Clustering Effect Research Articles

A particle swarm optimization-based deep clustering algorithm for power load curve analysis

To address the inflexibility of the convolutional autoencoder (CAE) in adjusting the network structure and the difficulty of accurately delineating complex class boundaries in power load data, a particle swarm optimization deep clustering method (DC-PSO) is proposed. First, a particle swarm optimization algorithm for automatically searching the optimal network architecture and hyperparameters of CAE (AHPSO) is proposed to obtain better reconstruction performance. Then, an end-to-end deep clustering model based on a reliable sample selection strategy is designed for the deep clustering algorithm to accurately delineate the category boundaries and further improve the clustering effect. The experimental results show that the DC-PSO algorithm exhibits high clustering accuracy and higher performance for the power load profile clustering.

Antenatal Care Visits, Institutional Births, and Associated Risk Factors in Afghanistan: Insights from the Multiple Indicator Cluster Survey 2022-2023.

Insufficient use of antenatal care (ANC) services and institutional births services can elevate the maternal mortality risk in limited resource settings. Hence, the key objective of this study was to evaluate the potential association between the frequency of ANC visits and institutional birth services in Afghanistan, while also identifying other sociodemographic factors that may exert influence. Furthermore, we explored factors associated with the attendance of women at ANC visits during their pregnancy. We employed data from the most recent Multiple Indicator Cluster Survey conducted in Afghanistan in 2022 to 2023 with a total of 8096 women aged 15 to 49. A complex survey weight-adjusted logistic regression model was used to examine factors related to institutional births, and a multinomial logistic regression model was fitted to assess the relationships between sociodemographic factors and ANC visits, adjusting for survey weights, cluster effects, and strata. Approximately 40% of the sample (n = 3247) had undergone 4 or more ANC visits, and 74.4% (n = 6,022) had opted for institutional birth. Women's higher education was found to be associated with ANC visits. The area of residence, wealth index, education levels of women, ownership of mobile phones, number of children, and number of ANC visits were associated with institutional births. Compared with women with no or one ANC visit, those with more than 3 visits had 31% higher odds (adjusted odds ratio, 1.31; 95% CI, 1.10-1.57) of accessing institutional births. Our findings indicate a significant association between ANC visits and use of institutional birth care. These findings carry implications for advancing safe motherhood and childbirth by enhancing women's social status.

Landslide Distribution and Development Characteristics in the Beiluo River Basin

The Beiluo River Basin, situated in the central region of the Loess Plateau, frequently experiences landslide geological disasters, posing a severe threat to local lives and property. Thus, establishing a detailed database of historical landslides and analyzing and revealing their development characteristics are of paramount importance for providing a foundation for geological hazard risk assessment. First, in this study, landslides in the Beiluo River Basin are interpreted using Google Earth and ZY-3 high-resolution satellite imagery. Combined with a historical landslide inventory and field investigations, a landslide database for the Beiluo River Basin is compiled, containing a total of 1781 landslides. Based on this, the geometric and spatial characteristics of the landslides are analyzed, and the relationships between the different types of landslides and landslide scale, stream order, and geomorphological types are further explored. The results show that 50.05% of the landslides have a slope aspect between 225° and 360°, 68.78% have a slope gradient of 16–25°, and 38.97% are primarily linear in profile morphology. Areas with a high landslide density within a 10 km radius are mainly concentrated in the loess ridge and hillock landform region between Wuqi and Zhidan Counties and in the loess tableland region between Fu and Luochuan Counties, with a significant clustering effect observed in the Fu County area. Loess–bedrock interface landslides are relatively numerous in the northern loess ridge and hillock landform region due to riverbed incision and the smaller thickness of loess in this area. Intra-loess landslides are primarily found in the southern loess tableland region due to headward erosion and the greater thickness of loess in this area. Loess–clay interface landslides, influenced by riverbed incision and the limited exposure of red clay, are mainly distributed in the northern part of the southern loess tableland region and on both sides of the Beiluo River Valley in Ganquan County. These results will aid in further understanding the development and spatial distribution of landslides in the Beiluo River Basin and provide crucial support for subsequent landslide susceptibility mapping and geological hazard assessment in the region.

