Miracle-dz/UniFAR-FTUs huggingface dataset:
Absolute “Two measurement” vs Relative quantum yield measurement techniques:An experimental analysis

In this work, we report a comparative study of quantum yield (QY) or quantum efficiency (QE) measurement using relative and absolute “two measurement” techniques for the high and low quantum efficiency materials to explore the measurement techniques for a wide range of applicability. We have experimentally determined the relative quantum efficiency of rhodamine 6G (a highly luminescent material) at 490 nm excitation wavelength using cuvette holder and compared the result with absolute quantum efficiency obtained using integrating sphere at the same excitation wavelength. Furthermore, to do the comparative study with low luminescent materials, cadmium selenide (CdSe) quantum dots have been fabricated and characterized. From the analysis, we observed very less variation in quantum efficiencies obtained from above stated measurement techniques. In this paper, we are also reporting the absolute quantum efficiency of fluorescein and rhodamine 6G at an excitation wavelength of 470 nm. These quantum efficiencies may be used as a reference for the quantum yield measurement of luminescent materials.
Learning the Relationship between Asthma and Meteorological Events by Using Machine Learning Methods

In this article, a new methodology is proposed by using the relationships between meteorological events and asthma cases of asthma patients in a region compared to other regions in a country. We focus on the impact of weather conditions on asthma in order to estimate asthma cases using machine learning methods based on meteorological events only. In order to increase the success of the estimates, in addition to the 10 features identified by the National Environmental Information Centers, we create some new semi-synthetic features by using the multiplication and addition operations on the features given after the scaling. Then, we use machine learning methods and the R-square coefficient approach to learn the effective features using the features obtained from publicly available data sets for Russia. After determining the effective features, we use three different machine learning algorithms: random forest, linear regression, and kernel ridge regression algorithms. We use transfer learning to store effective features obtained from a dataset for Russia and then apply them to a dataset for Kazakhstan. Our hypothesis is that a combination of the selected semi-synthetic properties of the random forest algorithm has the best performance accuracy for this application. The model successfully identifies (predicts) very high, high, medium, low or very low numbers of people with asthma for the first time in the region.
Fully Convolutional Multi-Scale ScSE-DenseNet for Automatic Pneumothorax Segmentation in Chest Radiographs

Automatic pneumothorax segmentation on chest X-ray images is very crucial for diagnosis and treatment as large pneumothorax could be fatal. The pneumothorax segmentation is challenging, as some small pneumothoraces can be subtle, and may overlap with the ribs and clavicles. Meanwhile, the shape variation of pneumothorax is also very large, which also makes the segmentation more difficult. In this paper, we propose a novel automated pneumothorax segmentation framework which consists of three modules: 1) a fully convolutional DenseNet (FC-DenseNet), 2) a spatial and channel squeeze and excitation module (scSE), and 3) a multi-scale module. In order to improve boundary segmentation accuracy, a novel spatial weighted cross-entropy loss function is proposed, which penalize the target, background and contour pixels with different weights. Extensive experiments are conducted on the 2213 chest X-ray images of testing data and the results suggest that proposed segmentation algorithm outperforms the state-of-the-art methods in terms of mean pixel-wise accuracy (MPA) of $0.93\pm 0.13$ and dice similarity coefficient (DSC) of $0.92\pm 0.14$ etc. Accordingly, the effectiveness of our method is corroborated.
Industrial Production and Field Evaluation of Transparent Electrodynamic Screen (EDS) Film for Water-Free Cleaning of Solar Collectors

Production and evaluation of transparent Electrodynamic Screen (EDS) films for application to water-free cleaning of PV modules and concentrating mirrors are reported. Medium-scale EDS films with ladder-structured Cu micro-wire electrodes on PET films were produced by a combination of flexographic printing and electroless copper plating in Kodak’s roll-to-roll manufacturing facility. The architecture of the stack includes 100-µm thick flexible glass as the front surface over the EDS film which is laminated on the solar collectors. Design of the field-test with EDS laminated PV Modules for their autonomous operation in solar fields are discussed and experimental data are presented.
Low Dose CT Image Denoising Using Multi-level Feature Fusion Network and Edge Constraints

