Indoor heterogeneous photochemistry of furfural drives emissions of nitrous acid

People spend approximately 80% of their time indoor, making the understanding of the indoor chemistry an important task for safety. The high surface-area-to-volume ratio characteristic of indoor environments leads the semi-volatile organic compounds (sVOCs) to deposit on the surfaces. Using a long path absorption photometer (LOPAP), this work investigates the formation of nitrous acid (HONO) through the photochemistry of adsorbed nitrate anions and its enhancement by the presence of furfural. Using a high-resolution proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS), this work also investigates the surface emissions of VOCs from irradiated films of furfural and a mix of furfural and nitrate anions. Among the emitted VOCs, 2(5H)-furanone/2-Butenedial was observed at high concentrations, leading to maleic anhydride formation after UV irradiation. Moreover, the addition of potassium nitrate to the film formed NO_x and HONO concentrations up to 10 ppb, which scales to ca. 4 ppb for realistic indoor conditions. This work helps to understand the high levels of HONO and NO_x measured indoors.     

Creating a Student Centered Learning Environment at the University of Denver

A team of faculty members at the University of Denver changed the learning environment in key courses in the Department of Engineering from predominately teacher centered to student centered. Through this funded project new grading methods were implemented, classrooms were renovated and wired with studio layouts to facilitate learning, the Engineering Circuits Laboratory was rewired and instrumented for automated data acquisition and reporting, and two new pedagogical approaches were developed. At the onset of the project, six goals were established related to student learning. The introduction of industry standard hardware and software provided students with unprecedented hands‐on experience and project related activities stimulated faculty innovations in other current and future courses. Assessment results indicate that the new grading system improved the clarity of expectations for students before assignments were given resulting in increased reported motivation for learning in many courses. Even though course GPAs did not always reflect higher achievement on graded work, faculty members firmly believe that deeper understanding was achieved because more complex material was assimilated.     

Immunoregulatory functions of paf-acether. VI. Dual effect on human B cell proliferation

The role of paf-acether (paf), a phospholipid cytokine, in the modulation of human B cell function was investigated. Paf, from 1×10⁻⁵ M to 10⁻⁶ M, decreased B cell proliferation induced by both phorbol myristate acetate (PMA) and anti-IgM antibodies (anti-IgM Ab). By contrast, 1×10⁻⁷ M to 1×10⁻⁹ M paf enhanced PMA triggered, but not anti-IgM triggered B cell proliferation. B cell proliferation was modulated between 24 and 72 hr of culture indicating that the effect of paf did not merely reflect a shift in proliferation kinetics. Interestingly, paf also enhanced the spontaneous proliferation of a Burkitt lymphoma-derived B cell line, Raji, which suggests that paf can directly act on B cells. The modulatory effect of paf on peripheral blood B cells was independent of PMA concentration, yet the effect on Raji cells was dependent upon cell density. The data suggest that paf is a potent modulator of B cell function, and may be involved in the control of humoral immune response. Based on a paper presented at the Third International Conference on Platelet-Activating Factor and Structurally Related Alkyl Ether Lipids, Tokyo, Japan, May 1989.     

Pilot proteomic analysis of cerebrospinal fluid in Alzheimer's disease

Proteomic analysis of cerebrospinal fluid (CSF) holds great promise in understanding the progression of neurodegenerative diseases, including Alzheimer's disease (AD). As one of the primary reservoirs of neuronal biomolecules, CSF provides a window into the biochemical and cellular aspects of the neurological environment. CSF can be drawn from living participants allowing the potential alignment of clinical changes with these biochemical markers. Using cutting-edge mass spectrometry technologies, we perform a streamlined proteomic analysis of CSF. We quantify greater than 700 proteins across 10 pairs of age- and sex-matched participants in approximately one hour of analysis time each. Using the paired participant study structure, we identify a small group of biologically relevant proteins that show substantial changes in abundance between cognitive normal and AD participants, which were then analyzed at the peptide level using parallel reaction monitoring experiments. Our findings suggest the utility of fractionating a single sample and using matching to increase proteomic depth in cerebrospinal fluid, as well as the potential power of an expanded study.     

