Recent publications
Quarantine guidelines that arose with the COVID-19 pandemic limited opportunities for social interaction, raising concerns about increases in intimate partner violence and cyberabuse while simultaneously restricting access to help. The current study assessed increases in cyberabuse, sexual aggression, and intimate partner violence victimization and perpetration during the first year of the COVID-19 pandemic in a U.S. nationally representative sample of young adults ages 18 to 35, recruited from a probability-based household panel. Data were collected between November 2020 and May 2021. Descriptive analyses were conducted to assess the prevalence of any self-reported increase in cyberabuse, sexual aggression, or intimate partner victimization or perpetration during the COVID-19 pandemic. Logistic regression models were run for each outcome measuring any increase compared to no increase. Approximately one in ten U.S. young adults ages 18 to 35 reported experiencing an increase in cyberabuse victimization (12.6%) and cyberabuse perpetration (8.9%) during the pandemic. Similar proportions were observed for increased sexual aggression victimization (11.8%) and perpetration (9.0%). More than one in five respondents (21.4%) reported that their intimate partner was more physically, sexually, or emotionally aggressive toward them during the pandemic. Conversely, 16.2% of respondents reported that they were more physically, sexually, or emotionally aggressive themselves toward an intimate partner, compared to their behavior before the onset of the pandemic. Having an intimate partner and staying at home more than usual during the pandemic were protective factors for both cyberabuse and sexual aggression victimization. Respondent age, education, and race and ethnicity were not associated with increased victimization or perpetration of cyberabuse or sexual aggression. However, women reported lower odds of increased sexual aggression perpetration than men. These findings improve understanding of changes to interpersonal abuse and associated risk factors during a period of social disruption.
Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted by identifying patterns in diseases recorded in hospital visits over time. Disease multimorbidity or simultaneous occurrence of multiple diseases is a growing global public health challenge as populations age and long-term conditions become more prevalent. We propose a graph analytics framework for analyzing disease multimorbidity in hospital visits. Within the framework, we propose a graph model to explain multimorbidity as a function of prevalence, category, and chronic nature of the underlying disease. We apply our model to examine and compare multimorbidity patterns in public hospitals in Arizona, U.S., during five six-month time periods before and during the pandemic. We observe that while multimorbidity increased by 34.26% and 41.04% during peak pandemic for mental disorders and respiratory disorders respectively, the gradients for endocrine diseases and circulatory disorders were not significant. Multimorbidity for acute conditions is observed to be decreasing during the pandemic while multimorbidity for chronic conditions remains unchanged. Our graph analytics framework provides guidelines for empirical analysis of disease multimorbidity using electronic health records. The patterns identified using our proposed graph model informs future research and healthcare policy makers for pre-emptive decision making.
We propose a compensatory interactive influence of conscientiousness and GMA in task performance such that conscientiousness is most beneficial to performance for low-GMA individuals. Drawing on trait by trait interaction theory and empirical evidence for a compensatory mechanism of conscientiousness for low GMA, we contrast our hypothesis with prior research on a conscientiousness-GMA interaction and argue that prior research considered a different interaction type. We argue that observing a compensatory interaction likely requires (a) considering the appropriate interaction form, including a possible curvilinear conscientiousness-performance relationship; (b) measuring the full conscientiousness domain (as opposed to motivation proxies); (c) narrowing the criterion domain to reflect task performance; and (d) appropriate psychometric scoring of variables to increase power and avoid type 1 error. In four employee samples (N1 = 300; N2 = 261; N3 = 1,413; N4 = 948), we test a conscientiousness-GMA interaction in two employee samples. In three of four samples, results support a nuanced compensatory mechanism such that conscientiousness compensates for low to moderate GMA, and high conscientiousness may be detrimental to or unimportant for task performance in high-GMA individuals.
Objectives:
The purpose of our study is to investigate the efficacy and safety of blood purification (BP) therapy in hypertriglyceridemia-induced acute pancreatitis.
Methods:
We searched PubMed, Embase, Cochrane Library, and Web of Science databases for articles published.
Results:
The analysis included 13 studies with 934 patients (263 of BP group, 671 of control group). There was no difference in efficacy and safety between the BP group and the control group (all P > 0.05). Compared with conventional treatment, BP had shorter hospital stay (mean difference, -4.96; 95% confidence interval [CI], -8.81 to -1.11; P = 0.01) in the case of similar mortality and complications. Meanwhile, insulin treatment showed similar mortality to BP, but fewer local complications (odds risk, 2.18; 95% CI, 1.13-4.20; P = 0.02) and shorter hospital stay (mean difference, 5.46; 95% CI, 0.64-10.29; P = 0.03).
