Effect of sediment load boundary conditions in predicting sediment Delta of Tarbela Reservoir in Pakistan

Setting precise sediment load boundary conditions plays a central role in robust modeling of sedimentation in reservoirs. In the presented study, we modeled sediment transport in Tarbela Reservoir using sediment rating curves (SRC) and wavelet artificial neural networks (WA-ANNs) for setting sediment load boundary conditions in the HEC-RAS 1D numerical model. The reconstruction performance of SRC for finding the missing sediment sampling data was at R-2 = 0.655 and NSE = 0.635. The same performance using WA-ANNs was at R-2 = 0.771 and NSE = 0.771. As the WA-ANNs have better ability to model non-linear sediment transport behavior in the Upper Indus River, the reconstructed missing suspended sediment load data were more accurate. Therefore, using more accurately-reconstructed sediment load boundary conditions in HEC-RAS, the model was better morphodynamically calibrated with R-2 = 0.980 and NSE = 0.979. Using SRC-based sediment load boundary conditions, the HEC-RAS model was calibrated with R-2 = 0.959 and NSE = 0.943. Both models validated the delta movement in the Tarbela Reservoir with R-2 = 0.968, NSE = 0.959 and R-2 = 0.950, NSE = 0.893 using WA-ANN and SRC estimates, respectively. Unlike SRC, WA-ANN-based boundary conditions provided stable simulations in HEC-RAS. In addition, WA-ANN-predicted sediment load also suggested a decrease in supply of sediment significantly to the Tarbela Reservoir in the future due to intra-annual shifting of flows from summer to pre- and post-winter. Therefore, our future predictions also suggested the stability of the sediment delta. As the WA-ANN-based sediment load boundary conditions precisely represented the physics of sediment transport, the modeling concept could very likely be used to study bed level changes in reservoirs/rivers elsewhere in the world

Microfluidic device for the separation of non-metastatic (MCF-7) and non-tumor (MCF-10A) breast cancer cells using AC dielectrophoresis

Dielectrophoretic devices are capable of the detection and manipulation of cancer cells in a label-free, cost-effective, robust, and accurate manner using the principle of the polarization of the cancer cells in the sample volume by applying an external electric field. This article demonstrates how a microfluidic platform can be utilized for high-throughput continuous sorting of non-metastatic breast cancer cells (MCF-7) and non-tumor breast epithelial cells (MCF-10A) using hydrodynamic dielectrophoresis (HDEP) from the cell mixture. By generating an electric field between two electrodes placed side-by-side with a micron-sized gap between them in an HDEP microfluidic chip, non-tumor breast epithelial cells (MCF-10A) can be pushed away, exhibiting negative DEP inside the main channel, while the non-metastatic breast cancer cells follow their course unaffected when suspended in cell medium due to having conductivity higher than the membrane conductivity. To demonstrate this concept, simulations were performed for different values of medium conductivity, and the sorting of cells was studied. A parametric study was carried out, and a suitable cell mixture conductivity was found to be 0.4 S/m. By keeping the medium conductivity fixed, an adequate AC frequency of 0.8 MHz was established, giving maximum sorting efficiency, by varying the electric field frequency. Using the demonstrated method, after choosing the appropriate cell mixture suspension medium conductivity and frequency of the applied AC, maximum sorting efficiency can be achieved

Secondary metabolite from Nostoc XPORK14A inhibits photosynthesis and growth of Synechocystis PCC 6803

Screening of 55 different cyanobacterial strains revealed that an extract from NostocXPORK14A drastically modifies the amplitude and kinetics of chlorophyll a fluorescence induction of SynechocystisPCC 6803 cells. After 2d exposure to the NostocXPORK14A extract, SynechocystisPCC 6803 cells displayed reduced net photosynthetic activity and significantly modified electron transport properties of photosystem II under both light and dark conditions. However, the maximum oxidizable amount of P700 was not strongly affected. The extract also induced strong oxidative stress in SynechocystisPCC 6803 cells in both light and darkness. We identified the secondary metabolite of NostocXPORK14A causing these pronounced effects on Synechocystis cells. Mass spectrometry and nuclear magnetic resonance analyses revealed that this compound, designated as M22, has a non-peptide structure. We propose that M22 possesses a dual-action mechanism: firstly, by photogeneration of reactive oxygen species in the presence of light, which in turn affects the photosynthetic machinery of SynechocystisPCC 6803; and secondly, by altering the in vivo redox status of cells, possibly through inhibition of protein kinases. We identified the secondary metabolite of NostocXPORK14A causing these pronounced effects on SynechocystisPCC6803 cells by inhibiting photosynthesis and growth. This compound, designated as M22, has a non-peptide structure. We propose that M22 possesses a dual action mechanism: first, by photo-generation of reactive oxygen species in the presence of light, which in turn affects the photosynthetic machinery of SynechocystisPCC 6803; and second, by altering the in vivo redox status of cells, possibly through inhibition of protein kinases

A Hybrid Deep Learning-Based Approach for Brain Tumor Classification

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time

A Hybrid Deep Learning-Based Approach for Brain Tumor Classification

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images