A dyeing clustering algorithm based on ant colony path-finding mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

Exploring the impacts of risk factors on mortality patterns of global Alzheimer’s disease and related dementias from 1990 to 2021

Alzheimer’s Disease and Related Dementias (ADRD) affect millions of people worldwide, with mortality rates influenced by several risk factors and exhibiting significant heterogeneity across geographical regions. This study aimed to investigate the impact of risk factors on global ADRD mortality patterns from 1990 to 2021, utilizing clustering and modeling techniques. Data on ADRD mortality rates, cardiovascular disease, and diabetes prevalence were obtained for 204 countries from the GBD platform. Additional variables such as HDI, life expectancy, alcohol consumption, and tobacco use prevalence were sourced from the UNDP and WHO. All the data were extracted for men, women, and the overall population. Longitudinal k-means clustering and generalized estimating equations were applied for data analysis. The findings revealed that cardiovascular disease had significant positive effects of 1.84, 3.94, and 4.70 on men, women, and the overall ADRD mortality rates, respectively. Tobacco showed positive effects of 0.92, 0.13, and 0.39, while alcohol consumption had negative effects of − 0.59, − 9.92, and − 2.32, on men, women, and the overall ADRD mortality rates, respectively. The countries were classified into five distinct subgroups. Overall, cardiovascular disease and tobacco use were associated with increased ADRD mortality rates, while moderate alcohol consumption exhibited a protective effect. Notably, tobacco use showed a protective effect in cluster A, as did alcohol consumption in cluster B. The effects of risk factors on ADRD mortality rates varied among the clusters, highlighting the need for further investigation into the underlying causal factors.

Wind-induced interference effect of complex building clusters on long-span roof structures

The presence of complex buildings introduces interference effects and complicates the wind field around the long-span roof structure. The complexity of the problem makes it challenging to predict wind load of long-span roof structures, resulting in a scarcity of design data available to engineers and designers. Thus, the influence of interference effect on mean and peak pressure coefficients of a long-span tri-centered cylindrical roof structure was investigated through wind tunnel tests conducted in an atmospheric boundary layer wind tunnel. The study focused on the interference effects caused by two cooling towers, chimney, and some low-rise buildings, considering all wind directions. The results show that the interference effects are the result of a combination of amplification effects from the adjacent cooling towers and chimney and shielding effects from the surrounding low-rise buildings. Due to the shielding effect of low-rise buildings, the wind suction on the roof is mitigated compared to that of a single building, while at the end of the roof, wind suction is amplified by cooling tower effects. The interference effect of the building clusters, however, amplifies the fluctuating wind pressure coefficient and the peak pressure coefficient on the roof. To accurately estimate the wind load of building components and envelopes, furthermore, this study investigates the influence mechanism of interference effect on non-Gaussian characteristics of wind pressure combining with fluctuating wind pressure spectrum, spatial correlation of fluctuating wind pressure, and probability distribution characteristics of standardized wind pressure coefficient. The spatial correlation of wind pressure in the roof interference area exhibits a strong association, and the probability density distribution characteristics of the standardized wind pressure coefficient significantly deviate from the Gaussian curve, with a peak factor exceeding 3.5. Thus, it can be concluded that the wind pressure within this region demonstrates significant non-Gaussian characteristics. Hence, the zone values of peak pressure coefficients are determined using the peak factor approach to inform the wind resistance design of the envelope structure.

