Open Hub
Open Hub

 Settings |  Report Duplicate

0
I Use This!
Very Low Activity

Commits : Listings

Analyzed about 2 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Section 1598,1603 updated
K.Srinivasan
More... over 1 year ago
Section 1603 updated
K.Srinivasan
More... over 1 year ago
1603. (THEORY and FEATURE) Governing Equation Discovery from Stock quote timeseries by SINDy (Sparse Identification of Non-Linear Dynamics) - Function for SINDy Lorenz Logistic Map fit 3 - corrections and ratelimit exception handling - related to 690,691,692,1257,1315,1316,1327,1348,1367,1375,1376,1436,1437,1438,1515,1517,1518,1601,1602 to and all sections on Computational Economics-Quantitative Finance and Stockquote TimeSeries Analysis, Climate and Weather analytics, Graphical Event Models (GEM), Causal Event Models (CEM), Chain Event Graphs(CEG), Chaos and Fractals, EventNet causality, Multifractal Detrended Fluctuation Analysis (MFDFA), Linear and Logistic regression, Global Warming - 6 May 2025
K.Srinivasan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------- 1603. (THEORY and FEATURE) Governing Equation Discovery from Stock quote timeseries by SINDy (Sparse Identification of Non-Linear Dynamics) - Function for SINDy Lorenz Logistic Map fit 3 - corrections and ratelimit exception handling - related to 690,691,692,1257,1315,1316,1327,1348,1367,1375,1376,1436,1437,1438,1515,1517,1518,1601,1602 to and all sections on Computational Economics-Quantitative Finance and Stockquote TimeSeries Analysis, Climate and Weather analytics, Graphical Event Models (GEM), Causal Event Models (CEM), Chain Event Graphs(CEG), Chaos and Fractals, EventNet causality, Multifractal Detrended Fluctuation Analysis (MFDFA), Linear and Logistic regression, Global Warming - 6 May 2025 ------------------------------------------------------------------------------------------------------------------------------- 1603.1 This update is continuation of section 1602 - python-src/SINDy.py has been updated to invoke fit() on lorenzx_1 L2 norm 1 dimensional Lorenz training data (which was incorrect y earlier - due to this error trend predicted by testlogs/SINDy.5May2025.jpg may not be a logistic map fit exactly) 1603.2 Stock quote ticker timeseries yahoo finance datasource throws ratelimit exceptions for repetitive calls which has been handled by try-except block and in case of an exception a random dummy timeseries dataset is returned. 1603.3 Following are the logs and graph plots for this update: testlogs/SINDy.6May2025.jpg (random timeseries are used as datasets due to yahoo finance ratelimit errors) testlogs/SINDy.log.6May2025 1603.4 After above changes SINDy model learns a Lorenz attractor training data by vector integral and based on it future trend of stock quote timeseries is predicted by SINDy. 1603.5 SINDy lorenz logistic map model prediction could be suitable for Matiyasevich Riemann Hypothesis Python program while loop LHS-RHS ratio timeseries which has been approximately upperbounded by Logistic map in section 1597.
K.Srinivasan
More... over 1 year ago
+1602. (THEORY and FEATURE) Governing Equation Discovery from Stock quote timeseries by SINDy (Sparse Identification of Non-Linear Dynamics) - Function for SINDy Lorenz Logistic Map fit 2 - further refinements, predictions and graphics - related to 690,691,692,1257,1315,1316,1327,1348,1367,1375,1376,1436,1437,1438,1515,1517,1518,1601 to and all sections on Computational Economics-Quantitative Finance and Stockquote TimeSeries Analysis, Climate and Weather analytics, Graphical Event Models (GEM), Causal Event Models (CEM), Chain Event Graphs(CEG), Chaos and Fractals, EventNet causality, Multifractal Detrended Fluctuation Analysis (MFDFA), Linear and Logistic regression, Global Warming - 5 May 2025
