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Purpose: The purpose of this paper is to identify workforce skills that protect an occupation from elimination due to automation technology. Design/methodology/approach: The authors apply a Gaussian process (GP) classifier, based on the level of non-automatable work activities in an occupation, to USA and Colombian occupational datasets. Findings: The authors find that communication, interpersonal relationship management and decision-making skills are most important in occupations that are resistant to automation. Research limitations/implications: The results are based on work activities data from the Occupational Information Network (O*NET) database developed for the USA labor market. This dataset does not capture significant differences in work activities, where they exist, for the same occupation between the two countries. The findings are also limited to Colombia. Readers should be careful to extrapolate the findings outside of this geography. Originality/value: The authors discover that automation is likely to be a global phenomenon that can only be slightly mitigated by cultural and political factors.

Wage gaps between "skilled" and "unskilled" earners in the United States are conventionally attributed to U.S. workers' "skill deficits," or lack of skills necessary to deal with technological change, particularly automation. This paper argues instead that skills deficit/automation claims have always been a weak explanation for post-1979 trends. Since the mid-1990s all indications are that there is no basis for considering automation to be a significant factor in wage suppression or the growth of wage inequality. Rather, inequality growth has been an outgrowth of developments in the labor market rather than product markets. The key dynamic undercutting the typical worker's wage growth has been the strengthening of employers' power relative to their white-collar and blue-collar workers. The cause has not been monopoly firms exercising their power in product markets by charging higher prices to consumers. Monopolization has indeed contributed to wage suppression, but even this factor has largely run through the labor market, as monopoly firms squeezed supplier chain firms which in turn undercut their own workers' wages while seeing a profit squeeze as well. The paper concludes that what is needed is a better balancing of power in the labor market rather than a perfecting of competition. [ABSTRACT FROM AUTHOR]

With emerging Automated Driving Systems (ADS) representing Automated Vehicles (AVs) of Level 3 or higher as classified by the Society of Automotive Engineers, several AV manufacturers are testing their vehicles on public roadways in the U.S. The safety performance of AVs has become a major concern for the transportation industry. Several ADS-equipped vehicle crashes have been reported to the National Highway Traffic Safety Administration (NHTSA) in recent years. Scrutinizing these crashes can reveal rare or complex scenarios beyond the normal capabilities of AV technologies called "edge cases." Investigating edge-case crashes helps AV companies prepare vehicles to handle these unusual scenarios and, as such, improves traffic safety. Through analyzing the NHTSA data from July 2021 to February 2023, this study utilizes an unsupervised machine learning technique, hierarchical clustering, to identify edge cases in ADS-equipped vehicle crashes. Fifteen out of 189 observations are identified as edge cases, representing 8 % of the population. Injuries occurred in 10 % of all crashes (19 out of 189), but the proportion rose to 27 % for edge cases (4 out of 15 edge cases). Based on the results, edge cases could be initiated by AVs, humans, infrastructure/environment, or their combination. Humans can be identified as one of the contributors to the onset of edge-case crashes in 60 % of the edge cases (9 out of 15 edge cases). The main scenarios for edge cases include unlawful behaviors of crash partners, absence of a safety driver within the AV, precrash disengagement, and complex events challenging for ADS, e.g., unexpected obstacles, unclear road markings, and sudden and unexpected changes in traffic flow, such as abrupt road congestion or sudden stopped traffic from a crash. Identifying and investigating edge cases is crucial for improving transportation safety and building public trust in AVs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Teachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open‐ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence‐level semantic representations of student responses to open‐ended questions, we propose a collaborative filtering‐based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. Lay Description: What is already known about this topic: Open‐ended questions are used by teachers in the domain of mathematics to assess their students understanding but automated support for these types of questions are limited in online learning platforms. Recent advancements in areas of machine learning and natural language processing have led to promising results in a range of domains and applications. What this paper adds: Emulating how teachers identify similar student answers can be used to build tools that support teachers in assessing and providing feedback to student open‐ended work. Implications for practice: Developing better automated supports can increase the amount of direct feedback students receive to guide their learning. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

