Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets

This paper determines the optimal bidding strategy of an electric vehicle (EV) aggregator participating in day-ahead energy and regulation markets using stochastic optimization. Key sources of uncertainty affecting the bidding strategy are identified and incorporated in the stochastic optimization model. The aggregator portfolio optimization model should include inevitable deviations between day-ahead cleared bids and actual real-time energy purchases as well as uncertainty for the energy content of regulation signals in order to ensure profit maximization and reliable reserve provision. Energy deviations are characterized as “uninstructed” or “instructed” depending on whether or not the responsibility resides with the aggregator. Price deviations and statistical characteristics of regulation signals are also investigated. Finally, a new battery model is proposed for better approximation of the battery charging characteristic. Test results with an EV aggregator representing one thousand EVs are presented and discussed.
Potency of papua local chickens as egg producers: a molecular review

This research aims to evaluate potency of Papua Local Chickens as egg producers through allelic frequency estimation of 24-bp indel (insertion-deletion) on the prolactin gene promoter (cPRL) owned by the population. A total of 68 Papua Local Chickens (ALP) are used in this research, and blood samples are taken individually for DNA isolation. DNA fragment target size 130 or 154 bp contain the 24-bp indel locus is successfully amplified using Polymerase Chain Reaction (PCR) method with a pair of specific primer. Electrophoresis analysis on 1.5 % agarose gel indicates three genotypes: II, ID and DD with the frequency of 8.82 %, 41.18 % and 50.00 %, respectively. The I and D allele frequencies are 0.29 and 0.71. This result estimate shows that the I allele frequency is categorized medium in Papua Local Chickens population. Therefore, the Papua Local Chickens is potential in producing eggs. Their egg producer potency can be increased by eliminating the broodiness behaviour of the hens in the population or by increasing the I allele frequency through a controlled mating.
BAL1 and BBAP Are Regulated by a Gamma Interferon-Responsive Bidirectional Promoter and Are Overexpressed in Diffuse Large B-Cell Lymphomas with a Prominent Inflammatory Infiltrate

ABSTRACT BAL1 is a transcription modulator that is overexpressed in chemoresistant, diffuse large B-cell lymphomas (DLBCLs). BAL1 complexes with a recently described DELTEX family member termed BBAP. Herein, we characterized BAL1 and BBAP expression in primary DLBCL subtypes defined by their comprehensive transcriptional profiles. BAL1 and BBAP were most abundant in lymphomas with a brisk host inflammatory response, designated host response (HR) tumors. Although these DLBCLs include significant numbers of tumor-infiltrating lymphocytes and interdigitating dendritic cells, BAL1 and BBAP were expressed primarily by malignant B cells, prompting speculation that the genes might be induced by host-derived inflammatory mediators such as gamma interferon (IFN-γ). In fact, IFN-γ induced BAL1 and BBAP expression in DLBCL cell lines; doxycycline-induced BAL1 also increased the expression of multiple IFN-stimulated genes, directly implicating BAL1 in an IFN signaling pathway. We show that BAL1 and BBAP are located on chromosome 3q21 in a head-to-head orientation and are regulated by a IFN-γ-responsive bidirectional promoter. BBAP regulates the subcellular localization of BAL1 by a dynamic shuttling mechanism, highlighting the functional requirement for coordinated BBAP and BAL1 expression. IFN-γ-induced BAL1/BBAP expression contributes to the molecular signature of HR DLBCLs and highlights the interplay between the inflammatory infiltrate and malignant B cells in these tumors.
Lean Flame Root Dynamics in a Gas Turbine Model Combustor

