric dynamics between solar minimum and maximum may play an important role in driving the observed total ozone solar cycle variation. To test this hypothesis, a simplified perturbation ozone transport model is applied to calculate the expected total ozone variation owing to dynamical forcing for the calculated geopotential height solar coefficients, climatological ozone mixing ratios, and zonal winds. For the summer season during which the solar regression coefficients are significant over the largest area, both the amplitude and latitude dependence of the observed solar cycle ozone variation are approximately consistent with the model estimates.
Performance Analysis of Wireless Cooperative Networks with Iterative Incremental Relay Selection

In this paper, an iterative incremental relay selection (IIRS) scheme is considered for wireless cooperative networks in order to increase the reliability of transmission. Different from the conventional incremental relay selection which incrementally selects the best relay for only one iteration; the IIRS scheme iteratively applies the incremental relaying and relay selection processes. To evaluate the performance of the proposed scheme, outage probability and average capacity of the system are investigated through analysis and simulation. This scheme provides (I+1) diversity order in a system of I relays as the highest diversity order which can be provided by all participate (AP) cooperative scheme. Also, it is shown that the IIRS scheme combats with the spectral efficiency loss resulted from applying all of the relays. As the cost of the improvement, it is seen that the average required feedback bits to implement the IIRS scheme leads to I×log2 (I+1) bits of low signal to noise ratio (SNR), while it leads to log2 (I+1) bits at higher SNRs which is acceptable for implementation. Considering the provided improvement along with the limited feedback reveals that the IIRS scheme can be applied as an efficient scheme compared to the other common cooperative schemes. Finally, numerical results indicate the validity of the analysis, especially at high SNRs. Review History: Received: 3 October 2015 Revised: 27 September 2016 Accepted: 25 October 2016 Available Online: 29 October 2016
Retrieval of snow freeboard of Antarctic sea ice using waveform fitting of CryoSat-2 returns

Abstract. In this paper we develop a CryoSat-2 algorithm to retrieve the surface elevation of the air–snow interface over Antarctic sea ice. This algorithm utilizes a two-layer physical model that accounts for scattering from a snow layer atop sea ice as well as scattering from below the snow surface. The model produces waveforms that are fit to CryoSat-2 level 1B data through a bounded trust region least-squares fitting process. These fit waveforms are then used to track the air–snow interface and retrieve the surface elevation at each point along the CryoSat-2 ground track, from which the snow freeboard is computed. To validate this algorithm, we compare retrieved surface elevation measurements and snow surface radar return power levels with those from Operation IceBridge, which flew along a contemporaneous CryoSat-2 orbit in October 2011 and November 2012. Average elevation differences (standard deviations) along the flight lines (IceBridge Airborne Topographic Mapper, ATM – CryoSat-2) are found to be 0.016 cm (29.24 cm) in 2011 and 2.58 cm (26.65 cm) in 2012. The spatial distribution of monthly average pan-Antarctic snow freeboard found using this method is similar to what was observed from NASA's Ice, Cloud, and land Elevation Satellite (ICESat), where the difference (standard deviation) between October 2011–2017 CryoSat-2 mean snow freeboard and spring 2003–2007 mean freeboard from ICESat is 1.92 cm (9.23 cm). While our results suggest that this physical model and waveform fitting method can be used to retrieve snow freeboard from CryoSat-2, allowing for the potential to join laser and radar altimetry data records in the Antarctic, larger (∼30 cm) regional differences from ICESat and along-track differences from ATM do exist, suggesting the need for future improvements to the method. Snow–ice interface elevation retrieval is also explored as a potential to obtain snow depth measurements. However, it is found that this retrieval method often tracks a strong scattering layer within the snow layer instead of the actual snow–ice interface, leading to an overestimation of ice freeboard and an underestimation of snow depth in much of the Southern Ocean but with promising results in areas such as the East Antarctic sector.
Performance Study on Data Discretization Techniques Using Nutrition Dataset

