Multi fingered robot hand in industrial robot application using tele-operation

This research focuses on the working and development of wireless robotic hand system. In this research previously developed models have been studied. After analysis of those models, a better approach has been presented in this research. The objective of this research is to design and develop a tele-operated robotic hand system. The robotic hand is intended for providing solutions to industrial problems like robot reprogramming, industrial automation and safety of the workers working in hostile environments. The robotic hand system works in the master slave configuration where Bluetooth is being used as the communication channel for the tele-operation. The master is a glove, embedded with sensors to detect the movement of every joint present in the hand, which a human operator can wear. This joint movement is transferred to the slave robotic hand which will mimic the movement of human operator. The robotic hand is a multi fingered dexterous and anthropomorphic hand. All the fingers are capable of performing flexion, extension, abduction, adduction and hence circumduction. A new combination of pneumatic muscles and springs has been used for the actuation purpose. As a result, this combination reduces the size of the robotic hand by decreasing the number of pneumatic muscles used. The pneumatic muscles are controlled by the opening and closing of solenoid valves. A novel technique has been used in the robotic hand for tendon routing, which gives the ability of independence to all finger joints. The heart of all the control mechanism of the system is mbed microcontroller. The designed system was tested at different module levels. The results show the successful establishment of communication between master and slave at a rate of 10 packets per second, which was sufficient for smooth motion of the system. The amount of torque produced at all the joints in the robotic hand has been presented in this research. The posture tests have been performed in which two fingers were actuated which followed the master. This system has achieved motion of fingers without any tendon coupling problem. The system is able to replace the human industrial workers performing dexterous tasks

Exploration of Two Cucurbitaceae Fruit (Muskmelon and Watermelon) Seeds for Presence of Phytochemicals, and Antioxidant and Antimicrobial Activities

Cucurbitaceae family fruits, especially melons, offers significant quantities of minerals carotenoids and phenolic compounds, contributing to their antioxidant activity. However, seeds of these fruits are usually discarded as waste by products. In current study, seeds of watermelon (Citrullus lanatus) and muskmelon (Cucumis melo) were separated, dried, grounded and extracted, with 70% ethanol, to investigate total phenolic content (TPC), flavonoid content (TFC), carotenoid content (TC) content, and total antioxidant activity (TAA). Further, antimicrobial activities of these extracts were tested against selected bacterial and fungus strains. Results showed that extracts of both cucurbits presented significant amounts of phytochemicals, with higher quantities presented by watermelon seeds. In watermelon seeds, TPC were found 156.50 mg/GAE 100 g, TFC 56.78 mg CE/100 g, TC 36.65 mg/100 g, and TAA 71%, and these amounts were significantly higher than those found in muskmelon seeds. Antimicrobial study results showed that extracts of both seeds exhibited significant zone of inhibitions against three bacterial and three fungal species, and these values were very comparable to the reference antimicrobial drug used, Ciprofloxacin. Findings of current research work provided significant grounds for presence of phytochemical bioactives in two melon fruits seeds, providing the basis for extraction and utilization of these bioactives, through processing and fortification different pharma foods

Survey of human-leopard (Panthera Pardus) conflict in Ayubia National Park, Pakistan

Wildlife populations are at a risk of extinction mainly because of human-wildlife conflict (HWC). The present study was designed to evaluate the ongoing HWC with special reference to Common Leopard (Panthera pardus) in Ayubia National park through field study as well as a literature-based approach. Questionnaire interview surveys were designed for wildlife officials working in the park and the locals who bear the cost for leopard conflict through livestock depredation and crop damage. The study showed that human-leopard conflict in the study area has been increasing. More than 60% of people considered livestock depredation as the major reason for their negative perception towards the common leopard. Among livestock, goats were more vulnerable which showed that leopards mostly preferred smaller prey. A number of reported human injuries and deaths on account of Human-Leopard conflict in the study area helped conclude that human-wildlife conflict is a significant issue. Mitigation measures may hence be recommended, such as livestock compensation schemes and community-based conservation approaches, etc. It is critical to avoid human-Leopard conflict not only to keep the public and their property safe but also to help conserve this important species of common leopard (Panthera pardus)

Identification and Phylogenetic Analysis of Channa Species from Riverine System of Pakistan Using COI Gene as a DNA Barcoding Marker

