Characteristic study, its identification and self-tuned approach to control hydro-power plants

The water time constant and mechanical time constant greatly influences the power and speed oscillations of hydro-turbine-generator unit. This paper discusses the turbine power transients in response to different nature and changes in the gate position. The work presented here analyses the characteristics of hydraulic system with an emphasis on changes in the above time constants. The simulation study is based on mathematical first-, second-, third- and fourth-order transfer function models. The study is further extended to identify discrete time-domain models and their characteristic representation without noise and with noise content of 10 & 20 dB signal-to-noise ratio (SNR). The use of self-tuned control approach in minimising the speed deviation under plant parameter changes and disturbances is also discussed

What makes brands achieve iconic status?

We propose that brands do not achieve iconic status by chance. This article focuses on how brands manage iconic status effectively. Drawing on an exploratory study of iconic brands, we identify a brand's ability to inspire consumers and connect with them on a personal level as well as its visual identity and presence in consumers' mind as critical elements of brand status. Consumers' perceptions of a sample of brands were investigated through in-depth interviews, followed by the examination of these brands' activities through case-study analyses. The alignment between brand strategies and the relevant features highlighted by consumers was then assessed. A comprehensive framework for achieving iconicity is presented and discussed.Working Pape

Control of vortex shedding behind circular cylinder for flows at low reynolds numbers

It has been observed by researchers in the past that vortex shedding behind circular cylinders can be altered, and in some cases suppressed, over a limited range of Reynolds numbers by proper placement of a second, much smaller, 'control' cylinder in the near wake of the main cylinder. Results are presented for numerical computations of some such situations. A stabilized finite element method is employed to solve the incompressible Navier-Stokes equations in the primitive variables formulation. At low Reynolds numbers, for certain relative positions of the main and control cylinder, the vortex shedding from the main cylinder is completely suppressed. Excellent agreement is observed between the present computations and experimental findings of other researchers. In an effort to explain the mechanism of control of vortex shedding, the streamwise variation of the pressure coefficient close to the shear layer of the main cylinder is compared for various cases, with and without the control cylinder. In the cases where the vortex shedding is suppressed, it is observed that the control cylinder provides a local favorable pressure gradient in the wake region, thereby stabilizing the shear layer locally

Tomato leaf disease detection using Taguchi-based Pareto optimized lightweight CNN

The prospect of food security becoming a global danger by 2050 due to the exponential growth of the world population. An increase in production is indispensable to satisfy the escalating demand for food. Considering the scarcity of arable land, safeguarding crops against disease is the best alternative to maximize agricultural output. The conventional method of visually detecting agricultural diseases by skilled farmers is time-consuming and vulnerable to inaccuracies. Technology-driven agriculture is an integral strategy for effectively addressing this matter. However, orthodox lightweight convolutional neural network (CNN) models for early crop disease detection require fine-tuning to enhance the precision and robustness of the models. Discovering the optimal combination of several hyperparameters might be an exhaustive process. Most researchers use trial and error to set hyperparameters in deep learning (DL) networks. This study introduces a new systematic approach for developing a less sensitive CNN for crop leaf disease detection by hyperparameter tuning in DL networks. Hyperparameter tuning using a Taguchi-based orthogonal array (OA) emphasizes the S/N ratio as a performance metric primarily dependent on the model’s accuracy. The multi-objective Pareto optimization technique accomplished the selection of a robust model. The experimental results demonstrated that the suggested approach achieved a high level of accuracy of 99.846% for tomato leaf disease detection. This approach can generate a set of optimal CNN models’ configurations to classify leaf disease with limited resources accurately

Kinetics of biodegradation of diethylketone by Arthrobacter viscosus

The performance of an Arthrobacter viscosus culture to remove diethylketone from aqueous solutions was evaluated. The effect of initial concentration of diethylketone on the growth of the bacteria was evaluated for the range of concentration between 0 and 4.8 g/l, aiming to evaluate a possible toxicological effect. The maximum specific growth rate achieved is 0.221 h-1 at 1.6 g/l of initial diethylketone concentration, suggesting that for higher concentrations an inhibitory effect on the growth occurs. The removal percentages obtained were approximately 88%, for all the initial concentrations tested. The kinetic parameters were estimated using four growth kinetic models for biodegradation of organic compounds available in the literature. The experimental data found is well fitted by the Haldane model (R2 = 1) as compared to Monod model (R2 = 0.99), Powell (R2 = 0.82) and Loung model (R2 = 0.95). The biodegradation of diethylketone using concentrated biomass was studied for an initial diethylketone concentration ranging from 0.8–3.9 g/l in a batch with recirculation mode of operation. The biodegradation rate found followed the pseudo-second order kinetics and the resulting kinetic parameters are reported. The removal percentages obtained were approximately 100%, for all the initial concentrations tested, suggesting that the increment on the biomass concentration allows better results in terms of removal of diethylketone. This study showed that these bacteria are very effective for the removal of diethylketone from aqueous solutions.The authors would like to gratefully acknowledge the financial support of this project by the Fundacao para a Ciencia e Tecnologia (FCT), Ministerio da Ciencia e Tecnologia, Portugal and Fundo Social Europeu (FSE). Cristina Quintelas thanks FCT for a Post-Doc grant

Lesion-Based Detection of Cardiovascular Diseases Using Deep Learning and Red Deer Optimization

