Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimality of (s, S) Policies in Inventory Models with Markovian Demand

This paper is concerned with a generalization of classical inventory models (with fixed ordering costs) that exhibit (s, S) policies. In our model, the distribution of demands in successive periods is dependent on a Markov chain. The model includes the case of cyclic or seasonal demand. The model is further extended to incorporate some other realistic features such as no ordering periods and storage and service level constraints. Both finite and infinite horizon nonstationary problems are considered. We show that (s, S) policies are also optimal for the generalized model as well as its extensions.
Introducing Regulatory Intermediaries

is as a two-party relationship between a regulator (R) and the targets of its regulation (T). This volume of regulation as a three-party system, in which diverse intermediaries (I) provide assistance to regulators and/or targets, drawing on their own capabilities, authority and legitimacy. Our framework paper for the volume, Theorizing regulatory intermediaries: The RIT model , sets out a general theoretical model for analyzing the roles and implications of regulatory intermediaries in diverse settings.
Novel methods for stress features identification using EEG signals

This paper introduces new methods to extract stress features from electroencephalogram (EEG) signals during two cognitive states; Closed-Eyes (CE) and Open-Eyes (OE) using Relative Energy Ratio (RER), Shannon Entropy (SE) and Spectral Centroids (SC). The group with the stress features was identified and classified using k-Nearest Neighbor (k-NN). The RER in term of Energy Spectral Density (ESD) for each frequency band (delta, theta, alpha and beta) in four different groups consisted of 180 EEG data were calculated and analyzed. Then, the SE was used to confirm the pattern of stress features. Meanwhile, SC was applied to the RER of each group and then the results were selected as input features to k-Nearest Neighbor (k-NN) for the classification purposes. The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The proposed method showed promising results where the combination of RER, SE and SC techniques with the training and testing of k-NN set at 70:30 able to detect and classify the group with the unique stress features at 88.89% accuracy
An APP Algorithm for Fading Channels using Forward-Only Prediction

A multiple-antenna receiver is proposed for achieving a diversity effect that partly overcomes the severity of continuous-time frequency-flat Rayleigh fading channels, and fast fading channels in particular. The error performance is further improved by bit interleaving and channel coding, where the encoders/channel is viewed as a serially concatenated system: a convolutional code (CC) constitutes the outer code, whereas a differential encoder and the fading channel (having truncated memory) form a joint inner code. In order to obtain a feasible detector structure, it is desirable to perform iterative decoding, which implies the need for an APP (a posteriori probability) algorithm that can operate over fading channels. For this purpose, the well-known BCJR algorithm (due to Bahl, Cocke, Jelinek, Raviv [1]) is generalized to handle channels having memory. Numerical results indicate that iterative decoding becomes more powerful when the exploited channel memory depth is extended, but the chief performance gain is due to space diversity provided by the multiple antennas.
Low power scan bypass technique with test data reduction

The exponential advance in semiconductor manufacturing technology is bringing heavy increase not only in power consumption but in test data volume as well. Moreover, power consumption in test mode is much higher than that in the functional operation mode. In this paper, a low power scan bypass technique is proposed to reduce both the test data volume and the test power consumption. The proposed technique can reduce both test data volume and power consumption with the minimal impact on area overhead. Unused segments, which consist of don't care bits, can be bypassed in the proposed scan bypass technique. In order to maximize the bypassing portion, scan cell ordering and pattern ordering are performed. Experimental results show that the proposed technique efficiently reduce test power and test data volume with a small overhead.
Hyperthermia Influences the Effects of Sodium Channel Blocking Drugs in Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Introduction Fever can increase the susceptibility to supraventricular and ventricular arrhythmias, in which sodium channel dysfunction has been implicated. Whether fever influences the efficacy of sodium channel blocking drugs is unknown. The current study was designed to investigate the temperature dependent effects of distinct sodium channel blocking drugs on the sodium currents in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Methods and Results hiPSC-CMs were generated from human skin fibroblasts of a healthy donor. The peak and late sodium currents (INa), steady-state activation, inactivation and recovery from inactivation of INa in hiPSC-CMs were analyzed using the whole-cell patch clamp technique. The effects of different concentrations of the antiarrhythmic drugs flecainide, lidocaine, ajmaline and the antianginal drug ranolazine on INa were tested at 36°C and 40°C. Increasing the temperature of the bath solution from 36°C to 40°C enhanced the inhibition of peak INa but reduced the inhibition of late INa by flecainide and lidocaine. By contrast, increasing the temperature reduced the effect of ajmaline and ranolazine on the peak INa but not late INa. None of the tested drugs showed temperature-dependent effects on the steady-state activation and inactivation as well as on the recovery from inactivation of INa in hiPSC-CMs. Conclusions Temperature variation from the physiological to the febrile range apparently influences the effects of sodium channel blockers on the sodium currents. This may influence their antiarrhythmic efficacy in patients suffering from fever.
PAC-Bayesian Learning and Domain Adaptation

