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A shared aperture antenna array including an array of antennas is disclosed. Elements of neighboring antennas are shared to create additional antennas. The shared elements include radiating patches and apertures. Each antenna shares an aperture with neighboring antennas. The array of antennas may be linear or two-dimensional. A phase shifting network with single-pole-single-throw reflective switches may be coupled to the antennas.
The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.
A shared aperture antenna array including an array of antennas is disclosed. Elements of neighboring antennas are shared to create additional antennas. The shared elements include radiating patches and apertures. Each antenna shares an aperture with neighboring antennas. The array of antennas may be linear or two-dimensional. A phase shifting network with single-pole-single-throw reflective switches may be coupled to the antennas.
With autonomous vehicles (AVs) still in the testing phase, researchers and planners must resort to simulation techniques to explore possible futures regarding shared and automated mobility. An agent-based discrete-event transport simulator, POLARIS, is used in this study to simulate travel in the 20-county Chicago region with a shared AV (SAV) mobility option. Using this framework, the effect of an SAV fleet on system performance when constrained to serve within geofences is studied under four distinct scenarios: service restricted to the city, to the city plus suburban core, to the core plus exurban areas, and to the entire region — along with the choice of dynamic ridesharing (DRS) versus solo travel in an SAV. Results indicate that service areas need a balanced mix of trip generators and attractors, and an SAV fleet’s empty VMT (eVMT) can be noticeably reduced through suitable geofencing and DRS. Geofences can also help lower response times, reduce systemwide VMT across all modes, and ensure uniform access to SAVs. DRS is most useful in lowering VMT and %eVMT that arises from sprawled land development, but with insufficient demand to share rides, savings from the use of geofences is higher. Geofences targeting neighborhoods with high trip density bring about low response times and %eVMT, but fleet sizes in these regions need to be designed for uniformly low response times throughout a large region, as opposed to maximizing vehicle use in a 24-hour day.
Addressing climate mitigation while meeting global electrification goals will require major transitions from fossil-fuel dependence to large-scale renewable energy deployment. However, renewables require significant land assets per unit energy and could come at high cost to ecosystems, creating potential conflicts between global climate mitigation and biodiversity conservation. Here, we explore the potential biodiversity implications of alternative future global electrification pathways as depicted under the Shared Socioeconomic Scenarios (SSPs), i.e., alternative trends in societal development. We examined the intersection of high-resolution estimates of global energy densities for ten renewable and conventional technologies with global richness data to estimate technology-specific biodiversity footprints (species per GWh), whereas a Cumulative Biodiversity Impact (CBI) score was used to assess land and biodiversity outcomes of alternative scenarios. Downscaled electricity generation scenarios (2020-2100) were also constrained by alternative land conservation and energy development policies. Unexpectedly, variation among SSPs did not exhibit a clear tradeoff between global climate mitigation and CBI. Rather, CBIs were an outcome of total infrastructure development to meet electricity demand (from population growth and GDP) and the total magnitude of renewable energy development and storage technologies. Renewables assembled along a spectrum from land sharing to sparing. At the land sharing end, biomass-powered electricity from dedicated crops contributed the most to biodiversity impacts due to low energy density, whereas land-sparing technologies (solar) caused more-intense land degradation, but in smaller areas. Our results suggest that local land conservation practices and strategies promoting energy diversification could have greater implications for future biodiversity conflicts than global socioeconomic drivers.
The concept of capacitive-sharing readout, described in detail in a previous study, offers the possibility for the development of high-performance three-coordinates (X--Y--U)-strip readout for Micro Pattern Gaseous Detectors (MPGDs) using simple standard PCB fabrication techniques. Capacitive-sharing (X--Y--U)-strip readout allows simultaneous measurement of the Cartesian coordinates x and y of the position of the particles together with a third coordinate u along the diagonal axis in a single readout PCB. This provides a powerful tool to address multiple-hit ambiguity and enable pattern recognition capabilities in moderate particle flux environment of collider or fixed target experiments in high energy physics HEP) and nuclear physics (NP). We present in this paper the performance of a 10 cm × 10 cm triple-GEM detector with capacitive-sharing (X--Y--U)-strip anode readout. Spatial resolutions of the order of $\sigma_{x}^{res}$ = 71.6 $\pm$ 0.8 $\mu$m for X-strips, $\sigma_{y}^{res}$ = 56.2 $\pm$ 0.9 $\mu$m for Y-strips and $\sigma_{u}^{res}$ = 75.2 $\pm$ 0.9 $\mu$m for U-strips have been obtained at a beam test at Thomas Jefferson National Accelerator Facility (Jefferson Lab). Modifications of the readout design of future prototypes to improve the spatial resolution and challenges in scaling to large-area MPGDs are discussed.
