Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust State Estimation Method for Unknown, Time-Varying and Featureless Aircraft Sensor Failures

Fault-tolerant state estimation is necessary for analytical redundancy in aviation safety, but complicated fault conditions pose a major challenge to reliable estimations. In this paper, we propose a new state-estimation method based on an intermittent-measurement Kalman filter, a maximum-likelihood estimation rule, and the Gaussian mixture reduction. This method is robust to time-varying and featureless faults, and requires no past history of innovation errors to perform fault detection and isolation, leading to the easy implementation and improved accuracy under severe sensor-failure conditions. With an example application of monitoring the rotation speeds of a two-spool jet engine in simulations, the effectiveness of the proposed method is verified.
Effect of Leguminous Cover Crops on Soil Biological Activity in Pots of Citrus Unshiu Marcovitch

Little is known about the effects of cover crops on soil properties in citrus orchards. To fill this gap, this work was aimed to determine the effects of leguminous cover crops on the chemical and biological properties of the soil and on the structure of the microbial community in pots of Citrus unshiu (Marcovitch). After amendment with cover crops, an increase in total organic C (TOC), total extractable C (TEC), and total N (TN) contents were observed irrespective of the type of soil. Substrate induced respiration (SIR), and potentially mineralisable nitrogen (PMN), tested three times in one year, were higher in soils with leguminous cover crops while no significant differences were observed in protease and deaminase activity. The effect on the chemical and biochemical properties of the soil was more evident in plots containing Trifolium subterraneum. No changes were observed in the microbial communities studied (_-proteobacteria, _-proteobacteria, nitrogen-fixing, and ammonia oxidizers) irrespective of the kind of cover crop or type of soil, neither were variations noted during the trial.
ANALYSIS TOTAL FLAVONOID CALCULATED AS GENISTEIN, ANTIOXIDANT ACTIVITY AND ANTI-INFLAMMATORY PROPERTIES OF CRANBERRY PLANT ETHANOL EXTRACT

Raw cranberries and cranberry fruit are abundant food sources of flavonoids such as proanthocyanidins, flavonols and quersetin. Reviews These compounds have demonstrated activity as a possible anti-cancer agents in vitro. In previous research has been done proving the interaction of test compounds quersetin computing that is nutritious content of the stems and leaves of cranberries with an antiinflammatory receptor H4R. Cytotoxicity properties of cranberries for this plant.This study reports on the phytochemical screening, total flavonoids calculated as quercetin, antioxidant activity as well as antiinflammatory of ethanol extract of cranberry. Antioxidant activity was carried out by DPPH method. Testing antiinflammatory activity performed by the method of induction of carrageenan and total flavonoids calculated as quercetin done by spectrophotometry. It was found that the herbs that we checked positive compounds containing alkaloids, flavonoids, tannins and polifenolat, triterpenoids and steroids, as well as monoterpenoid and sesquiterpenoid quinones, and negative for saponins. From the results of this experiment, the percentage content of total flavonoids in cranberry herbal at 0.099% w/w. IC50 of antioxidant activity of cranberry ethanol extract was found lower than its vitamin C as control positive. The antioxidant activy might be due to flavonoid content.The test results indicated that the anti-inflammatory activity of extracts of cranberry plants had inflammation inhibition percentage of 36.47%, 40.07% and 51.27%. It was found that extracts of a dose of 1000 mg/kg was the most good in the inhibition of inflammation. The results of this study support previous research that the cranberry extract can be used as an adjuvant therapy in the treatment of inflammation caused by cancer.
Large-scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines

Perturbation biology is a powerful approach to developing quantitative models of cellular behaviors and gaining mechanistic insights into disease development. In recent years, large-scale resources for phenotypic and mRNA responses of cancer cell lines to perturbations have been generated. However, similar large-scale protein response resources are not available, resulting in a critical knowledge gap for elucidating oncogenic mechanisms and developing effective cancer therapies. Here we generated and compiled perturbed expression profiles of ~210 clinically relevant proteins in >12,000 cancer cell-line samples in response to >150 drug compounds using reverse-phase protein arrays. We show that integrating protein response signals substantially increases the predictive power for drug sensitivity and aids in gaining insights into mechanisms of drug resistance. We build a systematic map of protein-drug connectivity and develop an open-access, user-friendly data portal for community use. Our study provides a valuable information resource for a broad range of quantitative modeling and biomedical applications. Highlights A large collection of cancer cell line protein responses to drug perturbations Perturbed protein responses greatly increase predictive power for drug sensitivity Build a systematic map of protein-drug connectivity based on response profiles Develop a user-friendly, interactive data portal for community use
Motion Planning of Planar Closed Chains Based on Structural Sets

