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The effect of yttrium on the 475 °C brittleness and microstructure stability of Fe–13Cr–6Al–2Mo-0.5Nb-0.15Zr alloy after long-term aging at 475 °C has been investigated. At the early stage of aging, the nanoscale metastable transition phase precipitates in large quantities. After aging for 10,000 h, the nanophase in the Y-free alloy has completely disappeared, and only a great deal of granular Laves phases are left, while a large number of nanoscale phases still exist in the grain of Y-containing alloys. This is because the addition of yttrium combines with vacancies, hinders the diffusion of Laves phase-forming elements, and has the result that the transformation of nanoscale phases Fe2(Mo0.3Nb0.7)Al to Laves phase is inhibited during the aging process of the Y-containing alloys. During aging, precipitation-free zones (PFZ) are formed in the surrounding area where the Laves phase and grain boundaries exist. The elongation of Y-containing alloys decreases with yttrium content, but it is still higher than that of Y-free alloy. The addition of yttrium improves the aging plasticity and weakens the degree of 475 °C brittleness of the alloy, and the effectiveness is diminished with the yttrium. This plays a key role in the further study of Fe–Cr–Al alloys for fuel cladding materials.

New neutron-absorbing Ni-based functional alloys that depend on the gadolinium and dysprosium neutron absorption units were fabricated using vacuum induction melting. The effects of Gd and Dy contents on the microstructure characteristics and properties of Ni-based alloys were investigated. Adding Gd, Dy, and Gd + Dy into Ni-based alloys formed Ni5Gd, Ni5Dy and Ni5(Gd,Dy), respectively, and their volume fraction increased with increasing of Gd/Dy content. Different type of elements, Gd or Dy, had no obvious influence on mechanical properties and corrosion resistance, while the amount of Gd and Dy elements played an important role. The shielding properties were calculated by the Monte Carlo method, and the alloys possessed high efficiency and high durability performance due to the high contents of Gd and Dy elements. The developed alloys are expected to be good functional materials for use in the field of nuclear shielding.

With the development of blockchain technology and the economy, the demand for data interaction and application collaboration between blockchains is increasing. Due to the differences in the data structure, interface protocols, consensus mechanisms, and even business models, new “chain silos” have been formed among blockchain applications, which limit the interoperability of asset exchange and business collaboration among blockchains. Based on the full analysis of blockchain technology characteristics and blockchain Internet development needs, this paper designs a blockchain cross-chain system based on the unified NFT identity identification technology, address tracking technology, and seamless interconnection of mobile security cross-chain technology, based on the “NFT + Cross-chain bridge”. Based on the service mode, verification mode, and security of Bridges, six model timeliness, smart contract robustness, and convergence radius were selected from 44 cross-chain Bridges for experimental verification. Finally, it is concluded that the overall performance of the blockchain cross-chain system based on “NFT + Cross-chain bridge” is far more universal and better security than the performance of ordinary Bridges.

Coniella vitis is a dominant phytopathogen of grape white rot in China, significantly impacting grape yield and quality. Previous studies showed that the growth and pathogenicity of C. vitis were affected by the environmental pH. Arrestin-like protein PalF plays a key role in mediating the activation of an intracellular-signaling cascade in response to alkaline ambient. However, it remains unclear whether palF affects the growth, development, and virulence of C. vitis during the sensing of environmental pH changes. In this study, we identified a homologous gene of PalF/Rim8 in C. vitis and constructed CvpalF-silenced strains via RNA interference. CvpalF-silenced strains exhibited impaired fungal growth at neutral/alkaline pH, accompanied by reduced pathogenicity compared to the wild-type (WT) and empty vector control (CK) strains. The distance between the hyphal branches was significantly increased in the CvpalF-silenced strains. Additionally, CvpalF-silenced strains showed increased sensitivity to NaCl, H2O2, and Congo red, and decreased sensitive to CaSO4. RT-qPCR analysis demonstrated that the expression level of genes related to pectinase and cellulase were significantly down-regulated in CvpalF-silenced strains compared to WT and CK strains. Moreover, the expression of PacC, PalA/B/C/F/H/I was directly or indirectly affected by silencing CvpalF. Additionally, the expression of genes related to plant cell wall-degrading enzymes, which are key virulence factors for plant pathogenic fungi, was regulated by CvpalF. Our results indicate the important roles of CvpalF in growth, osmotolerance, and pathogenicity in C. vitis.

