Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Speech Control Toy Car based on Single Chip Microcomputer

The speech recognition is a kind of technology, which makes a machine change the speech signals into homologous text or order by recognition and comprehend. The speech recognition is a cross-subject, and it is becoming to the key-technology of human-computer interface in information technology. It makes man can operate the computer through the speech order without the keyboard. Speech recognition technology has be used in fields such household electronic products and intellectual toys. The bus controlled by speech signals proposed in this paper will be more powerful than traditional toy bus. It will be structure simple, strong stability, high precise. More important, its speech control system will has especial attraction to children. This paper Introduces hardware system and software system of speech control intellectual toy bus.
Improving Search-based Automatic Program Repair with Neural Machine Translation

The challenge of automatically repairing bugs in programs to reduce debugging expenses and increase program quality is known as automated program repair. To overcome this issue, test-suite-based repair techniques use a specified test suite as an oracle and alter the input faulty program to pass the full test suite. GenProg is a well-known example of this kind of repair, in which genetic programming is used to reorder the statements already present in the faulty program. However, recent practical experiments suggest that GenProg’s performance, notably for Java, is not sufficient. Improved program dependability necessitates the use of automatic program repair techniques. Template-based program repair techniques have recently been combined with search-based techniques to solve program issues automatically. Although intriguing, it has two fundamental drawbacks: Its search space often lacks the correct solution, and the technique disregards program expertise, such as precise code language. Compared with the template-based program repair approach, existing neural-machine-translation-based approaches are not limited by these constraints due to their ability to learn and generate new solutions. We propose an approach that combines a search-based automatic program repair technique with a neural-machine-translation-based approach. More specifically, we use both redundancy assumption and sequence-to-sequence learning of correct patches as the source for potential fix statements that feed into a multiobjective evolutionary search algorithm to find test-suite-adequate patches. In this work, a novel framework called ARJANMT is introduced for automatically repairing Java programs. Two sets of controlled experiments are conducted on 410 bugs from two benchmarks to investigate the repairability and correctness of our proposed framework. A comparison between state-of-the-art automatic program repair frameworks is made. The experimental results indicate that combining those two types of repair techniques (search-based and neural-machine-translation-based) produces better results or fixes bugs that they previously were unable to fix individually.
Flexibility evaluation of integrating solar power into the Nigerian electricity grid

The study evaluated the capability of current thermal generation to back-up variation generation from solar energy penetration into the Nigerian power grid from a system operation perspective using security constraint unit commitment model. This provides an empirical evidence of the impacts that can tip the balance towards a sustainable future low carbon electricity mix. Through 10 and 20% solar energy penetration scenarios, greenhouse reduction of 1975.70 and 3590.03 lb/day, respectively, can be achieved. However, daily peak-valley net demand difference to be supplied by the thermal plants will increase from 702.5 to 857.5 and 1607.5 MW, numbers of daily start-up will increase from 23 to 30 and 25, daily system spinning reserve will increase from 723 to 757 and 815 MW and daily idle hours of the thermal plants will increase from 52 to 71 and 72 h, in a 10 and 20% solar energy integrated system, respectively. The daily operational revenue of the thermal plants will also reduce by 5.5 and 7.9% in a 10 and 20% solar energy integrated system, respectively. These are useful data in developing policy framework for the future electricity market as the country diversify her energy source and mitigate greenhouse effect.
Ability Test of IAA (Indole-3-Acetic Acid) Hormone-Producing Endophytic Bacteria from Lamongan Mangrove

Most of the plant-associated bacteria can synthesize active biological components of phytohormones such as auxin. This study aimed to examine the potency of 61 endophytic bacteria isolates from the mangroves at Kutang Beach, Lamongan in producing IAA hormone and to identify types of isolates effecting the concentration of IAA, morphological characteristics of isolates, as well as endophytic bacterial species that have the most potential to produce IAA hormone. Screening of endophytic bacteria isolates was performed using the colorimetric method and the production of IAA was carried out using the spectrophotometric method. IAA production by endophytic bacteria was analyzed descriptively and statistically. One-Way ANOVA was employed to determine the effect of the isolate type on the concentration of IAA. The most potential isolates to produce IAA hormone are identified by 16s rRNA gene marker. The screening results showed that 12 isolates of endophytic bacteria have the potential to produce IAA hormones (2.0-9.3 ppm), coded with LMG 7, 15, 31, 32, 43, 53, 54, 55, 56, 57, 62, and 63. The results of the One-Way ANOVA test suggested that the type of isolates did not affect the concentration of IAA produced by endophytic bacteria. The twelve isolates had different morphological characters and those were Gram-positive bacilli with cell sizes ranging from 1.5 µm - 3 µm. The highest concentration of IAA was produced by LMG 15 (9.3 ppm). LMG 15 was identified as Bacillus cereus strain LMG 15, having 99.33% similarity to Bacillus cereus strain IAM 12605.
Embedded Optical Fibre with Fibre Bragg Grating Influence on Additive Manufactured Polymeric Structure Durability

