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 fun...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.展开更多
We propose an approach for generating robust two-dimensional(2D)vortex clusters(VCs)in a Rydberg atomic system by utilizing parity-time(PT)symmetric optical Bessel potential.We show that the system supports novel mult...We propose an approach for generating robust two-dimensional(2D)vortex clusters(VCs)in a Rydberg atomic system by utilizing parity-time(PT)symmetric optical Bessel potential.We show that the system supports novel multicore VCs with four and eight cores,corresponding to topological charges 2 and 4,respectively.The stability of these VCs can be dynamically adjusted through the manipulation of the gain-loss component,Kerr nonlinearities,and the degree of nonlocality inherent in the Rydberg atoms.These VCs are confined within the first lattice well of the Bessel potential,and both the power and width of lights undergo a quasi-periodic breathing phenomenon,which is attributed to the power exchange between the light fields and Bessel potential.Both self-attractive and self-repulsive Kerr interactions can sustain robust VCs within this system.The insights presented here not only facilitate the creation and manipulation of 2D VCs through PT-symmetric potentials but also pave the way for potential applications in optical information processing and transmission.展开更多
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 consideri...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.展开更多
Recently,cell-free(CF)massive multipleinput multiple-output(MIMO)becomes a promising architecture for the next generation wireless communication system,where a large number of distributed access points(APs)are deploye...Recently,cell-free(CF)massive multipleinput multiple-output(MIMO)becomes a promising architecture for the next generation wireless communication system,where a large number of distributed access points(APs)are deployed to simultaneously serve multiple user equipments(UEs)for improved performance.Meanwhile,a clustered CF system is considered to tackle the backhaul overhead issue in the huge connection network.In this paper,taking into account the more realistic mobility scenarios,we propose a hybrid small-cell(SC)and clustered CF massive MIMO system through classifications of the UEs and APs,and constructing the corresponding pairs to run in SC or CF mode.A joint initial AP selection of this paradigm for all the UEs is firstly proposed,which is based on the statistics of estimated channel.Then,closed-form expressions of the downlink achievable rates for both the static and moving UEs are provided under Ricean fading channel and Doppler shift effect.We also develop a semi-heuristic search algorithm to deal with the AP selection for the moving UEs by maximizing the weight average achievable rate.Numerical results demonstrate the performance gains and effective rates balancing of the proposed system.展开更多
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 vari...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.展开更多
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 subse...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.展开更多
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
Remarkable progress has been made in infection prevention and control(IPC)in many countries,but some gaps emerged in the context of the coronavirus disease 2019(COVID-19)pandemic.Core capabilities such as standard cli...Remarkable progress has been made in infection prevention and control(IPC)in many countries,but some gaps emerged in the context of the coronavirus disease 2019(COVID-19)pandemic.Core capabilities such as standard clinical precautions and tracing the source of infection were the focus of IPC in medical institutions during the pandemic.Therefore,the core competences of IPC professionals during the pandemic,and how these contributed to successful prevention and control of the epidemic,should be studied.To investigate,using a systematic review and cluster analysis,fundamental improvements in the competences of infection control and prevention professionals that may be emphasized in light of the COVID-19 pandemic.We searched the PubMed,Embase,Cochrane Library,Web of Science,CNKI,WanFang Data,and CBM databases for original articles exploring core competencies of IPC professionals during the COVID-19 pandemic(from January 1,2020 to February 7,2023).Weiciyun software was used for data extraction and the Donohue formula was followed to distinguish high-frequency technical terms.Cluster analysis was performed using the within-group linkage method and squared Euclidean distance as the metric to determine the priority competencies for development.We identified 46 studies with 29 high-frequency technical terms.The most common term was“infection prevention and control training”(184 times,17.3%),followed by“hand hygiene”(172 times,16.2%).“Infection prevention and control in clinical practice”was the most-reported core competency(367 times,34.5%),followed by“microbiology and surveillance”(292 times,27.5%).Cluster analysis showed two key areas of competence:Category 1(program management and leadership,patient safety and occupational health,education and microbiology and surveillance)and Category 2(IPC in clinical practice).During the COVID-19 pandemic,IPC program management and leadership,microbiology and surveillance,education,patient safety,and occupational health were the most important focus of development and should be given due consideration by IPC professionals.展开更多
