Backup Mechanism Research Articles

Advancements in AI-Driven Disaster Recovery: Predictive Failure Detection and Automated Data Protection

This article explores the transformative impact of artificial intelligence (AI) on disaster recovery systems in information technology. It examines how AI-driven solutions are revolutionizing traditional approaches to data protection and business continuity through advanced predictive analytics, automated backup mechanisms, and intelligent recovery processes. The research highlights the significant improvements achieved in recovery times, with some implementations reporting up to 60% faster recovery compared to conventional methods. Key aspects discussed include AI-powered predictive failure detection, automated data backup mechanisms, and the acceleration of recovery times. The article also delves into the enhancement of data availability through AI-driven replication strategies and intelligent failover mechanisms, emphasizing their critical role in maintaining operational resilience. Additionally, it addresses the challenges and considerations in implementing these advanced systems, including integration with existing infrastructure and the necessary adaptation of IT personnel. The article concludes by exploring future directions in AI algorithms for disaster recovery, potential integrations with cloud-based solutions, and the broader applications of these technologies across various industries. This comprehensive analysis underscores the pivotal role of AI in shaping the future of disaster recovery and business continuity strategies in an increasingly digital world.

Dual roles of inflammatory programmed cell death in cancer: insights into pyroptosis and necroptosis.

Programmed cell death (PCD) is essential for cellular homeostasis and defense against infections, with inflammatory forms like pyroptosis and necroptosis playing significant roles in cancer. Pyroptosis, mediated by caspases and gasdermin proteins, leads to cell lysis and inflammatory cytokine release. It has been implicated in various diseases, including cancer, where it can either suppress tumor growth or promote tumor progression through chronic inflammation. Necroptosis, involving RIPK1, RIPK3, and MLKL, serves as a backup mechanism when apoptosis is inhibited. In cancer, necroptosis can enhance immune responses or contribute to tumor progression. Both pathways have dual roles in cancer, acting as tumor suppressors or promoting a pro-tumorigenic environment depending on the context. This review explores the molecular mechanisms of pyroptosis and necroptosis, their roles in different cancers, and their potential as therapeutic targets. Understanding the context-dependent effects of these pathways is crucial for developing effective cancer therapies.

ЕФЕКТИВНІ СХОВИЩА ДАНИХ ДЛЯ РІШЕНЬ МАШИННОГО НАВЧАННЯ

Awareness of the importance of using effective data warehouses to develop machine learning solutions in today's world is becoming increasingly relevant in connection with the growth of data volumes and the growing need for accurate and productive models. The article identifies major vital requirements for data storage systems to ensure effective data management in building machine learning solutions. The study underscores the practical implications of rapid data access and minimal latency, reliable storage systems with robust backup and recovery mechanisms, high-level security, flexibility in handling different data types, and cost-effectiveness. These factors are not just theoretical considerations but directly impact the efficiency and success of machine learning solutions. Together with describing the concepts of block storage and object storage, the article also compares major cloud platforms—AWS (Amazon Web Services), Microsoft Azure, and Google Cloud Platform (GCP)—each providing a comprehensive range of flexible and customizable storage, security, and data processing services. These platforms enable users to manage their information resources flexibly and efficiently, offering unique features that cater to the specific needs of machine learning applications. Understanding the nuances of each provider's offerings empowers engineers and researchers to make informed decisions that align with their unique requirements. The article provides a thorough overview of the key considerations and available options, guiding strategic decisions to support machine learning initiatives effectively. Also the importance of staying up to date with the evolution of major platforms offerings is emphasized. In conclusion, selecting the proper data storage solution is critical for enhancing the performance, security, and cost-efficiency of machine learning models.

