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Phase-change memory emerges as a top contender for non-volatile data storage applications. We report here a systematic change in local structure and crystallization kinetics of binary GeTe thin films using temperature-dependent resistivity measurements, which offers single-stage crystallization at around 187 °C, corroborated with x-ray diffraction. Furthermore, the change in chemical bonding upon crystallization is determined through x-ray photoelectron spectroscopy core level spectra, which reveals the existence of Ge and Te components that align with the GeTe crystal structure. Also, an investigation was carried out employing a UV–Vis–NIR spectrophotometer to explore the evolution of optical bandgaps (E g) , Tauc parameter (B) representing the local disorder, and Urbach energy (E u) of the GeTe material, as it undergoes the transition from a disordered amorphous state to a crystalline state. As crystallization progresses, a consistent shift of E g from 0.92 to 0.70 eV corresponds to as-deposited amorphous at room temperature and crystalline at 250 °C, respectively. In addition, the reduction in E u (from 199.87 to 141.27 meV) and a sudden increase of B around crystallization temperature is observed upon increasing temperature, indicating direct observation of enhanced medium-range order and distortion in short-range order, respectively, in GeTe thin films, revealing improved structural and optical properties. These enhancements make the GeTe material ideal for data storage applications of phase-change memory for next-generation computing technology. [ABSTRACT FROM AUTHOR]

In this study, we integrated an Indium Gallium Zinc Oxide (IGZO) channel with a superlattice of HfO2/ZrO2 (HZO) under low-thermal-budget microwave annealing to produce nearly wake-up-free ferroelectric capacitors. To eliminate the impact of trap-charges during the atomic layer deposition process, we conducted H2 plasma treatment to eliminate leak defects induced by carbon contamination and maintain neutrality to achieve high-quality IGZO/HZO interfaces, confirmed by x-ray photoelectron spectroscopy. The H2 plasma treatment improved polarization (Pr) and coercive field (Ec), reaching 2Pr: 40 μC/cm2 and Ec: 2.33 MV/cm, enabling a low-power writing speed of 30 ns with eight states (three bits per cell). The defect engineering method ensures endurance of up to 108 cycles and retains ten-year data storage at 90 °C. This research provides a new avenue for improving emerging oxide interfaces controlled by ferroelectric polarization. [ABSTRACT FROM AUTHOR]

Resistive Random Access Memory (RRAM) is considered one of the most promising candidates for big data storage. By using atomic layer deposition and magnetron sputtering, HfO2 thin films were prepared on ITO first, which exhibited good resistive switching (RS) characteristics in the structure of Ag/HfO2/ITO. By analyzing the RS mechanism, it is found that both metal conductive filaments and oxygen vacancy conductive filaments coexisted and Sn ion in ITO can influence the retention of RRAM. Furthermore, a device in the structure of Ag/HfO2/Pt was proposed and prepared, which exhibited excellent RS characteristics, including an ultrahigh switching ratio averaging up to 108 and low operating voltage. It is concluded that the difference in the work function between the top and bottom electrodes contributes to improving the switching ratio, reducing the operating voltage. In addition, the Ag/HfO2/Pt device is similar to the Ag/HfO2-based threshold switching selector in the structure and in characteristics of high switching ratio, besides non-volatile memory. Hence, the device is functionally equivalent to the combination of an RRAM and a threshold switching selector. It is the potential way to replace the conventional 1S1R structure memory. [ABSTRACT FROM AUTHOR]

Ge2Sb2Se4Te1 (GSST) exhibits unprecedented broadband transparency over the infrared wavelength range and has emerged as a promising functional material in photonic applications that operate in the optical fiber telecommunication wavelength band. In this work, GSST and graphene oxide (GO) are integrated into an optical fiber link to achieve all-fiber non-volatile multilevel photonic memory. The GSST and GO (GSST-GO) duo-layer hybrid structure is sandwiched between two optical fiber ferrules, where the GO acts as a localized heat source to initiate the phase transition of GSST upon optical excitation. The GSST-GO-coated fiber exhibits a low insertion loss of 0.8 dB and a maximum readout contrast of about 32%, with at least five distinguished memory states. The response time of the device is measured in the range between 2.5 and 9.5 μs. This work serves as a proof of concept on implementing the GSST-GO duo-layer hybrid structure in optical fiber platform to realize all-fiber non-volatile multi-bit channel control or data storage. [ABSTRACT FROM AUTHOR]

We like and need Information and Communication Technologies (ICTs) for data processing. This is measurable in the exponential growth of data processed by ICT, e.g., ICT for cryptocurrency mining and search engines. So far, the energy demand for computing technology has increased by a factor of 1.38 every 10 years due to the exponentially increasing use of ICT systems as computing devices. Energy consumption of ICT systems is expected to rise from 1500 TWh (8% of global electricity consumption) in 2010 to 5700 TWh (14% of global electricity consumption) in 2030 [A. S. G. Andrae, Eng. Appl. Sci. Lett. 3, 19–31 (2020)]. A large part of this energy is required for the continuous data transfer between separated memory and processor units, which constitute the main components of ICT computing devices in von-Neumann architecture. This, at the same time, massively slows down the computing power of ICT systems in von-Neumann architecture. In addition, due to the increasing complexity of artificial intelligence (AI) compute algorithms, since 2010, the AI training compute time demand for computing technology has increased tenfold every year, for example, from 1 × 10−6 to 1 × 10+4 Petaflops/day in the period from 2010 to 2020 [J. Wang, see https://ark-invest.com/articles/analyst-research/ai-training/ for information about the cost to train an AI interference system (2020)]. It has been theoretically predicted that ICT systems in the neuromorphic computer architecture will circumvent all of this through the use of merged memory and processor units. However, the core hardware element for this has not yet been realized so far. In this work, we discuss the perspectives for non-volatile resistive switches with hysteretic memristance as the core hardware element for merged memory and processor units in neuromorphic computers. [ABSTRACT FROM AUTHOR]

Owing to their ability for fast switching and the large property contrast between the crystalline and amorphous states that permits multi-level data storage, in-memory computing and neuromorphic computing, the investigation of phase change materials (PCMs) remains a highly active field of research. Yet, the continuous increase in electrical resistance (called drift) observed in the amorphous phase has so far hindered the commercial implementation of multi-level data storage. It was recently shown that the resistance drift is caused by aging-induced structural relaxation of the glassy phase, which is accompanied by a simultaneous decrease in enthalpy and fictive temperature. This implies that resistance is related to enthalpy relaxation. While the resistance is known to drift even at room temperature and below, evidence for enthalpy relaxation at room temperature in amorphous PCMs is still missing. Here, we monitor changes in enthalpy induced by long-term room-temperature aging in a series of PCMs. Our results demonstrate the simultaneity of resistance drift and enthalpy relaxation at room temperature, and thus provide further insights into the mechanism of resistance drift and its possible remediation. [ABSTRACT FROM AUTHOR]

