Miracle-dz/UniFAR-FTUs huggingface dataset:
A NEW ATTITUDE-BEHAVIOUR (AB) THEORY FOR ACCEPTABLE LEADERS IN WINNING ORGANIZATIONS

The organizational fate depends on a committed effective decision maker who can predict the future business situations based on various affecting parameters on the organizational business. The right decisions taken at right time can elevate an organization as winning organization and the decision maker can transform himself as an acceptable leader. Such decisions can also decide the fate of the organization along with the livelihood of its employees. A winning leader is an asset of an organization and the employees are directly and indirectly get the benefits of such leader. If the leader fails in predicting the future of a profit oriented organization or not for profit organizations, the employees are directly going to be the victims of such wrong decisions. Leaders should able to make correct judgement while taking decisions based on effective predictions by using predictive analysis framework. The attitude of the leader plays an important role while making decisions on solving organizational/individual problems. In Simon’s model of decision making, the attitude of the leader decides his ability of identifying a problem, finding alternative solutions, analysing these alternatives to find optimum alternative, and finally to implement the best alternative to practically solve the problem. Attitude is the mental status of a human being which represents the emotions based on his/her feelings at a given time and controls his/her instantaneous behaviour. In this paper, we have developed a theory on winning leaders actions based on their behaviour in organizations. It is argued that the behaviour of a leader depends on his/her attitude which may be positive or negative depends on the four factors identified as feelings, emotions, belief, and environment. Thus a supportive, effective, good environment creates positive attitude and hence winning leaders. The bad environment supports negative attitude due to wrong belief, negative emotions, and frustratiive feelings which are again depends on the present and past environment of the decision maker.
Basis function networks for interpolation of local linear models

In this paper, a new algorithm (LOLIMOT) for nonlinear dynamic system identification with local linear models is proposed. The input space is partitioned by a tree-construction algorithm. The local models are interpolated by overlapping local basis functions. The resulting structure is equivalent to a Sugeno-Takagi fuzzy system and a local model network and can therefore be interpreted correspondingly. The LOLIMOT algorithm is very simple, easy to implement, and fast. Moreover, this approach has the following appealing properties: it does not underlie the "curse of dimensionality", it reveals irrelevant inputs, it detects inputs that influence the output mainly in a linear way, and it applies robust local linear estimation schemes. The drawbacks are that only orthogonal cuts are performed and that the local estimation approach may lead to interpolation errors.
P RELIMINARY R EPORT ON UK-S AUDI 2017 F IELDWORK A T W ADI D ABSA , A SIR P ROVINCE , S AUDI A RABIA

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on Novel for Gout

Gout is a kind of arthritis which is caused by the accumulation of monosodium uric acid crystals especially in joints. 8.3 million People in the United States are affected with gout and Prevalence is approximately 20% in patients with a family history of gout. Controlling the acute flares has been the first priority in the management of gout. As there are limitations in approved therapies for gout especially with patients who frequently have multiple comorbidities. Because of the greater understanding of the pathophysiology of gout, it has resulted in the discovery of new therapies to treat and prevent gout flares and underlying hyperuricemia. Novel therapies will lower serum urate levels or treat and prevent acute gouty flares. This review describes various novel drugs for treating the gout and their safety and efficacy when compared to other traditional drugs used for gout.
The influence of cobalt population on the structural properties of CoxFe3−xO4

CoxFe3−xO4-based (x=0.05–1.6) nanoparticles prepared by combustion reaction were investigated using x-ray diffraction, Raman spectroscopy, and Mossbauer spectroscopy. The Mossbauer data revealed Co2+ in both tetrahedral (site A) and octahedral (site B) sites of the cubic ferrite structure. For x⩽0.4 the experimental data indicated the synthesis of a core-shell structure, with hematite as the shell and cobalt ferrite as the core of the nanoparticle. Differences in crystalline structure between the two phases support the evidences we found of a highly stressed core-shell interface, leading to symmetry reduction of the tetrahedral and octahedral sites.
(2019). Data linkage to monitor hepatitis C-associated end-stage liver disease and hepatocellular carcinoma inpatient stays in England. Journal of Viral Hepatitis.

