Storage System Research Articles

The Critical Role of Two-Factor Authentication (2FA) in Mitigating Ransomware and Securing Backup, Recovery, and Storage Systems

In today’s digital landscape, ransomware attacks have become one of the most significant cybersecurity threats. Securing backup, recovery, and storage systems is paramount to ensuring business continuity. Two-Factor Authentication (2FA) has proven to be a key strategy in defending against ransomware attacks by adding an extra layer of security. By requiring two forms of authentication, 2FA reduces the risk of unauthorized access to critical systems and data. This paper explores how 2FA enhances the security of backup systems, recovery processes, and storage devices, with a particular focus on its role in mitigating ransomware risks. It further discusses its impact on compliance with data protection regulations and operational efficiency.

Process Systems Engineering for Climate-Resilient Infrastructure: A Framework for Vulnerability Assessment and Optimization

This study explores the application of Process Systems Engineering (PSE) methodologies to enhance the climate resilience of infrastructure systems. As climate change increasingly challenges infrastructure, traditional designs often fail to address evolving long-term risks, leading to system vulnerabilities. PSE techniques, including vulnerability assessments, optimization models, sensitivity analysis, and scenario planning, offer a structured approach to identify weaknesses, assess adaptation strategies, and inform decision-making for climate-resilient infrastructure. Through a synthesis of existing research and case studies from energy, transportation, and water sectors, this study highlights the effectiveness of PSE methods in quantifying infrastructure vulnerabilities under various climate projections. Key findings show that investing in adaptive measures, such as upgrading energy storage systems and flood prevention infrastructure, provides long-term cost savings and resilience improvements. Despite challenges like data gaps and high uncertainty, the study demonstrates that PSE frameworks can significantly improve infrastructure design and planning, helping decision-makers develop strategies that are both cost-effective and adaptive to climate change.

Fast-Charging Lithium-Ion Batteries Enabled by Magnetically Aligned Electrodes.

With the increasing popularity of electric transportation over the past several years, fast-charging lithium-ion batteries are highly demanded for shortening electric vehicles' charging time. Extensive efforts have been made on material development and electrode engineering; however, few of them are scalable and cost-effective enough to be potentially incorporated into the current battery production. Here, we propose a facile magnetic templating method for preparing LiFePO4 (LFP) cathodes with vertically aligned graphene sheets to realize fast-charging properties at a practical loading of 20 mg cm-2. Graphene sheets decorated with Fe3O4 nanoparticles can be responsive to an external magnetic field and can maintain their vertical alignment during the electrode fabrication process. The vertically aligned graphene provides the magnetized LFP electrodes (m-LFP) with simultaneously improved electron and lithium-ion transport properties, achieving 110 and 76 mA h g-1 at 3C and 4C, respectively. Furthermore, magnetized Fe3O4 (m-Fe3O4) anodes were also prepared via the magnetic templating method to vertically align the Fe3O4 nanosheets inside, which outperforms the conventional graphite anodes at a high rate of 3C. Finally, by pairing the magnetized LFP cathode and Fe3O4 anode, we demonstrate the simultaneous fast-charging properties and good cycling stability in the m-LFP||Fe3O4 full cells. This study not only provides an effective methodology for achieving vertically aligned structures which can potentially be incorporated into industrial manufacturing but also brings insightful considerations for designing scalable fast-charging energy storage systems.

Elucidating Thermal Decomposition Kinetic Mechanism of Charged Layered Oxide Cathode for Sodium-Ion Batteries.

