Miracle-dz/UniFAR-FTUs huggingface dataset:
Monte Carlo simulation to reconstruct formaldehyde exposure levels from summary parameters reported in the literature.

OBJECTIVES This study presents a procedure allowing the numerical synthesis of exposure data reported in different ways in the literature, including summary parameters and single measurements. The procedure was applied to literature regarding formaldehyde exposure in the reconstituted wood panels industry, including oriented-strand board (OSB), medium density fibre board (MDF) and particle board (PB). METHODS For each publication providing summary parameters we estimated geometric means (GM) and geometric standard deviations (GSD) by assuming lognormality of exposure levels. Monte Carlo simulation was performed to re-create datasets from the sample sizes and estimated GMs and GSDs, allowing their subsequent formatting together with the single measurements. The precision and bias of the methods used to estimate GMs and GSDs were evaluated. RESULTS Altogether, the 13 articles included in our study yielded a final database of 874 data, of which 732 were simulated. For both area and personal data, exposures corresponding to MDF and PB were similar while OSB levels were lower. The most recent available personal levels (1985-1994) were highest in PB for jobs performed in the vicinity of the press (GM=0.63 mg m-3). Corresponding area levels were highest for PB in the main production zone (GM=0.43 mg m-3). Mixed-effects models fitted to area PB data explained 38% of the total variability. A 6-fold decrease in exposures from 1965 to 1995 was estimated. Replication of the simulation process yielded relative standard deviations of the calculated GMs and GSDs between 10 and 20%. The relative biases of the methods used to estimate GMs and GSDs varied across methods and decreased with higher sample sizes (from approximately 15% for n=5 to less than 5% for n=30, in absolute value). The precision also varied across methods and improved with higher sample sizes (from approximately 30% for n=5 to approximately 10% for n=30). DISCUSSION This methodology constitutes a new meta-analysis tool that should improve the interpretation of industrial hygiene literature data, but needs to be further validated.
Automated Phrase Mining from Massive Text Corpora

As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus and has various downstream applications including information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. None of the state-of-the-art models, even data-driven models, is fully automated because they require human experts for designing rules or labeling phrases. In this paper, we propose a novel framework for automated phrase mining, <inline-formula> <tex-math notation="LaTeX">$\mathsf{AutoPhrase}$</tex-math><alternatives> <inline-graphic xlink:href="shang-ieq1-2812203.gif"/></alternatives></inline-formula>, which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, <inline-formula><tex-math notation="LaTeX"> $\mathsf{AutoPhrase}$</tex-math><alternatives><inline-graphic xlink:href="shang-ieq2-2812203.gif"/></alternatives> </inline-formula> has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, <inline-formula><tex-math notation="LaTeX">$\mathsf{AutoPhrase}$ </tex-math><alternatives><inline-graphic xlink:href="shang-ieq3-2812203.gif"/></alternatives></inline-formula> can be extended to model single-word quality phrases.
An Integrative Review of Non-pharmacological Intervention in Elderly Patients with Mild Cognitive Impairment

The purpose of this study is to provide evidences for developing non-pharmacological intervention program in elderly patients with mild cognitive impairment by integrative review and analyze. An integrative review concept analysis method was used. According to the study result, relevant studies were 11, which are related to non-pharmacological intervention program in elderly patients with mild cognitive impairment. The quality of stduies were two groups nonrandomized studies were 9
Regime-Specific Predictability in Predictive Regressions

Predictive regressions are linear specifications linking a noisy variable such as stock returns to past values of a very persistent regressor with the aim of assessing the presence of predictability. Key complications that arise are the potential presence of endogeneity and the poor adequacy of asymptotic approximations. In this article, we develop tests for uncovering the presence of predictability in such models when the strength or direction of predictability may alternate across different economically meaningful episodes. An empirical application reconsiders the dividend yield-based return predictability and documents a strong predictability that is countercyclical, occurring solely during bad economic times. This article has online supplementary materials.
Developing human capital in Africa: carving a role for human resource professionals and practitioners

ABSTRACT Africa is a continent with remarkable economic potential yet the least developed largely due to its inadequate human capital to transform this potential into social and economic development. The focus on provision of classroom education as the cornerstone of human capital development in Africa has not produced the desirable outcomes expected. We see an important role in this process within the workplace and thus argue that the role of human resource (HR) professionals in human capital development in Africa deserves serious attention. The paper outlines why and how HR professionals can contribute to the development of Africa’s human capital. It also highlights the challenges HR professionals will face and the competencies they will need to address the challenges. The paper concludes with suggestions for further research.
Evaluation of the Incredible Years Teacher Classroom Management Program in a Regular Norwegian School Setting