Channa are the freshwater and important food fish species in Pakistan belonging to family Channidae. However, identification and phylogenetic analysis based on molecular tools of these species in Pakistan was not well known. Herein, the current investigation was conceptualized, which dealt with mitochondrial DNA sequences from three geographically distinct populations of this species from Pakistan’s water system. DNA from fin tissues was extracted. COI region of mtDNA was amplified using universal primers for fish. PCR products were sequenced. Phylogenetic analysis conducted in the present study, i.e. neighbor-joining (NJ) cladogram, maximum likelihood, K2P genetic divergence and histogram suggests that the studied species of family Channidae are genetically different. The K2P intraspecific divergences were lower than interspecific divergences. The clades in the evolutionary tree for three species were clearly separated

An Early Warning System for Earthquake Prediction from Seismic Data Using Batch Normalized Graph Convolutional Neural Network with Attention Mechanism (BNGCNNATT)

Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events

Antioxidant Effects of Amino Acids-Capped Silver Nanoprisms Against Cadmium-Induced Toxicity: Modified Silver Nanoprisms with Enhanced Potential

The surface functionality of nanomaterials (NMs) with suitable biomolecules may enhance their biocompatibility and make them more effective for biological applications. Furthermore, the functionalization of various materials with biomolecules would also yield more secure and biocompatible nanomaterials for different applications. The present research was designed to evaluate the amino acids-based surface functionality of silver nanoprisms (AgNPrs). Silver nanoprisms were prepared by chemical method and further capped with amino acids such as L-cystine (Cys), L-glycine (Gly), and L-tyrosine (Tyr). Characterization of the newly-synthesized NMs was performed by using various techniques. Prepared nanomaterials (NMs) were assessed for their in vitro antioxidant activity using diphenylpicrylhydrazyl (DPPH), ferric reducing power (FRP), and hydrogen peroxide (HP) scavenging assays. In vivo, the antioxidant potential of the same was evaluated in the cadmium-intoxicated Mus musculus model. Tyr-AgNPrs (p < 0.05), Cys-AgNPrs (p < 0.05), and Gly-AgNPrs (p > 0.05) showed enhanced DPPH scavenging activity. Whereas the Cys-AgNPrs displayed enhanced FRP activity and Tyr-AgNPrs displayed enhanced HP scavenging activity. The AgNPrs and cadmium-exposed mice displayed a decreased (p<0.05) catalase (CAT) activity in G2 and G3, whereas it increased in G4. The superoxide dismutase (SOD) activity was decreased in the G2 (p < 0.05) and G5 (p > 0.05) groups, whereas it increased in the G3 (p < 0.05), G4, and G6 groups of mice. The G2 showed a slightly decreased glutathione-s-transferase (GST) activity (p > 0.05). The levels of reduced glutathione (p<0.05) and metallothioneins (p>0.05) were elevated in the cadmium-intoxicated group. The results revealed that the cystine-AgNPrs and tyrosine-AgNPrs demonstrated higher antioxidant potential in comparison to other treatments. It is concluded that biomolecule-conjugated AgNPrs can work efficiently with more biocompatibility for various nanotechnological and biomedical applications

GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction

Generative adversarial networks (GAN), which are fueled by deep learning, are an efficient technique for image reconstruction using under-sampled MR data. In most cases, the performance of a particular model’s reconstruction must be improved by using a substantial proportion of the training data. However, gathering tens of thousands of raw patient data for training the model in actual clinical applications is difficult because retaining k-space data is not customary in the clinical process. Therefore, it is imperative to increase the generalizability of a network that was created using a small number of samples as quickly as possible. This research explored two unique applications based on deep learning-based GAN and transfer learning. Seeing as MRI reconstruction procedures go for brain and knee imaging, the proposed method outperforms current techniques in terms of signal-to-noise ratio (PSNR) and structural similarity index (SSIM). As compared to the results of transfer learning for the brain and knee, using a smaller number of training cases produced superior results, with acceleration factor (AF) 2 (for brain PSNR (39.33); SSIM (0.97), for knee PSNR (35.48); SSIM (0.90)) and AF 4 (for brain PSNR (38.13); SSIM (0.95), for knee PSNR (33.95); SSIM (0.86)). The approach that has been described would make it easier to apply future models for MRI reconstruction without necessitating the acquisition of vast imaging datasets