Shared micromobility has garnered widespread popularity in recent years, but limited attention has been given to the energy impacts of trips replaced by micromobility. This paper investigates the energy bounds of shared micromobility adoption. Travel demand data at the national and city level were analyzed to identify trips that can be served through micromobility, and scenarios with varying levels of micromobility adoption were evaluated. Results show that peak adoption of shared micromobility can reduce energy consumption from reported passenger travel by 1% at the national level and 2.6% at the city level, with micromobility-induced transit trips identified as the largest contributor for energy reduction. Sensitivity analysis was carried out to show how the energy impacts would change with various levels of key micromobility-related parameters, and results show distance threshold having a stronger influence on the energy impacts, compared to redistribution energy intensity.
Electricity market pricing and settlement are the key signals for real-time dispatch and long-term investment decisions. Regional transmission operators (RTOs) in the U.S. adopt uniform pricing scheme, which is based on the marginal costs of supplying an incremental MW of electric services. The marginal cost of an electric service is highly dependent on the constraints in the pricing models of RTOs. A slight difference in constraint modeling of pricing model on energy and ancillary services could result in drastically different market clearing prices (MCPs), cleared reserve quantities, and associated revenue. RTOs in the U.S. have various market designs and assumptions in ancillary services modeling in capacity sharing and reserve substitutes. This paper examines four combination models of capacity sharing and reserve substitutes and analyzes the associated market implications. The numerical results present that 1) cascading reserve requirements have direct impact on reserve pricing schemes 2) both cascading reserve requirements and sharing capacity have significant impact on reserve MCPs and locational marginal prices, and thus result in drastically different reserve revenue, energy revenue, generation cost, and generation profit.
Fatigue driving has been regarded as one of the most important factors that cause traffic accidents. This paper proposes a robust human-machine shared control strategy to improve the vehicle performance for different driver fatigue states. Firstly, the time-varying driver steering model is proposed to address the model mismatch caused by fatigue driving. And the driver fatigue evaluation system is established based on facial features to quantify driver fatigue levels. Based on the quantified fatigue levels, a novel strategy for allocating authorities of the driver and controller is developed for building the driver-vehicle interaction system. Then, to weaken the influence of parameter perturbations caused by the time-varying driver states, we design a fatigue-based shared controller through state feedback. The actuator saturation and system constraints are considered in the controller design through the robust set-invariance property to improve vehicle safety and driving comfort. The driver-in-the-loop platform is conducted to validate the effectiveness of the proposed shared steering controller. In conclusion, the experimental results show that the proposed strategy can adaptively optimize the human-machine authorities according to fatigue states and comprehensively improve vehicle performance.
The uncertainties of driver's behavior seriously affect road safety and bring significant challenges to the human-machine cooperative control. Here, this paper proposes a human-machine shared control framework considering driver's time-varying characteristics to improve the co-driving cooperation performance. Firstly, the driving intention is introduced to describe the driver's involvement level through using Gauss-Bernoulli restricted Boltzmann machine method. And the index of driving ability is proposed to evaluate driver skills based on path-tracking errors. Then, a novel human-machine authority allocation strategy is designed by combining the two driving behavior characteristics and used to construct the driver-vehicle interaction system. Subsequently, a T-S fuzzy robust state-feedback shared control system is developed considering time-varying driver behaviors and vehicle states. Finally, the proposed shared steering system is validated by the driver-in-the-loop test bench. The results show that the proposed control method can reduce human-machine conflicts and has obvious superiority in improving performance of driving comfort, path tracking, and vehicle stability for the co-driving vehicles.