Low-dose computed tomography image denoising is a challenging task that has been studied by many researchers. Current denoising methods based on deep learning tend to produce a blur effect on the final results, especially at high noise levels, which are prone to over-smoothed edges and loss of details. In this paper, we propose a deep learning approach based on deep convolutional and edge constraints to mitigate these problems. Firstly, to avoid the loss of shallow layers details while obtaining semantically-richer features information, we use dilated convolution instead of standard convolution, and fusion feature maps of different levels to aggregate information from different receptive field. Secondly, in order to improve the network's ability to distinguish between noise and image content, we have designed an attention block to adaptively recalibrate the information relationship of the fusion feature maps. Finally, we incorporate edge prior knowledge into LDCT image denoising task, guiding the network to pay more attention on texture and structure information by edge constraints loss. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.
How Gettering Affects the Temperature Sensitivity of the Implied Open Circuit Voltage of Multicrystalline Silicon Wafers

The temperature sensitivity of the open circuit voltage of a solar cell is mainly driven by changes in the intrinsic carrier concentration, but also by the temperature dependence of the limiting recombination mechanisms in the cell. This paper investigates the influence of recombination through metallic impurities on the temperature sensitivity of multicrystalline silicon wafers. Spatially resolved temperature dependent analysis is performed to evaluate the temperature sensitivity of wafers from different brick positions before and after being subjected to phosphorus diffusion gettering. Local spatial analysis is performed on intra-grain areas, dislocation clusters and grain boundaries. Large variations in temperature sensitivity is observed across the wafers both before and after gettering. The spatially resolved γ parameter is found to change with gettering, indicating that the gettering process alters the balance between different recombination mechanisms in the material. Features with low temperature sensitivity are observed across the wafers and correlated with dislocation clusters. The locations of these areas remain unchanged by the gettering process, suggesting that the cause for the low temperature sensitivity is not removed. Gettering is observed to have a complex effect on the temperature sensitivity of the dislocation clusters depending on the wafer position in the brick, with dislocations from the top wafer exhibiting lowest temperature sensitivity both before and after gettering.
Effect of Oasis-Ultra Matrix on the Healing Rate of Stage IV Pressure Wounds

Posters located in the same county as a level 1 trauma center provided 24/7 hand care. ConClusions: There is a discrepancy between emergency and elective hand care in New Jersey. Similar findings across Florida, Massachusetts, upstate New York, and Tennessee suggest a concerning trend of gaps in access to hand healthcare. As disproportionate availability between emergency and elective care lead to suboptimal patient care and unnecessary transfers, a nationwide system that can appropriately triage and treat patients is warranted. inTRoDuCTion: In abdominal based autologous breast reconstructions, abdominal closure is a time-consuming procedure. The time cost of the skin closure process is difficult to reduce. However, skin suture using absorbable sub-cuticular staples, which allow for increased rapid closure can provide equivalent healing capacity in various situations of incision closure. 1 The purpose of this study was to evaluate the economic efficacy and cosmetic satisfaction of wound closure with an absorbable subcuticular stapling system , and compare these results obtained with conventional dermal sutures in breast reconstruction patients. MATERiAls AnD METHoDs: The A total 94 patients undergoing autologous breast reconstruction were included the study. Of these, INSORB® was used in 64 patients and we compared 30 patient's clinical data as a conventional suture group. We provided a controlled surgical environment for evaluate effect of choice of closure material. For exclude the possibility of surgical procedure variety, we divide into free flap and pedicle flap groups and each group has stapler and control groups. For analysis of cost-effectivenss we applied endotracheal anesthesia expenses, and objective assessment by resident and questionnaire by patient was made for scar evaluation. REsulTs: Using paired t test, statistical analysis was made. An pedicle flap group represents 387.2hrs (stapler), 480.5hrs(control) (t-value-2.031, p-value 0.043) and free flap group represents 442.0hrs(stapler), 514.8hrs(control) (t-value-2.037, p-value 0.025) as average anesthesia time. A gap of time reduction is 93.3hrs in pedicle flap group and 72.8hrs in free flap group for absorbable stapler used groups. These reduce time of total general anesthesia in absorbable groups, which is equivalent to a 135.67$ in pedicle group and 128.35$ in free flap group. In regards to the expenses of INSORB®, only the group that used 1 stapler is significant. The complication rate, duration of healing period and the patient's satisfaction of the scar did not differ between each group. ConClusion: An absorbable staples are effective materials that allow cost-effective closure when used appropriately. While dealing with cutting-edge technology, …
The Users Emotional Study of Netease Cloud Music Based on LDA Model

