Deep Learning-Based Robotic Perception for Adaptive Facility Disinfection

Hospitals, schools, airports, and other environments built for mass gatherings can become hot spots for microbial pathogen colonization, transmission, and exposure, greatly accelerating the spread of infectious diseases across communities, cities, nations, and the world. Outbreaks of infectious diseases impose huge burdens on our society. Mitigating the spread of infectious pathogens within mass-gathering facilities requires routine cleaning and disinfection, which are primarily performed by cleaning staff under current practice. However, manual disinfection is limited in terms of both effectiveness and efficiency, as it is labor-intensive, time-consuming, and health-undermining. While existing studies have developed a variety of robotic systems for disinfecting contaminated surfaces, those systems are not adequate for intelligent, precise, and environmentally adaptive disinfection. They are also difficult to deploy in mass-gathering infrastructure facilities, given the high volume of occupants. Therefore, there is a critical need to develop an adaptive robot system capable of complete and efficient indoor disinfection. The overarching goal of this research is to develop an artificial intelligence (AI)-enabled robotic system that adapts to ambient environments and social contexts for precise and efficient disinfection. This would maintain environmental hygiene and health, reduce unnecessary labor costs for cleaning, and mitigate opportunity costs incurred from infections. To these ends, this dissertation first develops a multi-classifier decision fusion method, which integrates scene graph and visual information, in order to recognize patterns in human activity in infrastructure facilities. Next, a deep-learning-based method is proposed for detecting and classifying indoor objects, and a new mechanism is developed to map detected objects in 3D maps. A novel framework is then developed to detect and segment object affordance and to project them into a 3D semantic map for precise disinfection. Subsequently, a novel deep-learning network, which integrates multi-scale features and multi-level features, and an encoder network are developed to recognize the materials of surfaces requiring disinfection. Finally, a novel computational method is developed to link the recognition of object surface information to robot disinfection actions with optimal disinfection parameters

GRB/GW association: Long-short GRB candidates, time-lag, measuring gravitational wave velocity and testing Einstein's equivalence principle

Short-duration gamma-ray bursts (SGRBs) are widely believed to be powered by the mergers of compact binaries, such as binary neutron stars or possibly neutron star-black hole binaries. Though the prospect of detecting SGRBs with gravitational wave (GW) signals by the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO)/VIRGO network is promising, no known SGRB has been found within the expected advanced LIGO/VIRGO sensitivity range for binary neutron star systems. We find, however, that the two long-short GRBs (GRB 060505 and GRB 060614) may be within the horizon of advanced GW detectors. In the upcoming era of GW astronomy, the merger origin of some long-short GRBs, as favored by the macronova signature displayed in GRB 060614, can be unambiguously tested. The model-dependent time lags between the merger and the onset of the prompt emission of the GRB are estimated. The comparison of such time lags between model predictions and the real data expected in the era of the GW astronomy would be helpful in revealing the physical processes taking place at the central engine (including the launch of the relativistic outflow, the emergence of the outflow from the dense material ejected during the merger, and the radiation of gamma rays). We also show that the speed of GWs, with or without a simultaneous test of Einstein's equivalence principle, can be directly measured to an accuracy of $\sim 3\times 10^{-8}~{\rm cm~s^{-1}}$ or even better in the advanced LIGO/VIRGO era. The Astrophysical Journal, VolumeComment: 12 pages, 3 figures, published in The Astrophysical Journa

Consumer Demand for Ahi Poke (Raw Tuna Salad) in Hawaii

Ahi poke (raw tuna salad) has significant role in Hawaii culture and economy. A consumer survey in Hawaii was used to examine consumers’ purchasing intentions of ahi poke. A censored analysis was conducted to analyze the demand and tie with various consumer characteristics. Results show that many consumer eat ahi poke frequently and different consumer profiles will lead to large differences in their demand. Information obtained in study may help producers and retailers to better target their marketing strategies and increase sales.Ahi Poke, Demand, Hawaii, Tobit, Agribusiness, Agricultural and Food Policy, Consumer/Household Economics, Demand and Price Analysis, Marketing, Q13, D12,

Anodic behavior of semiconducting InSb single crystals in acidic solutions

Undoped, semiconducting single, n-type, InSb crystals were used to study the different etching behavior of the inverse {111} planes, to determine the apparent electron number of InSb under anodic dissolution (which corresponds to the sum of the absolute values of the oxidation numbers) and to investigate whether or not the inverse {111} planes show potential differences. The In {111}, Sb {111[repeating]}, {110} and {100} faces were highly polished, etched and immersed in various acidic solutions. Several new etch patterns of the inverse {111} faces were observed in giving other possibilities to distinguish both of the planes. The significant differences in potentials between the inverse {111} faces are exhibited in some acidic solutions. The potential of Sb {111[repeating]} is always less noble that of In {111}. The addition of oxidizing or reducing agents does not shift the sequence in potentials of the inverse {111} faces within at least a certain range of current density. The apparent electron number of InSb dissolving anodically in acidic solutions is close to 6 at low current densities. The deviation from the value of 6, for InSb dissolving anodically in 2 N HCl at current densities higher than ~40 ma/cm², is found to be due to surface disintegration after a dark protective film (mainly Sb₄O₅Cl₂) is formed on the surface of the specimen. Sb particles, colloidal in origin and embedded in this corrosion product, are responsible for the dark color. It is also found that the dissolution potentials of the inverse {111} faces of InSb are much closer to that of metallic Sb than to metallic In, indicating that the latter undergoes a larger chemical change during the InSb formation than Sb. From the observed Tafel behavior and the calculated activation energies of InSb undergoing anodic dissolution in 2 N HCl, within the current density range of ~3 x 10⁻² to ~30 ma/cm², it is concluded that the rate determining step is a one electron discharge and is the same on all four low-indexed crystallographic planes --Abstract, Pages ii-iii

Reduction and construction of Poisson quasi-Nijenhuis manifolds with background

We extend the Falceto-Zambon version of Marsden-Ratiu Poisson reduction to Poisson quasi-Nijenhuis structures with background on manifolds. We define gauge transformations of Poisson quasi-Nijenhuis structures with background, study some of their properties and show that they are compatible with reduction procedure. We use gauge transformations to construct Poisson quasi-Nijenhuis structures with background.Comment: to appear in IJGMM