Background: Shedding of glycocalyx is relevant to worse prognosis in surgical patients, and elevated levels of serum matrix metalloproteinase-9 (MMP-9) are associated with this phenomenon. This study aimed to investigate the dynamic alterations of serum glycocalyx components and MMP-9 during cardiopulmonary bypass (CPB), and evaluate their predictive capacities for prolonged intensive care unit (ICU) stay, as well as their correlation with coagulation dysfunction. Methods: This retrospective study analyzed serum levels of syndecan-1, heparan sulfate (HS), and MMP-9 at different time points during CPB, and assessed their association with prolonged ICU stay and coagulation dysfunction. Results: Syndecan-1, HS, and MMP-9 exhibited divergent changes during CPB. Serum levels of syndecan-1 (AUC = 78.0 %) and MMP-9 (AUC = 78.4 %) were validated as reliable predictors for prolonged ICU stay, surpassing the predictive value of creatinine (AUC = 70.0 %). Syndecan-1 (rho = 0.566, P

Since the establishment of ‘Careers for the Disabled’, China has been committed to promoting the program. Special higher education for the disabled is regarded as important for the protection of the rights of the disabled and for the achievement of equity and promotion of the development of special higher education. Currently, many colleges and universities in China provide higher education for the disabled. However, special higher education has problems such as a lack of capable teachers, which requires active inter-school cooperation. Thus, we propose the construction of an online education community for the disabled to overcome the existing problems and propose a methody of promoting the development of special higher education to a higher standard.

Electrocatalytic urea oxidation reaction (UOR) is regarded as an effective yet challenging approach for the degradation of urea in wastewater into harmless N2 and CO2. To overcome the sluggish kinetics, catalytically active sites should be rationally designed to maneuver the multiple key steps of intermediate adsorption and desorption. Herein, we demonstrate that metal-organic frameworks (MOFs) can provide an ideal platform for tailoring binary active sites to facilitate the rate-determining steps, achieving remarkable electrocatalytic activity toward UOR. Specifically, the MOF (namely, NiMn0.14-BDC) based on Ni/Mn sites and terephthalic acid (BDC) ligands exhibits a low voltage of 1.317 V to deliver a current density of 10 mA cm-2. As a result, a high turnover frequency (TOF) of 0.15 s-1 is achieved at a voltage of 1.4 V, which enables a urea degradation rate of 81.87% in 0.33 M urea solution. The combination of experimental characterization with theoretical calculation reveals that the Ni and Mn sites play synergistic roles in maneuvering the evolution of urea molecules and key reaction intermediates during the UOR, while the binary Ni/Mn sites in MOF offer the tunability for electronic structure and d-band center impacting on the intermediate evolution. This work provides important insights into active site design by leveraging MOF platform and represents a solid step toward highly efficient UOR with MOF-based electrocatalysts.

Decentralized networks bring us many benefits, but as networks evolve, many nodes either actively or passively become unreachable behind an NAT or a firewall. This has become a hindrance to the development of decentralized networks, where peer-to-peer communication data transfer between unreachable nodes cannot be accomplished, whether in decentralized file systems, decentralized social, or decentralized IoT. The existing scheme requires a series of centralized servers or requires network-wide flooding for consensus data, which can lead to the loss of decentralized nature of the network and cause flooding bottlenecks, contrary to the design concept of decentralization. In this paper, our proposed scheme uses a structured P2P overlay network to store the indexes of unreachable nodes in the whole network, so that the characteristics of a decentralized network are still maintained while ensuring the efficiency of lookup. When nodes communicate, the transmission channel is established so that both nodes continuously transmit data streams peer-to-peer without relying on the central server. Moreover, the scheme guarantees the security and privacy of nodes’ data transmission and the P2P overlay network without relying on centralized trusted institutions. Finally, we deploy a real cluster environment to verify the effectiveness of each module at different network sizes and prove the overall feasibility of the scheme. The scheme has certain advantages over existing solutions in terms of security, privacy, communication efficiency, device democracy, etc.

Abstract The Internet of Things (IoT) is a new technology rapidly developed in various fields in recent years. With the continuous application of the IoT technology in production and life, the network security problem of IoT is increasingly prominent. In order to meet the challenges brought by the development of IoT technology, this paper focuses on network security situational awareness. The network security situation awareness is basic of IoT network security. Situation prediction of network security is a kind of time series forecasting problem in essence. So it is necessary to construct a modification function that is suitable for time series data to revise the kernel function of traditional support vector machine (SVM). An improved network security situation awareness model for IoT is proposed in this paper. The sequence kernel support vector machine is obtained and the particle swarm optimization (PSO) method is used to optimize related parameters. It proves that the method is feasible by collecting the boundary data of a university campus IoT network. Finally, a comparison with the PSO-SVM is made to prove the effectiveness of this method in improving the accuracy of network security situation prediction of IoT. The experimental results show that PSO-time series kernel support vector machine is better than the PSO-Gauss kernel support vector machine in network security situation prediction. The application of the Hadoop platform also enhances the efficiency of data processing.

