Set Of Clusters Research Articles

Deployment of the Deliberative Model of Political Participation of EU Citizens in the Discourse of the European Democracy Future

The article investigates the process of deployment of the deliberative model of political participation of EU citizens in the discourse of the future of European democracy, which was officially defined by the EU at the organizational level (Conference on the Future of Europe), but the thematic content of the problem was not limited to any official documents, so every EU citizen got the opportunity to express an idea and participate in the discussion of the ideas of other subjects of deliberation at the first stage of the Conference (Multilingual digital platform). The study of the application of digital tools of political participation in the practices of deliberative democracy, applied during the Conference on the Future of Europe, proved that two formats of such tools were used: first, the format of the Multilingual Digital Platform functioned according to the digital model of the deployment of political participation; secondly, the format of the European Citizen Panels was based on a mixed model that combined the principles of digital and face-to-face communications in the deliberation process. At the same time, both formats most systematically reflect the nature of the transformation of the discursive model of political participation of EU citizens from dialogic to deliberative forms based on digital tools. The study of the forms of deliberation on the future of European democracy during the Conference on the Future of Europe and its results allows us to conclude that structurally, the problem of European democracy was defined by EU citizens as a set of interdependent dimensions promising for a common political agenda: elections to the European Parliament; EU federalization; political participation of citizens and consultations; institutional reforms; promotion of common European identity and common public space; protection and strengthening of democracy. The established set of markers, thematic areas and clusters of the deliberation of the future of democracy at the second European Citizen Panel was reflected in 39 guidelines proposed for the development of the final recommendations of the Conference: ensuring rights and (non)discrimination – 9 guidelines; protection of democracy and the rule of law – 5; reforming the EU – 10; formation of European identity – 9; citizens' participation – 6. In the final documents of the Conference, a consensus was reached on 5 proposals, which accumulated most of the recommendations of citizens regarding the future of European democracy, expressed during the deliberation process.

Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks

The advent of the fifth-generation (5 G) network integrates a spectrum of powerful technologies. Among these, Device-to-Device (D2D) communication stands as a focal point in research due to its expansive array of applications. Within the realm of 5 G, D2D communication offers support for high data rates and ultra-reliable, low-latency communications. While machine learning-based approaches have been devised to facilitate D2D communication in 5 G, ensuring robust connectivity and optimal network throughput remains a persisting challenge. Therefore, the bagging ensemble mean-shift Gaussian kernelized clustering-based D2D Connectivity (BEMSGKC-DC) algorithm is designed to enhance device-to-device communication in 5 G Cellular Networks with better packet delivery ratio and throughput. The proposed BEMSGKC-DC algorithm is proposed to enable massive device connectivity while improving the overall network performance. This can be achieved by employing bootstrap aggregating clustering where it clusters the devices via forming a set of base clusters i.e., Mean-Shift Gaussian kernelized clustering algorithm. To further enhance the performance of the algorithm, this paper also computes the resources of the devices in the clustering process using the Gaussian kernel function. All the weak cluster outcomes are merged to acquire the final clustering output with lower error. Through the resultant clustering of the device, the connectivity between D2D is ensured and thereby improves the data communication in the 5 G network. The result demonstrates that the BEMSGKC-DC algorithm optimizes the connectivity with lower energy, latency, and higher data delivery ratio and packet loss rate than the conventional algorithms.

Time Course of Recovery From Different Velocity Loss Thresholds and Set Configurations During Full-Squat Training.