Additive manufacturing (AM) polymers are applied in many branches of the industry due to the possibility of fast and accurate production of elements with various and complex shapes. Fibre Bragg grating sensors (FBG) are widely applied in structural health monitoring (SHM) systems. The main objective of this research is to perform analyses of the influence of embedded FBG sensors on AM polymer elements’ durability. Two polymers (M3 X and M3 Crystal) with different mechanical properties were analysed. The tests were performed on samples with FBG sensors embedded in (different alignment) and attached to the surfaces of the elements. Firstly, the samples were exposed to elevated or sub-zero temperatures under stable relative humidity levels. The strain in the samples was measured using fibre Bragg grating (FBG) sensors. The achieved results allow us to determine the relationships between strain and temperature for both materials and the differences in their mechanical response to the thermal loading. Then, the samples were subjected to a tensile test. A comparison of the tensile strength values was performed for the samples without and with embedded FBG sensors. The samples after the tensile tests were compared, showing differences in the mechanisms of failures related to the polymers and the thermal treatment influence on the material internal structure. Additionally, strain values measured by the FBG sensors were compared to the strain values achieved from the testing machine showing a good agreement (especially for M3 X) and indicating the differences in the materials’ mechanical properties. The achieved results allow us to conclude there is a lack of influence of embedded FBG sensors on the mechanical durability of AM polymers.
FLORISTIC DIVERSITY AND ETHNOBOTANY OF FAMILY ASTERACEAE OF DISTRICT BHIWANI (HARYANA), INDIA

The present paper deals with floristic diversity of plants belonging to family Asteraceae growing in district Bhiwani, Haryana, India. A sum of 36 species belonging to 26 genera of family Asteraceae was identified. Exploration was carried out in different seasons from 2014 to 2019. Local inhabitants were interviewed regarding ethnobotanical aspects of collected plants. Out of 36 species identified, 29 species have medicinal value used to treat various diseases and health issues. Ageratum conyzoides, Bidens pilosa, Eclipta alba, Erigeron canadensis and Tridax procumbens have pharmacological value. The genera Blumea, Gnaphalium and Sonchus were found dominating followed by Erigeron, Bidens, Ageratum and Launaea. Most of the species were seen in wild state but few occurred as common crop weeds like Ageratum, Cichorium, and Cirsium etc. while Glossocardia, Dicoma, Blainvillea etc. were found rarely and occurred mainly on hilly slopes. Parthenium hysterophorus was found to be the strongest weed followed by Verbesina in the areas under investigation. Nearly all the members were herbaceous and grow abundantly throughout the year in the area studied.
Integrating a Biorefinery into an Operating Kraft Mill

Kraft pulp and paper mills have several advantages for serving the emerging biorefinery industry as a source of raw materials. This review examines technologies for producing liquid biofuels, chemicals, and advanced materials from woody feedstocks to generate new sources of revenue. Market pull comes in part from government policies that drive substitution of petroleum-based products with biobased equivalents. Kraft mills have ample networks to supply feedstocks, whether these are forest residues or byproduct side streams. Pulp mills are well suited to expand sufficiently to accommodate production of value added platform chemicals that are in demand because of brand owner sustainability commitments.
Exploration of Erasure-Coded Storage Systems for High Performance, Reliability, and Inter-operability

EXPLORATION OF ERASURE-CODED STORAGE SYSTEMS FOR HIGH PERFORMANCE, RELIABILITY, AND INTER-OPERABILITY By Pradeep Subedi A Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2016. Director: Dr. Xubin He, Professor, Department of Electrical and Computer Engineering With the unprecedented growth of data and the use of low commodity drives in local disk-based storage systems and remote cloud-based servers has increased the risk of data loss and an overall increase in the user perceived system latency. To guarantee high reliability, replication has been the most popular choice for decades, because of simplicity in data management. With the high volume of data being generated every day, the storage cost of replication is very high and is no longer a viable approach. Erasure coding is another approach of adding redundancy in storage systems, which provides high reliability at a fraction of the cost of replication. However, the choice of erasure codes being used affects the storage efficiency, reliability, and overall system performance. At the same time, the performance and interoperability are adversely affected by the slower device components and complex central management systems and operations. To address the problems encountered in various layers of the erasure coded storage system, in this dissertation, we explore the different aspects of storage and design several xi techniques to improve the reliability, performance, and interoperability. These techniques range from the comprehensive evaluation of erasure codes, application of erasure codes for highly reliable and high-performance SSD system, to the design of new erasure coding and caching schemes for Hadoop Distributed File System, which is one of the central management systems for distributed storage. Detailed evaluation and results are also provided in this dissertation.
A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