In the United States of America, the Census Bureau (USCB) is implementing four major areas of innovation in preparation for the 2020 Census. The first area focuses on the maintenance of our frame of addresses using satellite imagery. The USCB will compare old and new satellite images to identify geographic areas that are stable and will not require in-person field staff updating. The second uses the Internet, including Identified (ID) and Non-Identified (Non-ID) processing, and cloud services to make it easier for people to self-respond. This Non-ID methodology has significant impacts on the way USCB staff execute our advertising campaign and promises to increase our self-response rates. The third uses existing administrative data to reduce cost. USCB will use existing data in many components of the 2020 Census including identification of vacant, non-existent, and occupied living quarters for non-respondents. The last area of innovation leverages technology to make field staff more efficient. Much of the field tasks will be automated including recruiting people, hiring staff, training staff, payroll and expense reporting, case assignments, case management, route optimization, quality control, and communications. The 2020 Census promises to be innovative, and improve over past censuses that relied heavily on paper questionnaires. [ABSTRACT FROM AUTHOR]

This paper explores how professional engineers recognize and make sense of product defects in their everyday work. Such activities form a crucial, if often overlooked, part of professional engineering practice. By detecting, recognizing and repairing defects, engineers contribute to the creation of value and the optimization of production processes. Focusing on early-career engineers in an advanced steel mill in the United States, we demonstrate how learning specific ways of seeing and attending to defects take shape around the increasing automation of certain aspects of engineering work. Practices of sensing defects are embodied, necessitating disciplined eyes, ears, and hands, but they are also distributed across human and non-human actors. We argue that such an approach to technical work provides texture to the stark opposition between human and machine work that has emerged in debates around automation. Our approach to sensing defects suggests that such an opposition, with its focus on job loss or retention, misses the more nuanced ways in which humans and machines are conjoined in perceptual tasks. The effects of automation should be understood through such shifting configurations and the ways that they variously incorporate the perceptual practices of humans and machines. [ABSTRACT FROM AUTHOR]

Occupational Applications This paper provides a historical overview of the rise of Work-Related Musculoskeletal Disorders (WMSDs) in the U.S. from the 1980s to the present, with a special focus on the two industries with the highest recorded rates: Meatpacking and Auto Assembly. The activities underlying this account amount to a major occupational health success story (although still incomplete) in the recognition of these disorders and the development of a nation-wide effort for prevention. An obstacle in creating the data presented here was that the system of recordkeeping changed in multiple ways, making it difficult to compare WMSD rates over time. The work summarized here evaluated the effect of these changes and combined temporal segments that were judged to be the most accurate to create an overall trend line. These results can aid in: 1) showing the overall history of the recognition and prevention of WMSDs, 2) understanding the effects of the recordkeeping changes, and 3) allowing international comparisons of the trends.

Aims: The primary aim of this paper was to outline the processes involved in building the Partners Arthroplasty Registry (PAR), established in April 2016 to capture baseline and outcome data for patients undergoing arthroplasty in a regional healthcare system. A secondary aim was to determine the quality of PAR's data. A tertiary aim was to report preliminary findings from the registry and contributions to quality improvement initiatives and research up to March 2019., Methods: Structured Query Language was used to obtain data relating to patients who underwent total hip or knee arthroplasty (THA and TKA) from the hospital network's electronic medical record (EMR) system to be included in the PAR. Data were stored in a secure database and visualized in dashboards. Quality assurance of PAR data was performed by review of the medical records. Capture rate was determined by comparing two months of PAR data with operating room schedules. Linear and binary logistic regression models were constructed to determine if length of stay (LOS), discharge to a care home, and readmission rates improved between 2016 and 2019., Results: The PAR captured 16,163 THAs and TKAs between April 2016 and March 2019, performed in seven hospitals by 110 surgeons. Manual comparison to operating schedules showed a 100% capture rate. Review of the records was performed for 2,603 random operations; 2,298 (88.3%) had complete and accurate data. The PAR provided the data for three abstracts presented at international conferences and has led to preoperative mental health treatment as a quality improvement initiative in the participating institutions. For primary THA and TKA surgeries, the LOS decreased significantly (p < 0.001) and the rate of home discharge increased significantly (p < 0.001) between 2016 and 2019. Readmission rates did not correlated with the date of surgery (p = 0.953)., Conclusion: The PAR has high rates of coverage (the number of patients treated within the Partners healthcare network) and data completion and can be used for both research purposes and quality improvement. The same method of creating a registry that was used in the PAR can be applied to hospitals using similar EMR systems. Cite this article: Bone Joint J 2020;102-B(7 Supple B):90-98.

