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Comprehensive Summary We present a facile synthetic strategy to create mesoporous Cu2O nanocrystals with tunable pore structures and surface functional groups of amine derivatives for efficient and preferable electrochemical conversion of CO2 into ethylene. The structural characteristics of these Cu2O nanocrystals can be manipulated using a set of amine derivatives, such as pyridine, 4,4'‐bipyridine, and hexamethylenetetramine, during the oxidative etching process of Cu nanocrystals by bubbling gaseous oxygen in N,N‐dimethylformamide solution. These amine derivatives not only serve as surface functional groups but also significantly affect the resulting pore structures. The synergistic effect of pore structure confinement and surface amine functionalization leads to the superb Faradaic efficiency (FE) of 51.9% for C2H4, respectively, together with the C2H4 partial current density of –209.4 mA·cm−2 at –0.8 V vs. reversible hydrogen electrode (RHE). The relatively high selectivity towards C2H4 was investigated using DFT simulations, where 4,4'‐bipyridine functionalized Cu2O seemed to favor the C2H4 formation with the low free energy of the intermediates. This study provides a feasible strategy to manipulate the pore structure and surface functionalization of mesoporous Cu2O nanocrystals by regulating the oxidative etching process, which sheds light on the rational preparation of high‐performance CO2RR electrocatalysts. [ABSTRACT FROM AUTHOR]

This study examines the past, present, and future of Supply Chain Resilience (SCR) research in the context of COVID-19. Specifically, a total of 1717 papers in the SCR field are classified into 11 thematic clusters, which are subsequently verified by a supervised machine learning approach. Each cluster is then analyzed within the context of COVID-19, leading to the identification of three associated capabilities (i.e., interconnectedness, transformability, and sharing) on which firms should focus to build a more resilient supply chain in the post-COVID world. The derived insights offer invaluable guidance not only for practicing managers, but also for scholars as they design their future research projects related to SCR for greatest impact. [ABSTRACT FROM AUTHOR]

Background: Dementia, mainly Alzheimer's disease (AD) and vascular dementia (VaD), remains a global health challenge. Previous studies have demonstrated the benefits of acupuncture therapy (AT) in improving dementia. Nevertheless, the therapeutic targets and integrated biological mechanisms involved remain ambiguous. Objective: To identify therapeutic targets and biological mechanisms of AT in treating dementia by integrated analysis strategy. Methods: By the identification of differentially expressed genes (DEGs) of AD, VaD, and molecular targets of AT active components, the acupuncture therapeutic targets associated with the biological response to AD and VaD were extracted. Therapeutic targets-based functional enrichment analysis was conducted, and multiple networks were constructed. AT-therapeutic crucial targets were captured by weighted gene co-expression network analysis (WGCNA). The interactions between crucial targets with AT active components were verified by molecular docking. Results: Our results demonstrated that 132 and 76 acupuncture therapeutic targets were associated with AD and VaD. AT-therapeutic crucial targets including 58 for AD and 24 for VaD were captured by WGCNA, with 11 in shared, including NMU, GRP, TAC1, ADRA1D, and SST. In addition, 35 and 14 signaling pathways were significantly enriched by functional enrichment analysis, with 6 mutual pathways including neuroactive ligand-receptor interaction, GABAergic synapse, calcium signaling pathway, cAMP signaling pathway, chemokine signaling pathway, and inflammatory mediator regulation of TRP channels. Conclusion: The improvement of AD and VaD by AT was associated with modulation of synaptic function, immunity, inflammation, and apoptosis. Our study clarified the therapeutic targets of AT on dementia, providing valuable clues for complementing and combining pharmacotherapy. [ABSTRACT FROM AUTHOR]

In this paper, we consider the inverse problem for identifying the initial value problem of the time–space fractional nonlinear diffusion equation. The uniqueness of the solution is proved by taking the fixed point theorem of Banach compression, and the ill-posedness of the problem is analyzed through the exact solution. The quasi-boundary regularization method is chosen to solve the ill-posed problem, and the error estimate between the regularization solution and the exact solution is given. Moreover, several numerical examples are chosen to prove the effectiveness of the quasi-boundary regularization method. Finally, our method can be used to solve high dimensional time–space fractional nonlinear diffusion equation, especially in cylindrical and spherical symmetric regions. [ABSTRACT FROM AUTHOR]

Supply chain finance (SCF) provides credit for small and medium-sized enterprises with low credit lines and small financing scales. The resulting financial credit data and related business transaction data are highly confidential and private. However, traditional SCF management schemes use third-party platforms and centralized designs that cannot achieve highly reliable secure storage and fine-grained access control. To address such a need, we propose Fabric-SCF, designing and implementing a Blockchain-based secure storage system by utilizing distributed consensus to realize data security, traceability, and immutability. The attribute-based access control model is deployed for access control, also utilizing smart contracts to define system processes and access policies to ensure the system's efficient operation. To verify the performance of Fabric-SCF, two sets of simulation experiments are designed its effectiveness. Experimental results show that Fabric-SCF achieves dynamic and fine-grained access control while maintaining high throughput in a simulated real-world operating scenario. [ABSTRACT FROM AUTHOR]