The safety of the P2-type layered transition metal oxides (P2-NaxTMO2), a promising cathode material for sodium-ion batteries (SIBs), is a prerequisite for grid-scale energy storage systems. However, previous thermal runaway studies mainly focused on morphological changes resulting from gas production detection and thermogravimetric analysis, while the structural transition and chemical reactions underlying these processes are still unclear. Herein, a comprehensive methodology to unveil an interplay mechanism among phase structures, interfacial microcrack, and thermal stability of the charged P2-Na0.8Ni0.33Mn0.67O2 (NNMO) and the P2-Na0.8Ni0.21Li0.12Mn0.67O2 (NNMO-Li) at elevated temperatures is established. Combining a series of crystallographic and thermodynamic characterization techniques, the specific chemical reactions occurring in the NNMO materials during thermal runaway are clarified firstand solidly proved that Li doping effectively hinders the dissolution of transition metal ions, reduces oxygen release, and enhances thermal stability at elevated temperatures. Importantly, based on Arrhenius and nonisothermal kinetic equations, the kinetic triplet model is successfully constructed to in-depth elucidate the thermal decomposition reaction mechanism of P2-NaxTMO2, demonstrating that such thermodynamic assessment provides a new perspective for building high-safety SIBs.

Automated Integration of AI Results into Radiology Reports Using Common Data Elements.

Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information. This article describes the application of CDEs as a standardized framework to embed AI-derived results into radiology reports. The authors defined a set of CDEs for measurements of the volume and attenuation of the liver and spleen. An AI system segmented the liver and spleen on non-contrast CT images of the abdomen and pelvis, and it recorded their measurements as CDEs using the Digital Imaging and Communications in Medicine Structured Reporting (DICOM-SR) framework to express the corresponding labels and values. The AI system successfully segmented the liver and spleen in non-contrast CT images and generated measurements of organ volume and attenuation. Automated systems extracted corresponding CDE labels and values from the AI-generated data, incorporated CDE values into the radiology report, and transmitted the generated image series to the Picture Archiving and Communication System (PACS) for storage and display. This study demonstrates the use of radiology CDEs in clinical practice to record and transfer AI-generated data. This approach can improve communication among radiologists and referring providers, harmonize data to enable large-scale research efforts, and enhance the performance of decision support systems. CDEs ensure consistency, interoperability, and clarity in reporting AI findings across diverse healthcare systems.

Curvature Memory in Electrically Stimulated Lipid Membranes.

We demonstrate, using non-equilibrium molecular dynamics simulations, that lipid membrane capacitance varies with surface charge accumulation linked to membrane shape and curvature changes. Specifically, we show that lipid membranes exhibit a hysteretic response when exposed to oscillatory electric fields. The electromechanical coupling in these membranes leads to hysteretic buckling, in which the membrane can spontaneously buckle in one of two distinct directions along the electric field, even for the same ionic charge accumulation at the water-membrane interface. In this regard, these binary buckled membrane states suggest potential applications in neuromorphic computing. Their bistable nature, characterized by two distinct and stable configurations, could serve as a foundation for implementing memory storage systems and logic operations. Furthermore, we introduce a circuit model that captures these dynamic effects, offering insights into emergent memory effects in electrically stimulated lipid membranes. Finally, this work presents lipid bilayers as dynamic, adaptable elements and suggests a new platform for exploring energy storage, information processing, and memory encoding at the lipid membrane level.

Liquid Metals for Advanced Batteries: Recent Progress and Future Perspective

ABSTRACTThe shift toward sustainable energy has increased the demand for efficient energy storage systems to complement renewable sources like solar and wind. While lithium‐ion batteries dominate the market, challenges such as safety concerns and limited energy density drive the search for new solutions. Liquid metals (LMs) have emerged as promising materials for advanced batteries due to their unique properties, including low melting points, high electrical conductivity, tunable surface tension, and strong alloying tendency. Enabled by the unique properties of LMs, four key scientific functions of LMs in batteries are highlighted: active materials, self‐healing, interface stabilization, and conductivity enhancement. These applications can improve battery performance, safety, and lifespan. This review also discusses current challenges and future opportunities for using LMs in next‐generation energy storage systems. image

Enhancing Smart Microgrid Resilience Under Natural Disaster Conditions: Virtual Power Plant Allocation Using the Jellyfish Search Algorithm