Cornejo-Daza, PJ, Villalba-Fernández, A, González-Badillo, JJ, and Pareja-Blanco, F. Time course of recovery from different velocity loss thresholds and set configurations during full-squat training. J Strength Cond Res 38(2): 221-227, 2024-The aims of the research were to examine the effects of (a) velocity loss (VL) thresholds and (b) set configuration, traditional or cluster, on time-course recovery. A randomized cross-over research design was conducted, in which 15 resistance-trained men performed 4 protocols consisting of 3 sets of 70% 1RM in full squat (SQ), differing in the VL incurred during the set assessed with a linear velocity transducer: (a) 20% (70-20), (b) 30% (70-30), (c) 40% (70-40), and in the set configuration (d) 20% of VL using a cluster methodology (70-CLU). Movement velocity against the load that elicited a 1 m·s -1 velocity at baseline measurements (V1-load) in SQ, countermovement jump (CMJ) height, and sprint time in 20 m (T20) were assessed at baseline (Pre) and postintervention (Post, 6 hour-Post, 24 hour-Post, and 48 hour-Post). The 70-20 protocol resulted in fewer total repetitions than the other protocols ( p = 0.001), whereas 70-CLU, 70-30, and 70-40 completed similar total repetitions. The 70-30 protocol significantly worsened T20 at 6 hours-Post, CMJ at 48 hours-Post, and V1-load at 6 hours-Post ( p < 0.05). The 70-40 protocol significantly impaired T20 at 6 hours-Post, and CMJ and V1-load at 24 hours-Post ( p < 0.05). No significant performance reductions were observed for 70-20 and 70-CLU at 6 hours-Post, 24 hours-Post, and 48 hours-Post. Protocols with higher VL resulted in more pronounced fatigue and a slower rate of recovery. Cluster sets (70-CLU) resulted in higher volume than protocols with a similar level of fatigue (70-20) and a quicker recovery than protocols with a similar volume (70-30 and 70-40).

Combined Accentuated Eccentric Loading and Rest Redistribution in High-Volume Back Squat: Acute Kinetics and Kinematics.

Chae, S, Long, SA, Lis, RP, McDowell, KW, Wagle, JP, Carroll, KM, Mizuguchi, S, and Stone, MH. Combined accentuated eccentric loading and rest redistribution in high-volume back squat: Acute kinetics and kinematics. J Strength Cond Res 38(4): 640-647, 2024-The purpose of this study was to explore acute kinetic and kinematic responses to combined accentuated eccentric loading and rest redistribution (AEL + RR). Resistance-trained men ( n = 12, 25.6 ± 4.4 years, 1.77 ± 0.06 m, and 81.7 ± 11.4 kg) completed a back squat (BS) 1 repetition maximum (1RM) and weight releaser familiarization session. Three BS exercise conditions (sets × repetitions × eccentric/concentric loading) consisted of (a) 3 × (5 × 2) × 110/60% (AEL + RR 5), (b) 3 × (2 × 5) × 110/60% (AEL + RR 2), and (c) 3 × 10 × 60/60% 1RM (traditional sets [TS]). Weight releasers (50% 1RM) were attached to every first repetition of each cluster set (every first, third, fifth, seventh, and ninth repetition in AEL + RR 5 and every first and sixth repetition in AEL + RR 2). The AEL + RR 5 resulted in significantly ( p < 0.05) greater concentric peak velocity (PV) (1.18 ± 0.17 m·s -1 ) and peak power (PP) (2,304 ± 499 W) compared with AEL + RR 2 (1.11 ± 0.19 m·s -1 and 2,148 ± 512 W) and TS (1.10 ± 0.14 m·s -1 and 2,079 ± 388 W). Furthermore, AEL + RR 5 resulted in significantly greater PV and PP across all 10 repetitions compared with TS. Although AEL + RR 5 resulted in significantly greater concentric mean force (MF) (1,706 ± 224 N) compared with AEL + RR 2 (1,697 ± 209 N) and TS (1,685 ± 211 N), no condition by set or repetition interactions existed. In conclusion, AEL + RR 5 increases PV and PP but has little effect on MF. Coaches might consider prescribing AEL + RR 5 to increase especially peak aspects of velocity and power outcomes.