This paper investigates the motion planning problem of planar m-link ( $m \geq 4$ ) closed chains among point obstacles with extension to arbitrary convex 2-D obstacles. The configuration space (C-space) of closed chains is embedded into two copies of m-3 dimensional tori. Two structural sets, the C-boundaries and the C-obstacles, are analyzed based upon the C-spaces of recursively constructed lower-dimensional closed chains. They contain essential structural information about the connectivity of the collision-free portion (C-free) of the C-space. By approximating each workspace obstacle by a set of points on the boundary after dilation, its corresponding C-obstacle is guaranteed to be covered by the C-obstacle of the convex hull of the point set. This permits a resolution-complete roadmap algorithm that puts specific bias for sampling the structural sets. Several benchmark examples are presented that compare the performance between our algorithm and the traditional algorithms. Animation videos and source codes are also provided which demonstrate the effectiveness of our method for closed chains of up to 20 links.
Performance Analysis Of Enhanced Mist-Assisted Cloud Computing Model For Healthcare System

Internet of Things (IoT) has become immensely popular and an integral part of our daily lives. We have seen a surge in the number of connected devices. There are billions of IOT devices and we enjoy the numerous facilities that are provided by them. The cloud, fog and mist computing jointly serves to provide a promising solution which can maintain security of the data generated by IoT devices. With time, this amount of data generated increases in volume and somewhat also in complexity. Hence, if we follow the traditional method of data transmission, we will encounter higher levels of energy consumption. This is because, computation consumes a smaller amount of energy as compared to data transmission. Thus energy consumption is to be taken on a serious note and our paper introduces a sleep scheduling algorithm by which we can work on energy efficiency of the system.
A Knowledge-Domain Analyser for Malware Classification

Malware or malicious software is a serious threat from decades. Different malware detection and analysis approaches have been developing, but new malware and evasion techniques are also generating with the same pace. In this paper we have proposed a framework called knowledge-domain mal-ware analyser which analyses and detects malware using machine learning classification techniques. To carry out our experiment, we have collected a data set from online sources which contains 100000 records and 35 attributes. Feature selection methods are performed before classification to include most relevant features for classification. We have used extra tree classifier and k-best feature selection method to select best and relevant features of the collected data set. Machine learning classification algorithms such as support vector machine, Logistic Regression and Naive Bayes are applied on the data set and a comparison of their detection accuracy is presented.
Artificial Neural Network simulation for Markovian Queuing Models in a Busy airport

Successful takeoff of a flight in a busy airport depends on how effectively various queues in the airports are managed. Due to globalization, the number of passengers flying is ever-increasing and due to this, the number of flights handled by busy airports is increasing exponentially. Queuing theory is generally used to design the number of servers in the existing and new systems to optimize the waiting cost. Accordingly, service providers decide whether to increase the number of service stations or not. However in the case of airport operations unlike other queuing systems number of runways cannot be increased to minimize the flight queue length on runways to reduce waiting cost. Flights waiting for takeoff and landing are resulting in high operating costs. Fuel costs during flight waiting on queues to take off and flight flying due to runway congestion for which flight waiting on the sky for landing are phenomenal. In most of the airports only a single runway is available and all the flights depend on that single runway. In this work flights waiting for the runway are considered as a single server queuing system and are modeled using ANN to predict the queue behavior and thereby maximize the use of runways effectively and minimize waiting cost.
Rice Quality Prediction using Computer Vision

The largest cultivated crop in India is rice. It means 70% population of India used to eat rice in their daily basis i.e. two times in a day. The easiest method one person can get contaminated by any kind of disease is food. We always get concerned about our health when we got to know about any kind of diseases and we started taking precautions. For example to avoid pollution in our day to day life we are using mask in our face and to avoid getting contaminated by water we are using bacteria free hand-wash and sanitizers. But when it comes to food we are buying it directly from the market and wash it with water and directly used it for cooking which means we are totally dependable on the food grains that we are buying from the market. Sometimes it happens that we found different small particles i.e. small stones, half sized rice, husk etc. after buying the rice packet, sometimes we found the unwanted products while eating also. We are not concerned about the quality of it. In this paper we have shown our concern towards the quality of rice. We belief a rice size should have more accurate, i.e. it must grow in its full size to provide its full proteins to everyone. We have used the image of rice to predict the quality. We have implemented feature extraction techniques and train our model by different machine learning techniques.
Hybrid Complementary FSS Element based on Fibonacci Spiral for Triple-band EMI Shielding Application

Electromagnetic interference (EMI) caused by microwave sources leads to the malfunction and faulty readings of many real-time systems. Hence it is essential to provide EMI shielding for sensitive electronic devises anwd chambers. An array of periodic elements offers advantages such as transparency and opacity at selected frequencies with lower weight. In this regard, a complementary frequency selective surfaces (CFSS) has been analyzed for EMI shielding application. The CFSS exhibits a highly stable response with three controllable passbands for normal and oblique incidences. Such response is more suitable for allowing selective frequency bands with strong stopband between them.
Nomenclature for factors of the HLA system, update October 2011 *

