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| 1 | clusterProfiler 4.0:A universal enrichment tool for interpreting omics data显示文摘Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet these requirements,we present here an updated version of our popular Bioconductor package,clusterProfiler 4.0.This package has been enhanced considerably compared with its original version published 9 years ago.The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases.It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization.Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists.We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms. | Tianzhi Wu Erqiang Hu Shuangbin Xu Meijun Chen Pingfan Guo Zehan Dai Tingze Feng Lang Zhou Wenli Tang Li Zhan Xiaocong Fu Shanshan Liu Xiaochen Bo Guangchuang Yu | 2021 | The Innovation2021，2，3： | 40 |
| 2 | Fintech investments in European banks:a hybrid IT2 fuzzy multidimensional decision‑making approach显示文摘Financial technology(Fintech)makes a significant contribution to the financial system by reducing costs,providing higher quality services and increasing customer satisfaction.Hence,new studies play an essential role to improve Fintech investments.This study evaluates Fintech-based investments of European banking services with an application of an original methodology that considers interval type-2(IT2)fuzzy decision-making trial and evaluation laboratory and IT2 fuzzy TOPSIS models.Empirical findings are controlled for consistency by applying the VIKOR method.Moreover,we conduct a sensitivity analysis by considering six distinct cases.This study contributes to the existing literature by identifying the most important Fintech-based investment alternatives to improve the financial performance of European banks.Our empirical findings illustrate that results are coherent,reliable,and identify“competitive advantage”as the most important factor among Fintech-based determinants.Moreover,“payment and money transferring systems”are the most important Fintech-based investment alternatives.It is recommended that,among Fintech-based investments,European banks should mainly focus on payment and money transferring alternatives to attract the attention of customers and satisfy their expectations.This is also believed to have a positive impact on the ease of bank’receivable collection.Another important point is that Fintech-based investments in money transferring systems could help to decrease costs. | Gang Kou Ozlem Olgu Akdeniz Hasan Dincer Serhat Yuksel | 2021 | Financial Innovation2021，7，1： | 38 |
| 3 | Mechanisms of Plant Responses and Adaptation to Soil Salinity显示文摘Soil salinity is a major environmental stress that restricts the growth and yield of crops.Understanding the physiological,metabolic,and biochemical responses of plants to salt stress and mining the salt tolerance-associated genetic resource in nature will be extremely important for us to cultivate salt-tolerant crops.In this review,we provide a comprehensive summary of the mechanisms of salt stress responses in plants,including salt stress-triggered physiological responses,oxidative stress,salt stress sensing and signaling pathways,organellar stress,ion homeostasis,hormonal and gene expression regulation,metabolic changes,as well as salt tolerance mechanisms in halophytes.Important questions regarding salt tolerance that need to be addressed in the future are discussed. | Chunzhao Zhao Heng Zhang Chunpeng Song Jian-Kang Zhu Sergey Shabala | 2020 | The Innovation2020，1，1： | 25 |
| 4 | Forecasting and trading cryptocurrencies with machine learning under changing market conditions显示文摘This study examines the predictability of three major cryptocurrencies—bitcoin,ethereum,and litecoin—and the profitability of trading strategies devised upon machine learning techniques(e.g.,linear models,random forests,and support vector machines).The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets,allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods.The classification and regression methods use attributes from trading and network activity for the period from August 15,2015 to March 03,2019,with the test sample beginning on April 13,2018.For the test period,five out of 18 individual models have success rates of less than 50%.The trading strategies are built on model assembling.The ensemble assuming that five models produce identical signals(Ensemble 5)achieves the best performance for ethereum and litecoin,with annualized Sharpe ratios of 80.17%and 91.35%and annualized returns(after proportional round-trip trading costs of 0.5%)of 9.62%and 5.73%,respectively.These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets,even under adverse market conditions. | Helder Sebastiao Pedro Godinho | 2021 | Financial Innovation2021，7，1： | 21 |