Vehicle automation is a revolutionary trend in urban mobility, and robotaxi—that is, a service providing shared autonomous vehicles (AVs)—are expected to reduce energy consumption and hinder the growth of car ownership. This study investigates consumers' intention to use robotaxi and to buy AVs, particularly focusing on the roles of perceived benefits to multiple stakeholders (i.e., users and pedestrians) and consumers' environmental awareness in the adoption process. Based on a nationwide survey in China, our empirical analysis reveals the following important findings: (1) users' perceptions of the benefits of AVs for pedestrians and users influence their attitude toward and intention to use robotaxi and to buy AVs; (2) perceived drawbacks are negatively related to intention to buy AVs and to use robotaxi, but not to attitude toward AVs; (3) attitude positively predicts intention to use robotaxi, but not intention to buy AVs; (4) intention to use robotaxi is positively associated with intention to buy AVs; (5) incorporating the perceived benefits of AVs for pedestrians significantly improves the behavioral model; and (6) environmental awareness strengthens the positive influence of perceived benefits on attitude, and exacerbates the negative association between perceived drawbacks and intention to use robotaxi. We contribute to the literature on AV adoption by furthering understanding of the influence of benefits to multiple stakeholders and environmental awareness; we also present key business and policy implications relating to facilitating the transition of AVs. • Perceived drawbacks are insignificantly related to attitude, but not AV adoption. • Positive attitude predicts intention to use a robotaxi, but not to buy an AV. • Intention to use a robotaxi contributes to intention to buy an AV. • Environmental awareness moderates several key linkages in the adoption mechanism. • Perceived benefits of AVs for pedestrians explains attitude and adoption intention. [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]

Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning (BCFL) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL. Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL. Finally, we summarize the industrial application scenarios of BCFL. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases (NDDs) remain a global health challenge. Previous studies have reported potential links between environmental factors and NDDs, however, findings remain controversial across studies and elusive to be interpreted as evidence of robust causal associations. In this study, we comprehensively explored the causal associations of the common environmental factors with major NDDs including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS), based on updated large-scale genome-wide association study data through two-sample Mendelian randomization (MR) approach. Our results indicated that, overall, 28 significant sets of exposure-outcome causal association evidence were detected, 12 of which were previously underestimated and newly identified, including average weekly beer plus cider intake, strenuous sports or other exercises, diastolic blood pressure, and body fat percentage with AD, alcohol intake frequency with PD, apolipoprotein B, systolic blood pressure, and forced expiratory volume in 1 s (FEV 1) with ALS, and alcohol intake frequency, hip circumference, forced vital capacity, and FEV 1 with MS. Moreover, the causal effects of several environmental factors on NDDs were found to overlap. From a triangulation perspective, our investigation provided insights into understanding the associations of environmental factors with NDDs, providing causality-oriented evidence to establish the risk profile of NDDs. • Exposome-wide causal associations of neurodegenerative diseases were conducted. • 28 sets of exposure-outcome causal associations were detected. • 12 sets of exposure-outcome causal associations were newly identified. • The effects of several environmental factors on neurodegenerative diseases overlap. [ABSTRACT FROM AUTHOR]

Stablecoins have enabled the rapid growth of decentralized payments and the birth of the new generation of payment system with cryptocurrencies and Blockchain technology. However, while some studies have investigated the price-performance or role of Stablecoins, a comprehensive overview focused on the full context, stabilization mechanisms, and payment applicability of Stablecoins is still missing. This paper first summarizes the definition and the current state of Stablecoins and then presents the relative ecosystem. More precisely, after summarizing the system structure and stability mechanism, we discuss its applicability for payment. The overview proposed in this paper represents the most comprehensive summary of research on Stablecoin. We identified that the asset-backed Stablecoins is the most efficient and widely used Stablecoins. On the other hand, the cryptocurrency-backed Stablecoins are currently the most balanced applicable to the original concept of Stablecoins. Again, the algorithm-backed Stablecoins have the most potential for development, though they hesitated without real collateral or deposit reserves and are potentially prone to collapse. Finally, we conclude and present possible future trends for Stablecoins. • This article represents the first overview of Stablecoins in a comprehensive perspective that investigates extensive ecosystem, mechanism, applicability as payment method, related monetary policy, use case, and risk considerations in this field. • Study focuses on the structure, stabilization mechanisms, and applicability of Stablecoins as a payment system. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) remains a global health challenge. Previous studies have reported linkages between AD and multiple behavioural risk exposures, however, the underlying biological mechanisms and crucial genes of gene expression patterns driven by behavioural risks on the onset or progression of AD remains ambiguous. In this study, we performed an integrated analysis on the influence of behavioural risks including smoking, excessive alcohol consumption, physical inactivity, and non-healthy dietary pattern on AD with a comprehensive strategy. Our results demonstrated that multiple behavioural risk exposures could independently or collectively influence diverse hierarchical levels of gene expression patterns through multiple biological mechanisms such as Wnt, mitogen-activated protein kinase (MAPK), AMP-activated protein kinase (AMPK), nuclear factor (NF)-κB, phosphatidylinositol 3-kinase (PI3K)-Akt, and insulin (INS) signalling pathways-mediated pathological processes, thereby prodromally or intermediately impacting AD. Our study provided insights into understanding the association of behavioural risk exposures with AD and informative support for further studies. [Display omitted] • Common behavioural risks may influence Alzheimer's disease (AD). • The changes in gene expression patterns by behavioural risks for AD were identified. • The integrated biological mechanisms of behavioural risks on AD were investigated. • The interactions of crucial genes of behavioural risks for AD were uncovered. • Diverse hierarchical levels of genes impacting AD by behavioural risks were revealed. [ABSTRACT FROM AUTHOR]

Knowledge reasoning technology can infer new knowledge based on the existing entity relationship information in the knowledge graph to complement the knowledge graph. In the existing knowledge reasoning methods based on multi-step relationship paths, the contributions of entities and relationships in the relationship paths are not distinguished, nor are multiple relationship paths comprehensively considered. To solve these problems, we propose a knowledge reasoning model based on Bi-directional Short-Term Memory and Attention Mechanism. Firstly, the neighborhood, entity semantic category and relationship path information are integrated in the knowledge graph, and the Neighborhood Semantic Path Network model (NSPN) is constructed to obtain a hybrid representation of the multi-step relationship path. Secondly, the loss function and training process of NSPN are introduced. Finally, comparative experiments are carried out on the public dataset, and the results show that our model is superior to other models in relation prediction tasks and link prediction tasks, improves the computational efficiency and data sparsity, and provides a new idea for knowledge reasoning methods. [ABSTRACT FROM AUTHOR]