Today's power utilities, around the world, own multiple substations that are connected together to form a complex energy network. The functions within and between these substations are being automated according to globally accepted power utility automation standard IEC 61850. This automation results in efficient operation and enhanced protection of power network with the aid of the communication system. Implementing the protection schemes modeled using communication configurations of standardized information exchange will lead to digital power grid. Designing an IEC-61850-based protection scheme to take care of the faults outside the substations is a challenge, as the typical local-area-network-based generic-object-oriented substation events and sampled-value messages need to be transmitted over a wide-area network. This paper presents communication configuration for line current differential protection schemes applied between two automated substations. It presents the simulation results of communication configuration network between two substations. Its performance is evaluated using a network simulator tool. This study intends to guide the development of a robust protection scheme with IEC-61850-based communication configuration. [ABSTRACT FROM PUBLISHER]

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While road safety in the United States (U.S.) has been continually improving since the 1970's, there are indications that these improvements are becoming increasingly difficult to sustain. Strategic Highway Safety Plans (SHSPs) are prepared by States to guide road safety management, however assessing the appropriateness of these plans remains a significant challenge, especially for the future in which they are to be applied. This study developed a new methodology to assess SHSPs from the perspectives of comprehensive system-based safety management and relevant future issues that can be applied before SHSPs are implemented, thereby avoiding long periods after implementation before assessing the appropriateness of the plans. A rating scale was developed and applied to assess 48 U.S. SHSPs against two key criteria: 1. a comprehensive framework for road safety, and 2. the anticipated changing, difficult and unpredictable nature of future transport and its context. The analysis concluded that current SHSPs have good national oversight with several strengths but were weak in some areas of content and did not address future challenges. Improvements are suggested to strengthen the plans’ thoroughness by being consistent with systems theory and practice, as well as ways that these SHSPs can be more resilient to future circumstances. Implementing the recommendations in this paper provides the opportunity to adopt a system-based safety management practice that has been successful in other hazardous industries. Doing so is expected to most efficiently and effectively continue the recent improvements to road safety, which is likely to be increasingly difficult otherwise. [ABSTRACT FROM AUTHOR]

Incorporating automation into wind turbine blade manufacturing is important for reducing costs to meet current offshore wind energy production goals in the United States. This work proposes a process for automating three operations in wind blade manufacturing: trimming to remove flashing left over after bonding two blade skins together, grinding to produce a desired leading‐edge shape, and sanding to prepare the blade for bonding overlamination or adding paint to the surface. The majority of this work focuses on the toolpath generation. The algorithms were tested on a 5‐m blade section, and the results were analyzed in terms of operation speed and accuracy. Finally, future work is discussed to improve the performance of the system. [ABSTRACT FROM AUTHOR]

Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients’ health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED. Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding. Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases. Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED. [ABSTRACT FROM AUTHOR]

Rationale: Previous studies have observed a positive association between automation risk and employment loss. Based on the job insecurity-health risk hypothesis, greater exposure to automation risk could also be negatively associated with health outcomes., Objective: The main objective of this paper is to investigate the county-level association between prevalence of workers in jobs exposed to automation risk and general, physical, and mental health outcomes., Methods: As a preliminary assessment of the job insecurity-health risk hypothesis (automation risk → job insecurity → poorer health), a structural equation model was used based on individual-level data in the two cross-sectional waves (2012 and 2014) of General Social Survey (GSS). Next, using county-level data from County Health Rankings 2017, American Community Survey (ACS) 2015, and Statistics of US Businesses 2014, Two Stage Least Squares (2SLS) regression models were fitted to predict county-level health outcomes., Results: Using the 2012 and 2014 waves of the GSS, employees in occupational classes at higher risk of automation reported more job insecurity, that, in turn, was associated with poorer health. The 2SLS estimates show that a 10% increase in automation risk at county-level is associated with 2.38, 0.8, and 0.6 percentage point lower general, physical, and mental health, respectively., Conclusion: Evidence suggests that exposure to automation risk may be negatively associated with health outcomes, plausibly through perceptions of poorer job security. More research is needed on interventions aimed at mitigating negative influence of automation risk on health., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The decline of manufacturing employment is frequently invoked as a key cause of worsening U.S. population health trends, including rising mortality due to "deaths of despair." Increasing automation-- the use of industrial robots to perform tasks previously done by human workers-- is one structural force driving the decline of manufacturing jobs and wages. In this study, we examine the impact of automation on age- and sex-specific mortality. Using exogenous variation in automation to support causal inference, we find that increases in automation over the period 1993-2007 led to substantive increases in all- cause mortality for both men and women aged 45-54. Disaggregating by cause, we find evidence that automation is associated with increases in drug overdose deaths, suicide, homicide, and cardiovascular mortality, although patterns differ by age and sex. We further examine heterogeneity in effects by safety net program generosity, labor market policies, and the supply of prescription opioids. [ABSTRACT FROM AUTHOR]

