Moringa oleiferia leaves extract: a natural antioxidant for retarding lipid peroxidation in cooked goat meat patties

The effective utilisation of Moringa oleiferia mature leaves (MOL) extract as an antioxidant in cooked goat meat patties during refrigerated storage was investigated, and its efficiency was evaluated against butylated hydroxytoluene (BHT). The extract exhibited high phenolic content (48.36 mg of gallic acid equivalent per g), flavonoid (31.42 mg g^?1 of sample) being the major component. Moringa oleiferia mature leaves extract showed excellent antioxidant activity as determined by radical‐scavenging activity of 1, 1‐diphenyl 2 picrylhydrazyl (DPPH). The IC₅₀ value of MOL extract for 2, 2‐diphenyl‐1‐picrylhydrazyl radical scavenging was 18.54 μg mL^?1. Total phenolic content (as gallic acid equivalent) significantly (P < 0.05) increased from 285.56 in control to 379.45 in patties with MOL extract. MOL extract (0.1%) when added to meat was found to retard lipid peroxidation of cooked goat meat patties as measured by TBARS number during refrigerated storage. The increase in TBARS number in MOL extract–treated samples was very slow and remained lowest (0.53 mg malonaldehyde per kg sample) up to 15 days. The antioxidant activity of MOL extract was found to be comparable to BHT. Addition of MOL extract did not affect any of the sensory attributes of patties. The MOL extract at a level of 100 mg/100 g meat was sufficient to protect goat meat patties against oxidative rancidity for periods longer than the most commonly used synthetic antioxidant like BHT.     

Delineation and mapping of coal mine fire using remote sensing data – a review

Various countries around the globe face numerous hazards due to the burning of coal on the surface as well as below ground. Countries like China, India, United States of America (USA), Australia, Indonesia, and many other countries have reported the burning of coal fires, and thus it is the urgent need to control the coal fire propagation to prevent the loss of energy resources. Coal is a fossil fuel that has a tendency to catch fire for many reasons; spontaneous combustion being the most frequent reasons for its burning. Other factors leading to coal fire include forest fires close to coal seams, natural hazards, old mining techniques, and external heat sources. The review work demonstrates the application of various satellite data in fire detection and mapping. The literature reveals that remote sensing plays an important role in facilitating quick and complete delineation of coal mine fires. Many algorithms have been developed around the world for fire detection from different satellite data. A comprehensive demonstration of different algorithms along with their merits and demerits are outlined. Comparative performances of the different algorithms with their case studies are also explained. It can be inferred from the various literature that it is very difficult to select a particular sensor algorithm for generating global fire products. Suggestions are given to further explore the possibility of improvement of fire detection algorithms.     

Two-tier pressure consolidation operation method for hydrogen refueling station cost reduction

An operation strategy known as two-tier “pressure consolidation” of delivered tube-trailers (or equivalent supply storage) has been developed to maximize the throughput at gaseous hydrogen refueling stations (HRSs) for fuel cell electric vehicles (FCEVs). The high capital costs of HRSs and the consequent high investment risk are deterring growth of the infrastructure needed to promote the deployment of FCEVs. Stations supplied by gaseous hydrogen will be necessary for FCEV deployment in both the near and long term. The two-tier pressure consolidation method enhances gaseous HRSs in the following ways: (1) reduces the capital cost compared with conventional stations, as well as those operating according to the original pressure consolidation approach described by Elgowainy et al. (2014) [1], (2) minimizes pressure cycling of HRS supply storage relative to the original pressure consolidation approach; and (3) increases use of the station's supply storage (or delivered tube-trailers) while maintaining higher state-of-charge vehicle fills.     

Life-cycle analysis of greenhouse gas emissions from hydrogen delivery: A cost-guided analysis

The cost of hydrogen delivery for transportation accounts for most of the current H₂ selling price; delivery also requires substantial amounts of energy. We developed harmonized techno-economic and life-cycle emissions models of current and future H₂ production and delivery pathways. Our techno-economic analysis of dispensed H₂ costs guided our selection of pathways for the life-cycle analysis. In this paper, we present the results of market expansion scenarios using existing capabilities (for example, those that use H₂ from steam methane reforming, chlor-alkali, and natural gas liquid cracker plants), as well as results for future electrolysis plants that use nuclear, solar, and hydroelectric power. Reductions in greenhouse gas emissions for fuel cell electric vehicles compared to conventional gasoline pathways vary from 40% reduction for fossil-derived H₂ to 20-fold for clean H₂. Supplemental tables with greenhouse gas emissions data for each step in the H₂ pathways enable readers to evaluate additional scenarios.     

Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is to predict the premature heart disease based on machine learning algorithms. This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself. Support Vector Machine (SVM) is used for segregating the CVD patients based on their symptoms and medical observation. The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time. A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.     

Indian Science Reports: a web-based scientometric portal for mapping Indian research competencies at overall and institutional levels

Scientometrics - The article presents an introduction to a newly created scientometric portal called Indian Science Reports, available at www.indianscience.net . The portal is designed to fulfil...     

Finding patterns in the degree distribution of real-world complex networks: going beyond power law

Pattern Analysis and Applications - The most important structural characteristics in the study of large-scale real-world complex networks in pattern analysis are degree distribution. Empirical...