Soot formation is one major challenge in developing clean and efficient utilizes based on hydrocarbon fuels. We report the physical and chemical characteristics of a set of soot samples obtained from coal and coal mixed with iron nitrate pyrolysis on a drop tube furnace at different reaction times. Yimin lignite was chosen for the experiments, using acid washing to remove its metals and adding iron by impregnation. The pyrolysis processes of the two coal samples were analyzed by TG-DTG, DSC and MS. Characterization was carried out with transmission electron microscopy(TEM), Raman spectrometer, X-ray photoelectron spectroscopy(XPS), and infrared spectroscopy (FTIR). The results show that the iron nitrate significantly reduces the yield of soot and decreases the sizes of the primary soot particles. This inhibition effect on soot production increase with reaction time. With the addition of iron nitrate, the carbon lamellae in soot are more graphitized, but the primary particle surface roughness increases. In addition, the addition of iron results in more aliphatic functional groups and C–O on the surface of the soot. • Iron inhibited soot formation and positively correlated with pyrolysis time. • Iron affects extra-core lamellae accumulation and reduces particle size. • Iron promotes soot oxidation, and divot-like structures appear on the surface. • Iron promotes C–O and aliphatic C–H formation during soot formation. [ABSTRACT FROM AUTHOR]

With the rapid advances in computing and networking technologies, there have led to the creation of a novel and booming set of payment services, known as cryptocurrencies or digital tokens. Many are available for exchanges worldwide, inviting investors to trade with costs, quality, and safety that vary widely. Nevertheless, Blockchain transaction data have complex time and space dependencies, and historical transaction data reflect the transaction trends of cryptocurrencies to a certain extent, thus identifying the illegal behaviors of transactions such as money laundering more at the earliest. In this article, we propose a novel cryptocurrency abnormal transaction detection method with spatio-temporal and global representation, namely CTDM. CTDM combines EvolveGCN with MGU and global representations to achieve better performance. In addition, CTDM needs fewer learning parameters through MGU, which leads to less training time. Experimental results show that the proposed CTDM method outperforms SOTA Blockchain abnormal transaction detection methods. [ABSTRACT FROM AUTHOR]

Soot formation is one major challenge in developing clean and efficient utilizes based on hydrocarbon fuels. In this work, ferric nitrate was selected as an iron additive to be loaded into pulverized coal, and the pyrolysis experiment with an inert atmosphere was conducted in a high-temperature drop tube furnace (DTF). The physical and chemical character of the soot obtained from coal samples with different iron content were analyzed to explore the effect of the soot format process. The results showed that the soot yield was reduced 7% and 23%, respectively, at the same conditions when the content of iron in the coal sample was 0.51% and 0.87%. After iron was added, the diameter of soot primary particles was reduced from 50.8 nm to 37.4 and 37.8 nm, and the D f of soot aggregates increased, indicating that iron inhibited the growth process of soot primary particles and promoted the agglomeration between particles. Thus, the degree of graphitization of soot increased. In addition, the content of C–O functional groups in soot derived from coal with iron addition increased significantly, which implied that the participation of iron enhanced the oxidation reaction of soot. • Iron inhibits the coal soot formation, and the average diameter of soot primary particles significantly reduced. • Iron promotes oxidation of soot, especially the amorphous carbon component. • Iron changes the structure of soot crystallites, and enhances the degree of graphitization of soot. • Iron promote C–O formation during the process of soot formation. [ABSTRACT FROM AUTHOR]

• A Blockchain-based secure storage and sharing scheme for ELRs in MOOCs learning systems is proposed. • Aimed to leverage Blockchain, the proposed scheme can support efficient conditional anonymity, secure storage, and share without requiring complex cryptographic computations. • The security analysis shows that the proposed scheme achieves legitimate security assurance, outperforming other similar works. Massive Open Online Courses (MOOCs) have become a paramount online learning approach for flexible learning methods and extensive learning courses. Different from the traditional learning method, MOOCs advocate completing the learning process through online devices. Electronic Learning Records (ELRs) are vital for learners as compelling evidence of the learning process, generally are stored in a cloud data center. However, with trustless third-party storage, the security and privacy of ELRs cannot be guaranteed. Due to such, we propose a Blockchain-based secure storage and sharing scheme for ELRs in MOOCs learning systems. Designed to take advantage of blockchain, the proposed solution can support efficient conditional anonymity, secure storage, and sharing without the need for sophisticated cryptographic calculations. The experimental results and the security analysis shows that the proposed scheme achieves legitimate security assurance and outperforms other similar works. [ABSTRACT FROM AUTHOR]

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study's findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach. [ABSTRACT FROM AUTHOR]

Pests are the main threats to crop growth, and the precision classification of pests is conducive to formulating effective prevention and governance strategies. In response to the problems of low efficiency and inadaptability to the large‐scale environment of existing pest classification methods, this paper proposes a new pest classification method based on a convolutional neural network (CNN) and an improved Vision Transformer model. First, the MMAlNet is designed to extract the characteristics of the identification object from different scales and finer granularity. Then, a classification model called DenseNet Vision Transformer (DNVT) combining a CNN and an improved vision transformer model is proposed. The proposed DNVT captures both long distance dependencies and local characteristic modelling capabilities, which can effectively improve pest classification accuracy. Finally, the ensemble learning algorithm is used to learn MMAlNet and DNVT classification forecasts for soft voting, further enhancing the classification accuracy of pests. The simulation experiment results on the D0 and IP102 datasets show that the proposed method attained a maximum classification of 99.89 and 74.20%, respectively, which is better than other state‐of‐the‐art methods and has a high practical application value. [ABSTRACT FROM AUTHOR]

