Miracle-dz/UniFAR-FTUs huggingface dataset:
Moisture curling in concrete with fine lightweight aggregates

Moisture gradient development of concrete that incorporates saturated fine lightweight aggregates (FLWA) is not well understood. When a concrete beam or slab is exposed to external drying, water is transmitted through the surface pores and an internal drying front forms. If additional internal water can be provided, the development of the drying front can be minimized and delayed especially during early-age drying periods. With a delay, the concrete material can gain sufficient strength to resist cracking and hypothetically reduce the rate and magnitude of moisture curling. In order to measure the impact of external drying on concrete moisture curling, a comprehensive BoxWilson design of experiments setup was constructed to examine three critical factors: water to cementitious ratio, FLWA content, and moist curing duration. The concrete mixtures were characterize by utilizing a high-aspect ratio beam geometry. The beams were placed on a near frictionless foundation perpendicular to their direction of curling in order to eliminate the effects of creep and self-weight. Unrestrained curling deflections were measured for the experimental combinations with the results indicating that moist curing duration and FLWA content have the highest impact on the concrete curling magnitude. In fact, greater curling occurred for mixtures that were moist cured longer. Concrete mixtures with 27% FLWA by volume of fine aggregates had a 50% reduction in unrestrained curling deflection relative to concrete mixture without FLWA. In order to observe the effects of creep and self-weight, several of the concrete mixtures from the unrestrained beam testing were tested on an elastic foundation. An analytical solution was developed to calculate the deflections and moments in the beams given a certain curling moment, which also allowed for beamfoundation separation. Experimental results indicate that very early-age drying of restrained beams, i.e. 24 hours after hydration begins, leads to lower curling magnitudes than longer moist curing e.g., 6.5 days. Both the larger surface porosity and the high creep values for early age concrete result in this curling behavior. After a 6.5 day moist curing duration, the concrete mixtures curled more and had significantly less creep with the concrete containing FLWA reducing the beam curling magnitude. In order to directly calculate the strain profile through the concrete beam specimen and the impact
Sinonasal inhalation of tobramycin vibrating aerosol in cystic fibrosis patients with upper airway Pseudomonas aeruginosa colonization: results of a randomized, double-blind, placebo-controlled pilot study

Rationale In cystic fibrosis (CF), the paranasal sinuses are sites of first and persistent colonization by pathogens such as Pseudomonas aeruginosa. Pathogens subsequently descend to the lower airways, with P. aeruginosa remaining the primary cause of premature death in patients with the inherited disease. Unlike conventional aerosols, vibrating aerosols applied with the PARI Sinus™ nebulizer deposit drugs into the paranasal sinuses. This trial assessed the effects of vibrating sinonasal inhalation of the antibiotic tobramycin in CF patients positive for P. aeruginosa in nasal lavage. Objectives To evaluate the effects of sinonasal inhalation of tobramycin on P. aeruginosa quantification in nasal lavage; and on patient quality of life, measured with the Sino-Nasal Outcome Test (SNOT-20), and otologic and renal safety and tolerability. Methods Patients were randomized to inhalation of tobramycin (80 mg/2 mL) or placebo (2 mL isotonic saline) once daily (4 minutes/nostril) with the PARI Sinus™ nebulizer over 28 days, with all patients eligible for a subsequent course of open-label inhalation of tobramycin for 28 days. Nasal lavage was obtained before starting and 2 days after the end of each treatment period by rinsing each nostril with 10 mL of isotonic saline. Results Nine patients participated, six initially receiving tobramycin and three placebo. Sinonasal inhalation was well tolerated, with serum tobramycin <0.5 mg/L and stable creatinine. P. aeruginosa quantity decreased in four of six (67%) patients given tobramycin, compared with zero of three given placebo (non-significant). SNOT-20 scores were significantly lower in the tobramycin than in the placebo group (P=0.033). Conclusion Sinonasal inhalation of vibrating antibiotic aerosols appears promising for reducing pathogen colonization of paranasal sinuses and for control of symptoms in patients with CF.
Short-term mortality risk in emergency department acute heart failure.

