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As qualitative research continues to adapt to technological advancements, the evaluation of data quality and quantity in online versus face-to-face focus groups becomes increasingly important. The purpose of this systematic review was to explore how the format of a focus group (online or in-person) influences the quality and quantity of data collected. Adhering to PRISMA guidelines, the review analyzes a total of 45 cross-sectional studies published between 2000 and 2023, providing both quantitative and qualitative comparisons across various metrics, including word count, idea expression, participant interaction, and statements of agreement or disagreement. Word count was generally higher in face-to-face focus groups, suggesting a tendency for more extensive verbal expression. However, the total number of ideas expressed did not significantly differ between the two modalities. Qualitatively, 62.5% of studies reported more detailed responses in face-to-face focus groups, while asynchronous online focus groups were found to elicit more expansive responses in some instances. In terms of the characteristics of focus groups, synchronous formats were more common for online focus groups, with the duration of sessions varying widely across the two modalities. These results suggest that although online and face-to-face focus groups exhibit distinct characteristics, they are similarly effective in yielding data of comparable quality, especially in terms of idea generation. Accordingly, combining the two formats within a single study is likely to maintain data integrity. Overall, the findings from this review offer valuable insights for researchers and practitioners across diverse disciplines and should help them to strategically design their focus group studies so as to align with specific research objectives and contexts. [ABSTRACT FROM AUTHOR]

In order to make the image copyright protection system can resist network attacks, and to solve the problems of unknown copyright owner, difficulty in data integrity verification and unauthorized redistribution, a digital image copyright protection method based on blockchain and zero trust mechanism was proposed. Firstly, after the copyright owner signs with the elliptic curve digital signature algorithm (ECDSA), the signature and copyright information are encrypted with the elliptic curve cryptography (ECC) algorithm, and the ciphertext and the generated image hashing are uploaded to the blockchain; Then, the smart contract is called to authenticate the copyright owner, the similarity of the hash value is used to detect the infringement of the verified image, the MD5 key generated by the image hashing value is used to encrypt the detected image, the ciphertext and encrypted image are uploaded to the interplanetary file system (IPFS), and the corresponding storage address is returned to the blockchain. Consumers can download ciphertext and encrypted images in IPFS as needed after completing asset evaluation and paying when calling the dual-smart contract to initiate copyright transaction. Experimental results show that the proposed method can not only authenticate the identity, but also realize double encryption of the image and copyright information. It can prevent the unauthorized use and redistribution of digital content, and improve the overall security of the image copyright protection system. [ABSTRACT FROM AUTHOR]

Summary: Data aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain‐based secure multifunctional data aggregation method for IoT‐Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions. [ABSTRACT FROM AUTHOR]

Background: Comprehensive details on Hepatitis C virus (HCV) infection in Myanmar are lacking. This study determined the prevalence of HCV antibodies and ribonucleic acid (RNA) and the distribution of HCV genotypes across different populations in Myanmar from 1990 to 2023. Material and methods: A systematic search in PubMed, Web of Science, Scopus, and local journals identified studies reporting on HCV antibodies, RNA, and genotypes, excluding clinical research related to liver disease prognosis. Screening and data extraction was done by two authors and study populations were categorized into low-risk, high-risk, liver disease patients, and refugees outside the country. The pooled prevalence was performed by Dersimonian and Laird method using the R program. The publication bias was shown by funnel plot, the Egger test was used to assess the symmetry of the plot, and the heterogeneity was examined by the Cochran Q test and I2 index. Results: Out of 135 reports screened for eligibility, 35 reports comprising 51 studies were included in which 33 studies provided data on HCV seroprevalence in 685,403 individuals, 8 studies reported HCV RNA prevalence in 25,018 individuals, and 10 studies examined HCV genotypes in 1,845 individuals. The pooled seroprevalence of HCV among low-risk, high-risk, liver disease patients and refugees were 2.18%, 37.07%, 33.84%, and 2.52% respectively. HCV RNA-positive rates in these groups were 1.40%, 5.25%, 24.96%, and 0.84% respectively. Seroprevalence studies showed publication bias (Egger test, p = 0.0001), while RNA studies did not (Egger test, p = 0.8392). HCV genotype 3 was predominant in all sub-groups in Myanmar. Conclusion: Our study shows Myanmar has intermediate HCV endemicity with lowest HCV prevalence of 2.18% in low-risk groups and highest prevalence of 37.07% in high- risk groups. However, the findings highlight the need for further epidemiological studies to understand actual disease burden and implement effective countermeasures to achieve the WHO's goal of HCV elimination by 2030. [ABSTRACT FROM AUTHOR]

Increasingly, studies use social media to recruit, enroll, and collect data from participants. This introduces a threat to data integrity: efforts to produce fraudulent data to receive participant compensation, e.g., gift cards. MOMENT is an online symptom-monitoring and self-care study that implemented safeguards to protect data integrity. Facebook, Twitter, and patient organizations were used to recruit participants with chronic health conditions in four countries (USA, Italy, The Netherlands, Sweden). Links to the REDCap baseline survey were posted to social media accounts. The initial study launch, where participants completed the baseline survey and were automatically re-directed to the LifeData ecological momentary assessment app, was overwhelmed with fraudulent responses. In response, safeguards (e.g., reCAPTCHA, attention checks) were implemented and baseline data was manually inspected prior to LifeData enrollment. The initial launch resulted in 411 responses in 48 hours, 265 of which (64.5%) successfully registered for the LifeData app and were considered enrolled. Ninety-nine percent of these were determined to be fraudulent. Following implementation of safeguards, the re-launch yielded 147 completed baselines in 3.5 months. Eighteen cases (12.2%) were found fraudulent and not invited to enroll. Most fraudulent cases in the re-launch (15 of 18) were identified by a single attention check question. In total, 96.1% of fraudulent responses were to the USA-based survey. Data integrity safeguards are necessary for research studies that recruit online and should be reported in manuscripts. Three safeguard strategies were effective in preventing and removing most of the fraudulent data in the MOMENT study. Additional strategies were also used and may be necessary in other contexts. [ABSTRACT FROM AUTHOR]

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems. [ABSTRACT FROM AUTHOR]

Digital holography has transformative potential for the measurement of stacked-chip microstructures due to its non-invasive, single-shot, full-field characteristics. However, significant light scattering and diffraction at steep edges in step microstructures cause the batwing effect, leading to measurement errors. Herein, we propose a standard-deviation-based adaptive median filter to eliminate batwing effects in step microstructure measurement using digital holography. The standard deviation determines the positions of the steps and the range of the batwing effect. During filtering, the filter window size varies: it adjusts according to the center's position within the batwing effect range and reduces outside this range to prevent distortion in other regions. Filtering weights are set to maintain information integrity while using larger filter windows. Experiments on the Standard Resolution Target USAF 1951 and the standard step height target show that our method successfully eliminates batwings while preserving the integrity of the remaining profile. [ABSTRACT FROM AUTHOR]