Core enterprises are entering into close collaborations with multi-level manufacturing service providers to create and share value. However, credible data sharing in collaborative manufacturing supply chains is rare, which can lead to uncontrollable production distortions and delayed operational adjustments. Driven by blockchain technology, this study proposes a systematic framework for credible manufacturing data sharing for a cross-enterprise collaborative manufacturing supply chain (CMSC). To develop the needed method, a blockchain network system with blockchain nodes, distributed ledger, and Raft-based distributed node ordering service system was first modelled. Then, two automatic data upload algorithms were studied to ensure the reliability of off-chain manufacturing data sources from core enterprises, suppliers, and customers. Then, two types of smart contracts were designed to ensure the standardisation and co-validation of manufacturing data storage and query processes in the blockchain network. A demonstrative case was studied to validate the proposed blockchain-based credible manufacturing data sharing method. The results show that our work is effective for credible data sharing in CMSC, which makes it easier for core enterprises to better operate the CMSC and further stimulate enterprises to create a credible social community. [ABSTRACT FROM AUTHOR]

Summary: The precise diagnosis and effective treatment of patients rely heavily on healthcare data. However, sharing healthcare information can be challenging due to the potential risks of unauthorized tampering and data leakage. To address these concerns and facilitate secure and efficient data access for stakeholders within and outside the healthcare system, this study introduces a patient‐enabled blockchain system (PEBS). Patient‐enabled blockchain system uses the Model View Controller (MVC) approach where the model manages the off‐chain and on‐chain data, the view is the user‐accessible module, and the controller acts as an interface between a user interface and storage layer. It enables patients to control their data by determining specific access permissions and executes various smart contracts for stakeholders' registration, authorization, data storage, query, and update operations. Patient‐enabled blockchain system incorporates Modified Proof‐of‐Authority (MPoA), which has been compared against various consensus algorithms such as Proof‐of‐Work (PoW), Proof‐of‐Authority (PoA), and Istanbul Byzantine Fault Tolerance (IBFT). Furthermore, the suggested system incorporates the utilization of the Interplanetary File System (IPFS) to address concerns related to performance and storage. We conducted an in‐depth analysis and comparison of the system's performance using key parameters such as transaction latency and throughput. Experiments are carried out using network sizes of 10 and 30, with transaction counts from 5 to 500. The experiments show that the highest latency for the proposed system is 58,105 ms, almost 4.8 times less than PoW, which is 283,575 and provides 2.7 times higher throughput (101 transactions per second) than PoW (38 transactions per second). [ABSTRACT FROM AUTHOR]

It is always a hot and difficult problem of how to realize multi-bit data storage by preparing multi-resistance memristors by a simple process compatible with standard CMOS processes. In this work, a tristable TaO x -based memristor with tunable oxygen vacancies is fabricated by physical vapor deposition, consisting of a high-resistance state and two low-resistance states. Its set and reset voltages are low −1.1V, −2.1V, and -1V, 3.3V, respectively. The XPS characterization demonstrates the presence of three valence states within the TaO x film. It has been suggested that different barriers lead to different switching voltages during the transition of the trivalent state, resulting in the formation of stable states. Interestingly, when increasing the concentration of oxygen during preparation to decrease the concentration of oxygen vacancies in TaO x film, the volt-ampere properties of memristor change from tristable to bistable, which further proves the above point. This work provides a viable strategy for switching between bistable and tristable states in memristors prepared from transition metal oxides. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become a pervasive phenomenon, with applications in a multitude of sectors, including healthcare, smart agriculture, smart cities, transportation, and water management. This has led to a significant generation of Big Data. In order to process this substantial volume of data efficiently, there is a pressing need for a platform capable of handling large quantities. However, real-time applications face challenges in cloud processing due to high latency. As a complementary infrastructure to the cloud, fog computing emerges as a viable solution by facilitating task processing, networking, and data storage in cloud data centers accessible to mobile users. The offloading of tasks represents a promising solution to the resource constraints inherent in IoT applications, particularly within the context of fog computing. This process entails the execution of particular components of mobile applications within a fog–cloud environment, to reduce execution time and energy consumption. The objective of our research is to optimize task offloading in IoT within heterogeneous environments, taking into account conflicting constraints. This optimization challenge is formulated as a multi-objective problem, with a particular focus on energy consumption and latency, as well as quality of service metrics. The proposed solution, TOF-NSGAII, is designed to respect the finite resources of fog computing, balancing workloads to meet the latency requirements of IoT tasks. The widely employed meta-heuristic, the non-dominated sorting genetic algorithm (NSGA-II), has been adapted to generate a set of non-dominated multi-objective task offloading optimization solutions, considering both energy consumption and latency. The experimental results demonstrate the efficacy of TOF-NSGAII in generating task offloading solutions that distribute executed tasks between fog and cloud computing environments in a judicious manner, based on their specific requirements. Furthermore, the generated non-dominated solutions demonstrate optimality in terms of energy consumption, with an average reduction of 12.18% compared to alternative approaches. It is noteworthy that our approach introduces only a marginal increase in latency, amounting to 0.38%, which can be considered negligible. [ABSTRACT FROM AUTHOR]

The growing popularity of the most current wave of decentralized systems, powered by blockchain technology, which act as data vaults and preserve data, ensures that, once stored, it stays preserved, considered to be one of the most promising safe and immutable storage methods. The authors of this research suggest an on-chain storage framework that stores files inside blockchain transactions using file transforming, chunking, and encoding techniques. This study investigates the performance of on-chain file storage using a simulated network blockchain environment. Test files of varying sizes were deployed. Performance metrics, including consumed time in chunking, encoding, and distributing chunks among block transactions, were measured and analyzed. An analysis of the collected data was conducted to assess the framework's performance. The result showed that selecting the appropriate chunk size significantly influences the overall performance of the system. We also explored the implications of our findings and offered suggestions for improving performance within the framework. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the impact on rural Veterans' access to social work services of a Department of Veterans Affairs (VA) national program to increase social work staffing, by Veterans' rurality, race, and complex care needs. Data Sources and Study Setting: Data obtained from VA Corporate Data Warehouse, including sites that participated in the social work program between October 1, 2016 and September 30, 2021. Study Design: The study outcome was monthly number of Veterans per 1000 individuals with 1+ social work encounters. We used difference‐in‐differences to estimate the program effect on urban, rural, and highly rural Veterans. Among rural and highly rural Veterans, we stratified by race (American Indian or Alaskan Native, Asian, Black, Native Hawaiian or Other Pacific Islander, and White) and complex care needs (homelessness, high hospitalization risk, and dementia). Data Collection: We defined a cohort of 740,669 Veterans (32,434,001 monthly observations) who received primary care at a participating site. Principal Findings: Average monthly social work use was 8.7 Veterans per 1000 individuals. The program increased access by 49% (4.3 per 1000; 95% confidence interval, 2.2–6.3). Rural Veterans' social work access increased by 57% (5.0; 3.6–6.3). Among rural/highly rural Veterans, the program increased social work access for those with high hospitalization risk by 63% (24.5; 18.2–30.9), and for Veterans experiencing homelessness, 35% (13.4; 5.2–21.7). By race, the program increased access for Black Veterans by 53% (6.1; 2.1–10.2) and for Asian Veterans by 82% (5.1; 2.2–7.9). Conclusions: At rural VA primary care sites with social work staffing below recommended levels, Black and Asian Veterans and those experiencing homelessness and high hospitalization risk may have unmet needs warranting social work services. [ABSTRACT FROM AUTHOR]