The following sequences have been submitted to the Nomenclature Committee since the September 2011 nomenclature update and, following agreed policy, have been assigned official allele designations Marsh et al. (2010). Full details of all sequences will be published in a forthcoming report. Below are listed the newly assigned sequences (Table 1) and confirmations of previously reported sequences (Table 2). The accession number of each sequence is given, and these can be used to retrieve the sequence files from the EMBL, GenBank or DDBJ data libraries. Although accession numbers have been assigned by the data libraries and most sequences are already available, there is still the possibility that an
Analysis of retinoblastoma protein function in the regulation of apoptosis = Analyse der Funktion des Retinoblastoma-Proteins in der Apoptose-Regulation

Ziel: Im Rahmen dieser Arbeit sollte die Rolle des Retinoblastoma-Proteins (RB) in der negativen Regulation von programmiertem Zelltod untersucht werden. Die Analyse des Einflusses verschiedener aktivitatserhaltender RB-Punktmutanten auf individuelle Apoptose-Signalwege sollte Aufschluss uber Mechanismen der Apoptose-Regulation durch RB geben. Thesen: Die negative Regulation von Zellwachstum und Apoptose sind zwei unabhangige Funktionen des Retinoblastoma-Proteins (RB). RB-vermittelter Zellzyklusarrest ist nicht grundsatzlich mit einer erhohten Resistenz gegenuber Apoptose-aktivierenden Stimuli verbunden RB-vermittelter Zellzyklusarrest kann die Aktivierung von Caspasen nach DNA Schaden hemmen. Die negative Regulation von Apoptose durch RB kann unabhangig von der Regulation von Genaktivitat erfolgen. Die Expression von Caspase-resistentem RB (RB-MI) verleiht murinen Fibroblasten-Zellen selektiven Schutz vor TNFR1-aktivierter Apoptose. Diese Resistenz lasst sich auf eine RB-MI-abhangige Unterbrechung des mitochondrialen Apoptose-Signalwegs zuruckfuhren, die durch die Abwesenheit von Caspase-Spaltprodukt des Bid-Proteins gekennzeichnet ist.
PWS: Potential Wafermap Scratch Defect Pattern Recognition with Machine Learning Techniques

Wafermap defect pattern detection and diagnosis provide useful clue to yield learning. However, most wafermaps have no special spatial patterns and are full of noises, which make pattern recognition difficult. Specially, recognizing scratch and line types of defect patterns is a challenging problem for process and test engineers and it takes a lot of manpower to identify such patterns, as potential defective dies may exist on the scratch contour and become discontinuity points. However, such potential defective dies may suffer from latent and leakage faults, which usually deteriorate quickly and need to be screened by burn-in test to improve quality. A possible solution is to locate the obscure defective dies in potential scratch patterns and mark them as faulty. As a result, the quality and reliability of products can be significantly improved and cost of final test can be reduced. In this paper, we propose a systematic methodology to search for potential scratch/line defect types in wafers. A five-phase method is developed to enhance wafermaps such that automatic defect pattern recognition can be carried with high accuracy. Experimental results show the proposed method can achieve more than 89% prediction accuracy for scratch/line types, and higher than 94% for all common wafer defect types.
Prediction Of Accuracy On Open Source Java Projects Using Class Level Refactoring

The refactoring approach is used to restructure the software system without altering its external functionalities. Our research objective is to analyze the accuracy of the software metrics using machine learning classifiers. After predicting the accuracy we can refactor the inaccurate source metrics by using different refactoring tools and machine learning algorithms. We have considered 30 code metrics from 3 open source java projects by using different framework like antlr4,junit and oryx at the class level and predict its accuracy using different machine learning classifiers. We have used Gaussian, Bernoulli and Multinomial classifiers to predict the accuracy of the software metrics. Statistical significant test reveals the mean accuracy for the above sources are 33.33%,39%,48.33% respectively. As per the prediction analysis we can correct the fault and improve the performance of our source metrics by conducting unit level testing which leads to software refactoring
Reliability analysis of P-type SOI FinFETs with multiple SiGe channels on the degradation of NBTI

SOI FinFETs are a multi-gate structure that can not only reduce the leakage current and improve the control of the device, but also inhibit the outflow of heat. Heat dissipation is an important issue for small chips, because high temperatures can let them unstable and even cause them to fail. In this paper, P-type multi-gate SOI FinFETs are used to analyze the reliability of negative bias temperature instability (NBTI) under room temperature. The results indicate that the estimated voltage for 10 years of lifetime is 0.0895V, and the threshold voltage (VTH) has a high dependence on the stress time. Some researchers have found that one cause of device degradation is the presence of interface defects (Nit) and oxide defects (Not). The appearance of Nit and Not caused by NBTI can be explained by the reaction-diffusion model (R-D model) [1]. In this experiment, the defects are successfully extracted by linear extrapolated ID to the midgap current (Img). The results show that both Nit and Not exist in the oxide layer, and Not is the major factor affecting degradation at room temperature.
Differential Roles for IL-15R α-Chain in NK Cell Development and Ly-49 Induction 1

