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Changes in precipitation patterns with climate change could have important impacts on human and natural systems. Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) report trends in daily precipitation patterns over the last five decades in the western United States, focusing on meteorological drought. They report that dry intervals (calculated at the annual or seasonal level) have increased across much of the southwestern U.S., with statistical assessment suggesting the results are statistically robust. However, Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) preprocess their annual (or seasonal) averages to compute 5‐year moving window averages before using established statistical techniques for trend analysis that assume independence about some fixed trend. Here we show that the moving window preprocessing violates that independence assumption and inflates the statistical significance of their trend estimates. This raises questions about the robustness of their results. We conclude by discussing the difficulty of adjusting for spatial structure when assessing time trends in a regional context. Plain Language Summary: A recent paper reports trends in drought in the western United States, in particular increases in drought in the southwestern United States, based on changes in the lengths of time intervals without precipitation. In this "comment" we note that the preprocessing approach used in the paper artificially increases the apparent statistical signal in the data and caution that the evidence for the trends reported is not as strong as presented in the paper. We conclude by discussing the difficulty of estimating trends in a statistically rigorous fashion across multiple weather stations. Key Points: Statistical uncertainty about trends in droughts in the southwestern US is larger than reported in a recent GRL letter [ABSTRACT FROM AUTHOR]

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

The primary objective of this study is to uncover and examine the patterns of scientific collaboration within the domain of tax evasion and tax avoidance spanning the years 1975 to 2022. To analyze the dissemination of knowledge on a worldwide scale, we investigated the interconnections among authors, journals, countries, and institutions. A total of 1456 publications were retrieved from the Web of Science repository. Bibliographic analysis and network visualization were conducted using CiteSpace. The publications analysed in this study consisted of 1,456 articles authored by 2478 scholars affiliated with 1355 institutions. The publications were distributed among 724 distinct journals and originated from 98 countries. The United States of America is found to be the most productive nation, with McGee, R.W. being recognised as the most prolific author. The League of European Research Universities is recognised as the most productive institution, whereas the Journal of Public Economics is identified as the most productive publication. The findings show that authors who exhibit high levels of productivity also tend to demonstrate a strong inclination toward collaboration. Furthermore, the findings reveal that the interest of the scholars in particular topics in this research has evolved over time. [ABSTRACT FROM AUTHOR]

The increasing availability of bridge data from the National Bridge Inventory (NBI) offers a great opportunity to perform predictive analytics (such as bridge deterioration prediction) using machine learning (ML) pipelines for supporting bridge asset management. However, data quality issues (e.g., outliers and missing values) can significantly affect ML pipelines, requiring upstream tasks to be performed for ensuring the validity, applicability, and generalizability of pipelines. Among the tasks, outlier removal and missing value imputation are the most challenging due to a highly laborious process, a lack of data governance, and a mixture of heterogenous data quality issues and data types. To address this challenge, this paper proposes an upstream workflow for enhancing the downstream predictive analytics of bridge-related ML pipelines. The proposed upstream workflow was developed based on the NBI data collected for all States in the United States, which includes a total of 617,084 observations/bridges. Existing bridge domain knowledge from multiple sources (such as the bridge design manual and regulations) was leveraged to remove outliers. Then, this study applied and compared 10 statistical and ML-based data imputation techniques to impute missing values. Statistical analysis and imputation evaluation of NBI data indicated that: (1) 19 and 15 out of the total 38 frequently used features or variables had outliers and missing values, respectively; (2) categorical features are generally more prone to data dropping due to inapplicable values, while numeric features are more subjected to outliers; and (3) ML-based data imputation is more suitable than statistical imputation for both numeric and categorical features, especially for features with high missing rate. The proposed workflow was validated on its capability of improving downstream predictive analytics for bridge deck condition prediction, increasing the balanced accuracy by 6.85%–9.76%. This paper contributes to the body of knowledge by offering a novel upstream workflow that can be utilized as a benchmark for guiding researchers and bridge engineering practitioners to handle NBI data quality issues for better preforming predictive analytics using ML pipelines. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During the COVID-19 pandemic, early childhood educators (ECEs) made a rapid pivot to remote instruction. Much of this instruction was facilitated by digital learning resources, but the nature and quality of these resources for children's language and literacy learning is an open question. This paper draws on a national U.S. survey of ECEs during the second pandemic school year (2020–2021), which asked them to report on the apps and websites they asked children to use during remote learning. We then engaged in a content analysis of the apps/websites used most frequently, evaluating their quality along several dimensions, including support for a full range of literacy competencies, equity, and accessibility. Survey results indicated that preschool children were asked to use a wide range of apps, but six were used most frequently: YouTube, Seesaw, Starfall, Epic, Boom Cards, and ABC Mouse. The content analysis indicated that most apps supported code-focused literacy skills, but had less capacity to foster oral language and comprehension. Apps also presented few opportunities to approximate the active, hands-on learning characteristic of in-person preschool. Our analysis points to the pressing need for teacher guidance in the selection and use of apps that provide comprehensive support for language and literacy. Prior State of Knowledge: Prior studies have investigated the quality of apps for young children's literacy learning, and several studies have investigated the nature and frequency of remote instruction for children attending preschool during COVID-19. Novel Contributions: The present study reports valuable information about which language and literacy apps early childhood educators asked children to use during remote learning and provides a comprehensive evaluation of those apps, including dimensions such as equity and accessibility that have been previously under-researched. Practical Implications: One important implication for administrators is that teachers need support in finding and learning to use apps that align more closely with early learning standards. Teachers can use the QuELLA rubric in this paper as a tool for evaluating apps. [ABSTRACT FROM AUTHOR]