A low-storage synchronization framework for blockchain systems

The advent of blockchain technology has brought major changes to traditional centralized storage. Therefore, various fields have begun to study the application and development of blockchain. However, blockchain technology has a serious shortcoming of data bloating. The reason is that blockchain technology achieves decentralization by storing complete blockchain data at each node, incurring a significant amount of blockchain data. Therefore, each node must spend significant amount of storage space and initialization synchronization time. To solve the above problems, in this research, we propose a secure and agile synchronization framework for low storage blockchains. First, we design a K-extreme segment algorithm, which reduces the synchronization time by returning only the first and last k blocks of each block segment at once to the local storage. Next, we decentrally store the block data of the blockchain by IPFS and establish a backup mechanism by IPFS-cluster. Finally, due to use of distributed storage, the nodes must request un-stored block data from IPFS, causing an increase in the throughput of the blockchain network. To avoid network congestion, we propose the working set algorithm to improve the hit ratio of the local storage and reduce the number of requests to decrease throughput. In summary, our experiments demonstrate that the ratio of full nodes to low storage nodes is significantly lower for nodes with higher storage limits compared to those with lower storage limits. In other words, a higher storage limit results in more low storage nodes which can be permitted to ensure that the blockchain network is robust and reliable. Therefore, our proposed framework can provide reliable low storage nodes for the blockchain. The node can reduce the local storage pressure and can still maintain the full functionality of blockchains.

Sustainable production and dissemination of genetically improved farmed tilapia seed in smallholder farming systems: A case study from Timor-Leste

CONTEXTTimor-Leste's National Aquaculture Development Strategy (NADS) 2012–30 aims to achieve an annual aquaculture production of 12,000 tons by 2030 to raise per capita fish consumption from 6.1 kg in 2010 to 15 kg. Genetically Improved Farmed Tilapia (GIFT) is identified as the most suitable species under Timor-Leste's existing farming systems and agro-ecological context. It is expected to account for two-thirds of total aquaculture production, requiring over 30 million fingerlings annually, necessitating a strong role of public and private sectors to develop a sustainable hatchery model for producing and supplying high quality GIFT fingerlings to fish farmers on a mass-scale. OBJECTIVEThe major objective was to conceptualize and apply a public-private partnership (PPP) model to produce and disseminate high-quality monosex GIFT seed by public and private hatcheries across Timor-Leste. METHODSModel GIFT hatcheries were established and monitored using a standard protocol developed by the R&D team. Data on total eggs, clutch size, incubation survival rate, swim-up fry, and monosex fry produced were recorded on a weekly basis. The performance of hatcheries was compared using descriptive statistics at a 5% level of significance. Economic analysis was conducted to compare production costs, gross profits, and net profits among hatcheries and nurseries. Enabling conditions and factors for effective PPP were identified. RESULTS AND CONCLUSIONSThe WorldFish, in collaboration with Timor-Leste's Ministry of Agriculture and Fisheries, upgraded an existing public hatchery in Gleno, Ermera, introduced GIFT in 2015, and developed it as a GIFT broodstock centre to supply quality brood fry to other hatcheries. To produce monosex seed on a mass-scale, four PPP model GIFT hatcheries were established between 2019 and 2023. Results from a public hatchery and two initially established PPP model hatcheries showed that the hatchery in Parlamento had significantly higher (P < 0.05) monthly production of eggs, swim-up fry, monosex fry, and clutch sizes. On average, over 177,000 monosex fry per month were produced by those three hatcheries. Local service providers (LSPs), self-employed youths who have been recruited and trained to facilitate input supply and output marketing, purchase GIFT fry from the hatcheries, nurse them to fingerling size, and supply them to farmers. To achieve high seed production and supply targets, scaling of PPP model hatcheries, nurseries, and LSPs along with effective training and technical back-up, favourable policies, institutional environments, and governance mechanisms, is necessary. SIGNIFICANCEThe PPP model GIFT hatcheries are scalable across inland farming systems in Timor-Leste and other countries with similar agro-ecological, socio-economic, and climatic contexts.

