Search

We consider the numerical taxonomy problem of fitting a positive distance function \({\mathcal {D}:{S\choose 2}\rightarrow \mathbb {R}_{\gt 0}}\) by a tree metric. We want a tree T with positive edge weights and including S among the vertices so that their distances in T match those in \(\mathcal {D}\). A nice application is in evolutionary biology where the tree T aims to approximate thebranching process leading to the observed distances in \(\mathcal {D}\) [Cavalli-Sforza and Edwards 1967]. We consider the total error, that is, the sum of distance errors over all pairs of points. We present a deterministic polynomial time algorithm minimizing the total error within a constant factor. We can do this both for general trees and for the special case of ultrametrics with a root having the same distance to all vertices in S. The problems are APX-hard, so a constant factor is the best we can hope for in polynomial time. The best previous approximation factor was O((log n)(log log n)) by Ailon and Charikar [2005], who wrote "determining whether an O(1) approximation can be obtained is a fascinating question." [ABSTRACT FROM AUTHOR]

In this study, an analysis of hourly air temperatures in four groups of 32 stations from the UK highland (5 stations), UK lowland (4 stations), Italian highland (11 stations), and Italian lowland (12 stations) at different altitudes was carried out over the period from 2002 to 2021. The study aimed to examine the trends of each hour of the day during this period, over different averaging time windows (10‐day, 30‐day, and 60‐day). The trends were computed using the Mann–Kendall trend test and Sen's slope estimator. The similarity of trends within and across the groups of stations was assessed using the hierarchical clustering with dynamic time warping technique. An additional analysis was conducted to show the correlation of trends among the group of stations using the correlation distance matrix. Hierarchical clustering and distance correlation analysis show trend similarities and correlations, also indicating dissimilarities among different groups. Using 30‐day averages, significant warming trends in specific months at the Italian stations are evident, especially in February, July, August, and December. The UK highland stations did not show statistically significant trends, but clear pattern similarities were found within the groups, especially in certain months. The ultimate goal of this article is to provide insights into temperature dynamics and climate change characteristics on regional and diurnal scales. [ABSTRACT FROM AUTHOR]

Introduction: The thrombo-inflammatory response and outcomes of community-acquired pneumonia (CAP) due to various organisms (non-COVID-19 CAP) versus CAP due to a single virus, SARS-CoV-2 (i.e., COVID-19) may differ. Methods: Adults hospitalized with non-COVID-19 CAP (December 1, 2021–June 15, 2023) or COVID-19 (March 2, 2020–June 15, 2023) in Canada. We compared non-COVID-19 CAP and COVID-19 baseline, thrombo-inflammatory response, and mortality. We measured plasma cytokine and coagulation factor levels in a sample of patients, did hierarchical clustering, and compared cytokine and coagulation factor levels. Results: In 2,485 patients (non-COVID-19 CAP, n = 719; COVID-19 patients, n = 2,157), non-COVID-19 CAP patients had significantly lower 28-day mortality (CAP vs. COVID-19 waves 1 and 2; 10% vs. 18% and 16%, respectively), intensive care unit admission (CAP vs. all waves; 15% vs. 39%, 37%, 33%, and 24%, respectively), invasive ventilation (CAP vs. waves 1, 2, and 3 patients; 11% vs. 25%, 20%, and 16%), vasopressor use (CAP 12% vs. 23%, 21%, and 18%), and renal replacement therapy use (CAP 3% vs. Omicron 7%). Complexity of hierarchical clustering aligned directly with mortality: COVID-19 wave 1 and 2 patients had six clusters at admission and higher mortality than non-COVID-19 CAP and Omicron that had three clusters at admission. Pooling all COVID-19 waves increased complexity with seven clusters on admission. Conclusion: Complex thrombo-inflammatory responses aligned with mortality of CAP. At a fundamental level, the human thrombo-inflammatory response to a brand new virus was “confused” whereas humans had eons of time to develop a more concise efficient thrombo-inflammatory host response to CAP. [ABSTRACT FROM AUTHOR]

This study examines the issue of understanding management accounting practices in Romanian financial and accounting service firms, a subject of significant relevance due to the influence of accounting on organizational decision-making and strategic planning. The research examines 21 distinct management accounting practices, utilizing hierarchical clustering analysis refined by the Bayesian Information Criterion (BIC) to discern patterns and classifications within these practices. The findings indicate eight unique clusters, distinguished by differences in cluster size, withincluster heterogeneity, total within-cluster sum of squares, and Silhouette scores, which explain the diversity of practices and their organizational implications. The research enhances the literature by delineating the present state of management accounting practices in Romania and providing a more refined comprehension of the interrelationships among these practices, as guided by institutional theory. This method surpasses other attempts by emphasizing the diversity within clusters, uncovering distinct traits and strategic consequences. The findings indicate that various groupings of practices demonstrate particular patterns of adoption and utilization, which might affect firms' strategic decision-making processes. These insights facilitate future research focused on examining the drivers and effects of accounting methods across various institutional contexts. [ABSTRACT FROM AUTHOR]

Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives. [ABSTRACT FROM AUTHOR]

This study investigates the metabolite diversity of twelve Japanese plum cultivars grown in South Africa to understand their differential organoleptic characteristics and nutritional properties. The goal is to differentiate or associate these plum cultivars based on their metabolic profiles. Metabolite profiling was conducted using gas chromatography-mass spectrometry (GC-MS) at different postharvest ripening stages. Different unsupervised machine learning algorithms were applied: hierarchical clustering, K-means clustering, Density-Based Spatial Applications with Noise, and principal component analysis (PCA). Results revealed that each cultivar contains a unique combination of 13 amino acids, 4 sugars (contributing to organoleptic characteristics), and numerous phenolic compounds and antioxidant activities (contributing to nutritional value). The levels of these compounds are cultivar-dependent and vary with postharvest stages. The number of clusters of plum cultivars varied with both the clustering algorithm and postharvest stages. However, certain cultivars were consistently grouped regardless of the clustering method, indicating similar characteristics and responses to storage and shelf-life conditions. Similar consistent groupings were observed after cold storage and shelf life. Our findings also showed that K-means clustering is the most effective model for plum cultivar differentiation based on the Silhouette Score and Davies-Bouldin Index. This study enhances our understanding of how metabolites evolve over different postharvest stages and provides a robust framework for differentiating plum cultivars, which can aid in sorting and grading operations. The research offers actionable insights to improve postharvest handling and storage practices, which are critical for maintaining the nutritional quality of plums, an important fruit for human health. [ABSTRACT FROM AUTHOR]

