Efficient Storage Management Research Articles

Advanced Data Modeling Techniques in SAP BW/4HANA: Optimizing for Performance and Scalability

The increasing demand for faster and more scalable data processing in business intelligence systems has led to the adoption of advanced data modeling techniques in SAP BW/4HANA. SAP BW/4HANA, built on the in-memory computing platform of SAP HANA, offers significant advantages in terms of performance and scalability. This paper explores the latest data modeling methodologies within SAP BW/4HANA to enhance system performance and accommodate the growing complexity of business data. Key strategies for optimizing data models include leveraging the power of HANA's in-memory capabilities, implementing optimized object structures, and utilizing data tiering for efficient storage management. The use of advanced modeling techniques such as composite providers, enhanced data flows, and the integration of SAP HANA's native capabilities for real-time analytics are also discussed. Furthermore, the paper addresses the challenges involved in managing large-scale data environments, ensuring data consistency, and implementing best practices for performance tuning. By focusing on these advanced approaches, organizations can achieve higher data throughput, reduced query response times, and greater scalability, enabling them to effectively handle large volumes of data across multiple systems. The implementation of these techniques not only enhances the operational efficiency of SAP BW/4HANA but also provides businesses with actionable insights that drive data-driven decision-making. The paper concludes by highlighting the future directions in data modeling within SAP BW/4HANA and the ongoing evolution of its features for emerging business needs.

Double-network aerogel-based eutectic composite phase change materials for efficient solar energy storage and building thermal management

Phase change materials (PCMs) have shown great promise in solar energy storage and thermal management of buildings. Nevertheless, the solid-liquid PCMs currently used in these applications face multiple challenges that need to be addressed, such as inadequate solar absorption capacity, leakage issues, and low phase change enthalpy. In this research, urea and sodium acetate trihydrate (SAT) were employed as the main PCMs, while disodium hydrogen phosphate dodecahydrate (DHPD) served as the nucleating agent. Additionally, we used polyvinyl alcohol/silica (PVA/SiO2) aerogel as a porous support structure. Carbon nanotubes (CNTs) were also introduced as photothermal conversion materials to fabricate a photothermal conversion-type double-network aerogel-based eutectic composite PCM (ECPCM). The prepared ECPCM exhibited excellent shape stability, high mechanical strength, and outstanding photothermal conversion performance. Moreover, the ECPCM demonstrated good flame retardancy, low supercooling, and the absence of phase separation issues. Furthermore, the ECPCM demonstrated efficient solar energy storage capacity and outstanding performance in building thermal management. This study offers a new perspective on the advancement of ECPCMs in solar energy storage and thermal management of buildings.

Development of the energy hub GEDERlab at Universitat Politècnica de València for encouraging the integration of renewable energy resources

Renewable energies stand out as fundamental solutions to address climate change, providing environmental benefits and promoting energy independence. However, renewable plants face challenges such as intermittency, initial costs, and visual effects on the surroundings. The GEDERlab is an innovative energy hub developed at the Universitat Politècnica de València (UPV) that integrates four renewable energies (solar, wind, biogas, and hydraulic) to tackle these challenges. The main objective is only to optimize complementarity among energies but also to develop strategies for efficient storage and optimal management. This project goes beyond research and it aims to become a replicable model at the municipal level. Therefore, the results are extrapolated to a rural municipality in the Valencian Community, to improve energy efficiency, promote circular economy principles and utilize waste as resources. So, in this paper, the components of the energy hub GEDERlab, their integration, and the control system are described to encourage the integration of renewable energy resources.

Efficient Data Storage Management and Reducing Deduplication in Cloud Computing

Efficient Data Storage Management and Reducing Deduplication in Cloud Computing

Faradaic and Non-Faradaic Self-Discharge Mechanisms in Carbon-Based Electrochemical Capacitors.

Electrochemical capacitors (ECs) play a crucial role in electrical energy storage, offering great potential for efficient energy storage and power management. However, they face challenges such as moderate energy densities and rapid self-discharge. Addressing self-discharge necessitates a fundamental understanding of the underlying processes. This review sets itself apart from other reviews by focusing on the basic principles of self-discharge processes in carbon-based ECs, particularly examining the nature of the process and the involvement of redox reactions. This study delineates the potential conditions for various self-discharge processes and proposes plausible criteria for differentiation, complemented by mathematical modeling. Additionally, the model selection, curve fitting, and effective tuning methods are explored to control self-discharge processes.

