Research Corporation of The University of Hawaii

Abstract Urban ecosystems are widely hypothesized to be more ecologically homogeneous than natural ecosystems. We argue that urban plant communities assemble from a complex mix of horticultural and regional species pools, and evaluate the homogenization hypothesis by comparing cultivated and spontaneously occurring urban vegetation to natural area vegetation across seven major U.S. cities. There was limited support for homogenization of urban diversity , as the cultivated and spontaneous yard flora had greater numbers of species than natural areas, and cultivated phylogenetic diversity was also greater. However, urban yards showed evidence of homogenization of composition and structure . Yards were compositionally more similar across regions than were natural areas, and tree density was less variable in yards than in comparable natural areas. This homogenization of biodiversity likely reflects similar horticultural source pools, homeowner preferences, and management practices across U.S. cities.

Mosquito-borne avian diseases, principally avian malaria (Plasmodium relictum Grassi and Feletti) and avian pox (Avipoxvirus sp.) have been implicated as the key limiting factor associated with recent declines of endemic avifauna in the Hawaiian Island archipelago. We present data on the relative abundance, infection status, and spatial distribution of the primary mosquito vector Culex quinquefasciatus Say (Diptera: Culicidae) across a mixed, residential-agricultural community adjacent to Hawai'i Volcanoes National Park on Hawai'i Island. We modeled the effect of agriculture and forest fragmentation in determining relative abundance of adult Cx. quinquefasciatus in Volcano Village, and we implement our statistical model in a geographic information system to generate a probability of mosquito capture prediction surface for the study area. Our model was based on biweekly captures of adult mosquitoes from 20 locations within Volcano Village from October 2001 to April 2003. We used mixed effects logistic regression to model the probability of capturing a mosquito, and we developed a set of 17 competing models a priori to specifically evaluate the effect of agriculture and fragmentation (i.e., residential landscapes) at two spatial scales. In total, 2,126 mosquitoes were captured in CO2-baited traps with an average probability of 0.27 (SE = 0.10) of capturing one or more mosquitoes per trap night. Twelve percent of mosquitoes captured were infected with P. relictum. Our data indicate that agricultural lands and forest fragmentation significantly increase the probability of mosquito capture. The prediction surface identified areas along the Hawai'i Volcanoes National Park boundary that may have high relative abundance of the vector. Our data document the potential of avian malaria transmission in residential-agricultural landscapes and support the need for vector management that extends beyond reserve boundaries and considers a reserve's spatial position in a highly heterogeneous landscape.

It has been shown that the rate of decomposition of methyl thiolate species on copper is accelerated by sliding on a methyl thiolate covered surface in ultrahigh vacuum at room temperature. The reaction produces small gas-phase hydrocarbons and deposits sulfur on the surface. Here, a new ReaxFF potential was developed to enable investigation of the molecular processes that induce this mechanochemical reaction by using density functional theory calculations to tune force field parameters for the model system. Various processes, including volumetric expansion/compression of CuS, CuS2, and Cu2S unit cells; bond dissociation of Cu–S and valence angle bending of Cu–S–C; the binding energies of SCH3, CH3, and S atoms on a Cu surface; and energy for the decomposition of methyl thiolate molecular species on copper, were used to identify the new ReaxFF parameters. Molecular dynamics simulations of the reactions of adsorbed methyl thiolate species at various temperatures were performed to demonstrate the validity of the new potential and to study the thermal reaction pathways. It was found that reaction is initiated by C–S bond scission, consistent with experiments, and that the resulting methyl species diffuse on the surface and combine to desorb ethane, also as found experimentally.

SUMMARY Nodamura virus multiplied and caused paralysis and death when injected into the thoraces of adult Aedes albopictus and Toxorhynchites amboinensis mosquitoes but not when similarly injected into Culex quinquefasciatus adults. A. albopictus also became infected after ingesting a Nodamura virus suspension or after immersion in a virus suspension as larvae, but they did not die. Head squash preparations of the injected insects, examined by indirect fluorescent antibody technique, showed large amounts of Nodamura virus antigen in the brain regardless of the mode of infection. Nodamura virus was isolated from and titrated in mosquito cell (AP-61) cultures. Comparative titrations indicate that this method of assay is more sensitive than intracerebral inoculation of infant mice.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

