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Augmented Reality (AR) is known to enhance user experience, however, it remains under-adopted in industry. We present an AR interaction system improving human-machine coordination in Internet of Things (IoT) and Industry 4.0 applications including manufacturing and assembly, maintenance and safety, and other highly-interactive functions. A driver of slow adoption is the computational complexity and inaccuracy in localization and rendering digital content. AR systems may render digital content close to the associated physical objects, but traditional object recognition and localization modules perform poorly when tracking texture-less objects and complex shapes, presenting a need for robust and efficient digital content rendering techniques. We propose a method of improving IoT-AR by integrating Deep Learning with AR to increase accuracy and robustness of the target object localization module, taking both color and depth images as input and outputting the target’s pose parameters. Quantitative and qualitative experiments prove this system’s efficacy and show potential for fusing these emerging technologies in real-world applications.

Conventional cybersecurity neglects the Internet of Things' physicality. We propose a cognitive protection system capable of using system models to ensure command safety while monitoring system performance and develop and test a cognitive firewall and cognitive supervisor. This system is tested in theory and practice for three threat models.

We present an Augmented Reality (AR) visualization and interaction tool for users to control Internet of Things (IoT) devices with hand gestures. Today, smart IoT devices are becoming increasingly ubiquitous with diverse forms and functions, yet most user controls over them are still limited to mobile devices and web interfaces. Recently, AR has been developed rapidly, and provided immersive solutions to enhance user experience of applications in many fields. Its capability to create immersive interactions allows AR to improve the way smart devices are controlled via more direct visual feedback. In this paper, we create a functional prototype of one such system, enabling seamless interactions with sound and lighting systems through the use of augmented hand-controlled interaction panels. To interpret users' intentions, we implement a standard 2D convolution neural network (CNN) for real-time hand gesture recognition and deploy it within our system. Our prototype is also equipped with a simple but effective object detector which can identify target devices within a proper range by analyzing geometric features. We evaluate the performance of our system qualitatively and quantitatively and demonstrate it on two smart devices.

We perform kinematic and dynamic source inversions of four interplate thrust earthquakes that occurred near the trench at the base of the continental wedge in Northern Chile and we compare them to deeper intraplate events. The magnitudes of these interplate events were between Mw 6.3 and Mw 6.5, with hypocentral depths varying between 17.8 km and 28 km. These earthquakes correspond to one foreshock and three aftershocks of the large 2014 Mw 8.2 Iquique earthquake. The geometry of the seismic rupture obtained from the kinematic and dynamic inversions were similar for the four earthquakes studied and their dynamic source parameters are in accordance with typical interplate earthquakes around Mw 6.5. We compare the dynamic rupture of these events with those of five intraplate, intermediate-depth earthquakes that occurred in Northern Chile, Japan, Argentina and Mexico with depths between 57 km and 250 km. We also compare these events with the Mw 6.9 Valparaiso earthquake that was also a shallow interplate thrust event. Results show that the main dynamic parameters of the interplate events (stress in the nucleation zone, overall stress drop and fracture energy rate) were smaller than those of the intraplate intermediate-depth earthquakes. Furthermore, we compare our results with those obtained with several methodologies commonly used to estimate values of stress drop, fault radius and corner frequency. We find some minor differences between them for most of the earthquakes analyzed. Finally, we infer from our results that the studied area, located in the subduction interface zone near the trench, is suitable for earthquake nucleation of small to moderate earthquakes and does not represent a barrier for a future large tsunamigenic rupture.

We demonstrate a new and novel approach to calculate Earth's geobiosphere emergy baseline (GEB). In this method we use gravitational potential energy dissipated in the generation of Earth's main renewable energy sources. From this gravitational perspective, we recognize three refinements to our understanding of Earths driving energies. First we acknowledge the recent literature suggests that Earth's geothermal energy is from two separate sources, decay of radioisotopes and primordial heat, thus warranting separate solar equivalence ratios (SERs). Second, tidal energy dissipation can be viewed as the loss of Earth's rotational kinetic energy (KE), due to gravitational interaction between Earth/Moon/Sun and frictional forces in Earth's oceans. Seen in this way we draw an equivalence between loss of Earth rotational KE and tidal energy dissipated. Third, Earth's rotational KE and primordial heat are coupled processes of the gravitationally induced accretion of Earth. The four sources of available energy to the geobiosphere, solar radiation, tidal energy dissipation, primordial heat, and radiogenic heat, are expressed as a ratio of gravitational emergy needed to produce them. After all four sources are expressed by their gravitational transformities, solar equivalences are computed by dividing their gravitational transformity by the gravitational transformity of solar radiation, resulting in solar equivalent ratios. Using solar equivalences, we combine the four sources to express the emergy driving all planetary phenomena. The method yields four different baselines depending on the allocation procedure used to assign gravitational exergy of Earth's accretion to it is rotational KE and primordial heat. The GEBs ranged between 11.1E+24 seJ J−1 and 13.8E+24 seJ J−1.

