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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]

This study presented new field measurement data on the particle size distributions, morphological characteristics, and elemental composition of particulate matter (PM)2.5 (particle diameter less than 2.5 mm) from 12 industrial boilers, including eight circulating fluidized bed boilers (CFB) and four bubbling fluidized bed boilers (BFB). Measurement in situ was taken with an electrical low-pressure impactor equipped with a two-stage dilution sampling system. Morphological characteristics were determinded by electron microscope (SEM), and elemental compositions of the samples were determinded by X-ray fluorescence (XRF) and inductively coupled plasma (ICP). The results show that before and after the dust removal, the number concentration distributions of PM2.5 display bimodal distributions, one peak is between 0.07 and 0.12 μm, and the other peak is between 0.32 and 0.48 μm. And the particle concentrations of PM2.5 depended mainly on submicron particles (less than 1 µm in diameter), formed by gasification-condensation mechanisms. The mass concentration distributions of PM2.5 showing no peak distribution and increase with the particle size. Compared to electrostatic precipitators (ESPs) and fabric filters (FFs) , the ESPs has a lower removal efficiency of PM2.5. The morphological characteristics of PM2.5 before and after dust removal contain irregular particles and spherical particles. The mass percentages of Na, K, and Fe in PM2.5 after the dust removal are nearly the same as those before the dust removal, but Si, Ca, and Al in PM2.5 decreased after dust removal and S in PM2.5 increased after dust removal. Although the hazardous elements Cr, V, Mn, and Ni in PM2.5 after dust removal did not change significantly, the Pb, Cd, As, Se, and Hg increased after dust removal. These data clearly show that the composition of PM2.5 changes as dust are removed by air pollution control devices. [ABSTRACT FROM AUTHOR]

Highlights • Corn straw was gasified and reformed with steam in a closed-loop reactor. • 10% Ni loadings on γ-A1 2 O 3 catalyst was synthesized and tested. • Reforming temperature, steam/biomass ratio, catalyst/biomass ratio was studied. • Syngas quality was upgraded by both the decoking and steam reforming reactions. Abstract A closed-loop gasification and catalytic reforming apparatus was applied for producing syngas from corn straw with upgraded syngas quality. Four nickel based catalysts at 10% and that at 5–15% loadings on γ-A1 2 O 3 were synthesized and tested. Effects of reforming temperature (700–900 °C), steam to biomass ratio (S/B, 0.5–2.5), catalyst to biomass ratio (C/B, 0.2–0.6) on the syngas quality and the carbon conversion and gasification efficiency were investigated. Incorporation of reforming reaction with 10% Ni/γ-A1 2 O 3 catalyst leads to the highest CO content (50.2%) and lowest CO 2 content (7.5%) amongst the four tested catalysts and amongst the Ni/γ-A1 2 O 3 catalysts at different Ni loading. The syngas was upgraded by both the decoking reaction in the pyrolytic chamber and the steam reforming and water shift reactions in the reforming chamber. [ABSTRACT FROM AUTHOR]

Highlights • Integrated gasification and catalytic reforming process with gas loop was developed. • Effects of support type, reforming temperature, S/B ratio, and C/B ratio was studied. • Ni/γ-Al 2 O 3 catalyst was used to upgrade quality and mitigate GHG potential of syngas. • Recirculated syngas can remove cokes on the used catalyst surface. Abstract The syngas that is produced from waste biomass often has high levels of CH 4 and CO 2 , which are greenhouse gases. This investigation presents an integrated gasification and catalytic reforming process with a closed gas loop that can improve quality of syngas from corn straw and mitigate CH 4 and CO 2 emission. The effects of the support type, reforming temperature, steam-to-biomass (S/B) ratio, and catalyst-to-biomass ratio (C/B) ratio on gas yield and composition were experimentally examined with waste corn straw as the feedstock gasified at 850 °C in the proposed closed-loop apparatus. Reformation of syngas using Ni/γ-Al 2 O 3 at 850 °C, S/B = 1 and C/B = 0.5 yielded 1.16 m3/kg of syngas, which contained 48.5% H 2 , 33.9% CO, 12.2% CO 2 and 5.3% CH 4 , corresponding to 71.0% and 81.5% enhancements of the first two and −77.8% and −58.1% reductions of the last two components. [ABSTRACT FROM AUTHOR]

