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Summary: The rise of the Internet of Things (IoT) has given rise to an era marked by interconnected devices and substantial data generation. This has led to an increased reliance on cloud computing for data processing and storage, primarily due to its cost‐effective pay‐for‐use model. However, this dependence has prompted critical inquiries into the optimal replication of data: what data to replicate, when to replicate it, and where to place new replicas strategically. Conventional cloud data replication often results in resource overutilization, performance bottlenecks, increased workloads, energy consumption, prolonged user wait times, and suboptimal response times. In response to these challenges, this paper introduces a novel approach named Multiobjective Optimization Harris Hawks Optimization with Salp Swarm Algorithm (MOHHOSSA). This approach employs multiobjective optimization (MOO) alongside Harris Hawks Optimization (HHO) and IoT‐based Salp Swarm Algorithm (SSA) for cloud computing environments. MOHHOSSA efficiently identifies data replication opportunities and strategically allocates them across nodes in cloud computing infrastructures. The algorithm aims to enhance key performance metrics, including energy consumption, carbon dioxide emission rate, and mean service time. Extensive experimental validation demonstrates MOHHOSSA's superior performance compared to alternative algorithms. It excels in optimizing energy efficiency, load distribution, mean service time, and the establishment of cost‐effective communication paths between nodes. This research represents a significant advancement in addressing challenges related to IoT data replication in cloud computing, ultimately promoting more sustainable, efficient, and responsive cloud‐based services. [ABSTRACT FROM AUTHOR]

The Earth System Grid Federation (ESGF) holds several petabytes of climate data distributed across millions of files held in data centers worldwide. Obtaining and manipulating the scientific information (climate variables) held in these files is non-trivial. The ESGF Virtual Aggregation is one of several solutions to providing an out-of-the-box aggregated and analysis ready view of those variables. Here we discuss the ESGF Virtual Aggregation in the context of the existing infrastructure, and some of those other solutions providing analysis ready data. We describe how it is constructed, how it can be used, and provide some performance evaluation. It will be seen that the ESGF Virtual Aggregation provides a sustainable solution to some of the problems encountered in producing analysis ready data, without the cost of data replication to different formats, albeit at the cost of more data movement within the analysis than some alternatives. If heavily used, it may also require more ESGF data servers than are currently deployed in data node deployments. The need for such data servers should be a component of ongoing discussions about the future of the ESGF and its constituent core services. [ABSTRACT FROM AUTHOR]

Data-intensive applications must be run on systems with high-performance processing and enough storage. When compared to conventional distributed systems like the data grid, cloud computing offers these features on a platform that is more flexible, scalable, and inexpensive. Moreover, retrieving data files is crucial to operate these services. Typically, accessing data causes the entire cloud workflow system to experience a bottleneck, thus significantly reducing system performance. Two key strategies that can enhance the efficiency of data-intensive applications are task scheduling and data replication. This research proposes a novel Data replication and scheduling in the cloud. Initially, the workflow management process is performed with 3 phases (1) workflow placement, (2) clustering of tasks, and (3) scheduling and replication. Initially, the workflow placement takes place. Then, the clustering of tasks is performed via an improved K-means algorithm. Finally, the tasks and datasets are replicated during the scheduling and replication phase. Further, the scheduling and replication are performed using the Self Modified Pelican Optimization Algorithm (SM-POA) based on the execution cost, migration cost, storage cost and replication. [ABSTRACT FROM AUTHOR]

Erasure coding is extensively deployed in today's data centers to tackle prevalent failures, because it can offer higher reliability at lower storage overhead than data replication. However, for each small write, erasure-coded storage systems have to perform a partial write to an entire erasure coding group, resulting in a time-consuming write-after-read. This paper presents DABRI, an erasure-coded hybrid write approach based on data delta for fast partial writes. DABRI uses data deltas that are the differences between latest data values and original data values, instead of parity deltas to recover the failed data. The data node sends the latest data instead of the parity delta to parity nodes for each partial write. The original data stored on the data node is read and sent to the parity nodes, only when the data stored on the parity nodes is insufficient to maintain the data reliability. This can bypass the computation of parity deltas and reduce the number of data reads. For a series of n partial writes to the same data, DABRI performs log-based updates for data and parity in the first write, performs in-place data updates and log-based parity updates for the last n-1 writes. In addition, the I/O between data nodes and parity nodes is scheduled for parallel I/O in each partial write. We implement an erasure-coded prototype storage system based on DABRI to perform performance evaluation. Experimental results running the real-world traces show that DABRI can significantly improve the I/O throughput, compared with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

This correction notice addresses three issues in the original article. First, the authors excluded data from nineteen national surveys that were biased towards urban areas. Second, they revised their treatment of non-responses in the surveys, incorporating measurement uncertainty due to non-response. Third, they standardized survey weights to preserve the sample size. The corrections did not significantly change the results of the analysis, and the conclusions of the article remain the same. The supplementary materials and replication data have been updated accordingly. [Extracted from the article]