Nowadays, cardiovascular disease is a very concerning health issue in human life. Medical imaging through MRI plays an important role in the detection of many diseases. Magnetic resonance imaging (MRI) is a non-invasive and sophisticated diagnostic tool for cardiovascular disease (CVD) that allows for full visualization of the heart and blood vessels. Through Magnetic resonance imaging, we get high-quality images of blood vessels, which helps in detecting various types of heart-related diseases. With the help of MRI, we can detect various types of heart-related diseases. It also gives us information about their early diagnosis and their preventive measures. Deep learning and its advanced features are proving to be very helpful in this work. Deep learning has brought many new changes in this field. The article presents the Red Deer Optimizer with Deep Learning (ACVD-RDODL) algorithm for automated cardiovascular disease identification using magnetic resonance imaging (MRI). The primary goal of the proposed approach is to use Deep Learning models on cardiac MRI to detect Cardiovascular issues. The dynamic histogram equalisation (DHE) based noise removal model is used in the given approach to pre-process the images. Additionally, the Attention Based Convolutional Gated Recurrent Unit Network (ACGRU) model is used in this approach to classify Cardiovascular diseas

Laser induced elastico mechano luminescence of SrAl2O4 : Eu phosphor

When γ – irradiated elastic mechano luminescent materials SrAl2O4 : Eu are exposed to 1060 nm infrared pulse of nanosecond duration from CO2 laser, then stress produced in the crystals excites visible luminescence due to piezoelectric field in SrAl2O4 : Eu phosphor because they are non – centro symmetric.  In the present investigation SrAl2O4 : Eu crystals are given laser shocks and ML intensity recorded. During laser induced shocks, ML intensity increases linearly with stress and attains a peak value at a particular time and then decays exponentially with time. A theoretical approach has been proposed to explain the experimental results.&nbsp

In silico de novo design of NNRTIs of HIV-1: Functional group based computational molecular modelling approach

1484-1493Seven novel lead compounds, acting as NNRTIs of HIV-1, are extracted from a database of, in silico de novo designed, 500 compounds. Functional group based computational molecular modelling techniques are used for such design of Acylthiocarbamate derivatives. Effect of structural characteristics on the antiviral activity of these derivatives has also been studied. Statistical regression techniques namely, Non-linear (Back Propagation Neural Network, Support Vector Machine) and linear (Multiple Linear) chemometric regression methods are used in developing the relationships of Kier-Hall Electrotopological State Indices (ERingA, EO8, EN9, EO14, ES16, EN17, EO19, ER, and ER1) with the HIV-1 antiviral activity. The relative potentials of these methods are also assessed and the results suggest that BPNN (r2 = 0.845, MSE = 0.142, q2 = 0.818) describes the relationship between the descriptors and antiviral activity in a relatively better manner than SVM-ε-radial (r2 = 0.844, MSE = 0.144, q2 = 0.807) and MLR (r2 = 0.836, MSE = 0.150, q2 = 0.805)

Gaussian filter and CNN based framework for accurate detection of brain tumor by analyzing MRI images

The diagnosis of cancer can be challenging and time-consuming due to the complex characteristics of tumors and inherent noise in medical imaging. The significance of early detection and localization of tumors must be considered. Radiological imaging techniques can detect and potentially forecast the presence of neoplastic growths at various phases. The expeditiousness of the diagnosis process can be notably enhanced by amalgamating these images with algorithms designed for segmentation and relegation. Early detection of tumors and accurate localization of their position are critical factors. Medical scans, when used with segmentation and relegation procedures, enable the prompt and precise detection of cancerous tumor regions. The identification of malignant tumors enables this achievement. The present article introduces a framework for detecting brain tumors based on a convolutional neural network (CNN). The initial step in processing brain magnetic resonance imaging (MRI) images involves the application of a Gaussian filter to eliminate any noise present. Subsequently, CNN and long short-term memory (LSTM) deep learning methodologies are employed to classify images. CNN has demonstrated improved accuracy in the classification and detection of brain tumors. CNN has achieved an accuracy of 99.25% in cancer image classification. The sensitivity and specificity of CNN are also 98.75% and 99.25%, respectively

The sequence of rice chromosomes 11 and 12, rich in disease resistance genes and recent gene duplications

Background: Rice is an important staple food and, with the smallest cereal genome, serves as a reference species for studies on the evolution of cereals and other grasses. Therefore, decoding its entire genome will be a prerequisite for applied and basic research on this species and all other cereals. Results: We have determined and analyzed the complete sequences of two of its chromosomes, 11 and 12, which total 55.9 Mb (14.3% of the entire genome length), based on a set of overlapping clones. A total of 5,993 non-transposable element related genes are present on these chromosomes. Among them are 289 disease resistance-like and 28 defense-response genes, a higher proportion of these categories than on any other rice chromosome. A three-Mb segment on both chromosomes resulted from a duplication 7.7 million years ago (mya), the most recent large-scale duplication in the rice genome. Paralogous gene copies within this segmental duplication can be aligned with genomic assemblies from sorghum and maize. Although these gene copies are preserved on both chromosomes, their expression patterns have diverged. When the gene order of rice chromosomes 11 and 12 was compared to wheat gene loci, significant synteny between these orthologous regions was detected, illustrating the presence of conserved genes alternating with recently evolved genes. Conclusion: Because the resistance and defense response genes, enriched on these chromosomes relative to the whole genome, also occur in clusters, they provide a preferred target for breeding durable disease resistance in rice and the isolation of their allelic variants. The recent duplication of a large chromosomal segment coupled with the high density of disease resistance gene clusters makes this the most recently evolved part of the rice genome. Based on syntenic alignments of these chromosomes, rice chromosome 11 and 12 do not appear to have resulted from a single whole-genome duplication event as previously suggested