Soil Chemical Analysis

The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

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The Environmental Protection Agency

Statistical Methods For Agricultural Workers

B6nassy, C., 1955. R6marques sur deux Aphelinid6s: Aphelinus mytilaspidis Le Baron et Aphytis proclia Walker. Annls l~piphyt. 6: 11-17. Lord, F. T. & MacPhee, A. W., 1953. The influence of spray programs on the fauna of apple orchards in Nova Scotia II. Oyster shell scale. Can. Ent. 79: 196-209. Pickett, A. D., 1946. A progress report on long term spray programs. Rep. Nova Scotia Fruit Grow. Ass. 83 : 27-31. Pickett, A. D., 1967. The influence of spray programs on the fauna of apple orchards in Nova Scotia XIV. Can. Ent. 97: 816-821. Tothill, J. D., 1918. The predacious mite Hemisarcoptes malus Shimer and its relation to the natural control of the oyster shell scale Lepidosaphes ulmi L. Agric. Gaz. Can. 5 : 234-239.

http://link.springer.com/content/pdf/10.1007/BF01976688.pdf

Soil enzymes: Kuprevich, V. F. & Shcherbakova, T. A.: Translated from the Russian edition (1966), published by the Indian National Scientific Documentation Centre, New Delhi 1971. 392 pp., offset printing from type script, paper cover. Obtainable from U.S. Department of Commerce, National Technical Information Service, Springfield, Va. 22151

This article introduces the radical approach of applying alkaline anion-exchange membranes (AAEMs) to meet the current challenges with regards to direct methanol fuel cells (DMFCs). A review of the literature is presented with regards to the testing of fuel cells with alkaline membranes (fuelled with hydrogen or methanol) and also to candidate alkaline anion-exchange membranes for such an application. A brief review of the directly related patent literature is also included. Current and future research challenges are identified along with potential strategies to overcome them. Finally, the advantages and challenges with the direct electrochemical oxidation of alternative fuels are discussed, along with how the application of alkaline membranes in such fuel cells may assist in improving performance and fuel efficiency.

https://epubs.surrey.ac.uk/1696/1/fulltext.pdf

Prospects for Alkaline Anion-Exchange Membranes in Low Temperature Fuel Cells†

Cloud Radio Access Network systems with mmWave Massive MIMO framework can be considered as a potential candidate for next generation wireless communications due to its promise of increased spectral efficiency and distributed signal processing capability. State-of-the-art compressive sensing algorithms like sparse Bayesian learning can exploit the inherent sparsity of the mm-Wave wireless channels to estimate the channel connecting the remote radio head and the user equipment in the wireless access link. The performance of the sparse Bayesian learning based channel estimation can be adversely affected by impairments due to optical fiber based front-haul channel and quantization noise. As a result, it is necessary to compensate for the performance degradation by applying methods which can combat the effects of the front-haul channel. Contemporary research has demonstrated the capability of deep learning algorithms in signal enhancement under low signal-to-noise ratio conditions, such as hybrid beamforming design, channel estimation as well as feedback of channel state in heterogeneous multi-antenna wireless systems. Motivated by their de-noising and signal prediction capabilities, convolutional long short term memory networks are employed in this work to jointly remove the quantization noise and optical fiber impairment due to the front-haul channel, which can improve the performance of sparse Bayesian learning in estimating the wireless access channel at the base-band processing unit. Computer simulation results show that the proposed methodology performs well under low signal-to-noise ratio conditions.

Deep Neural Network aided Sparse Bayesian Learning for Wireless Access Channel Estimation in mm-Wave Massive MIMO Cloud Radio Access Network Systems

Judicious application of chemical fertilizers can significantly enhance the biochemical constituents of agriculturally important crop plants. Present study deals with the effect of indigenous input with reduced dose of chemical fertilizer towards biochemical contents of mustard leaf; a field experiment was conducted in a randomized complete block design (RCBD) pattern with three replications during two consecutive winter seasons of 2011-2012 and 2012-2013 under old alluvial soil zone of Burdwan, West Bengal, India. Pseudomonas putida, Burkholderia cepacia, Burkholderia sp. and their mixture were used as indigenous biofertilizer for mustard cultivation. Results revealed that indigenous inoculation (with reduced dose of chemical fertilizer) significantly increased (p<0.05) the biochemical contents (chlorophyll, carotenoids, carbohydrate and protein) of mustard leaf as compared to uninoculated control (full recommended dose of NPK fertilizers). The comprehensive approach of plant growth promoting rhizobacteria (PGPR) in agriculturally important crops should be carried out to explore the hidden potential of PGPR and to promote the quality and yield of crop under field conditions.

