Application of Nonlinear Dynamical Methods for Arc Welding Quality Monitoring

Owing to its diverse, the stability of arc signals in high-powered submerged arc welding is not very salient, and weld defects are difficult to detect automatically. Aimed at this problem, this paper proposes a noise robustness algorithm for calibrating the singularity points and denoting the kinetics and stability of arc. Firstly, reconstruct a vector, which is the calculation of the approximate entropy in phase space, denotes the distortion of arc. Then, a algorithm for calculation is given based on reconstruction of chaotic time series in phase space. Finally, we apply the calculation of approximate entropy algorithm in phase space to flaw detection for arc signals, which is efficient proved by experimental results

Carbon productivity and economic growth patterns in China

This article discusses the changes in carbon productivity and economic growth patterns in China. We calculated carbon productivity using panel data from BRICS and G7 countries between 2001 and 2019 and developed a methodology to estimate economic growth patterns by combining carbon productivity and economic growth. As the world’s top carbon emitter, China can combat global climate change by increasing carbon productivity. We show that (i) China has a high growth rate of carbon productivity; however, the carbon productivity level only accounts for about 20% of developed countries. (ii) When determining economic growth patterns from a low-carbon perspective, China has transitioned from high-carbon type II to low-carbon type III. However, low-carbon economic growth is common in developed countries, and (iii) it can improve carbon productivity by reducing energy-averaged carbon emission factors. It assists the government in determining how to implement low-carbon economic development policies by examining economic growth from a low-carbon perspectiv

Managerial response to shareholder empowerment: evidence from majority- voting legislation changes

This paper studies how managers react to shareholder empowerment that makes the votes on shareholder proposals regarding majority-voting director elections binding. Exploiting staggered legislative changes that introduce such empowerment, we find that managers become more responsive by initiating majority voting through either management proposals or governance guidelines. Further results suggest compromised implementation: managers adopt provisions that give them greater control over the channel of implementation and allow them to retain directors who fail in elections. Managers show the greatest resistance to implementing majority-voting standards when shareholder value is likely to suffer more or benefit less from the legislation

Spatiotemporal Analysis of Human Mobility based on Land Use Types in the Greater Toronto Area during COVID-19 Pandemic

The 2019 Coronavirus disease COVID-19 is an infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It undoubtedly poses a huge challenge in terms of public health and social impact worldwide. The Ontario government implemented a series of non-pharmaceutical interventions (NPIs) prior to vaccination to prevent large-scale outbreaks in the Great Toronto Area (GTA), which is the most densely populated region in Ontario. Detecting and analyzing human mobility during the pandemic can help decision makers assess the effectiveness of policy implementation, in order to better respond to similar events in the future. Geotagged Twitter data serves as an important source of volunteered geographic information (VGI). Anonymized geotagged tweet in the GTA in 2020 using the Twitter Academic API are used to analyze inner-city human mobility. The results provide a longer-term insight into how human activity is affected by the pandemic as well as government orders. In this thesis, human mobility spatiotemporal patterns in the GTA are found to be close to patterns founded in the previous studies. People are affected more by the severeness of the first outbreak. More people stay at home rather than in commercial areas, schools, and workplaces. Human mobility in open spaces is affected by seasons besides policy effects. Human mobility in utility and transportation areas is related to the properties of the areas they connect. Most of the policies received significant reflections within one week of release, but milder policies resulted in insignificant human mobility changes. Human mobility patterns in most land use types have moderate correlation with the Google Community Mobility Report. Even so, some limitations still exist

From Authority-Respect to Grassroots-Dissent: Degree-Weighted Social Learning and Convergence Speed

Opinions are influenced by neighbors, with varying degrees of emphasis based on their connections. Some may value more connected neighbors' views due to authority respect, while others might lean towards grassroots perspectives. The emergence of ChatGPT could signify a new ``opinion leader'' whose views people put a lot of weight on. This study introduces a degree-weighted DeGroot learning model to examine the effects of such belief updates on learning outcomes, especially the speed of belief convergence. We find that greater respect for authority doesn't guarantee faster convergence. The influence of authority respect is non-monotonic. The convergence speed, influenced by increased authority-respect or grassroots dissent, hinges on the unity of elite and grassroots factions. This research sheds light on the growing skepticism towards public figures and the ensuing dissonance in public debate

Phosphodiesterase type 5 inhibitors and risk of melanoma: A meta-analysis

Background The association between phosphodiesterase type 5 (PDE5) inhibitors and melanoma risk is controversial. Objective We quantify the association between use of PDE5 inhibitors and melanoma. Methods We systematically searched PubMed, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, and ClinicalTrials.gov for studies that were conducted up to July 13, 2016, and evaluated the association between PDE5 inhibitors and skin cancer. Random effects meta-analyses were used to calculate the adjusted odds ratio (OR) with the 95% confidence interval (CI). Results Five observational studies were included. Compared with PDE5 inhibitor nonuse, PDE5 inhibitor use was slightly but significantly associated with an increased risk for development of melanoma (OR, 1.12; 95% CI, 1.03-1.21) and basal cell carcinoma (OR, 1.14; 95% CI, 1.09-1.19) but not squamous cell carcinoma. For melanoma risk, none of the prespecified factors (dose of PDE5 inhibitor, study design, and study region) significantly affected the results (P > .05). Our sensitivity analysis confirmed the stability of the results. Limitations We included only observational studies, which had some heterogeneities and inconsistent controlling for potential confounders. Conclusions Use of PDE5 inhibitors may be associated with a slightly increased risk for development of melanoma and basal cell carcinoma but not squamous cell carcinoma. However, further large well-conducted prospective studies with adequate adjustment for potential confounders are required for confirmation

MoViT: Memorizing Vision Transformers for Medical Image Analysis

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability. In this work, inspired by the human decision-making process of correlating new ``evidence'' with previously memorized ``experience'', we propose a Memorizing Vision Transformer (MoViT) to alleviate the need for large-scale datasets to successfully train and deploy transformer-based architectures. MoViT leverages an external memory structure to cache history attention snapshots during the training stage. To prevent overfitting, we incorporate an innovative memory update scheme, attention temporal moving average, to update the stored external memories with the historical moving average. For inference speedup, we design a prototypical attention learning method to distill the external memory into smaller representative subsets. We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available. More importantly, MoViT can reach a competitive performance of ViT with only 3.0% of the training data