Statistical modeling, parameter estimation and measurement planning for PV degradation

Photovoltaics (PV) degradation is a key consideration during PV performance evaluation. Accurately predicting power delivery over the course of lifetime of PV is vital to manufacturers and system owners. With many systems exceeding 20 years of operation worldwide, degradation rates have been reported abundantly in the recent years. PV degradation is a complex function of a variety of factors, including but not limited to climate, manufacturer, technology and installation skill. As a result, it is difficult to determine degradation rate by analytical modeling; it has to be measured. As one set of degradation measurements based on a single sample cannot represent the population nor be used to estimate the true degradation of a particular PV technology, repeated measures through multiple samples are essential. In this chapter, linear mixed effects model (LMM) is introduced to analyze longitudinal degradation data. The framework herein introduced aims to address three issues: 1) how to model the difference in degradation observed in PV modules/systems of a same technology that are installed at a shared location; 2) how to estimate the degradation rate and quantiles based on the data; and 3) how to effectively and efficiently plan degradation measurements

Innovative Solutions for Slope Stability Reinforcement and Characterization: Vol. I

Soil slope instability concerning highway infrastructure is an ongoing problem in Iowa, as slope failures endanger public safety and continue to result in costly repair work. Characterization of slope failures is complicated, because the factors affecting slope stability can be difficult to discern and measure, particularly soil shear strength parameters. While in the past extensive research has been conducted on slope stability investigations and analysis, this research consists of field investigations addressing both the characterization and reinforcement of such slope failures. The current research focuses on applying an infrequently-used testing technique comprised of the Borehole Shear Test (BST). This insitu test rapidly provides effective (i.e., drained) shear strength parameter values of soil. Using the BST device, fifteen Iowa slopes (fourteen failures and one proposed slope) were investigated and documented. Particular attention was paid to highly weathered shale and glacial till soil deposits, which have both been associated with slope failures in the southern Iowa drift region. Conventional laboratory tests including direct shear tests, triaxial compression tests, and ring shear tests were also performed on undisturbed and reconstituted soil samples to supplement BST results. The shear strength measurements were incorporated into complete evaluations of slope stability using both limit equilibrium and probabilistic analyses. The research methods and findings of these investigations are summarized in Volume 1 of this report. Research details of the independent characterization and reinforcement investigations are provided in Volumes 2 and 3, respectively. Combined, the field investigations offer guidance on identifying the factors that affect slope stability at a particular location and also on designing slope reinforcement using pile elements for cases where remedial measures are necessary. The research findings are expected to benefit civil and geotechnical engineers of government transportation agencies, consultants, and contractors dealing with slope stability, slope remediation, and geotechnical testing in Iowa

Modeling the role of irrigation in winter wheat yield, crop water productivity, and production in China

Irrigation plays an important role in increasing food production in China. The impact of irrigation on crop yield (Y), crop water productivity (CWP), and production has not been quantified systematically across regions covering the whole country. In this study, a GIS-based EPIC model (GEPIC) was applied to simulate Y and CWP for winter wheat (Triticum aestivum L.) in China at a grid resolution of 5arc-minutes and to analyze the impacts of reducing irrigation water on wheat production. The findings show that irrigation is especially important in improving CWP of winter wheat in the North China Plain (NCP), the "bread basket” of China. On average, the provincial aggregate CWP was 56% higher under the irrigated than that under the rainfed conditions. The intensification of water stress and the associated increase in environmental problems in much of the NCP require critical thoughts about reducing water allocation for irrigated winter wheat. Two scenarios for irrigation reduction in the NCP provinces are presented: reducing irrigation depth (S1), and replacing irrigated winter wheat by rainfed winter wheat (S2). The simulation results show that S1 and S2 have similar effects on wheat production when the reduction in irrigation water supply is below 20% of the current level. Above this percentage, S2 appears to be a better scenario since it leads to less reduction in wheat production with the same amount of water savin

A Boolean Model of the Pseudomonas syringae hrp Regulon Predicts a Tightly Regulated System

The Type III secretion system (TTSS) is a protein secretion machinery used by certain gram-negative bacterial pathogens of plants and animals to deliver effector molecules to the host and is at the core of the ability to cause disease. Extensive molecular and biochemical study has revealed the components and their interactions within this system but reductive approaches do not consider the dynamical properties of the system as a whole. In order to gain a better understanding of these dynamical behaviours and to create a basis for the refinement of the experimentally derived knowledge we created a Boolean model of the regulatory interactions within the hrp regulon of Pseudomonas syringae pathovar tomato strain DC3000 Pseudomonas syringae. We compared simulations of the model with experimental data and found them to be largely in accordance, though the hrpV node shows some differences in state changes to that expected. Our simulations also revealed interesting dynamical properties not previously predicted. The model predicts that the hrp regulon is a biologically stable two-state system, with each of the stable states being strongly attractive, a feature indicative of selection for a tightly regulated and responsive system. The model predicts that the state of the GacS/GacA node confers control, a prediction that is consistent with experimental observations that the protein has a role as master regulator. Simulated gene “knock out” experiments with the model predict that HrpL is a central information processing point within the network

A role for SUMO modification in transcriptional repression and activation

Since the discovery of the SUMO (small ubiquitin-like modifier) family of proteins just over a decade ago, a plethora of substrates have been uncovered including many regulators of transcription. Conjugation of SUMO to target proteins has generally been considered as a repressive modification. However, there are now a growing number of examples where sumoylation has been shown to activate transcription. Here we discuss whether there is something intrinsically repressive about sumoylation, or if the outcome of this modification in the context of transcription will prove to be largely substrate-dependent. We highlight some of the technical challenges that will be faced by attempting to answer this question

Topological data analysis of Escherichia coli O157:H7 and non-O157 survival in soils.

