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Looking back at 50 years of Journal of Consumer Research methods and interviewing some of the field's most respected methodologists, this article seeks to craft a core set of best practices for scholars in consumer research. From perennial issues like conceptual validity to emerging issues like data integrity and replicability, the advice offered by our experts can help scholars improve the way they approach their research questions, provide empirical evidence that instills confidence, use new tools to make research more inclusive or descriptive of the "real world," and seek to become thought leaders. [ABSTRACT FROM AUTHOR]

K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μ m of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μ m length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel. [ABSTRACT FROM AUTHOR]

This study investigates the Chinese equity premium puzzle, a phenomenon that has been the subject of several inquiries in recent years. Central to the exploration is the potential unreliability of the relative risk aversion rate derived from the GMM approach, largely due to the quality of Chinese consumption data and the "limited T" problem. To ensure a robust examination, we adopted two robust inference methods.Our results consistently indicate that the estimated relative risk aversion is notably low, regardless of the variations in consumption measures used. This finding challenges the prevailing notion of the equity premium puzzle in the global context, suggesting its possible non-existence in China. Beyond providing a novel perspective on the Chinese equity premium puzzle, our study underscores the paramount importance of data integrity and the selection of appropriate methodologies in financial research. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Cloud storage provides a convenient way of collaboration and sharing. An increasing number of users are becoming more open to sharing their data with others. Consequently, the integrity of shared data has started to draw significant attention due to the loss control of it. Remote data integrity techniques can solve this problem. To resist the collusion between the third party auditor and the cloud server, existing integrity auditing schemes utilize blockchain to replace the third party auditor to do some work. However, these schemes still involve collusion between the third party auditor and the miners and cannot guarantee the third party auditor to execute the auditing process honestly. Considering this, we propose a blockchain‐based transparent and certificateless data integrity auditing scheme. Specifically, we design the smart contract combining the commitment technology to generate the challenge information and record the auditing information. Besides, we combine certificateless cryptography and the blind technology to achieve the privacy‐preserving. The proposed scheme can resist collusion, improve the transparency of the auditing process, protect the data privacy and reduce the overhead of certificate management and key management. The security analysis and performance evaluation demonstrate the security and efficiency of the scheme. [ABSTRACT FROM AUTHOR]

The article analyzes current issues in protecting the information security of enterprises, which is an integral part of modern business activities. Emphasizing the importance of confidentiality, integrity, and availability of information assets, the authors highlight the need to implement comprehensive measures aimed at neutralizing both internal and external threats. By utilizing modern methods of encryption, authentication, and access management, enterprises are capable of integrating information security into their overall business strategy, ensuring its resilience. Focusing on the importance of personnel training, the researchers recommend employing enhanced monitoring mechanisms to timely detect threats, thereby maintaining business stability and competitiveness in the contemporary information landscape. Effective information security of business activities requires the implementation of comprehensive mechanisms that cover both legal and technical aspects. One of the main elements of such a system is the legal framework. Modern laws and regulations create a legal framework for the protection of information data. Information cooperation between business entities, government agencies and international organizations is one of the main components of the information security system. Cooperation between these entities allows for timely detection and neutralization of threats. It is necessary to develop mechanisms for monitoring and evaluating the effectiveness of information security measures. Modern businesses need tools that allow them to respond quickly to threats and adapt to new challenges. Solving these unresolved issues is important for creating a reliable system of ensuring information security of business activities in the mod ern information environment. [ABSTRACT FROM AUTHOR]

Detecting careless responding in survey data is important to ensure the credibility of study findings. Of several available detection methods, personal reliability (PR) is one of the best-performing indices. Curran, Journal of Experimental Social Psychology, 66, 4-19, (2016) proposed a resampled version of personal reliability (RPR). Compared to the conventional PR or even–odd consistency, in which just one set of scale halves is used, RPR is based on repeated calculation of PR across several randomly rearranged sets of scale halves. RPR should therefore be less affected than PR by random errors that may occur when a specific set of scale half pairings is used for the PR calculation. In theory, RPR should outperform PR, but it remains unclear whether it in fact does, and under what conditions the potential gain in detection accuracy is the most pronounced. We conducted two studies: a simulation study examined the performance of the conventional PR and RPR in detecting simulated careless responding, and a real data example study analyzed their performance when detecting human-generated careless responding. In both studies, RPR turned out to be a significantly better careless response indicator than PR. The results also revealed that using 25 resamples for the RPR computation is sufficient to obtain the expected gain in detection accuracy over the conventional PR. We therefore recommend using RPR instead of the conventional PR when screening questionnaire data for careless responding. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) data encompasses vital information that provides a comprehensive understanding of the structural health condition. However, data loss may occur due to faults in acquisition equipment or sensors, and it is essential to reconstruct missing data to ensure the integrity of the monitoring information. Although extensive researches have been conducted on the topic of data recovery, a suitable missing data recovery method that can effectively address the missing data for multiple-source monitoring variables has not been identified yet. In this study, we proposed a novel missing data recovery model based on a deep learning framework to recover the missing strain and acceleration data simultaneously for SHM of the quayside container crane (QCC). The framework combines dual-tree complex wave transform (DTCWT) and bidirectional long short-term memory with attention mechanism (Att-BiLSTM). The multiple-source monitoring data are decomposed into several subtime series using the dual-tree complex wavelet, then, the Att-BiLSTM network is used to assign different weights to each subsequence in order to capture valuable information from the complete data. The effectiveness of the proposed model is verified by case studies, and the comparison results of missing data recovery under different miss rates show that the proposed model simultaneously improves the accuracy of missing data recovery for multiple-source monitoring variables. [ABSTRACT FROM AUTHOR]

