Center for Information Technology Research in the Interest of Society

Intelligent vehicle cooperation based on reliable communication systems contributes not only to reducing traffic accidents but also to improving traffic flow. Adaptive cruise control (ACC) systems can gain enhanced performance by adding vehicle-vehicle wireless communication to provide additional information to augment range sensor data, leading to cooperative ACC (CACC). This paper presents the design, development, implementation, and testing of a CACC system. It consists of two controllers, one to manage the approaching maneuver to the leading vehicle and the other to regulate car-following once the vehicle joins the platoon. The system has been implemented on four production Infiniti M56s vehicles, and this paper details the results of experiments to validate the performance of the controller and its improvements with respect to the commercially available ACC system.

This study used microscopic simulation to estimate the effect on highway capacity of varying market penetrations of vehicles with adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC). Because the simulation used the distribution of time gap settings that drivers from the general public used in a real field experiment, this study was the first on the effects of ACC and CACC on traffic to be based on real data on driver usage of these types of controls. The results showed that the use of ACC was unlikely to change lane capacity significantly. However, CACC was able to increase capacity greatly after its market penetration reached moderate to high percentages. The capacity increase could be accelerated by equipping non-ACC vehicles with vehicle awareness devices so that they could serve as the lead vehicles for CACC vehicles.

ABSTRACT Organizational investment in information systems is often large and risky given the variety of information requirements placed on systems today. To make more informed decisions and to meet the challenge of developing systems that satisfy these demands, system developers need to achieve a better understanding of factors that ultimately lead to system usage. To enhance this understanding, we posit a holistic framework to examine several constructs suggested in the literature that lead to the behavioral intention to use an information system. Our framework includes situational involvement, intrinsic involvement, argument for change, perceived usefulness, ease of use, prior usage, and attitude constructs. We extend the Davis, Bagozzi, and Warshaw Technology Acceptance Model (TAM), which is founded on the Theory of Reasoned Action. A diverse sample from industry is used to test our model. Structural equation modeling is used to examine the entire pattern of intercorrelations among the constructs and to test related propositions. A hierarchical structure is used to compare the explanatory ability of TAM with our extension. Our model explains a large portion of the covariance among the constructs that lead to a user's behavioral intention to use an information system and compares favorably with TAM. The results indicate that (1) the direct effect of situational involvement on behavioral intention as well as attitude is significant in the negative direction, (2) attitude seems to play a mediating role, and (3) intrinsic involvement plays a significant role in shaping perceptions. Finally, we conclude that the user involvement construct needs to be separated into its psychological as well as its participative components for developers to understand its impact on the systems development process.

Metagenomics is a discipline that enables the genomic study of uncultured microorganisms. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats are expanding and transforming our view of the microbial world. Distilling meaningful information from the millions of new genomic sequences presents a serious challenge to bioinformaticians. In cultured microbes, the genomic data come from a single clone, making sequence assembly and annotation tractable. In metagenomics, the data come from heterogeneous microbial communities, sometimes containing more than 10,000 species, with the sequence data being noisy and partial. From sampling, to assembly, to gene calling and function prediction, bioinformatics faces new demands in interpreting voluminous, noisy, and often partial sequence data. Although metagenomics is a relative newcomer to science, the past few years have seen an explosion in computational methods applied to metagenomic-based research. It is therefore not within the scope of this article to provide an exhaustive review. Rather, we provide here a concise yet comprehensive introduction to the current computational requirements presented by metagenomics, and review the recent progress made. We also note whether there is software that implements any of the methods presented here, and briefly review its utility. Nevertheless, it would be useful if readers of this article would avail themselves of the comment section provided by this journal, and relate their own experiences. Finally, the last section of this article provides a few representative studies illustrating different facets of recent scientific discoveries made using metagenomics.

