Miracle-dz/UniFAR-FTUs huggingface dataset:
Droplets interaction effects on the heat removed by droplets impact onto heated wall in a polydispersed flow film boiling

A dispersed ﬂow of steam and droplets is formed inside the core of a pressurized water reactor after a loss of primary coolant accident (LOCA). This ﬂow is essential for cooling the core in a LOCA transient. In a previous study, we presented NECTAR, a mechanistic code that calculates heat and mass transfer of a dispersed ﬂow inside a fuel subchannel. This code was validated using experimental results obtained with a test bench named COLIBRI at low droplets’ volume fraction ( ∼ 10 − 4 ). With NECTAR, it was possible to determine that the heat removed by the impact of the drops on the wall is substantial. However, at higher droplet volume fractions, droplets interaction may occur and affect the calculation implemented in NECTAR. Therefore, this work presents numerical simulations of heat transfer by droplets impact using NECTAR for a straight vertical tube with different droplets’ volume fraction, from 10 − 4 to 0 . 6 , considering a wetted wall correction factor proposed by Breitenbach et al. [2]. This parameter was added to Gradeck et al. [3] for calculation of impinging droplet heat transfer. We observed negligible interaction for LOCA typical droplets’ volume fraction. These simulation results will be validated with experiments to be performed soon in COLIBRI experimental apparatus.
Virtual Reality Application on Road Markings’ Visibility Analysis

The most important function of road markings is to guide road users; therefore, their visibility, in relation to retroreflectivity (RL), plays an essential role in the markings’ performance at nighttime. Researchers have applied field experiments to determine the minimum acceptable levels of markings’ RL. However, field studies usually involve a large investment of time, personnel, space, and budget. This paper presents a preliminary study that adopted virtual reality (VR) techniques to establish an immersive driving environment for the RL analysis of yellow markings. The objective is to evaluate the minimum required marking visibility for road users, especially senior drivers, while driving at nighttime. The brightness feature created in the VR model does not simulate the optical behavior of markings’ glass beads, but it simulates the marking’s visibility in relation to detection distance at each test drive. This is an alternative method to correlate the field RL and the VR simulation model through the equivalent detection distances that were recorded by the observers who participated in both field and VR driving tests. The relationship between the field markings’ RL, 80 to 130 mcd/m2/lx, and simulated markings’ brightness was successfully established with its limitations of RL between 80 and 130 mcd/m2/lx. Three age clusters are involved in this study, namely 20–30, 40–50, and 60+ years old, with 30 people in each cluster. The simulation study found that the driver’s behavior in relation to reaction time is highly related to age cluster but not driving speed or RL. The average reaction times for the three age clusters are 0.438 s, 0.499 s, and 0.522 s, respectively. However, the perception reaction time 2.5 s was considered while calculating the required RL for markings. Results found that the minimum required yellow markings’ RL increases with driving speed, and it requires 80 mcd/m2/lx for 50 km/h and 130 mcd/m2/lx for 65 km/h to satisfy the 85 percentiles of the driving population in Taiwan.
Energy-Friendly Keyword Spotting System Using Add-Based Convolution

Wake-up keyword of a keyword spotting (KWS) system represents brand name of a smart device. Performance of KWS is also crucial for modern speech based human-device interaction. An on-device KWS with both high accuracy and low power consumption is desired. We propose a KWS with add-based convolution layers, namely Add TC-ResNet. Add-based convolution paves a new way to reduce power consumption of KWS system, as addition is more energy efﬁcient than multiplication at hardware level. On Google Speech Commands dataset V2, Add TC-ResNet achieves an accuracy of 97.1%, with 99% of multiplication operations are replaced by addition operations. The result is competitive to a state-of-the-art fully multiplication-based TC-ResNet KWS. We also investigate knowledge distillation and a mixed addition-multiplication design for the proposed KWS, which leads to further performance improvement.
Simulation Methodology Based on Wind and Thermal Performance for Early Building Optimization Design in Taiwan

