Storage Optimization Research Articles

Blockchain-based Scalable IoT Cloud for Electronic Health Record using Horizontal and Vertical Mining

The twin technology of Blockchain with IoT(BIoT) deployed in healthcare systems has fetched out the mutative potentiality for accessing and managing Electronic Health Records(EHRs). However, this divergence also has considerable challenges, primarily in terms of the protection of data, scalability, and interoperability. To overcome these challenges, this research paper presents a new approach, leveraging Blockchain technology and mining mechanisms, to launch a secure, scalable, and proficient IoT cloud ecosystem for the administration of Electronic Health Records. This research study includes Blockchain scalable IoT cloud architecture intended to address the scalability problem of storing and managing large amounts of EHR records produced through IoT sensors. The proposed mechanism implements both horizontal and vertical mining concepts for the optimization of data storage as well as the retrieval processes. Horizontal mining concentrates on allocating and distributing Electronic Health Records data to more than one node, which can ultimately sink the overload on each node in the network and increase the scalability. Vertical mining, on the other flip of the coin, prioritizes the transactions, and optimizing the data with each transaction can drastically improve the acceleration speed of retrieving the data access competency. Blockchain technology constructs the core technology of BEHRHVM, assuring data integrity, privacy, and transparency. All the EHR record transactions are authorized as mutable blocks on the Blockchain framework. This mechanism improved patient privacy while accessing the secure distribution of EHRs among authorized health providers. In addition, the proposed system is built to integrate with current healthcare systems and the Internet of Things.

Performance optimization of phase change energy storage combined cooling, heating and power system based on GA + BP neural network algorithm

Combined cooling, heating, and power systems present a promising solution for enhancing energy efficiency, reducing costs, and lowering emissions. This study focuses on improving operational stability by optimizing system design using the GA + BP neural network algorithm. By integrating phase change energy storage, specifically a box-type heat bank, the system effectively addresses load imbalance issues by aligning building thermoelectric demand with system output. This approach increases energy storage density, improves space utilization efficiency, and streamlines maintenance. The study evaluates the system's implementation in residential buildings across five climate zones, leveraging the GA + BP neural network algorithm to optimize energy, economic, and environmental aspects. The proposed full-load operation strategy, rather than the traditional hybrid operation strategy, reduces the need for frequent system adjustments, enhancing stability and efficiency. Results show a significant reduction in primary energy consumption, ranging from 7.74 × 103 to 2.08 × 106 kWh across the five cities studied. Energy optimization indexes improve by 0.27 %–3.78 % compared to electricity load operation and 1.94 %–8.58 % compared to thermal load operation. Comprehensive optimization indexes also demonstrate enhancements ranging from 5.78 % to 27.68 % and 2.67 % to 28.22 %, respectively. The full-load operation strategy proves highly effective for energy savings in residential buildings, offering innovative contributions to optimizing combined cooling, heating, and power systems, developing distributed energy systems, and promoting sustainable building development.

Optimal configuration of distributed energy storage considering intending island recovery in faulty distribution networks

With the rapid development of distributed generation, represented by photovoltaic power, the access of a large number of distributed generation poses threats to the security and reliable operation of islanded distribution networks. However, relying on the distributed energy storage system can stabilize the island power supply, which can effectively improve the reliability of the island distribution network. To this end, under the premise of knowing photovoltaic output and load forecast curve, this paper proposes a distributed energy storage optimization configuration method in the active islanding operation mode of multi-source distribution network, which satisfies the “N-1″ safety criterion. First, this paper establishes an optimization configuration model for distributed energy storage with multiple objectives, including minimizing the load shedding in the non-fault loss of power zone, the initial investment cost of distributed energy storage, the node voltage deviation and the system frequency offset. Then, aiming at the islanded power flow calculation process mentioned above, an improved trust region algorithm is proposed, and integrating this method with the harmony search algorithm and ”N-1″ safety criterion solves the model. Finally, the IEEE 33-node distribution system is used to test the performance of the proposed algorithm. The results show that the proposed improved trust region algorithm has the advantages of fast convergence speed and short time-consumption compared with the traditional trust region algorithm. And the proposed optimization configuration scheme for distributed energy storage can satisfy the “N-1″ safety criterion of the distribution network under different fault scenarios.

