Miracle-dz/UniFAR-FTUs huggingface dataset:
Secondary organic aerosol formation from photooxidation of furan: effects of NOx and humidity

Abstract. Atmospheric furan is a primary and secondary pollutant in the atmosphere, and its emission contributes to the formation of ultrafine particles. We investigate the effects of NOx level and humidity on the formation of secondary organic aerosol (SOA) generated from the photooxidation of furan in the presence of NaCl seed particles. SOA mass concentration and yield were determined under different NOx and humidity levels. A significant difference is observed both in the variation of SOA mass concentration and SOA yield with the initial experimental conditions. Varying VOC (volatile organic compound) ∕ NOx ratios over the range 48.1 to 8.2 contributes to the effective formation of SOA in the presence of NaCl seed particles, with the SOA mass concentration and SOA yield ranging from 0.96 to 23.46 µg m−3 and from 0.04 % to 1.01 %, respectively. We found that there was a favourable relationship between the SOA yields and NOx concentration. In particular, the increase in SOA yield with increasing NOx concentration was continuously observed at high NOx levels owing to a corresponding increase in the amount of low-volatility hydroxyl nitrates and dihydroxyl dinitrates that can partition into the particle phase. In addition, varying relative humidity (RH) from 5 % to 88 % increased the SOA yield from 1.01 % to 5.03 %. The enhanced SOA formation from humid conditions may result from the high OH concentration, rapid furan decay rate, enhanced carbonyl-rich products condensation, and the aqueous-phase reactions. Using hybrid quadrupole-orbitrap mass spectrometer equipped with electrospray ionization (HESI-Q Exactive-Orbitrap MS), three carbonyl-rich products and three kinds of organonitrates were identified in the collected SOA. Based on the HESI-Q Exactive-Orbitrap MS analysis and Fourier transform infrared spectroscopy (FTIR), the reaction mechanism of furan photooxidation was proposed. This study demonstrates the effects of NOx and humidity on SOA formation during the furan–NOx–NaCl photooxidation and provides new insights into the oxidation regime and SOA composition in furan photooxidation. The results also illustrate the importance of studying SOA formation over a comprehensive range of environmental conditions. Only such evaluations can induce meaningful SOA mechanisms to be implemented in air quality models.
Progress in Chemokine-Like Factor 1 Research over the Past 10 Years: From Promoting Inflammatory Cell Trafficking to Inhibiting AllergicAirway Inflammation

Chemokine-like factor 1 (CKLF1) is a cytokine, first described in 2001. CKLF1 is highly expressed in the lung and leukocytes. Recombinant CKLF1 has chemotactic activity on leukocytes, and stimulates the proliferation of murine skeletal muscle cells. Administration of CKLF1 in mice caused dramatic pathological changes in the lungs, including peribronchial leukocyte infiltration, epithelial shedding, collagen deposition, and proliferation of bronchial smooth muscle cells. Expression of CKLF1 mRNA in PBMCs and CKLF1 immunoreactivity in bronchial mucosa were found to be higher in an asthmatic group than in controls. A CKLF1-specific monoclonal antibody was generated using an intramuscular injection of pCDI-CKLF1 followed by electroporation in vivo, instead of a conventional protein immunization strategy. CCR4 is a functional receptor for CKLF1; this was confirmed using a chemotaxis assay, calcium flux assays, and receptor internalization. Two peptides from secreted CKLF1 in cell supernatants were obtained when recombinant CKLF1 was stably expressed in Drosophila S2 cells, termed CKLF1-C27 (C27) and CKLF1-C19 (C19). C27 and C19 had the same effect through CCR4 as the recombinant CKLF1 protein. Although with weaker chemotactic activity, C19 can inhibit chemotaxis induced by other chemokines, such as CKLF1 and TARC/CCL17. Chemically synthesized C19 peptide was injected intraperitoneally to inhibit allergic inflammation associated with asthma in mice, resulting in a significant reduction in AHR, airway eosinophilia, and the number of lymphocytes in BALF. Most recently, C19 peptide was used to treat murine allergic rhinitis. Intranasal administration of C19 reduced allergic symptoms, such as sneezing and rubbing, and serum concentrations of IgE. Mice treated with C19 or budesonide, a intranasal glucocorticoid steroid for the treatment of non-infectious rhinitis, for the treatment of non-infectious rhinitis, showed fewer eosinophils in the submucosa or peribronchiolar zone, while the nasal mucosa of untreated allergic mice displayed significant increases in eosinophils and conspicuous hyperplasia of the mucous glands
Observation on the effects of compound matrine injection on the toxic and side effects in chemotherapy of breast cancer