Recent individual studies have indicated that ultra-processed food (UPF) consumption may be associated with the incidence of chronic kidney disease (CKD). We conducted a systematic review and meta-analysis based on those longitudinal studies evaluating the relationship between UPF consumption and the risk of incident CKD, and synthesizing the results. PubMed, Embase, The Cochrane Library, Web of Science, and Scopus were searched from inception through 22 March 2023. Any longitudinal studies evaluating the relationship between UPF consumption and the risk of incident CKD were included. Two researchers independently conducted the literature screening and data extraction. RR and its 95% CI were regarded as the effect size. The Newcastle–Ottawa Scale (NOS) was applied to assess the quality of the studies included, and the effect of UPF consumption on the risk of incident CKD was analyzed with STATA version 15.1. This study's protocol was registered in PROSPERO (CRD42023411951). Four cohort studies with a total of 219,132 participants were included after screening. The results of the meta-analysis suggested that the highest UPF intake was associated with an increased risk of incident CKD (RR = 1.25; 95% CI: 1.18–1.33). High-dose UPF intake was associated with an increased risk of incident CKD. However, the underlying mechanisms remain unknown. Thus, more standardized clinical studies and further exploration of the mechanisms are needed in the future. [ABSTRACT FROM AUTHOR]

The main aim of this research work is to make it easier for the same group to share and store anonymous data on the cloud securely and effectively. This research work presents verifiable privacy-aware enhanced homomorphic (VPEH) for multiple participants; moreover, the enhanced homomorphic encryption mechanism provides end-to-end encryption and allows the secure computation of data without revealing any data in the cloud. The proposed algorithm uses homomorphic multiplication to compute the hashes product of challenges blocks that make it more efficient, furthermore an additional security model is incorporated to verify the shared data integrity. The VPEH mechanism is evaluated considering parameters such as tag generation, proof generation, and verification; model efficiency is proved by observing the marginal improvisation over the other existing models by varying the number of blocks and number of challenge blocks. [ABSTRACT FROM AUTHOR]

Recent advances in intelligent systems have significantly improved power management, load distribution, and resource management capabilities, far beyond past constraints. Despite these gains, the development of internetconnected technology has brought various vulnerabilities, leading to negative results. The integration of intelligent technology has unintentionally offered chances for hackers to enter networks and modify data sent to central systems for analysis. One of the most serious risks is the false data injection attack (FDIA), which may drastically impair analytical outcomes. Previous research has shown that standard approaches for recovering data affected by FDIA are unreliable and inefficient. This paper investigates the use of the proof of stake (PoS) consensus method in this framework improves data integrity and makes it easier to identify illegal changes. Participating nodes may reject or change block transactions, ensuring the ledger's correctness. Our results show that the PoS consensus method is exceptionally successful in creating and adding transactions to the blockchain. Furthermore, the PoS mechanism's simplicity in block formation enhances both time and energy efficiency, resulting in considerable benefits in operational performance. [ABSTRACT FROM AUTHOR]

Summary: Ensuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs. [ABSTRACT FROM AUTHOR]

This study aims to identify and analyze the strategies employed by public librarians to activate their civic roles in combating information disorders. Using discourse analysis, the research offers meaning to what is happening and what has happened when a librarian tries to rediscover their knowledge practices when a phenomenon like information disorders proliferates. Twelve public librarians showed interest in being part of the study. The results showed that among the strategies employed by public librarians to combat information disorders were providing instruction, applying information literacy, ensuring information integrity, self-regulation, critical examination through scholarly engagement, being role models, and using reliable sources. [ABSTRACT FROM AUTHOR]

Variable screening plays a crucial role in ultra-high-dimensional data analysis. In this article, we establish a sure independence screening procedure based on the multiscale graph correlation (MGC), a new frame to generalize distance correlation, which can identify the monotonous or non monotonic relationship between predictors and response, and also enjoy the same sure screening property as the DC-SIS. Besides, we extend the method to right-censored survival data in two ways, the Kaplan-Meier estimator and composite quantile, respectively, and build the corresponding sure screening properties. Through numerical simulation, the results show that MGC-based screening methods have better performance than other methods when complicated non linear relationships exist for both complete data and right-censored data. Furthermore, we apply the proposed methods to two real datasets to examine the ranking ability and model prediction accuracy. [ABSTRACT FROM AUTHOR]

IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity. [ABSTRACT FROM AUTHOR]