The speaker diarization is considered to be the process by which the speaker signal is segmented, and the speaker identity is grouped into homogenous regions. The central point behind this scheme is the ability to distinguish between the speaker signal and each speaker signal with the label. As mass communication and meetings grow quickly, the diarization of the speakers is burden to improve the readability of the speech transcript. To solve this problem, tangent weighted mel‐frequency cepstral coefficient (TMFCC) and the extended linear prediction with autocorrelation snapshot feature extraction and the speaker diarization approach proposes a deep convolutional neural network (DCNN) for clustering and optimization using sailfish optimizer. A new development in the HXLPS extraction method is the holoentropy with extended linear prediction with autocorrelation snapshot. TMFCC makes more efficient and improves the effectiveness of the proposed scheme using lesser energy frame and higher energy framework. When achieve this, the voice activity detection method can recognize speech and non‐speech signals. Therefore, every segmented signal is represented by the d‐vector. The label of the speaker signal is clustered according to the speaker label used in the DCNN. The evaluation methods, like tracking distance, false alarm rate, diarization error rate examine the effectiveness.
Prediction of Diabetes Mellitus using Machine Learning

Diabetes is one of the most fleetly growing habitual conditions, which has affected millions of people around the globe. Its opinion, vaticination, proper cure, and operation are pivotal. Data booby-trapping grounded soothsaying ways for data analysis of diabetes can help in the early discovery and vaticination of the complaint and the affiliated critical events similar as hypo/ hyperglycemia. Multitudinous ways have been developed in this sphere for diabetes discovery, vaticination and bracket. In this paper, we present a comprehensive review of the state-of-the- art in the area of diabetes opinion and vaticination using data mining. The end of this paper is twofold; originally, we explore and probe the data mining grounded opinion and vaticination result in the field of glycemic control for diabetes. Secondly, in the light of this disquisition, we give a comprehensive bracket and comparison of the ways that have been constantly used for opinion and vaticination of diabetes grounded on important crucial criteria. Also, we punctuate the challenges and unborn exploration directions in this area that can be considered in order to develop optimized results for diabetes discovery and vaticination.
Selecting training samples for ovarian cancer classification via a semi-supervised clustering approach

Machine learning techniques have shown great promise in digital pathology. However, a major bottleneck is the difficulty of annotating necessary amount of tissue to deal with several variability factors, namely chemical fixation, sample slicing, or staining. Usually, models are trained using sets of annotated small image patches, but then, the number of required patches may increase exponentially and yet they must represent such variability. This paper presents a method for automatic sample selection to train a classifier for ovarian cancer by integrating a novel soft clustering strategy. The method starts by classifying a large set of patches with a previously trained classifier and divide patches from the cancer class as highly and moderately confident. An unsupervised selection of moderately confident patches by a Probabilistic Latent Semantic Analysis (PLSA), picks samples from relevant and meaningful groups with maximum within-group variance. A new model is re-trained using the highly confident patches together with patches obtained from the associated PLSA. This strategy outperforms a model trained with a larger set of annotated patches while the training times and the number of samples are much more smaller. The strategy was evaluated in a set of patches from 18 patients with Serous Ovarian Cancer, obtaining a reduction of 54.62% in the training time and 73.66% in the number of samples, while recall rate improved from 0.69 to 0.73.
Synchronous and Futuristic Views on the Application of Silver Nanoparticles: A Journey towards Green Synthesis

Historically, silver has been recognized as a powerful antibacterial agent with a broad spectrum of functions, and it has been employed safely in healthcare for several years. It seems that the incorporation of silver into embedded medical devices may be advantageous when particular genetic features, including such antibacterial behavior, are needed for the device to function. According to current and prior bacterial studies, it appears that the toxicity against bacteria was significantly more significant than the toxicity against human cells. Silver nanoparticles are nanoparticles with sizes ranging between one and one hundred nanometers (nm) in size. When it comes to molecular diagnostics, therapies, and equipment that are employed in a wide range of medical procedures, silver nanoparticles have a number of unique qualities that make them very useful. The physical and chemical approaches are the two majority ways for the creation of silver nanoparticles. The challenge with chemical and physical approaches is that the synthesis is complicated and can result in harmful compounds being absorbed onto the surfaces of the materials. In order to address this, the biological technique offers a viable alternative solution. Bacteria, fungus, and plant extracts are the three principal biological systems concerned in this process. A complete overview of the mechanism of action, manufacture, and uses in the medical area, as well as the healthcare and ecological concerns that are believed to be produced by these nanoparticles, is provided in this paper. The emphasis is on the proper and effective synthesis of silver nanoparticles even while exploring their numerous promising utility and attempting to assess the current status in the debates over the toxicity concerns that these nanoparticles pose, as well as attempting to reflect the needs in the debates over the toxicity concerns that these nanoparticles pose.
Flight Theory and Aerodynamics: a Practical Guide for Operational Safety – Second edition C.E. Dole and J.E. Lewis John Wiley and Sons, Baffins Lane, Chichester, West Sussex P019 1UD, UK. 2000. 314pp. Illustrated. £51.95. ISBN 0-471-37006-1.