Conclusions:
In the treatment of hypertriglyceridemia-induced acute pancreatitis, BP methods are effective in accelerating triglyceride levels reduction and shortening hospital stay but do not affect the efficacy or reduce mortality significantly compared with conventional treatment. Insulin therapy has the same effect as BP but decreases incidence of complications and cost.
The focus of this study is the use of Natural Language Processing (NLP) to help improve AI-based recruitment systems. The goal is to train and tune NLP models to help speed up the screening, ranking, and matching of job candidates using resumes and job descriptions. To improve recruitment chatbots, advanced models like BERT and GPT are adopted to promote dynamic candidate interaction as well as initial interviews. The study also involves constructing an NLP-driven interview simulation tool based on Hiring Manager input, create which aids ensure better candidate suitability by emulating real interactions. The research then extends the semantic search algorithm to refine the selection of candidates from large HR databases. The study seeks to address the recruitment efficiency, candidate fit and engagement challenges by integrating these methods into a more effective and streamlined hiring process.
In our study, the primary goal was to gain insights into cognition by measuring spatial memorability for two different types of approaches to geometry in interior design (biomorphic design and non‐biomorphic rectilinear design). To better understand the processes behind the memorability differences, we also looked at how spatial memorability interacted with visual attention and spatial pleasantness. After extensive pre‐testing, two standardized photographic stimulus sets were created and used during the experiment, controlling for variables such as novelty, complexity, pleasantness, and the number and density of interior architectural elements. Each stimulus set contained equal numbers of photographs with biomorphic elements and photographs with non‐biomorphic elements. Subjects (N = 68 students, mean age = 25.4 years) viewed the first stimulus set, then were given a “distractor” task. Next, subjects viewed the second stimulus set, and for each photograph indicated whether the image was one they had seen or whether it was new. Visual attention for each photograph was monitored using eye‐tracking technology, and subjects also rated the pleasantness of each environment. The data were analyzed to test for the relative strength of memorability between environments with biomorphic elements and non‐biomorphic elements, as well as the links between recognition memory, visual attention, and pleasantness. The results suggest that interior spaces with biomorphic elements positively contribute to spatial memorability, are found to be more pleasant, and increase visual attention.
The primary objective of this paper is to offer a structured and comprehensive list of the barriers associated with implementation of artificial intelligence (AI) solutions in supply chain management (SCM). While the broader field of AI has made rapid advances in a relatively short period of time, there are significant barriers that still need to be addressed to harness the true potential of AI. SCM’s dependency on multi-actor collaboration, disparate data sources, unwillingness of actors to embrace AI, change management issues, and lack of AI governance framework poses significant barriers for successful implementation of AI. Drawn from extensive literature review as well as real-world experience, this paper systematically explores and compiles a robust list of barriers of AI implementation in supply chain functions by categorizing them and elaborating their impact at inter- and intra-organizational SCM. Lastly, the paper offers recommendations for practitioners, policymakers, researchers, and governments on how they can work together for AI to be successful.
Dynamic scheduling is one of the most important key technologies in production and flexible job shop is widespread. Therefore, this paper considers a dynamic flexible job shop scheduling problem considering setup time and random job arrival. To solve this problem, a dynamic scheduling framework based on the improved gene expression programming algorithm is proposed to construct scheduling rules. In this framework, the variable neighborhood search using four efficient neighborhood structures is combined with gene expression programming algorithm. And, an adaptive method adjusting recombination rate and transposition rate in the evolutionary progress is proposed. The test results on 24 groups of instances with different scales show that the improved gene expression programming performs better than the standard gene expression programming, genetic programming, and scheduling rules.
