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In general solutions and outcomes to an issue in image processing, a ton of trail and examination with an enormous assortment of test photographs is incorporated. Pediastrum, which comprises of pigments like carotene, xanthophyll, chlorophylls, and other nourishing elements like complex sugars, major and minor minerals, and protein, enzymes, fiber, is a minute coccal shaped colony forming green algae. In this research, an attempt was made to solve the problem of identifying and classifying different species of Pediastrum with the aid of CNN based deep learning model. The informational collection comprising of 12,000 algal pictures was being utilized by, AlexNet CNN (Convolution Neural Network) model for the training and validating purposes. The features like the presence of sporopollenin in the cell wall, the structures and the functions of the cells, and the structural properties of coenobia make the basis for automated classification by AlexNet deep learning model. The efficacy of the proposed approach is demonstrated by an experimental outcome of 99.54 percent classification accuracy with precision and Fl-score more than 0.98.

Depletion of petroleum fuels and their rising cost have directed towards an intensive search for substitute fuels. In the present work, a potential feedstock for biodiesel production is identified as sesame oil biodiesel. Raw sesame oil is collected from the market and biodiesel from raw oil is produced through the trans-esterification process in the laboratory. A highest biodiesel yield of 92.1% was achieved, when esterification process is performed with 10:1 ethanol to oil ratio and 2.3% sodium hydroxide at 60 °C for 90 min. Thermo-physical properties are investigated with the help of different experimental apparatus. Comparison with Bureau of Indian Standards (IS 15607:2016) reveals the suitability of sesame oil biodiesel used as an alternative fuel for compression-ignition engine. Experimental study is performed for different blends of sesame oil biodiesel and diesel fuel (SOEE20, SOEE40, SOEE60, SOEE80, SOEE100), mineral diesel and raw sesame oil to recognize the suitability of blends to be use as a fuel for diesel engine.

Nowadays, the bioremediation of toxic pollutants is a subject of interest in terms of health issues and environmental cleaning. In the present review, an eco-friendly, cost-effective approach is discussed for the detoxification of environmental pollutants by the means of natural purifier, i.e., blue–green algae over the conventional methods. Industrial wastes having toxic pollutants are not able to eliminate completely by existing the conventional techniques; in fact, these methods can only change their form rather than the entire degradation. These pollutants have an adverse effect on aquatic life, such as fauna and flora, and finally harm human life directly or indirectly. Cyanobacterial approach for the removal of this contaminant is an efficient tool for sustainable development and pollution control. Cyanobacteria are the primary consumers of food chain which absorbed complex toxic compounds from environments and convert them to simple nontoxic compounds which finally protect higher food chain consumer and eliminate risk of pollution. In addition, these organisms have capability to solve secondary pollution, as they can remediate radioactive compound, petroleum waste and degrade toxins from pesticides.

The rise in the earth’s surface and water temperature is part of the effect of climatic change that has been observed for the last decade. The rates of climate change are unprecedented, and biological responses to these changes have also been prominent in all levels of species, communities and ecosystems. Aquatic-terrestrial ecotones are vulnerable to climate change, and degradation of the emergent aquatic macrophyte zone would have contributed severe ecological consequences for freshwater, wetland and terrestrial ecosystems. Most researches on climate change effects on biodiversity are contemplating on the terrestrial realm, and considerable changes in terrestrial biodiversity and species’ distributions have been detected in response to climate change. This is unfortunate, given the importance of aquatic systems for providing ecosystem goods and services. Thus, if researchers were able to identify early-warning indicators of anthropogenic environmental changes on aquatic species, communities and ecosystems, it would certainly help to manage and conserve these systems in a sustainable way. One of such early-warning indicators concerns the expansion of emergent macrophytes in aquatic-terrestrial ecotones. Hence, this review highlights the impact of climatic changes towards aquatic macrophytes and their possible environmental implications.

Algae are photosynthetic eukaryotes that may range from unicellular to multicellular forms. Algae have been reported from almost all the ecological systems, including terrestrial, marine, and aquatic ecosystems. The manual classification of algae is a time-consuming method and requires great efforts with expertise due to the numerous families and genera. In the present study, an automated system is developed for the identification and classification of the 16 algal families with a data set of 80,000 images by a modified ResNeXt CNN (Convolution Neural Network) model. Cell differentiation by modified ResNeXt CNN topology is based on cell arrangement and morphological features including area, width, shape, and length of the cell. An experimental result of 99.97% classification accuracy demonstrates the effectiveness of the proposed method. The present investigation may open a new path in the future for the development of a time and a cost-effective, highly sensitive computer-based system for the identification and classification of different algae.

For identification of different Pediastrum species in a sample, the determination of microscopic feature and colony morphology are the preliminary steps before sending them to the higher genomic and proteomic level. Great efforts with high expertise are required for the time-consuming manual process. In the present study, the first time an effort has been done to address the problem for identification and classification of Pediastrum species with the help of convolutional neural networks (CNNs). The modified ResNeXt CNN (Convolution Neural Network) model is used for training and validation of the data set consisting of 42,000 algal images. Modified ResNeXt CNN topology differentiates cells based on the formation of coenobia, cell arrangement and feature and particularly the sculptures on the outer sporopollenin cell-wall layers. An experimental result of 98.45% classification accuracy and F1-score more than 0.98 demonstrates the effectiveness of the proposed method. In the future, such time and cost-effective facilities can be used as promising sources for phycological studies.

