Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

Evaluation of Lactobacillus spp. Based on Phenotypical Profile as Direct-Fed Microbial Candidate for Poultry Nutrition

The present study was conducted to isolate, identify and characterize a lactic acid bacteria strain from turkey ileum content (46-day-old). The new strain was phenotypical confirmed as Lactobacillus acidophilus (L. acidophilus) and conserved under the code IBNA 09. Bacterial profile of L. acidophilus was compared with other strains known as L. paracasei CCM 1837 and L. plantarum ATCC 8014, based on cultural, morphological, biochemical and enzymatic activity (amylase and cellulase). The strains appear as Gram positive bacilli, thin, non-spore-forming, isolated, diplo form, in short chains or in small irregular piles on Man Rogosa and Sharp (MRS) broth and agar medium. The identification and biochemical traits were performed by catalase assay, API 50 CHL V 5.1 soft (L. acidophilus biotype 2, 99.9% ID; good identification to the genus L. paracasei spp. paracasei 1 or 3, 48-51% ID; L. plantarum 1, 99.9% ID) and ABIS online (L. acidophilus ~ 88%; L. paracasei spp. paracasei, ~ 90%; L. plantarum, ~91%). The highest total score of extracellular amylase activity was recorded by L. acidophilus IBNA 09 at 24-48 h (5.10 ± 0.176 U/mL, 4.99 ± 0.409 U/mL), follow by L. paracasei CCM 1837(0.12 ± 0.002 U/mL, 0.15 ± 0.001 U/mL). During entire period, cellulase production was observed only for L. acidophilus (0.28 ± 0.019 U/mL), comparative with L. paracasei where the activity was observed in the first 24 h, respectively at 72 h for L. plantarum. These results suggest that L. acidophilus IBNA 09 possesses potential probiotic traits as a suitable candidate for amylase and cellulase production, and starter culture can improve cereal fermentation and the process of digestion in poultry nutrition.

Simultaneous Quantification of 25 Fentanyl Derivatives and Metabolites in Oral Fluid by Means of Microextraction on Packed Sorbent and LC–HRMS/MS Analysis

Fentanyl and fentalogs’ intake as drugs of abuse is experiencing a great increase in recent years. For this reason, there are more and more cases in which it is important to recognize and quantify these molecules and related metabolites in biological matrices. Oral fluid (OF) is often used to find out if a subject has recently used a psychoactive substance and if, therefore, the person is still under the effect of psychotropics. Given its difficulty in handling, good sample preparation and the development of instrumental methods for analysis are essential. In this work, an analytical method is proposed for the simultaneous determination of 25 analytes, including fentanyl, several derivatives and metabolites. OF was collected by means of passive drool; sample pretreatment was developed in order to be fast, simple and possibly semi-automated by exploiting microextraction on packed sorbent (MEPS). The analysis was performed by means of LC–HRMS/MS obtaining good identification and quantification of all the analytes in less than 10 min. The proposed method was fully validated according to the Scientific Working Group for Forensic Toxicology (SWGTOX) international guidelines. Good results were obtained in terms of recoveries, matrix effect and sensitivity, showing that this method could represent a useful tool in forensic toxicology. The presented method was successfully applied to the analysis of proficiency test samples.

Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs

Automatically identifying traffic signs is a challenging and time-consuming process. As the academic community pays more attention to traditional algorithms for vision-based detection, tracking, and classification, three main criteria drive the investigation, they are detection, tracking, and classification. It is capable of performing detection and identification operations to minimize traffic accidents and move towards autonomous cars. A novel method proposed in this paper is based on moment invariants and neural networks for performing detection and recognition with classification, and it also includes automatic detection and identification of traffic signs and traffic board text that uses colour segmentation. Aside from the proposed structure, it is also required to identify the potential graphic road marking with text. This research article contains two algorithms, which are used to accurately classify the board text. The detection through image segmentation and recognition can be done by using the CNN algorithm. Finally, the classification is performed by the SVM framework. Therefore, the proposed framework will be very accurate and reliable with high efficiency, which has been proven in many big dataset applications. The proposed algorithm is tested with various datasets and provided good identification rate compared to the traditional algorithm.