Possibility and stabilizing effect of Mo clusters in the Ni-based single-crystal superalloy

Nickel-based single-crystal superalloys are crucial materials for the preparation of aero-engine turbine blades. Many solute elements are added to superalloys for strengthening. However, the relationship between the clustering behavior of solute atoms and the properties of nickel-based single-crystal superalloys is still unclear. Herein, we conduct first-principles calculations on γ phases with Mo−Mo and Mo−Mo−Ru clusters to reveal the possibility and stabilizing mechanism of solute clusters. Introducing Mo lowers the total energy, binding energy, and formation energy of the γ phase due to the replacement of weak Ni−Ni interaction with strong Mo−Ni bonding. Note that the γ phase containing the Mo−Mo cluster is more stable than that containing a Mo single atom, possibly owing to a wide affecting range. The Ru atom added to the γ phase can further boost system stability, and it tends to form a Mo−Mo−Ru cluster. The stabilizing impact of the Mo−Mo−Ru cluster is demonstrated to be the replacement of weak Ni−Mo interaction by the strong Ru−Mo interaction, which may be derived from the enhanced d-orbital hybridization.

Effect of copper cluster on reaction pathways of carbon dioxide hydrogenation on magnesium hydride surface

Understanding the reaction mechanism of CO2 hydrogenation to produce value-added chemicals is essential for the rational design of catalysts to improve reaction activity and product selectivity. In this work, the first-principles calculations based on density functional theory were employed to study the effect of copper cluster on product selectivity in CO2 hydrogenation utilizing magnesium hydride as hydrogen source. The results show that on pristine MgH2 (0 0 1), CO2 molecule adsorbs on the surface with an O atom close to Mg atom, and the hydrogenation reaction follows formate pathway to yield CH3OH with an energy barrier only 1.09 eV for the rate-limiting step; however, the production of the most accessible C2-based product (CH2OH)2 needs to overcome an energy barrier up to 1.94 eV. On MgH2 (0 0 1) with Cu7 cluster, the C atom of CO2 molecule can strongly adsorb on a Cu atom of Cu7 cluster, and the hydrogenation more likely proceeds through reverse water-gas shift pathway, in which it is easy to generate C2 product C2H4 by the coupling of intermediate H2C*, with an energy barrier only 1.53 eV for the rate-limiting step. The findings utilizing transition metal cluster to change the reaction pathway and product selectivity of CO2 hydrogenation on magnesium hydride can provide novel perspectives for the design of catalysts.

Brain magnetic resonance image (MRI) segmentation using multimodal optimization

AbstractOne of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters.

Web‐based application to guide sampling on salmon farms for more precise estimation of sea louse abundance

AbstractObjectiveEfficiently managing sea lice on salmon farms through active surveillance, crucial for lice abundance estimation, is challenging due to the need for effective sampling schemes. To address this, we developed an application that considers infestation levels, farm structure, and management protocols, enhancing the precision of sampling strategies for sea louse abundance estimation.MethodsSimulation‐based methods are valuable for estimating suitable sample sizes in complex studies where standard formulae are inadequate. We introduce FishSampling, an open Web‐based application tailored to determine precise sample sizes for specific scenarios and objectives.ResultThe model incorporates factors such as sea lice abundance, farm pen numbers, potential clustering effects among these pens, and the desired confidence level. Simulation outcomes within this application provide practical advice on how to decide on the number of fish and pens to sample, under varying levels of assumed clustering.ConclusionThis approach can be used across the salmon aquaculture sector to improve sampling strategies for sea lice abundance estimation and balance surveillance costs against health objectives.

