Synthesis of multiarea grid power systems

This dissertation presents improved development in the formation of a generalized transmission loss (B)-matrix for a multiarea grid power system. In the procedure, the individual tie powers of each area are replaced by the net interchange, sneak and circulating powers. The latter two variables are directly eliminated in the power reference frame using actual impedances, unlike current methods that require the elimination of sneak and circulating currents, the formation of complex tie current model and the complex tie power model. Consequently, manipulation of large complex current, power and impedance matrices is avoided reducing both computer time and memory requirement. Further, the procedure not only provides a model for predicting individual tie powers, given generator and net interchange powers, but also provides coefficients that reflect the changes in the tie power flows with respect to the changes in generator and net interchange flows. The dissertation also presents a modified pool lambda dispatch method that could be used on-line for optimal coordination of generating sources in a multiarea grid power system. The classical fuel cost minimization problem is modified with the addition of a constraint equation that forms the basis for the definition of a common pool reference running cost. The solution algorithm is in a closed form rather than iterative and explicitly provides the individual area running costs in terms of the pool reference cost and the desired generation of each area. Thus, individual areas can be dispatched in a multiarea grid power system in the same manner as individual generators are dispatched in a single area. Finally, a procedural method of selecting and designing an acceptable optimum power system configuration from a group of system alternatives, in terms of a generalized conductance (G)-matrix is presented. Analysis of an arbitrary N area power system by the method presented herein can be very economical, since the dimension (2N-1)X(2N-1) of the (G)-matrix is substantially smaller than the actual network. Once optimal (G)-matrix is identified, the actual network in reference frame one, can be designed by a reverse transformation, reflecting the constraints set by members of the power pool

Far-infrared study of K giants in the solar neighborhood: Connection between Li enrichment and mass-loss

We searched for a correlation between the two anomalous properties of K giants: Li enhancement and IR excess from an unbiased survey of a large sample of RGB stars. A sample of 2000 low-mass K giants with accurate astrometry from the Hipparcos catalog was chosen for which Li abundances have been determined from low-resolution spectra. Far-infrared data were collected from the $WISE$ and $IRAS$ catalogs. To probe the correlation between the two anomalies, we supplemented 15 Li-rich K giants discovered from this sample with 25 known Li-rich K giants from other studies. Dust shell evolutionary models and spectral energy distributions were constructed using the code DUSTY to estimate different dust shell properties, such as dust evolutionary time scales, dust temperatures, and mass-loss rates. Among 2000 K giants, we found about two dozen K giants with detectable far-IR excess, and surprisingly, none of them are Li-rich. Similarly, the 15 new Li-rich K giants that were identified from the same sample show no evidence of IR excess. Of the total 40 Li-rich K giants, only 7 show IR excess. Important is that K giants with Li enhancement and/or IR excess begin to appear only at the bump on the RGB. Results show that K giants with IR excess are very rare, similar to K giants with Li enhancement. This may be due to the rapid differential evolution of dust shell and Li depletion compared to RGB evolutionary time scales. We also infer from the results that during the bump evolution, giants probably undergo some internal changes, which are perhaps the cause of mass-loss and Li-enhancement events. However, the available observational results do not ascertain that these properties are correlated. That a few Li-rich giants have IR excess seems to be pure coincidence.Comment: Accepted for Publication in Astronomy & Astrophysics, 6 figures, 5 tables, 19 page

IMMUNOMODULATORY EFFECT OF BHRINGRAJ SWARAS SHODHIT GANDHAK AND GODUGDHA SHODHIT GANDHAK: A COMPARATIVE INVITRO STUDY

Introduction: Gandhak is one among the mineral drug explained in Uparasa Varga. It possesses Rasayana property. It attains therapeutic properties with proper Shodhana processes by Godugdha and by Bhringraj Swaras. Assessment of immunomodulatory effect of Bhringraj Swaras Shodhit Gandhak and Godugdha Shodhit Gandhak may provide evidence base for its textual reference and analysis of these Shodhit Gandhak contributes to establish standards for quality control. Method: Shuddha Gandhak by Godugdha and by Bhringraj Swaras prepared as per Rasaratnasamuchchaya and subjected to Immunomodulatory activity by Neutrophil Function Assay test with four parameters NBT test, phagocytosis, candidacidal assay and Neutrophils locomotion (chemotaxis) test. Results: In Immuno-modulatory assay different concentrations of Godugdha Shodhit Gandhaka and Bhringraj Swaras Shodhit Gandhaka shown significant increase in phagocytic activity, candidacidal capacity, locomotion and activation of Neutrophils for phagocytosis. Conclusion: Godugdha Shodhit Gandhaka and Bhringraj Swaras Shodhit Gandhaka shown significant immunomodulatory effect. Statistically there is no significant difference between Godugdha Shodhit Gandhaka and Bhringraj Swaras Shodhit Gandhaka