Grain amaranth is an underutilized pseudo-cereal recognized as an important nutritional crop owing to its balanced nutrient profile. A total of two hundred and fifty-one accessions of grain amaranth identified as core, from a pool of nine-hundred accessions, were assessed for phenotypic performance and genetic diversity across two locations. The establishment of the core accession aimed to reduce the dataset and enhance the accessibility of germplasm resources. This study primarily aimed to identify elite accessions exhibiting desirable agro-morphological traits and on the basis of their variability. ANOVA and diversity indices demonstrated a significant amount of variability for all the quantitative and qualitative traits. Traits such as plant height, inflorescence length, and seed yield showed high GA and high heritability estimates for both locations, with the lowest being observed for days to maturity. The accessions were grouped into four primary clusters with majority of accessions i.e., 92 (37%) belonged to Cluster-I for Loc1 while for Loc2, Cluster-IV contained majority of the accessions i.e., 94 (38%). Principal Component Analysis resolved trait variability into 3 PCs, accounting for 65.01% for Loc1 and 63.47% for Loc2, respectively. All traits, except protein content, showed a significant positive correlation and high degree of variability in both locations. The present investigation identified elite accessions suited to two different environments that have the potential for use in future genetic improvement initiatives for grain amaranth genetic resources. [ABSTRACT FROM AUTHOR]

Cassava (Manihot esculenta Crantz) holds significant economic importance globally. Evaluating a diverse range of germplasm based on molecular characteristics not only enhances its preservation but also supports its utilization in breeding programs. In this study, we assessed genetic diversity and population structure among 155 cassava genotypes from Uganda using 5247 single nucleotide polymorphism (SNP) markers. Genotyping by sequencing (GBS) was employed for SNP discovery and to evaluate genetic diversity and population structure using the ADMIXTURE software. The cassava accessions comprised two populations: 49 accessions from Ugandan lines and 106 accessions resulting from crosses between South American and Ugandan lines. The average call rate of 96% was utilized to assess marker polymorphism. Polymorphic information content values of the markers ranged from 0.1 to 0.5 with an average of 0.4 which was moderately high. The principal component analysis (PCA) showed that the first two components captured ~ 24.2% of the genetic variation. The average genetic diversity was 0.3. The analysis of molecular variance (AMOVA) indicated that 66.02% and 33.98% of the total genetic variation occurred within accessions and between sub-populations, respectively. Five sub-populations were identified based on ADMIXTURE structure analysis (K = 5). Neighbor-joining tree and hierarchical clustering tree revealed the presence of three different groups which were primarily based on the source of the genotypes. The results suggested that there was considerable genetic variation among the cassava genotypes which is useful in cassava improvement and conservation efforts. [ABSTRACT FROM AUTHOR]

GARCH models can contain outliers, such as Additive Level Outliers (ALO) and/or Additive Volatility Outliers (AVO), so a robust parameter estimation is used to overcome the presence of outliers. This study uses Quasi-Maximum Likelihood Estimation (QMLE) and M-estimation to estimate the parameters of the GARCH model containing outliers. Hierarchical and K-means clustering algorithms are then used to cluster time-series data based on distances calculated from these parameter estimates. The proposed approach applies hierarchical and K-means clustering using distances derived from QMLE and M-estimation results for GARCH (1,1) models containing outliers for clustering with simulation data and a case study using stock data listed in the Indonesia Stock Exchange. The results show that hierarchical and K-means clustering with distance between two GARCH (1,1) models based on M-estimation produces good clusters with smaller C index than QMLE estimations for simulation data and the case study of stock data in Indonesia. [ABSTRACT FROM AUTHOR]

Cacao (Theobroma cacao L.) is a tropical tree species belonging to the Malvaceae, which originated in the lowland rainforests of the Amazon. It is a major agricultural commodity, which contributes towards the Gross Domestic Product of West African countries, where it accounts for about 70% of the world's production. Understanding the genetic diversity of genetic resources in a country, especially for an introduced crop such as cacao, is crucial to their management and effective utilization. However, very little is known about the genetic structure of the cacao germplasm from Sierra Leone and Togo based on molecular information. We assembled cacao germplasm accessions (235 from Sierra Leone and 141 from Togo) from different seed gardens and farmers' fields across the cacao-producing states/regions of these countries for genetic diversity and population structure studies based on single nucleotide polymorphism (SNP) markers using 20 highly informative and reproducible KASP–SNPs markers. Genetic diversity among these accessions was assessed with three complementary clustering methods, including model-based population structure, discriminant analysis of principal components (DAPC), and phylogenetic trees. STRUCTURE and DAPC exhibited some consistency in the allocation of accessions into subpopulations or groups, although some discrepancies in their groupings were noted. Hierarchical clustering analysis grouped all the individuals into two major groups, as well as several sub-clusters. We also conducted a network analysis to elucidate genetic relationships among cacao accessions from Sierra Leone and Togo. Analysis of molecular variance (AMOVA) revealed high genetic diversity (86%) within accessions. A high rate of mislabeling/duplicate genotype names was revealed in both countries, which may be attributed to errors from the sources of introduction, labeling errors, and lost labels. This preliminary study demonstrates the use of KASP–SNPs for fingerprinting that can help identify duplicate/mislabeled accessions and provide strong evidence for improving accuracy and efficiency in cacao germplasm management as well as the distribution of correct materials to farmers. [ABSTRACT FROM AUTHOR]

