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The purpose of this research is to propose a methodology utilizing machine learning techniques to support medical organizations in effectively managing risks. Specifically, the study aims to connect social media data to identify and assess potential threats, ultimately enabling healthcare management to make informed decisions for their organizations and clients. The research employs machine learning algorithms to analyze user-generated content on social media platforms, generating comprehensive visual representations of various risk categories and their magnitudes. Additionally, the study utilizes data simplification techniques, including categorization, to streamline data processing and enhance overall efficiency. A computational framework is also developed, incorporating closed-form connections for threat assessment and evaluation. The study further empirically analyses the Consumer Value Stores (CVS) established for medical care in the United States. The findings reveal that prevalent threats within the lower quartile of client messages about CVS services include operational, financial, and technological risks. The severity of these risks is distributed among high risk (21.8%), moderate risk (78%), and minimal risk (0.2%). The research also presents several metrics to demonstrate the robustness of the proposed framework, confirming its effectiveness in effectively identifying and addressing potential threats. This research provides insights that can help healthcare management make informed decisions and foster a safer and more secure environment for their organizations and the people they serve. [ABSTRACT FROM AUTHOR]

This article explores the issue of spurious regression in econometric models, focusing on the case of imports in the United States. It explains that a high coefficient of determination (R2) without significant parameters can indicate the presence of spurious regression. The article warns that spurious regression can lead to misleading conclusions and offers recommendations for avoiding it in econometric models. It also discusses the importance of studying time series stability, the limitations of ordinary least squares (OLS) method, and the Gauss-Markov theory. Additionally, the article examines factors affecting imports and suggests methods to avoid spurious regression, such as following econometric methodology, using appropriate sample sizes, and utilizing modern econometric tools. It emphasizes the need for careful model specification, estimation, testing, and forecasting to prevent spurious regression. [Extracted from the article]

Summary: We construct a latent employment estimate for the United States, which both reconciles the information from separate payroll and household surveys and incorporates the preliminary data revision process of the payroll data. We find that our reconciled latent employment series looks somewhat different than the initial release of payroll employment and is closer to the fully revised data that is benchmarked to a near census of employment. A real‐time exercise, however, suggests that the reconciled employment estimate is remarkably similar to the initial release of payroll employment with near zero weight on the household survey information. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool for studying brain activity in mobile subjects. Open-access fNIRS datasets are limited to simple and/or motion-restricted tasks. Here, we report a fNIRS dataset acquired on mobile subjects performing Fundamentals of Laparoscopic Surgery (FLS) tasks in a laboratory environment. Demonstrating competency in the FLS tasks is a prerequisite for board certification in general surgery in the United States. The ASTaUND data set was acquired over four different studies. We provide the relevant information about the hardware, FLS task execution protocols, and subject demographics to facilitate the use of this open-access data set. We also provide the concurrent FLS scores, a quantitative metric for surgical skill assessment developed by the FLS committee. This data set is expected to support the growing field of assessing surgical skills via neuroimaging data and provide an example of data processing pipeline for use in realistic, non-restrictive environments. [ABSTRACT FROM AUTHOR]

This research aims to determine research trends regarding archivists in Scopus-indexed journals. The research method used is bibliometric analysis, with data collection from the Scopus database, carried out on September 8, 2023, using the keyword "Archivist" from 2013 to 2022 and specifically for final publications with journal type. Data is processed and analyzed using Publish or Perish (PoP) and VOSviewer to display visualization results. The research results concluded that the number of publications regarding archivists in the last ten years was 1241 documents. American Archivist became the most dominant publication source by publishing 94 documents. Poole was the most prolific writer, and the United States was the most contributing country by publishing 439 documents. The document type in articles is the largest, with 1024. Social Science is the subject area most often discussed in archivist topics. The total number of publication citations regarding archivists is 4528. Publication trends based on the appearance of a minimum of ten keywords are divided into five clusters, with the most dominant keywords being "metadata treatment, archives, human, digitization, and librarians." The publication trend seen from the latest publication year discusses "artificial intelligence," which has 5 (five) related links: artificial intelligence-copyright, artificial intelligence-photography, artificial intelligence-automation, artificial intelligence-digitization, and artificial intelligence-metadata. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI's general applications and research frontiers in the detection and gradation of DR. Methods: Citation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software. Results: Overall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled "automated detection" was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning. Conclusion: Initial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required. [ABSTRACT FROM AUTHOR]