Background: Prostate cancer (PCa) is a leading cause of cancer-related death in men. Understanding the proteomic landscape associated with PCa risk can provide insights into its molecular mechanisms and pave the way for potential therapeutic interventions. Methods: A proteome-wide Mendelian randomization (MR) analysis was employed to determine associations between genetically predicted protein concentrations in plasma and PCa risk. From an initial list of 4,364 proteins, significant associations were identified and validated. Multiple sensitivity analyses were also conducted to enhance the robustness of our findings. Results: Of the 4,364 genetically predicted proteins, 308 exhibited preliminary associations with PCa risk. After rigorous statistical refinement, genetically predicted concentrations of 14 proteins showed positive associations with PCa risk, with odds ratios spanning from 1.55 (95% CI 1.28–1.87) for ATG4B to 2.67 (95% CI 1.94–3.67) for HCN1. In contrast, genetically predicted concentrations of ATG7, B2M, MSMB, and TMEM108 demonstrated inverse associations with PCa. The replication analysis further substantiated positive associations for MDH1 and LSM1, and a negative one for MSMB with PCa. A meta-analysis harmonizing primary and replication data mirrored these findings. Furthermore, the MVMR analysis pinpointed B2M and MSMB as having significant associations with PCa risk. Conclusion: The genetic evidence unveils a refined set of proteins associated with PCa risk. The findings underscore the potential of these proteins as molecular markers or therapeutic targets for PCa, calling for deeper mechanistic studies and exploration into their translational relevance. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks' inherent hairiness, and, in turn, the heterogeneous reporting structure. To address these issues, we propose a new artifact, referred to as ID-Card, whose goal is to provide a structured summary of research papers emphasizing replication-relevant information. We construct the ID-Card through a structured, iterative process based on design science. In this article: (i) we report on hands-on experiences of replication; (ii) we review the state-of-the-art and extract replication-relevant information: (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction; and (iv) we present the concept and structure of the ID-Card to mitigate the identified challenges. This study aims to create awareness of replication in NLP for RE. We propose an ID-Card that is intended to foster study replication but can also be used in other contexts, e.g., for educational purposes. [ABSTRACT FROM AUTHOR]

We use this article to introduce the Militarized Interstate Events (MIE) dataset, a new dataset for international conflict with a host of innovative features. The MIE data offers dyadic, event-level information for all militarized interstate confrontations from 1816 to 2014, including major wars as well as all threats, displays, and uses of force between two or more states. The data come with major innovations, including dyadic fatality estimates at the event level and recorded events for general conflicts alongside the battles of the wars of the last two centuries. We discuss how to use these conflict data and provide a replication that demonstrates the dataset's usefulness. [ABSTRACT FROM AUTHOR]

Incorporating cloud-based algorithms for disaster recovery (DR), it explores data replication, failover, virtual machine (VM) migration, and consistency algorithms. These algorithms play a pivotal role in safeguarding data and system continuity during unforeseen disruptions. Data replication ensures redundancy, failover algorithms swiftly transition to backup resources, VM migration facilitates resource optimization, and consistency algorithms maintain data integrity. Leveraging cloud technology enhances the effectiveness of these algorithms, providing robust DR solutions critical for business continuity in today's digital landscape. The recent growth in popularity of internet services on a massive scale has also raised the demand for stable underpinnings. Despite the fact that DR for big data is frequently overlooked in security research, the majority of existing approaches use a narrow, endpoint-centric approach. The significance of DR strategies has grown as cloud storage has become the norm for more data. But traditional cloud-centric DR techniques may be expensive, thus less expensive alternatives are being sought. There is persistent concern in the information technology (IT) community about whether or not cloud service providers (CPs) can guarantee data and service continuity in the event of a disaster. [ABSTRACT FROM AUTHOR]

Spreading quickly throughout populations, whether animal or human-borne, infectious illnesses provide serious risks and difficulties. Controlling their spread and averting disinformation requires effective risk assessment and epidemic identification. Technology-enabled data analysis on diseases allows for quick solutions to these problems. A Combinational Data Assessment Scheme intended to accelerate disease detection is presented in this paper. The suggested strategy avoids duplicate data replication by sharing data among edge devices. It uses indexed data gathering to improve early detection by using tree classifiers to discern between various kinds of information. Both data similarity and index measurements are considered throughout the data analysis stage to minimize assessment errors. Accurate risk detection and assessment based on information kind and sharing frequency are ensured by comparing non-linear accumulations with accurate shared edge data. The suggested system exhibits high accuracy, low mistakes, and decreased data repetition to improve overall effectiveness in illness detection and risk reduction. [ABSTRACT FROM AUTHOR]

Background: With the widespread adoption of Blood Establishment Computer Systems and other Blood Collection and Transfusion Service (BCTS) clinical information systems (CIS), electronic blood donor, product, and patient data are now routinely required for clinical, regulatory, operational, and quality needs. That data are often not readily accessible for such secondary use within CIS databases, particularly for applications with significant data availability requirements such as machine learning and artificial intelligence. Data replication provides one avenue by which CIS data can be made more readily available. Study Design and Methods: Members of the AABB's Information Systems Committee along with institutional information technology colleagues provided a multi‐institutional viewpoint on data replication through the lens of BCTS specific use cases. Case studies of informatics offerings leveraging such technologies were also elicited. Results: Six distinct use cases describe the potential role of data replication including the creation of data warehouses for frontline laboratory staff. Specific BCTS examples for each use case are presented to highlight the value of data replication, including visualization of critical inventory (O red blood cells, HLA‐compatible platelets) and utilization analytics for patient blood management. Two case studies describe the approach to implement such technologies to (1) optimize staffing via laboratory workload reporting and (2) improve access to blood via antigen‐negative blood product location services. Discussion: Data replication and warehousing can empower BCTS analytic offerings not otherwise natively available through one's CIS to improve patient care and laboratory operations. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of the Internet of things (IoT) and mobile communication technology, the amount of data related to industrial Internet of things (IIoT) applications has shown a trend of explosive growth, and hence edge‐cloud collaborative environment becomes one of the most popular paradigms to place the IIoT applications data. However, edge servers are often heterogeneous and capacity limited while having lower access delay, so there is a contradiction between capacity and latency while using edge storage. Additionally, when IIoT applications deployed crossing edge regions, the impact of data replication and data privacy should not be ignored. These factors often pose challenges to proposing an effective data placement strategy to take full advantage of edge storage. To address these challenges, an effective data placement strategy for IIoT applications is designed in this article. We first analyze the data access time and data placement cost in an edge‐cloud collaborative environment, with the consideration of data replication and data privacy. Then, we design a data placement strategy based on ϵ$$ \epsilon $$‐constraint and Lagrangian relaxation, to reduce the data access time and meanwhile limit the data placement cost to an ideal level. As a result, our proposed data placement strategy can effectively reduce data access time and control data placement costs. Simulation and comparative analysis results have demonstrated the validity of our proposed strategy. [ABSTRACT FROM AUTHOR]

