Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication between native and non-native speakers of English in noise

Non-fluency has a negative impact on speech understanding in noise, particularly when hearing protection devices are worn. In multi-national military operations where the communication language is English, it is important to understand the effects of non-native speech and accent on speech understanding. Twenty-four normal-hearing participants were divided into two groups: monolingual English speaking from birth (NA group), and those who learned English after the age of 10 (NN group). All participants completed the Language Experience and Proficiency Questionnaire (LEAP-Q; Marian et al., 2007) to confirm their group assignment. Two experimental sessions were completed, in which each participant was paired with an NA participant in one session and an NN participant in the other. The modified rhyme test (MRT) and speech perception in noise test (SPIN) were administered with each participant pair using two methods. In the first, participants spoke to each other using a communication headset (radio) in background noise of 80 dBA. In the second, the particpants wore the headset with the radio off and spoke to each other face-to-face in background noise levels of 55, 60 and 65 dBA. Performance was calculated as the percentage of correct responses. For the MRT, there was a main effect of talker for both the face-to-face (NA- 79.6%; NN-75.2%), and radio conditions (NA-87.2%; NN- 77.5%). There was also a main effect of background noise level for the face-to-face condition (81.1%, 78.3% and 72.8% for the lowest to highest noise levels, respectively). For the SPIN, there was a main effect of the listener in both the face-to-face (NA-70.9%; NN-54.1%) and radio conditions (NA-86.4%; NN-73.8%), as well as a main effect of background noise level for the face-to-face condition (68.4%, 63.5% and 55.7%). Overall, the results indicate that both NA and NN listeners perform poorly when listening to NN talkers.
Does early retirement increase poverty among Belgian elderly

A new administrative dataset on incomes (1991-2002) merged with the National Register and the Socio-economic survey of 2001 was used to study the poverty dynamics of Belgian elderly that are in the transition from work to retirement. It is shown that although static poverty rates turn around 12% in a given year, 33% of Belgian elderly is once confronted with poverty over a period of 12 years. The estimation of a discrete-time hazard model showed that poverty transitions are strongly related to the entry in different retirement pathways and social security systems and to changes in household composition. It was found that 1)the social security system of the self-employed, that is financially unsustainable, is not effective neither in combating poverty nor in insuring social risks, 2) entering the system of elderly unemployed and early retirement schemes before the age of 57 is associated with a strong risk of poverty entry.
Motivating Contributions to Public Information Goods : A Field Experiment on Wikipedia 1

We use a large-scale field experiment on Wikipedia to examine the effect of motivation on the contributions of domain experts to public information goods. We find that experts are more interested in contributing when we mention the private benefit of contribution, such as the likely citation of their work. More importantly, using cosine similarity, we find that greater matching accuracy between a recommended Wikipedia article and an expert’s paper abstract increases both contribution quantity and quality. Our results show the potential of combining machine learning tools with field experiments to study drivers of prosocial behavior.
Identification and Characterization of Bioactive Compounds Targeting Uropathogenic Escherichia coli from Sanjin Tablets

Sanjin Tablets are completely natural preparation with significant efficacy in treating urinary tract infection. To identify the bioactive compounds from Sanjin Tablets, we separated components capable of binding to the soluble proteins of uropathogenic Escherichia coli (UPEC) by affinity binding and characterized their identities using liquid chromatography-mass spectrometry (LC-MS) analysis. Our study discovered eight compounds with E. coli protein-binding capabilities, and all these compounds were tracked back to the original natural ingredients of Sanjin Tablets. These compounds presented essentially no antibacteria activity, indicating that they affect UPEC by means other than directly killing the cells. Further molecular modeling analysis predicted molecular targets for these compounds and mapped the residues potentially involved in compound-target interactions. All the predicted targets turned out to be critical proteins regulating the metabolisms of E. coli, suggesting that these compounds may affect metabolic pathways in UPEC and inhibit pathogenesis. These data will benefit future design of drugs with higher efficacy and specificity on targeting pathogenic bacteria.
On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

