Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of irrigation management on the abundance of leafhoppers (Homoptera: Cicadellidae) on grapes.

During a 3-yr study, we investigated the effects of grapevine, Vitis vinifera L. , irrigation management on the abundance of the western grape leafhopper, Erythroneura elegantula Osborn and a variegated grape leafhopper, E. variabilis Beamer, at two locations in the San Joaquin Valley of California. Irrigation treatments ranged from 40 to 120% replacement of the expected nonstressed crop evapotranspiration (EToc). Densities of both species were significantly lower on vines in the low irrigation treatments (0.4, 0.6, and 0.8 EToc), than on vines that received maximum water allotments (1.0 and 1.2 EToc). Leafhoppers tended to be more numerous on the shaded side of the vines than on the sunlit side. E. elegantula attained a maximum average density of 15 nymphs per leaf at Westside in 1984, whereas a maximum of 23 nymphs per leaf was recorded for E. variabilis at Kearney. There was greater variation in leafhopper densities in response to irrigation treatments at Westside than at Kearney, an effect believed to be the result of differential water holding capacities of the respective soils at each location. Restricted available water can affect vine water status and alter characteristics of the microenvironment, including canopy temperature and humidity. Both transient and long-term irrigation effects can contribute to fluctuations in leafhopper abundance.
Gebelerde Beden İmajı Algısı ve Distres Arasındaki İlişki

Amac: Arastirmanin amaci, gebelik deneyimi yasayan kadinlarin gebelikte beden imaji algisi ve distres arasindaki iliskiyi tanimlamak ve karsilastirmaktir. Gerec ve Yontemler: Tanimlayici ve iliski arayici nitelikte olan bu arastirma, Bayburt il merkezinde bulunan Bayburt Devlet Hastanesinin kadin dogum polikliniklerinde Mart 2015-Eylul 2015 tarihleri arasinda basvuran 467 gebe ile yurutulmustur. Veriler “Kisisel Bilgi Formu”, “Cok Yonlu Beden-Benlik Iliskileri Olcegi (CYBBIO)” ve “Tilburg Gebelikte Distres Olcegi (TGDO)” kullanilarak toplanmistir. Verilerin degerlendirilmesinde, tanimlayici analizler icin yuzdelik dagilimlar ve ortalama±standart sapma kullanilmistir. Olcme aracinin guvenirligini test etmek icin Cronbach alfa, olceklerin birbiri ile iliskisini degerlendirmek icin de Pearson Korelasyon testi kullanilmistir. Bulgular: Gebelerin TGDO’den aldiklari toplam puan ortalamasinin 20.02±6.28, CYBBIO’den aldiklari toplam puan ortalamasinin ise 3.44±0.41 oldugu saptanmistir. TGDO’nun es katilimi alt boyutu puan ortalamasi ile CYBBIO’nun gorunusu degerlendirme, saglik yonelim alt boyut ve toplam puan ortalamasi arasinda istatistiksel olarak negatif yonde anlamli bir iliski oldugu belirlenmistir. TGDO’nun olumsuz duygulanim alt boyutu puan ortalamasi ile CYBBIO’nun fiziksel yeterliligi degerlendirme, saglik degerlendirmesi alt boyut ve toplam puan ortalamasi arasinda istatistiksel olarak negatif yonde anlamli bir iliski oldugu saptanmistir. TGDO’nun toplam puan ortalamasi ile CYBBIO’nun gorunusu degerlendirme, fiziksel yeterliligi degerlendirme, saglik degerlendirmesi, saglik yonelim alt boyut ve toplam puan ortalamasi arasinda istatistiksel olarak negatif yonde anlamli bir iliski oldugu belirlenmistir. TGDO’nun kesme noktasina gore; gebelerin %13.1’inin distresinin oldugu, %86.9’unun distresinin olmadigi saptanmistir. Sonuc: Bu calismada gebelerin gebelikteki beden imajlarini olumsuz algilamalarinin gebelikteki distres duzeyini arttirdigi belirlenmistir.
Android malware detection based on multi-category and tag level ratio

The arrival of the mobile platforms has brought much convenience to our life, people start to get used to social events, online shopping, and business activities with mobile phones, especially those with the Android system. Since the open source of Android system, it has rapidly penetrated into various internet ecosystems, a large number of Android applications are released to the market every day. However, more and more malicious applications are being developed to steal user privacy and property. In this paper, firstly, based on the AndroidManifest file structure, a tag level ratio feature is proposed for detecting Android malicious applications. Secondly, a new hybrid feature selection strategy is used to preprocessing permission and API features. Finally, through single model and ensemble model, 1100 malicious applications and 1567 nonmalicious applications were detected. The result show that our method has a good accuracy.
Information Propagation Dynamics Model Based on Implicit Cluster Structure Network

