Formative reasons for state-to-state influences on firearm acquisition in the U.S.

Objectives: Firearm-related crimes and self-inflicted harms pose a significant threat to the safety and well-being of Americans. Investigation of firearm prevalence in the United States (U.S.) has therefore been a center of attention. A critical aspect in this endeavor is to explain whether there are identifiable patterns in firearm acquisition. Methods: We view firearm acquisition patterning as a spatio-temporal dynamical system distributed across U.S. states that co-evolves with crime rates, political ideology, income levels, population, and the legal environment. We leverage transfer entropy and exponential random graph models along with publicly available data, to statistically reveal the formative factors in how each state's temporal patterning of firearm acquisition influences other states. Results: Results help to explain how and why U.S. states influence each other in their firearm acquisition. We establish that state-to-state influences, or lack thereof, in firearm acquisition patterning are explained by states' percent of gun homicide, firearm law strictness, geographic neighborhood, and citizen ideology. Network-based characteristics, namely, mutuality and transitivity, are also important to explain such influence. Conclusions: Results suggest that state policies or programs that reduce gun homicides will also help suppress that state's influence on the patterning of firearm acquisition in other states. Furthermore, states with stricter firearm laws are more likely to influence firearm acquisition in other states, but are themselves shielded from the effects of other states' firearm acquisition patterns. These results inform future research in public health, criminology, and policy making.

Association of State Firearm Laws With Firearm Ownership and Mortality.

Introduction: Firearm injury is a leading cause of death among Americans. Because the right to bear arms is protected by the Second Amendment, policymakers must consider the impact of legislation on both firearm ownership and firearm harms. The current state of knowledge in firearm research majorly examines the impact of firearm legislation on firearm injuries and fatalities alone, and it relies on correlational analyses. The few studies that consider causal effects employ counterfactual-based inference. This study introduces information-theoretic tools to explore the role of firearm laws in mitigating firearm harms while maintaining citizens' right to bear arms. Methods: The authors study monthly time series from January 2000 to October 2019 for the implementation of firearm laws from RAND's State Firearm Law Database, firearm deaths by intent from the Centers for Disease Control and Prevention databases, and firearm ownership from an econometric model. The authors employ transfer entropy, an information-theoretic method that relies on Granger causality, to infer relationships from time series. Specifically, the authors examine transfer entropy from firearm restrictiveness to deaths per firearm owner, firearm ownership, and firearm deaths, independently. Results: On a national level, the authors uncover a negative association from firearm restrictiveness to deaths per firearm owner and a positive association from firearm restrictiveness to firearm ownership. On a regional level, the authors identify a negative association from firearm restrictiveness to deaths per firearm owner in the Northeast, a negative association from firearm restrictiveness to firearm ownership in the Midwest, and a negative association from firearm restrictiveness to firearm suicides in the South. Conclusions: The authors present an information-theoretic approach to study relationships in firearm research. This method provides preliminary evidence for the role of restrictive legislation in promoting safe firearm ownership. The authors find that firearm acquisition considerably increases after the implementation of restrictive firearm laws, and simultaneously, firearm deaths decrease. These effects vary with respect to death by intent and the geographic region the laws were implemented in.

Reverse social contagion as a mechanism for regulating mass behaviors in highly integrated social systems.

Mass behavior is the rapid adoption of similar conduct by all group members, with potentially catastrophic outcomes such as mass panic. Yet, these negative consequences are rare in integrated social systems such as social insect colonies, thanks to mechanisms of social regulation. Here, we test the hypothesis that behavioral deactivation between active individuals is a powerful social regulator that reduces energetic spending in groups. Borrowing from scaling theories for human settlements and using behavioral data on harvester ants, we derive ties between the hypermetric scaling of the interaction network and the hypometric scaling of activity levels, both relative to the colony size. We use elements of economics theory and metabolic measurements collected with the behavioral data to link activity and metabolic scalings with group size. Our results support the idea that metabolic scaling across social systems is the product of different balances between their social regulation mechanisms.

Adapting to the Abyss: Passive Ventilation in the Deep-Sea Glass Sponge Euplectella aspergillum.

We analyze the flow physics inside the body cavity and downstream the deep-sea glass sponge Euplectella aspergillum. We provide evidence that the helical skeletal motifs of the sponge give rise to a rich fluid dynamic field, allowing the organism to scavenge flow from the bottom of the sea and promoting a spontaneous, organized vertical flow within its body cavity toward the osculum. Our analysis points at a functional adaptation of the organism, which can passively divert flow through the osculum in unfavorable, low ambient currents, with no need for active pumping, with potential repercussions in functional ecology, as well as the design of chemical reactors, air-treatment units, and civil and aeronaval structures.

