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In the realm of biological macromolecules, entities such as nucleic acids and proteins are distinguished by their homochirality, consistently defined chain lengths, and integral sequence‐dependent functionalities. Historically, these refined attributes have eluded traditional synthetic polymers, which often exhibit wide variabilities in chain lengths, limited batch‐to‐batch reproducibility, and stochastic monomer arrangements. Bridging this divide represents a pivotal challenge within the domain of polymer science – a challenge that the burgeoning discipline of precision polymer chemistry is poised to address. Recent advancements have yielded precision polymers that boast prescribed monomer sequences and narrow molecular weight distributions, heralding a new era for developing model systems to decipher structure‐property correlations within functional polymers, analogous to those within biological matrices. This review discusses the innovative liquid‐phase and solid‐phase synthesis techniques for creating precision polymers and the advanced characterization tools essential for dissecting their structure and properties. We highlight potential applications in self‐assembly, catalysis, data storage, imaging, and therapy, and provide insights into the future challenges and directions of precision polymers. [ABSTRACT FROM AUTHOR]

In non-residential buildings, building energy management systems (BEMS) and the application of data hold significant promise in reducing energy consumption. Nevertheless, BEMS have different levels of complexity, benefit, and limitation. Despite the advanced technologies and improvements in building operation, there is a clear gap in the actual performance of buildings that has been attributed to the adoption of advanced technologies. Consequently, there is an increasing need for researchers and practitioners to study current practices in order to identify and address the challenges that compromise the core objectives of BEMS. For this reason, this paper aims to validate three research questions: (i) to examine the current state of BEMS and its functionalities; (ii) to analyze the type of control used; (iii) and to determine the availability of historical data compiled by BEMS and its application in non-residential buildings. A survey of 676 buildings and interviews with building professionals were conducted. The findings confirmed that most of the buildings applied BEMS with scheduled control. In addition, a lack of digitized data for analysis and predictions was detected. Indeed, only 0.60% of the investigated buildings implemented predictive control. Finally, using hierarchical clustering analysis, responses were grouped to analyze similarities between them. The study findings help to develop targeted actions for implementing predictive control in non-residential buildings. [ABSTRACT FROM AUTHOR]

Background: Blockchain technology provides a secure and decentralized platform for storing and transferring sensitive medical data, which can be utilized to enable remote medical consultations. Aim: A theoretical framework for creating a blockchain-based digital system created to facilitate telemedicine system. Results: This paper proposes a theoretical framework based on Hyperledger fabric for creating a blockchain-based digital entity to facilitate telemedicine services. The proposed framework utilizes blockchain technology to provide a secure and reliable platform for medical practitioners to interact remotely with patient transactions. Conclusion: The blockchain will serve as a one-stop digital service to secure patient data, ensure privacy, and facilitate payments. The proposed framework leverages the existing Hyperledger fabric platform to build a secure blockchain-assisted telemedicine platform. [ABSTRACT FROM AUTHOR]

A favorable business environment plays a crucial role in facilitating the high-quality development of a modern economy. In order to enhance the credibility and efficiency of business environment evaluation, this paper proposes a hybrid storage blockchain-based query efficiency enhancement method for business environment evaluation. Currently, most blockchain systems store block data in key-value databases or file systems with simple semantic descriptions. However, such systems have a single query interface, limited supported query types, and high storage overhead, which leads to low performance. To tackle these challenges, this paper proposes a query efficiency enhancement method based on hybrid storage blockchain. Firstly, data are stored in a hybrid data storage architecture combining on-chain and off-chain. Additionally, relational semantics are added to block data, and three index mechanisms are designed to expedite data access. Subsequently, corresponding query efficiency enhancement algorithms are designed based on the query types that are applicable to the aforementioned three index mechanisms, further refining the query processing. Finally, a comprehensive authentication query is implemented on the blockchain for the light client, and the user can verify the soundness and integrity of the query results. Experimental results on three open datasets show that the method proposed in this paper significantly reduces storage overhead, has shorter query latency for three different query types, and improves retrieval performance and verification efficiency. [ABSTRACT FROM AUTHOR]

