Miracle-dz/UniFAR-FTUs huggingface dataset:
Flavored E-cigarette Use and Progression of Vaping in Adolescents

In this longitudinal cohort study of adolescents who vape, use of e-cigarettes in nontraditional flavors predicted future vaping persistence and frequency. BrightcoveDefaultPlayer10.1542/6138648267001PEDS-VA_2019-0789 Video Abstract OBJECTIVES: Electronic cigarettes (e-cigarettes) are available in nontraditional flavors (eg, fruit and candy) that are banned in combustible cigarettes in the United States. Whether adolescent use of e-cigarettes in nontraditional flavors prospectively predicts continuation of vaping and progression to more frequent vaping is unknown. METHODS: High school students in Los Angeles, California, completed 5 semiannual surveys (2014–2017 [10th grade to 12th grade]). Among past-6-month e-cigarette users at survey waves 1 to 4 (N = 478), e-cigarette flavor (or flavors) used was coded into 2 mutually exclusive categories at each wave (use of ≥1 nontraditional flavors [fruit, candy, sweet or dessert, buttery, blends or combinations, and other] versus exclusive use of tobacco, menthol or mint, or flavorless). Flavor used during waves 1 to 4 was modeled as a time-varying, time-lagged regressor of vaping status and frequency outcomes 6 months later at waves 2 to 5. RESULTS: Across waves 1 to 4, there were 739 (93.8%) observations of nontraditional-flavor use and 49 (6.2%) observations of exclusive use of tobacco, mint or menthol, or flavorless e-cigarettes. Use of e-cigarettes in nontraditional flavors (versus only tobacco, mint or menthol, or flavorless) was positively associated with vaping continuation (64.3% vs 42.9%; adjusted odds ratio = 3.76 [95% confidence interval 1.20 to 10.31]) and past-30-day number of puffs per nicotine vaping episode (mean: 3.1 [SD 5.5] vs 1.5 [SD 3.8]; adjusted rate ratio = 2.41 [95% confidence interval 1.08 to 5.92]) 6 months later. Flavor used was not associated with the subsequent number of past-30-day vaping days or episodes per day. CONCLUSIONS: Adolescents who vaped e-cigarettes in nontraditional flavors, compared with those who exclusively vaped tobacco-flavored, mint- or menthol-flavored, or flavorless e-cigarettes, were more likely to continue vaping and take more puffs per vaping occasion 6 months later.
Technical Note: Film-Based Measurement of Gold Nanoparticle Dose Enhancement for 192 Ir.

PURPOSE The purpose of this work is to introduce a simple yet accurate technique to measure the dose enhancement factor (DEF) of a citrate-capped gold nanoparticle (GNP) solution using EBT3 film in an 192 Ir setup. METHODS DEF is the ratio of absorbed dose in a solution compared to absorbed dose in water, assuming identical irradiation parameters. Citrate-capped GNPs were synthesized. An acrylic apparatus was constructed such that the EBT3 film was placed in charged particle equilibrium within the GNP solution with 0.28%, 0.56%, and 0.77% gold by mass. Sets of 12 dose measurements were collected for each GNP concentration as well as for water. The expected value of DEF was also calculated with the effective mass absorption coefficient of the GNP solution and water for an 192 Ir spectrum. Furthermore, Burlin cavity correction factors were calculated and experimentally verified. Experimental verification of the cavity correction was performed by measuring DEF using stacks of 1, 3, and 5 sheets of film and extrapolating the DEF to 0 sheets of film. RESULTS Experimental cavity corrections agreed with those calculated with the Burlin cavity formalism. The calculated DEF was 1.013, 1.027, and 1.037 for the 0.28%, 0.56%, and 0.77% gold by mass GNP solutions, respectively. The corresponding uncorrected DEF measurement values were 1.013±0.006, 1.024±0.010, and 1.032±0.006, respectively. When applying the Burlin cavity formalism, the final corrected DEF measurement values were 1.016±0.006, 1.029±0.010, and 1.039±0.006, respectively. CONCLUSION The experimental cavity correction results agreed with the theoretical Burlin calculations, which allowed for the Burlin corrections to be performed for all GNP concentrations and measured DEF values. The adjusted DEF values agreed with the theoretical calculations. Thus, these results indicate that a Burlin cavity calculation can be applied to correct film-based DEF measurements for 192 Ir.
Comparative Study of the Temperature Sensitive, Cold Adapted and Attenuated Mutations Present in the Master Donor Viruses of the Two Commercial Human Live Attenuated Influenza Vaccines