In the paper a problem of assignment of tasks to machines is formulated and solved, where a criterion of data replication is used and a large size of data imposes additional constraints. This problem is met in practice when dealing with large genomic files or other types of vast data. The necessity of comparing all pairs of files within a big set of DNA sequencing results, which we collected, maintained, and analyzed within a national genomic project, brought us to the proposed results. This problem resembles that of generating a particular Steiner system, and a mechanism observed there is employed in one of our algorithms. Based on the problem complexity, we propose two heuristic algorithms, which work very well even for instances with tight constraints and a heterogeneous environment defined. In addition, we propose a simplified method, nevertheless capable of finding very good solutions and surpassing the algorithms in some special cases. The methods are validated in tests on a wide set of instances, where values of parameters reflect our real-world application and where their usefulness is proven. [ABSTRACT FROM AUTHOR]

According to an aptitude-treatment interaction experiment (Lehmann et al., Metacognition and Learning,11, 89–105, 2016, N = 47, published in Metacognition and Learning), perceptually disfluent texts facilitated retention and comprehension performance (but not transfer performance) only for learners with higher working memory capacity (WMC). No effects of WMC for a fluent text were found (albeit theoretically, fluency may be more advantageous for learners with lower WMC). The findings of our (pre-registered) direct replication (supervised online sample of N = 96) show a substantial deviation from the original results: In contrast to the interaction effect (disfluency and WMC) of the primary study, we obtained null results for disfluency, WMC, and their interaction for all learning outcomes. Our replication data are not indicative of WMC as a boundary condition moderating the disfluency effect on learning. We discuss discrepancies in the results of the primary study and our direct replication regarding particular methodological and analytical decisions, questioning the robustness and generalizability of Lehman et al.'s results beyond their primary study. [ABSTRACT FROM AUTHOR]

Tradescantia spathacea has been traditionally employes for treating various conditions, including coughs, fevers, wounds, inflammation, and oxidative-related diseases. This paper presents a comprehensive review of its traditional uses, alongside an examination of phytochemical and pharmacological studies aimed at validating ethnopharmacological practices. A systematic search across electronic databases using relevant keywords yielded 845 articles, of which 70 articles were reviewed. T. spathacea exhibits a broad spectrum of pharmacological activities, as evidenced by in vivo studies demonstrating hepatoprotective, anti-diabetic, anti-inflammatory and anticancer effects. In vitro studies further support its medicinal properties, including antimicrobial, anti-viral, antioxidant, anti-helminthic, antimutagenic, antigenotoxic and cytotoxic activities. While these modern pharmacological properties substantiate traditional uses, further research is essential to validate them and develop safe and effective therapeutic formulations. Further investigations should focus on bioassay-guided isolation of bioactive compounds, extensive pharmacological, clinical, and toxicological studies, incorporating adequate data replication, proper standardization and control groups, and the selection of reasonable doses or concentrations of extracts and controls. [ABSTRACT FROM AUTHOR]

Physically unclonable functions (PUFs) have emerged as the most effective method against counterfeiting, leveraging the intrinsic randomness of objects to avoid data replication. However, despite their efficacy, the complexity and high cost of most PUF‐based counterfeiting labels hinder their practical application. Here, a high‐capacity PUF label based on vertically aligned perovskite nanowire (NW) arrays is demonstrated. The NW arrays are conveniently constructed using a low‐cost solution process using an anodized aluminum oxide template. Each pixel within the label is composed of closely packed CsPbBr3 NW arrays of different lengths, resulting in varied lasing signals. The collected signal of each pixel emerged from the lasing output of the NW arrays, generating high‐density and nonuniform multiple‐mode lasing to facilitate information encoding. A quaternary encoding mechanism is used to encode the number of laser modes per pixel. Impressively, it exhibits a mapping resolution of 1600 pixels within 400 µm2, achieving an impressive encoding capacity (1.97 × 10963) and a pixel density (4 bits µm−2) in this compact area. This work shows a promising approach for anticounterfeiting applications because of its simplicity, low cost, and high encoding capacity. [ABSTRACT FROM AUTHOR]

Nowadays, a large amount of data is being generated and these data are usually stored in distributed environments such as cloud, fog, and edge environments. Data replication, which is commonly used to manage large amounts of data, improves overall accessibility and availability. Due to the importance of data replication, a lot of research has been done on replication methods in these environments. In this paper, a systematic review has been conducted to analyze the existing studies on data replication methods in cloud, fog, edge, and edge-cloud environments. The advantages, disadvantages, and some of the main challenges of the existing methods are explained and a taxonomy of data replication methods is provided. The selected papers are analyzed based on various criteria related to data replication, such as recovery, storage cost, load balancing, replica consistency, security, mobility, and data migration. Finally, the open issues and future research directions are discussed. [ABSTRACT FROM AUTHOR]

Gut microbiomes are diverse ecosystems whose drivers of variation remain largely unknown, especially in time and space. We analysed a dataset with over 900 red squirrel (Tamiasciurus hudsonicus) gut microbiome samples to identify the drivers of gut microbiome composition in this territorial rodent. The large‐scale spatiotemporal replication in the data analysed was an essential component of understanding the assembly of these microbial communities. We identified that the spatial location of the sampled squirrels in their local environment is a key contributor to gut microbial community composition. The non‐core gut microbiome (present in less than 75% of gut microbiome samples) had highly localised spatial patterns throughout different seasons and different study areas in the host squirrel population. The core gut microbiome, on the other hand, showed some spatial patterns, though fewer than in the non‐core gut microbiome. Environmental transmission of microbiota is the likely contributor to the spatiotemporal distribution observed in the North American red squirrel gut microbiome. [ABSTRACT FROM AUTHOR]

