Theoretically Catalytic Synthesis of 5-Nitro-1,2,4-Triazol-3-One in Inert Gas Clustered System (X₆, X = He, Ne)

Inert gas-clustered systems (Xn, X = He, Ne, Ar and n = 2 - 20) were established in this study and their stability as a result of interparticulate interaction was examined. Ferric chloride and ferrous oxides were used as catalysts to promote reaction, and 5-nitro-1,2,4-triazol-3-one (NTO) was theoretically synthesized under an inert gas (X6)-clustered environment in this study. The raw material, urea, initially underwent chlorination using chlorine as the reagent, followed by amination, formylation and nitration. Reaction routes closely related to the experimental processes were successfully constructed, and the corresponding energy barriers were estimated for each elementary reaction. The findings revealed that the average errors in the B3LYP/6-31G(d, p)-calculated geometry and vibrational frequency of NTO in an Ne6 system relative to the observed values were 0.83% and 1.84%, respectively. The neon gas-clustered system achieved greater stabilization, which results from the difference in self-consistent field energy (ESCF), than the corresponding stabilization acquired in a helium- or argon-based system. Ferric chloride serves as a good catalyst to reduce the energy barrier of the chlorination reaction, and ferrous oxide is suitable for catalyzing the amination, formylation and nitration reactions, although nitric acid is the better agent for nitration. The catalytic Ne6-clustered reaction system is suggested to be a more feasible pathway for the synthesis of NTO.

Clustered张拉整体结构的动力学建模

针对clustered张拉整体结构,提出了一种基于任意拉格朗日-欧拉(arbitrary Lagrangian-Eulerian, ALE)法的多体动力学模型。相较于传统的拉格朗日法模型,该研究中的模型具有更为简单的运动学约束。首先,引入了一种ALE时变长度索单元,其网格节点与物质点可以独立运动,提供了一种自然的方式描述结构中运动的滑轮和滑动绳索;其次,利用达朗贝尔原理,推导了该单元的广义力向量并计算了相应的雅可比矩阵;然后,建立了张拉整体系统的动力学方程,并利用广义α算法对其进行求解,选取节点的全局位置坐标和物质坐标作为广义坐标,其中全局位置坐标可以被不同物体共享,以减少动力学方程的自由度数和消除物体间的约束;最后,展示了一个数值算例,10层可折叠张拉整体塔架,对其折叠过程进行了准静态和动力学仿真,验证了模型的有效性。所提出的模型和算法可为clustered张拉整体结构的设计提供理论指导,具有工程意义。

Modified clustered comb pilot-aided fast time-varying channel estimation for OFDM system

As high-speed railway is booming worldwide, the communication system with fast-time varying channel has drawn great attention. The comb pilot based linear minimum mean square error （LMMSE） channel estimator is proved to be an effective method for fast time-varying channel estimation. In this paper, the clustered comb pilot-aided chan- nel estimation for orthogonal frequency-division multiplexing （OFDM） system is discussed, where the time varying channel is approximated by a basis expansion model （BEM）. A modified clustered comb pilot structure is proposed and justified to improve the estimation performance compared with the clustered comb pilot proposed by Tang. Based on the complex-exponential BEM （CE-BEM） model, a suboptimal-pilot structure is proposed. In addition, optimal pilot length is analyzed and simulated with a predefined total number of pilots. The simulation results show that the modi- fied clustered comb pilot can greatly reduce the estimation error especially with high Doppler spread. The suboptimal- pilot structure with guard pilot approximation is proven to be competitive. Optimal nonzero pilot lengths for different Doppler spread are obtained by simulation with a predefined channel order and fixed pilot subcarriers.

Time-domain clustered transmit power adaptation for OFDM system in fading channels

This paper proposes a time-domain clustered transmitter power adaptation scheme for orthogonal frequency division multiplexing (OFDM) system, which can significantly reduce the feedback amount during power adaptation comparison with conventional frequency-domain adaptation schemes. It was found that the cluster size plays an important role on the adaptation performance, especially for the vehicular environment. Simulation results showed that using Lagrange interpolation to obtain an explicit curve of Doppler frequency vs cluster size yields good trade-off between the resulted bit error rate (BER) and the amount of feedback.

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of CPES.Since security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection.The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion detection.Besides,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)model.The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects.The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

In the Internet of Things(IoT)based system,the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems(UCS).The UCS necessitates heterogeneity,management level,and data transmission for distributed users.Simultaneously,security remains a major issue in the IoT-driven UCS.Besides,energy-limited IoT devices need an effective clustering strategy for optimal energy utilization.The recent developments of explainable artificial intelligence(XAI)concepts can be employed to effectively design intrusion detection systems(IDS)for accomplishing security in UCS.In this view,this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System(BXAI-IDCUCS)model.The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment.The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization(EADSO)algorithm to accomplish this.Besides,deep neural network(DNN)is employed for detecting and classifying intrusions in the IoT network.Lastly,blockchain technology is exploited for secure inter-cluster data transmission processes.To ensure the productive performance of the BXAI-IDCUCS model,a comprehensive experimentation study is applied,and the outcomes are assessed under different aspects.The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%,a packet loss rate of 0.71%,a throughput of 92.95 Mbps,energy consumption of 0.0891 mJ,a lifetime of 3529 rounds,and accuracy of 99.38%.

Fixed-Time Cluster Optimization for Multi-Agent Systems Based on Piecewise Power-Law Design

Dear Editor,This letter focuses on the fixed-time(FXT)cluster optimization problem of first-order multi-agent systems(FOMASs)in an undirected network,in which the optimization objective is the sum of the objective functions of all clusters.A novel piecewise power-law control protocol with cooperative-competition relations is proposed.Furthermore,a sufficient condition is obtained to ensure that the FOMASs achieve the cluster consensus within an FXT.