In machine learning, Domain Adaptation (DA) arises when the distribution gen- erating the test (target) data differs from the one generating the learning (source) data. It is well known that DA is an hard task even under strong assumptions, among which the covariate-shift where the source and target distributions diverge only in their marginals, i.e. they have the same labeling function. Another popular approach is to consider an hypothesis class that moves closer the two distributions while implying a low-error for both tasks. This is a VC-dim approach that restricts the complexity of an hypothesis class in order to get good generalization. Instead, we propose a PAC-Bayesian approach that seeks for suitable weights to be given to each hypothesis in order to build a majority vote. We prove a new DA bound in the PAC-Bayesian context. This leads us to design the first DA-PAC-Bayesian algorithm based on the minimization of the proposed bound. Doing so, we seek for a \rho-weighted majority vote that takes into account a trade-off between three quantities. The first two quantities being, as usual in the PAC-Bayesian approach, (a) the complexity of the majority vote (measured by a Kullback-Leibler divergence) and (b) its empirical risk (measured by the \rho-average errors on the source sample). The third quantity is (c) the capacity of the majority vote to distinguish some structural difference between the source and target samples.
Clinical Features and Treatment Outcomes of Patients with Drug-Resistant and Drug-Sensitive Tuberculosis: A Historical Cohort Study in Porto Alegre, Brazil

Purpose To evaluate the clinical features and treatment outcomes of patients with pulmonary tuberculosis, stratified by level of drug resistance. Methods This was a historical cohort study based on data from the II National Anti-Tuberculosis Drug Resistance Survey (2006–2007) collected at eight participating health care facilities in Porto Alegre, southern Brazil. The cohort was followed for 3 years after the start of treatment. Results Of 299 cases of smear-positive pulmonary tuberculosis included in the study, 216 (72.2%) were diagnosed at five public primary health care units and 83 (27.8%) at three public hospitals. Among these cases, the prevalence of drug-resistant tuberculosis was 14.4%, and that of multidrug-resistant tuberculosis was 4.7%. Overall, 32.0% of drug-resistant and 2.0% of multidrug-resistant cases occurred in previously treated patients. The most common comorbidity in the sample was HIV infection (26.2%). There was no association between drug-resistant or multidrug-resistant tuberculosis and sociodemographic variables. Cure was achieved in 66.7% of patients, and the default rate was 21.2%. The 2-month sputum conversion rate was 34.2%, and the relapse rate was 16.9%. Patients with drug-resistant tuberculosis had lower rates of cure (45.2%) and 2-month sputum conversion (25%), as well as a higher relapse rate (30.7%). Conclusion These results highlight the urgent need for a more effective TB control program in this geographical setting, with a major emphasis on treatment of drug-resistant and multidrug-resistant tuberculosis.
Performing Isometric Force Control in Combination with a Cognitive Task: A Multidimensional Assessment

Introduction We used a multidimensional approach to study isometric force control in single and dual-task conditions. Methods Multiple measures of performance, efficiency, variability, and structural interference were calculated at low and higher force levels under single (force maintenance) and dual-task (force maintenance and reaction time) conditions. Results Reaction time and signal-to-noise ratio were larger in the dual-task conditions. They were also greater for the higher force condition, while sample entropy was lower. Perturbation analyses revealed smaller relative amplitude of downward perturbations for the higher force level. Discussion Attentional effort and efficiency are positively related when force level increases, and inversely related to entropy. These relations were presumably mediated by attentional investment. Behavioral perturbations show that attentional resources and structural interference models are not mutually exclusive to account for dual-task situation. Overall, the present study highlights the interest of a multidimensional assessment of force control.
Genderlectal variation in Korean?: An empirical sociolinguistic study of utterance-final tones