Electric power networks face critical challenges from extreme weather events and natural disasters, disrupting socioeconomic activities and jeopardizing energy security. This study presents an innovative approach incorporating virtual power plants (VPPs) within networked microgrids (MGs) to address these challenges. VPPs integrate diverse distributed energy resources such as solar- and wind-based generation, diesel generators, shunt capacitors, battery energy storage systems, and electric vehicles (EVs). These resources enhance MG autonomy during grid disruptions, ensuring uninterrupted power supply to critical services. EVs function as mobile energy storage units during emergencies, while shunt capacitors stabilize the system. Excess energy from distributed generation is stored in battery systems for future use. The seamless integration of VPPs and networked technologies enables MGs to operate independently under extreme weather conditions. Prosumers, acting as both energy producers and consumers, actively strengthen system resilience and efficiency. Energy management and VPP allocation are optimized using the jellyfish search optimization algorithm, enhancing resource scheduling during outages. This study evaluates the proposed approach’s resilience, reliability, stability, and emission reduction capabilities using real-world scenarios, including the IEEE 34-bus and Indian 52-bus radial distribution systems. Various weather conditions are analyzed, and a multi-objective function is employed to optimize system performance during disasters. The results demonstrate that networked microgrids with VPPs significantly enhance distribution grid resilience, offering a promising solution to mitigate the impacts of extreme weather events on energy infrastructure.

State switch control of magnetically suspended flywheel energy storage system in uninterrupted power supply system

The magnetically suspended flywheel energy storage system (MS-FESS) is an energy storage equipment that accomplishes the bidirectional transfer between electric energy and kinetic energy, and it is widely used as the power conversion unit in the uninterrupted power supply (UPS) system. First, the structure of the FESS-UPS system is introduced, and the working principles at different working states are described. Furthermore, the control strategy of the FESS-UPS is developed, and the switch oscillation of the FESS-UPS system between the charging and discharging states is analyzed. Then, the switch strategy using the angle compensation of the flux linkage is designed to control the FESS-UPS system among different working states, and the peak values of current and voltage at the switch moment are suppressed. Finally, the simulations and experiments are performed to validate the performances of the switch strategy used in the FESS-UPS system, and the results prove that the current/voltage peaks during the switching process are effectively mitigated, so the impact on the power converter caused by the state switch is suppressed. Therefore, the control performance of the UPS using the MS-FESS could be further improved, and the FESS-UPS could realize the fast and safe discharge/charge for the grid source and three-phase loads.

Analysis and Evaluation of a TCO2 Electrothermal Energy Storage System with Integration of CO2 Geological Storage

The goal to reduce greenhouse gas emissions necessitates the increase in RES utilization. To accomplish this goal, energy storage solutions are required. This study investigates the performance of an electrothermal energy storage system, the CEEGS, which consists of an above-surface energy storage system and a below-surface geological system. The focus is set initially on the analysis of the above-surface system to gain insight into its operation. Then, steady-state optimization is utilized to identify the operating conditions that maximize the system performance, before investigating the below-surface system integration and the effect that the geological conditions have on system performance. For the above-surface system, efficiency (ηR-T) up to 46.89% is calculated. For systems integrated with CO2 geological storage, two case studies are examined, presenting higher ηR-T compared to the above-surface system (Case study 1: 50.37%, Case study 2: 67.39%). The optimal ηR-T for Case study 2 is achieved for higher injection/production pressures and temperatures conditions and minimal ΔP and ΔT between injection and production. In conclusion, it is the selection of the geological storage conditions that contribute the most to the optimal ηR-T; thus, the selection of the appropriate geological storage formation is imperative.

