Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hie...Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hierarchical, use the distance function to measure the dissimilarities among actors. These distance functions need to fulfill various properties, including the triangle inequality (TI). However, in some cases, the triangle inequality might be violated, impacting the quality of the resulting clusters. With experiments, this paper explains how TI violates while performing traditional clustering techniques: k-medoids, hierarchical, DENGRAPH, and spectral clustering on social networks and how the violation of TI affects the quality of the resulting clusters.展开更多
Based on the analysis of the importance of professional cluster construction by ecological theory,with the change of social demand for talents,this paper explores the practice of environmental chemical professional cl...Based on the analysis of the importance of professional cluster construction by ecological theory,with the change of social demand for talents,this paper explores the practice of environmental chemical professional cluster construction in Pingdingshan University,including gradually perfecting teaching conditions and reforming teaching mode,breaking through the limitations of resources,integrating the boundaries of colleges and departments,integrating multiple resources,innovating systems and mechanisms,reconstructing professional clusters,decon-structing professional connotations,reorganizing curriculum systems,etc.,in order to better build the ecological chain network of education in application-oriented colleges and universities,realize the deep integration of industry and education,train future-oriented interdisciplinary applied talents of new engineering,and realize the construction of characteristic professional cluster in application-oriented colleges.展开更多
To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)stru...To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.展开更多
Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims...Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.展开更多
The valence states and coordination structures of doped heterometal atoms in two-dimensional(2D)nanomaterials lack predictable regulation strategies.Hence,a robust method is proposed to form unsaturated heteroatom clu...The valence states and coordination structures of doped heterometal atoms in two-dimensional(2D)nanomaterials lack predictable regulation strategies.Hence,a robust method is proposed to form unsaturated heteroatom clusters via the metal-vacancy restraint mechanism,which can precisely regulate the bonding and valence state of heterometal atoms doped in 2D molybdenum disulfide.The unsaturated valence state of heterometal Pt and Ru cluster atoms form a spatial coordination structure with Pt–S and Ru–O–S as catalytically active sites.Among them,the strong binding energy of negatively charged suspended S and O sites for H+,as well as the weak adsorption of positively charged unsaturated heterometal atoms for H*,reduces the energy barrier of the hydrogen evolution reaction proved by theoretical calculation.Whereupon,the electrocatalytic hydrogen evolution performance is markedly improved by the ensemble effect of unsaturated heterometal atoms and highlighted with an overpotential of 84 mV and Tafel slope of 68.5 mV dec^(−1).In brief,this metal vacancy-induced valence state regulation of heterometal can manipulate the coordination structure and catalytic activity of heterometal atoms doped in the 2D atomic lattice but not limited to 2D nanomaterials.展开更多
The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Inst...The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.展开更多
Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are sim...Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative accuracy.However,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering metric.This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering metric.Realistic traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity.The Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the clustering.OMNET++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in Python.The Scott’s formula estimation of the K number only matched the baseline when the road layout was simple.Moreover,the clustering algorithm required one iteration on average to converge when used with LET.展开更多
Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, t...Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.展开更多
Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effect...Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.展开更多
Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competi...Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.展开更多
The high-temperature pyrolysis process for preparing M–N–C single-atom catalyst usually results in high heterogeneity in product structure concurrently contains multiscale metal phases from single atoms(SAs),atomic ...The high-temperature pyrolysis process for preparing M–N–C single-atom catalyst usually results in high heterogeneity in product structure concurrently contains multiscale metal phases from single atoms(SAs),atomic clusters to nanoparticles.Therefore,understanding the interactions among these components,especially the synergistic effects between single atomic sites and cluster sites,is crucial for improving the oxygen reduction reaction(ORR)activity of M–N–C catalysts.Accordingly,herein,we constructed a model catalyst composed of both atomically dispersed FeN4 SA sites and adjacent Fe clusters through a site occupation strategy.We found that the Fe clusters can optimize the adsorption strength of oxygen reduction intermediates on FeN4 SA sites by introducing electron-withdrawing–OH ligands and decreasing the d-band center of the Fe center.The as-developed catalyst exhibits encouraging ORR activity with halfwave potentials(E1/2)of 0.831 and 0.905 V in acidic and alkaline media,respectively.Moreover,the catalyst also represents excellent durability exceeding that of Fe–N–C SA catalyst.The practical application of Fe(Cd)–CNx catalyst is further validated by its superior activity and stability in a metalair battery device.Our work exhibits the great potential of synergistic effects between multiphase metal species for improvements of singleatom site catalysts.展开更多
