Local Storage Research Articles

Assessment of the Impact of Herbal Drugs Used in Neurodegenerative Disorders: A Preclinical Review

Abstract: The use of herbal drugs may offer great potential opportunities in preventing and controlling neurodegenerative disorders, such as Alzheimer’s disease, Parkinson’s disease and Huntington’s disease. Herbal drugs contain multiple pharmacologically active constituents. The relative amounts and nature of these constituents vary due to diverse factors, such as plant source and plant parts, extraction methods, local environmental conditions, storage conditions, adulterations, and accidental or intentional contamination. After administration, they are put through the processes of absorption, distribution, metabolism, and excretion of the same as modern medicines. When handled by the body, they can show a combined effect and interact with modern drugs due to various factors, including similar transport protein interaction, metabolizing cytochrome P450 enzyme, and different transporter mechanisms. Herbal medicine can either induce or inhibit CYP450 enzymes. When herbs are combined with the drugs, either they mimic or oppose the effect of drugs. Many studies worldwide indicate the favorable properties of plant extracts or their bioactive compounds against neurodegenerative disorders, but several clinical concerns have appeared regarding the use of these combinations, which could be due to lack of evidence and scientific support for their effectiveness and safety of the patient.

In-Memory Distributed Mosaicking for Large-Scale Remote Sensing Applications with Geo-Gridded Data Staging on Alluxio

The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much larger scale than ever. Imagery mosaicking, which is critical for forming “One Map” with a continuous view for large-scale climate research, has drawn significant concern. However, despite employing distributed data processing engines such as Spark, large-scale data mosaicking still significantly suffers from a staggering number of remote sensing images which could inevitably lead to discouraging performance. The main ill-posed problem of traditional parallel mosaicking algorithms is inherent in the huge computation demand and incredible heavy data I/O burden resulting from intensively shifting tremendous RS data back and forth between limited local memory and bulk external storage throughout the multiple processing stages. To address these issues, we propose an in-memory Spark-enabled distributed data mosaicking at a large scale with geo-gridded data staging accelerated by Alluxio. It organizes enormous “messy” remote sensing datasets into geo-encoded gird groups and indexes them with multi-dimensional space-filling curves geo-encoding assisted by GeoTrellis. All the buckets of geo-grided remote sensing data groups could be loaded directly from Alluxio with data prefetching and expressed as RDDs implemented concurrently as grid tasks of mosaicking on top of the Spark-enabled cluster. It is worth noticing that an in-memory data orchestration is offered to facilitate in-memory big data staging among multiple mosaicking processing stages to eliminate the tremendous data transferring at a great extent while maintaining a better data locality. As a result, benefiting from parallel processing with distributed data prefetching and in-memory data staging, this is a much more effective approach to facilitate large-scale data mosaicking in the context of big data. Experimental results have demonstrated our approach is much more efficient and scalable than the traditional ways of parallel implementing.

Protocols with constant local storage and unreliable communication

Population protocols are a model of distributed computation intended for the study of networks of independent computing agents with dynamic communication structure. Each agent has a finite number of states, and communication occurs nondeterministically, allowing the involved agents to change their states based on each other's states.In the present paper we study unreliable models based on population protocols and their variations from the point of view of expressive power. We model the effects of message loss. We show that for a general definition of protocols with unreliable communication with constant-storage agents such protocols can only compute predicates computable by immediate observation (IO) population protocols (sometimes also called one-way protocols). Immediate observation population protocols are inherently tolerant to unreliable communication and keep their expressive power under a wide range of fairness conditions. We also prove that a large class of message-based models that are generally more expressive than IO becomes strictly less expressive than IO in the unreliable case.

Privacy-Preserving Serverless Computing Using Federated Learning for Smart Grids

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various home area networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This article proposes a federated learning based model with privacy preservation of smart grids data using serverless cloud computing. The model considers the blockchain-enabled dew servers in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.

