Set Of Clusters Research Articles

Supervised fusion content-based framework for breakdown detection in task-oriented conversational systems

Conversational agents (CAs) have been widely used for many domains, such as healthcare, education, and business. One main category of CAs is task-oriented CAs, which aim to help users to complete a set of specific tasks. However, task-oriented CAs can fail to answer the user’s question, which can lead to a breakdown in the dialogue (when it is not possible to complete a conversation with a CA). Breakdown detection is an essential task for developing better CAs. Several related studies have focused on breakdown detection using different sets of features, for example, topic transition, word-based similarity and clustering; but, the existing studies develop features mainly from the system’s outputs or user’s inputs, whereas the features can be extracted from both sides, as well as from the interaction between them. Therefore, in this work, we developed a new supervised fusion machine learning (ML) model that combines the prediction from two machine learning algorithms for breakdown detection CAs services system. We developed features from different groups focusing on both the user input and the system response. Then we select the optimal combined features. The features are based on sentence similarity, sentiment features, and count-based features. The developed fusion model is mainly based on the two best performances of the single classifiers (SVM and RF). We explore several single ML algorithms using different sets of features and the combined features. To verify the effectiveness of the proposed fusion model, we compared the proposed models against baseline methods using four sets of data. We conclude that the proposed fusion model with the combined features outperforms the baselines and all other models in terms of prediction accuracy and f-score measures.

Enhancing evaluation of bull fertility through multivariate analysis of sperm

Computer-assisted sperm analysis (CASA) has become the predominant tool for assessing bull semen in artificial insemination programs. Despite such popularity CASA's ability to predict fertility has been limited, especially when emphasis is based upon single motion characteristics. Our hypothesis is that numerical sets of CASA measures provide a more effective method to differentiate the potential fertilization capacity of bulls and that bulls can be clustered based upon sets of CASA measures. Therefore, we used CASA to evaluate frozen-thawed semen samples from 307 Holstein and 152 Jersey bulls sourced from USDA-ARS's National Animal Germplasm Program gene bank. Sperm was evaluated immediately after thawing and 30 min later. We evaluated sperm kinetic and morphometric means and variances to capture the structure of CASA data in relation to various sources of variation. These data were subjected to univariate and multivariate statistical methods to investigate animal and management factors affecting sperm characteristics measured by CASA. Clustering with K-means identified 4 clusters of bulls based upon each cluster's set of CASA parameters after thawing. There was little overlap among clusters for sets of CASA measures. At the extremes, bull cluster 1 (BC1, n = 180) and BC3 (n = 101) had different sire conception rates (SCR) -0.07 vs -1.29, respectively and sets of CASA measures. Interestingly, BC2 had CASA measures that could be perceived as negative, e.g., cell size at 8.18mm2 vs 6.37mm2 for BC4 and total motility of 29.7% vs 48.7% for BC3, but SCR for BC2 was higher (-0.79) than BC3 (-1.29). Despite such discrepancies for some BC2 CASA values it appears the potentially negative effects were offset by the levels of other CASA values. Our findings suggest improved approaches for using CASA could lie in evaluating multiple CASA measures as sets within specific numerical ranges rather than as independent measures.

Ensemble Clustering via Co-Association Matrix Self-Enhancement.

Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according to the base clusterings to achieve ensemble clustering. However, when the constructed CA matrix is of low quality, the performance will degrade. In this article, we propose a simple, yet effective CA matrix self-enhancement framework that can improve the CA matrix to achieve better clustering performance. Specifically, we first extract the high-confidence (HC) information from the base clusterings to form a sparse HC matrix. By propagating the highly reliable information of the HC matrix to the CA matrix and complementing the HC matrix according to the CA matrix simultaneously, the proposed method generates an enhanced CA matrix for better clustering. Technically, the proposed model is formulated as a symmetric constrained convex optimization problem, which is efficiently solved by an alternating iterative algorithm with convergence and global optimum theoretically guaranteed. Extensive experimental comparisons with 12 state-of-the-art methods on ten benchmark datasets substantiate the effectiveness, flexibility, and efficiency of the proposed model in ensemble clustering. The codes and datasets can be downloaded at https://github.com/Siritao/EC-CMS.