K.Srinivasan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------- 1602. (THEORY and FEATURE) Governing Equation Discovery from Stock quote timeseries by SINDy (Sparse Identification of Non-Linear Dynamics) - Function for SINDy Lorenz Logistic Map fit 2 - further refinements, predictions and graphics - related to 690,691,692,1257,1315,1316,1327,1348,1367,1375,1376,1436,1437,1438,1515,1517,1518,1601 to and all sections on Computational Economics-Quantitative Finance and Stockquote TimeSeries Analysis, Climate and Weather analytics, Graphical Event Models (GEM), Causal Event Models (CEM), Chain Event Graphs(CEG), Chaos and Fractals, EventNet causality, Multifractal Detrended Fluctuation Analysis (MFDFA), Linear and Logistic regression, Global Warming - 5 May 2025 ------------------------------------------------------------------------------------------------------------------------------- 1602.1 This update is continuation of section 1601 - python-src/SINDy.py has been updated to import matplotlib plot and math. SINDY_fit_lorenz() has been changed to compute L2 norm of 3D lorenz attractor points and make it one dimensional training data which is used to fit a Lorenz attractor SINDy model. 1602.2 Past weekly 5 year stock quote data is input to earlier Lorenz model to predict future trend (which could be for 5 years) - predicted future trend is plotted for earlier 7 stocks in 1601. 1602.3 Time interval for solve_ivp() Lorenz integral has been made proportionate to the size of predicted stock quote dataset. All 7 stocks are predicted by setting model="LorenzLogisticMap" flag - following are logs and graph plots for this update: testlogs/SINDy.5May2025.jpg testlogs/SINDy.log2.5May2025
K.Srinivasan
More... over 1 year ago
Section 701 updated
K.Srinivasan
More... over 1 year ago
1601. (THEORY and FEATURE) Governing Equation Discovery from Stock quote timeseries by SINDy (Sparse Identification of Non-Linear Dynamics) - Function for SINDy Lorenz Logistic Map fit - implementation - related to 690,691,692,1257,1315,1316,1327,1348,1367,1375,1376,1436,1437,1438,1515,1517,1518 to and all sections on Computational Economics-Quantitative Finance and Stockquote TimeSeries Analysis, Climate and Weather analytics, Graphical Event Models (GEM), Causal Event Models (CEM), Chain Event Graphs(CEG), Chaos and Fractals, EventNet causality, Multifractal Detrended Fluctuation Analysis (MFDFA), Linear and Logistic regression, Global Warming - 5 May 2025
K.Srinivasan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------- 1601. (THEORY and FEATURE) Governing Equation Discovery from Stock quote timeseries by SINDy (Sparse Identification of Non-Linear Dynamics) - Function for SINDy Lorenz Logistic Map fit - implementation - related to 690,691,692,1257,1315,1316,1327,1348,1367,1375,1376,1436,1437,1438,1515,1517,1518 to and all sections on Computational Economics-Quantitative Finance and Stockquote TimeSeries Analysis, Climate and Weather analytics, Graphical Event Models (GEM), Causal Event Models (CEM), Chain Event Graphs(CEG), Chaos and Fractals, EventNet causality, Multifractal Detrended Fluctuation Analysis (MFDFA), Linear and Logistic regression, Global Warming - 5 May 2025 ------------------------------------------------------------------------------------------------------------------------------- 1601.1 python-src/SINDy.py has been updated to import solve_ivp (vector integration) from SciPy and lorenz logistic map function from PySINDy. PySINDy has in-built support for Lorenz logistic map - defined as PDEs in https://pysindy.readthedocs.io/en/latest/examples/sindy-original-example/original_paper.html and https://pysindy.readthedocs.io/en/latest/api/pysindy.html#module-pysindy 1601.2 New function def SINDy_fit_lorenz(t,x,y,order=2,degree=5,threshold=0.0001) has been implemented that (*) vector-integrates Lorenz logistic map by solve_ivp() over a predefined time interval to get points along Lorenz attractor (*) and instantiates SINDy object with default arguments to invoke fit() on a stock quote timeseries - code for this function is based on documentation code examples in https://pysindy.readthedocs.io/en/latest/examples/sindy-original-example/original_paper.html and https://pysindy.readthedocs.io/en/latest/api/pysindy.html#module-pysindy (Non-default arguments to SINDy cause exceptions) 1601.3 At present arrays y (stockquotes timeseries) and t (time points) are made equal if unequal by padding zeros or by truncation to prevent runtime errors. Lorenz SINDy Model is printed along with coefficients, equations and score. 