Knowledge reasoning technology can infer new knowledge based on the existing entity relationship information in the knowledge graph to complement the knowledge graph. In the existing knowledge reasoning methods based on multi-step relationship paths, the contributions of entities and relationships in the relationship paths are not distinguished, nor are multiple relationship paths comprehensively considered. To solve these problems, we propose a knowledge reasoning model based on Bi-directional Short-Term Memory and Attention Mechanism. Firstly, the neighborhood, entity semantic category and relationship path information are integrated in the knowledge graph, and the Neighborhood Semantic Path Network model (NSPN) is constructed to obtain a hybrid representation of the multi-step relationship path. Secondly, the loss function and training process of NSPN are introduced. Finally, comparative experiments are carried out on the public dataset, and the results show that our model is superior to other models in relation prediction tasks and link prediction tasks, improves the computational efficiency and data sparsity, and provides a new idea for knowledge reasoning methods. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide empirical evidence for the sharing-based product service system (sharing-based PSS), a new type of PSS, to achieve sustainable performance in sharing economy context. A multiple case study method was applied, in which 20 semi-structured interviews were conducted with key respondents from three transport sharing economy companies (i.e., OfO, Mobike, and Man-bang) in China. We found that the structural and infrastructural aspects of the service delivery system design influence the achievement of sustainability for sharing economy platforms. Moreover, by applying contingency theory, this study identified goal congruence and application of information and communications technology as contingent factors that affect the relationship between the service delivery system design and the sustainability initiatives. This study provides insightful knowledge into the sharing-based PSS and therefore has enormous practical implications for sharing economy platforms. • We analyze sustainability of sharing-based product service systems (PSS). • 20 interviews with key people from three sharing economy companies were conducted. • Structural and infrastructural aspects affect sustainability of sharing economy firms. • Goal congruence and application of ICT as contingent factors in sharing economy. [ABSTRACT FROM AUTHOR]

In this paper, we study an inverse problem to identify the initial value problem of the homogeneous Rayleigh–Stokes equation for a generalized second-grade fluid with the Riemann–Liouville fractional derivative model. This problem is ill posed; that is, the solution (if it exists) does not depend continuously on the data. We use the Landweber iterative regularization method to solve the inverse problem. Based on a conditional stability result, the convergent error estimates between the exact solution and the regularization solution by using an a priori regularization parameter choice rule and an a posteriori regularization parameter choice rule are given. Some numerical experiments are performed to illustrate the effectiveness and stability of this method. [ABSTRACT FROM AUTHOR]

In this manuscript, we study the inverse problem for identifying the initial value of a time-fractional diffusion equation in an axisymmetric region. This is an ill-posed problem, i.e., the solution does not depend continuously on the data. We choose the Landweber iterative regularization method to solve this problem. Under the a priori and the a posteriori regularization parameter choice rules, we present the error estimates between the regularization solutions and the exact solution. We present some examples to show this method's effectiveness. [ABSTRACT FROM AUTHOR]

Fog computing, as a new distributed computing framework, extends the tasks originally done in the cloud data center to the edge of the network and brings more serious security challenges while providing convenience. Abnormal network traffic detection is an effective means to defense malicious behavior, can detect a variety of known attacks. Although the application of deep learning method in the field of network abnormal traffic detection is easier than traditional machine learning methods, there are still problems of poor recognition accuracy and false alarm rate. In this paper, we use the semi-supervised network anomaly detection model (NADLA) that combines the long-short-term memory neural network method and the self-encoder method to solve this problem. NADLA analyzes network traffic through the time characteristics and behavior characteristics of traffic, and optimizes the accuracy and false alarm rate of network traffic classification. In addition, we improved the preprocessing method to improve the sensitivity of the trained model to network abnormal traffic. The NADLA model is tested on NSL-KDD dataset, and the results show that the proposed model can improve the accuracy and F1-value of network anomaly traffic detection. [ABSTRACT FROM AUTHOR]

Medical data involves a large amount of personal information and is highly privacy sensitive. In the age of big data, the increasing informatization of healthcare makes it vital that medical information is stored securely and accurately. However, current medical information is subject to the risk of privacy leakage and difficult to share. To address these issues, this paper proposes a healthcare information security storage solution based on hyperledger fabric and the attribute-based access control framework. The scheme first utilizes attribute-based access control, which allows dynamic and fine-grained access to medical information, and then stores the medical information in the blockchain, which can be secured and tamper-proof by formulating corresponding smart contracts. In addition, this solution also incorporates IPFS technology to relieve the storage pressure of the blockchain. Experiments show that the proposed scheme combining access control of attributes and blockchain technology in this paper can not only ensure the secure storage and integrity of medical information but also has a high throughput when accessing medical information [ABSTRACT FROM AUTHOR]

Network intrusion detection, which takes the extraction and analysis of network traffic features as the main method, plays a vital role in network security protection. The current network traffic feature extraction and analysis for network intrusion detection mostly uses deep learning algorithms. Currently, deep learning requires a lot of training resources and has weak processing capabilities for imbalanced datasets. In this paper, a deep learning model (MFVT) based on feature fusion network and vision transformer architecture is proposed, which improves the processing ability of imbalanced datasets and reduces the sample data resources needed for training. Besides, to improve the traditional raw traffic features extraction methods, a new raw traffic features extraction method (CRP) is proposed, and the CPR uses PCA algorithm to reduce all the processed digital traffic features to the specified dimension. On the IDS 2017 dataset and the IDS 2012 dataset, the ablation experiments show that the performance of the proposed MFVT model is significantly better than other network intrusion detection models, and the detection accuracy can reach the state-of-the-art level. And, when MFVT model is combined with CRP algorithm, the detection accuracy is further improved to 99.99%. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence (AI) technology has enabled large-scale AI applications to land in the market and practice. However, plenty of security issues have been exposed to society while AI technology has brought many conveniences to humankind, especially for the chatbot with online learning. This paper proposes a speech censorship chatbot system with reinforcement learning, which is mainly composed of two parts: the aggressive speech censorship model and the speech purification model. The aggressive speech censorship can combine the context of user input sentences to detect aggressive speech and respond to the rapid evolution of aggressive speech. According to the situation of the chatbot that is polluted by large numbers of aggressive speech, the speech purification model has the capacity to "forget" the learned malicious data through reinforcement learning rather than rolling back to the early versions. In addition, by integrating few-shot learning, the speed of speech purification is accelerated while reducing the influence on the quality of replies. The experimental results show that our proposed method reduces the probability of generating aggressive speeches and that the integration of the few-shot learning improves the training speed rapidly while effectively slowing down the decline in BLEU values. [ABSTRACT FROM AUTHOR]