The present study intended to divulge the potential role of garlic-derived organosulfur compounds (OSCs) in targeting PCSK-9 and averting its interaction with the EGF-A portion of LDL-R via in-vitro and in-silico analysis. Our in-silico screening data showed that 3-(Propylsulfinyl)-L-alanine (PSA), S-Ethyl-L-cysteine (SEC), alliin, and S-Allyl-L-cysteine (SAC) exhibited higher binding energy (−7.05, −7.00, −6.65, and −6.31 Kcal/mol, respectively) against PCSK-9, among other selected OSCs. Further, the protein-protein interaction study of PCSK-9-OSCs-complex with EGF-A demonstrated a similar binding pattern with E-total values ranging from −430.01 to −405.6 Kcal/mol. These results were further validated via in-vitro analysis which showed that SEC, SAC, and diallyl trisulphide (DAT) exhibited the lowest IC50 values of 4.70, 5.26, and 5.29 µg/mL, respectively. In conclusion, the presented data illustrated that SEC, SAC, and DAT were the best inhibitors of PCSK-9 activity and may have the potential to improve the LDL-R function and lower the circulatory LDL-C level. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANET) are revolutionizing intelligent transportation systems (ITS), and as a result, research on their security is becoming increasingly important. As the primary security concern for VANET, authentication security is still quite difficult to achieve. Consequently, the prior knowledge of VANET is covered in this survey before outlining the primary security concerns. To set itself apart from previous surveys, this study suggests security properties and challenges among VANET. Next, the essential and significant features of a secure VANET system, such as confidentiality and integrity of data, and the availability of network systems have been reported, the authenticity of nodes and messages, and the refusal to deny data once it has been transmitted is detailed. Later, it outlined the requirement of the ITS which makes the survey unique. More importantly, the report on the most recent developments in VANET concentrates on the authentication schemes that have been proposed recently. The security features and authentication resistance against attacks, along with the overhead and efficiency of these schemes, are thoroughly examined and contrasted. A detailed analysis of V2V, V2I, and V2X authentication is been reported. Various cryptographic schemes have been discussed along with some advanced techniques such as Blockchain and hybrid schemes. An overview of the integration of 5G/6G networks is documented. Applications of VANET have been discussed in detail along with some open challenges in VANET. In summary, this work reviews a few lessons learned and explores different possibilities for future research. [ABSTRACT FROM AUTHOR]

To improve the data security and integrity of the outsourced data, storing multiple copies of data on multiple cloud servers is a good way. Many public Provable Data Possession (PDP) schemes in multiple cloud servers have been proposed in recent years. However, in some scenarios, the Data Owner (DO) may not want anyone (e.g. a stranger) to check the integrity of their data. Nevertheless, few schemes consider the fault's location function when the data auditing fails. Another problem is that anyone can make a challenge for the Cloud Server (CS) in the PDP schemes. Some access control strategies are necessary to reduce the waste of computation power resources of the CS. To solve these problems, we propose a certificateless and designed-verifier auditing scheme in multi-cloud storage environments. In our scheme, we utilize certificateless signature combined with a delegation key to achieve designed-verifier auditing. We design a secret Merkle Hash Tree (MHT) to locate the faults of CSs and data blocks. We utilize Zero-Knowledge Proof (ZKP) to achieve access control. Theoretical and experimental evaluation show that the proposed scheme is efficient and practical. [ABSTRACT FROM AUTHOR]

The article examines the legal basis for protecting information about personal and family life in the complex conditions of martial law, which provide for a number of specific restrictions and prohibitions defined by regulatory legal acts. It has been clarified that under martial law, the legal regulation of social relations objectively has certain peculiarities. In particular, it is possible to temporarily, for the period of the legal regime of martial law, restrict the constitutional rights and freedoms of a person and citizen, including those provided for in Article 32 of the Constitution of Ukraine, within the limits and scope necessary to ensure the possibility of introducing and implementing measures of the legal regime of martial law, which are provided for by the Law of Ukraine “On the Legal Regime of Martial Law”. Article 32 of the Constitution of Ukraine is considered in the context of guaranteeing a person’s right to non-interference in their personal and family life in order to determine the peculiarities of legal regulation of protecting information about personal and family life when introducing the legal regime of martial law. The peculiarities of legal regulation of social relations under martial law are studied. The state’s obligation to protect information about personal and family life is emphasized. Such protection involves creating a set of legal, administrative, organizational, technical, and other measures that ensure the preservation, integrity of information, as well as the proper order of access to information about personal and family life. In the context of this guarantee, three components have been identified that required concretization of content within the framework of the presented scientific study: information; personal life; family life of a person. During the study, the concept of “information” according to the Law of Ukraine No. 2657-XII “On Information” was taken as a basis. The content of the concept “personal life of a person”, a person’s right to personal life, a person’s right to the secrecy of personal life, a person’s rights to respect for their family life are considered. In the process of conducting the research, attention is focused on the fact that the legal regime of martial law cannot be used as an excuse for unreasonable restriction of personal rights. [ABSTRACT FROM AUTHOR]

Managing identities in metaverse environment is challenging due to the conflicting requirements of ensuring user privacy while enforcing accountability. Users may participate in the metaverse through multiple virtual identities or avatars for different applications/scenarios. Thus, virtual identities must be continuously generated and mapped with real identities. Further, user privacy needs to be protected while not ensuring blanket anonymity to indulge in objectionable or unlawful conduct. Ensuring privacy for the metaverse while still being able to pinpoint users for their actions in the metaverse is a significant research issue. Solving the aforementioned issue, we propose a third‐party identity management solution for the metaverse based on creating a decentralized oracle network (DON) providing identity authentication and mapping service. The proposed identity management framework is metaverse platform agnostic, ensuring users' privacy on the platform while still being able to uncover their real identities if warranted. The proposed framework is viable as evidenced by experimental results of DON and blockchain implementation. The DON based framework leverage the benefits of blockchain technology and introduces immutability, transparency, and traceability into the the metaverse environment. This ensures data integrity and minimizes the frequency of cyber attacks in the metaverse ecosystem. [ABSTRACT FROM AUTHOR]