The authors consider the private information retrieval (PIR) problem, in particular, the problem of ensuring secure queries to a database. Previously, the authors considered this problem for a cloud database in the presence of an active adversary who does not interfere with the execution of the PIR protocol but can carry out an attack with known open queries. In the proposed algorithms, bit number i is represented as an l-ary number with d digits. An algorithm for database placement on the cloud and an algorithm for bit querying with the use of permutations in the digits of a bit number, where bit number i is defined in a base-l number system, are proposed. The permutations are regarded as secret encryption keys. The communication complexity and the probability of guessing the bit number in a single attack with a known open query for bit number i, as well as in an attack with an unlimited number of known open queries, are estimated. [ABSTRACT FROM AUTHOR]

Liquefied natural gas (LNG) regasification terminals have specification that necessitates adjustment of heating value of LNG feed. One strategy to reduce heating value is to separate heavier hydrocarbons from LNG by utilizing LNG cold energy. The amount of cold energy utilized in recovering heavy hydrocarbon is much less resulting in wastage of remaining cold energy. To reverse this wastage, we propose a polygeneration process that encompasses an organic Rankine cycle to generate electricity and refrigeration for cold storage warehouse cooling and data center cooling by utilizing remaining cold energy. Based on process simulation and optimization, we found that a significant amount of cold energy is utilized resulting in an avoided CO2 emission, thereby enhancing sustainability of an LNG regasification terminal. [ABSTRACT FROM AUTHOR]

PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System. METHODS: Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage. To that end, a clinician annotated MM stage within over 5,000 short snippets of clinical notes, and annotated MM stage at MM treatment initiation for 200 patients. These were allocated into snippet- and patient-level development and validation sets. We developed MM stage extraction and roll-up algorithms within the development sets. After the algorithms were finalized, we validated them using standard measures in held-out validation sets. RESULTS: We developed algorithms for three different MM staging systems that have been in widespread use (Revised International Staging System [R-ISS], International Staging System [ISS], and Durie-Salmon [DS]) and for stage reported without a clearly defined system. Precision and recall were uniformly high for MM stage at the snippet level, ranging from 0.92 to 0.99 for the different MM staging systems. Performance in identifying for MM stage at treatment initiation at the patient level was also excellent, with precision of 0.92, 0.96, 0.90, and 0.86 and recall of 0.99, 0.98, 0.94, and 0.92 for R-ISS, ISS, DS, and unclear stage, respectively. CONCLUSION: Our MM stage extraction algorithm uses rule-based NLP and data aggregation to accurately measure MM stage documented in oncology notes and pathology reports in VA's national EHR system. It may be adapted to other systems where MM stage is recorded in clinical notes. [ABSTRACT FROM AUTHOR]

Owing to the increasing demands of high-density data storage double-halide perovskite-based resistive random access memory (RRAM) have recently emerged as a promising candidate in the forefront of next-generation optoelectronic memory applications. The ionic motion-based quick switching is the key feature of this kind of material, which plays a significant role in resistive switching (RS) applications. Recently, lead-free tin-based double-halide perovskites have been considered as favourable material due to their superior stability, functionality and eco-friendly nature. Here, we report the synthesis of cesium tin (IV) iodide (Cs2SnI6) perovskites. X-ray diffraction (XRD) pattern of the as-synthesized perovskite confirms the formation of Cs2SnI6 material. The crystallographic data corroborate the formation of a pure cubic phase, free of any other phase at room temperature. We also studied optical properties of the sample by using the ultraviolet–visible (UV) spectra and photoluminescence (PL) spectra. A broadband at around 580 nm is observed in the UV−Vis absorption spectra. The optical band gap of the sample is found to be 1.68 eV. Cs2SnI6 perovskite exhibited intense PL emission at ~540 nm. In this work, to fabricate a flexible Al/Cs2SnI6/ITO-PET memory device, we used Cs2SnI6 film as a switching layer and the device exhibits bipolar RS characteristics. [ABSTRACT FROM AUTHOR]

Hyperledger Fabric is a platform for permissioned blockchain networks that enables secure and auditable distributed data storage for enterprise applications. There is a growing interest in applications based on this platform, but its use requires the configuration of different blockchain parameters. Various configurations impact the system's non-functional qualities, especially performance and cost. In this article, we propose a Stochastic Petri Net to model the performance of the Hyperledger Fabric platform with different blockchain parameters, computer capacity, and transaction rates. We also present a set of case studies to demonstrate the feasibility of the proposed model. This model serves as a practical guide to help administrators of permissioned blockchain networks find the best performance for their applications. The proposed model allowed us to identify the block size that leads to a high mean response time (ranging from 1 to 25 seconds) caused by a change in the arrival rate. [ABSTRACT FROM AUTHOR]

Blockchain and Decentralized Applications (DApps) are increasingly important for creating trust and transparency in data storage and computation. However, on-chain transactions are often costly and slow. To overcome this challenge, off-chain nodes can be used to store and compute data. Unfortunately, this introduces the risk of untrusted nodes. To address this, authenticated data structures have been proposed, however, this ignores the compute of data from the raw data. We tackle this challenge by introducing zk-Oracle, which provides an efficient and trusted compute and storage off-chain. There is a challenge in using zero-knowledge proofs (zk-proof for short), which is the large proof generation time. We aim to overcome it with novel designs in zk-Oracle. zk-Oracle builds on zk-proofs technologies to achieve two goals. First, the computation of data structures from raw data and the corresponding proof generation is improved in terms of performance. Second, the verification on-chain is inexpensive and fast. Our experiments show that we can speed up zk-proof generation by up to 550 × faster than the baseline method. [ABSTRACT FROM AUTHOR]