Blockchain technology has unlocked the doors of building decentralized applications, where security plays a vital role. Any transaction ever created in a blockchain is recorded permanently. With the increasing usage of social media modalities today, many non-reputable sources create and publish fake and luring news. Due to the easy availability of the internet and excessive use of social media, fake news can spread like a flash. It causes the need to eliminate fake news posted on social media platforms. The main motive of the paper is to develop an ensemble model that assists in the automated identification of fake news. This paper addresses the identified gaps in detecting multi-modal fake news. We propose Fake News Network (FNNet) as a novel secure blockchain-based deep learning model to detect fake news. It uses blockchain and deep learning to assure data integrity and learn data representations, respectively. The proposed model consists of four layers: the node layer, the deep learning layer, the blockchain layer, and the network layer. This is an ensemble deep-learning model that leverages Bi-LSTM and CNN models, where the Bi-LSTM model captures the sequential data in both directions, and CNN captures the hidden features. We use Pheme, CrisisLexT6, and ISOT datasets to train the model. FNNet achieves an average accuracy of 86.93%, 92.22%, and 98.53% on Pheme, CrisisLexT9, and ISOT datasets, respectively. Our results show that the proposed model is robust and applicable to real-time datasets and social media networks to detect fake news effectively. [ABSTRACT FROM AUTHOR]

As automatic test technology advances, the number of programmable instruments in a single test system increases. Traditional wired communication methods have a limited range and involve complex cable layouts. Single-function wireless converters provide a viable alternative, but they have limitations. These include complicated configuration, issues with multi-system collaboration, and data blocking. This paper proposes a wireless ad hoc network platform for multi-bus instruments based on a low-cost ESP-12H WiFi module. The platform supports GPIB, RS232, RS485, and CAN bus interface instrument access. It features easy configuration, ad hoc networking, and self-repairing capabilities. A relay multi-hop network with a tree topology expands capacity and coverage. Additionally, a dynamic window-receiving mode and an improved multi-priority queue ensure data transmission integrity. The experimental results show that the platform's networking time is less than 10 s, and the coverage range reaches 50 m in complex indoor environments. It also shows good stability when running for a long time. However, due to hardware and software design limitations, the actual upload speeds fall short of the theoretical values. For example, RS232 and RS485 are about 10% slower than the theoretical values, and GPIB is about 80% slower. Further optimization is required in the future. [ABSTRACT FROM AUTHOR]

Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. An SSA-CNN-LSTM model is then established to extract effective data features from low-dimensional data with insufficient feature depth through structures such as convolutional layers and pooling layers in a CNN, determine the optimal hyperparameters, automatically optimize the model network size, solve the problem of the difficult determination of the neural network model size, and achieve accurate prediction of the fault rate of station electromechanical equipment. Finally, an engineering verification was conducted on the platform screen door (PSD) systems in stations on Shanghai Metro Lines 1, 5, 9, and 10. The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), malicious nodes can infiltrate the network at any time, compromising network performance. Developing a secure routing protocol is critical for ensuring reliable data transmission. LEACH (Low-Energy Adaptive Clustering Hierarchy) is a well-established hierarchical routing protocol within the WSN paradigm. However, LEACH lacks robustness against security attacks due to the absence of integrated security mechanisms, making it vulnerable in the presence of malicious nodes. On the other hand, Blockchain, an emerging technology, is best suited for Wireless Sensor Networks due to its distributed, decentralized, immutable, and traceability features. Integrating blockchain with WSNs significantly enhances security, data integrity, and decentralization within the network. Integrating blockchain technology with WSNs significantly enhances security, data integrity, and decentralization within the network. In this paper, we introduce a Blockchain-enabled secure LEACH protocol to ensure reliable data transmission in wireless sensor networks. The proposed hybrid blockchain framework includes three phases: Registration, Authentication, and Malicious Node Detection. By utilizing the autonomous execution capabilities of smart contracts without third-party intervention, we develop specific smart contracts for the registration of cluster heads and ordinary nodes, for authentication between cluster heads and their corresponding ordinary nodes, and for the identification and elimination of malicious cluster head nodes. A detailed security analysis of the proposed BSLEACH framework is provided to provide its robustness against different network attacks such as Man-in-the-Middle attack, Sybil attack, Replay attack and non-repudiation.In performance evaluation, it is observed that the proposed Blockchain-based Secured LEACH (BSLEACH) protocol outperforms other versions of LEACH such as Sleach (Secure low-energy adaptive clustering hierarchy) and Ms-leach (A routing protocol combining multi-hop transmissions and Single-hop transmissions) in the presence of malicious cluster heads and Sybil nodes in the network. The proposed solution improves the performance of the traditional LEACH protocol by identifying and removing malicious Cluster head units from the Private Blockchain network. [ABSTRACT FROM AUTHOR]

Background: Depression and anxiety are prevalent after stroke and associated with poor outcomes. We previously co-developed a stroke-specific self-management intervention, HEADS: UP (Helping Ease Anxiety and Depression after Stroke). The two studies reported here aimed to test the feasibility and acceptability of the HEADS: UP course and supporting materials, and research processes ahead of a definitive trial. Methods: We recruited community-dwelling stroke survivors (SS) ≥ 3 months post-stroke, with symptoms of mood disorder (Hospital Anxiety and Depression Scale ≥ 8). Participants could 'enrol' a family member/ 'other' to take part with them, if desired. Study 1 tested HEADS: UP delivered in-person, and informed optimisation of research processes and intervention delivery and materials. In a pragmatic response to Covid-related socialising restrictions, HEADS: UP was then adapted for online delivery, tested in Study 2. The primary outcome (both studies) was the feasibility (acceptability, fidelity) of the intervention and of research processes. Quantitative data (including patient-reported outcome measures (PROMs) assessing mood and quality of life) and qualitative data were collected pre-/post-intervention. Descriptive statistics were used to analyse quantitative data; a thematic framework approach was used to analyse qualitative data. Both studies received ethical approval prior to commencement. Results: Study 1 Feasibility: 13 (59.1%) of 22 potentially eligible stroke survivors consented; aged 66 (median, interquartile range (IQR) 14); male (n = 9; 69%); 28 (IQR 34) months post-stroke. Of these, n = 10 (76.9%) completed PROMS pre-intervention; n = 6 (46.2%) post-intervention. Acceptability: Nine (69.2%) of the 13 participants attended ≥ 4 core intervention sessions. Aspects of screening and data collection were found to be burdensome. Study 2 Feasibility: SS n = 9 (41%) of 22 potentially eligible stroke survivors consented; aged 58 years (median; IQR 12); male (n = 4; 44.4%); 23 (IQR 34) months post-stroke. Of these, n = 5 (55.6%) completed PROMS pre-intervention; n = 5 (55.6%) post-intervention. Acceptability: Five (55.6%) of the 9 participants attended ≥ 4 core sessions. They found online screening and data collection processes straightforward. [ABSTRACT FROM AUTHOR]