1601.4 Function def SINDy_fit(t,x,y,order=2,degree=5,threshold=0.0001) has been changed to increase degree of polynomial to 5. 1601.5 Function def stockquote_SINDy_model(ticker,period='5y',interval='1wk',model='Plain') has been updated to included new argument "model" which chooses either normal fit or Lorenz fit based on values "Plain" or "LorenzLogisticMap". Time range array t is separately assigned by hardcoding at present just before SINDy_fit_lorenz() is invoked. 1601.6 Magnificent seven stock quotes in IT sector (NASDAQ symbols for Amazon,Microsoft,Meta,Google,Tesla,Amazon,Nvidia) are analyzed by SINDy_fit_lorenz() and SINDy_fit() as below (along with corresponding model governing equations discovered and score) - logs excerpts from testlogs/SINDy.log.5May2025: 1601.6.1 Lorenz - stockquote_SINDy_model('MSFT',model='LorenzLogisticMap'): (x0)' = 217.412 1 + -0.614 x0 model coefficients: [[217.4121331 -0.61404112 0. ]] model equations: ['217.412 1 + -0.614 x0'] model score: 0.00998880388907486 1601.6.2 Plain - stockquote_SINDy_model('GOOG'): model equations: ['1.000 1', '-1.228 1 + -0.012 x + 0.035 y'] 1601.6.3 Lorenz - stockquote_SINDy_model('AAPL',model='LorenzLogisticMap'): (x0)' = 159.962 1 + -0.829 x0 model coefficients: [[159.96226582 -0.82868427 0. ]] model equations: ['159.962 1 + -0.829 x0'] model score: 0.012955994856997388 1601.6.4 Plain - stockquote_SINDy_model('AMZN'): model equations: ['1.000 1', '-2.166 1 + -0.030 x + 0.056 y'] 1601.6.5 Lorenz - stockquote_SINDy_model('META',model='LorenzLogisticMap'): (x0)' = 145.913 1 + -0.463 x0 model coefficients: [[145.91291066 -0.46338518 0. ]] model equations: ['145.913 1 + -0.463 x0'] model score: 0.002198506393575106 1601.6.6 Plain - stockquote_SINDy_model('NVDA'): model equations: ['1.000 1', '-0.948 1 + -0.031 x + 0.159 y + -0.001 x y'] 1601.6.7 Lorenz - stockquote_SINDy_model('TSLA',model='LorenzLogisticMap'): (x0)' = 199.508 1 + -0.134 x0 model coefficients: [[ 1.99508405e+02 -1.34281885e-01 0.00000000e+00]] model equations: ['199.508 1 + -0.134 x0'] model score: 0.00023945148672521732 1601.7 Stocks belonging to same sector often have similar governing equations (or polynomials of similar shape clustering together) due to similar extraneous factors common to all stocks in that sector (e.g news sentiment variable from linear regression fame-merit risk model in 1489.5 is an external factor depending not fully on intrinsic performance alone)
K.Srinivasan
More... over 1 year ago
Section 701 updated
K.Srinivasan
More... over 1 year ago
1598. (THEORY) Matrix Decomposition or Matrix Factorization and its connections to Coin Problem and Integer Factorization - related to related to 620,713,728,739,1158,1159,1161,1213,1214 and all sections on Coin Problem, Frobenius coin diophantine, Neuro Cryptocurrency, Computational Geometric Factorization, Set Partitions to Exact Square Tile Cover Reduction by Lagrange Sum of 4 Squares - Optimal denominations and Money Changing Problem, Integer Partitions, Equidepth-Equiheight V-Optimal Histograms and Set partition analytics, Drone Electronic Voting Machines,Catalytic computing,Tensor decomposition - 29 April 2025
K.Srinivasan
More... over 1 year ago
Updated BestPractices.txt
K.Srinivasan
More... over 1 year ago
Section 1450 updated
K.Srinivasan
More... over 1 year ago
Section 1236 updated
K.Srinivasan
More... over 1 year ago
Section 1489 updated
K.Srinivasan
More... over 1 year ago
Section 1489 updated
K.Srinivasan
More... over 1 year ago