AIMS: To review structural, chemical and metabolic brain changes, particularly those in the basal ganglia, in individuals who used methamphetamine, as well as in children with prenatal methamphetamine exposure. METHODS: Magnetic resonance imaging (MRI) and positron emission tomography (PET) studies that evaluated brain structural, chemical and metabolite changes in methamphetamine subjects, or children with prenatal methamphetamine exposure, were reviewed and summarized. Relevant pre-clinical studies that provided insights to the interpretations of these imaging studies were also reviewed. RESULTS: In adults who used methamphetamine, MRI demonstrates enlarged striatal volumes, while MR spectroscopy shows reduced concentrations of the neuronal marker N-acetylasparate and total creatine in the basal ganglia. In contrast, children with prenatal methamphetamine exposure show smaller striatal structures and elevated total creatine. Furthermore, PET studies consistently showed reduced dopamine transporter (DAT) density and reduced dopamine D(2) receptors in the striatum of methamphetamine subjects. PET studies also found lower levels of serotonergic transporter density and vesicular monoamine transporter (VMAT2) across striatal subregions, as well as altered brain glucose metabolism that correlated with severity of psychiatric symptoms in the limbic and orbitofrontal regions. CONCLUSION: Neuroimaging studies demonstrate abnormalities in brain structure and chemistry convincingly in individuals who used methamphetamine and in children with prenatal methamphetamine exposure, especially in the striatum. However, many important questions remain and larger sample sizes are needed to validate these preliminary observations. Furthermore, longitudinal studies are needed to evaluate the effects of treatment and abstinence on these brain changes and to determine whether imaging, and possibly genetic, markers can be used to predict treatment outcome or relapse.

Green industrial policy builds support for carbon regulation

Abstract This paper contrasts performances overall and by gender, ethnicity, and socioeconomic status (SES) for middle school students learning science through traditional scripted inquiry versus a design‐based, systems approach. Students designed and built electrical alarm systems to learn electricity concepts over a four‐week period using authentic engineering design methods. The contrast study took place in the eighth grade of an urban, public school district, with the systems approach implemented in 26 science classes (10 teachers and 587 students) and the scripted inquiry approach implemented in inquiry groups of 20 science classes (five teachers and 466 students). The results suggest that a systems design approach for teaching science concepts has superior performance in terms of knowledge gain achievements in core science concepts, engagement, and retention when compared to a scripted inquiry approach. The systems design approach was most helpful to low‐achieving African American students.

Cooperative adaptive cruise control (CACC) includes multiple concepts of communication-enabled vehicle following and speed control. Definitions and classifications are presented to help clarify the distinctions between types of automated vehicle-following control that are often conflated with each other. A distinction is made between vehicle-to-vehicle (V2V) CACC, based on vehicle–vehicle cooperation, and infrastructure-to-vehicle CACC, in which the infrastructure provides information or guidance to the CACC system (such as the target set speed value). In V2V CACC, communication provides enhanced information so that vehicles can follow their predecessors with higher accuracy, faster response, and shorter gaps; the result would be enhanced traffic flow stability and possibly improved safety. A further distinction is made between CACC, which uses constant-time-gap vehicle following (forming CACC strings), and automated platooning, which uses tightly coupled, constant-clearance, vehicle-following strategies. Although adaptive cruise control (ACC) and CACC are examples of Level 1 automation as defined by both SAE and NHTSA, the vehicle-following performance that can be achieved under each scenario is representative of the performance that should be expected at higher levels of automation. Implementation of CACC in practice will also require consideration of more than the lowest level of vehicle-following and speed regulation performance. Because CACC requires interactions between adjacent equipped vehicles, strategies are needed such as ad hoc, local, or global coordination to cluster CACC vehicles. Some of the challenges that must be overcome to implement the clustering strategies are discussed as well as strategies for separating CACC clusters as they approach their destinations, as potential traffic improvements from CACC will be negated if the vehicles cannot disperse effectively.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

As telehealth plays an even greater role in global health care delivery, it will be increasingly important to develop a strong evidence base of successful, innovative telehealth solutions that can lead to scalable and sustainable telehealth programs. This paper has two aims: (1) to describe the challenges of promoting telehealth implementation to advance adoption and (2) to present a global research agenda for personalized telehealth within chronic disease management. Using evidence from the United States and the European Union, this paper provides a global overview of the current state of telehealth services and benefits, presents fundamental principles that must be addressed to advance the status quo, and provides a framework for current and future research initiatives within telehealth for personalized care, treatment, and prevention. A broad, multinational research agenda can provide a uniform framework for identifying and rapidly replicating best practices, while concurrently fostering global collaboration in the development and rigorous testing of new and emerging telehealth technologies. In this paper, the members of the Transatlantic Telehealth Research Network offer a 12-point research agenda for future telehealth applications within chronic disease management.

The Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA, http://camera.calit2.net/) is a database and associated computational infrastructure that provides a single system for depositing, locating, analyzing, visualizing and sharing data about microbial biology through an advanced web-based analysis portal. CAMERA collects and links metadata relevant to environmental metagenome data sets with annotation in a semantically-aware environment allowing users to write expressive semantic queries against the database. To meet the needs of the research community, users are able to query metadata categories such as habitat, sample type, time, location and other environmental physicochemical parameters. CAMERA is compliant with the standards promulgated by the Genomic Standards Consortium (GSC), and sustains a role within the GSC in extending standards for content and format of the metagenomic data and metadata and its submission to the CAMERA repository. To ensure wide, ready access to data and annotation, CAMERA also provides data submission tools to allow researchers to share and forward data to other metagenomics sites and community data archives such as GenBank. It has multiple interfaces for easy submission of large or complex data sets, and supports pre-registration of samples for sequencing. CAMERA integrates a growing list of tools and viewers for querying, analyzing, annotating and comparing metagenome and genome data.

Over the last decade, significant progress has been made in automated driving systems (ADS). Given the current momentum and progress, ADS can be expected to continue to advance and a variety of ADS products will become commercially available within a decade. It is envisioned that automated driving technology will lead to a paradigm shift in transportation systems in terms of user experience, mode choices, and business models. In this paper, we start with a review of the state-of-the-art in the field of ADS and their deployment paths. It is followed by a discussion of the future prospects of ADS and their effects on various aspects of the transportation field. We then identify two specific use cases of ADS where the impacts can be significant – personal mobility services and vehicle automation for aging society. A survey of impact assessment studies and the associated methodologies for evaluating ADS is given, which is followed by concluding remarks at the end of the paper.

In this paper, we present a computational framework for automatic generation of provably correct control laws for planar robots in polygonal environments. Using polygon triangulation and discrete abstractions, we map continuous motion planning and control problems, specified in terms of triangles, to computationally inexpensive problems on finite-state-transition systems. In this framework, discrete planning algorithms in complex environments can be seamlessly linked to automatic generation of feedback control laws for robots with underactuation constraints and control bounds. We focus on fully actuated kinematic robots with velocity bounds and (underactuated) unicycles with forward and turning speed bounds.

Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. Sparse probe data represent the vast majority of the data available on arterial roads. This paper proposes a probabilistic modeling framework for estimating and predicting arterial travel-time distributions using sparsely observed probe vehicles. We introduce a model based on hydrodynamic traffic theory to learn the density of vehicles on arterial road segments, illustrating the distribution of delay within a road segment. The characterization of this distribution is essentially to use probe vehicles for traffic estimation: Probe vehicles report their location at random locations, and the travel times between location reports must be properly scaled to match the map discretization. A dynamic Bayesian network represents the spatiotemporal dependence on the network and provides a flexible framework to learn traffic dynamics from historical data and to perform real-time estimation with streaming data. The model is evaluated using data from a fleet of 500 probe vehicles in San Francisco, CA, which send Global Positioning System (GPS) data to our server every minute. The numerical experiments analyze the learning and estimation capabilities on a subnetwork with more than 800 links. The sampling rate of the probe vehicles does not provide detailed information about the location where vehicles encountered delay or the reason for any delay (i.e., signal delay, congestion delay, etc.). The model provides an increase in estimation accuracy of 35% when compared with a baseline approach to process probe-vehicle data.