Reliablity and Security for Fog Computing Systems

Fog computing (FC) is a distributed architecture in which computing resources and services are placed on edge devices closer to data sources. This enables more efficient data processing, shorter latency times, and better performance. Fog computing was shown to be a promising solution for addressing the new computing requirements. However, there are still many challenges to overcome to utilize this new computing paradigm, in particular, reliability and security. Following this need, a systematic literature review was conducted to create a list of requirements. As a result, the following four key requirements were formulated: (1) low latency and response times; (2) scalability and resource management; (3) fault tolerance and redundancy; and (4) privacy and security. Low delay and response can be achieved through edge caching, edge real-time analyses and decision making, and mobile edge computing. Scalability and resource management can be enabled by edge federation, virtualization and containerization, and edge resource discovery and orchestration. Fault tolerance and redundancy can be enabled by backup and recovery mechanisms, data replication strategies, and disaster recovery plans, with a residual number system (RNS) being a promising solution. Data security and data privacy are manifested in strong authentication and authorization mechanisms, access control and authorization management, with fully homomorphic encryption (FHE) and the secret sharing system (SSS) being of particular interest.

Optimal sizing of multi-energy microgrid with electric vehicle integration: Considering carbon emission and resilience load

To address the intermittency of renewable energy sources, global warming, and increasing load demands, this paper proposes the optimal sizing of a multi-energy microgrid (MEMG) consisting of electrical, thermal, cooling, and hydrogen networks. The system integrates multi-energy storage and EVs along with the resilience load to facilitate a robust operation scheme. The paper introduces an improved resilience backup mechanism for EVs using hybrid storage. Then, random samples for stochastic parameters are generated with Monte Carlo Simulations (MCS). To this end, a mixed-integer linear programming-based model is developed to minimize cost, emissions, and load shed. Then, a CPLEX solver is applied to solve it efficiently. The optimal MEMG with the hydrogen network reduces cost by 4% and emissions by 40%. The case study validates that hybrid storage (BES-HST-TST) can effectively reduce the electricity grid purchase to zero, making MEMG self-sufficient while yielding the least annual system cost (ASC) of 3855562$ and decreasing emissions to 8385 kg, resulting in economic savings, environmental sustainability, and increased utilization of renewables. Notably, V2G can save 0.7546% of MEMG extra cost incurred due to EVs integration and significantly reduces CO2 emissions by 4%. A novel finding is that the proposed hybrid storage backup mechanism can effectively minimize the extra cost of keeping the resilience load by retaining 31.46% of backup in hydrogen form. It mitigates risk associated with power outages while achieving the least ASC of 3858933$ and CO2 emissions of 8043 kg. These results can help realize a green, cost-effective, and efficient energy system.

Comparative Analysis of PCC and ECMP Methods in Load Balancing Using GNS3 Simulator

Judging from daily activities, human beings heavily rely on the internet for communication purposes. and exchange information using either social media applications or browsers, vonsistently fast internet speeds are incredibly beneficial for performing tasks and activities, particularly for students and professionals. A sluggish internet connection can be frustrating and may lead to interruptions in online activities and tasks if it persists. Hence, this study examines a comparative evaluation of two approaches, Per Connection Classifier (PCC) and Equal Cost Multi-Path (ECMP) in Load Balancing through GNS3 simulation. Load balancing, as a method for evenly distributing traffic loads, and failover, as a backup mechanism when the main connection experiences problems. GNS3 is a graphical network simulator program that can transmit more complex network topologies compared to other simulators, for example Cisco Packet Tracer. The primary aim of this study is to comprehend how efficiently both techniques distribute traffic loads, maintaining smooth internet access, and increasing reliability. The PCC method produces better throughput, delay and jitter compared to the ECMP method, even though it has slightly different values for each QoS parameter. In testing traffic distribution, the PCC method outperforms the ECMP method. The PCC method can distribute traffic evenly across both ISP lines when downloading and uploading data packets. Meanwhile, the ECMP method can only carry out download and upload activities on one traffic path.

