Oxygen functionalization-assisted anionic exchange toward unique construction of flower-like transition metal chalcogenide embedded carbon fabric for ultra-long life flexible energy storage and conversion

The metal-organic framework(MOF)derived Ni–Co–C–N composite alloys(NiCCZ)were“embedded”inside the carbon cloth(CC)strands as opposed to the popular idea of growing them upward to realize ultrastable energy storage and conversion application.The NiCCZ was then oxygen functionalized,facilitating the next step of stoichiometric sulfur anion diffusion during hydrothermal sulfurization,generating a flower-like metal hydroxysulfide structure(NiCCZOS)with strong partial implantation inside CC.Thus obtained NiCCZOS shows an excellent capacity when tested as a supercapacitor electrode in a three-electrode configuration.Moreover,when paired with the biomass-derived nitrogen-rich activated carbon,the asymmetric supercapacitor device shows almost 100%capacity retention even after 45,000 charge–discharge cycles with remarkable energy density(59.4 Wh kg^(-1)/263.8μWh cm^(–2))owing to a uniquely designed cathode.Furthermore,the same electrode performed as an excellent bifunctional water-splitting electrocatalyst with an overpotential of 271 mV for oxygen evolution reaction(OER)and 168.4 mV for hydrogen evolution reaction(HER)at 10 mA cm−2 current density along with 30 h of unhinged chronopotentiometric stability performance for both HER and OER.Hence,a unique metal chalcogenide composite electrode/substrate configuration has been proposed as a highly stable electrode material for flexible energy storage and conversion applications.

Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption of AI technologies in various applications.

Insider threat detection approach for tobacco industry based on heterogeneous graph embedding

In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

Teaching Reform and Practice of Embedded System Design Based on Outcome-Based Education

Embedded system design is the core course of the telecommunication major in engineering universities,which combines software and hardware through embedded development boards.Aiming at the problems existing in traditional teaching,this paper proposes curriculum teaching reform based on the outcome-based education(OBE)concept,including determining course objectives,reforming teaching modes and methods,and improving the curriculum assessment and evaluation system.After two semesters of practice,this method not only enhances students’learning initiative but also improves teaching quality.

Structural Research on Embedment of a Bare Deflated Spiral Case

Complete bearing spiral case has not been applied to large power stations in China so far. The proposal of applying complete bearing spiral case necessitates an analysis of the reliability of the spiral case structure and the security of units under various working conditions. In combination with practice of a project, this paper presents a three-dimensional nonlinear finite element static analysis of the concrete using a concrete smeared crack model by means of the well-known finite element method （FEM） software ABAQUS. The stress distribution of the spiral case and reinforcing bars, the range of damages in surrounding concrete, and the displacement of structure are quantified. The computational results indicate that the embedment method ensures the structure＇s safety in strength. At the same time, the result shows that this embedment is a kind of preponderant method for embedment in aspects of economy and technique of construction, and the application of this embedment method to the hydropower station is feasible provided that some proper engineering measures are taken to constrain the width of the concrete in accord with the code＇s requirements. The paper proves the security and reliability of the structural design of spiral case in hydropower station accordingly.

Crushing and embedment of proppant packs under cyclic loading: An insight to enhanced unconventional oil/gas recovery

Crushing and embedment are two critical downhole proppant degradation mechanisms that lead to a significant drop in production outputs in unconventional oil/gas stimulation projects. These persistent production drops due to the non-linear responses of proppants under reservoir conditions put the future utilization of such advanced stimulation techniques in unconventional energy extraction in doubt. The aim of this study is to address these issues by conducting a comprehensive experimental approach. According to the results, whatever the type of proppant, all proppant packs tend to undergo significant plastic deformation under the first loading cycle.Moreover, the utilization of ceramic proppants(which retain proppant pack porosity up to 75%), larger proppant sizes(which retain proppant pack porosity up to 15.2%) and higher proppant concentrations(which retain proppant pack porosity up to 29.5%) in the fracturing stimulations with higher in-situ stresses are recommended to de-escalate the critical consequences of crushing associated issues. Similarly, the selection of resin-coated proppants over ceramic and sand proppants may benefit in terms of obtaining reduced proppant embedment.In addition, selection of smaller proppant sizes and higher proppant concentrations are suggested for stimulation projects at depth with sedimentary formations and lower in-situ stresses where proppant embedment predominates. Furthermore, correlation between proppant embedment with repetitive loading cycles was studied.Importantly, microstructural analysis of the proppant-embedded siltstone rock samples revealed that the initiation of secondary induced fractures. Finally, the findings of this study can greatly contribute to accurately select optimum proppant properties(proppant type, size and concentration) depending on the oil/gas reservoir characteristics to minimize proppant crushing and embedment effects.