How information is spread has always been a hot issue in the research of online social networks. Empirical research shows that the propagation process based on the original network is quite different from the real propagation process. In this paper, we design a new model to solve this problem. By combining the original network structure and individual interest attributes, our model mines the implicit cluster structure network and then predicts the propagation process of hot event based on the implicit cluster structure network. The empirical results on the Sina microblogging data set show that the simulated propagation process based on the implicit cluster structure network is outstanding in fitting the infection rate curve in the real propagation process. The results show that the potential community structure is a key factor of information dissemination mechanism.
Blood Mixers for Transfusion Therapy: Blood Flow Estimation Based on PPG Technique

The photoplethysmography technique take advantage of the pulsatile phenomena inside the cardiovascular system. In addition, the blood volume can be discriminated from the sourounding tissues because of its optical reflectance property under a certain wavelengh of light source. In medical systems that used blood for donation the setup of apparatus can made the optimum environment to apply the photopletysmography tecnique. Therefore, in the presented paper the estimation of blood flow rate is determined by applying the described technique. This measurement can increase the redundance of electronical part that monitor the blood flow during the transfusion operation. The actual method that calculate the previous parameter is based on the mass sensor however, in the event of the inoperability of the force sensor, the mass of the blood packet may be greater or less than a predetermined volume. The article discusses possible options for the passage of blood flow through the tube, as well as modeling the real process of donation. It is part of the work devoted to the modernization and development of new equipment for transfusion therapy based on LLC Lidkor production.
Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems

Latent fingerprint identification is one of the most essential identification procedures in criminal investigations. Addressing this task is challenging as (i) it requires analyzing massive databases in reasonable periods and (ii) it is commonly solved by combining different methods with very complex data-dependencies, which make fully exploiting heterogeneous CPU-GPU systems very complex. Most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time. Indeed, the most accurate approach was designed for one single thread. This work introduces the fastest methodology for latent fingerprint identification maintaining high accuracy called Asynchronous processing for Latent Fingerprint Identification (ALFI). ALFI fully exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism for analyzing massive databases. Our approach reduces idle times in processing and exploits the inherent parallelism of comparing latent fingerprints to fingerprint impressions. We analyzed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results reveal that ALFI is in average 22x faster than the state-of-the-art algorithm, reaching a value of 44.7x for the best-studied case.
Self-Sensing and Feedback Control for a Twin Coil Spring-Based Flexible Ultrasonic Motor

We propose a twin coil spring-based soft actuator that can move forward and backward with extensibility and can bend left and right with flexibility. It is driven by two flexible ultrasonic motors, each consisting of a compact metallic stator and an elastic elongated coil spring. The position of the end effector is determined by the positional relationship of the two coils and can be kinetically controlled with a constant curvature model. In our design, the coil springs act not only as a flexible slider but also as a resistive positional sensor. Changes in the resistance between the stator and the coil spring end are converted to a voltage and used for position detection. Each flexible ultrasonic motor with the self-sensing is experimentally evaluated, and it has shown good response characteristics, high sensor linearity, and robustness, without losing flexibility and controllability. We build a twin coil spring-based flexible ultrasonic motor prototype and demonstrate feedback control of planar motion based on the constant curvature model.
A Bag-of-Words framework for natural disaster evaluation on Sentinel-2 image

The machine learning algorithms are an essential tool to measure the impact that severe weather has on the environment. Addressing the land cover changes generated by extreme natural phenomena, in this paper, we present the ability of the bag-of-words (BoW) framework for change detection in remote sensing images. Regarding this, we used Sentinel-2 images related to two case studies: the massive bushfires from Kangaroo Island, on January 2020, and 2019 Midwestern U.S floods. Our experimental results demonstrated that the proposed methodology can achieve promising performance for both fires and floods scenarios.
Multiple Vehicle Detection using C-OFDM Based RADAR for Advanced Collision Warning

This paper proposes the use of Coded- Orthogonal Frequency Division Multiplexing (C-OFDM) to detect a large number of vehicles on the road. The C-OFDM technique enables a high doppler tolerance over the conventional radar. It is very important to determine the moving vehicle and road-side parked vehicle to take the proper reaction according to the diverse traffic situation. Day-by-day growing vehicular speed and traffic congestion make the task more difficult to detect a large number of surrounding cars on road. More number of the vehicles lined up in a row also makes the situation challenging, as it creates a blind zone in a lane over the road. Various remote sensing systems are being evaluated in the present road traffic scenario. Authors have successfully developed the model to get the desired performance by utilizing the concept of coded-OFDM technology in RADAR signal processing. It also combats the interference while detecting multiple vehicles.
MYTHYA: Fake News Detector, Real Time News Extractor and Classifier