The monitoring of fog density is of great importance in meteorology and its applications in environment, aviation and transportation. Nowadays, vision-based fog estimation from images taken with surveillance cameras has made a great supplementary contribution to the scarcely traditional meteorological fog observation. In this paper, we propose a new Random Forest (RF) approach for image-based fog estimation. In order to reduce the impact of data imbalance on recognition, the StyleGAN2-ADA (generative adversarial network with adaptive discriminator augmentation) algorithm is used to generate virtual images to expand the data of low proportions. Key image features related to fog are extracted, and an RF method, integrated with the hierarchical and k-medoid clustering, is deployed to estimate the fog density. The experiment conducted in Sichuan in February 2024 shows that the improved RF model has achieved an average accuracy of fog density observation of 93%, 6.4% higher than the RF model without data expansion, 3–6% higher than the VGG16, the VGG19, the ResNet50, and the DenseNet169 with or without data expansion. What is more, the improved RF method exhibits a very good convergence as a cost-effective solution. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Foodborne pathogens cause around 47.8 million illnesses in the U.S. annually, with antimicrobial misuse in food production, particularly in poultry processing, contributing significantly to this public health challenge. Misuse of antimicrobials can contribute to antimicrobial resistance (AMR) and make the treatment of pathogens increasingly difficult. This emphasizes the need to investigate antimicrobial resistance in U.S. poultry. This study analyzes data from the NCBI Pathogen Isolates Browser (2015–2023) to explore the relationships between antimicrobial-resistant pathogens, AMR genes, and antimicrobials detected with resistance in pathogens isolated from chicken and turkey. Using principal component analysis and hierarchical clustering, we mapped and profiled regional and temporal patterns of antimicrobial resistance. Salmonella enterica was the most prevalent antimicrobial-resistant pathogen across both chicken and turkey, with notable outbreaks, particularly in the Northeast. Antimicrobial-resistant Campylobacter jejuni was more prevalent in chicken, particularly in California and Georgia, while Escherichia coli and Shigella were more prominent in turkey, with concentrated antimicrobial resistance in Texas for pathogen samples isolated from chicken. Resistance to tetracycline and streptomycin was widespread, with distinct regional clusters: antimicrobial resistance was concentrated in states like Minnesota for pathogens isolated from chicken, while AMR found in pathogens isolated from turkey was more evenly distributed across the Midwest. Key AMR genes, such as tet(A), mdsA, and mdsB, also followed similar patterns, peaking in 2019 and significantly declining by 2022. The observed decline in AMR cases may be linked to improved biosecurity measures and disruptions in detection due to the COVID-19 pandemic. This comprehensive study of antimicrobial resistance in U.S. poultry provides valuable insights into resistance trends, which provide useful information to inform targeted interventions and policies to mitigate AMR threats in the poultry production industry. For consumers, these findings emphasize the importance of proper food handling and cooking practices to reduce the risk of exposure to resistant pathogens. Regulatory authorities should focus on enforcing stricter antimicrobial usage policies and enhancing surveillance systems to sustain the reduction in AMR cases. [ABSTRACT FROM AUTHOR]

Natural neighbor is known for its ability to adaptively identify the nearest neighbors in a dataset, but it sometimes can not accurately cluster in datasets with density variations. To overcome this drawback, this paper proposes a hierarchical clustering algorithm based on natural local density peaks (HC-NLDP). HC-NLDP mainly consists of partition and merge. Firstly, HC-NLDP searches natural local density peaks to form sub-clusters. This effectively solves the problem that points in low-density regions are difficult to cluster accurately. Secondly, HC-NLDP computes the shared similarity between every pair of sub-clusters. The shared similarity describes the relevance between sub-clusters. When confronted with datasets exhibiting density variations, the shared similarity can ensure that HC-NLDP achieves superior clustering performance. Numerical experiments based on synthetic and real datasets demonstrate the effectiveness and superiority of HC-NLDP. [ABSTRACT FROM AUTHOR]

Introduction: Statistical shape analysis (SSA) with clustering is often used to objectively define and categorise anatomical shape variations. However, studies until now have often focused on simplified anatomical reconstructions, despite the complexity of studied anatomies. This work aims to provide insights on the anatomical detail preservation required for SSA of highly diverse and complex anatomies, with particular focus on the left atrial appendage (LAA). This anatomical region is clinically relevant as the location of almost all left atrial thrombi forming during atrial fibrillation (AF). Moreover, its highly patient-specific complex architecture makes its clinical classification especially subjective. Methods: Preliminary LAA meshes were automatically detected after robust image selection and wider left atrial segmentation. Following registration, four additional LAA mesh datasets were created as reductions of the preliminary dataset, with surface reconstruction based on reduced sample point densities. Utilising SSA model parameters determined to optimally represent the preliminary dataset, SSA model performance for the four simplified datasets was calculated. A representative simplified dataset was selected, and clustering analysis and performance were evaluated (compared to clinical labels) between the original trabeculated LAA anatomy and the representative simplification. Results: As expected, simplified anatomies have better SSA evaluation scores (compactness, specificity and generalisation), corresponding to simpler LAA shape representation. However, oversimplification of shapes may noticeably affect 3D model output due to differences in geometric correspondence. Furthermore, even minor simplification may affect LAA shape clustering, where the adjusted mutual information (AMI) score of the clustered trabeculated dataset was 0.67, in comparison to 0.12 for the simplified dataset. Discussion: This study suggests that greater anatomical preservation for complex and diverse LAA morphologies, currently neglected, may be more useful for shape categorisation via clustering analyses. [ABSTRACT FROM AUTHOR]

The incorporation of real-world data (RWD) into medical product development and evaluation has exhibited consistent growth. However, there is no universally adopted method of how much information to borrow from external data. This paper proposes a study design methodology called Tree-based Monte Carlo (TMC) that dynamically integrates patients from various RWD sources to calculate the treatment effect based on the similarity between clinical trial and RWD. Initially, a propensity score is developed to gauge the resemblance between clinical trial data and each real-world dataset. Utilizing this similarity metric, we construct a hierarchical clustering tree that delineates varying degrees of similarity between each RWD source and the clinical trial data. Ultimately, a Gaussian process methodology is employed across this hierarchical clustering framework to synthesize the projected treatment effects of the external group. Simulation result shows that our clustering tree could successfully identify similarity. Data sources exhibiting greater similarity with clinical trial are accorded higher weights in treatment estimation process, while less congruent sources receive comparatively lower emphasis. Compared with another Bayesian method, meta-analytic predictive prior (MAP), our proposed method's estimator is closer to the true value and has smaller bias. [ABSTRACT FROM AUTHOR]