Study on Cultural Consumption Characteristics of Museum Short Video Platforms under the Threshold of Identifier Economics

Purpose - This study examines museums’ operations on the Shakeology platform, elucidating the characteristics of museums’ cultural consumption in relation to identity, cultural identity, value identity, and the cluster effect of certified museums. The study explores how short video platforms can serve as effective tools for cultural communication and consumption promotion. Design/Methodology/Approach - The study utilizes qualitative research to investigate how museums utilize the Shakeology platform to create a diverse content ecosystem and improve audience engagement. Data is collected through the analysis of museum activities on the platform, including content types, interaction strategies, and audience engagement. It also examines how short video platforms differ from traditional media in terms of granular information processing and community interaction. Findings - Short video platforms are more effective in communication than traditional media due to their granular information processing and community interaction. Museums that focus on Shakeology can enhance consumers’ connection to cultural heritage, optimize market positioning, and improve audience interaction. Key characteristics of cultural consumption on the platform include enhanced cultural identity, value identity, and a significant cluster effect of certified museums, leading to a richer cultural experience for audiences. Research Implications - The study holds significant implications for museums aiming to utilize modern media for audience engagement and market positioning. By understanding cultural consumption dynamics on short video platforms, museums can customize content and interaction strategies to cultivate stronger audience connections. The study suggests that museums should continue to innovate and adapt to new media trends to enhance their cultural communication efforts.

Utilizing the GARCH Model for Analysis and Prediction of Stock Market Trends

This study utilizes the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model to analyze the Shanghai Stock Exchange Composite Index and the Shenzhen Composite Index. It computes the returns and logarithmic returns, fits the GARCH model with a student-t distribution, and calculates the standardized residuals of the stock. Tests for normality and heteroskedasticity of residuals are conducted, follows by the creation of auto-correlation and partial auto-correlation plots to aid in volatility forecasting. The findings indicate that both returns of stock distributions exhibit multiple peaks and significant fluctuations, revealing that there exists pronounced volatility clustering and strong abrupt changes. Historical fluctuations significantly affect current fluctuations, showing a clustering effect. Volatility in returns has been very persistent, reflecting strong speculation and elevated overall risk. Histograms and skewness assessments show a left-skewed distribution for both indices, with a kurtosis greater than three, indicating a "fat-tailed" distribution. The positive mean values imply that China's economy is growing constantly, while the low standard deviation implies the immaturity of China's stock market. These insights highlight the dynamic behaviors and risk profiles in Chinas major stock indices, offering a deeper understanding of their speculative nature and economic implications. The Advantages and disadvantages of GARCH models and limitations of a single model are acknowledged in the report.

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets.

Single-atom catalysts on ceria substrates: Exploring cluster and surface effects on methane activation

The activation of methane (CH4) is a critical step in its conversion to high value products, which require the use of catalysts due to the higher stability of the C-H bond. In this study, we combined density functional theory calculations and the unit bond index-quadratic exponential potential approximation to investigate the first CH4 dehydrogenation in the TM/(CeO2)10 system, where TM represents a single transition-metal specie (Fe, Co, Ni, Cu) supported in the compact (CeO2)10 cluster. In addition, substrate size effects will be addressed by comparing our results with previous surface science studies by our group. We found a direct correlation between the magnitude of the TM adsorption energy on the CeO2 substrates (hollow and bridge sites) and the magnitude of charge transfer from the TM adatoms to the substrates, while CH4 has a weaker physisorption binding energy to the ceria cluster and the surface substrates. Molecular fragments derived from CH4 (CH3 and H) bind through chemisorption interactions to the TM and O sites, respectively. The estimated energy barrier for the cleavage of the C-H bond is lower when the reaction occurs in the cluster rather than on the surface. The molecular fragments exhibited better stabilization on cluster substrates, resulting in an exergonic dehydrogenation reaction; whereas, for surfaces, the reaction was endergonic. Among the selected TM species, Fe and Co adatoms are promising candidates for the first CH4 dehydrogenation on CeO2 substrates, which can be explained by the magnitude of the metal-support interaction and stabilization of the CH3 specie, which results in the lowest barriers among the evaluated transition metals.