Land subsidence resulting from the withdrawal of groundwater is one of the problems of major importance to geosciences and geomechanics. In order to analyze the phenomena of land subsidence, this study attempts to constitute a more reliable prediction model to simulate the consolidation process of the aquifer system under the drawdown of groundwater. A fractional-derivative viscoelastic model is introduced to characterize the rheological properties of the aquitard, and the equation governing the consolidation process is established on the basis of the one-dimensional consolidation theory. The semianalytical solutions for the pore-water pressure, the settlement of aquitard, and the degree of consolidation are deduced using both finite sine transform and Laplace transform. Then, the comparisons of two special cases are performed to validate the correctness of the proposed model. The variations in the degree of consolidation and settlement of the aquitard are simulated to analyze the consolidation behavior of the aquifer system, and the influences of model parameters and the drawdown speed of groundwater are investigated. [ABSTRACT FROM AUTHOR]

Abstract Sharing economy has gained momentum in recent years. In this paper, a concept and a synthesised conceptual framework are developed for Sharing Economy-based Service Triad (SEST). Service triads have increasingly become an interesting topic in supply chain management literature. The study adopts a systematic literature review method carrying out content analysis of 100 peer-reviewed journal article focused on triad and sharing economy in order to propose a concept and develop a conceptual development of SEST. Case examples identified from secondary sources are used to illustrate the model. Based on the literature review and case example, the research first coins the term 'Sharing Economy–based Service Triad' (SEST) by differentiating it from traditional manufacturing triads and manufacturing service triads. Then, building on social-capital and balance theories, the study develops a conceptual framework, which proposes two types of strategy for sharing-economy platforms (commitment- and control based platform strategies). These strategies reveal five service-triad structures/archetypes, which lead to different outcomes (e.g., service quality and social capital), which eventually lead to triple bottom line performance and avoid negative externalities. Finally, the study argues that SEST should not only follow economic logic, i.e., economic benefits but also adopt a social and environmental sustainability logic given the negative externalities. Highlights • Presents an integrated conceptual framework that characterizes service-triad archetypes of the sharing economy-based economy to guide future research. • The conceptual framework presented proposes two types of strategies for access economy platforms (commitment- and control-based platform strategies). • Reveal five service-triad structures/archetypes that lead to different outcomes (i.e., service quality and social capital). [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]

SiGe materials on Si substrate are promising candidates to act as the bottom cell in a tandem structure, due to its high mobility, good process compatibility, adjustable lattice constant, and capability of absorbing light of wavelengths up to 1800 nm. This work demonstrates the improvement of short circuit current and efficiency for the SiGe solar cell, operating in a GaAsP-SiGe two-terminal configuration tandem device. A numerical model is proposed to predict the short circuit current of the SiGe cell. Experimentally, the highest short circuit current density is increased from 12.9 mA/cm 2 to 19.4 mA/cm 2 , with optimized fabrication process and device structure. The efficiency of the SiGe bottom cell is improved from 1.7% to 3.0% filtered by GaAsP. It is demonstrated that the cell has potential to achieve an efficiency of 4.6% at 20 suns illumination. Different mole fractions of Ge in the structure are confirmed from the X-ray diffraction line scanning image. [ABSTRACT FROM AUTHOR]

A compositionally graded silicon germanium (Si 1−x Ge x ) buffer layer is a leverage technology that grows III–V materials on silicon (Si) substrates with low threading dislocation density. This study determined average optical absorption coefficients of Si 1−x Ge x graded buffer layers at wavelengths beyond the band gap of Si by measuring the transmittances of Si 1−x Ge x on Si wafers. The results show that assuming linear changes of absorption coefficients of Si 1−x Ge x with the change of germanium (Ge) composition will significantly overestimates the absorption in the Si 1−x Ge x graded buffer layers. Using determined average absorption coefficients of graded buffer layers, an accurate optical model was developed for a fabricated planar Si 0.15 Ge 0.85 on Si solar cell. This model shows that significant amounts of light at the band edge of Si are absorbed by the Si substrate. However, the majority of the light absorbed by the Si substrate cannot contribute to the short-circuit current of the solar cell, as the generated electron-hole carriers are too far away from the p-n junction and thus have a very low probability of being collected by the p-n junction. By thinning the Si substrate from 675 µm to 200 µm, a 0.5 mA/cm 2 short-circuit current density ( J SC ) improvement under AM1.5 G spectrum was observed on the Si 0.15 Ge 0.85 solar cell. Further thinning the Si substrate to 50 µm should lead to a total J SC gain of 1.6 mA/cm 2 . This study provides a practical path for achieving a highly efficient two-terminal current matched GaAsP/SiGe tandem solar cell on Si substrate. [ABSTRACT FROM AUTHOR]