Objective To observe the effects of compound matrine injection on the toxic and side effects in chemotherapy of breast cancer. Methods 64 patients with breast cancer were randomly divided into two groups, 32 cases in each group. The control group received chemical treatment and routine post operative recovery treatment, while the observation group was added with compound matrine injection based on the treatment of the control group. The improvement of symptoms, recent therapeutic effects, incidences of adverse reactions and quality of life before and after treatment were observed. Results The clinical symptoms improved after treatment was 81.21% in the control group after treatment to improve the rate of 53.14%; adverse reactions observed group and the control group with significant difference; observation group improved quality of life and stability rates were 92.53% and 62.83% and significant different with the control group. Conclusion Compound matrine injection, with conspicuous recent therapeutic effects, can significantly relieve symptoms of chemotherapy of breast cancer, improve patients quality of life, so it is worthy of being popularized and applied in clinical chemotherapy.
A Novel Hybrid Full Adder Based on Gate Diffusion Input Technique, Transmission Gate and Static CMOS Logic

This research proposes a hybrid Full Adder (FA) cell using a combination of Gate Diffusion Input (GDI) technique, Transmission Gate (TG) and conventional Static CMOS (C-CMOS) logic. To test performance parameters, simulation has been conducted using Cadence Virtuoso in 65 nm technology. Moreover, a comparative analysis of the proposed design with 13 existing state of art FAs has been presented in this research to observe the performance improvements obtained by the proposed FA. In addition, the proposed and existing FAs have been cascaded to implement 4-bit, 8-bit, 16-bit and 32-bit adder to test their performance and feasibility in large structures. The proposed design exhibited remarkable performance both as single cell and cascaded mode. Besides, the proposed technique for FA design removes the defect of voltage degradation in GDI technique and low drivability of TG based design by utilizing C-CMOS logic in the output terminals.
Evaluation of Partnering Intensity on Highway Construction Projects Performance

Throughout the literature and current practice in highways construction projects, Partnering has been known as an effective management technique to improve performance project metrics; moreover, for some US transportation agencies (DOT), it has contributed to a change in the business behavior. Some organizations have evolved partnering into different intensity levels and incorporated into alternative contracting methods (ACMs). However, there are scarce studies that validate the improvement on partnered project performance metrics. This paper describes the elements to define the partnering intensity regarding the project characteristics and partnering tools. This study hypothesizes that a higher partnering intensity, the better project outcomes concerning cost, time and claims. The paper bases this assertion on the analysis of cost and time growth and claim cost of 20 partnered highway construction projects located in seven transportation agencies within the United States grouped into two categories: partnering intensity and the type of contracting methods. A higher partnering intensity is applied to complex projects and large collaborative procurement practices. The paper highlights several contributions of partnering intensity to attain a better understanding of organizational matureness to achieve improved partnering management practices.
Vision Based Advanced Driver Assistance System Using Deep Learning