This article analyzes US drone operations through a historical and ethnographic analysis of the remote split paradigm used by the US Air Force. Remote split refers to the globally distributed command and control of drone operations and entails a network of human operators and analysts in the Middle East, Europe, and Southeast Asia as well as in the continental United States. Though often viewed as a teleological progression of "unmanned" warfare, this paper argues that historically specific technopolitical logics establish the conditions of possibility for the work of war to be divisible into discreet and computationally mediated tasks that are viewed as effective in US military engagements. To do so, the article traces how new forms of authorized evidence and expertise have shaped developments in military operations and command and control priorities from the Cold War and the "electronic battlefield" of Vietnam through the Gulf War and the conflict in the Balkans to contemporary deployments of drone operations. The article concludes by suggesting that it is by paying attention to divisions of labor and human-machine configurations that we can begin to understand the everyday and often invisible structures that sustain perpetual war as a military strategy of the United States. [ABSTRACT FROM AUTHOR]

Debate over the effect of technology and automation on job creation or job destruction has been an ongoing debate in economics for some time. The recent developments in automation and the speed with which machine is replacing labor in some industries has worried many economists (Krugman, 2013, Graetz, G & G Michaels, 2015). While a number of recent studies present evidence on the negative effect of automation on employment by occupations (Oschinski M & R Wyonchi, 2017), none presents empirical evidence on the effect of automation on jobs at the macroeconomics level. This study utilizes the traditional model of the relationship between the real GDP growth and unemployment rate, estimated for US economy in 1962 and publicized as Okun's Law. The relationship implies that a one percent increase in GDP growth above the normal growth of GDP results in .4 percent decrease in unemployment rate. Although the relationship between GDP growth and unemployment rate may be affected by other economic variables in the short-run, a variable that may result in structural change in this relationship in the long-run is technology and its effect on unemployment. Technological advancement may lead to substitution of capital for labor, resulting in less response of GDP growth to unemployment rate than what Okun's law proposed. The main objective of this paper is to test whether such a structural change in the relationship has occurred or not. Using data for the last sixty years for eight industrialized countries, this paper compares the average response of unemployment rate to real GDP growth in three decades of 1955-1985, with the recent three decades of 1986-2015. [ABSTRACT FROM AUTHOR]

Clinical terminologies are considered a key technology for capturing clinical data in a precise and standardized manner, which is critical to accurately exchange information among different applications, medical records and decision support systems. An important step to promote the real use of clinical terminologies, such as SNOMED-CT, is to facilitate the process of finding mappings between local terms of medical records and concepts of terminologies. In this paper, we propose a mapping tool to discover text-to-concept mappings in SNOMED-CT. Name-based techniques were combined with a query expansion system to generate alternative search terms, and with a strategy to analyze and take advantage of the semantic relationships of the SNOMED-CT concepts. The developed tool was evaluated and compared to the search services provided by two SNOMED-CT browsers. Our tool automatically mapped clinical terms from a Spanish glossary of procedures in pathology with 88.0% precision and 51.4% recall, providing a substantial improvement of recall (28% and 60%) over other publicly accessible mapping services. The improvements reached by the mapping tool are encouraging. Our results demonstrate the feasibility of accurately mapping clinical glossaries to SNOMED-CT concepts, by means a combination of structural, query expansion and named-based techniques. We have shown that SNOMED-CT is a great source of knowledge to infer synonyms for the medical domain. Results show that an automated query expansion system overcomes the challenge of vocabulary mismatch partially.