In different scientific disciplines, large-scale data are generated with enormous storage requirements. Therefore, effective data management is a critical issue in distributed systems such as the cloud. As tasks can access a nearby site to access the required file, replicating the desired file to an appropriate location improves access time and reliability. Replicating the popular file to an appropriate site is a good choice, as tasks can get the necessary file from a nearby site. In this research, a novel data replication algorithm is proposed that is consisted of four main phases: 1- determining 20% of commonly used files, 2- computing five conflicting objectives (i.e., average service time, load variance, energy consumption, average response time and cost) 3- finding the near-optimal solution (i.e., suitable locations for new replica) by the PSO technique to acquire a trade-off among the desired objectives. 4- replica replacement considering a fuzzy system with three inputs (i.e., Number of accesses, size of replica and the last access time). The experimental results denote that the proposed replication algorithm outperforms the Profit oriented Data Replication (PDR) and Bee colony-based approach for Data Replication (BCDR) strategies in terms of energy consumption, average response time, load variance, number of connections, Hit ratio, Storage usage, and cost.

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.

In this modern day and age, data is being produced at an ever-increasing rate and as such the demand for more and more storage space is also ever escalating. These demands are met by major tech companies offering paid cloud storage solutions which are a risk due to centralized servers, running out of necessary storage (to the client) and the risk of a data breach. To solve some of the problems posed by the centralized cloud storage solutions, this paper presents a novel Peer-to-Peer (P2P) file storage system designed to provide a robust and scalable decentralized storage solution. The proposed system combines several techniques, including distributed hash tables (DHTs) for efficient peer discovery and data retrieval, redundancy and replication for improved data availability, and cryptographic techniques for secure and private data sharing. The system is evaluated through simulations and real-world experiments, demonstrating its effectiveness in providing a reliable and scalable file storage solution in a decentralized environment. The results show that the proposed system provides high data availability and is resilient to peer churn and network failures. Furthermore, the system offers improved security and privacy features, making it suitable for a wide range of applications, such as file sharing, content delivery, and cloud storage. Overall, the proposed P2P file storage system offers a promising approach to decentralized storage, with the potential to provide a more robust, scalable, and secure storage solution compared to traditional centralized storage systems. [ABSTRACT FROM AUTHOR]

A connection between diabetes and an increased risk of epilepsy has been suggested by observational studies. Animal studies have also shown that antihyperglycemic drugs can improve seizures. However, it is unclear whether antihyperglycemic drugs have a causal role in epilepsy in humans. To investigate this potential causal relationship, a Mendelian randomisation study was conducted using International League Against Epilepsy data as the discovery set and FinnGen data as the replication set. It was discovered that three antidiabetic drug target genes, ETFDH, CYP21A2 and CYP2D6, were involved in the occurrence of epilepsy. In particular, ETFDH was identified as a target gene in both the discovery set (inverse variance weighting [IVW], odds ratio [OR] = 1.018, 95% confidence interval [CI], 1.004–1.033, p = 0.009) and replication set (IVW, OR = 1.074, 95% CI, 1.034–1.114, p = 0.00016), and CYP21A2 was identified in the discovery set (IVW, OR = 1.029, 95% CI, 1.005–1.053, p = 0.016) and replication set (IVW, OR = 1.057, 95% CI, 1.001–1.116, p = 0.045) as having a causal association with an increased risk of epilepsy. Conversely, the CYP2D6 gene was found to be a protective factor for epilepsy in both the discovery set (IVW, OR = 0.0984, 95% CI, 0.969–0.998, p = 0.025) and replication set (IVW, OR = 0.977, 95% CI, 0.955–1.000, p = 0.046). A search of DrugBank revealed that metformin, an anti-glucose drug, is an inhibitor of the ETFDH gene and may have a potential therapeutic effect on epilepsy. [ABSTRACT FROM AUTHOR]

The UAV-borne video SAR (ViSAR) imaging system requires miniaturization, low power consumption, high frame rates, and high-resolution real-time imaging. In order to satisfy the requirements of real-time imaging processing for the UAV-borne ViSAR under limited memory and parallel computing resources, this paper proposes a method of embedded GPU-based real-time imaging processing for the UAV-borne ViSAR. Based on a parallel programming model of the compute unified device architecture (CUDA), this paper designed a parallel computing method for range-Doppler (RD) and map drift (MD) algorithms. By utilizing the advantages of the embedded GPU characterized with parallel computing, we improved the processing speed of real-time ViSAR imaging. This paper also adopted a unified memory management method, which greatly reduces data replication and communication latency between the CPU and the GPU. The data processing of 2048 × 2048 points took only 1.215 s on the Jetson AGX Orin platform to form a nine-consecutive-frame image with a resolution of 0.15 m, with each frame taking only 0.135 s, enabling real-time imaging at a high frame rate of 5 Hz. In actual testing, continuous mapping can be achieved without losing the scenes, intuitively obtaining the dynamic observation effects of the area. The processing results of the measured data have verified the reliability and effectiveness of the proposed scheme, satisfying the processing requirements for real-time ViSAR imaging. [ABSTRACT FROM AUTHOR]

In a distributed environment, replication is the most investigated phenomenon. Replication is a way of storing numerous copies of the same data at different locations. Whenever data is needed, it will be fetched from the nearest accessible copy, avoiding delays and improving system performance. To manage the replica placement strategy in the Cloud, three key challenges must be addressed. The challenges in determining the best time to make replicas were generated, the kind of files to replicate, as well as the best location to store the replicas. This survey conducts a review of 65 articles published on data replication in the cloud. The literature review examines a series of research publications and offers a detailed analysis. The analysis begins by presenting several replication strategies in the reviewing articles. Analysis of each contributor's performance measures is conducted. Moreover, this survey offers a comprehensive examination of data auditing systems. This work also determines the analytical evaluation of replication handling in the cloud. Furthermore, the evaluation tools used in the papers are examined. Furthermore, the survey describes a lot of research issues & limitations that might help researchers support better future work on pattern mining for data replication in the cloud. [ABSTRACT FROM AUTHOR]

