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In-Memory Column Index (IMCI) is the technology utilized by PolarDB-M to accelerate complex SQL queries, enhancing execution performance for intricate queries through columnar storage indexing. This improvement elevates PolarDB-M's real-time analytics capabilities.

Recently, I developed an in-depth data resource that introduces and categorizes the S&P 500, offering a clear, accessible analysis of various facets of the index. This includes breakdowns of company weight, unweighted versus weighted comparisons, the MAG7 and FAB5 groups, revenue-to-performance correlations, and sector-specific insights. To enhance clarity, I incorporated detailed charts and tables generated using SQL and Excel, utilizing comprehensive data cleanup and organization techniques. The data, collected over the past several months from multiple reputable sources, is tailored to provide valuable insights for students and others eager to expand their market knowledge. I welcome any feedback on the resource. https://lnkd.in/gGdEiB8R

Consider an array of integers representing various data points. The objective is to identify the number that occurs the least number of times in the array. This least occurring number holds significance in scenarios such as anomaly detection, outlier analysis, or resource allocation optimization.

Dynamic threshold panel data regression model Use dtp With (In) R Software https://ln.run/dtp_ Performs estimations of a dynamic panel data threshold effects model with endogenous and endogeneity (regression) regressors Use xtendothresdpd and xthenreg With STATA 18 https://ln.run/xthenreg

Generalised Joint Regression Models (Modelling) With Classic Bivariate Probit Use gjrm With (In) R Software https://ln.run/gjrm Generalized two-parameter (2-parameter) log-gamma regression Use glgamma2 With STATA 18 https://ln.run/glgamma2

Are you interested in data-driven control? Then, you should definitely check our latest research: “From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements” with Andrea Carron, Melanie Zeilinger, Johannes Köhler We've developed a novel framework, Data to Predictive Control (D2PC), that bridges the gap between data-driven techniques and predictive control for discrete-time stochastic linear systems. D2PC offers: A parameter identification method based on Expectation-Maximization that enables integration of pre-defined constraints. An asymptotically correct uncertainty quantification approach, allowing us to establish probabilistic sets over parameters. A robust dynamic output-feedback controller synthesis strategy. A predictive control scheme ensuring recursive feasibility and chance constraint satisfaction. D2PC marks a significant step forward in integrating data into robust and predictive control schemes. We demonstrate its effectiveness on a 10-dimensional spring-mass-damper system. Check out our paper for the full details: https://lnkd.in/dBQwu9mf Additionally, we offer an extensive code framework implementing our pipeline: https://lnkd.in/d5pdADE6 NCCR Automation ETH Foundation #ControlSystems #ControlTheory #ModelPredictiveControl #MPC

Equating the notched and smooth field solutions gives the equation to solve for alpha, see below. With “adjustable” notched data at N= 1e6 only one equation is required and it can be setup at any location within the bounds of the data for the linear fit. The example utilizes the 1e4 data. AI would do a much more thorough job using many test configurations. This, as I have been saying, is a simplified example to walk through the process. In actual cases the decay function may be more complex. Remember there will also be a decay function for steady stress. This energy solution is based on coupon test data. At N = 1e4 à B = 52/24 = 2.1667 At N = 1e6 à B = 20/9.231 = 2.1667 Adjusted 10 ksi 1e6 lower stress value to match B. At N = 1e4  

How MCDM can be used to evaluate effiency? Steps & Approaches.. 1 - Identifying criteria for efficency. 2 - Assigning Weights for each Criteria by reverting to Stakhholders/ Industry experts inputs or by applying AHP " Analytic hierarchy Process". 3 - Gatheing performance Data. 4 - Applying MCDM algorithms: -WSM "Weighted Sum Model". -Topsis " technique for order preference by Similarty to Ideal Solution". -Analytic Netwrok Process " measuring the interrelations between criteria and how this affects the score of each alternative. 5 - Analyzing Results to: -Ranking employees based on generated figures. -Identify Weakness and strengths. -Make informed decisions and take proactive actions to avoid performance drops.

Fit a GLM with lasso (or elastic net), snet or mnet regularization Use glmreg With (In) R Software https://ln.run/glmreg Consistent estimation of linear regression models using matched data Use msreg With STATA 18 https://ln.run/msreg