This paper empirically examines genderlectal variation in sentence-final boundary tones claimed by Lakoff (1975) on the basis of Seoul Korean data. Two studies are conducted in the research focusing on declaratives, the most frequently used sentence type, and additionally exclamatories. Study 1 is conducted based on six speakers' (3 males and 3 females) reading of 33 constructed dialogues, while study 2 examines recordings of three radio talk-shows cohosted by a male and a female. The two studies commonly found a clear difference in the use frequency of complex tones; the females produced a notably more frequent use of complex tones than the males, supporting Crystal's (1971) claim. However, no cross-gender difference was revealed in the inventory of tones. Shared attitudinal meaning of complex tones are proposed and their sociolinguistic implications are also discussed.
A permutation testing framework to compare groups of brain networks

Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.
Epstein-Barr virus-induced B-cell transformation: quantitating events from virus binding to cell outgrowth.

Epstein-Barr virus (EBV) infection and growth activation of human B cells is central to virus biology and disease pathogenesis, but is poorly understood in quantitative terms. Here, using virus at defined m.o. i. , the different stages of this process at the single-cell level are followed in vitro. Virus binding to the B-cell surface, assayed by quantitative PCR, is highly efficient, particularly at the low m.o. i. values that most likely reflect physiologic events in vivo. However, only 10-15 % of bound virus genomes reach the cell nucleus, as visualized by sensitive fluorescence in situ hybridization (FISH) assay; viral genomes acquired per cell nucleus range from 1 to >10, depending on the m.o. i. Thereafter, despite differences in initial genome load, almost all nuclear genome-positive cells then go on to express the virus-encoded nuclear antigen EBNA2, upregulate the cell activation antigen CD23 and transit the cell cycle. EBNA2-positive cells in the first cycle post-infection then grow out to lymphoblastoid cell lines (LCLs) just as efficiently as do cells limiting-diluted from already established LCLs. This study therefore identifies EBV genome delivery to the nucleus as a key rate-limiting step in B-cell transformation, and highlights the remarkable efficiency with which a single virus genome, having reached the nucleus, then drives the transformation programme.
Preparation and Mechanical Properties of Graphene Oxide: Cement Nanocomposites

We investigate the performance of graphene oxide (GO) in improving mechanical properties of cement composites. A polycarboxylate superplasticizer was used to improve the dispersion of GO flakes in the cement. The mechanical strength of graphene-cement nanocomposites containing 0.1–2 wt% GO and 0.5 wt% superplasticizer was measured and compared with that of cement prepared without GO. We found that the tensile strength of the cement mortar increased with GO content, reaching 1.5%, a 48% increase in tensile strength. Ultra high-resolution field emission scanning electron microscopy (FE-SEM) used to observe the fracture surface of samples containing 1.5 wt% GO indicated that the nano-GO flakes were well dispersed in the matrix, and no aggregates were observed. FE-SEM observation also revealed good bonding between the GO surfaces and the surrounding cement matrix. In addition, XRD diffraction data showed growth of the calcium silicate hydrates (C-S-H) gels in GO cement mortar compared with the normal cement mortar.
Concomitant Administration of Different Doses of Simvastatin with Ivabradine Influence on PAI-1 and Heart Rate in Normo- and Hypercholesterolaemic Rats

Ivabradine is a novel heart rate lowering agent that inhibits If ionic current in the sinus node and demonstrates antiischaemic and antianginal activity. The aim of the paper was to investigate the effect its dose-dependent drug-drug interaction with simvastatin inhibitor HMGCo-A has on PAI-1 blood level, heart rate and blood pressure. The experiments were performed in hyper- and normocholesterolemic Wistar rats receiving simvastatin (1 and 20 mg × kg−1 bw) with ivabradine (10 mg × kg−1 bw) during a 4-week period. Ivabradine exacerbated the decrease of PAI-1 in normocholesterolemic animals receiving simvastatin at a dose of 1 mg/kg bw and was not observed to have any significant influence on the PAI-1 values in rats receiving 20 mg × kg−1 bw simvastatin. Ivabradine, coadministered with simvastatin given at a dose of 20 mg × kg−1 bw, significantly slowed the heart rate in normocholesterolaemic and hypercholesterolaemic groups as compared to the group receiving ivabradine alone. Conclusion. The administration of ivabradine to normocholesterolaemic and hypercholesterolaemic rats receiving simvastatin significantly exacerbated the slowing of heart rate with no effect on blood pressure. The administration of ivabradine has been shown to demonstrate different effects on PAI-1 values depending on lipid disorders.
Quarter-square analog four-quadrant multiplier based on CMOS invertes and using low voltage high speed control circuits