The robust fluoride-free superhydrophobic thermal energy storage coating for efficient energy storage and thermal management

The robust fluoride-free superhydrophobic thermal energy storage coating for efficient energy storage and thermal management

Synergistic phase change and heat conduction of low melting-point alloy microparticle additives in expanded graphite shape-stabilized organic phase change materials

Organic phase change materials (PCMs) have great potential for efficient thermal energy storage and passive thermal management applications due to their non-corrosiveness, high heat storage capacity and stable operating temperature. However, low thermal conductivity and easy leakage seriously restrict the actual application. Meanwhile, heat transfer enhancers typically have a serious negative effect on the heat storage capacity of the PCMs. In this paper, a novel strategy for preparing high performance shape-stable composite phase change materials (CPCMs) is reported, utilizing paraffin wax (PW) as the energy storage material and expanded graphite (EG) as heat transfer enhancers and supporting material, especially in which low melting-point alloy (LMA) micro particles having same phase change temperature as PW are employed with two purposes: one is to provide extra latent heat, and another is to construct a hybrid three-dimensional thermal conduction network in microscale to achieve synergistic thermal conduction with EG. The resulting CPCMs exhibit excellent characteristics in energy storage capacity, thermal conductivity, and thermal cycling stability. The thermal conductivity of ternary CPCM can reach 5.842 W m−1 K−1 when the LMA and EG loading are 4.55 wt% and 9 wt%, which is about 16.4 times higher than that of pure PW, with no obvious volumetric latent heat reduction. This work provides a novel and promising approach for the preparation of CPCMs with high and fast storage properties.

Life cycle assessment of lab-scale solid sodium-ion batteries: A sustainable alternative to liquid lithium-ion batteries

This study presents a comprehensive life cycle assessment (LCA) of liquid lithium-ion batteries (liquid LIB), liquid sodium-ion batteries (liquid SIB), and solid sodium-ion batteries (solid SIB) to evaluate their environmental impacts in a laboratory scale. The results emphasize the potential environmental benefits of adopting solid SIBs as they show considerable savings in toxicity categories compared to conventional lithium-ion batteries. Moreover, solid SIBs demonstrated the lowest abiotic depletion potential and photochemical oxidation. However, they exhibited higher impacts in ozone depletion and eutrophication compared to liquid LIBs and liquid SIBs. Liquid LIBs showed the lowest global warming potential, making them more climate-friendly than the other battery types. Moreover, the results showed that, energetically, liquid LIBs are the most competitive option. To determine the conditions in which the solid SIBs improve the environmental behaviour of the liquid LIBs, an energetic and sensitivity analysis were conducted. Solid SIBs using renewable energy like concentrated solar power (CSP) and dematerializing part of its electrolyte exhibited reduced Cumulative Energy Demand and improved the environmental performance in all categories compared to conventional liquid LIBs. Furthermore, the study assessed the specific heat capacity (Cp) of the solid sodium electrolyte (NASICON), revealing its potential suitability for advanced battery technologies that require efficient heat storage and thermal management. Globally, this study highlights some environmental benefits of adopting solid SIBs, especially those manufactured with renewable energy, as a promising alternative to traditional lithium-ion batteries.

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Solving an energy resource management problem with a novel multi-objective evolutionary reinforcement learning method

Microgrids have become popular candidates for integrating diverse energy sources into the power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is a type of Unit Commitment problem, where a player operates a microgrid with diverse renewable generators integrated with an external supplier. Calculating the economic dispatch of each committed unit on a planning horizon is an NP-hard problem, and therefore, finding an exact solution is difficult. This paper presents a multi-objective solution to the ERM problem from the perspective of battery operation and external supplier dispatch. First, a novel multi-objective decision problem modeling is proposed that considers three objectives: cost, greenhouse gas emissions, and battery degradation. This framework involves a learning agent that controls the depth of discharge of a Lithium-Ion battery. To address the proposed problem, a new multi-objective algorithm called Multi-Objective Evolutionary Policy Search (MEPS) is introduced. The proposed algorithm uses NeuroEvolution of Augmenting Topologies structure to evolve artificial neural networks for estimating action-preference values considering multi-objective rewards. The MEPS performance is evaluated on both standard and newly-proposed benchmark problems, using the hypervolume as the evaluation metric. When compared to standard deep reinforcement learning, results showed that MEPS provides cost-effective, environmentally friendly, and efficient energy storage management solutions. Furthermore, MEPS effectively solves the proposed ERM problem by finding neural networks with a small number of nodes and connections, which are suitable for use in embedded control systems. Overall, MEPS proved to be a promising multi-objective approach in the transition to clean energy resources.