ABSTRACT This study examined whether the Incredible Years (IY) Teacher Classroom Management (TCM) program implemented as a school-wide preventive intervention at 1st to 3rd grade in a regular school setting reduces the development of problem behavior and improves social competence. Using a quasi-experimental pre-post design, the IYTCM was implemented in 21 schools and compared with 22 matched schools that did not receive the program. A total of 241 1st to 3rd grade teachers and 1518 students aged 6 to 8 years took part in the trial. Mixed-model analyses found small positive effects on changes in students’ social competence (dw = 0.19), while effects on change in students problem behavior were less than small (dw < 0.20). When the program is implemented as school-wide universal preventive intervention, results suggest a small preventive impact of the IYTCM program in regular school settings for some of the outcomes measured in the study.
Intracellular acidification enhances neutrophil phagocytosis in chronic haemodialysis patients: possible role of CD11b/CD18.

BACKGROUND We have demonstrated that uraemic neutrophils that exhibit a low intracellular pH (pHi) display enhanced phagocytosis. However, the underlying cellular mechanism is unclear. METHODS We used neutrophils from three groups of haemodialysis (HD) patients before dialysis (Groups A, B and C) and also from age- and sex-matched healthy individuals to determine pHi, phagocytosis and expression of CD11b, CD18, CD14 and toll-like receptors (TLR)-2 and TLR-4. The patients were categorized based on three consecutive monthly pre-dialysis plasma bicarbonate concentrations(P(HCO3)) and pH values; Groups A, B and C had a constant pre-dialysis P(HCO3) of </=21, 21-26 and >/=26 mmol/L (mEq/L), respectively. We also studied the effects induced by the correction of metabolic acidosis and monoclonal antibodies (mAbs) against CD11b/CD18 on neutrophils in Group A. Furthermore, we investigated the effect of intracellular acidification on uraemic neutrophils ex vivo. RESULTS We observed that the neutrophils in Group A exhibited significantly increased phagocytosis and expression of CD11b/CD18 compared with those in Groups B and C. Additionally, our ex vivo studies demonstrated that the mAbs against CD11b/CD18 partially blocked the enhancement of neutrophil phagocytosis in Group A. Moreover, the pHi of uraemic neutrophils is inversely correlated with phagocytosis and expression of CD11b/CD18. CONCLUSIONS HD patients with a low P(HCO3) exhibited low neutrophil pHi that in turn increased the expression of CD11b/CD18 compared with neutrophils with a normal or high pHi. This increased expression of CD11b/CD18 on the uraemic neutrophils may contribute to the pHi-mediated phagocytosis.
Protein Secondary Structure Reduction Methods Significantly Affect Prediction Accuracy

Protein secondary structure prediction is a critical step in determining the 3D structure of a protein which in turns determines the function of a protein. Five methods that reduce the DSSP (Dictionary of Secondary Structure of Proteins) secondary structures from eight classes into three classes are attempted in this work. A protein secondary structure classifier from amino acid sequences has been used to evaluate the five reduction methods under the same hardware, platforms, and environments to allow stringent and reliable comparison between the methods and then arrive at clear conclusions. Researchers in the filed of protein secondary structure prediction use typical three states of secondary structures, namely: alpha helices (H) beta strands (E), and coils (C). The adopted reduction schemes from the DSSP eight classes (states) to the three classes (H, E, and C) are usually performed by using one of the five assignments or reduction methods implemented in this research. This paper explains and discusses the effect of these reduction methods on the prediction accuracy.
Fingerprint Liveness Detection using Binarized Statistical Image Features

Recent experiments, reported in the third edition of Fingerprint Liveness Detection competition (LivDet 2013), have clearly shown that fingerprint liveness detection is a very difficult and challenging task. Although the number of approaches is large, none of them can be claimed as able to detect liveness of fingerprint traits with an acceptable error rate. In our opinion, in order to investigate at which extent this error can be reduced, novel feature sets must be proposed, and, eventually, integrated with existing ones. In this paper, a novel fingerprint liveness descriptor named “BSIF” is described, which, similarly to Local Binary Pattern and Local Phase Quantization-based representations, encodes the local fingerprint texture on a feature vector. Experimental results on LivDet 2011 data sets appear to be encouraging and make this descriptor worth of further investigations.
A Hierarchical Model for the Analysis of Efficiency and Speed-Up of Multi-core Cluster-Computers

