Action Research on Development and Application of AIoT Traffic Solution

AIoT solution based on the AI (Artificial Intelligent) and IoT (Internet of Things) is considered state-of-the-art technology and has emerged in various business environments. To enhance intelligent traffic quality, maximize energy saving and reduce carbon emission, this study applied an AIoT technology based on traffic counting modules and people behavior modules as traffic inference systems. Applications of the IoT technology based on WiFi, 3G/4G and NB-IoT (Narrowband IoT) was conducted gradually in key demonstration roads and cities worldwide, and the development and evaluation results were aligned to an action research framework. The five phases in the action research included designing, collecting data, analyzing data, communicating outcome, and acting phases. During the first two phases, problems of functional operations in traffic were verified and designed for network services by ICT (Information and Communication Technology) and IoT technologies to collection traffic big data. In the third phase, stakeholders may use basic statistic or further deep learning methods to solve traffic scheduling, order and road safety issues. During the fourth and fifth phases, the roles and benefits of stakeholders participating in the service models were evaluated, and issues and knowledge of the whole application process were respectively derived and summarized from technological, economic, social and legal perspectives. From an action research approach, AIoT-based intelligent traffic solutions were developed and verified and it enables MOTC (Ministry of Transportation and Communications) and stakeholders to acquire traffic big data for optimizing traffic condition in technology enforcement. With its implementation, it will ultimately be able to go one step closer to smart city vision. The derived service models could provide stakeholders, drivers and citizens more enhanced traffic services and improve policies’ work more efficiency and effectiveness

Fair Value, Historical Cost model, and Audit Fees: Evidence from Investment Properties

This study examines the effect of fair value model versus historical cost model for investment property on audit fees. Using China’s real state firms data from 2007-2014, controlling for other determinants of audit fees, this study finds that audit fees are higher for firms reporting investment property at the fair value model relative to those reporting investment property at the cost model. This study also finds that firm reporting investment properties at the fair value located in the cities with active markets leads to lower audit fees than those located in the remote areas with less active markets. This study does not find that investment property valued under the fair value model audited by industry specialist leads to higher audit fees than investment property audited by non-industry specialist. Finally, this study provides evidence that firms use external appraisers to monitor the fair value estimates of investment properties leads low audit fees. Overall, our result suggests that fair value measurements leads to lower audit fees in the developed regions relative to less developed regions

Environmental Dependence of the Stellar Initial Mass Function.

I present an in-depth study of how the stellar initial mass function (IMF) depends on the environmental density from both theoretical and observational aspects. In my theoretical work, I used Gadget-2, an SPH code with sink particles, to test the applicability of competitive accretion in an initially non-clustered environment and see how the resulting IMF depends on the environment. The results show that in a sheet-like geometry, as well as a uniform sphere, the accretion rates of individual sinks follow the Bondi-Hoyle accretion at high masses, resulting in continual flattening of the slope towards an asymptotic form Gamma = 1. The asymptotic limit is most rapidly reached when starting from a broad distribution of initial sink masses. In general the higher sink masses are found in simulations with flatter slopes. Although these simulations are highly idealized, the results suggest that competitive accretion may be relevant in a wider variety of environments than previously considered. In my observational work, I surveyed the stellar population in the L1641 region, the distributed star-forming region south of the Orion Nebula Cluster (ONC). 864 low-mass members of L1641 are identified through optical photometry and spectroscopy. Overall, L1641 may contain up to 1600 stars. Compared to the standard IMFs, L1641 is deficient in O and early B stars to a 3-4 sigma significance level. I then searched for high-to-intermediate mass members of L1641 to make a direct comparison with the ONC. I compared the spectral type distribution to that of the ONC and the K-band luminosity function to the Trapezium cluster. Based on Fisher's exact test, there is only 3% probability that the ONC and the southern region of L1641 were drawn from the same distribution. This supports the hypothesis that the uppermass IMF depends on the environmental density. Additionally, in an attempt to characterize the highly-extincted population in L1641 and ensure that the luminous members in high extinction regions are not missing, I observed 115 members of L1641 with the MMIRS Spectrograph in H and K band. The IR spectra, along with 2MASS photometry, are used to characterize their accretion luminosities, spectral-types and ages.PHDAstronomy and AstrophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99878/1/wenhsin_1.pd

