Clustering Algorithm In Order Research Articles

Application of information fusion technology to build knowledge service model of university smart archives

Abstract Based on the SERVQUAL evaluation model, this paper examines the characteristics and service model of university archive services and establishes a knowledge service model framework for university smart archives. The service framework utilizes information fusion technology to quantitatively analyze the archive situation through the combination of 6 dimensions and 23 secondary indicators. One of the key points is to combine the whitening weight function of upper and lower limit measurements and the central whitening weight function to form a grey clustering algorithm in order to solve the “cliff” effect of the whitening weight function of the traditional upper and lower limit measurements. After the development of the system was completed, the index weights were first calculated by means of a questionnaire and then practiced in the school. The results show that responsiveness has the highest weight, 0.8612; security is second, 0.6321. And tangibility is the last, only 0.1792. The first college scored 0.461 in C1, and the second college scored only 1.87 in E1, which pulled the average score of the first-level index to 4.52. After the practice, the survey proved that the system meets the needs of the college and can point out areas for archive improvement. Archives improvement direction.

Graph Theoretical Analysis of local ultraluminous infrared galaxies and quasars

We present a methodological framework for studying galaxy evolution by utilizing Graph Theory and network analysis tools. We study the evolutionary processes of local ultraluminous infrared galaxies (ULIRGs) and quasars and the underlying physical processes, such as star formation and active galactic nucleus (AGN) activity, through the application of Graph Theoretical analysis tools. We extract, process and analyze mid-infrared spectra of local (z ¡ 0.4) ULIRGs and quasars between 5-38μm through internally developed Python routines, in order to generate similarity graphs, with the nodes representing ULIRGs being grouped together based on the similarity of their spectra. Additionally, we extract and compare physical features from the mid-IR spectra, such as the polycyclic aromatic hydrocarbons (PAHs) emission and silicate depth absorption features, as indicators of the presence of star-forming regions and obscuring dust, in order to understand the underlying physical mechanisms of each evolutionary stage of ULIRGs. Our analysis identifies five groups of local ULIRGs based on their mid-IR spectra, which is quite consistent with the well established fork classification diagram by providing a higher level classification. We demonstrate how graph clustering algorithms and network analysis tools can be utilized as unsupervised learning techniques for revealing direct or indirect relations between various galaxy properties and evolutionary stages, which provides an alternative methodology to previous works for classification in galaxy evolution. Additionally, our methodology compares the output of several graph clustering algorithms in order to demonstrate the best-performing Graph Theoretical tools for studying galaxy evolution.

Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

This paper presents a dataset containing automatically collected source codes solving unique programming exercises of different types. The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers or benchmarking mutation testing frameworks, and more applications are yet to be discovered. We describe the architecture of the DTA system, aiming to provide detailed insight regarding how and why the dataset was collected. In addition, we describe the algorithms responsible for source code analysis in the DTA system. These algorithms use vector representations of programs based on Markov chains, compute pairwise Jensen–Shannon divergences of programs, and apply hierarchical clustering algorithms in order to automatically discover high-level concepts used by students while solving unique tasks. The proposed approach can be incorporated into massive programming courses when there is a need to identify approaches implemented by students.

Multicriteria Ordered the Profile Clustering Algorithm Based on PROMETHEE and Fuzzy c-Means

The purpose of multicriteria clustering is to locate groups of alternatives that have comparable qualities and have been examined across multiple criteria. An ordered profile clustering is a well-known problem, and the fuzzy c-means clustering (FCM) technique is one of the most broadly used in every field of life. At present, FCM is for the partitioning of information into numerous clusters which are still lacking priority relations. To address the problem of finding ranking in clusters based on multicriteria in the fuzzy environment, we propose a multicriteria ordered clustering algorithm based on the partial net outranking flow of the preference organization for enrichment evaluations method (PROMETHEE) and fuzzy c-means. Lastly, we apply the proposed algorithm to solve a real-world targeted clustering problem regarding the human development indexes. To test the efficacy of the proposed algorithm, a comparative analysis of ordered K-means clustering (OKM) and FCM is carried out with it.

A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network

In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contains abundant information about its users which could be exploited to create profiles of the users. For social commerce, the quality of the profiles of potential consumers determines whether the recommender system is a success or a failure. In our work, not only the user’s textual information but also the tags and the relationships between users have been considered in the process of building user profiling model. A topic model has been adopted in our system, and a feedback mechanism also been design in this paper. Then, we apply a collative filtering method and a clustering algorithm in order to obtain a high recommendation accuracy. We do an empirical analysis based on real data collected on a social network and an e-commerce platform. We find that the social network has an impact on e-commerce, so social commerce could be realized. Simulations show that our topic model has a better performance in topic finding, meaning that our profile-building model is suitable for a social commerce recommender system.

