No And No2 Modeling For Diesel Oxidation Catalyst At Different Thermal Aging Levels

Different DOC modeling methods are analyzed and compared. Detailed reaction mechanisms of DOC reaction are discussed and a new NO and NO2 modeling technique is proposed. Experiments are conducted measuring downstream DOC NO and NOx . DOC out NO and NO2 concentrations are modeled as a function of DOC temperature. A robust model is built such that with the input of real time NOx and DOC temperature information, DOC-out NO and NO2 concentrations can be precisely predicted. As is true with many catalysts, the DOC thermally ages as it operates, which reduces the thermal effectiveness level of the catalyst. This directly reduces the amount of HC, CO and NO it is able to oxidize, increasing the DOC-out NO percentage. In this thesis, aged DOC model is recalibrated in modeling DOC-out NO and NO 2concentrations. Correlations between HC light-off temperature shift and DOC-out NO and NO2 concentration shift are discovered and analyzed. A preliminary model of NO and NO2 concentrations as a function of HC light-off temperature and DOC temperature is built and validated against actual data

Sugar signalling mediates cluster root formation and phosphorus starvation-induced gene expression in white lupin

Cluster root (CR) formation contributes much to the adaptation to phosphorus (P) deficiency. CR formation by white lupin (Lupinus albus L.) is affected by the P-limiting level in shoots, but not in roots. Thus, shoot-derived signals have been expected to transmit the message of P-deficiency to stimulate CR formation. In this study, it is shown that sugars are required for a response to P starvation including CR formation and the expression of P starvation-induced genes. White lupin plants were grown in vitro on P-sufficient or P-deficient media supplemented with sucrose for 4 weeks. Sucrose supply stimulated CR formation in plants on both P-sufficient and P-deficient media, but no CR appeared on the P-sufficient medium without sucrose. Glucose and fructose also stimulated CR formation on the P-sufficient medium. On the medium with sucrose, a high concentration of inorganic phosphate in leaves did not suppress CR formation. Because sorbitol or organic acid in the media did not stimulate CR formation, the sucrose effect was not due to increased osmotic pressure or enriched energy source, that is, sucrose acted as a signal. Gene transcription induced by P starvation, LaPT1 and LaPEPC3, was magnified by the combination of P limitation and sucrose feeding, and that of LaSAP was stimulated by sucrose supply independently of P supply. These results suggest that at least two sugar-signalling mediating systems control P starvation responses in white lupin roots. One system regulates CR formation and LaSAP expression, which acts even when P is sufficient if roots receive sugar as a signal. The other system controls LaPT1 and LaPEPC3 expression, which acts when P is insufficient

A geometric description of the set of stabilizing PID controllers

This article developed a new method to described the set of stabilizing PID control. The method is based on D-parameterization with natural description of the set. It was found that the stability crossing surface is a ruled surface that is completely determined by a curve known as discriminant. The discriminant is divided into sectors at the cusps. Corresponding to the sectors, the stability crossing surface is divided into positive and negative patches. A systematic study is conducted to identify the regions with a fixed number of right half-plane characteristic roots. The crossing directions of characteristic roots for positive patches and negative patches are also studied. As a result, a systematic method is developed to identify the regions of PID parameter such that the system is stabilized

Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training

Regularization in modern machine learning is crucial, and it can take various forms in algorithmic design: training set, model family, error function, regularization terms, and optimizations. In particular, the learning rate, which can be interpreted as a temperature-like parameter within the statistical mechanics of learning, plays a crucial role in neural network training. Indeed, many widely adopted training strategies basically just define the decay of the learning rate over time. This process can be interpreted as decreasing a temperature, using either a global learning rate (for the entire model) or a learning rate that varies for each parameter. This paper proposes TempBalance, a straightforward yet effective layer-wise learning rate method. TempBalance is based on Heavy-Tailed Self-Regularization (HT-SR) Theory, an approach which characterizes the implicit self-regularization of different layers in trained models. We demonstrate the efficacy of using HT-SR-motivated metrics to guide the scheduling and balancing of temperature across all network layers during model training, resulting in improved performance during testing. We implement TempBalance on CIFAR10, CIFAR100, SVHN, and TinyImageNet datasets using ResNets, VGGs, and WideResNets with various depths and widths. Our results show that TempBalance significantly outperforms ordinary SGD and carefully-tuned spectral norm regularization. We also show that TempBalance outperforms a number of state-of-the-art optimizers and learning rate schedulers.Comment: NeurIPS 2023 Spotlight, first two authors contributed equall

Association Between Cortical Superficial Siderosis and Dementia in Patients With Cognitive Impairment: A Meta-Analysis

Background: It remains unclear whether cortical superficial siderosis (cSS) is associated with dementia and its subtypes. We thus performed a meta-analysis to evaluate the relationship between dementia and cSS.Methods: We searched EMBASE, PubMed, and Web of Science for relevant studies assessing risk of dementia and prevalence of cSS in patients with cognitive impairment. Fixed-effects and random-effects models were performed.Results: Seven eligible studies including 3,218 patients with definite cognitive impairment were pooled in meta-analysis. The prevalence of cSS was 3.4%. The pooled analysis demonstrates odds ratio for cSS and dementia to be 1.60 (95% CI 1.04–2.44; p = 0.031). Subgroup analysis further indicated a significant association between cSS and Alzheimer's disease (AD) (OR = 2.01, 95% CI 1.34–3.02; p < 0.001), but not non-AD dementia (OR = 0.700, 95% CI 0.435–1.128; p = 0.143).Conclusions: Our meta-analysis of available published data demonstrates an increased prevalence of dementia in the subjects with pre-existing cSS, especially for AD. These findings suggest cSS to be a candidate imaging indicator for AD. Further longitudinal research is needed to investigate the clinical relevance

Parallel and progressive approaches for skyline query over probabilistic incomplete database

The advanced productivity of the modern society has created a wide range of similar commodities. However, the descriptions of commodities are always incomplete. Therefore, it is difficult for consumers to make choices. In the face of this problem, skyline query is a useful tool. However, the existing algorithms are unable to address incomplete probabilistic databases. In addition, it is necessary to wait for query completion to obtain even partial results. Furthermore, traditional skyline algorithms are usually serial. Thus, they cannot utilize multi-core processors effectively. Therefore, a parallel progressive skyline query algorithm for incomplete databases is imperative, which provides answers gradually and much faster. To address these problems, we design a new algorithm that uses multi-level grouping, pruning strategies, and pruning tuple transferring, which significantly decreases the computational costs. Experimental results demonstrate that the skyline results can be obtained in a short time. The parallel efficiency for an Octa-core processor reaches 90% on high-dimensional, large databases.<br /