Tests for Epstein-Barr virus (EBV) capsid antibody were carried out on 115 children, aged 4-32 months, on admission to and discharge from the Junior Village nursery, Washington, D.C. Forty-three children positive on admission remained positive at discharge, 44/72 negative on admission remained negative at discharge, and 28/72 negative on admission converted to positive at discharge. Age, sex and season did not appear to influence the rate of EBV seroconversion among the nursery children. The only identifiable factor that significantly increased the rate of EBV seroconversion was the duration of nursery residency. The rates were 1/9 (11%), 1/29 (3%), and 11/16 (69%) among children residing for 1.5-2.4, 2.5-4.4 and 4.5-7.4 months, respectively. Children residing in the nursery for 1.5-4.4 months and children residing at home had similar EBV-seroconversion rates. For children residing in the nursery for 4.5-7.4 months, however, the observed EBV-seroconversion rate was much higher than the rate estimated for children living at home.

This paper describes a coverage-based path planning algorithm for multiple robotic agents with the application on the automated inspection of an unknown 2D environment. The proposed path planning algorithm determines a motion path that a robotic agent will follow to sweep and survey all areas of the unknown environment, which is enclosed by the known boundary. The 2D unknown environment is decomposed into a union of simplices using the principle of Delaunay triangulation. The area coverage is equivalent to design a path for a robotic agent to follow and visit all simplices subject to certain mission constraints. A hierarchical mission planner is designed to allocate mission tasks among multiple agents in each level and pass information down to the next level along the hierarchy. The proposed path planning algorithm has been tested and evaluated on the problem of planning path for two types of robotic agents - flying agents and crawling agents in a two-tier hierarchical mission planner to cover various unknown 2D environments.

In this paper, an approach to fault diagnosis in a nonlinear stochastic dynamic system is proposed using the interacting multiple particle filtering (IMPF) algorithm. The fault diagnostic approach is formulated as a hybrid multiple- model estimation scheme. The proposed diagnostic approach provides an integrated framework to estimate the system's current operational or faulty mode, as well as the unmeasured state variables in the system. Particle filtering algorithm is used to statistically model the underlying dynamics of a nonlinear/non-Gaussian stochastic system. A set of models is assumed to present the possible system behavior pattern or modes. A bank of particle filters runs in parallel, each based on a particular mode, to obtain mode-conditional estimates according to the probabilistically weighting scheme. The interaction among particle filters allows estimation from multiple filters to be fused in a principled manner. The simulation results on a highly nonlinear system are provided which demonstrate the effectiveness of the proposed method by comparing it with other nonlinear estimation techniques (extended Kalman filter (EKF) and unscented Kalman filter (UKF)-based).

Two cell lines (TA-42 and TA-9) established from Toxorhynchites amboinensis eggs and larvae, respectively, are described. The TA-42 cells are epithelioid in character, while the TA-9 cells are fibroblastic in appearance. Both were initiated and grown in MM/VPI2 medium. Both cell types form a monolayer on glass or plastic. Their origin and identity were confirmed by isozyme analyses. Their susceptibility to infection with 23 different arboviruses was tested. Eleven of the viruses replicated in the TA-42 cells, while 13 grew in the TA-9 cells. Highest postinfection virus titers in both cell lines were obtained with 2 orbiviruses, Changuinola and Corriparta. No cytopathic effect was produced in either cell line by any of the viruses.

In this paper, a fault detection, identification and estimation approach has been developed for the condition monitoring of the electro-hydraulic actuator (EHA) system using the multiple-model (MM) estimation algorithm. The MM estimation algorithm makes use of the extended Kalman filter (EKF) technique to generate estimates of states and key physical parameters, which are related to faults in the EHA system. The proposed fault detection and identification (FDI) is formulated as a hybrid interacting multiple-model estimation scheme. The interaction scheme between multiple models is introduced into the MM estimation algorithm to yield more robust detection and estimation. Estimates of the key physical parameters in the EHA system are assessed against baseline values and fused with the FDI results for higher level monitoring purposes. Two parameters of interests, namely torque motor equivalent resistance and the effective bulk modulus are investigated for the EHA system condition monitoring purpose. The simulation results highlight the considerable potential of the proposed technique for achieving improved condition monitoring of the EHA system.

