Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated analyses of transcriptome and metabolome provides new insights into the primary and secondary metabolism in response to nitrogen deficiency and soil compaction stress in peanut roots

Peanut (Arachis hypogaea L.) is an important oil crop globally because of its high edible and economic value. However, its yield and quality are often restricted by certain soil factors, especially nitrogen (N) deficiency, and soil compaction. To explore the molecular mechanisms and metabolic basis behind the peanut response to N deficiency and soil compaction stresses, transcriptome and metabolome analyses of peanut root were carried out. The results showed that N deficiency and soil compaction stresses clearly impaired the growth and development of peanut's aboveground and underground parts, as well as its root nodulation. A total of 18645 differentially expressed genes (DEGs) and 875 known differentially accumulated metabolites (DAMs) were identified in peanut root under differing soil compaction and N conditions. The transcriptome analysis revealed that DEGs related to N deficiency were mainly enriched in “amino acid metabolism”, “starch and sucrose metabolism”, and “TCA cycle” pathways, while DEGs related to soil compaction were mainly enriched in “oxidoreductase activity”, “lipids metabolism”, and “isoflavonoid biosynthesis” pathways. The metabolome analysis also showed significant differences in the accumulation of metabolisms in these pathways under different stress conditions. Then the involvement of genes and metabolites in pathways of “amino acid metabolism”, “TCA cycle”, “lipids metabolism”, and “isoflavonoid biosynthesis” under different soil compaction and N deficiency stresses were well discussed. This integrated transcriptome and metabolome analysis study enhances our mechanistic knowledge of how peanut plants respond to N deficiency and soil compaction stresses. Moreover, it provides new leads to further investigate candidate functional genes and metabolic pathways for use in improving the adaptability of peanut to abiotic stress and accelerating its breeding process of new stress-resistant varieties.
A Novel Approach in Improving I/O Performance of Small Meteorological Files on HDFS

With the cloud computing is becoming mature, many of its characteristics for meteorological data processing is extremely important. Since HDFS is designed for reading and writing large files, it’s difficult to be taken advantage of small meteorological files. In this paper, an improved approach on HDFS is proposed for small meteorological files, small files are to be merged, indexed, and blocks are compressed, the pressure of memory on master node occupied by metadata is relieved, the speed of reading and writing small files is increased, read speed is increased by 50%, and write speed is up to 3-4 times of the original, saving about 2/3 of storage space and computing performance has also been improved. Thus, meteorological data processing can make use of cloud computing platform more closely.
Face orientation detection in video stream based on harr-like feature and LQV classifier for civil video surveillance

Most face recognition and tracking techniques employed in surveillance and human-computer interaction (HCI) systems rely on the assumption of a frontal view of the human face. In alternative approaches, knowledge of the orientation angle of the face in captured images can improve the performance of techniques based on non-frontal face views. Face orientation detection plays important role in city surveillance video for the successive specific application, such as the face identification, face recognition and screening face snapshot image for saving the storage volume. In this paper-we propose a kind of method on face orientation by combining Haar-feature and LVQ technique. First, we execute the eye location based on the haar-like feature. Then, we divide the face image into several sub-images and statistical information of the binary sub-image at the eye location. After acquiring the statistical pixel distribution, we exploit the LVQ classifier to execute the classification on face orientation. According to the result, the algorithm we proposed can detect correctly up to 95%. By executing the face orientation classification, we can get the upright fontal face image with the best recognizable and distinctive quality for the further application.
The Hierarchical Hybrid Fuzzy‐Neural Network Based on Lasso Function and Its Application to Classification of Remote Sensing Images

In this paper, a new algorithm for the hierarchical hybrid fuzzy-neural network model is proposed. The Takagi-Sugeno model and triangular membership function are adopted in the fuzzy system, and the Lasso function of the coefficient contraction method is used to reduce the strong interaction among discrete input variables. In the end, an experimental test on surface features classification by using LANDSAT ETM+ remote sensing image data of Zhangping and Anxi in Fujian Province is conducted. Compared with other neural networks, the classification result with the proposed approach is the most accurate, which proves its feasibility and validity, and can be used as a new classification method for surface features on remote sensing images.
A radial basis function neural network based on artificial immune systems for remote sensing image classification

