,The Impact of Human-Automation Collaboration in Decentralized Multiple Unmanned Vehicle Control

For future systems that require one or a small team of operators to supervise a network of automated agents, automated planners are critical since they are faster than humans for path planning and resource allocation in multivariate, dynamic, time-pressured environments. However, such planners can be brittle and unable to respond to emergent events. Human operators can aid such systems by bringing their knowledge-based reasoning and experience to bear. Given a decentralized task planner and a goal-based operator interface for a network of unmanned vehicles in a search, track, and neutralize mission, we demonstrate with a human-on-the-loop experiment that humans guiding these decentralized planners improved system performance by up to 50%. However, those tasks that required precise and rapid calculations were not significantly improved with human aid. Thus, there is a shared space in such complex missions for human–automation collaboration

Operator Objective Function Guidance for a Real-time Unmanned Vehicle Scheduling Algorithm

Advances in autonomy have made it possible to invert the typical operator-to-unmanned-vehicle ratio so that asingle operator can now control multiple heterogeneous unmanned vehicles. Algorithms used in unmanned-vehicle path planning and task allocation typically have an objective function that only takes into account variables initially identified by designers with set weightings. This can make the algorithm seemingly opaque to an operator and brittle under changing mission priorities. To address these issues, it is proposed that allowing operators to dynamically modify objective function weightings of an automated planner during a mission can have performance benefits. A multiple-unmanned-vehicle simulation test bed was modified so that operators could either choose one variable or choose any combination of equally weighted variables for the automated planner to use in evaluating mission plans. Results from a human-participant experiment showed that operators rated their performance and confidence highest when using the dynamic objective function with multiple objectives. Allowing operators to adjust multiple objectives resulted in enhanced situational awareness, increased spare mental capacity, fewer interventions to modify the objective function, and no significant differences in mission performance. Adding this form of flexibility and transparency to automation in future unmanned vehicle systems could improve performance, engender operator trust, and reduce errors.Aurora Flight Sciences, U.S. Office of Naval Researc

Surveying Standard Model Flux Vacua on T6/Z2×Z2T^6/Z_2\times Z_2

We consider the SU(2)LxSU(2)R Standard Model brane embedding in an orientifold of T6/Z2xZ2. Within defined limits, we construct all such Standard Model brane embeddings and determine the relative number of flux vacua for each construction. Supersymmetry preserving brane recombination in the hidden sector enables us to identify many solutions with high flux. We discuss in detail the phenomenology of one model which is likely to dominate the counting of vacua. While Kahler moduli stabilization remains to be fully understood, we define the criteria necessary for generic constructions to have fixed moduli.Comment: 30 pages, LaTeX, v2: added reference

Intelligent Cooperative Control Architecture: A Framework for Performance Improvement Using Safe Learning

Planning for multi-agent systems such as task assignment for teams of limited-fuel unmanned aerial vehicles (UAVs) is challenging due to uncertainties in the assumed models and the very large size of the planning space. Researchers have developed fast cooperative planners based on simple models (e.g., linear and deterministic dynamics), yet inaccuracies in assumed models will impact the resulting performance. Learning techniques are capable of adapting the model and providing better policies asymptotically compared to cooperative planners, yet they often violate the safety conditions of the system due to their exploratory nature. Moreover they frequently require an impractically large number of interactions to perform well. This paper introduces the intelligent Cooperative Control Architecture (iCCA) as a framework for combining cooperative planners and reinforcement learning techniques. iCCA improves the policy of the cooperative planner, while reduces the risk and sample complexity of the learner. Empirical results in gridworld and task assignment for fuel-limited UAV domains with problem sizes up to 9 billion state-action pairs verify the advantage of iCCA over pure learning and planning strategies

Organic Matter Clogging Results in Undeveloped Hardpan and Soil Mineral Leakage in the Rice Terraces in the Philippine Cordilleras

Rice terraces in Cordillera, Philippines, a world cultural heritage site, are threatened by the risk of collapse. It is crucial to manage these rice terraces for their conservation, while simultaneously practicing traditional farming. We examined the soil environment and investigated its effects on rice terrace conservation, by focusing on the hardpan condition; infiltration process, which is related to the collapse of rice terraces; and soil nutrition conditions in these sites. Field survey and soil analysis revealed that in areas where the hardpan was not sufficiently developed and water infiltration was effectively suppressed, organic matter content was significantly high, suggesting organic matter clogging. In these rice terraces, the amounts of P, K, Ca, and Mn were significantly low, showing the mineral leaching under reductive soil conditions. Therefore, hardpan formation, rather than organic matter clogging, is essential for the suppression of infiltration and prevention of potential terrace collapse. Because hardpan formation or organic matter clogging cannot be identified from the surface of flooded rice paddies, it is difficult to identify the influencing factor. Thus, we suggest that the hard soil layer should be checked before the planting season and drainage is allowed after the cropping season in the rainy season