An integrated machine learning and metaheuristic approach for advanced packed bed latent heat storage system design and optimization

To tackle the challenge of waste heat recovery in the industrial sector, this research presents a novel design and optimization framework for Packed Bed Latent Heat Storage Systems (PBLHS). This features a Deep Learning (DL) model, integrated with metaheuristic algorithms. The DL model was developed to predict PBLHS performance, trained using data generated from a validated Computational Fluid Dynamics (CFD) model. The model exhibited a high performance with an R2 value of 0.975 and a low Mean Absolute Percentage Error (<9.14%). To enhance the ML model's efficiency and optimized performance, various metaheuristic algorithms were explored. The Harmony Search algorithm emerged as the most effective through an early screening and underwent further refinement. The optimized algorithm demonstrated its capability by rapidly producing designs that showcased an improvement in total efficiency of up to 85% over available optimized experimental PBLHS designs. This research underscores the potential of ML-integrated approaches in laying the groundwork for generalized design frameworks for TES systems, offering efficient and effective solutions for waste heat recovery.

Blockchain Data Storage Optimisations: A Comprehensive Survey

Blockchain offers immutability, transparency, and security in a decentralised way for many applications, including finance, supply chain, and the Internet of Things (IoT). Due to its popularity and widespread adoption, it has started to process an enormous number of transactions, placing an ever-growing demand for storage. As the technology gains more popularity, the storage requirements of blockchain will increase, necessitating storage optimisation solutions. Proposed solutions for blockchain storage efficiency range from reducing the degree of data replication to redacting or compressing data. Each of these storage optimisation categories involves a complex interplay with the timing of blockchain data processing and mining, yet no existing survey analyses these dimensions. This article surveys the state-of-the-art blockchain storage optimisations and categorises them into replication-based, redaction-based, and content-based optimisations. Replication-based optimisations focus on reducing duplication of blockchain data shared among participants after committing data on the blockchain ledger. Redaction-based optimisations allow users to modify or delete data already committed on the ledger in various ways, while content-based optimisations compress data before or after committing it to the ledger. We analyse and evaluate these solutions in the aspects of security, decentralisation, and scalability. We present the advantages and disadvantages of the existing blockchain storage optimisations and comprehensively compare them. Additionally, we discuss the opportunities and challenges for future work to optimise blockchain storage.

Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data

The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.

Multi-Time-Scale Energy Storage Optimization Configuration for Power Balance in Distribution Systems

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the “dual carbon” objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years.

Analysis of Grid-Connected Stability of VSG-Controlled PV Plant Integrated with Energy Storage System and Optimization of Control Parameters

In the static stability analysis of the grid-connected photovoltaic (PV) generation and energy storage (ES) system, the grid-side is often simplified using an infinite busbar equivalent, which streamlines the analysis but neglects the dynamic characteristics of the grid, leading to certain inaccuracies in the results. Furthermore, the control parameter design does not consider the coupling relationships among parameters, resulting in arbitrary values and the inability to achieve overall optimality. To address these issues, this paper presents a comprehensive parameter optimization method for the oscillation characteristics of grid-connected PV generation and ES systems in various frequency ranges. Firstly, a detailed modeling of the grid-connected PV generation and ES system is conducted, resulting in the derivation of the system’s small-signal model. This study investigates the impact of parameters related to PV arrays, ES units, and the virtual synchronous generator (VSG) on the system’s characteristic roots using participation factors, sensitivity analysis, and eigenvalue root trajectories. Subsequently, based on the analysis of system root trajectories and the Particle Swarm Optimization (PSO) algorithm, a holistic parameter optimization design method for the system’s oscillation modes is proposed, and the parameter optimization results are obtained. Finally, the accuracy of the theoretical analysis is validated through perturbation testing using both Matlab/Simulink models and the small-signal model.