An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the...An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.展开更多
The legacy of United States cluster munition use in Laos and Cambodia during the Second Indochina War is residual bomblets that unexpectedly detonate years later, killing and injuring children, farmers, and other civi...The legacy of United States cluster munition use in Laos and Cambodia during the Second Indochina War is residual bomblets that unexpectedly detonate years later, killing and injuring children, farmers, and other civilians. Cluster munitions release dozens of smaller bomblets that rain deadly ammunition on troops, armored tanks, and vegetation, effectively striking broad sections of war zone landscapes in one launch. While many bomblets detonate immediately, others fail to detonate and can lie dormant on the ground for years. The primary objectives of this study were to document the long-term consequences and impacts of the US Air Force bombing of Laos and Cambodia during the Second Indochina War (1959 to 1973). The historical lessons learned by United States should be shared with Russia and Ukraine governments and military. These countries need to discontinue the use of cluster bombs to prevent additional people living along the Russia-Ukraine border from having to live and die with the consequences of unexploded ordnance, including cluster bombs, for the next century.展开更多
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability...Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.展开更多
In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clu...In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.展开更多
Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of spatial modeling. This study i...Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of spatial modeling. This study investigates the application of hard and fuzzy clustering algorithms for domain delineation, using geological and geochemical data from two exploration campaigns at the eastern Kahang deposit in central Iran. The dataset includes geological layers (lithology, alteration, and mineral zones), geochemical layers (Cu, Mo, Ag, and Au grades), and borehole coordinates. Six clustering algorithms—K-means, hierarchical, affinity propagation, self-organizing map (SOM), fuzzy C-means, and Gustafson-Kessel—were applied to determine the optimal number of clusters, which ranged from 3 to 4. The fuzziness and weighting parameters were found to range from 1.1 to 1.3 and 0.1 to 0.3, respectively, based on the evaluation of various hard and fuzzy cluster validity indices. Directional variograms were computed to assess spatial anisotropy, and the anisotropy ellipsoid for each domain was defined to identify the model with the highest level of anisotropic discrimination among the domains. The SOM algorithm, which incorporated both qualitative and quantitative data, produced the best model, resulting in the identification of three distinct domains. These findings underscore the effectiveness of combining clustering techniques with variogram analysis for accurate domain delineation in geostatistical modeling.展开更多
Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of a...Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.展开更多
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.展开更多
The percolation fields constructed around the elements of a cluster system in the phase spaces of properties are studied.It is shown that such neighborhoods significantly increase the number of structure parameters of...The percolation fields constructed around the elements of a cluster system in the phase spaces of properties are studied.It is shown that such neighborhoods significantly increase the number of structure parameters of the system under study,expanding the possibilities of analytical description.To study the structure and properties of such systems in the proposed model,a three-dimensional continuum percolation problem with interacting elements is solved.The dependences of the structure and properties of clusters on the parameters of the generation processes of the cluster system are studied,and analytical dependences are obtained.展开更多
We investigated the ionization and dissociation processes of ammonia clusters ranging from dimer to pentamer induced by 800-nm femtosecond laser fields.Time-of-flight(TOF)mass spectra of the ammonia clusters were reco...We investigated the ionization and dissociation processes of ammonia clusters ranging from dimer to pentamer induced by 800-nm femtosecond laser fields.Time-of-flight(TOF)mass spectra of the ammonia clusters were recorded over a range of laser intensities from 2.1×10^(12)W/cm^(2) to 5.6×10^(12)W/cm^(2).The protonated ion signals dominate the spectra,which is consistent with the stability of the geometric structures.The ionization and dissociation channels of ammonia clusters are discussed.The competition and switching among observed dissociation channels are revealed by analyzing the variations in the relative ionic yields of specific protonated and unprotonated clusters under different laser intensities.These results indicate that the ionization of the neutral multiple-ammonia units,produced through the dissociation of cluster ions,may start to contribute,as well as the additional processes to consume protonated ions and/or produce unprotonated ions induced by the femtosecond laser fields when the laser intensity is above^4×10^(12)W/cm^(2).These findings provide deeper insights into the ionization and dissociation dynamics in multi-photon ionization experiments involving ammonia clusters.展开更多