The Economic Consequences of Voluntary Environmental Information Disclosure

A significant body of accounting and finance literature has witnessed an increase in the demandfor company’s environmental information over the past few decades. Using data of Australian companiesfrom 1998 to 2000, this study provides an analysis of environmental disclosure in companies’ annual reports.The evidence indicates that companies do respond to the increased demand for environmental disclosure byproviding more environmental-related information in their annual report. Although the requirement todisclose environmental information in annual reports has not kept pace with the legislative reform, there hasbeen a significant increase of these disclosures from 1998 to 2000. We also find that most of the disclosuresare covered in the Director’s Report across industries. This paper also evaluates the economic consequencesof these disclosures. The importance of the environmental disclosures to the value of the company isexamined by investigating the relationship between the environmental information disclosed in the annualreport and the company’s share price

MicroRNA inhibition using antimiRs in acute human brain tissue sections

Antisense inhibition of microRNAs is an emerging preclinical approach to pharmacoresistant epilepsy. A leading candidate is an "antimiR" targeting microRNA-134 (ant-134), but testing to date has used rodent models. Here, we develop an antimiR testing platform in human brain tissue sections. Brain specimens were obtained from patients undergoing resective surgery to treat pharmacoresistant epilepsy. Neocortical specimens were submerged in modified artificial cerebrospinal fluid (ACSF) and dissected for clinical neuropathological examination, and unused material was transferred for sectioning. Individual sections were incubated in oxygenated ACSF, containing either ant-134 or a nontargeting control antimiR, for 24 h at room temperature. RNA integrity was assessed using BioAnalyzer processing, and individual miRNA levels were measured using quantitative reverse transcriptase polymerase chain reaction. Specimens transported in ACSF could be used for neuropathological diagnosis and had good RNA integrity. Ant-134 mediated a dose-dependent knockdown of miR-134, with approximately 75% reduction of miR-134 at 1 μmol L-1 and 90% reduction at 3 μmol L-1 . These doses did not have off-target effects on expression of a selection of three other miRNAs. This is the first demonstration of ant-134 effects in live human brain tissues. The findings lend further support to the preclinical development of a therapy that targets miR-134 and offer a flexible platform for the preclinical testing of antimiRs, and other antisense oligonucleotide therapeutics, in human brain

Airway "Resistotypes" and Clinical Outcomes in Bronchiectasis.

Rationale: Chronic infection and inflammation shapes the airway microbiome in bronchiectasis. Utilizing whole-genome shotgun metagenomics to analyze the airway resistome provides insight into interplay between microbes, resistance genes, and clinical outcomes. Objectives: To apply whole-genome shotgun metagenomics to the airway microbiome in bronchiectasis to highlight a diverse pool of antimicrobial resistance genes: the "resistome," the clinical significance of which remains unclear. Methods: Individuals with bronchiectasis were prospectively recruited into cross-sectional and longitudinal cohorts (n = 280), including the international multicenter cross-sectional Cohort of Asian and Matched European Bronchiectasis 2 (CAMEB 2) study (n = 251) and two independent cohorts, one describing patients experiencing acute exacerbation and a further cohort of patients undergoing Pseudomonas aeruginosa eradication treatment. Sputum was subjected to metagenomic sequencing, and the bronchiectasis resistome was evaluated in association with clinical outcomes and underlying host microbiomes. Measurements and Main Results: The bronchiectasis resistome features a unique resistance gene profile and increased counts of aminoglycoside, bicyclomycin, phenicol, triclosan, and multidrug resistance genes. Longitudinally, it exhibits within-patient stability over time and during exacerbations despite between-patient heterogeneity. Proportional differences in baseline resistome profiles, including increased macrolide and multidrug resistance genes, associate with shorter intervals to the next exacerbation, whereas distinct resistome archetypes associate with frequent exacerbations, poorer lung function, geographic origin, and the host microbiome. Unsupervised analysis of resistome profiles identified two clinically relevant "resistotypes," RT1 and RT2, the latter characterized by poor clinical outcomes, increased multidrug resistance, and P. aeruginosa. Successful targeted eradication in P. aeruginosa-colonized individuals mediated reversion from RT2 to RT1, a more clinically favorable resistome profile demonstrating reduced resistance gene diversity. Conclusions: The bronchiectasis resistome associates with clinical outcomes, geographic origin, and the underlying host microbiome. Bronchiectasis resistotypes link to clinical disease and are modifiable through targeted antimicrobial therapy