We develop a simple hierarchical model for the performance analysis of compute clusters assembled from multi-core compute nodes connected by a (high-speed) network. The performance is described by the dimensionless speed-up and efficiency in dependence on important hardware and application parameters. The hardware parameters are the number of compute nodes and the bandwidth the network, together with the number of cores per node, the theoretical performance of each core and the bandwidth of the main memory. The application parameters are the total number of operations performed on a number of bytes and the total number of bytes communicated between the processing units. In order to exemplify our concept we apply it to the scalar product of vectors, matrix multiplication, Linpack and FFT. Our previous performance models are contained as special cases in the new more comprehensive approach.
GerNED: A German Corpus for Named Entity Disambiguation

Determining the real-world referents for name mentions of persons, organizations and other named entities in texts has become an important task in many information retrieval scenarios and is referred to as Named Entity Disambiguation (NED). While comprehensive datasets support the development and evaluation of NED approaches for English, there are no public datasets to assess NED systems for other languages, such as German. This paper describes the construction of an NED dataset based on a large corpus of German news articles. The dataset is closely modeled on the datasets used for the Knowledge Base Population tasks of the Text Analysis Conference, and contains gold standard annotations for the NED tasks of Entity Linking, NIL Detection and NIL Clustering. We also present first experimental results on the new dataset for each of these tasks in order to establish a baseline for future research efforts.
The impact of anxiety on processing efficiency : implications for the attentional control theory

The Attentional Control Theory (ACT) proposes that high-anxious individuals maintain performance effectiveness (accuracy) at the expense of processing efficiency (response time), in particular, the two central executive functions of inhibition and shifting. In contrast, research has generally failed to consider the third executive function which relates to the function of updating. In the current study, seventy-five participants completed the Parametric Go/No-Go and n-back tasks, as well as the State-Trait Anxiety Inventory in order to explore the effects of anxiety on attention. Results indicated that anxiety lead to decay in processing efficiency, but not in performance effectiveness, across all three Central Executive functions (inhibition, set-shifting and updating). Interestingly, participants with high levels of trait anxiety also exhibited impaired performance effectiveness on the n-back task designed to measure the updating function. Findings are discussed in relation to developing a new model of ACT that also includes the role of preattentive processes and dual-task coordination when exploring the effects of anxiety on task performance.
Mycobacterium abscessus-Induced Granuloma Formation Is Strictly Dependent on TNF Signaling and Neutrophil Trafficking

Mycobacterium abscessus is considered the most common respiratory pathogen among the rapidly growing non-tuberculous mycobacteria. Infections with M. abscessus are increasingly found in patients with chronic lung diseases, especially cystic fibrosis, and are often refractory to antibiotic therapy. M. abscessus has two morphotypes with distinct effects on host cells and biological responses. The smooth (S) variant is recognized as the initial airway colonizer while the rough (R) is known to be a potent inflammatory inducer associated with invasive disease, but the underlying immunopathological mechanisms of the infection remain unsolved. We conducted a comparative stepwise dissection of the inflammatory response in S and R pathogenesis by monitoring infected transparent zebrafish embryos. Loss of TNFR1 function resulted in increased mortality with both variants, and was associated with unrestricted intramacrophage bacterial growth and decreased bactericidal activity. The use of transgenic zebrafish lines harboring fluorescent macrophages and neutrophils revealed that neutrophils, like macrophages, interact with M. abscessus at the initial infection sites. Impaired TNF signaling disrupted the IL8-dependent neutrophil mobilization, and the defect in neutrophil trafficking led to the formation of aberrant granulomas, extensive mycobacterial cording, unrestricted bacterial growth and subsequent larval death. Our findings emphasize the central role of neutrophils for the establishment and maintenance of the protective M. abscessus granulomas. These results also suggest that the TNF/IL8 inflammatory axis is necessary for protective immunity against M. abscessus and may be of clinical relevance to explain why immunosuppressive TNF therapy leads to the exacerbation of M. abscessus infections.
Sustaining school-based improvement: considering emotional responses to change

