Abstract:A hot-box solar cooker designed for domestic applications had its thermal performance initially evaluated from experimental measurements, and then numerically using artificial neural networks (ANNs). Ambient temperature, temperature of the cooker and that of a known volume of water were measured as functions of incident solar radiation intensity and time of day. The cooker output power and efficiency, and the exergy output and efficiency computed from experimental measurements, constituted the four cooker thermal performance parameters. Experimental results show that the cooker has peak energy output of 21.3 W and peak energy efficiency of about 12 %. Peak exergy out was 1.4 %, while peak exergy efficiency was about 0.9 %. ANNs were then employed to estimate the four cooker performance parameters, for arbitrary inputs. A feed-forward neural network based on the Levenberg-Marquardt backpropagation algorithm was developed that used the five experimentally measured variables as inputs, while the four computed thermal performance parameters were the targets. Statistical parameters were used to rate the performance of ANNs in estimating the four experimental thermal performance parameters. Linear regressions were used to find relationships between ANN estimated and experimental energy/exergy outputs, and efficiencies. Results show good correlations between ANN predictions and experimentally measured cooker performance parameters. Correlation coefficients for all the four thermal parameters were very close to unity, with relatively low root-mean square errors (rmse) of only up to ±0.20. Peak mean absolute errors (mae) were about 0.10 W for cooker power, while mean absolute percentage errors (mape) were in the range of 0.57−1.25 %. High values of the correlation coefficients and relatively low error values show that the ANN model developed here successfully estimates cooker power, energy efficiency, exergy and exergy efficiencies with some good degree of accuracy.
Abstract:Objectives: Traditional oriental medicine provides a wide range of techniques for human health screening. Some of them, such as pulse diagnostics, are currently widely used especially in Asia regions. However, the question of these techniques utility for population screening is relevant. The study describes the experience of using the Mongolian and Tibetan medicine methods.\nMethods: More than 800 participants voluntarily undertook in the study. Participants filled out questionnaires and attended medical examination with a clinician trained in Tibetan and Mongolian medicine. To analyze these experiments, we used correlation analysis methods, Student\'s t-criterion.\nResults: The results of the analysis show a correlation between the Tibetan and Mongolian medicine questionnaires. A reliable positive correlation for \"wind\" humor between the Tibetan questionnaire and the clinician\'s diagnosis was found. \nConclusion: We described an information model based on Tibetan and Mongolian medicine techniques. It is possible to use this model for population screening. However, additional experiments are required to obtain a representative sample for the whole population.
Abstract:The present study reports the histopathological, haematological and biochemical changes in broiler chicken naturally infected with Eimeria acervulina. A polymerase chain reaction (PCR) was used to identify this species in 225 gut samples of broiler chicken from different farms in North Eastern (NE) region of India. Postmortem examination revealed greyish white transeversely elongated area on the serous surface, oedema, together with necrosis and sloughing of intestinal epithelium.Haematological changes included a decrease in haemoglobin,(Hb) and packed cell volume. The value of mean corpuscular haemoglobin concentration (MCHC), on the otherhand increased slightly. Biochemical changes showed a significant increase in the level of glucose, cholesterol, Alaninine amino transferase (ALT), Asparase amino transferase (AST) and Alkaline phosphatase. The PCR assay was based on internal transcribed spacer (ITS1) region of the ribosomal DNA ofEimeria sp which has shown interspecies variation that enables to differentiate the species. The six isolates of Eimeria acervulina obtained were sequenced and a phylogenetic tree wasprepared. The sequences of the six isolates were searched for matching with isolates available in the Gen Bank database for sequence similarity using nucleotide blast. The sequence analysis showed that the newly isolates of E. acervulina had 99% Similarity with isolate of Turkey origin.
Abstract:Yang recently introduced algebraic Routley--Meyer-style semantics for basic substructural logics. We extend his investigation to fuzzy logics. First, we recall the basic substructural core fuzzy logic MIAL (Mianorm logic) and its axiomatic extensions, together with their algebraic semantics. Next, we introduce two kinds of ternary relational semantics, called here \nUrquhart-style and Fine-style Routley--Meyer semantics, for them as algebraic Routley--Meyer-style semantics.
Abstract:In this paper study focuses on a portable compressed air-driven power mechanical generation apparatus that a green clean energy and clear air power generation apparatus driven by compressed air. This invention design approach concept can provide real-time power electricity or LED lighting requirement application. A novel approach to air kinetic energy can operate to transfer to power energy for electricity useful. Modularization experimental testing, design, and assembly are adopted in this power generation electricity source apparatus to enhance portability. The gas source can drive the micro-rotor speed is between 5,400 rpm and 32,400 rpm, which power is sufficient to have closed to 40 W LED lighting. It is a good gas dynamics output power can be converted to green clean energy.