The power of battery energy storage systems: unlocking home efficiency

Abstract This project explores the integration of battery energy storage systems (BESS) in residential settings to optimize energy management with a novel focus on standalone BESS configurations independent of solar photovoltaic (PV) systems. The objective is to analyze electricity price patterns, evaluate different BESS configurations, and develop strategies for efficient charging and discharging. This enables homeowners to store excess electricity during low-demand periods and discharge it during peak demand, reducing grid reliance and saving on electricity costs. The analysis reveals that a standalone BESS can reduce electricity costs by leveraging low-demand periods with electricity prices averaging 0.4 DKK/kWh, compared to peak prices exceeding 0.8 DKK/kWh. For June 2023, optimal charging (6:00–14:00, 18:00–22:00) and discharging (14:00–18:00, 23:00–6:00) periods were identified, leading to potential cost savings of up to 30 %. The findings highlight the potential of BESS to transform energy management and suggestions such as a net metering program are proposed to mitigate these challenges.

Process Integration and Optimization of the Integrated Energy System Based on Coupled and Complementary “Solar-Thermal Power-Heat Storage”

Within the context of “peak carbon and carbon neutrality”, reducing carbon emissions from coal-fired power plants and increasing the proportion of renewable energy in electricity generation have become critical issues in the transition to renewable energy. Based on the principles of cascaded energy utilization, this paper improves the coupling methodology of an integrated solar thermal and coal-fired power generation system based on existing research. A parabolic trough collector field and a three-tank molten salt thermal energy storage system are connected in series and then in parallel with the outlet of the reheater. ASPEN PLUS V14 and MATLAB R2018b software were used to simulate a steady-state model and numerical model, respectively, so as to study the feasibility of the improved complementary framework in enhancing the peak load capacity of coal-fired units and reducing their carbon emissions. Actual solar radiation data from a specific location in Inner Mongolia were gathered to train a neural network predictive model. Then, the peak-shaving performance of the complementary system in matching load demands under varying hours of thermal energy storage was simulated. The findings demonstrate that, under constant boiler load conditions, optimizing the complementary system with a thermal energy storage duration of 5 h and 50 min results in an energy utilization efficiency of 88.82%, accompanied by a daily reduction in coal consumption by 36.49 tonnes. This indicates that when operated under the improved coupling framework with optimal parameters, the peak regulation capabilities of coal-fired power units can be improved and carbon emission can be reduced.

Storage and Query of Drug Knowledge Graphs Using Distributed Graph Databases: A Case Study

Background: Distributed graph databases are a promising method for storing and conducting complex pathway queries on large-scale drug knowledge graphs to support drug research. However, there is a research gap in evaluating drug knowledge graphs’ storage and query performance based on distributed graph databases. This study evaluates the feasibility and performance of distributed graph databases in managing large-scale drug knowledge graphs. Methods: First, a drug knowledge graph storage and query system is designed based on the Nebula Graph database. Second, the system’s writing and query performance is evaluated. Finally, two drug repurposing benchmarks are used to provide a more extensive and reliable assessment. Results: The performance of distributed graph databases surpasses that of single-machine databases, including data writing, regular queries, constrained queries, and concurrent queries. Additionally, the advantages of distributed graph databases in writing performance become more pronounced as the data volume increases. The query performance benefits of distributed graph databases also improve with the complexity of query tasks. The drug repurposing evaluation results show that 78.54% of the pathways are consistent with currently approved drug treatments according to repoDB. Additionally, 12 potential pathways for new drug indications are found to have literature support according to DrugRepoBank. Conclusions: The proposed system is able to construct, store, and query a large graph of multisource drug knowledge and provides reliable and explainable drug–disease paths for drug repurposing.