| 5 | A systematic review of blockchain显示文摘Blockchain is considered by many to be a disruptive core technology.Although many researchers have realized the importance of blockchain,the research of blockchain is still in its infancy.Consequently,this study reviews the current academic research on blockchain,especially in the subject area of business and economics.Based on a systematic review of the literature retrieved from the Web of Science service,we explore the top-cited articles,most productive countries,and most common keywords.Additionally,we conduct a clustering analysis and identify the following five research themes:“economic benefit,”“blockchain technology,”“initial coin offerings,”“fintech revolution,”and“sharing economy.”Recommendations on future research directions and practical applications are also provided in this paper. | Min Xu Xingtong Chen Gang Kou | 2019 | Financial Innovation2019，5，1： | 21 |
| 6 | Cryptocurrency trading:a comprehensive survey显示文摘In recent years,the tendency of the number of financial institutions to include crypto-currencies in their portfolios has accelerated.Cryptocurrencies are the first pure digital assets to be included by asset managers.Although they have some commonalities with more traditional assets,they have their own separate nature and their behaviour as an asset is still in the process of being understood.It is therefore important to summarise existing research papers and results on cryptocurrency trading,including available trading platforms,trading signals,trading strategy research and risk management.This paper provides a comprehensive survey of cryptocurrency trading research,by covering 146 research papers on various aspects of cryptocurrency trading(e.g.,cryptocurrency trading systems,bubble and extreme condition,prediction of volatility and return,crypto-assets portfolio construction and crypto-assets,technical trading and others).This paper also analyses datasets,research trends and distribution among research objects(contents/properties)and technologies,concluding with some promising opportunities that remain open in cryptocurrency trading. | Fan Fang Carmine Ventre Michail Basios Leslie Kanthan David Martinez-Rego Fan Wu Lingbo Li | 2022 | Financial Innovation2022，8，1： | 21 |
| 7 | Blockchain-based sharing services:What blockchain technology can contribute to smart cities显示文摘Background:The notion of smart city has grown popular over the past few years.It embraces several dimensions depending on the meaning of the word“smart”and benefits from innovative applications of new kinds of information and communications technology to support communal sharing.Methods:By relying on prior literature,this paper proposes a conceptual framework with three dimensions:(1)human,(2)technology,and(3)organization,and explores a set of fundamental factors that make a city smart from a sharing economy perspective.Results:Using this triangle framework,we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services.Conclusions:This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework.We hope it can stimulate interest in theory and practice to foster discussions in this area. | Jianjun Sun Jiaqi Yan Kem Z.K.Zhang | 2016 | Financial Innovation2016，2，1： | 20 |
| 8 | The transition from traditional banking to mobile internet finance:an organizational innovation perspective-a comparative study of Citibank and ICBC显示文摘The development of Financial Technology(FinTech)in areas such as mobile Internet,cloud computing,big data,search engines,and blockchain technology have significantly changed the financial industry.FinTech is expected to overturn the traditional banking business model,forcing banks to upgrade and transform.This study adopts a comparative case study method to contrast and analyze the Industrial and Commercial Bank of China(ICBC)and Citibank.It analyzes the strategies,organizations,HR systems,and product innovations adopted by these two banks in response to the impact of FinTech.This paper proposes an“electric vehicle”mode for ICBC and an“airplane mode”for Citibank.Further,it describes the difficulties encountered by the Chinese banking industry and proposes some feasible ways to upgrade.“Technology power”will become the core competitive concept for the financial institutions of the future. | Zhuming Chen Yushan Li Yawen Wu Junjun Luo | 2017 | Financial Innovation2017，3，1： | 19 |
