Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques

In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm Optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm Optimization, with a mean absolute percentage error value of 6.15%.
Bloodstream Infections Caused by Serratia marcescens: Have a High Prevalence of ESBL and Carbapenemase Production in Pediatric Patients

et al. Bloodstream infections caused by Serratia marcescens : Have a high prevalence of esbl and carbapenemase production in pediatric patients. J Pediatr Inf 2022;16(3):e173-e178. mevcut-Abstract Objective: In this study, it was aimed to present a cohort study conduct-ed retrospectively in order to examine the unexpected Serratia marcescens bacteremia prevalence in a children’s hospital in Türkiye. Material and Methods: S. marcescens was isolated in the blood cultures of 45 patients at a 20-month period. Demographic features and clinical findings of the 45 patients including age, sex, underlying diseases, white blood cell (WBC), C-reactive protein (CRP), serum albumin level, length of hospital stay and length of pediatric intensive care unit stay, portal of entry, duration of central venous catheter, results of antimicrobial susceptibility testing and 28-day all-cause mortality were examined. BSI. Definitions used to characterize antimicrobial resistant bacteria were classified as multidrug-resistant (MDR), extensively drug-resistant (XDR) and pandrug-resistant (PDR). Results: Twelve patients (26.9%) had a malignancy. Mean hospitalization duration was 42.7 ± 41.4 (3-171) days. Central venous catheters (CVCs), including chemo-ports and Hickman catheters, were present in 43 patients during episodes of BSI. Twenty-four patients had primary BSI while 21 patients had catheter-related BSI. Mean length of CVC presence be-Available direnç fore catheter-related BSI was 46.02 ± 50.96 (1-200) days. During bacteremia episodes, 24 catheters (55.8%) were removed. Four patients (8.9%) died during the bacteremia episode. Laboratory findings of the deceased patients were as follows: mean WBC was 17.200/mm³; mean serum CRP level was 147.2 mg/L; and mean serum albumin level was 2.43 g/dL. Among all cases, 57.8% of the S. marcescens isolates produced ESBL and 40% produced carbapenemase. We classified these isolates as non-MDR (42.2%), MDR (31.1%), XDR (24.4%) and PDR (2.2%). The most common regimes received for XDR isolates were high-dose prolonged meropenem, amikacin, levofloxacin, and tigecycline. Conclusion: It is a great concern that S. marcescens isolates are intrinsi-cally resistant to polymyxins and produce ESBL and carbapenemase. Our mortality rate was reduced by high-dose prolonged-infusion of meropenem and early catheter removal.
Note on Open String/D-brane System and Noncommutative Soliton

This is a summary of a series of papers hep-th/9910263,0005283,0010066 written with B. Chen, T. Matsuo and K. Murakami on a p-p', (p<p^{\prime}) open string with B_{ij} field, which has led us to the explicit identification of the Dp-brane with the noncommutative projector soliton via the gaussian damping factor. A lecture given at Summer Institute 2000, FujiYoshida, Yamanashi, Japan, at August 7-14, 2000.
The Kondo Lattice Model

In this lecture, we review the experimental situation of heavy Fermions with emphasis on the existence of a quantum phase transition (QPT) and related non-Fermi liquid (NFL) effects. We overview the Kondo lattice model (KLM) which is believed to describe the physics of those systems. After recalling the existing theories based on large-N expansion and various N=2 schemes, we present two alternative approaches: (i) a spin fluctuation-Kondo functional integral approach treating the spin-fluctuation and Kondo effects on an equal footing, and (ii) a supersymmetric theory enlarging the usual fermionic representation of the spin into a mixed fermionic-bosonic representation in order to describe the spin degrees of freedom as well as the Fermi-liquid type excitations. This kind of approaches may open up new prospects for the description of the critical phenomena associated to the quantum phase transition in Heavy-Fermion systems.
The Odd Isotope Fractions of Barium in the Strongly r-process-enhanced (r-II) Stars

We determined the fodd,Ba values, 0.46 ± 0.08, 0.51 ± 0.09, 0.50 ± 0.13, and 0.48 ± 0.12, that correspond to the r-contribution 100% for four r-II stars, CS 29491-069, HE 1219-0312, HE 2327-5642, and HE 2252-4225, respectively. Our results suggest that almost all of the heavy elements (in the range from Ba to Pb) in r-II stars have a common origin, that is, from a single r-process (the main r-process). We found that the fodd,Ba has an intrinsic nature, and should keep a constant value of about 0.46 in the main r-process yields, which is responsible for the heavy element enhancement of r-II stars and of our Galaxy chemical enhancement. In addition, except for the abundance ratio [Ba/Eu] the fodd,Ba is also an important indicator, which can be used to study the relative contributions of the r- and s-processes during the chemical evolution history of the Milky Way and the enhancement mechanism in stars with peculiar abundances of heavy elements.
Scaling Analysis of Surfactant Templated Polyacrylamide Gel Surfaces

