The Future Impacts of Autonomous Aid on Disaster Relief Efforts

Natural disasters and other catastrophes have significantly increased in recent years (Oishi & Komiya, 2017). Currently, resources available to succor in response to cataclysms are limited. Humans save lives, while ultimately risking their own. In order to bypass this risk, autonomous robot programming is essential. Research and advocacy regarding autonomous aid in the scientific community has yet to be fully addressed (Oishi & Komiya, 2017). Because of this, first responders, firefighters, and policemen are perpetually endangered. Furthermore, technological contributions would also eliminate human error, promote productivity, and stimulate collaboration on matters unable to be solved autonomously. A robot prototype, designed to retrieve 6x6х6 inch cubes, was programmed to a controller, but also operated autonomously. Despite the controller being beneficial for specific functions, autonomous programming proved to be advantageous when used applicably. While the initial task was direct, the knowledge acquired from the project possesses the potential to enhance future ventures seeking to aid disaster relief

Explaining Reinforcement Learning to Mere Mortals: An Empirical Study

We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization, saliency maps (the focus of attention), and a more recent explanation type, reward-decomposition bars (predictions of future types of rewards). We designed a 124 participant, four-treatment experiment to compare participants' mental models of an RL agent in a simple Real-Time Strategy (RTS) game. Our results show that the combination of both saliency and reward bars were needed to achieve a statistically significant improvement in mental model score over the control. In addition, our qualitative analysis of the data reveals a number of effects for further study.Comment: 7 page

Total- and Monomethyl-Mercury and Major Ions in Coastal California Fog Water: Results from Two Years of Sampling on Land and at Sea

Marine fog water samples were collected over two summers (2014–2015) with active strand collectors (CASCC) at eight coastal sites from Humboldt to Monterey counties in California, USA, and on four ocean cruises along the California coastline in order to investigate mercury (Hg) cycling at the ocean-atmosphere-land interface. The mean concentration of monomethylmercury (MMHg) in fog water across terrestrial sites for both years was 1.6 ± 1.9 ng L-1 (\u3c0.01–10.4 ng L-1, N = 149), which corresponds to 5.7% (2.0–10.8%) of total Hg (HgT) in fog. Rain water samples from three sites had mean MMHg concentrations of 0.20 ± 0.12 ng L-1 (N = 5) corresponding to 1.4% of HgT. Fog water samples collected at sea had MMHg concentrations of 0.08 ± 0.15 ng L-1 (N = 14) corresponding to 0.4% of HgT. Significantly higher MMHg concentrations in fog were observed at terrestrial sites next to the ocean relative to a site 40 kilometers inland, and the mean difference was 1.6 ng L-1. Using a rate constant for photo-demethylation of MMHg of -0.022 h-1 based on previous demethylation experiments and a coastal-inland fog transport time of 12 hours, a mean difference of only 0.5 ng L-1 of MMHg was predicted between coastal and inland sites, indicating other unknown source and/or sink pathways are important for MMHg in fog. Fog water deposition to a standard passive 1.00 m2 fog collector at six terrestrial sites averaged 0.10 ± 0.07 L m-2 d-1, which was ∼2% of typical rainwater deposition in this area. Mean air-surface fog water fluxes of MMHg and HgT were then calculated to be 34 ± 40 ng m-2 y-1 and 546 ± 581 ng m-2 y-1, respectively. These correspond to 33% and 13% of the rain fluxes, respectively

Body Composition Estimation in Youth Athletes: Agreement Between Two-Component Methods

Body composition techniques such as skinfold measurements, air displacement plethysmography, and underwater weighing are commonly performed in athletic populations, particularly in youth athletes who may not have access to other laboratory methods. However, little is known whether such body composition estimates can be directly compared across techniques. PURPOSE: To determine the agreement between common two-component (2C) body composition techniques. METHODS: 90 youth athletes (Males: 39; Females: 51; Age: 18.2 ± 2.4 years; Height: 172.0 ± 9.9 cm; Body Mass: 69.0 ± 12.5 kg; Underwater Weighing [UWW] Body Fat Percentage [%BF]: 20.2 ± 7.0%) participated in this study. 2C estimates of %BF were determined via UWW, air displacement plethysmography (ADP), and 7-site skinfold (SKF) using the applicable Jackson-Pollock equation. Body mass was measured via calibrated scale. Agreement between methods was quantified using Lin’s concordance correlation coefficients (CCC). Estimates of body fat percentage were also compared between techniques using paired samples t-tests (α \u3c 0.05) and equivalence testing, with the threshold of equivalence set at ± 2% body fat. RESULTS: Mean ± SD %BF estimates were 20.2 ± 7.0% (UWW), 18.7 ± 7.3% (ADP), and 16.1 ± 7.2% (SKF). Mean differences between methods were 1.6% [95% CI: 0.8, 2.3] for UWW vs. ADP, 4.1% [95% CI: 3.4, 4.8] for UWW vs. SKF, and 2.6% [95% CI: 1.9, 3.2] for ADP vs. SKF. Paired-samples t-tests revealed significant differences between %BF estimates for each comparison. Likewise, no methods were found to be equivalent, based on a ± 2% BF equivalence range. CCC values were 0.855 for UWW vs. ADP, 0.759 for UWW vs. SKF, and 0.844 for ADP vs. SKF. CONCLUSION: This study suggests limited agreement between 2C %BF estimates derived from three common assessment techniques. Hypothesis testing revealed significant differences between methods, and the magnitude of these differences resulted in non-equivalence at ± 2% BF. Based on these results, it appears that direct comparisons between 2C %BF estimates from these different techniques should be avoided if possible. Though the magnitude of the differences between techniques may be acceptable in certain contexts, coaches and clinicians should strive to utilize the same assessment methodology when examining and comparing body composition results across time