Policy makers and practitioners increasingly believe that medical evidence plays a critical role in improving care and health outcomes and lowering costs. However, public understanding of the role of evidence-based care may be different. Public deliberation is a process that convenes diverse citizens and has them learn about and consider ethical or values-based dilemmas and weigh alternative views. The Community Forum Deliberative Methods Demonstration project, sponsored by the Agency for Healthcare Research and Quality, obtained informed public views on the role of evidence in health care decisions through seventy-six deliberative groups involving 907 people overall, in the period August-November 2012. Although participants perceived evidence as being essential to high-quality care, they also believed that personal choice or clinical judgment could trump evidence. They viewed doctors as central figures in discussing evidence with patients and key arbiters of whether to follow evidence in individual cases. They found evidence of harm to individuals or the community to be more compelling than evidence of effectiveness. These findings indicate that increased public understanding of evidence can play an important role in advancing evidence-based care by helping create policies that better reflect the needs and values of the public.

In 2015, the Pacific marine heat wave, low river flows, and record high water temperatures in the Columbia River Basin contributed to a near-complete failure of the adult migration of endangered Snake River sockeye salmon (Oncorhynchus nerka, NOAA Fisheries 2016). These extreme weather events may become the new normal due to anthropogenic climate change, with catastrophic consequences for endangered species. Existing anthropogenic pressures may amplify vulnerability to climate change, but these potential synergies have rarely been quantified. We examined factors affecting survival of endangered sockeye (Oncorhynchus nerka) and threatened Chinook salmon (O. tshawytscha) as they migrated upstream through eight dams and reservoirs to spawning areas in the Snake River Basin. Our extensive database included histories of 17,279 individual fish that migrated since 2004. A comparison between conditions in 2015 and daily temperatures and flows in a regulated basin forced by output from global climate models showed that 2015 did have many characteristics of projected future mean conditions. To evaluate potential salmon responses, we modeled migration timing and apparent survival under historical and future climate scenarios (2040s). For Chinook salmon, adult survival from the first dam encountered to spawning grounds dropped by 4-15%, depending on the climate scenario. For sockeye, survival dropped by ~80% from their already low levels. Through sensitivity analyses, we observed that the adult sockeye migration would need to shift more than 2 weeks earlier than predicted to maintain survival rates typical of those seen during 2008-2017. Overall, the greater impacts of climate change on adult sockeye compared with adult Chinook salmon reflected differences in life history and environmental sensitivities, which were compounded for sockeye by larger effect sizes from other anthropogenic factors. Compared with Chinook, sockeye was more negatively affected by a history of juvenile transportation and by similar temperatures and flows. The largest changes in temperature and flow were projected to be upstream from the hydrosystem, where direct mitigation through hydrosystem management is not an option. Unfortunately, Snake River sockeye have likely lost much of their adaptive capacity with the loss of the wild population. Further work exploring habitat restoration or additional mitigation actions is urgently needed.

Tank-style water heaters provide a critical utility but often waste energy, as low temperatures encourage the formation of harmful bacteria. We develop low-cost hardware to capture training data from a single home’s hot water consumption and present a system capable of proactively anticipating demand while remaining sensitive to health concerns by combining a flow-predictive autoregressive convolutional neural network with a Cognitive Supervisor for minimizing Legionella formation. The predictive performance is evaluated for different receptive fields, and the best-performing model successfully tracks water demand one week into the future, providing robust input for the Supervisor to reform Legionella growth with minimal energy expenditure. First-order estimates find U.S. homeowner savings of $ 10 B in heating costs and 80MMT of C O 2 without increased health risks. This system uniquely combines predictive and protective elements to augment new and incumbent water heater installations including those used in low to middle-high income countries, enhancing global impact.