Abstract:RNAi method is often used to silence expressed genes in the cell. This is composed of miRNA and siRNA, some involving antisense RNA binding mechanisms. Similarly, instead of using RNA as an antisense nucleotide, this study utilizes DNA strands for binding to the target mRNA. This method is known to be a more powerful method of knocking down the expression of genes than using RNAi method; RNA is quite unstable than DNA molecules by the existence of 2’ OH group. In this experiment, histone H1 and H2A antagonists were used to silence the histone expression. When histone is insufficiently expressed, the chromosome and nucleosome may lose its folding balance and potential disruption of scaffolds is expected. The interference DNA was identically made with PCR primers. The forward primer and reverse primer was each made for H1 and H2A. As a result, the DAPI nuclear stain showed that the disrupting effects were powerful. Compared to control groups, the primer treated group had nuclear fragments that were observed out of the nuclear membrane. This indicates that DNA interference targeting histone genes can be used for regulation of cell cycle and proliferation.
Abstract:This manuscript aims to interpret the theory of complex interval-valued hesitant fuzzy set (CIvHFS) as a generalization of interval-valued complex fuzzy set (IvCFS) and interval-valued hesitant fuzzy set (IvHFS), to manage the doubtful and complex data in real-life problems. CIvHFS holds the membership grade in the form of a finite subset of different interval-values of the unit disc in the complex plane. The operational laws of the interpreted theory are also discussed. Additionally, the vector similarity measures (VSMs) and weighted vector similarity measures (WVSMs) such as Jaccard similarity measure (JSM), Dice similarity measure (DSM), and Cosine similarity measure (CSM) are interpreted based on CIvHFSs. We also interpreted the hybrid VSM and weighted hybrid VSM for CIvHFSs. Moreover, some generalized exponential and non-exponential based similarity measures (SMs) are explored for CIvHFSs. The VSMs are applied to pattern recognition and medical diagnosis to assess the competence and achievability of the proposed SMs. We also presented some examples utilizing the proposed SMs. To assess the dependability and rationality of the established SMs we compared the proposed SMs with some existing SMs. The edges and graphical representation of the proposed SMs with existing SMs are also deduced.
Abstract:Several issues are there to prevent the traditional classifiers from getting an acceptable performance level while learning from multi-class problems. One of the core issues is the imbalanced distribution of samples, which in unification with incongruous optimization benchmarks and data overlapping phenomena dramatically decrease the performance of the underlying classifier. The joint impact of imbalance distribution and sample overlapping near the class boundaries compromise over the classifier performance beyond the expectation level and become even more challenging with the increasing number of classes in the multi-class environment. Learning from imbalanced data studied extensively in the research community, however, the overlapping issues and the co-occurrence impact of overlapping with data imbalance have received comparatively less attention, even though their joint impact is more thoughtful on classifiers\' performance. This paper introduces SVM++, a modified version of Support Vector Machine (SVM) to enhance the learning from the complex scenarios of multi-class problems with the imbalance and overlapping data with a shared attribute in the overlapped region. The proposed techniques is implemented using three steps. In the first step, an algorithm is designed to divide the training set into the overlapped and non-overlapped region at the data preprocessing level. In the second step, the overlapped data further filtered into the Critical-1 and Critical-2 region. In the third step, the SVM++ transforms the Critical-1 region sample, sharing similar characteristics into higher dimensional space by altering the SVM kernel mapping function based on the mean of the maximum and minimum distance. For the experiment, we use 30 real datasets with varying imbalance ratio and the overlapping degree to compare the novelty of the SVM++ with the existing classifiers. Experimental results highlight the superiority of the proposed SVM++ on a collection of benchmark datasets to its standard counterpart classifiers.
Abstract:Demand Response (DR) in smart grid is an energy management technique which enables the end users of electricity to schedule their daily consumption pattern for cost benefits. The energy consumption scheduling provides a path to lower the peak demands of power and reducing the chances of grid instability. In this paper we propose Linear Programming (LP) formulation of DR optimization problem for a single home in a Real Time Pricing (RTP) scenario. In RTP model of pricing, the cost of electricity changes for every hour of the day. The proposed method aims to achieve a balanced daily energy consumption schedule with reduced cost by evenly distributing the demand to low price time slots. Shifting maximum demands to low price periods may overload the grid during those periods hence to avoid grid instability a power limit is set as an optimization constraint for every timeslot. The proposed optimization problem is solved using primal barrier method.
Abstract:The present paper is aimed to a brief ecological study on three species (S1, S2, S3) neutralism with mortality rates. Here all the three species posses limited resources. Neutralism is an absence of any interaction between members of a mixed population. i.e, The species may be living side by side but are unaware of each other and also cause no harm or nor beneficial to each other. A real life example is certain plant species, bacteria, animal species which do not affect each other. The model equations of the system constitute a set of three first order non-linear simultaneous differential equations. Criteria for the asymptotic stability of all the eight critical points are established. The system would be stable if all the characteristic roots are negative, in case they are real, and have negative real parts, in case they are complex.