Data mining has been widely used in medical and health care domain as the predictive models. Data preprocessing is one of the important steps in data mining process as it consumes about sixty percent of the data mining project effort. Data discretization is one of the pre-processing methods. It makes learning process faster and more accurate. In this paper we proposed the nutrition data classification modeling using two discretization techniques i.e. Boolean Reasoning and Entropy Algorithm. Both techniques are selected from detail study of fifty discretization techniques available to date. The purpose of this work is to compare the performance of different data discretization techniques and to find the most suitable discretization techniques for the nutrition data set. The nutrition data set are obtained from a survey conducted and it contains 160 attributes and 820 records. Both techniques are used to discretize the nutrition data set and the classification performance of both techniques in terms of accuracy and the number of rules evaluated. The experimental results showed that Boolean Reasoning performs better than Entropy Algorithm which gives higher classification accuracy in nutrition data set.
The turn in recent economics and return of orthodoxy

This paper examines change on the economics research frontier, and asks whether the current competition between new research programs may be supplanted by a new single dominant approach in the future. The paper discusses whether economics tends to be dominated by a single approach or reflect a pluralism of approaches, and argues that historically it has alternated between the two. It argues that orthodoxy usually emerges from heterodoxy, and interprets the division between orthodoxy and heterodoxy in terms of a core-periphery distinction. Regarding recent economics, the paper maps out two different types of combinations of new research programs as being synchronic or diachronic in nature. It treats the new research programs as a new kind of heterodoxy, and asks how a new orthodoxy might arise out of this new heterodoxy and traditional heterodoxy. It discusses this question by advancing two views regarding how the two different types of combinations in the new research programs might consolidate along the lines of three shared commitments with traditional heterodoxy to form a new orthodoxy in economics.
Modelling the demand for new nitrogen fixation by terrestrial ecosystems

Abstract. Continual input of reactive nitrogen (N) is required to support the natural turnover of N in terrestrial ecosystems. This N demand can be satisfied in various ways, including biological N fixation (BNF) (the dominant pathway under natural conditions), lightning-induced abiotic N fixation, N uptake from sedimentary substrates, and N deposition from natural and anthropogenic sources. We estimated the global new N fixation demand (NNF), i.e. the total new N input required to sustain net primary production (NPP) in non-agricultural terrestrial ecosystems regardless of its origin, using a N-enabled global dynamic vegetation model (DyN-LPJ). DyN-LPJ does not explicitly simulate BNF; rather, it estimates total NNF using a mass balance criterion and assumes that this demand is met from one source or another. The model was run in steady state and then in transient mode driven by recent changes in CO2 concentration and climate. A range of values for key stoichiometric parameters was considered, based on recently published analyses. Modelled NPP and C : N ratios of litter and soil organic matter were consistent with independent estimates. Modelled geographic patterns of ecosystem NNF were similar to other analyses, but actual estimated values exceeded recent estimates of global BNF. The results were sensitive to a few key parameters: the fraction of litter carbon respired to CO2 during decomposition and plant-type-specific C : N ratios of litter and soil. The modelled annual NNF increased by about 15 % during the course of the transient run, mainly due to increasing CO2 concentration. The model did not overestimate recent terrestrial carbon uptake, suggesting that the increase in NNF demand has so far been met. Rising CO2 is further increasing the NNF demand, while the future capacity of N sources to support this is unknown.
SDN-based Solution of Path Restoration for Smart Grids

Due to the heterogeneity and the nature of distributed control, the existing electric power data communication network based on Synchronous Digital Hierarchy (SDH) has been unable to meet the requirements of dynamic resource allocation and fine-grained traffic monitoring. The OpenFlow-based Software Defined Network (SDN) allows operators dynamically control and monitor the whole network using the software running on the operating system of the centralized controller (e.g., NOX controller). Thus, in this paper, we propose the SDN-based fault-tolerant solution for the electric power data communication network. The feasibility and efficiency of the proposed solution are verified, and the performances of the dynamic end-to-end path restoration, such as the CPU utilization of NOX and end-to-end delay, are quantitatively evaluated in our experimental platform. The experimental results indicate that the dynamic end-to-end path restoration can be achieved within tens of milliseconds by using our SDN-based solution.
Bayesian Estimation and Model Selection for the Weekly Colombian Exchange Rate