High temperature stress is one of the major environmental factors that affect the growth and development of plants. Although WRKY transcription factors play a critical role in stress responses, there are few studies o...High temperature stress is one of the major environmental factors that affect the growth and development of plants. Although WRKY transcription factors play a critical role in stress responses, there are few studies on the regulation of heat stress by WRKY transcription factors,especially in tomato. Here, we identified a group I WRKY transcription factor, SlWRKY3, involved in thermotolerance in tomato. First, SlWRKY3 was induced and upregulated under heat stress. Accordingly, overexpression of SlWRKY3 led to an increase, whereas knock-out of SlWRKY3 resulted in decreased tolerance to heat stress. Overexpression of SlWRKY3 accumulated less reactive oxygen species(ROS), whereas knock-out of SlWRKY3 accumulated more ROS under heat stress. This indicated that SlWRKY3 positively regulates heat stress in tomato. In addition,SlWRKY3 activated the expression of a range of abiotic stress-responsive genes involved in ROS scavenging, such as a SlGRXS1 gene cluster.Further analysis showed that SlWRKY3 can bind to the promoters of the SlGRXS1 gene cluster and activate their expression. Collectively, these results imply that SlWRKY3 is a positive regulator of thermotolerance through direct binding to the promoters of the SlGRXS1 gene cluster and activating their expression and ROS scavenging.展开更多
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t...Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.展开更多
Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedra...Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds.展开更多
Using archival Fermi-LAT data with a time span of~12 yr,we study the population of Millisecond Pulsars(MSPs)in Globular Clusters(GlCs)and investigate their dependence on cluster dynamical evolution in the Milky Way.We...Using archival Fermi-LAT data with a time span of~12 yr,we study the population of Millisecond Pulsars(MSPs)in Globular Clusters(GlCs)and investigate their dependence on cluster dynamical evolution in the Milky Way.We show that theγ-ray luminosity(L_(γ))and emissivity(i.e.,ε_(γ)=L_(γ)/M,with M the cluster mass)are good indicators of the population and abundance of MSPs in GlCs,and they are highly dependent on the dynamical evolution history of the host clusters.Specifically speaking,the dynamically older GlCs with more compact structures are more likely to have larger L_(γ)andε_(γ),and these trends can be summarized as strong correlations with cluster stellar encounter rateΓand the specific encounter rate(Λ=Γ/M),with L_(γ)∝Γ^(0.7±0.11)andε_(γ)∝Λ^(0.73±0.13)for dynamically normal GlCs.However,as GlCs evolve into deep core collapse,these trends are found to be reversed,implying that strong encounters may have lead to the disruption of Low-Mass X-ray Binaries and ejection of MSPs from core-collapsed systems.Besides,the GlCs are found to exhibit largerε_(γ)with increasing stellar mass function slope(ε_(γ)∝10^((0.52±0.1)α)),decreasing tidal radius(ε_(γ)∝R_(t)^(-10±0.22))and distances from the Galactic Center(GC,ε_(γ)∝R_(gc)^(-1.13±0.21)).These correlations indicate that,as GlCs losing kinetic energy and spiral in toward the GC,tidal stripping and mass segregation have a preference in leading to the loss of normal stars from GlCs,while MSPs are more likely to concentrate to cluster center and be deposited into the GC.Moreover,we gaugeε_(γ)of GlCs is~10-1000 times larger than the Galactic bulge,the latter is thought to reside thousands of unresolved MSPs and may be responsible for the GC 7-ray excess,which supports that GlCs are generous contributors to the population of MSPs in the GC.展开更多
To date,there is still a lack of a comprehensive explanation for caged dynamics which is regarded as one of the intricate dynamic behaviors in amorphous alloys.This study focuses on Pd_(82)Si_(18)as the research objec...To date,there is still a lack of a comprehensive explanation for caged dynamics which is regarded as one of the intricate dynamic behaviors in amorphous alloys.This study focuses on Pd_(82)Si_(18)as the research object to further elucidate the underlying mechanism of caged dynamics from multiple perspectives,including the cage's lifetime,atomic local environment,and atomic potential energy.The results reveal that Si atoms exhibit a pronounced cage effect due to the hindrance of Pd atoms,resulting in an anomalous peak in the non-Gaussian parameters.An in-depth investigation was conducted on the caged dynamics differences between fast and slow Si atoms.In comparison to fast Si atoms,slow Si atoms were surrounded by more Pd atoms and occupied lower potential energy states,resulting in smaller diffusion displacements for the slow Si atoms.Concurrently,slow Si atoms tend to be in the centers of smaller clusters with coordination numbers of 9 and 10.During the isothermal relaxation process,clusters with coordination numbers 9 and 10 have longer lifetimes,suggesting that the escape of slow Si atoms from their cages is more challenging.The findings mentioned above hold significant implications for understanding the caged dynamics.展开更多