This work reports the formation of a novel adsorbent, prepared by activating bentonite with cinnamic acid, which is highly efficient to remove dyes from wastewater. The adsorption efficiency of the cinnamic acid activated bentonite was compared with unmodified bentonite by removing methyl orange and rhodamine-B from polluted water. The characterization was performed through X-ray diffraction (XRD) Fourier transform infrared (FTIR) and scanning electron microscopy (SEM). The results indicated that acidic pH and low temperature were more suitable for the selected dyes adsorption. The analysis of the data was done by the Langmuir and Freundlich isotherms; the Freundlich isotherm showed more suitability for the equilibrium data. The data were further analyzed by pseudo-first and pseudo-second-order models to study adsorption kinetics. The results showed that methyl orange and rhodamine-B adsorption obeyed pseudo-order kinetics. The results obtained from this research suggested that acid activation of bentonite with cinnamic acid increased the surface area of the clay and hence enhanced its adsorption efficiency. The maximum adsorption efficiency for the removal of methyl orange and rhodamine-B was up to 99.3 mg g−1 and 44.7 mg g−1, respectively, at 25 °C. This research provides an economical modification technique of bentonite, which makes it cost-effective and a good adsorbent for wastewater treatment.

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall.

With the development of data transactions, data security issues have become increasingly important. For example, the copyright authentication and provenance of data have become the primary requirements for data security defense mechanisms. For this purpose, this paper proposes a data provenance system with retention of reference relations (called RRDP), which can enhance the security of data service in the process of publishing and transmission. The system model for data provenance with retention of reference relations adds virtual primary keys using reference relations between data tables. Traditional provenance algorithms have limitations on data types. This model has no such limitations. Added primary key is auto-incrementing integer number. Multi-level encryption is performed on the data watermarking to ensure the secure distribution of data. The experimental results show that the data provenance system with retention of reference relations has good accuracy and robustness of the provenance about common database attacks.

Abstract The graphitic carbon nitride (g-C3N4) which is a two-dimensional conjugated polymer has drawn broad interdisciplinary attention as a low-cost, metal-free, and visible-light-responsive photocatalyst in the area of environmental remediation. The g-C3N4-based materials have excellent electronic band structures, electron-rich properties, basic surface functionalities, high physicochemical stabilities and are “earth-abundant.” This review summarizes the latest progress related to the design and construction of g-C3N4-based materials and their applications including catalysis, sensing, imaging, and white-light-emitting diodes. An outlook on possible further developments in g-C3N4-based research for emerging properties and applications is also included.

Atom-thick two-dimensional materials usually possess unique properties compared to their bulk counterparts. Their properties are significantly affected by defects, which could be uncontrollably introduced by irradiation. The effects of electromagnetic irradiation and particle irradiation on 2H MoS 2 two-dimensional nanolayers are reviewed in this paper, covering heavy ions, protons, electrons, gamma rays, X-rays, ultraviolet light, terahertz, and infrared irradiation. Various defects in MoS 2 layers were created by the defect engineering. Here we focus on their influence on the structural, electronic, catalytic, and magnetic performance of the 2D materials. Additionally, irradiation-induced doping is discussed and involved.

Graphene is a kind of typical two-dimensional material consisting of pure carbon element. The unique material shows many interesting properties which are dependent on crystallographic orientations. Therefore, it is critical to determine their crystallographic orientations when their orientation-dependent properties are investigated. Raman spectroscopy has been developed recently to determine crystallographic orientations of two-dimensional materials and has become one of the most powerful tools to characterize graphene nondestructively. This paper summarizes basic aspects of Raman spectroscopy in crystallographic orientation of graphene nanosheets, determination principles, the determination methods, and the latest achievements in the related studies.

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.

Recently, Android malicious code has increased dramatically and the technology of reinforcement is increasingly powerful. Due to the development of code obfuscation and polymorphic deformation technology, the current Android malicious code static detection method whose feature selected is the semantic of application source code can not completely extract malware’s code features. The Android malware static detection methods whose features used are only obtained from the AndroidManifest.xml file are easily affected by useless permissions. Therefore, there are some limitations in current Android malware static detection methods. The current Android malware dynamic detection algorithm is mostly required to customize the system or needs system root permissions. Based on the Dendritic Cell Algorithm (DCA), this paper proposes an Android malware algorithm that has a higher detection rate, does not need to modify the system, and reduces the impact of code obfuscation to a certain degree. This algorithm is applied to an Android malware detection method based on oriented Dalvik disassembly sequence and application interface (API) calling sequence. Through the designed experiments, the effectiveness of this method is verified for the detection of Android malware.