Fake news is a kind of exploitative journalism which is completely deprived of facts. It spreads disinformation, lies or hoaxes by means of traditional print media, televisions or radio media and nowadays through social media. The spreading of fake news on social media has changed the course of elections and thus impacted the future. Automating fake news detection is essential to maintain the integrity of news and journalism. In this Paper, a system that uses Gradient Boosted Decision Tree and Convolutional Neural network is provided to detect the stance of the news headlines and classify them as real or fake. By combining these two models systems gets the accuracy of 97.59%.
Proposal of a cost-effective wireless Power Transfer system from Solar using Aerial Technology

The proposed system focusses on reducing the complexity and power loss during transmission of electricity. Solar energy will harvest electricity and can avoid shedding down of power supply as well as can reduce the complexity since no wire is required for transmission. Through this system, focusing to supply electricity in remote areas where cable connection does not reach. This system is eco-friendly, cheap and portable. Our system uses sunlight to produce power and shall transfer to devices with the help of aerial to avoid breakage of electric lines and poles as well as transformer explosion.
Hardware Implementation of Pixel Comparison and Error Detection in Image

Image processing methods are widely used for industrial and research-based applications. Recently, the image processing system concern about hardware implementation to store data efficiently and effectively. So, in this paper the image comparison model is used to detect the errors that are concealed in the image. In this comparison model, two images i.e., the test image is compared with a reference image for real time industrial use. The out image contains identified and highlighted error patches. This model is executed on hardware using VerilogHDL. Xilinx ISE Design Suite and MATLAB software platforms are used for simulation purposes.
MHM: A Novel Collaborative Spectrum Sensing Method based on Markov-chains and Harmonic Mean for 5G Networks

Cognitive radios and spectrum sensing are considered fundamental for spectrum optimization in 5G networks. Collaborative spectrum sensing improves detection by collecting data from different nodes and increasing the amount of information available for accurate channel state detection. However, malicious nodes can report wrong information, disturbing the collaborative sensing results and network operation. This paper presents two techniques: (1) a Markov chain-based technique that improves spectrum sensing accuracy while reducing the reporting control traffic; (2) a harmonic mean-based technique that discards less relevant sensing reports, mitigating Byzantine attacks. The two techniques were evaluated in a simulation scenarios based on rural areas. The results show that the proposed techniques increase the accuracy of a classic hard-combining fusion technique, reducing false positives and reporting overhead while improving network resilience to malicious nodes.
A community based cross-sectional study on the health system responsiveness in the urban field practicing area of tertiary health care centre, Hyderabad

Background: According to WHO, responsiveness is an important goal of the health system, in addition to the two predominant goals of improving health and fairness of financing. Responsiveness includes non-medical aspects of health care. As the progress to universal health coverage is gaining pace, the present study has attempted to study the domains of responsiveness in the government and private health services and health care providers.Methods: A community based cross-sectional study for a period of 3 months in the households of the urban field practising area. Sampling technique was simple random sampling. Assuming the prevalence of 50% and allowable error of 5%, 400 households were surveyed. KISH table method was used at household level. Study tool was World Health Survey responsiveness module questionnaire for the eight responsiveness domains-prompt attention, dignity, communication, autonomy, confidentiality, choice, quality of basic facilities and social support (for inpatients). Results: The mean age (yrs) of the study subjects is 46.078±13.998. 68.25% (273) were males.31. 75% (127) were females. 46.25% (185) were using government services and 53.75% (215) were using private services. The mean waiting time (min) in the public health facilities was 135.2±111.2 which was more than private facilities, 62.4±40.8. Conclusions: All the responsiveness domains (except confidentiality) were found to be positively associated (p<0.05) with the government health services. Proportion of people rating the responsiveness domains from most important to the least important showed prompt attention (52%) and dignity (30%) as the most important domains.
A Fast SNR-based Vibration Events Detection Algorithm for AETA Geo-acoustic Data