ABSTRACT A swirl-stabilised flame close to blow-off conditions in a gas turbine model combustor is investigated using large eddy simulation. The sub-grid combustion is modelled using a presumed probability density function approach along with flamelets. Good comparisons between the computed and measured statistics are observed. This allows for a detailed investigation of the flame behaviour. Two distinct stages are noted for the flame behaviour. The flame has a steady and stable flame root anchored near the entrance to the burner, yielding a “V” shaped flame in Stage 1, and a transient lift-off event is observed in Stage 2. These two stages switch from one to the other, giving the unstable flame behaviour, as observed in the experimental studies. Further analysis of the simulations shows that large-scale scalar mixing plays a prominent role in the stabilisation of the flame and the entrainment of inflammable mixtures near the flame root location initiates the lift-off event.
O modelo Lindley-Weibull com proporção de cura: uma abordagem bayesiana

In this work we propose a new long-term survival model called LindleyWeibull with a cure rate (LWcr). We consider that the event of interest can be caused by M different latent risk factors. We assume that M follows a discrete Lindley distribution and the activation time of j-th factor follows a Weibull distribution. For the estimation of the parameters of interest we developed a Bayesian approach with non-informative prior distributions. To obtain the parameter estimates based on their posterior distributions we used Monte Carlo Markov Chain (MCMC) methods. In order to detect influential observations it was used the Bayesian method of influence analysis of case deletion based on ψ-divergence. We apply the proposed model to simulated and an actual dataset as an illustration,
Nitric oxide synthase inhibitor L‐NAME affects Arabidopsis root growth, morphology, and microtubule organization

The presence of evolutionarily conserved NOS or NOS‐like enzymes in land plants different than those in animals is still unclear, despite their activity has been revealed in cytosol and some organelles. At the same time, the emerging evidence for the importance of L‐arginine‐dependent pathways of NO synthesis in plant cells is still accumulating. The aim of our study was to reveal physiological effects on growth and differentiation processes, and microtubular cytoskeleton organization of the competitive mammalian NO synthase inhibitor Nω‐nitro‐L‐arginine methylester (L‐NAME). Thus, the treatment of Arabidopsis with L‐NAME (50–1 mM) caused dose‐ and time‐dependent inhibition of primary roots growth. Moreover, the morphology of primary roots under the influence of L‐NAME also underwent changes. L‐NAME (>100 µM) induced the formation of novel over‐elongated root hairs in shortened elongation zone, while in higher concentrations (500 µM) it caused a slight swelling of epidermal cells in differentiation zone. L‐NAME also provoked microtubule reorganization in epidermal cells of different root growth zones. Thus, L‐NAME at concentrations of 50–1 mM induced cortical microtubules randomization and/or depolymerization in epidermal cells of the root apex, meristem, transition, elongation, and differentiation zones after 2 h of treatment. Disordered microtubules in trichoblasts could initiate the formation of actively elongating root hairs that reveals longitudinal microtubules ensuring their active growth at 24 h of treatment. Therefore, L‐NAME inhibits primary root growth, induces the differentiation processes in roots, reorganizes cortical microtubules in epidermal root cells suggesting the importance of L‐arginine‐dependent pathways of NO synthesis in plants.
Interim analysis of the effects of exenatide treatment on A1C, weight and cardiovascular risk factors over 82 weeks in 314 overweight patients with type 2 diabetes

Aim: Exenatide, an incretin mimetic for the adjunct treatment of type 2 diabetes (DM2), reduced A1C and weight in 30‐week placebo‐controlled trials. This analysis examined the effects of exenatide on glycaemic control and weight over an 82‐week period in patients with DM2 unable to achieve adequate glycaemic control with sulphonylurea (SU) and/or metformin (MET).
Defining the rest interval associated with the main sleep period in actigraph scoring