In order to study the role of metal sodium in the spray pyrolysis of biomass tar, this paper designs a sodium-containing naphthalene pyrolysis system (NSS) and a pure naphthalene pyrolysis system (PNS) using naphthalene as the carbon source and sodium chloride as the sodium metal donor for comparison. This enables an exploration of the effect of sodium on the initial nucleation of carbon fumes formed by naphthalene pyrolysis using reaction molecular dynamics (ReaxFF MD). The simulation results show that NSS undergoes pyrolysis reactions earlier and faster than PNS at the same temperature. Simulated at 3250 K temperature for 2 ns, the naphthalene pyrolysis consumption rate of the NSS was faster than that of the PNS, and the addition of sodium atoms during the condensation process provided more active sites and accelerated the condensation of macromolecular products. Moreover, Na+ and carbon rings form a Na+-π structure to promote the bending of graphite lamellae to facilitate the formation of carbon nuclei. Molecular dynamics simulations were used to simulate the formation of carbon nuclei during the initial stage of naphthalene pyrolysis, revealing that the mechanism of sodium salt catalyzed the acceleration of organic matter pyrolysis from a microscopic visualization perspective. [ABSTRACT FROM AUTHOR]

News media agencies are known to publish misinformation, disinformation, and propaganda for the sake of money, higher news propagation, political influence, or other unfair reasons. The exponential increase in the use of social media has also contributed to the frequent spread of fake news. This study extends the concept of symmetry into deep learning approaches for advanced natural language processing, thereby improving the identification of fake news and propaganda. A hybrid HyproBert model for automatic fake news detection is proposed in this paper. To begin, the proposed HyproBert model uses DistilBERT for tokenization and word embeddings. The embeddings are provided as input to the convolution layer to highlight and extract the spatial features. Subsequently, the output is provided to BiGRU to extract the contextual features. The CapsNet, along with the self-attention layer, proceeds to the output of BiGRU to model the hierarchy relationship among the spatial features. Finally, a dense layer is implemented to combine all the features for classification. The proposed HyproBert model is evaluated using two fake news datasets (ISOT and FA-KES). As a result, HyproBert achieved a higher performance compared to other baseline and state-of-the-art models. [ABSTRACT FROM AUTHOR]

The low-temperature heat-exchange section of boiler tails differs from other sections by absorbing latent heat. The subcooling of water vapor droplets in natural gas flue gas in boiler tails is becoming increasingly important. However, little is known about heat transfer and the subcooling mechanism. We report an airtight condensation cavity table and use thermal imaging to monitor the surface temperature and time-dependent size of single droplets. We observed a temperature gradient in droplets; three distinct size distributions; a change in infiltration mode from Wenzel to Cassie when non-condensing gas was introduced; and accelerated departure of droplets in a sound field. [ABSTRACT FROM AUTHOR]

With the rapid development of network technology, a variety of new malicious attacks appear while attack methods are constantly updated. As the attackers exploit the vulnerabilities of popular third-party components to invade target websites, further improving the classification accuracy of malicious network traffic is the key to improving the performance of abnormal traffic detection. Existing intrusion detection systems may suffer from incomplete feature extraction and low classification accuracy. Thus, this paper proposes an efficient hybrid parallel deep learning model (HPM) for intrusion detection based on margin learning. First, HPM constructs two parallel CNN architectures and fuses the spatial features obtained through full convolution. Secondly, the temporal information of the fused features is parsed separately using two parallel LSTMs. Finally, the extracted spatial-temporal features are fed into the CosMargin classifier for classification detection after global convolution and global pooling. Besides, this paper proposes an improved traffic feature extraction method, which not only reduces redundant features but also speeds up the convergence speed of the network. In the experiment, our HPM has achieved 99% detection accuracy of each malicious class, ranging from 5%–10% improvement with other models, which demonstrates the superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Objective to examine the association between different patterns of impaired lung function with the incident risk of dementia and magnetic resonance imaging (MRI)-based brain structural features. Methods in UK Biobank, a total of 308,534 dementia-free participants with valid lung function measures (forced expiratory volume in 1 s [FEV1] and forced vital capacity [FVC]) were included. Association was assessed using Cox proportional hazards regression model. Furthermore, the association between impaired lung function and brain MRI biomarkers related to cognitive function was analysed among 30,159 participants. Results during a median follow-up of 12.6 years, 3,607 incident all-cause dementia cases were recorded. Restrictive impairment (hazard ratio [HR], 1.42; 95% confidence interval [CI], 1.27–1.60) and obstructive impairment (HR, 1.28; 95% CI, 1.15–1.42) were associated with higher risk of all-cause dementia. The restricted cubic splines indicated FEV1% predicted and FVC % predicted had reversed J-shaped associations with dementia. Participants with impaired lung function have higher risks of all-cause dementia across all apolipoprotein E (APOE) risk categories, whereas associations were stronger among those of low APOE risk (P for interaction = 0.034). In addition, restrictive and obstructive impairment were linked to lower total (β: −0.075, SE: 0.021, P fdr = 0.002; β: −0.033, SE: 0.017, P fdr = 0.069) and frontoparietal grey matter volumes, higher white matter hyperintensity, poorer white matter integrity, lower hippocampus (β: –0.066, SE: 0.024, P fdr = 0.017; β: –0.051, SE: 0.019, P fdr = 0.019) and other subcortical volumes. Conclusions participants with restrictive and obstructive impairments had a higher risk of dementia. Brain MRI indices further supported adverse effects and provided insight into potential pathophysiology biomarkers. [ABSTRACT FROM AUTHOR]

