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Artificial intelligence will be deployed increasingly in more systems that affect public health, safety, and welfare. These systems will better utilize scarce resources; prevent disasters; and increase safety, reliability, comfort, and convenience. Despite the technological challenges and public fears, these systems will improve the quality of life of millions of people worldwide.
The era of atmospheric testing defies a simple description. Beginning with the goal of proving fission, the United States’ test program grew in complexity as scientists explored thermonuclear reactions, met the military demand for a variety of weapons for an increasing number of delivery platforms, and reacted to the ever-growing public fear of fallout. And, of course, political considerations were always present, first in the prosecution of World War II and later in the Cold War. To understand the atmospheric test program, then, is to understand how science, politics, and the public shaped our society in ways that are still difficult to fully understand.
As the frequency and duration of grid outages increase, backup power systems are becoming more important for ensuring that critical infrastructure continues to provide essential services. Most facilities rely on diesel generators, which may be ineffective during long outages owing to limited fuel supplies and high generator failure rates. Distributed energy resources such as solar, storage, and combined-heat-and-power systems, coupled with on-site biofuel production, offer an alternative source of on-site generation that can provide both cost savings and resilience (i.e., the ability to respond to catastrophic events with longer-term consequences). A mixed-integer linear program minimizes costs and maximizes resilience at a wastewater treatment plant in Wilmington, North Carolina. We find that the plant can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. When paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared with the diesel-only solution. This analysis informed a decision by the Cape Fear Public Utility Authority to allocate funds for the implementation of a combined-heat-and-power system at the wastewater treatment plant in fiscal year 2023. Finally, the benefits of deploying hybrid combined-heat-and-power technologies and the utilization of on-site biofuel production extend, on a national scale, to thousands of wastewater treatment facilities and other types of critical infrastructure.
In the realm of online identity, social media has emerged as a rich and dynamic source of user-generated content, making it an invaluable resource for understanding public sentiment on a wide range of topics. Individuals often share their raw emotions and candid opinions on these platforms without fear of judgment or backlash. In this study, we conduct a sentiment analysis on user comments collected from various online platforms, with a specific focus on discussions surrounding autonomous vehicles. Leveraging the capabilities of large language models (LLMs), we classify each comment into one of five sentiment categories: Very Negative, Negative, Neutral, Positive, and Very Positive. Our approach demonstrates the effectiveness of LLMs in capturing nuanced contextual sentiment, offering a scalable and state-of-the-art alternative to traditional manual annotation methods. The results reveal key trends and insights into public perception, enabling a deeper understanding of how autonomous vehicle technologies are received by the online community. Our findings underscore the dynamic nature of public sentiment, which is shaped not only by advances in autonomous vehicle technology but also by contextual events such as regulatory developments, political adjustment and safety incidents.
The societal perception of nuclear technology is ambivalent. Many people recognize the great benefits of nuclear technology such as the ability of nuclear power plants to generate massive amounts of electricity with very low carbon emissions as well as the contributions of nuclear medicine as a powerful means of diagnostics and treatment of many illnesses. However, there are profound fears and reservations concerning the use of nuclear power, mostly due to the connection with nuclear weapons, severe nuclear accidents (e.g., Three Mile Island, Chernobyl, and Fukushima), and legacy of nuclear wastes. Addressing the public concerns related to nuclear waste can have a big impact on the public acceptability of nuclear technology.