H[formula omitted]-RAID: Improving the reliability of SSD RAID with unified SSD and HDD hybrid architecture

With the increasing development of SSD (Solid-State Drives) technology, SSD RAID (Redundant Arrays of Independent Disks) has been widely deployed in enterprise data centers. However, the inherent write endurance issue of SSD seriously affects the reliability of the array. Meanwhile, compared with conventional HDD-based RAID, SSD RAID exhibits very different failure characteristics, such as correlated failure (Balakrishnan et al., 2010) under RAID-5. In this paper, we present a Hybrid High reliability RAID architecture, named H2-RAID, by equipping each SSD with an extra HDD as the backup to improve the reliability of SSD RAID. Considering the relatively longer write latency of HDD, in H2-RAID, we first propose an HDD-aware backup mechanism to smartly aggregate random writes into sequential writes to decrease performance degradation. In addition, to cope with the scenarios of SSD failure, an HDD-aware reconstruction method is further proposed to guarantee the reliability and the online transaction processing performance. We build a novel Markov process-based mathematical model to analyze the reliability of different architectures, and the theoretical results prove the reliability of H2-RAID is much higher than that of traditional SSD RAID. To more accurately evaluate the performance influence of HDD on H2-RAID, we develop a simulator based on Disksim and the experimental results show H2-RAID significantly increases the reliability compared with SSD array (under RAID-5) while with little performance loss on average.

Experimental Validation of a Remedial Action via Hardware-in-the-Loop System Against Cyberattacks Targeting a Lab-Scale PV/Wind Microgrid

This paper experimentally validates the effectiveness of a primary/backup framework in preventing/mitigating the impacts of false data injection (FDI) cyberattacks targeting a lab-scale wind/photovoltaic (PV) microgrid. As the primary mecha-nism, an artificial intelligence-based false data detection algorithm is proposed to forecast the upcoming measurements and alert the operator about the accuracy of sensors readings, directly collected from the field devices. Given that some FDI attacks are designed to bypass the utilized detection methods, it is essential to be prepared for such circumstances. Hence, as the backup mechanism, a remedial action scheme (RAS) is also introduced to mitigate the impacts of such malicious cyberattacks and keep the functionality of the microgrid. The proposed framework (i.e., FDI model, detection approach, and remedial action scheme) is developed as a hardware-in-the-loop (HIL) testbed within the cyber-physical structure of the smart microgrid. The experimental results validate 1) the negative impacts of modeled FDI attacks on the lab-scale microgrid, 2) the effectiveness of the developed false data detection technique, and 3) the efficiency of the proposed RAS to keep the normal operation of the targeted microgrid when the detection system fails to recognize the attack.

A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

For the application of intelligent and green transportation systems (e.g., autonomous driving), traffic congestion is a severe challenge. So far, when traffic congestion is perceived for a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestion should be tackled with re-routing since the extra overhead (e.g., travel time, fuel consumption, and CO2 emission) caused by specific congestion might be lower than that of re-routing. Against this backdrop, a Prediction-based Route Guidance Method (PRGM) is proposed for intelligent and green transportation systems. To begin with, PRGM involves a novel hybrid and dynamic system architecture based on the collaboration of vehicle clusters and the cloud platform. Notably, a backup mechanism between adjacent cluster heads is designed to avoid the problem that the data might be lost during dynamic clustering. Furthermore, PRGM involves a novel traffic congestion control strategy, which is based on four procedures: perception about traffic congestion with three indexes (i.e., speed index, dense index, acceleration index); judgment about congestion type with four defined congestion types; prediction about congestion duration considering the formation of congestion (i.e., why and how the congestion is formed); route planning about vehicles considering congestion duration and the extra time overhead of re-routing. Simulations are performed, and they show that the proposed PRGM not only can perceive traffic congestion more precisely and timely but also can reduce the travel time, fuel consumption, and CO2 emission of vehicles.

RHINO directs MMEJ to repair DNA breaks in mitosis.