Assessment of Youth Athlete Body Composition using Bioimpedance Techniques as Compared to a Three-Compartment Model

Body composition is believed to contribute to success in many sports. For this reason, assessment of body composition with various devices is commonly performed. The agreement between devices warrants exploration, particularly in groups with limited data, such as youth athletes. PURPOSE: To determine the agreement between a 3-compartment model (3C) and bioelectrical impedance analysis (BIA) devices for assessing body composition in youth athletes. METHODS: The body composition of 90 youth athletes was evaluated (51 F, 39 M; age: 18.2±2.4 y; body mass: 69.0±12.5 kg; height: 172.0±9.9 cm; BMI: 23.2±3.2 kg/m2, BF%: 19.7±6.9%). 3C values were produced using body volume from an underwater weighing system, body water from bioimpedance spectroscopy (ImpediMed SFB7), and body mass from a calibrated scale. Additionally, three BIA techniques were performed: a consumer-grade standing hand-to-foot analyzer (InBody H2ON; BIAINBODY), a consumer-grade standing foot-to-foot analyzer (Tanita BF-680W; BIATANITA), and a laboratory-grade supine hand-to-foot analyzer (RJL Quantum IV; BIARJL). Bioimpedance from BIARJL was inserted into the Matias FFM equation for athletes. BIA BF% and FFM values were compared to 3C values using paired t-tests, Pearson correlations, and the standard error of the estimate (SEE). RESULTS: 3C BF% estimates did not differ from BIAINBODY (-0.9%, 95% CI: -2.1, 0.2) or BIARJL (0.2%, 95% CI: -0.8, 1.2%). However, BF% was underestimated by BIATANITA relative to 3C (-2.7%, 95% CI: -4.1, -1.2). All BIA BF% estimates were significantly correlated with 3C (r: 0.59 to 0.73; R2: 0.35 to 0.53, pINBODY (0.8 kg, 95% CI: -0.1, 1.6) or BIARJL (0.1 kg, 95% CI: -0.6, 0.9). However, FFM was overestimated by BIATANITA relative to 3C (1.8 kg, 95% CI: 0.7, 2.8). All BIA FFM estimates were significantly correlated with 3C (r: 0.92 to 0.97; R2: 0.85 to 0.93, pCONCLUSION: This study demonstrated potentially acceptable agreement between 3C BF% and FFM estimates and those from BIAINBODY and BIARJL, with the athlete-specific equation used with BIARJL demonstrating the best performance. In contrast, the consumer-grade foot-to-foot analyzer underestimated BF% and overestimated FFM. These findings may help inform practical and accurate body composition estimation in youth athletes

Effects of Mesenchymal Stem Cell and Growth Factor Delivery on Cartilage Repair in a Mini-Pig Model

We have recently shown that mesenchymal stem cells (MSCs) embedded in a hyaluronic acid (HA) hydrogel and exposed to chondrogenic factors (transforming growth factor–β3 [TGF-β3]) produce a cartilage-like tissue in vitro. The current objective was to determine if these same factors could be combined immediately prior to implantation to induce a superior healing response in vivo relative to the hydrogel alone

Reachability analysis for AWS-based networks

Cloud services provide the ability to provision virtual networked infrastructure on demand over the Internet. The rapid growth of these virtually provisioned cloud networks has increased the demand for automated reasoning tools capable of identifying misconfigurations or security vulnerabilities. This type of automation gives customers the assurance they need to deploy sensitive workloads. It can also reduce the cost and time-to-market for regulated customers looking to establish compliance certification for cloud-based applications. In this industrial case-study, we describe a new network reachability reasoning tool, called Tiros, that uses off-the-shelf automated theorem proving tools to fill this need. Tiros is the foundation of a recently introduced network security analysis feature in the Amazon Inspector service now available to millions of customers building applications in the cloud. Tiros is also used within Amazon Web Services (AWS) to automate the checking of compliance certification and adherence to security invariants for many AWS services that build on existing AWS networking features