Animal-robot interaction-an emerging field at the intersection of biology and robotics.

The field of animal-robot and organism-robot interaction systems (ARIS, ORIS) is a currently rapidly emerging field in biorobotics. In this special issue we aim for providing a comprehensive overview of the cutting-edge advancements and pioneering breakthroughs within this scientific and engineering discipline. Therefore, we collected scientific articles that delineate and expound upon the complexity of these remarkable biohybrid systems. These configurations stand as engineered conduits, facilitating the accurate investigation and profound exploration of the multifaceted interactions between robotic devices and biological entities, including various fish species, honeybees and plants. Also the human factor plays a role in this collection, as we also include a philosophical perspective on such systems as well as an augmented reality setup that brings humans into the loop with living fish. Within our editorial purview, we categorize the scientific contributions based on their focal points, differentiating between examinations of singular agent-to-agent interactions, extensions to the social stratum, and further expansions to the intricate levels of swarm dynamics, colonies, populations, and ecosystems. Considering potential applications, we delve into the multifaceted domains wherein these biohybrid systems might be applied. This discourse culminates in a tentative glimpse into the future trajectories these technologies might traverse, elucidating their promising prospects for both scientific advancement and societal enrichment. In sum, this special issue aims at facilitating the convergence of diverse insights, at encapsulating the richness of the ARIS and ORIS domain, and at charting a course toward the untapped prospects lying at the nexus of biology and robotics.

Modeling State Firearm Law Adoption Using Temporal Network Models.

Policy Points Promoting healthy public policies is a national priority, but state policy adoption is driven by a complex set of internal and external factors. This study employs new social network methods to identify underlying connections among states and to predict the likelihood of new firearm-related policy adoption given changes to this interstate network. This approach could be used to assess the likelihood that a given state will adopt a specific new firearm-related law and to identify points of influence that could either inhibit or promote wider diffusion of specific laws. CONTEXT: US states are largely responsible for the regulation of firearms within their borders. Each state has developed a different legal environment with regard to firearms based on different values and beliefs of citizens, legislators, governors, and other stakeholders. Predicting the types of firearm laws that states may adopt is therefore challenging. METHODS: We propose a parsimonious model for this complex process and provide credible predictions of state firearm laws by estimating the likelihood they will be passed in the future. We employ a temporal exponential-family random graph model to capture the bipartite state law-state network data over time, allowing for complex interdependencies and their temporal evolution. Using data on all state firearm laws over the period 1979-2020, we estimate these models' parameters while controlling for factors associated with firearm law adoption, including internal and external state characteristics. Predictions of future firearm law passage are then calculated based on a number of scenarios to assess the effects of a given type of firearm law being passed in the future by a given state. FINDINGS: Results show that a set of internal state factors are important predictors of firearm law adoption, but the actions of neighboring states may be just as important. Analysis of scenarios provide insights into the mechanics of how adoption of laws by specific states (or groups of states) may perturb the rest of the network structure and alter the likelihood that new laws would become more (or less) likely to continue to diffuse to other states. CONCLUSIONS: The methods used here outperform standard approaches for policy diffusion studies and afford predictions that are superior to those of an ensemble of machine learning tools. The proposed framework could have applications for the study of policy diffusion in other domains.

Commute Booster: A Mobile Application for First/Last Mile and Middle Mile Navigation Support for People With Blindness and Low Vision.

OBJECTIVE: People with blindness and low vision face substantial challenges when navigating both indoor and outdoor environments. While various solutions are available to facilitate travel to and from public transit hubs, there is a notable absence of solutions for navigating within transit hubs, often referred to as the "middle mile". Although research pilots have explored the middle mile journey, no solutions exist at scale, leaving a critical gap for commuters with disabilities. In this paper, we proposed a novel mobile application, Commute Booster, that offers full trip planning and real-time guidance inside the station. METHODS AND PROCEDURES: Our system consists of two key components: the general transit feed specification (GTFS) and optical character recognition (OCR). The GTFS dataset generates a comprehensive list of wayfinding signage within subway stations that users will encounter during their intended journey. The OCR functionality enables users to identify relevant navigation signs in their immediate surroundings. By seamlessly integrating these two components, Commute Booster provides real-time feedback to users regarding the presence or absence of relevant navigation signs within the field of view of their phone camera during their journey. RESULTS: As part of our technical validation process, we conducted tests at three subway stations in New York City. The sign detection achieved an impressive overall accuracy rate of 0.97. Additionally, the system exhibited a maximum detection range of 11 meters and supported an oblique angle of approximately 110 degrees for field of view detection. CONCLUSION: The Commute Booster mobile application relies on computer vision technology and does not require additional sensors or infrastructure. It holds tremendous promise in assisting individuals with blindness and low vision during their daily commutes. Clinical and Translational Impact Statement: Commute Booster translates the combination of OCR and GTFS into an assistive tool, which holds great promise for assisting people with blindness and low vision in their daily commute.

