Experiencing flow with instant messaging and its facilitating role on creative behaviors

The goal of this study is to explore how experiencing flow with IM, an Internet-based communication technology, can facilitate an individual’s perception on creativity. We argue that certain capabilities in IM, telepresence and perceived control, provide users a means to experience flow. We further investigate whether flow and its outcomes, positive affect and exploratory behavior have any influence on the perception of creativity of the users. Using an online survey with 207 observations we found that user’s perceived control over the technology and its ability to transport the user to a virtual environment were shown to facilitate the experience of flow. We found that flow in itself does not influence perceived expected creativity in its users but does indirectly influence perceived expected creativity through exploratory behavior and positive affect. From these initial findings we believe that our study provides implications for both users and designers on IT. Furthermore, managers can apply the findings during implementation and use of interactive information technologies for communications in the workplace.     

Recent Advances in Poly (Amide-B-Ethylene) Based Membranes for Carbon Dioxide (CO2) Capture: A Review

Polyamide-b-ethylene (Pebax) is a promising material for membrane-based gas separation application with excellent CO₂ capturing potential. Pebax is a rubbery elastomer which offers good mechanical support with its hard crystalline phase and excellent gas transport through its amorphous polyether phase. This review article includes recent advances in Pebax based membrane synthesis, solvent selection for membrane synthesis, compatible fillers with Pebax matrix and the improved gas separation performance of the prepared membranes. The literature review shows that Pebax based membranes are a good candidate for separation of CO₂ from flue gases and can be used for commercial applications.     

Emerging Functions for snoRNAs and snoRNA-Derived Fragments

The widespread implementation of mass sequencing has revealed a diverse landscape of small RNAs derived from larger precursors. Whilst many of these are likely to be byproducts of degradation, there are nevertheless metabolically stable fragments derived from tRNAs, rRNAs, snoRNAs, and other non-coding RNA, with a number of examples of the production of such fragments being conserved across species. Coupled with specific interactions to RNA-binding proteins and a growing number of experimentally reported examples suggesting function, a case is emerging whereby the biological significance of small non-coding RNAs extends far beyond miRNAs and piRNAs. Related to this, a similarly complex picture is emerging of non-canonical roles for the non-coding precursors, such as for snoRNAs that are also implicated in such areas as the silencing of gene expression and the regulation of alternative splicing. This is in addition to a body of literature describing snoRNAs as an additional source of miRNA-like regulators. This review seeks to highlight emerging roles for such non-coding RNA, focusing specifically on “new” roles for snoRNAs and the small fragments derived from them.     

Diagnosing bot infections using Bayesian inference

Prior research in botnet detection has used the bot lifecycle to build detection systems. These systems, however, use rule-based decision engines which lack automated adaptability and learning, accuracy tunability, the ability to cope with gaps in training data, and the ability to incorporate local security policies. To counter these limitations, we propose to replace the rigid decision engines in contemporary bot detectors with a more formal Bayesian inference engine. Bottleneck, our prototype implementation, builds confidence in bot infections based on the causal bot lifecycle encoded in a Bayesian network. We evaluate Bottleneck by applying it as a post-processing decision engine on lifecycle events generated by two existing bot detectors (BotHunter and BotFlex) on two independently-collected datasets. Our experimental results show that Bottleneck consistently achieves comparable or better accuracy than the existing rule-based detectors when the test data is similar to the training data. For differing training and test data, Bottleneck, due to its automated learning and inference models, easily surpasses the accuracies of rule-based systems. Moreover, Bottleneck’s stochastic nature allows its accuracy to be tuned with respect to organizational needs. Extending Bottleneck’s Bayesian network into an influence diagram allows for local security policies to be defined within our framework. Lastly, we show that Bottleneck can also be extended to incorporate evidence trustscore for false alarm reduction.     

Grey is the new RGB: How good is GAN-based image colorization for image compression?

Multimedia Tools and Applications - GAN-based image colorization techniques are capable of producing highly realistic color in real-time. Subjective assessment of these approaches has demonstrated...     

Slow Off-Rate Modified Aptamer (SOMAmer) Proteomic Analysis of Patient-Derived Malignant Glioma Identifies Distinct Cellular Proteomes

Malignant gliomas derive from brain glial cells and represent >75% of primary brain tumors. This includes anaplastic astrocytoma (grade III; AS), the most common and fatal glioblastoma multiforme (grade IV; GBM), and oligodendroglioma (ODG). We have generated patient-derived AS, GBM, and ODG cell models to study disease mechanisms and test patient-centered therapeutic strategies. We have used an aptamer-based high-throughput SOMAscan^® 1.3K assay to determine the proteomic profiles of 1307 different analytes. SOMAscan^® proteomes of AS and GBM self-organized into closely adjacent proteomes which were clearly distinct from ODG proteomes. GBM self-organized into four proteomic clusters of which SOMAscan^® cluster 4 proteome predicted a highly inter-connected proteomic network. Several up- and down-regulated proteins relevant to glioma were successfully validated in GBM cell isolates across different SOMAscan^® clusters and in corresponding GBM tissues. Slow off-rate modified aptamer proteomics is an attractive analytical tool for rapid proteomic stratification of different malignant gliomas and identified cluster-specific SOMAscan^® signatures and functionalities in patient GBM cells.     

Deep introspective SLAM: deep reinforcement learning based approach to avoid tracking failure in visual SLAM

Autonomous Robots - Reliable and consistent tracking is essential to realize the dream of power-on-and-go autonomy in mobile robots. Our investigation with state-of-the-art visual navigation and...     

A review on extrusion-based 3D-printed nanogenerators for energy harvesting

Journal of Materials Science - Energy harvesting technologies now play a significant role in the successful deployment of self-powered electronic devices. Researchers are working on small-scale...