| 9 | A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance显示文摘Background:Supply chain finance(SCF)is a series of financial solutions provided by financial institutions to suppliers and customers facing demands on their working capital.As a systematic arrangement,SCF utilizes the authenticity of the trade between(SMEs)and their“counterparties”,which are usually the leading enterprises in their supply chains.Because in these arrangements the leading enterprises are the guarantors for the SMEs,the credit levels of such counterparties are becoming important factors of concern to financial institutions’risk management(i.e.,commercial banks offering SCF services).Thus,these institutions need to assess the credit risks of the SMEs from a view of the supply chain,rather than only assessing an SME’s repayment ability.The aim of this paper is to research credit risk assessment models for SCF.Methods:We establish an index system for credit risk assessment,adopting a view of the supply chain that considers the leading enterprise’s credit status and the relationships developed in the supply chain.Furthermore,We conducted two credit risk assessment models based on support vector machine(SVM)technique and BP neural network respectly.Results:(1)The SCF credit risk assessment index system designed in this paper,which contained supply chain leading enterprise’s credit status and cooperative relationships between SMEs and leading enterprises,can help banks to raise their accuracy on predicting a small and medium enterprise whether default or not.Therefore,more SMEs can obtain loans from banks through SCF.(2)The SCF credit risk assessment model based on SVM is of good generalization ability and robustness,which is more effective than BP neural network assessment model.Hence,Banks can raise the accuracy of credit risk assessment on SMEs by applying the SVM model,which can alleviate credit rationing on SMEs.Conclusions:(1)The SCF credit risk assessment index system can solve the problem of banks incorrectly labeling a creditworthy enterprise as a default enterprise,and thereby improve the credit rating status in the process of SME financing.(2)By analyzing and comparing the empirical results,we find that the SVM assessment model,on evaluating the SME credit risk,is more effective than the BP neural network assessment model.This new assessment model based on SVM can raise the accuracy of classification between good credit and bad credit SMEs.(3)Therefore,the SCF credit risk assessment index system and the assessment model based on SVM,is the optimal combination for commercial banks to use to evaluate SMEs’credit risk. | Lang Zhang Haiqing Hu Dan Zhang | 2015 | Financial Innovation2015，1，1： | 17 |
| 10 | Overview of business innovations and research opportunities in blockchain and introduction to the special issue显示文摘Blockchain has become a new frontier of venture capitals that has attracted the attention of banks,governments,and other business corporations.The recent blockchain related attempts included legal blockchains by Fadada.com and Microsoft and pork tracking blockchains by Walmart and IBM.Blockchain is poised to become the most exciting invention after the Internet;while the latter connects the world to enable new business models based on online business processes,the former will help resolve the trust issue more efficiently via network computing.In this paper,we give an overview on blockchain research and development as well as introduce the papers in this special issue.We show that while blockchain has enabled Bitcoin,the most successful digital currency,its widespread adoption in finance and other business sectors will lead to many business innovations as well as many research opportunities. | J.Leon Zhao Shaokun Fan Jiaqi Yan | 2016 | Financial Innovation2016，2，1： | 17 |
| 11 | Analysis and outlook of applications of blockchain technology to equity crowdfunding in China显示文摘Equity crowdfunding via the Internet is a new channel of raising money for startups.It features low barriers to entry,low cost,and high speed,and thus encourages innovation.In recent years,equity crowdfunding in China has experienced some developments.However,some problems remain unsolved in practice.Blockchain is a decentralized and distributed ledger technology to ensure data security,transparency,and integrity.Because it cannot be tampered with or forged,the technology is deemed to have great potential in the finance industry.This study examines current problems in the practice of equity crowdfunding in China.Based on the analysis of the characteristics of blockchain technology,this study further explores its practical applications in equity crowdfunding.1)Blockchain technology may be a secure,efficient,low-cost solution for the registration of stocks and shares of a firm financed by crowdfunding;2)Blockchain technology simplifies the transaction and transfer of crowdfunding equities,and thus facilitates their circulation;3)Blockchain technology enables peer to peer transactions between investors and entrepreneurs,and solves the problems of regulatory compliance and security of fund management;Blockchain technology can be used to develop a voting system for crowdfunders,which enables them to be involved in corporate governance.This helps protect the rights and interests of small investors;5)Blockchain technology helps regulators know about market conditions,and supports regulatory activities such as managing investors and fighting money laundering. | Huasheng Zhu Zach Zhizhong Zhou | 2016 | Financial Innovation2016，2，1： | 15 |