In a subtropical climate like that of Taiwan, the high temperature and humid environmental conditions often result in discomfort and health effects for building occupants. With regard to building geometry, the wind environment and thermal comfort assessment, which can enhance energy efficiency and the comfort and health of occupants, both ought to be considered as soon as possible in the design process. In view of the limited comprehensive design evaluation methods and design workflows regarding wind and thermal performance currently available, this research aims to develop an early decision support workflow that includes suggested performance evaluation methods and design optimization processes. The results of our case study show that the building had clear performance results using the proposed evaluation methods, making it easier for architects to understand and compare alternatives. Appropriate analysis and visualization of the results also effectively assisted architects in determining design solutions and making relevant decisions. The methods and results in this article can facilitate performance-based buildings for healthy and energy-efficient built environments.
Learning an Empirical Digital Twin from Measurement Images for a Comprehensive Quality Inspection of Solar Cells

Measurement images of solar cells contain information about their material‐ and process‐related quality beyond current–voltage characteristics. This information is currently only partially used because most algorithms look for human‐defined image features or defects. Herein, a purely data‐driven method is proposed to derive the essential image information in terms of the electrical quality within a comprehensive and meaningful representation. This representation is denoted as the empirical digital twin of the cell. Using it, solar cells can be classified according to their defects visible in the measurement images. For this purpose, a human‐in‐the‐loop approach to efficiently develop a classification scheme is presented. Therefore, a convolutional neural network combining various measurement data of a sample by correlating them with quality parameters is designed. The digital twin is an intermediate representation of the network capturing the quality‐relevant defect signatures from the images. Human experts can analyze this representation space to identify defect clusters that relate to different process errors, such as finger interruptions and shunts. How the representations are usable to derive sorting criteria for quality inspection is shown. Finally, how the empirical digital twin and the sorting scheme can be used for segmenting the defects without additional label effort is demonstrated.
Temporal Convolutional Network with Frequency Dimension Adaptive Attention for Speech Enhancement

Despite much progress, most temporal convolutional networks (TCN) based speech enhancement models are mainly focused on modeling the long-term temporal contextual dependencies of speech frames, without taking into account the distribution information of speech signal in frequency dimension. In this study, we propose a frequency dimension adaptive attention (FAA) mechanism to improve TCNs, which guides the model selectively emphasize the frequency-wise features with important speech information and also improves the representation capability of network. Our extensive experimental investigation demonstrates that the proposed FAA mechanism is able to consistently provide signiﬁcant improvements in terms of speech quality (PESQ), intelligibility (STOI) and three other composite metrics. More promisingly, it has better generalization ability to real-world noisy environment.
Impact of Grain Subsidy Reform on the Land Use of Smallholder Farms: Evidence from Huang-Huai-Hai Plain in China

Smallholder farms have played an essential role in agricultural production and food security. In order to increase farm size, the Chinese government announced a reform of the grain subsidy program in 2015. Under the reform, 20% of the aggregate input subsidy, as well as the pilot subsidy to large-scale farmers and the incremental part of the agricultural support and protection subsidy budget, were used to support increasing farm size. This study evaluated the impact of China’s grain subsidy reform on the land use of smallholder farms to investigate whether the reform achieved its goal. Based on 2063 samples obtained from the 2013–2015 Survey for Agriculture and Village Economy data in Huang-Huai-Hai Plain, we conducted a difference-in-difference model to solve the problem of missing counterfactual states in policy evaluation. Farms from Henan and Shandong were assigned to the treatment group, and farms from Hebei were assigned to the control group. The results revealed that the average treatment effect on the treated of the impact of the grain subsidy reform on the wheat-sown area was −25% (0.10 ha). Furthermore, there was heterogeneity in regard to the subsidy reform effects in different sown-area groups. The reform had the most significant impact on the smallest farmers. We also found that China’s grain subsidy reform had a significant and positive effect on the amount of outflow land area, while the impact of subsidy reform on land tenure was insignificant. Our findings suggest that while encouraging large-scale farms, it is necessary to take into account farmers’ small-scale operations and gradually promote the transformation of small-scale operations to large-scale operations. The Chinese government should strengthen the supervision of land use to achieve the goal of ensuring food security.
A novel approach for characterizing frozen soil damage based on mesostructure