Abundant information can be mined from AETA geo-acoustic data. In this paper, a fast SNR-based vibration events detection algorithm is proposed by improving traditional STA/LTA algorithm for these data. Background noise sampling and different update strategies during different states are applied to improve detection performance. Furthermore, we studied relative method to update the eigenvalues in $O(1)$ time when an element in the sliding window is operated so data stream can be detected in $O(N)$ time. Finally, in the experience part, white Gaussian noise with SNR range from 3 to 10dB is added to the geo-acoustic data of 72 AETA stations around the Longmenshan fault zone from October to November 2019 and result is measured by start point relative error, end point relative error and detected energy ratio. Result shows that the proposed method achieves considerable detection effect for vibration events in AETA geo-acoustic data with low computation complexity.
Resource-Efficient Quantum Computing by Breaking Abstractions

Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.
Community Detection Based on Individual Topics and Network Topology in Social Networks

Detecting community structures is an important research topic in social network analysis. Unfortunately, the fundamental factors that drive the generation of social networks (i.e., the network topology and content) and community structures have not been well investigated. In this paper, according to the natural characteristics of social networks, we reveal that individual topics play a core role in community generation. If two individuals are in the same community and are interested in similar topics, it is more likely that a link will form between them. Otherwise, the probability of generating a link depends on the relationships between their communities and the topics they talk about. Based on the above observations, a novel generative community detection model is proposed that simulates the generation of the network topology and network content by considering individual topics. Moreover, our model utilizes a topic model to generate network content. The model is evaluated on two real-world datasets. The experimental results show that the community detection results outperform all the state-of-the-art baselines. In addition to accurate community detection results, we identify each individual topic distribution and the most popular users corresponding to different topics in each community.
An Energy-Aware SDN/NFV Architecture for the Internet of Things

The Internet of Things (IoT) is an essential component of emerging network applications like smart cities, where billions of IoT devices are connected to transport massive network traffic. A programmable network such as softwaredefined networking (SDN) can cope with such data explosion and constrained network resources. Furthermore, Network Function Virtualization (NFV) can enable on-demand network functions deployment. Thus, SDN and NFV can complement each other to empower an architecture for the IoT. In this paper, we first design an SDN/NFV enabled IoT node for dynamic deployment of network functions. The proposed IoT node is then used to develop an SDN/NFV architecture to realize on-demand network functions like data aggregation. Next, we define an Integer Linear programming (ILP) problem to optimize the energy consumption of the IoT nodes by activating an optimal number of NFV nodes and optimally assigning regular nodes to those activated NFV nodes. Finally, we design a heuristic and evaluate it in the Cooja simulator. Extensive evaluation confirms that the proposed solution outperforms its counterparts in communication energy consumption and packet delivery ratio.
Toward the Bleaching of the Black Boxes: Minimalist Machine Learning

In the field of machine learning, there exist effective models that achieve remarkable results. However, it is criticized from the perspective of the end user that many successful models behave like “black boxes. ” In the desire for achieving excellent performances, the models become more complicated and less explainable. In this context, a major challenge has emerged: it is necessary to provide the intelligent models and algorithms with the property to be explained. In this article, a new paradigm is proposed: the minimalist machine learning. The conceptual cornerstone of the new paradigm is the assumption that it is possible to make the representation of the data of any problem of classification of patterns reduced to the Cartesian plane. The proposal is to design algorithms that are capable of achieving intelligent pattern classification in two dimensions (the plane) effectively, regardless of the number of attributes that the patterns contain.
New Algorithm to Discriminate Phase Distribution of Gas-Oil-Water Pipe Flow With Dual-Modality Wire-Mesh Sensor

Three-phase gas-oil-water flow is an important type of flow present in petroleum extraction and processing. This paper reports a novel threshold-based method to visualize and estimate the cross-sectional phase fraction of gas-oil-water mixtures. A $16\times 16$ dual-modality wire-mesh sensor (WMS) was employed to simultaneously determine the conductive and capacitive components of the impedance of fluid. Then, both electrical parameters are used to classify readings of WMS into either pure substance (gas, oil or water) or two-phase oil-water mixtures (foam is neglected in this work). Since the wire-mesh sensor interrogates small regions of the flow domain, we assume that the three-phase mixture can be segmented according to the spatial sensor resolution (typically 2–3 mm). Hence, the proposed method simplifies a complex three-phase system in several segments of single or two-phase mixtures. In addition to flow visualization, the novel approach can also be applied to estimate quantitative volume fractions of flowing gas-oil-water mixtures. The proposed method was tested in a horizontal air-oil-water flow loop in different flow conditions. Experimental results suggest that the threshold-based method is able to capture transient three-phase flows with high temporal and spatial resolution even in the presence of water-oil dispersion regardless of the continuous phase.