• Cracking experiments were performed with pyrene instead of coal tar. The reactants are simple and the process can be controlled. • The effect of Na on the soot formation process was analyzed by a combination of experiments and simulations. • Na promoted the oxidation of soot, promoted the formation of five-membered rings in the microchip layer, and increased the curvature of the microchip layer. This work aims to investigate the effects of Na on the characteristics of coal-derived soot. Soot, which has long been regarded as an unwelcome source of air pollution, has been classified as a pollutant. However, with the proposed clean and efficient utilization of coal, soot has been found to be effectively inhibited by adding metal catalysts and changing the reaction environment, and it can even realize the transformation of pollutants to functional materials. To understand the effect on physiochemical properties, pyrene was used as a coal tar substitute, and the structural transformation of soot was thoroughly investigated by DFT calculation method in combination with elemental analysis, transmission electron microscopy, X-ray diffraction, Raman spectroscopy, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The presence of Na changed the π electron distribution of benzene ring, promoted the dehydrogenation condensation and oxidation exfoliation of the surface microchip layer, and accelerated the condensation of PAHs and the formation of five-membered ring in the microchip layer. Na participates in the oxidation and growth process of soot, which can regulate the micromorphology of soot. With the participation of Na, the diameter of soot particles decreases, the microcrystal length and curvature become larger, and the internal holes increase. [ABSTRACT FROM AUTHOR]

In this paper, the inverse problem for identifying the initial value of a time fractional nonhomogeneous diffusion equation in a columnar symmetric region is studied. This is an ill-posed problem, i.e., the solution does not depend continuously on the data. The fractional Tikhonov regularization method is applied to solve this problem and obtain the regularization solution. The error estimations between the regularization solution and the exact solution are also obtained under the priori and the posteriori regularization parameter choice rules, respectively. Some examples are given to show this method's effectiveness. [ABSTRACT FROM AUTHOR]

In order to study the effect of a high twist rate propeller on the flow field characteristics of pseudoplastic non-Newtonian fluids, the numerical simulation method was used to analyze the mixing flow field of pseudoplastic non-Newtonian fluids at different concentrations in this paper. By changing the rotational speed and the blade installation height, the vorticity, turbulent energy, mixing power consumption, mixing time and mixing energy of the flow field were analyzed. By analyzing and comparing the research results, it was found that increasing the mixing propeller speed can effectively improve the mixing effect. Single-layer arrangement of mixing propeller is not suitable to be placed close to the bottom of the tank, and the mixing of the upper flow field is weaker. Under the same conditions, when the viscosity of pseudoplastic non-Newtonian fluid is increased, the high vorticity and high turbulence energy area is reduced to the mixing propeller area, and the time required for mixing 1.25% CMC solution is 246 times longer than that for mixing 0.62% CMC solution and the required mixing energy also increases sharply. The accuracy of the numerical simulation was verified by experiments. Considering the mixing effect and the mixing power consumption, the single-layer arrangement propeller is more suitable for mixing pseudoplastic non-Newtonian fluids with mass fraction of 0.62% CMC or below. This study can provide a reference for the practical application of propeller mixers to mix pseudoplastic non-Newtonian fluids. [ABSTRACT FROM AUTHOR]

With the rapid development of network technology, a variety of new malicious attacks appear while attack methods are constantly updated. As the attackers exploit the vulnerabilities of popular third-party components to invade target websites, further improving the classification accuracy of malicious network traffic is the key to improving the performance of abnormal traffic detection. Existing intrusion detection systems may suffer from incomplete feature extraction and low classification accuracy. Thus, this paper proposes an efficient hybrid parallel deep learning model (HPM) for intrusion detection based on margin learning. First, HPM constructs two parallel CNN architectures and fuses the spatial features obtained through full convolution. Secondly, the temporal information of the fused features is parsed separately using two parallel LSTMs. Finally, the extracted spatial-temporal features are fed into the CosMargin classifier for classification detection after global convolution and global pooling. Besides, this paper proposes an improved traffic feature extraction method, which not only reduces redundant features but also speeds up the convergence speed of the network. In the experiment, our HPM has achieved 99% detection accuracy of each malicious class, ranging from 5%–10% improvement with other models, which demonstrates the superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is traditionally considered a glycolytic tumor with a poor prognosis while lacking targeted therapies. Here we show that high expression of dihydrolipoamide S-succinyltransferase (DLST), a tricarboxylic acid (TCA) cycle enzyme, predicts poor overall and recurrence-free survival among TNBC patients. DLST depletion suppresses growth and induces death in subsets of human TNBC cell lines, which are capable of utilizing glutamine anaplerosis. Metabolomics profiling reveals significant changes in the TCA cycle and reactive oxygen species (ROS) related pathways for sensitive but not resistant TNBC cells. Consequently, DLST depletion in sensitive TNBC cells increases ROS levels while N-acetyl-L-cysteine partially rescues cell growth. Importantly, suppression of the TCA cycle through DLST depletion or CPI-613, a drug currently in clinical trials for treating other cancers, decreases the burden and invasion of these TNBC. Together, our data demonstrate differential TCA-cycle usage in TNBC and provide therapeutic implications for the DLST-dependent subsets. Shen and Korm et al. investigate the role of dihydrolipoamide S-succinyltransferase (DLST), a tricarboxylic acid (TCA) cycle enzyme, in the metabolic heterogeneity of triple negative breast cancer (TNBC) cell lines. They show that either depletion or inhibition of DLST in the dependent cells leads to decreased invasion and metastasis in vitro and in vivo. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is traditionally considered a glycolytic tumor with a poor prognosis while lacking targeted therapies. Here we show that high expression of dihydrolipoamide S-succinyltransferase (DLST), a tricarboxylic acid (TCA) cycle enzyme, predicts poor overall and recurrence-free survival among TNBC patients. DLST depletion suppresses growth and induces death in subsets of human TNBC cell lines, which are capable of utilizing glutamine anaplerosis. Metabolomics profiling reveals significant changes in the TCA cycle and reactive oxygen species (ROS) related pathways for sensitive but not resistant TNBC cells. Consequently, DLST depletion in sensitive TNBC cells increases ROS levels while N-acetyl-L-cysteine partially rescues cell growth. Importantly, suppression of the TCA cycle through DLST depletion or CPI-613, a drug currently in clinical trials for treating other cancers, decreases the burden and invasion of these TNBC. Together, our data demonstrate differential TCA-cycle usage in TNBC and provide therapeutic implications for the DLST-dependent subsets. Shen and Korm et al. investigate the role of dihydrolipoamide S-succinyltransferase (DLST), a tricarboxylic acid (TCA) cycle enzyme, in the metabolic heterogeneity of triple negative breast cancer (TNBC) cell lines. They show that either depletion or inhibition of DLST in the dependent cells leads to decreased invasion and metastasis in vitro and in vivo. [ABSTRACT FROM AUTHOR]