Increasing concerns about the trustworthiness of research have prompted calls to scrutinise studies' Individual Participant Data (IPD), but guidance on how to do this was lacking. To address this, we developed the IPD Integrity Tool to screen randomised controlled trials (RCTs) for integrity issues. Development of the tool involved a literature review, consultation with an expert advisory group, piloting on two IPD meta‐analyses (including 73 trials with IPD), preliminary validation on 13 datasets with and without known integrity issues, and evaluation to inform iterative refinements. The IPD Integrity Tool comprises 31 items (13 study‐level, 18 IPD‐specific). IPD‐specific items are automated where possible, and are grouped into eight domains, including unusual data patterns, baseline characteristics, correlations, date violations, patterns of allocation, internal and external inconsistencies, and plausibility of data. Users rate each item as having either no issues, some/minor issue(s), or many/major issue(s) according to decision rules, and justification for each rating is recorded. Overall, the tool guides decision‐making by determining whether a trial has no concerns, some concerns requiring further information, or major concerns warranting exclusion from evidence synthesis or publication. In our preliminary validation checks, the tool accurately identified all five studies with known integrity issues. The IPD Integrity Tool enables users to assess the integrity of RCTs via examination of IPD. The tool may be applied by evidence synthesists, editors and others to determine whether an RCT should be considered sufficiently trustworthy to contribute to the evidence base that informs policy and practice. [ABSTRACT FROM AUTHOR]

The development of pharmaceutical products is the critical bridge that moves a potential new medicine from academic discovery to applied treatment of patients. It translates an idea for a new drug to bench-level research on how it can be manufactured, formulated, characterized and controlled for use in non-clinical and early clinical trials. From pre-clinical R&D discovery work through the commercial launch, substantial R&D CMC data is generated to develop and optimize cGMP manufacturing and testing operations, while also supporting product comparability, elucidating product / impurity structures, assessing critical quality attributes, developing the drug delivery mode, and developing the product formulation for long-term stability. Significant R&D CMC work continues post-approval to support continuous improvement and market expansion of the commercial product. These activities are crucial elements of Product Lifecycle Management, and taken together, they comprise Pharmaceutical Quality or Chemistry, Manufacturing and Controls (CMC). The objective of this paper is to mitigate the regulatory ambiguity of R&D quality systems with practical, risk-based examples and recommendations when conducting supportive CMC studies for biological products. Making sound strategic CMC decisions under any circumstances assumes data from R&D studies are reliable, traceable, and complete. While there are specific regulatory guidelines on phase-appropriate cGMP activities, none exist for quality practices in R&D CMC laboratories conducting non-cGMP studies. Hindsight is not the time to discover that R&D studies lack key elements that would otherwise have allowed the data to be directly presented to regulators, if needed. There is a strong prospective business interest in protecting considerable investments made for CMC R&D studies. Therefore, establishment of a robust and stage-appropriate R&D laboratory quality system is essential for companies seeking to capitalize on prior knowledge, protect investments, and be prepared for accelerated approval pathways. [ABSTRACT FROM AUTHOR]

Background: C-reactive protein (CRP) and high-sensitivity C-reactive protein (hsCRP) are measures of inflammation used in diagnosis, to guide treatment decisions, and in disease prediction. Variability in measured CRP and hsCRP may affect their clinical utility but estimates of within-subject variability are based on limited data. Methods: A systematic review and meta-analysis was performed to estimate longitudinal within-subject variability of CRP and hsCRP over any time period. Follow-up studies of any design in adults or children, with repeated measures of CRP or hsCRP were sought. Multiple databases were searched from inception to November 2022. Titles and abstracts were screened in duplicate. Full text screening and data extraction were performed by one reviewer and verified by a second. Risk of bias was assessed with a modified Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) tool. Intraclass correlation coefficient (ICC) results were pooled with a meta-analysis and coefficient of variation (CV) results were described by median and range. Results: Of 2675 studies identified, 60 met the inclusion criteria: 34 reported CRP and 26 reported hsCRP. For CRP, median CV was 0.41 (range 0.11 to 0.89), and the pooled estimate of ICC was 0.55 (95% CI 0.35 to 0.74). For hsCRP, median CV was 0.44 (range 0.27 to 0.76) and the pooled estimate of ICC was 0.62 (95% CI 0.58 to 0.67). Limitations: Assessment of variability was not the main aim of many of the included papers, and it is possible that some relevant papers have been missed. Many of the papers included had low numbers of participants and/or low numbers of repeated measurements. Conclusions: Estimated within-subject variability is high for both CRP and hsCRP, but estimates are based on small numbers of participants and measurements. There is a need for better estimates of within-subject variability from analysis of larger numbers of repeated measurements in larger numbers of subjects. [ABSTRACT FROM AUTHOR]

