Estimating Water Yields in Utah by Principal Component Analysis

The basic hydrologic data required to determine the water yield are usually unavailable for small basins and streams while increasing emphasis is being placed on their development. Therefore, some methods and techniques for estimating the amount of water available for development of these small units is needed. The purpose of this study is to use the concepts and techniques of statistical analysis to develop equations which are useful in estimating the water yield of watersheds for which no stream flow records are available. The approach is an extension of earlier studies at Utah State University (1, 10) in which physiographic and topographic parameters were related to mean annual runoff of Utah watersheds. Previous studies used multiple regression techniques primarily. The work reported herein utilizes the same data as in the earlier work but analysis is based on the multivariate technique of principal component analysis. Results and evaluations derived from the principal component analysis are compared with those obtained from multiple regression analysis

Cancer risks associated with germline PALB2 pathogenic variants: An international study of 524 families

PURPOSE To estimate age-specific relative and absolute cancer risks of breast cancer and to estimate risks of ovarian, pancreatic, male breast, prostate, and colorectal cancers associated with germline PALB2 pathogenic variants (PVs) because these risks have not been extensively characterized. METHODS We analyzed data from 524 families with PALB2 PVs from 21 countries. Complex segregation analysis was used to estimate relative risks (RRs; relative to country-specific population incidences) and absolute risks of cancers. The models allowed for residual familial aggregation of breast and ovarian cancer and were adjusted for the family-specific ascertainment schemes. RESULTS We found associations between PALB2 PVs and risk of female breast cancer (RR, 7.18; 95% CI, 5.82 to 8.85; P = 6.5 × 10-76), ovarian cancer (RR, 2.91; 95% CI, 1.40 to 6.04; P = 4.1 × 10-3), pancreatic cancer (RR, 2.37; 95% CI, 1.24 to 4.50; P = 8.7 × 10-3), and male breast cancer (RR, 7.34; 95% CI, 1.28 to 42.18; P = 2.6 3 1022). There was no evidence for increased risks of prostate or colorectal cancer. The breast cancer RRs declined with age (P for trend = 2.0 × 10-3). After adjusting for family ascertainment, breast cancer risk estimates on the basis of multiple case families were similar to the estimates from families ascertained through population-based studies (P for difference = .41). On the basis of the combined data, the estimated risks to age 80 years were 53% (95% CI, 44% to 63%) for female breast cancer, 5% (95% CI, 2% to 10%) for ovarian cancer, 2%-3% (95% CI females, 1% to 4%; 95% CI males, 2% to 5%) for pancreatic cancer, and 1% (95% CI, 0.2% to 5%) for male breast cancer. CONCLUSION These results confirm PALB2 as a major breast cancer susceptibility gene and establish substantial associations between germline PALB2 PVs and ovarian, pancreatic, and male breast cancers. These findings will facilitate incorporation of PALB2 into risk prediction models and optimize the clinical cancer risk management of PALB2 PV carriers

Identification of Wiener and Hammerstein Systems with Rate Saturation

This paper proposes the use of the coherence function for distinguishing between a Wiener structure and a Hammerstein structure for systems with rate saturation nonlinearity. This is an important extension in the application of the coherence function from the usual case of static nonlinearity to cover dynamic nonlinearity. The test capitalizes on the same set of data generated when estimating the best linear approximation of the system. The identification of the system parameters through the best linear approximation is also analyzed in terms of the sensitivity and convergence properties. The effectiveness of the approach is illustrated through simulations. The results show that the proposed approach combining the structure identification and parameter estimation outperforms an existing method

Enhance Cascaded H-Bridge Multilevel Inverter with Artificial Intelligence Control

This paper proposed a 7-level Cascaded H-Bridge Multilevel Inverter (CHBMI) with two diffenrent controller, ie, PID and Artificial Neural Network (ANN) controller to improve the output voltage performance and achieve a lower Total Harmonic Distortion (THD). A PWM generator is connected to the 7-level CHBMI to provide switching of the MOSFET. The reference signal waveform for the PWM generator is set to be sinusoidal to obtain an ideal AC output voltage waveform from the CHBMI. By tuning the PID controller as well as the self-learning abilities of the ANN controller, switching signals towards the CHBMI can be improved. Simulation results from the general CHBMI together with the proposed PID and ANN controller based 7-level CHBMI models will be compared and discussed to verifyl the proposed ANN controller based 7-level CHBMI achieved a lower output voltage THD value with a better sinusoidal output performance

Enhance Cascaded H-Bridge Multilevel Inverter with Artificial Intelligence Control

This paper proposed a 7-level Cascaded H-Bridge Multilevel Inverter (CHBMI) with two diffenrent controller, ie, PID and Artificial Neural Network (ANN) controller to improve the output voltage performance and achieve a lower Total Harmonic Distortion (THD). A PWM generator is connected to the 7-level CHBMI to provide switching of the MOSFET. The reference signal waveform for the PWM generator is set to be sinusoidal to obtain an ideal AC output voltage waveform from the CHBMI. By tuning the PID controller as well as the self-learning abilities of the ANN controller, switching signals towards the CHBMI can be improved. Simulation results from the general CHBMI together with the proposed PID and ANN controller based 7-level CHBMI models will be compared and discussed to verifyl the proposed ANN controller based 7-level CHBMI achieved a lower output voltage THD value with a better sinusoidal output performance