Miracle-dz/UniFAR-FTUs huggingface dataset:
Geophysical and Geochemical Constraints on Neogene‐Recent Volcanism in the North American Cordillera

Widespread recent volcanic rocks occur across the Cordillera landward of the current/recent volcanic arc Mexico to Alaska and most other subduction backarcs. We conclude that most are produced by partial melt in the upper asthenosphere where two conditions are met: (1) thin lithosphere, shallow hot asthenosphere. Most of the Cordillera is uniformly hot with thin lithosphere such that the hot asthenosphere extends up to a sufficiently shallow depth to intersects the wet solidus; (2) wet upper asthenosphere. There is substantial water in the upper asthenosphere that reduces the solidus sufficiently for partial melting. We integrate geochemical analyses that constrain the partial melt source temperature and depth, seismic velocities that define the upper mantle temperature and partial melt zones, and seismic receiver functions that define the lithosphere‐asthenosphere boundary (LAB). Geochemical data for the Canadian Cordillera are integrated with data from the western United States. Upper mantle xenoliths indicate a dry strong lithosphere, <50 ppm H2O. A wet asthenosphere source, >250 ppm, is indicated by the volcanics, facilitating small‐scale convection. Geochemical equilibration averages ∼1,350°C, at ∼65 km. Receiver functions also define the LAB at ∼65 km for the western Cordillera, deeper in the eastern US Cordillera, and asthenosphere tomography velocities indicate ∼1,350°C. Low velocities above ∼150 km suggest a few percent partial melt that percolates upward and ponds at the base of the lithosphere until enough accumulates to locally penetrate upward. The 65‐km depth may be controlled by the spinel‐garnet phase transition. Mechanisms are discussed for the spatial distribution of recent volcanics.
A Review of Cellulose Coarse-Grained Models and Their Applications

Cellulose is the most common biopolymer and widely used in our daily life. Due to its unique properties and biodegradability, it has been attracting increased attention in the recent years and various new applications of cellulose and its derivatives are constantly being found. The development of new materials with improved properties, however, is not always an easy task, and theoretical models and computer simulations can often help in this process. In this review, we give an overview of different coarse-grained models of cellulose and their applications to various systems. Various coarse-grained models with different mapping schemes are presented, which can efficiently simulate systems from the single cellulose fibril/crystal to the assembly of many fibrils/crystals. We also discuss relevant applications of these models with a focus on the mechanical properties, self-assembly, chiral nematic phases, conversion between cellulose allomorphs, composite materials and interactions with other molecules.
Development of a three dimensional (3D) knitted scaffold for myocardial tissue engineering. Part I: mechanical performance of the knitted structures

Abstract The aim of this study was to design a knitted scaffold for myocardial tissue engineering applications. The textile structure of pile loop knit fabric (terry fabric) was selected as a three dimensional (3 D) scaffold. The candidate fabrics were knitted from textured polyethylene terephthalate (PET) yarns. Biocompatible porous structures were obtained and the mechanical properties of the fabrics were analyzed. The values of the stiffness (Young’s modulus) of the structures were observed to range from 124.09 to 167.01 kPa in the direction of wales, and from 40.16 to 85.28 kPa in the direction of courses at 20% strain, which can be stated not to be far from those reported for the human myocardium. Furthermore, the knit structures were observed to have a pore network mainly consisting of macropores of 100 − 400 µm that were well interconnected with micropores (<60 µm). In this respect, the developed scaffolds could potentially be used as biocompatible materials for the applications of tissue engineering and regenerative medicine.
Low-dose Exposure to Black Carbon and Cadmium Significantly Induce Lung Injury by Promoting Cellular Apoptosis

PMs has adverse biological effects on major living organs in the body, including lungs. The complex composition of PM2.5, including carbon black and heavy metals, cause toxic effects to the lung. The synergistic effects of CB and Cd were less investigated in previous study. In our research, we deciphered the combination of CBs and Cd enhanced the cytotoxicity in BEAS-2B cells in vitro and in vivo. In the molecular level, the intracellular level of Cd increased, as a result of the cell membrane damage, causing high expression of MT-1 in BEAS-2B cells. Moreover, the expression of BAX increased, and the expression of BCL-2 decreased. Collectively, our data suggests the apoptosis effect of synergistic effects of CB and Cd exposure.
Developing a P-12 English Language Learner Collection in an Academic Library That Reflects Its Community