Information maximization-based clustering of histopathology images using deep learning

Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis. However, the pancreas remains so deep inside the body that experts sometimes struggle to detect cancer in it. Computer-aided diagnosis can come to the aid of pathologists in this scenario. It assists experts by supporting their diagnostic decisions. In this research, we carried out a deep learning-based approach to analyze histopathology images. We collected whole-slide images of KPC mice to implement this work. The pancreatic abnormalities observed in KPC mice develop similar histological features to human beings. We created random patches from whole-slide images. Then, a convolutional autoencoder framework was used to embed these patches into an integrated latent space. We applied ‘information maximization’, a deep learning clustering technique to cluster the identical patches in an unsupervised manner since our dataset does not have annotation. Moreover, Uniform manifold approximation and projection, a nonlinear dimension reduction technique was utilized to visualize the embedded patches in a 2-dimensional space. Finally, we calculated a few internal cluster validation metrics to determine the optimal cluster set. Our work concentrated on patch-based anomaly detection in the whole slide histopathology images of KPC mice.

Deep asymmetric nonnegative matrix factorization for graph clustering

Graph clustering is a fundamental technique in machine learning that has widespread applications in various fields. Deep Nonnegative Matrix Factorization (DNMF) was recently emerged to cope with the extraction of several layers of features, and it has been demonstrated to achieve remarkable results on unsupervised tasks. While DNMF has been applied for analyzing graphs, the effectiveness of the current DNMF approaches for graph clustering is generally unsatisfactory: these methods are intrinsically data representation models, and their objective functions do not capture cluster structures, also ignores direction which is crucial in the directed graph clustering problems. To overcome these downsides, this paper proposes a graph-specific DNMF model based on the Asymmetric NMF which can handle undirected and directed graphs. Inspired by hierarchical graph clustering and graph summarization approaches, the Deep Asymmetric Nonnegative Matrix Factorization (DAsNMF) is introduced for the directed graph clustering problem. In a pseudo-hierarchical clustering setting, DAsNMF decomposes the input graph to extract low-level to high-level node representations and graph representations (summarized graphs). In addition, the asymmetric cosine and PageRank-based similarities are imposed on the proposed model to preserve the local and global graph structures. The learning process is formulated as a unified optimization problem to jointly train representation learning model and clustering model. The extensive experimental studies validate the effectiveness of the proposed method on directed graphs.

A note on clustering aggregation for binary clusterings

We consider the clustering aggregation problem in which we are given a set of clusterings and want to find an aggregated clustering which minimizes the sum of mismatches to the input clusterings. In the binary case (each clustering is a bipartition) this problem was known to be NP-hard under Turing reductions. We strengthen this result by providing a polynomial-time many-one reduction. Our result also implies that no 2o(n)⋅|I′|O(1)-time algorithm exists that solves any given clustering instance I′ with n elements, unless the Exponential Time Hypothesis fails. On the positive side, we show that the problem is fixed-parameter tractable with respect to the number of input clusterings.

Enhanced destruction of cluster satellites by major mergers

ABSTRACT Using a set of clusters in dark matter only cosmological simulations, we study the consequences of merging of clusters and groups of galaxies (with mass ratio larger than 5:1) to investigate the tidal impact of mergers on the satellite haloes. We compare our results to a control sample of clusters that have had no major mergers over the same time period. Clusters that undergo major mergers are found to have a significant enhancement in destruction of their subhaloes of ∼10–30 per cent, depending on how major the merger is. Those with mass ratios less than 7:1 showed no significant enhancement. The number of destroyed subhaloes are measured for the cluster members that were inside the virial radius of clusters before the merger begins. This means preprocessed galaxies brought in by the merger are deliberately excluded, allowing us to clearly see the enhanced destruction purely as a result of the distorted and disturbed tidal field of the cluster during the merger. We also consider secondary parameters affecting the destruction of those satellites but find that the major mergers are the dominant factor. These results highlight how major mergers can significantly impact the cluster population, with likely consequences for the formation of intracluster light, and enhancement of tidal features in the remaining satellites.

Endowing networks with desired symmetries and modular behavior.

Symmetries in a network regulate its organization into functional clustered states. Given a generic ensemble of nodes and a desirable cluster (or group of clusters), we exploit the direct connection between the elements of the eigenvector centrality and the graph symmetries to generate a network equipped with the desired cluster(s), with such a synthetical structure being furthermore perfectly reflected in the modular organization of the network's functioning. Our results solve a relevant problem of designing a desired set of clusters and are of generic application in all cases where a desired parallel functioning needs to be blueprinted.