OBJECTIVES Few tools exist that provide objective accurate prediction of short-term mortality risk in patients presenting with acute heart failure (AHF). The purpose was to describe the accuracy of several biomarkers for predicting short-term death rates in patients diagnosed with AHF in the emergency department (ED). METHODS The Biomarkers in ACute Heart failure (BACH) trial was a prospective, 15-center, international study of patients presenting to the ED with nontraumatic dyspnea. Clinicians were blinded to all investigational markers, except troponin and natriuretic peptides, which used the local hospital reference range. For this secondary analysis, a core lab was used for all markers except troponin. This study evaluated patients diagnosed with AHF by the on-site emergency physician (EP). RESULTS In the 1,641 BACH patients, 466 (28.4%) had an ED diagnosis of AHF, of whom 411 (88.2%) had a final diagnosis of AHF. In the ED-diagnosed HF patients, 59% were male, 69% had a HF history, and 19 (4.1%) died within 14 days of their ED visit. The area under the curve (AUC) for the 14-day mortality receiver operating characteristic (ROC) curve was 0.484 for brain natriuretic peptide (BNP), 0.586 for N-terminal pro-B-type natriuretic peptide (NT-proBNP), 0.755 for troponin (I or T), 0.742 for adrenomedullin (MR-proADM), and 0.803 for copeptin. In combination, MR-proADM and copeptin had the best 14-day mortality prediction (AUC = 0.818), versus all other markers. CONCLUSIONS MR-proADM and copeptin, alone or in combination, may provide superior short-term mortality prediction compared to natriuretic peptides and troponin. Presented results are explorative due to the limited number of events, but validation in larger trials seems promising.
Supercritical Carbon Dioxide and Microwave-Assisted Extraction of Functional Lipophilic Compounds from Arthrospira platensis

Arthrospira platensis biomass was used in order to obtain functional lipophilic compounds through green extraction technologies such as supercritical carbon dioxide fluid extraction (SFE) and microwave-assisted extraction (MAE). The temperature (T) factor was evaluated for MAE, while for SFE, pressure (P), temperature (T), and co-solvent (ethanol) (CS) were evaluated. The maximum extraction yield of the obtained oleoresin was (4.07% ± 0.14%) and (4.27% ± 0.10%) for SFE and MAE, respectively. Extracts were characterized by gas chromatography mass spectrometry (GC-MS) and gas chromatography flame ionization detector (GC-FID). The maximum contents of functional lipophilic compounds in the SFE and MAE extracts were: for carotenoids 283 ± 0.10 μg/g and 629 ± 0.13 μg/g, respectively; for tocopherols 5.01 ± 0.05 μg/g and 2.46 ± 0.09 μg/g, respectively; and for fatty acids 34.76 ± 0.08 mg/g and 15.88 ± 0.06 mg/g, respectively. In conclusion, the SFE process at P 450 bar, T 60 °C and CS 53.33% of CO2 produced the highest yield of tocopherols, carotenoids and fatty acids. The MAE process at 400 W and 50 °C gives the best extracts in terms of tocopherols and carotenoids. For yield and fatty acids, the MAE process at 400 W and 70 °C produced the highest values. Both SFE and MAE showed to be suitable green extraction technologies for obtaining functional lipophilic compounds from Arthrospira platensis.
Evaluation of Explosive Power Performance in Ski Jumpers and Nordic Combined Competitive Athletes: A 19-Year Study

Abstract Janura, M, Cabell, L, Svoboda, Z, and Elfmark, M. Evaluation of explosive power performance in ski jumpers and Nordic combined competitive athletes: A 19-year study. J Strength Cond Res 30(1): 71–80, 2016—Between 1992 and 2010, a total of 334 males participated in this study that assessed the differences and relationships between anthropometric variables and lower limb muscle strength in young and adult ski jumpers (n = 207) and Nordic combined (NC, n = 127) athletes. All athletes completed a maximal vertical jump from an in-run position and a maximal relative isometric force (MRIF) of the knee extensor measurement in a laboratory setting. The body mass index (BMI) in young competitors was lower than in adult groups (NC: p < 0.001; ski jumping [SJ]: p < 0.001). Similarly, the MRIF in both limbs was lower for both disciplines in the groups of young competitors. The vertical jump height (VJH) was lower for young competitors than for adults (NC: p ⩽ 0.05; SJ: p < 0.001). When comparing SJ and NC athletes, BMI was lower in SJ athletes. In addition, the adult SJ competitors exhibited greater values of bilateral MRIF (p ⩽ 0.05) and VJH (p < 0.01). There was a strong positive correlation in MRIF between the left and right lower limbs (p < 0.001) for all groups of SJ and NC athletes; therefore, it was determined to be sufficient to measure the MRIF on a single limb. Application of the new training methods (e.g., less emphasis on maximum resistance exercises) resulted in improved explosive power in ski jumpers even at lower-body weights. These changes are in accordance with the change in ski jump techniques.
Determination of minimal steady‐state plasma level of diazepam causing seizure threshold elevation in rats