In this study, the relationship between the computed tomography (CT) number and the density of frozen soil was established on a macro and mesoscopic scale, aimed at overcoming the shortcomings of the mesoscopic damage behavior predicted by previous macroscopic research on frozen soil. In addition, considering the influence of stress level and temperature, a mesoscopic structural damage variable was proposed, which could reflect the damage characteristics of frozen soil. On this basis, combining the meso-damage of frozen soil with the macro-mechanical response, the mechanism of the damage evolution of frozen soil under stress was studied. It was found that the variation law of porosity was consistent with the law of the mesoscopic damage expansion in frozen soil. In the initial stage of stress loading, compaction was observed before meso-crack expansion. The internal structure exhibited a strengthening effect, by which the damage was reduced. The macroscopic mechanical properties were characterized by an elastic strain phase. As the stress continued to increase, an inflection point of the damage value appeared, and the damage gradually increased. The starting point of the meso-fracture expansion curve could represent the stress yield point on the macroscopic mechanical curve, and also represented the inflection point of the volume change of the material from the contraction to the dilatancy. When the damage value was gradually increased and became equal to the initial value, the deviatoric stress reached the peak of the macroscopic mechanical curve.
Effectiveness of Green Infrastructure Location Based on a Social Well-Being Index

Urban Green Infrastructure (GI) provides promising opportunities to address today’s pressing issues in cities, mainly resulting from uncurbed urbanization. GI has the potential to make significant contributions to make cities more sustainable by satisfying the growing appetite for higher standards of living as well as helping cities adapt to extreme climate events. To leverage the potentials of GI, this article aims to investigate the effectiveness of GI that can enhance social welfare benefits in the triple-bottom line of urban sustainability. First, publicly available data sets representing social demographic, climate, and built environmental elements are collected and indexed to normalize its different scales by the elements, which is termed as the “Social Well-being Index. ” Second, a random forest regressor was applied to identify the impacts of variables on the indexed scores by region. As a result, both the Seoul and Gyeonggi-do models found the most significant relationship with the type of GI to prevent pollutants and disasters, followed by GI types to conserve and improve the environment in Seoul and GI types to serve activity spaces in Gyeonggi-do. Furthermore, variables such as population, number of pollutants, and employment rate in Seoul were found significant and employment rate, population, and air pollution were significant in Gyeonggi-do. Finally, a scenario analysis is conducted to investigate the impacts of the overall index score with additional GI facilitation according to the model’s findings. This article can provide effective strategies for implementing policies about GI by considering regional conditions. The analytical processes in this article can provide useful insights into preparing effective ecological and environmental improvement policies accordingly.
Compact Quantum‐Dot Microbeads with Sub‐Nanometer Emission Linewidth

Fluorescent microbeads are widely used for applications in life sciences and medical diagnosis. The spectral contrast and sharpness of photoluminescence are critical in the utilities of microbeads for imaging and multiplexing. Here, microbeads capable of generating single‐peak laser emission with a sub‐nanometer linewidth are demonstrated. The microbeads are made of quantum dots that are tightly packed and crosslinked via ligand exchange for high optical gain and refractive index as well as material stability. Bright single‐mode lasing with no photobleaching is achieved with particle diameters as small as 1.5 µm in the air. Sub‐nm lasing emission is maintained even inside high‐index surroundings, such as organic solvents and biological tissues. Feasibility of intracellular tagging and multi‐color imaging in vivo is demonstrated.
Analysis and Design of Class E Frequency Multiplier With Shunt Linear and Nonlinear Capacitances at Any Duty Cycle