Data‐Based Modeling of the Magnetosheath Magnetic Field

AbstractA quantitative model of the magnetosheath (MS) magnetic structure is developed, using a multi‐year set of Geotail, Themis, Cluster, and MMS magnetometer and plasma instrument data. The MS database is created using an identification algorithm, based on observed magnetic field magnitudes and proton densities, normalized by their concurrent interplanetary values, followed by additional filtering with the help of standard bow shock (BS) and magnetopause (MP) models. The model architecture is based on the toroidal/poloidal formalism and a coordinate system that naturally accounts for the tailward flaring of both boundaries. The magnetic field expansions include 960 free coefficients, derived by fitting the model to a grand data set, split into independent training and validation subsets with 1,291,380 and 411,933 1‐min records, respectively. The model faithfully reproduces basic types of the interplanetary magnetic field (IMF) wrapping around the MP. Regular IMF sectors result in strongly dawn‐dusk asymmetric draping, with much larger magnitudes at the quasi‐perpendicular dusk side of BS, and weaker at the quasi‐parallel dawn side, where the MS field lines are bent and dragged tailward. Except in the case of the flow‐aligned IMF orientation, the subsolar field steadily grows toward the MP, and the effect is clearly IMF Bz‐dependent: the field and its gradient are larger (smaller) for northward (southward) IMF Bz, implying a pile‐up of the magnetic flux in the first case and stronger reconnection in the second. Model distributions of the MS field magnitude reveal local depressions, associated with polar cusps near the high‐latitude limits of data coverage.

Ages and metallicities of stellar clusters using S-PLUS narrow-band integrated photometry: the Small Magellanic Cloud

ABSTRACT The Magellanic Clouds are the most massive and closest satellite galaxies of the Milky Way (MW), with stars covering ages from a few Myr up to 13 Gyr. This makes them important for validating integrated light methods to study stellar populations and star formation processes, which can be applied to more distant galaxies. We characterized a set of stellar clusters in the Small Magellanic Cloud (SMC), using the Southern Photometric Local Universe Survey. This is the first age (metallicity) determination for 11 (65) clusters of this sample. Through its seven narrow bands, centred on important spectral features, and five broad bands, we can retrieve detailed information about stellar populations. We obtained ages and metallicities for all stellar clusters using the Bayesian spectral energy distribution fitting code bagpipes. With a sample of clusters in the colour range −0.20 &amp;lt; r − z &amp;lt; +0.35, for which our determined parameters are most reliable, we modeled the age–metallicity relation of SMC. At any given age, the metallicities of SMC clusters are lower than those of both the Gaia Sausage-Enceladus disrupted dwarf galaxy and the MW. In comparison with literature values, differences are Δlog(age) ≈ 0.31 and Δ[Fe/H] ≈ 0.41, which is comparable to low-resolution spectroscopy of individual stars. Finally, we confirm a previously known gradient, with younger clusters in the centre and older ones preferentially located in the outermost regions. On the other hand, we found no evidence of a significant metallicity gradient.

Pangenome of water caltrop reveals structural variations and asymmetric subgenome divergence after allopolyploidization.

Water caltrop (Trapa spp., Lythraceae) is a traditional but currently underutilized non-cereal crop. Here, we generated chromosome-level genome assemblies for the two diploid progenitors of allotetraploid Trapa. natans (4x, AABB), i.e., diploid T. natans (2x, AA) and Trapa incisa (2x, BB). In conjunction with four published (sub)genomes of Trapa, we used gene-based and graph-based pangenomic approaches and a pangenomic transposable element (TE) library to develop Trapa genomic resources. The pangenome displayed substantial gene-content variation with dispensable and private gene clusters occupying a large proportion (51.95%) of the total cluster sets in the six (sub)genomes. Genotyping of presence-absence variation (PAVs) identified 40 453 PAVs associated with 2570 genes specific to A- or B-lineages, of which 1428 were differentially expressed, and were enriched in organ development process, organic substance metabolic process and response to stimulus. Comparative genome analyses showed that the allotetraploid T. natans underwent asymmetric subgenome divergence, with the B-subgenome being more dominant than the A-subgenome. Multiple factors, including PAVs, asymmetrical amplification of TEs, homeologous exchanges (HEs), and homeolog expression divergence, together affected genome evolution after polyploidization. Overall, this study sheds lights on the genome architecture and evolution of Trapa, and facilitates its functional genomic studies and breeding program.