Where Will Older Adults Reside: Understanding the Distribution of Naturally Occurring Retirement Communities in Australia

Most older individuals prefer to age in place during their later years; however, achieving this aspiration presents significant challenges. Naturally Occurring Retirement Communities (NORCs) represent a potential option for promoting healthy aging, both from the perspective of meeting seniors’ real needs and cost-effectiveness. This article aims to analyze the distribution of NORCs in Australia and compares census data from 2011 to 2021 to understand the overall distribution patterns and changes across the nation, by providing a localized analysis of the hotspot distribution of NORCs in eight Greater Capital Cities. The study employs methods of geovisualization, Global Moran’s I, and Getis-Ord Gi* analysis to examine the spatial correlations and clustering effects of NORCs. The results indicate that NORCs are rapidly growing in Australia, with their distribution primarily influenced by sea change and urbanization. Understanding the trends in NORC distribution can assist the government in developing effective and localized policies and interventions to help older Australians to better age in place.

HSeq2Seq: Hierarchical graph neural network for accurate mobile traffic forecasting

Hierarchical graph neural networks (HGNNs) provide a feasible method for modeling complex spatiotemporal dependencies during mobile traffic forecasting. However, most existing studies have adopted spatial node clustering methods to construct a coarsened graph that overlooks the temporal correlation within the original station graph. Furthermore, existing state-of-the-art methods fail to fully exploit the cross-regional feature impacts on stations, which limits their ability to model nonlocal spatial dependency.To overcome these limitations, we proposed a hierarchical sequence-to-sequence (HSeq2Seq) approach that combines HGNNs with a sequence-to-sequence architecture (Seq2Seq) for mobile traffic forecasting. First, a spatiotemporal node clustering method was designed to construct the hierarchical structure. Second, a convolution encoder was employed to extract the local spatiotemporal features from a hierarchical perspective, and a novel attention-based feature fusion module was built to capture the nonlocal spatiotemporal features by identifying both the intra- and cross-regional feature impacts on the stations. Finally, a recurrent decoder is introduced to reweight the spatiotemporal features and recursively produce the final prediction.Extensive experiments on two real-world datasets demonstrate that our model outperforms state-of-the-art methods, while the ablation study also verifies the effectiveness of the spatiotemporal node clustering and the attention-based feature fusion module.

Coupled zoning and spatial heterogeneity of human activities and natural endowments based on self-organizing map and random forest: A case study of the agro-pastoral ecotone in Gansu, China

Regional zoning is an important way for humans to understand geographical space，but the existing research on regional zoning focuses on natural conditions, ignoring the interdependent characteristics of human and nature, and the driving mechanism of regional differentiation is not sufficiently explained. In this study, using algorithms such as self-organizing feature map, clustering quality index, structural similarity index measure (SSIM) and random forest, the human activity (HA)–natural endowment (NE) coupled zoning in the agro-pastoral ecotone in Gansu, China (AEGC), was conducted to explore the driving mechanism of regional differentiation. Results show the following: (1) The HA-NE coupled zoning has the best clustering effect when implementing the partitioning scheme with a classification number of 4. Accordingly, the AEGC can be divided into four regions with significant differences between HAs and NEs. (2) From the perspective of spatial distribution, HA–NE coupled zoning, climate zoning, geographic zoning, and vegetation zoning are similar, whereas HA zoning is different. The results of SSIM showed that HA–NE coupled zoning takes all types of factors into consideration (SSIM mean 0.708), and the zoning results are better than single type zonings. HAs have strong independence (SSIM mean 0.576), and geographic conditions are closely related to all other factors (SSIM mean 0.671). (3) Elevation is the most important driver of regional differentiation in the AEGC, with a contribution degree of 22.36%; other important drivers include land use intensity, precipitation, and normalized difference vegetation index, with contribution degree distributions of 17.38%, 16.34%, and 15.79%. Furthermore, the dominant factors of regional differentiation in each sub-region are different. This study emphasizes that regional characteristic factors and uneven spatial distribution factors are important drivers of regional differentiation, and land use intensity has become an important force influencing geographic space. Policy recommendations for zonal governance are made based on the inherent conditions of different regions. This study may provide a reference for scientific regional zoning and cognitive regional differentiation.