Automatic Rain Sensing Wiper using Arduino

Now a days the technology will enhanced to focusing on autonomous vehicle on different implementation. In all likelihood major of accidents occur due to the disturbance of driver. Sometime many cases the deficiency of proper vision accountable for road accident during heavy rain falls. The usual wiper system requires driver’s attention to switch on the wiper system during rainfall. Whereas in traffic condition, driver should not be unfocused by manual adjustment of switching the wiper system which may lead to accident. In this framework we proposed a weather recognized method to construction an automatic rain sensing wiper on the wind screen during rain so as to avoid frenzy of driver. In this project we used Arduino along with a rain sensor, an LCD 16x2 module, and a servo motor. the rainfalls is measured via water rain sensor is present in automatic wiper system to collect information via sensor the wiper will start rotating then dispatch to Arduino. our method exhibited good identification ability of raindrops and encouraging results for rainfall discernment . In order to keep away from condemning situation this automatic wiper system provides changeable wiping speed formed on precipitation intensity. The state of the art in this paper was not only money-making but also highly dispatch and more accurate and economically inexpensive which can be implemented in all low and middle intensity vehicles.

Proteomic Identification and Meta-Analysis in Salvia hispanica RNA-Seq de novo Assemblies

While proteomics has demonstrated its value for model organisms and for organisms with mature genome sequence annotations, proteomics has been of less value in nonmodel organisms that are unaccompanied by genome sequence annotations. This project sought to determine the value of RNA-Seq experiments as a basis for establishing a set of protein sequences to represent a nonmodel organism, in this case, the pseudocereal chia. Assembling four publicly available chia RNA-Seq datasets produced transcript sequence sets with a high BUSCO completeness, though the number of transcript sequences and Trinity “genes” varied considerably among them. After six-frame translation, ProteinOrtho detected substantial numbers of orthologs among other species within the taxonomic order Lamiales. These protein sequence databases demonstrated a good identification efficiency for three different LC-MS/MS proteomics experiments, though a seed proteome showed considerable variability in the identification of peptides based on seed protein sequence inclusion. If a proteomics experiment emphasizes a particular tissue, an RNA-Seq experiment incorporating that same tissue is more likely to support a database search identification of that proteome.

Determining the Intangible: Detecting Land Abandonment at Local Scale

Precisely determining agricultural land abandonment (ALA) in an area is still difficult, even with recent progress in data collection and analysis. It is especially difficult in fragmented areas that need more tailor-made methods. The aim of this research was to determine ALA using airborne laser scanning (ALS) data, which are available in Poland with 4 to 6 points per square metre resolution. ALS data were processed into heat maps and modified with chosen kernel functions: triweight and Epanechnikov. The results of ALS data processing were compared to the control method, i.e., visual interpretation of an orthophotomap. This study shows that ALS data modelled with kernel functions allow for a good identification of ALA. The accuracy of results shows 82% concordance as compared to the control method. When comparing triweight and Epanechnikov functions, higher accuracy was achieved when using the triweight function. The research shows that ALS data processing is a promising method of detection of ALA and could provide an alternative to well-known methods such as the analysis of satellite images.

Development and Evaluation of Inexpensive Ultrasound Using A-Mode and M-Mode Signals to Identify Lung Depth and Avoid Risk of Pneumothorax in Acupuncture

Purpose To increase patient safety, ultrasound detection acupuncture (UDA) has been developed, which can detect a safe depth for acupuncturists to avoid causing pneumothorax. This study aims to develop and evaluate a single-transducer ultrasound for acupuncture (UFA) to promote UDA. Methods Special A-mode and M-mode signals were analyzed to identify the depth of the lung. Six subjects were recruited to test the reliability and validity of UFA on GB21, LV14 and BL43 acupuncture points. Results The result showed UFA’s coefficient of variation was less than 0.2 and no difference in age, gender, and BMI of the subjects statistically, demonstrating excellent reliability. However, the content validity index of 0.51 did not meet expectations. UFA has good reliability, but it cannot reach the level of medical ultrasound. UFA uses A-mode and the seashore sign of M-mode to make a good identification of the lung, and it would be useful in the promotion of UDA.