An algorithmic approach to identification of gray areas: Analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model

Machine learning (ML) models have become a key component in modern world services. In decision-making domains where human expertise is crucial, for example, for manually scoring biological signal data, human uncertainties undermine experts’ trust in the outcomes of these models. The field of sleep staging in particular, which requires experts to score complex biological signal is notably impacted by scoring uncertainties. Data consisting of an ensemble of independent scorers are collected to estimate inter scorer agreement and the uncertainty associated with manual scoring. However, scorers’ uncertainty lacks statistical modeling, which poses difficulties in validating ML algorithms and leads to issues of reliability and explainability. From the ensemble of scorers, uncertainty zones, called gray areas, are highlighted by samples where scorers disagree. The objective of our work is to provide a framework introducing and inferring gray areas. We present a flexible and easy-to-use multi-objective method based on multinomial mixture models clustering the different levels of scorer agreement and summarize the results into two sets of high-agreement and gray area clusters, which are called supra-clusters. The threshold is selected according to the maximization of the distance between two distributions of scorers agreement measure. Effective results were obtained by the method after it was fitted on simulated data. Additionally, the method was applied to a real case of uncertainty analysis in the sleep staging domain. A series of actual sleep stages scored by an ensemble of 10 independent scorers for a dataset of 50 participants was used.

Effects of different cluster-set rest intervals during plyometric-jump training on measures of physical fitness: A randomized trial.

The optimal intra-set rest for cluster sets (CLS) during plyometric-jump training (PJT) to improve physical fitness remains unclear. The objective of this quasi-experimental study was to compare the effects of PJT with traditional (TRS) vs. CLS structures, using different intra-set rests, on the physical fitness of healthy participants. Forty-seven recreationally active young men performed 3-5 sets of 10-12 repetitions of upper- and lower-body PJT exercises twice a week for six weeks using different set configurations: TRS group (no intra-set rest), and the CLS10, CLS20 and CLS30 groups with 10, 20 and 30 s of intra-set rest, respectively, while the total rest period was equated. Pretest-posttest measurements were carried out 48 h before and after the intervention and the rating of fatigue (ROF) was also assessed using a numerical scale (0-10 points) 20 min after the first and last (i.e., 12th) session. There was no significant difference in the mean energy intake between groups (p > 0.05). The repeated measures ANOVA revealed that all groups showed similar improvements (p < 0.05) in body mass, body mass index, fat-free mass, one repetition maximum (dynamic strength) and repetitions to failure (muscular endurance) in back squat and chest press, handgrip strength, standing long jump, 20 m sprint, 9-m shuttle run (change of direction speed), and ROF. Of note, the ROF was lower for the CLS20 and CLS30 groups, independent from the training effect. The physical fitness of recreationally active young men improved after 6 weeks of PJT involving intra-set rest intervals of 0 s, 10 s, 20 s, or 30 s. However, an intra-set rest of 20 s and 30 s seems to induce lower exercise-induced fatigue perception.

Impact of cluster set resistance training on strength, functional capacity, metabolic and inflammatory state in older hemodialysis subjects: A randomized controlled clinical trial

