Miracle-dz/UniFAR-FTUs huggingface dataset:
Talent or Popularity? Analysis of the Superstar Phenomenon and Player Value Determination in the English Premier League

Chapter 1: Introduction . 1 Chapter 2: Literature Review of Superstar Economics.7 ● Alternative Explanations To The Superstar Phenomenon.10 ● Related Empirical Literature to Soccer.14 ● Background of The English Premier League (EPL). 26 Chapter 3: Econometric Model for Superstars in the English Premier League.30 ● Data and Main Variables.30 ● Descriptive Statistics.37 ● Results.44 Chapter 4: Conclusion.51 Bibliography.59 Abstract: The superstar phenomenon in our economy has wide implications and fosters debate on the issues of wealth and income inequality. This paper analyzes the superstar phenomenon in England’s top soccer league, the English Premier League (EPL), and what variables are most important to superstar compensation. Past superstar literature cites the importance of the debate of talent versus popularity. Using a dataset of the top 12% highest-paid players in the EPL by annual salary in 2021-2022, this paper creates an econometric model of talent and popularity variables using two different dependent variables, one being the annual salary of players and the other the industry-renowned transfermarkt.com market value. This paper found that the control variables, such as a player’s position and what team they’re on are most important to salary compensation. “Talent” variables such as goals, match rating, and control variable age were most important for market value. Popularity, measured by how many Instagram followers a player has, was not important for both salary and market value. The combined talent and popularity variable, The superstar phenomenon in our economy has wide implications and fosters debate on the issues of wealth and income inequality. This paper analyzes the superstar phenomenon in England’s top soccer league, the English Premier League (EPL), and what variables are most important to superstar compensation. Past superstar literature cites the importance of the debate of talent versus popularity. Using a dataset of the top 12% highest-paid players in the EPL by annual salary in 2021-2022, this paper creates an econometric model of talent and popularity variables using two different dependent variables, one being the annual salary of players and the other the industry-renowned transfermarkt.com market value. This paper found that the control variables, such as a player’s position and what team they’re on are most important to salary compensation. “Talent” variables such as goals, match rating, and control variable age were most important for market value. Popularity, measured by how many Instagram followers a player has, was not important for both salary and market value. The combined talent and popularity variable, the video game FIFA 21 rating, was the only variable significant to both salary and market value. This study suggests that talent comes first and popularity comes second.
Molecular identi ﬁ cation, life cycle characterization, and hatchery seed production of dwarf oysters from the Ebro Delta (Spain)

– Dwarf oysters in the Ebro Delta are only observed growing on local pen shells, Pinna nobilis L. , currently under extinction risk. The species identi ﬁ cation of these populations is uncertain, given recent genetic classi ﬁ cations of dwarf oysters of Ostrea stentina species complex. Hence, the ﬁ rst objective of this study was to con ﬁ rm the identity of dwarf oysters associated to P. nobilis in the Ebro Delta. Then, we aimed to assess the viability of hatchery and nursery production of O. stentina for potential conservation programs using diets based on a mix of live microalgae species or the commercial Shell ﬁ sh Diet 1800 ® . Our results showed that COI sequences obtained were speci ﬁ c to O. stentina . Signi ﬁ cant differences in the growth and survivaloflarvaebetweendiettreatmentswereobservedin3days.Forthelivediet,thelarvalperiodlastedfrom 15to22days(299.2±4 m m)whereasittookupto36daysusingtheShell ﬁ shDiet1800 ® (280±7.2 m m),withan overall mortality of 72.3 vs. 99.3%, respectively for each diet. Besides, no seed survival was observed for the commercialdiet after a 10-day-ﬁ xation attempt.In contrast, ca.16% oftheindividuals onthe livediet that were allowed to settle reached the juvenile stageandcould be released to the environment. Histological examination of obtained seed did not present any sign of disease and showed gonadal development for both sexes at ca. 6 months of age (16.1 – 19.1 mm). The growth curve obtained was ﬁ tted to a Schnute-Richards growth model which returned ages of up to 10 years for maximum local sizes of 45 mm. We conclude that although the Shell ﬁ shDiet1800 ® isnotanadequatedietfor O.stentina ,itshatcheryproductionisfeasibleandmightprovide support to future conservation actions.
Reducing Query Latency for Information Retrieval

As the world is moving towards Big Data, NoSQL (Not only SQL) databases are gaining much more popularity. Among the other advantages of NoSQL databases, one of their key advantage is that they facilitate faster retrieval for huge volumes of data, as compared to traditional relational databases. This project deals with one such popular NoSQL database, Apache HBase. It performs quite efficiently in cases of retrieving information using the rowkey (similar to a primary key in a SQL database). But, in cases where one needs to get information based on non-rowkey columns, the response latency is higher than what we observe in the previous case. This project discusses an approach which aims towards decreasing this latency. It also compares the performance of the existing approach and the proposed approach for various scenarios. REDUCING QUERY LATENCY FOR INFORMATION RETRIEVAL 4
The Determinants of Cash Holdings: The case of France

