Engineering of geometrical configurations in dual-atom catalysts for electrocatalytic applications

Geometrical configurations play a crucial role in dual-atom catalysts(DACs)for electrocatalytic applications.Significant progress has been made to design DACs electrocatalysts with various geometri-cal configurations,but in-depth understanding the relationship between geometrical configurations and metal-metal interaction mechanisms for designing targeted DACs is still required.In this review,the recent progress in engineering of geometrical configurations of DACs is systematically summarized.Based on the polarity of geometrical configuration,DACs can be classified into two different types that are homonuclear and heteronuclear DACs.Furthermore,with regard to the geometrical configurations of the active sites,homonuclear DACs are identified into adjacent and bridged configurations,and heteronuclear DACs can be classified into adjacent,bridged,and separated configurations.Subsequently,metal-metal interactions in DACs with different geometrical configurations are introduced.Additionally,the applications of DACs in different electrocatalytic reactions are discussed,including the oxygen reduction reaction(ORR),oxygen evolution reaction(OER),hydrogen evolution reaction(HER),and other catalysis.Finally,the future challenges and perspectives for advancements in DACs are high-lighted.This review aims to provide inspiration for the design of highly effcient DACs towards energy relatedapplications.

Review of the Configuration and Transient Stability of Large-scale Renewable Energy Generation through Hybrid DC Transmission

Based on the complementary advantages of Line Commutated Converter(LCC)and Modular Multilevel Converter(MMC)in power grid applications,there are two types of hybrid DC system topologies:one is the parallel connection of LCC converter stations and MMC converter stations,and the other is the series connection of LCC and MMC converter stations within a single station.The hybrid DC transmission system faces broad application prospects and development potential in large-scale clean energy integration across regions and the construction of a new power system dominated by new energy sources in China.This paper first analyzes the system forms and topological characteristics of hybrid DC transmission,introducing the forms and topological characteristics of converter-level hybrid DC transmission systems and system-level hybrid DC transmission systems.Next,it analyzes the operating characteristics of LCC and MMC inverter-level hybrid DC transmission systems,provides insights into the transient stability of hybrid DC transmission systems,and typical fault ride-through control strategies.Finally,it summarizes the networking characteristics of the LCC-MMC series within the converter station hybrid DC transmission system,studies the transient characteristics and fault ridethrough control strategies under different fault types for the LCC-MMC series in the receiving-end converter station,and investigates the transient characteristics and fault ride-through control strategies under different fault types for the LCC-MMC series in the sending-end converter station.

Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

Sliding and damming properties of granular debris with different geometric configurations and grain size distributions

Granular debris plays a significant role in determining damming deposit characteristics. An indepth understanding of how variations in grain size distribution(GSD) and geometric configurations impact the behavior of granular debris during the occurrence of granular debris is essential for precise assessment and effective mitigation of landslide hazards in mountainous terrains. This research aims to investigate the impact of GSD and geometric configurations on sliding and damming properties through laboratory experiments. The geometric configurations were categorized into three categories based on the spatial distribution of maximum volume: located at the front(Type Ⅰ), middle(Type Ⅱ), and rear(Type Ⅲ) of the granular debris. Our experimental findings highlight that the sliding and damming processes primarily depend on the interaction among the geometric configuration, grain size, and GSD in granular debris. Different sliding and damming mechanisms across various geometric configurations induce variability in motion parameters and deposition patterns. For Type Ⅰ configurations, the front debris functions as the critical and primary driving component, with energy dissipation primarily occurring through inter-grain interactions. In contrast, Type Ⅱ configurations feature the middle debris as the dominant driving component, experiencing hindrance from the front debris and propulsion from the rear, leading to complex alterations in sliding motion. Here, energy dissipation arises from a combination of inter-grain and grain-substrate interactions. Lastly, in Type Ⅲ configurations, both the middle and rear debris serve as the main driving components, with the rear sliding debris impeded by the front. In this case, energy dissipation predominantly results from grainsubstrate interaction. Moreover, we have quantitatively demonstrated that the inverse grading in damming deposits, where coarse grain moves upward and fine grain moves downward, is primarily caused by grain sorting due to collisions among the grains and between the grain and the base. The impact of grain on the horizontal channel further aids grain sorting and contributes to inverse grading. The proposed classification of three geometric configurations in our study enhances the understanding of damming properties from the view of mechanism, which provides valuable insights for related study about damming granular debris.