I argue that the contemporary organization of professional work indicates the limits of answering typical questions about automation and technology in work to understand the practices of experts. Examining the case of automation in medicine, specifically Stereotaxis technology, I analyze a colligation of data on decisions about the uptake of this robotic technology among United States surgeons. I examine data including: procedure efficiency over time, the technology's affordances for the preservation of surgeons' bodily capital, changes in the profession's demographic profile, physician social networks, and clinical researchers' published assessments. The data suggest that automation of a central task can support and enhance the work of individual experts. They also show that using robotics does not improve efficiency. This case thus challenges the assumption that automation displaces work, at least in the case of professionals. And so, because Stereotaxis becomes more of a complement to surgeons' work as opposed to a substitute, this case points towards the importance of focusing attention less on job automation than on task automation. The case also highlights that users of the technology (physicians) value it differently than do purchasers (administrators). In addition, it identifies the differing considerations motivating their decisions to adopt or not adopt the technology. And in light of the finding that robotics use is more common among the rank-and-file than the elite, it may be that some professionals perceive automation to afford mobility opportunities. Based on these findings I propose new questions for scholarship on medicine, work, and automation, including those around "expert" versus "unskilled" labor, the body, and workplace divisions of labor. [ABSTRACT FROM AUTHOR]

The rationalization of human life in work, feeling, and relationships is amplified by artificial intelligence (AI), apps and automation, challenging interpersonal workers not only in how and whether they do their work, but also how they understand themselves as human. Given these trends, how do interpersonal workers interpret the humanness of their work? To answer this question, I focus on the interactive service work I call "connective labor," relying on 80+ in-depth interviews and 300+ hours of ethnographic observations with teachers, therapists and primary care physicians in the San Francisco Bay Area and mid-Atlantic United States, as well as with less advantaged practitioners such as sex workers, hairdressers, and home health care aides. I found that these interpersonal workers differentiated themselves from AI, automated agents, and robots in three ways: (1) by describing and defending their work as not rote, (2) taking pains to prove that they were not robots, and (3) justifying their judgments as safe, unique, and worthwhile. Much of their case rested on the unpredictability of humans, in terms of feelings, secrets, and mistakes. These findings have implications for race, class, and gender inequality, as advantage shaped how people were able to demand, perform, or experience their humanness in the ways that the proliferation of algorithmic technologies made salient. [ABSTRACT FROM AUTHOR]

The article employs a conceptual framework to study the impact of technological changes on the productivity and wages of skilled and non-skilled workers in the U.S. Topics discussed include comparison of productivity between skilled workers and less-skilled workers, changes in the demand for skills, and the association between automation and inequality.

Objective In this study, we used human factors (HF) methods and principles to design a clinical decision support (CDS) that provides cognitive support to the pulmonary embolism (PE) diagnostic decision-making process in the emergency department. We hypothesised that the application of HF methods and principles will produce a more usable CDS that improves PE diagnostic decision-making, in particular decision about appropriate clinical pathway. Materials and methods We conducted a scenario-based simulation study to compare a HF-based CDS (the so-called CDS for PE diagnosis (PE-Dx CDS)) with a web-based CDS (MDCalc); 32 emergency physicians performed various tasks using both CDS. PE-Dx integrated HF design principles such as automating information acquisition and analysis, and minimising workload. We assessed all three dimensions of usability using both objective and subjective measures: effectiveness (eg, appropriate decision regarding the PE diagnostic pathway), efficiency (eg, time spent, perceived workload) and satisfaction (perceived usability of CDS). Results Emergency physicians made more appropriate diagnostic decisions (94% with PE-Dx; 84% with web-based CDS; p<0.01) and performed experimental tasks faster with the PE-Dx CDS (on average 96 s per scenario with PE-Dx; 117 s with web-based CDS; p<0.001). They also reported lower workload (p<0.001) and higher satisfaction (p<0.001) with PE-Dx. Conclusions This simulation study shows that HF methods and principles can improve usability of CDS and diagnostic decision-making. Aspects of the HF-based CDS that provided cognitive support to emergency physicians and improved diagnostic performance included automation of information acquisition (eg, auto-populating risk scoring algorithms), minimisation of workload and support of decision selection (eg, recommending a clinical pathway). These HF design principles can be applied to the design of other CDS technologies to improve diagnostic safety. [ABSTRACT FROM AUTHOR]