Background: Surgery is an indispensable component of a functional healthcare system. To date there is limited information regarding how many people die during the perioperative period globally. This study describes a protocol for a systematic review and multilevel meta-regression to evaluate time trends regarding the odds of perioperative mortality among adults undergoing a bellweather surgical procedure while accounting for higher order clustering at the national level. Methods: Published studies reporting the number of perioperative deaths from bellweather surgical procedures among adults will be identified from MEDLINE, Embase, Cochrane CENTRAL, LILACS and Global Index Medicus. The primary outcome will be the rate of perioperative mortality across time and the secondary outcome will be investigating cause of death over time as a proportion of overall perioperative mortality. Two reviewers will independently conduct full text screening and extract the data. Disagreements will first be resolved via consensus. If consensus cannot be reached a third reviewer will be included to arbitrate. Due to human resource limitations, a risk of bias appraisal will not be conducted. From the included studies a multilevel meta-regression will be constructed to synthesize the results. This model will conceptualize patients as nested in studies which are in turn nested within countries while taking into account potential confounding variables at all levels. Discussion: The systematic review and multilevel meta-regression that will be conducted based on this protocol will provide synthesized global evidence regarding the trends of perioperative mortality. This eventual study may help policymakers and other key stakeholders with benchmarking surgical safety initiatives as well as identify key gaps in our current understanding of global perioperative mortality. Trial registration: Systematic review registration: PROSPERO registration number 429040. [ABSTRACT FROM AUTHOR]

Semi‐synthetic cannabinoids (SSCs) are derivatives of phytocannabinoids with slight chemical modifications. SSCs have appeared as legal alternatives to tetrahydrocannabinol (Δ9‐THC) in recent years. This study investigates the prevalence of SSCs in seized drug samples from Danish police and custom authorities seized in Eastern Denmark in the period 2018–2023. Screening data obtained by gas chromatography–mass spectrometry (GC–MS) were reprocessed to enable detection of SSCs. Seized drug samples were categorized into six types of formulations. Δ8‐THC was the first SSC observed and appeared in 2019 followed by hexahydrocannabinol (HHC), tetrahydrocannabidiol (H4‐CBD), hexahydrocannabinol acetate (HHC‐O‐Acetate), hexahydrocannabiphorol (HHCP) and tetrahydrocannabiphorol (Δ9‐THCP). Only one sample positive for SSCs was observed before the third quarter of 2021, with positive samples increasing from third quarter of 2022. Over the study period, a total of 15% (n = 216) of seized cannabis products were positive for SSCs. HHC was the most frequently identified SSC and found in 10% (n = 137) of samples, followed by H4‐CBD at 4% (n = 53), Δ8‐THC at 3% (n = 44), and HHC‐O‐Acetate, HHCP, and THCP each at 1% (n = 10–20). SSCs appeared in 56% of E‐cigarette products, 20% of hashish, 17% of concentrates, 10% of edibles, and 10% of plant materials. In conclusion, SSCs represent a new type of cannabinoids with a rapidly growing popularity and with specific compounds dominating at different periods. Some of the observed trends were likely influenced by the scheduling of HHC in May of 2023 in Denmark. [ABSTRACT FROM AUTHOR]

The application of healthcare systems has led to an explosive growth in personal electronic health records (EHRs). These EHRs are generated from different healthcare institutions and stored in cloud data centers, respectively. However, data owners lose the authority to control and track their private and sensitive EHRs. In fact, data owners cannot establish rules for EHRs exchanging and sharing, nor can they verify the integrity of EHRs stored in semi-trusted clouds. Hence, an individual-centric access control framework is required to realize data access control. In this study, we construct a data access control framework, which integrates decentralized smart contracts and role-based access control (RBAC) to provide fine-grained data access control services. The key ideas of this schme includes: (1) a fine-grained access control framework for EHRs is proposed to achieve trusted access control; (2) a personalized policies definition mechanism is adopted to achieve patient-centric data access control; (3) a integrity checking mechanism for the shared EHRs is implemented to ensure the availability of medical records. Finally, we analyze the security properties of this scheme and develop a prototype system to evaluate its performance. Both theoretical analysis and experiment results demonstrate that this scheme can provide fine-grained access control and efficient integrity checking services for EHRs. [ABSTRACT FROM AUTHOR]