Dual-strategy-encapsulated phase change materials with thermal immune functions for efficient energy storage and all-climate battery thermal management

The utilization of phase-change materials (PCMs) has garnered great interest in purposes of energy storage and thermal management due to its lightweight, high-energy efficiency, and cost-competitiveness. However, the intrinsic limitations of low thermal conductivity and leakage in PCMs impede their usage in high-power-density energy harvesting applications. Here, we designed multifunctional phase-change composites (PCCs) via a dual-encapsulation strategy to realize all-climate thermal managements for lithium-ion batteries. For PCMs, polyethylene glycol was interpenetrated into porous polyurethane network (PEG@PU) to enclose a considerable amount of PEG. Then, the PCMs were infiltrated into the highly oriented graphite frameworks (HOGF). A series of processes enabled to form multi-layered structures as well as alleviate the leakage issue of PEG, and thereby improving the thermal storage ability. The so-obtained PCCs exhibited excellent electrothermal properties (134.9 °C at 1.8 V) and high electromagnetic interference performances (larger than 90 dB). These findings presented that the thermal managements of PCCs can be utilized as active preheating or passive cooling system in a battery thermal management system for the all-climate demands.

Shape-Stable Hybrid Emulsion Gel with Sodium Acetate Trihydrate and Paraffin Wax for Efficient Solar Energy Storage and Building Thermal Management.

Organic-inorganic composite phase change materials (PCMs) are promising in the fields of solar energy storage and building thermal management. However, combining inorganic with organic PCMs meets a great challenge. In the current work, a shape-stable hybrid emulsion gel (EGel/GO) is developed via Pickering emulsion polymerization, which seamlessly combines sodium acetate trihydrate (SAT) in the water phase with paraffin wax (PW) in the oil phase. The polymer dual-phase cross-linking in EGel/GO forms a supporting framework that effectively enhances the material's shape stability, slows the loss of crystalline water in hydrates, and reduces supercooling. The addition of graphene oxide (GO) enhances EGel/GO-0.5's optical absorption properties, resulting in photothermal conversion efficiency as high as 89.1%. Furthermore, EGel/GO not only has high latent heat of 225.08 J/g but also has almost no leakage and no phase separation. The Pickering emulsion polymerization method paves a broad avenue for combining organic with inorganic PCMs, which is an ideal choice for the effective utilization of solar energy and building energy storage.

High-power-density packed-bed thermal energy storage using form-stable expanded graphite-based phase change composite

Thermal energy storage is highlighted as a crucial strategy for energy saving and utilization, in which domain, latent heat storage using phase change materials has gained great potential for efficient heat storage and thermal management applications. A strategy for developing high energy-storage-density and power-density latent heat storage units, through the compression-induced assembly of expanded graphite based stearic acid composites and the macro encapsulation method by using polyethylene shells, is demonstrated. The fabricated composite shows a satisfactory phase change enthalpy of 161.24 ± 0.5 J g−1, and enhances thermal conductivity to 13.4 ± 0.8 W m−1 K−1. The resulting heat storage unit also exhibits form-stable, leakage-proof, good homogeneity, and high-power-density behaviors. A 0.462 kWh proof-of-concept prototype of the packed-bed latent-heat-storage system by using 492 heat storage units has demonstrated its feasibility in fast heat charging/discharging operations. The outlet air temperature in the discharging process can maintain above 30 °C for over 1.74 h with a heat storage utilization efficiency of 90.3 ± 6.1% and an effective discharging efficiency of 93.5 ± 9.4%, under a volumetric flow rate of 30 m3 h−1 and heat storage temperature of 27–86 °C. The maximum and average power density, and effective energy density are obtained as 20.7 ± 1.6 kW m−3, 14.2 ± 0.9 kW m−3, 24.8 ± 2.5 kWh m−3, respectively, with a discharging threshold temperature of 30 °C. This high-power-density apparatus using form-stable heat storage units has realized hourly rapid heat charging-discharging processes, showing its prospective potential of low-temperature heat storage and thermal management.

