Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient In Vitro Propagation , Antioxidant and Antimicrobial Activities of Aphyllorchis Montana ( Reichenb . f . )

Abbreviations: IAA (Indol-3-acetic acid), BM-TM (BM-1Terrestrial orchid medium), IBA (Indole3-butyric acid), NAA (αNaphthaleneacetic acid), TDZ (Thidiazuron), BA (6-Benzyl adenine), KIN (6-Furfurylaminopurine), 2-iP (2-Isopentenyladenine), GA3 (Gibberellic acid), Zt (Zeatin), DPPH (2, 2-diphenyl-1-picrylhydrazyl), ABTS (2, 2’azinobis (3-ethylbenzothiozoline-6-sulfonic acid) diammonium salt), TPTZ (2, 4, 6-tripyridyl-S-triazine), EDTA (Ethylenediamine tetraacetic acid). Ganesan Mahendran Research scholar, Department of Botany, Bharathiar University, Coimbatore641046, India.
Robust Methods for Multivariate Functional Data Analysis

In the present work we have proposed a robust multivariate functional principal component analysis (RMFPCA) method, that is efficient in estimation and fast in computation, to achieve dimension reduction of dataset and to develop tools for detection of functional outliers. We intend to develop smooth principal functions as M-type smoothing spline estimators by using penalized M-regression with a bounded loss function. The proposed method is more efficient since it makes maximal use of the normally observed measurements by separately downweighing abnormally observed measurements in a single curve. Using natural cubic splines formulation the computation of the proposed method becomes fast for functional data. We have described accompanying diagnostic plots that can be used to detect possible outliers. Simulations are conducted to investigate the effectiveness of the proposed robust multivariate functional principal component analysis (PCA) based on MM estimation, in which we compare our proposed methodology with classical multivariate functional PCA. The estimation of a functional coefficient in a regression setting where the response is a scalar and explanatory variables are sampling points of a continuous process is considered. Multivariate linear model is considered for several applications, for instance chemometrics where some chemical variables have to be predicted by a digitized signal such as the Near Infrared Reflectance (NIR) spectroscopic information. These methods do not really take into account the functional nature of the data. Further it is typical to have outlying observations in such datasets. Fitting functional parameter by using functional regression is vulnerable to unusual data. Therefore from functional point of view a robust functional principal component regression (RFPCR) is proposed for regressing scalar response on the space, spanned by small number of eigenfunctions of the functional predictor. Before running a
Mismatch unemployment : evidence from Germany 2000-2010

"This paper provides detailed empirical evidence on the scope of mismatch in Germany in the past decade, using a comprehensive administrative data set that allows for disaggregation at the levels of industry, occupation and region. The findings suggest that regional mismatch did not play an important role in explaining movements of aggregate unemployment. Across industries and occupations, there was a decrease in mismatch unemployment from over 5 percent to below 4 percent (on the highest disaggregation level), whereas the share of mismatch unemployment (across industries and occupations) within total unemployment remains almost unchanged between 2000 and 2010. The results provide no evidence that the Hartz reforms have substantially reduced mismatch, in line with the fact that reallocation across occupations appears not to have been eased. " (Author's abstract, IAB-Doku) ((en))
An Effective Progress for Extreme Deviation Spying with Flawed Data Labels

: The flaw detection will detect data objects that are inconsistent with normal dataset. In additional to normal data, there exist negative outliers in many applications and data will be imperfectly labeled. This paper represents an outlier detection approach to address data with imperfect data labels into learning. Our past approach works in two steps. In the first step, we develop a pseudo training dataset by computing possible values of each example based on its local action. We introduce kernel k -means clustering method and kernel Local Outlier Factor-based method to compute the likelihood values. In the next step, we introduce the obtained possible values and limited abnormal examples into SVDD-based learning to produce a more accurate classification for global outlier detection. The proposed system has three approaches. They are Naive Bayes approach, Logistic regression and . For classification of dataset we go for these two approaches which makes easy to find outliers.
Taxation and Fiscal Reform in Ghana

To finance basic pro-poor services, the Government of Ghana must mobilize more public revenue. But tax reform has been highly controversial in Ghana. An attempt to introduce VAT in 1995 failed after widespread protests. Although a second attempt to introduce VAT in 1998 succeeded, strong resistance to tax reform remains, and the total tax base remains narrow and over-dependent on petroleum taxes. This paper argues that slow progress in public expenditure reform, in particular in raising spending on basic services, has impeded the public's willingness to pay taxes, since many people see little benefit. This is especially the case in the poorer regions, which in addition have borne the brunt of the rising petroleum tax. The paper concludes that much more attention must be given to the political economy of fiscal policy in Ghana, if lasting improvements to the fiscal system are to be achieved.
What Has Been Said, and What Remains to Be Said, About the Balanced Scorecard?