Variable selection in modelling clustered data via within‐cluster resampling

AbstractIn many biomedical applications, there is a need to build risk‐adjustment models based on clustered data. However, methods for variable selection that are applicable to clustered discrete data settings with a large number of candidate variables and potentially large cluster sizes are lacking. We develop a new variable selection approach that combines within‐cluster resampling techniques with penalized likelihood methods to select variables for high‐dimensional clustered data. We derive an upper bound on the expected number of falsely selected variables, demonstrate the oracle properties of the proposed method and evaluate the finite sample performance of the method through extensive simulations. We illustrate the proposed approach using a colon surgical site infection data set consisting of 39,468 individuals from 149 hospitals to build risk‐adjustment models that account for both the main effects of various risk factors and their two‐way interactions.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

BackgroundSingle-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable.ResultsHere, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results—or partitions—on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html.ConclusionsCluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Students’ Perception of Electronic Feedback: Lecturer, Peer, and Self-Electronic Feedback on Writing Academic With Different Students’ Writing Achievement

This study investigates the critical role of writing as a fundamental skill requiring continuous refinement, particularly in professional settings where it underpins daily business, academic, and corporate interactions. Focused on English as a Foreign Language (EFL) students, 30 participants from Universitas Islam Negeri Intan Lampung engaged in a clustered class setting. Utilizing a Likert-Scale questionnaire and SPSS version 26 for analysis, the research assessed students' perceptions of electronic feedback provided by instructors and peers. Findings indicate that while a majority of students received feedback on grammar, organization, and vocabulary, there's a notable gap in self-feedback, with many feeling uncertain about error identification. While 75% acknowledge the instructor's role in offering electronic feedback, 69% stress the importance of peer feedback. Interestingly, 75% deem self-feedback less impactful, citing uncertainty in error identification. These insights underscore the significance of targeted training to enhance English writing proficiency across diverse socio-cultural backgrounds.

Orthogonal multimodality integration and clustering in single-cell data

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

Approximate balancing weights for clustered observational study designs.

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure of covariate imbalance, subject to an additional penalty on the variance of the weights. We tailor the approximate balancing weights optimization problem to the clustered observational study setting by deriving an upper bound on the mean square error and finding weights that minimize this upper bound, linking the level of covariate balance to a bound on the bias. We implement the procedure by specializing the bound to a random cluster-level effects model, leading to a variance penalty that incorporates the signal-to-noise ratio and penalizes the weight on individuals and the total weight on groups differently according to the the intra-class correlation.

Development and validation of a novel biomarker panel for Crohn's disease and rheumatoid arthritis diagnosis and treatment.

Crohn's disease (CD) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases. However, the molecular mechanisms linking these two diseases remain unclear. To identify shared core genes between CD and RA, we employed differential gene analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Functional annotation of these core biomarkers was performed using consensus clustering and gene set enrichment analysis. We also constructed a protein-protein network and a miRNA-mRNA network using multiple databases, and potential therapeutic agents targeting the core biomarkers were predicted. Finally, we confirmed the expression of the genes in the biomarker panel in both CD and RA using quantitative PCR. A total of five shared core genes, namely C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 9 (CXCL9), aquaporin 9 (AQP9), secreted phosphoprotein 1 (SPP1), and metallothionein 1M (MT1M), were identified as core biomarkers. These biomarkers activate classical pro-inflammatory and immune signaling pathways, influencing immune cell aggregation. Additionally, testosterone was identified as a potential therapeutic agent targeting the biomarkers identified in this study. The expression of genes in the biomarker panel in CD and RA was confirmed through quantitative PCR. Our study revealed some core genes shared between CD and RA and established a novel biomarker panel with potential implications for the diagnosis and treatment of these diseases.