Proteases have characteristics of biotechnological interest and have thus become important industrial enzymes. We report the production of thermostable protease and its characterization in Bacillus species, which is a thermotolerant bacterium; Bacillus subtilis is widely used for isolating protease enzyme. Gelatin was used as the substrate in nutrient agar medium for screening and showed the maximum zone of activity (22mm) after overnight incubation and addition of the indicator. Under submerged fermentation conditions, a high level of protease production was found at 45°C after 36h at pH 10, with continuous agitation (180rpm). The presence of galactose and peptone in the medium enhanced enzyme production by 0.5% when compared with other carbon and nitrogen sources. Thus, such additions can augment protease production and their application in various industries.

The high volumes of the alerts occurring in the network makes it a challenging job for human operators to understand them and take appropriate actions. The more complex is the network, the more are the events and alerts generated by the appliances. This paper shares insights on various Event Correlation techniques and algorithms implemented in the Network Management Solutions for an Ethernet Network. The aim of this paper is to design a framework and algorithm which ensure to a large extent; efficient and reliable handling of massive amounts of machine data received in varying formats in a flexible and cost-efficient manner. Techniques like Event filtering, Event correlation, event augmentation, Event aggregation and Root cause analysis has been effectively utilised in the given framework. Result shows the proposed framework and algorithm is reducing 50% time for computation and efforts.

The present investigation describes simple and effective method for synthesis of silver nanoparticles via green route. Solanum trilobatum Linn extract were prepared by both conventional and homogenization method. We optimized the production of silver nanoparticles under sunlight, microwave and room temperature. The best results were obtained with sunlight irradiation, exhibiting 15–20 nm silver nanoparticles having cubic and hexagonal shape. Biosynthesized nanoparticles were highly toxic to various bacterial strains tested. In this study we report antibacterial activity against various Gram negative (Klebsiella pneumoniae, Vibrio cholerae and Salmonella typhi) and Gram positive (Staphylococcus aureus, Bacillus cereus and Micrococcus luteus) bacterial strains. Screening was also performed for any antifungal properties of the nanoparticles against human pathogenic fungal strains (Candida albicans and Candida parapsilosis). We also demonstrated that these nanoparticles when mixed with shampoo enhance the anti-dandruff effect against dandruff causing fungal pathogens (Pityrosporum ovale and Pityrosporum folliculitis). The present study showed a simple, rapid and economical route to synthesize silver nanoparticles and their applications hence has a great potential in biomedical field.

The present study indicates the effect of fungicides (approved by WHO) and their behavior on nitrogen fixer of rice eco system Nostoc sp. Application of plant protecting chemicals at recommended levels braced up the growth of blue green algae thereby enhancing heterocyst formation and nitrogenase activity. Nostoc sp demoed varying degrees of sensitivity to fungicides. Biomass yield, protein, carbohydrate content reduced after 3pg/mL concentration. Heterocyst damage was observed from 4μg/mL, Proline content increased with increase in fungicide concentration, utmost yellowing of the culture started from 4μg/mL. The decreasing order of the toxicity to Nostoc sp with fungicides was MancozebEdiphenphosCarbendazimHexaconazole.

The self-control study on thirty normal subjects of both genders (mean age 25.83 +/- 3.41 years) were taken in a self control study group and were tested for three types of Nostril breathing practices and Breath Awareness (BA) effects. Namely verbal recall performance of numerical data such as Digit Span Forward (DSF) and Digit Span Backward (DSB) as well associate learning memory function using Wechsler Memory Scale. The interventions included Right Nostril Breathing (RNB), Left Nostril Breathing (LNB), Alternate Nostril Breathing (ANB) and Breathe Awareness for duration of 30 minutes daily, four consecutive days. The Repeated Measure ANOVA analysis revealed a significant increase in both DSF and DSB recall performance due to RNB at P0.001 level and increased DSB score due to ANB at P0.014 level with a non- significant increase due to LNB suggests that the RNB facilitates both DSF and DBF recall performance. However, the LNB effect on left hemisphere helps to restore the memory function of right hemisphere. This study concludes that the RNB enhances numerical data retrieval mostly as a result of left brain activation.

The purpose of the study was to find out the effect of hostile and favourable spectators on the performances of motor tasks. For the purpose of this study a total of 50 students of LNUPE were selected and randomly divided into two groups A and B, 25 each (15 male and 10 female). These subjects were tested on the selected variables such as bent knee sit ups, pull-ups (male)/flexed arm hang (female), bass stick test (crosswise), field goal speed test and dribbling test. Total 200 students of LNUPE were selected as spectators and were asked to first act as hostile spectators and then as favourable ones in order to create the two atmospheres for the study. Hostile spectators tried to disturb the subjects while they were performing by whistling, taunting, booing, shouting and hooting them whereas favourable spectators helped in facilitating the performance by clapping, cheering and appreciating the performance efforts in order to create an appropriate atmosphere. These variables were particularly chosen because of their administrative feasibility and they could be conducted very close to the spectators as the size of the spectators was a smaller one. ‘t Test’ was employed at 0.05 level of significance to compare the performance of both the groups under hostile and favourable situations types of spectators on the performance of motor tasks. The statistical findings revealed that the performance of pull ups (male)/flexed arm hang (female), bass stick test (static balance) and field goal speed test showed a significance differences on comparison whereas bent knee sit-ups and dribbling test showed no significant differences on comparison of performances when performed under the presence of hostile and favourable spectators.