Online Hybrid Neural Network for Stock Price Prediction: A Case Study of High-Frequency Stock Trading in the Chinese Market

Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time-series data. In this paper, we developed an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future, we only use the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing only involves updating the current data into the process. We evaluated the performance of O-LGT by analyzing high-frequency limit order book (LOB) data from the Chinese market. It shows that, in most cases, our model achieves a similar speed with a much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 12 to 64 times faster than the existing high-accuracy neural network models for LOB data from the Chinese market.

Efficient Storage Management for Social Network Events Based on Clustering and Hot/Cold Data Classification

Social network events are related to the national economy and people’s livelihood, so timely perception and processing of massive social network events data are becoming increasingly important for public opinion analysis. How to fully exploit the access feature of event information to store and manage social network events is of great significance for accurate and real-time query analysis. We propose a social network event storage management method based on microblog text clustering and hot/cold data classification. First, for the microblog text data, we construct a keyword provenance graph by using the information entropy to measure the weight of the edge between keyword nodes. Then, we cluster the events using the provenance-based community partition (PCP) with local modularity to improve the event clustering accuracy. In addition, we can further filter noisy data via incremental clustering, enable hot/cold event data classification and dynamic migration, and compress cold data to save space on a hybrid storage architecture. The experimental results show that the clustering purity can reach more than 93% and the query time can be reduced by more than 70% using clustering and hybrid storage policy.

Monitoring Dewatering Fish Spawning Sites in the Reservoir of a Large Hydropower Plant in a Lowland Country Using Unmanned Aerial Vehicles

This paper presents research concerning dewatered areas in the littoral zones of the Kaunas hydropower plant (HPP) reservoir in Lithuania. It is a multipurpose reservoir that is primarily used by two large hydropower plants for power generation. As a result of the peaking operation regime of the Kaunas HPP, the large quantity of water that is subtracted and released into the reservoir by the Kruonis pumped storage hydropower plant (PSP), and the reservoir morphology, i.e., the shallow, gently sloping littoral zone, significant dewatered areas can appear during drawdown operations. This is especially dangerous during the fish spawning period. Therefore, reservoir operation rules are in force that limit the operation of HPPs and secure other reservoir stakeholder needs. There is a lack of knowledge concerning fish spawning locations, how they change, and what areas are dewatered at different stages of HPP operation. This knowledge is crucial for decision-making and efficient reservoir storage management in order to simultaneously increase power generation and protect the environment. Current assessments of the spawning sites are mostly based on studies that were carried out in the 1990s. Surveying fish spawning sites is typically a difficult task that is usually carried out by performing manual bathymetric measurements due to the limitations of sonar in such conditions. A detailed survey of a small (approximately 5 ha) area containing several potential spawning sites was carried out using Unmanned Aerial Vehicles (UAV) equipped with multispectral and conventional RGB cameras. The captured images were processed using photogrammetry and analyzed using various techniques, including machine learning. In order to highlight water and track changes, various indices were calculated and assessed, such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Visible Atmospherically Resistant Index (VARI), and Normalized Green-Red Difference Index (NGRDI). High-resolution multispectral images were used to analyze the spectral footprint of aquatic macrophytes, and the possibility of using the results of this study to identify and map potential spawning sites over the entire reservoir (approximately 63.5 km2) was evaluated. The aim of the study was to investigate and implement modern surveying techniques to improve usage of reservoir storage during hydropower plant drawdown operations. The experimental results show that thresholding of the NGRDI and supervised classification of the NDWI were the best-performing methods for the shoreline detection in the fish spawning sites.

An Efficient Storage Structure and Management for Distributed Ledgers in Blockchain Systems: An Exploration Based on Purely Theoretical Approach

In this paper, an efficient data structure for storage, management, and security in blockchain systems is presented. This efficient structure is based on the AVL (Adelson-Velsky and Landis) tree and make an extension for the purpose of management and security. We first present a new storage structure for efficient data search based on the AVL tree. Next we expand this data structure and apply to the management and security of the blockchain ledger. The way of management for distributed account/data is divided into two operations, namely splitting and merging. This paper also makes a discussion about the significance of these two operations. We show a detailed description and make mathematical analysis with proof for the performance of all mechanisms proposed in this paper. In addition, a mechanism of dual authentication is also proposed and to provide a better tamper-proof.