IL-15Rα-deficient (IL-15Rα−/−) mice lack NK cells. However, when bone marrow (BM) progenitors from IL-15Rα−/− mice were cultured with IL-7, stem cell factor and flt3 ligand, followed by IL-15, they were able to differentiate into functional NK cells, indicating that IL-15Rα is not critical for NK cell development. Whereas NK cells generated in vitro from IL-15Rα−/− BM progenitors expressed CD94/NKG2, they failed to express Ly-49 receptors. In keeping with this, when IL-15Rα−/− BM cells were transferred into wild type recipients, they gave rise to NK cells in vivo, but with greatly reduced expression of Ly-49 receptors. Furthermore, the small numbers of NK cells found in IL-15−/− as well as IL-15Rα−/− but not flt3 ligand−/− mice expressed much lower levels of Ly-49 receptors than those from wild type mice. These results indicate a novel role for IL-15Rα-chain in Ly-49 induction on developing NK cells.
Acoustic Systems for Information Transfer in Audible Range

The paper presents existing digital information transfer systems using acoustic waves of an audible frequency range as a carrier. Such systems form alternatives to systems using electromagnetic (EM) waves, since in practice there are situations when the use of EM waves is impossible or undesirable. Examples of conditions for justified use of information transfer acoustic systems through the air channel are shown, various types and features of the used types of acoustic waves modulation in the audible range are analyzed.
Identification of Key Sentences in the Task of Text Duplicate Detection

The paper considers the problems of detecting duplicates of large text documents. To reduce the verification time, it is proposed to submit the document being verified with a set of key sentences. Key sentences are selected for parts of the text and are used to search for matches over the Internet. As a criterion for choosing key sentences, the largest sum of the weights of the words included in the sentence is calculated, taking into account the global frequency of words. Studies have shown that the use of key offers can significantly reduce the number of queries to search engines. At the same time, good duplicate text detection results are preserved.
Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

High density Wi-Fi networks require load balancing in order to ensure quality of service (QoS). In the traditional Wi-Fi networks, the wireless stations learn the access points (APs) load and make the association decisions themselves leading to uneven load distribution among the APs. The new paradigm, software defined networking (SDN), has a centralized architecture, which allows to manage, measure and control high density Wi-Fi networks easily. In this paper we propose a QoS-aware load balancing strategy (QALB) for software defined Wi-Fi networks (SD-Wi-Fi), as a solution to address the problem of Wi-Fi congestion among the OpenFlow enabled APs (OAPs). The SDN controller selects a load level up to which the association decisions are made by the OAPs without consulting the controller. The wireless stations from an overloaded OAP are handed to an underloaded OAP by considering multi-metrics such as the packet loss rate, received signal strength indicator (RSSI) and throughput. An emulation platform and a large-scale-low-cost testbed with the same settings are constructed to evaluate the performance of our load balancing strategy. The results show that in comparison to four non-static schemes such as, channel measurement based access selection scheme (CMAS), (DL-SINR) downlink-signal to interference plus noise ratio AP selection scheme (DASA), mean probe delay scheme (MPD) and RSSI scheme, the proposed QALB, optimizes the throughput up to 16%, reduces the average frame delay up to 19%, minimizes the number of re-transmissions by 49%, reduces the number of handoffs by 15%, improves the degree of load balancing by 22% and minimizes the re-association times by 38%, in high density SD-Wi-Fi.
Video Content Analysis for Compliance Audit in Finance and Security Industry

The quality and accessibility of modern financial service have been quickly and dramatically improved, which benefits from the fast development of information technology. It has also witnessed the trend for applying artificial intelligence related technology, especially machine learning to the finance and security industry ranging from face recognition to fraud detection. In particular, deep neural networks have proven to be far superior to traditional algorithms in various application scenarios of computer vision. In this paper, we propose a deep learning-based video analysis system for automated compliance audit in stock brokerage, which in general consists of five modules here: 1) Video tampering and integrity detection; 2) Objects of interest localization and association; 3) Analysis of presence and departure of personnel in a video; 4) Face image quality assessment; and 5) Signature action positioning. To the best of our knowledge, this is the first work that introduces remote automated compliance audit system for the dual-recorded video in finance and security industry. The experimental results suggest our system can identify most of the potential non-compliant videos and has greatly improved the working efficiency of the auditors and reduced human labor costs. The collected dataset in our experiment will be released with this paper.