Assessment of soil fertility in Xinjiang oasis cotton field based on big data techniques

Assessing soil fertility through traditional methods has faced challenges due to the vast amount of meteorological data and the complexity of heterogeneous data. In this study, we address these challenges by employing the K-means algorithm for cluster analysis on soil fertility data and developing a novel K-means algorithm within the Hadoop framework. Our research aims to provide a comprehensive analysis of soil fertility in the Shihezi region, particularly in the context of oasis cotton fields, leveraging big data techniques. The methodology involves utilizing soil nutrient data from 29 sampling points across six round fields in 2022. Through K-means clustering with varying K values, we determine that setting K to 3 yields optimal cluster effects, aligning closely with the actual soil fertility distribution. Furthermore, we compare the performance of our proposed K-means algorithm under the MapReduce framework with traditional serial K-means algorithms, demonstrating significant improvements in operational speed and successful completion of large-scale data computations. Our findings reveal that soil fertility in the Shihezi region can be classified into four distinct grades, providing valuable insights for agricultural practices and land management strategies. This classification contributes to a better understanding of soil resources in oasis cotton fields and facilitates informed decision-making processes for farmers and policymakers alike.

Outranking-based approaches for multiple criteria partially ordered clustering: A review of existing algorithms, new proposals, and experimental comparison

We consider clustering problems that involve categorizing alternatives into partially ordered, initially undefined groups based on their performance across multiple criteria. To achieve this, we use an outranking relation model to reflect the Decision Maker's preferences. We examine various algorithms that not only group the alternatives but also order the clusters in different ways. This analysis includes innovative approaches that use distances in the space of outranking relations or detailed relation profiles, and apply orthogonal non-negative factorization to outranking matrices. Additionally, we discuss a set of measures, including two novel ones, for assessing the effectiveness of clustering when the groupings are partially ordered. Our findings are based on comprehensive computational experiments on real-world and simulated datasets. Beyond evaluating various methods using four quality metrics and computational efficiency, we explore the influence of accessible preference structures and ordering techniques on the clustering outcomes.

Updated Surveillance Metrics and History of the COVID-19 Pandemic (2020-2023) in the Middle East and North Africa: Longitudinal Trend Analysis.

This study updates the COVID-19 pandemic surveillance in the Middle East and North Africa (MENA) we first conducted in 2020 with 2 additional years of data for the region. The objective of this study is to determine whether the MENA region meets the criteria for moving from a pandemic to endemic. In doing so, this study considers pandemic trends, dynamic and genomic surveillance methods, and region-specific historical context for the pandemic. These considerations continue through the World Health Organization (WHO) declaration of the end of the public health emergency for the COVID-19 pandemic on May 5, 2023. In addition to updates to traditional surveillance data and dynamic panel estimates from the original study by Post et al, this study used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether regional weekly speed of COVID-19 spread was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data from September 4, 2020, to May 12, 2023. The speed of COVID-19 spread for the region had remained below the outbreak threshold for 7 continuous months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant and positive, the weekly shift parameters suggested the coefficients had most recently turned negative, meaning the clustering effect of new COVID-19 cases became even smaller in the 2 weeks around the WHO declaration. From December 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of the speed of spread equal to 10 became entirely insignificant from October 2022 onward. The COVID-19 pandemic had far-reaching effects on MENA, impacting health care systems, economies, and social well-being. Although COVID-19 continues to circulate in the MENA region, the rate of transmission remained well below the threshold of an outbreak for over 1 year ahead of the WHO declaration. COVID-19 is endemic in the region and no longer reaches the threshold of the pandemic definition. Both standard and enhanced surveillance metrics confirm that the pandemic had transitioned to endemic by the time of the WHO declaration.