Actigraphy is increasingly used for sleep monitoring. However, there is a lack of standardized methodology for data processing and analysis, which often makes between study comparisons difficult, if not impossible, and thus open to flawed interpretation. This study evaluated a manual method for detection of the rest interval in actigraph data collected with Actiwatch 2. The rest interval (time in bed), defined as the bedtime and rise time and set by proprietary software, is an essential requirement for the estimation of sleep indices. This study manually and systematically detected the rest interval of 187 nights of recording from seven healthy males and three females, aged 13.5±0.7 (mean ± standard deviation) years. Data were analyzed for agreement between software default algorithm and manual scoring. Inter-rater reliability in manual scoring was also tested between two scorers. Data showed consistency between default settings and manual scorers for bedtime and rise time, but only moderate agreement for the rest interval duration and poor agreement for activity level at bedtime and rise time. Manual detection of rest intervals between scorers showed a high degree of agreement for all parameters (intraclass correlations range 0.864 to 0.995). The findings demonstrate that the default algorithm on occasions was unable to detect rest intervals or set the exact interval. Participant issues and inter-scorer issues also made difficult the detection of rest intervals. These findings have led to a manual detection protocol to define bedtime and rise time, supplemented with an event diary.
Subjective evaluation of common singing skills using the rank ordering method

This paper presents the results of two experiments on singing skill evaluation, where human subjects judge the subjective quality of previously unheard melodies. The aim of this study is to explore the criteria that human subjects use in judging singing skill and the stability of their judgments, as a basis for developing an automatic singing skill evaluation scheme. The experiments use the rank ordering method, where the subjects ordered a group of given stimuli according to their preferred rankings. Experiment 1 uses real, a capella singing as the stimuli, while experiment 2 uses the fundamental frequency (F0) sequence extracted from the singing. In experiment 1, 88.9% of the correlation between the subjects' evaluations was significant at the 5% level. Results of experiment 2 show that the F0 sequence is significant in only certain cases, so that the judgment and its stability in experiment 1 should be attributed to other factors of real singing.
Development and Analysis of Interior Permanent Magnet Synchronous Motor with Field Excitation Structure

I am submitting herewith a dissertation written by Seong Taek Lee entitled “Development and Analysis of Interior Permanent Mag net Synchronous Motor with Field Excitation Structure. ” I have examined the final el ectronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy , with a major in Electrical Engineering.
Continuous Value Function Approximation for Sequential Bidding Policies

Market-based mechanisms such as auctions are being studied as an appropriate means for resource allocation in distributed and multiagent decision problems. When agents value resources in combination rather than in isolation. they must often deliberate about appropriate bidding strategies for a sequence of auctions offering resources of interest. We briefly describe a discrete dynamic programming model for constructing appropriate bidding policies for resources exhibiting both complementarities substitutability. We then introduce a continuous approximation of this model, assuming that money (or the numeraire good) is infinitely divisible. Though this has the potential to reduce the computational cost of computing policies, value functions in the transformed problem do not have a convenient closed form representation. We develop grid-based approximations for such value functions, representing value functions using piecewise linear approximations. We show that these methods can offer significant computational savings with relatively small cost in solution quality.
A new modeling approach by global and local characterization

In this paper, we introduce a new approach for modeling closed surfaces. After working with several methods, we think that none really takes into account both the local and global characteristics of an object within a single coherent model. For instance, parametric surfaces endure changes of topology with difficulty and can mainly be used for local modifications of an object’s surface. On the opposite, implicit surfaces allow a good global characterization of an object, thanks to a skeleton. However, the latter quickly shows its limits when it comes to small variations on an object’s surface. After exposing several limits of classical existing approaches, we present the concept of a double characterization of an object, using two skeletons (intern and extern) and transition layers that link these two entities. The intern and extern skeletons define the object’s topology, morphology and geometry in a different way than with the usual definition of a skeleton. In the end of this paper, we use this modeling approach in a reconstruction context.
Probabilistic Opacity in Refinement-Based Modeling