Background: Comorbidities in primary care do not occur in isolation but tend to cluster together causing various clinically complex phenotypes. This study aimed to distinguish phenotype clusters and identify the risks of all‐cause mortality in primary care. Methods: The baseline cohort of the LIPIDOGEN2015 sub‐study involved 1779 patients recruited by 438 primary care physicians. To identify different phenotype clusters, we used hierarchical clustering and investigated differences between clinical characteristics and mortality between clusters. We then performed causal analyses using causal mediation analysis to explore potential mediators between different clusters and all‐cause mortality. Results: A total of 1756 patients were included (mean age 51.2, SD 13.0; 60.3% female), with a median follow‐up of 5.7 years. Three clusters were identified: Cluster 1 (n = 543) was characterised by overweight/obesity (body mass index ≥ 25 kg/m2), older (age ≥ 65 years), more comorbidities; Cluster 2 (n = 459) was characterised by non‐overweight/obesity, younger, fewer comorbidities; Cluster 3 (n = 754) was characterised by overweight/obesity, younger, fewer comorbidities. Adjusted Cox regression showed that compared with Cluster 2, Cluster 1 had a significantly higher risk of all‐cause mortality (HR 3.87, 95% CI: 1.24–15.91), whereas this was insignificantly different for Cluster 3. Causal mediation analyses showed that decreased protein thiol groups mediated the hazard effect of all‐cause mortality in Cluster 1 compared with Cluster 2, but not between Clusters 1 and 3. Conclusion: Overweight/obesity older patients with more comorbidities had the highest risk of long‐term all‐cause mortality, and in the young group population overweight/obesity insignificantly increased the risk in the long‐term follow‐up, providing a basis for stratified phenotypic risk management. [ABSTRACT FROM AUTHOR]

The research focuses on incorporating microgrids into engineering curricula for achieving voltage stability in today's power systems. This helps to meet the increasing demand for engineers to integrate distributed power generation and renewable energy sources. Some limitations of the current literature include the absence of models outlining approaches to microgrid education and limited insight into teaching strategies for electrical power systems. The research used a quantitative methodology to survey 100 engineering students enrolled in a microgrid modeling class to achieve the study's objectives. The data analysis involved machine learning models such as Random Forest, Gradient Boosting, K-Means, hierarchical clustering, and regression models. The major findings identified exam score as the most significant determiner of student performance (weight ≈ 0.40). Based on the clustering analysis, it was found that microgrid systems can be grouped into four operational states. It was also seen that linear regression models were highly accurate and better than other highly complex models, like Decision Tree, with a model accuracy of R2 ≈ 0.4. One of the study's major strengths is the potential impact of the proposed framework for integrating microgrids into engineering education on the professional training of engineers. This framework, based on theoretical knowledge and practical experience as well as on developing advanced analytical skills, can significantly enhance the professional training of engineers to deal with the complexities of contemporary power systems, including microgrids and sustainable energy progress. [ABSTRACT FROM AUTHOR]

The sociology of elites has long considered families as the unit of analysis in studies of power dynamics between elite dynasties and their transmission of wealth and prestige over generations. However, the assumption that families are cohesive units with common goals and agendas does not hold, especially for large and powerful family dynasties. Internal conflicts and clan rivalries throughout history suggest that independent clans, rather than families, are the more appropriate level for aggregation. The increasing availability of large-scale genealogical datasets and advances in social network analysis allow this more fine-grained perspective to be implemented even without historical documentation on observed clan structures. This paper builds on socio-anthropological conceptualizations of kinship and on hierarchical clustering techniques to present a new method for identifying independent clans within families that relies only on network-dependent terms. I use simulated data and an empirical kinship network of families of early modern Basel, Switzerland to compare a clan detector algorithm's performance with common community detection techniques. The historical accuracy of the clan structures detected is further assessed with various status indicators. The analyses show that the proposed clan detector algorithm is more suitable for identifying historically accurate clans than the traditional approaches. The application of the new method to the kinship network of Basel families sheds light on the city's stratification into high- and low-status societies in which elite families were also divided into privileged and less privileged clans. • New method for identifying independent clans within large family dynasties. • The algorithm's performance is assessed with simulated and empirical data. • Comparison with other community detection techniques. • The detected clans in the empirical network are historically accurate. [ABSTRACT FROM AUTHOR]

Amid geopolitical tensions over 5G technology, concerns about foreign firms like Huawei collaborating with academia have surfaced. This paper examines Huawei's role in Canadian research, analyzing its impact on network robustness and research themes over time. Robustness in network research has been extensively explored, yet there remains a notable gap in understanding the influence of geopolitical factors and foreign corporate presence, such as Huawei's, on these networks. The main results of this research show that: (1) The 5G network exhibits a decreasing trend in network robustness, with the potential for fragmentation increasing over time; (2) The impact of Huawei's removal on the network's Largest Connected Component (LCC) is relatively minor; (3) The network retains its small-world properties irrespective of Huawei's presence, and its removal has a minor impact on knowledge transfer efficiency; (4) Huawei's removal does not significantly affect network centralization, nor does it influence the prevailing trend observed over time; (5) Hierarchical clustering and specificity analysis identify Huawei's strategic focus on the silicon and optical photonic domain within the 5G research; (6) The collaboration-topic network shows a high degree of robustness, suggesting that Canada's research contributions in these areas are unaffected by the absence Huawei. This study provides a nuanced view of Huawei's role in Canadian 5G research, suggesting that while the company is a significant player, its impact is in general neither singular nor irreplaceable within the academic network. [ABSTRACT FROM AUTHOR]

In this article, we present various algorithms to partition the nodes of a network into groups that show the property of nestedness. Since perfect nestedness is a rare phenomenon, we consider the task from a data mining perspective, and we search for groups having high-level of nestedness. We utilize both agglomerative and divisive hierarchical clustering procedures and compare them on several benchmark and real-life networks. Furthermore, we propose different metrics derived from the results of our algorithms. We show that average-linkage and complete-linkage clustering can recover the largest fully nested clusters, and that the cluster size-weighted mean nestedness was a more stable metric for measuring clustering performance. Our proposed algorithms allow us to create multiple resolution views of nestedness-based clustering of networks, extending the field of graph-based data mining. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence models for the fault diagnosis of marine machinery increased expeditiously within the shipping industry. This relates to the effectiveness of artificial intelligence in capturing fault patterns in marine systems that are becoming more complex and where the application of traditional methods is becoming unfeasible. However, despite these advances, the lack of fault labelling data is still a major concern due to confidentiality issues, and lack of appropriate data, for instance. In this study, a method based on histogram similarity and hierarchical clustering is proposed as an attempt to label the distinct anomalies and faults that occur in the dataset so that supervised learning can then be implemented. To validate the proposed methodology, a case study on a main engine of a tanker vessel is considered. The results indicate that the method can be a preliminary option to classify and label distinct types of faults and anomalies that may appear in the dataset, as the model achieved an accuracy of approximately 95% for the case study presented. [ABSTRACT FROM AUTHOR]