Nonhomologous end-joining (NHEJ) and homologous recombination (HR) are the primary pathways for repairing DNA double-strand breaks (DSBs) during interphase, whereas microhomology-mediated end-joining (MMEJ) has been regarded as a backup mechanism. Through CRISPR-Cas9-based synthetic lethal screens in cancer cells, we identified subunits of the 9-1-1 complex (RAD9A-RAD1-HUS1) and its interacting partner, RHINO, as crucial MMEJ factors. We uncovered an unexpected function for RHINO in restricting MMEJ to mitosis. RHINO accumulates in M phase, undergoes Polo-like kinase 1 (PLK1) phosphorylation, and interacts with polymerase θ (Polθ), enabling its recruitment to DSBs for subsequent repair. Additionally, we provide evidence that MMEJ activity in mitosis repairs persistent DSBs that originate in S phase. Our findings offer insights into the synthetic lethal relationship between the genes POLQ and BRCA1 and BRAC2 and the synergistic effect of Polθ and poly(ADP-ribose) polymerase (PARP) inhibitors.

Increased IKKϵ protein stability ensures efficient type I interferon responses in conditions of TBK1 deficiency.

TBK1 and IKKϵ are related, crucial kinases in antiviral immune signaling pathways downstream of cytosolic nucleic acid receptors such as cGAS and RIG-I-like receptors. Upon activation, they phosphorylate the transcription factors IRF3 and IRF7 and thereby initiate the expression of type I interferons and antiviral effectors. While point mutation-induced loss of TBK1 kinase activity results in clinical hyper-susceptibility to viral infections, a complete lack of TBK1 expression in humans is unexpectedly not associated with diminished antiviral responses. Here, we provide a mechanistic explanation for these so-far unexplained observations by showing that TBK1 controls the protein expression of its related kinase IKKϵ in human myeloid cells. Mechanistically, TBK1 constitutively diminishes the protein stability of IKKϵ independent of TBK1 kinase activity but dependent on its interaction with the scaffold protein TANK. In consequence, depletion of TBK1 protein but not mutation-induced kinase deficiency induces the upregulation of IKKϵ. Due to the functional redundancy of the kinases in cGAS-STING and RIG-I-like receptor signaling in human myeloid cells, enhanced IKKϵ expression can compensate for the loss of TBK1. We show that IKKϵ upregulation is crucial to ensure unmitigated type I interferon production in conditions of TBK1 deficiency: While the type I interferon response to Listeria monocytogenes infection is maintained upon TBK1 loss, it is strongly diminished in cells harboring a kinase-deficient TBK1 variant, in which IKKϵ is not upregulated. Many pathogens induce TBK1 degradation, suggesting that loss of TBK1-mediated destabilization of IKKϵ is a critical backup mechanism to prevent diminished interferon responses upon TBK1 depletion.

Research on the Reading Promotion Model of Universities Based on Smart Libraries

With the development of digital technology, smart libraries have become a new model of digital reading services and have received widespread attention. This article analyzes the role of smart libraries in promoting reading in universities, and explores the management mechanism of smart libraries and measures to address issues such as security, data protection, and privacy protection. Regarding security issues, it is recommended to strengthen network security awareness training and adopt multi-level security measures; For data protection and privacy protection, it is recommended to strengthen data classification and permission control, establish secure backup and recovery mechanisms, and take measures such as encrypted transmission and storage. In addition, this article also proposes methods for establishing talent cultivation and management mechanisms, including conducting specialized training programs, actively introducing high-end talents, establishing compensation incentives, career promotion, and role model recognition mechanisms, as well as strengthening team management measures.

DESIGN AND RESEARCH OF AUTOMATIC STATION BUFR DATA EMERGENCY BACKUP PLATFORM BASED ON HYBRID PROGRAMMING

Relying on the domestic meteorological transmission and communication system and NAS backup mechanism, based on the relevant standards and specifications of the meteorological big data cloud platform, in the case that the “Tianqing” interface cannot obtain data in a timely and effective manner, detailed requirements, detailed research, and clear implementation steps are made to provide users with reliable meteorological data services. Through the design and research of automatic station BUFR data rain express emergency backup system in this paper, the BUFR data for cell turn decoding, the specific factor value processing, can provide users with continuous data service, for business personnel in flood season normal work to provide basic support. The application shows that the sustainability of the data service is enhanced, and it provides a strong guarantee for the acquisition of rainfall elements in flood season.