Exploring social-ecological system resilience in South China Karst:Quantification,interaction and policy implication

China’s Grain to Green Program(GTGP),which is one of the largest payments for ecosystem services(PES)in the world,has made significant ecological improvements to the environment.However,current understanding of its outcomes on the social-ecological system(SES)remains limited.Therefore,taking the South China Karst as an example,a SES resilience evaluation index system was constructed followed by an exploratory spatial analysis,root mean square error,and Self-Organizing Feature Map to clarify the spatiotemporal changes and relationship of SES resilience,achieve the zoning of SES resilience and provide restoration measures.The results showed an upward trend in social resilience from 2000 to 2020,especially its subsystem of social development.Regional ecological resilience was stable,owing to a slightly declined ecosystem services and increased landscape pattern.Spatially,nearly half of the counties exhibited a distribution mismatch in SES resilience.There was an obvious inverted U-shaped relationship of SES resilience,indicating a clear threshold effect,and the constraint relation-ship of SES resilience eased over time,demonstrating the effectiveness of the ecological restoration program.GTGP played a positive role in reducing regional SES trade-off,but this positive effect was limited,reflecting the limitations of overemphasizing the conversion from farmland to forest and grassland.Regional SES resilience can be divided into four clusters,which were the key optimization zone for social system,the SES resilience safety zone,the key restoration zone for SES resilience,and the key optimization zone for ecological system.Adaptive adjustments for the GTGP in these zones should be taken to achieve maximum SES benefits in the future.

Systematic study on heavy-particle radioactivity of superheavy nuclei ^(297–300)119

In the current study,we examined every possible cluster-daughter combination in the heavy-particle decay of isotopes ^(297-300)119 and computed the decay half-lives using the modified generalized liquid drop model(MGLDM)with the preformation factor depending on the disintegration energy.The predicted half-life of every heavy cluster(Z_(C)≥32)was within the experimentally observable limits.These results aligned with the predictions of Poenaru et al.[Phys.Rev.Lett.107,062503(2011)]that superheavy nuclei(SHN)with Z>110 will release heavy particles with a penetrability comparable to or greater than theα-decay.The half-lives predicted using the MGLDM for clusters^(89)Rb,^(91)Rb,and^(92)Rb from parents^(297)119,^(299)119,and^(300)119,respectively,agreed with the predictions of Poenaru et al.[Eur.Phys.J.A 54,14(2018)].It was found that the isotopes of heavy clusters Kr,Rb,Sr,Pa,In,and Cd had half-lives comparable to theαhalf-life;and isotopes of clusters I,Xe,and Cs had the minimum half-life(10^(-14)s).These observations revealed the role of the shell closure(Z=82,N=82,and N=126)of the cluster and daughter nuclei in heavy-cluster radioactivity.We predicted that isotope ^(297,299)119 decayed by 4αdecay chains and isotope^(300)119 decayed by 6αdecay chains,while^(298)119 decayed by continuousαdecay chains.The predicted half-lives and modes of decay of the nuclei in the decay chains of^(297-300)119 agreed with the experimental data,proving the reliability of our calculations.The present study determined the most favorable heavy-cluster emissions from these nuclei and provided suitable projectile-target combinations for their synthesis.

System error iterative identification for underwater positioning based on spectral clustering

The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning.

Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.

Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

Research on typical operating conditions of hydrogen production system with off-grid wind power considering the characteristics of proton exchange membrane electrolysis cell

Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System ContainingWind-Storage Combined Unit

To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.

Bruton’s tyrosine kinase inhibitors in primary central nervous system lymphoma:New hopes on the horizon

In this editorial,we comment on the article by Wang et al.This manuscript explores the potential synergistic effects of combining zanubrutinib,a novel oral inhibitor of Bruton’s tyrosine kinase,with high-dose methotrexate(HD-MTX)as a therapeutic intervention for primary central nervous system lymphoma(PCNSL).The study involves a retrospective analysis of 19 PCNSL patients,highlighting clinicopathological characteristics,treatment outcomes,and genomic biomarkers.The results indicate the combination’s good tolerance and strong antitumor activity,with an 84.2%overall response rate.The authors emphasize the potential of zanubrutinib to modulate key genomic features of PCNSL,particularly mutations in myeloid differentiation primary response 88 and cluster of differentiation 79B.Furthermore,the study investigates the role of circulating tumor DNA in cerebrospinal fluid for disease surveillance and treatment response monitoring.In essence,the study provides valuable insights into the potential of combining zanubrutinib with HD-MTX as a frontline therapeutic regimen for PCNSL.The findings underscore the importance of exploring alternative treatment modalities and monitoring genomic and liquid biopsy markers to optimize patient outcomes.While the findings suggest promise,the study’s limitations should be considered,and further research is needed to establish the clinical relevance of this therapeutic approach for PCNSL.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.

Modeling of the Multi-Target Locating and Tracking in the Field Artillery System

A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied. A general modeling structure of the system is established. Based on concepts of cluster and closed ball, an algorithm is put forward for multi sensor multi target data fusion and an optimal solution for state estimation is presented. The simulation results prove the algorithm works well for the multi stationary target locating and the multi moving target tracking under the condition of the sparse target environment. Therefore, this method can be directly applied to the field artillery C 3I system.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Simultaneous Partitioning and Scheduling Algorithm for Clustered Architecture

Clustered architecture is selected for high level synthesis,and a simultaneous partitioning and scheduling algorithm are proposed.Compared with traditional methods,circuit performance can be improved.Experiments show the efficiency of the method.