Annual Daily Irradiance Analysis of Clusters in Mexico by Machine Learning Algorithms

The assessment of solar resources involves the utilization of physical or satellite models for the determination of solar radiation on the Earth’s surface. However, a critical aspect of model validation necessitates comparisons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges associated with establishing and maintaining solar radiation measurement networks—characterized by their expense, logistical complexities, limited station availability and the imperative consideration of climatic criteria for siting—countries endowed with substantial climatic diversity face difficulties in station placement. In this investigation, the measurements of annual solar irradiation, from meteorological stations of the National Weather Service in Mexico, were compared in different regions clustered by similarities in altitude, TL Linke, albedo and cloudiness index derived from satellite images; the main objective is to find the best ratio of annual solar irradiation in a set of clusters. Employing machine learning algorithms, this research endeavors to identify the most suitable model for predicting the ratio of annual solar irradiation and to determine the optimal number of clusters. The findings underscore the efficacy of the L-method as a robust technique for regionalization. Notably, the cloudiness index emerges as a pivotal feature, with the Random Forest algorithm yielding superior performance with a R2 score of 0.94, clustering Mexico into 17 regions.

A global clustering of terrestrial food production systems.

Food production is at the heart of global sustainability challenges, with unsustainable practices being a major driver of biodiversity loss, emissions and land degradation. The concept of foodscapes, defined as the characteristics of food production along biophysical and socio-economic gradients, could be a way addressing those challenges. By identifying homologues foodscapes classes possible interventions and leverage points for more sustainable agriculture could be identified. Here we provide a globally consistent approximation of the world's foodscape classes. We integrate global data on biophysical and socio-economic factors to identify a minimum set of emergent clusters and evaluate their characteristics, vulnerabilities and risks with regards to global change factors. Overall, we find food production globally to be highly concentrated in a few areas. Worryingly, we find particularly intensively cultivated or irrigated foodscape classes to be under considerable climatic and degradation risks. Our work can serve as baseline for global-scale zoning and gap analyses, while also revealing homologous areas for possible agricultural interventions.

Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms

There are many applications of anomaly detection in the Internet of Things domain. IoT technology consists of a large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to the Internet, they frequently meet with the challenges of illegitimate access in the form of intrusions, anomalies, fraud, etc. Identifying these illegitimate accesses can be an exciting research problem. In numerous applications, either fuzzy clustering or rough set theory or both have been successfully employed. As the data generated in IoT domains are high-dimensional, the clustering methods used for lower-dimensional data cannot be efficiently applied. Also, very few methods were proposed for such applications until today with limited efficacies. So, there is a need to address the problem. In this article, mixed approaches consisting of nano topology and fuzzy clustering techniques have been proposed for anomaly detection in the IoT domain. The methods first use nano topology of rough set theory to generate CORE as a subspace and then employ a couple of well-known fuzzy clustering techniques on it for the detection of anomalies. As the anomalies are detected in the lower dimensional space, and fuzzy clustering algorithms are involved in the methods, the performances of the proposed approaches improve comparatively. The effectiveness of the methods is evaluated using time-complexity analysis and experimental studies with a synthetic dataset and a real-life dataset. Experimentally, it has been found that the proposed approaches outperform the traditional fuzzy clustering algorithms in terms of detection rates, accuracy rates, false alarm rates and computation times. Furthermore, nano topological and common Mahalanobis distance-based fuzzy c-means algorithm (NT-CM-FCM) is the best among all traditional or nano topology-based algorithms, as it has accuracy rates of 84.02% and 83.21%, detection rates of 80.54% and 75.37%, and false alarm rates of 7.89% and 9.09% with the KDDCup’99 dataset and Kitsune Network Attack Dataset, respectively.

Application of the Typology Approach for Energy Renovation Planning of Public Buildings’ Stocks at the Local Level: A Case Study in Greece