Online social networks (OSNs) are a major component of societal digitalization. OSNs alter how people communicate, make decisions, and form or change their beliefs, attitudes, and behaviors. Thus, they can now impact social groups, financial systems, and political communication at scale. As one type of OSN, social media platforms, such as Facebook, Twitter, and YouTube, serve as outlets for users to convey information to an audience as broad or targeted as the user desires. Over the years, these social media platforms have been infected with automated accounts, or bots, that are capable of hijacking conversations, influencing other users, and manipulating content dissemination. Although benign bots exist to facilitate legitimate activities, we focus on bots designed to perform malicious acts through social media platforms. Bots that mimic the social behaviors of humans are referred to as social bots. Social bots help automate sociotechnical behaviors, such as "liking" tweets, tweeting/retweeting a message, following users, and coordinating with or even competing against other bots. Some advanced social bots exhibit highly sophisticated traits of coordination and communication with complex organizational structures. This article presents a detailed survey of social bots, their types and behaviors, and how they impact social media, identification algorithms, and their coordination strategies in OSNs. The survey also discusses coordination in areas such as biological systems, interorganizational networks, and coordination games. Existing research extensively studied bot detection, but bot coordination is still emerging and requires more in-depth analysis. The survey covers existing techniques and open research issues on the analysis of social bots, their behaviors, and how social network theories can be leveraged to assess coordination during online campaigns.
Background
Seasonal influenza vaccine (SIV) uptake in the US remains suboptimal, requiring new and innovative strategies.
Objective
To evaluate the impact of a behavioral Peer Comparison (PC) intervention on SIV uptake in community pharmacies across the US.
Methods
A cluster randomized study was conducted across a national network of Walmart community pharmacies (> 4,500 sites) during the 2019/20 influenza season. Clusters consisted of 416 markets, each containing an average of 11 pharmacies. All pharmacies in a market were randomly assigned to either no intervention or the PC intervention, a software-delivered communication informing on-site staff, including pharmacists and pharmacy technicians, of their pharmacy’s weekly performance, measured as SIV doses administered, relative to peer pharmacies within their market. The outcome was the pharmacy-level cumulative SIV doses administered during the intervention period (September 1, 2019 to February 29, 2020). Linear regression models were used to estimate the PC impact, with multi-way cluster-robust standard errors (SE) estimated by market and state.
Results
A total of 4,589 pharmacies were enrolled in the study, with 2,297 (50.1%) randomized to the control and 2,292 (49.9%) randomized to the PC. Overall, relative to control pharmacies, PC pharmacies administered 3.7% (95% CI: -0.3% to 7.9%) additional SIV doses. Among large format pharmacies, PC pharmacies administered 4.1% (95% CI: 0.1% to 8.3%) additional SIV doses, relative to controls. Historically low performing large format PC pharmacies administered 6.1% (95% CI: 0.5% to 11.9%) additional SIV doses, relative to controls. No significant treatment effects were observed among small format pharmacies.
Conclusion
Our findings demonstrate that PCs can improve SIV uptake among large format community pharmacies, with historically low performing pharmacies potentially exhibiting the greatest relative impact. Wide-scale implementation of PCs in community pharmacies may help to further improve SIV uptake in these settings.
The United States inland waterway transportation system is comprised of 12,000 miles of navigable waterways that connect and move freight between 38 states and the global supply chain. When investing in inland waterway infrastructure, engineering managers should aim to maximize all benefits associated with the investment, including flood protection, water supply, hydropower generation, recreation, and environmental impact benefits. In this article, we formulate an initial qualitative value model for inland waterway infrastructure investment decisions, based on a value-focused thinking approach that enables engineering managers to holistically evaluate project investment alternatives. For circumstances involving limited resources, a portfolio optimization model is formulated to maximize the total value associated with project investments, while considering budget and minimally acceptable benefit constraints. To demonstrate an application of our approach, we present a case study based on the McClellan Kerr Arkansas River Navigation System.
We developed a novel multiobjective markdown system and deployed it across many merchandising units at Walmart. The objectives of this system are to (1) clear the stores’ excess inventory by a specified date, (2) improve revenue by minimizing the discounts needed to clear shelves, and (3) reduce the substantial cost to relabel merchandise in the stores. The underlying mathematical approach uses techniques such as deep reinforcement learning, simulation, and optimization to determine the optimal (marked-down) price. Starting in 2019, after six months of extensive testing, we implemented the new approach across all Walmart stores in the United States. The result was a high-performance model with a price-adjustment policy tailored to each store. Walmart increased its sell-through rate (i.e., the number of units sold during the markdown period divided by its inventory at the beginning of the markdown) by 21% and reduced its costs by 7%. Benefits that Walmart accrues include demographics-based store personalization, reductions in operating costs with limited numbers of price adjustments, and a dynamic time window for markdowns.
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