Multiplexing technology creates several orthogonal data channels and dimensions for high‐density information encoding and is irreplaceable in large‐capacity information storage, and communication, etc. The multiplexing dimensions are constructed by light attributes and spatial dimensions. However, limited by the degree of freedom of interaction between light and material structure parameters, the multiplexing dimension exploitation method is still confused. Herein, a 7D Spin‐multiplexing technique is proposed. Spin structures with four independent attributes (color center type, spin axis, spatial distribution, and dipole direction) are constructed as coding basic units. Based on the four independent spin physical effects, the corresponding photoluminescence wavelength, magnetic field, microwave, and polarization are created into four orthogonal multiplexing dimensions. Combined with the 3D of space, a 7D multiplexing method is established, which possesses the highest dimension number compared with 6 dimensions in the previous study. The basic spin unit is prepared by a self‐developed laser‐induced manufacturing process. The free state information of spin is read out by four physical quantities. Based on the multiple dimensions, the information is highly dynamically multiplexed to enhance information storage efficiency. Moreover, the high‐dynamic in situ image encryption/marking is demonstrated. It implies a new paradigm for ultra‐high‐capacity storage and real‐time encryption. [ABSTRACT FROM AUTHOR]

For the problem that massive data and cloud computing in the industrial Internet of Things cannot meet the requirements of low latency, a cloud hybrid network architecture is proposed. In the single user scenario, RS coding is used to realize data redundancy, and the security evaluation method is put forward on this basis. By using the ant colony algorithm, the data transmission delay and security objective function are established to obtain the best allocation scheme. In the multi-user scenario, the SD-CFIIoT architecture is constructed by combining SDN and fog computing technology. The optimal solution is found by applying the ant colony algorithm to solve the objective function of the data transmission delay. Simulation results show that when the security constraint is 0.9, the data transmission delay of RS is 5.04 s, which is 0.18 s less than LDPC; 0.19 s less than MBR. When the safety constraint is 0.9, the delay of the cloud mixed structure is 5.82 seconds; 1.53 seconds less than the cloud core structure and 1.20 seconds less than the fog core structure. When the data volume reached 80 MB, the delay of SD-CFIIoT was 2.20 s, 6.61 s lower than the fog structure and 15.80 s lower than the cloud structure. The simulation results prove the effectiveness of the proposed scheme, which can ensure data security while realizing low delay data transmission, improve the efficiency of the Internet of Things, and then improve the industrial production efficiency. This has a certain positive significance to the industrial modernization and intelligent development. [ABSTRACT FROM AUTHOR]

Digitally‐encoded poly(phosphodiesters) (d‐PPDE) with highly complex primary structures are evaluated for layer‐by‐layer (LbL) assembly. To be easily decoded by mass spectrometry (MS), these digital polymers contain many different monomers: 2 coding units allowing binary encryption, 1 cleavable spacer allowing controlled MS fragmentation, and 3 mass tags allowing fragment identification. These complex heteropolymers are therefore composed of 6 different motifs. Despite this strong sequence heterogeneity, it is found that they enable a highly controlled LbL film formation. For instance, a regular growth is observed when alternating the deposition of negatively‐charged d‐PPDE and positively‐charged poly(allyl amine hydrochloride) (PAH). Yet, in this approach, the interdistance between consecutive coded d‐PPDE layers remains relatively small, which may be an issue for data storage applications, especially for the selective decoding of the stored information. Using poly(sodium 4‐styrene sulfonate) (PSS) as an intermediate non‐coded polyanion, it is shown that a controlled interdistance between d‐PPDE layers can be easily achieved, while still maintaining a regular LbL growth. Last but not least, it is found in this work that d‐PPDE of relatively small molecular weight (i.e., significantly smaller than those of PAH and PSS) still enables a controlled LbL assembly. [ABSTRACT FROM AUTHOR]