DeepLabV3, IBCO-based ALCResNet: A fully automated classification, and grading system for brain tumor

Brain tumors, which are uncontrolled growths of brain cells, pose a threat to people worldwide. However, accurately classifying brain tumors through computerized methods has been difficult due to differences in size, shape, and location of the tumors and limitations in the medical field. Improved precision is critical in detecting brain tumors, as small errors in human judgments can result in increased mortality rates. This paper proposes a new method for improving early detection and decision-making in brain tumor severity using learning methodologies. Clinical datasets are used to obtain benchmark images of brain tumors, which undergo pre-processing, data augmentation with a Generative Adversarial Network, and classification with an Adaptive Layer Cascaded ResNet (ALCResNet) optimized with Improved Border Collie Optimization (IBCO). The abnormal images are then segmented using the DeepLabV3 model and fed into the ALCResNet for final classification into Meningioma, Glioma, or Pituitary. The IBCO algorithm-based ALCResNet model outperforms other heuristic classifiers for brain tumor classification and severity estimation, with improvements ranging from 1.3% to 4.4% over COA-ALCResNet, DHOA-ALCResNet, MVO-ALCResNet, and BCO-ALCResNet. The IBCO algorithm-based ALCResNet model also achieves higher accuracy than non-heuristic classifiers such as CNN, DNN, SVM, and ResNet, with improvements ranging from 2.4% to 3.6% for brain tumor classification and 0.9% to 3.8% for severity estimation. The proposed method offers an automated classification and grading system for brain tumors and improves the accuracy of brain tumor classification and severity estimation, promoting more precise decision-making regarding diagnosis and treatment

State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images

Brain tumors have become a leading cause of death around the globe. The main reason for this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately, magnetic resonance images (MRI) are utilized to diagnose tumors in most cases. The performance of a Convolutional Neural Network (CNN) depends on many factors (i.e., weight initialization, optimization, batches and epochs, learning rate, activation function, loss function, and network topology), data quality, and specific combinations of these model attributes. When we deal with a segmentation or classification problem, utilizing a single optimizer is considered weak testing or validity unless the decision of the selection of an optimizer is backed up by a strong argument. Therefore, optimizer selection processes are considered important to validate the usage of a single optimizer in order to attain these decision problems. In this paper, we provides a comprehensive comparative analysis of popular optimizers of CNN to benchmark the segmentation for improvement. In detail, we perform a comparative analysis of 10 different state-of-the-art gradient descent-based optimizers, namely Adaptive Gradient (Adagrad), Adaptive Delta (AdaDelta), Stochastic Gradient Descent (SGD), Adaptive Momentum (Adam), Cyclic Learning Rate (CLR), Adaptive Max Pooling (Adamax), Root Mean Square Propagation (RMS Prop), Nesterov Adaptive Momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN. The experiments were performed on the BraTS2015 data set. The Adam optimizer had the best accuracy of 99.2% in enhancing the CNN ability in classification and segmentation

Genetic diversity in cytochrome c oxidase I gene of <i>Anopheles</i> mosquitoes

<p>Genetic diversity in cytochrome c oxidase I (coxI) among 7 species of <i>Anopheles</i> mosquitoes from Pakistan, and 37 species from different geographical regions of the world, was recorded. Automatic Barcode Gap Discovery (ABGD) analysis revealed a clear gap between intraspecific and interspecific distances of 7 species from Pakistan. However, genetic distances of 37 <i>Anopheles</i> species failed to adequately differentiate species in a global context. Intraspecific and interspecific divergences for 7 <i>Anopheles</i> species of Pakistan varied from 0.0% to 2.5% (mean = 0.49%) and 8% to 22.3% (mean = 12.77%), respectively. Similarly, intraspecific distances for 37 species from different parts of world ranged from 0.0% to 11.2% (mean = 0.65%) while values of interspecific divergences ranged from 3.4% to 35% (mean = 11.75%). Although phylogenetic tree revealed separate clades for 7 <i>Anopheles</i> species of Pakistan, it failed to produce separate clades for 37 species of the world. It is concluded that although standard barcode region is helpful for identifying <i>Anopheles</i> mosquitoes, combination of multi-locus approaches and morphology may be required to accurately identify species in this genus.</p