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Background: Cigarette smoking remains the leading cause of preventable disease and death in the United States. Primary care offers an ideal setting to reach adults who smoke cigarettes and improve uptake of evidence-based cessation treatment. Although U.S. Preventive Services Task Force Guidelines recommend the 5As model (Ask, Advise, Assess, Assist, Arrange) in primary care, there are many barriers to its implementation. Automated, comprehensive, and proactive tools are needed to overcome barriers. Our team developed and preliminarily evaluated a proactive electronic visit (e-visit) delivered via the Electronic Health Record patient portal to facilitate evidence-based smoking cessation treatment uptake in primary care, with promising initial feasibility and efficacy. This paper describes the rationale, design, and protocol for an ongoing Hybrid Type I effectiveness-implementation trial that will simultaneously assess effectiveness of the e-visit intervention for smoking cessation as well as implementation potential across diverse primary care settings. Methods: The primary aim of this remote five-year study is to examine the effectiveness of the e-visit intervention vs. treatment as usual (TAU) for smoking cessation via a clinic-randomized clinical trial. Adults who smoke cigarettes are recruited across 18 primary care clinics. Clinics are stratified based on their number of primary care providers and randomized 2:1 to either e-visit or TAU. An initial baseline e-visit gathers information about patient smoking history and motivation to quit, and a clinical decision support algorithm determines the best evidence-based cessation treatment to prescribe. E-visit recommendations are evaluated by a patient's own provider, and a one-month follow-up e-visit assesses cessation progress. Main outcomes include: (1) cessation treatment utilization (medication, psychosocial cessation counseling), (2) reduction in cigarettes per day, and (3) biochemically verified 7-day point prevalence abstinence (PPA) at six-months. We hypothesize that patients randomized to the e-visit condition will have better cessation outcomes (vs. TAU). A secondary aim evaluates e-visit implementation potential at patient, provider, and organizational levels using a mixed-methods approach. Implementation outcomes include acceptability, adoption, fidelity, implementation cost, penetration, and sustainability. Discussion: This asynchronous, proactive e-visit intervention could provide substantial benefits for patients, providers, and primary care practices and has potential to widely improve reach of evidence-based cessation treatment. Trial registration: NCT05493254. [ABSTRACT FROM AUTHOR]

Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual's likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care. [ABSTRACT FROM AUTHOR]

Policing in many parts of the world—the United States in particular—has embraced an archetypal model: a conception of the police based on the tenets of individuated archetypes, such as the heroic police "warrior" or "guardian." Such policing has in part motivated moves to (1) a reallocative model: reallocating societal resources such that the police are no longer needed in society (defunding and abolishing) because reform strategies cannot fix the way societal problems become manifest in (archetypal) policing; and (2) an algorithmic model: subsuming policing into technocratic judgements encoded in algorithms through strategies such as predictive policing (mitigating archetypal bias). This paper begins by considering the normative basis of the relationship between political community and policing. It then examines the justification of reallocative and algorithmic models in light of the relationship between political community and police. Given commitments to the depth and distribution of security—and proscriptions against dehumanizing strategies—the paper concludes that a nonideal-theory priority rule promoting respect for personhood (manifest in community and dignity-promoting policing strategies) is a necessary condition for the justification of the above models. [ABSTRACT FROM AUTHOR]

This case study demonstrates the application of an unsupervised topic modeling algorithm to 7,773 English-language articles published in the Library Quarterly from 1931 to 2015. The analysis of 85 years of the journal's output follows an exploratory data analysis framework to generate novel hypotheses about the history of LIS using topic modeling, a method for identifying clusters of co-occurring words within large collections of text. The paper closely examines two topics that suggest differences in gender representation in the journal to propose and support a new hypothesis regarding the historical inclusion of gendered objects of study in LIS literature. [ABSTRACT FROM AUTHOR]

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC). [ABSTRACT FROM AUTHOR]