Background: Dengue virus (DENV) is endemic to many parts of the world and has serious health and socioeconomic effects even in high-income countries, especially with rapid changes in the climate globally. We explored the literature on dengue vector control methods used in high-income, city settings and associations with dengue incidence, dengue prevalence, or mosquito vector densities. Methods: Studies of any design or year were included if they reported effects on human DENV infection or Aedes vector indices of dengue-specific vector control interventions in high-income, city settings. Results: Of 24 eligible sources, most reported research in the United States (n = 8) or Australia (n = 5). Biocontrol (n = 12) and chemical control (n = 13) were the most frequently discussed vector control methods. Only 6 sources reported data on the effectiveness of a given method in reducing human DENV incidence or prevalence, 2 described effects of larval and adult control on Aedes DENV positivity, 20 reported effectiveness in reducing vector density, using insecticide, larvicide, source reduction, auto-dissemination of pyriproxyfen and Wolbachia, and only 1 described effects on human-vector contact. Conclusions: As most studies reported reductions in vector densities, rather than any effects on human DENV incidence or prevalence, we can draw no clear conclusions on which interventions might be most effective in reducing dengue in high-income, city areas. More research is needed linking evidence on the effects of different DENV vector control methods with dengue incidence/prevalence or mosquito vector densities in high-income, city settings as this is likely to differ from low-income settings. This is a significant evidence gap as climate changes increase the global reach of DENV. The importance of community involvement was clear in several studies, although it is impossible to tease out the relative contributions of this from other control methods used. Author summary: Dengue virus is present in many parts of the world and has serious health and socioeconomic effects even in high-income countries, especially with rapid changes in the climate globally. In this study, we explored the literature on dengue vector control methods used in high-income, city settings and associations with number of human dengue infections, and the density of mosquitoes. We found 24 papers with relevant results. Most of these described studies in the United States or Australia. Most were about various forms of biological or chemical control of the mosquitoes. Few papers discussed effects on human dengue infection. We were unable to draw any clear conclusions on how effective mosquito control methods were as we could identify little research on this subject in this specific setting. More research is needed on this topic, particularly as climate change will make more areas of the world vulnerable to dengue infection. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

Redistribution for income equality is a contentious issue, with advocates citing increased economic stability and skeptics fearing potential negative impacts on economic growth. The United States (U.S.) faces notably high income inequality compared to the Organisation for Economic Co-operation and Development (OECD) average, with wealth concentrated in the top brackets. This paper critically examines the existing U.S. redistribution methods, including the taxation system and welfare programs, highlighting challenges such as the lack of tax progressiveness in top brackets and limited redistribution compared to European counterparts. Based on this overview, the study evaluates alternative redistribution strategies, including wealth and business taxes. The paper compares theory with insights from pilot programs in other countries. Finally, the paper argues for a universal basic income (UBI) set at per-capita welfare spending, positing that this approach can empower individuals to escape poverty traps, enhance productivity, and introduce a choice mechanism for welfare consumption. Ultimately, this research seeks to propose the most effective and feasible income redistribution policies for the U.S. to bridge the income inequality gap. [ABSTRACT FROM AUTHOR]