DNA is considered as a prospective candidate for the next‐generation data storage medium, due to its high coding density, long cold‐storage lifespan, and low energy consumption. Despite these advantages, challenges remain in achieving high‐fidelity, fully integrated, and cost‐efficient DNA storage system. In this study, a homemade digital microfluidic (DMF)‐based compact DNA data storing pipeline is orchestrated to complete the entire process from the synthesis to the sequencing. The synthetic half employs phosphoramidite chemistry on 200 nm magnetic beads (MBs), where the dimethyltrityl protecting group is removed by droplet manipulation of trichloroacetic acid. The sequencing counterpart relies on pyrophosphate releasing originated from polymerase‐catalyzed primer extension, which leads to photon‐countable chemiluminescence (CL) signal in 2.5‐μL drops of trienzyme cascading reactions. Further by DNA denaturation, repeated pyrosequencing plus plurality voting can improve the nucleobase accuracy beyond 95 %. As a proof‐of‐concept trial, semantic information is saved in DNA via the Huffman coding algorithm plus the Reed‐Solomon error‐correction, and then robustly retrieved from this streamlined platform. As a result, it took a net total of approximately 6.5 h to writing and reading 8 bytes of data, that equal to a storaging speed of 49 min/byte, much quicker than the previously reported 2.8–4.2 h/byte. This bead‐based miniaturized device promises an unattended protocol for achieving high‐throughput, full‐packaged, and above all, neatly precision DNA storage. [ABSTRACT FROM AUTHOR]

Reservoir sedimentation poses a significant challenge to freshwater management, leading to declining storage capacity and inefficient reservoir operations for various purposes. However, trustworthy and independently verifiable information on declining storage capacity or sedimentation rates around the world is sparse and suffers from inconsistent metadata and curation to allow global-scale archiving and analyses. The G lobal R eservoir I nventory of L ost S torage by S edimentation (GRILSS) dataset addresses this challenge by providing organized, well-curated and open-source data on sedimentation rates and capacity loss for 1,015 reservoirs in 75 major river basins across 54 countries. This publicly accessible dataset captures the complexities of reservoir sedimentation, influenced by regional factors such as climate, topography, and land use. By curating the information from numerous sources with disparate formats in a homogenized data structure, GRILSS serves as an invaluable resource for water managers, policymakers, and researchers for improved sediment management strategies. The open-source nature of GRILLS promotes collaboration and contributions from the global community to grow the dataset. By providing essential reference data on sedimentation to understand the global challenge of reservoir sedimentation, this GRILLS dataset represents a gateway for the global community to share sedimentation and storage loss data for sustainable operation of world's reservoirs for future generations. [ABSTRACT FROM AUTHOR]

Due to the primogenic effect, the valence shells of divalent iron Fe(II) ([Ar]3d6) and trivalent lanthanides Ln(III) ([Xe]4fn) are compact enough to induce spin-state equilibrium for the 3d-block metal and atom-like luminescence for the 4f-block partner in Fe(II)–Ln(III) dyads. In the specific case of homoleptic pseudo-octahedral [Fe(II)N6] units, programming spin crossover (SCO) around room temperature at normal pressure requires the design of unsymmetrical didentate five-membered ring chelating N∩N′ ligands, in which a five-membered (benz)imidazole heterocycle (N) is connected to a six-membered pyrimidine heterocycle (N′). Benefiting from the trans influence, the facial isomer fac-[Fe(II)(N∩N′)3]2+ is suitable for inducing SCO properties at room temperature in solution. Its connection to luminescent [LnN6O3] chromophores working as non-covalent podates in the triple-stranded [Fe(II)Ln(L10)3]5+ helicates (Ln = Nd, Eu) controls the facial arrangement around Fe(II). The iron-based SCO behaviour of the 3d–4f complex mirrors that programmed in the mononuclear scaffold. Because of the different electronic structures of high-spin and low-spin [Fe(II)N6] units, their associated absorption spectra are different and modulate the luminescence of the appended lanthanide luminophore via intramolecular intermetallic energy transfers. It thus becomes possible to detect the spin state of the Fe(II) center, encoded by an external perturbation (i.e. writing), by lanthanide light emission (i.e. reading) in a single molecule and without disturbance. Shifting from visible emission (Ln = Eu) to the near-infrared domain (Ln = Nd) further transforms a wavy emitted signal intensity into a linear one, a protocol highly desirable for future applications in data storage and thermometry. [ABSTRACT FROM AUTHOR]

Summary: Blockchain (BC) technology has been incorporated into the infrastructure of different kinds of applications that require transparency, reliability, security, and traceability. However, the BC still has privacy issues because of the possibility of privacy leaks when using publicly accessible transaction information, even with the security features offered by BCs. Specifically, certain BCs are implementing security mechanisms to address data privacy to prevent privacy issues, facilitates attack‐resistant digital data sharing and storage platforms. Hence, this proposed review aims to give a comprehensive overview of BC technology, to shed light on security issues related to BC, and to emphasize the privacy requirements for existing applications. Many proposed BC applications in asset distribution, data security, the financial industry, the Internet of Things, the healthcare sector, and AI have been explored in this article. It presents necessary background knowledge about BC and privacy strategies for obtaining these security features as part of the evaluation. This survey is expected to assist readers in acquiring a complete understanding of BC security and privacy in terms of approaches, ideas, attributes, and systems. Subsequently, the review presents the findings of different BC works, illustrating several efforts that tackled privacy and security issues. Further, the review offers a positive strategy for the previously described integration of BC for security applications, emphasizing its possible significant gaps and potential future development to promote BC research in the future. [ABSTRACT FROM AUTHOR]

To securely share the data between users, encryption schemes with keyword searches in various settings have been proposed. Many studies design schemes in a designated receiver setting where a data owner specifies which receivers could download the data in advance at the time the data are uploaded. In this setting, it is not easy to extend the scheme to support environments with multiple data owners. Moreover, there was no scheme considering the situation in which a newly enrolled user accesses data that were uploaded prior to his enrollment. On the other hand, schemes designed in an undesignated receiver setting support multiple data owners and allow data to be accessed by all users in the system, regardless of the time the data were uploaded. However, most of them are not secure against collusion attacks involving an untrusted server and revoked users. In this paper, we propose a full‐accessible multiparty searchable encryption (FA‐MPSE) scheme for data‐sharing systems. Our scheme supports the property that we call full-accessibility, and any users in the system can access all data in the storage. In addition, our scheme is secure against collision attacks so that the revoked users who collaborate with the server can not access the stored data. Furthermore, our scheme provides all the essential properties of MPSE, such as query privacy, query unforgeability, full‐revocability, and unlinkability, and its security is proven in a formal security model. We provide the comparison result with the related schemes to show that our scheme has a comparative advantage. [ABSTRACT FROM AUTHOR]

With the rapid growth of modern information data, DNA has emerged as a novel medium for data storage due to its durability, replicability, sustainability, and high density. This study focuses on the compatibility, high capacity, and random access of DNA data storage systems. A calcium phosphate (CaP) mineralized fluorescent SiO2 DNA (FSD@CaP) microspheres is developed by encapsulating fluorescence‐labeled encoding DNA files onto the surface of the SiO2 microspheres that are intrinsically sized and fluorescent as addressed two‐dimensional (2D) barcodes. The various sizes of SiO2 and various fluorescence‐labeled encoding DNA files forming 2D barcodes of FSD@CaP. The 2D barcodes enhance the system's compatibility while allowing multiplexed random access to DNA files. The high‐capacity DNA storage permits effective single access to the DNA file using a minimal number of FSD@CaP microspheres, without impacting the matrix of the system. Ultimately, random access to arbitrary DNA files within a complex pool of distinct 2D barcode‐tagged FSD@CaP microspheres is demonstrated by utilizing a flow cytometer for multiplexed sorting. Consequently, the strategy of this storage system provides a scalable concept for compatibility and multiplexed random access to DNA data sets. [ABSTRACT FROM AUTHOR]