This study addresses the Vehicle Routing Problem with Time Windows (VRPTW) in the context of ice distribution by introducing a novel mathematical model that incorporates practical constraints essential for real-world applications. These constraints include customer retention strategies and quality preservation methods, which are important for maintaining customer satisfaction and product integrity. The objective is to minimize the total costs, including fuel expenses, standard and bonus driver wages, missed delivery penalties, and costs related to a quality preservation strategy. Given the NP-hard nature of this problem, this study proposes a hierarchical cluster-first-route-second approach and a Differential Evolution (DE) algorithm to solve large-scale problems. The effectiveness of these methods was examined and compared through test cases involving various problem sizes using real-world data from an ice distribution company in Thailand. The results show that the hierarchical cluster-first-route-second approach is more effective for the practical problem. Using capacitated K-means clustering, this hierarchical approach groups customers, enabling the solution of manageable subproblems through Mixed-Integer Linear Programming (MILP). The proposed method not only provides cost-effective and scalable solutions, but also outperforms traditional methods in terms of computation time and feasibility for large-scale applications. This study offers significant theoretical contributions by extending VRPTW models and providing practical implications for optimizing distribution strategies in competitive market environments, leading to substantial cost reductions and enhanced operational efficiency. [ABSTRACT FROM AUTHOR]

We analyzed literature data on the composition of 479 glasses used to stabilize radioactive wastes, covering a wide range and including 51 oxides and few fluorides. The most common glass constituents included SiO2, B2O3, Na2O, and Fe2O3 in varying amounts, with a predominance of borosilicate glasses. Seven families of waste radioactive glasses were observed, including borosilicates, silicates, boroaluminosilicates, iron phosphates, aluminosilicates, sodium iron phosphates, and boroaluminates. These data were used to estimate an average composition associated with a sodium borosilicate glass. Multivariate exploratory methods were used to analyze and classify the compositions of the waste radioactive glasses using multidimensional scaling and hierarchical clustering. Four main clusters were observed, the largest with 417 glasses, mainly silicates, borosilicates, aluminosilicates, and boroaluminosilicates. The results of this work have shown that it is possible to map radioactive waste glasses according to their composition, promoting a road map for future applications with specific properties. [ABSTRACT FROM AUTHOR]

In this study, an implementation of hierarchical clustering methods was conducted in schools' admission data. We aim to demonstrate that the hierarchical clustering method can be used to help analyze the membership changes of each cluster based on its achievement number of new students from different months period observations. This method can be used by decision-makers to make a strategy for each school which has decreasing achievement from the previous period. In this paper, we employ the hierarchical clustering method to cluster admission performance achievement from fifty Telkom Schools. Instead of clustering admission in one period directly, this paper tried to analyze the movement of clustering membership from one period to another. We observed the movement membership of the group from three categories period, such as monthly, quarterly, and semesterly. The experimental results demonstrate that the monthly scenario was the best clustering result. The monthly scenario achieves the best score for all metrics such as the Dunn index, Silhouette score, Davies-Bouldin index, and Calinski-Harabasz compared to the quarter and semester scenario. There are four schools which are consistent in the first cluster and seven schools which are consistent in the second cluster in all scenarios and all periods. [ABSTRACT FROM AUTHOR]

Abstract One method for estimating floods in areas lacking statistical data is the use of regional frequency analysis based on machine learning. In this study, statistical and clustering-based approaches were evaluated for flood estimation in the Karkheh watershed. The hydrological homogeneity of the obtained zones was then assessed using linear moments and heterogeneity adjustment methods proposed by Hosking and Wallis. Then, the ZDIST statistic was used to calculate the three-parameter distributions for stations within each hydrologically homogeneous cluster. These parameters were computed using linear moments, and floods with different return periods at each station were estimated using regional relationships. The results indicated the creation of two clusters in this area, with five stations in cluster one and 11 stations in cluster two. The statistical homogeneity values for clusters one and two were calculated as 0.33 and 0.17, respectively, indicating the homogeneity of each region. Generalized Pearson type III and generalized extreme value distributions were selected as the best regional distributions for clusters 1 and 2, respectively. The results also showed that floods could be estimated for return periods of 2, 5, 25 years, and more. The highest estimated flood is predicted at the Jelugir-e Majin station, where the flood with a 2-year return period reaches 1034 m3 s−1. This increases to 5360 m3 s−1 for a 100-year return period. The approach presented in this study is recommended for similar regions lacking complete information.

Classical approaches to plant disease diagnosis assume a single pathogen/single disease paradigm. Here, we revisit the presumed role of Fusarium oxysporum as the causal agent of wilting and yellowing of chickpea plants on smallholder farms in Ethiopia. Contrary to expectations, detection of Fusarium DNA using conserved PCR primers failed to associate the pathogen with symptomatic plants. Instead, culture-independent sequencing of microbial communities nominated unexpected pathogens and revealed patchiness in the assembly of common microbial consortia. Surprisingly, tests of differential enrichment identified Phytophthora as the most common disease-associated taxon. More generally, across all field sites, multilevel pattern analysis identified indicator taxa whose patterns of co-occurrence demarcate discrete microbial communities and are consistent with a range of specific interactions, including mutualism and antagonism. Taken together, these data indicate that soilborne chickpea disease in Ethiopia has heterogeneous origins and that despite decades of emphasis and disease resistance breeding, the role of Fusarium as the frequent agent of chickpea disease in Ethiopia remains enigmatic.

