Miracle-dz/UniFAR-FTUs huggingface dataset:
Production of Thermostable Organic Solvent Tolerant Keratinolytic Protease from Thermoactinomyces sp. RM4: IAA Production and Plant Growth Promotion

There are several reports about the optimization of protease production, but only few have optimized the production of organic solvent tolerant keratinolytic proteases that show remarkable exploitation in the development of the non-polluting processes in biotechnological industries. The present study was carried with aim to optimize the production of a thermostable organic solvent tolerant keratinolytic protease Thermoactinomyces sp. RM4 utilizing chicken feathers. Thermoactinomyces sp. RM4 isolated from the soil sample collected from a rice mill wasteyard site near Kashipur, Uttrakhand was identified on the basis of 16S rDNA analysis. The production of organic solvent tolerant keratinolytic protease enzyme by Thermoactinomyces sp. RM4 was optimized by varying physical culture conditions such as pH (10.0), temperature (60°C), inoculum percentage (2%), feather concentration (2%) and agitation rate (2 g) for feather degradation. The result showed that Thermoactinomyces sp. RM4 potentially produces extra-cellular thermostable organic solvent tolerant keratinolytic protease in the culture medium. Further, the feather hydrolysate from keratinase production media showed plant growth promoting activity by producing indole-3-acetic acid itself. The present findings suggest that keratinolytic protease from Thermoactinomyces sp. RM4 offers enormous industrial applications due to its organic solvent tolerant property in peptide synthesis, practical role in feather degradation and potential function in plant growth promoting activity, which might be a superior candidate to keep ecosystem healthy and functional.
Disparities in Cardiovascular Disease Risk in the United States

This is a comprehensive narrative review of the literature on the current science and evidence of population-level differences in risk factors for heart disease among different racial and ethnic population in the United States (U.S.). It begins by discussing the importance of population-level risk assessment of heart disease in light of the growth rate of specific minority populations in the U.S. It describes the population-level dynamics for racial and ethnic minorities: a higher overall prevalence of risk factors for coronary artery disease that are unrecognized and therefore not treated, which increases their likelihood of experiencing adverse outcome and, therefore, potentially higher morbidity and mortality. It discusses the rate of Acute Coronary Syndrome (ACS) in minority communities. Minority patients with ACS are at greater risk of myocardial infarction (MI), rehospitalization, and death from ACS. They also are less likely than non-minority patients to receive potentially beneficial treatments such as angiography or percutaneous coronary intervention. This paper looks at the data surrounding the increased rate of heart disease in racial and ethnic minorities, where the risk is related to the prevalence of comorbidities with hypertension or diabetes mellitus, which, in combination with environmental factors, may largely explain CHF disparity. The conclusion is that it is essential that healthcare providers understand these various communities, including nuances in disease presentation, risk factors, and treatment among different racial and ethnic groups. Awareness of these communities’ attributes, as well as differences in incidence, risk factor burdens, prognosis and treatment are necessary to mitigate racial and ethnic disparities in heart disease.
STATISTICAL AND NEURAL NETWORK CLASSIFIERS FOR CITRUS DISEASE DETECTION USING MACHINE VISION

The citrus industry is an important constituent of Florida’s overall agricultural economy. Proper disease control measures must be undertaken in citrus groves to minimize losses. Technological strategies using machine vision and artificial intelligence are being investigated to achieve intelligent farming, including early detection of diseases in groves, selective fungicide application, etc. This research used a texture analysis method termed the color co-occurrence method (CCM) to determine whether classification algorithms could be used to identify diseased and normal citrus leaves. Normal and diseased citrus leaf samples with greasy spot, melanose, and scab were collected in the field and brought to the laboratory for the development of suitable segmentation and classification algorithms. Four feature models were created for classification analysis using varying subsets of a 39-variable texture feature set. The classification strategies used were based on a Mahalanobis minimum distance classifier, using the nearest neighbor principle, as well as neural network classifiers based on the back-propagation algorithm and radial basis functions. The leaf sample discriminant analysis using the Mahalanobis statistical classifier and the CCM textural analysis achieved classification accuracies of over 95% for all classes (99% mean accuracy) when using hue and saturation texture features. Likewise, a back-propagation neural network algorithm achieved accuracies of over 90% for all classes (95% mean accuracy) when using hue and saturation features. It was concluded that the Mahalanobis statistical classifier and the back-propagation neural network classifier performed equally well when using ten hue and saturation texture features selected through a stepwise variable reduction method. Future studies will seek to apply the developed algorithms in a natural citrus grove environment.
EFFECTS OF STRATEGIC ALLIANCE ON COMPETITIVE ADVANTAGE IN AIRTEL KENYA LIMITED