The aim of this study is to address the paradox that while the Balanced Scorecard (BSC) tool enjoys widespread penetration in the corporate world, at a theoretical level it has received many and varied criticisms. The hypothesis is that the BSC is based on a solid theoretical model that is applicable to companies. This hypothesis is analysed by means of a literature review. This review has been done over 955 documents containing the same expression related to BSC in the ISI database. Finally, after some filter criteria, 309 articles have been analyzed. The conclusion of the study is that the assumed hypothesis has been confirmed. The BSC is a model that has evolved and been honed over the years to the point where it currently has a solid and applicable theoretical foundation, which is made possible by implementing it in various sectors and countries. However, there are still some questionable aspects with regard to certain elements of the model (perspectives, indicators and cause and effect relationship).
Data-adaptive doubly robust instrumental variable methods for treatment effect heterogeneity

We consider the estimation of the average treatment effect in the treated as a function of baseline covariates, where there is a valid (conditional) instrument. We describe two doubly robust (DR) estimators: a locally efficient g-estimator, and a targeted minimum loss-based estimator (TMLE). These two DR estimators can be viewed as generalisations of the two-stage least squares (TSLS) method to semi-parametric models that make weaker assumptions. We exploit recent theoretical results that extend to the g-estimator the use of data-adaptive fits for the nuisance parameters. A simulation study is used to compare standard TSLS with the two DR estimators' finite-sample performance, (1) when fitted using parametric nuisance models, and (2) using data-adaptive nuisance fits, obtained from the Super Learner, an ensemble machine learning method. Data-adaptive DR estimators have lower bias and improved coverage, when compared to incorrectly specified parametric DR estimators and TSLS. When the parametric model for the treatment effect curve is correctly specified, the g-estimator outperforms all others, but when this model is misspecified, TMLE performs best, while TSLS can result in large biases and zero coverage. Finally, we illustrate the methods by reanalysing the COPERS (COping with persistent Pain, Effectiveness Research in Self-management) trial to make inference about the causal effect of treatment actually received, and the extent to which this is modified by depression at baseline.
Detection of a tall ridge at the core–mantle boundary from scattered PKP energy

The seismic wavefield mainly contains reflected, refracted and direct waves but energy related to elastic scattering can also be identified at frequencies of 1 Hz and higher. The scattered, highfrequency seismic wavefield contains information on the small-scale structure of the Earth's crust, mantle and core. Due to the high thermal conductivity of mantle materials causing rapid dissipation of thermal anomalies, the Earth's small-scale structure most likely reveals details of the composition of the interior, and, is therefore essential for our understanding of the dynamics and evolution of the Earth. Using specific ray configurations we can identify scattered energy originating in the lower mantle and under certain circumstances locate its point of origin in the Earth allowing further insight into the structure of the lowermost mantle. Here we present evidence, from scattered PKP waves, for a heterogeneous structure at the core-mantle boundary (CMB) beneath southern Africa. The structure rises approximately 80 km above the CMB and is located at the eastern edge of the African LLSVP.Mining-related and tectonic seismic events in South Africa, with m from 3.2 to 6.0 recorded at epicentral distances of 119.3° to 138.8° from Yellowknife Array (YKA) (Canada), show large amplitude precursors to PKP arriving 3-15 s prior to themain phase.We use array processing to measure slowness and backazimuth of the scattered energy and determine the scatterer location in the deep Earth. To improve the resolution of the slowness vector at the medium aperture YKA we present a new application of the F-statistic. The high-resolution slowness and backazimuth measurements indicate scattering from a structure up to 80 km tall at the CMB with lateral dimensions of at least 1200 km by 300 km, at the edge of the African Large Low Shear Velocity Province. The forward scattering nature of the PKP probe indicates that this is velocity-type scattering resulting primarily from changes in elastic parameters. The PKP scattering data are in agreement with dynamically supported dense material related to the Large Low Shear Velocity Province.
Models and dynamic optimisation for the asset and liability management of pension funds

