Strange stars with different quark mass scalings

We investigate the stability of strange quark matter and the properties of the corresponding strange stars, within a wide range of quark mass scaling. The calculation shows that the resulting maximum mass always lies between 1.5 solor mass and 1.8 solor mass for all the scalings chosen here. Strange star sequences with a linear scaling would support less gravitational mass, and a change (increase or decrease) of the scaling around the linear scaling would lead to a larger maximum mass. Radii invariably decrease with the mass scaling. Then the larger the scaling, the faster the star might spin. In addition, the variation of the scaling would cause an order of magnitude change of the strong electric field on quark surface, which is essential to support possible crusts of strange stars against gravity and may then have some astrophysical implications.Comment: 5 pages, 6 figures, 1 table. accepted by M

The value premium and time-varying volatility

Numerous studies have documented the failure of the static and conditional capital asset pricing models to explain the difference in returns between value and growth stocks. This paper examines the post-1963 value premium by employing a model that captures the time-varying total risk of the value-minus-growth portfolios. Our results show that the time-series of value premia is strongly and positively correlated with its volatility. This conclusion is robust to the criterion used to sort stocks into value and growth portfolios and to the country under review (the US and the UK). Our paper is consistent with evidence on the possible role of idiosyncratic risk in explaining equity returns, and also with a separate strand of literature concerning the relative lack of reversibility of value firms' investment decisions

Advancing Agro-Based Research

Taking the next sums up Universiti Putra Malaysia (UPM) approach to research. The university now aims to create an environment that inspires innovative research following its selection as a research university by the Higher Education Ministry in November 2006

Commodity strategies based on momentum, term structure, and idiosyncratic volatility

This article demonstrates that momentum, term structure, and idiosyncratic volatility signals in commodity futures markets are not overlapping, which inspires a novel triple-screen strategy. We show that simultaneously buying contracts with high past performance, high roll-yields, and low idiosyncratic volatility, and shorting contracts with poor past performance, low roll-yields, and high idiosyncratic volatility yields a Sharpe ratio over the 1985 to 2011 period that is five times that of the S&P-GSCI. The triple-screen strategy dominates the double-screen and individual strategies and this outcome cannot be attributed to overreaction, liquidity risk, transaction costs, or the financialization of commodity futures markets

Designing organometallic compounds for catalysis and therapy

Bioorganometallic chemistry is a rapidly developing area of research. In recent years organometallic compounds have provided a rich platform for the design of effective catalysts, e.g. for olefin metathesis and transfer hydrogenation. Electronic and steric effects are used to control both the thermodynamics and kinetics of ligand substitution and redox reactions of metal ions, especially Ru II. Can similar features be incorporated into the design of targeted organometallic drugs? Such complexes offer potential for novel mechanisms of drug action through incorporation of outer-sphere recognition of targets and controlled activation features based on ligand substitution as well as metal- and ligand-based redox processes. We focus here on η 6-arene, η 5-cyclopentadienyl sandwich and half-sandwich complexes of Fe II, Ru II, Os II and Ir III with promising activity towards cancer, malaria, and other conditions. © 2012 The Royal Society of Chemistry

Estimating Discrete Markov Models From Various Incomplete Data Schemes

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a case, the estimation of transition probabilities is straightforwardly made by counting one-step moves from a given state to another. In many real-life problems, however, the inference is much more difficult as state sequences are not fully observed, namely the state of each individual is known only for some given values of the time variable. A review of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms to perform Bayesian inference and evaluate posterior distributions of the transition probabilities in this missing-data framework. Leaning on the dependence between the rows of the transition matrix, an adaptive MCMC mechanism accelerating the classical Metropolis-Hastings algorithm is then proposed and empirically studied.Comment: 26 pages - preprint accepted in 20th February 2012 for publication in Computational Statistics and Data Analysis (please cite the journal's paper

Graphene transistors are insensitive to pH changes in solution

We observe very small gate-voltage shifts in the transfer characteristic of as-prepared graphene field-effect transistors (GFETs) when the pH of the buffer is changed. This observation is in strong contrast to Si-based ion-sensitive FETs. The low gate-shift of a GFET can be further reduced if the graphene surface is covered with a hydrophobic fluorobenzene layer. If a thin Al-oxide layer is applied instead, the opposite happens. This suggests that clean graphene does not sense the chemical potential of protons. A GFET can therefore be used as a reference electrode in an aqueous electrolyte. Our finding sheds light on the large variety of pH-induced gate shifts that have been published for GFETs in the recent literature

FFN: a Fine-grained Chinese-English Financial Domain Parallel Corpus

Large Language Models (LLMs) have stunningly advanced the field of machine translation, though their effectiveness within the financial domain remains largely underexplored. To probe this issue, we constructed a fine-grained Chinese-English parallel corpus of financial news called FFN. We acquired financial news articles spanning between January 1st, 2014, to December 31, 2023, from mainstream media websites such as CNN, FOX, and China Daily. The dataset consists of 1,013 main text and 809 titles, all of which have been manually corrected. We measured the translation quality of two LLMs -- ChatGPT and ERNIE-bot, utilizing BLEU, TER and chrF scores as the evaluation metrics. For comparison, we also trained an OpenNMT model based on our dataset. We detail problems of LLMs and provide in-depth analysis, intending to stimulate further research and solutions in this largely uncharted territory. Our research underlines the need to optimize LLMs within the specific field of financial translation to ensure accuracy and quality.Comment: a simplified version of this paper is accepted by International Conference on Asian Language Processing 202