Green building energy: Patents analysis and analytical hierarchy process evaluation

In the dynamic sphere of building energy systems, this study explores advancements in energy integration, storage technologies, management practices, and occupant behavior, assessing sustainable energy practices, including emerging technologies like fuel cells and energy storage systems. It underscores the significance of efficient energy management, considering both renewable and conventional energy mechanisms. The study comprises four key strata: (i) a thorough literature review of recent energy trends, (ii) a comparative study of global energy patents using the World Intellectual Property Organization (WIPO) database, (iii) a comprehensive analysis of building-energy patents, and (iv) expert-guided Analytic Hierarchy Process (AHP) evaluation. These realms encompass five primary sources: (i) energy-efficient building design, (ii) intelligent building automation, (iii) optimizing energy systems integration, (iv) energy storage, and (v) energy management and optimization. Findings reveal energy storage's dominance, with water energy storage and emerging hydrogen technology leading the trajectory. Global energy patent scrutiny underscores China, the United States, and Japan as influential players in optimizing energy markets. The research shapes energy futures, identifies gaps, and drives sustainable energy practices within the built environment, serving as a compass for policymakers and researchers.

Comprehensive Measurement, Monitoring, Verification Planning Enables Safe CO2 Storage, Risk Reduction, and Operating Cost Optimisation

Comprehensive Measurement, Monitoring, Verification Planning Enables Safe CO2 Storage, Risk Reduction, and Operating Cost Optimisation

Impact of Columnar Storage Optimizations in Redshift and BigQuery

With the data explosion, companies are utilizing "new age" data warehouses, such as Amazon Redshift and Google BigQuery, to process this information. Space-efficient storage structures (specifically column-oriented storage formats) are the path to improved query performance and lower costs. Columnar storage stores the data sorted by columns and not rows so that only the columns being queried are accessed when a query is made. Fewer disk reads mean faster execution time, and that's good for analytical processing

Standalone hybrid power-hydrogen system incorporating daily-seasonal green hydrogen storage and hydrogen refueling station

In light of the recent developments in the green hydrogen industry, it is essential to investigate its compatibility and integration with existing electrical grids. This task becomes more challenging when considering its implementation in off-grid sites that solely rely on renewable energy. In cases where off-grid sites should provide electricity and hydrogen to a variety of consumers, it is compulsory to have electrical and hydrogen storage devices with long-term storage capabilities. Consequently, a precise programming technique is necessary to manage the storage and consumption of hydrogen and electricity. To investigate such a model, this paper presents a connected electricity-hydrogen grid in which all the required energy is supplied by solar and hydroelectric units. This off-grid system supplies the necessary energy for industrial and residential loads, electric vehicle charging stations, fuelcell car refueling stations, and natural gas pipelines. The power-to-hydrogen (P2H) and hydrogen-to-power (H2P) systems are used to facilitate energy flow between the electrical and hydrogen sectors. A daily-seasonal hydrogen storage is integrated as well. The proposed method contributes by integrating and improving the modeling, management, optimization, and operation of the daily-seasonal hydrogen storage and electrical storage. Its primary objective is to supply electricity and hydrogen to various consumers using solely renewable energy, even in the face of renewable energy fluctuations and outages. The model is mathematically expressed as a mixed integer linear optimization, implemented in GAMS software, and solved by the CPLEX solver. It aims at maximizing profit from selling electricity and hydrogen to the users. Results demonstrate that P2H and H2P units have a complementary operation with the solar system. When there is abundant solar energy, the P2H converts excess clean energy to green hydrogen and H2P operates when solar energy is not available. Seasonal storage also stores hydrogen from spring to autumn and discharges it in winter because of higher hydrogen prices in this season. Such an operation increases the annual profit by about 9%. When the hydroelectricity unit faces an outage in hours 8 to 13, the fuelcell uses the stored hydrogen and comes into operation to compensate hydropower outage. The net present value of profit amounts to $275,179 per year. The total hydrogen required is approximately 49,870 kg per year, with a total electricity requirement of 1190 MWh per year. The levelized revenue from hydrogen is achieved at around $4 per kilogram, while the levelized revenue from electricity is about $0.058 per kilowatt-hour. It is concluded that the integration of seasonal storage effectively reduces costs, boosts profits, and efficiently manages renewable energy fluctuations across different seasons.

Research on interval optimization of power system considering shared energy storage and demand response

Considering the low utilization rate of energy storage system under uncertainty of source-load and the coarse demand response mechanism, an interval optimization model of power systems based on shared energy storage and refined demand response is proposed. The dual-side uncertainty of source-load is expressed by interval numbers, and the refined demand response mechanism and shared energy storage optimization model for different building load are analyzed. Then, the source-grid-load-storage interval optimization model with shared energy storage is solved and analyzed. In order to improve the accuracy of the optimization results, sensitivity analysis is performed on the linearized segmentation number of the objective function under dual-side uncertainty of source-load. Finally, by comparing the simulation with the traditional centralized energy storage system, the results show that the dual-side uncertainty of source-load can be effectively handled using interval optimization. Considering shared energy storage and demand response, it can effectively improve the energy storage utilization rate and system operation economy, and realize the source-grid-load-storage synergistic interaction.