Digital disparities have been a concern among different communities across the U.S. The lack of an integrated approach to evaluate digital connectivity limits our understanding of where and what the differences are to address digital equity. This research utilized broadband internet open data and other government datasets to evaluate the status of digital connectivity across the U.S. where K-12 schools and public libraries are located. This work is funded by the University of North Texas Libraries' Dean's Innovation Grant (DIG) 2021–2022 award. The research data and products are open for others to use and replicate. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) consists of complex and dynamically aggregated elements or smart entities that need decentralized supervision for data exchanging throughout different networks. The artificial bee colony (ABC) is utilized in optimization problems for the big data in IoT, cloud and central repositories. The main limitation during the searching mechanism is that every single food site is compared with every other food site to find the best solution in the neighboring regions. In this way, an extensive number of redundant comparisons are required, which results in a slower convergence rate, greater time consumption and increased delays. This paper presents a solution to optimize search operations with an enhanced ABC (E-ABC) approach. The proposed algorithm compares the best food sites with neighboring sites to exclude poor sources. It achieves an efficient mechanism, where the number of redundant comparisons is decreased during the searching mechanism of the employed bee phase and the onlooker bee phase. The proposed algorithm is implemented in a replication scenario to validate its performance in terms of the mean objective function values for different functions, as well as the probability of availability and the response time. The results prove the superiority of the E-ABC in contrast to its counterparts. [ABSTRACT FROM AUTHOR]

Cybersecurity researchers have contributed to the automated extraction of CTI from textual sources, such as threat reports and online articles describing cyberattack strategies, procedures, and tools. The goal of this article is to aid cybersecurity researchers in understanding the current techniques used for cyberthreat intelligence extraction from text through a survey of relevant studies in the literature. Our work finds 11 types of extraction purposes and 7 types of textual sources for CTI extraction. We observe the technical challenges associated with obtaining available clean and labeled data for replication, validation, and further extension of the studies. We advocate for building upon the current CTI extraction work to help cybersecurity practitioners with proactive decision-making such as in threat prioritization and mitigation strategy formulation to utilize knowledge from past cybersecurity incidents. [ABSTRACT FROM AUTHOR]

In real-time rendering applications, mesh rendering quality suffers from limited GPU memory capacity and display resolution. Due to the increased complexity of models and the demand for higher display resolutions, people have started building commodity workstations with multiple GPUs at a low cost. As a result, more GPU memory is available across multiple GPUs, and a higher display resolution can be achieved by connecting each GPU to a display monitor, resulting in a large tiled display configuration. However, a multi-GPU workstation may not efficiently handle a complex model that cannot fit into the GPU memory, due to (1) the unified configuration treating GPUs as one hardware entity and requiring the same data replicated in all GPUs, and (2) the lack of scalability to reduce, balance, and stream data dynamically between the CPU and GPUs as well as among the GPUs. In this work, we present a fine-grained parallel rendering approach that integrates a view-dependent LOD selection strategy with the inter-GPU load balancing method to ensure each GPU handles the portion of data it rasterizes, without data replication. A new multi-GPU out-of-core method minimizes the amount of data transferred from the CPU to each GPU by taking the advantage of frame-to-frame coherence. A comprehensive evaluation is presented to understand the efficiency and scalability of the execution components over extremely large scenes. [ABSTRACT FROM AUTHOR]

Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster. However, the amount of data is increasing at a rapid pace, which leads to challenges in storage, analysis, and various processing tasks. In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo-distributed data centers. The goal of the proposed framework is to enable efficient use of the distributed data blocks for various data analysis tasks. The architecture of the proposed framework is composed of a grid of geo-distributed data centers connected to a data controller (DCtrl). The DCtrl is responsible for organizing and managing the block replicas across the geo-distributed data centers. We use the BDMS system as the installed system on the distributed data centers. BDMS stores the big data file as a set of random sample data blocks, each being a random sample of the whole data file. Then, DCtrl distributes these data blocks into multiple data centers with replication. In analyzing a big data file distributed based on the proposed framework, we randomly select a sample of data blocks replicated from other data centers on any data center. We use simulation results to demonstrate the performance of the proposed framework in big data analysis across geo-distributed data centers. [ABSTRACT FROM AUTHOR]

One of the main problems of big distributed systems, like IoT, is the high access time to data objects. Replicating the data objects on various servers is a traditional strategy. Replica placement, which can be implemented statically or dynamically, is generally crucial to the effectiveness of distributed systems. Producing the minimum number of data copies and placing them on appropriate servers to minimize access time is an NP-complete optimization problem. Various heuristic techniques for efficient replica placement in distributed systems have been proposed. The main objectives of this research are to decrease the cost of data processing operations, decrease the number of copies, and improve the accessibility of the data objects. In this study, a discretized and group-based gray wolf optimization algorithm with swarm and evolutionary features was developed for the replica placement problem. The proposed algorithm includes swarm and evolutionary features and divides the wolves' population into subgroups, and each subgroup was locally searched in a different solution space. According to experiments conducted on the standard benchmark dataset, the suggested method provides about a 40% reduction in the data access time with about five replicas. Also, the reliability of the suggested method during different executions is considerably higher than the previous methods. [ABSTRACT FROM AUTHOR]