This paper compares the differences in power generation and resident experience between S-shape and M-shape photovoltaic (PV) systems at the same location with the same number of modules. First, the sky anisotropy model (Hay-Davies-Klucher-Reindl, HDKR) is used to simulate solar irradiance (direct, diffuse, and reflection radiation) received by solar modules based on the laws of movement between the Earth and the Sun in the solar system. Next, the annual output power of two different PV system arrangements is calculated based on the solar irradiation received. Furthermore, because the solar module receives three types of solar radiation, interference is added to each of the three parts one by one to simulate shadows cast by moving objects such as birds, leaves, or clouds. Subsequently, off-grid and grid-connected power consumption models are established, and two different photovoltaic power generation systems are used at the power generation end. Lastly, the photovoltaic systems were tested based on residents’ electricity consumption habits, considering both off-grid and grid-connected households. Among them, the grid-connected model adopts the Particle Swarm Optimization (PSO) algorithm to generate an intelligent and flexible power consumption strategy. The results, based on data from Naha, Japan, show that with the same number of modules, the M-shape arrangement generates less annual power than the S-shape. In summer, though, the M-shape can generate more power, and the two structures have similar immunity to disturbances. The S-shape and M-shape have little difference in the power consumption experience. However, the general applicability of these results to other regions remains uncertain, and further testing is needed to account for environmental variations.

A history of leptospirosis research in Hawaii is presented, beginning with the first published work in 1937. This account traces the leading researchers who described the organism and the disease, the diagnostic tests developed and used, the reservoir animals identified, methods of disease transmission discovered, prevention programs developed in the state, and research into more effective disease detection and prevention.

This paper discusses the use of unsupervised learning and localized modeling to identify nonlinear dynamical systems from empirical series data. A finite-order nonlinear autoregressive (AR) model is constructed to capture the system dynamics. The embedded input space for the nonlinear AR model is partitioned into overlapped regions that are fine enough so that localized modeling techniques, such as local linear modeling, can approximate system dynamics well in each region. Subsequently, unsupervised learning, such as hierarchical clustering analysis, is used for partitioning the embedded input space to achieve the tradeoff between the model complexity and the approximation error. The performance of the proposed approach is evaluated on two numerical examples: (i) time series prediction; (ii) identification of SISO system. Simulation results demonstrate that the proposed approach can capture the nonlinear system dynamics well.

Purpose The purpose of this paper is to understand the dynamics of inclusive approaches to housing development programs directed at supporting low-income communities. Design/methodology/approach This study uses a mixed-methods approach by employing a combination of case study and survey methods, whereby the development process is studied through qualitative approaches and specific determinant comparisons of quantitative Z -tests. This study provides data from key informants: end-users (ten occupants), leaders of community-based organizations (2), and supporting non-governmental organizations (2). Findings These results indicate that an inclusive approach is more likely be able to provide low-income households with access to a variety of key resources that are identified as housing development priorities, particularly when compared with the supply-side approaches currently being promoted. Practical implications This study helps to encourage policymakers to think about more targeted and facilitative processes to meet the needs of public housing in Indonesia, a challenge that has resulted in ironic effects, and has not met the important challenges in providing access that is adequate for the people of Indonesia. Originality/value The current study provides data that provide evidence of positive value of inclusive approach to response the equitable issues in housing provision, particularly in Indonesia.

OBJECTIVE: To analyze the clinical characteristics of relapsing polychodritis (RP). METHODS: Clinical features and laboratory data of 131 patients with RP were analyzed retrospectively. RESULTS: The average age at onset was (44 ± 14) years (ranging from 9 to 76). Male to female ratio was 1.22:1. The most common onset symptom was respiratory system symptom (61.8%), followed by auricle lesions (16.0%), articular cartilage (15.3%), ocular region (7.6%), internal ear system (5.3%), nasal cartilages (4.6%), skin (2.3%) and nervous system (0.8%). During the whole course of the disease, 81.7% of the patients endured a respiratory system involvement; 77.9% and 56.5% of the patients suffered joint symptoms and nasal cartilages lesions, respectively; the involvement of auricle lesions, ocular region and internal ear system were 54.2%, 35.1% and 33.6%. Comparatively, the occurrence rates of skin lesion (10.7%), neurologic disorder (9.2%) and kidney system lesions (1.5%) were low. Analyzing of the laboratory examination data showed that 38.2% and 43.5% of the patients had an increase in white blood count (WBC) and platelet, 86.3% and 82.4% patients showed raised erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) values. CONCLUSIONS: RP is a multi-system involvement disease, which has complicated clinical manifestations. Respiratory tract is the most common site involved, and respiratory system involvement is a prediction of unfavourable prognosis.