Background: Clinical trials play a crucial role in biomedical research, and it is important to register them in public registries to ensure transparency and prevent research waste. In this study, we wished to determine what steps need to be taken to identify every clinical trial run in India that has been registered in any of the (non-Indian) World Health Organization-recognised primary registries. Of the 16 registries, we studied all except that of the European Union, which will be studied separately. Methods: Two methodologies were employed for each registry, except for four that did not facilitate one or the other method. Methodology A involved downloading all the records in a registry and querying them. Methodology B involved conducting a search via the registry website. Results: Only four registries provided consistent results with both methodologies. Seven registries had different results from the two methodologies. Of these, in four cases, in Methodology A one field indicated that the study ran in India, while another indicated otherwise. Conclusions: The above-mentioned ambiguities should be addressed by the concerned registries. Overall, this study reinforces the need for improved data accuracy and transparency in clinical trial registries and emphasizes the importance of resolving complications faced by users while navigating the registries. Ensuring accurate and comprehensive registration of clinical trials is essential for meta-research and the use of such data by a variety of stakeholders. [ABSTRACT FROM AUTHOR]

Data auditing permits data owners to confirm the reliability of their personal information without obtaining it from an unsafe cloud. Advertising cloud storage has widely adopted this characteristic. Data integrity is more difficult to maintain, and data through the cloud is not always secure. The ability to access data remotely through cloud storage makes it necessary to protect it against unauthorized access, hackers, and other malicious activity. In order to overcome these concerns, a hybrid Advanced Encryption Standard- Elliptic Curve Cryptography (AES-ECC) algorithm and Merkle Hash (MHT) Tree is developed. The user data is sent to the cloud service provider via blockchain for storage purposes. The key generation and encryption are carried out using a hybrid AES-ECC encryption technique, which is used to generate both private and public keys. The encrypted user file is then divided into several blocks. The tags for the split encrypted blocks are then generated using SHA256, which provides the hash value of each smaller file. Cloud Service Provider (CSP) constructs an MHT by employing generated hashtags to return the proof to user information after establishing hashtags for encrypted file blocks in the blockchain storage. Merkle hash tree is frequently employed to validate data integrity. Furthermore, the third-party auditor determines if the proofs generated by CSP match one from the user to check the tamper. The simulation analysis shows that the accuracy, encryption, decryption, key generation, and execution time reached for the proposed approach is 96%, 190 s, 124 s, 5.9 s, and 440 s. The proposed algorithm effectively validates data integrity and provides improved data security in blockchain environment. [ABSTRACT FROM AUTHOR]

In the era of digital proliferation, individuals opt for cloud servers to store their data due to the diverse advantages they offer. However, entrusting data to cloud servers relinquishes users' control, potentially compromising data confidentiality and integrity. Traditional auditing methods designed to ensure data integrity in cloud servers typically depend on Trusted Third Party Auditors. Yet, many of these existing auditing approaches grapple with intricate certificate management and key escrow issues. Furthermore, the imminent threat of powerful quantum computers poses a risk of swiftly compromising these methods in polynomial time. To overcome these challenges, this paper introduces a Quantum‐based Secure Key Communication and Key Generation Scheme QSKCG for Outsourced Data in the Cloud. Leveraging Elliptic Curve Cryptography, the BB84 secure communication protocol, certificateless signature, and blockchain network, the proposed scheme is demonstrated through security analysis, affirming its robustness and high efficiency. Additionally, performance analysis underscores the practicality of the proposed scheme in achieving post‐quantum security in cloud storage. [ABSTRACT FROM AUTHOR]

In the envisioned 6G landscape, data sharing is expected to become increasingly prevalent, giving rise to digital marketplaces that foster cooperation among organizations for collecting, sharing, and processing data for analysis. These marketplaces serve as connectors between data producers and consumers, empowering multi-tenancy scenarios for seamless and secure data sharing both within and outside organizations. Given that 6G networks promise ultra-low latency, enhanced connectivity, and massive data throughput, the need for robust data access control mechanisms becomes imperative. These mechanisms ensure security and trust among entities, particularly in multi-tenant environments where multiple organizations share infrastructure and data resources. In this paper, we have designed and implemented a novel access control mechanism tailored for a distributed data streaming system developed by Nokia Bell Labs. Our approach leverages fine-grained policies, dynamic enforcement, and transparency mechanisms to enhance trust between data owners and consumers. By facilitating secure multi-tenancy data sharing, our solution contributes to the seamless exchange of data across diverse entities within the next-generation communication ecosystem. We demonstrate that our proposed access control mechanism incurs minimal overhead while ensuring data confidentiality and integrity. The introduction of such advancements in data sharing markets strengthens the overall ecosystem by providing heightened transparency and enhanced control over data, promoting collaboration and innovation in the 6G era. [ABSTRACT FROM AUTHOR]

The integrity and clarity of information have long been regarded as the cornerstones of advertising strategy. However, recent game advertising has taken a different approach. Specifically, incomplete game videos, especially those showcasing losing gameplay, are more likely to stimulate players' interest compared to complete videos of winning gameplay. This study, through five experiments, uncovers a dual-pathway mechanism behind this phenomenon. Firstly, information gaps resulting from incomplete videos trigger curiosity drive, compelling viewers to seek more information and reinforcing their willingness to engage in gaming. Secondly, witnessing failures in game demonstrations activates components of downward social comparison and competitive motivation. These findings offer valuable insights into the complex dynamics of game advertising, shedding light on the effects of information gaps, curiosity, and social comparison. They provide valuable implications for advertising strategies within the gaming industry. [ABSTRACT FROM AUTHOR]

Background: Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation. Methods: We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically. Results: Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis. Conclusion: Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data. [ABSTRACT FROM AUTHOR]

Background: The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and accuracy remains a significant challenge due to variations in data recording and reporting standards. Methods: We developed lab2clean, a novel algorithm aimed at automating and standardizing the cleaning of retrospective clinical laboratory results data. lab2clean was implemented as two R functions specifically designed to enhance data conformance and plausibility by standardizing result formats and validating result values. The functionality and performance of the algorithm were evaluated using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. Results: lab2clean effectively reduced the variability of laboratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial laboratory data records. The evaluation highlighted significant improvements in the conformance and plausibility of lab results, confirming the algorithm's efficacy in handling large-scale data sets. Conclusions: lab2clean addresses the challenge of preprocessing and cleaning clinical laboratory data, a critical step in ensuring high-quality data for research outcomes. It offers a straightforward, efficient tool for researchers, improving the quality of clinical laboratory data, a major portion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models. Future developments aim to broaden its functionality and accessibility, solidifying its vital role in healthcare data management. [ABSTRACT FROM AUTHOR]