Blockchain technology is effecting a transformation in land transportation, enhancing both the efficiency and security of supply chains. This paper delineates the principal attributes of blockchain technology, including decentralization, transparency, and security, and their ramifications for transportation systems. The integration of blockchain technology can enhance traceability, optimize operational efficiency, and cultivate trust among stakeholders while guaranteeing data integrity. It presents opportunities for regulatory compliance, cost reduction, and sustainability, particularly when integrated with IoT devices for enhanced monitoring. Nevertheless, obstacles such as technical complexities and regulatory ambiguities may impede the comprehensive adoption of this technology. Future research should concentrate on the practical implementation and scalability of blockchain technology, with the transportation sector assuming an active role in the development of applications that will facilitate more efficient and sustainable supply chains. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the present meta-analysis was to systematically determine the overall complication rate and incidence of cryoshock in patients undergoing cryoablation of the liver. Methods: A systematic review and meta-analysis adhering to the PRISMA guidelines and focusing on studies of cryotherapy for liver malignancies published after 2000 were conducted. PubMed, Web of Science, Embase, and Scopus were systematically searched for articles reporting incidences of adverse events associated with percutaneous cryoablation in patients with liver malignancies. Data extraction and screening were independently conducted by two reviewers, who resolved discrepancies through consensus. Statistical analysis was performed to assess heterogeneity and pooled complication rates and included a moderator analysis to explore factors influencing the occurrence of complications. Results: The initial search yielded 4,145 articles, of which 26 met our inclusion criteria. From these 26 articles, pooled data on 4,029 patients were extracted. Variance between studies reporting cryoshock was low (I2 = 13.15%), while variance among studies reporting major complications was high (I2 = 82.52%). The pooled weighted proportion of major complications was 4.71% while that of cryoshock was as low as 0.265%. Moderator analysis identified publication year as the only moderator for major complications and no moderator for the occurrence of cryoshock. Conclusion: Analysis of currently available evidence indicates that cryoablation has a relative safe profile with a pooled incidence of major complications below 5%. Cryoshock occurred in less than 0.3% of procedures and was not reported for liver lesions smaller than 3 cm. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) are extensively utilized to capture the spatial–temporal relationships among human body parts for skeleton-based action recognition. However, due to the inefficient information propagation caused by redundant sampling of video frames in the temporal domain, we focus on refining temporal graphs through key frame selection. To this end, we propose a multi-stage key frame selection (MSKFS) method, aiming to find the most representative frames as the graph nodes to learn compact temporal graph representations of human skeletons for action recognition. The MSKFS progressively selects key frames in two stages: (1) salient posture frame selection based on the global dynamics of body parts and (2) key frame refinement and alignment according to intra-frame correlations. The first stage captures the most salient information and aligns the corresponding information of skeleton sequences within the same category. The second stage enriches the subtle information for the integrity of the information derived by the salient frames. Moreover, variational inference is applied to differentiate the key frame refinement and alignment procedure, allowing the end-to-end optimization of arbitrary graph-based models to represent the obtained compact graph for skeleton-based action recognition. Our MSKFS method achieves state-of-the-art performances on two challenging action recognition datasets. [ABSTRACT FROM AUTHOR]

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j's strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability. [ABSTRACT FROM AUTHOR]

Swarm of Unmanned Aerial Vehicles (UAVs) broaden the field of application in various fields like military surveillance, crop monitoring in agriculture, combat operations, etc. Unfortunately, they are becoming increasingly susceptible to security attacks, such as jamming, information leakage and spoofing, as they become more common and in more demand. So, there is a wider need for UAVs, which requires the design of strong security procedures to fend off such attacks and security dangers. Even though several studies focused on security aspects, many questions remain unanswered, particularly in the areas of secure UAV‐to‐UAV communication, support for perfect forward secrecy and non‐repudiation. In a battle situation, it is extremely important to close these gaps. The security requirements for the UAV communication protocol in a military setting were the focus of this study. In this paper, we present the issues faced by the UAV swarm, especially during military surveillance operations. To secure the communication link in UAV, a new protocol for UAV Swarm communication is proposed with anonymous secure messaging token‐based protocol (ASMTP). The proposed protocol secures UAV‐to‐base station communication and safeguards the metadata of the sender and receiver nodes. The proposed model maintains the confidentiality, integrity and availability of data in the UAV Swarm and achieves robustness. In addition, it provides a different strategy for the cybersecurity gaps in the swarm of UAVs during military surveillance and combat operations. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are rapidly being integrated into various fields, significantly impacting and facilitating many aspects of human life. However, the increasingly prominent security issues associated with WSNs have become a significant challenge. This paper provides an in-depth analysis of the security challenges faced by WSNs in resource constrained and open communication environments. As a key component of the Internet of Things (IoT), a WSN can perceive, collect and transmit physical environmental data in real-time, and is widely used in military, medical, agricultural and other fields. However, the insecurity of communication channels and unauthorized user access pose severe threats to network security and data integrity. To address these challenges, this paper proposes a provably secure two-factor authentication protocol. This protocol utilizes a Chebyshev chaotic map and a two-factor authentication mechanism, which not only enhances security in WSNs but also improves authentication efficiency. The protocol is validated through security proof and performance experiments, demonstrating excellent security, functionality and efficiency. This provides strong support for secure and efficient communication in WSNs across various application scenarios. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%. [ABSTRACT FROM AUTHOR]

Traceability in the food industry has become essential to ensuring safety, quality, and regulatory compliance. Traditional traceability methods often lack transparency, efficiency, and security, leading to challenges in verifying product quality and adherence to health regulations. This paper addresses these challenges by presenting a unique blockchain-based framework/system to enhance the traceability of the food grain. Integrating sensors, Raspberry Pi units, IPFS, and Ethereum Blockchain creates a transparent and auditable supply chain, empowering every participant within the supply chain to verify quality and adherence to healthful regulations. The suggested framework combines machine learning (ML) with blockchain technology. ML is responsible for distinguishing between valid and invalid data within the agri-food supply chain in this setup. At the same time, blockchain ensures that only valid data is stored, maintaining its security and privacy. This is crucial for consumer trust and enabling regulatory bodies to conduct efficient online inspections and ensure adherence to best practices. Finally, the proposed system is evaluated using various performance metrics. In terms of scalability, as the volume of data transactions increases, the system's scalability improves. The framework shows faster transaction commitments, reduced propagation delays, higher throughput, and lower latency with higher transaction volumes. Additionally, the security analysis confirms that the proposed system effectively addresses critical security and privacy concerns, including confidentiality, data integrity, availability, non-repudiation, and protection against cyber-attacks. The proposed blockchain-based traceability framework for food grains has shown substantial possibility in reducing fraud and improving transparency and consumer trust. [ABSTRACT FROM AUTHOR]