During the summer of 2018, the upward-pointing Wyoming Cloud Lidar (WCL) was deployed on board the University of Wyoming King Air (UWKA) research aircraft for the Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign. This paper describes the generation of calibrated attenuated backscatter coefficients and aerosol extinction coefficients from the WCL measurements. The retrieved aerosol extinction coefficients at the flight level strongly correlate (correlation coefficient, rr > 0.8) with in situ aerosol concentration and carbon monoxide (CO) concentration, providing a first-order estimate for converting WCL extinction coefficients into vertically resolved CO and aerosol concentration within wildfire smoke plumes. The integrated CO column concentrations from the WCL data in nonextinguished profiles also correlate (rr = 0.7) with column measurements by the University of Colorado Airborne Solar Occultation Flux instrument, indicating the validity of WCL-derived extinction coefficients. During BB-FLUX, the UWKA sampled smoke plumes from more than 20 wildfires during 35 flights over the western United States. Seventy percent of flight time was spent below 3 km above ground level (AGL) altitude, although the UWKA ascended up to 6 km AGL to sample the top of some deep smoke plumes. The upward-pointing WCL observed a nearly equal amount of thin and dense smoke below 2 km and above 5 km due to the flight purpose of targeted fresh fire smoke. Between 2 and 5 km, where most of the wildfire smoke resided, the WCL observed slightly more thin smoke than dense smoke due to smoke spreading. Extinction coefficients in dense smoke were 2–10 times stronger, and dense smoke tended to have larger depolarization ratio, associated with irregular aerosol particles. [ABSTRACT FROM AUTHOR]

Background: Pediatric traumatic brain injury (TBI) affects about 475,000 children in the United States annually. Studies from the 1990s showed worse mortality in pediatric TBI patients not transferred to a pediatric trauma center (PTC), but did not examine mild pediatric TBI. Evidence-based guidelines used to identify children with clinically insignificant TBI who do not require head CT were developed by the Pediatric Emergency Care Applied Research Network (PECARN). However, which patients can be safely observed at a non-PTC is not directly addressed.Methods: A systematic review of the literature was conducted, focusing on management of pediatric TBI and transfer decisions from 1990 to 2020.Results: Pediatric TBI patients make up a great majority of preventable transfers and admissions, and comprise a significant portion of avoidable costs to the health care system. Majority of mild TBI patients admitted to a PTC following transfer do not require ICU care, surgical intervention, or additional imaging. Studies have shown that as high as 83% of mild pediatric TBI patients are discharged within 24 hrs.Conclusions: An evidence-based clinical practice algorithm was derived through synthesis of the data reviewed to guide transfer decision. The papers discussed in our systematic review largely concluded that transfer and admission was unnecessary and costly in pediatric patients with mild TBI who met the following criteria: blunt, no concern for NAT, low risk on PECARN assessment, or intermediate risk on PECARN with negative imaging or imaging with either isolated, nondisplaced skull fractures without ICH and/or EDH, or SDH <0.3 cm with no midline shift. [ABSTRACT FROM AUTHOR]

Algorithmic vision, the computational process of making meaning from digital images or visual information, has changed the relationship between the image and the human subject. In this paper, I explicate on the role of algorithmic vision as a technique of algorithmic governance, the organization of a population by algorithmic means. With its roots in the United States post-war cybernetic sciences, the ontological status of the computational image undergoes a shift, giving way to the hegemonic use of automated facial recognition technologies towards predatory policing and profiling practices. By way of example, I argue that algorithmic vision reconfigures the philosophical links between vision, image, and truth, paradigmatically changing the way a human subject is represented through imagistic data. With algorithmic vision, the relationship between subject and representation challenges the humanistic discourse around images, calling for a critical displacement of the human subject from the center of an analysis of how computational images make meaning. I will explore the relationship between the operative image, the image that acts but is not seen by human eyes, and what Louise Amoore calls an "emergent subject," a subject that is made visible through algorithmic techniques (2013). Algorithmic vision reveals subjects to power in a mode that requires a new approach towards analyzing the entanglement and invisiblization of the human in automated decision-making systems. [ABSTRACT FROM AUTHOR]

Significant wave height prediction for the following hours is a necessity for the planning and operation of wave energy devices. For a site-specific and short-term prediction, classical numerical wave forecasting methods may not be justified as exhaustive climatological data and huge computational power are needed. In this paper, a combination of a decomposition approach and long short-term memory network was presented to forecast the significant wave heights. An improved version of complete ensemble empirical mode decomposition algorithm and recurrence quantification analysis were applied to separate the original time series into deterministic and stochastic components. Each decomposed series was forecasted by the long short-term memory network and the final predicted significant wave heights were obtained by integrating the deterministic and stochastic predictions. Wave data measured at three buoy stations along the eastern coast of the United States were utilized to verify the hybrid model. The performance of the proposed method in three different wave height ranges was evaluated. The results suggested that the hybrid model outperformed the stand-alone long short-term memory network adjusted on the unseparated signal; in particular, for longer lead times and larger wave heights. [ABSTRACT FROM AUTHOR]