Introduction: Persons with chronic hepatitis C (HCV) infection are at increased risk of end-stage liver disease (ESLD) and hepatocellular carcinoma (HCC). The impact of hepatitis treatment scale-up and elimination strategies on ESLD and HCC incidence is a critical measure of progress towards WHO targets. Method: Data from national laboratory surveillance of HCV diagnoses were linked to inpatient care records in Hospital Episode Statistics (HES). For persons first diagnosed with HCV between 1998-2016, we describe the characteristics of persons with ESLD and HCC and estimate incidence. Results: Of persons diagnosed with HCV between 1998 and 2016 (104,674), 9.1 % (9,525) had an admission for ESLD and 2.5% (2,610) for HCC. The majority of persons with ESLD and HCC were male (70.7% and 82.7%) and of white ethnicity (89.9% and 82.7%). Crude incidence of ESLD and HCC admission was 10.4 and 3.2 per 1,000 person years respectively. When compared to 2011-2013, incidence of ESLD and HCC admissions in 2014-2017 were lower (ESLD incidence rate ratio (IRR): 0.81; 95% Confidence interval (CI): 0.76-0.86; HCC IRR: 0.90; 95% CI: 0.82-1.00, p=0.045). Data linkage showed considerable underreporting of HCV in HES coding for ESLD and HCC (16.0% and 11.3% respectively) Conclusion: We found a decline in incidence of ESLD and HCC-related inpatient admissions since 2011-2013. Linked analysis is required for the continued monitoring of ESLD and HCC inpatient incidence. However, HES data quality issues around completeness of identifiers contribute to uncertainty in linkage and may limit our ability to robustly monitor progress towards WHO elimination goals.
ournal of C osmology and A stroparticle P hysics J On the consistency of warm inﬂation

Conditions are obtained for the existence of a warm inﬂationary attractor in the system of equations describing an inﬂaton coupled to radiation. These conditions restrict the temperature dependence of the dissipative terms and the size of thermal corrections to the inﬂaton potential, as well as the gradient of the inﬂaton potential. When these conditions are met, the evolution approaches a slow-roll limit and only curvature ﬂuctuations survive on super-horizon scales. Formulae are given for the spectral indices of the density perturbations and the tensor/scalar density perturbation amplitude ratio in warm inﬂation.
ATLAS NOTE b -tagging in dense environments

This note describes the performance of the common ATLAS b -tagging algorithms in the context of the dense environments that can be found in the decay of boosted top quarks into jets and addresses the problems related to such conditions. The results of these studies lead to the development of a new multivariate-analysis-based b -tagging algorithm called MVb. The training of this new b -tagging algorithm is modiﬁed with respect to that of the current tools to take the conditions of boosted topologies better into account. Its performance is signiﬁcantly improved relative to the standard algorithms for high p T b -jets and jets contained in dense environments. function of the b -tagging e ﬃ ciency ε b . Each working point on these curves corresponds to a di ﬀ erent cut on the discriminant of these taggers, Also their relative performance with respect to MV1, the current ATLAS default b -tagger, is shown. The evaluation is performed using jets that are either from simulated t ¯ t events produced according to the Standard Model predictions (a) or from events in which a hypothetical Kaluza-Klein gluon with the mass m g KK = 2 . 5 TeV decays to a boosted t ¯ t system (b). The basic di ﬀ erence of these two samples is the distribution of their top quark p T . While the top quarks produced according to the SM production mechanisms are mainly of low transverse momentum, a large fraction of the top quarks from the g KK → t ¯ t samples have a transverse momentum above 400 GeV and provide therefore an ideal scenario to test the performance of the MVb and MVbCharm taggers, which are developed exactly for such environments. The MVb tagger shows a similar performance compared to the MV1 tagger in the SM t ¯ t sample for working points corresponding to the interesting range of b -tagging e ﬃ ciencies (0 . 65 < ε b < 0 . 85). As the MVb tagger uses not only the IP3D weight as input, but also the IP2D weight, the jet width and the number of tracks with a large impact parameter signiﬁcance, its performance is strongly improved with respect to the MV1 and JetFitterCombNN algorithm for jets having no reconstructed vertex. Also working points corresponding to b -tagging e ﬃ ciencies between 50% and 60% show an improved light-ﬂavour rejection rate (up to a factor of 1 . 3). In events where a high mass resonance of the KK-gluon decays into boosted top-antitop pairs, the performance of MVb is even better, by a factor of 1.5-2.5 (depending on the chosen working point).
Bifurcations and exact solutions for a nonlinear surface wind waves equation