According to the latest energy efficiency European directive (EED 2023/1791/EU), the expected energy renovation rate of at least 3% of the buildings’ floor area each year towards nearly zero-energy buildings (nZEBs) is extended to include public buildings not only of the central government (as per the first EED 2012/27/EU) but also of regional and local authorities. This poses a great challenge, especially for Municipalities that often manage large building stocks with high energy demands. In response to this challenge, this paper presents the application of the so-called “typology approach” for conducting public buildings’ energy renovation plans at the local level. A computational survey is initially introduced to decide the optimal set of building-stock clustering criteria among all possible combinations, involving the minimization of the RMSE index regarding the primary energy consumption of each building. For a representative building from each identified typology, the key performance indicators (KPIs) are computed for alternative energy-upgrading scenarios. Exploiting the IMPULSE Interreg-MED project tools, the KPIs from each representative building are at first extrapolated to all buildings of the examined stock and, finally, a gradual energy renovation plan is automatically produced based on user-defined decision parameters including the required annual renovation rate. The methodology is applied for the case of the Municipality of Hersonissos in Greece. For the specific 44-buildings’ stock it was found that the optimal clustering set included four criteria, building use, construction year, heating, and a cooling system, leading to 15 building typologies. Finally, assuming a 7% renovation rate per year, a 12-year gradual renovation (nZEB transformation) plan is obtained foreseeing an 85% CO2 emissions’ reduction.

Aquimarina aquimarini sp. nov. and Aquimarina spinulae sp. nov., novel bacterial species with versatile natural product biosynthesis potential isolated from marine sponges.

This study describes two Gram-negative, flexirubin-producing, biofilm-forming, motile-by-gliding and rod-shaped bacteria, isolated from the marine sponges Ircinia variabilis and Sarcotragus spinosulus collected off the coast of Algarve, Portugal. Both strains, designated Aq135T and Aq349T, were classified into the genus Aquimarina by means of 16S rRNA gene sequencing. We then performed phylogenetic, phylogenomic and biochemical analyses to determine whether these strains represent novel Aquimarina species. Whereas the closest 16S rRNA gene relatives to strain Aq135T were Aquimarina macrocephali JAMB N27T (97.8 %) and Aquimarina sediminis w01T (97.1 %), strain Aq349T was more closely related to Aquimarina megaterium XH134T (99.2 %) and Aquimarina atlantica 22II-S11-z7T (98.1 %). Both strains showed genome-wide average nucleotide identity scores below the species level cut-off (95 %) with all Aquimarina type strains with publicly available genomes, including their closest relatives. Digital DNA-DNA hybridization further suggested a novel species status for both strains since values lower than 70 % hybridization level with other Aquimarina type strains were obtained. Strains Aq135T and Aq349T grew from 4 to 30°C and with between 1-5 % (w/v) NaCl in marine broth. The most abundant fatty acids were iso-C17 : 03-OH and iso-C15 : 0 and the only respiratory quinone was MK-6. Strain Aq135T was catalase-positive and β-galactosidase-negative, while Aq349T was catalase-negative and β-galactosidase-positive. These strains hold unique sets of secondary metabolite biosynthetic gene clusters and are known to produce the peptide antibiotics aquimarins (Aq135T) and the trans-AT polyketide cuniculene (Aq349T), respectively. Based on the polyphasic approach employed in this study, we propose the novel species names Aquimarina aquimarini sp. nov. (type strain Aq135T=DSM 115833T=UCCCB 169T=ATCC TSD-360T) and Aquimarina spinulae sp. nov. (type strain Aq349T=DSM 115834T=UCCCB 170T=ATCC TSD-361T).

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

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 stimulus and fatigue. J Strength Cond Res 38(4): 648-655, 2024-The purpose of this study was to examine acute stimulus and fatigue 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 greater total volume load (sets × repetitions × eccentric + concentric loading) (6,630 ± 1,210 kg) when compared with AEL + RR 2 (5,944 ± 1,085 kg) and TS (5,487 ± 1,002 kg). In addition, AEL + RR 5 led to significantly ( p < 0.05) greater rating of perceived exertion (RPE) after set 2 and set 3 and lower blood lactate (BL) after set 3 and 5, 15, and 25 minutes postexercise than AEL + RR 2 and TS. There was a main effect of condition for BL between AEL + RR 5 (5.11 ± 2.90 mmol·L -1 ), AEL + RR 2 (6.23 ± 3.22 mmol·L -1 ), and TS (6.15 ± 3.17 mmol·L -1 ). In summary, AEL + RR 5 results in unique stimulus and fatigue responses. Although it may increase perceived exertion, coaches could use AEL + RR 5 to achieve greater back squat total volume load while reducing BL accumulation.