Shiga toxin-producing E. coli O157:H7 and non-O157 have been implicated in many foodborne illnesses caused by the consumption of contaminated fresh produce. However, data on their persistence in soils are limited due to the complexity in datasets generated from different environmental variables and bacterial taxa. There is a continuing need to distinguish the various environmental variables and different bacterial groups to understand the relationships among these factors and the pathogen survival. Using an approach called Topological Data Analysis (TDA); we reconstructed the relationship structure of E. coli O157 and non-O157 survival in 32 soils (16 organic and 16 conventionally managed soils) from California (CA) and Arizona (AZ) with a multi-resolution output. In our study, we took a community approach based on total soil microbiome to study community level survival and examining the network of the community as a whole and the relationship between its topology and biological processes. TDA produces a geometric representation of complex data sets. Network analysis showed that Shiga toxin negative strain E. coli O157:H7 4554 survived significantly longer in comparison to E. coli O157:H7 EDL 933, while the survival time of E. coli O157:NM was comparable to that of E. coli O157:H7 EDL 933 in all of the tested soils. Two non-O157 strains, E. coli O26:H11 and E. coli O103:H2 survived much longer than E. coli O91:H21 and the three strains of E. coli O157. We show that there are complex interactions between E. coli strain survival, microbial community structures, and soil parameters

Terahertz Reconfigurable Metasurface for Dynamic Non-Diffractive Orbital Angular Momentum Beams using Vanadium Dioxide

Funding: This work was supported in part by the Natural Science Foundation of Beijing under Grant 4202047, in part by the Beijing Nova Program under Grant 181100006218039, and in part by the 111 Project (B17007). 10.13039/501100004826-Natural Science Foundation of Beijing Municipality (Grant Number: 4202047) 10.13039/501100005090-Beijing Nova Program (Grant Number: Z181100006218039) 10.13039/501100013314-Higher Education Discipline Innovation Project (Grant Number: B17007)Peer reviewedPublisher PD

Accurate Modeling of the Effects of Fringing Area Interface Traps on Scanning Capacitance Microscopy Measurement

Scanning capacitance microscopy (SCM) is a dopant profile extraction tool with nanometre spatial resolution. While it is based on the high-frequency MOS capacitor theory, there are crucial second-order effects which make the extraction of dopant profile from SCM data a challenging task. Due to small size of the SCM probe, the trapped charges in the interface traps at the oxide-silicon dioxide interface surrounding the probe significantly affect the measured SCM data through the fringing electric field created by the trapped charges. In this paper, we present numerical simulation results to investigate the nature of SCM dC/dV data in the presence of interface traps. The simulation takes into consideration the traps response to the ac signal used to measure dC/dV as well as the fringing field of the trapped charge surrounding the probe tip. In the study, we present an error estimation of experimental SCM dopant concentration extraction when the interface traps and fringing field are ignored. The trap distribution in a typical SCM sample is also investigated

Smaller, Faster, Greener: Compressing Pre-trained Code Models via Surrogate-Assisted Optimization

Large pre-trained models of code have been adopted to tackle many software engineering tasks and achieved excellent results. However, their large model size and expensive energy consumption prevent them from being widely deployed on developers' computers to provide real-time assistance. A recent study by Shi et al. can compress the pre-trained models into a small size. However, other important considerations in deploying models to have not been addressed: the model should have fast inference speed and minimal energy consumption. This requirement motivates us to propose Avatar, a novel approach that can reduce the model size as well as inference latency and energy consumption without compromising effectiveness (i.e., prediction accuracy). Avatar trains a surrogate model to predict the performance of a tiny model given only its hyperparameters setting. Moreover, Avatar designs a new fitness function embedding multiple key objectives, maximizing the predicted model accuracy and minimizing the model size, inference latency, and energy consumption. After finding the best model hyperparameters using a tailored genetic algorithm (GA), Avatar employs the knowledge distillation technique to train the tiny model. We evaluate Avatar and the baseline approach from Shi et al. on three datasets for two popular software engineering tasks: vulnerability prediction and clone detection. We use Avatar to compress models to a small size (3 MB), which is 160$\times$ smaller than the original pre-trained models. Compared with the original models, the inference latency of compressed models is significantly reduced on all three datasets. On average, our approach is capable of reducing the inference latency by 62$\times$, 53$\times$, and 186$\times$. In terms of energy consumption, compressed models only have 0.8 GFLOPs, which is 173$\times$ smaller than the original pre-trained models.Comment: 12 pages, a working-in-progress versio