DeepQC: A Deep Learning System for Automatic Quality Control of In-situ Soil Moisture Sensor Time Series Data

Amidst changing climate, real-time soil moisture monitoring is vital for the development of in-season decision support tools to help farmers manage weather related risks. Precision Sustainable Agriculture (PSA) recently established a real-time soil moisture monitoring network across the central, Midwest, and eastern U.S., but field-scale sensor observations often come with data gaps and anomalies. To maintain the data quality needed for development of decision tools, a quality control system is necessary. The International Soil Moisture Network (ISMN) introduced the Flagit module for anomaly detection in soil moisture observations. However, under certain conditions, Flagit's quality control approaches may underperform in identifying anomalies. Recently deep learning methods have been successfully applied to detect anomalies in time series data in various disciplines. However, their use in agriculture has not been yet investigated. This study focuses on developing a Bi-directional Long Short-Term Memory (LSTM) model, referred to as DeepQC, to identify anomalies in soil moisture data. Manual flagged PSA observations were used for training, validation, and testing the model, following an 80:10:10 split. The study then compared the DeepQC and Flagit based estimates to assess their relative performance. Flagit corrected flagged 95.5% of the corrected observations and 50.3% of the anomaly observations, indicating its limitations in identifying anomalies. On the other hand, the DeepQC correctly flagged 99.7% of the correct observations and 95.6% of the anomalies in significantly less time, demonstrating its superiority over Flagit approach. Importantly, DeepQC's performance remained consistent regardless of the number of anomalies. Given the promising results obtained with the DeepQC, future studies will focus on implementing this model on national and global soil moisture networks.Comment: 9 pages, 8 figure

THE USE OF GAMIFICATION TO TEACH CYBERSECURITY AWARENESS IN INFORMATION SYSTEMS

This paper investigates the impact of gamification in teaching and learning cybersecurity awareness. The increasing rate of cyber-attacks and data breaches in recent times, have made cybersecurity awareness a critical learning objective in Information Systems (IS) curriculum globally. However, teaching and learning cybersecurity awareness can be challenging, especially to smaller colleges and universities who have meagre resources. Moreover, learning cybersecurity principles requires understanding of concepts that are usually unfamiliar to students in the IS major. In order to effectively deliver the desired learning objectives in cybersecurity awareness, IS educators can adopt pedagogical approaches, e.g., gamification, that are interactive, fun and appealing to students. Gamification which has been defined as the use of game components to deliver learning objectives in a given area, offer an alternative that is affordable, easy to learn and requires very little to no overhead cost. Currently, the authors are designing 3 gamified activities that can be used to teach and learn cyber security awareness. We intend to validate the effectiveness of these activities using experimental approaches. Students will be randomly selected from universities in Northern Pennsylvania, USA, and divided into experimental and control group. Experimental group will be asked to complete the gamified activities. Data will be collected using questionnaire. Data analysis will be by means of statistical approaches such as ANOVA, paired t-test of factor analysis. We hope that the results of our study will support the use of gamification in teaching and learning cybersecurity awareness

Galactic Foreground Constraints from the Python V Cosmic Microwave Background Anisotropy Data

We constrain Galactic foreground contamination of the Python V cosmic microwave background anisotropy data by cross correlating it with foreground contaminant emission templates. To model foreground emission we use 100 and 12 $\mu$m dust emission templates and two point source templates based on the PMN survey. The analysis takes account of inter-modulation correlations in 8 modulations of the data that are sensitive to a large range of angular scales and also densely sample a large area of sky. As a consequence the analysis here is highly constraining. We find little evidence for foreground contamination in an analysis of the whole data set. However, there is indication that foregrounds are present in the data from the larger-angular-scale modulations of those Python V fields that overlap the region scanned earlier by the UCSB South Pole 1994 experiment. This is an independent consistency cross-check of findings from the South Pole 1994 data.Comment: 15 pages, 1 figure, ApJ accepted versio