Reversible solid oxide cell coupled to an offshore wind turbine as a poly-generation energy system for auxiliary backup generation and hydrogen production

The coupling of a reversible Solid Oxide Cell (rSOC) with an offshore wind turbine is investigated to evaluate the mutual benefits in terms of local energy management. This integrated system has been simulated with a dynamic model under a control algorithm which manages the rSOC operation in relation to the wind resource, implementing a local hydrogen storage with a double function: (i) assure power supply to the wind turbine auxiliary systems during power shortages, (ii) valorize the heat produced to cover the desalinization system needs. With an export-based strategy, which maximize the rSOC capacity factor, up to 15 tons of hydrogen could be produced for other purposes. The results show the compatibility between the auxiliary systems supply of a 2.3 MW wind turbine and a 120/21 kWe rSOC system which can cover the auxiliaries demand during wind shortages or maintenance. The total volume required by such a system occupy less than the 2%, if compared with the turbine tower volume. Additionally, thermal availability exceeds the desalination needs, representing a promising solution for small-scale onsite desalination in offshore environments.

Monitoring System of the ATLAS ITk Laboratory

The ATLAS ITk Upgrade project, culminating in the installation into the experiment in 2026, enters this year its production period. Cooperating laboratories dealing with the strip part of the project needs to meet various conditions in clean rooms and testing environments to ensure safety for production components during assembly and measurement procedures. One of the ITk laboratories, located in Prague, prepared for modules’ production is presented as a model case. For this purpose, dedicated DAQ software called ITSDAQ is used together with the slow control monitoring system based on RS232/GPIB standards, MySQL database entry and Grafana visualization platform. Data are stored in the local database storage, a subset of them and the test results are also sent into the ITk Production Database. Such an integrated tool offers real-time plotting of crucial parameters and the possibility of receiving an immediate notification in case of exceeding any threshold.

INVMFS: An Efficient File System for NVRAM-Based Intermittent Computing Devices

Developing toward battery-less, energy-harvesting designs is a promising direction for sensor-scale devices. The recent introduction of byte-addressable, nonvolatile memory (NVRAM) enables intermittent computing, which preserves as much program progress across power interruptions as possible using fine-grained checkpoints. Sensing applications strongly demand local storage for near-data processing, and there is no exception for intermittent computing devices. While checkpointing on program progress is being studied recently, there is little work regarding how sensor file systems support intermittent computing. On power recovery, although the program progress is reverted to the latest checkpoint, the storage state stays at the instant of power loss. To remedy this problem, we present a lightweight file system for efficient operations on files and checkpoints. Our design exploits the byte addressability of NVRAM and uses as much in-place byte writing as possible while guaranteeing the safety of checkpoint operations. We evaluated our design against two prior checkpoint-enabled file systems, and results show that on average, our design reduced the total time overhead and energy consumption by 77% and 78%, respectively.

Assured data deletion in cloud computing: security analysis and requirements

With the rapid development of cloud storage, more data owners store their data on the remote cloud to reduce the heavy local storage overhead. Cloud storage provides clients with a storage space that they may outsource and use on a pay-as-you-go basis. Due to data ownership separation and management, local data owners lose control over their data. Hence, all the operations over the outsourced data such as data transfer, update, and deletion, will be executed by the remote cloud server. As a result of that, various security challenges appear in terms of data privacy and integrity. In addition to data deletion that becomes an important security challenge, once a cloud user intends to delete his data, it must be sure that data is deleted from all cloud storage sources and prevent the cloud server from reserving the data maliciously for economic interests. In this paper, we present and discuss several types of research that use different technologies to solve assured deletion problems and verification the deletion result. The paper also presents a thorough analysis of the surveyed protocols in terms of fine-grained, security, performance, and requirements of remote cloud storage design.