To ensure the privacy, integrity, and security of the user data and to prevent the unauthorized access of data by illegal users in the blockchain storage system is significant. Blockchain networks are widely used for the authentication of data between the data user and the data owner. However, blockchain networks are vulnerable to potential privacy risks and security issues concerned with the data transfer and the logging of data transactions. To overcome these challenges and enhance the security associated with blockchain storage systems, this research develops a highly authenticated secure blockchain storage system utilizing a rider search optimized deep Convolution Neural Network(CNN) model. The architecture integrates the Ethereum blockchain, Interplanetary File System (IPFS), data users, and owners, in which the Smart contracts eliminate intermediaries, bolstering user-owner interactions. In tandem, blockchain ensures immutable transaction records, and merging IPFS with blockchain enables off-chain, distributed storage of data, with hash records on the blockchain. The research accomplishes privacy preservation through six-phase network development: system establishment, registration, encryption, token generation, testing, and decryption. Parameters for secure transactions are initialized, user registration provides genuine user transaction credentials, and encryption guarantees data security, employing optimized Elliptic Curve Cryptography (ECC). Further, the optimized ECC algorithm is developed utilizing a novel rider search optimization that utilizes search and rescue characteristics of human, and rider characteristics for determining the shorter key lengths. Token generation involves issuing digital tokens on a blockchain platform, followed by testing using a deep CNN classifier to detect anomalies and prevent unauthorized data access during the test phase. The decryption of data is conducted for registered users. The developed rider search optimized deep CNN model attains 96.68% accuracy, 96.68% sensitivity, 96.68% specificity for models and ECC encryption with rider search optimization attains 0.0117 ms Decryption time, 4.583 ms Encryption time, 83.28% Genuine User Detection rate(GUD), 364.80 kbs Memory usage, 0.843 s responsiveness for 50 users, which is more efficient. [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]

Resistive switching (RS) for nonvolatile data storage is a highly relevant field of research. Up to now, RS devices are fabricated via semiconductor processing technologies. This poses the question of whether integration of chemically tailored nanoparticles, either consisting of valence change or phase change materials, can be integrated in nanoelectrode configurations in order to explore their functionality for RS applications. This review discusses the RS properties of such nanoparticles by means of selected examples of both nanoparticle assemblies as well as on the individual particle level. Although this field of research is rather unexplored, it becomes evident that chemically tailored nanoparticles bear great potential for RS applications. [ABSTRACT FROM AUTHOR]

This paper addresses the computational methods and challenges associated with prime number generation, a critical component in encryption algorithms for ensuring data security. The generation of prime numbers efficiently is a critical challenge in various domains, including cryptography, number theory, and computer science. The quest to find more effective algorithms for prime number generation is driven by the increasing demand for secure communication and data storage and the need for efficient algorithms to solve complex mathematical problems. Our goal is to address this challenge by presenting two novel algorithms for generating prime numbers: one that generates primes up to a given limit and another that generates primes within a specified range. These innovative algorithms are founded on the formulas of odd-composed numbers, allowing them to achieve remarkable performance improvements compared to existing prime number generation algorithms. Our comprehensive experimental results reveal that our proposed algorithms outperform well-established prime number generation algorithms such as Miller-Rabin, Sieve of Atkin, Sieve of Eratosthenes, and Sieve of Sundaram regarding mean execution time. More notably, our algorithms exhibit the unique ability to provide prime numbers from range to range with a commendable performance. This substantial enhancement in performance and adaptability can significantly impact the effectiveness of various applications that depend on prime numbers, from cryptographic systems to distributed computing. By providing an efficient and flexible method for generating prime numbers, our proposed algorithms can develop more secure and reliable communication systems, enable faster computations in number theory, and support advanced computer science and mathematics research. [ABSTRACT FROM AUTHOR]

Circularly polarized luminescence (CPL) materials hold significant promise in multidisciplinary fields such as circularly polarized organic light‐emitting diodes, biological probes, data storage, and information encryption. However, these cutting‐edge applications also put forward higher requirements for the design of CPL materials, requiring large dissymmetry factor and high emission quality. For this purpose, diverse approaches have been explored to generate and enhance CPL emission. Among them, energy transfer (ET) strategy stands out as it can be readily implemented in a wide range of CPL materials. The present work overviews latest advances in energy transfer for generating and modulating CPL, involving small organic molecules, polymers, metal complexes, liquid crystals, as well as new‐emerging chiral luminescent materials. It is anticipated that the review article will garner increased attention toward energy transfer systems and facilitate the advancement of CPL materials. [ABSTRACT FROM AUTHOR]

Background: Alcohol use disorders (AUD) are prevalent and responsible for substantial morbidity and mortality; yet efficacious treatments are underused. Previous studies have identified demographic and clinical predictors of medication fills, yet these studies typically do not include patients who were prescribed a medication but did not fill it. Objectives: To examine rates of and factors associated with prescription order and prescription fill for medications for AUD (MAUD) among individuals diagnosed with AUD in outpatient settings. Design: In a cross-sectional analysis, we used multivariate logistic regression to identify factors associated with prescription order and fill. Patients: We used data from the Optum Labs Data Warehouse that linked 2016–2021 de-identified claims and electronic health record (EHR) data, allowing us to observe prescription orders and whether they were filled. We identified 14,674 patients aged ≥ 18 who had an index outpatient encounter with an AUD diagnosis in the EHR. Key Measures: We computed the proportion for whom a MAUD prescription was ordered within 1 year of index visit, and for whom one was filled within 30 days of the order. Key Results: 5.8% of the sample had a MAUD prescription order within 1 year of their index visit. Among those with an order, 87% filled their MAUD prescription within 30 days of receipt (i.e., 5.1% of full sample). After multivariable adjustment, receipt of a MAUD prescription order was more likely for patients who were female (adjusted odds ratio (aOR) [95%CI] = 1.44 [1.24–1.67]), or had moderate or severe AUD (1.74 [1.50–2.01]). Patients receiving an order were more likely to fill it if they had a comorbid mental disorder (1.64 [1.09–2.49]). Conclusions: The low rate of prescription orders was notable. Low use of MAUD appears to result chiefly from prescription order decisions, rather than from prescription fill decisions made by patients. [ABSTRACT FROM AUTHOR]

Background: Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system. Objective: The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements. Methods: We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash. Results: The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software. Conclusion: A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement. [ABSTRACT FROM AUTHOR]