Kinematic and Spatial Substructure in NGC 2264

We present an expanded kinematic study of the young cluster NGC 2264 based upon optical radial velocities measured using multi-fiber echelle spectroscopy at the 6.5 meter MMT and Magellan telescopes. We report radial velocities for 695 stars, of which approximately 407 stars are confirmed or very likely members. Our results more than double the number of members with radial velocities from F{\H u}r{\'e}sz et al., resulting in a much better defined kinematic relationship between the stellar population and the associated molecular gas. In particular, we find that there is a significant subset of stars that are systematically blueshifted with respect to the molecular ($^{13}$CO) gas. The detection of Lithium absorption and/or infrared excesses in this blue-shifted population suggests that at least some of these stars are cluster members; we suggest some speculative scenarios to explain their kinematics. Our results also more clearly define the redshifted population of stars in the northern end of the cluster; we suggest that the stellar and gas kinematics of this region are the result of a bubble driven by the wind from O7 star S Mon. Our results emphasize the complexity of the spatial and kinematic structure of NGC 2264, important for eventually building up a comprehensive picture of cluster formation.Comment: Accepted to AJ. 38 pages, 5 Figures 3 Table

Supported Zinc Oxide Photocatalyst for Decolorization and Mineralization of Orange G Dye Wastewater under UV365 Irradiation

To solve the environmental challenge of textile wastewater, a UV/ZnO photocatalytic system was proposed. The objective of this study was to prepare a photocatalytic system by utilizing both cold cathode fluorescent light (CCFL) UV irradiation and steel mesh supported ZnO nanoparticles in a closed reactor for the degradation of azo dye C.I. Orange G (OG). Various operating parameters such as reaction time, preparation temperature, mixing speed, ZnO dosage, UV intensity, pH, initial dye concentration, and service duration were studied. Results presented efficient color and total organic carbon (TOC) removal of the OG azo dye by the designed photocatalytic system. The optimal ZnO dosage for color removal was 60 g m−2. An alkaline pH of 11.0 was sufficient for photocatalytic decolorization and mineralization. The rate of color removal decreased with the increase in the initial dye concentration. However, the rate of color removal increased with the increase in the UV intensity. The steel mesh supported ZnO can be used repeatedly over 10 times without losing the color removal efficiency for 120 min reaction time. Results of Fourier transform infrared (FTIR) and ion chromatography (IC) indicated the breakage of N=N bonds and formation of sulfate, nitrate, and nitrite as the major and minor products. The observation indicated degradation of dye molecules

Adopting IoT Technology to Optimize Intelligent Water Management

Intelligent water management (IWM) has been used to study the supply and demand of tap water in Taiwan. This research aims to enhance existing and future water utility management. Leveraging the supervisory control and data acquisition (SCADA) technology that connects sensors to a distributive infrastructure, the system detects leaks, assesses quality, monitors discharge, and manages assets of water utility. In this paper, we propose a prototype of urban intelligent water system by installing an intelligent water meter. Three steps are undertaken to demonstrate the IWM: 1) choose the way of data transmission; 2) establish communication equipment and generate cloud database; and 3) apply big data analyses and value-added applications. By intelligently managing the water supply system, it generates benefits of saving water, saving energy and optimizing water resources dispatching

Improving protein secondary structure prediction based on short subsequences with local structure similarity

<p>Abstract</p> <p>Background</p> <p>When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.</p> <p>Results</p> <p>In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an <it>n-</it>gram pattern of amino acids that reflects the sequence variation in a protein’s evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.</p> <p>On a large non-redundant dataset of 8,297 protein chains (<it>DsspNr-25</it>), the average <it>Q</it>₃ of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (<it>EVA Set_1</it> and <it>EVA_Set2</it>), the average <it>Q</it>₃ of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases.</p> <p>Conclusions</p> <p>Our experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at <url>http://bio-cluster.iis.sinica.edu.tw/SymPred/</url>.</p