IsoqurPEKS: An Isogeny-Based Quantum-Resistant Public-Key Encryption Scheme with Keyword Search

Since the convenience and advancement of cloud applications, many users (e.g., companies or individuals) adopt remote cloud services to reduce the local storage overload and computing consumption. However, before transferring them to the cloud server, users always encrypt outsourced data for the privacy of important data, which deprives flexible usage of these data. Public key encryption with keyword search (PEKS) undoubtedly offers a precise resolution to this issue. Unfortunately, most PEKS schemes cannot fight against quantum computing attackers, which is increasingly a research hotspot. To achieve postquantum security and privacy-preserving search function, we propose a quantum-resistant PEKS scheme named IsoqurPEKS. Our proposed instantiation satisfies basic semantic security indistinguishable against chosen keyword attack (IND-CKA), and IsoqurPEKS is proved to be secure under the security model. Furthermore, we compare IsoqurPEKS with the other eight current PEKS schemes with respect to security properties, communication, and computation costs. The comparison results indicate that the proposed scheme has the best security and performance among the nine PEKS schemes.

Plant biomass allocation is mediated by precipitation use efficiency in arid and semiarid ecosystems

AbstractThe arid and semiarid grassland ecosystems are characterized by limited water resources and are generally vulnerable to climate change. Understanding how plants in arid and semiarid ecosystems respond to global climatic variation is crucial for ecological restoration under a changing climate. Although the effects of climate on aboveground biomass (AGB) and belowground biomass (BGB) have been widely explored, how AGB and BGB respond to climatic variation is seldom disentangled. There is still a need to understand how plant communities respond to global climate change. In this study, we conducted a transect survey across grasslands in Inner Mongolia to capture changes in AGB and BGB in plant communities. Then, we used structural equation modelling (SEM) to explore the relationship between environmental factors and the root:shoot ratio to understand how plant communities respond to ecological drought under global climate change. Our results showed that low precipitation use efficiency (LPUE) results in a high root:shoot (HRS) ratio, and BGB was more sensitive to environmental changes. By contrast, high precipitation use efficiency (HPUE) led to a low root:shoot (LRS) ratio, and environmental factors had a greater impact on AGB. For the LPUE pattern, soil water content (SWC), pH, and soil total nitrogen (STN) mainly affected the HRS ratio. Soil water content and STN influenced the HRS ratio through a positive effect on BGB . For the HPUE pattern, SWC, STN, and plant abundance (PA) predominantly regulate the LRS ratio, while biodiversity (plant abundance) affects the LRS ratio by positively affecting AGB. Our results highlight the differential impact of precipitation use efficiency on aboveground and belowground biomass allocation. This is important for monitoring the impact of drought events on plant biomass, improving productivity assessment models in arid and semi‐arid regions, and assessing local carbon storage accurately.

A global super-grid: sociotechnical drivers and barriers

BackgroundOne way to design an electricity system wholly based on renewables is referred to as the global Super-grid, a vision of a transmission network of unprecedented geographical scope that uses advanced technology to balance spatially and temporally varying supply and demand across the globe. While proponents, since the 1960s, have argued that a global Super-grid is technologically possible and socially desirable, and significant technical progress has been made since the 1990s, development is slow with new transmission lines being built predominantly with established technology and within the boundaries of single countries. The aim of this study is to explore sociotechnical drivers and barriers of global Super-grid development.ResultsA main driver is the century old ideas that larger grids are more efficient and contribute to cooperation and peace. Over the last decades, the level of technical knowledge and networks of proponent have grown. The Super-grid also benefits from the potential opportunity of building on existing grids. Barriers stem from the scale of investments needed to experiment, path dependences in established industry and competition from novel smaller scale solutions based on local production, energy storage and smart grid technology. Other barriers originate in the organisational and institutional complexities of international electricity trade, and in the lack of trust at local and global levels, which hinder the development of necessary coordination.ConclusionsThe analysis suggests that if the Super-grid is to become part of a future electricity system, the discourse needs to open up, move beyond simplistic ideas of efficiency and ‘technocratic internationalism’, and take into account a broader set of social benefits, risks and trade-offs.