Diazepam, administered by the intravenous, oral, or rectal routes, is widely used for the management of acute seizures. Dosage forms for delivery of diazepam by other routes of administration, including intranasal, intramuscular, and transbuccal, are under investigation. In predicting what dosages are necessary to terminate seizures, the minimal exposure required to confer seizure protection must be known. Here we administered diazepam by continuous intravenous infusion to obtain near–steady‐state levels, which allowed an assessment of the minimal levels that elevate seizure threshold.
Inference of quantized neural networks on heterogeneous all-programmable devices

Neural networks have established as a generic and powerful means to approach challenging problems such as image classification, object detection or decision making. Their successful employment foots on an enormous demand of compute. The quantization of network parameters and the processed data has proven a valuable measure to reduce the challenges of network inference so effectively that the feasible scope of applications is expanded even into the embedded domain. This paper describes the making of a real-time object detection in a live video stream processed on an embedded all-programmable device. The presented case illustrates how the required processing is tamed and parallelized across both the CPU cores and the programmable logic and how the most suitable resources and powerful extensions, such as NEON vectorization, are leveraged for the individual processing steps. The crafted result is an extended Darknet framework implementing a fully integrated, end-to-end solution from video capture over object annotation to video output applying neural network inference at different quantization levels running at 16 frames per second on an embedded Zynq UltraScale+ (XCZU3EG) platform.
Three-Dimensional Rotational Angiography in Congenital Heart Disease: Estimation of Radiation Exposure

Objectives: There is an increasing use of three-dimensional rotational angiography (3D-RA) during catheterization of congenital heart disease. Dose-area-product (DAP) measured by the angiography system and computed-tomography dose index (CTDI) do not appear practical for dose assessment. Hence, we performed real dose measurements in anthropomorphic phantoms. Methods: Three different anthropomorphic phantoms (10 kg, 19 kg and 73 kg bodyweight) equipped with thermoluminescent dosimeters (TLD) were used. We used a typical standard diagnostic program and a low-dose program. The effective dose (ED) was calculated according to the International Commission on Radiological Protection (ICRP) 103. The 3D distribution of radiation in the body was assessed. Results: ED for the male 10 kg phantom was 0.192 mSv in the diagnostic program and 0.050 mSv (male) in the low-dose program. The 19 kg phantom received an ED of 0.205 mSv (male) in the diagnostic program. In the low-dose program the ED reached 0.058 mSv (male). The male adult 73 kg phantom was exposed with an ED of 0.730 mSv in the diagnostic program and 0.282 mSv in the low-dose program. ED for the female phantoms was slightly higher for both acquisition-programs. Dose distribution was inhomogeneous with a dose maximum in the esophageal region behind the heart, whereas in the brain, intestine and gonads we found nearly no radiation. Conclusions: 3D-RA imaging in the interventional catheter laboratory is possible with an effective dose lower than 1 mSv. With its potential to reduce fluoroscopic time and the number of control angiographies in catheterization and intervention in complex anatomy, it can decrease the radiation dose.
An approach to classify flue-cured tobacco leaves using deep convolutional neural networks

Convolutional Neural Network (CNN) is a Multi-Layer Perceptron Neural Network (MLP), specially designed for classification and identification of image data. MLPs are very useful but very slow for learning image features. Even for small images MLPs takes a lot of time to learn the features. On contrary, Convnets detects the features locally and propagate them to the neighboring layer so that the learning process is easier and efficient. Image reduction is a process normally used to reduce the number of learning parameters. The present paper is aimed at designing a new technique to convolve the input image, using Deep CNN algorithm and then reduce the image dimension by pooling techniques. The new technique is applied for image classification of flue-cured tobacco leaves. About 120 samples of cured tobacco leaves are taken for training the CNN and reduced the image dimensions from 1450×1680 to 256×256 RGB. Here four hidden layer CNN is considered and performed convolution and pooling on input images with sixteen, thirty two and sixty four feature kernels on first three hidden layers and fourth layer is connected to output layer. Max pooling technique is used in the model and classified them into three major classes' class-1, class-2 and class-3 with a global efficiency of 85.10% on the test set consisting about fifteen images of each group. Results from the proposed model are compared with other existing models and shown that the model performs better even with small training set.
GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media

Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its sheer size and multi-dimensional nature. Nevertheless, how to obtain quality mobility models from the highly sparse and complex GeoSM data remains a challenge that cannot be readily addressed by existing techniques. We propose GMove, a group-level mobility modeling method using GeoSM data. Our insight is that the GeoSM data usually contains multiple user groups, where the users within the same group share significant movement regularity. Meanwhile, user grouping and mobility modeling are two intertwined tasks: (1) better user grouping offers better within-group data consistency and thus leads to more reliable mobility models; and (2) better mobility models serve as useful guidance that helps infer the group a user belongs to. GMove thus alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group-level movement regularity. Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. With such correlations as auxiliary knowledge, it performs sampling-based augmentation to alleviate text sparsity and produce high-quality HMMs. Our extensive experiments on two real-life data sets demonstrate that GMove can effectively generate meaningful group-level mobility models. Moreover, with context-aware location prediction as an example application, we find that GMove significantly outperforms baseline mobility models in terms of prediction accuracy.
VirtualGrasp: Leveraging Experience of Interacting with Physical Objects to Facilitate Digital Object Retrieval

