From VIB‑to VB‑Group Transition Metal Disulfides:Structure Engineering Modulation for Superior Electromagnetic Wave Absorption

The laminated transition metal disulfides(TMDs),which are well known as typical two-dimensional(2D)semiconductive materials,possess a unique layered structure,leading to their wide-spread applications in various fields,such as catalysis,energy storage,sensing,etc.In recent years,a lot of research work on TMDs based functional materials in the fields of electromagnetic wave absorption(EMA)has been carried out.Therefore,it is of great significance to elaborate the influence of TMDs on EMA in time to speed up the application.In this review,recent advances in the development of electromagnetic wave(EMW)absorbers based on TMDs,ranging from the VIB group to the VB group are summarized.Their compositions,microstructures,electronic properties,and synthesis methods are presented in detail.Particularly,the modulation of structure engineering from the aspects of heterostructures,defects,morphologies and phases are systematically summarized,focusing on optimizing impedance matching and increasing dielectric and magnetic losses in the EMA materials with tunable EMW absorption performance.Milestones as well as the challenges are also identified to guide the design of new TMDs based dielectric EMA materials with high performance.

Defect chemistry engineering of Ga-doped garnet electrolyte with high stability for solid-state lithium metal batteries

Ga-doped Li_(7)La_(3)Zr_(2)O_(12)(Ga-LLZO)has long been considered as a promising garnet-type electrolyte candidate for all-solid-state lithium metal batteries(ASSLBs)due to its high room temperature ionic conductivity.However,the typical synthesis of Ga-LLZO is usually accompanied by the formation of undesired LiGaO_(2) impurity phase that causes severe instability of the electrolyte in contact with molten Li metal during half/full cell assembly.In this study,we show that by simply engineering the defect chemistry of Ga-LLZO,namely,the lithium deficiency level,LiGaO_(2) impurity phase is effectively inhibited in the final synthetic product.Consequently,defect chemistry engineered Ga-LLZO exhibits excellent electrochemical stability against lithium metal,while its high room temperature ionic conductivity(~1.9×10^(-3)S·cm^(-1))is well reserved.The assembled Li/Ga-LLZO/Li symmetric cell has a superior critical current density of 0.9 mA·cm^(-2),and cycles stably for 500 hours at a current density of 0.3 mA·cm^(-2).This research facilitates the potential commercial applications of high performance Ga-LLZO solid electrolytes in ASSLBs.

Ultra-Net Emission and Solid Waste Transfer of Air Pollutants in Steel, Power and Cement Industries—The Idea of Construction and Key Technology of Denitration Engineering Transformation and By-Product Formation

In this paper, an integrated desulfurization and denitrification technology is proposed for ultra-low emissions of SO2 and NOx in the steel, power and cement industries. A cost-effective and operationally efficient control strategy is realized through a forced oxidation-absorption-reduction process, which reduces equipment investment and operating costs. The technology was adapted to continuous and intermittent denitrification in different temperature zones, promoting the recycling of desulfurization and denitrification products. The study also explored the use of a highly active absorbent obtained by the hydration reaction of coal ash and lime from a power company for the desulfurization and denitrification of sintered flue gases in iron and steel mills, which produces by-products that can be used as retarding agents in the cement industry, resulting in a circular economy. The article emphasizes the importance of improving the lime digestion process and developing new denitrification agents for environmentally safe and cost-effective flue gas treatment.

Defect Engineering:Can it Mitigate Strong Coulomb Effect of Mg^(2+)in Cathode Materials for Rechargeable Magnesium Batteries?

Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,the sluggish diffusion kinetics of bivalent Mg^(2+)in the host material,related to the strong Coulomb effect between Mg^(2+)and host anion lattices,hinders their further development toward practical applications.Defect engineering,regarded as an effective strategy to break through the slow migration puzzle,has been validated in various cathode materials for RMBs.In this review,we first thoroughly understand the intrinsic mechanism of Mg^(2+)diffusion in cathode materials,from which the key factors affecting ion diffusion are further presented.Then,the positive effects of purposely introduced defects,including vacancy and doping,and the corresponding strategies for introducing various defects are discussed.The applications of defect engineering in cathode materials for RMBs with advanced electrochemical properties are also summarized.Finally,the existing challenges and future perspectives of defect engineering in cathode materials for the overall high-performance RMBs are described.

Research on the Reform of Practical Teaching of Internet of Things Engineering in the Context of New Engineering

The emergence of new engineering disciplines has resulted in the growing trend of cross-discipline,and the enhancement of students’technical application ability has become the main teaching objective of engineering disciplines.For this reason,the Internet of Things(IoT)engineering program should be actively reformed,providing students with sufficient opportunities to improve their practical skills.This paper identifies the challenges within practical teaching of IoT engineering,delves into effective strategies for practical IoT teaching within the context of emerging engineering disciplines,and presents practical teaching experiences from the School of Information Engineering at Hainan University of Science and Technology as a case study.The aim is to offer guidance and insights to educators in this field.

