Delineating shallow ground water irrigated areas in the Atankwidi Watershed (Northern Ghana, Burkina Faso) using Quickbird 0.61 - 2.44 meter data

The major goal of this research was to delineate the shallow groundwater irrigated areas (SGI) in the Atankwidi Watershed in the Volta River Basin of West Africa. Shallow ground water irrigation is carried out using very small dug-wells all along the river banks or shallow dug-outs all along the river bed. Each of these dug-wells and dug-outs are highly fragmented small water bodies that irrigate only a fraction of an acre. However, these are contiguous dug-wells and dug-outs that are hundreds or thousands in number. Very high spatial resolution (VHSR) Quickbird imagery (0.61 to 2.44 m) was used to identify: (a) dug-wells that hold small quantities of water in otherwise dry stream; and (b) dug-outs that are just a meter or two in depth but have dug-out soils that are dumped just next to each well. The Quickbird VHSR imagery was found ideal to detect numerous: (i) dug-wells through bright soils that lay next to each dug-well, and (ii) water bodies all along the dry stream bed. We used fusion of 0.61 m Quickbird panchromatic data with 2.44 Quickbird multispectral data to highlight SGI and delineate their boundaries. Once this was achieved, classification techniques using Quickbird imagery was used within the delineated areas to map SGI and other land use/land cover (LULC) areas. Results obtained showed that SGI is practiced on a land area of 387 ha (1.4%), rainfed areas is 15638 ha (54.7%) and the remaining area in other LULC. These results were verified using field-plot data which showed an accuracy of 92% with errors of omissions and commissions less than 10%.Key words: Shallow groundwater, Quickbird, remote sensing, irrigated areas, Atankwidi Watershed, Ghana

On Interval-Valued Intuitionistic Fuzzy Hyper BCK-Ideals of Hyper BCK Algebras

In this paper, we apply the concept of an interval-valued intuitionistic fuzzy set to hyper BCK-ideals in hyper BCK-algebras. The notion of an interval-valued intuitionistic fuzzification of (strong, weak, s-weak) hyper BCK-ideals is introduced, and related properties are investigated. Characterizations of an interval-valued intuitionistic fuzzification of hyper BCK-ideals are established

Tuning spin one channel to exotic orbital two-channel Kondo effect in ferrimagnetic composites of LaNiO3 and CoFe2O4

We report the tuning from spin one channel (1CK) to orbital two-channel Kondo (2CK) effect by varying CoFe2O4 (CFO) content in the composites with LaNiO3 (LNO) along with the presence of ferrimagnetism. Although there is no signature of resistivity upturn in case of pure LNO, all the composites exhibit a distinct upturn in the temperature range 30-80 K. For composite with lower percentage of CFO (10 %), the electron spin plays the key role in the emergence of resistivity upturn which is affected by external magnetic field. On the other hand, when the CFO content is increased (15%), the upturn shows strong robustness against high magnetic field (14 T) and a crossover in temperature variation from lnT to T^1/2 at the Kondo temperature, indicating the appearance of orbital 2CK effect. The orbital 2CK effect is originated due to the scattering of conduction electrons from the structural two-level systems which is created at the interfaces between the two phases (LNO and CFO) of different crystal structures as well as inside the crystal planes. A negative magnetoresistance (MR) is observed at low temperature (< 30 K) for composites containing both lower (10 %) and higher percentage (15 %) of CFO. We have analyzed the negative MR using Khosla and Fisher semi-empirical model based on spin dependent scattering of conduction electrons from localized spins.Comment: 14 pages including supplementary materials and 12 figure

Multi-class Cervical Cancer Classification using Transfer Learning-based Optimized SE-ResNet152 model in Pap Smear Whole Slide Images

Among the main factors contributing to death globally is cervical cancer, regardless of whether it can be avoided and treated if the afflicted tissues are removed early. Cervical screening programs must be made accessible to everyone and effectively, which is a difficult task that necessitates, among other things, identifying the population\u27s most vulnerable members. Therefore, we present an effective deep-learning method for classifying the multi-class cervical cancer disease using Pap smear images in this research. The transfer learning-based optimized SE-ResNet152 model is used for effective multi-class Pap smear image classification. The reliable significant image features are accurately extracted by the proposed network model. The network\u27s hyper-parameters are optimized using the Deer Hunting Optimization (DHO) algorithm. Five SIPaKMeD dataset categories and six CRIC dataset categories constitute the 11 classes for cervical cancer diseases. A Pap smear image dataset with 8838 images and various class distributions is used to evaluate the proposed method. The introduction of the cost-sensitive loss function throughout the classifier\u27s learning process rectifies the dataset\u27s imbalance. When compared to prior existing approaches on multi-class Pap smear image classification, 99.68% accuracy, 98.82% precision, 97.86% recall, and 98.64% F1-Score are achieved by the proposed method on the test set. For automated preliminary diagnosis of cervical cancer diseases, the proposed method produces better identification results in hospitals and cervical cancer clinics due to the positive classification results

