The Impact of“Fintech+Financial Regulation”Matching Level on Financial Risk:Evidence from China

Preventing financial risk is an important topic that academic circles and the government have paid attention to for a long time.The development of fintech and the improvement of financial regulation will affect the level of financial risk.The relationship between the degree of matching between fintech and financial regulation and financial risk is explored,which is crucial for reducing financial risk.Panel data from 31 provinces in China from 2011 to 2020 is used to explore the impact of fintech and financial regulatory matching levels on financial risk.The study finds that the improved matching level between fintech and financial regulation helps reduce financial risk.The degree of matching between fintech and financial regulation affects financial risk through financial efficiency.

Phase separation and transcriptional regulation in cancer development

Liquid-liquid phase separation,a novel biochemical phenomenon,has been increasingly studied for its medical applications.It underlies the formation of membrane-less organelles and is involved in many cellular and biological processes.During transcriptional regulation,dynamic condensates are formed through interactions between transcriptional elements,such as transcription factors,coactivators,and mediators.Cancer is a disease characterized by uncontrolled cell proliferation,but the precise mechanisms underlying tumorigenesis often remain to be elucidated.Emerging evidence has linked abnormal transcriptional condensates to several diseases,especially cancer,implying that phase separation plays an important role in tumorigenesis.Condensates formed by phase separation may have an effect on gene transcription in tumors.In the present review,we focus on the correlation between phase separation and transcriptional regulation,as well as how this phenomenon contributes to cancer development.

Transcriptional regulation in the development and dysfunction of neocortical projection neurons

Glutamatergic projection neurons generate sophisticated excitatory circuits to integrate and transmit information among different cortical areas,and between the neocortex and other regions of the brain and spinal cord.Appropriate development of cortical projection neurons is regulated by certain essential events such as neural fate determination,proliferation,specification,differentiation,migration,survival,axonogenesis,and synaptogenesis.These processes are precisely regulated in a tempo-spatial manner by intrinsic factors,extrinsic signals,and neural activities.The generation of correct subtypes and precise connections of projection neurons is imperative not only to support the basic cortical functions(such as sensory information integration,motor coordination,and cognition)but also to prevent the onset and progression of neurodevelopmental disorders(such as intellectual disability,autism spectrum disorders,anxiety,and depression).This review mainly focuses on the recent progress of transcriptional regulations on the development and diversity of neocortical projection neurons and the clinical relevance of the failure of transcriptional modulations.

Compartmentalized regulation of organelle integrity in neurodegenerative diseases:lessons from the Drosophila motor neuron

Neurons are highly polarized,morphologically asymmetric,and functionally compartmentalized cells that contain long axons extending from the cell body.For this reason,their maintenance relies on spatiotemporal regulation of organelle distribution between the somatodendritic and axonal domains.Although some organelles,such as mitochondria and smooth endoplasmic reticulum,are widely distributed throughout the neuron,others are segregated to either the somatodendritic or axonal compartment.For example,Golgi outposts and acidified lysosomes are predominantly present in the somatodendritic domain and rarely distributed along the axon,whereas newly formed autophagosomes and synaptic vesicles are mainly distributed in the distal axon(Britt et al.,2016).

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

Dynamic regulation of the irrigation-nitrogen-biochar nexus for the synergy of yield,quality,carbon emission and resource use efficiency in tomato

Integrated water and fertilizer management is important for promoting sustainable development of facility agriculture,and biochar plays an important role in guaranteeing food production,as well as alleviating water shortages and the overuse of fertilizers.The field experiment had twelve treatments and a control(CK)trial including two irrigation amounts(I1,100%ETm;I2,60%ETm;where ETm is the maximum evapotranspiration),two nitrogen applications(N1,360 kg ha^(−1);N2,120 kg ha^(−1))and three biochar application levels(B1,60 t ha^(−1);B_(2),30 t ha^(−1)and B3,0 t ha^(−1)).A multi-objective synergistic irrigation-nitrogen-biochar application system for improving tomato yield,quality,water and nitrogen use efficiency,and greenhouse emissions was developed by integrating the techniques of experimentation and optimization.First,a coupled irrigation-nitrogen-biochar plot experiment was arranged.Then,tomato yield and fruit quality parameters were determined experimentally to establish the response relationships between irrigation-nitrogen-biochar dosage and yield,comprehensive quality of tomatoes(TCQ),irrigation water use efficiency(IWUE),partial factor productivity of nitrogen(PFPN),and net greenhouse gas emissions(NGE).Finally,a multi-objective dynamic optimization regulation model of irrigation-nitrogen-biochar resource allocation at different growth stages of tomato was constructed which was solved by the fuzzy programming method.The results showed that the application of irrigation and nitrogen to biochar promoted increase in yield,IWUE and PFPN,while it had an inhibitory effect on NGE.In addition,the optimal allocation amounts of water and fertilizer were different under different scenarios.The yield of the S1 scenario increased by 8.31%compared to the B_(1)I_(1)N_(2) treatment;TCQ of the S2 scenario increased by 5.14%compared to the B_(2)I_(2)N_(1) treatment;IWUE of the S3 scenario increased by 10.01%compared to the B1I2N2 treatment;PFPN of the S4 scenario increased by 9.35%compared to the B_(1)I_(1)N_(2) treatment;and NGE of the S5 scenario decreased by 11.23%compared to the B_(2)I1N1 treatment.The optimization model showed that the coordination of multiple objectives considering yield,TCQ,IWUE,PFPN,and NGE increased on average from 4.44 to 69.02%compared to each treatment when the irrigation-nitrogen-biochar dosage was 205.18 mm,186 kg ha^(−1)and 43.31 t ha^(−1),respectively.This study provides a guiding basis for the sustainable management of water and fertilizer in greenhouse tomato production under drip irrigation fertilization conditions.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