Construction and Identification of HSP70 Antisense RNA Expression Vector for Genetic Engineering Male Sterility in Plant

[ Objective] In order to study the relation between the HSPTO gene and male sterility of plant further. [ Methods ] Anther specific expression promoter Osg6B of rice was coloned by PCR then connected with HSP70 antisense fragment to construct HSPTO antisense expression vector. The expression vector was identified by PCR experiment and enzyme digestion. [ Result] The sequence of coloned Osg6B promoter had 97% homology to the published sequence, and the cis-regulatory element in promoter area was integrated. HSP70 antisense expression vector driven by the promoter Osg6B was confired by colony PCR and enzyme digestion. [ Conclusion] The construction of expression vector would lay solid foundation for utilization of genetic engineering male sterility of plant.

Chloroplast Genetic Engineering in Higher Plants

Chloroplast genetic engineering, with several advantages over nuclear genetic engineering, is now regarded as an attractive new technology in basic and applied research, including deepening our understanding of plastid genome, engineering plant metabolic system, generating transplastomic plants with higher resistance to insect, disease, drought and herbicide and bioproducing of antibodies and vaccines. In this review, the principle and operating system for chloroplast genetic engineering and its application in higher plants have been discussed.

Advances in Cold Tolerance Genes and Their Application in Genetic Engineering of Plant for Cold Tolerance

Low temperature is one of the main environmental stress factors influenc- ing plant growth and development and crop yield. Cold tolerance genes and progress of their application in genetic engineering of plant for cold tolerance were reviewed comprehensively and systematically from the aspect of genes that are in- volved in biosynthesis of osmotic substances, genes coding fatty acid desaturation enzymes, antifreeze protein genes, genes coding antioxidant enzymes and so on, aiming at laying the foundation for genetic improvement of cold tolerance and breeding of plants.

A novel type of neural networks for feature engineering of geological data:Case studies of coal and gas hydrate-bearing sediments

The nature of the measured data varies among different disciplines of geosciences.In rock engineering,features of data play a leading role in determining the feasible methods of its proper manipulation.The present study focuses on resolving one of the major deficiencies of conventional neural networks(NNs)in dealing with rock engineering data.Herein,since the samples are obtained from hundreds of meters below the surface with the utmost difficulty,the number of samples is always limited.Meanwhile,the experimental analysis of these samples may result in many repetitive values and 0 s.However,conventional neural networks are incapable of making robust models in the presence of such data.On the other hand,these networks strongly depend on the initial weights and bias values for making reliable predictions.With this in mind,the current research introduces a novel kind of neural network processing framework for the geological that does not suffer from the limitations of the conventional NNs.The introduced single-data-based feature engineering network extracts all the information wrapped in every single data point without being affected by the other points.This method,being completely different from the conventional NNs,re-arranges all the basic elements of the neuron model into a new structure.Therefore,its mathematical calculations were performed from the very beginning.Moreover,the corresponding programming codes were developed in MATLAB and Python since they could not be found in any common programming software at the time being.This new kind of network was first evaluated through computer-based simulations of rock cracks in the 3 DEC environment.After the model’s reliability was confirmed,it was adopted in two case studies for estimating respectively tensile strength and shear strength of real rock samples.These samples were coal core samples from the Southern Qinshui Basin of China,and gas hydrate-bearing sediment(GHBS)samples from the Nankai Trough of Japan.The coal samples used in the experiments underwent nuclear magnetic resonance(NMR)measurements,and Scanning Electron Microscopy(SEM)imaging to investigate their original micro and macro fractures.Once done with these experiments,measurement of the rock mechanical properties,including tensile strength,was performed using a rock mechanical test system.However,the shear strength of GHBS samples was acquired through triaxial and direct shear tests.According to the obtained result,the new network structure outperformed the conventional neural networks in both cases of simulation-based and case study estimations of the tensile and shear strength.Even though the proposed approach of the current study originally aimed at resolving the issue of having a limited dataset,its unique properties would also be applied to larger datasets from other subsurface measurements.

TRAFFIC ENGINEERING IN IP BACKBONE NETWORK

This paper discusses traffic engineering with multi protocol label switching (MPLS) in a backbone of Internet data center (IDC) network. The traditional SPF routing limitations are analyzed and the traffic engineering concept is given. MPLS, resource reservation protocol (RSVP) and enhanced link status Protocols intermediate system to intermediate system (IS IS) are reviewed to provide a background for traffic engineering, the general issues of designing an MPLS system of IDC network for traffic engineering are then discussed. Finally a practical example with MPLS traffic engineering is shown.