Abstract Accurate monitoring of the depth of anesthesia (DoA) is essential for ensuring patient safety and effective anesthesia management. Existing methods, such as the Bispectral Index (BIS), are limited in real-time accuracy and robustness. Current methods have problems in generalizability across diverse patient datasets and are sensitive to artifacts, making it difficult to provide reliable DoA assessments in real time. This study proposes a novel method for DoA monitoring using EEG signals, focusing on accuracy, robustness, and real-time application. EEG signals were pre-processed using wavelet denoising and discrete wavelet transform (DWT). Features such as Permutation Lempel–Ziv Complexity (PLZC) and Power Spectral Density (PSD) were extracted. A random forest regression model was employed to estimate anesthetic states, and an unsupervised learning method using the Hurst exponent algorithm and hierarchical clustering was introduced to detect transitions between anesthesia states. The method was tested on two independent datasets (UniSQ and VitalDB), achieving an average Pearson correlation coefficient of 0.86 and 0.82, respectively. For the combined dataset, the model demonstrated an R-squared value of 0.70, a RMSE of 6.31, a MAE of 8.38, and a Pearson correlation of 0.84, showcasing its robustness and generalizability. This approach offers a more accurate and reliable real-time DoA monitoring tool that could significantly improve patient safety and anesthesia management, especially in diverse clinical environments.

Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.

This paper proposes navigation of multiple autonomous underwater vehicles (AUVs) by employing machine learning approach for wide area surveys in underwater environment. Wide area survey in underwater environment is affected by low data rate.We consider two AUVs moving in formation through clustering followed by selection of optimal path that is affected by low data rate and limited acoustical underwater communication. A state compression approach using machine learning based acoustical localization and communication (ML-ALOC) is proposed to overcome the low data rate issue in which AUV states are approximated by Hierarchical clustering followed by an optimal selection approach using Convolutional Neural Network (CNN). The performance of the proposed state compression algorithm is compared with particle state compression algorithm based on K-Means clustering at each iteration followed by Akaike information criterion (AIC) pursuing extensive simulations, in which two AUVs navigate through trajectory. It is observed from the simulations that the proposed ML-ALOC system provides better estimates when compared with acoustical localization and communication (ALOC) system using particle clustering for state compression scheme.

Conventional k-means algorithms often face significant computational burdens and have a high dependence on the number of predefined clusters k. Therefore, this paper proposes the k ∗ -means algorithm, which incorporates the concept of the perceptron classification algorithm to transform the distance-based clustering task into a classification problem, significantly improving clustering efficiency. Moreover, this paper combines the k ∗ -means algorithm with hierarchical clustering methods that can automatically identify the number of clusters. An initial clustering is performed using a large pre-set number of clusters with the k ∗ -means algorithm, followed by merging the sub-clusters through hierarchical clustering. Experimental results show that the proposed k ∗ -means method has significant advantages when handling large-scale datasets. It greatly reduces the number of distance calculations and performs better in terms of runtime compared to the latest accelerated k-means algorithms. And the k ∗ -means algorithm, when combined with hierarchical clustering, shows notable performance on both the four synthetic datasets and the four real datasets. Future work could explore leveraging parallelization techniques to further enhance its scalability and efficiency on even larger datasets. [ABSTRACT FROM AUTHOR]

The goal of this paper is to estimate the true but unknown number of clusters K in multivariate data. The contributions are two folds. The first is to narrow the search space for the estimates k ^ to 1 ≤ k ^ ≤ K max . We propose a new method for finding K max , which is better than the existing ones. The second is to propose three indices for computing k ^ within the range 1 ≤ k ^ ≤ K max : The R-Index, the FB Index, and the CSum Index. All three indices are based on the L-curve (the plot of W k vs. k), where W k is the total within-cluster-similarity (withinness), for values of k in the above range. We give the rationale for each method. We investigate the performance of these three indices and compare them with six of the most commonly used indices using both real benchmark datasets and a challenging synthetic data of varying sample sizes ( n = 200 to n = 3600 ) and varying number of true clusters K ranging from K = 2 to K = 36 . We use both the Hierarchical clustering and the k-Means clustering algorithms, but the approach can also be used with other clustering methods. The three indices are shown to outperform the existing ones. An additional advantage of our indices is computational complexity, where it is shown that they take much less time to compute than the existing ones. [ABSTRACT FROM AUTHOR]

• DNA-methylation based immunological cell-type estimates are associated with depression severity over time. • Immunomethylomics shows applicability to inflammation and depression. • The combination of cell-type estimates, cytokines, and hsCRP reflect current depression severity. • Hierarchical clustering on immunomethylomic estimates is a valuable tool for immunological profiling. Alterations in DNA methylation and inflammation could represent valid biomarkers for the stratification of patients with major depressive disorder (MDD). This study explored the use of DNA-methylation based immunological cell-type profiles in the context of MDD and symptom severity over time. In 119 individuals with MDD, DNA-methylation was assessed on whole blood using the Illumina Infinium MethylationEPIC 850 k BeadChip. Quality control and data processing, as well as cell type estimation was conducted using the RnBeads package. The cell type composition was estimated using epigenome-wide DNA methylation signatures, applying the Houseman method, considering six cell types (neutrophils, natural killer cells (NK), B cells, CD4+ T cells, CD8+ T cells and monocytes). Two cytokines (IL-6 and IL-1β) and hsCRP were quantified in serum. We performed a hierarchical cluster analysis on the six estimated cell-types and tested the differences between these clusters in relation to the two cytokines and hsCRP, depression severity at baseline, and after 6 weeks of treatment (celecoxib/placebo + vortioxetine). We performed a second cluster analysis with cell-types and cytokines combined. ANCOVA was used to test for differences across clusters. We applied the Bonferroni correction. After quality control, we included 113 participants. Two clusters were identified, cluster 1 was high in CD4+ cells and NK, cluster 2 was high in CD8+ T-cells and B-cells, with similar fractions of neutrophils and monocytes. The clusters were not associated with either of the two cytokines and hsCRP, or depression severity at baseline, but cluster 1 showed higher depression severity after 6 weeks, corrected for baseline (p = 0.0060). The second cluster analysis found similar results: cluster 1 was low in CD8+ T-cells, B-cells, and IL-1β. Cluster 2 was low in CD4+ cells and natural killer cells. Neutrophils, monocytes, IL-6 and hsCRP were not different between the clusters. Participants in cluster 1 showed higher depression severity at baseline than cluster 2 (p = 0.034), but no difference in depression severity after 6 weeks. DNA-methylation based cell-type profiles may be valuable in the immunological characterization and stratification of patients with MDD. Future models should consider the inclusion of more cell-types and cytokines for better a prediction of treatment outcomes. [ABSTRACT FROM AUTHOR]