A Self‑Powered,Highly Embedded and Sensitive Tribo‑Label‑Sensor for the Fast and Stable Label Printer

Label-sensor is an essential component of the label printer which is becoming a most significant tool for the development of Internet of Things(IoT).However,some drawbacks of the traditional infrared label-sensor make the printer fail to realize the high-speed recognition of labels as well as stable printing.Herein,we propose a selfpowered and highly sensitive tribo-label-sensor(TLS)for accurate label identification,positioning and counting by embedding triboelectric nanogenerator into the indispensable roller structure of a label printer.The sensing mechanism,device parameters and deep comparison with infrared sensor are systematically studied both in theory and experiment.As the results,TLS delivers 6 times higher signal magnitude than traditional one.Moreover,TLS is immune to label jitter and temperature variation during fast printing and can also be used for transparent label directly and shows long-term robustness.This work may provide an alternative toolkit with outstanding advantages to improve current label printer and further promote the development of IoT.

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

Recent Study of Drag Embedment Plate Anchors in China

试验性、理论的研究拖最近在天津执行的埋入板锚大学在这篇研究论文被总结，它包含与学习相关的一系列重要话题拖锚。为测量轨道和运动方向的技术在土壤拖锚，为测量动人的埋入的技术削尖并且逆行链状的形状嵌入拖线，穿入机制和运动学的行为拖锚，最终的埋入深度拖锚，有任意的吸虫节的锚的运动方向，反向的链状的性质嵌入拖线， interactional 性质在之间拖锚和安装现在的工作显著地在设计和分析减少无常拖埋入板抛锚，并且对改进这种新类型的申请有益在近海的设计拖锚。

On Some Embedment of Groups into Wreath Products

In this paper, we showed how groups are embedded into wreath products, we gave a simpler proof of the theorem by Audu (1991) (see [1]), also proved that a group can be embedded into the wreath product of a factor group by a normal subgroup and also proved that a factor group can be embedded inside a wreath product and the wreath product of a factor group by a factor group can be embedded into a group. We further showed that when the abstract group in the Universal Embedding Theorem is a p-group, cyclic and simple, the embedding becomes an isomorphism. Examples were given to justify the results.

Characterization of the fracture compressibility and its permeability for shale under the effects of proppant embedment and compaction:A preliminary study

Hydraulic fracturing technology plays a key role in improving the recovery rate of shale gas.The improvement of permeability in relation to hydraulic fracturing depends on changes brought about by the proppant on the fracture structure in reservoirs.Then it is of great significance to describe the microscopic changes during this process by means of an accurate theoretical model.In this study,based on the heterogeneity of shale fracture and the compaction and embedment of a proppant,we proposed a permeability model to examine the combined effects of a proppant and stress to describe the change mechanism in permeability.Further,changes in fracture width and porosity were considered,and a calculation model of fracture compressibility under proppant compaction and embedment was proposed.The difference from previous studies is that the compressibility and permeability of supported fractures can be further quantified and analyzed by this model.Moreover,its rationality was verified by publicly released test data.The results show that,the compressive effect of stress and the embedding of proppant both have a negative impact on shale permeability.

Calculation method of proppant embedment depth in hydraulic fracturing

For the issue of proppant embedment in hydraulic fracturing,a new calculation method of embedment depth considering elastic-plastic deformation was proposed based on the mechanism of proppant embedment into rocks by combining proppant embedment constitutive equations and contact stresses on the rock-proppant system.And factors affecting embedment depth of proppant were analyzed using the new method.Compared with the elastic embedment model,the results calculated by the new method match well with the experimental data,proving the new method is more reliable and more convenient to make theoretical calculation and analysis.The simulation results show the process of proppant embedment into rocks is mainly elastic-plastic.The embedment depth of monolayer proppants decreases with higher proppant concentration.Under multi-layer distribution conditions,increasing the proppant concentration will not change its embedment depth.The larger the proppant embedment ratio,the more the stress-bearing proppants,and the smaller the embedment depth will be.The embedment depth under higher closure stress is more remarkable.The embedment depth increased with the drawdown of fluid pressure in the fracture.Increasing proppant radius or the ratio of proppant Young’s modulus to rock Young’s modulus can reduce the proppant embedment depth.