Phylogenomic early warning signals for SARS-CoV-2 epidemic waves

Epidemic waves of coronavirus disease 2019 (COVID-19) infections have often been associated with the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Rapid detection of growing genomic variants can therefore serve as a predictor of future waves, enabling timely implementation of countermeasures such as non-pharmaceutical interventions (social distancing), additional vaccination (booster campaigns), or healthcare capacity adjustments. The large amount of SARS-CoV-2 genomic sequence data produced during the pandemic has provided a unique opportunity to explore the utility of these data for generating early warning signals (EWS). We developed an analytical pipeline (Transmission Fitness Polymorphism Scanner - designated in an R package mrc-ide/tfpscanner) for systematically exploring all clades within a SARS-CoV-2 virus phylogeny to detect variants showing unusually high growth rates. We investigated the use of these cluster growth rates as the basis for a variety of statistical time series to use as leading indicators for the epidemic waves in the UK during the pandemic between August 2020 and March 2022. We also compared the performance of these phylogeny-derived leading indicators with a range of non-phylogeny-derived leading indicators. Our experiments simulated data generation and real-time analysis. Using phylogenomic analysis, we identified leading indicators that would have generated EWS ahead of significant increases in COVID-19 hospitalisations in the UK between August 2020 and March 2022. Our results also show that EWS lead time is sensitive to the threshold set for the number of false positive (FP) EWS. It is often possible to generate longer EWS lead times if more FP EWS are tolerated. On the basis of maximising lead time and minimising the number of FP EWS, the best performing leading indicators that we identified, amongst a set of 1.4 million, were the maximum logistic growth rate (LGR) amongst clusters of the dominant Pango lineage and the mean simple LGR across a broader set of clusters. In the case of the former, the time between the EWS and wave inflection points (a conservative measure of wave start dates) for the seven waves ranged between a 20-day lead time and a 7-day lag, with a mean lead time of 5.4 days. The maximum number of FP EWS generated prior to a true positive (TP) EWS was two and this only occurred for two of the seven waves in the period. The mean simple LGR amongst a broader set of clusters also performed well in terms of lead time but with slightly more FP EWS. As a result of the significant surveillance effort during the pandemic, early detection of SARS-CoV-2 variants of concern Alpha, Delta, and Omicron provided some of the first examples where timely detection and characterisation of pathogen variants has been used to tailor public health response. The success of our method in generating early warning signals based on phylogenomic analysis for SARS-CoV-2 in the UK may make it a worthwhile addition to existing surveillance strategies. In addition, the method may be translatable to other countries and/or regions, and to other pathogens with large-scale and rapid genomic surveillance. This research was funded in whole, or in part, by the Wellcome Trust (220885_Z_20_Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. KOD, OB, VBF and EMV acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. RMC acknowledges funding from the Wellcome Trust Collaborators Award (206298/Z/17/Z).

The Criminal Justice System in Environmental Crime by Corporations

Environmental law has advanced quickly, not only in relation to the role of law as a means of development (a tool of social engineering) with the function of an agent of development or agent of change, but also more prominently about the function of law as protection, control, and legal certainty for society (social control) with the role of agent of stability. This legal research is normative legal research with a focus on positive legal norms (ius constitutum), that is, it focuses more on the application of positive legal norms and principles using a statutory approach (statutes approach), which is pertinent to the study of how legal issues are formulated in this legal research. Law Number 11 of 2020 Concerning Job Creation’s environmental cluster setting, particularly the elimination of criminal penalties, is extremely inappropriate. Due to the repeal of criminal penalties in Law Number 11 of 2020 Concerning Job Creation, administrative sanctions are not subject to stringent regulation. Legislation that prioritizes environmental and health issues and establishes a legal framework for the environment is required. This legislation should be written utilizing the omnibus process once more.&#x0D; Keywords: criminal system reconstruction, environmental law, Law Number 11 of 2020

Civil Aeronautical Ad Hoc Network Zero-Overhead Clustering Algorithm Based on Realtime Position Information of the Aircraft