With the continuous development of cloud computing, the application of cloud storage has become more and more popular. To ensure the integrity and availability of cloud data, scholars have proposed several cloud data auditing schemes. Still, most need help with outsourced data integrity, controlled outsourcing, and source file auditing. Therefore, we propose a controlled delegation outsourcing data integrity auditing scheme based on the identity-based encryption model. Our proposed scheme allows users to specify a dedicated agent to assist in uploading data to the cloud. These authorized proxies use recognizable identities for authentication and authorization, thus avoiding the need for cumbersome certificate management in a secure distributed computing system. While solving the above problems, our scheme adopts a bucket-based red–black tree structure to efficiently realize the dynamic updating of data, which can complete the updating of data and rebalancing of structural updates constantly and realize the high efficiency of data operations. We define the security model of the scheme in detail and prove the scheme's security under the difficult problem assumption. In the performance analysis section, the proposed scheme is analyzed experimentally in comparison with other schemes, and the results show that the proposed scheme is efficient and secure. [ABSTRACT FROM AUTHOR]

A browser‐in‐the‐middle (BITM) attack occurs when an attacker intercepts and manipulates communication between a user's web browser and the website they are attempting to visit. This approach is risky, as it allows the attacker to eavesdrop on crucial information, such as login passwords or financial details, leading to unauthorized access and potential data breaches. Moreover, the attacker can inject malicious content into the conversation, compromising the integrity of the data shared between the user's browser and the website. The article delves into the BITM attack as a strategy employed in phishing assaults. The article emphasizes precautionary measures to safeguard against BITM attacks. Additionally, it offers insights into how users can defend themselves and respond to an attack. The significance of user awareness in avoiding phishing attempts is a key highlight of the article. [ABSTRACT FROM AUTHOR]

The research relevance is determined by the growing use of blockchain technology in various industries and the need to address technical challenges and security and fraud issues. The research aims to analyse the application of blockchain technology in various fields and identify key technical challenges and opportunities for fraud prevention. The study results confirm the great potential of blockchain technology in addressing key aspects of security, transparency, and efficiency in various fields. Blockchain can significantly improve quality and business processes in industries such as healthcare, finance, and agriculture. Technical challenges related to the scalability and security of blockchain technology were also identi- fied. Particularly in industries where data volumes are large and processing largescale transactions requires increased scaling, performance and speed issues may arise. Additionally, insufficient security mechanisms can compromise data integrity and lead to misuse. Fraud issues were also identified as a significant threat that requires further research and development. The results of the study highlight the importance of further development and development of new algorithms and approaches to ensure scalability, improve security, and develop mechanisms for detecting and preventing fraud in the context of blockchain. [ABSTRACT FROM AUTHOR]

The Kunming‐Montreal Global Biodiversity Framework lays out an ambitious set of goals and targets aimed at halting and reversing biodiversity loss. The extent of natural ecosystems has been selected as one of a small set of headline indicators against which countries will report progress under this framework. We evaluate the effectiveness with which this indicator is expected to capture the overall scope of the ecosystem‐focused component of Goal A, and interlinkages with the species‐focused component of this same goal, using extensive global data on the integrity, connectivity, and plant species composition of forests. Results generated for all forest‐supporting countries demonstrate that consideration of these additional factors can profoundly alter understanding of the state of forest biodiversity relative to that based on extent alone. Employment of ecosystem extent as a headline indicator must therefore be augmented by appropriate use of component and complementary indicators addressing other key dimensions of ecosystem change. [ABSTRACT FROM AUTHOR]

To solve the problems of the existing construction quality inspection with time-consuming information retrieval and data integrity concerns, according to the requirement analysis of engineering quality acceptance management, the traceability and immutability of blockchain and the efficiency and responsiveness of information processing of human-machine interaction (HMI), the construction quality inspection system based on blockchain and HMI was proposed. The proposed system was verified by the real-world case, and the performance evaluation was conducted by read/write latency and data storage. The results show that under ideal network conditions, the read and write latencies for construction quality data are below 1.5 seconds and 3.5 seconds, respectively. The storage capacity for accumulating construction quality acceptance transaction data is within the manageable range. The results align with the established performance requirements for blockchain systems as outlined in existing research. [ABSTRACT FROM AUTHOR]

RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data. [ABSTRACT FROM AUTHOR]

Addressing the critical challenge of ensuring data integrity in decentralized systems, this paper delves into the underexplored area of data falsification probabilities within Merkle Trees, which are pivotal in blockchain and Internet of Things (IoT) technologies. Despite their widespread use, a comprehensive understanding of the probabilistic aspects of data security in these structures remains a gap in current research. Our study aims to bridge this gap by developing a theoretical framework to calculate the probability of data falsification, taking into account various scenarios based on the length of the Merkle path and hash length. The research progresses from the derivation of an exact formula for falsification probability to an approximation suitable for cases with significantly large hash lengths. Empirical experiments validate the theoretical models, exploring simulations with diverse hash lengths and Merkle path lengths. The findings reveal a decrease in falsification probability with increasing hash length and an inverse relationship with longer Merkle paths. A numerical analysis quantifies the discrepancy between exact and approximate probabilities, underscoring the conditions for the effective application of the approximation. This work offers crucial insights into optimizing Merkle Tree structures for bolstering security in blockchain and IoT systems, achieving a balance between computational efficiency and data integrity. [ABSTRACT FROM AUTHOR]

Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools. [ABSTRACT FROM AUTHOR]

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments. [ABSTRACT FROM AUTHOR]

The use of physical unclonable functions (PUFs) linked to the manufacturing process of the electronic devices supporting applications that exchange critical data over the Internet has made these elements essential to guarantee the authenticity of said devices, as well as the confidentiality and integrity of the information they process or transmit. This paper describes the development of a configurable PUF/TRNG module based on ring oscillators (ROs) that takes full advantage of the structure of modern programmable devices offered by Xilinx 7 Series families. The proposed architecture improves the hardware efficiency with two main objectives. On the one hand, we perform an exhaustive statistical characterization of the results derived from the exploitation of RO configurability. On the other hand, we undertake the development of a new version of the module that requires a smaller amount of resources while considerably increasing the number of output bits compared to other proposals previously reported in the literature. The design as a highly parameterized intellectual property (IP) module connectable through a standard interface to a soft- or hard-core general-purpose processor greatly facilitates its integration into embedded solutions while accelerating the validation and characterization of this element on the same electronic device that implements it. The studies carried out reveal adequate values of reliability, uniqueness, and unpredictability when the module acts as a PUF, as well as acceptable levels of randomness and entropy when it acts as a true random number generator (TRNG). They also illustrate the ability to obfuscate and recover identifiers or cryptographic keys of up to 4096 bits using an implementation of the PUF/TRNG module that requires only an array of 4 × 4 configurable logic blocks (CLBs) to accommodate the RO bank. [ABSTRACT FROM AUTHOR]