Modelling and kinetics of the toluene/methylcyclohexane‐based hydrogen storage system

AbstractThe coupling of toluene and methylcyclohexane is one of the most promising liquid organic hydrogen carriers. In this study, the kinetics models of toluene hydrogenation and methylcyclohexane dehydrogenation are established. The reactions were carried out in fixed bed reactors, and the kinetics parameters were fitted with the obtained data. In this experiment, hydrogenation and dehydrogenation reactions were carried out under ambient pressure. Ni/SGA_CnTES catalyst was employed for toluene hydrogenation, the fitting activation energy is 36.27 kJ/mol, and the preexponential factor is 1212 s−1. Meanwhile, the catalyst of Pt/MgAl2O4 was for methylcyclohexane dehydrogenation, the fitting activation energy is 83.92 kJ/mol, and the preexponential factor is 7.28 × 106 s−1. Aspen Plus was used to simulate on a larger scale and determine the optimal process conditions: the optimum reaction conditions for toluene hydrogenation were determined to be 185°C and 0.3 MPa, and the optimized hydrogen flow rate is 3970 kg/d (The molar ratio is 5.88). Additionally, the optimal conditions for methylcyclohexane dehydrogenation are 325°C and 0.15 MPa.

Temperature Gradients as a Data Storage Principle

In this work, we analyze the thermodynamic principles underlying modern data storage systems, including Random Access Memory (RAM), hard disk drive (HDD), flash memory, magnetic RAM (MRAM), ferroelectric RAM (FeRAM), and phase-change RAM (PCRAM), as well as other less well-known data storage mechanisms. The analysis is conducted in the context of data storage and processing in relation to Landauer’s principle, with special emphasis on hysteresis. Analogous to how heat engines are characterized by thermodynamic cycles, data storage systems are examined in terms of the hysteresis loop of their fundamental data unit. We explore the role of heat in data storage systems. Afterward, we introduce the concept of temperature gradient memory (TeGraM) along with a detailed layout of a realizable device. Experimental results demonstrating this technology are also presented.

Nanomaterials for Zinc Batteries—Aerogels

Aqueous zinc batteries, mainly including Zn-ion batteries (ZIBs) and Zn–air batteries (ZABs), are promising energy storage systems, but challenges exist at their current stage. For instance, the zinc anode in aqueous electrolyte is impacted by anodic dendrites, hydrogen and oxygen precipitation, and some other harmful side reactions, which severely affect the battery’s lifespan. As for traditional cathode materials in ZIBs, low electrical conductivity, slow Zn2+ ion migration, and easy collapse of the crystal structure during ion embedding and migration bring challenges. Also, the slower critical oxygen reduction reaction (ORR), for example, in ZABs shows unsatisfactory results. All these issues greatly hindered the development of zinc batteries. Aerogel materials, characterized by their high specific surface area, unique open-pore structure formed by nanoporous structures, and excellent physicochemical properties, have a positive role in cathode modification, electrode protection, and catalytic reactions in zinc batteries. This manuscript provides a systematic review of aerogel materials, highlighting advancements in their preparation and application for zinc batteries, aiming to promote the future progress and development of aerogel nanomaterials and zinc batteries.

Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication

Outsourcing storage to the cloud can save storage costs and is commonly used in businesses. It should fulfill two major goals: storage efficiency and data confidentiality. Encrypted deduplication can achieve both goals via performing deduplication to eliminate the duplicate data within encrypted data. Traditional encrypted deduplication generates the encryption key on the client side, which poses a risk of offline brute-force cracking of the outsourced data. Server-aided encryption schemes have been proposed to strengthen the confidentiality of encrypted deduplication by distributing the encryption process to dedicated servers. Existing schemes rely on expensive cryptographic primitives to provide a decentralized setting on the dedicated servers for scalability. However, this incurs substantial performance slowdown and can not be applied in practical encrypted deduplication storage systems. In this paper, we propose a new decentralized server-aided encrypted deduplication approach for outsourced storage, called ECDedup, which leverages secret sharing to achieve secure and efficient key management. We are the first to use the coding matrix as the encryption key to couple the encryption and encoding processes in encrypted deduplication. We also propose a acceleration scheme to speed up the encryption process of our ECDedup. We prototype ECDedup in cloud environments, and our experimental results based on the real-world backup datasets show that ECDedup can improve the client throughput by up to 51.9% compared to the state-of-the-art encrypted deduplication schemes.

