Abstract: A thin film transistor (TFT) array substrate is disclosed. In one aspect, the substrate includes a buffer layer formed over a substrate, a storage capacitor formed in the buffer layer and including a first electrode and a second electrode surrounding and insulated from the first electrode and a driving TFT formed over the buffer layer.

Abstract: A thin film transistor (TFT) array substrate is disclosed. In one aspect, the substrate includes a buffer layer formed over a substrate, a storage capacitor formed in the buffer layer and including a first electrode and a second electrode surrounding and insulated from the first electrode and a driving TFT formed over the buffer layer.

Abstract: A computer-implemented initial run module processes manifest variable data using computer-defined model specification parameters stored in a database to provide initial estimates of weights that are associated with latent variables. The initial run module employs a unique value-based weighting partial least squares computer-implemented process. A final run module then operates upon the manifest variable data to determine the importance of the predictor values that are then used to control the industrial, manufacturing or commercial process. The final run module implements a unique patient partial least squares regression model utilizing a boosting learning technique.

Abstract: A computer-implemented initial run module processes manifest variable data using computer-defined model specification parameters stored in a database to provide initial estimates of weights that are associated with latent variables. The initial run module employs a unique value-based weighting partial least squares computer-implemented process. A final run module then operates upon the manifest variable data to determine the importance of the predictor values that are then used to control the industrial, manufacturing or commercial process. The final run module implements a unique patient partial least squares regression model utilizing a boosting learning technique.

Abstract: A computer-implemented apparatus and method for determining the impacts of predetermined customer characteristics associated with measured physical attributes. A manifest variable database is utilized for storing manifest variable data that is indicative of the measured physical attributes. A partial least squares determinator is connected to the manifest variable database for determining statistical weights based upon the stored manifest variable data. A weights database which is connected to the partial lease squares determinator is utilized for storing the determined weights. A latent variable determinator is connected to the weighing database for determining scores for latent variables based upon the stored manifest variables and upon the stored weights. The latent variables are indicative of the predetermined customer characteristics. Additionally, a latent variable database is connected to the latent variable score determinator for storing the determined latent variable scores.