Purpose Emotions of school leaders influence school culture and structure. Understanding emotions is under-researched and under-theorized in non-western contexts, especially during educational change. The purpose of this paper is to understand the nature of the leadership team’s (LT’s) emotional responses to change, their coping strategies and conditions that maintain their commitment to change. Design/methodology/approach The study used intrinsic case study research, drawing on data from interviews and a focus group that illuminated perceptions of the LT in a school. The data set was analyzed following the general inductive approach. Findings The LT’s experienced three critical incidents (CI) of educational change that provoked a range of intense negative and positive emotions, a national curriculum reform. Despite the team’s attempt to cope with the national curriculum reform (i.e. CI1), negative emotions and unsupportive conditions challenged their commitment to change. In CI2, supportive conditions and effective personal coping strategies helped elicit positive emotions, which led to sustained commitment to change. Emotions experienced during the capacity-building program (i.e. CI3) were predominantly positive due to support from the school principal and coaches, resulting in sustained commitment to change. Research limitations/implications Findings from this small-scale case study in Lebanon are not generalizable to other contexts. The time lag could have affected the recollection of experiences. All participants were female, and their experiences might not reflect those of other school members affected by the changes. Practical implications Examining emotions during change uncovers insight into school leaders’ subjective experience, facilitates a more nuanced understanding of change, and supports change implementation. Considering emotions during change informs the development of tailored interventions that provide effective support. Originality/value This study examines how emotions affect the success of educational change. Contrary to common understanding, change does not always generate negative emotions that impede implementation. School-based improvement creates structural and cultural conditions for effective change as it considers practitioners’ socio-emotional needs, eliciting positive emotions.
Taylor-Rule Consistent Estimates of the Natural Rate of Interest

We estimate the natural rate of interest for the US and the euro area in a semi-structural model comprising a Taylor rule. Our estimates feature key elements of Laubach and Williams (2003), but are more consistent with using conventional policy rules: we model inflation to be stationary, with the output gap pinning down deviations of inflation from its objective (rather than relative to a random walk). We relax some constraints on the correlation of latent factor shocks to make the original unobserved-components framework more amenable to structural interpretation and to reduce filtering uncertainty. We show that resulting natural rate metrics are more consistent with estimates from structural models. JEL Classification: C11, E32, E43, E52
Information seeking behavior of the poor: the study of parents’ school choice decisions

The purpose of this study is to investigate information seeking behavior of immigrants in disadvantaged communities in the state of CO, USA, using school choice decisions as a problematic situation. The study investigated the extent to which immigrant families in poor neighborhoods took advantage of school choice policies and the extent to which these decisions were mediated by information seeking activities. ,Quantitative study using survey questionnaire was used. The study design used everyday life information seeking behavior (Savolainen, 1995) and information-poverty lived-experiences of poor people (Chatman and Pendelton, 1995) as a framework and theoretical lens, respectively. Parents of school-age children who met the criteria of “poor” based on the federal poverty guideline were recruited to participate in the study. ,The study found that only 21 per cent (24 families out of 113) participated in school choice programs. Within the smaller group of parents that took advantage of school choice policy, 72 per cent gathered information before choosing a school, and of this group, about 80 per cent asked friends followed by school visit and a phone call to schools as information sources. Library use as sources of information was also selected by 61 per cent of the respondents. One important finding of this study is that although a majority of the families are aware of school choice policy, their level of participation is low and that is largely due to their economic and life circumstances. ,Access to quality education is widely investigated and multiple entities have a stake in it, including parents, policymakers, researchers and school districts. The social implications of this study are significant in that the mere presence and awareness of school choice policy did not translate into increased participation by parents from disadvantaged communities for whom the policy was designed to benefit. Consistent with findings in the extant literature, parents in low socio-economic status also value quality education for their children. To address the issue of low participation in school choice, cities need to work toward strengthening schools and library systems in poor neighborhoods instead of diverting resources away. ,Based on two theoretical accounts – i.e. the information-poverty lived-experiences of poor people, or the outsiders, (Chatman, 1999, 1996), and theory of everyday life information seeking (Savolainen, 1995) – this study investigated how immigrants and disadvantaged communities seek, acquire and use information to navigate school choice policy in the city of Aurora, CO. The findings of this study are relevant for educators, policymakers, libraries, school districts, cities, counties and parents to determine the necessary policy measures that are required to increase school choice participation by immigrants and disadvantaged communities.
Topology Preserving Non-negative Matrix Factorization for Face Recognition

In this paper, a novel topology preserving non-negative matrix factorization (TPNMF) method is proposed for face recognition. We derive the TPNMF model from original NMF algorithm by preserving local topology structure. The TPNMF is based on minimizing the constraint gradient distance in the high-dimensional space. Compared with L2 distance, the gradient distance is able to reveal latent manifold structure of face patterns. By using TPNMF decomposition, the high-dimensional face space is transformed into a local topology preserving subspace for face recognition. In comparison with PCA, LDA, and original NMF, which search only the Euclidean structure of face space, the proposed TPNMF finds an embedding that preserves local topology information, such as edges and texture. Theoretical analysis and derivation given also validate the property of TPNMF. Experimental results on three different databases, containing more than 12 000 face images under varying in lighting, facial expression, and pose, show that the proposed TPNMF approach provides a better representation of face patterns and achieves higher recognition rates than NMF.