The rapid growth of the urban rail transit industry has resulted in a surge in the volume of operational data generated by urban rail trains. However, traditional full-data storage methods have become inadequate in handling the escalating demands for data processing and storage. To address this, this paper presents an innovative optimization strategy that utilizes the data hopping technology for data processing and storage. This strategy aims to significantly reduce processing demands for urban rail train operational data, minimizing the occupation of storage resources, by comparing data from adjacent time sequences to accurately identify and store characteristics of data changes. In subsequent site experiments, the proposed approach achieved a 97.5% reduction in data processing demands and a 97% saving in storage space, while ensuring data security and stability. The study outcomes not only support the endeavor to effectively ease the burden on data transmission and storage, but also provide a robust data foundation for the intelligent operation and maintenance of urban rail transit.

There is an urgent need to increase the global data storage capacity, as current approaches lag behind the exponential growth of data generation driven by the Internet, social media, and cloud technologies. In addition to increasing storage density, new solutions should provide long‐term data archiving that goes far beyond traditional magnetic memory, optical disks, and solid‐state drives. Here, a concept of energy‐efficient, ultralong, high‐density data archiving is proposed, based on optically active atomic‐size defects in a radiation resistance material, silicon carbide (SiC). The information is written in these defects by focused ion beams and read using photoluminescence or cathodoluminescence. The temperature‐dependent deactivation of these defects suggests a retention time minimum over a few generations under ambient conditions. With near‐infrared laser excitation, grayscale encoding and multi‐layer data storage, the areal density corresponds to that of Blu‐ray discs. Furthermore, it is demonstrated that the areal density limitation of conventional optical data storage media due to the light diffraction can be overcome by focused electron‐beam excitation. [ABSTRACT FROM AUTHOR]

In this paper, we first analyze a real storage system consisting of 72 SSDs utilizing either Hardware RAID (HW-RAID) or Software RAID (SW-RAID), and show that SW-RAID is up to 7× faster. We then reveal that with an increasing number of SSDs, the limited I/O parallelism in SAS controllers and multi-enclosure handshaking overheads cause a significant performance drop, minimizing the total I/O Per Second (IOPS) of a 144-SSD system to less than a single SSD. Second, we disclose the most important architectural parameters that affect a large-scale storage system. Third, we propose a framework that models a large-scale storage system and estimates the system IOPS and system resource usage for various architectures. We verify our framework against a real system and show its high accuracy. Lastly, we analyze a use case of a 240-SSD system and reveal how our framework guides architects in storage system scaling. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Swirling spin configurations with a non-trivial topological state are popularly known as magnetic skyrmions. They are often observed in magnetic thin films with perpendicular magnetic anisotropy and Dzyaloshinskii–Moriya interaction (DMI). Due to their small size and non-collinear spin texture, they are being considered as a potential candidate for spintronic devices such as data storage devices, and logic-based devices. These skyrmions have distinct properties and interactions, that can be utilized to control their movement by applying an external electric current. In this work, we have successfully demonstrated the operation of skyrmion-based universal NAND and NOR logic gates. We have further performed arithmetic addition of two and three single-bit by using a half and full adder, fabricated based on the XOR and AND gates. We have observed that the presence of only uniform DMI is not enough to achieve all logic operations. The presence of a negative DMI at the edges is crucial in achieving proper logic operations. The findings are very important for the realization of skyrmion-based logic and computational operations. [ABSTRACT FROM AUTHOR]

Regeneration code technique has been widely used in the field of data storage with the advantages of high fault tolerance and low redundancy overhead, but the redundancy technique based on regeneration codes needs to download multiple coded blocks from other providers for repairing the failed data. Considering the heterogeneity of link bandwidth between nodes, the available bandwidth capacity of links varies greatly in real networks, minimization of network traffic does not necessarily imply minimization of regeneration time. Moreover, existing regeneration code repair schemes for bandwidth heterogeneity are difficult to support exact repair regeneration codes. Due to the specific mathematical structure of exact repair regeneration codes, their parallel repair is difficult to achieve. Therefore, ERC-TREE is proposed as a repair framework for exact repair regeneration codes under bandwidth heterogeneous networks. This framework efficiently takes advantage of the available bandwidth between providers by constructing an optimal tree to achieve exact repair of failed node data. The simulation experiment shows the feasibility of tree repair for exact repair regeneration codes in heterogeneous bandwidth environments. In the scenario with a significant difference in bandwidth, ERC-TREE reduces the data repair time by 78%compared to star repair. [ABSTRACT FROM AUTHOR]

