Miracle-dz/UniFAR-FTUs huggingface dataset:
Agricultural Policy Strategy, Instruments and Implementation: A Review and the Road Ahead

For 40 years, India’s agricultural growth rate has averaged less than one-third of the government’s modest target of 4%. The sector’s performance has been about the same before and after the economic reforms in the early 1990s. The reforms that brought a dramatic acceleration of growth in urban sectors have essentially had no effect on agriculture. Slow agricultural growth has had ill-effects on food security, food price inflation and poverty reduction because of the inadequate level and composition of public expenditure. Agricultural education, research, extension and a wide range of ancillary public institutions have also suffered. Agricultural growth always demands massive public goods provision and that in turn requires a radical reorientation of central, state and district government activities. This paper advocates a new integrated, technology-led strategy to pull out of, what looks like, a vicious circle that agriculture is now caught in. hree of India’s many current problems are rooted in its agricultural and allied sectors. These are decelerating growth in real agricultural gross domestic product (GDP) and land productivity, increasing food insecurity among the poor and an increased level and persistence of food inflation. They have an adverse impact on the growth of the economy and employment and offset efforts to reduce poverty. Alleviating these problems and their consequences require accelerating the growth of agriculture through “continuous” location-specific generation and transfer of land augmenting and labour using technical change, and ensuring a critical mass of professional human resources. It also requires boosting public expenditure, revamping farm support and input pricing, and radically reorienting the existing multi-institutional model of organisation and management. The reforms of the early 1990s were not accompanied by these changes and they did not therefore accelerate the agricultural growth rate.
Private Equity and Asset Characteristics: The Case of Agricultural Production

Unlike most other mature industries, family firms, partnerships, and cooperatives dominate the agricultural production sector, with few corporations and limited access to capital derived from a source other than retained earnings and existing owners. However, the use of external equity capital in agriculture has increased dramatically since 1990. This funding source allows farms to exploit entrepreneurial opportunities not easily financed by debt. Following O. Williamson, we view debt and equity as alternative governance structures and argue that transaction cost economics offers insights on firms' financial structure beyond those provided by agency theory. We relate capital structure to asset specificity, a particularly important attribute in agricultural production. We construct an international dataset of agricultural companies receiving external private equity and show that the attributes of the assets involved in production are important determinants of financial structure. Copyright © 2013 John Wiley & Sons, Ltd.
Developing Secure and Efficient Electronic Commerce Applications with eTRON Architecture

Many e-commerce applications are prevalent in today’s world. New features and enhanced security, both are required for successful launch and deployment of e-commerce applications. In this paper, we focus on a generic e-commerce architecture, called eTRON which incorporates – at its nucleus – the tamper-resistant eTRON chip, equipped with functions for mutual authentication, secure communication, and robust access control. With a view to exploring and assessing eTRON’s efficacy and applicability in various e-commerce applications, we present an electronic ticket system developed with the eTRON architecture. Firsthand results from the electronic ticket system and critical analysis of salient eTRON features prove eTRON to be a secure and efficient architecture for developing various e-commerce applications.
Evaluation of Minimal Residual Disease in Acute Myeloid Leukemia with NPM1 Marker

Background: Minimal residual disease (MRD) tests provide early identification of hematologic relapse and timely management of acute myeloid leukemia (AML) patients. Approximately, 50% of AML patients do not have clonal chromosomal aberrations and categorize as a cytogenetically normal acute myeloid leukemia (CN-AML). About 60% of adult CN-AML has a mutation in exon 12 of NPM1 gene. This mutation is specific for malignant clone and potentially is a good marker of MRD. In this retrospective study, we set up a quantitative test for quantifying NPM1 type A mutation and AML patients carrying this mutation at the time of diagnosis, were followed-up. Materials and Methods : We prepared plasmids containing a cDNA fragment of NPM1 and ABL genes by PCR cloning. The plasmids were used to construct standard curves. Eleven patients were analyzed using established method. Serial PB and/or BM samples (n=71) were taken in 1-3 months intervals (mean 1.5-month intervals) and median follow-up duration after chemotherapy was 11 months (5-28.5 months). Results: In this study, we developed RNA-based RQ-PCR to quantitation of NPM1 mutation A with sensitivities of 10(-5). The percent of NPMmut/ABL level showed a range between 132 and 757 with median of 383.5 in samples at diagnosis. The median NPMmut transcript level log reduction was 3 logs. Relapse occurred in 54.5% of patients (n=6), all cases at diagnosis demonstrated the same mutation at relapse. In patients who experienced relapse, log reduction levels of NPM1 mRNA transcript after therapy were 4 (n=2), 3 (n=2) and 1 log (n=2). Totally, NPMmut level showed less than 5 log reduction in all of them, whereas this reduction was 5-6 logs in other patients. Conclusion: Despite the limitations of this study in terms of sample size and duration of follow-up, it showed the accuracy of set up for detection of mutation and this marker has worth for following-up at different stages of disease. Because of high frequency, stability, specificity to abnormal clone and high sensitivity, NPM1 is a suitable marker for monitoring of NPMc+ AML patients.
The Role of Functional and Wellness Values in Visitors' Evaluation of Spa Experiences

