BEHAVIORS, VALUES, AND POSITIVE REINFORCEMENT: DEVELOPING A STRONG WORKPLACE SAFETY CULTURE
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Equity, Diversity, and Inclusion (EDI) committees and Codes of Conduct (CoC) have become common in laboratories and physics departments across the country. However, very often these EDI committees and CoC are not equipped to provide practical consequences for violations, and therefore are mostly performative in nature. A considerable effort has been devoted by various groups within APS units and beyond the APS in developing instead what are now called Community Guidelines. Community Guidelines help implement the core principles in CoC, by setting expectations for participation in in-person events and virtual communication. When further accompanied by accountability and enforcement processes, they develop into Community Agreements. This White Paper discusses the elements necessary to create and implement an effective Community Agreement, reviews examples of Community Agreements in physics, and argues that physics collaborations, physics departments, and ultimately as many physics organizations as possible, however large or small, should have a Community Agreement in place. We advocate that Community Agreements should become part of the bylaws of any entity that has bylaws.
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Transportation electrification is forecast to bring millions of new electric vehicles to roads worldwide this decade. Planning to support those vehicles depends on detailed scenarios of their electricity demand in both uncontrolled and controlled or smart charging scenarios. In this work, we present a novel modeling approach to enable rapid generation of demand estimates that represent the impact of controlled charging for large-scale scenarios with millions of individual drivers. To model the effect of load modulation control on aggregate charging profiles, we propose a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control under a range of electricity rate schedules, achieving small errors (2.5%–4.5%) while accelerating computations by more than 4000 times. To generate the uncontrolled charging demand for scenarios with residential, workplace, and public charging we use statistical representations of a large data set of real charging sessions. We demonstrate the methodology by generating diverse sets of scenarios for California's charging demand in 2030 which consider multiple charging segments and controls, each run locally in under 50 s. We further demonstrate support for rate design by modeling the large-scale impact of a new, custom rate schedule for workplace charging.
Small, advanced reactors may require few, if any, safety-related human actions (HAs) and fewer HAs to monitor and control the plant. In comparison to large light water reactors, these are significant changes that have implications for many aspects of an applicant's human factors engineering (HFE), including control room design, plant staffing, and the management of safety functions. To support the Nuclear Regulatory Commission's (NRC) ability to evaluate these changes, information needs to be developed addressing the characteristics and potential issues. The objectives of our research were to (1) identify when a traditional main control room (MCR) may not be necessary, (2) identify workplace design alternatives to traditional MCRs, and (3) develop guidance for reviewing an applicant's workplace designs with and without a MCR. We determined that the safety question isn't so much justifying why a design has no MCR, but rather verifying that important human actions can be accurately and reliably performed under a range of challenging conditions using the HSIs provided regardless of their location. We developed guidance to review alternatives to MCRs based on HFE analyses for determining workplace location and design.
The WELL Building Standard (WELL) is currently one of the most comprehensive building certification programs that aim to enhance the health and well-being of building occupants. However, there is a lack of systematic evaluation of the effectiveness of WELL in achieving its goal. This study investigates the impact of WELL certification on occupant satisfaction with the workplace and occupant perceived health, well-being, and productivity. More than 1300 pre- and post-occupancy survey responses provided by the nearly same cohort of occupants from six companies in North America were quantitatively analyzed. The results showed that transitioning to WELL certified offices from non-WELL certified offices had a positive impact on occupant satisfaction with the workplace and occupant perceived health, well-being, and productivity, with increases in means from pre-to post-occupancy being highly statistically significant. The majority of the studied occupant satisfaction parameters as well as occupant perceived mental health had large effect sizes. While they improved from pre-to post-occupancy, the analysis revealed small effect sizes for occupant perceived physical health and self-assessed productivity. The majority of the effect sizes for the perceived well-being parameters were large and medium. In addition to analyzing the survey responses in aggregate, the responses were examined at the individual company level to confirm the by-company and aggregate findings aligned.
Abstract For workplaces which cannot operate as telework or remotely, there is a critical need for routine occupational SARS-CoV-2 diagnostic testing. Although diagnostic tests including the CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel (CDC Diagnostic Panel) (EUA200001) were made available early in the pandemic, resource scarcity and high demand for reagents and equipment necessitated priority of symptomatic patients. There is a clearly defined need for flexible testing methodologies and strategies with rapid turnaround of results for (1) symptomatic, (2) asymptomatic with high-risk exposures and (3) asymptomatic populations without preexisting conditions for routine screening to address the needs of an on-site work force. We developed a distinct SARS-CoV-2 diagnostic assay based on the original CDC Diagnostic Panel (EUA200001), yet, with minimum overlap for currently employed reagents to eliminate direct competition for limited resources. As the pandemic progressed with testing loads increasing, we modified the assay to include 5-sample pooling and amplicon target multiplexing. Analytical sensitivity of the pooled and multiplexed assays was rigorously tested with contrived positive samples in realistic patient backgrounds. Assay performance was determined with clinical samples previously assessed with an FDA authorized assay. Throughout the pandemic we successfully tested symptomatic, known contact and travelers within our occupational population with a ~ 24–48-h turnaround time to limit the spread of COVID-19 in the workplace. Our singleplex assay had a detection limit of 31.25 copies per reaction. The three-color multiplexed assay maintained similar sensitivity to the singleplex assay, while tripling the throughput. The pooling assay further increased the throughput to five-fold the singleplex assay, albeit with a subtle loss of sensitivity. We subsequently developed a hybrid ‘multiplex-pooled’ strategy to testing to address the need for both rapid analysis of samples from personnel at high risk of COVID infection and routine screening. Herein, our SARS-CoV-2 assays specifically address the needs of occupational healthcare for both rapid analysis of personnel at high-risk of infection and routine screening that is essential for controlling COVID-19 disease transmission. In addition to SARS-CoV-2 and COVID-19, this work demonstrates successful flexible assays developments and deployments with implications for emerging highly transmissible diseases and future pandemics.