This letter reports on the performance improvement of an epitaxially grown SiGe on Si solar cell by optimizing the back surface field (BSF). First, a Si0.18Ge0.82 on silicon (Si) solar cell was fabricated with a 0.25 μm BSF layer. A 25 mV open-circuit voltage ( VOC) improvement was observed on this BSF solar cell compared with the reference solar cell without BSF layer. Then, a Si0.18Ge0.82 on Si solar cell with double BSF layers was designed and fabricated. The measured efficiency of this solar cell is 3.4% when filtered by a GaAs0.79P0.21 top cell. To the best of the authors' knowledge, the 3.4% efficiency reported here is the highest efficiency for SiGe on Si solar cells when filtered by a GaAs0.79P0.21 top cell. The previous best reported efficiency for high Ge composition SiGe on Si solar cell was only 1.7% when filtered by a GaAs0.79P0.21 top cell. [ABSTRACT FROM AUTHOR]

A GaAsP/SiGe tandem solar cell on Si substrate has been further fabricated using light trapping techniques, such as texturing and adding a back surface reflector, and thinning the Si substrate. This is of importance to increase the Jsc of the Si0.18Ge0.82 bottom cell in this tandem system since bottom cell is current limiting. The Jsc of the bottom cell has been increased by relative 7.4%. This current improvement leads to a predicted efficiency of near 21% for this tandem device, with an absolute efficiency improvement of 0.3% over previous results without light trapping processes. The current of the bottom cell can be further improved by optimizing the bottom cell structure and texturing process, further thinning the Si substrate and increasing Ge concentration. (© 2016 WILEY-VCH Verlag GmbH &Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Objective: Insulin-like growth factor-1 (IGF-1) is reported to be neuroprotective in the setting of Parkinson’s disease (PD), and there is increasing interest in the possible association of serum IGF-1 levels with PD patients, but with conflicting results. Therefore, we conducted a meta-analysis to evaluate the association of serum IGF-1 levels in de novo, drug naïve PD patients compared with healthy controls. Methods: Pubmed, ISI Web of Science, OVID, EMBASE, and Cochrane library databases from 1966 to October 2014 were utilized to identify candidate studies using Medical Subjective Headings without language restriction. A random-effects model was chosen, with subgroup analysis and sensitivity analysis conducted to reveal underlying heterogeneity among the included studies. Results: In this meta-analysis, we found that PD patients had higher serum IGF-1 levels compared with healthy controls (summary mean difference [MD] = 17.75, 95%CI = 6.01, 29.48). Subgroup analysis demonstrated that the source of heterogeneity was population differences within the total group. Sensitivity analysis showed that the combined MD was consistent at any time omitting any one study. Conclusions: The results of this meta-analysis demonstrate that serum IGF-1 levels were significantly higher in de novo, drug-naïve PD patients compared with healthy controls. Nevertheless, additional endeavors are required to further explore the association between serum IGF-1 levels and diagnosis, prognosis and early therapy for PD. [ABSTRACT FROM AUTHOR]

Silicon germanium (Si 1− x Ge x ) is a material with high mobility and relatively low bandgap making it an attractive candidate for the bottom subcell in a multi-junction solar cell. Optical constants of Si 1− x Ge x films grown on silicon substrates with germanium (Ge) compositions of 77%, 82%, 85% and 88% from wavelength of 400 nm to 1450 nm at room temperature are reported. Spectroscopic ellipsometry is used to obtain the real part of refractive index for the whole wavelength range and the extinction coefficients for short wavelengths. Optical transmittance ratio of two structures being different only in the thicknesses of the Si 1− x Ge x films is used to extract the extinction coefficients for long wavelengths. We demonstrate that the optical constants of Si 1− x Ge x films grown on silicon substrates with the aid of graded buffer layers are similar to that of their corresponding bulk materials. However, the absorption coefficients of Si 1− x Ge x with 88% Ge determined by us at around 1000 nm is three times higher than previously interpolated results. The main reason for this discrepancy may be due to the inaccuracy of interpolation in the wavelength range where the material's bandgap changes very quickly. In addition, it should be noted that the Ge composition of the Si 1− x Ge x films we are able to obtain are determined to be ±1% which is much more accurate than the previous experimentally determined results which have an accuracy of ±5%. [ABSTRACT FROM AUTHOR]