The Expression Pattern of Non-apoptotic Cell Death Pathway in Osteosarcoma: Necroptosis and Autophagy as Backup Mechanisms for Therapeutics Strategy

Background: Among the primary bone tumors, osteosarcoma accounts for a malignant tumor with a high rate of progression and poor prognosis. Despite the achievement of combined therapy regimens in improving patients’ overall survival, patients with osteosarcoma confront the chemoresistance obstacle. Objectives: This study aimed at determining the expression pattern of autophagy and necroptosis pathways mediators in osteosarcoma tumors. Methods: The expression level of autophagy main mediators such as autophagy-associated protein 5 (ATG5), Beclin 1 (BECN1), and microtubule-associated protein 1A/1B-light chain 3 (LC3), necroptosis biomarkers such as receptor-interacting protein kinases (RIPK1 and RIPK3), and mixed lineage kinase domain-like (MLKL) were evaluated in 80 bone tissues including 60 bone tumors (40 malignant tumors and 20 benign tumors) and 20 margin tissues, using real-time PCR. The correlations of gene expression levels with the patient’s clinical and pathological features were considered. Results: Based on our data, ATG5, BECN1 and LC3 expression were down-regulated in osteosarcoma tumors compared to margin tissues. Also, malignant osteosarcoma tumors showed a significant decrease in the expression level of RIPK1 and MLKL as necroptosis regulators, which revealed a correlation with tumor malignancy. In addition, the higher expression levels of BECN1, LC3, RIPK1, and MLKL were observed in tumor tissues of patients under the chemotherapy regimen, indicating the relevance of autophagy and necroptosis pathways with the patient’s response to therapy. Conclusions: Reduction in the expression level of autophagy and necroptosis mediators in high-grade osteosarcoma tumors indicates the possible impact of these pathways on the rate of proliferation and growth of osteosarcoma tumor cells and can emphasize the importance of cell death alternative pathways for treatment when apoptosis machinery is mutated and cause chemoresistance.

Malaria parasite heme biosynthesis promotes and griseofulvin protects against cerebral malaria in mice

Heme-biosynthetic pathway of malaria parasite is dispensable for asexual stages, but essential for mosquito and liver stages. Despite having backup mechanisms to acquire hemoglobin-heme, pathway intermediates and/or enzymes from the host, asexual parasites express heme pathway enzymes and synthesize heme. Here we show heme synthesized in asexual stages promotes cerebral pathogenesis by enhancing hemozoin formation. Hemozoin is a parasite molecule associated with inflammation, aberrant host-immune responses, disease severity and cerebral pathogenesis. The heme pathway knockout parasites synthesize less hemozoin, and mice infected with knockout parasites are protected from cerebral malaria and death due to anemia is delayed. Biosynthetic heme regulates food vacuole integrity and the food vacuoles from knockout parasites are compromised in pH, lipid unsaturation and proteins, essential for hemozoin formation. Targeting parasite heme synthesis by griseofulvin—a FDA-approved antifungal drug, prevents cerebral malaria in mice and provides an adjunct therapeutic option for cerebral and severe malaria.

