Recovery of Coal Values from Middling and Rejects by Froth Flotation and Mozley Mineral Separation

The recovery of coals values from Middling and Rejects carries out by using Froth flotation and Mozley Mineral Separation. The middling and rejects are the waste products from gravity beneficiation process, it has been noted that most of washery plants are selling this product at low cost because they have less values.The independent variables selected for Mozley Mineral Separator and their ranges were indicated in the parentheses as follow, water flow rates (400, 600, 800ml/s), amplitude (1.25, 1.5, 1.75inch) and collection time (30, 40, 60 s) while the independent variables for froth flotation were; Pulp density (10, 12.5, 15 %), collector dosage (39.3, 44.4, 49.5 g/t) and frother dosage (61.8, 65.3, 68.8 g/t). The number of experimental runs and regression equation determined by using Design Expert softwareThe d80 for middling and rejects samples were 10.5mm and 12.89mm respectively. The ash contents for the middling sample treated by froth flotation decrease from 37% to 15.85% at the reagent concentration of 49.5g/t collector, 65.3g/t frother and pulp density of 10%. The froth flotation results of middling sample shown to have a great reduction of ash contents. The overall optimum middling recovery and yield for washery grade I and II attain at reagent concentration and pulp density of 47.703g/t, 68.568g/t and 13.2% for collector, frother and pulp density respectively. The feed of reject coal was 71% and the ash contents reduced to 28.87% with the recovery of 0.85%. The analysis through Mozley mineral separator did not show significant changes in the reduction of ash from both middling and rejects. The ash contents achieved were above the scope of the studies for recovering of coal values. The experiments for middling and reject by froth flotation and Mozley mineral separator may be carried out by varying other parameters as well as the type of methods

EFFECT OF VEGETATION STRUCTURE ON UNDERCANOPY SOLAR RADIATION USING LIDAR REMOTE SENSING

Estimation of under-canopy radiation is crucial for characterizing vegetation–energy interactions and for a better understanding of its implications for ecosystem studies and forestry applications. Under-canopy radiation regimes are difficult to model due to the complex interaction of light with vegetation structure. Also, measuring radiation under the canopy over large areas is challenging using traditional field-based procedures. In this context, LiDAR remote sensing shows great potential for radiation estimation because it directly measures the three-dimensional canopy structure. The primary aim of this dissertation is to improve the understanding of under-canopy light regime using discrete return LiDAR and estimate solar radiation in forests with different structural characteristics. Based on the availability of LiDAR data, research sites were chosen in the coniferous forests of Sierra National Forest (SNF), California, and a chronosequence of mixed deciduous forest plots located in the Smithsonian Environmental Research Center (SERC), Maryland. First, LiDAR-derived digital surface models with and without vegetation canopy were used to assess the first-order effect of vegetation on solar radiation in SNF. The results showed a significant difference (p value < 0.001) in insolation values between the two surface models, with the mean solar irradiation over the bare surface almost three times higher than vegetation canopy surface. Next, a ray-tracing method was used to estimate beam radiation using LiDAR point clouds, and estimates were compared with in situ pyranometer measurements across three forest plots in SERC and were found to be in good agreement (RMSE = 13.94 W/m2). Lastly, LiDAR-derived vertical light transmittance values were compared with measurements from field-based PAR sensors, across five forest plots in SERC and were found to be in good agreement (R2 = 0.84). These results suggest that LiDAR remote sensing can provide reliable fine-scale estimates of beam radiation and vertical transmittance values under the vegetation canopy without the need for extensive ground measurements. This information provides a better understanding of radiation variability under the canopy and can help potentially improve the estimates from a range of land surface models such as snowmelt and hydrological models, and possibly help downscale general circulation model (GCM) predictions