Abstract The percentage of English Language Learners (ELLs) has grown in public schools across the United States in recent decades. Curriculum materials libraries associated with teacher education programs must modify their collections to better prepare teachers for work in these increasingly diverse preschool to twelfth grade (P-12) schools. This article describes how librarians at a research institution in one of the country’s largest school districts approached a collection development project in support of young ELLs and the educators who work with them. Prior to purchasing any new materials, the librarians assessed the current collection, surveyed the literature and current practices regarding ELL collections, and researched characteristics of the local school district. Age-appropriate non-English language materials can help to support the continued development of home languages and culture, while nontraditional youth formats, including graphic novels and hi-lo books, provide ELLs with the opportunity to read developmentally appropriate materials with less text intimidation.
Evaluation and path analysis for yield and its components in some genotypes of lentil (Lens culinaris Medikus) under Upper Egypt condition

Evaluation of lentil genotypes is important to choose selection criteria for improvement of seed yield. This investigation was conducted at El-Mataana, Agricultural Research Station, Luxor governorate, ARC, Egypt to assess the genotypic variability, correlation and path analysis of some lentil genotypes. Twenty-two genotypes of lentil including one as the check variety (Giza 9) were tested in a Randomized Complete Block Design (RCBD) with three replicates for three years; 2017/2018, 2018/2019 and 2019/2020. For each season and over seasons, significant (p˂0.01) differences were observed among genotypes for all the studied traits; days to flowering, plant height, no of pods plant, 100-seed weight, seed yield plant and harvest index. Also, the differences among years and years × genotypes interactions were significant (p˂0.01) for all the studied traits. The combined mean demonstrated that the genotypes X2012-S-291 and Flip 2003-37L produced the highest seed yield plant, no of pods plant, 100-seed weight and earliest compared to the check variety (Giza 9). These genotypes were promising and could be used as new lentil cultivars i.e. parents in breeding programmes to produce high-yielding varieties for Upper Egypt. Estimates of broad sense heritability were high for all traits under investigation except seed yield plant was moderate. Seed yield plant was correlated positively with all the studied traits except days to 50% flowering. Path analysis illustrated that 100-seed weight and number of pods plant were the two important traits for seed yield improvement.
Research on target tracking algorithm based on information entropy feature selection and example weighting

An improved algorithm based on information entropy feature selection and sample weighting is proposed to solve the drift problem when the multi-instance learning (MIL) target tracking algorithm is updating classifier. Algorithm based on Boosting framework, adopt the method of MIL as sample selection, the positive and negative samples collected around the target area, to extract the image feature is compressed and weak classifier. Based on maximum entropy principle from a number of weak classifier to select several optimal classifier, the resulting strong classifier can be used to track the location of the next frame target. The results show that the algorithm is robust to target occlusion and fast motion.
Influences of Fracture Aperture on the Water-Shutoff Performance of Polyethyleneimine Cross-Linking Partially Hydrolyzed Polyacrylamide Gels in Hydraulic Fractured Reservoirs

In previous study, we have discussed the gelation performance of the polyethyleneimine (PEI) cross-linking partially hydrolyzed polyacrylamide (HPAM) gel system. The major goals of this paper are to investigate the conventional application performance of the PEI cross-linking HPAM gel system, including injectivity and sealing charactersic through core flowing experiments. Results show that polymer gels formualted with a combination of 2.0 wt % HPAM (Mw = 8000 kDa) and 0.35 wt % PEI can achieve the critical pressure gradient above 500 psi/ft on average and with a maximum at 1136.38 psi/ft through the core flowing experiments with apertures ranging from 0.080 to 0.200 cm. The pressure gradient and brine permeability reduction factors (Frrw) are also attractive. The gradient pressure is inline with expectations of the gradient pressure predicting curve obtained by Seright for the chromium(III) acetate cross-linking HPAM gel. A strong adhesive force of stretching 2 cm width rather than breakage in fracture pr.
Efficient tuning of potential parameters for liquid–solid interactions

Abstract Spherical and cylindrical water droplets on silicon surface are studied to tune the silicon–oxygen interaction. We use molecular dynamics simulations to estimate the contact angle of two different shaped droplets. We found that the cylindrical droplets are independent of the line tension as their three phases curvature is equal zero. Additionally, we compare an analytical model, taking into account or not the Tolman length and we show that for spherical small size droplets, this length is important to be included, in contrast to cylindrical droplets in which the influence of the Tolman length is negligible. We demonstrate that the usual convenient way to exclude linear tension in the general case can give wrong results. Here, we consider cylindrical droplets, since their contact angle does not depend on the droplet size in the range of few to 10ths of nanometres. The droplets are stabilised due to the periodic boundary conditions. This allows us to propose a new parameterisation for nanoscale droplets, which is independent the size of the droplets or its shape, minimising at the same time the calculation procedure. With the proposed methodology, we can extract the epsilon parameter of the interaction potential between a liquid and a solid from the nanoscaled molecular simulation with only as input the macrosized experimental wetting angle for a given temperature.
Multi-factor joint normalisation for face recognition in the wild