Asian corn borer, Ostrinia furnacalis (Guenée), is a key corn pest in the Asian-Western Pacific countries. It overwinters as full-grown larvae in plant stalks or in a spun-silk covering located in the plant debris in the temperate regions of China. Supercooling point (SCP) and survival rate after low sub-zero temperature treatment were assessed for field-collected populations in the laboratory using a cool bath with a 1°C/min cooling rate until −40°C. Mean SCPs were varied among geographical populations, with a significant decline from −22.7°C of Haikou, the multivoltine tropical population in the south, to −28.5°C of Gongzhuling, the univoltine temperate population in the northeast of China. In addition, there was more than 1°C difference in SCP between Gongzhuling univoltine and bivoltine populations that were from the same geographic origin. Mean SCPs of the Guangzhou population fluctuated over the year, with significantly lower SCPs in winter than in other seasons, which correlated with a significantly higher proportion of diapausing larvae in winter than in other seasons. Over 41% of overwintering larvae from the northeast population could withstand to be supercooled for a few minutes to the low sub-zero temperature of −40°C, but only 6.7% of their southern counterparts did so. The findings from this study suggest that O. furnacalis mostly takes advantage of freeze avoidance as diapausing larvae for overwintering in the southern region, whereas it exhibits freeze tolerance in diapause in the northeastern region. [ABSTRACT FROM AUTHOR]

Introductions Transcranial sonography (TCS) of the substantia nigra is a new and promising method to diagnose Parkinson's disease (PD) but its effectiveness is controversial. Methods All 55 PD patients involved in the study underwent single photon emission computed tomography (SPECT) imaging using the labeled dopamine transporter radiotracer 99m Tc-TRODAT-1 to assess nigrostriatal dopaminergic function. The echogenicity of the substantia nigra was measured by TCS in all patients who received DAT-SPECT scanning. Finally, statistical analysis was carried out to determine the diagnostic accuracy of TCS as well as its correlation with 99m Tc-TRODAT-1 SPECT, its positive predictive value (PPV), and negative predictive value (NPV). Results Contralateral striatal 99m Tc-TRODAT-1 uptake was significantly reduced compared to ipsilateral striatal uptake, and had a negative correlation with UPDRS-Ⅲ(r = −0.334, p = 0.013), disease duration (r = −0.393, p = 0.003) and H–Y stage (r = −0.330, p = 0.014). After TCS measurement, the contralateral SN echogenic area was similar to the ipsilateral SN echogenic area (27.77 ± 13.19 vs 25.98 ± 11.94 mm 2 , p = 0.402, n = 24). No correlation was identified between TCS and UPDRS-Ⅲ (r = 0.383, p = 0.065), disease duration (r = 0.371, p = 0.075) or H–Y stage (r = 0.259, p = 0.222). The sensitivity and specificity of SN TCS for the diagnosis of PD were calculated as 64.70% and 60% according to DAT-SPECT, respectively, while the positive predictive value and negative predictive value was calculated as 91.67% and 20%, respectively. Conclusions Compared to DAT-SPECT, TCS is a non-radioactive and convenient procedure to perform. In our investigation, TCS had no correlation with DAT-SPECT. However, the high positive predictive value of TCS highlights its possible utility as a routine diagnostic test. [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]

Low bandgap Si (1− x ) Ge ( x ) solar cells are designed, fabricated, characterized and analyzed for the purpose of acting as the bottom cell in a GaAsP–SiGe tandem solar cell. The development of the SiGe cell under a GaAsP cell can lead to a 34% relative increase in efficiency over that of a silicon solar cell. This work focuses on making a SiGe bottom cell that can generate 21 mA/cm 2 of short circuit current ( J sc ) at 1 Sun from photons beyond 780 nm and with a 450 mV band gap–voltage offset ( W oc ) under 20 Suns illumination. Numerical and analytical methods are introduced to demonstrate the SiGe solar cells׳ performance limits and to examine the trade-offs among I – V performance, cell structure and material composition. Material compositions are confirmed with energy-dispersive X-ray spectroscopy (EDS) and electrochemical capacitance voltage profiling (ECV). First principles analysis shows that a Si .15 Ge .85 cell with a 5 µm base can produce the expected current and an efficiency of 8.8% under 20 Suns. By increasing base doping, we achieve a W oc of 435 mV under 20 Suns with a Si .18 Ge .82 cell. The best 1 Sun short circuit current measured from a Si .12 Ge .88 cell at wavelengths beyond 780 nm without anti-reflection coating, back surface reflector, or back texturing is 12.9 mA/cm 2 . Adding a back surface field to the structure will lead to a higher V oc and lower W oc . Implementing light trapping with a higher base angle of pyramids can lead to the target J sc of 21 mA/cm 2 . [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]

Highlights: [•] Litchi stink bug is the most significant pest in litchi and longan orchards. [•] Anastatus japonicas is an efficient enemy of Tessaratoma papillosa. [•] A. japonicus has been mass reared and successful used as a biocontrol agent. [•] Many factors constraint the utilization of A. japonicas. [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]