The idea of the present note is to shed light on the different motives for holding cash in France 1 . For that purpose, we first establish cash demand equations to identify the determinants of cash holding and, in a second step, try to estimate the domestic transactions and hoarding shares as well as the foreign shares by means of seasonal methods. The cash demand equations reveal that different domestic and foreign motives for holding cash play a crucial role. The second part finds low and decreasing transaction balances involving a downward trend in the active circulation of cash in France. In contrast, cash hoardings as well as holdings outside the euro area increased. There is also a net demand from countries within the euro area. Interestingly, it seems that the two foreign parts are mainly driven by the small and medium denominations, at least in the last years.
NFC-Enabled IoT: Enhancing Website Interaction and Automation

: The integration of Near Field Communication (NFC) technology with the Internet of Things (IoT) has the potential to revolutionize a wide range of industries and applications. NFC, a wireless communication technology that allows for the exchange of data between devices over short distances, can be used to connect IoT devices and enable new forms of interaction and automation. By integrating NFC with IoT, it is possible to enhance the functionality and connectivity of IoT devices, and enable new forms of user engagement and automation. For example, NFC-enabled IoT devices can be controlled and monitored remotely, and can also communicate with other devices and systems. Additionally, NFC can be used to securely authenticate and authorize IoT devices, and to protect against unauthorized access and tampering. Despite the potential benefits, the integration of NFC and IoT also poses challenges such as security, data privacy, and interoperability. Ensuring the security of NFC-enabled IoT devices and the data they generate is crucial, as any breach can have serious consequences for individuals and organizations. As NFC and IoT continue to grow and evolve, it is important for industry leaders and governments to work together to address these challenges and ensure the safe and responsible implementation of this technology.
The Generalized Inflection S-Shaped Software Reliability Growth Model

In this paper, the new generalized inflection S-shaped software reliability growth model is proposed. It is a very flexible finite failure Poisson process that possesses two distinguishing features: 1) includes as special cases the popular inflection S-shaped model, Goel generalized nonhomogenous Poisson process, and Goel–Okumoto model and 2) differently than these latter models, allows for modeling nonmonotonic failure rate per fault functions. The properties of the generalized inflection S-shaped model are discussed and intuitive arguments are provided to justify its structure. Maximum-likelihood estimators of model parameters are formulated and their properties are summarized. A special attention is devoted to the nonexistence issue of maximum-likelihood estimates. The problem of estimating the optimal release time of a software product is also addressed. Affordability and flexibility of the proposed model are demonstrated via four applicative examples, based on real sets of software reliability data. Attained results show that the generalized inflection S-shaped model provides outputs that may significantly differ from those provided by the models nested within it. As a side result, the developed examples also show that the nonexistence issue of maximum-likelihood estimates, in the case of finite failure Poisson processes, cannot be considered an oddity and that its occurrence is not necessarily related to model complexity.
Ground penetrating radar for underground sensing in agriculture: a review

Abstract Belowground properties strongly affect agricultural productivity. Traditional methods for quantifying belowground properties are destructive, labor-intensive and pointbased. Ground penetrating radar can provide non-invasive, areal, and repeatable underground measurements. This article reviews the application of ground penetrating radar for soil and root measurements and discusses potential approaches to overcome challenges facing ground penetrating radar-based sensing in agriculture, especially for soil physical characteristics and crop root measurements. Though advanced data-analysis has been developed for ground penetrating radar-based sensing of soil moisture and soil clay content in civil engineering and geosciences, it has not been used widely in agricultural research. Also, past studies using ground penetrating radar in root research have been focused mainly on coarse root measurement. Currently, it is difficult to measure individual crop roots directly using ground penetrating radar, but it is possible to sense root cohorts within a soil volume grid as a functional constituent modifying bulk soil dielectric permittivity. Alternatively, ground penetrating radarbased sensing of soil water content, soil nutrition and texture can be utilized to inversely estimate root development by coupling soil water flow modeling with the seasonality of plant root growth patterns. Further benefits of ground penetrating radar applications in agriculture rely on the knowledge, discovery, and integration among differing disciplines adapted to research in agricultural management.
Positive streamer corona, single filament, transient glow, dc glow, spark, and their transitions in atmospheric air