We propose VirtualGrasp, a novel gestural approach to retrieve virtual objects in virtual reality. Using VirtualGrasp, a user retrieves an object by performing a barehanded gesture as if grasping its physical counterpart. The object-gesture mapping under this metaphor is of high intuitiveness, which enables users to easily discover, remember the gestures to retrieve the objects. We conducted three user studies to demonstrate the feasibility and effectiveness of the approach. Progressively, we investigated the consensus of the object-gesture mapping across users, the expressivity of grasping gestures, and the learnability and performance of the approach. Results showed that users achieved high agreement on the mapping, with an average agreement score [35] of 0.68 (SD=0.27). Without exposure to the gestures, users successfully retrieved 76% objects with VirtualGrasp. A week after learning the mapping, they could recall the gestures for 93% objects.
Co-occurrence of linguistic and biological diversity in biodiversity hotspots and high biodiversity wilderness areas

As the world grows less biologically diverse, it is becoming less linguistically and culturally diverse as well. Biologists estimate annual loss of species at 1,000 times or more greater than historic rates, and linguists predict that 50–90% of the world’s languages will disappear by the end of this century. Prior studies indicate similarities in the geographic arrangement of biological and linguistic diversity, although conclusions have often been constrained by use of data with limited spatial precision. Here we use greatly improved datasets to explore the co-occurrence of linguistic and biological diversity in regions containing many of the Earth’s remaining species: biodiversity hotspots and high biodiversity wilderness areas. Results indicate that these regions often contain considerable linguistic diversity, accounting for 70% of all languages on Earth. Moreover, the languages involved are frequently unique (endemic) to particular regions, with many facing extinction. Likely reasons for co-occurrence of linguistic and biological diversity are complex and appear to vary among localities, although strong geographic concordance between biological and linguistic diversity in many areas argues for some form of functional connection. Languages in high biodiversity regions also often co-occur with one or more specific conservation priorities, here defined as endangered species and protected areas, marking particular localities important for maintaining both forms of diversity. The results reported in this article provide a starting point for focused research exploring the relationship between biological and linguistic–cultural diversity, and for developing integrated strategies designed to conserve species and languages in regions rich in both.
Submarine measurement of the Newtonian gravitational constant.

We have measured the Newtonian gravitational constant using the ocean as an attracting mass and a research submersible as a platform for gravity measurements. Gravitational acceleration was measured along four continuous profiles to depths of 5000 m with a resolution of 0.1 mGal. These data, combined with satellite altimetry, sea surface and seafloor gravity measurements, and seafloor bathymetry, yield an estimate of C=(6.677±0.013)×10 -11 m 3 s -2 kg -1 ; the fractional uncertainty is 2 parts in 1000
Review of Intrinsic Motivation in Simulation-based Game Testing

This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored.
Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications. To fully leverage the nearly unlimited corpora and capture linguistic information of multifarious levels, large-size LMs are required; but for a specific task, only parts of these information are useful. Such large-sized LMs, even in the inference stage, may cause heavy computation workloads, making them too time-consuming for large-scale applications. Here we propose to compress bulky LMs while preserving useful information with regard to a specific task. As different layers of the model keep different information, we develop a layer selection method for model pruning using sparsity-inducing regularization. By introducing the dense connectivity, we can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. In model training, LMs are learned with layer-wise dropouts for better robustness. Experiments on two benchmark datasets demonstrate the effectiveness of our method.
Testing for arbitrary interference on experimentation platforms

Experimentation platforms are essential to large modern technology companies, as they are used to carry out many randomized experiments daily. The classic assumption of no interference among users, under which the outcome for one user does not depend on the treatment assigned to other users, is rarely tenable on such platforms. Here, we introduce an experimental design strategy for testing whether this assumption holds. Our approach is in the spirit of the Durbin–Wu–Hausman test for endogeneity in econometrics, where multiple estimators return the same estimate if and only if the null hypothesis holds. The design that we introduce makes no assumptions on the interference model between units, nor on the network among the units, and has a sharp bound on the variance and an implied analytical bound on the Type I error rate. We discuss how to apply the proposed design strategy to large experimentation platforms, and we illustrate it in the context of an experiment on the LinkedIn platform.