1597. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 6 - Non-linear dynamics fit of while loop LHS-RHS ratio timeseries by SINDy - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591,1596 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorandom generator, CPU Branch Prediction, Sparse Indentification of Non-Linear Dynamics (SINDy) - 18 April 2025
K.Srinivasan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------- 1597. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 6 - Non-linear dynamics fit of while loop LHS-RHS ratio timeseries by SINDy - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591,1596 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorandom generator, CPU Branch Prediction, Sparse Indentification of Non-Linear Dynamics (SINDy) - 18 April 2025 ------------------------------------------------------------------------------------------------------------------------------- 1597.1 python-src/MatiyasevichRH.py has been updated to import SINDy.py (PySINDy wrapper) for analyzing Matiyasevich Riemann Hypothesis Python program while loop condition LHS/RHS ratio series evolution through Sparse Identification of Non-Linear Dynamics(SINDy) which is recently used widely to discover governing equations underneath complex systems and NeuronRain already applies it to discover governing equations for Stock market timeseries and Weather timeseries which are known to be non-linear chaotic systems. 1597.2 NeuronRain SINDy implementation SINDy.SINDy_fit() is invoked for LHS/RHS ration series after every iteration. 1597.3 Function def SINDy_fit(t,x,y,order=2,degree=3,threshold=0.0001) has been fine-tuned to include more parameters for differentiation method (order of Finite difference), feature library (degree of Polynomial) and STLSQ (threshold) in SINDy object instantiation. This change in SINDy.py also applies to Weather and Stock market Non-Linear Dynamics models making them more refined and configurable (without above additional parameters some coefficients in governing equations may not be printed). 1597.4 After 10000 iterations following are the SINDy governing equations and Higuchi fractal dimensions (from testlogs/MatiyasevichRH.log.18April2025) - x' and y' denote first derivatives of x and y axis - first derivative of LHS/RHS ratio is fit to a degree 3 polynomial (y)' = 0.007 y + 0.230 y^2 + 0.277 y^3 after 10000 iterations: ------------------------- iteration: 10000 -------------------------- LHS/RHS: 0.0069894135097758985 Higuchi Fractal Dimension of the 1-dimensional timeseries: 1.842788109500823 SINDy non-linear dynamics fit of LHS-RHS ratio series so far: ================ SINDy non-linear dynamics governing equation discovery ========= (x)' = 1.000 1 (y)' = 0.007 y + 0.230 y^2 + 0.277 y^3 model coefficients: [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [0. 0. 0.00725937 0. 0. 0.23032554 0. 0. 0. 0.2773797 ]] model equations: ['1.000 1', '0.007 y + 0.230 y^2 + 0.277 y^3'] -------------------- 1597.5 Following are graphics for Logistic map upperbound of while loop LHS/RHS series: testlogs/MatiyasevichRH.LHSRHSRatio.18April2025.jpg - unmagnified 10000 iterations testlogs/MatiyasevichRH.LHSRHSRatio.magnified.18April2025.jpg - magnified logistic map and LHS/RHS ratio oscillations for first 450 iterations 1597.6 While loop condition - while p**2*(m-f0) < f3 - is indeed a chaotic competition between 3 logistic maps because of following feedback statements within the loop: (*) d=2*n*d-4*(-1)**n*h (plays an important role in making LHS negative often due to exponentiatiion of -1 and RHS has no such limitation) (*) h=f0 (*) f0=2*n*h (*) f3=(2*n+3)*f3 which make f0 and m recurrent maps for LHS (f0 <- 2*n*f0, m <- m + d and d <- 2*n*d-4*(-1)**n*h) and f3 a recurrent map for RHS (f3 <- (2*n+3)*f3) respectively. Earlier observation makes SINDy a suitable choice for analyzing while loop LHS/RHS ratio seriesas a non-linear system. 1597.7 Logistic map upperbound oscillates between 0.4 and 0.8 while while loop LHS/RHS ratio series oscillates between -0.1 and 0.1 from earlier graphics for 10000 iterations. LHS/RHS ratio must reach 1.0 for while loop to exit and Riemann Hypothesis to be false.