The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre-defined number of iterations. The classification results are evaluated by comparing with that of the k-means center selection procedures and other results from the literature using remote sensing imagery. It is shown that aiNet-RBF NN algorithm outperform other algorithms and provides an effective option for remote sensing image classification.
A transient chaotic associative memory model with temporary stay function

When chaotic dynamics is imparted to the neurons that compose the associative memory model, they search for stored patterns in a pattern space chaotically. However, this model has the deficiency that judgment of whether the stored pattern has been recollected or not is difficult because its behavior is always chaotic. Because all dynamics of the chaotic neurons are chaotic, chaotic transition is repeated. The transient‐chaotic associative network (TCAN) that Lee proposed changes from the state of chaos to the state of stability (nonchaos) transiently. Additionally, it has fast recollection speed, and has large memory capacity. However, the states of TCAN do not change chaotically. Based on these results, this paper proposes a transient chaotic associative memory model with a temporary stay function (TCAMMwithTSF) which has two capabilities: one is fast speed as the states of the model converge to a stored pattern, like TCAN, and the other is the ability to search the stored pattern in a pattern space chaotically, like chaotic neural networks. Finally, the characteristics and usefulness of TCAMMwithTSF are verified by a simulation study. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(2): 29–36, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21077
Electromagnetic brain mapping

There has been tremendous advances in our ability to produce images of human brain function. Applications of functional brain imaging extend from improving our understanding of the basic mechanisms of cognitive processes to better characterization of pathologies that impair normal function. Magnetoencephalography (MEG) and electroencephalography (EEG) (MEG/EEG) localize neural electrical activity using noninvasive measurements of external electromagnetic signals. Among the available functional imaging techniques, MEG and EEG uniquely have temporal resolutions below 100 ms. This temporal precision allows us to explore the timing of basic neural processes at the level of cell assemblies. MEG/EEG source localization draws on a wide range of signal processing techniques including digital filtering, three-dimensional image analysis, array signal processing, image modeling and reconstruction, and, blind source separation and phase synchrony estimation. We describe the underlying models currently used in MEG/EEG source estimation and describe the various signal processing steps required to compute these sources. In particular we describe methods for computing the forward fields for known source distributions and parametric and imaging-based approaches to the inverse problem.
Serum steroid profiling of hepatocellular carcinoma associated with hyperadrenocorticism in dogs: A preliminary study

Background Hepatocellular carcinoma (HCC) is one of the most common primary liver tumors in humans and dogs. Excessive adrenocortical hormone exposure may cause steroid hepatopathy, which may develop into HCC. In our previous study, hyperadrenocorticism (HAC) was a highly concurrent disease in dogs with HCC. Therefore, this study hypothesized that adrenal steroid alterations might be involved in hepatocarcinogenesis and aimed to specify the relationship between HAC and HCC in dogs. Materials and methods This study included 46 dogs brought to the Hokkaido University Veterinary Teaching Hospital between March 2019 and December 2020. Owners gave their signed consent for blood collection on their first visit. A total of 19 steroids (14 steroids and 5 metabolites) in the baseline serum of 15 dogs with HCC, 15 dogs with HAC, and 10 dogs with both diseases were quantitatively measured using the developed liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS) method. Results In each group, 11 steroids were detected higher than 50%. The detection rate of steroid hormones did not significantly differ between the groups (p > 0.05). Principle component analysis (PCA) showed that the steroid profiles of the three groups were comparable. Median steroid hormone concentrations were not significantly different between the study diseases (p > 0.05). Conclusion The developed LC/MS/MS was useful for measuring steroid hormones. Although it was clear that HAC was concurrent in dogs with HCC, none of the serum steroids was suggested to be involved in HCC.
Tracking Individual Travel Behavior Using Mobile Phone