Studying the effect of moisture content and temperature of corn stored in ware - houses and out – doors for production and deter mination of Aflatoxin B1

This experiment was conducted at the drying and thrushing of maize cob mill . Ministry of Agriculture , Baghdad at the Mycotoxins Laboratory , Plant Protection Dep.. Coll . of Agriculture , Uni . of Baghdad. This study aimed to study the effect of moisture content and temperature of corn stored in ware- houses and out – doors for production and determination of Aflatoxin B1. 1200 Kg of corn crops were used for our experiment, and were divided each 600 Kg stored out – doors and in doors respectively . Samples had been taken for (7) months by using stratified random sampling method. Results showed that the production of Aflatoxin B1in ware –houses was higher than that of out – door storage in three periods ( the middle of December , the middle of January, the middle of February ) . The temperature effect was stronger than moisture content and the Aflatoxin B1 was increased with higher temperature between ( 13- 32.8) wareas the decrease moisture content of corn in out –door was higher than that of where –houses storage ratio (5.8) .According to The above results , it was concluded that out- door storage was suitable for storing grain corn when compared to warehouse storage from the point of secretion of Aflatoxins. Whereas, the warehouse storage was better than out- door storage for moisture was being lost in local storage.

Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called "preparation range" to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.

Researching basic searchable encryption schemes in databases that support SQL

Currently, many users prefer to outsource data to third-party cloud servers in order to mitigate the load of local storage. However, storing sensitive data on remote servers creates security challenges and is a source of concern for data owners. With ever-growing security and privacy concerns, it is becoming increasingly important to encrypt data stored remotely. However, the use of traditional encryption prevents the search operation in the encrypted data. One approach to solving this problem is searchable encryption. Solutions for search in secure databases cover a wide range of cryptographic techniques, although there is still no dominant solution. Designing secure search systems is a balance between security, functionality, performance, and usability. Therefore, this paper provides an overview of some of the important current secure search solutions. The main searchable encryption systems of databases that support SQL are considered. The strengths and weaknesses of the analyzed systems and the techniques implemented in them are highlighted. A comparative analysis of some characteristics of the compared systems is given. Attention is drawn to the fact that the ability to perform search operations in encrypted data leads to a complication of systems, an increase in the amount of required memory and query execution time. All this indicates the openness of the protected search problem and the need for further research in this direction to ensure secure work with remote databases and data warehouses.

A centralized local energy storage modular multilevel converter

The energy storage modular multilevel converter (MMC-ES) has been widely studied for its excellent performance in solving the problems of power difference, voltage fluctuation and effective improvement of power quality in the grid caused by the integration of new energy caused by new energy grid connection. Aiming at the problems that energy storage units of the traditional distributed MMC-ES are scattered, inconvenient to assemble and maintain, complex system control, and the traditional centralized MMC-ES has poor capacity selection flexibility, in this paper, a centralized local energy storage modular multilevel converter (MMC-CLES) between the distributed MMC-ES and the centralized MMC-ES is proposed. On the premise of not affecting the advantages of MMC-ES, by reducing the number of energy storage units, the switching loss is reduced, and the problems of complex SOC (state of charge) balance control strategy, large power conversion system loss, the AC side current imbalance caused by the grid connection of new energy sources and high system cost caused by too many energy storage units are solved. Matlab/simulink simulation shows that compared with traditional distributed MMC-ES, MMC-CLES reduces a total of 52 power devices, and under the condition of three-phase voltage unbalance, MMC-CLES effectively suppresses power fluctuations, the SOC balance control and the fast balance of the three-phase current are realized. The unbalance degree of the three-phase current is 8.16%, and the total harmonic distortion rate of the current is 3.11%.