Background: Selection of suitable predators for the control of the spider mite, Panonychus citri is always a challenge due to the lack of their reliable predicted efficacy. In this laboratory study, biological control potential of Neoseiulus californicus, N. cucumeris and Scapulaseius newsami, against P. citri using the "Relative Control Potential" (RCP) metric to compare their efficacies was evaluated. The functional response type was determined at different prey densities (after 24 h). Results: The type II functional responses were observed from all the tested predators feeding on 3 different life stages of P. citri, except N. cucumeris, which was fed only on eggs. All predatory mites preferred eggs of P. citri with a higher attack rate (1.397) in N. cucumeris, followed by N. californicus (1.238) and S. newsami (0.975). Handling time was also lower in N. cucumeris than other mites, with a trend as: for N. cucumeris, eggs > adults > immature stages; for N. californicus, immature stages > eggs > adults, while for S. newsami, eggs > immature stages > adults. The integration of greenhouse abundance and fecundity (%) data resulted in a higher RCP for N. cucumeris than other predators. Conclusion: Neoseiulus cucumeris was selected as the most suitable predatory mite for the control of P. citri with a higher RCP and potential to maximum reproduction. S. newsami, which was used for the first time, gave almost similar results compared to N. californicus but with lower efficacy. [ABSTRACT FROM AUTHOR]

Secondary population outbreaks of Panonychus citri (McGregor) (Acari: Tetranychidae) are triggered by synthetic chemical applications (dose and method), which also elicited a change in mites' behavioral responses. This study aimed to understand the dispersal pattern of P. citri and how changes in dispersal behavior may influence secondary pest outbreaks in the field with or without chemicals. We found positive density and time-dependent dispersal within the inoculated leaflet. Dispersion from inoculated leaflets to the last leaflet depends on initial density and time. A significant difference was observed in the composite dispersal index data and preferred midrib region. The minimum dispersal was observed by P. citri in no direct contact with treated surfaces, whereas attraction was observed on treated surfaces (right). All chemicals gave different dispersal and feed disruption responses depending on the treatment application pattern. The maximum number of mites dispersed and avoid surfaces treated with abamectin and vegetable oil, respectively. Vegetable and EnSpray 99 had a positive impact on toxicity, repellency, and irritancy. The fecundity rate of P. citri boosted with a high dose and direct exposure. Panonychus citri colonization as a single individual or gregarious distribution resulted in a rapid fecundity rate, which may explain why citrus orchards were severely damaged and how suddenly a whole citrus plantation can be highly infested. This study concluded that change in treatment application patterns leads to a change in the behavioral responses in P. citri. [ABSTRACT FROM AUTHOR]

Objective We aimed to prospective investigate the association between cardiometabolic diseases (CMDs) with dementia, and to examine whether genetic factors and CMDs jointly contribute to the incidence of dementia. Methods We used data from the UK biobank of 204,646 adults aged 37-73 free of dementia at baseline. Genetic risk for dementia including APOE ε4 status and polygenic risk score (PRS) categorized as low, intermediate, and high. CMDs including ischemic heart disease (IHD), stroke, and type 2 diabetes (T2D) were confirmed by touchscreen questionnaires, medical examinations, and hospital inpatient records. Results Over the follow-up (median: 12.5 years), 5,750 participants developed dementia. The HRs (95% CI) of those with APOE ε4 carriers and high PRS were 3.16 (3.00-3.33) and 1.50 (1.41-1.60), respectively. The risk of dementia was 70% higher among those with CMDs (HR: 1.70; 95% CI: 1.60-1.82). In joint effect analyses, compared to no CMDs and APOE ε4 non-carriers, the HRs (95% CIs) of dementia were 3.53 (3.31-3.76)/2.06 (1.89-2.23) in participants with only APOE ε4 carriers and CMDs, and 5.06 (4.64-5.53) for those with APOE ε4 carriers plus CMDs. Compared to no CMDs and low PRS, the HRs (95% CIs) of dementia were 1.29 (1.19-1.40)/1.60 (1.48-1.73) in participants with only intermediate and high PRS, and 2.00 (1.79-2.23)/2.63 (2.38-2.92) for those with intermediate, and high PRS plus CMDs. Moreover, there were significant additive and multiplication interactions between CMDs and APOE ε4 carriers of dementia, but only multiplication interaction was observed for PRS. Conclusions CMDs were associated with higher risk of dementia regardless of genetic risk for dementia. [ABSTRACT FROM AUTHOR]