A short overview of the lead iodide residue impact and regulation strategies in perovskite solar cells

Lead iodide(PbI2) is a vital raw material for preparing perovskite solar cells(PSCs),and it not only takes part in forming the light absorption layer but also remains in the grain boundary as a passivator.In other words,the PbI2 content in the precursor and as formed film will affect the efficiency and stability of the PSCs.With moderate residual PbI2,it passivates the bulk/surface defects of perovskite,reduces the interfacial recombination,promotes the perovskite stability,minimizes the device hysteresis,and so on.Deficient PbI2 residue will reduce the interfacial passivation effect and device performance.In addition to facilitating the non-radiative recombination,over PbI2 residue can also lead to electronic insulation in the grain boundary and deteriorate the device performance.However,the impact and regulation of PbI2 residue on the device performance and stability is still not fully understood.Herein,a comprehensive and detailed review is presented by discussing the PbI2 residue impact and its regulation strategies(i.e., elimination,facilitation and conversion of the residue PbI2) to manipulate the PbI2 content,distribution and forms.Finally,we also show future outlooks in this field,with an aim to help further the progression of high-efficiency and stable PSCs.

Rice melatonin deficiency causes premature leaf senescence via DNA methylation regulation

In a study of DNA methylation changes in melatonin-deficient rice mutants,mutant plants showed premature leaf senescence during grain-filling and reduced grain yield.Melatonin deficiency led to transcriptional reprogramming,especially of genes involved in chlorophyll and carbon metabolism,redox regulation,and transcriptional regulation,during dark-induced leaf senescence.Hypomethylation of mCG and mCHG in the melatonin-deficient rice mutants was associated with the expression change of both protein-coding genes and transposable element-related genes.Changes in gene expression and DNA methylation in the melatonin-deficient mutants were compensated by exogenous application of melatonin.A decreased S-adenosyl-L-methionine level may have contributed to the DNA methylation variations in rice mutants of melatonin deficiency under dark conditions.

Flexible bidirectional pulse charging regulation achieving long-life lithium-ion batteries

Typical application scenarios,such as vehicle to grid(V2G)and frequency regulation,have imposed significant long-life demands on lithium-ion batteries.Herein,we propose an advanced battery life-extension method employing bidirectional pulse charging(BPC)strategy.Unlike traditional constant current charging methods,BPC strategy not only achieves comparable charging speeds but also facilitates V2G frequency regulation simultaneously.It significantly enhances battery cycle ampere-hour throughput and demonstrates remarkable life extension capabilities.For this interesting conclusion,adopting model identification and postmortem characterization to reveal the life regulation mechanism of BPC:it mitigates battery capacity loss attributed to loss of lithium-ion inventory(LLI)in graphite anodes by intermittently regulating the overall battery voltage and anode potential using a negative charging current.Then,from the perspective of internal side reaction,the life extension mechanism is further revealed as inhibition of solid electrolyte interphase(SEI)and lithium dendrite growth by regulating voltage with a bidirectional pulse current,and a semi-empirical life degradation model combining SEI and lithium dendrite growth is developed for BPC scenarios health management,the model parameters are identified by genetic algorithm with the life simulation exhibiting an accuracy exceeding 99%.This finding indicates that under typical rate conditions,adaptable BPC strategies can extend the service life of LFP battery by approximately 123%.Consequently,the developed advanced BPC strategy offers innovative perspectives and insights for the development of long-life battery applications in the future.