A numerical survey of parameters to reach ignition condition for axial compression of a large-sized field reversed configuration

Field reversed configuration(FRC)is widely considered as an ideal target plasma for magnetoinertial fusion.However,its confinement and stability,both proportional to the radius,will deteriorate inevitably during radial compression.Hence,we propose a new fusion approach based on axial compression of a large-sized FRC.The axial compression can be made by plasma jets or plasmoids converging onto the axial ends of the FRC.The parameter space that can reach the ignition condition while preserving the FRC's overall quality is studied using a numerical model based on different FRC confinement scalings.It is found that ignition is possible for a large FRC that can be achieved with the current FRC formation techniques if compression ratio is greater than 50.A more realistic compression is to combine axial with moderate radial compression,which is also presented and calculated in this work.

Configuration Synthesis and Grasping Performance Analysis of Multi-Loop Coupling Capture Mechanism for Launch and Recovery of Torpedo-Shaped Autonomous Underwater Vehicle

Research of capture mechanisms with strong capture adaptability and stable grasp is important to solve the problem of launch and recovery of torpedo-shaped autonomous underwater vehicles(AUVs).A multi-loop coupling capture mechanism with strong adaptability and high retraction rate has been proposed for the launch and recovery of torpedo-shaped AUVs with different morphological features.Firstly,the principle of capturing motion retraction is described based on the appearance characteristics of torpedo-shaped AUVs,and the configuration synthesis of the capture mechanism is carried out using the method of constrained chain synthesis.Secondly,the screw theory is employed to analyze the degree of freedom(DoF)of the capture mechanism.Then,the 3D model of the capture mechanism is established,and the kinematics and dynamics simulations are carried out.Combined with the capture orientation requirements of the capture mechanism,the statics and vibration characteristics analyses are carried out.Furthermore,considering the capture process and the underwater working environment,the motion characteristics and hydraulics characteristics of the capture mechanism are analyzed.Finally,a principle prototype is developed and the torpedo-shaped AUVs capture experiment is completed.The work provides technical reserves for the research and development of AUV capture special equipment.

Spatiotemporal variations,influencing factors,and configurational promotion paths of energy efficiency in China’s cities

The promotion of energy efficiency(EE)helps address energy constraints and promote environmental sustainability.This study comprehensively explores the spatiotemporal variations,influencing factors,and configuration promotion paths of EE in 284 Chinese cities during 2003‒2019 using the global super-efficiency minimum distance to strong efficient frontier(G-S-MinDS),exploratory spatial data analysis(ESDA),multiscale geographically weighted regression(MGWR),and fuzzy set qualitative comparative analysis(fsQCA)methods.The findings are:①China’s cities have an annual average EE of 0.658 with a growth rate of 0.53%,showing considerable promotion potential.②Industrial structure optimization,population agglomeration,economic development,and increased green coverage contribute positively,while government intervention and openness hinder China’s urban EE.③Four configurational promotion paths for enhancing China’s urban EE are identified,where among those paths population density is a core condition,while government intervention is not.This study provides valuable insights into substantially improving urban EE,emphasizing the need for targeted policies to address energy and environmental crises in China.

Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

Effect of the Particle Packing Configuration on Fixed Bed Performance

Fixed-bed reactors are generally considered the optimal choice for numerous multi-phase catalytic reactions due to their excellent performance and stability.However,conventional fixed beds often encounter challenges related to inadequate mass transfer and a high pressure drop caused by the non-uniform void fraction distribution.To enhance the overall performance of fixed beds,the impact of different packing configurations on performance was investigated.Experimental and simulation methods were used to investigate the fluid flow and mass transfer performances of various packed beds under different flow rates.It was found that structured beds exhibited a significantly lower pressure drop per unit length than conventional packed beds.Furthermore,the packing configurations had a critical role in improving the overall performance of fixed beds.Specifically,structured packed beds,particularly the H-2 packing configuration,effectively reduced the pressure drop per unit length and improved the mass transfer efficiency.The H-2 packing configuration consisted of two parallel strips of particles in each layer,with strips arranged perpendicularly between adjacent layers,and the spacing between the strips varied from layer to layer.