Clustering is an important means to solve the poor scalability of aeronautical ad hoc networks (AANET). To improve the stability and performance of AANET and avoid unnecessary waste of resources caused by civil aircraft in communication, we proposed a zero-overhead clustering algorithm according to the real-time position of the aircraft based on the known trajectory. Firstly, the route and trajectory models are used to obtain geographical coordinates by the aircraft positioning algorithm. On this basis, the geographical cluster and cluster head region are divided in order to complete the cluster setting. Considering the aircraft maintenance cluster generation time updates, we use the communication sub-cluster generation algorithm to control the size of the cluster, and also, the flexibility of cluster hops is guaranteed by the subsidiary cluster members. The continuity of communication and the scalability of the cluster are maintained by the gateway node, thereby forming a network structure and increasing the stability of clusters. Finally, the actual route data are used to simulate the performance of the algorithm. The experimental and analytical results show that clustering and maintenance of the algorithm have zero overhead. Additionally, compared with the traditional algorithm, our proposed method can maintain a reasonable number of clusters, reduce the frequency of cluster head replacement, reduce the number of cluster members entering and leaving the cluster and avoid the loss of control of cluster heads to cluster members. So, it has important application value in the field of civil aviation.

A comparison of morning priming exercise using traditional-set and cluster-set configurations on afternoon explosive performance.

The impact of two priming exercise protocols using traditional (TS) or cluster-set (CS) arrangements on explosive performance 6 hours later were examined. Sixteen male collegiate athletes performed three testing sessions (one baseline without any prior exercise in the morning and two experimental sessions) separated by 72 hours. Participants completed two morning (9-11 am) priming protocols in a randomized order, either using a TS (no rest between repetitions) or CS (30 seconds of rest between repetitions) configuration. The protocols consisted of 3 sets × 3 repetitions of barbell back squat at 85% of 1 repetition maximum, with 4 minutes of rest between sets. In the afternoon (3-5 pm) of each trial, after a 6-hour rest period, a physical test battery was conducted that replicated baseline testing, including countermovement jump, 20-meter straight-line sprint, and T-test abilities. Across both conditions, participants exhibited increased countermovement jump height, 20-meter sprint time and T-test time compared to baseline (P < 0.05). Improvements in countermovement jump height (+4.4 ± 5.4%; P = 0.008) and 20-meter sprint time (+1.3 ± 1.7%; P = 0.022), but not T-test time (+1.1 ± 3.3%; P = 0.585), were significantly greater for CS than TS. In conclusion, compared to a traditional set arrangement, a morning-based priming protocol using a cluster-set configuration led to superior explosive performance benefits in the afternoon.

Towards Optimization of Privacy-Utility Trade-Off Using Similarity and Diversity Based Clustering

Most data owners publish personal data for information consumers, which is used for hidden knowledge discovery. But data publishing in its original form may be subjected to unwanted disclosure of subjects' identities and their associated sensitive information, and therefore, data is usually anonymized before publication. Many anonymization techniques have been proposed, but most of them often sacrifice utility for privacy, or vice versa, and explicitly disclose sensitive information when original data have skewed distributions. To address these technical problems, we propose a novel anonymization method using similarity and diversity-based clustering that effectively preserves both the subjects' privacy and anonymous-data utility. We identify influential attributes from the original data using a machine learning algorithm that assists in preserving a subject's privacy in imbalanced clusters, and that remained unexplored in previous research. The objective function of the clustering process considers both similarity and diversity in the attributes while assigning records to clusters, whereas most of the existing clustering-based anonymity techniques consider either similarity or diversity, thereby sacrificing either privacy or utility. Attribute values in each cluster set are minimally generalized to effectively achieve both competing goals. Extensive experiments were conducted on four real-world benchmark datasets to prove the feasibility of proposed method. The experimental results showed that the common and AI-based privacy risks were reduced by 13.01% and 24.3% respectively in contrast to existing methods. Data utility was augmented by 11.25% and 20.21% on two distinct metrics compared to its counterparts. The complications (e.g., # of iterations) of the clustering process were 2.25× lower than the state-of-the-art methods.