India is having one of the highest road casualty rates of almost nearly 1.5 lakh a year. The rate of road related deaths come down by a good margin if Advanced Driver Assistance System(ADAS) becomes mandatory for the vehicles. Since Indian car manufacturers focus on cost cutting, only Anti-lock Braking System(ABS) and airbags are offered as standard. With the advent of deep learning methods and computer vision, the use of an on-board camera and real time processing of captured images of road scenes makes it possible to achieve a wide range of ADAS features. In this paper a deep learning and computer vision based approach is used to develop a multi-functional ADAS system. The proposed system combines various objectives such as Vehicle Detection, Pedestrian Detection, Traffic Sign Board Detection, Traffic Light Detection and Blind-spot Vehicle Detection. The detection model is implemented using Tensorflow and various models are trained and tested using different detection algorithms and deep neural network(DNN) architectures. Networks such as SSD Inception, SSD Mobilenet, Faster RCNN Inception are trained on the same hardware set-up in order to perform a comparative study. The optimal model is selected based on a trade-off between detection time and accuracy. The model developed can detect 35 classes of objects that are commonly seen while driving a vehicle. The optimal model can be implemented on a suitable hardware and converted to a low-cost portable vehicle accessory.
Human Activity Recognition Using Accelerometer, Gyroscope and Magnetometer Sensors: Deep Neural Network Approaches

Nowadays, smartphone with various extraordinary and notable sensors makes new invigorating open entryways for Data Mining and Machine Learning; other than makes another exploration field for Human Activity Recognition a.k. a. HAR. Such development advanced the manufacture of an assortment of useful datasets, which encourages the investigation of certainties of different research area. The MHEALTH, a multivariate time series dataset is such an optional dataset that was arranged in order to encourage the investigation with respect to HAR. The dataset includes data with respect to twelve human activities oppressing ten volunteers. Sensors were utilized for the information amassing process to be specific Accelerometer, Gyroscope, Magnetometer and Electrocardiogram signal. This paper plays out a relative report on HAR process as far as work of four unique information preprocessing strategies joined by six popular classifiers entitled as Random Forest, Support Vector Machine, Naive Bayes, along with three deep learning approaches, Multilayer Perceptron, Deep Convolutional Neural Network and Long-Short Term Memory. Information preprocessing strategies that were utilized on the referenced dataset are disposal of invalid name occurrences, consistency of unequal classes, low-pass filtering and Principle Component Analysis. The objective of this examination is to break down execution of various classifiers as far as the referenced information preprocessing techniques and furthermore distinguishing the procedure for which the classifiers display prevalent precision.
Sensing resolution enhancement of long-range BDTS using D-SVD algorithm

This paper presents the sensing resolution improvement of Brillouin distributed temperature sensor (BDTS) using signal processing technique. The sensor is designed for 70 km sensing range using optical time domain reflectometry (OTDR) technique. We have compared the performance of designed sensor in terms of sensing resolution using different denoising algorithms such as dichotomize singular value decomposition (D-SVD) algorithm, Fourier-Wavelet Regularized Deconvolution (ForWaRD) algorithm and Lifting Wavelet algorithm. The temperature resolution is obtained as 11 K using D-SVD algorithm at 70 km distance. However, the temperature resolution as 29 K is observed without using denoising algorithm at same distance. An improvement of temperature resolution as 18 K is observed using D-SVD algorithm. In addition, the improvement of sensing resolution is observed in D-SVD algorithm compared to other denoising algorithms.
Improving Clone Detection Precision Using Machine Learning Techniques

Code clones or similar segments of code in a software project can be detected by using a clone detection tool. Due to modifications applied after copying and pasting of the cloned code, the current code clone detection tools face challenges to accurately detect clones with heavy modifications (i.e., Type-3 clones or clones with added/deleted/modified statements). One challenge is because the clone results contain several false positives. In this paper, we propose an approach for increasing the precision of code clone detection using machine learning techniques. By training a decision tree on 19 clone class metrics, we use the trained decision tree as a clone filter by placing it in the last step in the clone detection pipeline. This aims to remove false positive clone classes reported by a clone detection tool. We found that the decision tree clone filter is helpful for decreasing the number of false positive clone classes in iClones, a well-known code clone detector. After training the decision tree on 537 clone classes in JFreeChart and evaluating it on the test data set, it could improve iClone's precision from 0.94 to 0.98. The findings show that decision tree can be used effectively for filtering false positive clones. Nonetheless, we found that the filter is only effective for Java and does not offer satisfying performance when running on a Django Python project.
Methods for Intelligent Data Analysis Based on Keywords and Implicit Relations: The Case of "ISTINA" Data Analysis System