Background and aimsHemodialysis-associated neuromuscular dysfunction appears to be counteracted by resistance training (RT) in older patients with chronic kidney disease (CKD). Thus, the aim of the present study was to evaluate the impact of cluster-set RT protocol on anthropometric parameters, functional capacities, and biochemical variables in older patients with CKD. Methods and resultsSeventy-eight older patients (age: 57.55 ± 4.06 years) with CKD undergoing maintenance hemodialysis participated, and were randomly divided into control group (CG, N = 26), traditional RT (RT, N = 26) and cluster-set RT (RT-CS, N = 26) groups. Participants completed 24 weeks of RT three times per week, 1 h and 30 min before the hemodialysis session. Patients from the RT-CS group displayed increased adherence as compared to the RT group (66.35 % versus 61.73 %, p < 0.0001). There was an improvement of all anthropometric variables, handgrip strength, timed up and-go (TUG) and six-minute walking test (6MWT) following both training protocols when compared to control group and pre-intervention values. Fasting blood glucose decreased for both RT and RT-CS groups as compared with pre-intervention, without differences between training protocols and CG. Glycated hemoglobin, inflammatory cytokines, and triglycerides decreased in RT and RT-CS groups as compared with pre-intervention and CG, without differences between them. Furthermore, the RT-CS protocol resulted in a greater number of people who were responsive to training when compared to traditional training. ConclusionsRT-CS is a clinically valuable tool to improve anthropometric parameters, handgrip strength, TUG, 6MWT, fasting blood glucose, and cytokines in CKD older patients.

A persistent excess of galaxy-galaxy strong lensing observed in galaxy clusters

Context. Previous studies have revealed that the estimated probability of galaxy-galaxy strong lensing in observed galaxy clusters exceeds the expectations from the Λ cold dark matter cosmological model by one order of magnitude. Aims. We aim to understand the origin of this excess by analyzing a larger set of simulated galaxy clusters, and investigating how the theoretical expectations vary under different adopted prescriptions and numerical implementations of star formation and feedback in simulations. Methods. We performed a ray-tracing analysis of 324 galaxy clusters from the THREE HUNDRED project, comparing the GADGET-X and GIZMO-SIMBA runs. These simulations, which start from the same initial conditions, were performed with different implementations of hydrodynamics and galaxy formation models tailored to match different observational properties of the intracluster medium and cluster galaxies. Results. We find that galaxies in the GIZMO-SIMBA simulations develop denser stellar cores than their GADGET-X counterparts. Consequently, their probability for galaxy-galaxy strong lensing is higher by a factor of ∼3. This increment is still insufficient to fill the gap with observations as a discrepancy by a factor ∼4 still persists. In addition, we find that several simulated galaxies have Einstein radii that are too large compared to observations. Conclusions. We conclude that a persistent excess of galaxy-galaxy strong lensing exists in observed galaxy clusters. The origin of this discrepancy with theoretical predictions is still unexplained in the framework of the cosmological hydrodynamical simulations. This might signal a hitherto unknown issue with either the simulation methods or our assumptions regarding the standard cosmological model.

Federated learning with SARIMA-based clustering for carbon emission prediction

Driven by the global Sustainable Development Goals, predicting the long-term trend of carbon emissions in the industrial sector has important reference value for managing national macroeconomic and reducing carbon emissions. To address the potential privacy leakage problem when using multisource industrial big data for carbon emission prediction, a federated learning method was introduced for carbon emission prediction research. However, owing to the variability in individual data providers, using all clients indiscriminately to train the federated model may hinder the computational efficiency of federated learning. To improve the accuracy and computational efficiency of the prediction model, a federated learning method based on SARIMA clustering was proposed. First, a trend was fitted to all clients based on the SARIMA model and the clients were grouped into different clusters based on the fitting results. Second, a federated bidirectional long short-term memory calculation was implemented for different client combinations using a federated averaging algorithm. Extensive experimental results on real datasets show that combining SARIMA and the federated averaging algorithm protects the data privacy of clients and improves the convergence speed and accuracy of the federated learning carbon emission prediction model. Within the optimal set of clusters (Cluster 0), the mean absolute error and mean square error improved by 63.32% and 79.27%, respectively, and the convergence speed of the model improved by 73.17%. This study further enriches the methodological system of carbon emission prediction and provides methodological support for relevant departments to use multisource big data in the industry for carbon emission prediction.

Foliar application of Ascophyllum nodosum on improvement of photosynthesis, fruit setting percentage, yield and quality of tomato (Solanum lycopersicum L.)

