Melanogenesis Inhibitory Activity of Two Generic Drugs: Cinnarizine and Trazodone in Mouse B16 Melanoma Cells

More than 200 generic drugs were screened to identify the inhibitory activity on melanogenesis in mouse B16 melanoma cells. Cinnarizine and trazodone were identified as melanogenesis inhibitors. The inhibitory effects of the two drugs on cell survival, melanogenesis, and tyrosinase activity were investigated. The results showed that both cinnarizine and trazodone inhibited melanogenesis in B16 cells by a dose-dependent manner at the non-cytotoxic concentrations. Based on the results of the present study, seeking new melanogenesis inhibitors from generic drugs is an alternative approach to developing new depigmenting agents in cosmeceuticals. Moreover, cinnarizine and trazodone were proven to be good candidates as skin-whitening agents for treatment of skin hyperpigmentation

Establishing National Carbon Emission Prices for China

The purpose of the paper is to establish national carbon emissions prices for the People’s Republic of China, which is one of the world’s largest producers of carbon emissions. Several measures have been undertaken to address climate change in China, including the establishment of a carbon trading system. Since 2013, eight regional carbon emissions markets have been established, namely Beijing, Shanghai, Guangdong, Shenzhen, Tianjin, Chongqing, Hubei and Fujian. The Central Government announced a national carbon emissions market, with power generation as the first industry to be considered. However, as carbon emissions prices in the eight regional markets are very different, for a variety of administrative reasons, it is essential to create a procedure for establishing a national carbon emissions price. The regional markets are pioneers, and their experience will play important roles in establishing a national carbon emissions market, with national prices based on regional prices, turnovers and volumes. The paper considers two sources of regional data for China’s carbon allowances, which are based on primary and secondary data sources, and compares their relative strengths and weaknesses. The paper establishes national carbon emissions prices based on the primary and secondary regional prices, for the first time, and compares both national prices and regional prices against each other. The carbon emission prices in Hubei, Guangdong, Shenzhen and Tianjin are highly correlated with the national prices based on the primary and secondary sources. Establishing national carbon emissions prices should be very helpful for the national carbon emissions market that is under construction in China, as well as for other regions and countries worldwide

Pricing carbon emissions in China

The purpose of the paper is to provide a clear mechanism for determining carbon emissions pricing in China as a guide to how carbon emissions might be mitigated to reduce fossil fuel pollution. The Chinese Government has promoted the development of clean energy, including hydroelectric power, wind power, and solar energy generation. In order to involve companies in carbon emissions control, a series of regional and provincial carbon markets have been established since 2013. Since China’s carbon market was established in 2013 and mainly run domestically, and not necessarily using market principles, there has been almost no research on China’s carbon price and volatility. This paper provides an introduction to China’s regional and provincial carbon markets, proposes how to establish a national market for pricing carbon emissions, discusses how and when these markets might be established, how they might perform, and the subsequent prices for China’s regional and national carbon markets. Power generation in manufacturing consumes more than other industries, with more than 40% of total coal consumption. Apart from manufacturing, the northern China heating system also relies on fossil fuels, mainly coal, which causes serious pollution. In order to understand the regional markets well, it is necessary to analyze the energy structure in these regions. Coal is the primary energy source in China, so that provinces that rely heavily on coal receive a greater number of carbon emissions permits from the Chinese Government. In order to establish a national carbon market for China, a detailed analysis of eight important regional markets will be presented. The four largest energy markets, namely Guangdong, Shanghai, Shenzhen and Hubei, traded around 82% of the total volume and 85% of the total value of the seven markets in 2017, as the industry structure of the western area is different from that of the eastern area. The China National Development and Reform Commission has proposed a national carbon market, which can attract investors and companies to participate in carbon emissions trading. This importantissue will be investigated in the paper

A method for computing Lucas sequences

AbstractMost of public-key cryptosystems rely on one-way functions, which can be used to encrypt and sign messages. Their encryption and signature operations are based on the computation of exponentiation. Recently, some public-key cryptosystems are proposed and based on Lucas functions, and the Lucas sequences are performed as S = V(d)modN. In this paper, we will transform the concept of addition chains for computing the exponentiation evaluations to the Lucas chains for computing the Lucas sequences. Theoretically, the shorter Lucas chain for d is generated, the less computation time for evaluating the value V(d) is required. Therefore, we proposed a heuristic algorithm for evaluating a shorter Lucas chain and then use it to compute the Lucas sequence with less modular multiplications

On the Influencing Factors of Dictionary App Interface Design for the Elders

AbstractEnglish learning is becoming one of the popular movements towards the Globalization. In recent years especially, more people use smartphones to learn English. However, it was found in the current market that most dictionary apps were designed for the younger generation and neglected the needs of the elderly. The issue of memory over-load turned out to be the critical problem of the usability for the elderly, due to the complex menu structures. Thus this study is meant to explore a suitable menu structure for the senior user, and provide suggestions for the relative researches.The study results are:1.Gender: There is no significant between male and female in the operating performance.2.Menu structure: the performance of the hybrid structure is superior to the linear structure.3.Display mode: There is no significant between the horizontal and vertical display modes in operating performance.4.Task Complexity: A positive ratio between task complexity and menu topological structure was revealed, the harder the task complexity, the better performance of mixed structure can be expected