Effect of Ni-doping on magnetism and superconductivity in Eu0.5K0.5Fe2As2

The effect of Ni-doping on the magnetism and superconductivity in Eu0.5K0.5Fe2As2 has been studied through a systematic investigation of magnetic and superconducting properties of Eu0.5K0.5(Fe1-xNix)2As2 (x = 0, 0.03, 0.05, 0.08 and 0.12) compounds by means of dc and ac magnetic susceptibilities, electrical resistivity and specific heat measurements. Eu0.5K0.5Fe2As2 is known to exhibit superconductivity with superconducting transition temperature Tc as high as 33 K. The Ni-doping leads to a rapid decrease in Tc; Tc is reduced to 23 K with 3% Ni-doping, and 8% Ni-doping suppresses the superconductivity to below 1.8 K. In 3% Ni-doped sample Eu0.5K0.5(Fe0.97Ni0.03)2As2 superconductivity coexists with short range ordering of Eu2+ magnetic moments at Tm ~ 6 K. The suppression of superconductivity with Ni-doping is accompanied with the emergence of a long range antiferromagnetic ordering with TN = 8.5 K and 7 K for Eu0.5K0.5(Fe0.92Ni0.08)2As2 and Eu0.5K0.5(Fe0.88Ni0.12)2As2, respectively. The temperature and field dependent magnetic measurements for x = 0.08 and 0.12 samples reflect the possibility of a helical magnetic ordering of Eu2 moments. We suspect that the helimagnetism of Eu spins could be responsible for the destruction of superconductivity as has been observed in Co-doped EuFe2As2. The most striking feature seen in the resistivity data for x = 0.08 is the reappearance of the anomaly presumably due to spin density wave transition at around 60 K. This could be attributed to the compensation of holes (K-doping at Eu-site) by the electrons (Ni-doping at Fe site). The anomaly associated with spin density wave further shifts to 200 K for x = 0.12 for which the electron doping has almost compensated the holes in the system.Comment: 9 pages, 10 figure

Improving International Development Evaluation through Geospatial Data and Analysis

Increasing availability of new types of data strengthens geospatial research in different scientific fields and opens up opportunities to better measure results and evaluate the impacts of development interventions. This article presents examples where geospatial approaches have been applied in evaluations and thus demonstrate the potential use in informing policy design through scientifically sound evidence as well as learning. The authors illustrate innovative ways of employing geospatial data and analysis in impact evaluations of international development cooperation. These interventions are concerned with topics such as biodiversity conservation, land degradation, sustainable use of natural resources, and disaster risk management. Recent methodological developments in the field of remote sensing and machine learning show significant potential to transform the vast body of new data into meaningful evidence aimed to improve policy and program design. The application and potential of methods are discussed in light of increasing importance of concerns over global climate change and climate change adaptation. The authors call for enhancing mutual interaction between the geospatial research disciplines and the development evaluation community to jointly contribute to finding solutions for tackling pressing social and environmental challenges

RISK FACTOR ASSESSMENT FOR ACNE VULGARIS IN HUMAN AND IMPLICATIONS FOR PUBLIC HEALTH INTERVENTIONS IN NORTH CENTRAL INDIA: A SURVEY-BASED STUDY

Background: Acne vulgaris is a very common dermatological problem of adolescence since the time immemorial. However, it is neither life threatening nor is a physical disability, but acne affects social and psychological functioning. Acne vulgaris is multifactorial, apart from basic factor of hormonal change and bacterial outbreak; there are several other factors that may influence the prevalence of acne.Methods: In the present study, populations were assessed for influence of various factors on acne prevalence. This cross-sectional study was a population based field study intending to discern the factors that influence the prevalence of acne in adolescents. The study carried out from April 2016 to October 2016 in north central India. For this survey, questionnaires were design to cover all the required information regarding occurrence of acne that include factors like gender, age, skin type, complexion, season of occurrence, dietary habit etc.Results: Acne vulgaris appears to be influenced by gender, age, seasonal variations, breakout area, complexion, skin types and dietary habits. Further, the influence of dietary habit on acne, by the consumption of dairy products or high-carbon diet has also been evaluated. Apart from depicting the vulnerable range of age (p=0.003288), sensitivity on various skin types (p=0.00039) and complexion (p=0.001355) on the basis of gender; This Field study on Acne Vulgaris, also reveals that the season has inordinate role in acne pervasiveness (p=0.115731).Conclusion: This study is helpful in categorizing the risk factors and evidencing the afflictions of acne in population thus, contributing health care planning. Â

Polymer Gear Fault Classification Using EMD-DWT Analysis Based on Combination of Entropy and Hjorth Features

339-346Polymer gears have proven to be an adequate replacement for traditional metal gears in various applications. They are lighter, have less inertia, and are much quieter than their metal counterparts. Polymer gears, however, are rarely employed because there is a lack of failure data. Hence, there is tremendous scope for fault detection of polymer gears. In this paper, a novel technique of polymer gear fault detection is proposed following the double decomposition of vibration signals. The experimentally acquired vibration signals are processed through two steps of decomposition, i.e., empirical mode decomposition and discrete wavelet transform based Time-Frequency decomposition. Subsequently, entropy features (EF), Hjorth parameter (HP), and a combination of EF and HP are extracted. A combination of these feature sets is used to train the classifier: support vector machine (SVM), ensemble learning, and decision tree. Among all classification methods, the ensemble learning classifier reached the maximum classification accuracy of 99.2 % using a combination of EF and HP features. Furthermore, EMD and DWT are compared with the proposed double decomposition method (EMD-DWT) for accuracy validation. The experiments demonstrated that the proposed EMD-DWT method is efficient and yields promising results for classifying polymer gear faults

