Measurement of Microbial Protein Supply in Murrah Buffaloes (Bubalus bubalis) Using Urinary Purine Derivatives Excretion and PDC Index

Dipu, M.T.,George, S.K.,Singh, P.,Verma, A.K.,Mehra, U.R. Asian Australasian Association of Animal Productio 2006 Animal Bioscience Vol.19 No.3

A study was conducted to predict the rumen microbial protein production based on urinary excretion of purine derivatives in buffaloes fed a diet of wheat straw and concentrate (40:60) at four fixed levels of feed intake. (95, 80, 60 and 40% of preliminary voluntary feed intake) following experimental protocol of IAEA (Phase I). The buffaloes were allocated according to a $4{\times}4$ latin square design. The urinary allantoin, uric acid, total PD excretion (mmol/d) in treatments L-95, L-80, L-60 and L-40 was 20.13, 16.00, 12.96 and 9.17; 1.88, 2.12, 2.11 and 2.15; 22.01, 18.12, 15.07 and 11.32, respectively and were significantly (p<0.05) different among treatments except for uric acid. The rate of PD excretion (mmol/d) was positively correlated with the digestible organic matter intake. Variations were observed in PD and creatinine concentration in spot samples collected at 6-hour interval. However, daily PD:Creatinine ratio (PDC index) appears to be a reasonably good predictor of microbial-N supply. The contribution of basal purine excretion to total excretion of purine derivatives (PD) was determined in pre-fasting period followed by a fasting period of 6 d (Phase II). Daily PD and creatinine excretion (mmol/kg $W^{0.75}$) during fasting averaged 0.117 and 0.456 respectively for buffaloes. The excretion rates of PD decreased significantly (p<0.01) during fasting compare to pre-fasting period, the urinary creatinine excretion remained almost similar. Except for creatinine, plasma concentration of target parameters significantly (p<0.01) declined during fasting. Likewise, glomerular filtration rate (GFR) and renal clearance of allantoin and uric acid also decreased. Based on the PD excretion rates during fasting and at different levels of feed intake obtained in this study, a relationship between daily urinary PD excretion (Y-mmol) and microbial purine absorption (X-mmol) was developed for buffaloes as Y = 0.74X+0.117 kg $W^{0.75}$. The microbial N supply (g/kg DOMI) remained statistically similar irrespective of dietary treatment. The results showed that excretion of urinary purine derivatives is positively correlated with the levels of feed intake in Murrah buffaloes and thus, estimation of urinary purine derivatives and PDC index could be used to determine microbial nitrogen supply when there is large variation in level of feed intake.

Machine Learning Enabled Steady-State Security Predictor as Deployed for Distribution Feeder Reconfiguration

Sarkar Dipu,Gunturi Sravan Kumar 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.3

The distribution network is reconfi gured by modifying the topology arrangement of the network feeders. Because the voltage stability of the distribution networks can diff er within a range following network reconfi guration, the calculation of steady-state voltage stability plays a signifi cant role in real-time feeder reconfi guration. Examining the state of security and estimating it for the next operational confi guration is crucial for making real-time decisions. Online security evaluation needs minimal complexity and computing time. Standard methods of assessing the distribution network’s steady-state voltage stability can be insuffi cient for online and real-time environments due to their high complexity and long computing period. This study proposes a machine learning (ML) approach for classifying confi guration states and adopts the decision tree technique to interpret the online applications in the feeder reconfi guration. For the classifi cation, the single line equivalent L-index voltage stability and switching confi gurations of the feeders are employed as training information for the ML models. A modifi ed IEEE 14-bus and 30-bus test systems verifi es the feasibility of the suggested solution. Once trained, the proposed system provides a quick and accurate classifi cation for unknown confi gurations of the specifi c security state in 0.3 seconds.

Influence of Level of Feed Intake on Concentration of Purine Derivatives in Urinary Spot Samples and Microbial Nitrogen Supply in Crossbred Bulls

George, S.K.,Dipu, M.T.,Mehra, U.R.,Verma, A.K.,Singh, P. Asian Australasian Association of Animal Productio 2006 Animal Bioscience Vol.19 No.9

The potential of the spot urine sampling technique as an alternative to performing a total urine collection to predict the microbial nitrogen supply was evaluated in crossbred bulls. In a completely randomized design, 20 growing crossbred bulls were assigned four levels of feed intake (120, 100, 80 and 60% of voluntary dry matter intake) on diets comprised of wheat straw and concentrate mixture (50:50). After three months of experimental feeding, a metabolism trial was conducted for ten days, during which spot urine collections were performed every 6 h post feeding on days 9 and 10. The daily urinary excretion of allantoin (A) and purine derivatives (PD) decreased with the reduction in feed intake while creatinine (C) excretion remained similar in animals fed at different levels. The microbial nitrogen (MN) supply calculated from the PD excreted in total urine (35.08 to 72.08 g/d) was higher at increased levels of feed intake. PD concentration in spot urine samples had poor correlation with feed intake except at 12 h post feeding. A/C ratio and PD/C ratio in spot urine samples remained similar irrespective of sampling time and significantly (p<0.01) correlated with daily urinary PD excretion, digestible organic matter intake and dry matter (DM) intake. However, no significant differences were evident in these ratios among animals fed at levels 120, 100 and 80% of voluntary dry matter intake (VDMI) at different times post feeding. These results suggests that the spot urine sampling technique to predict the microbial protein supply is not suitable for detecting small differences in MN supply and hence, estimation of PD excreted in total urine (mmol/d) is necessary to assess precisely the MN supply in crossbred bulls.