Influenza viruses cause annual, seasonal infection across the globe. Vaccination represents the most effective strategy to prevent such infections and/or to reduce viral disease. Two major types of influenza vaccines are approved for human use: inactivated influenza vaccines (IIVs) and live attenuated influenza vaccines (LAIVs). Two Master Donor Virus (MDV) backbones have been used to create LAIVs against influenza A virus (IAV): the United States (US) A/Ann Arbor/6/60 (AA) and the Russian A/Leningrad/134/17/57 (Len) H2N2 viruses. The mutations responsible for the temperature sensitive (ts), cold-adapted (ca) and attenuated (att) phenotypes of the two MDVs have been previously identified and genetically mapped. However, a direct comparison of the contribution of these residues to viral attenuation, immunogenicity and protection efficacy has not been conducted. Here, we compared the In vitro and in vivo phenotype of recombinant influenza A/Puerto Rico/8/34 H1N1 (PR8) viruses containing the ts, ca and att mutations of the US (PR8/AA) and the Russian (PR8/Len) MDVs. Our results show that PR8/Len is more attenuated in vivo than PR8/AA, although both viruses induced similar levels of humoral and cellular responses, and protection against homologous and heterologous viral challenges. Our findings support the feasibility of using a different virus backbone as MDV for the development of improved LAIVs for the prevention of IAV infections.
Association Between Fatty Acids Profile and Cerebral Blood Flow: An Exploratory fNIRS Study on Children with and without ADHD

Polyunsaturated fatty acids (PUFAs) biostatus has been proposed as possible attention deficit hyperactivity disorder (ADHD) diagnosis biomarker. The present exploratory study aimed to investigate the association between PUFAs biostatus and cerebral cortex metabolism measured by functional Near Infrared Spectroscopy (fNIRS) in a sample of children with and without ADHD. 24 children with ADHD and 22 typically developing (TD) peers, aged 8–14, were recruited. Linoleic, arachidonic, docosahexaenoic and eicosapentaenoic acids levels were evaluated in whole blood. All children underwent fNIRS while performing an n-back working memory task. Between groups comparisons revealed lower levels of arachidonic acid in children with ADHD and stronger NIRS signal in TD participants, especially when completing more difficult tasks. Correlations conducted between fNIRS activation and PUFA biostatus revealed several associations between hemodynamic changes in the frontoparietal regions and fatty acids profile across participants. This result was also confirmed by the multiple hierarchical regression analyses that remarked an inverse effect of eicosapentaenoic acid levels on oxyhemoglobin values in right frontoparietal region. Such preliminary findings, if confirmed, would suggest that PUFAs could play a role in atypical neurodevelopment.
Sensor-based Threats to Internet-of-Things and Deep Learning enabled Predictive Analytics

Predictive maintenance (PdM) is a modern solution which facilitates in predicting faults in a component powered by state-of-the-art machine learning algorithms. Particularly, PdM plays a prominent role in Aircraft Engine Health Monitoring (EHM) systems since early detection of defects in an aircraft engine can save valuable time, cost, and human lives. PdM uses IoT sensor data for predicting Remaining Useful Life (RUL) of an engine. However, IoT sensors are well-known for their susceptibility to cyber-attacks which possess a vital threat for aircraft PdM. In this work, we utilize the Long Short-Term Memory (LSTM), a deep learning approach for RUL prediction of an aircraft engine. Afterward, we apply the false data injection attack (FDIA), replay, and denial of service (DoS) attacks on aircraft engine sensor data and evaluate their impact on the LSTM-based RUL prediction. Our experimental results demonstrate that the cyber-attacks on IoT sensors strongly defects the RUL prediction. Furthermore, the results show that increasing the number of layers in LSTM also improves the resiliency of LSTM-based PdM against the FDIA attacks.
STUDIES OF RARE-EARTH SUBSTITUTED NICKEL ZINC FERRITES