Prototype Engineering Test Platform of ITER Magnet Gravity Support

ITER magnet gravity support （GS） has been redesigned as a structure of pre- assembled nmlti-flexible plates instead of the original welded structure. In the past several years, engineering tests of the new structure have been proposed. A prototype engineering test plat- form is being developed. In order to apply the loads/load combinations onto the test mock-up, seven hydraulic bolt tensioners in three directions have been applied to simulate various loads （forces and moments）, through which the deformation of bolts, flexible plates and clamp blocks, the stress distribution in the flexible plates, the friction between the contact surface, etc. can be monitored/tested. The measurement and control system includes seven sets of synchronization controller, a 16-channel strain gauge, 25 sets of displacement sensors, etc. Principles of EDC220 digital controller and development of multi-channel control software are also demonstrated.

Traffic Engineering in Dynamic Hybrid Segment Routing Networks

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet routing.In SR architecture,the paths that the packets choose to route on are indicated at the ingress router.Compared with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress router.Despite the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical reasons.Updating partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable choice.For the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR network.WASAR algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering algorithm.Secondly,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)algorithm.Thirdly,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network performance.In the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in WASAR.The extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

Magnetic resonance imaging-three-dimensional printing technology fabricates customized scaffolds for brain tissue engineering

Conventional fabrication methods lack the ability to control both macro- and micro-structures of generated scaffolds. Three-dimensional printing is a solid free-form fabrication method that provides novel ways to create customized scaffolds with high precision and accuracy. In this study, an electrically controlled cortical impactor was used to induce randomized brain tissue defects. The overall shape of scaffolds was designed using rat-specific anatomical data obtained from magnetic resonance imaging, and the internal structure was created by computer- aided design. As the result of limitations arising from insufficient resolution of the manufacturing process, we magnified the size of the cavity model prototype five-fold to successfully fabricate customized collagen-chitosan scaffolds using three-dimensional printing. Results demonstrated that scaffolds have three-dimensional porous structures, high porosity, highly specific surface areas, pore connectivity and good internal characteristics. Neural stem cells co-cultured with scaffolds showed good viability, indicating good biocompatibility and biodegradability. This technique may be a promising new strategy for regenerating complex damaged brain tissues, and helps pave the way toward personalized medicine.

Conifer genetic engineering: Particle bombardment and Agrobacterium-mediated gene transfer and its application in future forests

Many important advances in forest biotechnology have been made. The use of genetic transformation and the ap-plications of transgenic trees in modern forestry is now an important field. Two basic methodologies particle bombardment and Agrobacterium-mediated transformation have been used on conifers. However, routine procedures exist for only a limited number of conifers. As a result only a few species have been successfully transformed into stable transgenic plants. The use of a particle bombardment has been more successful and transgenic plants have been produced in Picea abies, Picea glauca, Picea mariana, and Pinus radiata, although the level of production of stable transgenic plants is lower than that of Agrobacte-rium. At present, breeding programs have been directed toward improving bole shape, growth rate, wood properties, and quality, as well as toward improving root and shoot performance, pest resistance, stress tolerance, herbicide resistance, and ability to resist stresses, which will drive forestry to enter a new era of productivity and quality. This article provides a brief overview of the current state of knowledge on genetic transformation in conifers.

Genetic engineering and lignin biosynthetic regulation in forest tree species

Genetic engineering of forest tree species is regarded as a strategy to reduce worldwide pressure on natural forests, to conserve genetic resources and ameliorate stress on global climate, and to meet growing demand for forest wood and timber products. Genetic engineering approaches toward the control or management of fungal pathogens, arthropod herbivores, bacterial and viral diseases, the use of pest resistance genes, and weed competitors are being studied. Although the production of transgenic trees is relatively recent and only a few species have been successfully genetically engineered in forest tree species, very useful and valuable information is available on the application of transgenic trees. Genes involved in important agricultural traits such as herbicide resistance, insect resistance, and wood quality have been isolated and have been used to genetically engineer trees. New technologies of plant molecular biology and genomics now make it possible high-efficient genetic improvement of forest trees. Genetic engineering promises to expand greatly the potential for genetic manipulation as new genes of commercial interest are discovered and utilized. Lignification is a process essential to the nature and evolution of vascular plants that is still poorly understood, even though it has been studied for more than a century. Recent studies on mutant and transgenic plants indicate that lignification may be far more flexible than previously realized. Rines with a mutation affecting the biosynthesis of the major lignin precursor, coniferyl alcohol, show a high level of an unusual subunit, dihydroconiferyl alcohol. It is also unusual as a plant polymer in that there are no plant enzymes for its degradation. These results have significant implications regarding the tradiational definition of lignin, and highlight the need for a better understanding of the lignin precursor biosynthetic pathway. In this review, we describe the progress made recently in genetic engineering of forest tree species.