Investigation on mechanical properties regulation of rock-like specimens based on 3D printing and similarity quantification

3D printing is widely adopted to quickly produce rock mass models with complex structures in batches,improving the consistency and repeatability of physical modeling.It is necessary to regulate the mechanical properties of 3D-printed specimens to make them proportionally similar to natural rocks.This study investigates mechanical properties of 3D-printed rock analogues prepared by furan resin-bonded silica sand particles.The mechanical property regulation of 3D-printed specimens is realized through quantifying its similarity to sandstone,so that analogous deformation characteristics and failure mode are acquired.Considering similarity conversion,uniaxial compressive strength,cohesion and stress–strain relationship curve of 3D-printed specimen are similar to those of sandstone.In the study ranges,the strength of 3D-printed specimen is positively correlated with the additive content,negatively correlated with the sand particle size,and first increases then decreases with the increase of curing temperature.The regulation scheme with optimal similarity quantification index,that is the sand type of 70/140,additive content of 2.5‰and curing temperature of 81.6℃,is determined for preparing 3D-printed sandstone analogues and models.The effectiveness of mechanical property regulation is proved through uniaxial compression contrast tests.This study provides a reference for preparing rock-like specimens and engineering models using 3D printing technology.

Research on the Legal Framework of Content Regulations for Network Platforms

In the era of the Internet,various network platforms have evolved into new hubs for information dissemination.Currently,China has established a platform-centered content regulation framework,wherein platforms proactively enforce content regulations in accordance with legal censorship obligations.Additionally,platform policies and user agreements augment their authority in content regulation.The platforms can achieve cost-effective and highly efficient content regulation by leveraging their strategic advantages enabled by their own technical capabilities and extensive coverage.The platform self-regulation model,however,still faces challenges.First,accurately evaluating content remains a formidable task;second,ensuring effective platform publicity through self-regulation poses difficulties;third,users may potentially face disadvantages due to the platform’s right of self-regulation;and fourth,digital copyright owners face challenges when defending digital copyright disputes under the safe harbor rule.Therefore,it is imperative to establish,review,and revise the legal framework for content regulation of network platforms in order to enhance the efficiency of their governance systems.The formulation of the legal framework for content regulation of network platforms may encompass the following aspects:rationalizing obligations pertaining to platform content regulations,enhancing supervision over platform self-regulation,and establishing a dual-track responsibility system for digital copyright content regulation.This will ensure a harmonious balance among public interests,users’personal rights and interests,and commercial benefits through regulating the content on network platforms.

Exploring the combination of biochar‐amended soil and automated irrigation technology for water regulation and preservation in green infrastructure

Biochar is a carbon sink material with the potential to improve water retention in various soils.However,for the long‐term maintenance of green infrastructure,there is an additional need to regulate the water contents in the covers to maintain vegetation growth in semiarid conditions.In this study,biochar‐amended soil was combined with subsurface drip irrigation,and the water preservation characteristics of this treatment were investigated through a series of one‐dimensional soil column tests.To ascertain the best treatment method specific to semiarid climatic conditions,the test soil was amended with 0%,1%,3%,and 5%biochar.Automatic irrigation devices equipped with soil moisture sensors were used to control the subsurface water content with the aim of enhancing vegetation growth.Each soil column test lasted 150 h,during which the volumetric water contents and soil suction data were recorded.The experimental results reveal that the soil specimen amended with 3%biochar is the most water‐saving regardless of the time cost.Soil with a higher biochar content(e.g.,5%)consumes a more significant amount of water due to the enhancement of the water‐holding capacity.Based on the experimental results,it can be concluded that the appropriate ratio can be determined within 1%–3%,which can reduce not only the amount of irrigated/used water but also the time cost.Such technology can be explored for water content regulation in green infrastructure and the development of barriers for protecting the environment around deep underground waste containment.

Primary and Secondary Microplastic and Nanoplastic Regulations: Perspectives on Water Industry Impacts

Plastic pollution, both primary and secondary, is an environmental challenge, prompting regulatory actions at global and regional levels. With increasing research about the impacts of micro- and nanoplastics on ecosystems and human health, governments worldwide have undertaken efforts to address this issue. The global community has advanced regulatory efforts that have led to the implementation of national laws or regulations in numerous countries. These measures ban or restrict the use of primary and secondary microplastics and nanoplastics. An overview of global and European regulatory frameworks is presented to provide context for the United States (US). In the endeavor to document plastics regulation development in the US, a description of regulatory agencies and their roles in advancing policy and rules as well as future direction and areas for improvement in regulating plastics are introduced. A discussion of what emerging regulations mean to utilities and industries is also provided along with a comparison of regulatory frameworks.