By using the method of dynamical systems, for the nonlinear surface wind waves equation, which is given by Manna, we study its dynamical behavior to determine all exact explicit traveling wave solutions. To guarantee the existence of the aforementioned solutions, all parameter conditions are determined. Our procedure shows that the nonlinear surface wind waves equation has no peakon solution. Copyright © 2015 John Wiley & Sons, Ltd.
A Python Code for Simulating Single Tactile Receptors and the Spiking Responses of Their Afferents

This work presents a pieces of Python code to rapidly simulate the spiking responses of large numbers of single cutaneous tactile afferents with millisecond precision. To simulate the spike responses of all the major types of cutaneous tactile afferents, we proposed an electromechanical circuit model, in which a two-channel filter was developed to characterize the mechanical selectivity of tactile receptors, and a spike synthesizer was designed to recreate the action potentials evoked in afferents. The parameters of this model were fitted using previous neurophysiological datasets. Several simulation examples were presented in this paper to reproduce action potentials, sensory adaptation, frequency characteristics and spiking timing for each afferent type. The results indicated that the simulated responses matched previous neurophysiological recordings well. The model allows for a real-time reproduction of the spiking responses of about 4,000 tactile units with a timing precision of <6 ms. The current work provides a valuable guidance to designing highly realistic tactile interfaces such as neuroprosthesis and haptic devices
Correlation of Phytochemical, Metal Contents and Antioxidant Activities of Black Glutinous Rice Bran and Potential Chemoprevention

In this work total anthocyanin content (TAC), total flavonoid content (TFC), total phenolic content (TPC) and minerals found in five black glutinous rice cultivars (MS, SK, PY, PC and KK) from Thailand were analyzed. The antioxidant activity of anthocyanin-rich black glutinous rice bran extracts against nitric oxide radical (NO˙), superoxide radical (O2˙Ż) and lipid peroxyl radical (LOO˙) was also determined. Potential chemopreventive property of rice bran extract was screened based on cellular bioassays for phase II detoxification enzyme induction. Quinone reductase (QR) induction in murine hepatoma cells was used as a marker for this effect. Rice bran extract of cultivar KK had the highest TAC, of SK the highest TFC and of PC the highest TPC. The best antioxidants against NO˙, O2˙Ż and LOO˙ were cultivars KK, MS, and SK, respectively. Overall, TAC, TFC and TPC had a combinatorial effect on the antioxidant activities of all extracts; none of them dominated. Minerals may not play a role in the antioxidant activity of the extracts because most correlations between them and the antioxidant activity were unpredictable. However, rice bran contained high mass fractions of some essential minerals on dry mass basis, including Zn (103-133 µg/g), Se (11-18 µg/g) and Cu (3.8-7.1 µg/g). Chemopreventive study indicated that PC cultivar was the most potent chemopreventor with the lowest concentration of an inducer needed to double the QR activity (CD value) of 0.7 µg/mL. These findings showed that black glutinous rice bran is rich in phytochemicals and some essential minerals, and has a potential chemopreventive property.
Evidence of powerful relativistic jets in narrow-line Seyfert 1 galaxies