Tracking type data collection methods using mobile phone have been developed for monitoring and analyzing individual travel behavior. Previous methods using PHS (Personal Handy phone System) assumed that the location data of base stations were known. This paper shows a location positioning method of a mobile object when the location data of base stations are not available. Instead of base stations, signal strength vectors (reference vectors) are observed at many places in a target area. A mobile object also observes a signal strength vector (target vector). The similarity index of a reference vector and a target vector is used for calculating the location position of a mobile object. This method is applied to tracking travel behavior of tourists in Kobe Kitano area.
Effects of Case and Traditional Writing Assignments on Writing Products and Processes

This study investigates the effects of case and traditional assignments on the writing products and processes of community college students. Specifically, each of 57 first-year business students in three sections of a business composition course wrote in response to either (a) two traditional assignments, (b) two short case scenario assignments, or (c) two lengthy, elaborated case assignments. Participants' letters were scored using a performance criteria rating scale for determining both overall quality and specific trait quality. Results indicate that the case assignments generally produced more effective writing products than did traditional paradigm assignments. Results also indicate that the elaborated case assignments generally produced better writing products than did the short case scenarios. However, results also suggest that the writing of participants who already possess business-related experience was not as affected by assignment type as the writing of inexperienced participants. Finally, qualitative measures suggest that the writing processes and attitudes of participants. completing the case assignments were highly sensitive to audience and context, whereas the processes and attitudes of participants completing the traditional assignments were highly sensitive to organization, format, and correctness.
Modeling for Minimization of Loneliness of People on the Social NetworkingWebsites

In the current scenario of human life, use of Internet is an integral part of human being. Internet users are rapidly increasing worldwide. Use of internet is directly or indirectly affecting the life of human being as there are several advantages and disadvantages for using the services of internet especially for the online shopping, reading, searching articles, watching movies, net surfing, etc. In the present work, a real case study of domain of Indian people is considered and questionnaire for Internet Addiction Test is prepared and statistical analysis is used to find out some of important observations related to human being which are using the internet services. Although in India, rate of internet users is very poor however Internet Addiction Test shows some significant results which minimize the loneliness of human being due to vast use of social networking sites. A model is also proposed by the use of most popular object-oriented modeling platform independent language known as the Unified Modeling Language. One can easily develop the software code through proposed model by the use of any object-oriented programming language. Systematic steps are represented through model for minimization of loneliness of people which is converted into happiness by the use of the social networking websites. The proposed model is also validated by generation of valid test cases through Finite State Machine.
An extension for dynamic lot-sizing heuristics

This paper presents an efficient procedure to extend dynamic lot-sizing heuristics that has been overlooked by inventory management literature and practice. Its intention is to show that the extension improves the results of basic heuristics significantly. We first present a comprehensive description of the extension procedure and then test its performance in an extensive numerical study. Our analysis shows that the extension is an efficient tool to improve basic dynamic lot-sizing heuristics. The results of the paper may be used in inventory management to assist researchers in selecting dynamic lot-sizing heuristics and may be of help for practitioners as decision support.
A semi-automatic annotation tool for cooking video

In order to create a cooking assistant application to guide the users in the preparation of the dishes relevant to their profile diets and food preferences, it is necessary to accurately annotate the video recipes, identifying and tracking the foods of the cook. These videos present particular annotation challenges such as frequent occlusions, food appearance changes, etc. Manually annotate the videos is a time-consuming, tedious and error-prone task. Fully automatic tools that integrate computer vision algorithms to extract and identify the elements of interest are not error free, and false positive and false negative detections need to be corrected in a post-processing stage. We present an interactive, semi-automatic tool for the annotation of cooking videos that integrates computer vision techniques under the supervision of the user. The annotation accuracy is increased with respect to completely automatic tools and the human effort is reduced with respect to completely manual ones. The performance and usability of the proposed tool are evaluated on the basis of the time and effort required to annotate the same video sequences.
Semantic-Preserved Index Constructing for RDF-Based Resource in Structured P2P Network