This article uses ten case studies to illustrate the information needs, various communication approaches, and the communicator's role in explaining environmental health risks from a variety of hazards, to a variety of audiences, over time frames from days to years, using in person consultation, lectures, zooms, and email formats. Events often had a long history before the communication began and may have had a long tail afterward. Audiences may be public officials, companies, workers, communities, or individuals. Each individual may have their own understanding or mental model regarding the hazard, exposure, and risk. The communicator's role or intention may be to reassure an audience that has unrealistic exaggerated concerns or fears or to protect a client if the fears are realistic. Or it may be altruistic to inform a complacent audience to take the risks it faces more seriously. Although risk assessment research has advanced the techniques for communicating abstruse probabilities to audiences with low numeracy, in my experience, audiences are unimpressed by precise–sounding probability numbers, and are more interested in whether exposure is occurring or may occur and how to stop it. Often audiences have reason to be outraged and may be more concerned about punishing wrong doers than about the hazard itself, particularly when the exposure is past and cannot be undone. Thus, there is a difference between discussing the riskiness of a situation (risk communication) and what you are going to do about the situation (risk management). Risk communication is successful when the audience responds as intended, calming down or taking action. These case studies are drawn from a large number of risk communication experiences that I and my Rutgers colleagues have engaged in over the past four decades. Through the 20th century, New Jersey was the most densely industrialized State in United States. New Jersey experienced growth of the chemical and petrochemical industries and the unfortunately profligate disposal of toxic wastes. Having the most Superfund sites of any state is a dubious distinction, but New Jersey also has the most experience in evaluating and responding to these hazards.
United States critical infrastructure entities are increasingly targeted by motivated and capable threat actors and must be prepared to assess and treat a diverse range of cyber risks. Consequently, this necessitates some form of analytical process to evaluate risks and inform cyber security investment decisions. A potential solution for structuring cyber risk evaluation exists within the field of sovereign credit ratings – where agencies employ mature approaches that integrate quantitative and qualitative data to produce a singular value of assessment. Adapting such approaches, we present a novel criterion and methodology for measuring and communicating the likelihood element of cyber risk. The methodology is composed of three sequential phases: a quantitative baseline organized by distinct capability frames, a bounded qualitative adjustment per frame, and a greater-bounded qualitative adjustment spanning the entire process. The process culminates in publication of a cyber capability rating that communicates a critical infrastructure entity’s ability and willingness to mitigate discontinuous function due to cyberattack.
Abstract Historical assessment of radiation effects at low doses (below 0.2 Sv) are generally the result of back extrapolation from higher doses, which are known to have a linear relation between risk and dose. There are multiple counter-examples, and some literature argues that a threshold, nonlinear, or even a beneficial effect (hormeisis) can occur from radiation below these doses. The common theme found in all of these studies stems from the traditional approach of correlating disease rates to stimulus and then effectively curve-fitting the result toward zero dose. What has not been considered in general are the personal stress levels of the exposed individuals due to fear of cancer from low doses. The increased levels of cortisol due to the psychological stress from fear or depression has been shown in the literature to increase cancer probability. The extent to which low-dose exposed individuals were highly fearful or stressed from the radiation exposure would then give rise to elevated cancer based on stress rather than a fundamental radiogenic mechanism. If the population under epidemiological study is aware of a potential historical exposure (no matter how small) and has then lived under stress from fear or depression due to that exposure, the psychosomatic effects will bias the epidemiology accordingly and so should be quantified and accounted for as done with the effects of smoking. Health Phys. 129(0):000-000; 2025
Nuclear technology's controversial status has been shaped by its early use in military applications, fear driven by high-profile accidents, and unresolved waste management challenges. There have, on the other hand, been periodic claims that a “nuclear renaissance” is imminent, driven by different dynamics at different times, most recently related to growing energy demands and increasing concern over carbon emissions. In this article, we respond to urgent calls for more nuclear engineers, resulting from the latest rallying cry around nuclear energy, by reframing the problem using a systems thinking approach that illuminates new pathways for nuclear workforce development via interdisciplinary pedagogies. By building nuclear education into a wide variety of disciplines, we argue, the nuclear workforce could become more resilient to ebbs and flows in energy markets and public opinion. Widening nuclear education beyond nuclear engineers could also reduce the isolation and compartmentalization that has limited the possibilities of nuclear technology. We show how growing and diversifying the overall nuclear workforce could create a wide variety of career opportunities outside STEM and enable greater specialization within STEM, since nuclear engineers and other specialists could be freed up to focus on technological and infrastructural innovations. We argue that interventions into nuclear education should establish new connections and applications of the nuclear sciences in diverse areas of expertise to develop a broad range of professionals who can contribute to a more stable nuclear workforce, bringing what we call “critical and creative nuclear energy literacies” to long-standing and systemic challenges around nuclear energy.
This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. Here, the result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.