Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN’s basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.     

A Secure Communication Protocol for Unmanned Aerial Vehicles

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles. Multiple UAVs and autopilot systems support it, and it provides bi-directional communication between the UAV and Ground Control Station. The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS. In order to increase the transfer speed and efficiency, the Mavlink does not encrypt the messages. As a result, the protocol is vulnerable to various security attacks such as Eavesdropping, GPS Spoofing, and DDoS. In this study, we tackle the problem and secure the Mavlink communication protocol. By leveraging the Mavlink packet’s vulnerabilities, this research work introduces an experiment in which, first, the Mavlink packets are compromised in terms of security requirements based on our threat model. The results show that the protocol is insecure and the attacks carried out are successful. To overcome Mavlink security, an additional security layer is added to encrypt and secure the protocol. An encryption technique is proposed that makes the communication between the UAV and GCS secure. The results show that the Mavlink packets are encrypted using our technique without affecting the performance and efficiency. The results are validated in terms of transfer speed, performance, and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink protocol. Our achieved results have significant improvement over the literature and Mavlink in terms of security.     

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.     

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Scientometrics - In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata,...     

An efficient double exponentially weighted moving average Benjamini‐Hochberg control chart to control false discovery rate

A control chart based on double exponentially weighted moving average and Benjamini‐Hochberg multiple testing procedure is proposed that controls the false discovery rate (FDR). The proposed control chart is based on probabilities (or P values) to accept or reject the null hypothesis of the underlying process is in control. To make a decision, instead of using only the current probability, previous “m” probabilities are considered. The performance of the control chart is evaluated in terms of average run length (ARL) using Monte Carlo simulations. Procedure for estimation of parameters used in the control chart is also discussed. The proposed control chart is compared with previous control charts and found to be more efficient in controlling the false discovery rate.     

Translating the impact of knowledge management into knowledge‐based innovation: The neglected and mediating role of knowledge‐worker satisfaction

The purpose of knowledge management is innovation. However, this study proposes that the existence of knowledge management in the knowledge‐worker's work environment can nurture the overall satisfaction of knowledge worker. As the ultimate purpose of knowledge management is innovation performance, therefore, the satisfaction of knowledge worker as an outcome of knowledge management should be greater innovation performance. Consequently, the purpose of this study is to test the mediating role of satisfaction of knowledge worker between knowledge management and innovation. The study collected the data from 306 knowledge workers (engineers and managers) of software houses from Pakistan. The SmartPLS 3 Version 2.7 software that uses the PLS‐SEM (Partial Least Square‐Structural Equation Modelling) technique was used. The results indicate that satisfaction of knowledge worker mediates between two knowledge management processes (knowledge creation and knowledge sharing) and innovation significantly. However, it does not mediate between knowledge utilization and innovation significantly.     

Effect of soaking medium on the physicochemical properties of parboiled glutinous rice of selected Laotian cultivars

The effect of various soaking mediums, viz. water (control), 3% NaCl and 0.2% acetic acid, and without soaking on the physicochemical properties of parboiled selected glutinous (TDK8 and TDK11) and non-glutinous (Doongara) was investigated in the present study. Results showed that the chemistry of soaking had a significant effect on the head rice yield (HRY), grain hardness, crystallinity, color, pasting and thermal properties, textural attributes, and glycemic index of these rice varieties. Soaking with NaCl and acetic acid significantly increased the grain hardness and HRY than control and without soaking treatments. Acetic acid and NaCl soaking significantly affected crystalline regions of starch resulting in reduced crystallinity in X-ray diffraction analysis and thermal endotherms in DSC analysis. NaCl soaking induced swelling of starch granules resulting in high peak and final viscosities. However, acetic acid restricted swelling resulting in reduced peak and final viscosities. NaCl and acetic acid soakings also resulted in increased hardness and adhesiveness of cooked grains than normal water soaked and un-soaked parboiled rice samples. Interestingly, change in textural attributes was prominent in parboiled glutinous rice. The color difference value for fresh parboiled samples was significantly lower for acetic acid soaked samples compared to NaCl soaked and un-soaked samples probably due to bleaching effect of acetic acid. Moreover, parboiling also resulted in significant reduction in glycemic index of glutinous rice. These findings revealed the potential application of parboiling with modified soaking techniques to improve the grain quality.     

Single-Shot Characterization of Enzymatic Reaction Constants K(m) and k(cat) by an Acoustic-Driven, Bubble-Based Fast Micromixer

In this work we present an acoustofluidic approach for rapid, single-shot characterization of enzymatic reaction constants K(m) and k(cat). The acoustofluidic design involves a bubble anchored in a horseshoe structure which can be stimulated by a piezoelectric transducer to generate vortices in the fluid. The enzyme and substrate can thus be mixed rapidly, within 100 ms, by the vortices to yield the product. Enzymatic reaction constants K(m) and k(cat) can then be obtained from the reaction rate curves for different concentrations of substrate while holding the enzyme concentration constant. We studied the enzymatic reaction for β-galactosidase and its substrate (resorufin-β-D-galactopyranoside) and found K(m) and k(cat) to be 333 ± 130 μM and 64 ± 8 s(-1), respectively, which are in agreement with published data. Our approach is valuable for studying the kinetics of high-speed enzymatic reactions and other chemical reactions.     

Sargassum swartzii extracts ameliorate memory functions by neurochemical modulation in a rat model

Food Science and Biotechnology - Recently, considerable attention has been paid to drug exploration from natural sources for treating memory loss, a major manifestation of various neurodegenerative...     

Selenium (Se) improves drought tolerance in crop plants – a myth or fact?

Climate change has emerged as one of the most complex challenges of the 21st century and has become an area of interest in the past few decades. Many countries of the world have become extremely vulnerable to the impacts of climate change. The scarcity of water is a serious concern for food security of these countries and climate change has aggravated the risks of extreme events like drought. Oxidative stress, caused by a variety of active oxygen species formed under drought stress, damages many cellular constituents, such as carbohydrates, lipids, nucleic acids and proteins, which ultimately reduces plant growth, respiration and photosynthesis. Se has become an element of interest to many biologists owing to its physiological and toxicological importance. It plays a beneficial role in plants by enhancing growth, reducing damage caused by oxidative stress, enhancing chlorophyll content under light stress, stimulating senesce to produce antioxidants and improving plant tolerance to drought stress by regulating water status. Researchers have adopted different strategies to evaluate the role of selenium in plants under drought stress. Some of the relevant work available regarding the role of Se in alleviating adverse effect of drought stress is discussed in this paper. © 2015 Society of Chemical Industry