This paper concerns a quarter-square analog four-quadrant multiplier, based on proprietary architecture using four CMOS inverters. The most important difference with respect to already published own circuit implementation is the use of inverter ‘core’ of the circuit with completely redesigned auxiliary and steering blocks. The proposed circuit solution is suitable for RF applications in communication systems due to simple architecture comprising building blocks with RF CMOS transistors having sufficiently large biasing currents. Simulation results done on the basis of 180nm CMOS UMC Foundry Design Kit are also presented.
The Effects of Encouraging Student-Faculty Interaction on Academic Success, Identity Development, and Student Retention in the First Year of College

The study presented in this dissertation was designed to investigate the effects of a brief intervention encouraging student-faculty interaction among college students on their academic achievement, college adjustment and intent to withdraw. Additionally, the effects of identity style on academic achievement, college adjustment, and student-faculty interaction were examined. Two hundred and five first year students participated in a four-part study, measuring the frequency and quality of student-faculty interaction, college adjustment, and identity development at three different time points. Students were randomly assigned to one of three experimental groups; only one group received the advice to meet with faculty outside of the classroom at the beginning of the fall semester. Intent to withdraw was measured at the end of the semester, and first-semester GPA was obtained during the spring semester. Results indicated a significant effect of the experimental treatment on one key outcome variable: level of institutional commitment. Students who received the advice to interact with faculty reported higher institutional commitment than the other groups, and in turn, institutional commitment was the only predictor of intent to withdraw. Academic achievement in the first semester was predicted by first generation status, quality and frequency of student-faculty interaction, and academic efficacy. In terms of identity development, students using the information orientation were better equipped to handle the demands of the first year in college than those using the diffuse orientation.
Three Prompts for Collective Action in the Context of Digital Media

Research dealing with the nexus of collective action, political participation, and digital media confronts three challenges: conceptualizing digital media as an influence on human behavior, finding common ground among new theories, and connecting together individual-level models with structural-level theories. This article addresses these challenges as a theoretical undertaking. It argues that the digital media environment should be understood as a change in the context for action rather than as an individual-level variable, and that this changed context is relevant to behavior because it expands opportunities for action. This expansion involves a range of structural possibilities for viable collective action that entail at least three paths: organizational prompts, social prompts, and self-initiation. There are theoretical reasons to expect that individual-level attributes including age, education, ideology, and personality may differentially affect people’s susceptibility to these prompts. Future research may profit from refinements to behavioral models that account for possible differences across structurally different prompts for action.
Comparison Analysis of Coal Biodesulfurization and Coal's Pyrite Bioleaching with Acidithiobacillus ferrooxidans

Acidithiobacillus ferrooxidans (A. ferrooxidans) was applied in coal biodesulfurization and coal's pyrite bioleaching. The result showed that A. ferrooxidans had significantly promoted the biodesulfurization of coal and bioleaching of coal's pyrite. After 16 days of processing, the total sulfur removal rate of coal was 50.6%, and among them the removal of pyritic sulfur was up to 69.9%. On the contrary, after 12 days of processing, the coal's pyrite bioleaching rate was 72.0%. SEM micrographs showed that the major pyrite forms in coal were massive and veinlets. It seems that the bacteria took priority to remove the massive pyrite. The sulfur relative contents analysis from XANES showed that the elemental sulfur (28.32%) and jarosite (18.99%) were accumulated in the biotreated residual coal. However, XRD and XANES spectra of residual pyrite indicated that the sulfur components were mainly composed of pyrite (49.34%) and elemental sulfur (50.72%) but no other sulfur contents were detected. Based on the present results, we speculated that the pyrite forms in coal might affect sulfur biooxidation process.
The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems.We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost.In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development.