The Adult Literacy Program at Hawaii State Hospital utilized techniques drawn from the Morningside Model of Generative Instruction. In a study involving psychiatric inpatients, participants were taught reading, mathematics, or both over a 6- to 8-month time span. Using the Woodcock-Johnson Psychoeducational Battery-Revised, it was determined that nearly half of the participants demonstrated academic gains during the study period. Further, a behavioral observation system indicated that participants were on-task 80% of the observation time and staff engaged in positive interactions nearly 20% of the observation time. This study is the first of its kind to document any efficacy for academic instruction with a psychiatric inpatient population.

Astronomical solutions form the backbone of accurate dating for geology and paleoclimate studies. Beyond ∼50 Ma, however, the chaos inherent in the solar system makes it impossible to calculate a single unique astronomical solution. Geological data have been used to constrain this chaos in order to arrive at a geologic time scale up to the end-Cretaceous. Here, we adopt and extend this approach into the latest Cretaceous, by re-analyzing the Zumaia and Sopelana composite proxy records from the Maastrichtian. We find that the filtered sum total light reflectance (L*) record is most compatible with the astronomical solution ZB20a. However, these results are sensitive to parameter choices in our algorithm, which we describe in detail. Nevertheless, we present evidence in favor of solution ZB20a for cyclostratigraphy during the latest Cretaceous. Periods with very long eccentricity nodes (VLNs) (low amplitude in the short eccentricity cycle) in the astronomical solutions that coincide with large amplitudes in the short eccentricity-related peaks in the filtered sum proxy record rule out alternatives.

In this paper, a strategy of failure detection, identification and reconfigurable scheme for a dynamic system is proposed. The proposed scheme provides detection and identification of sensor, actuator and/or system component failures, dynamic system state estimation and system performance recovery. Fault detection and identification is carried out using radial basis function (RBF) neural network and interacting multiple model (IMM) estimation. The RBF-NN is used to form a statistical model of nominal or faulty data and estimate the mode-conditional probability densities as the choice of likelihood function. The IMM mechanism carries out the interaction among mode-based filters, update the mode probability and provide the overall state estimate as the control input. Eigenstructure assignment (EA) technique is used for the reconfigurable controller design. The proposed approach is evaluated using an aircraft example, and the results obtained show that it can reliably and accurately detect, identify the faults and recover the impaired dynamic performance to the desired one

A novel multiphysics-decision tree learning algorithm is presented for (1) estimating transport properties in the variably saturated subsurface governed by explicitly coupled equations for water, heat, and solute transport; and (2) providing reduced order simulation of time-dependent pressure head, temperature, and concentration with subsurface properties and/or changing surface boundary conditions. We demonstrate that the proposed algorithm results in about one order of magnitude less error in estimated parameters than the traditional multiphysics numerical inversion. We further show that the multiphysics-decision tree learning algorithm reduces the computational burden associated of traditional parameter estimation with reductions in the number Jacobian sensitivity calculations by as much as 90% and the number of iterations required for convergence by up to an order of magnitude. A natural outcome following convergence of the proposed learning algorithm is the reduced order set of supervised decision tree learning models for predicting the pressure head (Random Forest), and the temperature and concentration (Ensemble Gradient Boosting) given knowledge of time, depth, and remaining pair of state variables. The supervised reduced-order modeling is extended to unsupervised machine learning for the simultaneous prediction of state variables by training a Self-Organized Map using the joint multiphysics-decision tree learning property estimates, stochastic boundary conditions, and subsurface state field measurements. The reduced-order machine learning models provide a computationally efficient alternative for studying the effects of changing subsurface water, heat, and solute transport properties and/or surface boundary conditions on coupled subsurface pressure head, temperature, and concentration.

This is the simulation data used for the paper "A Possible Mechanism for 'Late Phase' in Stellar White-Light Flares" that was accepted for publication on ApJ.