Face recognition has become very challenging in unconstrained conditions due to strong intra ‐ personal variations, such as large pose changes. Face normalisation can help to resolve these problems and effectively improve the face recognition performance in unconstrained conditions by converting non ‐ frontal faces to frontal ones. However, there are other complex facial variations in addition to pose, such as illumination and expression, which will also influence face recognition performance. The authors propose a well ‐ designed generative adversarial network ‐ based multi ‐ factor joint normalisation network (MFJNN) to normalise multiple factors simultaneously. First, a multi ‐ encoder generator and a feature fusion strategy are designed and implemented in the MFJNN to realise the joint normalisation of multiple factors in addition to pose. Second, a convolutional neural network ‐ based (CNN ‐ based) network is applied in the MFJNN, which allows the MFJNN to simultaneously realise image synthesis and facial representation learning. Moreover, an identity perceptive loss is introduced based on the CNN ‐ based network to produce reliable identity ‐ preserving features of the input face images. The experimental results demonstrate that the proposed method can synthesise multi ‐ factor normalisation results with identity preservation
Risk Factors for a Severe Course of COVID-19 in Persons Aged 18 to 61.

Discussion This study on registered stem cell donors aged between 18 and 61 years enables exploration of risk factors for a severe course of COVID-19 in persons of working age with relatively few health impairments. The participants reported lower prevalence of diabetes mellitus, arterial hypertension, and nicotine abuse than in the general population (1). Precisely for this group, the information on risk factors for COVID-19 remains incomplete to date. Age was even more closely associated with the occurrence of severe respiratory infections and hospitalization than has been CORRESPONDENCE
Thoughts on the Reform of Higher Vocational Dance Teaching Based on Big Data

In the fast-developing information age, the current teaching level can no longer meet the current students’ demand for knowledge. Based on the current teaching system and platform, this paper uses modern programming and big data information database as the basis, and combines some shortcomings of current college education methods and levels to design a set of remote dance information teaching system. On the basis of the B/S network structure, a new online and offline interactive teaching model for the three roles of teachers, students and administrators established by using big data has successfully enriched the diversity of higher vocational education methods.
Security risks in cyber physical systems—A systematic mapping study

The increased need for constant connectivity and complete automation of existing systems fuels the popularity of Cyber Physical Systems (CPS) worldwide. Increasingly more, these systems are subjected to cyber attacks. In recent years, many major cyber‐attack incidents on CPS have been recorded and, in turn, have been raising concerns in their users' minds. Unlike in traditional IT systems, the complex architecture of CPS consisting of embedded systems integrated with the Internet of Things (IoT) requires rather extensive planning, implementation, and monitoring of security requirements. One crucial step to planning, implementing, and monitoring of these requirements in CPS is the integration of the risk management process in the CPS development life cycle. Existing studies do not clearly portray the extent of damage that the unattended security issues in CPS can cause or have caused, in the incidents recorded. An overview of the possible risk management techniques that could be integrated into the development and maintenance of CPS contributing to improving its security level in its actual environment is missing. In this paper, we are set out to highlight the security requirements and issues specific to CPS that are discussed in scientific literature and to identify the state‐of‐the‐art risk management processes adopted to identify, monitor, and control those security issues in CPS. For that, we conducted a systematic mapping study on the data collected from 312 papers published between 2000 and 2020, focused on the security requirements, challenges, and the risk management processes of CPS. Our work aims to form an overview of the security requirements and risks in CPS today and of those published contributions that have been made until now, towards improving the reliability of CPS. The results of this mapping study reveal (i) integrity authentication and confidentiality as the most targeted security attributes in CPS, (ii) model‐based techniques as the most used risk identification and assessment and management techniques in CPS, (iii) cyber‐security as the most common security risk in CPS, (iv) the notion of “mitigation measures” based on the type of system and the underline internationally recognized standard being the most used risk mitigation technique in CPS, (v) smart grids being the most targeted systems by cyber‐attacks and thus being the most explored domain in CPS literature, and (vi) one of the major limitations, according to the selected literature, concerns the use of the fault trees for fault representation, where there is a possibility of runtime system faults not being accounted for. Finally, the mapping study draws implications for practitioners and researchers based on the findings.
The role of trajectories of stress and social support in underrepresented students’ educational outcomes