New food sources and production systems:a comparison of international regulations and China’s advancements in novel foods with synthetic biology

The global shift towards sustainable food systems has sparked innovations in food sources and production systems,including cell-based meat,plant-based food products,precision fermentation,and 3D food printing.These advancements pose regulatory challenges and opportunities,with China emerging as a critical player in adopting and regulating new food technologies.This review explores the international landscape of new food sources and production systems(NFPS),focusing on China’s role and regulatory approaches compared to global practices.Through this comparative analysis,we aim to contribute to the ongoing dialogue on food safety regulation,offering insights and recommendations for policymakers,industry stakeholders,and researchers engaged in the global food system’s evolution.This comprehensive overview underscores the dynamic nature of regulatory frameworks governing NFPS,highlighting the international efforts to ensure food safety,consumer protection,and the sustainable evolution of the food industry.

Electronic structure and spin state regulation of vanadium nitride via a sulfur doping strategy toward flexible zinc-air batteries

Owing to the distinctive structural characteristics,vanadium nitride(VN)is highly regarded as a catalyst for oxygen reduction reaction(ORR)in zinc-air batteries(ZABs).However,VN exhibits limited intrinsic ORR activity due to the weak adsorption ability to O-containing species.Here,the S-doped VN anchored on N,S-doped multi-dimensional carbon(S-VN/Co/NS-MC)was constructed using the solvothermal and in-situ doping methods.Incorporating sulfur atoms into VN species alters the electron spin state of vanadium in the S-VN/Co/NS-MC for regulating the adsorption energy of vanadium sites to oxygen molecules.The introduced sulfur atoms polarize the V 3d_(z)^(2) electrons,shifting spin-down electrons closer to the Fermi level in the S-VN/Co/NS-MC.Consequently,the introduction of sulfur atoms into VN species enhances the adsorption energy of vanadium sites for oxygen molecules.The*OOH dissociation transitions from being unspontaneous on the VN surface to a spontaneous state on the S-doped VN surface.Then,the ORR barrier on the S-VN/Co/NS-MC surface is reduced.The S-VN/Co/NS-MC demonstrates a higher half-wave potential and limiting current density compared to the VN/Co/N-MC.The S-VN/Co/NS-MC-based liquid ZABs display a power density of 195.7 m W cm^(-2),a specific capacity of 815.7 m A h g^(-1),and a cycling stability exceeding 250 h.The S-VN/Co/NS-MC-based flexible ZABs are successfully employed to charge both a smart watch and a mobile phone.This approach holds promise for advancing the commercial utilization of VN-based catalysts in ZABs.

Comparative proteomics analysis reveals the domesticated Lepista sordida primordium differentiation regulation mechanism and the subsequent different development patterns in the pileus and stipe

The wild Lepista sordida is a kind of precious and rare edible fungus.An excellent strain of it by artificial domestication was obtained,which was high-yield and high in iron content.In this study,high-throughput comparative proteomics was used to reveal the regulatory mechanism of its primordium differentiation in the early fruiting body formation.The mycelium before the primordium differentiation mainly expressed high levels of mitochondrial functional proteins and carbon dioxide concentration regulatory proteins.In young mushrooms,the highly expressed proteins were mainly involved in cell component generation,cell proliferation,nitrogen compound metabolism,nucleotide metabolism,glutathione metabolism,and purine metabolism.The differential regulation patterns of pileus and stipe growth to maturity were also revealed.The highly expressed proteins related to transcription,RNA splicing,the production of various organelles,DNA conformational change,nucleosome organization,protein processing,maturation and transport,and cell detoxification regulated the pileus development and maturity.The proteins related to carbohydrate and energy metabolism,large amounts of obsolete cytoplasmic parts,nutrient deprivation,and external stimuli regulated the stipe development and maturity.Multiple CAZymes regulated nutrient absorption,morphogenesis,spore production,stress response,and other life activities at different growth and development stages.

Transcriptional regulation of MdPIN7 by MdARF19 during gravityinduced formation of adventitious root GSA in self-rooted apple stock

Self-rooted apple stock is widely used for apple production.However,the shallowness of the adventitious roots in self-rooted apple stock leads to poor performance in the barren orchards of China.This is because of the considerable difference in the development of a gravitropic set-point angle(GSA)between self-rooted apple stock and seedling rootstock.Therefore,it is crucial to study the molecular mechanism of adventitious root GSA in self-rooted apple stock for breeding self-rooted and deep-rooted apple rootstock cultivars.An apple auxin response factor MdARF19 functioned to establish the adventitious root GSA of self-rooted apple stock in response to gravity and auxin signals.MdARF19 bound directly to the MdPIN7 promoter,activating its transcriptional expression and thus regulating the formation of the adventitious root GSA in 12-2 self-rooted apple stock.However,MdARF19 influenced the expression of auxin efflux carriers(MdPIN3 and MdPIN10)and the establishment of adventitious root GSA of self-rooted apple stock in response to gravity signals by direct activation of MdFLP.Our findings provide new information on the transcriptional regulation of MdPIN7 by auxin response factor MdARF19 in the regulation of the adventitious root GSA of self-rooted apple stock in response to gravity and auxin signals.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.