Phase‐change random access memory is anticipated to break the bottleneck of the “storage wall” due to its advantages in simultaneous data storage and in‐memory computing. However, operation speed constrains its application scenarios. Antimony (Sb) thin film has ultrafast phase change speeds, low power consumption, and a straightforward chemical composition. In this study, silicon (Si) doping is employed to enhance the stability of pure Sb while achieving both ultrafast operational speeds and superior thermal stability concurrently. By utilizing optoelectronic hybrid phase change memory, the SET and RESET operation speeds can reach as fast as 26 and 13 ps, respectively, when using Si‐doped Sb films. The absence of the Si─Sb bond results in simple cubic nuclei within the amorphous film, which is posited as the structural basis for the high operational speed. These novel insights into ultrafast speed and phase mechanisms are poised to have valuable evidence for future high‐speed memory designs. [ABSTRACT FROM AUTHOR]

A ferroic order, ferrospintronic (FSp) order, arising in some magnetic materials lacking inversion symmetry is reported on. Emerging from a macroscopic spontaneous symmetry breaking of both the time reversal T and the spatial inversion, while maintaining the symmetry, the order parameter is given by the dipolar moment of the spin density PS$P_{\text{S}}$. Herein a model is advanced that fulfills the symmetry requirements mentioned earlier and its properties are investigated. With the aid of a mean‐field theory, its stability against thermal fluctuations is studied and evidence of truly ferroic states that result from breaking its symmetry is provided. Three predictions that can be checked experimentally to distinguish this class of materials from other compounds are provided. In the first place, it is illustrated that FSp systems host the potential for multiferroic behavior. Second, it is shown that the generic FSp system responds under strain by generating spin currents that can be used in spintronic devices, therefore showing a piezospintronic effect. Finally, it is proven that domain walls in the FSp order parameter lead to magnetoresistive effects. All of the findings mentioned earlier are a significant breakthrough in spintronics and multiferroic phenomena and they have wide‐ranging implications for advancing materials and technologies, particularly in computing and energy harvesting. [ABSTRACT FROM AUTHOR]

The continued expansion and development of the business requires great computing power and massive data storage systems. Cloud services deliver these resources in a simple, flexible and secure way. There is now a wide range of similar cloud services with different capabilities, which requires a recommendation system. Recommendation based on Quality of Service (QoS) is the first generation of service recommendation systems that only takes into account the rating information of all users without distinction. However, these systems suffer from many shortcomings, such as cold start and data sparsity issues, as well as poor accuracy and reliability of recommendation results. To address these issues and improve the quality of recommendations, a new generation of recommender systems has emerged, such as context‐aware, domain‐specific, and trust‐aware recommender systems. These systems now focus more on how to leverage social data generated from user interactions with each other in social networks to recommend more suitable and reliable services in response to user needs. Due to the importance of considering trust in cloud environments, this study aims to provide an overview of the research on trust‐based cloud service recommendation approaches proposed so far and highlights the current trend towards use new technologies such as deep learning to deal with certain challenges. [ABSTRACT FROM AUTHOR]

Hierarchical count histograms involve the publication of count statistics at various granularities based on a predefined hierarchy within a dimension table in a data warehouse. This task finds extensive applications in on-line analytical processing (OLAP) scenarios. This paper focuses on the rigorous privacy-preserving constraint when dealing with an untrusted server. We conduct a systematic investigation of this task and uncover the limitations of the straightforward baseline approach using local differential privacy, as it fails to strike an optimal balance between privacy and utility. We are thus motivated to propose DP-HORUS, a novel crypto-assisted Differentially Private framework for Hierarchical cOunt histogRams under Untrusted Server. DP-HORUS consists of a series of novel designs, including (1) Encrypted hierarchical tree (EHT) structure, which maintains the concept hierarchy in the input data; (2) Random matrix (RM), which reduces communication and computational cost; (3) To further boosted the utility, we propose DP-HORUS+ encompassing two additional modules of histograms structure (HS) and hierarchical consistency (HC), which are respectively introduced to reduce the noise caused by data sparsity and to ensure the hierarchy consistency; (4) To further boost the robust performance, we propose a series of schemes for workload queries based on DP-HORUS. We provide both theoretical analysis and extensive empirical study on both real-world and synthetic datasets, which demonstrates the superior utility of the proposed methods over the state-of-the-art solutions while ensuring strict privacy guarantee. [ABSTRACT FROM AUTHOR]

AbstractThe incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven classification-based anomaly detection based on the sensor data collected in manufacturing processes. However, one critical challenge is that newly presented defect category may manifest as the manufacturing process continues, resulting in monitoring performance deterioration of previously trained machine learning models. Hence, there is an increasing need for empowering machine learning models to learn continually. Among all continual learning methods, memory-based continual learning has the best performance but faces the constraints of data storage capacity. To address this issue, this paper develops a novel pseudo replay-based continual learning framework by integrating class incremental learning and oversampling-based data generation. Without storing all the data, the developed framework could generate high-quality data representing previous classes to train machine learning model incrementally when new category anomaly occurs. In addition, it could even enhance the monitoring performance since it also effectively improves the data quality. The effectiveness of the proposed framework is validated in three cases studies, which leverages supervised classification problem for anomaly detection. The experimental results show that the developed method is very promising in detecting novel anomaly while maintaining a good performance on the previous task and brings up more flexibility in model architecture. [ABSTRACT FROM AUTHOR]

Background: Total hip, knee and shoulder arthroplasties (THKSA) are increasing due to expanding demands in ageing population. Material surveillance is important to prevent severe complications involving implantable medical devices (IMD) by taking appropriate preventive measures. Automating the analysis of patient and IMD features could benefit physicians and public health policies, allowing early issue detection and decision support. The study aimed to demonstrate the feasibility of automated cohorting of patients with a first arthroplasty in two hospital data warehouses (HDW) in France. Methods: The study included adult patients with an arthroplasty between 2010 and 2019 identified by 2 data sources: hospital discharge and pharmacy. Selection was based on the health insurance thesaurus of IMDs in the pharmacy database: 1,523 distinct IMD references for primary THSKA. In the hospital discharge database, 22 distinct procedures for native joint replacement allowing a matching between IMD and surgical procedure of each patient selected. A program to automate information extraction was implemented in the 1st hospital data warehouse using natural language processing (NLP) on pharmacy labels, then it was then applied to the 2nd hospital. Results: The e-cohort was built with a first arthroplasty for THKSA performed in 7,587 patients with a mean age of 67.4 years, and a sex ratio of 0.75. The cohort involved 4,113 hip, 2,630 knee and 844 shoulder surgical patients. Obesity, cardio-vascular diseases and hypertension were the most frequent medical conditions. Discussion: The implementation of an e-cohort for material surveillance will be easily workable over HDWs France wild. Using NLP as no international IMD mapping exists to study IMD, our approach aims to close the gap between conventional epidemiological cohorting tools and bigdata approach. Conclusion: This pilot study demonstrated the feasibility of an e-cohort of orthopaedic devices using clinical data warehouses. The IMD and patient features could be studied with intra-hospital follow-up and will help analysing the infectious and unsealing complications. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance. [ABSTRACT FROM AUTHOR]