Music sharing and social functions span the boundaries of language and cultural communication between people, and through the development of the Internet, the choice of music has been upgraded from the traditional record player mode to the discovery music mode of the music social platform. Based on the emotional needs users in “music social”, this study selects Netease cloud music reviews as data set. Through the optimized LDA model, Word2vec word vector model and sentiment dictionary, the topics are quantified from the perspectives of semantics and emotions respectively. In this way, we can explore the distribution characteristics of users’ emotions under various music. At the same time, the music platform can conduct targeted emotional marketing and personalized recommendation through the emotional needs of users, which can promote user retention and improve marketing efficiency.
Gait classification of patients with Fabry's disease based on normalized gait features obtained using multiple regression models

Diagnosis of Fabry disease (FD) remains a challenge mostly due to its rare occurrence and phenotipical variability, with considerable delay between onset and clinical diagnosis. It is then of extreme importance to explore biomarkers capable of assisting the earlier diagnosis of FD. There is growing evidence supporting the use of gait assessment in the diagnosis and management of several neurological diseases. In fact, gait abnormalities have previously been observed in FD, justifying further investigation. The aim of this study is to evaluate the effectiveness of different machine learning strategies when distinguishing patients with FD from healthy controls based on normalized gait features. Gait features of an individual are affected by physical characteristics including age, height, weight, and gender, as well as walking speed or stride length. Therefore, in order to reduce bias due to inter-subject variations a multiple regression (MR) normalization approach for gait data was performed. Four different machine learning strategies - Support Vector Machines (SVM), Random Forest (RF), Multiple Layer Perceptrons (MLPs), and Deep Belief Networks (DBNs) - were employed on raw and normalized gait data. Wearable sensors positioned on both feet were used to acquire the gait data from 36 patients with FD and 34 healthy subjects. Gait normalization using MR revealed significant differences in percentage of stance phase spent in foot flat and pushing ($p < 0.05$), with FD presenting lower percentages in foot flat and higher in pushing. No significant differences were observed before gait normalization. Support Vector Machine was the superior classifier achieving an FD classification accuracy of 78.21% after gait normalization, compared to 71.96% using raw gait data. Gait normalization improved the performance of all classifiers. To the best of our knowledge, this is the first study on gait classification that includes patients with FD, and our results support the use of gait assessment on the clinical assessment of FD.
Recognizing Nested Named Entity in Biomedical Texts: A Neural Network Model with Multi-Task Learning

Many named entities usually contain nested entities in biomedical texts. Nested entities pose challenge to the task of named entity recognition. Traditional methods try to solve the problem as a graph-structure prediction problem. However, these methods fail to sufficiently capture the boundaries information between nested entities, which limits the performance of the task. In this paper, we take a different view by solving each unique entity type as a separate task, using multi-task learning with dispatched attention to facilitate information exchange between tasks. Results on GENIA corpus show that the proposed method is highly effective, obtaining the best results in the literature.
Toward >25% Efficient Monolithic Epitaxial GaAsP/Si Tandem Solar Cells

We report here on progress made in the development of >20% efficient monolithic epitaxial III-V/Si tandem solar cells. Following our prior demonstration of a GaAs0.75P0. 25/Si tandem cell with verified AM1.5G efficiency of 20.1%, we have undertaken intensive efforts aimed at optimization of the top and bottom subcells, involving both materials quality improvement and detailed device structure refinement. To date we have thus demonstrated 21.8% AM1.5G GaAs0.75P0. 25/Si tandem cells making use of an optimized, but still defect-limited GaAsP top cell combined with a simple diffusion-processed (emitter and back-surface field) Si bottom cell. With recent development of a low dislocation density III-V-on-Si epitaxial materials platform, as well as bottom cell enhancements for photon management, analytical performance loss analysis and efficiency projections indicate that >25% is achievable in the near-term, and 30% within reach with additional holistic optimization.
A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market

Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market. Stock markets are influenced by many risks of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market. Because it’s fluctuations, there’s a need and automation tool to predict bitcoin on the stock market. This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency. The Method that we apply on this research, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.
Finite Element Analysis of Multi-Phase Squirrel Cage Induction Motor to Develop the Optimum Torque