By responding to the current emphasis on health and wellness in modern society, the spa industry has experienced a rapid growth in the last two decades. To understand the determinants of revisitation and long-term attitudinal loyalty, this study investigated how visitors perceive the potential value of spa experiences. The purpose of this study was to examine the role of two distinct dimensions of perceived value (functional and wellness values) in spa visitors' decision-making process for future behavior. Based on the framework of “quality-perceived value-satisfaction-behavioral intention”, a field survey was conducted to test the mediating role of functional and wellness values in the case of Jinan Red Ginseng Spa located in South Korea. The findings provide further information to explain the underlying mechanisms for increasing spa visitors' behavioral intention to revisit and recommend.
Are Particulate Matter Exposures Associated with Risk of Type 2 Diabetes?

Background Although studies have found that diabetes mellitus (DM) modifies the impact of exposures from air pollution on cardiovascular outcomes, information is limited regarding DM as an air pollution-associated outcome. Objectives Using two prospective cohorts, the Nurses’ Health Study (NHS) and the Health Professionals Follow-Up Study (HPFS), we investigated the relationship of incident type 2 DM with exposures to particulate matter (PM) <2.5 μm (PM2.5), PM <10 μm (PM10), and PM between 2.5 and 10 μm in aerodynamic diameter (PM10–2.5) in the previous 12 months and the distance to roadways. Methods Cases were reported and confirmed through biennial and supplemental questionnaires of diagnosis and treatment information. During follow-up from 1989 to 2002, questionnaires provided information on time-varying covariates and updated addresses. Addresses were geocoded and used to assign air pollution exposures from spatiotemporal statistical models. Results Among participants living in metropolitan areas of the northeastern and midwestern United States, there were 3,784 incident cases of DM in the NHS, and 688 cases in the HPFS. Pooled results from random effects meta-analysis of cohort-specific models adjusted for body mass index and other known risk factors produced hazard ratios (HRs) for incident DM with interquartile range (IQR) increases in average PM during the 12 months before diagnosis of 1.03 [95% confidence interval (CI), 0.96–1.10] for PM2.5, 1.04 (95% CI, 0.99–1.09) for PM10, and 1.04 (95% CI, 0.99–1.09) for PM10–2.5. Among women, the fully adjusted HR for living < 50 m versus ≥ 200 m from a roadway was 1.14 (95% CI, 1.03–1.27). Conclusions Overall, results did not provide strong evidence of an association between exposure to PM in the previous 12 months and incident DM; however, an association with distance to road (a proxy marker of exposure to traffic-related pollution) was shown among women.
Adaptive decode-and-forward cooperative diversity with multiple-antenna terminals

In this paper, an adaptive decode-and-forward (DF) cooperative diversity with multiple-antenna terminals is proposed. The adaptive algorithm is based on instantaneous channel state information (ICSI). Because of time variability of wireless channel, the instantaneous bit error rate (BER) performance of cooperation is not always better than non-cooperation. At the beginning of each frame, whether DF cooperation or direct transmission will be adopted during the frame is decided according to ICSI. Simulation results show that the Adaptive DF cooperative diversity can improve the average BER performance than traditional DF cooperative diversity. Moreover, it consumes less energy and time, and occupies less channel resource.
How Effective Are Different Welfare-to-Work Approaches? Five-Year Adult and Child Impacts for Eleven Programs. National Evaluation of Welfare-to-Work Strategies.