Privacy-Preserving Threshold Spatial Keyword Search in Cloud-Assisted IIoT

Cloud-assisted Industrial Internet of Things (IIoT) systems are increasingly deployed in various applications such as location-based services. Outsourcing data to cloud servers can help minimize local data storage and computation overheads, but it may introduce security and privacy concerns. Therefore, privacy-preserving spatial keyword search has been extensively explored in the literature. However, existing solutions still reveal the order of the spatio-textual similarity values between the query point and all data objects, and do not support searching for arbitrary geometric regions. To solve these issues, in this article we propose a privacy-preserving threshold spatial keyword search (TSKS) scheme. Specifically, we use the polynomial fitting technology, vector space model, and randomizable matrix multiplication technology to allow the cloud server to find relevant objects that are within some arbitrary geometric range and contain all query keywords. Finally, formal security analysis proves that our scheme can protect the privacy of data sets and queries, and extensive experiments demonstrate that our scheme is efficient and practical.

Structural heterogeneity of soil clods: Correlating Weibull parameters to fracture surface topography

Planes of weakness (i.e. cracks) existing in the soil matrix propagate, creating fracture surfaces and fragmenting the clod into smaller clods. The cracking process defines the fracture topography (i.e., roughness) that control many processes from flow exchange between macropore and matrix to local carbon storage and turnover. To date, useful relations between the surface topography of fractures and hydro-mechanical soil properties are missing because of technical and conceptual limitations. The objective was to test the assumption that the Weibull modulus of clod tensile strength can be related to the surface topography of fractures that resulted from the breakage of standardized soil clods. For carrying out standardized indirect tensile strength tests, a tension testing machine was constructed to prompt and monitor aggregate rupture. A total of 280 clods of 4 size-classes from two contrasting soils were considered. Clods were from the finer-textured Bt horizon of a Haplic Luvisol and the coarser-textured C horizon with lower amount of organic compounds of a Haplic Regosol. The natural clods were standardized in a cylindrical shape, in order to capture differences in the structural heterogeneity in terms of particle binding forces and porosity quantified by the Weibull modulus (m) of tensile strength. The stronger hierarchical organisation of the structure was quantified by smaller values of m (Bt: 3.1 vs C: 3.6) and the mass fractal dimension (Bt: 2.93 vs C: 2.98), and by higher values of the friability (m−1) (Bt: 0.14 vs C: 0.06). From these results, the random forest ensemble learning algorithm showed that the spatial heterogeneity of the organo-mineral pore coating (O-M) (shape factor of frequency distribution Bt: 0.58 vs C: 0.69), and not the organic carbon content (OC) (Bt: 0.48 vs C: 0.1 %), was the most important variable for predicting tensile strength (importance O-M: 0.21 vs OC: 0.09). Furthermore, the greater the spatial heterogeneity of the clod (m values ca. 1.4 for Bt) the lower the fracture surface roughness (ca. 1.8 µm2 µm−2) when compared to a more homogeneous clod (m values ca. 3.6, fracture surface roughness ca. 2.7 µm2 µm−2 for C). According to the random forest ensemble learning algorithm, porosity was the most important variable (variable importance 0.95) for predicting the rupture surface roughness followed by the spatial heterogeneity of the organo-mineral pore coatings, water content, and soil texture. Fracture surface roughness decreased when increasing porosity or decreasing the shape factor of the frequency distribution of organo-mineral coatings. Results confirmed that the Weibull modulus of clod tensile strength can be related with clod fracture surface roughness resulting from standardized clod breakage tests. This sheds light on the Weibull modulus as a distinct macro property for linking soil hydraulic and mechanical properties. This is of special importance in the parameterization of coupled models that combine the spatial heterogeneity of soil physical properties with the effects of hydrostatic forces on the soil structure.