Modern medicine is increasingly interested in advanced sensors to detect and analyze biochemical indicators.Ion sensors based on potentiometric methods are a promising platform for monitoring physiological ions in bio...Modern medicine is increasingly interested in advanced sensors to detect and analyze biochemical indicators.Ion sensors based on potentiometric methods are a promising platform for monitoring physiological ions in biological subjects.Current semi-implantable devices are mainly based on single-parameter detection.Miniaturized semi-implantable electrodes for multiparameter sensing have more restrictions on the electrode size due to biocompatibility considerations,but reducing the electrode surface area could potentially limit electrode sensitivity.This study developed a semi-implantable device system comprising a multiplexed microfilament electrode cluster(MMEC)and a printed circuit board for real-time monitoring of intra-tissue K^(+),Ca^(2+),and Na^(+)concentrations.The electrode surface area was less important for the potentiometric sensing mechanism,suggesting the feasibility of using a tiny fiber-like electrode for potentiometric sensing.The MMEC device exhibited a broad linear response(K^(+):2–32 mmol/L;Ca^(2+):0.5–4 mmol/L;Na^(+):10–160 mmol/L),high sensitivity(about 20–45 mV/decade),temporal stability(>2weeks),and good selectivity(>80%)for the above ions.In vitro detection and in vivo subcutaneous and brain experiment results showed that the MMEC system exhibits good multi-ion monitoring performance in several complex environments.This work provides a platform for the continuous real-time monitoring of ion fluctuations in different situations and has implications for developing smart sensors to monitor human health.展开更多
BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in a...BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner,and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma(HCC).AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography(CECT)to predict the presence of VETC+in HCC.METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers.Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase.Radiomics features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set.The model’s performance was validated on two separate test sets.Receiver operating characteristic(ROC)analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets.The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features.ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics nomogram.RESULTS The study included 190 individuals from two independent centers,with the majority being male(81%)and a median age of 57 years(interquartile range:51-66).The area under the curve(AUC)for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825,0.788,and 0.680 in the training set and the two test sets.A total of 13 features were selected to construct the Rad-score.The nomogram,combining clinicalradiological and combined radiomics features could accurately predict VETC+in all three sets,with AUC values of 0.859,0.848 and 0.757.Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram,incorporating clinicalradiological features and combined radiomics features,in the identification of VETC+HCC.展开更多
With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of th...With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.展开更多
Open clusters are the basic building blocks that serve as a laboratory for the study of young stellar populations in the Milky Way.Variable stars in open clusters provide a unique way to accurately probe the internal ...Open clusters are the basic building blocks that serve as a laboratory for the study of young stellar populations in the Milky Way.Variable stars in open clusters provide a unique way to accurately probe the internal structure,temporal and dynamical evolutionary stages of individual stars and the host cluster.The most powerful tool for such studies is time-domain photometric observations.This paper follows the route of our previous work,concentrating on a photometric search for variable stars in NGC 884.The target cluster is the companion of NGC869,forming the well-known double cluster system that is gravitationally bound.From the observation run in 2016 November,a total of 9247 B-band CCD images and 8218Ⅴ-band CCD images were obtained.We detected a total of 15 stars with variability in visual brightness,including five Be stars,three eclipsing binaries,and seven of unknown types.Two new variable stars were discovered in this work.We also compared the variable star content of NGC 884 with its companion NGC 869.展开更多
文摘Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hierarchical, use the distance function to measure the dissimilarities among actors. These distance functions need to fulfill various properties, including the triangle inequality (TI). However, in some cases, the triangle inequality might be violated, impacting the quality of the resulting clusters. With experiments, this paper explains how TI violates while performing traditional clustering techniques: k-medoids, hierarchical, DENGRAPH, and spectral clustering on social networks and how the violation of TI affects the quality of the resulting clusters.