Pricing Carbon Emissions in China

The purpose of the paper is to provide a clear mechanism for determining carbon emissions pricing in China as a guide to how carbon emissions might be mitigated to reduce fossil fuel pollution. The Chinese Government has promoted the development of clean energy, including hydroelectric power, wind power, and solar energy generation. In order to involve companies in carbon emissions control, a series of regional and provincial carbon markets have been established since 2013. Since China’s carbon market was established in 2013 and mainly run domestically, and not necessarily using market principles, there has been almost no research on China’s carbon price and volatility. This paper provides an introduction to China’s regional and provincial carbon markets, proposes how to establish a national market for pricing carbon emissions, discusses how and when these markets might be established, how they might perform, and the subsequent prices for China’s regional and national carbon markets. Power generation in manufacturing consumes more than other industries, with more than 40% of total coal consumption. Apart from manufacturing, the northern China heating system also relies on fossil fuels, mainly coal, which causes serious pollution. In order to understand the regional markets well, it is necessary to analyze the energy structure in these regions. Coal is the primary energy source in China, so that provinces that rely heavily on coal receive a greater number of carbon emissions permits from the Chinese Government. In order to establish a national carbon market for China, a detailed analysis of eight important regional markets will be presented. The four largest energy markets, namely Guangdong, Shanghai, Shenzhen and Hubei, traded around 82% of the total volume and 85% of the total value of the seven markets in 2017, as the industry structure of the western area is different from that of the eastern area. The China National Development and Reform Commission has proposed a national carbon market, which can attract investors and companies to participate in carbon emissions trading. This important issue will be investigated in the paper

Variability of morphology in beat-to-beat photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment

We investigated the beat-to-beat fluctuation of the photoplethysmography (PPG) waveform. The motivation is that morphology variability extracted from the arterial blood pressure (ABP) has been found to correlate with baseline condition and short-term surgical outcome of the patients undergoing liver transplant surgery. Numerous interactions of physiological mechanisms regulating the cardiovascular system could underlie the variability of morphology. We used the unsupervised manifold learning algorithm, Dynamic Diffusion Map, to quantify the multivariate waveform morphological variation. Due to the physical principle of light absorption, PPG waveform signals are more susceptible to artifact and are nominally used only for visual inspection of data quality in clinical medical environment. But on the other hand, the noninvasive, easy-to-use nature of PPG grants a wider range of biomedical application, which inspired us to investigate the variability of morphology information from PPG waveform signal. We developed data analysis techniques to improve the performance and validated with the real-life clinical database

A Shallow Ritz Method for Elliptic Problems with Singular Sources

In this paper, a shallow Ritz-type neural network for solving elliptic equations with delta function singular sources on an interface is developed. There are three novel features in the present work; namely, (i) the delta function singularity is naturally removed, (ii) level set function is introduced as a feature input, (iii) it is completely shallow, comprising only one hidden layer. We first introduce the energy functional of the problem and then transform the contribution of singular sources to a regular surface integral along the interface. In such a way, the delta function singularity can be naturally removed without introducing a discrete one that is commonly used in traditional regularization methods, such as the well-known immersed boundary method. The original problem is then reformulated as a minimization problem. We propose a shallow Ritz-type neural network with one hidden layer to approximate the global minimizer of the energy functional. As a result, the network is trained by minimizing the loss function that is a discrete version of the energy. In addition, we include the level set function of the interface as a feature input of the network and find that it significantly improves the training efficiency and accuracy. We perform a series of numerical tests to show the accuracy of the present method and its capability for problems in irregular domains and higher dimensions

Integration of Social Media News Mining and Text Mining Techniques to Determine a Corporate’s Competitive Edge

Market globalization have triggered much more severe challenges for corporates than ever before. Thus, how to survive in this highly fluctuating economic atmosphere is an attractive topic for corporate managers, especially when an economy goes into a severe recession. One of the most consensus conclusions is to highly integrate a corporate’s supply chain network, as it can facilitate knowledge circulation, reduce transportation cost, increase market share, and sustain customer loyalty. However, a corporate’s supply chain relations are unapparent and opaque. To solve such an obstacle, this study integrates text mining (TM) and social network analysis (SNA) techniques to exploit the latent relation among corporates from social media news. Sequentially, this study examines its impact on corporate operating performance forecasting. The empirical result shows that the proposed mechanism is a promising alternative for performance forecasting. Public authorities and decision makers can thus consider the potential implications when forming a future policy

Environment Diversification with Multi-head Neural Network for Invariant Learning

Neural networks are often trained with empirical risk minimization; however, it has been shown that a shift between training and testing distributions can cause unpredictable performance degradation. On this issue, a research direction, invariant learning, has been proposed to extract invariant features insensitive to the distributional changes. This work proposes EDNIL, an invariant learning framework containing a multi-head neural network to absorb data biases. We show that this framework does not require prior knowledge about environments or strong assumptions about the pre-trained model. We also reveal that the proposed algorithm has theoretical connections to recent studies discussing properties of variant and invariant features. Finally, we demonstrate that models trained with EDNIL are empirically more robust against distributional shifts.Comment: In Proceedings of 36th Conference on Neural Information Processing Systems (NeurIPS 2022