In the present time, NiZn ferrites represent an important class of soft magnetic materials because of their vast technological applications, ranging from microwave to radio frequencies such as microwave devices, computer memories and magnetic recording [1]. In higher frequency range they exhibit high saturation magnetisation and initial permeability, sufficiently high Curie temperature and, on the other hand, low coercivity, magnetic and dielectric loss, high resistivity and subsequently low eddy current losses. The magnetic properties are most important for ferrites depending on the processing conditions, microstructure, chemical composition and the type of the small additives [2, 3]. The effects of different amounts of La2O3 on some properties of Ni0.42Zn0. 58LaxFe2-xO4 ferrite have been investigated. Even small rare-earth (RE) additives play an important role in the modification of the structure and intrinsic/extrinsic magnetic properties of ferrites due to the magneto-crystalline anisotropy in the rare-earth doped compounds and the RE-Fe interaction [4].
A Unified Framework for Ontology Driven Web Mining and Semantic Clustering of Web Documents

The objective of the paper is to propose a framework for web mining. An architectural pattern for multilayered architecture that implements ontology driven web mining and semantic clustering of web documents is proposed. The paper discusses a multilayered architecture for semantic clustering of web documents. The multilayered architecture maps to an architectural pattern. The mapping from pattern to implementation is also discussed in the paper. Semantic clustering is supported by including ontology as background knowledge. The architecture provides a unified and universal framework for ontology driven web mining and semantic clustering of web documents. KeywordsArchitecture pattern, Ontology, Web mining, Clustering.
MARI LEHTI-POLOJÄRVI ELECTRICAL IMPEDANCE TOMOGRAPHY APPLIED TO STEM CELLS IN HYDROGEL SCAFFOLD

MARI LEHTI-POLOJÄRVI: Electrical impedance tomography applied to stem cells in hydrogel scaffold Tampere University of Technology Master of Science Thesis, 72 pages, 3 Appendix pages December 2014 Master’s Degree Programme in Electrical Engineering Major: Medical Physics Examiners: Professor Jari Hyttinen, postdoctoral researchers Edite Figueiras and Niina Onnela
Boron toxicity induces altered expression of miRNAs in French bean (Phaseolus vulgaris L.)

© 2018 M.N. Jyothi, et al. This is an open access article distributed under the terms of the Creative Commons Attribution License -NonCommercialShareAlikeUnported License (http://creativecommons.org/licenses/by-nc-sa/3.0/). *Corresponding Author Nagesh Babu R, Post Graduate Department of Biochemistry, Maharani’s Science College for Women, Palace Road, Bangalore-560001, India. E-mail: nageshbabur@gmail.com Boron toxicity induces altered expression of miRNAs in French bean (Phaseolus vulgaris L.)
Hand–object configuration estimation using particle filters for dexterous in-hand manipulation

We consider the problem of in-hand dexterous manipulation with a focus on unknown or uncertain hand–object parameters, such as hand configuration, object pose within hand, and contact positions. In particular, in this work we formulate a generic framework for hand–object configuration estimation using underactuated hands as an example. Owing to the passive reconfigurability and the lack of encoders in the hand’s joints, it is challenging to estimate, plan, and actively control underactuated manipulation. By modeling the grasp constraints, we present a particle filter-based framework to estimate the hand configuration. Specifically, given an arbitrary grasp, we start by sampling a set of hand configuration hypotheses and then randomly manipulate the object within the hand. While observing the object’s movements as evidence using an external camera, which is not necessarily calibrated with the hand frame, our estimator calculates the likelihood of each hypothesis to iteratively estimate the hand configuration. Once converged, the estimator is used to track the hand configuration in real time for future manipulations. Thereafter, we develop an algorithm to precisely plan and control the underactuated manipulation to move the grasped object to desired poses. In contrast to most other dexterous manipulation approaches, our framework does not require any tactile sensing or joint encoders, and can directly operate on any novel objects, without requiring a model of the object a priori. We implemented our framework on both the Yale Model O hand and the Yale T42 hand. The results show that the estimation is accurate for different objects, and that the framework can be easily adapted across different underactuated hand models. In the end, we evaluated our planning and control algorithm with handwriting tasks, and demonstrated the effectiveness of the proposed framework.
Comparative evaluation of approaches to modelling kinetics of microbial thermal death as in the case of Alicyclobacillus acidoterrestris