The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL. In each of these four cities, SAEV simulations are performed using local projected electricity prices from the Regional Energy Deployment System (ReEDS) for a handful of supply side scenarios. A coordinated charging model is introduced that seeks to reduce fleet charging costs in response to time-varying electricity prices and increasing shares of personal trip demand served (between 1% and 25% of all metro trips served by the SAEV fleet). Simulation results are presented across various scenarios assuming fleetwide coordination to minimize charging energy costs while constrained by offering adequate mobility service to fleet customers. The results indicate that the SAEV charging load is highly flexible; energy costs were shown to reduce between 13% and 46% across a wide range of simulated scenarios. In addition, these savings were realized without detrimentally impacting the fleet’s ability to service trips.
Biospecimen Sharing Programs (BSPs) have been organized by NASA Ames Research Center since the 1960s with the goal of maximizing utilization and scientific return from rare, complex and costly spaceflight experiments. BSPs involve acquiring otherwise unused biological specimens from primary space research experiments for distribution to secondary experiments. Here we describe a collaboration leveraging Ames expertise in biospecimen sharing to magnify the scientific impact of research informing astronaut health funded by the NASA Human Research Program (HRP) Human Health Countermeasures (HHC) Element. The concept expands biospecimen sharing to one-off ground-based studies utilizing analogue space platforms (e.g., Hindlimb Unloading (HLU), Artificial Gravity) for rodent experiments, thereby significantly broadening the range of research opportunities with translational relevance for protecting human health in space and on Earth.
Biospecimen Sharing Programs (BSPs) have been organized by NASA Ames Research Center since the 1960s with the goal of maximizing utilization and scientific return from rare, complex and costly spaceflight experiments. BSPs involve acquiring otherwise unused biological specimens from primary space research experiments for distribution to secondary experiments. Here we describe a collaboration leveraging Ames expertise in biospecimen sharing to magnify the scientific impact of research informing astronaut health funded by the NASA Human Research Program (HRP) Human Health Countermeasures (HHC) Element. The concept expands biospecimen sharing to one-off ground-based studies utilizing analogue space platforms (e.g., Hind limb Unloading (HLU), Artificial Gravity) for rodent experiments, thereby significantly broadening the range of research opportunities with translational relevance for protecting human health in space and on Earth. In this presentation, we will report on biospecimens currently being acquired from HHC Award Head-Down Tilt as a Model for Intracranial and Intraocular Pressures, and Retinal Changes during Spaceflight, and their availability. The BSP add-on to the project described herein has already yielded for HHC-funded investigators more than 4,700 additional tissues that would otherwise have been discarded as waste, with additional tissues available for analysis. Young (3-mo old) male and female rats and Older (9-mo old) male rats are being exposed to HLU for either 7, 14, 28, or 90 days. Additional groups are exposed to 90 days of unloading followed by either 7, 14, 28 days or 90 days of recovery (normal loading). Comparisons are made with non-suspended controls. Unused tissues are: Skin, Lungs, Thymus, Adrenals, Kidneys, Spleen, Hindlimb Muscles (Soleus, Extensor Digitorum Longus, Tibialis Anterior, Plantaris Gastrocnemius), Fat Pads, Reproductive Organs, and Intestines. Tissues are harvested, weighed, preserved then archived (with metadata) using a sample tracking system (CryoTrack). Preservation techniques include snap-freezing and RNALatersnap-freezing. Specimens were weighed at the time of dissection, and organ mass: body mass ratios analyzed to determine unloading effects across conditions and durations. The results corroborate previously reported effects of short-term exposure to microgravity or unloading exposure on various organs, and provide new insights into adaptation to long-duration unloading relevant to sustained spaceflight exposures on ISS. Supported by the Human Research Program (HRP) Human Health Countermeasures (HHC) Element and NASA Grant NNX13AD94G (CAF).
