Microcantilever surface modification for chem-/bio-sensing

One focus of the microcantilever (MCL) sensing area is to develop a novel surface modification approach to increase the microcantilever bending amplitudes and thus further improve sensitivities. In this dissertation, enzyme incorporated using the Layer by-Layer (LbL) process, LbL deposition of micro-, nano-hydrogel particles and electrophoretic deposition (EPD) of micro-, nano-hydrogel particles were applied to prepare a multilayer or thin hydrogel films on the surface of microcantilevers. Prior to applying to the microcantilevers, LbL and electrophoretic deposition techniques were also applied to gold coated silicon wafer surfaces to investigate the feasibility and deposition behavior using these techniques. The multilayers prepared through self-assembling of poly(styrenesulfonate) (PSS), poly(ethylenimine) (PEI), and organophosphorus hydrolase (OPH), responded to organic phosphorus compounds such as paraoxon, parathion, and dimethyl phosphate at different bending amplitudes and bending rates. The bending mechanism investigation suggested that the conformational change of the OPH might be the primary contributor of the MCL bending. The micro-, nano- hydrogel particle deposition on the silicon wafer and microcantilever through LbL process was investigated and discussed based on the observation and characterization using optical microscope, SEM and AFM techniques. A pseudo-3D mechanism was promoted to explain the hydrogel particle deposition process. The research on the EPD demonstrated that the technique was a convenient and reliable approach to deposit a uniform and continuous hydrogel thin film on the microcantilever devices. The bending responses of hydrogel coated microcantilever correlated with changes in environmental pH, demonstrating the feasibility of this hydrogel film for micro-sensor development

Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation. Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.Comment: code URL typo, code is available at https://github.com/hackerekcah/distinct-events-asc.gi

Thermal storage/management system with phase change materials for building

Peer reviewe

Dynamic simulation of steam generation system in solar tower power plant

Concentrated solar power (CSP) plant with thermal energy storage can be operated as a peak load regulation plant. The steam generation system (SGS) is the central hub between the heat transfer fluid and the working fluid, of which the dynamic characteristics need to be further investigated. The SGS of Solar Two power tower plant was selected as the object. The mathematical model with lumped parameter method was developed and verified to analyze its dynamic characteristics. Five simulation tests were carried out under the disturbances that the solar tower power plant may encounter under various solar irradiations and output electrical loads. Both dynamic and static characteristics of SGS were analyzed with the response curves of the system state parameters. The dynamic response and time constants of the working fluids out of SGS was obtained when the step disturbances are imposed. It was indicated that the disturbances imposed to both working fluids lead to heat load reassignment to the preheater, evaporator and superheater. The proposed step-by-step disturbance method could reduce the fluid temperature and pressure fluctuations by 1.5 °C and 0.03 MPa, respectively. The results could be references for control strategies as well as the safe operation of and SGS.Peer reviewe

A New Theory of Technology Usage

Benbasat and Barki (2007) suggest that the traditional perception-intention-usage framework has fulfilled its original purpose and has demonstrated its deficiencies in a number of important respects. Thus, they call for researchers to step outside its limited confines and move toward a new theory that takes into account the constantly changing context of IT. Answering the call, this paper presents a preliminary effort to develop the theory of technology usage (TTU), review its research base, and discuss how it can be applied to different contexts of IT. The current paper also emphasizes that IT should be categorized into productivity-oriented, pleasure-oriented, and dual-purposed, as it evolves from a single-user system in an organizational context to a multi-user system in a social and leisure setting. Moreover, a two-step procedure is devised for such trichotomization. The TTU incorporates the core concepts of the needs-based perspective on behaviors, and maps these concepts in a way that permits prediction and understanding of usage of these three IT categories. The paper is concluded with discussions of implications and directions for future research

Social Media Attention and the “Death” of Cryptocurrency: A Hazard Model Perspective

This paper studies the survival of cryptocurrencies and their association with the social media attention they receive. The death of a cryptocurrency is defined based on the discontinuation of trading activities and modeled using Kaplan – Meier Survivor Function and the Cox survival regressions. Using data collected from coinmarketcap.com and bitcointalk.org, we find that social media attention is a very relevant influencer for the death hazard. Specifically, the death hazard of a cryptocurrency is estimated to increase by 0.5% - 1% for each additional trading day without any social media mention. We also find that high-quality social media mentions are more effective in reducing the death hazard. The theoretical and practical implications of the findings are discussed in the paper

Building Up Knowledge through Meta-analysis: A Review and Reinterpretation

In the last two decades, researchers have increasingly conducted meta-analyses in the information systems (IS) field. As such, we need to ensure that researchers conduct such analyses in a sound and accurate way, use appropriate and effective meta-analytic techniques, and produce reliable and valid results. Nevertheless, few papers on conducting a meta-analysis in the IS field exist. In this paper, we review and re-interpret the procedures, issues, and techniques in conducting a meta-analysis in the IS field. By doing so, we make important contributions to helping IS researchers expand their baseline knowledge of meta-analyses and, thus, more effectively design and conduct them in the future