The Interaction between Short- and Long-Term Energy Storage in an nZEB Office Building

The establishment of near-autonomous micro-grids in commercial or public building complexes is gaining increasing popularity. Short-term storage capacity is provided by means of large battery installations, or, more often, by the employees’ increasing use of electric vehicle batteries, which are allowed to operate in bi-directional charging mode. In addition to the above short-term storage means, a long-term storage medium is considered essential to the optimal operation of the building’s micro-grid. The most promising long-term energy storage carrier is hydrogen, which is produced by standard electrolyzer units by exploiting the surplus electricity produced by photovoltaic installation, due to the seasonal or weekly variation in a building’s electricity consumption. To this end, a novel concept is studied in this paper. The details of the proposed concept are described in the context of a nearly Zero Energy Building (nZEB) and the associated micro-grid. The hydrogen produced is stored in a high-pressure tank to be used occasionally as fuel in an advanced technology hydrogen spark ignition engine, which moves a synchronous generator. A size optimization study is carried out to determine the genset’s rating, the electrolyzer units’ capacity and the tilt angle of the rooftop’s photovoltaic panels, which minimize the building’s interaction with the external grid. The hydrogen-fueled genset engine is optimally sized to 40 kW (0.18 kW/kWp PV). The optimal tilt angle of the rooftop PV panels is 39°. The maximum capacity of the electrolyzer units is optimized to 72 kW (0.33 kWmax/kWp PV). The resulting system is tacitly assumed to integrate to an external hydrogen network to make up for the expected mismatches between hydrogen production and consumption. The significance of technology in addressing the current challenges in the field of energy storage and micro-grid optimization is discussed, with an emphasis on its potential benefits. Moreover, areas for further research are highlighted, aiming to further advance sustainable energy solutions.

Scheduling optimization of tier-captive four-way shuttle-based storage and retrieval systems with multiple lifts

The four-way shuttle-based storage and retrieval system (FSS/RS) handles retrieval transactions through shuttles moving horizontally in four directions and lifts moving vertically. The scheduling process and working route of both are important problems for daily operations. This article constructs a scheduling model for the tier-captive FSS/RS with multiple lifts and designs a two-stage optimization heuristic algorithm (OHA). A heuristic assignment algorithm is proposed to determine the retrieval request sequence, and the route-generation stage adopts an improved A-star algorithm with a multi-phase conflict avoidance method to generate shuttle conflict-free routes. Simulation results demonstrate that OHA can reliably generate high-quality solutions, with a low degree of fluctuation in the results, not exceeding 0.5%. The proposed scheduling method outperforms its competitors in all instances, reducing the makespan by 8%. Moreover, the evidence shows that the proposed method can obtain the most competitive scheduling scheme in FSS/RS with more shuttles and lifts.

Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets.

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)-a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration.

Phase-field modeling for energy storage optimization in ferroelectric ceramics capacitors during heat treatment process

Ferroelectric ceramic capacitors have potential advantages in energy storage performance, such as high energy storage density and fast discharge speed, making them widely applicable in different energy storage devices. During heat treatment, ferroelectric ceramics undergo an evolution of grain growth leading to changes in dielectric properties. Optimizing the energy storage properties of ferroelectric ceramics during heat treatment is a crucial issue. In this work, a phase field modeling for dielectric breakdown coupled with a grain growth model is developed to give a fundamental understanding of the effect of grain growth on dielectric breakdown. In addition, this work proposes a breakdown detection method to shorten the computational time of the model. The results indicate the existence of a local maximum of the breakdown field strength during grain growth, leading to a local maximum of the energy storage density. It is found that the dielectric breakdown strength and the pattern of the final breakdown path are significantly influenced by the grain size and distribution. The model is also applicable to various dielectrics and provides guidance for the design of high-energy storage dielectrics.