ix CHAPTER ONE : INTRODUCTION . 1 1.1 Background of the Study . 1 1.1.1 Strategic Alliance . 2 1.1.2 Competitive Advantage . 3 1.1.3 Telecommunication Industry in Kenya . 4 1.1.4 Airtel Networks Kenya Limited. 5 1.2 Research Problem . 6 1.3 Research Objectives . 8 1.4 Value of the Study . 8 CHAPTER TWO : LITERATURE REVIEW . 9 2.
Recurrent neural network language model for English-Indonesian Machine Translation: Experimental study

At recent time, the statistical based language model and neural based language model are still dominating the researches in the field of machine translation. The statistical based machine translation today is the fastest one but it has a weakness in term of accuracy. In contrast, the neural based network has higher accuracy but has a very slow computation process. In this research, a comparison between neural based network that adopts Recurrent Neural Network (RNN) and statistical based network with n-gram model for two-way English-Indonesian Machine Translation (MT) is conducted. The perplexity value evaluation of both models show that the use of RNN obtains a more excellent result. Meanwhile, Bilingual Evaluation Understudy (BLEU) and Rank-based Intuitive Bilingual Evaluation Score (RIBES) values increase by 1.1 and 1.6 higher than the results obtained using statistical based.
An image scrambling encryption using chaos-controlled Poker shuffle operation

Image scrambling is an important technique in digital image encryption and digital image watermarking. This paper presents a novel image scrambling method using Poker shuffle, which is controlled dynamically by chaotic system. Compared with other scrambling techniques such as algebraic permutations and chaotic permutations, the proposed method has properties of nonlinearity, non-analytic formula and large key space. Moreover, its scrambling performance is satisfied and can deal with non-square image. All these features show that the proposed method is more secure and efficient for image scrambling encryption.
An accurate model of inversion carrier effective mobility considering scattering mechanisms for nanoscale MOS devices

Electrical characteristics of metal oxide-semiconductor field effect transistor (MOSFET) are numerically studied here. Carriers scattering in the inversion channel of MOSFET dominates the drain current. In this work, carrier scattering in the inversion channel has been measured in terms of mobility. Mobility for ultra-short channel device is far different from the mobility of long channel device. By calculating several important device parameters, such as the threshold voltage, threshold voltage roll-off, sub-threshold swing, the effect of channel length on mobility and drain current, MOSFET with nanoscale channel length is simulated. This outlines an easy approach to model carrier transport in MOS devices using modified semi-classical expressions so that the evaluated results are consistent with that derived quantum mechanically. For this, an existing current-voltage (I–V) compact model is utilized and modified by appending a simplified threshold voltage derivation and a more precise carrier mobility model which is valid for short channel device. As a verification of the proposed model, both the mobility and the drain current are compared with the available experimental and simulated data.
REMOVING REDUNDANCY IN HIGH-PRECISION NEWTON ITERATION

This paper presents new algorithms for several high-precision operations in the power series ring C[[x]]. Compared to computing n coefficients of a product in C[[x]], computing n coefficients of a reciprocal in C[[x]] takes 1.5+o(1) times longer; a quotient or logarithm, 2.16666 . +o(1) times longer; a square root, 1.83333 . +o(1) times longer; an exponential, 2.83333 . +o(1) times longer. Previous algorithms had worse constants. The same ideas apply to high-precision computations in R, Q p , etc.
An annual study of inorganic and organic nitrogen and phosphorus and silicic acid in the southeastern Beaufort Sea