Healthcare 4.0 promises enormous breakthroughs in the Internet of Medical Device (IoMD) environment, allowing billions of devices to communicate seamlessly. The introduction of sixth-generation (6G) networks with IoMD improves smart healthcare services by enabling more efficient, robust, sustainable, low cost, and ubiquitous solutions, as well as improving sensing, communication, and interaction capabilities in context-aware settings. However, providing the secure authentication of these devices while also safeguarding data integrity presents considerable challenges. The current systems that rely on centralized servers are vulnerable to unwanted access and modification. To overcome this challenge, the use of Physical Unclonable Function (PUF) and blockchain technology can be made. In this paper, PUF-based authentication technique is integrated with blockchain technology to secure IoMD network in healthcare 4.0. To make it energy efficient as IoMD considered as constrained device, a novel consensus mechanism "Proof-of-Energy" has been introduced which will increase the transaction processing speed while reducing energy consumption. The experimental result shows the proposed model exhibits better security, efficiency and performance in this direction. [ABSTRACT FROM AUTHOR]

The eHealth sector has witnessed significant growth due to technological advancements, facilitating care delivery in patients' homes and moving away from traditional hospital settings. Blockchain and the Internet of Things (IoT) play pivotal roles in enhancing healthcare services, offering features such as remote patient monitoring, streamlined electronic medical record (EMR) management, drug traceability, and effective disease control, particularly during events like the COVID-19 pandemic. The growing utilization of IoT devices brings about security challenges, including concerns related to data integrity and device authentication. This paper proposes the integration of blockchain technology as a robust solution. Leveraging its decentralized and tamper-resistant features, blockchain establishes trust among diverse IoT devices, ensuring the integrity of IoT data. Additionally, smart contracts enhance device authentication, fortifying overall security by addressing vulnerabilities associated with centralization. Regarding the management of eHealth, this survey begins with an overview of the industry, highlighting IoT-related challenges in healthcare. It explores various IoT applications in eHealth and discusses how blockchain can effectively address obstacles in healthcare management through IoT. Notably, the paper provides insights into examining consensus algorithm parameters within blockchain systems, clarifying the methodology used to assess and optimize these critical components. The survey extends to a thorough review of existing research on integrating blockchain-based IoT in eHealth. Finally, it presents an overview of challenges and potential solutions for implementing blockchain-based IoT in the eHealth sector. This comprehensive survey aims to empower stakeholders by providing insights to enhance patient care in this dynamic and evolving field. [ABSTRACT FROM AUTHOR]

Background and Objective: The apnoea‐hypopnoea index (AHI) and oxygen desaturation index (ODI) encounter challenges in capturing the intricate relationship between obstructive sleep apnoea (OSA) and cardiovascular disease (CVD) risks. Although novel hypoxic indices have been proposed to tackle these limitations, there remains a gap in comprehensive validation and comparisons across a unified dataset. Methods: Samples were derived from the Sleep Heart Health Study (SHHS), involving 4485 participants aged over 40 years after data quality screening. The study compared several key indices, including AHI, ODI, the reconstructed hypoxic burden (rHB), the percentage of sleep time with the duration of respiratory events causing desaturation (pRED_3p) and the sleep breathing impairment index (SBII), in relation to CVD mortality and morbidity risks. Adjusted Cox proportional models were employed to calculate hazard ratios (HRs) for each index, and comparisons were performed. Results: SBII and pRED_3p exhibited significant correlations with both CVD mortality and morbidity, with SBII showing the highest adjusted HR (95% confidence interval) for mortality (2.04 [1.25, 3.34]) and pRED_3p for morbidity (1.43 [1.09‐1.88]). In contrast, rHB was only significant in predicting CVD mortality (1.63 [1.05–2.53]), while AHI and ODI did not show significant correlations with CVD outcomes. The adjusted models based on SBII and pRED_3p exhibited optimal performance in the CVD mortality and morbidity datasets, respectively. Conclusion: This study identified the optimal indices for OSA‐related CVD risks prediction, SBII for mortality and pRED_3p for morbidity. The open‐source online platform provides the computation of the indices. [ABSTRACT FROM AUTHOR]

Introduction: Despite increasing incidence of impacted fetal head at cesarean birth and associated injury, it is unclear which techniques are most effective for prevention and management. A high quality evidence review in accordance with international reporting standards is currently lacking. To address this gap, we aimed to identify, assess, and synthesize studies comparing techniques to prevent or manage impacted fetal head at cesarean birth prior to or at full cervical dilatation. Material and Methods: We searched MEDLINE, Emcare, Embase and Cochrane databases up to 1 January 2023 (PROSPERO: CRD420212750016). Included were randomized controlled trials (any size) and non‐randomized comparative studies (n ≥ 30 in each arm) comparing techniques or adjunctive measures to prevent or manage impacted fetal head at cesarean birth. Following screening and data extraction, we assessed risk of bias for individual studies using RoB2 and ROBINS‐I, and certainty of evidence using GRADE. We synthesized data using meta‐analysis where appropriate, including sensitivity analyses excluding data published in potential predatory journals or at risk of retraction. Results: We identified 24 eligible studies (11 randomized and 13 non‐randomized) including 3558 women, that compared vaginal disimpaction, reverse breech extraction, the Patwardhan method and/or the Fetal Pillow®. GRADE certainty of evidence was low or very low for all 96 outcomes across seven reported comparisons. Pooled analysis mostly showed no or equivocal differences in outcomes across comparisons of techniques. Although some maternal outcomes suggested differences between techniques (eg risk ratio of 3.41 [95% CI: 2.50–4.66] for uterine incision extension with vaginal disimpaction vs. reverse breech extraction), these were based on unreliable pooled estimates given very low GRADE certainty and, in some cases, additional risk of bias introduced by data published in potential predatory journals or at risk of retraction. Conclusions: The current weaknesses in the evidence base mean that no firm recommendations can be made about the superiority of any one impacted fetal head technique over another, indicating that high quality training is needed across the range of techniques. Future studies to improve the evidence base are urgently required, using a standard definition of impacted fetal head, agreed maternal and neonatal outcome sets for impacted fetal head, and internationally recommended reporting standards. [ABSTRACT FROM AUTHOR]