This document reviews and applies recently developed techniques for Bayesian estimation and model selection in the context of Time Series modeling for Stochastic Volatility. After the literature review on Generalized Conditional Autoregressive models, Stochastic Volatility models, and the relevant results on Markov chain Monte Carlo methods (MCMC), an example applying such techniques is shown. The methodology is used with a series of Weekly Colombian - USA Exchange Rate on seven different models. The GARCH model, which uses Type-IV Pearson distribution, is favored for the selecting technique, Reversible Jump MCMC, over other models, including Stochastic Volatility Models with a Student-t distribution.
Combining site and regional flood information using a Bayesian Monte Carlo approach

Regression‐based methods are widely used in flood regionalization. Since the generalized least squares (GLS) regression model properly accounts for both model and sampling errors, realistic estimates of uncertainty can be obtained. This study presents a general Bayesian approach for inferring the GLS regional regression model and for combining with any available site information to obtain the most accurate flood quantiles. A robust block Metropolis scheme is developed to sample the posterior distribution of the GLS regression model parameters. A particular feature of the sampler is the use of a uniform proposal for the regional model error variance to avoid convergence difficulties. A simple but general procedure, based on importance sampling, is developed to combine any kind of site information with regional information. The benefit of this approach is that all available information is fully exploited, and uncertainty is rigorously quantified as well as being minimized. Two case studies are presented. A synthetic case study illustrates the complex nature of the posterior distribution for the regional model error variance with its shape dependent on how much sampling error dominates the total error in the regional GLS model. A second case study involving 24 sites from the east coast of Australia is presented for the log Pearson III model. It demonstrates the significant benefit of combining site and regional information and the quantification of uncertainty.
Listing Requirements Lose IPO-Screening Functions: Evidence from the Emerging Growth Enterprise Market of China

Using two multivariate regression models based on prior studies, this paper aims to examine whether the listing requirements of the GEMC are able to help the exchange to screen high quality IPO firms. It suggests that the approved IPO companies have better performances than failed ones, but listing requirements of the GEMC are unable to screen high quality issuers to go public, because the majority of listed companies performed poorly rather than better after their IPOs. This result is against previous findings that regard an IPO market as a screening device.
Consumption and reaccumulation of forest fuels in oak shelterwood stands managed with prescribed fire

In the shelterwood-burn technique, a moderate- to high-intensity growing-season prescribed fire is essential to achieve desired oak regeneration goals. These levels of fire intensity are dependent on the increased fuel loadings created by the preceding first removal cut. However, the loadings of forest fuels and their fluctuation during implementation of the shelterwood-burn technique have not been studied. From 1994 to 1997, three mature uncut oak stands, three oak shelterwoods, and three burned oak shelterwoods in central Virginia were examined to determine fuel loadings before, during, and after implementation of the shelterwoodburn technique. Prior to the first removal cut, total fuel loadings averaged 10.5 tons/acre and this was evenly divided between small and large fuels. The harvest increased total fuel loadings to between 20 and 25 tons/ acre with nearly all this increase occurring in medium and large fuels. The prescribed burns consumed virtually all the small fuels and about half the medium/large fuels, but loadings for all size classes were 80 percent of pre-treatment levels within 3 years after the fires. It appears that forest fuel loadings in oak shelterwoods return to their initial levels within 5 years post-fire.
Multitarget Direct Localization Using Block Sparse Bayesian Learning in Distributed MIMO Radar

The target localization in distributed multiple-input multiple-output (MIMO) radar is a problem of great interest. This problem becomes more complicated for the case of multitarget where the measurement should be associated with the correct target. Sparse representation has been demonstrated to be a powerful framework for direct position determination (DPD) algorithms which avoid the association process. In this paper, we explore a novel sparsity-based DPD method to locate multiple targets using distributed MIMO radar. Since the sparse representation coefficients exhibit block sparsity, we use a block sparse Bayesian learning (BSBL) method to estimate the locations of multitarget, which has many advantages over existing block sparse model based algorithms. Experimental results illustrate that DPD using BSBL can achieve better localization accuracy and higher robustness against block coherence and compressed sensing (CS) than popular algorithms in most cases especially for dense targets case.
TOTAL PHYSICAL RESPONSE AND KEYWORD METHOD

