A decision support system for environmental effects monitoring

The Environmental Effects Monitoring (EEM) Statistical Assessment Tool (SAT) Decision Support System (DSS) has been developed to provide a user-friendly data analysis, display and decision support tool for Canada's federal environmental effects monitoring program for the pulp and paper and mining industries. The target users include industries, consultants, regional EEM coordinators, National EEM Office and scientists involved in EEM-related research. The tool allows the assessment of the effects of effluent from industrial or other sources on fish and benthic populations. Effect endpoints, which are used as indicators of potentially important effluent effects, are measured at effluent-exposed sites and are compared statistically to measures at reference sites, in order to determine if changes have occurred and the magnitude of the changes. The main driver of the EEM-SAT DSS is its rule-based expert system. The results are used in assessing the adequacy of existing regulations for protecting aquatic environments.     

Experiences in developing and applying a software engineering technology testbed

A major problem in empirical software engineering is to determine or ensure comparability across multiple sources of empirical data. This paper summarizes experiences in developing and applying a software engineering technology testbed. The testbed was designed to ensure comparability of empirical data used to evaluate alternative software engineering technologies, and to accelerate the technology maturation and transition into project use. The requirements for such software engineering technology testbeds include not only the specifications and code, but also the package of instrumentation, scenario drivers, seeded defects, experimentation guidelines, and comparative effort and defect data needed to facilitate technology evaluation experiments. The requirements and architecture to build a particular software engineering technology testbed to help NASA evaluate its investments in software dependability research and technology have been developed and applied to evaluate a wide range of technologies. The technologies evaluated came from the fields of architecture, testing, state-model checking, and operational envelopes. This paper will present for the first time the requirements and architecture of the software engineering technology testbed. The results of the technology evaluations will be analyzed from a point of view of how researchers benefitted from using the SETT. The researchers just reported how their technology performed in their original findings. The testbed evaluation showed (1) that certain technologies were complementary and cost-effective to apply; (2) that the testbed was cost-effective to use by researchers within a well-specified domain of applicability; (3) that collaboration in testbed use by researchers and the practitioners resulted comparable empirical data and in actions to accelerate technology maturity and transition into project use, as shown in the AcmeStudio evaluation; and (4) that the software engineering technology testbed’s requirements and architecture were suitable for evaluating technologies and accelerating their maturation and transition into project use.

Fiber-optic epoxy composite cure sensor. I. Dependence of refractive index of an autocatalytic reaction epoxy system at 850 nm on temperature and extent of cure

We discuss the behavior of the refractive index of a typical epoxy-aromatic diamine system. Near 850 nm the index of refraction is found to be largely controlled by the density of the epoxy. Models are derived to describe its dependence on temperature and extent of cure. Within the range of temperatures studied, the refractive index decreases linearly with increasing temperature. In addition, as the epoxy is cured, the refractive index increases linearly with conversion to the gel point. From then on, shrinkage in the volume of the epoxy is restricted by local viscosity. Therefore the linear relationship between the refractive index and the extent of cure does not hold beyond the gel point.     

Fiber-optic epoxy composite cure sensor. II. Performance characteristics

The performance of a fiber-optic epoxy composite cure sensor, as previously proposed, depends on the optical properties and the reaction kinetics of the epoxy. The reaction kinetics of a typical epoxy system are presented. It is a third-order autocatalytic reaction with a peak observed in each isothermal reaction-rate curve. A model is derived to describe the performance characteristics of the epoxy cure sensor. If a composite coupon is cured at an isothermal temperature, the sensor signal can be used to predict the time when the gel point occurs and to monitor the cure process. The sensor is also shown to perform well in nonstoichiometric epoxy matrices. In addition the sensor can detect the end of the cure without calibration.     

Application of virtual reality for peritoneal dialysis exchange learning in patients with end-stage renal disease and cognitive impairment

Virtual Reality - Cognitive impairment is not uncommon in patients with end-stage renal disease and can make it more difficult for these patients to carry out peritoneal dialysis (PD) on their own....     