Based on 2D finite element analysis (FEA), This paper presents an optimization strategy to get maximum induced torque of squirrel cage three phase induction motor of a constant outer stator diameter. The optimization study includes two main steps. The first step is to obtain the optimum air gap flux density while the second step is to obtain the optimum split ratio(inner to outer stator diameter) of the motor. The stator design of the three phase motor is modified and optimized to operate as a six phase asymmetrical induction motor and average values of full load torque of both motors are compared. For three phase motor, effect of changing design parameters such rotor bars, poles and stack length on the optimum design values is investigated. A real class A three phase squirrel cage four pole induction motor is considered to apply the optimization study. The motor is designed using ANSYS MAXWELL 2D software. All numerical results are analyzed using MATLAB software.
Reversible Data Hiding in JPEG Images Based on Negative Influence Models

Reversible data hiding (RDH) can be used to imperceptibly embed data into images in a reversible manner. Many RDH schemes have been developed for uncompressed images. However, JPEG compressed images are more widely used in our daily lives. The existing RDH techniques for JPEG images may cause significant distortion or a large increase in the file size of marked images. In this paper, a novel RDH scheme for JPEG images is proposed. First, the negative influence models of data embedding, including image visual distortion model and file size change model, are mathematically established. Then, a negative index for each frequency is defined as the weighted sum of the normalized average image visual distortion and the normalized average file size change per 1-bit hidden data, and the frequencies with small negative indices will be used for data embedding with a high priority. The weighting factor can be adjusted according to the user’s preference for less image distortion or smaller file size. Lastly, secret data is embedded into non-zero quantized AC coefficients of the selected frequencies in ascending order of zero-run length. Extensive experiments conducted on typical images and a well-known image database show that the presented negative influence models are effective, and the proposed RDH scheme based on the models can achieve low image distortion and small increase in the file size of marked images.
Aboveground Biomass Mapping Using ALOS-2/PALSAR-2 Time-Series Images for Borneo's Forest

Global carbon cycle research and climate change mitigation measures require a means for large-scale monitoring of forest aboveground biomass (AGB). L-band synthetic aperture radar (SAR) is one of promising means, although its signal is saturated at 75–150 Mg ha−1 of AGB, making it difficult to monitor high biomass forest. The Advanced Land Observing Satellite-2/Phased Array L-band SAR-2 (PALSAR-2) conducts ScanSAR mode observations that cover the whole tropical region about nine times a year. We investigated whether such time-series data is effective in overcoming the signal saturation issue. In Borneo, we adopted a spaceborne Light Detection and Ranging (LiDAR) Ice, Cloud, and Land Elevation/Geoscience Laser Altimeter System (GLAS)-derived AGB data for training and validation data, then developed an AGB estimation model using the Random Forest algorithm. As a result, we improved the saturation issue, and estimated the AGB up to 280 Mg ha−1 with a root mean square error (RMSE) of 62.8 Mg ha−1. Such an AGB range covers 83% of Borneo's forests. The developed model was applied to create an AGB map of Borneo with a 250-m resolution as of 2016. Total AGB of Borneo was calculated at 12.8 Gt with an average of 173.3 Mg ha−1. This article showed the PALSAR-2 time-series data to be highly useful in the AGB mapping of high carbon stock forests. However, we needed to correct the difference in observation years between GLAS and PALSAR-2 using a simple biomass growth model, but the accuracy will be improved by using future LiDAR and SAR sensors.
An Automatic Extraction Architecture of Urban Green Space Based on DeepLabv3plus Semantic Segmentation Model

Urban green space plays an important role in maintaining the balance of ecological environment and the sustainable development of the city. Extracting urban green space from remote sensing imagery can provide fast and accurate reference for urban planning and management. Deep learning semantic segmentation is a new exploration for image processing including remote sensing (RS) images these years. This paper describes a multilevel architecture which targets urban green space extraction from GF-2 imagery. The pillar of the architecture is semantic segmentation model (DeepLabv3plus) that is used for satellite imagery classification. In this paper, we take 19687 256 pixels × 256 pixels slices from Gaofen-2(GF-2) satellite images of three different cities and ground truth as training samples, and data of another city in Hebei province is taken for model verification. Then, five comparative methods are carried out for monitoring urban green space distribution, including ML (Maximum likelihood), SVM (Support Vector Machine), Object-oriented method, FCN and U-Net. The accuracy indexes of each method are obtained by calculating the difference between the extraction result and the ground truth. The results shows that the architecture with DeepLabv3plus outperforms the other five methods allowing us to better extract urban green space, in particular eliminating interference from farmland pixels. The architecture allows us to reach the target extraction accuracy of 89.46%. This paper is also an exploration of applying artificial intelligence technology to remote sensing image processing.