[1] Water column samples from the Mackenzie Shelf, the Beaufort Sea, and the Amundsen Gulf were obtained during 2003–2004 to investigate nutrient dynamics in an Arctic ecosystem influenced by a large river and flaw lead polynya. Nutrient inventories in the upper water column showed a significant seasonal drawdown of nitrate (NO3−) and silicic acid (Si(OH)4) (1:1.75 mol/mol) and subsequent accumulation of nitrite (NO2−) and ammonium (NH4+). Dissolved organic nitrogen and phosphorus concentrations (DON and DOP) were elevated in surface waters during the time of peak phytoplankton growth (bloom and postbloom phases) and covaried in a 17:1 molar ratio. Vertical profiles showed the typical middepth maxima in NO3−, PO43−, and Si(OH)4 at salinity 32–33.1. The concentration of DOP (0.76 ± 0.27 μmol P L−1) was also 2 times higher in this layer compared to the surface average and inversely correlated with the water mass tracer N* [N* = (NO3− − 16 × PO43− + 2.9) × 0.87], suggesting that Pacific-derived waters were a source of the enrichment. Below the mixed layer, DON was generally constant with depth, although averaged profiles (like those of urea) suggested the presence of subsurface maxima at 50 m and between 250 and 300 m. Regeneration ratios varied with depth and were approximately 9.0 mol −O2/mol NO3− and 122.5 mol −O2/mol PO43− in shallow Pacific-derived waters (halocline layer) and 17.4 and 193.5 in deep Atlantic waters (Atlantic layer), respectively. Deep waters of the Amundsen Gulf contained an excess of 1.7 μmol NO3− L−1, 0.12 μmol PO43− L−1, and 6.2 μmol Si(OH)4 L−1 and a deficit in O2, relative to waters of similar density in the Beaufort Sea, in proportions consistent with the remineralization ratios derived from oxygen-nutrient regressions. Enhanced export of particulate matter from the overlying polynya and subsequent remineralization at depth are hypothesized to create this nutrient enrichment.
Clusters and Entrepreneurship

This paper examines the role of regional clusters in regional entrepreneurship. We focus on the distinct influences of convergence and agglomeration on growth in the number of start-up firms as well as in employment in these new firms in a given region-industry. While reversion to the mean and diminishing returns to entrepreneurship at the region-industry level can result in a convergence effect, the presence of complementary economic activity creates externalities that enhance incentives and reduce barriers for new business creation. Clusters are a particularly important way through which location-based complementarities are realized. The empirical analysis uses a novel panel dataset from the Longitudinal Business Database of the Census Bureau and the U.S. Cluster Mapping Project (Porter, 2003). Using this dataset, there is significant evidence of the positive impact of clusters on entrepreneurship. After controlling for convergence in start-up activity at the region-industry level, industries located in regions with strong clusters (i.e. a large presence of other related industries) experience higher growth in new business formation and start-up employment. Strong clusters are also associated with the formation of new establishments of existing firms, thus influencing the location decision of multiestablishment firms. Finally, strong clusters contribute to start-up firm survival.
System Level Performance Evaluation of LTE-V2X Network

Vehicles are among the fastest growing type of connected devices. Therefore, there is a need for Vehicle-toEverything (V2X) communication i.e. passing of information from a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) and vice versa. In this paper, the main focus is on the communication between vehicles and road side units (RSUs) commonly referred to as V2I communication in a multi-lane freeway scenario. Moreover, we analyze network related bottlenecks such as the maximum number of vehicles that can be supported when coverage is provided by the Long Term Evolution Advanced (LTE-A) network. The performance evaluation is assessed through extensive system-level simulations. Results show that new resource allocation and interference mitigation techniques are needed in order to achieve the required high reliability requirements, especially when network load is high.
Consumer Demand for Meat Cuts and Seafood

Household at-home consumption of different types and cuts of meat and fish products is investigated by estimating a large censored demand system with a two-step procedure using ACNielsen's Homescan data. We find different price and expenditure elasticities between low-income and high-income households. High income households are less responsive to price changes, and the substitution patterns also differ between the low- and high-income households. Whereas the uncompensated elasticities suggest a mixture of gross substitutes and complements among the products for both low- and high-income households, the compensated elasticities suggest net substitution is the obvious pattern for the low-income households.
The Bulls and Bears in the Cross-Section of Stock Returns: Exploring an Asymmetric Semivariance Risk Factor

Many financial decision-makers seem to regard risk as the variability of returns below some pre-specified target and treat above-target variability as a sweetener. The disutility from losses also appears to be larger than the utility from gains. Using some simple metrics of downside bearishness and upside bullishness constructed from semivariances, this paper tests for the empirical content of this asymmetry. Some of these simple metrics are priced in the U.S. stock market. In particular, exploring a composite metric of asymmetric risk reveals that non-linearity in the covariation of stock returns with bullish and bearish states of the market carries a significant price. Also, market premium for bearishness is larger in magnitude than that for bullishness, lending support to the existence of loss aversion in the aggregate. While small-cap stocks tend to be more bearish than bullish, the asymmetric risk effect is not spuriously driven by the size effect. Finally, some results consistent with an aymmetric-risk-based explanation for the puzzles of return momentum and reversal are presented.
The Inflation Tax in a Real Business Cycle Model