School of Computational Science and Information Technology,Florida State University, Tallahassee, Florida 32306-4120(Dated: February 1, 2008)Surfaces of surfactant-templated polyacrylamide hydrogels were imaged by atomic force mi-croscopy (AFM), and the surface morphology was studied by numerical scaling analysis. The tem-plated gels were formed by polymerizing acrylamide plus a cross-linker in the presence of surfactants,which were then removed by soaking in distilled water. Gels formed in the presence of over 20%surfactant (by weight) formed clear, but became opaque upon removal of the surfactants. Untem-plated gels formed and remained clear. The surface morphology of the gels was studied by severalone- and two-dimensional numerical scaling methods. The surfaces were found to be self-aﬃne onshort length scales, with a roughness (Hurst) exponent in the range 0.85 to 1, crossing over to aconstant root-mean-square surface width on long scales. Both the crossover length between thesetwo regimes and the saturation value of the surface width increased signiﬁcantly with increasingsurfactant concentration, coincident with the increase in opacity. We propose that the changes inthe surface morphology are due to a percolation transition in the system of voids formed uponremoval of the surfactants from the bulk.
Evaluation of simulation-based scatter correction for 3-D PET cardiac imaging

Quantitative imaging of the human thorax poses one of the most difficult challenges for three-dimensional (3-D) (septaless) positron emission tomography (PET), due to the strong attenuation of the annihilation radiation and the large contribution of scattered photons to the data. In [/sup 18/F] fluorodeoxyglucose (FDG) studies of the heart with the patient's arms in the field of view, the contribution of scattered events can exceed 50% of the total detected coincidences. Accurate correction for this scatter component is necessary for meaningful quantitative image analysis and tracer kinetic modeling. For this reason, the authors have implemented a single-scatter simulation technique for scatter correction in positron volume imaging. Here, they describe this algorithm and present scatter correction results from human and chest phantom studies.
When and Why Consumers React Negatively to Brand Acquisitions: A Values Authenticity Account

Brand acquisitions are a popular growth strategy. However, both anecdotal evidence and initial empirical evidence suggest that acquisitions can harm the acquired brand. This article proposes and tests a theoretical framework that aims to explain when and why consumers react negatively to acquired brands. Across ten studies using different methods, research designs, product categories, and brands, the authors demonstrate that these negative brand reactions can be explained by the perceived loss of a brand's unique values. Building on this values authenticity account, they document that the negative effect of acquisitions depends on the acquired brand's values, brand age, leadership continuity, and the alignment between acquiring and acquired brands. The findings offer important theoretical and managerial implications, helping managers predict and mitigate the negative effects of acquisitions for brands.
Impact of the 3D source geometry on time-delay measurements of lensed type-Ia supernovae

It has recently been proposed that gravitationally lensed type-Ia supernovae can provide microlensing-free time-delay measurements provided that the measurement is taken during the achromatic expansion phase of the explosion and that color light curves are used rather than single-band light curves. If verified, this would provide both precise and accurate time-delay measurements, making lensed type-Ia supernovae a new golden standard for time-delay cosmography. However, the 3D geometry of the expanding shell can introduce an additional bias that has not yet been fully explored. In this work, we present and discuss the impact of this effect on time-delay cosmography with lensed supernovae and find that on average it leads to a bias of a few tenths of a day for individual lensed systems. This is negligible in view of the cosmological time delays predicted for typical lensed type-Ia supernovae but not for the specific case of the recently discovered type-Ia supernova iPTF16geu, whose time delays are expected to be smaller than a day.
Amyloid Beta in Aging and Alzheimer’s Disease

Alzheimer’s disease (AD), is a progressive neurodegenerative disease that affects behavior, thinking, learning, and memory in elderly individuals. AD occurs in two forms, early onset familial and late-onset sporadic; genetic mutations in PS1, PS2, and APP genes cause early onset familial AD, and a combination of lifestyle, environment and genetic factors causes the late-onset sporadic form of the disease. However, accelerated disease progression is noticed in patients with familial AD. Disease-causing pathological changes are synaptic damage, and mitochondrial structural and functional changes, in addition to increased production and accumulation of phosphorylated tau (p-tau), and amyloid beta (Aβ) in the affected brain regions in AD patients. Aβ is a peptide derived from amyloid precursor protein (APP) by proteolytic cleavage of beta and gamma secretases. APP is a glycoprotein that plays a significant role in maintaining neuronal homeostasis like signaling, neuronal development, and intracellular transport. Aβ is reported to have both protective and toxic effects in neurons. The purpose of our article is to summarize recent developments of Aβ and its association with synapses, mitochondria, microglia, astrocytes, and its interaction with p-tau. Our article also covers the therapeutic strategies that reduce Aβ toxicities in disease progression and discusses the reasons for the failures of Aβ therapeutics.
On the causal gauge principle