Objectives. In 2016, the Second Panel on Cost-effectiveness in Health and Medicine updated the seminal work of the original panel from 2 decades earlier. The Second Panel had an opportunity to reflect on the evolution of cost-effectiveness analysis (CEA) and to provide guidance for the next generation of practitioners and consumers. In this article, we present key topics for future research and policy. Methods. During the course of its deliberations, the Second Panel discussed numerous topics for advancing methods and for improving the use of CEA in decision making. We identify and consider 7 areas for which the panel believes that future research would be particularly fruitful. In each of these areas, we highlight outstanding research needs. The list is not intended as an exhaustive inventory but rather a set of key items that surfaced repeatedly in the panel’s discussions. In the online Appendix , we also list and expound briefly on 8 other important topics. Results. We highlight 7 key areas: CEA and perspectives (determining, valuing, and summarizing elements for the analysis), modeling (comparative modeling and model transparency), health outcomes (valuing temporary health and path states, as well as health effects on caregivers), costing (a cost catalogue, valuing household production, and productivity effects), evidence synthesis (developing theory on learning across studies and combining data from clinical trials and observational studies), estimating and using cost-effectiveness thresholds (empirically representing 2 broad concepts: opportunity costs and public willingness to pay), and reporting and communicating CEAs (written protocols and a quality scoring system). Conclusions. Cost-effectiveness analysis remains a flourishing and evolving field with many opportunities for research. More work is needed on many fronts to understand how best to incorporate CEA into policy and practice.

Down syndrome (DS) results from triplication of human chromosome 21. Neuropathological hallmarks of DS include atypical central nervous system development that manifests prenatally and extends throughout life. As a result, individuals with DS exhibit cognitive and motor deficits, and have delays in achieving developmental milestones. To determine whether different mouse models of DS recapitulate the human prenatal and postnatal phenotypes, here, we directly compared brain histogenesis, gene expression and behavior over the lifespan of three cytogenetically distinct mouse models of DS: Ts1Cje, Ts65Dn and Dp(16)1/Yey. Histological data indicated that Ts65Dn mice were the most consistently affected with respect to somatic growth, neurogenesis and brain morphogenesis. Embryonic and adult gene expression results showed that Ts1Cje and Ts65Dn brains had considerably more differentially expressed (DEX) genes compared with Dp(16)1/Yey mice, despite the larger number of triplicated genes in the latter model. In addition, DEX genes showed little overlap in identity and chromosomal distribution in the three models, leading to dissimilarities in affected functional pathways. Perinatal and adult behavioral testing also highlighted differences among the models in their abilities to achieve various developmental milestones and perform hippocampal- and motor-based tasks. Interestingly, Dp(16)1/Yey mice showed no abnormalities in prenatal brain phenotypes, yet they manifested behavioral deficits starting at postnatal day 15 that continued through adulthood. In contrast, Ts1Cje mice showed mildly abnormal embryonic brain phenotypes, but only select behavioral deficits as neonates and adults. Altogether, our data showed widespread and unexpected fundamental differences in behavioral, gene expression and brain development phenotypes between these three mouse models. Our findings illustrate unique limitations of each model when studying aspects of brain development and function in DS. This work helps to inform model selection in future studies investigating how observed neurodevelopmental abnormalities arise, how they contribute to cognitive impairment, and when testing therapeutic molecules to ameliorate the intellectual disability associated with DS. This article has an associated First Person interview with the first author of the paper., Editor's choice: Comparison of corticogenesis, gene expression and behavior in three mouse models of Down syndrome revealed major differences between the models as well as significant limitations in each strain for understanding neurobiological changes in the human phenotype.

Introduction Post-traumatic stress disorder (PTSD) has been linked to a variety of adverse mental and physical health outcomes including distressed relationships. Involving romantic partners in PTSD treatment appears to be a promising new avenue for PTSD treatment; however, additional research is necessary to clarify veteran preferences for inclusion of significant others in treatment and relationship characteristics that may influence such preferences. Accordingly, the present study was designed to evaluate Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn (OEF/OIF/OND) veterans' desire to include romantic partners in trauma-focused care (n = 74) and to explore psychological and relationship variables associated with preference for partner inclusion in PTSD treatment. Materials and methods This study surveyed male (N = 74) OEF/OIF/OND combat veterans seeking mental health services at a Veterans Health Administration PTSD treatment program. Relationships between PTSD symptoms, romantic relationship functioning, and interest in including their romantic partner in PTSD treatment were examined. Results Consistent with previous research, OEF/OIF/OND veterans seeking treatment at a specialty PTSD program report low relationship satisfaction. The majority of participants reported that PTSD symptoms interfere with relationship functioning; specifically, numbing symptoms were a significant predictor of PTSD-related relationship impairment. A minority (26%) of participants reported a desire to include their romantic partner in PTSD treatment. Greater behavioral avoidance and communication difficulties were associated with increased interest in including a romantic partner in PTSD treatment. Conclusion We confirmed that OEF/OIF/OND veterans seeking PTSD treatment experience PTSD-related problems in romantic relationships, low-relationship satisfaction, and relationship satisfaction was positively associated with perceived communication and problem-solving skills. A minority of veterans were interested in involving significant others in their PTSD treatment; however, in the present study, veterans were not given information about the various ways that a romantic partner might be involved in treatment, and they were not presented with specific conjoint therapies (e.g., Cognitive-Behavioral Conjoint Therapy for PTSD, Strategic Approach Therapy). For veterans with PTSD, relationship distress, and communication difficulties, conjoint psychotherapies may offer a way of increasing engagement in PTSD treatment by parsimoniously addressing multiple treatment targets at once (PTSD symptoms, relationship distress, communication problems) and providing veteran-centered care.