Biological approaches of termite management: A review

For increased crop production, the role of chemical termiticides cannot be neglected as they have provided the efficient way to achieve green revolution. But the present scenario has forced mankind to search for alternative options. While keeping in mind the concept of sustainable agriculture, pest management including termites and other phyto-diseases etc. needs to be focused. For the achievement of the above stated goal, eco-friendly and cost-effective strategies need to be emphasized. Biopesticidal agents that mainly comprise of herbal and microbial formulations are known to exhibit anti termite activity and have a pivotal role in the production of organic food products. In order to reduce the chemical consumption, the vast area of biological alternatives needs to be explored as they provide us with many beneficial aspects like sustainability, suitable application, biodegradable nature, target specificity etc. Further, the bioactive components of such biological agents can later be used as commercially viable termiticides in the form of formulations. These herbal and microbial termiticides are effective and have immense scope to be used in future for sustainable development

PHARMACOLOGICAL STUDIES OF OCIMUM BASILICUM L.

Ocimum basilicum (O. basilicum) L. belongs to family Lamiaceae which comprises the most employed medicinal plants as a worldwide source of spices and also as a consolidated source of extracts. The phytochemical constituents of sweet basil essential oil have been investigated for several therapeutic importance from many regions of the world. They include terpenoids, alkaloids, flavonoids, tannins, saponin glycosides and ascorbic acid. These compounds have been reported to exhibit antibacterial and antifungal, antidyspepsia, anti-inflammatory, antioxidant, antiulcer, antiviral, insecticidal, wound-healing etc. activities. The plant parts of O. basilicum have been widely used in preparation traditional medicine. The plants also been used as a folk remedy to treat various ailments such as feverish illness, poor digestion, nausea, abdominal cramps, gastro-enteritis, migraine, insomnia, depression, gonorrhea, dysentery etc. Externally, they have been applied for the treatment of acne, loss of smell, insect stings, snake bites and skin infections. The present review is aimed to cover the pharmacological investigations on this important medicinal herb. Key Words: Ocimum basilicum, phytochemical constituents, antiinflammatory, antioxidant, antiviral, insecticida

Fault Location Identification By Machine Learning

As the fault based analysis techniques are becoming more and more powerful, there is a need to streamline the existing tools for better accuracy and ease of use. In this regard, we propose a machine learning assisted tool that can be used in the context of a differential fault analysis. In particular, finding the exact fault location by analyzing the XORed output of a stream cipher/ stream cipher based design is somewhat non-trivial. Traditionally, Pearson\u27s correlation coefficient is used for this purpose. We show that a machine learning method is more powerful than the existing correlation coefficient, aside from being simpler to implement. As a proof of concept, we take two variants of Grain-128a (namely a stream cipher, and a stream cipher with authentication), and demonstrate that machine learning can outperform correlation with the same training/testing data. Our analysis shows that the machine learning can be considered as a replacement for the correlation in the future research works

Determination of quality kinetics, microbiology, and sensory properties of shelf-stable chicken-wing sauce

Introduction: The development of shelf-stable foods is necessary to reduce dependence on refrigeration during their storage and distribution. Current trends in shelf-stable foods have seen a continuous rise in consumer demand that triggers research studies in the formulation, shelf stability, processing, and manufacturing of sauces frequently used by the food service industry. This study evaluated the shelf-life stability of chicken wing sauce with different flavors (hot, lemon pepper, sweet chili, teriyaki, and mild).Methods: All sauce formulations were developed and thermally processed to pasteurize using the hot-fill-and-hold (87.75°C for 5 min) method in a portable container which was then kept at ambient temperature (18.35°C ± 2) for 12 months. The samples were drawn periodically and analyzed for color, rheology, sensory, and microbial load.Results: The study’s findings revealed that sauces with different flavors (hot, lemon pepper, sweet chili, teriyaki, and mild) significantly declined in color and appearance, including viscosity, after ten months of storage. A very similar trend was noticed in textural changes. With the advancement of storage time, textural changes became prominent in lemon pepper and sweet chili sauce compared to hot teriyaki and mild sauces. Microbial analyses indicated the absence of pathogenic organisms, and no microbial activity was observed throughout the storage for up to 12 months. Among all sauces studied in this research project, lemon pepper exhibited a drastic decline in flavor, including some rancidity development after seven months of storage.Discussion: Extension of the shelf life and overall quality of the most commonly used sauces in the food service industry is of paramount importance. A better understanding of the changes in the physicochemical properties of sauces during storage can help food processors understand the expected changes