Recent applications and growth on the internet have generated a lot of popularity and adoption of cloud computing which aims to assure the various computing resources. Data storage is one of the primary resources offered by the cloud; however, considering the multiple users in the particular cloud raises major concerns due to security. Recent researches shown great potential for providing efficient data sharing with multiple users. However, tracing of the data provider is still concerned to be a major issue. Hence, this research work proposes identifiable data sharing for multiple users (IDSMU) mechanism which aims to provide security for multiple users in a particular cloud group. At first, IDSMU creates the general participants (GP)-key for secure access to data. Further, IDSMU creates the trusted participants (TP) based on the reputation which further helps in creating the key generation. A novel signature scheme is used for identifying the participants; IDSMU is evaluated on computation count and efficiency is proved by comparing with an existing model considering computation count. [ABSTRACT FROM AUTHOR]

Featured Application: Increasing reproducibility for active learning-aided systematic screening is essential and our checklist can be used to evaluate reproducibility and data efficiency of software. In the screening phase of a systematic review, screening prioritization via active learning effectively reduces the workload. However, the PRISMA guidelines are not sufficient for reporting the screening phase in a reproducible manner. Text screening with active learning is an iterative process, but the labeling decisions and the training of the active learning model can happen independently of each other in time. Therefore, it is not trivial to store the data from both events so that one can still know which iteration of the model was used for each labeling decision. Moreover, many iterations of the active learning model will be trained throughout the screening process, producing an enormous amount of data (think of many gigabytes or even terabytes of data), and machine learning models are continually becoming larger. This article clarifies the steps in an active learning-aided screening process and what data is produced at every step. We consider what reproducibility means in this context and we show that there is tension between the desire to be reproducible and the amount of data that is stored. Finally, we present the RDAL Checklist (Reproducibility and Data storage for Active Learning-Aided Systematic Reviews Checklist), which helps users and creators of active learning software make their screening process reproducible. [ABSTRACT FROM AUTHOR]

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Multiplexing technology creates several orthogonal data channels and dimensions for high‐density information encoding and is irreplaceable in large‐capacity information storage, and communication, etc. The multiplexing dimensions are constructed by light attributes and spatial dimensions. However, limited by the degree of freedom of interaction between light and material structure parameters, the multiplexing dimension exploitation method is still confused. Herein, a 7D Spin‐multiplexing technique is proposed. Spin structures with four independent attributes (color center type, spin axis, spatial distribution, and dipole direction) are constructed as coding basic units. Based on the four independent spin physical effects, the corresponding photoluminescence wavelength, magnetic field, microwave, and polarization are created into four orthogonal multiplexing dimensions. Combined with the 3D of space, a 7D multiplexing method is established, which possesses the highest dimension number compared with 6 dimensions in the previous study. The basic spin unit is prepared by a self‐developed laser‐induced manufacturing process. The free state information of spin is read out by four physical quantities. Based on the multiple dimensions, the information is highly dynamically multiplexed to enhance information storage efficiency. Moreover, the high‐dynamic in situ image encryption/marking is demonstrated. It implies a new paradigm for ultra‐high‐capacity storage and real‐time encryption.

The sharing of pathological data is highly important in various applications, such as remote diagnosis, graded diagnosis, illness treatment, and specialist system development. However, ensuring reliable, secure, privacy-preserving, and efficient sharing of pathological data poses significant challenges. This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing. Additionally, it employs conditional proxy re-encryption (C-PRE) and public key encryption with equality test technology to control the scope and preserve the privacy of shared data. To assess the practicality of our solution, we implemented a prototype system using Hyperledger Fabric and conducted evaluations with various metrics. We also compared the solution with relevant schemes. The results demonstrate that the proposed solution effectively meets the requirements for pathological data sharing and is practical in production scenarios.