Given a probabilistic transition system (PTS) A partially observed by an attacker, and an ω-regular predicate ϕ over the traces of A, measuring the disclosure of the secret ϕ in A means computing the probability that an attacker who observes a run of A can ascertain that its trace belongs to ϕ. In the context of refinement, we consider specifications given as Interval-valued Discrete Time Markov Chains (IDTMCs), which are underspecified Markov chains where probabilities on edges are only required to belong to intervals. Scheduling an IDTMC S produces a concrete implementation as a PTS and we define the worst case disclosure of secret ϕ in S as the maximal disclosure of ϕ over all PTSs thus produced. We compute this value for a subclass of IDTMCs and we prove that refinement can only improve the opacity of implementations.
1 The Corporate Social Performance-- Financial Performance Link

Strategic managers are consistently faced with the decision of how to allocate scarce corporate resources in an environment that is placing more and more pressures on them. Recent scholarship in strategic management suggests that many of these pressures come directly from sources associated with social issues in management, rather than traditional arenas of strategic management. Using a greatly-improved source of data on corporate social performance, this paper reports the results of a rigorous study of the empirical linkages between financial and social performance. CSP is found to be positively associated with prior financial performance, supporting the theory that slack resource availability and CSP are positively related. CSP is also found to be positively associated with future financial performance, supporting the theory that good management and CSP are positively related. Post-print version of an article published in Strategic Management Journal 18(4): 303-319 (1997 April). doi: 10.1002/(SICI)1097-0266(199704)18:4<303::AID-SMJ869>3.0. CO;2-G
Sirtuins as Regulators of the Yeast Metabolic Network

There is growing evidence that the metabolic network is an integral regulator of cellular physiology. Dynamic changes in metabolite concentrations, metabolic flux, or network topology act as reporters of biological or environmental signals, and are required for the cell to trigger an appropriate biological reaction. Changes in the metabolic network are recognized by specific sensory macromolecules and translated into a transcriptional or translational response. The protein family of sirtuins, discovered more than 30 years ago as regulators of silent chromatin, seems to fulfill the role of a metabolic sensor during aging and conditions of caloric restriction. The archetypal sirtuin, yeast silent information regulator2 (SIR2), is an NAD+ dependent protein deacetylase that interacts with metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase and alcohol dehydrogenase, as well as enzymes involved in NAD(H) synthesis, that provide or deprive NAD+ in its close proximity. This influences sirtuin activity, and facilitates a dynamic response of the metabolic network to changes in metabolism with effects on physiology and aging. The molecular network downstream Sir2, however, is complex. In just two orders, Sir2’s metabolism related interactions span half of the yeast proteome, and are connected with virtually every physiological process. Thus, although it is fundamental to analyze single molecular mechanisms, it is at the same time crucial to consider this genome-scale complexity when correlating single molecular events with complex phenotypes such as aging, cell growth, or stress resistance.
Therapeutic Targeting of Telomerase

Telomere length and cell function can be preserved by the human reverse transcriptase telomerase (hTERT), which synthesizes the new telomeric DNA from a RNA template, but is normally restricted to cells needing a high proliferative capacity, such as stem cells. Consequently, telomerase-based therapies to elongate short telomeres are developed, some of which have successfully reached the stage I in clinical trials. Telomerase is also permissive for tumorigenesis and 90% of all malignant tumors use telomerase to obtain immortality. Thus, reversal of telomerase upregulation in tumor cells is a potential strategy to treat cancer. Natural and small-molecule telomerase inhibitors, immunotherapeutic approaches, oligonucleotide inhibitors, and telomerase-directed gene therapy are useful treatment strategies. Telomerase is more widely expressed than any other tumor marker. The low expression in normal tissues, together with the longer telomeres in normal stem cells versus cancer cells, provides some degree of specificity with low risk of toxicity. However, long term telomerase inhibition may elicit negative effects in highly-proliferative cells which need telomerase for survival, and it may interfere with telomere-independent physiological functions. Moreover, only a few hTERT molecules are required to overcome senescence in cancer cells, and telomerase inhibition requires proliferating cells over a sufficient number of population doublings to induce tumor suppressive senescence. These limitations may explain the moderate success rates in many clinical studies. Despite extensive studies, only one vaccine and one telomerase antagonist are routinely used in clinical work. For complete eradication of all subpopulations of cancer cells a simultaneous targeting of several mechanisms will likely be needed. Possible technical improvements have been proposed including the development of more specific inhibitors, methods to increase the efficacy of vaccination methods, and personalized approaches. Telomerase activation and cell rejuvenation is successfully used in regenerative medicine for tissue engineering and reconstructive surgery. However, there are also a number of pitfalls in the treatment with telomerase activating procedures for the whole organism and for longer periods of time. Extended cell lifespan may accumulate rare genetic and epigenetic aberrations that can contribute to malignant transformation. Therefore, novel vector systems have been developed for a ‘mild’ integration of telomerase into the host genome and loss of the vector in rapidly-proliferating cells. It is currently unclear if this technique can also be used in human beings to treat chronic diseases, such as atherosclerosis.
Estimating Linear Probability Functions: A Comparison of Approaches