| 12 | Analysis of Typical Automakers’ Strategies for Meeting the Dual-Credit Regulations Regarding CAFC and NEVs显示文摘The parallel corporate average fuel consumption(CAFC)and new energy vehicle(NEV)credit schemes that have been introduced by the Ministry of Industry and Information Technology of China is an innovative attempt to simultaneously regulate conventional gasoline vehicles(CGVs)and NEVs in the passenger vehicle sector that is expected to function as a long-term management mechanism for CGVs to be more energy-efficient and NEVs to be well-promoted.This will have a significant impact on trends in China’s automotive industry and automakers’business decisions.Taking the cases of four typical automakers with different levels of average fuel economy in their CGVs and advanced NEV production,scenario analysis has been applied to generate these automakers’alternatives in relation to compliance with the dual-credit regulations in force from 2017 to 2020 based on the Interim Measures on the Joint Management of CAFC and NEV Credits(Draft).These automakers’alternative approaches to compliance are compared.Further,in view of the financial losses as a result of halted production if they fail to comply,the values of CAFC and NEV credits and corresponding influencing factors are analyzed from the automakers’perspective.Finally,the most cost-effective compliance strategies for these automakers are summarized and suggested improvements in the regulations are proposed for the government. | Yue Wang Fuquan Zhao Yinshuo Yuan Han Hao Zongwei Liu | 2018 | Automotive Innovation2018，1，1： | 14 |
| 13 | Orderly Porous Covalent Organic Frameworks-based Materials:Superior Adsorbents for Pollutants Removal from Aqueous Solutions显示文摘Covalent organic frameworks(COFs)are a new type of crystalline porous polymers known for chemical stability,excellent structural regularity,robust framework,and inherent porosity,making them promising materials for capturing various types of pollutants from aqueous solutions.This review thoroughly presents the recent progress and advances of COFs and COF-based materials as superior adsorbents for the efficient removal of toxic heavy metal ions,radionuclides,and organic pollutants.Information about the interaction mechanisms between various pollutants and COF-based materials are summarized from the macroscopic and microscopic standpoints,including batch experiments,theoretical calculations,and advanced spectroscopy analysis.The adsorption properties of various COF-based materials are assessed and compared with other widely used adsorbents.Several commonly used strategies to enhance COF-based materials’adsorption performance and the relationship between structural property and sorption ability are also discussed.Finally,a summary and perspective on the opportunities and challenges of COFs and COF-based materials are proposed to provide some inspiring information on designing and fabricating COFs and COF-based materials for environmental pollution management. | Xiaolu Liu Hongwei Pang Xuewei Liu Qian Li Ning Zhang Liang Mao Muqing Qiu Baowei Hu Hui Yang Xiangke Wang | 2021 | The Innovation2021，2，1： | 12 |
| 14 | Carbon neutrality:Toward a sustainable future显示文摘Carbon neutrality refers to net-zero carbon dioxide(CO2)emissions attained by balancing the emission of CO2 with its removal so as to stop its increase in the atmosphere that causes global warming.As of February 2021,124 countries had pledged to achieve carbon neutrality by 2050 or 2060.This is a remarkable development reached after the annual United Nations Conference of the Parties of 1995,in particular those of Kyoto(1997),Bonn(2001),Bali(2007),and Paris(2015),with progressively more concrete binding commitments to emission reduction by the parties(countries). | Jing M.Chen | 2021 | The Innovation2021，2，3： | 10 |
| 15 | Predicting the daily return direction of the stock market using hybrid machine learning algorithms显示文摘Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks. | Xiao Zhong David Enke | 2019 | Financial Innovation2019，5，1： | 9 |
| 16 | New Lunar Samples Returned by Chang’e-5:Opportunities for New Discoveries and International Collaboration显示文摘On December 16^(th),2020 at 17:59 UTC,the sample capsule of Chang’e-5 successfully landed at Dorbod Banner,Inner Mongolia,China.It is a milestone for the Chinese Lunar Exploration Program(CLEP),which has achieved the goals of its first three phases:orbiting,landing,and sample return.China becomes the third country to return samples from the Moon after the United States and the Soviet Union.Forty-four years since the Luna 24 mission in 1976,new lunar samples have been returned to Earth. | Wei Yang Yangting Lin | 2021 | The Innovation2021，2，1： | 8 |