For the past 30 years, federal and state policymakers have been legislating various types of programs to increase employment among welfare recipients. How people can best move from welfare to work, however, has been the subject of long-standing debate. This report, summarizing the long-term effects of 11 mandatory welfare-to-work programs on welfare recipients and their children, represents a major advance in resolving this debate. The findings are the final ones from the National Evaluation of Welfare-to-Work Strategies (NEWWS), a multi-year study of alternative approaches to helping welfare recipients find jobs, advance in employment, and leave public assistance.
Score Analyzer: Automatically Determining Scores Difficulty Level for Instrumental e-Learning

Nowadays, huge sheet music collections exist on the Web, allowing people to access public domain scores for free. However, beginners may be lost in finding a score appropriate to their instrument level, and should often re-ly on themselves to start out on the chosen piece. In this instrumental e-Learning context, we propose a Score Analyzer prototype in order to automatically extract the difficulty level of a MusicXML piece and suggest advice thanks to a Musical Sign Base (MSB). To do so, we first review methods related to score performance information retrieval. We then identify seven criteria to characterize technical instrumental difficulties and propose methods to extract them from a MusicXML score. The relevance of these criteria is then evaluated through a Principal Components Analysis and compared to human estimations. Lastly we discuss the integration of this work to @-MUSE, a collaborative score annotation platform based on multimedia contents indexation.
Beyond the Stars: Improving Rating Predictions using Review Text Content

Online reviews are an important asset for users deciding to buy a product, see a movie, or go to a restaurant, as well as for businesses tracking user feedback. However, most reviews are written in a free-text format, and are therefore difficult for computer systems to understand, analyze, and aggregate. One consequence of this lack of structure is that searching text reviews is often frustrating for users. User experience would be greatly improved if the structure and sentiment conveyed in the content of the reviews were taken into account. Our work focuses on identifying this information from free-form text reviews, and using the knowledge to improve user experience in accessing reviews. Specifically, we focused on improving recommendation accuracy in a restaurant review scenario. In this paper, we report on our classification effort, and on the insight on user-reviewing behavior that we gained in the process. We propose new ad-hoc and regression-based recommendation measures, that both take into account the textual component of user reviews. Our results show that using textual information results in better general or personalized review score predictions than those derived from the numerical star ratings given by the users.
Sticking with Your Vote: Cognitive Dissonance and Political Attitudes

Cognitive dissonance theory predicts that the act of voting for a candidate leads to a more favorable opinion of the candidate in the future. We find support for the empirical relevance of cognitive dissonance to political attitudes. We examine the presidential opinion ratings of voting-age eligibles and ineligibles two years after the president's election. We find that eligibles show two to three times greater polarization of opinions than comparable ineligibles. We find smaller effects when we compare polarization in opinions of senators elected during high turnout presidential campaign years with senators elected during nonpresidential campaign years. (JEL D72)
Classifying Dialogue Acts in Multi-party Live Chats

We consider the task of classifying chat contributions by dialogue act in a multi-party setting. This extends the problem significantly over the 1-1 chat scenario due to the semiasynchronous and “entangled” nature of the contributions by chat participants. We experiment with a number of machine learning approaches, using different categories of features: lexical, contextual, structural, keyword and dialogue interaction information. For evaluation, we developed gold-standard data using online forums from the USA Library of Congress. We found that, for multi-party dialogues, features based on 1-gram and keywords produced best performance, while features exploiting structure and interaction did not perform as well as previously reported results over 1-to-1 chats.
Simulations of minor mergers - II. The phase-space structure of thick discs

We analyse the phase-space structure of simulated thick discs that are the result of a 5:1 mass-ratio merger between a disc galaxy and a satellite. Our main goal is to establish what would be the imprints of a merger origin for the Galactic thick disc. We find that the spatial distribution predicted for thick-disc stars is asymmetric, seemingly in agreement with recent observations of the Milky Way thick disc. Near the Sun, the accreted stars are expected to rotate more slowly, to have broad velocity distributions and to occupy preferentially the wings of the line-of-sight velocity distributions. The majority of the stars in our model thick discs have low eccentricity orbits (in clear reference to the pre-existing heated disc) which give rise to a characteristic (sinusoidal) pattern for their line-of-sight velocities as a function of galactic longitude. The z-component of the angular momentum of thick-disc stars provides a clear discriminant between stars from the pre-existing disc and those from the satellite, particularly at large radii. These results are robust against the particular choices of initial conditions made in our simulations.
Information and Accuracy in Pricing: Evidence from the NCAA Men’s Basketball Betting Market

In this paper, we test whether accuracy in the wagering markets for NCAA Division I men׳s basketball improves when there are more betting lines available for a given game. The empirical evidence indicates that when the money line is offered, it has no impact on the accuracy of the sides line as it is a redundant security, but the offering of the totals line impacts the accuracy of the sides line. We interpret this as evidence that the totals line provides additional information to uninformed bettors that improves the accuracy of the sides line in its role as a price.
Isolation of Saccharomyces Cerevisiae from Pineapple and Orange and Study of Metal’s Effectiveness on Ethanol Production