Background/Objectives: The healthcare sector is rapidly growing, aiming to promote health, provide treatment, and enhance well-being. This paper focuses on the organ donation and transplantation system, a vital aspect of healthcare. It offers a comprehensive review of challenges in global organ donation and transplantation, highlighting issues of fairness and transparency, and compares centralized architecture-based models and blockchain-based decentralized models. Methods: This work reviews 370 publications from 2016 to 2023 on organ donation management systems. Out of these, 85 publications met the inclusion criteria, including 67 journal articles, 2 doctoral theses, and 16 conference papers. About 50.6% of these publications focus on global challenges in the system. Additionally, 12.9% of the publications examine centralized architecture-based models, and 36.5% of the publications explore blockchain-based decentralized models. Results: Concerns about organ trafficking, illicit trade, system distrust, and unethical allocation are highlighted, with a lack of transparency as the primary catalyst in organ donation and transplantation. It has been observed that centralized architecture-based models use technologies such as Python, Java, SQL, and Android Technology but face data storage issues. In contrast, blockchain-based decentralized models, mainly using Ethereum and a subset on Hyperledger Fabric, benefit from decentralized data storage, ensure transparency, and address these concerns efficiently. Conclusions: It has been observed that blockchain technology-based models are the better option for organ donation management systems. Further, suggestions for future directions for researchers in the field of organ donation management systems have been presented. [ABSTRACT FROM AUTHOR]

Context: Phosphodiester bonds, which form the backbone of DNA, are highly stable in the absence of catalysts. This stability is crucial for maintaining the integrity of genetic information. However, when exposed to catalytic agents, these bonds become susceptible to cleavage. In this study, we investigated the role of different metal dications (Ca2⁺, Mg2⁺, Zn2⁺, Mn2⁺, and Cu2⁺) in promoting the hydrolysis of phosphodiester bonds. A minimal DNA model was constructed using two pyrimidine nucleobases (cytosine and thymine), two deoxyribose units, one phosphate group, and one metallic dication coordinated by six water molecules. The results highlight that Cu2⁺ is the most efficient in lowering the energy barrier for bond cleavage, with an energy barrier of 183 kJ/mol, compared to higher barriers for metals like Zn2⁺ (202 kJ/mol), Mn2⁺ (202 kJ/mol), Mg2⁺ (210 kJ/mol), and Ca2⁺ (223 kJ/mol). Understanding the interaction between these metal ions and phosphodiester bonds offers insight into DNA stability and organic data storage systems. Methods: DFT calculations were employed using Gaussian 16 software, applying the B3LYP hybrid functional with def2-SVP basis sets and GD3BJ dispersion corrections. Full geometry optimizations were performed for the initial and transition states, followed by identifying energy barriers associated with phosphodiester bond cleavage. The optimization criteria included maximum force, root-mean-square force, displacement, and energy convergence thresholds. [ABSTRACT FROM AUTHOR]

In many industrial applications, datasets are often obtained in a sequence associated with a series of similar but different tasks. To model these datasets, a machine‐learning algorithm, which performed well on the previous task, may not have as strong a performance on the current task. When the architecture of the algorithm is trained to adapt to new tasks, often the whole architecture needs to be revised and the old knowledge of modeling can be forgotten. Efforts to make the algorithm work for all the relevant tasks can cost large computational resources and data storage. Continual learning, also called lifelong learning or continual lifelong learning, refers to the concept that these algorithms have the ability to continually learn without forgetting the information obtained from previous task. In this work, we provide a broad view of continual learning techniques and their industrial applications. Our focus will be on reviewing the current methodologies and existing applications, and identifying a gap between the current methodology and the modern industrial needs. This article is categorized under:Technologies > Artificial IntelligenceFundamental Concepts of Data and Knowledge > Knowledge RepresentationApplication Areas > Business and Industry [ABSTRACT FROM AUTHOR]

In today's era, the pharmaceutical industry has integrated blockchain to secure the sensitive information of medicines, wherein public and private blockchains are used to preserve the security and privacy of the medicine supply chain data. However, conventional blockchains often limit scalability due to limited storage. Moreover, blockchain has loopholes; for example, it is not able to prove the validity of the data prior to being stored in the blockchain, which leads to fake data being added to the blockchain. As a result, it causes an issue of data provenance. Motivated by this, the proposed framework incorporated artificial intelligence (AI) algorithms to enhance the efficiency of the medicine supply chain data. The proposed framework integrated machine learning (ML) and blockchain, where ML classifies the valid and invalid data of the medicine supply chain, whereas blockchain stores only valid data and maintains its security and privacy. This identification helps the blockchain to verify medicine supply chain data before adding it to the blockchain. Additionally, we employed an InterPlanetary file system (IPFS) that saves medicine supply chain data and computes its hash to offer decentralized storage. Further, this hash data is stored on a private Hyperledger Fabric blockchain, which requires minimal storage instead of storing an entire large file. This minimal storage optimizes the process of data storage and retrieval in the Hyperledger Fabric blockchain, which enhances the scalability of the proposed framework. Finally, the result of the proposed framework is assessed in two phases: ML and blockchain, wherein the ML model's performance is measured by statistical measures and the blockchain‐based result is assessed using several performance parameters such as throughput is around (618 transactions per second), latency (0.12 s), response time (11 s) and data rate (282 Mbps). [ABSTRACT FROM AUTHOR]

DNA has emerged as a promising storage medium to meet the soaring need for archival data storage because of its exceptional storage density and stability. However, current DNA‐based data storage systems are incompetent of achieving high‐quality random multiplexed access and frequently accessed data storage, which impedes its practical applications. Here, a dual‐mode storage system is proposed that combines DNA‐based archival data and nanodot‐based active data. This novel data‐storage system is constructed by writing the active and archival data on the same substrate through a facile two‐step process involving scanning probe lithography (SPL), DNA synthesis, and chemical immobilization. The data files are categorized and stored orderly in different microregions of the substrate to achieve efficient random access. On each microregion, the nanodot array stores not only the concise information for the archival DNA data but also contains the corresponding primer sequence. Such interrelation between active and archival data allows for facilely data reading by efficient microscopic modalities and in situ polymerase chain reaction (PCR). Facilitated by the integration of nanodot and DNA, this novel dual‐mode storage system demonstrates efficient data access and the potential of excellent storing capacity, paving the way for the advancement of DNA‐based data storage. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for fabricating three-dimensional (3D) microstructures of cobalt–platinum (Co-Pt) permanent magnets using a localized electrochemical deposition (LECD) technique. The method involves the use of an electrolyte and a micro-nozzle to control the deposition process. However, traditional methods face significant challenges in controlling the thickness and uniformity of deposition layers, particularly in the manufacturing of magnetic materials. To address these challenges, this paper proposes a method that integrates machine learning algorithms to optimize the electrochemical deposition parameters, achieving a Co:Pt atomic ratio of 50:50. This optimized ratio is crucial for enhancing the material's magnetic properties. The Co-Pt microstructures fabricated exhibit high coercivity and remanence magnetization comparable to those of bulk Co-Pt magnets. Our machine learning framework provides a robust approach for optimizing complex material synthesis processes, enhancing control over deposition conditions, and achieving superior material properties. This method opens up new possibilities for the fabrication of 3D microstructures with complex shapes and structures, which could be useful in a variety of applications, including micro-electromechanical systems (MEMSs), micro-robots, and data storage devices. [ABSTRACT FROM AUTHOR]