| 17 | Progress in Automotive Transmission Technology显示文摘Much progress has been made in the development of automotive transmissions over the past 20 years,e.g.,an increased speed number,expanded ratio spread and improved efficiency and shift quality.Automotive transmissions are moving toward electrification in response to stringent legislation on emissions and the pressing demand for better fuel economy.This paper reviews progress in automotive transmission technology.Assisted by computer-aided programs,new transmission schemes are constantly being developed.We therefore first introduce the synthesis of the transmission scheme and parameter optimization.We then discuss the progress in the transmission technology of a conventional internal combustion engine vehicle in terms of new layouts;improved efficiency;noise,vibration and harshness technology;and the shifting strategy and control technology.As the major development trend,transmission electrification is subsequently discussed;this discussion includes the configuration design,energy management strategy,hybrid mode shifting control,single-speed and multi-speed electric vehicle transmission and distributed electric drive.Finally,a summary and outlook are presented for conventional automotive transmissions,hybrid transmissions and electric vehicle transmissions. | Xiangyang Xu Peng Dong Yanfang Liu Hui Zhang | 2018 | Automotive Innovation2018，1，3： | 8 |
| 18 | Recent Progress in Automotive Gasoline Direct Injection Engine Technology显示文摘Gasoline direct injection(GDI)engines are currently the dominant powertrains for passenger cars.With the implementation of increasingly stringent fuel consumption and emission regulationsworldwide,GDI engines are facing challenges owing to high particulate matter emissions and a tendency to knock,leading to a change in the research and design(R&D)issues compared with those in the twentieth century.This paper reviews the progress in research regarding GDI engine technologies over the past 20 years,focusing on combustion system configurations,and also highlights common issues in GDI R&D,including pre-ignition and deto-knock,soot formation and PM emissions,injector deposits and gasoline compression ignition(GCI).First,an overview of recent developments in the field as driven by regulations is provided,following which progress in injection and combustion systems is examined.Third,the review addresses the occurrence and mechanism of deto-knock and considers means of suppressing this phenomenon.The fourth section discusses soot formation mechanisms and particulate matter emission characteristics of GDI engines and describes the application of gasoline particulate filter(GPF)after-treatment.The subsequent section summarizes studies regarding injector deposit formation,as well as pioneering research into GCI combustion modes.Finally,a summary and future prospects for GDI engine technologies are provided. | Shijin Shuai Xiao Ma Yanfei Li Yunliang Qi Hongming Xu | 2018 | Automotive Innovation2018，1，2： | 8 |
| 19 | OPTICAL PROPERTIES OF SKIN,SUBCUTANEOUS,AND MUSCLE TISSUES:A REVIEW显示文摘The development of optical methods in modern medicine in the areas of diagnostics,therapy,and surgery has stimulated the investigation of optical properties of various biological tissues,since the efficacy of laser treatment depends on the photon propagation and fluence rate distribution within irradiated tissues.In this work,an overview of published absorption and scattering properties of skin and subcutaneous tissues measured in wide wavelength range is presented.Basic principles of measurements of the tissue optical properties and techniques used for processing of the measured data are outlined. | ALEXEY N.BASHKATOV ELINA A.GENINA VALERY V.TUCHIN | 2011 | Journal of Innovative Optical Health Sciences2011，4，1： | 8 |
| 20 | Driving-Cycle-Aware Energy Management of Hybrid Electric Vehicles Using a Three-Dimensional Markov Chain Model显示文摘This study developed a new online driving cycle prediction method for hybrid electric vehicles based on a three-dimensional stochastic Markov chain model and applied the method to a driving-cycle-aware energy management strategy.The impacts of different prediction time lengths on driving cycle generation were explored.The results indicate that the original driving cycle is compressed by 50%,which significantly reduces the computational burden while having only a slight effect on the prediction performance.The developed driving cycle prediction method was implemented in a real-time energy management algorithm with a hybrid electric vehicle powertrain model,and the model was verified by simulation using two different testing scenarios.The testing results demonstrate that the developed driving cycle prediction method is able to efficiently predict future driving tasks,and it can be successfully used for the energy management of hybrid electric vehicles. | Bolin Zhao Chen Lv Theo Hofman | 2019 | Automotive Innovation2019，2，2： | 7 |
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