Estimates of health insurance coverage in the United States rely on household-based surveys, and these surveys seek to improve data quality amid a changing health insurance landscape. We examine postcollection processing improvements to health insurance data in the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), one of the leading sources of coverage estimates. The implementation of updated data extraction and imputation procedures in the CPS ASEC marks the second stage of a two-stage improvement and the beginning of a new time series for health insurance estimates. To evaluate these changes, we compared estimates from two files that introduce the updated processing system with two files that use the legacy system. We find that updates resulted in higher rates of health insurance coverage and lower rates of dual coverage, among other differences. These results indicate that the updated data processing improves coverage estimates and addresses previously noted limitations of the CPS ASEC. [ABSTRACT FROM AUTHOR]

Federal consent decrees have been used to reform police organizations in the United States for more than two decades. Agencies having completed consent decrees and the data accumulated in the process, however, have rarely been examined. Meanwhile, the approach has become controversial as some see consent decrees as restrictive and costly to police and others view them as promising for improving police accountability and performance. Considering the confusion and controversies surrounding consent decrees, this paper provides a review of related policies, processes, and outcomes to develop a more accurate understanding of the nature of consent decrees and the specific areas that need to be addressed in order to bring about meaningful changes in policing. [ABSTRACT FROM AUTHOR]

Although they share many common qualities in design and operation, mesonetworks across the United States were established independently and organically over the last several decades. In numerous instances, the unique ways each network matured and developed new protocols has led to important lessons learned. These experiences have been shared in informal ways among various network operators over the years to promote reliable operation. As existing networks begin to introduce new sensors and technologies, and as new networks come online, there is a common need for guidance on best practices. This paper aims to formally provide recommendations to improve and harmonize the various aspects of operating a "mesonet," including siting, sensors, maintenance, quality assurance, and data processing. [ABSTRACT FROM AUTHOR]

In this paper, the author argues that the conflict between the copious amount of digital data processed by public organisations and the need for policy-relevant insights to aid public participation constitutes a 'public information paradox'. Machine learning (ML) approaches may offer one solution to this paradox through algorithms that transparently collect and use statistical modelling to provide insights for policymakers. Such an approach is tested in this paper. The test involves applying an unsupervised machine learning approach with latent Dirichlet allocation (LDA) analysis of thousands of public comments submitted to the United States Transport Security Administration (TSA) on a 2013 proposed regulation for the use of new full body imaging scanners in airport security terminals. The analysis results in salient topic clusters that could be used by policymakers to understand large amounts of text such as in an open public comments process. The results are compared with the actual final proposed TSA rule, and the author reflects on new questions raised for transparency by the implementation of ML in open rule-making processes. [ABSTRACT FROM AUTHOR]

As part of a suite of quality control methods applied to Canadian and American weather radar data before their assimilation into a numerical weather prediction model, the combination of thresholded depolarization ratio and a speckle filter was applied to American data with the purpose of identifying and removing non-precipitation echoes. This polarimetric quality control replaces a set of image-analysis-based methods used in a previous study and based on reflectivity information only. The old and new quality-controlled results were objectively assessed using meteorological aerodrome report (METAR)-based precipitation occurrence observations and a set of five common contingency table skill scores with all available Next Generation Weather Radar (NEXRAD) Level II data from the contiguous United States for August 2016. The new quality control yields consistently improved skill scores, indicating higher quality radar data for downstream application. The process whereby the radar data are quality controlled and assessed comprises a framework with the ability to monitor the impacts of quality control to radar data quality over time. In turn, this allows for the introduction of changes to data acquisition and processing with the ability to monitor the impacts on data quality: a scientific evidence-based quality assurance process as part of change management. [ABSTRACT FROM AUTHOR]

Copyright of Trayectorias is the property of Universidad Autonoma de Nuevo Leon 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.)