Ferritin Protein Regulates the Degradation of Iron Oxide Nanoparticles

Proteins implicated in iron homeostasis are assumed to be also involved in the cellular processing of iron oxide nanoparticles. In this work, the role of an endogenous iron storage protein—namely the ferritin—is examined in the remediation and biodegradation of magnetic iron oxide nanoparticles. Previous in vivo studies suggest the intracellular transfer of the iron ions released during the degradation of nanoparticles to endogenous protein cages within lysosomal compartments. Here, the capacity of ferritin cages to accommodate and store the degradation products of nanoparticles is investigated in vitro in the physiological acidic environment of the lysosomes. Moreover, it is questioned whether ferritin proteins can play an active role in the degradation of the nanoparticles. The magnetic, colloidal, and structural follow‐up of iron oxide nanoparticles and proteins in lysosome‐like medium confirms the efficient remediation of potentially harmful iron ions generated by nanoparticles within ferritins. The presence of ferritins, however, delays the degradation of particles due to a complex colloidal behavior of the mixture in acidic medium. This study exemplifies the important implications of intracellular proteins in processes of degradation and metabolization of iron oxide nanoparticles.     

Analytical‐Based Methodologies for Examining the In Vitro Absorption,Distribution, Metabolism,and Elimination (ADME) of Silver Nanoparticles

The clinical applications of silver nanoparticles (AgNPs) remain limited due to the lack of well‐established methodologies for studying their nanokinetics. Hereby, the primary goal is to adapt a suite of analytical‐based methodologies for examining the in vitro absorption, distribution, metabolism, and elimination of AgNPs. Vero 76 and HEK 293 cells are exposed to ≈10‐nm spherical AgNPs⁺ and AgNPs^? at relevant concentrations (0–300 µg mL^?1) and times (4–48 h). Absorption: Inductively coupled plasma optical emission spectroscopy (ICP‐OES) demonstrates that the two AgNP formulations are not bioequivalent. For example, different bioavailabilities (C _maximum < 20.7 ± 4% and 6.82 ± 0.4%), absorption times (T _maximum > 48 and ≈24 h), and absorption rate laws (first‐ and zeroth‐order at 300 µg mL^?1) are determined in Vero 76 for AgNPs⁺ and AgNPs^?, respectively. Distribution: Raman and CytoViva hyperspectral imaging show different cellular localizations for AgNPs⁺ and AgNPs^?. Metabolism: Cloud point extraction (CPE)‐tangential flow filtration (TFF) reveal that ≤ 11% ± 4% of the administered, sublethal AgNPs release Ag⁺ and contribute to the observed cytotoxicity. Elimination: ICP‐OES‐CPE suggests that AgNPs are cleared via exocytosis.     

Spontaneously Embedded Scattering Structures in a Flexible Substrate for Light Extraction

A flexible hazy substrate (FHS) with embedded air bubbles to increase light extraction efficiency of organic light‐emitting diodes (OLEDs) is reported. In order to embed the air bubbles in the flexible substrate, micropatterned substrates are fabricated by plasma treatment, and then coated with a planarization layer. During the planarization layer coating, air bubbles are trapped between the substrate and the planarization layer. The haze of the FHS can be controlled from 1.7% to 68.4% by changing the size of micropatterns by adjusting the plasma treatment time. The FHS shows average haze of 68.4%, average total transmittance of 90.3%, and extremely flat surface with average roughness (R _a) of 1.2 nm. Rigorous coupled‐wave analysis and finite‐difference time‐domain simulations are conducted to demonstrate that the air bubbles in the substrate can effectively extract photons that are trapped in the substrate. The FHS increases the power efficiency of OLEDs by 22% and further increases by 91% combined with an external extraction layer. Moreover, the FHS has excellent mechanical flexibility. No defect has been observed after 10 000 bending cycles at bending radius of 4 mm.     

The use of coevolution and the artificial immune system for ensemble learning

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm. An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles may be found using only implicit diversity promotion techniques.