Resource index constructing method directly influences resource retrieval efficiency in structured P2P network, a new approach to semantic-preserved index constructing for RDF-based resource is proposed. Firstly, each class in resource concept structure is assigned a class ID, RDF-based resource index is constructed by two-dimensional value consisted of class ID; secondly, the mapping from two-dimensional index to one-dimensional routing space of structured P2P network is achieved by Hilbert space filling curve; at last, resource query is realized by embedding the process of searching into the nodes of structured P2P networks and using the data search mechanism of networks. Algorithm analysis shows that this approach supports multidimensional semantic retrieval, resolves the problems that only support exact query based on single key words and the irrelevance between logical location and physical location because of ignoring semantic information, and improves RDF-based resource semantic retrieval efficiency in structured P2P network.
Forming Gas Post Metallization Annealing of Recessed AlGaN/GaN-on-Si MOSHFET

In this study, the effects of forming gas post metallization annealing (PMA) on recessed AlGaN/GaN-on-Si MOSHFET were investigated. The device employed an ICPCVD SiO2 film as a gate oxide layer on which a Ni/Au gate was evaporated. The PMA process was carried out at 350 o C in forming gas
An Efficient One-pot Synthesis of Dihydropyrano[c]chromenes and Amino-2-chromenes under Solvent-free Conditions

1,8-Diazabicyclo[5.4. 0]-undec-7-en-8-ium acetate, a basic ionic liquid, showed excellent catalytic activity in the three-component condensation reaction of aldehydes and malononitrile with α- or β-naphthol or 4-hydroxycoumarin. The products, amino-4H-chromenes and dihyropyrano[c]chromenes respectively, were obtained under mild conditions without any additional organic solvents in good to excellent yields and short reaction times. The catalyst could be recycled several times with only slight loss of activity.
Efficient evaluation of XML twig queries with keyword constraints

Abstract Efficient query processing has been a critical issue for XML repositories, and recently researchers have advocated the style of full‐text search against XML documents as well. In this paper, we consider the XML query which can be represented as a query tree with twig patterns, and might consist of keyword constraints. In the proposed approach, the correspondence of each element‐keyword pair and the associated score is pre‐computed to reduce the computation loading at run time. During query processing, we will first quickly retrieve data for each path fragment represented in the query tree, which consists of only parent‐child relationships. We then construct the glued paths and match them to formulate the twig pattern. This approach focuses on where the data need to be “glued” and thus is very efficient. We demonstrate, via an extensive experimental study, that the proposed technique has great advantages over the existing approaches.
A subpicosecond jitter PLL for clock generation in 0.12-μm digital CMOS

A fully integrated subpicosecond jitter phase-locked loop (PLL)-based frequency synthesizer in a standard digital 0.12-/spl mu/m CMOS technology with 1.5-V supply is presented. Two differentially tuned LC-VCOs are implemented to support different standards for serial data transmission. A fully differential charge pump and an active loop filter are used for reduction of charge-pump current mismatch. Operating with a 311-MHz reference clock, the PLL achieves typically 860-fs integrated jitter, and a phase noise of -115 dBc/Hz at 1-MHz offset, on a 2.488-GHz output. The power consumption is 35 mW, and the area is 0.7 mm/sup 2/.
Handset Antenna Design For Mobile Communication System

Monopole slot antenna is suitable for handset applications due to its simple planar structure. Besides that, it is compact, low cost, low profile and easy to manufacture. It also has wide bandwidth and omnidiractional charatriesticts. The monopole antenna is obtained by cutting monopole slot at the edge of the ground plane of the antenna. These slots operate as the radiating elements of the antenna. The objective of the project is to design a multiband planar monopole slot antenna. This design tool used is the Zeland IE3D software. The proposed antenna consists of two slots cut at the edge of a 100×40 mm ground plan. The antenna occupies an area of 50×40mm. A microstrip feedline is used to feed the antenna. The proposed antenna can operate in GSM 850, GSM900, GSM1800, and GSM1900. It has two wide bands centered at 862MHZ and 1768MHZ. The frequency range of the lower band is from 811MHZ to 981MHZ. The frequency range upper band is from 1572MHZ to 2503MHZ. The antenna has high return loss with 2:1 VSWR. The bandwidth percentage of the lower band is 18.97% whereas the upper band is 45.68% at -10dB return loss. The efficacy of the antenna is greater than 70%.