Abstract The current study examined the potential impact of trajectories of perceived stress and social support from parents, friends, romantic partners, and natural mentors (i.e., supportive adults from youths’ everyday lives) throughout underrepresented students’ college careers on their subsequent educational outcomes. Participants were underrepresented college students (n = 340) who were surveyed five times across their 4 years of college. The outcomes of interest (i.e., on-time graduation, pursuit of career goals) were assessed during the final wave of data collection. Longitudinal latent profiles were estimated based on fluctuations in perceived stress and social support over time. Three latent profiles of social support and perceived stress emerged: Early Stress Spike, Late Support Increase, and Decreasing Social Support. Results suggested that participants in the Decreasing Social Support group contained the fewest on-time graduating students, and students in this profile were less likely to be pursuing career goals than the Late Support Increase group.
Case study of property value transfer attributed to transit: spatial and temporal hedonic price impact of light rail in Minnesota’s Twin Cities

Does the proximity of transit increase property values, and can one example apply to another? Using a spatial and temporal hedonic price framework, a study of light rail transit’s impact on residential property values compares the station area property impacts of the Green and Blue lines in Saint Paul and Minneapolis. The study also points to when “value transfer” (used here to describe the comparison and application of property value impacts near transit) is appropriate, and what practitioners should keep in mind to maximize the effectiveness of the exercise. In the case of value transfer for transit, the study finds that intimate local knowledge matters more than special modeling specifications to appropriately capture the impacted properties and transfer the return of investment.
End-to-end drug entity recognition and adverse effect relation extraction via principal neighbourhood aggregation network

Drug entity and adverse effect relation extraction is a critical task that aims to recognize drug entity and extract adverse effect relation from the unstructured medical text. Many works have used statistical learning approach, traditional or new deep learning approach to solve similar pharmacovigilance problem. Recent works tended to employ the graph convolutional network to enhance the ability of drug and adverse effect information extraction. However, the injective problem generated by a single aggregator and the weak generalization of the summation aggregator cause the graph neural network lack the ability to extract sufficient relation information. To solve these problems, we propose a new end-to-end model named recognizing Drug entities and extracting Adverse effect relations via the Principal Neighbourhood Aggregation network (DAPNA). Moreover, we compared the DAPNA with baseline models on the Adverse Drug Effect (ADE) dataset using multiple metrics. The experimental results demonstrate the method proposed in this paper achieves state-of-the-art results and can be applied to other drug and adverse effect information extraction tasks.
Variability of PM10 in a Global Atmosphere Watch Station near the equator

Particulate Matter or aerosol is associated with climate change and global warming through the effect called radiative forcing. Biomass burning aerosol consists of Black Carbon, which has a positive radiative forcing that warms the atmosphere. The goal of this study is to ascertain biomass burning in Sumatera Island’s effect on aerosol concentration. We used Particulate Matter with diameter less than 10 µm (PM10) concentration in the near equator Global Atmosphere Watch Bukit Kototabang (GAW-BKT) and biomass burning in Sumatera Island from Visible Infrared Imaging Radiometer Suite (VIIRS) hotspot count. The statistical and spatial analysis was done for 2014-2018, which include the severe 2015 biomass-burning season. From the analysis of PM10 concentration and hotspot (R2=0.85) and air mass trajectories, biomass burning in Sumatera is the source of PM10 in GAW-BKT. Statistical analysis showed that hotspot in Sumatera, as well as PM10 concentration, follows a monsoonal pattern where the 90th percentile of the hotspot and PM10 concentration fell on dry season. The seasonal variabilities of particulates suggest the strong contribution of biomass open burning which occurred annually with different intensities.
Defending against sparse adversarial attacks using impulsive noise reduction filters

Deep Neural Networks (DNNs) have been deployed in many real-world applications in various domains, both industry and academic, and have proven to deliver outstanding performance. However, DNNs are vulnerable to adversarial attacks, that are small perturbations embedded in an image. As a result, introduction of DNNs into safety-critical systems, such as autonomous vehicles, unmanned aerial vehicles or healthcare devices, would introduce very high risk of limiting their capabilities to recognize and interpret the environment in which they are used and therefore would lead to devastating consequences. Thus, robustness enhancement of DNNs by development of defense mechanisms is a matter of the utmost importance. In this paper, we evaluated a set of state-of-the-art denoising filters designed for impulsive noise removal as defensive solutions. The proposed methods are applied as a pre-processing step, in which the adversarial patterns in the source image are removed before performing classification task. As a result, the pre-processing defense block can be easily integrated with any type of classifier, without any knowledge about utilized training procedures or internal architecture of the model. Moreover, the evaluated filtering methods can be considered as universal defensive techniques, as they are completely unrelated with the internal aspects of the selected attack and can be applied against any type of adversarial threats. The experimental results obtained on German Traffic Sign Recognition Benchmark (GTSRB) have proven that the denoising filters provide high robustness against sparse adversarial attacks and do not significantly decrease the classification performance on non-altered data.