In the field of the Internet of Things (IoT) and wireless communication networks, secure data transmission remains a critical challenge due to the inherent vulnerabilities and dynamic nature of these networks. Traditional routing protocols often fail to provide adequate security and efficiency, leading to data breaches and communication delays. To address these issues, we propose a Blockchain-Based Greedy Routing Protocol (BBGRP) that integrates blockchain technology with a greedy forwarding strategy to enhance the security and reliability of data transmission in wireless networks. The BBGRP leverages blockchain's decentralized and tamper-resistant ledger to validate the integrity and authenticity of data packets, while the greedy routing approach optimizes the selection of the most efficient transmission paths. Our method incorporates smart contracts to automate route verification, ensuring secure and real-time decision-making processes. Experimental results show that the proposed protocol significantly reduces the packet loss rate by 27.3% and improves data transmission speed by 35.6% compared to traditional routing protocols. Additionally, BBGRP demonstrates a 42.8% enhancement in overall network throughput and a 21.7% decrease in end-to-end latency. The security analysis confirms the robustness of BBGRP against common attacks, such as Sybil and man-in-the-middle attacks, by achieving a 98.4% attack detection rate. These results highlight the potential of blockchain-based routing protocols to revolutionize secure data transmission in IoT and other wireless communication environments. [ABSTRACT FROM AUTHOR]

Rolling bearing is the key component of rotating machinery, and its vibration signal usually exhibits nonlinear and nonstationary characteristics when failure occurs. Multiscale permutation entropy (MPE) is an effective nonlinear dynamics analysis tool, which has been successfully applied to rolling bearing fault diagnosis in recent years. However, MPE ignores the deep amplitude information when measuring the complexity of the time series and the original multiscale coarse-graining is insufficient, which requires further research and improvement. In order to protect the integrity of information structure, a novel nonlinear dynamic analysis method termed refined composite multiscale slope entropy (RCMSlE) is proposed in this paper, which introduced the concept of refined composite to further boost the performance of MPE in nonlinear dynamical complexity analysis. Furthermore, RCMSlE utilizes a novel symbolic representation that takes full account of mode and amplitude information, which overcomes the weaknesses in describing the complexity and regularity of bearing signals. Based on this, a GWO-SVM multi-classifier is introduced to fulfill mode recognition, and then a new intelligent fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. The experimental results show that the proposed method can not only accurately identify different fault types and degrees of rolling bearing, but also has a short computation time and better performance than other comparative methods. • Refined composite multiscale slope entropy (RCMSlE) is proposed for complexity measure of time series. • The feature extraction performance of RCMSlE outperforms the compared algorithms is confirmed by simulation experiments. • A novel fault diagnosis method for rolling bearing based on RCMSlE and GWO-SVM is proposed. • The effectiveness and superiority of the proposed method is verified by bearing test data analysis. [ABSTRACT FROM AUTHOR]

Introduction: There is a growing concern about the threat of distractions in online learning environments. It has been suggested that mindfulness may attenuate the effects of distraction. The extent to which this translates to academic performance is under investigation. We aimed to investigate the relationship between task-irrelevant visual distraction, time pressure, and mindful self-regulated learning in the context of a low-stake computer-based assessment. Methods: The study sampled 712 registered users of Prolific.co who were prescreened, current undergraduate university students. After data quality screening, 609 were retained for analyses. A 2 x 2 between-subjects design was used. Participants were randomly assigned to the following groups: (1) a control condition, (2) a distract condition, (3) a time pressure condition, or (4) a distract and time pressure condition. All participants completed reading comprehension questions, demographic questions, and the Mindful Self-Regulated Learning Scale. Results: Presenting a visual distraction increased self-reported distraction and having a clock present increased self-reported time pressure. The distraction did not have a statistically significant effect on test performance. Mindfulness was negatively correlated with test performance, self-reported distraction, and self-reported time pressure. Discussion: Continuous task-irrelevant visual distractions may not be distracting enough to influence low-stakes testing performance, but they do influence self-perceptions. [ABSTRACT FROM AUTHOR]

This study aims to identify and analyze the challenges of implementing cognitive computing in small construction projects, where decision-making, process optimization, and sustainability enhancements are crucial yet challenging. The research adopts a mixed-methods approach, integrating a thorough literature review, quantitative evaluation, and structural equation modeling (SEM) to explore the relationships between the identified barriers and the effective application of cognitive computing. The findings reveal significant hurdles, including complexity in customization (β = 0.327, t = 9.848, p < 0.001), data integrity and integration issues (β = 0.389, t = 14.534, p < 0.001), financial and cultural constraints (β = 0.295, t = 7.850, p < 0.001), and ethical and privacy concerns (β = 0.319, t = 8.963, p < 0.001). These barriers impede the seamless adoption of cognitive computing technologies. This research contributes novel insights into the specific challenges faced by small construction projects and provides practical recommendations to overcome these obstacles. By addressing these challenges, this study offers valuable guidance for stakeholders aiming to leverage cognitive computing to improve project outcomes in the construction industry. The novelty of this research lies in its focus on small-scale projects, a relatively underexplored area, and its comprehensive analysis of the multifaceted barriers that hinder the successful implementation of cognitive computing. [ABSTRACT FROM AUTHOR]

This paper selected 14 days of observation data from 7 GNSS basic stations in different environments along a conventional railway. Based on evaluation indicators such as data integrity, signal-to-noise ratio, multipath effect, ionospheric delay, and cycle slip, the quality of every frequency observation data from BDS-2, BDS-3, GPS, and Galileo satellites was analyzed. The impact of different railway environments on the observation data quality of reference stations was explored. The results show that, 7 GNSS basic stations in different railway environments have consistency in various evaluation indicators, the data integrity of daily observation data is greater than 98%, the pseudorange multipath effect is less than 0.32 m, the average SNR value is between 41~50 dB-Hz, the average IOD-RMS value is less than 0.10 m/ min, and the average CSR value is less than 50, all indicators can meet the demand for high-precision positioning along the railway. The performance of Beidou-3 satellites B1C and B2a new signals is equivalent to or even better than that of GPS and Galileo satellites. When selecting GNSS basic stations along the railway line, it is advisable to choose open environments such as building roofs, and choose the weak ground shelter or the environment near the line when the location is difficult. [ABSTRACT FROM AUTHOR]

Data collection is the primary link in the simulation execution process. The integrity and efficiency of data acquisition have a significant impact on the final effect and efficiency of the entire training simulation activity. However, in the existing distributed simulation system based on high level architecture (HLA), centralized data collection is difficult to handle massive data in a single step, which will affect the normal advancement of simulation while distributed data collection will cause a large number of redundant data, and the development of the collection module does not have universality. In response to the above problems, based on weak distributed data acquisition structure, maniple collection members are used to realize parallel data collection, and the distribution strategy of collection tasks in formulated among multiple members by the non-dominated sorting genetic algorithm-II (NSGA-II) to achieve a balanced distribution of data collection loads. The experimental results on simulation result and real system show that the proposed method can significantly improve the efficiency of data collection while reducing the central processing unit (CPU) and memory consumption during the execution of data collection members. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) has evolved significantly, automating daily activities by connecting numerous devices. However, this growth has increased cybersecurity threats, compromising data integrity. To address this, intrusion detection systems (IDSs) have been developed, mainly using predefined attack patterns. With rising cyber-attacks, improving IDS effectiveness is crucial, and machine learning is a key solution. This research enhances IDS capabilities by introducing binary attack identification and multiclass attack categorization for IoT traffic, aiming to improve IDS performance. Our framework uses the 'BoT-IoT' and 'TON-IoT' datasets, which include various IoT network traffic and cyber-attack scenarios, such as DDoS and data infiltration, to train machine learning and ensemble models. Specifically, it combines three machine learning models-decision tree, resilient backpropagation (RProp) multilayer perceptron (MLP), and logistic regression-into ensemble methods like voting and stacking to improve prediction accuracy and reduce detection errors. These ensemble classifiers outperform individual models, demonstrating the benefit of diverse learning techniques. Our framework achieves high accuracy, with 99.99% for binary classification on the BoT-IoT dataset and 97.31% on the ToN-IoT dataset. For multiclass classification, it achieves 99.99% on BoT-IoT and 96.32% on ToN-IoT, significantly enhancing IDS effectiveness against IoT cybersecurity threats. [ABSTRACT FROM AUTHOR]