The book is the latest in the AIAA series 'Progress in Aeronautics'. It alms to review the state of art and the future prospects for aerostructures in six important areas. This is a brave undertaking for two reasons. It is likely to become dated very rapidly, and if the topic is crucially important then details will be too sensitive for publication. The first danger is largely avoided. All the coverage is very topical and there are over 800 references, most of which have been published in the past three years! The second danger is unavoidable. There is no coverage for example on using structures and materials to improve stealth characteristics. The authors mostly come from the US Air Force Labs at Wright Patterson and Kirtland, and NASA Langley, so the emphasis is on Military and Space Technology, with almost nothing on civil aircraft. However, the authors clearly know their subjects and the material is authentic and from a series of reports and contract studies. This is reflected in the modest quality of the diagrams and photos but a fault well worth tolerating.
Compact Superconducting Radio-frequency Accelerators and Innovative RF Systems

We will present several new technical and design breakthroughs that enable the creation of a new class of compact linear electron accelerators for industrial purposes. Use of Superconducting Radio-Frequency (SRF) cavities allow accelerators less than 1.5 M in length to create electron beams beyond 10 MeV and with average beam powers measured in 10’s of KW. These machines can have the capability to vary the output energy dynamically to produce brehmstrahlung x-rays of varying spectral coverage for applications such as rapid scanning of moving cargo for security purposes. Such compact accelerators will also be cost effective for many existing and new industrial applications. Examples include radiation crosslinking of plastics and rubbers, creation of pure materials with surface properties radically altered from the bulk, modification of bulk or surface optical properties of materials, sterilization of medical instruments animal solid or liquid waste, and destruction of organic compounds in industrial waste water effluents. Small enough to be located on a mobile platform, such accelerators will enable new remediation methods for chemical and biological spills and/or in-situ crosslinking of materials. We will describe one current design under development at Fermilab including plans for prototype and value-engineering to reduce costs. We will also describemore » development of new nano-structured field-emitter arrays as sources of electrons, new methods for fabricating and cooling superconducting RF cavities, and a new novel RF power source based on magnetrons with full phase and amplitude control. « less
Management of pain due to cervical multilevel disk bulges and spinal stenosis with a focused vibro‐percussion wave treatment: A case report

A patient presenting with low back pain received 18 treatments of FDA‐approved low‐frequency vibro‐percussion wave stimulation known as Khan Kinetic Treatment (KKT). Following KKT, he demonstrated improvement in pain, function, quality of life, sleep, and trunk range of motion with no adverse events.
Recent advances in the psychopharmacology of major depressive disorder

SUMMARY This review highlights some of the recent advances in the psychopharmacology of major depressive disorder (MDD). We synthesise evidence on emerging pharmacological therapies targeting the serotonergic system, before exploring several novel treatment targets: the glutamatergic system, the GABAergic system and inflammation. When describing new treatment avenues, we examine the evidence base and how far these new treatments are from routine practice.
Adaptive state of charge estimation for battery packs

Rechargeable batteries as an energy source in electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids are receiving more attention with the worldwide demand for greenhouse reduction. In all of these applications, the battery management system needs to have an accurate online estimation of the state of charge (SOC) of the battery pack. This estimation is difficult, especially after substantial aging of batteries. In order to overcome this problem, this work addresses SOC estimation of Li-ion battery packs using fuzzyimproved extended Kalman filter (fuzzyIEKF) from new to aged cells. In the proposed approach, a fuzzy method with a new class of membership function has been introduced and used to make the approximate initial value to estimate SOC. Later on, the IEKF method, considering the unit single model for the battery pack, is applied to estimate the SOC for the long working time of the pack. This approach uses a model adaptive algorithm to update each single cell’s model in the battery pack. The algorithm’s fast response and low computational burden, makes on-board estimation practical. A LiFePO4 single cell/battery pack consists of single/120 cells connected in series with a nominal voltage 3.6V/432 V is used to implement the experiments/simulations to verify the SOC estimation method’s accuracy. The obtained results by the federal test procedure (FTP75) and the new European driving cycle (NEDC) reveal that the proposed approach’s SOC and voltage estimation error do not exceed 1.5%.