Biospecimen Sharing Programs (BSPs) have been organized by NASA Ames Research Center since the 1960s with the goal of maximizing utilization and scientific return from rare, complex and costly spaceflight experiments. BSPs involve acquiring otherwise unused biological specimens from primary space research experiments for distribution to secondary experiments. Here we describe a collaboration leveraging Ames expertise in biospecimen sharing to magnify the scientific impact of research informing astronaut health funded by the NASA Human Research Program (HRP) Human Health Countermeasures (HHC) Element. The concept expands biospecimen sharing to one-off ground-based studies utilizing analogue space platforms (e.g., Hindlimb Unloading (HLU), Artificial Gravity) for rodent experiments, thereby significantly broadening the range of research opportunities with translational relevance for protecting human health in space and on Earth. In this presentation, we will report on biospecimens currently being acquired from HHC Award Head-Down Tilt as a Model for Intracranial and Intraocular Pressures, and Retinal Changes during Spaceflight, and their availability. The BSP add-on to the project described herein has already yielded for HHC-funded investigators more than 4,700 additional tissues that would otherwise have been discarded as waste, with additional tissues available for analysis. Young (3-mo old) male and female rats and Older (9-mo old) male rats are being exposed to HLU for either 7, 14, 28, or 90 days. Additional groups are exposed to 90 days of unloading followed by either 7, 14, 28 days or 90 days of recovery (normal loading). Comparisons are made with non-suspended controls. Unused tissues are: Skin, Lungs, Thymus, Adrenals, Kidneys, Spleen, Hindlimb Muscles (Soleus, Extensor Digitorum Longus, Tibialis Anterior, Plantaris Gastrocnemius), Fat Pads, Reproductive Organs, and Intestines. Tissues are harvested, weighed, preserved then archived (with metadata) using a sample tracking system (CryoTrack). Preservation techniques include snap-freezing and RNALatersnap-freezing. Specimens were weighed at the time of dissection, and organ mass:body mass ratios analyzed to determine unloading effects across conditions and durations. The results corroborate previously reported effects of short-term exposure to microgravity or unloading exposure on various organs, and provide new insights into adaptation to long-duration unloading relevant to sustained spaceflight exposures on ISS.
The OpenMP specification recently introduced support for unified shared memory, allowing implementation to leverage underlying system software to provide a simpler GPU offloading model where explicit mapping of variables is optional. Support for this feature is becoming more available in different OpenMP implementations on several hardware platforms. A deeper understanding of the different implementation’s execution profile and performance is crucial for applications as they consider the performance portability implications of adopting a unified memory offloading programming style. This work introduces a benchmark tool to characterize unified memory support in several OepnMP compilers and runtimes, with emphasis on identifying discrepancies between different OpenMP implementations as to how they various memory allocation strategies interact with unified shared memory. The benchmark tool is used to characterize OpenMP compilers on three leading High Performance Computing platforms supporting different CPU and device architectures. The benchmark tool is used to assess the impact of enabling unified shared memory on the performance of memory-bound code, highlighting implementation differences that should be accounted for when applications consider performance portability across platforms and compilers.
Abstract AI-advised Decision Making is a form of human-autonomy teaming in which an AI recommender system suggests a solution to a human operator, who is responsible for the final decision. This work seeks to examine the importance of judgement and shared situation awareness between humans and automated agents when interacting together in the form of a recommender systems. We propose manipulating both human judgement and shared situation awareness by providing the human decision maker with relevant information that the automated agent (AI), in the form of a recommender system, uses to generate possible courses of action. This paper presents the results of a two-phase between-subjects study in which participants and a recommender system jointly make a high-stakes decision. We varied the amount of relevant information the participant had, the assessment technique of the proposed solution, and the reliability of the recommender system. Findings indicate that this technique of supporting the human’s judgement and establishing a shared situation awareness is effective in (1) boosting the human decision maker’s situation awareness and task performance, (2) calibrating their trust in AI teammates, and (3) reducing overreliance on an AI partner. Additionally, participants were able to pinpoint the limitations and boundaries of the AI partner’s capabilities. They were able to discern situations where the AI’s recommendations could be trusted versus instances when they should not rely on the AI’s advice. This work proposes and validates a way to provide model-agnostic transparency into recommender systems that can support the human decision maker and lead to improved team performance.
Abstract With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high-throughput access to a HEP-specific GNN and report on the outcome.
Suicide is a leading cause of death worldwide, and non-fatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium. The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via mtCOJO, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders and other known risk factors. Results: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with non-psychiatric traits remained largely unchanged. Conclusions: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.