The daunting issues that are promptly faced worldwide are the sophisticated cyber-attacks in all kinds of organizations and applications. The development of cloud computing pushed organizations to shift their business towards the virtual machines of the cloud. Nonetheless, the lack of security throughout the programmatic and declarative levels explicitly prone to cyber-attacks in the cloud platform. The exploitation of web pages and the cloud is due to the uncrated open web application security projects (OWASP) fragilities and fragilities in the cloud containers and network resources. With the utilization of advanced hacking vectors, the attackers attack data integrity, confidentiality, and availability. Hence, it’s ineluctable to frame the application security-based technique for the reduction of attacks. In concern to this, we propose a novel Deep learning-based secured advanced web application firewall to overcome the lack of missing programmatic and declarative level securities in the application. For this, we adopted the MobileNet-based technique to ensure the assurance of security. Simulations are effectuated and analyzed the robustness with the statistical parameters such as accuracy, precision, sensitivity, and specificity and made the comparative study with the existing works. Our proposed technique surpasses all the other techniques and provides better security in the cloud. [ABSTRACT FROM AUTHOR]

Flash memory, ubiquitous in diverse electronic devices, confronts persistent challenges stemming from inherent errors that jeopardize data integrity. This research situates itself at the intersection of these challenges and advancements, proposing an inventive error correction coding framework that harnesses the unique capabilities of analysis with a hybrid error control coding (HECC) approach. In the proposed work, a mathematical model aimed at enhancing the flash memory by identifying the error pattern within the pages using the discrete fourier transform (DFT). By incorporating distinctive DFT mathematical properties, the proposed technique intends to improve flash memory error correction beyond traditional methods. The flash storage defect detection and rectification results with hybrid error correction coding achieved bit error rate (BER) of 4.3e-6, latency 14.1, mean 15.1 and standard deviation 1.0. Error correction efficiency 98% and storage overhead 10%. With this approach results are significantly improving the error correction efficiency, reduce storage overhead and enhanced adaptability to diverse error patterns. [ABSTRACT FROM AUTHOR]

The online services provided by the Service Provider (SP) have brought significant convenience to people's lives. Nowadays, people have grown accustomed to obtaining diverse services via the Internet. However, some SP utilize or even tamper with personal data without the awareness or authorization of the Data Provider (DP), a practice that seriously undermines the authenticity of the DP's authorization and the integrity of personal data. To address this issue, we propose a Verifiable Authorization Information Management Scheme (VAIMS). During the authorization process, the authorization information and personal data fingerprints will be uploaded to the blockchain for permanent record, and then the SP will store such authorization information and personal data. The DP generates corresponding authorization fingerprints based on the authorization information and stores them independently. Through the authorization information and authorization fingerprints on the chain, the DP can verify the authenticity of the authorization information stored by the SP at any time. Meanwhile, by leveraging the personal data fingerprint on the blockchain, the DP can check whether the personal data stored by the SP has been tampered with. Additionally, the scheme incorporates database technology to accelerate data query. We implemented a VAIMS prototype on Etherum, and experiments demonstrate that the scheme is effective. [ABSTRACT FROM AUTHOR]

In the technologically advanced landscape, companies increasingly leverage cutting-edge technology to enhance operations. However, this progression also amplifies the risk of cyberthreats. Cybersecurity emerges as a pivotal element for safeguarding digital assets, encompassing networks, financial data, intellectual property (IP) and personal information. This note underscores the significance of shielding digital assets from cyberassaults, providing actionable guidance for individuals and businesses. Cybersecurity regulations play a vital role, establishing protective frameworks for sensitive data, IP and critical infrastructure. Compliance with these standards bolsters confidence, national security and global cooperation, elevating cybersecurity consciousness. Amid our digitally driven culture, prioritising cybersecurity ensures the safety and efficiency of digital assets. Implementing robust protocols fortifies individuals and organisations against online threats, facilitating secure navigation of the digital realm while upholding data integrity. [ABSTRACT FROM AUTHOR]

Background Public health surveillance is vital for monitoring and controlling disease spread. In the Philippines, an effective surveillance system is crucial for managing diverse infectious diseases. The Newcomb-Benford Law (NBL) is a statistical tool known for anomaly detection in various datasets, including those in public health. Methods Using Philippine epidemiological data from 2019 to 2023, this study applied NBL analysis. Diseases included acute flaccid paralysis, diphtheria, measles, rubella, neonatal tetanus, pertussis, chikungunya, dengue, leptospirosis and others. The analysis involved Chi-square tests, Mantissa Arc tests, Mean Absolute Deviation (MAD) and Distortion Factor calculations. Results Most diseases exhibited nonconformity to NBL, except for measles. MAD consistently indicated nonconformity, highlighting potential anomalies. Rabies consistently showed substantial deviations, while leptospirosis exhibited closer alignment, especially in 2021. Annual variations in disease deviations were notable, with acute meningitis encephalitis syndrome in 2019 and influenza-like illness in 2023 having the highest deviations. Conclusions The study provides practical insights for improving Philippine public health surveillance. Despite some diseases showing conformity, deviations suggest data quality issues. Enhancing the PIDSR, especially in diseases with consistent nonconformity, is crucial for accurate monitoring and response. The NBL's versatility across diverse domains emphasizes its utility for ensuring data integrity and quality assurance. [ABSTRACT FROM AUTHOR]

In the evolving landscape of the Internet of Things (IoT), ensuring the security and integrity of data transmission remains a paramount challenge. Routing Protocol for Low-Power and Lossy Networks (RPL) is commonly utilized in IoT networks to facilitate efficient data routing. However, RPL networks are susceptible to various security threats, with Sybil and flood attacks being particularly detrimental. Sybil attacks involve malicious nodes generating multiple fake identities to disrupt network operations, while flood attacks overwhelm network resources by inundating them with excessive traffic. This paper proposes a novel mitigation strategy leveraging Bloom filters and hash chains to enhance the security of RPL-based IoT networks against sybil and flood attacks. Extensive simulation and performance analysis demonstrate that this solution significantly reduces the impact of sybil and flood attacks while maintaining a low power consumption profile and low computational overhead. [ABSTRACT FROM AUTHOR]

