The socioeconomic and environmental impacts of the climate policies in China

This thesis is intended to analyse the policy effects of the simulated Chinese climate policies. Without considering any benefits and influential factors, the carbon tax could effectively reduce the carbon emissions at the price of the welfare loss. Considering its ancillary (health) benefit, the tax will induce less emission reduction and welfare loss. The primary (climate) benefit of the tax will increase the carbon emissions, decrease the carbon intensity, and induce an economic boom. The induced technological change (ITC) of the carbon tax will have negative impacts on the carbon emissions, and it will increase the real GDP (RGDP) but decrease the household welfare. The inequality impacts of the carbon tax depend on the distribution of the climate damages and the payments of the abatement costs. Recycling the tax revenues will also affect the inequality impacts of the tax. Under the impacts of the projected urbanisation, the carbon tax will induce more emission reduction, less RGDP loss, and more household welfare loss. With the same amount of the targeted emission reduction as the carbon tax, the emission trading scheme (ETS) policy will induce the higher household welfare compared to the carbon tax.Open Acces

Histone Deacetylase Inhibitors: The Epigenetic Therapeutics That Repress Hypoxia-Inducible Factors

Histone deacetylase inhibitors (HDACIs) have been actively explored as a new generation of chemotherapeutics for cancers, generally known as epigenetic therapeutics. Recent findings indicate that several types of HDACIs repress angiogenesis, a process essential for tumor metabolism and progression. Accumulating evidence supports that this repression is mediated by disrupting the function of hypoxia-inducible factors (HIF-1, HIF-2, and collectively, HIF), which are the master regulators of angiogenesis and cellular adaptation to hypoxia. Since HIF also regulate glucose metabolism, cell survival, microenvironment remodeling, and other alterations commonly required for tumor progression, they are considered as novel targets for cancer chemotherapy. Though the precise biochemical mechanism underlying the HDACI-triggered repression of HIF function remains unclear, potential cellular factors that may link the inhibition of deacetylase activity to the repression of HIF function have been proposed. Here we review published data that inhibitors of type I/II HDACs repress HIF function by either reducing functional HIF-1α levels, or repressing HIF-α transactivation activity. In addition, underlying mechanisms and potential proteins involved in the repression will be discussed. A thorough understanding of HDACI-induced repression of HIF function may facilitate the development of future therapies to either repress or promote angiogenesis for cancer or chronic ischemic disorders, respectively

Challenge keyword generation for voice-based authentication

This disclosure describes use of challenge keywords for use in voice authentication systems. Verification keywords are generated for a particular user and serve to verify the authenticity of the user. With user permission, keyword generation is performed based on analysis of previous voice-based interactions of the user. A uniqueness score is assigned to words uttered previously by the user in interactions with the voice interface. The uniqueness score for a word is determined based on a rarity of usage of the word in the user’s speech and a distinctiveness of user pronunciation of the word

Voice input for authentication

Recent years have seen a proliferation of systems designed to respond to voice, e.g., smart speakers, smartphones, and other voice-activated devices. Such systems are convenient and intuitive to use. However, they also open the possibility for malicious actors to gain control of sensitive information through fraudulent use of speech commands. For example, a malicious actor can spoof the voice recognition system through a recording of the user’s voice or by using a computer to synthesize the speech mimicking the user. The techniques of this disclosure thwart such attacks by providing a random phrase for the user to repeat and verifying the resulting audio and transcription for authenticity. Spoofed audio sounds choppy and exhibits sharp transitions, while authentic audio is smooth and lacks unnatural artifacts. A trained machine learning model is used to tell the difference