Abstract. Understanding the causes of variation in functional plant traits is a central issue in ecology, particularly in the context of global change. Spectroscopy is increasingly used for rapid and non-destructive estimation of foliar traits, but few studies have evaluated its accuracy when assessing phenotypic variation in multiple traits. Working with 24 chemical and physical leaf traits of six European tree species growing on strongly contrasting soil types (i.e. deep alluvium versus nearby shallow chalk), we asked (i) whether variability in leaf traits is greater between tree species or soil type, and (ii) whether field spectroscopy is effective at predicting intraspecific variation in leaf traits as well as interspecific differences. Analysis of variance showed that interspecific differences in traits were generally much stronger than intraspecific differences related to soil type, accounting for 25 % versus 5 % of total trait variation, respectively. Structural traits, phenolic defences and pigments were barely affected by soil type. In contrast, foliar concentrations of rock-derived nutrients did vary: P and K concentrations were lower on chalk than alluvial soils, while Ca, Mg, B, Mn and Zn concentrations were all higher, consistent with the findings of previous ecological studies. Foliar traits were predicted from 400 to 2500 nm reflectance spectra collected by field spectroscopy using partial least square regression, a method that is commonly employed in chemometrics. Pigments were best modelled using reflectance data from the visible region (400–700 nm), while all other traits were best modelled using reflectance data from the shortwave infrared region (1100–2500 nm). Spectroscopy delivered accurate predictions of species-level variation in traits. However, it was ineffective at detecting intraspecific variation in rock-derived nutrients (with the notable exception of P). The explanation for this failure is that rock-derived elements do not have absorption features in the 400–2500 nm region, and their estimation is indirect, relying on elemental concentrations covarying with structural traits that do have absorption features in that spectral region ( constellation effects ). Since the structural traits did not vary with soil type, it was impossible for our regression models to predict intraspecific variation in rock-derived nutrients via constellation effects. This study demonstrates the value of spectroscopy for rapid, non-destructive estimation of foliar traits across species, but highlights problems with predicting intraspecific variation indirectly. We discuss the implications of these findings for mapping functional traits by airborne imaging spectroscopy.
Incoherent Detection of Orthogonal Polarizations via an Antenna Coupled MKID: Experimental Validation at 1.55 THz

There is an increasing demand for large format detector arrays with large bandwidths and high antenna efficiencies for future THz astronomical radiometric applications. For direct detection instruments, it is also desired to have antennas with dual polarization reception in order to increase the received power from incoherent sources, thereby improving the observing speed of the instrument. The main goal of this work is the validation of the incoherent detection of two orthogonal polarizations by a leaky lens antenna, coupled to a single microwave kinetic inductance detector (MKID). The resonant frequency of the MKID changes depending on the absorbed power over a distributed transmission line. The proposed antenna is composed of two crossed leaky wave slots feeding a silicon extended hemispherical lens. The slots are coupled to four aluminum (Al) coplanar waveguide (CPW) lines that incoherently absorb the incoming THz radiation. The antenna and the power absorbing CPW lines are embedded inside the MKID, allowing efficient radiation detection at THz frequencies where no lossless superconductors are available. The proposed dual-polarized device absorbs power incrementally over four different CPWs incoherently and is therefore simulated in reception (deriving a plane-wave response) similarly to what is done in distributed absorbers. We compare numerically and experimentally the proposed dual-polarized leaky lens coupled MKID and its single polarization counterpart and show that the dual polarized device receives twice as much power as the single-polarized one. Eventually, the dual-polarized device, when used with air-bridges, provides the same angular selectivity and twice the throughput of the single-polarized one.
Research and Application of FTA and Petri Nets in Fault Diagnosis in the Pantograph-Type Current Collector on CRH EMU Trains

A fault tree is established based on structural analysis, working principle analysis, and failure mode and effects analysis (FMEA) of the pantograph-type current collector on the Chinese Rail High-Speed Electric Multiple Unit (CRH EMU) train. To avoid the deficiencies of fault tree analysis (FTA), Petri nets modelling is used to address the problem of data explosion and carry out dynamic diagnosis. Relational matrix analysis is used to solve the minimal cut set equation of the fault tree. Based on the established state equation of the Petri nets, initial tokens and enable-transfer algorithms are used to express the fault transfer process mathematically and improve the efficiency of fault diagnosis inferences. Finally, using a practical fault diagnosis example for the pantographs on CRH EMU trains, the proposed method is proved to be reasonable and effective.
Analysis of ATR-FTIR Absorption-Reflection Data from 13 Polymeric Fabric Materials Using Chemometrics

We used both correlation and covariance-principal component analysis (PCA) to classify the same absorption-reflectance data collected from 13 different polymeric fabric materials that was obtained using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR). The application of the two techniques, though similar, yielded results that represent different chemical properties of the polymeric substances. On one hand, correlation-PCA enabled the classification of the fabric materials according to the organic functional groups of their repeating monomer units. On the other hand, covariance-PCA was used to classify the fabric materials primarily according to their origins; natural (animal or plant) or synthetic. Hence besides major chemical functional groups of the repeat units, it appears covariance-PCA is also sensitive to other characteristic chemical (inorganic and/or organic) or biochemical material inclusions that are found in different samples. We therefore recommend the application of both covariance-PCA and correlation-PCA on datasets, whenever applicable, to enable a broader classification of spectroscopic information through data mining and exploration.
HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes

Hypoglycemia is common and potentially dangerous among those treated for diabetes. Electronic health records (EHRs) are important resources for hypoglycemia surveillance. In this study, we report the development and evaluation of deep learning-based natural language processing systems to automatically detect hypoglycemia events from the EHR narratives. Experts in Public Health annotated 500 EHR notes from patients with diabetes. We used this annotated dataset to train and evaluate HYPE, supervised NLP systems for hypoglycemia detection. In our experiment, the convolutional neural network model yielded promising performance $Precision=0.96 \pm 0.03, Recall=0.86 \pm 0.03, F1=0.91 \pm 0.03$ in a 10-fold cross-validation setting. Despite the annotated data is highly imbalanced, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE could be used for EHR-based hypoglycemia surveillance and to facilitate clinicians for timely treatment of high-risk patients.
Small-signal modeling of series and parallel resonant converters

A small-signal modeling technique based on the extended describing function concept is applied to series-resonant converters (SRCs) and parallel-resonant converters (PRCs). The models developed include both frequency control and phase-shift control. The small-signal equivalent circuit models are also derived and implemented in PSPICE. The models are in good agreement with measurement data. The high-frequency dynamics of resonant converters around the beat frequency can be accurately modeled. These simple analytical models can be employed in the control loop design of resonant converters. <<ETX>>
Eelgrass restoration by seed maintains genetic diversity: case study from a coastal bay system

Genetic diversity is positively associated with plant fitness, stability, and the provision of ecosystem services. Preserving genetic diversity is therefore considered an important component of ecosystem restoration as well as a measure of its success. We examined the genetic diversity of restored Zostera marina meadows in a coastal bay system along the USA mid-Atlantic coast using microsatellite markers to compare donor and recipient meadows. We show that donor meadows in Chesapeake Bay have high genetic diversity and that this diversity is maintained in meadows restored with seeds in the Virginia coastal bays. No evidence of inbreeding depression was detected (FIS −0.2 to 0) in either donor or recipient meadows, which is surprising because high levels of inbreeding were expected following the population contractions that occurred in Chesapeake Bay populations due to disease and heat stress. Additionally, there was no evidence for selection of genotypes at the restoration sites, suggesting that as long as donor sites are chosen carefully, issues that diminish fitness and survival such as heterosis or out-breeding depression can be avoided. A cluster analysis showed that, in addition to the Chesapeake Bay populations that acted as donors, the Virginia coastal bay populations shared a genetic signal with Chincoteague Bay populations, their closest neighbor to the north, suggesting that natural recruitment into the area may be occurring and augmenting restored populations. We hypothesize that the high genetic diversity in seagrasses restored using seeds rather than adult plants confers a greater level of ecosystem resilience to the restored meadows.
Assessment of mastitic infection in bovine milk using ATP bioluminescence

Few choices exist for a mobile, rapid, and nonsubjective assessment of mastitic infection in bovine milk. This project evaluated the effectiveness of using the Biotrace® raw milk quality ATP bioluminescence assay to serve this role. Milk samples with various somatic cell counts (13,000 2,500,000) and signs of mastitic infection were obtained from the Kansas State University Dairy Teaching and Research Center. Within 24 hr, raw milk samples were evaluated for microbial numbers and relative light units (RLU). The printed test procedure was modified to evaluate accurately clinical mastitic milk samples. As somatic cell count increased in raw milk, the RLU value increased. In addition, RLU values differentiated among milk samples with various levels of mastitic infection (none, subclinical, and clinical). Repeatability of the ATP bio-luminescence method was very good (CV = 4.76%). These results suggest that the Biotrace® raw milk quality test kit can served effectively as a nonsubjective, rapid assay to determine the degree of mastitic infection in bovine milk.
Establishment of reference or baseline conditions of chemical indicators in New Zealand streams and rivers relative to present conditions

The management of streams and rivers can be aided by knowledge of reference conditions. Data from >1000 sites across New Zealand was used to develop a technique to estimate median ammoniacal-N, clarity, Escherichia coli, filterable reactive phosphorus, nitrate-N, suspended solids, and total nitrogen and phosphorus values under reference conditions for streams and rivers as classified by the River Environment Classification (REC). The REC enabled us to account for natural variation in climate, topography and geology when estimating reference conditions. Values for minimally disturbed sites (i.e. <5% in intensive agriculture) were generally within the confidence limits for estimated reference values. Metrics that described: (1) the percentage of anthropogenic contribution to analyte values; and (2) the degree of enrichment beyond the reference conditions, showed that lowland sites classified as warm-wet, warm-dry or cool-dry exhibited the greatest anthropogenic input and enrichment. The consideration of natural variation by REC class informs the setting of water quality objectives through avoiding water quality limits or targets that are either too restrictive, and impossible to meet (e.g. below reference conditions), or too high, such that they have little ecological benefit. We recommend reference conditions be considered by regulatory authorities when assessing water quality impacts, objectives and limits.
Alzheimer’s Disease and Animal Models in Retrospect