In information analysis systems that are working with big data, there often arises a need to classify objects and calculate the degree of thematic proximity between two objects. One of the natural sources of data for solving such problems are keywords that are attributed to objects of the system. In this paper, a model for calculating the degree of thematic proximity between two keywords as well as between two sets of keywords is described. This model is based on contextual proximity between keywords, which means the number of sets where the two keywords are present together. When calculating the final proximity coefficient, such properties of keywords as abstractness degree and thematic belonging are taken into account. Various ways to use the developed model for solving practical tasks are described, on the example of "ISTINA" scientometric data analysis system in Lomonosov Moscow State University.
Ultra-Low Contact Resistivity of < $0.1~\Omega\cdot$ mm for Au-Free TixAly Alloy Contact on Non-Recessed i-AlGaN/GaN

A robust process for Au-free ohmic contact formation is demonstrated by a direct contact of Ti<sub>x</sub>Al<sub>y</sub> alloy film on non-recessed i-AlGaN/GaN. Using this novel Ti<sub>x</sub>Al<sub>y</sub> alloy instead of multilayers as contact metals, an ultra-low contact resistivity of <inline-formula> <tex-math notation="LaTeX">$ < 0.1~\Omega \cdot $ </tex-math></inline-formula> mm is achieved for Ti<sub>5</sub>Al<sub>1</sub> alloy on i-AlGaN/GaN after 880 °C/60s annealing.
Conveyors for Streaming Many-To-Many Communication

We report on a software package that offers high-bandwidth and memory-efficient ways for a parallel application to transmit numerous small data items among its processes. The package provides a standalone library that can integrated into any SHMEM, UPC, or MPI application. It defines a simple interface to parallel objects called conveyors, and it provides a variety of conveyor implementations. Often the most efficient type of conveyor is an asynchronous three-hop conveyor, which makes heavy use of fast intranode communication. This type also uses the least memory internally. Conveyors of this type scale well to 100,000 processes and beyond. Our experience with conveyors applied to irregular algorithms at scale has convinced us of the necessity and profitability of message aggregation. The conveyor interface is a low-level C API that is intended to guide future hardware and runtime improvements and to be a foundation for future parallel programming models.
Image Based Tomato Leaf Disease Detection

Leaf diseases are the major problem in agricultural sector, which affects crop production as well as economic profit. Early detection of diseases using deep learning could avoid such a disaster. Currently, Convolutional Neural Network (CNN) is a class of deep learning which is widely used for the image classification task. We have performed experiments with the CNN architecture for detecting disease in tomato leaves. We trained a deep convolutional neural network using PlantVillage dataset of 14,903 images of diseased and healthy plant leaves, to identify the type of leaves. The trained model achieves test accuracy of 99.25%.
Conceptual Design of Solar Rechargeable Airplane

In this paper, main goal focused on conceptual definition of automated design system tool, which could be for optimal design of the solar rechargeable aircraft for defined environmental conditions and as target to achieve continuous flight in scope of defined mission (flight path). This computer aided design system consists of many subsystems, including environmental, aircraft aerodynamics and flight dynamics, solar power energy system, which in turn will be configured by one of the main blocks - the multi-objective optimization subsystem. Also in the structure of the design utility there is a validation subsystem that provides a solution check and output of simulation results.
Approximate Query Answering in Complex Gaussian Mixture Models

Gaussian mixture models are widely used in a diverse range of research fields. If the number of components and dimensions grow high, the computational costs for answering queries become unreasonably high for practical use. Therefore approximation approaches are necessary to make complex Gaussian mixture models more usable. The need for approximation approaches is also driven by the relatively recent representations that theoretically allow unlimited number of mixture components (e.g. nonparametric Bayesian networks or infinite mixture models). In this paper we introduce an approximate inference algorithm that splits the existing algorithm for query answering into two steps and uses the knowledge from the first step to reduce unnecessary calculations in the second step while maintaining a defined error bound. In highly complex mixture models we observed significant time savings even with low error bounds.
Urgent Tsunami Computing