STORAGE MANAGEMENT OF NARCOTICS AND PSYCHOTROPIC DRUGS

The storage management of narcotics and psychotropic drugs is a critical aspect of pharmaceutical practice, encompassing a set of stringent guidelines and protocols designed to ensure the safety, security, and regulatory compliance of these controlled substances. This abstract provides an overview of the key considerations in the storage of narcotics and psychotropic drugs, focusing on the challenges faced by healthcare professionals, regulatory frameworks governing storage practices, and emerging technologies that contribute to enhanced storage management. The article delves into the specific requirements for storing narcotics and psychotropic drugs, emphasizing the importance of controlled environments, secure storage facilities, and robust inventory control systems. It explores the impact of temperature, humidity, and light on the stability and efficacy of these substances, highlighting the need for precise environmental monitoring and control measures. Regulatory compliance is a cornerstone of storage management, and the abstract outlines the international and national regulations that govern the handling and storage of narcotics and psychotropic drugs. It discusses the role of regulatory bodies in setting standards, conducting inspections, and enforcing compliance to prevent diversion, theft, or misuse of these controlled substances. Furthermore, the abstract addresses the challenges faced by healthcare institutions and pharmacies in balancing the need for accessibility of these medications with the imperative to maintain strict security measures. It explores best practices in designing storage facilities, implementing access controls, and training personnel to ensure a seamless yet secure workflow. As technology continues to evolve, the abstract discusses innovative solutions that contribute to efficient storage management. This includes the integration of electronic monitoring systems, automation, and data analytics to enhance real-time tracking, reduce errors, and improve overall accountability in the storage of narcotics and psychotropic drugs.

High-performance thermal energy storage and thermal management via starch-derived porous ceramics-based phase change devices

Low thermal conductivity and leakage of phase change materials (PCMs) have severely limited their applications in thermal energy storage and thermal management of electronic devices. Here, we propose starch-derived porous SiC ceramics to achieve high thermal conductivity and prevent leakage of PCMs simultaneously. Porous SiC ceramics with high thermal conductivity of 30 W/m-K at high porosity of 80% are obtained, benefiting from directional pore structures and dense grains enabled via facile directional freeze-drying of starch combined with liquid silicon infiltration technology. Thermal conductivity and thermal energy storage density of SiC/paraffin composite PCMs (CPCMs) attenuated only slightly by 2.75% and 2.80% after 500 repeated heating-cooling cycles, respectively, confirming their longevity and good stability. The phase change enthalpy achieves 331.56 J/g with high thermal conductivity of 24.27 W/m-K maintained by replacing paraffin with LiOH-LiF eutectics. When applying into transient cooling of high-power chips, the chip temperature is 10 ℃ lower if replacing traditional copper by porous SiC/paraffin CPCMs as a cooling medium. Our work demonstrates a promising route to realize efficient thermal energy storage and thermal management of high-power electronics via starch-derived porous ceramics-based phase change devices.

Analysis of Mango Marketing Channels in Siraha, Nepal

The Siraha district of Nepal experiences surplus mango production during the season that ends up being wasted or sold at throw-away prices due to imperfect marketing. Thus, this survey research was conducted to investigate the market channels of mango, post-harvest mechanisms, constraints, marketing margins, market shares, consumer purchasing behaviors, and patterns of purchasing in Siraha.The research survey was done with thirty producers, five wholesalers, thirty-two retailers, and twenty-five consumers within the Golbazaar and Dhangadimai municipalities through a semi-structured questionnaire. Mango trading was dominated by males. It was revealed that the market demand for mango in Siraha was produced in Siraha itself(91.49%). The wholesalers supplied 30.66% of their mango to retailers, and 14.73% to consumers. Gulab Khas and Banaganapalli were the most imported varieties from India. Each variety of mango provided an almost equal margin, although Bombay was slightly higher than others. The market share was dominated by the Maldah variety as most of the farmers cultivated this variety.It was found that in addition to the varieties and arrival time, the distance to markets and farming practices were significant factors influencing the profitability of mango marketing. There is great potential to industrialize and substitute the Indian mango in the market through commercial production of mango, efficient storage, and post-harvest management, and in increasing the efficiency of the mango marketing system through subsidies and aids to the mango growing farmers from concerned stakeholders. A collaborative approach is required among all stakeholders for sustainable mango marketing and establishing it as an economic profession.