Work by the Zurich school of causal (Epstein-Glaser) renormalization has shown that renormalizability in the presence of massless or massive gauge fields (as primary entities) explains gauge invariance and, in some instances, the presence of a Higgs-like particle, without the Brout-Englert-Higgs-Kibble (BEHK) mechanism. We review that work, in a pedagogical vein, with a pointer to go beyond.
Likelihood‐Based Inference for the Finite Population Mean with Post‐Stratification Information Under Non‐Ignorable Non‐Response

We describe models and likelihood‐based estimation of the finite population mean for a survey subject to unit non‐response, when post‐stratification information is available from external sources. A feature of the models is that they do not require the assumption that the data are missing at random (MAR). As a result, the proposed models provide estimates under weaker assumptions than those required in the absence of post‐stratification information, thus allowing more robust inferences. In particular, we describe models for estimation of the finite population mean of a survey outcome with categorical covariates and externally observed categorical post‐stratifiers. We compare inferences from the proposed method with existing design‐based estimators via simulations. We apply our methods to school‐level data from California Department of Education to estimate the mean academic performance index (API) score in years 1999 and 2000. We end with a discussion.
Statistics of heat transfer in mesoscopic circuits

A method to calculate the statistics of energy exchange between quantum systems is presented. The generating function of this statistics is expressed through a Keldysh path integral. The method is first applied to the problem of heat dissipation from a biased mesoscopic conductor into the adjacent reservoirs. We then consider energy dissipation in an electrical circuit around a mesoscopic conductor. We derive the conditions under which measurements of the fluctuations of heat dissipation can be used to investigate higher-order cumulants of the charge counting statistics of a mesoscopic conductor.
Performance Sex Differences in CrossFit®

CrossFit® has a unique standard for workout of the day for women and men. Scaling is used to set difficulty levels for women in CrossFit® gyms and competitions. This type of scaling is applied for weightlifting (60–82% of men’s load); however, there are usually no differences in difficulty settings for gymnastics and monostructural metabolic conditioning. Performance analysis is essential for every sports discipline, and statistical data comparing men’s and women’s results from athletics, running, swimming, weightlifting, etc., are available. However, CrossFit® lacks these statistics. The aim of our study was to analyze how the performances of men and women differed at the 2021 CrossFit Games®. Our sample comprised 40 female (age 27.8 ± 5.1) and 40 male participants (age 27.2 ± 3.7) competing in the Rx division. Data obtained from all events were analyzed using effect size and percentage. In 14 out of 15 events, men achieved better results than women. Even with the implementation of scaling, women’s results differed by 0.1–33.1% (effect size from small to large). Scaling for women is designed according to general strength and power differences; however, primarily because of anatomic and physiological differences, men attain better results. However, CrossFit Games® events are always unique, and the events rarely repeat; therefore, our study does not provide firm conclusions. As our study is the first to compare CrossFit Games® performance between the sexes, further research is needed.
The dynamics of entrepreneurial networking logics: evidence from United Kingdom high-tech start-ups

PurposeThis study aims to explore how entrepreneurial firms' networking logics may change under different types of perceived uncertainty. The arrival of new knowledge from the entrepreneurial firm's network may alter the perceived technology and market uncertainty that in turn determines how the firm adopts or combines the two opposing logics of causation and effectuation. Focusing on the roles of external advisors recruited by the firms, the study probes the details of the cyclical process and the mechanism through which networking logics are altered.Design/methodology/approachIn this study the authors conducted a 3-year longitudinal multiple case study of 12 United Kingdom (UK) high-tech start-ups from prefounding to A-round funding with 54 semistructured interviews and meeting observations.FindingsThe knowledge of external advisors with distinct experience often reshapes the entrepreneurial firm's perceptions of uncertainty, leading to logics change in network development. The authors identify two types of knowledge brought by external advisors and discover how these can influence three networking logic pathways under different levels of technology and market uncertainty.Originality/valueThe study is one of the first to map the paths of changing logics along with different types of uncertainty in the context of entrepreneurial network development. The study unpacks one of the key mechanisms of networking logic changes: the knowledge and expertise of those advisors recruited by the entrepreneurial firms. The process model of changing logics contributes to the effectuation literature and entrepreneurial network research.
Leading isospin-breaking corrections to pion, kaon and charmed-meson masses with Twisted-Mass fermions