Availability and accessibility of data objects in a reasonable time is a main issue in distributed systems like cloud computing services. As a result, the reduction of data-related operation times in distributed systems such as data read/write has become a major challenge in the development of these systems. In this regard, replicating the data objects on different servers is one commonly used technique. In general, replica placement plays an essential role in the efficiency of distributed systems and can be implemented statically or dynamically. Estimation of the minimum number of data replicas and the optimal placement of the replicas is an NP-complete optimization problem. Hence, different heuristic algorithms have been proposed for optimal replica placement in distributed systems. Reducing data processing costs as well as the number of replicas, and increasing the reliability of the replica placement algorithms are the main goals of this research. This paper presents a discrete and swarm-evolutionary method using a combination of shuffle-frog leaping and genetic algorithms to data-replica placement problems in distributed systems. The experiments on the standard dataset show that the proposed method reduces data access time by up to 30% with about 14 replicas; whereas the generated replicas by the GA and ACO are, respectively, 24 and 30. The average reduction in data access time by GA and ACO 21% and 18% which shows less efficiency than the SFLA–GA algorithm. Regarding the results, the SFLA–GA converges on the optimal solution before the 10th iteration, which shows the higher performance of the proposed method. Furthermore, the standard deviation among the results obtained by the proposed method on several runs is about 0.029, which is lower than other algorithms. Additionally, the proposed method has a higher success rate than other algorithms in the replica placement problem. [ABSTRACT FROM AUTHOR]

Summary: Data replication is a well‐known technique in cloud systems for enhancing availability and performance. Various strategies and surveys have been proposed in this respect. These surveys include comprehensive analysis and classifications. However, to the best of the authors' knowledge, there is no survey concentrating on strategies designed for interconnected cloud systems. In this article, we provide an in‐depth analysis of existing data replication strategies in cloud systems, covering single and interconnected clouds. We also highlight data correlation‐aware strategies as well as their key steps. Furthermore, we examine the major strategies' features such as: (i$$ i $$) addressed replication issues, (ii$$ ii $$) orientation towards the provider and the consumer, (iii$$ iii $$) consideration of the service level agreement, (iv$$ iv $$) consideration of cost and economic aspects, and (v$$ v $$) evaluation tools. Finally, we provide a performance analysis through extensive simulations of several replication strategies dedicated for single and interconnected clouds. [ABSTRACT FROM AUTHOR]

Kawasaki disease (KD) is an acute pediatric vasculitis that predominantly affects children under the age of 5 years. To date, genome-wide association studies (GWAS) have identified several KD susceptibility genes (e.g., BLK, CD40, FCGR2A, BCL2L11 , and IGHV), which are mainly involved in B cell immunity. In this study, we aimed to identify additional KD susceptibility genes mainly involved in B cell development and functions by analyzing our previous GWAS data and conducting a replication study using new sample. Initially, we selected 30 single nucleotide polymorphisms (SNPs) in B-cell-related genes that were significantly (P < 0.01) associated with KD in our previous GWAS analysis of 247 KD cases with complete type and 1,000 healthy controls. Replication study was performed by genotyping the new 837 KD case samples with Fluidigm system and comparing them with 3,553 control genotypes. Among the 30 candidate SNPs, two were significantly associated with KD (P < 0.001) in the replication study. An even greater association between these SNPs and KD was observed in the combined analysis of GWAS and replication samples: odds ratio (OR) = 1.97 (P = 8.61 × 10−6) for rs2270699 (nonsynonymous SNP: c.10588C > T, p.Arg3530Trp) in the heparan sulfate proteoglycan 2 (HSPG2) gene and OR = 1.28 (P = 1.34 × 10−6) for rs3130992 (intronic SNP) in both the corneodesmosin (CDSN) and psoriasis susceptibility 1 candidate 1 (PSORS1C1) genes. These results suggest that the B-cell-related genes, HSPG2 and CDSN or PSORS1C1 , play a role in the development of KD. [ABSTRACT FROM AUTHOR]

Phenotypic characterization of cowpea germplasm collections is an essential step for the cowpea genetic improvement. The objectives of the study were to determine the phenotypic diversity and characterize cowpea mutants and accessions for grain yield and yield components, to identify their superior mutants and accessions, and to determine the correlation between measured characteristics. Thirty-one cowpea genotypes (16 Namibian mutants, seven International Institute of Tropical Agriculture (IITA) genotypes and eight South Africa genotypes) were planted in five different environments in South Africa during the 2021/2022 cropping season. Trials were laid out in randomised complete block designs with replications and the data was collected for grain yield and agronomic traits. Significant (P ≤ 0.05) genotype, environment and genotype by environment interaction effects were observed for all grain yield and yield components. Moderate to high broad-sense heritability (H2) (> 50%) was observed for all yield components. The grain yield (GY) was significant and positive correlated pod weight per pod (PWP), number of seeds per plant (NSPPx), number of pods per plant (NPP), harvest index (HI), and seed weight per plant (SWP) suggesting the possibility of simultaneous improvement of these traits. In the principal component analysis, Namibian mutant genotypes ShL2P7, ShR4P1, NKR1P3 and BrR11P2 were associated with high NSPPx, PWP and GY. The clustered heat map identified a group of Namibian mutants (NKR1P3, BrR11P2, ShL2P7, ShR2P11 and ShR10P12), IITA genotype (98K-476–8) and South African genotypes (Glenda, Dr Saunders, Enchore, and Oloyin) which was associated with high GY, PWP, NSPPx, NPP, SWP and HI. The identified superior cowpea mutants for grain yield and yield components can be used to develop high yielding varieties. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) generates a plethora of data nowadays, and cloud computing has been introduced as an efficient solution to IoT data management. A cloud resource administrator usually adopts the replication strategy to guarantee the reliability of IoT data. This mechanism can significantly reduce data access time, and evidently, more replicas of data increase the data storage cost. Furthermore, the process of selecting mini clouds for replica allocation and sorting replicas in mini clouds is considered an NP-hard problem. Therefore, this paper proposes an approach based on the harmony search (HS) algorithm to allocate replicas to the IoT data in the cloud computing environment in order to mitigate the data access cost. The HS algorithm was employed in the proposed approach to determine the best location for data replication in the cloud computing environment. According to the implementation results, the proposed approach outperformed the other methods and managed to significantly decrease data access time and delay as well as energy consumption. [ABSTRACT FROM AUTHOR]