In this thesis, a modelling and dynamic optimisation framework for the asset and liability management of pension funds is given. The framework consists on one hand, of a model for the pension funds' future expected cash-flows. On the other hand dynamic optimisation methods that solve the problem of optimally investing the pension fund wealth on the financial markets are given. With the obtained investment strategy the pension funds' goals should be achieved within the given financial market environment. In order to specifically address the issues of the Swiss pension funds, the main issues and problems are analysed. It is found, that pension funds operate under a confl.ict of interests between young active members, retired passive members, and the management. In order to tackle the observed problems, a cash-flow based description of the pension funds' expected future payments is developed. Payments to pensioners, active members after their retirement, and active members as they leave the pension fund before retiring are temporally sorted and their expected values found, by taking into account their probability of occurring. Two concepts for describing future expected outflows are defined: obligations and liabilities. This leads to the so called bucket structure where payments within a given time interval are aggregated. This gives a clear indication of the structure of the pension fund which now allows for an asset management which is adjusted to the current obligations or liabilities structure. The pension funds' assets are modelled by means of returns on the financial markets and the resulting portfolio dynamics. Stochastic models for the asset returns based on external economic factors arc given, as well as models for stochastic volatilities and dynamic correlations bctween assets.
Distribution patterns of Syllidae (Annelida: Polychaeta) from seagrass ( Zostera marina and Z. noltei ) meadows in the Ensenada de O Grove (Galicia, NW Spain)

This paper describes the distribution and composition of the syllid fauna inhabiting seagrass meadows in the Ensenada de O Grove (NW Spain). Samples were collected on muddy sediments colonized by either Zostera marina L. , Zostera noltei Hornemann or by a mixed meadow with both species. Syllids were dominant (13340 individuals; 37% of total polychaete abundance), including 22 species (12 genera). The mixed meadows housed the highest number of species and the Z. noltei meadow had practically no syllids. The dominant species were Exogone naidina, Parapionosyllis elegans, Parexogone hebes and Prosphaerosyllis campoyi ( > 80% of total abundance). Carnivores (mainly species of Parapionosyllis, Amblyosyllis , and Streptosyllis ) were dominant, especially in muddy sand with either Z. marina or Z. noltei and sandy mud with a mixed meadow. The most important abiotic variables for explaining the composition and distribution of the syllid fauna were bottom water salinity, sorting coefficient and carbonate content. The highest number of species was recorded at sites with a high salinity and carbonate content and the lowest at sites with a high sorting coefficient.
Emerging function of mTORC 2 as a core regulator in glioblastoma : metabolic reprogramming and drug resistance

Glioblastoma (GBM) is the most common malignant primary brain cancer of adults, accounting for more than 45% of malignant primary brain and CNS tumors. GBM is also one of the most lethal cancers, with a median survival of only 15 months for patients despite combined treatment, including surgery, radiotherapy, and chemotherapy. GBM was one of the first cancer types profiled by The Cancer Genome Atlas project and is now one of the most genomically well-characterized forms of human cancer for which a central function of dysregulated growth factor receptor signaling has been demonstrated. Receptor tyrosine kinase gene amplification and mutations, PI3K catalytic and regulatory subunit genetic mutations, and PTEN gene deletion and mutation all result in constitutive PI3K pathway activation in the majority of GBMs, thus rendering the downstream effect or mechanistic target of rapamycin (mTOR) a compelling GBM drug target. mTOR is a serine threonine kinase that exists in two distinct complexes, mTOR complexes I and II (mTORC1 and mTORC2), which differ in terms of their regulation, function, and responsiveness to the allosteric inhibitor, rapamycin. mTORC1, which contains mTOR kinase in complex with six known components including Raptor, links upstream growth factor receptor signaling to downstream protein translation and cell proliferation through PI3K. mTORC1 integrates growth factor receptor signaling into amino acid and energy status to ensure sufficient nutrients and ATP to enable tumor proliferation and cell growth. mTORC1 also regulates protein degradation, ribosome biogenesis, glucose, lipid and nucleotide metabolism, as well as autophagy. In contrast to that of mTORC1, the function of mTORC2 remains poorly understood. This study reviews mTOR signaling in GBM with a focus on the newly identified central function of mTORC2 in GBM pathogenesis.
Parameter estimation of exponentially damped sinusoids using second order statistics

In this contribution, we present a new approach for the estimation of the parameters of exponentially damped sinusoids based on the second order statistics of the observations. The method may be seen as an extension of the minimum norm principal eigenvectors method (see [1]) to cydo-correlation statistics domain. The proposed method exploits the nullity property of the cy do-correlation of stationary processes at non-zero cyclo-frequencies [2], This property allows in a pre-processing step to get rid from stationary additive noise. This approach presents many advantages in comparison with existing higher order statistics based approaches [3]: (i) First it deals only with second order statistics which require generally few samples in contrast to higher-order methods, (ii) it deals either with Gaussian and non-Gaussian additive noise, and (iii) also deals either with white or temporally colored (with unknown autocorrelation sequence) additive noise. The effectiveness of the proposed method is illustrated by some numerical simulations.
EQUILIBRIUM AND THERMODYNAMIC MODELING FOR THE BIOSORPTION OF CU (II) IONS ON LIME (CITRUS AURANTIFOLIA) PEEL BIOSORBENTS