The theoretical efficiency of the class E frequency multiplier with arbitrary output frequency can reach 100% by controlling the phase of the output current to satisfy the ZVS/ZVDS condition. It is necessary to select the appropriate value of the shunt capacitance of the frequency multiplier for satisfying these two conditions. In general, the shunt capacitance of the transistor consists of the linear external capacitance and the nonlinear parasitic capacitance. Therefore, the conventional design method of treating shunt capacitance as purely linear is not accurate enough. In this brief, a class E frequency multiplier with shunt linear and nonlinear capacitances at any duty cycle is analyzed and designed, and the design curve is given in detail. The simulation and experimental results show high agreement with the theoretical analysis result.
Pain Assessment during Gnathological Treatment of Temporomandibular Myofascial Pain through Mandibular Repositioning Splints Designed after a Posture-Stabilometric Evaluation

The aim of the study is to evaluate the effectiveness of two complementary mandibular repositioning splints (SVED and MORA) designed after a preliminary patients’ posture-stabilometric evaluation in treatment for temporomandibular myofascial pain (TMP) using the Pain-Intensity Numeric Rating Scale (PI-NRS) assessment. Eighty-six consecutive sportive non-agonistic subjects with TMP were randomly divided in two groups. Forty-two wear mandibular repositioning splints designed by a posture-stabilometric evaluation, thus constituting the test group. The other 44 subjects were not subjected to any treatment, thus represented the control group. To record pain intensity, subjects in both groups were asked to fill in a PI-NRS which ranged from 0 (no pain) to 10 (worst imaginable pain). In the test group, PI-NRS was assessed three times: before starting therapy (t0), after 4 months (t1) and after 8 months (t2) of treatment. Instead, in the control group PI-NRS was recorded during the first visit (t0) and after 8 months (t2). The Friedman test showed a statistically significant decrease in PI-NRS mean scores after 4 (t1) and 8 (t2) months from the start of the gnathological therapy with mandibular repositioning splints, (p < 0.001) in test group. There is enough evidence to assess that occlusal splint therapy for mandibular repositioning (MORA and SVED) designed through a posture-stabilometric evaluation could be considered in the treatment of temporomandibular myofascial pain.
Prais–Winsten Algorithm for Regression with Second or Higher Order Autoregressive Errors

There is no available Prais–Winsten algorithm for regression with AR(2) or higher order errors, and the one with AR(1) errors is not fully justified or is implemented incorrectly (thus being inefficient). This paper addresses both issues, providing an accurate, computationally fast, and inexpensive generic zig-zag algorithm.
Contribution of Grape Skins and Yeast Choice on the Aroma Profiles of Wines Produced from Pinot Noir and Synthetic Grape Musts

The aroma profile is a key component of Pinot noir wine quality, and this is influenced by the diversity, quantity, and typicity of volatile compounds present. Volatile concentrations are largely determined by the grape itself and by microbial communities that produce volatiles during fermentation, either from grape-derived precursors or as byproducts of secondary metabolism. The relative degree of aroma production from grape skins compared to the juice itself, and the impact on different yeasts on this production, has not been investigated for Pinot noir. The influence of fermentation media (Pinot noir juice or synthetic grape must (SGM), with and without inclusion of grape skins) and yeast choice (commercial Saccharomyces cerevisiae EC1118, a single vineyard mixed community (MSPC), or uninoculated) on aroma chemistry was determined by measuring 39 volatiles in finished wines using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC-MS). Fermentation medium clearly differentiated the volatile profile of wines with and without yeast, while differences between EC1118 and MSPC wines were only distinct for Pinot noir juice without skins. SGM with skins produced a similar aroma profile to Pinot noir with skins, suggesting that grape skins, and not the pulp, largely determine the aroma of Pinot noir wines.
Vertical grid refinement for stratocumulus clouds in the radiation scheme of the global climate model ECHAM6.3-HAM2.3-P3