Summary: The heterogeneous, geographically distributed infrastructure of fog computing poses challenges in data replication, data distribution, and data mobility for fog applications. Fog computing is still missing the necessary abstractions to manage application data, and fog application developers need to re‐implement data management for every new piece of software. Proposed solutions are limited to certain application domains, such as the IoT, are not flexible in regard to network topology, or do not provide the means for applications to control the movement of their data. In this paper, we present FReD, a data replication middleware for the fog. FReD serves as a building block for configurable fog data distribution and enables low‐latency, high‐bandwidth, and privacy‐sensitive applications. FReD is a common data access interface across heterogeneous infrastructure and network topologies, provides transparent and controllable data distribution, and can be integrated with applications from different domains. To evaluate our approach, we present a prototype implementation of FReD and show the benefits of developing with FReD using three case studies of fog computing applications. [ABSTRACT FROM AUTHOR]

Multistores are data management systems that enable query processing across different and heterogeneous databases; besides the distribution of data, complexity factors like schema heterogeneity and data replication must be resolved through integration and data fusion activities. Our multistore solution relies on a dataspace to provide the user with an integrated view of the available data and enables the formulation and execution of GPSJ queries. In this paper, we propose a technique to optimize the execution of GPSJ queries by formulating and evaluating different execution plans on the multistore. In particular, we outline different strategies to carry out joins and data fusion by relying on different schema representations; then, a self-learning black-box cost model is used to estimate execution times and select the most efficient plan. The experiments assess the effectiveness of the cost model in choosing the best execution plan for the given queries and exploit multiple multistore benchmarks to investigate the factors that influence the performance of different plans. [ABSTRACT FROM AUTHOR]

Objective: Low back pain is a prevalent and debilitating condition worldwide, with significant implications for individuals' quality of life and productivity. The aim of this study was to assess the relationship between mood instability and the risk of developing chronic low back pain, using a rigorously designed mendelian randomization methodology. Method: The study incorporated both univariate and multivariate mendelian randomization to analysis the causal relationship between mood instability and the risk of developing chronic low back pain. The data on mood instability from the Integrative Epidemiology Unit (IEU) opened Genome-Wide Association Studies (GWAS) project (IEU-opened GWAS project). Data on low back pain were collected from two sources: One source is the IEU open GWAS project (discovery data). Another source is a GWAS meta-analysis (replication data). Inverse variance weighted method, weighted median method, MR-Egger regression, and mendelian randomization pleiotropy residual sum and outlier method were used for mendelian randomization analysis. Result: The univariable mendelian randomization analysis shows a statistically significant correlation between mood instability and the risk of low back pain. Several methods were performed, including inverse variance weighting (discovery data: odds ratio= 3.544, 95% confidence interval= 1.785-7.039, p = 0.000; replication data: odds ratio= 3.167, 95% confidence interval= 2.476-4.052, p= 0.000), MREgger (discovery data: odds ratio= 7.178, 95% confidence interval= 0.057-909.525, p = 0.429; replication data: odds ratio= 2.262, 95% confidence interval= 0.580-8.825, p = 0.246), weighted median (discovery data: odds ratio= 2.730, 95% confidence interval= 1.112-6.702, p = 0.028; replication data: odds ratio= 3.243, 95% confidence interval= 2.378-4.422, p = 0.000), MR-PRESSO (discovery data: odds ratio= 3.544, 95% confidence interval= 1.785-7.039, p = 0.001; replication data: odds ratio= 3.167, 95% confidence interval= 2.476-4.052, p= 0.000) methods. The results were consistent across these methods. The results obtained from discovery data are consistent with those obtained from discovery data. In the multivariable mendelian randomization, after adjusting for various covariates such as body mass index, current tobacco smoking, alcohol intake frequency, Total body bone mineral density, and vigorous physical activity, there is a consistent correlation between mood instability and chronic low back pain. Conclusion: This study provides robust evidence supporting a causal relationship between mood instability and the development of low back pain. Our findings suggest that addressing mood instability may play a crucial role in prevention and management strategies for individuals experiencing low back pain. [ABSTRACT FROM AUTHOR]

Objectives: South Africa is experiencing both HIV and hypertension epidemics. Data were compiled for a study to identify effects of HIV and high systolic blood pressure on mortality risk among people aged 40-plus in a rural South African area experiencing high prevalence of both conditions. We aim to release the replication data set for this study. Data description: The research data comes from the 2010-11 Ha Nakekela (We Care) population-based survey nested in the Agincourt Health and socio-Demographic Surveillance System (AHDSS) located in the northeast region of South Africa. An age-sex-stratified probability sample was drawn from the AHDSS. The public data set includes information on individual socioeconomic characteristics and measures of HIV status and blood pressure for participants aged 40-plus by 2019. The AHDSS, through its annual surveillance, provided mortality data for nine years subsequent to the survey. These data were converted to person-year observations and linked to the individual-level survey data using participants' AHDSS census identifier. The data can be used to replicate Houle et al. (2022) — which used discrete-time event history models stratified by sex to assess differential mortality risks according to Ha Nakekela measures of HIV-infection, HIV-1 RNA viral load, and systolic blood pressure. [ABSTRACT FROM AUTHOR]

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments. [ABSTRACT FROM AUTHOR]

Methodological issues pertaining to transparency and analytic error have been widely documented for publications featuring analysis of complex sample survey data. The availability of numerous public use datasets to researchers without adequate training in using these data likely contributes to these problems. In an effort to introduce standards for reporting analyses of survey data and promote replication, we propose the Preferred Reporting Items for Complex Sample Survey Analysis (PRICSSA), an itemized checklist to guide researchers publishing analyses using complex sample survey data. PRICSSA is modeled after other checklists (e.g. PRISMA, CONSORT) that have been widely adopted for other research designs. The PRICSSA items include a variety of survey characteristics, such as data collection dates, mode(s), response rate, and sample selection process. In addition, essential analytic information—such as sample sizes for all estimates, missing data rates and imputation methods (if applicable), disclosing if any data were deleted, specifying what survey weight and sample design variables were used along with method of variance estimation, and reporting design-adjusted standard errors/confidence intervals for all estimates—are also included. PRICSSA also recommends that authors make all corresponding software code available. Widespread adoption of PRICSSA will help improve the quality of secondary analyses of complex sample survey data through transparency and promote scientific rigor and reproducibility. [ABSTRACT FROM AUTHOR]

