Business Analysis and Future Development of an Electric Vehicle Company -- Tesla

The boom in electric vehicles in recent years has caught the attention of many companies that are investing or will be investing in the industry due to the increasing demand for electric cars. Tesla as a leader of the electric vehicles (EVs) industry, its development is of vital significance for referential value. Previous research on electric vehicle acceptance and behavioral intention of purchase is comprehensive, which could enable the EVs industry to understand consumer psychology. However, there is little analysis of the business strategy and future development of specific companies. When it comes to sustainability, almost every company has a path that is best suited to. This paper presents a comprehensive review of the historical background of Tesla, followed by in-depth states on its current strategy and future analysis. Given recommendations on its future development, Tesla could engage more in other different industries to increase the source of revenue and invest more into the development of autonomous public transportation, such as electric car-sharing services (ECS). These will help Tesla move steadily into the next stage

Identification of neprilysin as a potential target of arteannuin using computational drug repositioning

The discovery of arteannuin (qinghaosu) in the 20th Century was a major advance for medicine. Besides functioning as a malaria therapy, arteannuin is a pharmacological agent in a range of other diseases, but its mechanism of action remains obscure. In this study, the reverse docking server PharmMapper was used to identify potential targets of arteannuin. The results were checked using the chemical-protein interactome servers DRAR-CPI and DDI-CPI, and verified by AutoDock Vina. The results showed that neprilysin (also known as CD10), a common acute lymphoblastic leukaemia antigen, was the top disease-related target of arteannuin. The chemical-protein interactome and docking results agreed with those of PharmMapper, further implicating neprilysin as a potential target. Although experimental verification is required, this study provides guidance for future pharmacological investigations into novel clinical applications for arteannuin

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for the same purpose. Distributed learning based on divide-and-conquer provides a promising way to settle the data silos, but it suffers from several challenges, including autonomy, privacy guarantees, and the necessity of collaborations. This paper focuses on developing an adaptive distributed kernel ridge regression (AdaDKRR) by taking autonomy in parameter selection, privacy in communicating non-sensitive information, and the necessity of collaborations in performance improvement into account. We provide both solid theoretical verification and comprehensive experiments for AdaDKRR to demonstrate its feasibility and effectiveness. Theoretically, we prove that under some mild conditions, AdaDKRR performs similarly to running the optimal learning algorithms on the whole data, verifying the necessity of collaborations and showing that no other distributed learning scheme can essentially beat AdaDKRR under the same conditions. Numerically, we test AdaDKRR on both toy simulations and two real-world applications to show that AdaDKRR is superior to other existing distributed learning schemes. All these results show that AdaDKRR is a feasible scheme to defend against data silos, which are highly desired in numerous application regions such as intelligent decision-making, pricing forecasting, and performance prediction for products.Comment: 46pages, 13figure

Solar carbon fuel via photoelectrochemistry

A promising strategy to mitigate both energy shortage and global warming is the conversion of CO2 into chemicals that can be used as fuels (chemical fuels) by utilizing renewable energy sources. Up to date, solar-driven CO2 reduction has been achieved with photochemical (PC) and photoelectrochemical (PEC) systems or electrochemical cells combined with a photovoltaic system (PV-EC). This study is intended to compare and highlight the state-of-the-art PEC systems for CO2 reduction and show the limitation factors that still hinder their widespread utilization. The review starts with a description of semiconducting photocatalyst properties and fundamental understanding of PEC CO2 reduction process. Then, the most significant performance metrics used for evaluation of PEC systems are explained in details. In addition, recent progress in PEC CO2 reduction systems is summarized and classified in different categories according to the chemical product. Different strategies such as doping, combination of two or more semiconductors, synthesis of nanostructured materials, passivation layers and co-catalysts that enhance light absorption, chemical stability, charge transfer and reduce ohmic losses and overpotentials of photoactive materials are reviewed. Besides the improvement of photocatalysts, research progress on the front of PEC reactor design, combined with the development of advanced modelling tools and characterization techniques are expected to bring PEC CO2 reduction a step closer to commercialization