NETLAB-An Internet based laboratory for electrical engineering education

This article describes an Internet based laboratory (NETLAB) developed at Zhejiang University for electrical engi- neering education. A key feature of the project is the use of real experimental systems rather than simulation or virtual reality. NELTAB provides remote access to a wide variety of experiments, including not only basic electrical and electronic experiments but also many innovative control experiments. Students can effectively use the laboratory at any time and from anywhere. NETLAB has been in operation since July 2003.

Performance Evaluation of Traffic Engineering Signal Protocols in IPV6 MPLS Networks

This paper studies the performance of Traffic Engineering (TE) signal protocols used for load balancing in Multi-Protocol Label Switching (MPLS) networks, namely;Constraint Based Routed Label Distribution Protocol LDP (CR-LDP) and Resource Reservation Protocol (RSVP). Furthermore, the performance of an MPLS network uses these TE signal protocols is compared to that of a conventional Internet Protocol (IP) network. Different applications including voice, video, File Transfer Protocol (FTP) and Hyperlink Text Transfer Protocol (HTTP) are used for the performance evaluation. Simulation results show superior performance of the MPLS network with CR-LDP TE signal protocol in all tested applications.

Eradication of malaria through genetic engineering:the current situation

Malaria is an intra-cellular parasitic protozoon responsible for millions of deaths annually.Host and parasite genetic factors are crucial in affecting susceptibility to malaria and progression of the disease.Recent increased deployment of vector controls and new artemisinin combination therapies have dramatically reduced the mortality and morbidity of malaria worldwide.However, the gradual emergence of parasite and mosquito resistance has raised alarm regarding the effectiveness of current artemisinin-based therapies.In this review,mechanisms of anti-malarial drug resistance in the Plasmodium parasite and new genetically engineered tools of research priorities are discussed.The complexity of the parasite lifecycle demands novel interventions to achieve global eradication.However,turning laboratory discovered transgenic interventions into functional products entails multiple experimental phases in addition to ethical and safety hurdles.Uncertainty over the regulatory status and public acceptance further discourage the implementation of genetically modified organisms.

Traffic Engineering Based on Deep Reinforcement Learning in Hybrid IP/SR Network

Segment Routing(SR)is a new routing paradigm based on source routing and provide traffic engineering(TE)capabilities in IP network.By extending interior gateway protocol(IGP),SR can be easily applied to IP network.However,upgrading current IP network to a full SR one can be costly and difficult.Hybrid IP/SR network will last for some time.Aiming at the low flexibility problem of static TE policies in the current SR networks,this paper proposes a Deep Reinforcement Learning(DRL)based TE scheme.The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution.As a result,the network congestion can be mitigated and the performance of the network is improved.Simulation results show that our proposed scheme can improve the throughput of the network by up to 9%than existing schemes.

An Algorithm for Target Detection of Engineering Vehicles Based on Improved CenterNet

Aiming at the problems of low target image resolution,insufficient target feature extraction,low detection accuracy and poor real time in remote engineering vehicle detection,an improved CenterNet target detection model is proposed in this paper.Firstly,EfficientNet-B0 with Efficient Channel Attention(ECA)module is used as the basic network,which increases the quality and speed of feature extraction and reduces the number of model parameters.Then,the proposed Adaptive Fusion Bidirectional Feature Pyramid Network(AF-BiFPN)module is applied to fuse the features of different feature layers.Furthermore,the feature information of engineering vehicle targets is added by making full use of the high-level semantic and low-level fine-grained feature information of the target,which overcomes the problem that the original CenterNet network did not perform well in small target detection and improve the detection accuracy of the network.Finally,the tag coding strategy and bounding box regression method of CenterNet are optimized by introducing positioning quality loss.The accuracy of target prediction is increased by joint prediction of center position and target size.Experimental results show that the mean Average Precision(mAP)of the improved CenterNet model is 94.74%on the engineering vehicle dataset,and the detection rate is 29 FPS.Compared with the original CenterNet model based on ResNet-18,the detection accuracy of this model is improved by 16.29%,the detection speed is increased by 9 FPS,and the memory usage is reduced by 43 MB.Compared with YOLOv3 and YOLOv4,the mAP of this model is improved by 19.9%and 5.61%respectively.The proposed method can detect engineering vehicles more quickly and accurately in far distance.It has obvious advantages in target detection compared with traditional methods.