The blockchain‐enabled healthcare system assists in tackling the inherent issues of centralized healthcare systems, such as data stewardship, single point of failure, and data integrity. However, existing blockchain‐enabled healthcare systems suffer from high‐energy consumption, low transaction throughput, and poor scalability. This work proposes the healthcare framework ParallelChain to tackle the low scalability and high‐energy consumption issue. ParallelChain divides the network into clusters, enabling linear scalability of nodes. The parallel execution of transactions in each cluster improves the throughput. The leader selection process of ParallelChain does not require solving computer‐intensive puzzles, making it more energy efficient than proof‐of‐work. The performance of ParallelChain is validated using metrics such as data transfer, processing time, consensus delay, block congestion, and message exchange. It shows that the amount of data transmitted is 4.6 times more in Bitcoin than ParallelChain with varying nodes. The number of messages exchanged is reduced by 48%, and the processing time is reduced by 2.17 and 1.7 times for varying nodes and block sizes, respectively. [ABSTRACT FROM AUTHOR]

Security risks are increasingly concerning as the Internet of Things (IoT) expands. Authentication, access control, and authorization present significant challenges for resource-constrained IoT devices. Traditional authentication methods often require enhancements for these devices, but Blockchain technology presents a potential solution. Decentralized and distributed, Blockchain eliminates a single point of failure and relies on Elliptic Curve Cryptography (ECC) for robust security. We have introduced a cutting-edge solution to fortify communication security within IoT devices across supply chain ecosystems. By harnessing the power of Blockchain technology, our framework incorporates smart contracts, adheres to ES256 encryption standards, and seamlessly integrates with Infura API. These components establish stringent access controls, ensure data integrity, and enhance transparency throughout supply chain processes. The framework's robust architecture facilitates swift and secure transactions, bolsters traceability efforts, and effectively mitigates potential security risks. With its scalable design and reliable functionality, this framework emerges as a pivotal asset for optimizing IoT device communication within dynamic supply chain environments. The use of ProVerif in our analysis provides a formal guarantee of the correctness of our access control mechanisms. [ABSTRACT FROM AUTHOR]

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project's overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load. [ABSTRACT FROM AUTHOR]

Summary: With the development of the Internet of Things (IoT), the number of terminal devices is rapidly growing and at the same time, their security is facing serious challenges. For the industrial control system, there are challenges in detecting and preventing botnet. Traditional detection methods focus on capturing and reverse analyzing the botnet programs first and then parsing the extracted features from the malicious code or attacks. However, their accuracy is very low and their latency is relatively high. Moreover, they sometimes even cannot recognize the unknown botnets. The machine learning based detection methods rely on manual feature engineering and have a weak generalization. The deep learning‐based methods mostly rely on the system log, which does not take into account the multisource information such as traffic. To address the above issues, from the perspective of the botnet features, this paper proposes an intelligent detection method over parallel CNN‐LSTM, integrating the spatial and temporal features to identify botnets. Experimental demonstrate that the accuracy, recall, and F1‐score of our proposed method achieve up to over 98%, and the precision, 97.8%, is not the highest but reasonable. It reveals compared with the existing start‐of‐the‐art methods, our proposed method outperforms in the botnet detection. Our methodology's strength lies in its ability to harness the multifaceted information present in IoT traffic, offering a more nuanced and comprehensive analysis. The parallel CNN‐LSTM architecture ensures that spatial and temporal data are processed concurrently, preserving the integrity of the information and enabling a more robust detection mechanism. The result is a detection system that not only performs exceptionally well in a controlled environment but also holds promise for real‐world application, where the rapid and accurate identification of botnets is paramount. [ABSTRACT FROM AUTHOR]

False data injection (FDI) attacks have significantly threatened the security of the cyberphysical system (CPS). To ensure the stability of the CPS under FDI attacks, resilient model predictive control (MPC) has been extensively researched. However, most of the existing resilient MPC methods fail to consider the network resource limitation of the CPS, which may not be applicable in some real systems. Therefore, in this article, a resilient self-triggered (ST) MPC strategy is developed for a discrete-time nonlinear CPS under FDI attacks, which can not only ensure the system’s stability but also reduce resource consumption via decreasing the update frequency of MPC and economizing the utilization of the protection resource. First, an input signal reconstruction mechanism is designed based on key control data selection, which could reconstruct a feasible control sequence for the CPS even if the original ST control sequence is tampered with by FDI attacks. Then, a resilient ST-MPC algorithm based on the input signal reconstruction mechanism is proposed to weaken the adverse effects of FDI attacks and reduce resource consumption simultaneously. Moreover, the recursive feasibility of the resilient ST-MPC mechanism and input-to-state stability of the controlled system are respectively proved. Finally, the performance of the resilient ST-MPC mechanism is shown through a cart–damper–string system and an intelligent vehicle system. [ABSTRACT FROM AUTHOR]

This topical issue, Computational Approaches for Cyber Social Threats, showcases cutting-edge research that employs computational methods to tackle the pressing challenges of cyber social threats—such as fake news, disinformation, cyberbullying, hate speech, and online radicalization. In an increasingly interconnected digital landscape, these threats significantly jeopardize societal stability by eroding public trust, intensifying polarization, and widening social divides. The theme "Information Integrity During Crises" is highlighted to underscore the critical role of reliable information during global crises, when the spread of misinformation and disinformation is particularly pervasive. With a collection of nine outstanding papers, this topical issue advances our comprehension of how computational tools can address cyber social threats and protect information integrity during crises. [ABSTRACT FROM AUTHOR]