SUMMARYIt was found that, after colonies of Nostoc sphaeroides Kütz. in exponential phase of growth were transferred to fresh complete BG-110 medium, the hormogonia differentiated independent of the type of preculture used. This provided evidence that the hormogonia differentiation was not directly related to phosphorus and potassium status, nor to the osmotic effect of the media. In contrast, all the cultures in the stationary growth phase had no hormogonium differentiation after being transferred to fresh medium. However, the incomplete media for preculture seemed to favor the liberation of hormogonia from colonies to the medium. The result showed that the morphology and ultrastructures of the vegetative filament, the main stage of Nostocacean life history, determine its adaptability in changing environments, while the hormogonium remains as a propagule of the species. [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]

Investigates the effects of melatonin treatment on cardiac sarcolemmal membrane function in alloxan-injected rats. Isolation of sarcolemmal membranes; Adenosine triphosphatase assays; Dihydropyridine-binding assay.

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]

• Iron inhibits the formation of soot significantly in the process of coal pyrolysis. • After addition of iron, microstructure defects in soot derived from coal pyrolysis increase and the size of the graphite layers become smaller. • Iron may inhibit carbonyl formation and promote C–O formation during the process of soot formation. The addition of metal to fuel can effectively control the soot emission. However, there are few studies on the effect of metallic additive to coal on soot emissions. In this study, the effect of iron on the characteristics of coal-derived soot was analyzed. Soot particles were obtained at different temperatures from coal pyrolysis in a drop tube furnace without and with the addition of iron nitrate (Fe(NO 3) 3) into the coal, respectively. The microstructure and functional group information of soot derived from coal were obtained through Raman spectroscopy, infrared spectroscopy and XPS. The results showed that the yield of soot derived from coal pyrolysis with iron addition was reduced by 48–68% than that derived from coal pyrolysis without iron addition at different temperature. With the iron addition, the number of holes in the graphite layers in soot produced by coal pyrolysis was increased, while the valued of sp2/sp3 and the length of graphite layer were decreased. At the same time, the C–O and COO-functional groups in the soot increased, and the carbonyl content decreased. These phenomena indicated that the addition of iron can significantly inhibit the soot formation in the coal pyrolysis process, increase the defects of graphite layers in soot, inhibit the carbonyl formation, and promote the formation of C–O during the process of soot formation. [ABSTRACT FROM AUTHOR]

In this work, polyvinylidene fluoride (PVDF)/polymethyl methacrylate (PMMA) alternating multilayered materials were fabricated. Firstly, the scratch damage behaviors of PVDF side was investigated experimentally. The results presented that the maximum of critical normal load of scratch damage (F cr) was obtained by 32-layer material, which was 40% higher than that of pure PVDF and very close to that of pure PMMA. This indicated that the scratch resistance of PVDF could be significantly improved via multilayer assembly with PMMA. Furthermore, numerical modeling revealed that with the multiplication of layers, the decrease of tensile stress imposed on the first PVDF layer led to the delay of scratch damage, whereas the decrease of the distance between the first PMMA layer and the scratch tip promoted its early damage. Therefore, the optimum scratch performance was determined by the critical thickness of PVDF and PMMA layers, which was significant for designing scratch-resistant materials. Image 1 • A scratch resistance modification strategy for fluoropolymer was proposed. • Critical normal load of scratch damage was dramatically improved through multilayer assembly. • Good agreement was achieved between experiments and numerical modeling. • Mechanism for scratch damage was proposed. [ABSTRACT FROM AUTHOR]

The development of vehicular ad hoc networks (VANETs) is facing great challenges. Due to the open environment in VANETs, the false information sent by malicious vehicles not only affects the fairness of information interaction but also seriously threatens the driving safety of normal vehicles. Therefore, the study of trust evaluation and management in VANETs has become hot topics in recent years. In this paper, we propose a trust management model of VANETs based on blockchain. In this model, a hidden markov model (HMM) based vehicle trust evaluation method that improve the accuracy on the detection of malicious behavior is proposed. Besides, a trust management method based on the alliance chain is designed, which greatly improves the efficiency of trust updating and querying on the premise of security. Simulation results show that the model is promising and feasible, effective in the aspects of trust evaluation and trust management. [Display omitted] • We analyze the issues associated to the problem stated, and put forward the system model, attack model, and assumptions of the VANETs. • We propose a vehicle trust evaluation algorithm based on the HMM model with higher accuracy and better application value in the current environment of extreme computing performance. • We propose a trust management method based on hyperledger fabric, which improves the overall efficiency of the trust management and ensures better security. [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]