Objective. The purpose of the article is to present the main capabilities and advantages of implementing and using SIEM systems.Method. System analysis method was used.Result. The main systems of the SIEM class are described, their main capabilities, advantages and disadvantages are listed, and various options for constructing such systems and principles of data collection are considered.Conclusion. Studying the functioning of systems of this type allows us to assess the possibility of their use in the construction of security systems of various scales and architectures. To make maximum use of the capabilities of SIEM systems, it is necessary to adapt and configure it to specific information security requirements. The prospect for further research will be the use of hybrid approaches based on intermediate storage using data streaming.

Abstract Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required.

In the evolving landscape of the digital era, edge computing emerges as an essential paradigm, especially critical for low-latency, real-time applications and Internet of Things (IoT) environments. Despite its advantages, edge computing faces severe limitations in storage capabilities and is fraught with reliability issues due to its resource-constrained nature and exposure to challenging conditions. To address these challenges, this work presents a tailored storage mechanism for edge computing, focusing on space efficiency and data reliability. Our method comprises three key steps: relation factorization, column clustering, and erasure encoding with compression. We successfully reduce the required storage space by deconstructing complex database tables and optimizing data organization within these sub-tables. We further add a layer of reliability through erasure encoding. Comprehensive experiments on TPC-H datasets substantiate our approach, demonstrating storage savings of up to 38.35% and time efficiency improvements by 3.96x in certain cases. Furthermore, our clustering technique shows a potential for additional storage reduction up to 40.41%.

Background: Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data. Methods: WSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN). Results: A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection “Whole Slide Images as Non-fungible Tokens Project” on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API. Conclusion: Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.

The field of business management involves a large amount of data and information sources, including market data, customer data, supply chain data, etc. In order to quantify and analyze different resources, help enterprises better plan and allocate resources, and improve resource utilization efficiency, a clustering analysis based digital resource integration algorithm for business management is studied. Build a business management digital resource integration framework, including data layer, integration layer, and storage layer, to integrate and store data from different sources of business management databases, thereby facilitating unified management and utilization of digital resources by enterprises. The data layer collects data from different business management databases and stores it in the database according to different sources; The integration layer preprocesses the collected data, simply fixes errors and missing information in the data, and improves data quality. Adopting a feature extraction method based on the projection direction uncorrelation strategy of the labeled power set conversion method, the useful feature information of digital resources in enterprise management can be effectively extracted; Based on the two-step clustering analysis method, business management digital resources are clustered according to similar characteristics to complete the classification and integration of business management digital resources, and improve the efficiency of resource utilization; The storage layer adopts the Security Information Diffusion Algorithm (IDA) storage model to store integrated and classified digital resources managed by enterprises, ensuring data security and effectively preventing data leakage and illegal access. The experimental results show that the digital resource structure of business management integrated by this algorithm is clear, with a data redundancy of less than 8% and a difference of less than 11%. The time consumption for data integration is less than 2.11 minutes, indicating good resource integration ability. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]