By using a ballast resistor having resistance of 10 MΩ, varying the gap distance from 23 mm to 0 mm and using a fixed dc voltage at 14 kV, the streamer corona, single filament, transient glow, dc glow, and spark modes and their transitions are demonstrated in positive needle-to-plate air discharge at atmospheric pressure. The electrical characteristics, the rotational temperature, and vibrational temperature of N2, as well as the temporal behavior of streamer propagation in these discharge modes, are investigated. First, to the best of our knowledge, the transient glow mode between the single filament mode and the dc glow mode, operated in a stable repetitive fashion, is reported for the first time in positive dc air discharges. The pulse repetition frequency ranges from 7.5 to 15 kHz. The current density and the rotational temperature are in the range of 27–105 A/mm2 and 600–850 K, respectively. Its temporal behavior reveals that after the primary streamer arrives at the cathode, the secondary streamer initiates within several nanoseconds near the anode and then propagates at a high speed of 105–106 m/s. There is no transition to spark even after the secondary streamer arrives at the cathode. Second, the transition from single filament to transient glow is characterized by the sudden decrease in the pulse repetition frequency and the abrupt increase in the current amplitude, the pulse width, and the gas temperature. Third, the transition from transient glow to dc glow is identified visibly by the formation of typical glow structure (positive column, Faraday dark space, and negative glow), which is accompanied by the transition of the discharge current from nanosecond pulse to dc. In addition, both the ballast resistor and the stray capacitor exert significant influence on the transition of discharge modes.By using a ballast resistor having resistance of 10 MΩ, varying the gap distance from 23 mm to 0 mm and using a fixed dc voltage at 14 kV, the streamer corona, single filament, transient glow, dc glow, and spark modes and their transitions are demonstrated in positive needle-to-plate air discharge at atmospheric pressure. The electrical characteristics, the rotational temperature, and vibrational temperature of N2, as well as the temporal behavior of streamer propagation in these discharge modes, are investigated. First, to the best of our knowledge, the transient glow mode between the single filament mode and the dc glow mode, operated in a stable repetitive fashion, is reported for the first time in positive dc air discharges. The pulse repetition frequency ranges from 7.5 to 15 kHz. The current density and the rotational temperature are in the range of 27–105 A/mm2 and 600–850 K, respectively. Its temporal behavior reveals that after the primary streamer arrives at the cathode, the secondary streamer i.
ADOPTION OF TECHNOLOGY ON RECRUITMENT PROCESS A STUDY OF PERCEPTIONS AND ATTITUDES OF HR PROFESSIONALS

: This research paper examines the transformative effects of technology on the recruiting process. With advancements in technology recruitment practices have evolved significantly. The objective of this study is to provide a comprehensive overview of the impact of technology on various stages of recruitment, including sourcing, screening, interviewing, and onboarding. Additionally, the paper explores the advantages, challenges, and potential ethical considerations associated with the integration of technology in recruiting. The findings of this research shed light on the opportunities and limitations of technology-driven recruitment and offer insights for organizations to optimize their hiring strategies in the digital era. By providing a comprehensive overview of the impact of technology on the recruiting process, this research paper serves as a valuable resource for professionals in human resources, talent acquisition, and organizational leadership, helping them understand the opportunities, challenges, and best practices associated with integrating technology into their recruitment strategies.
Exact solution of the one phase inverse Stefan problem

Exact solution of inverse one phase Stefan problem is represented in the form of linear combination of integral error functions. Heat flux function is reconstructed and coefficients of solution function are found exactly. Test problem was considered for engineering purposes and it was shown that by collocation method the error for three points does not exceed 0.01%. Error estimate was calculated by maximum principle.
uvby β photometry of early type open cluster and ﬁeld stars (cid:3),(cid:3)(cid:3)

Context. The β Cephei stars and slowly pulsating B (SPB) stars are massive main sequence variables. The strength of their pulsational driving strongly depends on the opacity of iron-group elements. As many of those stars naturally occur in young open clusters, whose metallicities can be determined in several fundamental ways, it is logical to study the incidence of pulsation in several young open clusters. Aims. To provide the foundation for such an investigation, Strömgren-Crawford u v b yβ photometry of open cluster target stars was carried out to determine e ﬀ ective temperatures, luminosities, and therefore cluster memberships. Methods. In the course of three observing runs, u v b yβ photometry for 168 target stars was acquired and transformed into the standard system by measurements of 117 standard stars. The list of target stars also included some known cluster and ﬁeld β Cephei stars, as well as β Cephei and SPB candidates that are targets of the asteroseismic part of the Kepler satellite mission. Results. The u v b yβ photometric results are presented. The data are shown to be on the standard system, and the properties of the target stars are discussed: 140 of these are indeed OB stars, a total of 101 targets lie within the β Cephei and / or SPB star instability strips, and each investigated cluster contains such potential pulsators. Conclusions. These measurements will be taken advantage of in a number of subsequent publications.
Evidence accumulation in deep RL agents powered by a cognitive model

