Teff (Eragrostis tef) processing,utilization and future opportunities: a review

The demand for food production has been constantly increasing due to rising population. In developed countries, for example, the emergence of regional production of old grains that are rarely utilized, along with the production of commonly consumed grains, has gained importance in recent years. These grains, known collectively as ancient or heirloom grains, have offered both farmers and consumers novel ways of cultivation and products with interesting taste, characteristics and nutritional value. Among the 30 000 plant species known, only five cereals currently provide more than 50% of the world's energy intake – bread wheat (Triticum aestivum), rice (Oryza sativa), sorghum (Sorghum bicolor), millets (Panicum sp.) and maize (Zea mays). The excessive utilization of these selected species has a great potential to cause genetic losses and difficulty in bridging future agricultural demands. Teff (Eragrostis tef), an ancient grain extensively cultivated in countries like Eritrea and Ethiopia, provides promising alternatives for new food uses since its nutritional value is significantly higher than most others cereal grains. The absence of gluten allows flexibility in food utilization since it can be directly substituted to gluten-containing products. The grain also offers an excellent balance of essential amino acids and minerals, which can fulfil the recommended daily intake and eliminates the need for fortification and enrichment. This review provides a general overview of the physical properties and nutritional composition of teff grains related to processing and applications in the food and feed industries. The current status of teff utilization, as well as the challenges in production and commercialization, and future opportunities is presented and discussed.     

Composing RESTful Services and Collaborative Workflows: A Lightweight Approach

The use of RESTful Web services has gained momentum in the development of distributed applications based on traditional Web standards such as HTTP. In particular, these services can integrate easily into various applications, such as mashups. Composing RESTful services into Web-scale workflows requires a lightweight composition language that's capable of describing both the control and data flow that constitute a workflow. The authors address these issues with Bite, a lightweight and extensible composition language that enables the creation of Web-scale workflows and uses RESTful services as its main composable entities.     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Fusion of thermal infrared and visible spectra for robust moving object detection

The detection of moving objects is a crucial step for many video surveillance applications whether using a visible camera (VIS) or an infrared (IR) one. In order to profit from both types, several fusion methods were proposed in the literature: low-level fusion, medium-level fusion and high-level fusion. The first one is the most used for moving objects’ detection in IR and VIS spectra. In this paper, we present an overview of the different moving object detection methods in IR and VIS spectra and a state of the art of the low-level fusion techniques. Moreover, we propose a new method for moving object detection using low-level fusion of IR and VIS spectra. In order to evaluate quantitatively and qualitatively our proposed method, three series of experiments were carried out using two well-known datasets namely “OSU Color-Thermal Database” and “INO-Database”; the results of these evaluations show promising results and demonstrate the effectiveness of the proposed method.     

TMT-HCC: A tool for text mining the biomedical literature for hepatocellular carcinoma (HCC) biomarkers identification

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. New insights into the pathogenesis of this lethal disease are urgently needed. Chromosomal copy number alterations (CNAs) can lead to activation of oncogenes and inactivation of tumor suppressors in human cancers. Thus, identification of cancer-specific CNAs will not only provide new insight into understanding the molecular basis of tumor genesis but also facilitate the identification of HCC biomarkers using CNA.     

Multi-breath dry powder inhaler for delivery of cohesive powders in the treatment of bronchiectasis

A series of co-engineered macrolide–mannitol particles were successfully prepared using azithromycin (AZ) as a model drug. The formulation was designed to target local inflammation and bacterial colonization, via the macrolide component, while the mannitol acted as mucolytic and taste-masking agent. The engineered particles were evaluated in terms of their physico-chemical properties and aerosol performance when delivered via a novel high-payload dry powder Orbital^? inhaler device that operates via multiple inhalation manoeuvres. All formulations prepared were of suitable size for inhalation drug delivery and contained a mixture of amorphous AZ with crystalline mannitol. A co-spray dried formulation containing 200?mg of 50:50?w/w AZ: mannitol had 57.6%?±?7.6% delivery efficiency with a fine particle fraction (≤6.8?µm) of the emitted aerosol cloud being 80.4%?±?1.1%, with minimal throat deposition (5.3?±?0.9%). Subsequently, it can be concluded that the use of this device in combination with the co-engineered macrolide–mannitol therapy may provide a means of treating bronchiectasis.     

Tunning the Physical Properties of PVDF/PVC/Zinc Ferrite Nanocomposites Films for More Efficient Adsorption of Cd (II)

This work deals with the synthesis of ZnFe₂O₄ NPs and studies the effect of addition on the physical properties PVDF/PVC blend. XRD affirmed the formation of ZnFe₂O₄ NPs and HRTEM shows that the size of the prepared ZnFe₂O₄ NPs ranged from 20 to 55 nm. The effect of ZnFe₂O₄ on the behavior of PVDF/PVC was studied through XRD, ATR-FTIR, FESEM and UV–Visible spectroscopy. XRD revealed that the addition of ZnFe₂O₄ NPs enhanced the crystallinity of PVDF/PVC blend system and also confirmed the incorporation of ZnFe₂O₄ NPs by appearing a diffraction peak at 2θ equals 35°. ATR-FTIR affirmed the interaction between blend sample and ZnFe₂O₄ NPs by appearing new bands 554 cm^?1 and 421 cm^?1 which are corresponded to ZnFe₂O₄ NPs functional group with appearing a new band at 603 cm^?1. FESEM showed that the addition of ZnFe₂O₄ to PVDF/PVC blend improved surface properties, for example, roughness average has been increased from 319 to 414 nm while maximum height increased from 260 to 473 nm for PVDF/PVC and PVDF/PVC/10% ZnFe₂O₄, respectively. Optical properties and band gap calculations revealed that addition of ZnFe₂O₄ NPs changes the structure of polyblend samples which results due to the formation of localized states. The removal efficiency of Cd (II) by using PVDF/PVC/10% ZnFe₂O₄ reached about 50% at pH 6 after 60 min. the absorption mechanism as well as kinetics isotherm have been studied. It is found that adsorption of Cd (II) occurred through the Langmuir mechanism and fellow pseudo-second order isotherm.