Critical embedment depth of a rigid retaining wall against overturning in unsaturated soils considering intermediate principal stress and strength nonlinearity

The overturning stability is vital for the retaining wall design of foundation pits, where the surrounding soils are usually unsaturated due to water draining. Moreover, the intermediate principal stress does affect the unsaturated soil strength; meanwhile, the relationship between the unsaturated soil strength and matric suction is nonlinear. This work is to present closed-form equations of critical embedment depth for a rigid retaining wall against overturning by means of moment equilibrium. Matric suction is considered to be distributed uniformly and linearly with depth. The unified shear strength formulation for unsaturated soils under the plane strain condition is adopted to characterize the intermediate principal stress effect, and strength nonlinearity is described by a hyperbolic model of suction angle. The result obtained is orderly series solutions rather than one specific answer; thus, it has wide theoretical significance and good applicability. The validity of this present work is demonstrated by comparing it with a lower bound solution. The traditional overturning designs for rigid retaining walls, in which the saturated soil mechanics neglecting matric suction or the unsaturated soil mechanics based on the Mohr-Coulomb criterion are employed, are special cases of the proposed result. Parametric studies about the intermediate principal stress, matric suction and its distributions along with two strength nonlinearity methods on a new defined critical buried coefficient are discussed.

Morphological features of allogenic nerve segment in rats after subcutaneous embedment

BACKGROUND ： Some studies demonstrate that allogenic peripheral nerve segment embedded subcutaneously significantly reduce the infiltration of lymphocyte and decrease immunological reaction.OBJECTIVE ： To observe the gross shape, optical and electron microscope results of allogenic nerve segment in rats 2 weeks after subcutaneous embedment, and compare with subcutaneous emdedment of autologous nerve segment. DESIGN ： A randomized and controlled experiment.SETTING ： Department of Orthopaedics of Fifth People＇s Hospital of Zhengzhou; Department of Orthopaedics,First Hospital Affiliated to Chongqing Medical University.MATERIALS ： Totally 30 adult healthy Wistar male rats, with body mass of （200±20） g, were enrolled. Ten rats were chosen as the donors of allogenic nerve transplantation. The other 20 rats were randomly divided into 2 groups： allogenic nerve embedment group and autologous nerve embedment group, with 10 rats in each one. JEM-1220 transmission electron microscope （Japan） and Olympus BX50 optical microscope （Japan） were used. METHODS ： This experiment was carried out at the laboratory of Orthopaedic Department, Chongqing Medical University from October 2000 to April 2002. ① Sciatic nerve of donor rats for allogenic nerve transplantation was cut off at 5 mm distant from pelvic strait.15 mm sciatic nerve segment was chosen from lateral part as graft, allogenic nerve embedment group： 15 mm sciatic nerve form the donor rats was embedded in the posterior part of right legs. Autologous nerve embedment group： 15 mm sciatic nerve segment of autologous left side was embedded in the posterior side of right legs. ② Nerve segment embedded subcutaneously was taken out at postoperative 2 weeks and performed gross observation; then 5 samples chosen randomly respectively from 2 groups and given haematoxylin-eosin staining and observation under optical microscope （×400）;The other 5 samples were made into ultrathin sections （0.5μm）and observed under transmission electron microscope（×17 000）. MAIN OUTCOME MEASURES ： Gross shape, optical and electron microscope results of nerve segments of rats between two groups at 2 weeks after subcutaneous embedment. RESULTS ： ① Results of gross observation： Appearance of nerve segment was similar between 2 groups. ② Results of optical observation： medullary sheath denaturation, axonotmesis, vascular engorgement, desmoplasia of adventitia and infiltration of inflammatory cells were all found in both 2 groups. Inflammatory reaction was a little more severe in the allogenic nerve embedment group than in the autologous nerve embedment groups.③Results of electron microscope ： Similar cataplasia and denaturation of medullary sheath and cataplasia of Schwann cell were all found in the 2 groups. CONCLUSION： Some inflammatory reaction occurs after allogenic nerve embedment, but the activity of Schwann cell is similar to that of peripheral nerve after autologous nerve embedment.

Upper bound seismic rotational stability analysis of gravity retaining walls considering embedment depth

Stability analysis of gravity retaining wall was currently based on the assumption that the wall had no embedment depth. The effect of earth berm was usually neglected. The present work highlighted the importance of embedment depth when assessing the seismic stability of gravity retaining walls with the pattern of pure rotation. In the framework of upper bound theorem of limit analysis, pseudo-static method was applied into two groups of parallel rigid soil slices methods in order to account for the effect of embedment depth on evaluating the critical acceleration of wall-soil system. The present analytical solution is identical to the results obtained from using limit equilibrium method, and the two methods are based on different theory backgrounds. Parameter analysis indicates that the critical acceleration increases slowly when the ratio of the embedment depth to the total height of the wall is from 0 to 0.15 and increases drastically when the ratio exceeds 0.15.