Strategy for the Identification of Optimal Network Distribution Through Network Reconfiguration Using Graph Theory Techniques − Status and Technology Review

Konwar Pushpanjalee,Sarkar Dipu 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.6

Nowadays, power system reconfi guration has received much interest and has achieved signifi cant progress. Reconfi guration can be accomplished by modifying the status of the sectionalizing switches or tie switches. The main objective of a network reconfi guration strategy is to reduce power loss, relieve overloading and improve load end voltage profi le. This study reviews the advancement of power system reconfi guration research using graph theory including optimization-based approaches. In the context of determining the optimal radial network using graph theory-based strategies are also discussed. Network reconfi guration mechanism, which includes diff erent graph theory analysis such as Prim’s Minimal Spanning Tree, Dijkstra’s Shortest Path Algorithm, Kruskal’s Maximal Spanning Tree, Edmonds’ Maximal Spanning Tree are presented. Four diff erent code for the above graph theory techniques which already been implemented by previous researchers have been simulated and generate candidate solutions for 14 node system. The simulations demonstrate step by step procedure of the network reconfi guration mechanism. Moreover, simulation results verify that the graph theory approach can give good solution for Network reconfi guration with optimal radial network, less power loss and higher level of load voltage.

Aerosol indirect effect during successive contrasting monsoon seasons over Indian subcontinent using MODIS data

Panicker, A.S.,Pandithurai, G.,Dipu, S. Pergamon Press ; Elsevier [distribution] 2010 Atmospheric environment Vol.44 No.15

Aerosol indirect effect (AIE) was estimated over six Indian regions, which have been identified as main source regions of absorbing aerosol emissions, for four successive contrasting monsoon years, 2001 (normal monsoon rainfall year), 2002 (drought year), 2003 (excess monsoon rainfall year) and 2004 (below normal rainfall year). The AIE has been estimated both for fixed cloud liquid water path (CLWP) and for fixed cloud ice path (CIP) bins, ranging from 1 to 350 gm<SUP>-2</SUP> at 25 gm<SUP>-2</SUP> intervals obtained from Moderate resolution imaging spectroradiometer (MODIS). In 2002 and 2004, AIE found to be of positive (Twomey effect) in majority of the fixed CLWP and CIP bins, while in 2001 and 2003 majority of the bins were found to be showing negative indirect effect (Anti-Twomey effect). Changes in circulation patterns during contrasting monsoon seasons, bringing up air mass containing aerosols of different source origins may be the main reason for this positive and negative AIE. The study suggests that AIE could be one of the factors in modulating Indian summer monsoon. However, further research on this topic is to be carried out to establish the relationship between AIE and Indian monsoon rainfall and also AIE values may be parameterized in climate models for better prediction of monsoon.

NN-based Prediction Interval for Nonlinear Processes Controller

Mohammad Anwar Hosen,Abbas Khosravi,H. M. Dipu Kabir,Michael Johnstone,Douglas Creighton,Saeid Nahavandi,Peng Shi 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.9

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

On the contribution of black carbon to the composite aerosol radiative forcing over an urban environment

Panicker, A.S.,Pandithurai, G.,Safai, P.D.,Dipu, S.,Lee, Dong-In Elsevier 2010 Atmospheric environment Vol.44 No.25

<P><B>Abstract</B></P><P>This paper discusses the extent of Black Carbon (BC) radiative forcing in the total aerosol atmospheric radiative forcing over Pune, an urban site in India. Collocated measurements of aerosol optical properties, chemical composition and BC were carried out for a period of six months (during October 2004 to May 2005) over the site. Observed aerosol chemical composition in terms of water soluble, insoluble and BC components were used in Optical Properties of Aerosols and Clouds (OPAC) to derive aerosol optical properties of composite aerosols. The BC fraction alone was used in OPAC to derive optical properties of BC aerosols. The aerosol optical properties for composite and BC aerosols were separately used in SBDART model to derive direct aerosol radiative forcing due to composite and BC aerosols. The atmospheric radiative forcing for composite aerosols were found to be +35.5, +32.9 and +47.6Wm<SUP>−2</SUP> during post-monsoon, winter and pre-monsoon seasons, respectively. The average BC mass fraction found to be 4.83, 6.33 and 4μgm<SUP>−3</SUP> during the above seasons contributing around 2.2 to 5.8% to the total aerosol load. The atmospheric radiative forcing estimated due to BC aerosols was +18.8, +23.4 and +17.2Wm<SUP>−2</SUP>, respectively during the above seasons. The study suggests that even though BC contributes only 2.2–6% to the total aerosol load; it is contributing an average of around 55% to the total lower atmospheric aerosol forcing due to strong radiative absorption, and thus enhancing greenhouse warming.</P>