In 2008, the Fermi Gamma-ray Space Telescope has revealed - for the first time - high-energy (E > 100 MeV) gamma rays from a few Narrow-Line Seyfert 1 Galaxies (NLS1s). Later, in 2009 and 2010, two multifrequency campaigns on one of these sources, PMN J0948+0022 (z=0.585), definitely confirmed the presence, in sources of this type, of a relativistic jet very similar and with comparable power to those in blazars. However, these sources are neither blazars nor radio galaxies, as proven by their optical spectrum and by their very compact radio morphology. Moreover, since NLS1s are generally hosted in spiral galaxies, this casts a significant doubt on the paradigm of the correlation between jets and elliptical host galaxies. These findings pose intriguing challenges to the current knowledge of jet systems and on how these structures are generated. The current status of the researches in this field is reviewed and ongoing work is outlined.
Dendroclimatology of Yellow-Cedar (Callitropsis nootkatensis) and Temperature Variability on the Western Slopes of the North Cascades in Washington State, USA, from 1333 to 2015 CE

ABSTRACT Long-term paleoclimate reconstructions of temperature provide context for the magnitude of recent anthropogenic warming, help quantify the climate response to radiative forcing, and better characterize the range of natural variability. In North America, temperature-sensitive tree-ring proxy data remain sparse, which limits the spatial and temporal extent of these reconstructions. Here we present an analysis of yellow-cedar (Callitropsis nootkatensis) growth in Washington State (USA) and its relationship to climate. Combining empirical statistical analysis with a process model of xylogenesis, we show that tree-ring chronologies from three high-elevation sites in the North Cascades are primarily controlled by temperature. We then use these chronologies to reconstruct summer temperatures over the period 1333 to 2015 CE, adding a new proxy to the North American network of temperature-sensitive trees. Comparison with an existing large-scale spatial gridded reconstruction suggests this species offers important local and regional information on past temperatures.
Estimating and validating koala Phascolarctos cinereus density estimates from acoustic arrays using spatial count modelling

Abstract Context. It is notoriously difficult to estimate the size of animal populations, especially for cryptic or threatened species that occur in low numbers. Recent advances with acoustic sensors make the detection of animal populations cost effective when coupled with software that can recognise species-specific calls. Aims. We assess the potential for acoustic sensors to estimate koala, Phascolarctos cinereus, density, when individuals are not identified, using spatial count models. Sites were selected where previous independent estimates of density were available. Methods. We established acoustic arrays at each of five sites representing different environments and densities of koalas in New South Wales. To assess reliability, we compared male koala density estimates derived from spatial count modelling to independently derived estimates for each site. Key results. A total 11 312 koala bellows were verified across our five arrays. Koalas were detected at most of our sample locations (96–100% of sensors; n = 130), compared with low detection rates from rapid scat searches at trees near each sensor (scats at <2% of trees searched, n = 889, except one site where scats were present at 69% of trees, n = 129). Independent estimates of koala density at our study areas varied from a minimum of 0.02 male koalas ha−1 to 0.32 ha−1. Acoustic arrays and the spatial count method yielded plausible estimates of male koala density, which, when converted to total koalas (assuming 1:1 sex ratio), were mostly equivalent to independent estimates previously derived for each site. The greatest discrepancy occurred where the acoustic estimate was larger (although within the bounds of uncertainty) than the independent mark–recapture estimate at a fragmented, high koala-density site. Conclusions. Spatial count modelling of acoustic data from arrays provides plausible and reliable estimates of koala density and, importantly, associated measures of uncertainty as well as an ability to model spatial variations in density across an array. Caution is needed when applying models to higher-density populations where home ranges overlap extensively and calls are evenly spread across the array. Implications. The results add to the opportunities of acoustic methods for wildlife, especially where monitoring of density requires cost-effective repeat surveys.
Developing a thermo-regulative system for nonwoven textiles using microencapsulated organic coconut oil