In the United States of America, the Census Bureau (USCB) is implementing four major areas of innovation in preparation for the 2020 Census. The first area focuses on the maintenance of our frame of addresses using satellite imagery. The USCB will compare old and new satellite images to identify geographic areas that are stable and will not require in-person field staff updating. The second uses the Internet, including Identified (ID) and Non-Identified (Non-ID) processing, and cloud services to make it easier for people to self-respond. This Non-ID methodology has significant impacts on the way USCB staff execute our advertising campaign and promises to increase our self-response rates. The third uses existing administrative data to reduce cost. USCB will use existing data in many components of the 2020 Census including identification of vacant, non-existent, and occupied living quarters for non-respondents. The last area of innovation leverages technology to make field staff more efficient. Much of the field tasks will be automated including recruiting people, hiring staff, training staff, payroll and expense reporting, case assignments, case management, route optimization, quality control, and communications. The 2020 Census promises to be innovative, and improve over past censuses that relied heavily on paper questionnaires. [ABSTRACT FROM AUTHOR]

We introduce automation into a standard model of capital accumulation and show that (i) there is the possibility of perpetual growth, even in the absence of technological progress; (ii) the long-run economic growth rate declines with population growth, which is consistent with the available empirical evidence; (iii) there is a unique share of savings diverted to automation that maximizes long-run growth; and (iv) automation explains around 14% of the observed decline of the labor share over the last decades in the United States. [ABSTRACT FROM AUTHOR]

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production. [ABSTRACT FROM AUTHOR]

The last decades have witnessed a revolution in manufacturing production characterized by increasing technology adoption and a strong expansion of international trade. Simultaneously, the income distribution has exhibited both polarization and concentration among the richest. Combining datasets from the U.S. Census Bureau, the U.S. Internal Revenue Service, the International Federation of Robotics, EU KLEMS, and COMTRADE, we study the causal effect of industrial automation on income inequality in the U.S. during 2010-2015. We exploit spatial and time variations in exposure to robots arising from past differences in industry specialization across U.S. metropolitan areas and the evolution of robot adoption across industries. We document a robust positive impact of robotics on income for only the top 1 percent of taxpayers, which is largest for top income fractiles. Therefore, industrial automation fuels income inequality and, particularly, top income inequality. According to our estimates, one more robot per thousand workers results in relative increments of the total taxable income accruing to fractiles P99 to P99.9, P99.9 to P99.99 and P99.99 to P100, of 2.1 percent, 3.5 percent and 5.9 percent, respectively. We also find that robotization leads to increased exports to high-income and upper-middle-income economies, and that this is one of the key mechanisms behind the surge in top income inequality. [ABSTRACT FROM AUTHOR]

Background: Fitness technologies such as wearables and sit-stand desks are increasingly being used to fight sedentary lifestyles by encouraging physical activity. However, adherence to such technologies decreases over time because of apathy and increased dismissal of behavioral nudges. Objective: To address this problem, we introduced shared autonomy in the context of sit-stand desks, where user input is integrated with robot autonomy to control the desk and reduce sedentary behavior and investigated user reactions and preferences for levels of automation with a sit-stand desk. As demographics affect user acceptance of robotic technology, we also studied how perceptions of nonvolitional behavior change differ across cultures (United States and India), sex, familiarity, dispositional factors, and health priming messages. Methods: We conducted a web-based vignette study in the United States and India where a total of 279 participants watched video vignettes of a person interacting with sit-stand desks of various levels of automation and answered questions about their perceptions of the desks such as ranking of the different levels of automation. Results: Participants generally preferred either manual or semiautonomous desks over the fully autonomous option (P<.001). However, participants in India were generally more amenable to the idea of nonvolitional interventions from the desk than participants in the United States (P<.001). Male participants had a stronger desire for having control over the desk than female participants (P=.01). Participants who were more familiar with sit-stand desks were more likely to adopt autonomous sit-stand desks (P=.001). No effects of health priming messages were observed. We estimated the projected health outcome by combining ranking data and hazard ratios from previous work and found that the semiautonomous desk led to the highest projected health outcome. Conclusions: These results suggest that the shared autonomy desk is the optimal level of automation in terms of both user preferences and estimated projected health outcomes. Demographics such as culture and sex had significant effects on how receptive users were to autonomous intervention. As familiarity improves the likelihood of adoption, we propose a gradual behavior change intervention to increase acceptance and adherence, especially for populations with a high desire for control. [ABSTRACT FROM AUTHOR]