Due to depletion interactions, a few of colloidal spheres will be packed into cluster or clusters, even a phase transition may take place if the volume fraction of system is large enough. In a binary colloidal system,...Due to depletion interactions, a few of colloidal spheres will be packed into cluster or clusters, even a phase transition may take place if the volume fraction of system is large enough. In a binary colloidal system, if the mole fraction of one component is very small, then it can be taken as the impurity of the other component. In this work, the effect of impurity on critical conditions of colloidal cluster nucleation was studied by Carnahan-Starling state equation and the principle of entropy maximum. The results show that, even the mole fraction of small-spheres is very small, the critical volume fraction is obvious smaller than that of one component system, so the influence on critical volume fraction from impurity is very huge and cannot be ignored. In addition, it is also found that, the larger the volume fraction of the system is, the larger cluster density can be packed, however, the critical size of nucleating cluster is almost independent of the density of the cluster.展开更多
This paper investigates the cluster consensus problem for second-order multi-agent systems by applying the pinning control method to a small collection of the agents. Consensus is attained independently for different ...This paper investigates the cluster consensus problem for second-order multi-agent systems by applying the pinning control method to a small collection of the agents. Consensus is attained independently for different agent clusters according to the community structure generated by the group partition of the underlying graph and sufficient conditions for both cluster and general consensus are obtained by using results from algebraic graph theory and the LaSalle Invariance Principle. Finally, some simple simulations are presented to illustrate the technique.展开更多
An advanced fuzzy C-mean （FCM） algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, m...An advanced fuzzy C-mean （FCM） algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, modified similarity measure was considered to gather nodes having similar characteristics. The similarity measure was needed to contain locafi0nal prices as well as regional coherency. In order to consider the two properties simultaneously, distance measure of fuzzy C-mean algorithm had to be modified. Regional clustering algorithm for interconnected power systems was designed based on the modified fuzzy C-mean algorithm. The proposed algorithm produces proper classification for the interconnected power system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system.展开更多
基金supported in part by the National Natural Science Foundation of China(62373231,61973201)the Fundamental Research Program of Shanxi Province(202203021211297)Shanxi Scholarship Council of China(2023-002)。
文摘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.
基金Project supported by the National Natural Science Foundation of China(Grant No.62275075)the Science and Technology Research Program of the Education Department of Hubei Province,China(Grant No.B2022188)+2 种基金the Natural Science Foundation of Hubei Province,China(Grant No.2023AFC042)the Training Program of Innovation and Entrepreneurship for Undergraduates of Hubei Province,China(Grant No.S202210927003)the Medical Project of Hubei University of Science and Technology(Grant No.2023YKY08)。
文摘We propose an approach for generating robust two-dimensional(2D)vortex clusters(VCs)in a Rydberg atomic system by utilizing parity-time(PT)symmetric optical Bessel potential.We show that the system supports novel multicore VCs with four and eight cores,corresponding to topological charges 2 and 4,respectively.The stability of these VCs can be dynamically adjusted through the manipulation of the gain-loss component,Kerr nonlinearities,and the degree of nonlocality inherent in the Rydberg atoms.These VCs are confined within the first lattice well of the Bessel potential,and both the power and width of lights undergo a quasi-periodic breathing phenomenon,which is attributed to the power exchange between the light fields and Bessel potential.Both self-attractive and self-repulsive Kerr interactions can sustain robust VCs within this system.The insights presented here not only facilitate the creation and manipulation of 2D VCs through PT-symmetric potentials but also pave the way for potential applications in optical information processing and transmission.
基金This work was supported by the National Natural Science Foundation of China(61903086,61903366,62001115)the Natural Science Foundation of Hunan Province(2019JJ50745,2020JJ4280,2021JJ40133)the Fundamentals and Basic of Applications Research Foundation of Guangdong Province(2019A1515110136).
文摘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.
基金This work was supported by the China National Key Research and Development Plan(No.2020YFB1807204).