Alzheimer’s disease (AD) is one of the neurodegenerative diseases that affect millions of people worldwide. AD could rob patients of their ability to recall, reason and carry out executive functions. Pathophysiological studies of AD have revealed the gradual loss of neurons, function and ultimate death of neurons (apoptosis). Mutations, oxidative stress, excitotoxicity, infectious diseases are among the principal causes of neuronal degeneration. Despite the prescription of a wide range of drugs to treat AD, the emergence of effective treatments to halt the progress or reverse this disease has remained elusive for years. Series of preclinical studies have been developed to ensure better understanding of the neurobiology of AD and engender the discovery of new drugs. This review provides an overview on the pathophysiology, pharmacotherapy and preclinical models of AD in an attempt to bring together current research efforts, challenges, achievements and prospect for the discovery of drugs to treat AD. Pathophysiological evidences of this neurodegenerative disease has shown the involvement of multiple neural mechanisms. So far, the research approaches and treatment of this disease still remain largely unsatisfactory. However, there are possibilities of surmounting current challenges with new technology, diagnostic criteria and translational approach that effectively reflect clinical etiology of AD in experimental animals.
The energy-differences based exact criterion for testing approximations to the functional for the kinetic energy of non-interacting electrons

Gradient-dependent approximations to the functional of the kinetic energy of non-interacting electrons (Ts[ρ]), which reflect various properties of the exact functional, are considered. For specially constructed pairs of electron densities, for which the analytic expression for the differences of Ts[ρ] is known, it is shown that the accuracy of the quantities derivable from a given approximation to Ts[ρ]: energy differences and their functional derivatives, does not reflect that of Ts[ρ] itself. The comparisons between the exact values of the kinetic energy in such cases are proposed as an independent condition/criterion for appraisal of approximations to Ts[ρ].
Kanji Acquisition among Language Minority Students in Japan: A Comparative Study of Japanese-as-a-Second-Language Students Born in Japan

Although Japan has an increasing number of children who are learning Japanese as their second language ( JSL students), relatively little is understood with regards to their acquisition of the Japanese language. Since acquisition of kanji (i.e., Chinese characters used in Japanese) is considered a critical skill for academic success at school in Japan, this study examined the reading and writing of kanji among JSL students, focusing on students who were born in Japan but raised in non-Japanese speaking homes. A set of kanji reading and writing tests were administered to 27 4th grade JSL students, and their performance was compared to that of their Japanese native-speaking (NS) counterparts. While the oral proficiency of the JSL students was found to be equivalent to that of the native speakers, there was a significant difference in kanji reading between the JSL and NS students even though no differences were found in kanji writing. An error analysis indicated that the JSL students had more missing answers, and more errors associated with meaning in kanji reading. Among the various background factors, only the frequency of reading in Japanese outside of school was found to be significantly influential over the students’ kanji reading. With respect to kanji writing, in addition to the frequency of reading in Japanese, the amount of practice of kanji writing and the frequency of reading outside of the school in the students’ first language (L1) were found to be significantly influential. This article is available in Working Papers in Educational Linguistics (WPEL): https://repository.upenn. edu/wpel/vol26/iss1/1 Kanji Acquisition among Language Minority Students in Japan: A Comparative Study of Japanese-as-a-Second-Language Students
USING COMBINATION OF PLANAR AND HEIGHT FEATURES FOR DETECTING BUILT-UP AREAS FROM HIGH-RESOLUTION STEREO IMAGERY

Abstract. Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slope and building height are often used to optimize the results, but extracted from auxiliary data (e.g. LIDAR data, DSM). Moreover, the auxiliary data must be acquired around the same time as image acquisition. Otherwise, built-up area detection accuracy is affected. Stereo imagery incorporates both planar and height information unlike single remotely sensed images. Stereo imagery acquired by many satellites (e.g. Worldview-4, Pleiades-HR, ALOS-PRISM, and ZY-3) can be used as data source of identifying built-up areas. A new method of identifying high-accuracy built-up areas from stereo imagery is achieved by using a combination of planar and height features. The digital surface model (DSM) and digital orthophoto map (DOM) are first generated from stereo images. Then, height values of above-ground objects (e.g. buildings) are calculated from the DSM, and used to obtain raw built-up areas. Other raw built-up areas are obtained from the DOM using Pantex and Gabor, respectively. Final high-accuracy built-up area results are achieved from these raw built-up areas using the decision level fusion. Experimental results show that accurate built-up areas can be achieved from stereo imagery. The height information used in the proposed method is derived from stereo imagery itself, with no need to require auxiliary height data (e.g. LIDAR data). The proposed method is suitable for spaceborne and airborne stereo pairs and triplets.