Background: Patients with limited English proficiency frequently receive substandard health care. Asynchronous telepsychiatry (ATP) has been established as a clinically valid method for psychiatric assessments. The addition of automated speech recognition (ASR) and automated machine translation (AMT) technologies to asynchronous telepsychiatry may be a viable artificial intelligence (AI)-language interpretation option. Objective: This project measures the frequency and accuracy of the translation of figurative language devices (FLDs) and patient word count per minute, in a subset of psychiatric interviews from a larger trial, as an approximation to patient speech complexity and quantity in clinical encounters that require interpretation. Methods: A total of 6 patients were selected from the original trial, where they had undergone 2 assessments, once by an English-speaking psychiatrist through a Spanish-speaking human interpreter and once in Spanish by a trained mental health interviewer-researcher with AI interpretation. 3 (50%) of the 6 selected patients were interviewed via videoconferencing because of the COVID-19 pandemic. Interview transcripts were created by automated speech recognition with manual corrections for transcriptional accuracy and assessment for translational accuracy of FLDs. Results: AI-interpreted interviews were found to have a significant increase in the use of FLDs and patient word count per minute. Both human and AI-interpreted FLDs were frequently translated inaccurately, however FLD translation may be more accurate on videoconferencing. Conclusions: AI interpretation is currently not sufficiently accurate for use in clinical settings. However, this study suggests that alternatives to human interpretation are needed to circumvent modifications to patients' speech. While AI interpretation technologies are being further developed, using videoconferencing for human interpreting may be more accurate than in-person interpreting. [ABSTRACT FROM AUTHOR]

Approximately 11% of bridges in the United States are categorized as structurally deficient and there is a marked need of more accurately evaluating true structural capacity. Structural Health Monitoring (SHM) systems can provide a timely indication of the need for maintenance, repair, rehabilitation and replacement of bridges and can greatly improve the apportionment and management of limited resources. This paper presents an Automated Ambient Traffic (AAT) approach for determining load rating of bridges monitored by the BECAS SHM system under ambient traffic. The AAT approach was developed through a process integration of truck detection, bridge model calibration, and bridge load rating: (1) the quasi-static bridge strain response and the characteristics of associated trucks are collected; (2) multiple trucks are randomly sampled from a historic Weigh-In-Motion (WIM) database; and (3) for each combination of strain response and truck selection, an Finite Element (FE) model is calibrated and used to calculate a load rating. Sampling strategies were discussed for appropriately quantifying the influence of uncertainties of truck characteristics on the calibration and load rating results. To demonstrate this approach, a sample three-span, five-girder, and two-lane steel girder/concrete deck (I-80) bridge was utilized. A load rating of the I-80 Bridge using the Traditional Known Truck (TKT) approach was performed to provide benchmark results. The results of the calibration and load rating using the AAT approach were derived using three different sampling strategies and compared to those using the TKT approach. The sampling strategy, selecting strain response with a spectrum of higher peak girder strains, associated trucks with a spectrum of higher gross vehicle weights, and two truck events on south and north lanes respectively for a calibration, resulted in the best calibration and load rating results. It was concluded that the AAT approach using the BECAS SHM system is a reliable method for continuously estimating the load carrying capacity of bridges. [ABSTRACT FROM AUTHOR]

People around the world who seek to interact with large organisations increasingly find they must do so via mediated and automated communication. Organisations often deploy both mediated and automated platforms, such as instant messaging and interactive voice response systems (IVRs), for efficiency and cost-savings. Customer and client responses to these systems range from delight to frustration. To better understand the factors affecting people's satisfaction with these systems, we conducted a representative U.S. national survey (N = 1321). We found that people overwhelmingly like and trust in-person customer service compared to mediated and automated modalities. As to demographic attitude predictors, age was important (older respondents liked mediated systems less), but income and education were not strong attitude predictors. For personality variables, innovativeness was positively associated with mediated system satisfaction. However, communication apprehensiveness, which we expected to be related to satisfaction, was not. We conclude by discussing implications for the burgeoning field of human-machine communication, as well as social policy, equity, and the pullulating digital services divide. [ABSTRACT FROM AUTHOR]

The present study, addresses a research question on the perception of fairness of job losses due to automation technologies amongst general stakeholders. Jennifer Kim (2018) has shown that job displacement due to automation is perceived to be fairer in the United States when compared to outsourcing. The study by Jennifer Kim (2018) is replicated in this study on a sample drawn from the population of the Indian labour force, limiting it to the millennials and the IT industry. The results indicated that all job displacement is perceived to be unfair, despite the motive behind it. This is contrary to the results obtained from the sample from the United States. Also, there was no statistically significant difference between the perception of fairness towards the job displacement due to automation and outsourcing. Though there was no direct effect of the motive behind the job displacement/lay-off on the perception of fairness, it was found that there were significant indirect effects from the identified 4 mediators. [ABSTRACT FROM AUTHOR]