Information can be shared across the Internet using cloud computing, a powerful paradigm for meeting the needs of individuals and organizations. To minimize access time and maximize load balancing for data nodes (DNs), a dynamic data replication algorithm is necessary. Even so, few of the existing algorithms consider each objective holistically during replication. An improved ant lion optimizer (ALO) algorithm and a fuzzy system are used in this paper to determine dynamically the number of replicas and the DNs for replication. Further, it balances the trade-offs among different objectives (e.g., service time, system availability, load, and monetary cost). The ALO algorithm has been widely applied to solve complex optimization problems due to its simplicity in implementation. However, ALO has premature convergence and can thus easily get trapped into the local optimum solution. In this paper, to overcome the shortcomings of ALO by balancing exploration and exploitation, a hybrid ant lion optimizer with Tabu search algorithm (ALO-Tabu) is proposed. There are several improvements of the ALO, in which the appropriate solutions are selected for the initial population based on chaotic maps (CMs) and opposition-based learning (OBL) strategies. On the other hand, there are many CMs, OBLs, and random walk strategies that make it difficult to select the best one for optimization. Generally, they are selected manually, which is time-consuming. As a result, this paper presents a hyper-heuristic ALO (HH-ALO-Tabu) that automatically chooses CMs, OBLs, and random walk strategies depending on the differential evolution (DE) algorithm. Based on 20 well-known test functions, the experiment results and statistical tests show that HH-ALO-Tabu can solve optimization problems effectively. [ABSTRACT FROM AUTHOR]

In different scientific disciplines, large-scale data are generated with enormous storage requirements. Therefore, effective data management is a critical issue in distributed systems such as the cloud. As tasks can access a nearby site to access the required file, replicating the desired file to an appropriate location improves access time and reliability. Replicating the popular file to an appropriate site is a good choice, as tasks can get the necessary file from a nearby site. In this research, a novel data replication algorithm is proposed that is consisted of four main phases: 1- determining 20% of commonly used files, 2- computing five conflicting objectives (i.e., average service time, load variance, energy consumption, average response time and cost) 3- finding the near-optimal solution (i.e., suitable locations for new replica) by the PSO technique to acquire a trade-off among the desired objectives. 4- replica replacement considering a fuzzy system with three inputs (i.e., Number of accesses, size of replica and the last access time). The experimental results denote that the proposed replication algorithm outperforms the Profit oriented Data Replication (PDR) and Bee colony-based approach for Data Replication (BCDR) strategies in terms of energy consumption, average response time, load variance, number of connections, Hit ratio, Storage usage, and cost. [ABSTRACT FROM AUTHOR]

This article discusses the outcomes of a study on percutaneous arteriovenous fistula creation (PAVF) using the 4Fr WavelinQ device. The study found that the device had a high success rate, with 97.3% of patients achieving successful AVF creation. The cumulative and functional patency rates at 24 months were 91.7% and 92.7%, respectively. The study suggests that PAVF creation using the 4Fr WavelinQ device is a durable technique and may be a viable alternative to surgical AVF creation. However, further research and comparisons between different devices are needed. [Extracted from the article]

The RNA N7-methyltransferase (MTase) activity of SARS-CoV-2's nsp14 protein is essential for viral replication and is a target for the development of new antivirals. Nsp14 uses S-adenosyl methionine (SAM) as the methyl donor to cap the 5' end of the SARS-CoV-2 mRNA and generates S-adenosyl homocysteine (SAH) as the reaction byproduct. Due to the central role of histone MTases in cancer, many SAM/SAH analogs with properties of cell permeability have recently been developed for the inhibition of these MTases. We have succeeded in identifying two such compounds (SGC0946 and SGC8158) that display significant antiviral activity and bind to the SARS-CoV-2 nsp14 N7-MTase core. Unexpectedly, crystal structures of SGC0946 and SGC8158 with the SARS-CoV-2 nsp14 N7-MTase core identify them as bi-substrate inhibitors of the viral MTase, co-occupying both the SAM and RNA binding sites; positing novel features that can be derivatized for increased potency and selectivity for SARS-CoV-2 nsp14. Taken together, the high-resolution structures and the accompanying biophysical and viral replication data provide a new avenue for developing analogs of SGC0946 and SGC8158 as antivirals. Author summary: SARS-CoV-2 and its variants have led to millions of deaths globally. The RNA N7-methyltransferase activity of SARS-CoV-2's nsp14 protein is essential for viral replication and represents a promising target for antiviral drug development. In this study, we identified two compounds (SGC0946 and SGC8158) with significant antiviral activity that bind to the SARS-CoV-2 nsp14 N7-MTase core. Co-crystal structures of these compounds with the N7-MTase core were determined, revealing features that can be modified for increased potency and selectivity. These findings provide an avenue for developing SGC0946/SGC8158 analogs as antivirals. [ABSTRACT FROM AUTHOR]

Onion is one of the most important salad and vegetable plants in all parts of Pakistan. Onions in storage are being affected by different types of diseases, but Blue Mold of Onions is one of the most important detrimental post-harvest diseases of onions. The research was conducted in the Plant Pathology Laboratory at Sindh Agriculture University, Tando Jam, to manage Penicillium expansum by applying different Botanical extracts, Viz., Caramon, Garlic and Neem. The efficacy of all three botanicals was tested on storage onion with three replications and data was collected at four days' intervals. After statistical analysis, it was observed that all three botanicals cause significant reductions in the rot severity of onion in storage: among the three botanicals, Neem was found most effective in reducing rot severity, followed by garlic and caramon. [ABSTRACT FROM AUTHOR]