A Low-Storage Blockchain Framework Based on Incentive Pricing Strategies

Nowadays, blockchain bloat is an endangering issue caused by inefficient transaction storage mechanisms. Based on the Distributed File System (DFS), the blockchain network can reduce the local storage to solve the blockchain bloat problem. However, storing all blocks on DFS is not durable or scalable. Hence, classifying blocks into hot and cold was adopted in previous works. The blockchain nodes can reduce the time consumption and storage consumption by storing hot blocks locally. However, the previous works are not able to periodically check block integrity and do not provide a reward mechanism to encourage DFS system nodes to store blocks. We extend previous works based on the InterPlanetary File System (IPFS) and design an innovative scheme to incentivize IPFS nodes. The IPFS nodes are regulated with smart contracts and behave under the pricing strategy controls to increase profit. By adopting proof of retrievability, we guarantee the integrity of the blocks. Further, the redundant scheme extends our pricing strategy to improve the durability of our proposed framework. A load-balancing pricing strategy and a general pricing strategy are provided in the framework to reward the DFS nodes. Extensive experiments are presented to demonstrate that the latency and throughputs of our model are competitive, while still maintaining data integrity in the system. The additional increased throughput takes only 0.167% of that produced by the original Bitcoin and the upload latency takes only 6.67% of the mining time of the Bitcoin Mainnet. Furthermore, our load-balancing pricing strategy achieves the effectiveness to ensure the redundancy of blocks and reduces the overall storage consumption up to 97% using the load-balancing pricing strategy, compared to the non-load-balancing pricing strategy.

Potential of Load Shifting in a Parking Garage with Electric Vehicle Chargers, Local Energy Production and Storage

The electrification of the transport sector is of crucial importance for a successful transition to a fossil-free society. However, the electricity grid constitutes a bottleneck. This article provides a case study based on a real-world parking garage with a smart grid infrastructure, called Dansmästaren. The analysis shows how renewable energy sources, energy storage technologies, and smart charging of electric vehicles can smooth out the load curve of the parking garage and relieve the electric grid during peak hours. Dansmästaren is located in Uppsala, Sweden, and equipped with 60 charging points for electric vehicles, a PV system, and a battery storage system. The study utilizes an energy flow model to show the potential of a realistically dimensioned smart energy system, that can benefit the parking facility in itself and the local distribution grid in a city, Uppsala, with grid capacity challenges. The results suggest that the parking garage demand on the local grid can be significantly lowered by smarter control of its relatively small battery energy storage. Moreover, further smart control strategies can decrease demand up to 60% during high load hours while still guaranteeing fully charged vehicles at departure in near future scenarios. The study also shows that peak shaving strategies can lower the maximum peaks by up to 79%. A better understanding of the potential of public infrastructures for electric vehicle charging helps to increase knowledge on how they can contribute to more sustainable cities and a fossil-free society.

Stochastic multi-objectives optimal scheduling of energy hubs with responsive demands in smart microgrids

Energy hubs (EHs) are progressively being implemented in microgrids for local generation, transfer, and storage of various forms of energy. However, issues have emerged around developing an operating schedule for the different energy resources incorporated in the EHs to ensure the operation at minimum possible costs and emissions. This paper proposes a stochastic-based multi-objectives optimization model for optimal day-ahead scheduling of microgrids based on EHs. The proposed model simultaneously manages the non-dispatchable distributed generator units, such as wind turbines and photovoltaic systems, and energy storage systems, such as compressed air energy storage (CAES) and battery energy storage systems. Moreover, the thermal network is also established by introducing solar heat collectors, heat generated during the discharge of CAES, and thermal energy storage. Furthermore, the proposed model also considers the uncertainties of wind speed, solar radiation, and residential loads. A demand response program (DRP) is also employed to the residential and plug-in electric vehicles load to flatten the load curve and to perform more cost-effectively. A total reduction of 9.87 % and 21.41 % is achieved in the cost and emission amount, respectively, after applying the DRP. Furthermore, a decrease of 13.41 % and 45.04 % is also achieved for the costs of imported power and operation of the CAES, respectively. Different case studies are performed, and a comparative study with other existing research is conducted to demonstrate the effectiveness of the proposed approach. The simulation results show the efficacy of the proposed stochastic approach for optimal day-ahead scheduling.