Empirical models describing the factors influencing the levels of computerization of the operations of nurseries and greenhouses in the United States were estimated using the data collected from a regional survey in the Southern States and two national surveys in the United States. The models measure the significant effects of the socioeconomic characteristics of managers and operational characteristics of firms to the level of computer use among nurseries and greenhouses. Results show that the socioeconomic characteristics of the participating owners and managers had exercised limited influence in the decisions involving the computerization of horticulture operations. The operational characteristics of the horticulture operations included in the surveys which have significant influences on the level of computerization included the number of workers hired, level of automation, annual gross sales, wholesale sales, years since establishment, and location. The recent Great Recession encouraged the participating firms to increase the use of computers in their operations. [ABSTRACT FROM AUTHOR]

Firms that are capable, undertake strategic renewal to ensure and reinforce a sustainable competitive advantage. Digitalisation is a major technological change; a strategic renewal that can deliver such an advantage. In this article, we adopt the theories of path dependence and strategic renewal to analyse the implementation and utilisation of digitalisation in the USA manufacturing industry based on qualitative interview data collected among policymakers, researchers and manufacturers. The analysis identified and described five US strengths (i.e. technology leaders, innovative tolerant culture, technology agnostic, market driven, speed) contributing industrial renewal through digitalisation, and five US weaknesses (i.e. technology driven, disorganised convergence, not part of the cultural identity, competitive not collaborative, short term perspective) inhibiting it. The strengths and weaknesses were further considered as they pertain to individuals, businesses and national policy. The study highlighted the systemic nature of the identified strengths and weaknesses, and suggested that in addition to policymakers, also research community and businesses should further strengthen what is already strong in the innovation system to indirectly mitigate the weaknesses. [ABSTRACT FROM AUTHOR]

This study investigates the extent to which job process automation, which has resulted in wage inequality and job polarization in the USA and has affected US interstate migration over the past two decades. The level of automation in each state is calculated using data on the degree of automation of each occupation. In particular, this study examines how the difference in the levels among states explains the movement of migrants. The results show that people move to states with more automation in skilled occupations and less automation in unskilled occupations. This finding implies that automation has a complementary (substitution) effect on skilled (unskilled) occupations. The results also show that the former effect is larger and more robust than the latter one. Further analyses use migration flow data classified into several subgroups and find that both skilled and unskilled workers in most occupations move to states with more automation in skilled occupations and less automation in unskilled occupations. [ABSTRACT FROM AUTHOR]

The displacement of jobs via mechanization and automation has long been understood as uncomfortable for labourers but also an intrinsic part of a process of 'creative destruction' leading to further growth in capitalist economies. This article argues that a seismic shift is currently underway in the dynamics of the labour market with regard to automation. Technologies of automation are capable of a rapidly rising proportion of all of the tasks that capital is willing to pay for and that humans are capable of doing. We highlight the geographically differentiated implications of this ongoing transition, and emphasize that geographers are importantly situated to analyse the political and economic implications of what is likely the start of a radical restructuring of the relationships between labouring, resource distribution, and indeed human ethics. [ABSTRACT FROM AUTHOR]

Today's US cybersecurity milieu can be traced, in part, to the encounter between electronic technology and border control during the 1970s. Federal officials argued at the time that, due to the end of the Bracero Program and passage of the Immigration and Nationality Act of 1965, "illegal aliens" constituted a "silent" and "invisible invasion" threatening the US nation. In the science of cybernetics, officials found the conceptual tools to organize border enforcement operations and institute order over border life. Intrusion detection systems installed on the southern border area in 1970 highlight how actors attempted to control racialized populations by managing information and communication processes. Cybernetics and intrusion detection systems, this article argues, aimed to draw an electronic "line in the sand." They were put to work in making the nation's boundaries both on the ground and on human bodies; in doing so, they reproduced US racial sorting logics while recoding them into bits of data. The "electronic fence," as the system was known, was part and parcel of an infrastructure of technologies and knowledges targeting Mexicans. Tracing discourse networks in government publications and memoranda, technical reports, and newspapers, the essay charts the role of automation in US nation-making and in the management of racialized bodies. [ABSTRACT FROM AUTHOR]