• FDD tools are increasingly adopted in commercial buildings. • Using FDD tools to correct hunting faults can mitigate issues cost-effectively. • We developed and implemented automated control hunting correction in FDD tools. • The algorithm was tested in nine VAV boxes and successfully eliminated hunting faults. • We discussed challenges and future research areas for scaling the technology up. Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology. [ABSTRACT FROM AUTHOR]

The topic of algorithmic fairness is of increasing importance to the Human–Computer Interaction research community following accumulating concerns regarding the use and deployment of Artificial Intelligence-based systems. How we conduct research on algorithmic fairness directly influences our inferences and conclusions regarding algorithmic fairness. To better understand the methodological decisions of studies focused on people's perceptions of algorithmic fairness, we systematic analysed relevant papers from the CHI and FAccT conferences. We identified 200 relevant papers published between 1993 and 2022 and assessed their study design, participant sample, and geographical location of participants and authors. Our results highlight that studies are predominantly cross-sectional, cover a wide range of participant roles, and that both authors and participants are primarily from the United States. Based on these findings, we reflect on the potential pitfalls and shortcomings in how the community studies algorithmic fairness. • Mapping of CHI and FAccT papers on algorithmic fairness published 1993–2022. • Over one-third of papers miss details on participant locality and compensation. • Algorithmic fairness research largely restricted to US and other Western countries. • Limited cross-country collaboration, most study samples from a single country. • Remote studies are most common, longitudinal studies are relatively rare. [ABSTRACT FROM AUTHOR]

The past two decades have witnessed an increase in ethnicity-related peer aggression, driven by the intensification of the migration phenomenon and rise of information and communication technologies. The goal of the current study is to extend the scope of previous reviews on peer aggression to examine the emerging evidence on the role of technological tools in the prevention, detection, and handling of ethnicity-related peer aggression among young people. We have identified 14 relevant papers published between 2005 and 2020 that help us answer the following research questions: What forms of aggression among young ethnic minorities do these technological interventions try to address? What types of technological interventions are being used? The results indicate that the technological tools are being used to tackle both intergroup as well as intragroup peer aggression, in which ethnic minorities youth is involved (as a victim, perpetrator or bystander). Most studies have focused on adolescents and young adults in the US or in Europe. The technological tools reported on, include: online games, videos, social media, and chat-based programmes, or machine learning algorithms aimed at tackling online and offline peer aggression. They can be used either as stand - alone tools, or as part of intervention programmes (at the school, family or community level). These findings can be useful to improve and elaborate future digital technologies developed to address ethnicity-related peer aggression. • The review maps the existing literature on the role of technological tools in tackling ethnicity-related peer aggression. • All forms of peer aggression are considered in which ethnic minorities youth was involved. • Three groups of technological interventions are identified and categorized into intra- or intergroup ethnicity-related peer aggression. • The digital tools presented include: online games, video, social media, chat-based programmes, and machine learning algorithms. • Our study reveals some follow-up research questions needed to expand current knowledge on digital technologies. [ABSTRACT FROM AUTHOR]

The Geostationary Operational Environmental Satellite-R (GOES-R) series provides new opportunities for continuous observation of precipitation at large scales with a high resolution. An operational quantitative precipitation estimation (QPE) product has been produced based on multi-channel measurements from the Advanced Baseline Imager (ABI) aboard the GOES-16 (formerly known as GOES-R). This paper presents a comprehensive evaluation of this GOES-16 QPE product against a ground reference QPE product from the National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor (MRMS) system over the continental United States (CONUS) during the warm seasons of 2018 and 2019. For the first time, the accuracy of GOES-16 QPE product was quantified using the gauge-corrected MRMS (GC-MRMS) QPE product, and a number of evaluation metrics were applied to adequately resolve the associated errors. The results indicated that precipitation occurrence and intensity estimated by the GOES-16 QPE agreed with GC-MRMS fairly well over the eastern United States (e.g., the probability of detection was close to 1.0, and the Pearson's correlation coefficient was 0.80 during September 2019), while the discrepancies were noticeable over the western United States (e.g., the Pearson's correlation coefficient was 0.64 for the same month). The performance of GOES-16 QPE was downgraded over the western United States, in part due to the limitations of the GOES-16 rainfall retrieval algorithm over complex terrains, and in part because of the poor radar coverage analyzed by the MRMS system. In addition, it was found that the GOES-16 QPE product significantly overestimated rainfall induced by the mesoscale convective systems in the midwestern United States, which must be addressed in the future development of GOES satellite rainfall retrieval algorithms. [ABSTRACT FROM AUTHOR]