Evidence accumulation is thought to be fundamental for decision-making in humans and other mammals. Neuroscience studies suggest that the hippocampus encodes a low-dimensional ordered representation of evidence through sequential neural activity. Cognitive modelers have proposed a mechanism by which such sequential activity could emerge through the modulation of recurrent weights with a change in the amount of evidence. Here we integrated a cognitive science model inside a Reinforcement Learning (RL) agent and trained the agent to perform a simple evidence accumulation task inspired by the behavioral experiments on animals. We compared the agent’s performance with the performance of agents equipped with GRUs and RNNs. We found that the agent based on a cognitive model was able to learn much faster and generalize better while having significantly fewer parameters. This study illustrates how integrating cognitive models and deep learning systems can lead to brain-like neural representations that can improve learning.
Long term Azithromycin role in severe Chronic Obstructive Pulmonary Disease

- Exacerbations of COPD have a major impact on the course of the disease. Frequent exacerbations can result in lower quality of life, faster in decline in lung function, higher mortality rate and has a major impact on the health care budget. Maintenance treatment with azithromycin during a period of one year resulted in a significant decrease in COPD exacerbations during that year. However, there is little information about the long-term effect of azithromycin maintenance treatment for more than one year. Aim of the study is to identify the Role of Azithromycin in COPD. Methodology-Cross sectional study. Result and conclusion: There was a significant reduction in acute exacerbation and length of hospital stay in patients with Severe COPD on Azithromycin.
: Ir/Responsible Use of Machine Learning in Research Recommendation Tools

Research practices continually adapt to developments in technology, and contending with the vast body of scholarly literature is no exception. As digital workflows have reduced the barriers to creating and publishing scholarship, researchers have scrambled to find ways to avoid “drinking from a firehose” and reduce information overload. Recommendations from colleagues about what sources are most important or worthwhile to read have always played a pivotal role in guiding researchers’ attention, and they will continue to do so. But most of them have also become familiar with commercial services that help subscribers discover various forms of content tailored to their preferences and interests. Machine learning, the technology that powers recommendation engines for Spotify, Netflix, Amazon and the like, machine learning, can likewise be applied to generate research recommendations, and demand for such services in the academic sector has increased.1 Understanding how these tools function in a general sense has become critical for library and information professionals, both to competently advise researchers on literature management and to use them effectively in their own scholarly work. In this paper I will give a brief overview of how machine learning algorithms work by pattern recognition. I then discuss how the use of machine learning influences the function of tools that recommend scholarly sources to researchers. Two general mechanisms are explored: one in which algorithms are designed to identify similarity and connection from the literature itself, and one in which they attempt to characterize the preferences and intentions of the user. I will compare their suitability for solving a particular research problem, ineffectual searches of unfamiliar literature in the course of interdisciplinary research, and outline their strengths and drawbacks. Finally, I discuss how librarians and other information professionals can leverage these insights when assisting researchers to reduce their search effort with machine learning responsibly.
Bulletproofs With Stochastic Equation Sets

Bulletproofs are general-purpose Zero Knowledge Proof protocols that allow a Prover to demonstrate to a Verifier knowledge of a solution to a set of equations, represented as a Rank 1 Constraint System. Wepresent a protocol extending the standard Bulletproof protocol, which introduces a second layer of interactivity to the protocol, by allowing the Verifier to choose the set of equations after the Prover has already committed to portions of the solution. We show that such Verifier-chosen (or stochastically-chosen) equation sets can be used to design smaller equation sets with less variables that have the same proving-power as their larger, deterministic counterparts but are, in practice, orders of magnitude faster both in proof generation and in proof verification, and even reduce the size of the resulting proofs. We demonstrate this with an example from a real-world application. Our method maintains all the classical benefits of the Bulletproof approach: efficient proof generation, efficient proof checking, extremely short proofs, and the ability to use Fiat-Shamir challenges in order to turn an interactive proof into a non-interactive proof.
Volatile organic compound fluxes in the San Joaquin Valley – spatial distribution, source attribution, and inventory comparison

The San Joaquin Valley is an agricultural region in California that suffers from poor air quality. Since traffic 10 emissions are decreasing, other sources of volatile organic compounds (VOCs) are gaining importance in the formation of secondary air pollutants. Using airborne eddy covariance, we conducted direct, spatially resolved flux observations of a wide range of VOCs in the San Joaquin Valley during June 2021 at 23-36°C. Through landcover-informed footprint disaggregation, we were able to attribute emissions to sources and identify tracers for distinct source types. VOC mass fluxes were dominated by alcohols, mainly from dairy farms, while oak isoprene and citrus monoterpenes were important sources 15 of reactivity. Comparisons with two commonly used inventories showed that isoprene emissions in the croplands were overestimated, while dairy and highway VOC emissions were generally underestimated in the inventories, and important citrus and biofuel VOC point sources were missing from the inventories. This study thus presents unprecedented insights into the VOC sources in an intensive agricultural region and provides much needed information for the improvement of inventories, air quality predictions and regulations.