Microbial death kinetics modelling is an integral stage of developing the food thermal sterilisation regimes. At present, a large number of models have been developed. Their properties are usually being accepted as adequate even beyond boundaries of experimental microbiological data zone. The wide range of primary models existence implies the lack of universality of each ones. This paper presents a comparative assessment of linear and nonlinear models of microbial death kinetics during the heat treatment of the Alicyclobacillus acidoterrestris spore form. The research allowed finding that single-phase primary models (as adjustable functions) are statistically acceptable for approximation of the experimental data: linear – the Bigelow’ the Bigelow as modified by Arrhenius and the Whiting-Buchanan models; and nonlinear – the Weibull, the Fermi, the Kamau, the Membre and the Augustin models. The analysis of them established a high degree of variability for extrapolative characteristics and, as a result, a marked empirical character of adjustable functions, i.e. unsatisfactory convergence of results for different models. This is presumably conditioned by the particularity and, in some cases, phenomenology of the functions themselves. Consequently, there is no reason to believe that the heat treatment regimes, developed on the basis of any of these empirical models, are the most effective. This analysis is the first link in arguing the necessity to initiate the research aimed at developing a new methodology for determining the regimes of food thermal sterilisation based on analysis of the fundamental factors such as ones defined spore germination activation and their resistance to external impact.
The effects of red meat consumption and high-intensity resistance training of skeletal muscle strength, muscle mass and functional status in healthy older adults

With the older adult representing an increasingly large percentage of the Western world, attempts are being sought to improve their healthy aging through various modes of prevention. The age-associated declines that occur in the physiological and functional systems along with levels of physical activity and quality of life have the potential to be attenuated and ameliorated with various forms of healthrelated interventions. It has been suggested that intake of dietary protein in the elderly may be too low to sustain normal muscle mass and red meat intake declines in the over 65 y age group in Australia. The present study examined the effects of a high-intensity resistance training program and two levels of red meat intake on skeletal muscle strength, body composition and other health-related markers in healthy, community-dwelling older adults. Twenty-eight healthy male and female subjects with mean age (±SD) of 67 ±3 y and randomized to either a moderate (400g/wk) or high (800g/wk) red meat diet, completed a supervised twice weekly, twelve week high-intensity resistance training program of the lower extremities. The moderate meat diet represented the usual intake for older Australians over 65 y. Diet histories, body composition assessments, mid-thigh CT scans, grip strength, lower extremity performance, physical activity levels, one-mile walk test, fasted blood samples and morning urine samples were taken at baseline and twelve weeks. Four-repetition maximum strength testing of the lower extremity was undertaken at pre-, midand post-
Measuring the Impact of Regulation on Firms

This paper introduces a new measure of firm-level regulation. Contrary to the conventional wisdom, we find that more regulation increases labor and capital inputs. Productivity decreases, which is consistent with a model of regulation inducing non-productive investment. We employ two empirical strategies to identify the causal impact of regulation on firms, first, utilizing structural breaks and industry level regulation changes, and second, computing predicted industry level regulation measures as instruments. We conduct an event study using the surprise 2016 US election results. Firms with higher Dodd-Frank exposure exhibited higher returns following an increase in the probability of repeal. ∗The authors wish to thank Nick Bloom, Emanuele Colonnelli, Caroline Hoxby, Christos Makridis, Holger Mueller and Luigi Pistaferri for helpful comments and discussions as well as comments from seminars at Stanford University and the All-California Labor Economics Conference at SIEPR. Kaiji Gong is grateful to SIEPR and the Dixon and Carol Doll Family Foundation for generous financial support. We are grateful to Dalya Elmalt, Katerina Nikalexi and Jun Xu for superb research assistance. †Department of Economics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. rkgong@ust.hk ‡Department of Finance, Booth School of Business, University of Chicago, 5807 South Woodlawn Ave, Chicago IL 60637. constantine.yannelis@chicagobooth.edu
Efficient Scans on Modern Hardwarepdfsubject