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Introduction: Agenda-setting is a collaborative communication strategy used by a clinician before or at the start of a clinical encounter to work together with the patient to "elicit, propose, and organize" topics to be discussed during the encounter. While clinical visit agenda-setting has been acknowledged as an important element of patient-centered communication, the effectiveness of agenda-setting interventions in improving healthcare outcomes is unclear. To our knowledge, no systematic review has examined clinical visit agenda-setting interventions. Methods and analysis: The primary aim of the systematic review will be to assess the effects of agenda-setting interventions on outcomes relating to the clinical encounter itself, patients, and clinicians, as well as any other study-specified outcomes. Our secondary aims will be to examine the characteristics and delivery attributes of agenda-setting interventions, as well as how agenda-setting has been operationalized and measured. We will search selected databases (APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Cumulative Index to Nursing and Allied Health Literature, MEDLINE via PubMed, ProQuest, Scopus, and Web of Science) and gray literature from inception until date of search. All studies comparing a clinical visit agenda-setting intervention with either usual care or another agenda-setting intervention will be included. Two independent reviewers will complete article screening and data extraction, with a third independent reviewer resolving any conflicts. We will assess all studies' methodological quality and the quality of their evidence using standardized criteria. If a sufficient number of studies report the same outcomes, we will pool their results and perform a meta-analysis of those outcomes. We will also synthesize all results qualitatively, regardless of whether we are able to complete a meta-analysis. PROSPERO registration number: CRD42023468045 [ABSTRACT FROM AUTHOR]

As a direct result of advancements in digital technology and the Internet, the copyright protection and information integrity of multimedia that are being published across the Internet have emerged as a major and urgent issue that needs to be addressed. The technique of digital watermarking may be used to protect intellectual property. In terms of authentication, resilience, storage, and capacity of digital watermarking information, there is still room for development. Blockchain's potential in video copyright protection and management applications has motivated researchers. Copyright owners and consumers may now communicate directly via copyright protection apps built on the blockchain, eliminating the need for distributers and the associated fees. Nonetheless, the current blockchain–based video watermarking solutions require storing a significant number of coordinates depending on the watermark size and are susceptible to video frame attacks on the video frame texture region. This study proposes an enhanced video copyright management approach that incorporates digital watermarking, the blockchain, and a perceptual hash function. Watermark information is stored on a blockchain structure, which also acts as a timestamp for verification purposes. To verify watermark data without the source video, a perceptual hash function is employed to compute a hash value based on the structural information of video frames. The contribution is in learning how to extract a hash function from a small number of video frames that still adequately represent a large amount of video content while also reducing the number of unnecessary video frames and the amount of computation required to summarize and index that content in a blockchain. This expedites the dissemination of copyrighted works and increases their security and readability, hence facilitating their circulation on the Internet. Our experimental results demonstrate that this approach is memory efficient, as it only needs to store one key for each key frame, regardless of the size of the watermark. Additionally, the overall robustness is greatly improved by using the blockchain's random hash function. Therefore, new and important advancements in video watermarking have been realized because of this effort. [ABSTRACT FROM AUTHOR]

In the quest for computational efficiency, binary neural networks (BNNs) have emerged as a promising paradigm, offering significant reductions in memory footprint and computational latency. In traditional BNN implementation, the first and last layers are typically full-precision, which causes higher logic usage in field-programmable gate array (FPGA) implementation. To solve these issues, we introduce a novel approach named Ponte (Represent Totally Binary Neural Network Toward Efficiency) that extends the binarization process to the first and last layers of BNNs. We challenge the convention by proposing a fully binary layer replacement that mitigates the computational overhead without compromising accuracy. Our method leverages a unique encoding technique, Ponte::encoding, and a channel duplication strategy, Ponte::dispatch, and Ponte::sharing, to address the non-linearity and capacity constraints posed by binary layers. Surprisingly, all of them are back-propagation-supported, which allows our work to be implemented in the last layer through extensive experimentation on benchmark datasets, including CIFAR-10 and ImageNet. We demonstrate that Ponte not only preserves the integrity of input data but also enhances the representational capacity of BNNs. The proposed architecture achieves comparable, if not superior, performance metrics while significantly reducing the computational demands, thereby marking a step forward in the practical deployment of BNNs in resource-constrained environments. [ABSTRACT FROM AUTHOR]

Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information transmission, this paper proposes a high-quality image compression algorithm based on unsupervised learning. Among them, a content-weighted autoencoder network is proposed to achieve image compression coding on the basis of a smaller bit rate to solve the entropy rate optimization problem. Binary quantizers are used for coding quantization, and importance maps are used to achieve better bit allocation. The compression rate is further controlled and optimized. A multi-scale discriminator suitable for the generative adversarial network image compression framework is designed to solve the problem that the generated compressed image is prone to blurring and distortion. Finally, through training with different weights, the distortion of each scale is minimized, so that the image compression can achieve a higher quality compression and reconstruction effect. The experimental results show that the algorithm model can save the details of the image and greatly compress the memory of the image. Its advantage is that it can expand and compress a large number of images quickly and efficiently and realize the efficient processing of image compression. [ABSTRACT FROM AUTHOR]

Accurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks. [ABSTRACT FROM AUTHOR]