In recent years, multi-objective optimization evolutionary algorithms (MOEAs) have been proven to be effective methods for solving multi-objective optimization problems (MOPs). However, most existing MOEAs that are limited by the shape of the Pareto fronts (PFs) are only suitable for solving a certain type of problem. Therefore, in order to ensure the generality of the algorithm in practical applications and overcome the constraints brought by the shapes of PFs, a new adaptive MOEA (CAVA-MOEA) based on hierarchical clustering and vector angle to solve various MOPs with irregular PFs is proposed in this article. Firstly, a set of adaptively generated clustering centers is used to guide the population to converge quickly in many search directions. Secondly, the vector angle-based selection further exploits the potential of the clustering algorithm, which keeps a good balance between diversity and convergence. The proposed CAVA-MOEA is tested and analyzed on 24 MOPs with regular PFs and 18 MOPs with irregular PFs. The results show that CAVA-MOEA has certain competitive advantages compared with the other six advanced algorithms in solving MOPs with irregular PFs. [ABSTRACT FROM AUTHOR]

Accurate soil nutrient data are crucial for precise fertilizer recommendations in intelligent agriculture. However, the process of soil testing, which includes collecting samples, determining available nutrients and interpreting results, is expensive. To address this challenge, spatial interpolation methods are commonly used to predict soil fertility. Yet, existing techniques like IDW (Inverse Distance Weighting) and OK (Ordinary Kriging) face limitations, making it difficult to achieve highly accurate estimates. Therefore, this paper introduces NCAMS (Neighbor Cluster Adaptive Model with Spatial Color Block), a novel interpolation approach that automatically identifies nearby points crucial for estimating soil nutrient values at a given location. In our approach, we not only consider spatial correlation but also incorporate the soil variables of sampled points. Delaunay triangulation and hash functions further divide data points into distinct clusters, with our model automatically selecting specific clusters. Moreover, our interpolation method integrates IDW and OK without requiring extensive training on real-world data. Extensive experiments on four real-world datasets, conducted through cross-validation, demonstrate the superior performance of our approach compared to eight state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The aquaculture industry in China is not complete without the freshwater industry. The scientific assessment of the regional competitiveness of the freshwater aquaculture industry has significant theoretical and practical implications for fostering the industry's sustainable development. Based on the diamond model, this study developed a competitiveness evaluation index system for Chinese freshwater aquaculture industry which included six primary indicators and sixteen secondary indicators. The primary indicators included resource endowment, production capacity, science and technology promotion capacity, related and supporting industries, market demand and guiding development policy. The study analyzed freshwater aquaculture industrial competitiveness during 2012–2020 and classified the Chinese 18 major freshwater aquaculture provinces into five competitive levels. The results showed that the development of related and supporting industries has the greatest contribution to the competitiveness, followed by resource endowment and market demand capacity. The analysis and tier classification showed that the competitiveness of Jiangsu Province and Hubei Province has always been at the forefront, and the province of Guangdong, Fujian, Hunan, Jiangxi, Zhejiang, Anhui, Liaoning, and Guangxi were above the middle level. Moreover, the results indicated that the competitiveness of most provinces showed an upward trend.

Abstract In the pharmaceutical industry, there is an abundance of regulatory documents used to understand the current regulatory landscape and proactively make project decisions. Due to the size of these documents, it is helpful for project teams to have informative summaries. We propose a novel solution, MedicoVerse, to summarize such documents using advanced machine learning techniques. MedicoVerse uses a multi-stage approach, combining word embeddings using the SapBERT model on regulatory documents. These embeddings are put through a critical hierarchical agglomerative clustering step, and the clusters are organized through a custom data structure. Each cluster is summarized using the bart-large-cnn-samsum model, and each summary is merged to create a comprehensive summary of the original document. We compare MedicoVerse results with established models T5, Google Pegasus, Facebook BART, and large language models such as Mixtral 8 $$\times $$ × 7b instruct, GPT 3.5, and Llama-2-70b by introducing a scoring system that considers four factors: ROUGE score, BERTScore, business entities and the Flesch Reading Ease. Our results show that MedicoVerse outperforms the compared models, thus producing informative summaries of large regulatory documents.

Abstract Background Trauma induced coagulopathy remains to be an important cause of high transfusion requirements and mortality and shock induced endotheliopathy (SHINE) has been implicated. Methods European multicenter observational study of adult trauma patients with injury severity score ≥ 16 arriving within 2 h from injury to the trauma centers. Admission blood samples obtained were used for analysis of the SHINE biomarkers (syndecan-1, soluble thrombomodulin, adrenaline) and extensive analysis of coagulation, -and fibrinolytic factors together with collection of clinical data. Hierarchical clustering of the SHINE biomarkers was used to identify the SHINE phenotypes. Results The 313 patients clustered into four SHINE phenotypes. Phenotype 2, having the highest glycocalyx shedding, encompassing 22% of the whole cohort, had severe coagulopathy with lower levels of prothrombin, FV, IX, X, XI and severe hyperfibrinolysis with higher plasmin – alpha 2-antiplasmin (PAP) – and tPA levels and lower alpha2 – antiplasmin levels. This phenotype had significantly higher transfusion requirements and higher mortality (39% vs. 23%, 15% and 14%) but similar injury severity score (ISS) compared to the others phenotypes. Conclusions Hierarchical clustering identified four SHINE phenotype in a cohort of trauma patients. Trauma induced coagulopathy was confined to only one of the SHINE phenotypes, encompassing 22% of the total cohort. This phenotype was characterized by severe hypocoagulability and hyperfibrinolysis, which translated to significantly higher transfusion requirements and higher mortality compared to the other SHINE phenotypes with similar injury severity, warranting further investigation.