The article introduce and demarcate the problem of an overreliance on online access to court records in the U.S. It highlights a "democratic divide," which offers a meaningful way to explore this problem in an information society. It further explores several ways to redress the democratic divide and restore more accountability and usability to court records.

Zahlreiche Autoren, Autorinnen und kritische Initiativen (z. B. DORA) kritisieren den zu hohen und schädlichen Einfluss quantitativer Daten, welche akademische Instanzen für Evaluationszwecke heranziehen. Wegen des großen Einflusses der globalen Zitatdatenbanken von Thomson Reuters (bzw. Clarivate Analytics) auf die Bewertung der wissenschaftlichen Leistungen von Forscherinnen und Forschern habe ich extensive qualitative und quantitative Fallstudien zur Datenqualität des Social Sciences Citation Index (SSCI) durchgeführt, d. h. die Originaleinträge mit den SSCI-Datensätzen verglichen. Diese Fallstudien zeigten schwerste – nie in der Literatur erwähnte – Fehler, Verstümmelungen, Phantomautoren, Phantomwerke (Fehlerrate in der Fallstudie zu Beebe 2010, Harvard Law Review: 99 Prozent). Über die verwendeten Datenerfassungs- und Indexierungsverfahren von TR bzw. Clarivate Analytics ist nur wenig bekannt. Ein Ergebnis meiner Untersuchungen: Bei der Indexierung von Verweisen in Fußnoten (wie in den Rechtswissenschaften, gerade auch der USA, vorgeschrieben) scheinen die verwendeten Textanalyse-Anwendungen und -Algorithmen völlig überfordert. Eine Qualitätskontrolle scheint nicht stattzufinden. Damit steht der Anspruch des SSCI als einer multidisziplinären Datenbank zur Debatte. Korrekte Zitate in den Fußnoten des Originals können zu Phantom-Autoren, Phantom-Werken und Phantom-Referenzen degenerieren. Das bedeutet: Sämtliche Zeitschriften und Disziplinen, deren Zeitschriften und Büchern dieses oder ähnliche Zitierverfahren verwenden (Oxford-Style), laufen Gefahr, aufgrund starker Zitatverluste falsch, d. h. unterbewertet, zu werden. Wie viele UBOs (Unidentifiable Bibliographic Objects) sich in den Datenbanken SCI, SSCI und AHCI befinden, wäre nur mit sehr aufwändigen Prozeduren zu klären. Unabhängig davon handelt es sich, wie bei fast allen in meinen Untersuchungen gefundenen fatalen Fehlern, eindeutig um endogene Fehler in den Datenbanken, die nicht, wie oft behauptet, angeblich falsch zitierenden Autorinnen und Autoren zugeschrieben werden können, sondern erst im Laufe der Dateneingabe und -verarbeitung entstehen. Numerous authors and critical initiatives (such as DORA) criticize the excessive and damaging influence of quantitative data that academic administrators use for evaluation purposes. Due to the great impact of Thomson Reuters' (TR) or Clarivate Analytics' global citation databases on the evaluation of researchers' scientific achievements, I have conducted extensive qualitative and quantitative case studies on the data quality of the Social Sciences Citation Index (SSCI), in an innovative way by comparing the original references cited with the SSCI records indexed entries. These case studies revealed the most serious – to my knowledge, never mentioned in the literature before – errors, mutilations, phantom authors, phantom works (error rate: 99 % in the case study for Beebe 2010, Harvard Law Review). Little is known about the data collection and indexing techniques used by TR and Clarivate Analytics. A result of my investigations: When indexing references in footnotes (as prescribed in the scientific discipline of law, especially in the USA), the text analysis applications and algorithms seem to be completely overextended; quality control seems not to be in place. This raises the claim of the SSCI to be of a high quality multidisciplinary database. Correct citations in the footnotes of the original can degenerate into phantom authors, phantom works, and phantom references. This means that all journals and disciplines with journals and books using this or similar citation method (Oxford Style) run the risk of being misrepresented (i.e. undervalued) due to heavy citation losses. How many unidentifiable bibliographic objects (UBOs) are in the SCI, SSCI and AHCI databases would only be possible with very complex procedures. Either way, as with almost all the fatal errors found in my investigations, it is clearly a matter of endogenous databases errors that can not be attributed, as so often claimed, to allegedly misquoted authors, but induced only in the course of data entry and processing. [ABSTRACT FROM AUTHOR]