Migrants' mental health recovery in Italian reception facilities.

BACKGROUND: Forced migration leaves deep marks on the psychological well-being of migrants, with post-traumatic stress disorder (PTSD) and other psychological conditions being prevalent among them. While research has clarified the extent to which pre-migration trauma is a predictor of mental health outcomes, the role of post-migration stressors in the settlement environment are yet to be fully characterized. METHODS: We monitored mental health of a cohort of 100 asylum-seekers during their 14-day COVID-19-related quarantine in reception facilities in Rome, Italy, through the administration of six questionnaires (a demographic survey, the WHO-5 well-being index, the Primary Care PTSD Screen for Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5), the Harvard Trauma Questionnaire, the Trauma and Loss Spectrum-Self Report, and the LiMEs-Italian version). Through the combination of statistical analysis and supervised learning, we studied the impact of the first contact with the reception system on asylum-seekers' mental health and sought for possible risk and shielding factors for PTSD. RESULTS: We find that sheltering in refugee centers has a positive impact on migrants' mental health; asylum-seekers with PTSD reported more traumatic events and personality characteristics related to loss and trauma; life events are predictors of PTSD in asylum-seekers. CONCLUSIONS: We identify past traumatic experiences as predictors of PTSD, and establish the positive role the immediate post-migration environment can play on migrants' psychological well-being. We recommend for host countries to implement reception models that provide effective protection and integration of asylum-seekers, similar to those in the Italian system.

Traumatic experiences before and during migration can impact the psychological well-being of migrants. This can result in post-traumatic stress disorder (PTSD), a disorder in which prior experience of traumatic events can lead to severe anxiety. We asked migrants to fill in questionnaires about their well-being during a 14-day quarantine in reception facilities in Rome, Italy. We evaluated whether the migrants had symptoms of PTSD and the impact of the quarantine on their mental health. We found that migrants who had experienced past traumatic events were more likely to have PTSD, and that staying in a safe and welcoming place in Italy helped improve their mental wellbeing. These findings underline the importance of designing suitable policies to support migrants' mental health when they arrive in host countries.

Macro-Fiber Composite-Based Tactors for Haptic Applications.

Haptic technology is a critical component of human-computer interfaces. Traditional haptic actuators are often unable to provide the broad frequency range and latency that is required in many advanced applications. To address this problem, we propose a new type of tactor based on macro-fiber composites (MFCs), composites of piezoelectric fibers. We propose a physics-based model for the actuation of the tactors, calibrated and validated through experiments. As our tactors are intended for haptic applications, we consider the role of skin on their response, an aspect seldom analyzed in the literature. In our experiments, we simulate the presence of the skin with a rubber membrane in contact with the tactor, with varying pre-stretch, mimicking different indentations of the tactor on the skin. The MFC-based tactor can always generate vibration amplitudes higher than skin discrimination thresholds, over the range of frequencies of interest for haptics, with a latency much smaller than traditional actuators. We theoretically investigate the effect of the skin on tactor vibrations, highlighting the individual roles of skin stiffness and damping and their combined effect across a series of pre-stretches. Our tactor shows promise in haptic applications, including assistive technologies and real-time feedback systems for training, safety, and monitoring.

Virtual reality as a means to explore assistive technologies for the visually impaired.

Visual impairment represents a significant health and economic burden affecting 596 million globally. The incidence of visual impairment is expected to double by 2050 as our population ages. Independent navigation is challenging for persons with visual impairment, as they often rely on non-visual sensory signals to find the optimal route. In this context, electronic travel aids are promising solutions that can be used for obstacle detection and/or route guidance. However, electronic travel aids have limitations such as low uptake and limited training that restrict their widespread use. Here, we present a virtual reality platform for testing, refining, and training with electronic travel aids. We demonstrate the viability on an electronic travel aid developed in-house, consist of a wearable haptic feedback device. We designed an experiment in which participants donned the electronic travel aid and performed a virtual task while experiencing a simulation of three different visual impairments: age-related macular degeneration, diabetic retinopathy, and glaucoma. Our experiments indicate that our electronic travel aid significantly improves the completion time for all the three visual impairments and reduces the number of collisions for diabetic retinopathy and glaucoma. Overall, the combination of virtual reality and electronic travel aid may have a beneficial role on mobility rehabilitation of persons with visual impairment, by allowing early-phase testing of electronic travel aid prototypes in safe, realistic, and controllable settings.