Chemical sensor based on a novel capacitive microwave flexible transducer with polymer nanocomposite-carbon nanotube sensitive film

Microsystem Technologies - This study presents the results on the feasibility of a resonant planar chemical capacitive sensor in the microwave frequency range suitable for gas detection and...     

Dynamic and scalable multi-level trust management model for Social Internet of Things

The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by offering them the ability to connect to the Internet and communicate. Integrating the social component into IoT gave rise to the Social Internet of Things (SIoT), which has helped overcome various issues such as heterogeneity and navigability. In this kind of environment, participants compete to offer a variety of attractive services. Nevertheless, some of them resort to malicious behaviour to spread poor-quality services. They perform so-called Trust-Attacks and break the basic functionality of the system. Trust management mechanisms aim to counter these attacks and provide the user with an estimate of the trust degree they can place in other users, thus ensuring reliable and qualified exchanges and interactions. Several works in literature have interfered with this problem and have proposed different Trust-Models. The majority tried to adapt and reapply Trust-Models designed for common social networks or peer-to-peer ones. That is, despite the similarities between these types of networks, SIoT ones present specific peculiarities. In SIoT, users, devices and services are collaborating. Devices entities can present constrained computing and storage capabilities, and their number can reach some millions. The resulting network is complex, constrained and highly dynamic, and the attacks-implications can be more significant. In this paper, we propose DSL-STM a new dynamic and scalable multi-level Trust-Model, specifically designed for SIoT environments. We propose multidimensional metrics to describe and SIoT entities behaviours. The latter are aggregated via a Machine Learning-based method, allowing classifying users, detecting attack types and countering them. Finally, a hybrid propagation method is suggested to spread trust values in the network, while minimizing resource consumption and preserving scalability and dynamism. Experimentation made on various simulated scenarios allows us to prove the resilience and performance of DSL-STM.

     

Performance-friendly rule extraction in large water data-sets with AOC posets and relational concept analysis

In this paper, we consider data analysis methods for knowledge extraction from large water data-sets. More specifically, we try to connect physico-chemical parameters and the characteristics of taxons living in sample sites. Among these data analysis methods, we consider formal concept analysis (FCA), which is a recognized tool for classification and rule discovery on object–attribute data. Relational concept analysis (RCA) relies on FCA and deals with sets of object–attribute data provided with relations. RCA produces more informative results but at the expense of an increase in complexity. Besides, in numerous applications of FCA, the partially ordered set of concepts introducing attributes or objects (AOC poset, for Attribute–Object–Concept poset) is used rather than the concept lattice in order to reduce combinatorial problems. AOC posets are much smaller and easier to compute than concept lattices and still contain the information needed to rebuild the initial data. This paper introduces a variant of the RCA process based on AOC posets rather than concept lattices. This approach is compared with RCA based on iceberg lattices. Experiments are performed with various scaling operators, and a specific operator is introduced to deal with noisy data. We show that using AOC poset on water data-sets provides a reasonable concept number and allows us to extract meaningful implication rules (association rules whose confidence is 1), whose semantics depends on the chosen scaling operator.     

Distributed Virtual Reality environments based on rewriting systems

Ideally, virtual worlds should be dynamic, mutable, and complex in order to be attractive for immersed users. As such worlds can be designed easily by rewriting techniques, we propose a distributed Virtual Reality (VR) system that is based on an interactive animation system using a rewriting technique for geometric and behavioral modeling. The emphasis is on concepts and extensions for the integration of user immersion, user interaction, and networking into a rewriting-based animation system, Finally, the modeling of a ball game with two immersed users, as well as a virtual park, serve as case studies to illustrate the proposed concepts and extensions.