基金Supported by Education and Teaching Reform Research Project of Pingdingshan University(2021-JY55,2020-JY05)Key Scientifie Research Project of Col-leges and Universities in Henan Province(22B180011)+2 种基金Project of Henan Sci-ence and Technology Department(232102320262)Ideological and Political Theories Teaching in Key Demonstration Courses at School Level in Pingdings-han College in 2022-Comprehensive Experiment of Environmental BiologyIde-ological and Political Theories Teaching in Demonstration Courses at School Level in Pingdingshan College in 2023-Ecological Engineering.
文摘Based on the analysis of the importance of professional cluster construction by ecological theory,with the change of social demand for talents,this paper explores the practice of environmental chemical professional cluster construction in Pingdingshan University,including gradually perfecting teaching conditions and reforming teaching mode,breaking through the limitations of resources,integrating the boundaries of colleges and departments,integrating multiple resources,innovating systems and mechanisms,reconstructing professional clusters,decon-structing professional connotations,reorganizing curriculum systems,etc.,in order to better build the ecological chain network of education in application-oriented colleges and universities,realize the deep integration of industry and education,train future-oriented interdisciplinary applied talents of new engineering,and realize the construction of characteristic professional cluster in application-oriented colleges.
基金financially funded by Natural Science Basic Research Program of Shaanxi(grant number 2022JM-239)Key Research and Development Project of Shaanxi Provincial(grant number 2021LLRH-05–08)。
文摘To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.
基金This research was funded by the National Natural Science Foundation of China(Grant No.72001190)by the Ministry of Education’s Humanities and Social Science Project via the China Ministry of Education(Grant No.20YJC630173)by Zhejiang A&F University(Grant No.2022LFR062).
文摘Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.
基金supported by the National Natural Science Foundation of China(22205209,52202373 and U21A200972)China Postdoctoral Science Foundation(2022M722867)Key Research Project of Higher Education Institutions in Henan Province(23A530001)。
文摘The valence states and coordination structures of doped heterometal atoms in two-dimensional(2D)nanomaterials lack predictable regulation strategies.Hence,a robust method is proposed to form unsaturated heteroatom clusters via the metal-vacancy restraint mechanism,which can precisely regulate the bonding and valence state of heterometal atoms doped in 2D molybdenum disulfide.The unsaturated valence state of heterometal Pt and Ru cluster atoms form a spatial coordination structure with Pt–S and Ru–O–S as catalytically active sites.Among them,the strong binding energy of negatively charged suspended S and O sites for H+,as well as the weak adsorption of positively charged unsaturated heterometal atoms for H*,reduces the energy barrier of the hydrogen evolution reaction proved by theoretical calculation.Whereupon,the electrocatalytic hydrogen evolution performance is markedly improved by the ensemble effect of unsaturated heterometal atoms and highlighted with an overpotential of 84 mV and Tafel slope of 68.5 mV dec^(−1).In brief,this metal vacancy-induced valence state regulation of heterometal can manipulate the coordination structure and catalytic activity of heterometal atoms doped in the 2D atomic lattice but not limited to 2D nanomaterials.