Money is incorporated into a real business cycle model using a cash-in-advance constraint. The model is used to analyze whether the business cycle is different in high-inflation and low-inflation economies and to analyze the impact of variability in the growth rate of money. The welfare cost of the inflation tax is measured and the steady-state properties of high and low inflation economies are compared. The welfare cost of a sustained (10 percent) inflation is estimated to be between 0.11 percent and 0.4 percent of GNP. The features of the business cycle are the same in high and low inflation economies, but the steady-state paths may be quite different. Copyright 1989 by American Economic Association.
The Spirit of the Laws in Mozambique

Mozambique has been hailed as a success story by the international community, which has watched it evolve through a series of violent political upheavals: from colonialism, through socialism, to its current democracy. As Juan Obarrio shows, however, this view neglects a crucial element in Mozambique's transition to the rule of law: the re-establishment of traditional chieftanship and customs entangled within a history of colonial violence and civil war. Drawing on extensive historical records and ethnographic fieldwork, he examines the role of customary law in Mozambique to ask a larger question: what is the place of law in the neoliberal era, in which the juridical and the economic are deeply intertwined in an ongoing state of structural adjustment? Having made the transition from a people's republic to democratic rule in the 1990s, Mozambique offers a fascinating case of postwar reconstruction, economic opening, and transitional justice, one in which the customary has played a central role. Obarrio shows how its sovereignty has met countless ambiguities within the entanglements of local community, nation-state, and international structures. Ultimately, he looks toward local rituals and relations as producing an emergent kind of citizenship in Africa, which he dubs "customary citizenship," forming not a vestige of the past but a yet ill-defined political future.
Portable LCD Image Quality: Effects of Surround Luminance

Liquid crystal display (LCD) has become a major technology in a variety of display application markets from small sized portable displays to large sized televisions. Portable LCD devices such as smart phones and mobile phones are used in a diverse range of viewing conditions. We usually experience images on a mobile phone with a huge loss in contrast under bright outdoor viewing conditions; thus, viewing condition parameters such as surround effects, correlated colour temperature and ambient lighting have become of significant importance. (Katoh et al., 1998; Moroney et al., 2002) Recently, auxiliary attributes determining the mobile imaging were examined and the surround luminance and ambient illumination effects were considered as the first major factor. (Li et al., 2008) Surround and ambient lighting effects on colour appearance modelling have been extensively studied to understand the nature of colour perception under various ambient illumination levels (Liu & Fairchild, 2004, 2007; Choi et al., 2007; Park et al., 2007); thus, this study intends to figure out characteristics of the human visual system (HVS) in spatial frequency domain by means of analysing the contrast discrimination ability of HVS. In consequence, we propose an image quality evaluation method and a robust image enhancement filter based on the measured contrast sensitivity data of human observers under various surround luminance levels. The former is to quantify the observed trend between surround luminance and contrast sensitivity and to propose an image quality evaluation method that is adaptive to both surround luminance and spatial frequency of a given stimulus. The non-linear behaviour of the HVS was taken into account by using contrast sensitivity function (CSF). This model can be defined as the square root integration of multiplication between display modulation transfer function (MTF) and CSF. It is assumed that image quality can be determined by considering the MTF of an imaging system and the CSF of human observers. The CSF term in the original SQRI model (Barten, 1990) is replaced by the surround adaptive CSF quantified in this study and it is divided by the Fourier transform of a given stimulus. The latter is a robust image enhancement filter which compensates for the effects of surround luminance on our contrast perceiving mechanism. Precisely, the surround luminance adaptive CSF is used as a guide for determination of the adaptive enhancement gain in the proposed algorithm.
Identification of Trans-Golgi Network Proteins in Arabidopsis thaliana Root Tissue

Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.
Irish Households, Assessing the Impact of the Economic Crisis

The impact of the changing economic environment on Irish households has been significant, with net worth falling 30 per cent since 2006. This article uses Quarterly Financial Accounts data to investigate how they have adjusted to the vastly different economic climate with which they are now faced. It finds that households’ portfolio composition has shifted considerably, due largely to falling asset values. Results also suggest that households are now entering a long period of debt reduction. The consequence of deleveraging is a reduction in household consumption; a process that cannot proceed without an increase in the household savings rate. In the wake of the economic slowdown, there is a risk that increased savings could be a drag on consumption and bank lending in the future, with negative implications for the speed of economic recovery.