Early language learning for children is increasingly common, and the majority of parents and the public do not see it as superfluous or overburdening children. Moreover, teaching a foreign language to very young children has been an increasingly dominant trend in most globalized societies. While there is abundant literature that supports teaching a foreign language at an early age through language immersion programs, little is known about the efficiency of strategies used to explicitly teach new vocabulary words in a foreign language to young learners. This empirical investigation aimed to assess and compare the efficiency of two mnemonics that have been traditionally used to explicitly teach new foreign language words: the Keyword Method (KWM) and the Total Physical Response (TPR). Results indicate that the KWM is more effective than TPR in teaching new vocabulary words in a foreign language to early elementary school children.
Osteosarcoma of Mandible

Osteosarcomas (OS) are malignant neoplasms of the bone that commonly affect the long bones with rare presentation in jaws. Osteosarcomas of jaws represent about 6 to 8% of all OS, with an incidence of approximately 1 in 1.5 million persons per year. Although the exact cause of OS is still unknown, defects in the retinoblastoma (RB) and p53 genes play an important role in the process. It is characterized histologically by anaplastic stroma with direct osteoid production. Here, we report a case of OS in a 30-year-old female, who came with a massive bony swelling in the right mandibular region.
Integral Sliding Modes with Nonlinear -Control for Time-Varying Minimum-Phase Underactuated Systems with Unmatched Disturbances

This paper presents a methodology for controlling nonlinear time-varying minimum-phase underactuated systems affected by matched and unmatched perturbations. The proposed control structure consists of an integral sliding mode control coupled together with a global nonlinear -control for rejecting vanishing and nonvanishing matched perturbations and for attenuating the unmatched ones, respectively. It is theoretically proven that, using the proposed controller, the origin of the free-disturbance nonlinear system is asymptotically stabilized, while the matched disturbances are rejected whereas the -gain of the corresponding nonlinear system with unmatched perturbation is less than a given disturbance attenuation level with respect to a given performance output. The capability of the designed controller is verified through a flexible joint robot manipulator typically affected by both classes of external perturbations. In order to assess the performance of the proposed controller, an existing sliding modes controller based on a nonlinear integral-type sliding surface is also implemented. Both controllers are then compared for trajectory tracking tasks. Numerical simulations show that the proposed approach exhibits better performance.
Vine growth and yield response of Alphonse Lavallee (V. vinifera L.) grapevines to plant growth promoting rhizobacteria under alkaline condition in soilless culture

High carbonate content in soil negatively affect plant growth, because the availability of nutrients is restricted due to high pH. The present investigations were carried out to reveal possible alleviating effects of the exogenous root inoculation PGPRs on development and physiology of soilless-grown grapevines cultivated under alkaline stress in controlled glass house. pH of growth medium was increased from 7.5 to the values ranging from 7.9 (control) to 8.1 (A18) according to the bacterial inoculations by NaHCO3 supplementations. Bacteria inoculations did not result in statistically significant differences in pH values of growth media. The bacterial population density found in the rhizosphere of grapevines ranged from 6 × 10 8 CFU mL −1 (M-3) to 9 × 10 8 CFU mL −1 (Ca-637). The highest value of shoot thickness was obtained from Ca-637 (5.3 mm), followed by A18 (5.2 mm), while M3 did not significantly affected the shoot thickness. The greatest pruning residue per vine was obtained from A18 treatment (81.5 g), followed by Ca-637 (80.8 g) while the lowest value was determined in control. Vine yield was the greatest with A18 (1128 g) treatment and was followed by Ca 637 (1059 g). Considering the general observations, root inoculation of PGPRs A18 and Ca-637 may be recommended in enhancing bioremediation of alkali growth media.