Optimization of Squeeze Cast Process Parameters Using Taguchi and Grey Relational Analysis

AbstractThe near-net shape manufacturing capabilities of squeeze casting process have greater potential to achieve smooth uniform surface and internal soundness in the cast components. In squeeze casting process, casting density and surface finish is influenced majorly by process variables. Proper control of the process variables is essential to achieve better results. Hence in the present work an attempt made using taguchi method to analyze the squeeze cast process variables such as squeeze pressure, die and pouring temperature considering at three different levels using L9 orthogonal array. Pareto analysis of variance performed on each response to find out optimum process parameter levels and significant contribution of each individual process parameter towards surface roughness and density of LM20 alloy. Grey relation analysis used as a multi-response optimization technique to obtain the single optimal process parameter setting for both the responses surface roughness and casting density

Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs

SIMULTANEOUS DETERMINATION OF METFORMIN, LINAGLIPTIN IN JENTADUETO AND METFORMIN, SAXAGLIPTIN IN KOMBIGLYZE BY LC-MS METHOD

Objective: The objective of the present investigation was to develop a novel, simple and economic method for the determination of metformin (MET), linagliptin (LIN) and saxagliptin (SAX) in jentadueto and kombiglyze sample by employing the liquid chromatography and mass spectrometric method for estimation in bulk and pharmaceutical dosage form in presence of degradation products.Methods: The chromatographic separation was achieved by using the mobile phase composition of methanol and ammonium acetate buffer pH 4.5 (85:15 % v/v) on the Hypurity advance C-18 column at a flow rate of 0.5 ml/min. Ion signals m/z 130.10/70.10, 473.10/420.40 and 316.30/180.20†for metformin, linagliptin and saxagliptin respectively measured in positive ion mode. The detailed validation of the method was performed as per ICH guidelines.Results: The results of all validation parameters found within acceptance limits. The linearity of the drugs was found to be in the concentration range of 50–5000 ng/ml for all the drugs. Accuracy of the drugs was found to be from 94-102% and precision was found 4.67% RSD for all three drugs. The validated method was employed for the determination of drugs in the formulation and also determined the drugs in the presence of degradation products under stress conditions.Conclusion: The method was developed and validated as per guidelines. Hence, this method can be used for the simultaneous determination of metformin, linagliptin and metformin, saxagliptin in bulk and combined dosage forms

Labelled Classifier with Weighted Drift Trigger Model using Machine Learning for Streaming Data Analysis

The term “data-drift” refers to a difference between the data used to test and validate a model and the data used to deploy it in production. It is possible for data to drift for a variety of reasons. The track of time is an important consideration. Data mining procedures such as classification, clustering, and data stream mining are critical to information extraction and knowledge discovery because of the possibility for significant data type and dimensionality changes over time. The amount of research on mining and analyzing real-time streaming data has risen dramatically in the recent decade. As the name suggests, it’s a stream of data that originates from a number of sources. Analyzing information assets has taken on increased significance in the quest for real-time analytics fulfilment. Traditional mining methods are no longer effective since data is acting in a different way. Aside from storage and temporal constraints, data streams provide additional challenges because just a single pass of the data is required. The dynamic nature of data streams makes it difficult to run any mining method, such as classification, clustering, or indexing, in a single iteration of data. This research identifies concept drift in streaming data classification. For data classification techniques, a Labelled Classifier with Weighted Drift Trigger Model (LCWDTM) is proposed that provides categorization and the capacity to tackle concept drift difficulties. The proposed classifier efficiency is contrasted with the existing classifiers and the results represent that the proposed model in data drift detection is accurate and efficient

Unravelling the Components of a Multi-Thermal Coronal Loop Using Magnetohydrodynamic Seismology

Coronal loops, constituting the basic building blocks of the active Sun, serve as primary targets to help understand the mechanisms responsible for maintaining multi-million Kelvin temperatures in the solar and stellar coronae. Despite significant advances in observations and theory, our knowledge on the fundamental properties of these structures is limited. Here, we present unprecedented observations of accelerating slow magnetoacoustic waves along a coronal loop that show differential propagation speeds in two distinct temperature channels, revealing the multi-stranded and multi-thermal nature of the loop. Utilizing the observed speeds and employing nonlinear force-free magnetic field extrapolations, we derive the actual temperature variation along the loop in both channels, and thus are able to resolve two individual components of the multi-thermal loop for the first time. The obtained positive temperature gradients indicate uniform heating along the loop, rather than isolated footpoint heating.Comment: accepted for publication in Ap