In recent days, liquid formulations of brown seaweed extract, Ascophyllum nodosum used as a biostimulant in agriculture. Various studies suggest that A. nodosum enhanced the growth and yield of agriculturally important crops, but still, there is a lack of information about the biostimulation effects on photosynthesis, flowering and fruit setting of tomato. Hence, the present study aimed to know the effect of foliar application of A. nodosum on photosynthesis, flowering, fruit setting, yield and quality of tomato. A biostimulant product, MC Set with A. nodosum extract applied to tomato as a foliar spray at rates of three different concentrations such as 1.0 L ha−1 (MS 1), 2.0 L ha−1 (MS 2), 3.0 L ha−1 (MS 3) for six times during flowering of 2nd (30 Days after transplanting – DAT), 3rd (40 DAT) and 4th (50 DAT) cluster and fruit setting of 2nd (60 DAT), 3rd (70 DAT) and 4th (80 DAT) cluster respectively. The MC Set treatments enhanced the plant photosynthesis, flower number and fruit number per cluster, yield and quality traits of tomato. However, the middle concentration MS 2 showed highest photosynthetic rate, stomatal conductance, SPAD value, flower and fruit in 2nd, 3rd and 4th cluster. It also had better average fruit weight and yield per plant and hectare and enhanced the quality parameters such as total soluble solids, ascorbic acid content, lycopene and total sugars compared to control and other two concentrations of MS Set. Hence, using A. nodosum extract on tomato growth could be a better sustainable crop production method.

Ensemble clustering via fusing global and local structure information

Ensemble clustering is aimed at obtaining a robust consensus result from a set of weak base clusterings. Most existing methods rely on a co-association (CA) matrix that describes the frequency at which pairs of samples are clustered into the same class and exhibits a symmetrical property. However, these methods typically focus on either the global or local structure of the CA matrix and do not consider using both types of information simultaneously to improve ensemble clustering performance. To address this issue, we propose a novel scheme to Fuse both the global Structure and the local structure information for Ensemble Clustering, namely FSEC, in this paper. Specifically, FSEC integrates both global structural information and local structural information into a learning framework by self-expressive and CA matrix self-enhancement models respectively. Moreover, FSEC embeds a Hadamard product fusion term to maximize the commonalities between global structure and local structure. The objective function optimization problem is solved by using the alternating direction method of multipliers (ADMM). Experimental results demonstrate that the proposed FSEC outperforms many state-of-the-art methods of ensemble clustering.

Motif-to-Core Nucleation in a Decahedral Evolution Pattern.

The atomic precision of ultrasmall metal nanoclusters has opened the door to elucidating the structural evolution principles of metal nanomaterials at the molecular level. Here, we report a novel set of super-atomic Ag clusters, including [Ag19(TBBT)16(DPPP)4]+ (Ag19), [Ag22(DMAT)8(DPPM)4Cl8]2+ (Ag22), Ag26(SPh3,5-CF3)15(DPPF)4Cl5 (Ag26), and [Ag30(DMAT)12(DPPP)4Cl8]2+ (Ag30). The core structures of these clusters correspond to one decahedral Ag7, perpendicular bi-decahedrons, three-dimensional penta-decahedrons, and hexa-decahedrons, respectively. The Ag atoms in AgS2 blocks show a strong correlation with the decahedral cores: the five equatorial Ag atoms in the decahedral Ag7 core of Ag19 all adopt the AgS2 coordination, while the Ag atoms in AgS2 blocks of Ag22, Ag26, and Ag30 unexceptionally constitute additional decahedral structures with the core Ag atoms. Specifically, two and four core Ag atoms of Ag26 and Ag30 clusters occupy positions that highly resemble that of Ag (in AgS2 motifs) of Ag22. The strong structural correlation demonstrates the motif-to-core evolution of the surface Ag (on AgS2) to build extra-decahedral blocks. Density functional theory calculations indicate that the 2e, 4e, 6e, and 8e clusters (from Ag19 to Ag30) adopt 1S2, 1S21P2, 1S21P4, and 1S21P6 electron configurations, all of which feature excellent super-atomic characters.