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego‐motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego‐motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real‐time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed‐loop system, conditions are provided under which exponential stability can be guaranteed. In the closed‐loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision‐in‐the‐loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

This paper contains two main results. First, we revisit the well-known visibility-based pursuit-evasion problem, and show that in contrast to deterministic strategies, a single pursuer can locate an unpredictable evader in any simply connected polygonal environment, using a randomized strategy. The evader can be arbitrarily faster than the pursuer, and it may know the position of the pursuer at all times, but it does not have prior knowledge of the random decisions made by the pursuer. Second, using the randomized algorithm, together with the solution to a problem called the "lion and man problem" as subroutines, we present a strategy for two pursuers (one of which is at least as fast as the evader) to quickly capture an evader in a simply connected polygonal environment. We show how this strategy can be extended to obtain a strategy for a polygonal room with a door, two pursuers who have only line-of-sight communication, and a single pursuer (at the expense of increased capture time).

An array of technology-based interventions has increasingly become available to support family caregivers, primarily focusing on health and well-being, social isolation, financial, and psychological support. More recently the emergence of new technologies such as mobile and cloud, robotics, connected sensors, virtual/augmented/mixed reality, voice, and the evermore ubiquitous tools supported by advanced data analytics, coupled with the integration of multiple technologies through platform solutions, have opened a new era of technology-enabled interventions that can empower and support family caregivers. This paper proposes a conceptual framework for identifying and addressing the challenges that may need to be overcome to effectively apply technology-enabled solutions for family caregivers. The paper identifies a number of challenges that either moderate or mediate the full use of technologies for the benefit of caregivers. The challenges include issues related to equity, inclusion, and access; ethical concerns related to privacy and security; political and regulatory factors affecting interoperability and lack of standards; inclusive/human-centric design and issues; and inherent economic and distribution channel difficulties. The paper concludes with a summary of research questions and issues that form a framework for global research priorities.

Colorectal cancer (CRC) is the fourth most common cancer amongst men and women. Between 3 and 6% of all CRCs are attributed to well-defined inherited syndromes, including Lynch syndrome, familial adenomatous polyposis (FAP), MUTYH-associated polyposis (MAP), and several hamartomatous polyposis conditions. Identification of these patients through family history and appropriate genetic testing can provide estimates of cancer risk that inform appropriate cancer screening, surveillance and/or preventative interventions. This narrative review examines the hereditary colorectal cancer and polyposis syndromes, their genetic basis, clinical management, and evidence supporting cancer screening.

The unusually high demand for metals in the brain, along with insufficient understanding of how their dysregulation contributes to neurological diseases, motivates the study of how inorganic chemistry influences neural circuitry. We now report that the transition metal copper is essential for regulating rest–activity cycles and arousal. Copper imaging and gene expression analysis in zebrafish identifies the locus coeruleus–norepinephrine (LC-NE) system, a vertebrate-specific neuromodulatory circuit critical for regulating sleep, arousal, attention, memory and emotion, as a copper-enriched unit with high levels of copper transporters CTR1 and ATP7A and the copper enzyme dopamine β-hydroxylase (DBH) that produces NE. Copper deficiency induced by genetic disruption of ATP7A, which loads copper into DBH, lowers NE levels and hinders LC function as manifested by disruption in rest–activity modulation. Moreover, LC dysfunction caused by copper deficiency from ATP7A disruption can be rescued by restoring synaptic levels of NE, establishing a molecular CTR1–ATP7A–DBH–NE axis for copper-dependent LC function. Copper contributes to regulating zebrafish rest–activity cycles through the locus coeruleus system by modulating the biosynthesis of norepinephrine; brain copper deficiency leads to lower levels of both synaptic norepinephrine and daytime activity.

The COVID-19 pandemic demonstrates the critical need to reimagine and repair the broken systems of global health. Specifically, the pandemic demonstrates the hollowness of the global health rhetoric of equity, the weaknesses of a health security-driven global health agenda, and the negative health impacts of power differentials not only globally, but also regionally and locally. This article analyses the effects of these inequities and calls on governments, multilateral agencies, universities, and NGOs to engage in true collaboration and partnership in this historic moment. Before this pandemic spreads further - including in the Global South - with potentially extreme impact, we must work together to rectify the field and practice of global health.