This study explores implementation of Data Lake for Big data analytics marked by intervention of quasi-outsourcing personnel. Though an agentic interventionist, it takes place in an active Multinational Consumer Goods company (MCGC) of socially connected environment. The study being a case study of qualitative narrative inquiry toes the perspective of structural analysis. This study finds that the implementers utilise the lived experience to jump start their cases in view of adaptability to overcome stumbling blocks that circumvent implementation of data lake. The study concludes that the implementation of the Data Lake with quasi-outsourcing personnel was a successful strategy for the MCGC (Case) inasmuch as the decision to use the internal team, despite the longer term, proved to be correct, as it resulted in financial savings. Also, the standardization and centralization of data enabled a significant reduction in operational work, freeing up time for analytical and strategic activities. Finally, this study registers the struggles for implementation of data lake that would serve accounting information systems and broader environment of data usages. The triumphs and tribulations faced during the implementation points to several implications to partitioners, academia and policy makers. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud. Design/methodology/approach: This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design. Findings: For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning. Originality/value: The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients' health issues at any time and from any location. Medical IoT devices monitor patients' health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0 and big data technology, the Industrial Internet of Things (IIoT) is hampered by inherent issues such as privacy, security, and fault tolerance, which pose certain challenges to the rapid development of IIoT. Blockchain technology has immutability, decentralization, and autonomy, which can greatly improve the inherent defects of the IIoT. In the traditional blockchain, data is stored in a Merkle tree. As data continues to grow, the scale of proofs used to validate it grows, threatening the efficiency, security, and reliability of blockchain-based IIoT. Accordingly, this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data. To solve this problem, a new Vector Commitment (VC) structure, Partition Vector Commitment (PVC), is proposed by improving the traditional VC structure. Secondly, this paper uses PVC instead of the Merkle tree to store big data generated by IIoT. PVC can improve the efficiency of traditional VC in the process of commitment and opening. Finally, this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments. This mechanism can greatly reduce communication loss and maximize the rational use of storage space, which is of great significance for maintaining the security and stability of blockchain-based IIoT. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the role of research data preservation for enhanced data usage among agricultural researchers in Tanzania. Specifically, the study aimed to examine the data preservation methods used by agriculture researchers, find out how long agriculture researchers preserve their agriculture research data, and determine factors that influence agriculture researchers on their choice of data preservation methods for use. The study employed a cross-sectional research design. The study employed both qualitative and quantitative approaches. A survey was conducted to collect data in 11 research institutions. A simple random sampling technique was used to select 204 respondents from the study area while purposive sampling techniques were used to select 11 agriculture research institutions including 10 Tanzanian Agricultural Research Institution (TARI) centers, and Sokoine University of Agriculture (SUA). Also, 12 respondents were selected purposively for an in-depth interview as key informants. The study adopted Data Curation Centre (DCC) Lifecycle Model to explain data preservation process. Findings indicated that a majority of more than 90% of researchers preferred to preserve their data using different storage devices such as field notebooks, computers, and institutional libraries. Moreover, findings indicated that about 74% of agricultural researchers preferred to preserve their data for more than 6 years after the end of the project. Findings also indicated factors that influence researchers in the choice of data preservation methods to be easy to reach, cost-effective storage devices, support to use the devices, adequate infrastructure for data preservation, and reliable power supply. It can be concluded that there is yet a great role of research data preservation in enhancing data usage among researchers in Tanzania. It is recommended that the government should establish an agricultural research data bank to guarantee permanent availability of data at all times when needed. [ABSTRACT FROM AUTHOR]

Increasing popularity in passive acoustic monitoring and the ease with which researchers can accumulate large quantities of acoustic data has resulted in challenges for audio recording storage, archiving, and management. Reductions in file size can be achieved by lowering sample rate and compressing to different formats; however, how these processes affect audio data quality, and the resulting interpretation of wildlife data is not well understood. We investigated the effect of sampling rate and lossy compression of audio recordings to MP3 from their native WAV format on the performance of four commonly applied avian bioacoustic applications: community listening, distance estimation, automated recognition, and acoustic indices. Compression to MP3 decreased the number of detections, including a reduction in total abundance of individuals when transcribing audio files for community listening and lower precision and recall for automated recognisers. Sampling rate reduction introduced systematic bias to acoustic indices and had an influence on precision and recall for recognisers as well. We recommend against the use of MP3 compression to reduce file volume and suggest other lossless forms of audio compression where an exact copy of the original recording can be recovered. [ABSTRACT FROM AUTHOR]

In today's digital landscape, the exponential growth of data heightens security risks associated with traditional centralized storage systems. Utilizing blockchain technology, a shift towards decentralized data storage provides a more secure and private alternative. Central to our work is the exploration of symmetry in data management, a concept woven into the fabric of our proposed solution to challenge the inherency in InterPlanetary File System (IPFS) technology. Through the strategic utilization of smart contract-invoked random functions, our blockchain-based solution fragments and securely stores data in order to ensure a symmetrical balance between confidentiality and integrity. Our research endeavors are to contribute a robust, ethically grounded data storage framework fostering advancements in secure data sharing. The implications of this paper are significant in addressing contemporary challenges of data management within the expansive realm of big data. [ABSTRACT FROM AUTHOR]