In Baden-Württemberg, the Baden State Library in Karlsruhe and the Württemberg State Library Stuttgart are tasked with collecting printed and electronic media and web publication deposit copies. In various projects, both libraries have been closely collaborating with the Baden-Württemberg Library Service Centre (BSZ) and the German National Library (DNB). In this way, successful data storage of substantial parts of the federal state's text-based analogue and digital cultural heritage is secured. [ABSTRACT FROM AUTHOR]

Magnetic skyrmion random switching and structural stability are critical limitations for storage data applications. Enhancing skyrmions' magnetic properties could improve their thermal structural stability. Hence, micromagnetic calculation was carried out to explore the thermal nucleation and stability of skyrmions in magnetic nanodevices. Different magnetic properties such as uniaxial magnetic anisotropy energy (Ku), saturation magnetization (Ms) and Dzyaloshinskii—Moriya interaction (DMI) were used to assess the thermal stability of skyrmions in magnetic nanowires. For some values of Ms and Ku, the results verified that the skyrmion structure is stable at temperatures above 800 K, which is higher than room temperature. Additionally, manipulating the nanowire geometry was found to have a substantial effect on the thermal structural stability of the skyrmion in storage nanodevices. Increasing the nanowire dimensions, such as length or width, enhanced skyrmions' structural stability against temperature fluctuations in nanodevices. Furthermore, the random nucleation of the skyrmions due to the device temperature was examined. It was shown that random skyrmion nucleation occurs at temperature values greater than 700 K. These findings make skyrmion devices suitable for storage applications. [ABSTRACT FROM AUTHOR]

Electrically tunable optical devices present diverse functionalities for manipulating electromagnetic waves by leveraging elements capable of reversibly switching between different optical states. This adaptability in adjusting their responses to electromagnetic waves after fabrication is crucial for developing more efficient and compact optical systems for a broad range of applications, including sensing, imaging, telecommunications, and data storage. Chalcogenide‐based phase‐change materials (PCMs) have shown great promise due to their stable, nonvolatile phase transition between amorphous and crystalline states. Nonetheless, optimizing the switching parameters of PCM devices and maintaining their stable operation over thousands of cycles with minimal variation can be challenging. Herein, the critical role of PCM pattern as well as electrical pulse form in achieving reliable and stable switching is reported on, extending the operational lifetime of the device beyond 13000 switching events. To achieve this, a computer‐aided algorithm that monitors optical changes in the device and adjusts the applied voltage in accordance with the phase transformation process is developed, thereby significantly enhancing the lifetime of these reconfigurable devices. The findings reveal that patterned PCM structures show significantly higher endurance compared to blanket PCM thin films. [ABSTRACT FROM AUTHOR]

Hyperspectral infrared atmospheric sounding data, characterized by their high vertical resolution, play a crucial role in capturing three-dimensional atmospheric spatial information. The hyperspectral infrared atmospheric detectors HIRAS/HIRAS-II, mounted on the FY3D/EF satellite, have established an initial global coverage network for atmospheric sounding. The collaborative observation approach involving multiple satellites will improve both the coverage and responsiveness of data acquisition, thereby enhancing the overall quality and reliability of the data. In response to the increasing number of channels, the rapid growth of data volume, and the specific requirements of multi-satellite joint observation applications with infrared hyperspectral sounding data, this paper introduces an efficient storage and indexing method for infrared hyperspectral sounding data within a distributed architecture for the first time. The proposed approach, built on the Kubernetes cloud platform, utilizes the Google S2 discrete grid spatial indexing algorithm to establish a grid-based hierarchical model for unified metadata-embedded documents. Additionally, it optimizes the rowkey design using the BPDS model, thereby enabling the distributed storage of data in HBase. The experimental results demonstrate that the query efficiency of the Google S2 grid-based embedded document model is superior to that of the traditional flat model, achieving a query time that is only 35.6% of the latter for a dataset of 5 million records. Additionally, this method exhibits better data distribution characteristics within the global grid compared to the H3 algorithm. Leveraging the BPDS model, the HBase distributed storage system adeptly balances the node load and counteracts the detrimental effects caused by the accumulation of time-series remote sensing images. This architecture significantly enhances both storage and query efficiency, thus laying a robust foundation for forthcoming distributed computing. [ABSTRACT FROM AUTHOR]

Based on the spring, spring MVC and MyBatis structures of the cloud platform SSM framework, an information management platform for power grid material supply chain is built. The data layer uses a variety of sensors to collect power grid material supply chain information, and the information is fed back to the data storage layer after being integrated by the logical reorganization function of the persistence layer. The data storage layer uses the multi-sensor supply chain information fusion method based on paste progress to fuse the information and store it in the database. The business logic layer calls the information in the database and uses the improved k-means clustering algorithm to detect the abnormal supply chain data information. After calculation and data control by the control layer, the data management results are displayed through the presentation layer. The experimental results show that the absolute error of data fusion is very low. It can effectively cluster data information and distinguish outlier anomaly information at the same time, and the effect of information management is good. [ABSTRACT FROM AUTHOR]

As terahertz (THz) technology has been widely considered as a key candidate for future sixth‐generation wireless communication networks, THz modulators show profound significance in wireless communication, data storage, and imaging. In special, dynamic tuning of THz waves through 2D electron gas (2DEG) incorporated with a hybrid design of metasurface has attracted keen interest due to the combined merits of deliberate structural design, rapid switching speed and the process compatibility. Current meta‐modulator enables very high modulation depth but encounter limited bandwidth. In this paper, by taking into account the co‐functional effects of temperature and voltage‐dependent dynamic control on transmission amplitude, a 2DEG‐based GaN‐on‐Si modulator with two switchable operational states (or four modes) of active wave control is proposed. Under cryogenic temperature conditions, the proposed device exhibits prominent 2D plasmons characteristics with switchable transitions between the gated mode and ungated mode for active control. Under room temperature conditions, the proposed device exhibits non‐resonance broadband spectra characteristics with tunable transitions between the linearity mode and depletion mode for transmission control. The scheme provides an option for the development of the actively tunable THz meta‐modulator and paves a way for the robust multifunctionality of electrically controllable THz switching, and biosensors. [ABSTRACT FROM AUTHOR]