Econometric Study: E-commerce Adoption on Inventory Management for Manufacturing Companies in Nigeria

The study examined the effect of Business-to-Business E-commerce adoption on inventory management using manufacturing companies in Nigeria under the category of consumer goods as a case study. Inefficient inventory management leading to a rise in costs and a decrease in customer satisfaction level is one of the major challenges encountered by Nigerian manufacturing companies but this can be resolved through the implementation of e-commerce hence the study focussed on inventory management variables which are; inventory accuracy, stockout frequency, inventory turnover rate, demand forecasting accuracy, storage optimization, inventory holding costs, supplier reliability, inventory audit frequency, obsolete inventory and inventory replenishment. The explosion of e-commerce in the logistics sector has posed a challenge that must be effectively managed to susstain the volume of goods across long distances, the interface between e-commerce and in particular inventory management, for instance via stocks and warehousing has become so important to be overlooked. The study was carried out in Nigeria and the population of the study include staff of 20 manufacturing companies listed on the Nigerian Stock Exchange as at 2023. Simple random sampling technique was used to select 718 respondents. Data was collected through a structured questionnaire and was analysed using both one sample t-test analysis and multivariant regression analysis. E-commerce has positive and significant effect on stockout frequency (P = .000), inventory turnover rate (P=0.000), demand forecasting accuracy (P=0.000), storage optimization (P=0.000) and inventory replenishment (P=0.000). The findings revealed significant improvements in inventory accuracy, reduced stockout frequency, increased inventory turnover rate, and improved demand forecasting accuracy. Evidently, these findings imply that the adoption of e-commerce is essential in optimizing inventory management processes and thus recommends that manufacturing companies should further utilize digital tools to enable better forecasting in inventory management and efficient stock handling.

A review of time-memory trade-off techniques

This paper delves into the discussion of time-memory trade-off techniques in the field of cryptanalysis. The method was initially introduced by Hellman in 1980, subsequently, DP trade-off, rainbow trade-off, and checkpoint trade-off have been proposed to enhance the efficiency of cryptographic attacks. This paper elaborates on the concept of rainbow trade-off and their variants and presents optimizations in terms of storage and runtime speed for time-memory trade--off methods. Ingenious storage optimization significantly reduces the storage overhead of pre-computed tables, and the rapid advancement of implementation platforms achieves speed optimization for the online phase. Through these optimization measures, time-memory trade-off methods exhibit even more remarkable performance in practical applications. For researchers and practitioners in the field of cryptography, the content of this paper provides valuable references and insights for their work.

High-Performance All-Textile Triboelectric Nanogenerator toward Intelligent Sports Sensing and Biomechanical Energy Harvesting.

Merging textiles with advanced energy harvesting technology via triboelectric effects brings novel insights into self-powered wearable textile electronics. However, fabrication of a comfortable textile-based triboelectric nanogenerator (TENG) with high outputs remains challenging. Herein, we propose a highly flexible, tailorable, single-electrode all-textile TENG (t-TENG) with both wear comfort and high outputs. A dielectric modulated porous composite coating containing poly(vinylidene fluoride)-hexafluoropropylene copolymer and barium titanate nanoparticles is constructed on conductive fabric to counterpart with highly positive glass fiber fabric through knotted yarn bonding, maintaining the superiority of textiles and strong triboelectricity. Through the synergistic optimization of charge storage via dielectric modulation and charge dissipation offset by electrical poling, remarkable outputs (261 V, 1.5 μA, and 12.7 nC) are obtained from a miniaturized, lightweight t-TENG (2 × 2 cm2, 130 mg) with an instantaneous power density of 654.48 mW·m-2, as well as excellent electrical robustness and device durability over 20,000 cycles. The t-TENG also exhibits a high sensitivity of 3.438 V·kPa-1 in the force region (1-10 N), demonstrating great potential in TENG-based intelligent sports sensing applications for monitoring and correcting the basketball shooting hand and foot arch posture. Furthermore, over 110 light-emitting diode arrays can be lightened up by gently tapping this miniaturized t-TENG. It also offers a wearable power source scheme through integrating the single-electrode device into clothing and utilizing the skin as the grounded electrode, revealing its ease of integration and biomechanical energy harvesting capability. This work provides an attractive paradigm for next-generation textile electronics with well-balanced device performance and wear comfort.