A fault tolerant optimal relay node selection algorithm for Wireless Sensor Networks using modified PSO

Wireless Sensor Networks (WSNs) have energy-constraints that restricts to achieve prolonged network lifetime. To optimize energy consumption of sensor nodes, clustering is one of the efficient techniques for minimization of energy conservation in WSNs. This technique sends the collected data towards the SINK based on cluster head (CH) nodes that leads to the saving of energy. WSNs have been faced a crucial issue of fault tolerance and the overall data communication is collapsed due to the failure of cluster head. Various fault-tolerance clustering methods are available for WSNs, but they are not selected the backup nodes properly. The backup nodes’ closeness or location to the other remaining nodes is not considered in these methods. They may increase network overhead with the backup nodes accessibility. A fault-tolerance cluster-based routing method is presented in this paper that aims on providing fault tolerance for relay selection in addition to the data aggregation method for clustered WSNs. The proposed method utilizes backup mechanism & the Particle Swarm Optimization (PSO) to achieve this. Based on the distance from sink, residual energy, and link delay parameters, the CHs are chosen and the network is categorized into the clusters. The Backup CHs are selected by estimating the centrality among the nodes. As a part of intra-cluster communication for reducing the aggregation overhead among CHs, the Aggregator (AG) nodes are deployed in every cluster. So that they act as the bridge between the member nodes and CHs. These AG nodes aggregates the information from member nodes and deliver it to the CHs. The PSO with modified fitness function is used to identify the best relays between AG and member nodes. The proposed mechanism is compared with existing techniques such as EM-LEACH AI-Sodairi and Ouni (2018), QEBSR Rathee et al. (2019), QOS-IHC Singh and Singh (2019), and ML-SEEP Robinson et al. (2019). The simulation results proved that the proposed mechanism reduces overhead by 55% and improve the energy consumption & throughput by 40% & 60% respectively.

Stablecoins as a new form of digital money: Emission, circulation, regulation and risk management

Stablecoins as a new form of digital money, in contrast to the traditional forms of money prevailing today, maintain their stability by being tied to basic reserves (national currencies, goods or digital assets). Basically, they circulate in developed countries (primarily in the USA, EU countries or financial centers of Asia) in order to carry out exchange transactions with digital currencies or increase trade turnover and lending volumes. Over the time, they can become not only a reliable and inclusive means of payment, which will be actively used in transactions both nationally and internationally, but also widely used as a store of value. However, the integration of stablecoins with a modern monetary and payment system can only happen if there is a proper and coordinated regulation of all monetary authorities. Based on the analysis of the economic nature, the current standards and models for regulating the emission and circulation of stablecoins, as well as the existing international practice of managing their risks, it has been concluded that it is necessary to abandon the prohibitive approach to the use of this form of digital money. Instead of banning the use of stablecoins as a means of payment and savings, the Bank of Russia should justify and propose acceptable models for regulating the issue and circulation of stablecoins. Such models should provide for clear back-up mechanisms for the issue of stablecoins and requirements for their issuers to minimize the risks of both holders and issuers of stablecoins.

Riboflavin integrates cellular energetics and cell cycle to regulate maize seed development.

SummaryRiboflavin is the precursor of essential cofactors for diverse metabolic processes. Unlike animals, plants can de novo produce riboflavin through an ancestrally conserved pathway, like bacteria and fungi. However, the mechanism by which riboflavin regulates seed development is poorly understood. Here, we report a novel maize (Zea mays L.) opaque mutant o18, which displays an increase in lysine accumulation, but impaired endosperm filling and embryo development. O18 encodes a rate‐limiting bifunctional enzyme ZmRIBA1, targeted to plastid where to initiate riboflavin biosynthesis. Loss of function of O18 specifically disrupts respiratory complexes I and II, but also decreases SDH1 flavinylation, and in turn shifts the mitochondrial tricarboxylic acid (TCA) cycle to glycolysis. The deprivation of cellular energy leads to cell‐cycle arrest at G1 and S phases in both mitosis and endoreduplication during endosperm development. The unexpected up‐regulation of cell‐cycle genes in o18 correlates with the increase of H3K4me3 levels, revealing a possible H3K4me‐mediated epigenetic back‐up mechanism for cell‐cycle progression under unfavourable circumstances. Overexpression of O18 increases riboflavin production and confers osmotic tolerance. Altogether, our results substantiate a key role of riboflavin in coordinating cellular energy and cell cycle to modulate maize endosperm development.