The rise of automation, artificial intelligence (AI), and autonomous systems raises important questions about the future role of humans and the field of human factors/ergonomics in workplaces. This paper builds on Dr. Peter Hancock’s 2023 ‘Are Humans Still Necessary?’ article published in the Ergonomics journal. Using a multi-method approach that included a debate, opinion polling, roundtable discussions, and AI queries, the current effort examined the necessity of human involvement in future work environments. Debate team members presented arguments for and against the need for human workers, considering human factors, technology, and socioeconomic factors. Observations indicate that while AI may handle routine tasks, humans will likely remain essential for complex decision making, creativity, and ethical considerations. The paper advocates for viewing workplace dynamics as collaborative human-AI partnerships rather than competition, highlighting the need for a transdisciplinary approach in which human factors/ergonomics professionals play a vital role in enhancing these relationships.
Advances on differentiating between malicious intent and natural “organizational evolution” to explain observed anomalies in operational workplace patterns suggest benefit from evaluating collective behaviors observed in the facilities to improve insider threat detection and mitigation (ITDM). Advances in artificial neural networks (ANN) provide more robust pathways for capturing, analyzing, and collating disparate data signals into quantitative descriptions of operational workplace patterns. In response, a joint study by Sandia National Laboratories and the University of Texas at Austin explored the effectiveness of commercial artificial neural network (ANN) software to improve ITDM. Overall, this research demonstrates the benefit of learning patterns of organizational behaviors, detecting off-normal (or anomalous) deviations from these patterns, and alerting when certain types, frequencies, or quantities of deviations emerge for improving ITDM. Evaluating nearly 33,000 access control data points and over 1,600 intrusion sensor data points collected over a nearly twelve-month period, this study's results demonstrated the ANN could recognize operational patterns at the Nuclear Engineering Teaching Laboratory (NETL) and detect off-normal behaviors—suggesting that ANNs can be used to support a data-analytic approach to ITDM. Several representative experiments were conducted to further evaluate these conclusions, with the resultant insights supporting collective behavior-based analytical approaches to quantitatively describe insider threat detection and mitigation.
The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior such as travel surveys and travel demand models are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky; and Des Moines, Iowa which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.
In March 2021, the cumulative sale of plug-in electric vehicles (PEVs), including plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV), reached 1.8 million in the United States (Argonne National Laboratory 2021). However, PEV adoption is still in its infancy; its market share has just reached around 3% of new light-duty vehicle (LDV) sales by the end of 2020 (Alliance for Automotive Innovation 2021). Current trends suggest that PEV market share in the United States is increasing. The U.S. Energy Information Administration's (EIA's) 2020 Annual Energy Outlook forecasts PEV registrations to exceed 8 million vehicles by 2030 (AEO 2020). PEV adoption is expected to be led by states that are regulating the sale of zero emission vehicles (ZEVs) (California Air Resources Board). California continues to push for more aggressive ZEV regulations; the state recently issued an executive order aimed at 100% of LDV sales being ZEVs by 2035 (Office of Governor Newsom). At the federal level, the Biden administration has shown great ambition in encouraging broader electric vehicle (EV) adoption, including setting the goal of installing 500,000 new chargers nationwide (The White House 2021). Access to charging infrastructure is consistently cited as one of the primary barriers to the increased sale of PHEVs and BEVs (Carley et al. 2019). In the United States, PEV charging options are often described using a pyramid structure, with residential charging as the foundation, workplace charging in the middle, and public charging on top (Figure 1). The existing electricity system, which generates, transmits, and distributes electric fuel to residential households, has helped PEVs partially overcome the "chicken and egg" conundrum that has haunted other alternative fuels. Viable home access to electric charging is also an important equity issue, because non-residential PEV charging options (e.g., workplace or public charging stations) are generally more expensive. Households without residential charging access may experience higher total cost of PEV ownership if non-residential charging options are more costly.
This literature review is divided into two parts. The first part looks at new developments and emerging research specifically related to remote work since the start of the COVID-19 pandemic. This encompasses research and reporting on the impacts of COVID-19 on the workplace, more speculative writing on the possible future of remote and hybrid work, and impacts of remote work during COVID-19 for diversity, equity, and inclusion. The second part focuses on more fundamental research done on remote collaboration and remote work tools prior to COVID-19, which addresses in more detail how different types, aspects, or stages of work can best be supported using virtual collaboration tools. In both sections, we review literature that directly focuses on remote scientific collaboration, which is somewhat limited, as well as the broader literature on remote and hybrid work, which is relevant to a wide variety of workplaces, including scientific ones.
Prism, the Lab’s Lesbian, Gay, Bisexual, Transgender, and Queer+ (LGBTQ+) Employee Resource Group (ERG), fosters an inclusive workplace culture that supports the LGBTQ+ employee base. Prism provides and promotes LGBTQ+ visibility among staff and with management about current workplace and social issues that affect the LGBTQ+ community. This report also details 2022 accomplishments and ongoing initiatives.