基金sponsored by the National Natural Science Foundation of P.R.China(Nos.62102194 and 62102196)Six Talent Peaks Project of Jiangsu Province(No.RJFW-111)Postgraduate Research and Practice Innovation Program of Jiangsu Province(Nos.KYCX23_1087 and KYCX22_1027).
文摘The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.
文摘Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative accuracy.However,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering metric.This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering metric.Realistic traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity.The Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the clustering.OMNET++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in Python.The Scott’s formula estimation of the K number only matched the baseline when the road layout was simple.Moreover,the clustering algorithm required one iteration on average to converge when used with LET.
基金supported in part by NUS startup grantthe National Natural Science Foundation of China (52076037)。
文摘Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.
文摘Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.
基金supported by the NationalNatural Science Foundation of China(No.61972118)the Key R&D Program of Zhejiang Province(No.2023C01028).
文摘Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.
基金supported by the National Natural Science Foundation of China(22109100,22075203)Guangdong Basic and Applied Basic Research Foundation(2022A1515011677)+1 种基金Shenzhen Science and Technology Project Program(JCYJ2021032409420401)Natural Science Foundation of SZU(000002111605).
文摘The high-temperature pyrolysis process for preparing M–N–C single-atom catalyst usually results in high heterogeneity in product structure concurrently contains multiscale metal phases from single atoms(SAs),atomic clusters to nanoparticles.Therefore,understanding the interactions among these components,especially the synergistic effects between single atomic sites and cluster sites,is crucial for improving the oxygen reduction reaction(ORR)activity of M–N–C catalysts.Accordingly,herein,we constructed a model catalyst composed of both atomically dispersed FeN4 SA sites and adjacent Fe clusters through a site occupation strategy.We found that the Fe clusters can optimize the adsorption strength of oxygen reduction intermediates on FeN4 SA sites by introducing electron-withdrawing–OH ligands and decreasing the d-band center of the Fe center.The as-developed catalyst exhibits encouraging ORR activity with halfwave potentials(E1/2)of 0.831 and 0.905 V in acidic and alkaline media,respectively.Moreover,the catalyst also represents excellent durability exceeding that of Fe–N–C SA catalyst.The practical application of Fe(Cd)–CNx catalyst is further validated by its superior activity and stability in a metalair battery device.Our work exhibits the great potential of synergistic effects between multiphase metal species for improvements of singleatom site catalysts.
基金supported by grants from the National Key Research&Development Plan,China (Grant Nos.2021YFD1200201,2022YFD1200502)National Natural Science Foundation of China(31972426,31991182)+3 种基金Key Project of Hubei Hongshan Laboratory(Grant No.2021hszd007)Wuhan Major Project of Key Technologies in Biological Breeding (Grant No.2022021302024852)Fundamental Research Funds for the Central Universities,China (Grant No.2662022YLPY001)International Cooperation Promotion Plan of Shihezi University (Grant No.GJHZ202104)。
文摘High temperature stress is one of the major environmental factors that affect the growth and development of plants. Although WRKY transcription factors play a critical role in stress responses, there are few studies on the regulation of heat stress by WRKY transcription factors,especially in tomato. Here, we identified a group I WRKY transcription factor, SlWRKY3, involved in thermotolerance in tomato. First, SlWRKY3 was induced and upregulated under heat stress. Accordingly, overexpression of SlWRKY3 led to an increase, whereas knock-out of SlWRKY3 resulted in decreased tolerance to heat stress. Overexpression of SlWRKY3 accumulated less reactive oxygen species(ROS), whereas knock-out of SlWRKY3 accumulated more ROS under heat stress. This indicated that SlWRKY3 positively regulates heat stress in tomato. In addition,SlWRKY3 activated the expression of a range of abiotic stress-responsive genes involved in ROS scavenging, such as a SlGRXS1 gene cluster.Further analysis showed that SlWRKY3 can bind to the promoters of the SlGRXS1 gene cluster and activate their expression. Collectively, these results imply that SlWRKY3 is a positive regulator of thermotolerance through direct binding to the promoters of the SlGRXS1 gene cluster and activating their expression and ROS scavenging.