A linear probability function permits the estimation of the probability of the occurrence or non-occurrence of a discrete event. Nerlove and Press (p. 3–9) outline several statistical problems that arise if such a function is estimated via OLS. In particular, heteroskedasticity inherent in such a regression model leads to inefficient estimates of parameters (Amemiya 1973, Horn and Horn). Moreover, without restrictions on the conventional OLS model, probability estimates lying outside the unit (0–1) interval are possible (Nerlove and Press). Goldberger and Kmenta suggest two approaches for alleviating the heteroskedasticity problems inherent in the OLS regression model. Logit analysis will also alleviate heteroskedasticity problems and ensure that estimated probabilities will lie within the unit interval (Amemiya 1974, Hauck and Donner, Hill and Kau, Horn and Horn, Horn, Horn, and Duncan, Theil 1970).
On output-processing in face detectors

A face detector will classify many thousands of windows within an image, generating multiple positive classifications in the proximity of a face. Consequently, practical detectors must perform output-processing to give a single hypothesis per face. This problem is considered in this paper, in order to facilitate comparisons of detectors and provide practitioners with guidelines for constructing these systems. This is achieved first by reviewing previous approaches and introducing a generic framework to encompass these, then through a systematic exploration of several of the choices involved in constructing such a system.
Sentiment Analysis of Movie Reviews using POS tags and Term Frequencies

Sentiment analysis and opinion mining play an important role in judging and predicting people's views. Recently, sentiment analysis has focused on assigning positive and negative polarities to opinions. More methods are being devised to find the weightage of a particular expression in a sentence, whether the particular expression gives the sentence a positive, negative or a neutral meaning. Most of the work on sentiment analysis in the past has been carried out by determining the strength of a subjective expression within a sentence using the parts of speech. Sentiment analysis tries to classify opinion sentences in a document on the basis of their polarity as positive or negative, which can be used in various ways and in many applications for example, marketing and contextual advertising, suggestion systems based on the user likes and ratings, recommendation systems etc. This paper presents a novel approach for classification of online movie reviews using parts of speech and machine learning
MADMX: A Strategy for Maximal Dense Motif Extraction

We develop, analyze, and experiment with a new tool, called MADMX, which extracts frequent motifs from biological sequences. We introduce the notion of density to single out the "significant" motifs. The density is a simple and flexible measure for bounding the number of don't cares in a motif, defined as the fraction of solid (i.e., different from don't care) characters in the motif. A maximal dense motif has density above a certain threshold, and any further specialization of a don't care symbol in it or any extension of its boundaries decreases its number of occurrences in the input sequence. By extracting only maximal dense motifs, MADMX reduces the output size and improves performance, while enhancing the quality of the discoveries. The efficiency of our approach relies on a newly defined combining operation, dubbed fusion, which allows for the construction of maximal dense motifs in a bottom-up fashion, while avoiding the generation of nonmaximal ones. We provide experimental evidence of the efficiency and the quality of the motifs returned by MADMX.