A novel biosorbent, derived from dried peels of lime (Citrus aurantifolia ) was used for the removal of Cu (II) ions from aqueous solutions. The effects of pH, biosorbent dosage and temperature on the biosorption of Cu (II) ions were investigated in batch mode. At optimal conditions, with pH 5, 0.4 g/L of biosorbent dosage, 100 mg/L of initial Cu (II) concentration and temperature 80 ⁰C, the maximum biosorption capacity obtained was 100 mg/g. Equilibrium data was best described by both the Langmuir and Freundlich model, indicating the involvement of more than one mechanism for the sorption process. Thermodynamic studies revealed that the biosorption process is favorable, spontaneous and endothermic in nature. The outcome of this study revealed that dried lime peel powder has good potential to be used as a low cost and environmentally friendly biosorbent for the removal of Cu (II) ions from aqueous solutions.
Variations in soil moisture content in a rangeland catchment

Soil water studies for California rangelands have focused on near-surface hydrologic processes, limiting our understanding of spatial-temporal dynamics of the water regime below the root zone. Soil moisture content and potential were monitored for 16 months in 12 locations in an annual grass dominated 20 ha catchment. The data collected were analyzed by ANOVA to determine significant spatial and temporal differences in soil moisture. Further analysis identified variables that influenced the amount of moisture present at a particular subsurface location. It was determined that there were significant differences in the amount of soil moisture present along the vertical profile of each site and between sites. Soil texture, type of vegetation cover, and elevation were the significant variables that influenced the soil moisture status.
Permeability of continental crust influenced by internal and external forcing

The permeability of continental crust is so highly variable that it is often considered to defy systematic characterization. However, despite this variability, some order has been gleaned from globally compiled data. What accounts for the apparent coherence of mean permeability in the continental crust (and permeability–depth relations) on a very large scale? Here we argue that large-scale crustal permeability adjusts to accommodate rates of internal and external forcing. In the deeper crust, internal forcing – fluxes induced by metamorphism, magmatism, and mantle degassing – is dominant, whereas in the shallow crust, external forcing – the vigor of the hydrologic cycle – is a primary control. Crustal petrologists have long recognized the likelihood of a causal relation between fluid flux and permeability in the deep, ductile crust, where fluid pressures are typically near-lithostatic. It is less obvious that such a relation should pertain in the relatively cool, brittle upper crust, where nearhydrostatic fluid pressures are the norm. We use first-order calculations and numerical modeling to explore the hypothesis that upper-crustal permeability is influenced by the magnitude of external fluid sources, much as lower-crustal permeability is influenced by the magnitude of internal fluid sources. We compare model-generated permeability structures with various observations of crustal permeability.
Improved Methods for Statistical Modelling of Monophonic Music

N-Gram based models have been used for a variety of musical tasks including computer-assisted composition, machine improvisation, music information retrieval, stylistic analysis and cognitive modelling. We present an application-independent evaluation of some recent techniques for improving the performance of a subclass of n-gram models on a range of monophonic music data. We have applied these techniques incrementally to eight melodic datasets using cross entropy computed by 10-fold cross-validation on each dataset as our performance metric. The results demonstrate that significant and consistent improvements in performance are afforded by several of the evaluated techniques. We discuss the results in terms of previous research carried out in the field of data compression and with natural language and music corpora and conclude by presenting some important directions for future research.
The State of Communal Farmers’ Livelihoods and Vulnerability in Rural Zimbabwe after the Fast Track Land Reform Program (FTLRP): A Case Study of the Hurungwe District

Land ownership is a recognised human right. Being in possession of land can be the foundation for livelihood strategies. Land ownership also has a relationship with vulnerability patterns especially among those communities that depend on farming for their survival. Using a mixed methods approach, this study interrogated the Zimbabwe Fast Track Land Reform Programme (FTLRP) and its impact on rural livelihoods and vulnerability patterns before and after the year 2000. The sustainable livelihoods framework was employed as a theoretical framework in unpacking the study. The study findings pointed to asset ownership structure as important in determining vulnerability and livelihoods among communal farmers. Those endowed with more assets significantly produced more from their land based activities. The findings also showed that the challenges of the Zimbabwean economy invariably impacted on vulnerability and livelihood patterns. Apparently, any land reform without the necessary support (through finance, relevant assets and the necessary infrastructure) was generally not effective in reducing poverty. Land reform consequently must be complemented by programmes that enabled land beneficiaries to access financing and support in the form of assets that assisted them to utilise their land more productively. It was also noted that due to constrained livelihoods, some beneficiaries ended up resorting to relying on the natural resources through cutting down of trees for resale and engaging in unregulated mining activities. Climate change was a reality as most respondents had a perception that droughts were becoming more frequent and this affected their livelihoods. Any land reform programme hence must be comprehensive in order to have any significant impact on communal farmers’ livelihoods patterns.