Evaluation of Indigenous Rhizobacterial Strains With Reduced Dose of Chemical Fertilizer towards Biochemical Constituents of Mustard Leaf (Brassica Campestris)

Speech Enhancement using tensor decomposition-based source separation and convolutional, bidirectional recurrent neural network (CNN-biRNN) architecture is investigated in this paper. An acoustic receiver comprising uniform linear array (ULA) of microphone sensors is considered, where the ULA performs CANDECOMP/PARAFAC (CP) tensor decomposition to separate the individual speech source signals from the received mixture of multi-channel signals, followed by single channel de-reverberation by a variant of the CNN-biRNN referred to as DenseNet-biLSTM to enhance the target speech signal-of-interest (SOI). While the source separation module based on CP-tensor decomposition is responsible for extracting the target SOI, the subsequent deep learning framework based on DenseNet-biLSTM enhances the extracted SOI by performing de-noising and de-reverberation. It is demonstrated by computer simulations that the proposed approach leads to good performance under multiple interfering speakers and reverberation.

Speech Enhancement using Multi-Microphone Array based Source Separation and Deep Learning

Herbal products are cheaper and effective than chemical based mosquito repellent, therefore an attempt has been made to prepare a 100 % herbal product based on traditional practices and rural wisdom. Present study was conducted for effective utilization of Basak (Adhatoda zeylanica Medik. syn. A. vasica Nees) as a mosquito repellent. Seven varieties of products were prepared from its leaves with different natural compositions. Study results revealed that maximum percentage of weight loss with V7 variety followed by V1 and lowest in variety V6. Almost all varieties of bio-cake burned within 35 – 45 minutes except variety V5, which took 60 minutes to complete burning. Variety V6 showed highest ash content (7.70 g) and lowest ash was recorded for V5 (1.67 g) after complete burning. The emission of CO2, CO and O3 was recorded as V2>V4>V3>V5 � V7>V1>V6; V5>V6>V1>V7>V4>V2>V3 and V5>V3 � V4 � V7>V2>V1>V6, respectively during burning of bio-cake. On the other hand knock down experiment demonstrated that almost all varieties of bio-cakes are effective as mosquito repellent. But only two varieties (V6 and V7) caused more than 50 % knock down. Finally the emission of gaseous product was compared with a synthetic mosquito coil.

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Efficacy of Adhatoda zeylanica Medik. leaf as mosquito repellent

Resource scheduling in cloud computing is one of the impactful area of research. Cloud service providers maintain its efficacy through proper resource management schemes. Users experience seamless cloud services when cloud service provider manages its resources efficiently. Another aspect in resource management is energy efficient schemes. Energy efficiency largely depends on employment of minimum number of resource servers. This article discusses energy efficient resource scheduling mechanism on multi-layer prioritized resource requests. Resource requests are segregated into three categories and those are scheduled on resource servers based on their fast availability. The work focuses on scheduling resource requests on previously active servers as much as possible before activating any idle servers. Proposed scheduling scheme has been tested on a standard benchmark datasets and performance of the proposed method establishes its efficacy.*

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Jayanta Kumar Datta

Papers

Deep Neural Network aided Sparse Bayesian Learning for Wireless Access Channel Estimation in mm-Wave Massive MIMO Cloud Radio Access Network Systems

Evaluation of Indigenous Rhizobacterial Strains With Reduced Dose of Chemical Fertilizer towards Biochemical Constituents of Mustard Leaf (Brassica Campestris)

Speech Enhancement using Multi-Microphone Array based Source Separation and Deep Learning

Efficacy of Adhatoda zeylanica Medik. leaf as mosquito repellent

Prioritized Energy Efficient Resource Scheduling in Cloud Computing