Fame through surprise: How fame-seeking mass shooters diversify their attacks.

Mass shootings are becoming more frequent in the United States, as we routinely learn from the media about attempts that have been prevented or tragedies that destroyed entire communities. To date, there has been limited understanding of the modus operandi of mass shooters, especially those who seek fame through their attacks. Here, we explore whether the attacks of these fame-seeking mass shooters were more surprising than those of others and clarify the link between fame and surprise in mass shootings. We assembled a dataset of 189 mass shootings from 1966 to 2021, integrating data from multiple sources. We categorized the incidents in terms of the targeted population and shooting location. We measured "surprisal" (often known as "Shannon information content") with respect to these features, and we scored fame from Wikipedia traffic data-a commonly used metric of fame. Surprisal was significantly higher for fame-seeking mass shooters than non-fame-seeking ones. We also registered a significant positive correlation between fame and surprisal controlling for the number of casualties and injured victims. Not only do we uncover a link between fame-seeking behavior and surprise in the attacks but also we demonstrate an association between the fame of a mass shooting and its surprise.

Beyond the Bristol book: Advances and perspectives in non-smooth dynamics and applications.

Non-smooth dynamics induced by switches, impacts, sliding, and other abrupt changes are pervasive in physics, biology, and engineering. Yet, systems with non-smooth dynamics have historically received far less attention compared to their smooth counterparts. The classic "Bristol book" [di Bernardo et al., Piecewise-smooth Dynamical Systems. Theory and Applications (Springer-Verlag, 2008)] contains a 2008 state-of-the-art review of major results and challenges in the study of non-smooth dynamical systems. In this paper, we provide a detailed review of progress made since 2008. We cover hidden dynamics, generalizations of sliding motion, the effects of noise and randomness, multi-scale approaches, systems with time-dependent switching, and a variety of local and global bifurcations. Also, we survey new areas of application, including neuroscience, biology, ecology, climate sciences, and engineering, to which the theory has been applied.

Exploring a COVID-19 Endemic Scenario: High-Resolution Agent-Based Modeling of Multiple Variants.

Our efforts as a society to combat the ongoing COVID-19 pandemic are continuously challenged by the emergence of new variants. These variants can be more infectious than existing strains and many of them are also more resistant to available vaccines. The appearance of these new variants cause new surges of infections, exacerbated by infrastructural difficulties, such as shortages of medical personnel or test kits. In this work, a high-resolution computational framework for modeling the simultaneous spread of two COVID-19 variants: a widely spread base variant and a new one, is established. The computational framework consists of a detailed database of a representative U.S. town and a high-resolution agent-based model that uses the Omicron variant as the base variant and offers flexibility in the incorporation of new variants. The results suggest that the spread of new variants can be contained with highly efficacious tests and mild loss of vaccine protection. However, the aggressiveness of the ongoing Omicron variant and the current waning vaccine immunity point to an endemic phase of COVID-19, in which multiple variants will coexist and residents continue to suffer from infections.

Dipole- and vortex sheet-based models of fish swimming.

Elucidating the hydrodynamics of fish swimming is critical to identifying the processes underlying fish orientation and schooling. Due to their mathematical tractability, models based on potential flow are preferred in the study of bidirectional interactions of fish with their surroundings. Dipole-based models that assimilate fish to pairs of vortices are particularly enticing, but yet to be thoroughly validated. Here, we embark on a computational fluid dynamics (CFD) campaign informed by experimental data to validate the accuracy of dipole-based models. The locomotory patterns of a fish undergoing carangiform swimming are reconstructed from existing experimental data, which are used as inputs to CFD simulations of a fish swimming in a channel flow. We demonstrate that dipole-based models are accurate in capturing key features of the fluid flow, but cannot predict the elongated flow streamlines around the fish that are evident in CFD. To address this issue, we propose an alternative model that replaces each vortex in the pair with a sheet along the fish length. Using a pair of vortex sheets that span approximately 80% of the fish body length with a separation distance of approximately 50% of the body width, the model is successful in predicting the fluid flow around the swimming fish for a range of background flow speeds and channel widths. The proposed model shows improved accuracy at the cost of a mildly increased computational effort, thereby constituting an ideal basis for research on fish hydrodynamics.

Activity-driven network modeling and control of the spread of two concurrent epidemic strains.

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain-phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible-exposed-infectious-removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.

Collective response of fish to combined manipulations of illumination and flow.