基金the National Natural Science Foundation of China(Grant No.62101579).
文摘Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.11864040,11964037,and 11664038)。
文摘Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds.
基金supported by the Youth Program of the National Natural Science Foundation of China No.12003017。
文摘Using archival Fermi-LAT data with a time span of~12 yr,we study the population of Millisecond Pulsars(MSPs)in Globular Clusters(GlCs)and investigate their dependence on cluster dynamical evolution in the Milky Way.We show that theγ-ray luminosity(L_(γ))and emissivity(i.e.,ε_(γ)=L_(γ)/M,with M the cluster mass)are good indicators of the population and abundance of MSPs in GlCs,and they are highly dependent on the dynamical evolution history of the host clusters.Specifically speaking,the dynamically older GlCs with more compact structures are more likely to have larger L_(γ)andε_(γ),and these trends can be summarized as strong correlations with cluster stellar encounter rateΓand the specific encounter rate(Λ=Γ/M),with L_(γ)∝Γ^(0.7±0.11)andε_(γ)∝Λ^(0.73±0.13)for dynamically normal GlCs.However,as GlCs evolve into deep core collapse,these trends are found to be reversed,implying that strong encounters may have lead to the disruption of Low-Mass X-ray Binaries and ejection of MSPs from core-collapsed systems.Besides,the GlCs are found to exhibit largerε_(γ)with increasing stellar mass function slope(ε_(γ)∝10^((0.52±0.1)α)),decreasing tidal radius(ε_(γ)∝R_(t)^(-10±0.22))and distances from the Galactic Center(GC,ε_(γ)∝R_(gc)^(-1.13±0.21)).These correlations indicate that,as GlCs losing kinetic energy and spiral in toward the GC,tidal stripping and mass segregation have a preference in leading to the loss of normal stars from GlCs,while MSPs are more likely to concentrate to cluster center and be deposited into the GC.Moreover,we gaugeε_(γ)of GlCs is~10-1000 times larger than the Galactic bulge,the latter is thought to reside thousands of unresolved MSPs and may be responsible for the GC 7-ray excess,which supports that GlCs are generous contributors to the population of MSPs in the GC.
基金Project supported by the National Natural Science Foundation of China (Grant No.51701071)the Natural Science Foundation of Hunan Province,China (Grant Nos.2022JJ50115 and 2021JJ30179)the Research Foundation of the Education Bureau of Hunan Province,China (Grant No.22A0522)。
文摘To date,there is still a lack of a comprehensive explanation for caged dynamics which is regarded as one of the intricate dynamic behaviors in amorphous alloys.This study focuses on Pd_(82)Si_(18)as the research object to further elucidate the underlying mechanism of caged dynamics from multiple perspectives,including the cage's lifetime,atomic local environment,and atomic potential energy.The results reveal that Si atoms exhibit a pronounced cage effect due to the hindrance of Pd atoms,resulting in an anomalous peak in the non-Gaussian parameters.An in-depth investigation was conducted on the caged dynamics differences between fast and slow Si atoms.In comparison to fast Si atoms,slow Si atoms were surrounded by more Pd atoms and occupied lower potential energy states,resulting in smaller diffusion displacements for the slow Si atoms.Concurrently,slow Si atoms tend to be in the centers of smaller clusters with coordination numbers of 9 and 10.During the isothermal relaxation process,clusters with coordination numbers 9 and 10 have longer lifetimes,suggesting that the escape of slow Si atoms from their cages is more challenging.The findings mentioned above hold significant implications for understanding the caged dynamics.