Constructing long-cycling crystalline C_(3)N_(4)-based carbonaceous anodes for sodium-ion battery via N configuration control

Carbon nitrides with two-dimensional layered structures and high theoretical capacities are attractive as anode materials for sodium-ion batteries while their low crystallinity and insufficient structural stability strongly restrict their practical applications.Coupling carbon nitrides with conductive carbon may relieve these issues.However,little is known about the influence of nitrogen(N)configurations on the interactions between carbon and C_(3)N_(4),which is fundamentally critical for guiding the precise design of advanced C_(3)N_(4)-related electrodes.Herein,highly crystalline C_(3)N_(4)(poly(triazine imide),PTI)based all-carbon composites were developed by molten salt strategy.More importantly,the vital role of pyrrolic-N for enhancing charge transfer and boosting Na+storage of C_(3)N_(4)-based composites,which was confirmed by both theoretical and experimental evidence,was spot-highlighted for the first time.By elaborately controlling the salt composition,the composite with high pyrrolic-N and minimized graphitic-N content was obtained.Profiting from the formation of highly crystalline PTI and electrochemically favorable pyrrolic-N configurations,the composite delivered an unusual reverse growth and record-level cycling stability even after 5000 cycles along with high reversible capacity and outstanding full-cell capacity retention.This work broadens the energy storage applications of C_(3)N_(4) and provides new prospects for the design of advanced all-carbon electrodes.

Study on the Fine Configuration of Reservoir in River Facies Oilfield in Bohai Sea Area

The geological conditions of offshore shallow water delta oil reservoirs were complex, with limited well data and large well spacing. Taking A Oilfield in the Bohai Sea Area, China as an example, the target sand body was formed in a shallow water delta sedimentary environment, with well-developed underwater distributary channels and frequent branching and diversion. The reservoir was strong non-uniformity and uneven plane water cut pressure. To this end, based on the existing work of predecessors, combined with seismic, logging, and production dynamics data, and based on the genesis mechanism of shallow water delta reservoirs, the boundary of composite river channels was identified through seismic facies, and logging facies were used to subdivide them into single river levels within the composite river channels. Then, seismic waveform characteristics were applied to track and characterize the plane distribution of single river channels, guiding the efficient development of offshore shallow water delta oil fields and achieving increased storage and production in Bohai Oilfield, China.

Research on Visualization and Interactive Design of Plant Configuration Virtual Reality in the Context of Digitalization

This study aims to explore the application of digital technology in landscape design,focusing on the research of virtual reality visualization and interactive design in the process of plant configuration.Through an in-depth analysis of digital technology,the study outlines its important role in landscape design,especially in the application of plant configuration.The current application status of virtual reality technology in landscape design is discussed,as well as how interactive design can enhance user experience and participation.Furthermore,the achievements and challenges of digital technology in landscape design are summarized.Finally,it proposes future research directions and suggestions,aiming to provide new ideas and methods for practice and research in the field of landscape design and promote the further application and development of digital technology in landscape design.

Feasibility Analysis of IMA Configuration Design Tool

With the widespread application of integrated modular aviation(IMA)electronic systems,the requirements of IMA characteristic analysis and configuration generation have also improved significantly."Configuration and design system based on AADL models(real-time,reliability,interactive complexity,and schedulability)analysis of the AADL model"mainly get through key characteristic analysis for optimization,combined with input of component parameter information and constraint information,assist designers in generating system configuration information,and form a comprehensive modular avionics system resource configuration strategy generation platform.Also,the tool can generate configuration plans for avionics system software and hardware physical architecture configuration.The analysis of the avionics system model mainly involves designers inputting the existing system model architecture for multi characteristic analysis,it can realize the multi-character analysis based on software intensive system architecture models for software dense systems.Inter-complexity and schedulability analysis,and according to characteristic constraint analysis、system model assessment parameters and system architecture constraint parameters to generate configuration scheme information that meets the requirements.The system can meet domestic and foreign software-intensive systems and meet its quality requirements.

Integrated Experimental and Simulation Investigation of Breakdown Voltage Characteristics Across Electrode Configurations in SF_(6) Circuit Breakers

This study investigates the breakdown voltage characteristics in sulfur hexafluoride(SF6)circuit breakers,employing a novel approach that integrates both experimental investigations and finite element simulations.Utilizing a sphere-sphere electrode configuration,we meticulously measured the relationship between breakdown voltage and electrode gap distances ranging from 1 cm to 4.5 cm.Subsequent simulations,conducted using COMSOL Multiphysics,mirrored the experimental setup to validate the model’s accuracy through a comparison of the breakdown voltage-electrode gap distance curves.The simulation results not only aligned closely with the experimental data but also allowed the extraction of detailed electric field strength,electric potential contours,and electric current flow curves at the breakdown voltage for gap distances extending from 1 to 4.5 cm.Extending the analysis,the study explored the electric field and potential distribution at a constant voltage of 72.5 kV for gap distances between 1 to 10 cm,identifying the maximum electric field strength.A comprehensive comparison of five different electrode configurations(sphere-sphere,sphere-rod,sphere-plane,rod-plane,rod-rod)at 72.5 kV and a gap distance of 1.84 cm underscored the significant influence of electrode geometry on the breakdown process.Moreover,the research contrasts the breakdown voltage in SF6 with that in air,emphasizing SF6’s superior insulating properties.This investigation not only elucidates the intricate dynamics of electrical breakdown in SF6 circuit breakers but also contributes valuable insights into the optimal electrode configurations and the potential for alternative insulating gases,steering future advancements in high-voltage circuit breaker technology.

Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability information.Self-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts.However,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources.The process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and error-prone.Nevertheless,systems’complexity can be reduced while the development process of systems by reusing elements or components.FMs are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system components.While current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been considered.In this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse process.Evaluation results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

Zinc–Bromine Rechargeable Batteries:From Device Configuration,Electrochemistry,Material to Performance Evaluation

Zinc–bromine rechargeable batteries(ZBRBs)are one of the most powerful candidates for next-generation energy storage due to their potentially lower material cost,deep discharge capability,non-flammable electrolytes,relatively long lifetime and good reversibility.However,many opportunities remain to improve the efficiency and stability of these batteries for long-life operation.Here,we discuss the device configurations,working mechanisms and performance evaluation of ZBRBs.Both non-flow(static)and flow-type cells are highlighted in detail in this review.The fundamental electrochemical aspects,including the key challenges and promising solutions,are discussed,with particular attention paid to zinc and bromine half-cells,as their performance plays a critical role in determining the electrochemical performance of the battery system.The following sections examine the key performance metrics of ZBRBs and assessment methods using various ex situ and in situ/operando techniques.The review concludes with insights into future developments and prospects for high-performance ZBRBs.

Environmental Analysis Using Integrated GIS and Spatial Configurations in Israel

The scope for environmental analysis constitutes a critical factor in recent times, yet demanding importance due to the concerns of environmental sustainability. The study aims at analysing the prospects of implementing an integrated GIS and spatial configuration for environment analysis in Israel. The study adopts an empirical study design to consider the multi-dimensional utilisation of an integrated GIS and spatial configuration for environment analysis. The study considers the materials and methods of the GIS system modelling as well, consisting of satellite imagery, GPS-based location identification, Esri ArcGIS, CyberGIS, and BIM integration to present a comprehensive system for the environmental analysis of Israel. The results of the study indicate that the threats of natural disasters and climate change can be identified based on the synergy of spatial data within an integrated GIS modelling. In many cases, it is also used in collaboration with a BIM to ensure that planning and decision-making processes are sustainable, economically beneficial and environmentally considered. Thus, it is concluded that environmental analysis through the projection of visually represented satellite imagery within an integrated GIS with spatial configurations in Israel can minimise the conflicts between the infrastructural designs, human activities, and environmental sustainability.

Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

A self-adaptive resource provisioning on demand is a critical factor in cloud computing.The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests.Therefore,a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload.In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy(CARSS)Framework that formally addresses these issues and is more expressive than traditional approaches.The decision making in CARSS is based on more than one factors.TheMAPE-K based framework determines the state of the resources based on their current utilization.Timed-Arc Petri Net(TAPN)is used to model system formally and behaviour is expressed in TCTL,while TAPAAL model checker verifies the underline properties of the system.

Self-adaptive bulk/surface engineering of Bi_(x)O_(y)Br_(z) towards enhanced photocatalysis:Current status and future challenges

The bulk/surface states of semiconductor photocatalysts are imperative parameters to maneuver their performance by significantly affecting the key processes of photocatalysis including light absorption,separation of charge carrier,and surface site reaction.Recent years have witnessed the encouraging progress of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z) for photocatalytic applications spanning various fields.However,despite the maturity of current research,the interaction between the bulk/surface state and the performance of Bi_(x)O_(y)Br_(z) has not yet been fully understood and highlighted.In this regard,a timely tutorial overview is quite urgent to summarize the most recent key progress and outline developing obstacles in this exciting area.Herein,the structural characteristics and fundamental principles of Bi_(x)O_(y)Br_(z)for driving photocatalytic reaction as well as related key issues are firstly reviewed.Then,we for the first time summarized different self-adaptive engineering processes over Bi_(x)O_(y)Br_(z)followed by a classification of the generation approaches towards diverse Bi_(x)O_(y)Br_(z)materials.The features of different strategies,the up-to-date characterization techniques to detect bulk/surface states,and the effect of bulk/surface states on improving the photoactivity of Bi_(x)O_(y)Br_(z)in expanded applications are further discussed.Finally,the present research status,challenges,and future research opportunities of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z)are prospected.It is anticipated that this critical review can trigger deeper investigations and attract upcoming innovative ideas on the rational design of Bi_(x)O_(y)Br_(z)-based photocatalysts.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.