In 2016, the European Union ("EU") created heightened data privacy rights for its citizens by enacting the General Data Privacy Regulation ("GDPR"). The most drastic change from the previous regulation, enacted in 1995, lies within the expanded territorial scope. The change now subjects companies to fines for violations of the regulation, even if those companies are not domiciled in the EU. Data privacy has always been considered a fundamental human right in the EU; however, within the United States, there is no fundamental right to privacy. Rather, the country's privacy laws are based on a complicated sectoral structure that often leads the country's citizens confused as to what rights they actually have. This paper will review the EU and United States' fundamental differences in privacy laws, the changes implemented by the GDPR (including the expanded territorial scope), the compliance plans of some major players within the United States, and what the future looks like for American businesses that hold or process the data of EU citizens under the GDPR. [ABSTRACT FROM AUTHOR]

Weather surveillance radars are increasingly used for monitoring the movements and abundances of animals in the airspace. However, analysis of weather radar data remains a specialised task that can be technically challenging. Major hurdles are the difficulty of accessing and visualising radar data on a software platform familiar to ecologists and biologists, processing the low‐level data into products that are biologically meaningful, and summarizing these results in standardized measures. To overcome these hurdles, we developed the open source R package bioRad, which provides a toolbox for accessing, visualizing and analyzing weather radar data for biological studies. It provides functionality to access low‐level radar data, process these data into meaningful biological information on animal speeds and directions at different altitudes in the atmosphere, visualize these biological extractions, and calculate further summary statistics. The package aims to standardize methods for extracting and reporting biological signals from weather radars. Here we describe a roadmap for analyzing weather radar data using bioRad. We also define weather radar equivalents for familiar measures used in the field of migration ecology, such as migration traffic rates, and recommend several good practices for reporting these measures. The bioRad package integrates with low‐level data from both the European radar network (OPERA) and the radar network of the United States (NEXRAD). bioRad aims to make weather radar studies in ecology easier and more reproducible, allowing for better inter‐comparability of studies. [ABSTRACT FROM AUTHOR]

This paper examines the potential effects of the Dodd–Frank Act of 2010 on banks' noninterest expenses. Using data on U.S. bank holding companies from 1995 through 2016, we test whether noninterest expenses increase following the passage of the Dodd–Frank Act or in relation to the number of banking regulations implemented after Dodd–Frank. We analyze subsamples of banks above and below $10 billion in total assets and consider total noninterest expenses, salaries, non-salary expenses, and specific subcategories of non-salary expenses: legal, consulting, auditing, and data processing. Non-salary expenses for both large and small banks show a one-time increase after Dodd–Frank, while salary expenses tend to increase with regulations. The results indicate that total noninterest expenses for the banking system are higher on average by more than $50 billion per year compared to before the Dodd–Frank Act. [ABSTRACT FROM AUTHOR]