In view of the anticipated shortage of the traditional supplies of fossil fuels, there is a great deal of interest in the production of ethanol as an alternative biofuel in recent years. The main objective of this research work was to isolate and characterize stress tolerant, high potential ethanol producing yeast strains from various fruit peel. Two yeast isolates from pineapple (Pa) and orange (Or) have been isolated, characterized on the basis of morphological and physic-chemical characters and optimized on ethanol producing capability using sugarcane molasses as substrate. Ethanol production percentage was estimated by Conway method. Isolates were thermotolerant, pH tolerant, ethanol tolerant as well as osmotolerant. They were resistant to Chloramphenicol (30 μg/disc) and Nalidixic acid (30 μg/disc). The isolates showed no killer toxin activity against E. coli. The highest production capacity of the yeasts was found to be 7.39% and 5.02% for Pa and Or, respectively, at pH 5.0, 30 °C temperature in media with an initial reducing sugar concentration of 6.5% for Pa and 5.5% for Or (shaking). Addition of metal ions increased the rate of ethanol production highest to 10.61% by KH2PO4. This study revealed that indigenous yeast isolates could be used to benefit the fuel demand and industrial alcohol industries.
First-principles DFT + U study of radiation damage in UO 2 : f electron correlations and the local energy minima issue

The present highlight reviews recent advances in first-principles modelling of radiation damage in UO2. It focuses on the influence of strong correlations and the problem of metastable states that occur with some approximations that localize electrons, in particular the density functional theory (DFT)+U approximation. It gives an illustration that DFT+U calculations quantitatively describe atomic transport phenomena in strongly-correlated uranium dioxide, provided that one circumvents the DFT+U local energy minima issue that affects f -electron systems. The occupation matrix control (OMC) scheme is one of the techniques developed to tackle the metastable state issue. We demonstrate here its efficiency on perfect and defective UO2 through the study of oxygen diffusion. We use OMC to calculate UO2 bulk properties, defect formation energies, migration energy barriers, and we show that in order to avoid the metastable states and systematically reach the ground state of uranium dioxide with DFT+U, the monitoring of occupation matrices of the correlated orbitals on which the Hubbard term is applied is crucial. The presence of metastable states can induce significant differences in the calculated total energies, which explains the origin of the discrepancies in the results obtained by various authors on crystalline and defect-containing UO2. Also, for the bulk fluorite structure of UO2, we show that the widely used Dudarev approach of the DFT+U systematically yields the first metastable state when no control is done on the orbital occupancies. As for oxygen diffusion, the calculated migration energy relating to the interstitialcy mechanism compares very favourably to experimental data. Also, vacancy migration and Frenkel pair formation energies are shown to agree well with existing data.
RL-DOT: A Reinforcement Learning NPC Team for Playing Domination Games

In this paper, we describe the design of reinforcement-learning-based domination team (RL-DOT), a nonplayer character (NPC) team for playing Unreal Tournament (UT) Domination games. In RL-DOT, there is a commander NPC and several soldier NPCs. The running process of RL-DOT consists of several decision cycles. In each decision cycle, the commander NPC makes a decision of troop distribution and, according to that decision, sends action orders to other soldier NPCs. Each soldier NPC tries to accomplish its task in a goal-directed way, i.e., decomposing the final ultimate task (attacking or defending a domination point) into basic actions (such as running and shooting) that are directly supported by UT application programming interfaces (APIs). We use a Q-learning-style algorithm to learn the optimal decision-making policy. We carefully choose some opponent policies for our illustrative experiments. In these experiments, RL-DOT shows a distinct learning characteristic, which illustrates its efficiency in playing UT Domination games.
The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition

This paper reports on a new methodology for the automatic assessment of emotional responses. More specifically, emotions are elicited in agreement with a bidimensional spatial localization of affective states, that is, arousal and valence dimensions. A dedicated experimental protocol was designed and realized where specific affective states are suitably induced while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), ElectroDermal Response (EDR), and ReSPiration activity (RSP), are simultaneously acquired. A group of 35 volunteers was presented with sets of images gathered from the International Affective Picture System (IAPS) having five levels of arousal and five levels of valence, including a neutral reference level in both. Standard methods as well as nonlinear dynamic techniques were used to extract sets of features from the collected signals. The goal of this paper is to implement an automatic multiclass arousal/valence classifier comparing performance when extracted features from nonlinear methods are used as an alternative to standard features. Results show that, when nonlinearly extracted features are used, the percentages of successful recognition dramatically increase. A good recognition accuracy (>;90 percent) after 40-fold cross-validation steps for both arousal and valence classes was achieved by using the Quadratic Discriminant Classifier (QDC).