This study explores burnout among nursing students in Bangalore, India, focusing on Exhaustion and Disengagement scores. A cross-sectional design was applied using the Oldenburg Burnout Inventory modified for nursing students, collecting data using a survey that was conducted between October and December 2023. The sample consisted of 237 female nursing students from the Bachelor of Science in Nursing program at Bangalore College of Nursing, South India. The study integrated the t-distributed Stochastic Neighbor Embedding (t-SNE) procedure for data simplification into three t-SNE components, used in a hierarchical clustering analysis, which identified distinct student profiles: “High-Intensity Study Group” and “Altruistic Aspirants”. While burnout scores were generally high, students with high study hours (“High-Intensity Study Group”) reported greater Exhaustion, with a mean score of 26.78 (SD = 5.26), compared to those in the “Altruistic Aspirants” group, who reported a mean score of 25.00 (SD = 4.48), demonstrating significant differences (p-value = 0.005). Conversely, those motivated by altruism (“Altruistic Aspirants”) showed higher Disengagement, with a mean score of 19.78 (SD = 5.08), in contrast to “High-Intensity Study Group”, which reported a lower mean of 17.84 (SD = 4.74) (p-value = 0.002). This segmentation suggests that burnout manifests differently depending on the students’ academic load and intrinsic motivations. This study underscores the need for targeted interventions that address specific factors characterizing the clusters and provide information for designing future research and interventions. This study was not registered.

Abstract This paper processes a novel hierarchical high-dimensional clustering algorithm based on the Active Learning Method (ALM), which is a fuzzy-learning algorithm. The hierarchical part of the algorithm is composed of two phases: divisible and agglomerative. The divisible phase, a zooming-in-process, searches for sub-clusters in already-found clusters hierarchically. At each level of the hierarchy, the clusters are found by an ensemble clustering method based on the density of data. This part of the algorithm blurs each data point as multiple one-dimensional fuzzy membership functions called ink-drop patterns; then, it accumulates the ink-drop patterns of all data points on every dimension separately. Next, it performs one-dimensional density partitioning to produce an ensemble of clustering solutions; after that, combining the results is done based on a novel consensus method with the aid of prime numbers. An agglomerative phase is a bottom-up approach that merges clusters based on a novel distance metric, named $${K}^{2}$$ K 2 -nearest neighbor. The algorithm is named as the Hierarchical High-Dimensional Unsupervised Active Learning Method (HiDUALM) and is explained in more detail throughout this paper. Although the classical clustering methods are not suitable for high-dimensional data clustering, the proposed method solves the problems related to speed and memory using ensemble learning, while due to its hierarchy and the use of different distance criteria, different levels of the cluster provide the clause. Experiments on synthetic and real-world datasets are presented to show the effectiveness of the proposed-clustering algorithm.

Short-cycle agricultural product sales forecasting significantly reduces food waste by accurately predicting demand, ensuring producers match supply with consumer needs. However, the forecasting is often subject to uncertain factors, resulting in highly volatile and discontinuous data. To address this, a hierarchical prediction model that combines RF-XGBoost is proposed in this work. It adopts the Random Forest (RF) in the first layer to extract residuals and achieve initial prediction results based on correlation features from Grey Relation Analysis (GRA). Then, a new feature set based on residual clustering features is generated after the hierarchical clustering is applied to classify the characteristics of the residuals. Subsequently, Extreme Gradient Boosting (XGBoost) acts as the second layer that utilizes those residual clustering features to yield the prediction results. The final prediction is by incorporating the results from the first layer and second layer correspondingly. As for the performance evaluation, using agricultural product sales data from a supermarket in China from 1 July 2020 to 30 June 2023, the results demonstrate superiority over standalone RF and XGBoost, with a Mean Absolute Percentage Error (MAPE) reduction of 10% and 12%, respectively, and a coefficient of determination (R2) increase of 22% and 24%, respectively. Additionally, its generalization is validated across 42 types of agricultural products from six vegetable categories, showing its extensive practical ability. Such performances reveal that the proposed model beneficially enhances the precision of short-term agricultural product sales forecasting, with the advantages of optimizing the supply chain from producers to consumers and minimizing food waste accordingly. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice. [ABSTRACT FROM AUTHOR]

To address the symmetry-related resilience issues of stations and lines in urban rail transit networks, we propose a two-stage robust optimization-based approach for urban rail transit network planning. In this context, resilience is conceptualized as the ability of the network to maintain its operational symmetry under normal and disruptive conditions. Firstly, we used passenger flow distributions as decision variables to construct a two-stage symmetry-based urban rail transit network planning model, aiming to simultaneously minimize the total cost and total operating time of the network while preserving its functional symmetry. Secondly, we designed a hybrid evolutionary algorithm with chromosomes having a two-layer encoding structure, where the Niched Pareto Genetic Algorithm served as the main algorithmic framework, and a Large Neighborhood Search mechanism was designed to optimize the connectivity gene layer of individuals, ensuring the symmetry of network connectivity. Finally, we conducted computational verification on randomly generated instances to confirm the effectiveness of the model and algorithm. The experimental results demonstrated that our method could find two sets of Pareto optimal solutions for cost preference and time preference, thereby preserving the operational symmetry of the network under normal and damaged conditions, as well as reducing the total operating time. This effectively improved the overall efficiency and resilience of the network. Our designed hybrid evolutionary algorithm converged to satisfactory objective values in the early iterations, exhibiting strong search and optimization performance and effectively solving the two-stage symmetry-based urban rail transit network planning model. [ABSTRACT FROM AUTHOR]

Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups' samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness. [ABSTRACT FROM AUTHOR]

To enhance the precision of the music recommendation environment system, a novel design approach has been introduced, utilizing multi-label propagation and hierarchical clustering analysis for a dual music recommendation environment. First of all, the process model of music recommendation environmental system is built based on music recognition system, which is composed of music signal preprocessing module, music model, sound model and music recognizer; second, on the basis of further study on the clustering validity, a new clustering validity function is established by describing the intra-class compactness and inter-class separation of clustering through fuzzy similarity relation; finally, the validity of the proposed music double recommendation environmental system using multi-label propagation hierarchical clustering analysis is verified by simulation experiment. The results show that the recommendation method based on comprehensive evaluation of user characteristics is suitable for single-category users, while the recommendation method based on multi-category evaluation is suitable for multi-category users. This approach offers an effective and precise means to enhance the accuracy and customization of music recommendation systems, thereby increasing user satisfaction. [ABSTRACT FROM AUTHOR]