Threats to privacy abound in modern society, but individuals currently enjoy little meaningful legal protection for their privacy interests. We argue that the Genetic Information Nondiscrimination Act (GINA) offers a blueprint for preventing employers from breaching employee privacy. GINA has faced significant criticism since its enactment in 2008: commentators have dismissed the law as ill-conceived, unnecessary, and ineffective. While we concede that GINA may have failed to alleviate anxieties about medical genetic testing, we assert that it has unappreciated value as an employee-privacy statute. In the era of big data, protections for employee privacy are more pressing than protections against genetic discrimination. Instead of failed legislation, GINA could represent the future of employment law. [ABSTRACT FROM AUTHOR]

Reports on a new way to evaluate and support Internet software and shareware that are to be obtained by libraries for their computers. Solution offered by a program for Internet software evaluation devised by Morris Library, Southern Illinois University; Training procedures; Information about Internet shareware availability.

Reports on the University of Wisconsin- Whitewater Library's shift from using LS/2000 system to a second-generation integrated automation system called KeyNOTIS. Description of the terminal network setup and public terminals setup for the new system utilizing existing equipment; Description of database conversion problems encountered; Selection of an automation implementation team.

There is an abundance of literature about the impact of the new electronic communications technologies and most of the authors attest to its value and theoretical benefits. Many organizations are working in this area and producing novel implementations, but for the general company there is little that is of help in planning for these systems. In order to overcome this deficiency, this article presents a detailed analysis of electronic data interchange (EDI) undertaken by a large number of U.S. companies. The results suggest a model of strategic implementation which illustrates how factors such as the nature of markets and distribution channels, organizational structure and process and buyer power influence the implementation of EDI with suppliers and customers. Five generic EDI strategies have been identified and the impact of market share and IT expertise on these are discussed. Finally, the authors give their views on some of the long-term issues and suggest some possible outcomes. [ABSTRACT FROM AUTHOR]

This author considers ways in which computers will effect marketing efforts and impact U.S. society. He predicts future implications of what he terms "the five big C's:" the Computer, Communications, Convenience, Credit, and the Conglomerate. The introduction of toll-free telephone lines, the expansion of consumer credit up to roughly $300 billion as of 1968, and the expanded use of credit cards are discussed. The testimony of Senator Robert F.Kennedy to the House Banking and Currency Committee on the "Truth in Lending Bill" is presented.

Current literature is of little value in determining the nature and extent of system support being provided to marketing executives in major companies in the United States. This article describes the current state of market-oriented management information system development and utilization. [ABSTRACT FROM AUTHOR]

Practically all the nations of the world use the metric system of weights and measures. Yet the United States continues to offer goods, produced according to nonmetric standards, in the world markets in competition with metric products from other countries. The marketing aspects of the metric problem are now more important than ever: efforts to increase export trade; Britain's decision to move to the metric system; and widespread utilization of electronic data processing equipment, which uses the decimal system in calculations. [ABSTRACT FROM AUTHOR]

While automation exists today in only a small number of institutions, the author believes it will have a profound influence on the future of American marketing institutions. He goes on to analyze this impact on both the wholesale level and retail level. [ABSTRACT FROM AUTHOR]

The article discusses a study which analyzes the consequences of introducing electronic data processing (EDP) in several large offices in the U.S. The introduction of computers in offices revolutionizes the office system, alters the nature of the office work, increases labor productivity, upgrades the structure of the labor force, and changes its sex composition. Office automation reduced total labor requirements, but EDP has developed the skill composition of labor force. The application of EDP in office works of several companies included in the study had significant impact on office employment. However, office automation may be responsible for the decrease in growth rate of clerical jobs and that it may be reducing employment opportunities for women employees.

In assessing the impact of automation on workers and their jobs, little systematic attention has been paid to comparing its effects with those of more conventional changes in methods and organization of work. In this study of experience in an insurance company, changes in various job aspects, and workers' attitudes toward these changes, in the departments directly affected by the introduction of an electronic computer were compared with the effects of other types of changes that occurred at the same time in departments whose work was largely unaffected by the computer's introduction. In general, the changes in working environment and job satisfaction appear to have been quite similar between the two groups of employees. [ABSTRACT FROM AUTHOR]