Tsunamis pose a hazard that may strike a coastal population within a short amount of time. To effectively forecast and warn for tsunamis, extremely fast simulations are needed. However, until recently such urgent tsunami simulations have been infeasible in the context of early warning and even for high-resolution rapid post-event assessment. The implementation of efficient tsunami numerical codes using Graphical Processing Units (GPUs) has now allowed much faster simulations, which have opened a new avenue for carrying out simulations Faster Than Real Time (FTRT). This paper discusses the need for urgent computing in computational tsunami science, and presents workflows for two applications, namely FTRT itself and Probabilistic Tsunami Forecasting (PTF). PTF relies on a very high number of FTRT simulations addressing forecasting uncertainty, whose full quantification will be made more and more at reach with the advent of exascale computing resources.
Segmentation of Prostate Sonoelastography Images Using Quantitative Elasticity Measures

Non-invasive ultrasound imaging is an emerging approach for detection of prostate cancer. However appropriate segmentation of ultrasound images of prostate tissue is challenging due to their inherent complex structure and heterogeneity. To overcome such limitation, sonoelastography is a promising method for cancer detection based on variation in tissue stiffness. In this study, a scaling based segmentation algorithm has been developed for sonoelastography (SE) images of prostate. Proposed algorithm converts the input color SE image in the form of a parametric image by representing pixels in terms of elasticity values. Further, segmentation of images was performed through Otsu's multilevel method. Segmentation results are verified by expert clinician justifying the validation of the method for probable use in segmentation of SE images.
AD-Link: An Adaptive Approach for User Identity Linkage

User identity linkage (UIL) refers to linking accounts of the same user across different online social platforms. The state-of-the-art UIL methods usually perform account matching using user account's features derived from the profile attributes, content and relationships. They are however static and do not adapt well to fast-changing online social data due to: (a) new content and activities generated by users; as well as (b) new platform functions introduced to users. In particular, the importance of features used in UIL methods may change over time and new important user features may be introduced. In this paper, we proposed AD-Link, a new UIL method which (i) learns and assigns weights to the user features used for user identity linkage and (ii) handles new user features introduced by new user-generated data. We evaluated AD-Link on real-world datasets from three popular online social platforms, namely, Twitter, Facebook and Foursquare. The results show that AD-Link outperforms the state-of-the-art UIL methods.
A Suspended Thick-Winding Inductor for Integrated Voltage Regulator Applications

In this letter, a suspended thick-winding inductor is demonstrated for integrated voltage regulator applications. A suspended thick-winding inductor with an area of 2.9 mm<sup>2</sup> and an inductance of 25 nH is designed and fabricated. The inductor has a winding thickness of <inline-formula> <tex-math notation="LaTeX">$65~\mu \text{m}$ </tex-math></inline-formula> and a DC resistance of 121 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega $ </tex-math></inline-formula>. A quality factor >30 is maintained at 100–200 MHz. A peak inductor efficiency of 97.8% is analytically calculated for voltage conversion from 1.8 V to 1.1 V at 100 MHz.
A Survey on Wired, Wireless, and Internet of Things Routing Protocols

Delivery of data through the shortest path in a computer network is always a key area of research for academic as well as in industry. The routing protocols are extensively used in networking today to identify the shortest path. As technology is evolving in the wireless networks, Internet of Things (IoT), etc., routing protocols also need to be modified. The paper provides an overview of how routing protocols have changed from wired to wireless networks and for IoTs, different categories of the routing for wired and wireless networks. Though wired network provides fast and reliable communication as compared to wireless, due to the need of the internet everywhere, the wireless is preferred over wired. All three categories use the different IEEE standard for data transfer. The analysis of various routing protocols given in this paper may help the upcoming researchers to get a quick review of the existing protocol for wired, wireless and IoT networks.