Due to the high affinity with water molecules, amide compounds are easily contaminated by moisture; therefore, the water interference effect cannot be totally excluded from the amide‐involved reactions. Thus, the perfect solution is to use the interference effect but not shield it in a real application. In this work, we introduced different contents of sodium acrylate (AAS) to scavenge water from the monomers of N‐isopropylacrylamide (NIPAm) when copolymerized with TPA‐Vinyl‐4CN. Herein, water molecules play a role as nucleophilic reagents to attack highly active functional groups as –C=C–CN from TPA‐Vinyl‐4CN, leading to a blue emissive TPA‐Vinyl‐2CHO. From this study, we made a deep awareness of the interactions between three reaction partners of AAS and NIPAm as well as TPA‐Vinyl‐4CN. Our results clearly demonstrated the fact that water can be perfectly used and controlled by the water absorbent of AAS, developing a new approach to synthesizing multiple emission‐coloured polymers by using only one luminogen of TPA‐Vinyl‐4CN. [ABSTRACT FROM AUTHOR]

With the increase in the number of Internet of Things (IoT) applications, the reliance on robust networking, reliable communications, and efficient and secure data storage is increasing day by day. Many of these applications such as cellular communications, transportation networks and Internet of Medical Things generate huge volumes of data that need to be stored and analyzed to generate insights and make decisions related to applications. Security of data storage is a paramount concern, particularly in the current cyber security threat landscape. It is important to design novel techniques and algorithms to protect the data against these upcoming attacks. This paper provides a brief overview of security challenges faced by the data storage mechanisms. In comparison to the existing review papers in this area, the work in this paper identifies three major security techniques for data storage and conducts a comprehensive review of the recent advancements related to these techniques. These techniques include Blockchain algorithms, encryption mechanisms, and deduplication algorithms for data storage. Finally, the work highlights future opportunities and challenges related to secure data storage in future networks.

In order to ensure the consistency and completeness of monitoring data in the safety monitoring system of coal mines, a design scheme for continuous transmission of disconnected data was proposed in response to faults in the monitoring substation equipment. The methods for fault recognition of data transmission, data preprocessing, data storage strategies, and data transmission protocols were studied. Based on the principle of invariable nonstorage, analog data with invalid values was cleaned up by bit storage compression of discrete transient data. Two-level storage of data was achieved by using FLASH and ferroelectric memory, and the data storage format and strategy were described. Network transmission protocols and bus transmission protocols were formulated separately to implement data continuation functions. The experimental results showed that after the main communication of the substation was interrupted, the monitoring data could be continuously stored for 7 days, and the correct rate of data continuation after the end of the failure reached 100%. This solution effectively solved the problem of missing data caused by equipment failure in the substation, ensured the integrity of monitoring data, and improved the reliability of the monitoring system.

The high-capacity and long-lasting storage features of DNA have enabled researchers to demonstrate its data storage capabilities. Current challenges becoming apparent are high storage costs and slow retrieval of the data. Enzymatic read-in and read-out hold promise for lower prices for DNA synthesis and data retrieval. New fast read-out options include direct optic readout and sequence-mediated signals. Graphene for fast sequencing along with DNA wires and nanocellulose as chassis boost application. The storage of digital data is becoming a worldwide problem. DNA has been recognized as a biological solution due to its ability to store genetic information without alteration over long periods. The first demonstrations of high-capacity long-lasting DNA digital data storage have been shown. However, high storage costs and slow retrieval of the data must be overcome to make DNA data storage more applicable and marketable. Herein, we discuss the issues and recent advances in DNA data storage methods and highlight pathways to make this technology more applicable to real-world digital data storage. We envision that a combination of molecular biology, nanotechnology, novel polymers, electronics, and automation with systematic development will allow DNA data storage sufficient for everyday use. [ABSTRACT FROM AUTHOR]