Collective behavior is ubiquitous among fish, yet, its hows and whys are yet to be completely elucidated. It is known that several environmental factors can dramatically influence collective behavior, by eliciting behavioral adaptations in the individuals and altering physical pathways of social interactions in the group. Yet, empirical research has mostly focused on the quantification of the role of one factor at a time, with a paucity of studies designed to explore the multi-sensory basis of collective behavior. We investigated collective behavior of zebrafish (Danio rerio) pairs swimming in a water channel under combined manipulations of illumination (bright and dark) and flow conditions (absence and presence). The ability of the pair to orient and school increased in the presence of the flow and when fish were allowed to visually interact under bright illumination. Shoaling, instead, was only modulated by the illumination, so that fish swam at higher relative distances in the dark, irrespective of the flow. We also found evidence in favor of a modulatory effect of flow and illumination on the formation of the pair. Specifically, in the bright illumination, fish swam more side-by-side against a flow than in placid water; likewise, in the presence of a flow, they spent more time side-by-side in the bright illumination than in the dark. These findings point at a rich interplay between flow and illumination, whose alterations have profound effects on collective behavior.

A spatiotemporal model of firearm ownership in the United States.

Firearm injury is a major public health crisis in the United States, where more than 200 people sustain a nonfatal firearm injury and more than 100 people die from it every day. To formulate policy that minimizes firearm-related harms, legislators must have access to spatially resolved firearm possession rates. Here, we create a spatiotemporal econometric model that estimates monthly state-level firearm ownership from two cogent proxies (background checks per capita and fraction of suicides committed with a firearm). From calibration on yearly survey data that assess ownership, we find that both proxies have predictive value in estimation of firearm ownership and that interactions between states cannot be neglected. We demonstrate use of the model in the study of relationships between media coverage, mass shootings, and firearm ownership, uncovering causal associations that are masked by the use of the proxies individually.

Spatiotemporal patterns of firearm acquisition in the United States in different presidential terms.

This study develops mathematical tools and approaches to investigate spatiotemporal patterns of firearm acquisition in the U.S. complemented by hypothesis testing and statistical analysis. First, state-level and nation-level instant background check (BC) data are employed as proxy of firearm acquisition corresponding to 1999-2021. The relative-phase time-series of BC in each U.S. state is recovered and utilized to calculate the time-series of the U.S. states' synchronization degree. We reveal that U.S. states present a high-level degree of synchronization except in 2010-2011 and after 2018. Comparing these results with respect to a sitting U.S. president provides additional information: specifically, any two presidential terms are characterized by statistically different synchronization degrees except G. W. Bush's first term and B. H. Obama's second term. Next, to detail variations of BC, short-time Fourier transform, dimensionality reduction techniques, and diffusion maps are implemented within a time-frequency representation. Firearm acquisition in the high frequency band is described by a low-dimensional embedding, in the form of a plane with two embedding coordinates. Data points on the embedding plane identify separate clusters that signify state transitions in the original BC data with respect to different time windows. Through this analysis, we reveal that the frequency content of the BC data has a time-dependent characteristic. By comparing the diffusion map at hand with respect to a presidential term, we find that at least one of the embedding coordinates presents statistically significant variations between any two presidential terms except B. H. Obama's first term and D. J. Trump's pre-COVID term. The results point at a possible interplay between firearm acquisition in the U.S. and a presidential term.

Correction: Modeling multi-sensory feedback control of zebrafish in a flow.

[This corrects the article DOI: 10.1371/journal.pcbi.1008644.].

Urban Determinants of COVID-19 Spread: a Comparative Study across Three Cities in New York State.

The ongoing pandemic is laying bare dramatic differences in the spread of COVID-19 across seemingly similar urban environments. Identifying the urban determinants that underlie these differences is an open research question, which can contribute to more epidemiologically resilient cities, optimized testing and detection strategies, and effective immunization efforts. Here, we perform a computational analysis of COVID-19 spread in three cities of similar size in New York State (Colonie, New Rochelle, and Utica) aiming to isolate urban determinants of infections and deaths. We develop detailed digital representations of the cities and simulate COVID-19 spread using a complex agent-based model, taking into account differences in spatial layout, mobility, demographics, and occupational structure of the population. By critically comparing pandemic outcomes across the three cities under equivalent initial conditions, we provide compelling evidence in favor of the central role of hospitals. Specifically, with highly efficacious testing and detection, the number and capacity of hospitals, as well as the extent of vaccination of hospital employees are key determinants of COVID-19 spread. The modulating role of these determinants is reduced at lower efficacy of testing and detection, so that the pandemic outcome becomes equivalent across the three cities.