文摘Recently,cell-free(CF)massive multipleinput multiple-output(MIMO)becomes a promising architecture for the next generation wireless communication system,where a large number of distributed access points(APs)are deployed to simultaneously serve multiple user equipments(UEs)for improved performance.Meanwhile,a clustered CF system is considered to tackle the backhaul overhead issue in the huge connection network.In this paper,taking into account the more realistic mobility scenarios,we propose a hybrid small-cell(SC)and clustered CF massive MIMO system through classifications of the UEs and APs,and constructing the corresponding pairs to run in SC or CF mode.A joint initial AP selection of this paradigm for all the UEs is firstly proposed,which is based on the statistics of estimated channel.Then,closed-form expressions of the downlink achievable rates for both the static and moving UEs are provided under Ricean fading channel and Doppler shift effect.We also develop a semi-heuristic search algorithm to deal with the AP selection for the moving UEs by maximizing the weight average achievable rate.Numerical results demonstrate the performance gains and effective rates balancing of the proposed system.
文摘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.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘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.
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
基金The National Natural Science Foundation of China,Grant/Award Number:52178080Major Research Project of the Hospital Management Research Institute of the National Health Commission,Grant/Award Number:GY2023011National Institute of Hospital Administration Management of China,Grant/Award Number:GY2023049。
文摘Remarkable progress has been made in infection prevention and control(IPC)in many countries,but some gaps emerged in the context of the coronavirus disease 2019(COVID-19)pandemic.Core capabilities such as standard clinical precautions and tracing the source of infection were the focus of IPC in medical institutions during the pandemic.Therefore,the core competences of IPC professionals during the pandemic,and how these contributed to successful prevention and control of the epidemic,should be studied.To investigate,using a systematic review and cluster analysis,fundamental improvements in the competences of infection control and prevention professionals that may be emphasized in light of the COVID-19 pandemic.We searched the PubMed,Embase,Cochrane Library,Web of Science,CNKI,WanFang Data,and CBM databases for original articles exploring core competencies of IPC professionals during the COVID-19 pandemic(from January 1,2020 to February 7,2023).Weiciyun software was used for data extraction and the Donohue formula was followed to distinguish high-frequency technical terms.Cluster analysis was performed using the within-group linkage method and squared Euclidean distance as the metric to determine the priority competencies for development.We identified 46 studies with 29 high-frequency technical terms.The most common term was“infection prevention and control training”(184 times,17.3%),followed by“hand hygiene”(172 times,16.2%).“Infection prevention and control in clinical practice”was the most-reported core competency(367 times,34.5%),followed by“microbiology and surveillance”(292 times,27.5%).Cluster analysis showed two key areas of competence:Category 1(program management and leadership,patient safety and occupational health,education and microbiology and surveillance)and Category 2(IPC in clinical practice).During the COVID-19 pandemic,IPC program management and leadership,microbiology and surveillance,education,patient safety,and occupational health were the most important focus of development and should be given due consideration by IPC professionals.
文摘An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.
文摘The legacy of United States cluster munition use in Laos and Cambodia during the Second Indochina War is residual bomblets that unexpectedly detonate years later, killing and injuring children, farmers, and other civilians. Cluster munitions release dozens of smaller bomblets that rain deadly ammunition on troops, armored tanks, and vegetation, effectively striking broad sections of war zone landscapes in one launch. While many bomblets detonate immediately, others fail to detonate and can lie dormant on the ground for years. The primary objectives of this study were to document the long-term consequences and impacts of the US Air Force bombing of Laos and Cambodia during the Second Indochina War (1959 to 1973). The historical lessons learned by United States should be shared with Russia and Ukraine governments and military. These countries need to discontinue the use of cluster bombs to prevent additional people living along the Russia-Ukraine border from having to live and die with the consequences of unexploded ordnance, including cluster bombs, for the next century.
文摘Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.
基金partially supported by the National Natural Science Foundation of China(62161016)the Key Research and Development Project of Lanzhou Jiaotong University(ZDYF2304)+1 种基金the Beijing Engineering Research Center of Highvelocity Railway Broadband Mobile Communications(BHRC-2022-1)Beijing Jiaotong University。
文摘In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.