We examine methods for detecting and disrupting electronic arc faults, proposing an approach leveraging Internet of Things connectivity, artificial intelligence, and adaptive learning. We develop Deep Neural Networks (DNNs) taking Fourier coefficients, Mel-Frequency Cepstrum data, and Wavelet features as input for differentiating normal from malignant current measurements. We further discuss how hardware-accelerated signal capture facilitates real-time classification, enabling our classifier to reach 99.95% accuracy for binary classification and 95.61% for multi-device classification, with trigger-to-trip latency under 200 ms. Finally, we discuss how IoT supports aggregate and user-specific risk models and suggest how future versions of this system might effectively supervise multiple circuits. Keywords: Emerging applications and technology; Intelligent infrastructure; Ambient intelligence; Embedded intelligence; Distributed sensing; Arc fault detection; Real-time

For young adults with acute lymphoblastic leukemia, pediatric-based regimens are likely to provide the following when compared to hyper-CVAD regimens: better disease control, less hospitalization time, diminished acute toxicities, decreased financial cost, more quality-adjusted life years, and fewer adverse late effects, such as infertility, myelodysplasia, and second malignant neoplasms. There are also reasons to expect less cardiac and cognitive dysfunction after pediatric regimens. The improved quality and quantity of life associated with pediatric regimens renders them preferable to hyper-CVAD regimens for the treatment of Philadelphia-negative B-precursor or T-cell acute lymphoblastic leukemia and lymphoblastic lymphoma in young adults.

When developing cyber-physical systems such as automated vehicles, safety and cybersecurity analyses are often conducted separately. However, unlike in the IT world, safety hazards and cybersecurity threats converge in cyber-physical systems; a malicious party can exploit cyber-threats to create extremely hazardous situations, whether in autonomous vehicles or nuclear plants. We propose a framework for integrated system-level analyses for functional safety and cyber security. We present a generic model named Threat Identification and Refinement for Cyber-Physical Systems (TIRCPS) extending Microsoft’s six classes of threat modelling including Spoofing, Tampering, Repudiation, Information Disclosure, Denial-of-Service and Elevation Privilege (STRIDE). TIRCPS introduces three benefits for developing complex systems: first, it allows the refinement of abstract threats into specific ones as physical design information becomes available; Second, the approach provides support for constructing attack trees with traceability from high-level goals and hazardous events to threats. Third, TIRCPS formalizes the definition of threats such that intelligent tools can be built to automatically detect most of a system’s vulnerable components requiring protection. We present a case study on an automated-driving system to illustrate the proposed approach. The analysis results of a hierarchical attack tree with cyber threats traceable to highlevel hazardous events are used to design mitigation solutions.

Identifying and dating first human colonization of new places is challenging, especially when group sizes were small and material traces of their occupations were ephemeral. Generating reliable reconstructions of human colonization patterns from intact archaeological sites may be difficult to impossible given post-depositional taphonomic processes and in cases of island and coastal locations the inundation of landscapes resulting from post-Pleistocene sea-level rise. Paleoenvironmental reconstruction is proving to be a more reliable method of identifying small-scale human colonization events than archaeological data alone. We demonstrate the method through a sediment-coring project across the Lesser Antilles and southern Caribbean. Paleoenvironmental data were collected informing on the timing of multiple island-colonization events and land-use histories spanning the full range of human occupations in the Caribbean, from the initial forays into the islands through the arrival and eventual domination of the landscapes and indigenous people by Europeans. In some areas, our data complement archaeological, paleoecological, and historical findings from the Lesser Antilles and in others amplify understanding of colonization history. Here, we highlight data relating to the timing and process of initial colonization in the eastern Caribbean. In particular, paleoenvironmental data from Trinidad, Grenada, Martinique, and Marie-Galante (Guadeloupe) provide a basis for revisiting initial colonization models of the Caribbean. We conclude that archaeological programs addressing human occupations dating to the early to mid-Holocene, especially in dynamic coastal settings, should systematically incorporate paleoenvironmental investigations.