K.Srinivasan
More... over 1 year ago
Section 1272 updated
K.Srinivasan
More... over 1 year ago
1596. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 5 - Bounding while loop LHS-RHS ratio timeseries by Logistic map - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorandom generator, CPU Branch Prediction - 16,17 April 2025
K.Srinivasan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------- 1596. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 5 - Bounding while loop LHS-RHS ratio timeseries by Logistic map - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorandom generator, CPU Branch Prediction - 16,17 April 2025 ------------------------------------------------------------------------------------------------------------------------------- 1596.1 python-src/ChaosAttractor.py has been updated to change the location RPy2 import. Maximum size of an integer is raised to 150000 digits through sys.set_int_max_str_digits(150000) 1596.2 python-src/MatiyasevichRH.py has been updated to import Ulam-Neumann Logistic map pseudorandom generator ChaosPRG() from ChaosAttractor and Numeric Python. Logs reflect trends for 2 sets of iterations: 1) 23677 - testlogs/MatiyasevichRH.log1.17April2025 without LHSRHSRatio graphics and 2) 10000 - testlogs/MatiyasevichRH.log2.17April2025 with LHSRHSRatio graphics. Chaotic pseudorandom sequence from ChaosPRG() for Logistic Map rx(1-x) for initial condition 0.000001 and r=3.5699340 (when bifurcation tends to get chaotic) is generated for 10000 iterations and plotted alongside Matiyasevich program while loop LHS-RHS ratio timeseries. 1596.3 Logs for 23677 iterations - testlogs/MatiyasevichRH.log1.17April2025 - is a grepped stripped-down excerpt due to 2.5 GB size. Pearson Correlation Coefficient is computed between while loop LHS-RHS ratio and Logistic map chaotic pseudorandom sequence. Convergence test has been made optional by a boolean flag. 1596.4 Following logs and plots for above code changes reveal how Matiyasevich while loop LHS-RHS ratio timeseries is upperbounded by Logistic map at r=3.5699340 for first 10000 iterations (or) LHS/RHS <= LogisticMap at r=3.5699340 and initial condition=0.000001 - Logistic map upperbound of LHS/RHS ratio timeseries implies RHS of while loop is ahead of LHS (despite chaos) for 10000 iterations atleast which if extrapolated asymptotically would imply Riemann Hypothesis is True: - testlogs/MatiyasevichRH.LHSRHSRatio.17April2025.jpg - Graphics for Logistic map (heavy oscillations are shown as a thick red band) upperbounding Matiyasevich program while loop LHS-RHS ratio timeseries (blue) for 10000 iterations - testlogs/MatiyasevichRH.log1.17April2025 (grepped - 23677 iterations) - testlogs/MatiyasevichRH.log2.17April2025 (10000 iterations)
K.Srinivasan
More... over 1 year ago
Section 1272 updated
K.Srinivasan
More... over 1 year ago
Section 1591 updated
K.Srinivasan
More... over 1 year ago
Section 1591 updated
K.Srinivasan
More... over 1 year ago
Section 1562 updated
K.Srinivasan
More... over 1 year ago
Section 1562 updated
K.Srinivasan
More... over 1 year ago
1595. (THEORY and FEATURE) Nachiketas Formal LLM - Therapeutic Data Commons and PrimeKG medical knowlege graph integration - Changes to Wikipedia Question-Answering algorithm for better accuracy 24 - PrimeKG Knowledge graph link prediction for drug discovery - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1543,1585,1589,1594 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, Question-Answering, Embedding in vectorspace, Machine Translation, Transformers Attention Model,Named Entity Recognition and Meaning Representation, Complexity models of Question-Answering (multiple choice,open-ended,adaptive and static) - LTF,PTF,Switching Circuits,TQBF,Markov Chains, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression, Drug Discovery, AI drug prescriptions, Link prediction, Six-degrees of separation - 11 April 2025
K.Srinivasan
More... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------- 1595. (THEORY and FEATURE) Nachiketas Formal LLM - Therapeutic Data Commons and PrimeKG medical knowlege graph integration - Changes to Wikipedia Question-Answering algorithm for better accuracy 24 - PrimeKG Knowledge graph link prediction for drug discovery - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1543,1585,1589,1594 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, Question-Answering, Embedding in vectorspace, Machine Translation, Transformers Attention Model,Named Entity Recognition and Meaning Representation, Complexity models of Question-Answering (multiple choice,open-ended,adaptive and static) - LTF,PTF,Switching Circuits,TQBF,Markov Chains, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression, Drug Discovery, AI drug prescriptions, Link prediction, Six-degrees of separation - 11 April 2025 -------------------------------------------------------------------------------------------------------------------------------- 1595.1 python-src/KnowledgeGraph.py has been updated to define new function link_prediction() that computes likelihood of a future link between two vertices on the knowledge graph through various standard link prediction algorithms implemented in NetworkX - Resource Allocation Index, Jaccard Coefficient, Adamic-Adar index, Preferential Attachment, Soundarajan-Hopcroft, CCPA score. 1595.2 Earlier function link_preduction() is invoked within create_PrimeKG_knowledge_graph() in the context of drug discovery that predicts likelihood of a connection between a pharma drug and a disease. 1595.3 More information in python-src/testlogs/KnowledgeGraph.log.11April2025 for PrimeKG Drug-Disease graph link prediction example.
K.Srinivasan
More... over 1 year ago
Section 1594 updated
K.Srinivasan
More... over 1 year ago
Section 1594 updated
K.Srinivasan
More... over 1 year ago