In recent years, hardware characteristics, software systems, and application patterns have all changed in a way that emphasizes the importance of scan tasks in modern mainmemory database systems. Main memory is available cheaply and can hold significant amounts of data nowadays. In-memory computing has matured and has resulted in key business platforms such as SAP HANA. Expectations for ad-hoc querying defeat the use of pre-built index structures and mandate scans instead. At the same time, it is still unclear how the latest advances on the hardware side can best be leveraged to support efficient scans. Growing SIMD widths, for instance, appeal with theoretical speed-ups of up 16× or more. But existing strategies for predicate evaluation and scans are not prepared for such large SIMD widths. In fact, even for smaller SIMD sizes it is not clear how, e. g. , predicates that involve multiple data types and widths can be realized most efficiently. This thesis studies and evaluates methods that make use of modern hardware technologies to accelerate scans, in particular SIMD-Scan, BitWeaving and ByteSlice. Starting from SIMD-Scan, methods to further reduce memory bandwidth are explored by skipping parts of the input efficiently or by reducing the output size by handling intermediate results more efficiently. In den vergangenen Jahren haben sich Hardund Software sowie die Anforderungen an die Software so geändert, dass der Scan – das durchsuchen einer Spalte in einer Datenbank – in In-Memory-Datenbanken immer wichtiger wird. Hauptspeicher ist günstig und kann heute große Mengen an Daten halten. In-Memory-Datenbanken sind verbreitet und ausgereift und werden an Schlüsselstellen in der Wirtschaft verwendet, wie etwa SAP HANA. Die Anforderungen an diese Datenbanken können oft nicht durch vorberechnete Indices erfüllt werden und benötigen anstelle dessen Scans. Gleichwohl ist es immer noch unklar wie man die neusten Errungenschaften in der Hardware am besten für diese Scans nutzen kann. Breitere SIMD-Register zum Beispiel, könnten theoretisch eine Beschleunigung um das 16-fache erreichen. Aber bestehende Scan-Methoden sind nicht auf solch breite Register vorbereitet. Selbst für kleinere SIMD-Breiten ist es nicht klar wie Prädikate mit mehreren Datentypen am effizientesten realisiert werden können. Diese Masterarbeit beschäftigt sich mit Methoden, die diese moderne Hardware ausnutzen um Scans zu Beschleunigung, mit einem Fokus auf SIMD-Scan, BitWeaving und ByteSlice. Vom Startpunkt des SIMD-Scan werden weitere Möglichkeiten untersucht um die benötigte Speicherbandbreite zu reduzieren, zum Beispiel durch Überspringen von Teilen der Eingabe oder durch Reduzierung der Ausgabemenge durch effizientere Verwaltung von Zwischenergebnissen.
An Approach to Identify VLF/LF Cloud-to-ground Return Stroke Electric Field Waveform Based on Convolutional Neural Network

The identification of lightning return stroke waveform plays an important role in the accurate location of lightning. Very low frequency/low frequency (VLF/LF) electromagnetic signals are widely used in lightning detection systems. The signals in this frequency band have a long propagation distance and are prone to attenuation distortion, which makes it more possible to misclassify the return stroke waveforms when using methods based on some specific waveform characteristic. The convolutional neural network is robust enough and performs well at discovering hidden patterns in images. In this paper, the residual convolutional neural network model is trained to obtain the waveform classifier. The lightning waveform data dataset is collected by lightning electric field measuring meters deployed in various provinces. After training, the classification accuracy of this classifier reaches 97.2% in the test set, and the accuracy reaches 86.75% using the traditional method based on waveform characteristics. The result proves the superior performance of the residual convolutional neural network model. By exploring the interpretability of the model, it is also proved that the better performance of the convolution network model comes from making full use of waveform information.
Is there a “rowhammer” for MLC NAND Flash SSDs? An analysis of filesystem attack vectors

Rowhammer demonstrated that non-physical hardwareweakness-based attacks can be devastating. In a recent paper, Cai et al. [2] propose that similar attacks can be performed on MLC NAND flash-based SSDs, with potentially devastating effects as well. In this paper, we discuss the requirements for a successful, full-system, local privilege attack on SSDs and show a filesystem based attack vector, which we demonstrate. In particular, to motivate the assumptions of the filesystem-level attack, we show the attack primitive that an attacker can obtain by making use of cell-to-cell interference is quite weak, and therefore requires a carefully crafted attack at the OS layer for successful exploitation.