Aggregation induced photodynamic therapy enhancement based on linear and nonlinear excited FRET of fluorescent organic nanoparticles

A binary molecule can self-assemble to form fluorescent organic nanoparticles (FONs) based on the Aggregation-Induced Emission Enhancement (AIEE) property and subsequently, presents an efficient fluorescence resonance energy transfer (FRET) to generate singlet oxygen under linear and nonlinear light sources. Biologically, this FON-photosensitizer is much more phototoxic to cancer cells than to normal cells without significant dark toxicity. Eventually, a new approach, called FON FRET-PDT or AIEE FRET-PDT, to promote the PDT effect is expected

Leveling Maintenance Mechanism by Using the Fabry-Perot Interferometer with Machine Learning Technology

This study proposes a method for maintaining parallelism of the optical cavity of a laser interferometer using machine learning. The Fabry-Perot interferometer is utilized as an experimental optical structure in this research due to its advantage of having a brief optical structure. The supervised machine learning method is used to train algorithms to accurately classify and predict the tilt angle of the plane mirror using labeled interference images. Based on the predicted results, stepper motors are fixed on a plane mirror that can automatically adjust the pitch and yaw angles. According to the experimental results, the average correction error and standard deviation in 17-grid classification experiment are 32.38 and 11.21 arcseconds, respectively. In 25-grid classification experiment, the average correction error and standard deviation are 19.44 and 7.86 arcseconds, respectively. The results show that this parallelism maintenance technology has essential for the semiconductor industry and precision positioning technology

An Economy-wide Analysis of Impacts of WTO Tiered Formula for Tariff Reduction on Taiwan

In this study we use Taiwan as a case study to provide an economy-wide analysis of impacts on Taiwan of WTO tariff reduction schemes with different combinations of thresholds and reduction rates. The model we utilized in this study is Taiwan General Equilibrium Model with a WTO module (TAIGEM-WTO, hereafter) that is a multi-sectoral computable general equilibrium (CGE) model of the Taiwan's economy derived from Australian ORANI model (Dixon, Parmenter, Sutton and Vincent, 1982). Simulation results show that results are more sensitive to the scheme of tariff-reduction (i.e., Category 1, 2, and 3) than the tiered levels (i.e., A, B, C, and D) and as a strategy we should pay more attention to the arguments related to the amounts of tariff-reduction. Moreover, changes in nominal average tariff rates are more sensitive and shocks to the economy are more severe when we change the tariff reduction categories rather than the tiered levels. This conclusion also applies to the tiered reduction case when only sensitive products are considered. Finally, simulations with sector's bound rate calculated using arithmetic means have bigger effects than those using import values as weights. Therefore, sector's bound rate using import values as weights would be preferred.International Relations/Trade,

Development of High-Productivity Continuous Ethanol Production using PVA-Immobilized Zymomonas mobilis in an Immobilized-Cells Fermenter

Ethanol as one of renewable energy was being considered an excellent alternative clean-burning fuel to replace gasoline. Continuous ethanol fermentation systems had offered important economic advantages compared to traditional systems. Fermentation rates were significantly improved, especially when continuous fermentation was integrated with cell immobilization techniques to enrich the cells concentration in fermentor. Growing cells of Zymomonas mobilis immobilized in polyvinyl alcohol (PVA) gel beads were employed in an immobilized-cells fermentor for continuous ethanol fermentation from glucose. The glucose loading, dilution rate, and cells loading were varied in order to determine which best condition employed in obtaining both high ethanol production and low residual glucose with high dilution rate. In this study, 20 g/L, 100 g/L, 125 g/L and 150 g/L of glucose concentration and 20% (w/v), 40% (w/v) and 50% (w/v) of cells loading were employed with range of dilution rate at 0.25 to 1 h-1. The most stable production was obtained for 25 days by employing 100 g/L of glucose loading. Meanwhile, the results also exhibited that 125 g/L of glucose loading as well as 40% (w/v) of cells loading yielded high ethanol concentration, high ethanol productivity, and acceptable residual glucose at 62.97 g/L, 15.74 g/L/h and 0.16 g/L, respectively. Furthermore, the dilution rate of 4 hour with 100 g/L and 40% (w/v) of glucose and cells loading was considered as the optimum condition with ethanol production, ethanol productivity and residual glucose obtained were 49.89 g/L, 12.47 g/L/h, and 2.04 g/L, respectively. This recent study investigated ethanol inhibition as well. The present research had proved that high sugar concentration was successfully converted to ethanol. These achieved results were promising for further study

On the Momentum Dependence of the Flavor Structure of the Nucleon Sea

Difference between the $\bar u$ and $\bar d$ sea quark distributions in the proton was first observed in the violation of the Gottfried sum rule in deep-inelastic scattering (DIS) experiments. The parton momentum fraction $x$ dependence of this difference has been measured over the region $0.02 < x < 0.35$ from Drell-Yan and semi-inclusive DIS experiments. The Drell-Yan data suggested a possible sign-change for $\bar d(x)-\bar u(x)$ near $x \sim 0.3$, which has not yet been explained by existing theoretical models. We present an independent evidence for the $\bar d(x)-\bar u(x)$ sign-change at $x \sim 0.3$ from an analysis of the DIS data. We further discuss the $x$-dependence of $\bar d(x)-\bar u(x)$ in the context of meson cloud model and the lattice QCD formulation.Comment: 5 pages, 5 figures, final versio

Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation

Recently, deep convolutional neural networks (CNNs) have achieved many eye-catching results. However, deploying CNNs on resource-constrained edge devices is constrained by limited memory bandwidth for transmitting large intermediated data during inference, i.e., activation. Existing research utilizes mixed-precision and dimension reduction to reduce computational complexity but pays less attention to its application for activation compression. To further exploit the redundancy in activation, we propose a learnable mixed-precision and dimension reduction co-design system, which separates channels into groups and allocates specific compression policies according to their importance. In addition, the proposed dynamic searching technique enlarges search space and finds out the optimal bit-width allocation automatically. Our experimental results show that the proposed methods improve 3.54%/1.27% in accuracy and save 0.18/2.02 bits per value over existing mixed-precision methods on ResNet18 and MobileNetv2, respectively

Anthropomorphism of AI-based Intelligent Customer Service, and Its Affective and Behavioral Consequences

Recently, as many users turn to social media to interact with service providers, organizations apply Artificial intelligence (AI) to improve the efficiency and effectiveness of the operation. This type of customer service system is called intelligent customer service (ICS) which one of the most commonly adopted tools is chatbot. Since chatbot is AI-empowered, whether this system can effectively interact with customers and solve their problems is critical. However, the quality of ICS has received significant attention recently, and a lack of systematic study on the outcomes of anthropomorphism leaves this question unanswered in an ICS context. Based on a cognitive-affective-behavioral framework, this study attempts to understand whether anthropomorphism can promote desired behaviors (including usage and citizen-ship behaviors) through enhancing affective out-comes, such as satisfaction and identity. Data collected from 183 chatbot-ICS users, this study illustrates how anthropomorphism can increase quality, enhance satisfaction and identity. Furthermore, we also show that satisfaction and identity lead to further usage and citizenship behaviors. This highlights the importance of increasing anthropomorphism for the chatbot-ICS