文摘Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of spatial modeling. This study investigates the application of hard and fuzzy clustering algorithms for domain delineation, using geological and geochemical data from two exploration campaigns at the eastern Kahang deposit in central Iran. The dataset includes geological layers (lithology, alteration, and mineral zones), geochemical layers (Cu, Mo, Ag, and Au grades), and borehole coordinates. Six clustering algorithms—K-means, hierarchical, affinity propagation, self-organizing map (SOM), fuzzy C-means, and Gustafson-Kessel—were applied to determine the optimal number of clusters, which ranged from 3 to 4. The fuzziness and weighting parameters were found to range from 1.1 to 1.3 and 0.1 to 0.3, respectively, based on the evaluation of various hard and fuzzy cluster validity indices. Directional variograms were computed to assess spatial anisotropy, and the anisotropy ellipsoid for each domain was defined to identify the model with the highest level of anisotropic discrimination among the domains. The SOM algorithm, which incorporated both qualitative and quantitative data, produced the best model, resulting in the identification of three distinct domains. These findings underscore the effectiveness of combining clustering techniques with variogram analysis for accurate domain delineation in geostatistical modeling.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Science and Technology Development Plan in Jilin Province of China(20230203135SF)+1 种基金National Natural Science Foundation of China(41875119)Special Fund for Innovative Development of China Meteorological Administration(CXFZ2022J007)。
文摘Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.
文摘Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
文摘The percolation fields constructed around the elements of a cluster system in the phase spaces of properties are studied.It is shown that such neighborhoods significantly increase the number of structure parameters of the system under study,expanding the possibilities of analytical description.To study the structure and properties of such systems in the proposed model,a three-dimensional continuum percolation problem with interacting elements is solved.The dependences of the structure and properties of clusters on the parameters of the generation processes of the cluster system are studied,and analytical dependences are obtained.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.92261201,12134005,12334011)。
文摘We investigated the ionization and dissociation processes of ammonia clusters ranging from dimer to pentamer induced by 800-nm femtosecond laser fields.Time-of-flight(TOF)mass spectra of the ammonia clusters were recorded over a range of laser intensities from 2.1×10^(12)W/cm^(2) to 5.6×10^(12)W/cm^(2).The protonated ion signals dominate the spectra,which is consistent with the stability of the geometric structures.The ionization and dissociation channels of ammonia clusters are discussed.The competition and switching among observed dissociation channels are revealed by analyzing the variations in the relative ionic yields of specific protonated and unprotonated clusters under different laser intensities.These results indicate that the ionization of the neutral multiple-ammonia units,produced through the dissociation of cluster ions,may start to contribute,as well as the additional processes to consume protonated ions and/or produce unprotonated ions induced by the femtosecond laser fields when the laser intensity is above^4×10^(12)W/cm^(2).These findings provide deeper insights into the ionization and dissociation dynamics in multi-photon ionization experiments involving ammonia clusters.
文摘Due to depletion interactions, a few of colloidal spheres will be packed into cluster or clusters, even a phase transition may take place if the volume fraction of system is large enough. In a binary colloidal system, if the mole fraction of one component is very small, then it can be taken as the impurity of the other component. In this work, the effect of impurity on critical conditions of colloidal cluster nucleation was studied by Carnahan-Starling state equation and the principle of entropy maximum. The results show that, even the mole fraction of small-spheres is very small, the critical volume fraction is obvious smaller than that of one component system, so the influence on critical volume fraction from impurity is very huge and cannot be ignored. In addition, it is also found that, the larger the volume fraction of the system is, the larger cluster density can be packed, however, the critical size of nucleating cluster is almost independent of the density of the cluster.
基金Project supported by the National Natural Science Foundation of China (Grant No. 70571059)
文摘This paper investigates the cluster consensus problem for second-order multi-agent systems by applying the pinning control method to a small collection of the agents. Consensus is attained independently for different agent clusters according to the community structure generated by the group partition of the underlying graph and sufficient conditions for both cluster and general consensus are obtained by using results from algebraic graph theory and the LaSalle Invariance Principle. Finally, some simple simulations are presented to illustrate the technique.
基金Work supported by the Second Stage of Brain Korea 21 ProjectsWork(2010-0020163) supported by Priority Research Centers Program through the National Research Foundation (NRF) funded by the Ministry of Education,Science and Technology of Korea
文摘An advanced fuzzy C-mean （FCM） algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, modified similarity measure was considered to gather nodes having similar characteristics. The similarity measure was needed to contain locafi0nal prices as well as regional coherency. In order to consider the two properties simultaneously, distance measure of fuzzy C-mean algorithm had to be modified. Regional clustering algorithm for interconnected power systems was designed based on the modified fuzzy C-mean algorithm. The proposed algorithm produces proper classification for the interconnected power system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system.