Abstract. In this study, we implement a vertical grid refinement scheme in the radiation routine of the global aerosol–climate model ECHAM-HAM, aiming to improve the representation of stratocumulus clouds and address the underestimation of their cloud cover. The scheme is based on a reconstruction of the temperature inversion as a physical constraint for the cloud top. On the refined grid, the boundary layer and the free troposphere are separated and the cloud's layer is made thinner. The cloud cover is recalculated either by conserving the cloud volume (SC-VOLUME) or by using the Sundqvist cloud cover routine on the new grid representation (SC-SUND). In global climate simulations, we find that the SC-VOLUME approach is inadequate, as there is a mismatch, in most cases, between the layer of the inversion and the layer of the stratocumulus cloud, which prevents its application and is itself likely caused by an overly low vertical resolution. Additionally, we find that the occurrence frequency of stratocumulus clouds is underestimated in ECHAM-HAM, limiting a priori the potential benefits of a scheme like SC-VOLUME targeting only cloud amount when present. With the SC-SUND approach, the possibility for new clouds to be formed on the refined grid results in a large increase in mean total cloud cover in stratocumulus regions. In both cases, however, the changes exerted in the radiation routine are too weak to produce a significant improvement in the simulated stratocumulus cloud cover. We investigate and discuss the reasons behind this. The grid refinement scheme could be used more effectively for this purpose if implemented directly in the model's cloud microphysics and cloud cover routines, but other possible ways forward are also discussed.
Serum Anti TTG Level as a Predictor for Severity of Intestinal Damage in Children with Celiac Disease

AbstractIntroduction: This study aimed at evaluating how the patient’s clinical manifestations and his/her anti TTG level are correlated with the intensity of histological damage based on the classification of Marsh Oberhuber.Methods: This cross-sectional study was performed on 186 children suspected to celiac disease who referred to gastroenterology clinic between 2014 and 2015 and had Anti TTG >20. All patients underwent upper gastrointestinal endoscopy and multiple biopsies of duodenum were taken; histological classification was performed based on Marsh Oberhuber criteria. Finally, the relationship between serum levels of Anti TTG and histologic findings was assessed based on Marsh Oberhuber criteria.Results: There was a statistically significant difference between Anti TTG and different classes of grading March (P=0.01). Moreover, the intensity of intestinal damage based on Marsh Oberhuber criteria was significantly higher in children who referred with gastrointestinal complaints, the classic form of celiac disease and growth disorder. Anti TTG level of 148 IU/ml was the best positive cutoff point suggested by the ROC. For anti TTG level of 148 IU/ml, sensitivity, specificity, positive predictive value and negative predictive value were 46.8%, 82.4%, 91.7%, and 27.2%, respectively.Conclusion: Anti TTG level of 148 IU/ml has a high positive predictive value and a low negative predictive value for histologic changes more than grade 1 in Marsh classification.
Are there consistent abnormalities in event-related EEG oscillations in patients with Alzheimer's disease compared to other diseases belonging to dementia?

Cerebrospinal and structural-molecular neuroimaging in-vivo biomarkers are recommended for diagnostic purposes in Alzheimer's disease (AD) and other dementias; however, they do not explain the effects of AD neuropathology on neurophysiological mechanisms underpinning cognitive processes. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association reviewed the field literature and reached consensus on the event-related electroencephalographic oscillations (EROs) that show consistent abnormalities in patients with significant cognitive deficits due to Alzheimer's, Parkinson's (PD), Lewy body (LBD), and cerebrovascular diseases. Converging evidence from oddball paradigms showed that, as compared to cognitively unimpaired (CU) older adults, AD patients had lower amplitude in widespread delta (>4 Hz) and theta (4-7 Hz) phase-locked EROs as a function of disease severity. Similar effects were also observed in PD, LBD, and/or cerebrovascular cognitive impairment patients. Non-phase-locked alpha (8-12 Hz) and beta (13-30 Hz) oscillations were abnormally reduced (event-related desynchronization, ERD) in AD patients relative to CU. However, studies on patients with other dementias remain lacking. Delta and theta phase-locked EROs during oddball tasks may be useful neurophysiological biomarkers of cognitive systems at work in heuristic and intervention clinical trials performed in AD patients, but more research is needed regarding their potential role for other dementias.