We present a lattice computation of the isospin-breaking corrections to pseudoscalar meson masses using the gauge configurations produced by the European Twisted Mass collaboration with $N_f = 2 + 1 + 1$ dynamical quarks at three values of the lattice spacing ($a \simeq 0.062, 0.082$ and $0.089$ fm) with pion masses in the range $M_\pi \simeq 210 - 450$ MeV. The strange and charm quark masses are tuned at their physical values. We adopt the RM123 method based on the combined expansion of the path integral in powers of the $d$- and $u$-quark mass difference ($\widehat{m}_d - \widehat{m}_u$) and of the electromagnetic coupling $\alpha_{em}$. Within the quenched QED approximation, which neglects the effects of the sea-quark charges, and after the extrapolations to the physical pion mass and to the continuum and infinite volume limits, we provide results for the pion, kaon and (for the first time) charmed-meson mass splittings, for the prescription-dependent parameters $\epsilon_{\pi^0}$, $\epsilon_\gamma(\overline{MS}, 2~\mbox{GeV})$, $\epsilon_{K^0}(\overline{MS}, 2~\mbox{GeV})$, related to the violations of the Dashen's theorem, and for the light quark mass difference $(\widehat{m}_d - \widehat{m}_u)(\overline{MS}, 2~\mbox{GeV})$.
A New Model for Remaining Useful Life Prediction Based on NICE and TCN-BiLSTM under Missing Data

The Remaining Useful Life (RUL) prediction of engineering equipment is bound to face the situation of missing data. The existing methods of RUL prediction for such cases mainly take “data generation—RUL prediction” as the basic idea but are often limited to the generation of one-dimensional test data, resulting in the extraction of the prediction network. Therefore, this paper proposes a multivariate degradation device based on Nonlinear Independent Components Estimation (NICE) and the Temporal Convolutional Network–Bidirectional Long Short-term Memory (TCN-BiLSTM) network for the RUL prediction requirements in the case of missing data. First, based on the NICE network, realistic data are generated through reversible sampling; then, the filling of multivariate missing data is completed. Next, the filled multivariate degradation data are processed to generate multivariate degradation data and predicted labels for constructing the training set and test set. Based on this, a residual life prediction model integrating TCN and the BiLSTM network is proposed. To evaluate the proposed method, this paper takes an example of the RUL prediction of aeroengines to perform multivariate degradation data-filling and prediction tasks. The results demonstrate the superiority and potential application value of the method.
Big data and decision quality: the role of management accountants’ data analytics skills

Purpose Existing research on the relationship between big data and organizational decision quality is still few and far between, and what does exist often assumes direct effects of big data on decision quality. More recent research indicates that such direct effects may be too simplistic, and in particular, an organization’s overall human skills are often not considered sufficiently. Inspired by the knowledge-based view, we therefore propose that interactions between three aspects of big data usage and management accountants’ data analytics skills may be key to reaching high-quality decisions. The purpose of this study is to test these predictions based on a survey of US firms. Design/methodology/approach The authors draw on survey data from 140 US firms. This survey has been conducted via MTurk in 2020. Findings The results of the study show that the quality of big data sources is associated with higher perceived levels of decision quality. However, according to the results, the breadth of big data sources and a data-driven culture only improve decision quality if management accountants’ data analytics skills are highly developed. These results point to the important, but so far unexamined role of an organization’s management accountants and their skills for translating big data into high-quality decisions. Practical implications The present study highlights the importance of an organization’s human skills in creating value out of big data. In particular, the findings imply that management accountants may need to increasingly draw on data analytics skills to make the most out of big data for their employers. Originality/value This study is among the first, to the best of the authors’ knowledge, to provide empirical proof of the relevance of an organization’s management accountants and their data analytics skills for reaching desirable firm-level outcomes. In addition, this study thus adds to the further advancement of the knowledge-based view by providing evidence that in contemporary big-data environments, interactions between tacit and explicit knowledge seem crucial for driving desirable firm-level outcomes.
Entropic uncertainty relations and entanglement

We discuss the relationship between entropic uncertainty relations and entanglement. We present two methods for deriving separability criteria in terms of entropic uncertainty relations. In particular, we show how any entropic uncertainty relation on one part of the system results in a separability condition on the composite system. We investigate the resulting criteria using the Tsallis entropy for two and three qubits.