Organic coconut oil was investigated as a bio-based phase change material in core, and melamine formaldehyde was used as shell material to fabricate microencapsulated phase change material for thermo-regulation in nonwoven textiles. The microcapsules were synthesized using in situ polymerization method. The produced microcapsules (microencapsulated phase change material) were applied by knife coating in different ratios (1:5 and 1.5:5; MPCM: coating paste by wt.) to 100% polypropylene nonwoven, porous, and hydrophilic layer of a laminated, spunbond, and double-layer fabric. The coated layer was confined within two layers of the fabric to develop a thermo-regulative system on the nonwoven fabric to regulate the body temperature in surgeries. The two layers were composed by applying heat (140°C) and pressure (12 kg/cm2). Organic coconut oil, the fabricated microcapsule, and the composite fabrics were characterized by Fourier transform infrared spectroscopy, differential scanning calorimetry, and scanning electron microscopy. Scanning electron microscopy results revealed that spherical and uniform microcapsules were obtained with an approximate particle size of 2–6 µm. Differential scanning calorimetry results indicated that microencapsulated phase change material and the composite fabrics possessed significant melting enthalpies of 72.9 and 8.4–11.4 J/g, respectively, at peak melting temperatures between 21.6 and 22.8°C within human comfort temperature range. The utilization of coconut oil as a phase change material and the composite integration of this phase change material to a nonwoven fabric bring forward a novelty for future applications.
Buchberger stratification on the space of polynomials

In this paper, we consider the stratification on the space of polynomials, with a fixed set of monomials, induced from the notion of G-types, which encodes the process of Buchberger algorithm for obtaining Gröbner bases. We show that this indeed gives a finite stratification by locally closed subspaces, from which we deduce several finiteness results on Gröbner bases.
Applying Behavioral Contagion Theory to Examining Young Adults’ Participation in Viral Social Media Challenges

Viral social media challenges have erupted across multiple social media platforms. While social media users participate in prosocial challenges designed to support good causes, like the Ice Bucket Challenge, some challenges (e.g., Cinnamon Challenge) can also potentially be dangerous. To understand the influential factors, experiences, and reflections of young adults who participated in a viral social media challenge in the past, we conducted interviews with 30 college students (ages 18-27). We applied behavioral contagion theory as a qualitative lens to understand whether this theory could help explain the factors that contributed to their participation. We found that behavior contagion theory was useful but not fully able to explain how and why young social media users engaged in viral challenges. Thematic analyses uncovered that overt social influence and intrinsic factors (i.e., social pressure, entertainment value, and attention-seeking) also played a key role in challenge participation. Additionally, we identified divergent patterns between prosocial and potentially risky social media challenges. Those who participated in prosocial challenges appeared to be more socially motivated as they saw more similarities between themselves and the individuals that they observed performing the challenges and were more likely to be directly encouraged by their friends to participate. In contrast, those who performed potentially risky challenges often did not see similarities with other challenge participants, nor did they receive direct encouragement from peers; yet, half of these participants said they would not have engaged in the challenge had they been more aware of the potential for physical harm. We consider the benefits and risks that viral social media challenges present for young adults with the intent of optimizing these interactions by mitigating risks, rather than discouraging them altogether.
Socially-Aware Artificial Intelligence for Fair Mobility

Mobility systems have a fundamental impact on well-being. It is thus crucial to address the disproportional benefits that their design can lead to. In my research, I explore the trade-off between utility and fairness in transport network design and argue that AI can be used to create networks that achieve different notions of fairness.
Identifying Bias in Data Using Two-Distribution Hypothesis Tests

As machine learning models become more widely used in important decision-making processes, the need for identifying and mitigating potential sources of bias has increased substantially. Using two-distribution (specified complexity) hypothesis tests, we identify biases in training data with respect to proposed distributions and without the need to train a model, distinguishing our methods from common output-based fairness tests. Furthermore, our methods allow us to return a "closest plausible explanation" for a given dataset, potentially revealing underlying biases in the processes that generated them. We also show that a binomial variation of this hypothesis test could be used to identify bias in certain directions, or towards certain outcomes, and again return a closest plausible explanation. The benefits of this binomial variation are compared with other hypothesis tests, including the exact binomial. Lastly, potential industrial applications of our methods are shown using two real-world datasets.