The availability of raw Global Navigation Satellites System (GNSS) measurements in Android smartphones fosters advancements in high-precision positioning for mass-market devices. However, challenges like inconsistent pseudo-range and carrier phase observations, limited dual-frequency data integrity, and unidentified hardware biases on the receiver side prevent the ambiguity resolution of smartphone GNSS. Consequently, relying solely on GNSS for high-precision positioning may result in frequent cycle slips in complex conditions such as deep urban canyons, underpasses, forests, and indoor areas due to non-line-of-sight (NLOS) and multipath conditions. Inertial/GNSS fusion is the traditional common solution to tackle these challenges because of their complementary capabilities. For pedestrians and smartphones with low-cost inertial sensors, the usual architecture is Pedestrian Dead Reckoning (PDR)+ GNSS. In addition to this, different GNSS processing techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) have also been integrated with INS. However, integration with PDR has been limited and only with Kalman Filter (KF) and its variants being the main fusion techniques. Recently, Factor Graph Optimization (FGO) has started to be used as a fusion technique due to its superior accuracy. To the best of our knowledge, on the one hand, no work has tested the fusion of GNSS Post-Processed Kinematics (PPK) and PDR on smartphones. And, on the other hand, the works that have evaluated the fusion of GNSS and PDR employing FGO have always performed it using the GNSS Single-Point Positioning (SPP) technique. Therefore, this work aims to combine the use of the GNSS PPK technique and the FGO fusion technique to evaluate the improvement in accuracy that can be obtained on a smartphone compared with the usual GNSS SPP and KF fusion strategies. We improved the Google Pixel 4 smartphone GNSS using Post-Processed Kinematics (PPK) with the open-source RTKLIB 2.4.3 software, then fused it with PDR via KF and FGO for comparison in offline mode. Our findings indicate that FGO-based PDR+GNSS–PPK improves accuracy by 22.5% compared with FGO-based PDR+GNSS–SPP, which shows smartphones obtain high-precision positioning with the implementation of GNSS–PPK via FGO. [ABSTRACT FROM AUTHOR]

Blockchain technology has revolutionized numerous industries, including that of financial accounting. However, its potential to support environmental, social, and corporate governance (ESG) objectives remains underexplored. This paper addresses this gap by investigating how blockchain's decentralized and tamper-resistant characteristics can enhance green financial instruments, investment strategies, and climate-related financial disclosures. By leveraging these unique features of blockchain and applying knowledge discovery from data (KDD) methods, we uncover patterns and establish rules that highlight blockchain's role in promoting transparency, accountability, and sustainability within the financial sector. Through a comprehensive analysis of literature, case studies, and real-world examples, this paper not only presents a balanced perspective on the integration of blockchain into financial accounting but also underscores its transformative potential in advancing ESG initiatives. The use of KDD provides novel insights into the effectiveness and implementation strategies of blockchain for ESG, making this study a pioneering resource for academics, professionals, and policymakers seeking to understand and harness blockchain's impact on ESG in financial accounting. [ABSTRACT FROM AUTHOR]

Cloud storage has attracted widespread attention due to its advantages such as convenience, low cost, and easy scalability. With the explosion of cloud data volumes, security and performance issues are becoming major concerns. Countermeasures like data deduplication and integrity auditing techniques have been extensively studied. However, traditional auditable deduplication protocols face significant challenges related to confidentiality, reliability, and efficiency. Aiming to address these challenges, a blockchain-based auditable deduplication scheme for multi-cloud storage is proposed in this paper. Specifically, this scheme leverages blockchain technology and bilinear pairing cryptosystem to achieve an auditable deduplication mechanism that not only saves storage space but also checks data integrity. Furthermore, convergent encryption and threshold secret sharing algorithms are utilized to enhance storage confidentiality and system reliability. Batch auditing and anti-repeated auditing techniques are also adopted to improve auditing efficiency. Finally, security analysis and performance evaluation reveal that the proposed scheme achieves the expected security and performance requirements. [ABSTRACT FROM AUTHOR]

INTRODUCTION: With the rapid development of the economy, more and more devices and sensors are connected to the Internet, and a large amount of data is transmitted in the network. However, this large-scale data transmission involves the problem of information security, especially in the transport layer. Therefore, there is an urgent need to study and design an information security data enhancement security strategy for the transport layer of ubiquitous networks (i.e., IoT). OBJECTIVES: This thesis aims to research and create a data enhancement security strategy for the transport layer of the Ubiquitous Web to ensure the confidentiality and integrity of data transmitted in the Ubiquitous Web. Specific objectives include evaluating the advantages and disadvantages of current ubiquitous network transport layer lifting security techniques, proposing a new lifting security strategy applicable to the transport layer of ubiquitous networks, and verifying the feasibility and security of the proposed standard. METHODS: First, a detailed study and evaluation of the current Ubiquitous Network Transport Layer Elevated Security Techniques is conducted, including analyzing and comparing the existing elevated security algorithms and protocols. Then, based on the obtained research results, a new lifting security strategy applicable to the transport layer of ubiquitous networks is proposed. The design process takes into account the characteristics and requirements of ubiquitous networks, such as resource constraints, dynamics of network topology, and cooperative communication of multiple devices. Subsequently, the feasibility and security of the proposed standard are verified through simulations and experiments. In the experiments, real ubiquitous network devices and network environments are used to evaluate the performance and attack resistance of the enhanced security algorithms. RESULTS: Through the research and analysis of ubiquitous network transport layer lifting security techniques, some limitations of the existing lifting security algorithms are identified, such as high resource consumption, insufficient security, and limited ability to adapt to the characteristics of ubiquitous networks. Therefore, this thesis proposes a new lifting security strategy applicable to the transport layer of ubiquitous networks. The experimental results show that the standard can guarantee data confidentiality and integrity while possessing high efficiency and attack resistance. In addition, the proposed standard meets the needs of resource-constrained devices in ubiquitous networks and can operate properly under multiple network topologies and cooperative device communications. CONCLUSION: This thesis proposes a new elevated security strategy applicable to ubiquitous networks through the study and design of transport layer elevated security techniques for ubiquitous networks. This standard can effectively protect the confidentiality and integrity of data transmitted in ubiquitous networks with high efficiency and attack resistance. The proposed standard is expected to provide a feasible solution for the information security of ubiquitous networks and a more reliable guarantee for developing and applying ubiquitous networks. Future work can further improve and optimize this enhanced security strategy and validate and apply it in a wider range of ubiquitous network environments. [ABSTRACT FROM AUTHOR]