基金The authors would like to acknowledge financial support from the National Key R&D Program of China(Nos.2021YFF1200700 and 2021YFA0911100)the National Natural Science Foundation of China(Nos.T2225010,32171399,and 32171456)+4 种基金the Fundamental Research Funds for the Central Universities,Sun Yat-Sen University(No.22dfx02)Pazhou Lab,Guangzhou(No.PZL2021KF0003)The authors also would like to thank the funding support from the Opening Project of Key Laboratory of Microelectronic Devices&Integrated Technology,Institute of Microelectronics,Chinese Academy of Sciences,and State Key Laboratory of Precision Measuring Technology and Instruments(No.pilab2211)QQOY would like to thank the China Postdoctoral Science Foundation(No.2022M713645)JL would like to thank the National Natural Science Foundation of China(No.62105380)and the China Postdoctoral Science Foundation(No.2021M693686).
文摘Modern medicine is increasingly interested in advanced sensors to detect and analyze biochemical indicators.Ion sensors based on potentiometric methods are a promising platform for monitoring physiological ions in biological subjects.Current semi-implantable devices are mainly based on single-parameter detection.Miniaturized semi-implantable electrodes for multiparameter sensing have more restrictions on the electrode size due to biocompatibility considerations,but reducing the electrode surface area could potentially limit electrode sensitivity.This study developed a semi-implantable device system comprising a multiplexed microfilament electrode cluster(MMEC)and a printed circuit board for real-time monitoring of intra-tissue K^(+),Ca^(2+),and Na^(+)concentrations.The electrode surface area was less important for the potentiometric sensing mechanism,suggesting the feasibility of using a tiny fiber-like electrode for potentiometric sensing.The MMEC device exhibited a broad linear response(K^(+):2–32 mmol/L;Ca^(2+):0.5–4 mmol/L;Na^(+):10–160 mmol/L),high sensitivity(about 20–45 mV/decade),temporal stability(>2weeks),and good selectivity(>80%)for the above ions.In vitro detection and in vivo subcutaneous and brain experiment results showed that the MMEC system exhibits good multi-ion monitoring performance in several complex environments.This work provides a platform for the continuous real-time monitoring of ion fluctuations in different situations and has implications for developing smart sensors to monitor human health.
基金The study was reviewed and approved by the Second Hospital of Shandong University Institutional Review Board,IRB No.KYLL-2023LW044.
文摘BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner,and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma(HCC).AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography(CECT)to predict the presence of VETC+in HCC.METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers.Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase.Radiomics features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set.The model’s performance was validated on two separate test sets.Receiver operating characteristic(ROC)analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets.The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features.ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics nomogram.RESULTS The study included 190 individuals from two independent centers,with the majority being male(81%)and a median age of 57 years(interquartile range:51-66).The area under the curve(AUC)for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825,0.788,and 0.680 in the training set and the two test sets.A total of 13 features were selected to construct the Rad-score.The nomogram,combining clinicalradiological and combined radiomics features could accurately predict VETC+in all three sets,with AUC values of 0.859,0.848 and 0.757.Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram,incorporating clinicalradiological features and combined radiomics features,in the identification of VETC+HCC.
基金supported by the Key Technology Projects of the China Southern Power Grid Corporation(STKJXM20200059)the Key Support Project of the Joint Fund of the National Natural Science Foundation of China(U22B20123)。
文摘With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.
基金the National Natural Science Foundation of China(NSFC)through grants 12003022,12373035,12233009 and 12173047support from the Youth Innovation Promotion Association of the CAS(grant No.2022055)。
文摘Open clusters are the basic building blocks that serve as a laboratory for the study of young stellar populations in the Milky Way.Variable stars in open clusters provide a unique way to accurately probe the internal structure,temporal and dynamical evolutionary stages of individual stars and the host cluster.The most powerful tool for such studies is time-domain photometric observations.This paper follows the route of our previous work,concentrating on a photometric search for variable stars in NGC 884.The target cluster is the companion of NGC869,forming the well-known double cluster system that is gravitationally bound.From the observation run in 2016 November,a total of 9247 B-band CCD images and 8218Ⅴ-band CCD images were obtained.We detected a total of 15 stars with variability in visual brightness,including five Be stars,three eclipsing binaries,and seven of unknown types.Two new variable stars were discovered in this work.We also compared the variable star content of NGC 884 with its companion NGC 869.