Power Sharing of Different Distributed Generator Sets in Microgrid Based on Consensus Control

ABSTRACTMicrogrids incorporate renewable energy sources, battery energy storage systems (BESS), and local loads to operate either with the main grid or independently. In microgrids, virtual synchronous generators (VSGs) and droop control are commonly used to regulate renewable energy sources, providing frequency and voltage control. The VSG and droop control adjust active power based on frequency deviation. However, their effectiveness depends on parameters and does not fully address power‐sharing issues. Traditionally, power sharing is handled by secondary control via the microgrid central controller (MGCC), introducing a centralized, hierarchical approach. To enhance decentralization in microgrids, this paper proposes a consensus based power sharing scheme and its corresponding control for the distributed generators (DGs). The output power levels of DG units are determined by their capacities, ensuring efficient operation. Unlike existing approaches using a hierarchical framework, this work integrates primary and secondary control into a unified distributed control framework. Building on VSG control for microgrid voltage regulation, consensus control is added to manage the distribution of active power. A rigorous theoretical analysis is conducted to validate the proposed method. The method is capable of ensuring active power sharing without relying on an MGCC, and the sharing is also based on the ratio of rated capacities. The controller proposed in this paper achieves the expected control performance, effectively distributing active power sharing in the microgrid. Finally, the effectiveness and performance of the proposed method are verified through comprehensive case studies.

Scalable and High-Performance Large-Scale Dynamic Graph Storage and Processing System

Existing in-memory graph storage systems that rely on DRAM have scalability issues because of the limited capacity and volatile nature of DRAM. The emerging persistent memory (PMEM) offers us a chance to solve these issues through its larger capacity and non-volatile characteristics. However, simply adapting existing DRAM-based graph storage systems to PMEM would result in inefficient PMEM stores and accesses, including high read and write amplification to PMEM, imbalanced work division for PMEM accesses, and costly remote PMEM access across NUMA nodes. These issues severely limit the performance of large graph processing. In this paper, we aim to achieve scalable and high-performance graph processing in PMEM. We first propose an XPLine-friendly graph storage model that uses vertex-centric graph buffering, hierarchical vertex buffer managing, and in-place vertex block merging to optimize PMEM graph storage. Furthermore, we develop a scalable graph processing model that leverages multi-threaded work dividing and NUMA-friendly graph accessing to optimize PMEM graph accesses. Based on these techniques, we implement XPGraph , a PMEM-based graph storage system for large-scale evolving graphs, and several variants for different system settings. Our experiments demonstrate that XPGraph surpasses the state-of-the-art in-memory graph storage system on a PMEM-based system by 3.07 × to 4.99 × in update performance and up to 5.87 × in query performance, and performs much better in highly parallel multi-threaded scenarios.

Deep Reinforcement Learning Based Optimal Operation of Low-Carbon Island Microgrid with High Renewables and Hybrid Hydrogen–Energy Storage System

Hybrid hydrogen–energy storage systems play a significant role in the operation of islands microgrid with high renewable energy penetration: maintaining balance between the power supply and load demand. However, improper operation leads to undesirable costs and increases risks to voltage stability. Here, multi-time-scale scheduling is developed to reduce power costs and improve the operation performance of an island microgrid by integrating deep reinforcement learning with discrete wavelet transform to decompose and mitigate power fluctuations. Specifically, in the day-ahead stage, hydrogen production and the hydrogen blending ratio in gas turbines are optimized to minimize operational costs while satisfying the load demands of the island. In the first intraday stage, rolling adjustments are implemented to smooth renewable energy fluctuations and increase system stability by adjusting lithium battery and hydrogen production equipment operations. In the second intraday stage, real-time adjustments are applied to refine the first-stage plan and to compensate for real-time power imbalances. To verify the proposed multi-stage scheduling framework, real-world island data from Shanghai, China, are utilized in the case studies. The numerical simulation results demonstrate that the proposed innovative optimal operation strategy can simultaneously reduce both the costs and emissions of island microgrids.