In recent years, the surge in the number of users in recommender systems has brought unprecedented opportunities and challenges to research on recommender systems. The development of the graph neural network makes social recommendation based on graph data more advantageous, but it also brings problems with parameterization. Although the recommender system contains a large amount of data, such as users, items, and user-item interactions, the social relationship data of users in social recommendation are still sparse. To solve the problem of parameterization, we propose a novel quaternion-based knowledge graph neural network for social recommendation (QSoR), which applies hypercomplex space to the social recommendation. Without affecting the recommendation quality, QSoR utilizes the expressibility of the quaternion and weight sharing mechanism of the Hamilton product to reduce the parameters during training. To solve the problem of the sparsity of users' social relationship data, we propose a quaternion-based explicit and implicit social relationships integration algorithm, which obtains users' implicit social relationships from the user-item graph as a supplement for users' social information. We design a double-end attention mechanism when obtaining the user's preferences and social relationships, which makes full use of the item information and the interaction information between the user and the neighbor nodes. In this way, we can obtain users' personalized preferences, distinguish the importance of user relationships, and obtain neighbors with a high degree of influence on users, thus improving the accuracy of recommendation results. The social relationship graph and the user-item graph are effectively combined in the quaternion space to make personalized recommendations for users. Experiments on three public datasets, Yelp, Last-FM, and Epinions, demonstrate the validity of the proposed model QSoR. • We propose a novel quaternion-based knowledge graph neural network for social recommendation (QSoR). • We design a double-end attention mechanism to obtain user preferences and user social relationships in the quaternion space. • We propose a quaternion-based user explicit and implicit social relationships integration algorithm. [ABSTRACT FROM AUTHOR]

Correction to: Egypt J Biol Pest Control (2021) 31:143 https://doi.org/10.1186/s41938-021-004... Following publication of article [1], the authors flagged the following two errors in the affiliations information: Muhammad Irfan Akram had been incorrectly assigned affiliation 4 instead of 3; Izhar Hussain had been incorrectly assigned affiliation 3 instead of 4. The affiliation information has since been corrected in the published article. [Extracted from the article]

Coal-derived carbon black is the main composition of PM 2.5 , used in the rubber industry and energy storage fields. The formation and structure control of carbon black have attracted significant attention. Alkali metals have influence on the dynamic formation of coal-derived carbon black. To analyze the mechanism of Na in the formation of carbon black, a high-temperature drop tube furnace was used to analysis of carbon black from the pyrolysis of acid-washed coal and Na-containing coal at 1250 °C. DFT was applied to simulate the stepwise inhibitory effect of Na on the polymerization of PAHs into carbon black. The results show that Na promotes the oxidative cracking of large PAHs into small PAHs during the formation of coal-derived carbon black (reaction energy barrier is reduced by 17.7%), and inhibits the condensation of small PAHs molecules to form carbon black molecules (reaction energy barrier is increased by 74.7%), which causes the length of carbon black is shortened by 14.3%, and its curvature is increased by 1.7%. Na could change the spatial structure of formed carbon black-graphite crystallites, which increases the spacing of carbon black by approximately 6.2% to 16.3%. Na enhances the electrochemical performance of carbon black by 2 to 3 times. [Display omitted] • Experimental and DFT study of the catalytic effect of Na on carbon black. • Na promotes oxidation of PAHs and fracture of carbon black crystallites. • Na inhibits the polymerization of PAHs into carbon black particles. • Na changes carbon black structure, increasing the spacing by 6.2% ~ 16.3%. • The electrochemical performance of carbon black is enhanced 2–3 times by Na. [ABSTRACT FROM AUTHOR]

Simple Summary: Bactocera dorsalis is an insect pest that causes substantial losses to fruit crops. It can be potentially controlled by the parasitoid wasp, Spalangiaendius Walker (Hymenoptera: Pteromalidae). S. endius is also used to control Musca domestica and is commercially produced. We studied the parasitism capacity of S. endius as a pupal parasite of Bactocera dorsalis after switching hosts. We mass-reared S. endius for more than 50 generations on M. domestica, and then allowed them to parasitize B. dorsalis to study the parasitism capacity of S. endius. More M. domestica were parasitized than B. dorsalis at different host densities. The S. endius colony, which was reared on M. domestica can be used to control B. dorsalis at a low density of B. dorsalis. The parasitism capacity of S. endius could be improved. The result showed that parasitoid-pest ratio should be 1:25 in order to maintain a relatively stable parasitism rate for controlling B. dorsalis. The rate of S. endius parasitizing B. dorsalis was decreased by parasitoid age. These results will help to optimize the use of S. endius, reared on M. domestica, for control of B. dorsalis. We studied the parasitism capacity of Spalangia endius as a pupal parasite of Bactocera dorsalis after switching hosts. We used pupae of B. dorsalis and M. domestica as the hosts and studied parasitism by S. endius in the laboratory. The parasitism capacities were compared at different host densities and different parasitoid ages. The two functional responses of S. endius fitted a Holling Type II equation. More M. domestica were parasitized than B. dorsalis at all the densities. The ability of S. endius to control M. domestica was α/Th (parasitism capacity) = 32.1950, which was much stronger than that of control B. dorsalis, which was α/Th = 4.7380. The parasitism rate of wasps that had parasitized B. dorsalis had decreased by the emergence time of parasitoids. These results suggest that the parasitoid-pest ratio should be 1:25 to maintain a relatively stable parasitism rate for control of B. dorsalis. The S. endius colony reared on M. domestica successfully controlled a low-density population of B. dorsalis in the lab. We provide evidence suggesting that the parasitism capacity of S. endius needs to be improved. [ABSTRACT FROM AUTHOR]