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

AbstractEnsemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an ensemble clustering algorithm based on the consistency cluster consensus approach and the MapReduce model. In addition, the proposed algorithm is equipped with a new membership similarity measure. The consistency cluster consensus is defined as a new agreement function for the consensus of the results of the basic clustering methods. Besides, the proposed similarity measure consists of two factors: one is cluster similarity and the another is membership similarity. The process of the proposed ensemble clustering method is summarized in four steps. In the first step, partitions are generated by applying a number of individual clustering algorithms to the data. The second step is to convert the primary clusters from the partitions to a binary representation. The third step is to identify the candidate primary clusters for the consensus, where a subset with highest similarity between the clusters is emphasized. In the fourth step, the consistency cluster consensus is performed through a new agreement function. In addition, we use the MapReduce model to implement basic clustering algorithms to reduce computational complexity. Our method is evaluated on several real‐world datasets and their performance was compared with other clustering ensemble methods, such as TWCE and RDPC‐DSS. Experiments show that our method is more efficient and accurate and is 7% to 15% better than the best method compared.

Multiscale entropy analysis of astronomical time series

Context.The multiscale entropy assesses the complexity of a signal across different timescales. It originates from the biomedical domain and was recently successfully used to characterize light curves as part of a supervised machine learning framework to classify stellar variability.Aims.We aim to explore the behavior of the multiscale entropy in detail by studying its algorithmic properties in a stellar variability context and by linking it with traditional astronomical time series analysis methods and metrics such as the Lomb-Scargle periodogram. We subsequently use the multiscale entropy as the basis for an interpretable clustering framework that can distinguish hybrid pulsators with bothp- and g-modes from stars with onlyp-mode pulsations, such asδScuti (δSct) stars, or from stars with onlyg-mode pulsations, such asγDoradus (γDor) stars.Methods.We calculate the multiscale entropy for a set ofKeplerlight curves and simulated sine waves. We link the multiscale entropy to the type of stellar variability and to the frequency content of a signal through a correlation analysis and a set of simulations. The dimensionality of the multiscale entropy is reduced to two dimensions and is subsequently used as input to the HDBSCAN density-based clustering algorithm in order to find the hybrid pulsators within sets ofδSct andγDor stars that were observed byKepler.Results.We find that the multiscale entropy is a powerful tool for capturing variability patterns in stellar light curves. The multiscale entropy provides insights into the pulsation structure of a star and reveals how short- and long-term variability interact with each other based on time-domain information only. We also show that the multiscale entropy is correlated to the frequency content of a stellar signal and in particular to the near-core rotation rates ofg-mode pulsators. We find that our new clustering framework can successfully identify the hybrid pulsators with bothp- andg-modes in sets ofδSct andγDor stars, respectively. The benefit of our clustering framework is that it is unsupervised. It therefore does not require previously labeled data and hence is not biased by previous knowledge.

P-433 Comparative analysis of serum obtained in early pregnancy using Fourier transform-infrared spectroscopy to predict tubal ectopic pregnancy and miscarriage

Abstract Study question Can Fourier transform-infrared (FT-IR) spectroscopy be used to discriminate adverse early pregnancy outcomes such as tubal ectopic pregnancy and miscarriage from live normally-sited pregnancies? Summary answer Biochemical information acquired from serum samples using FT-IR spectroscopy does not discriminate adverse early pregnancy outcomes from live normally-sited pregnancies. What is known already Ectopic pregnancy and miscarriage are associated with maternal morbidity and mortality. Current management of pregnancy of unknown location and viability may involve serial blood human chorionic gonadotrophin-beta measurements and several ultrasound scans. Multiple interventions and length of time to diagnosis are costly and have a profound psychological impact on women. Vibrational spectroscopic methods such as Fourier transform-infrared (FT-IR) spectroscopy have been increasingly shown to discriminate disease states such as cancer from controls utilising the ‘metabolomic signature’ of molecules within a biological sample. It is hypothesised that this signature will differ between live normally-sited pregnancies, and tubal ectopic pregnancy and miscarriage. Study design, size, duration A total of 380 women were recruited to the prospective cohort study, Ectopic Pregnancy Diagnosis sTudy (ExPeDITe) between November 2018 and November 2021. Inclusion criteria comprised pregnant women ≥18 years presenting with pain and/or bleeding at < 10 weeks of gestation. A sample of blood and demographic data were collected at the time of consent, and a full history was taken. Early pregnancy outcomes were collected and classified according to ESHRE guidance. Participants/materials, setting, methods Serum samples prepared from collected blood (n = 340) were diluted 1:4 to prevent oversaturation of spectra and quality controls were created from the pooled diluted serum. Samples were transferred to silicon plates and dried at 37.5 °C. Samples were subjected to FT-IR spectroscopy and spectral data were submitted to baseline correction, smoothing, and vector normalisation prior to multivariate statistical analysis including Principal Component Analysis (PCA) and Partial Least Squares – Discriminant Analysis (PLS-DA). Main results and the role of chance Infrared spectral data collected from live normally-sited pregnancies, tubal ectopic pregnancies, pregnancies of unknown location, and miscarriages displayed similar infrared signatures, exhibiting bands associated to vibrational modes related to the main molecular constituents commonly found in most biological samples such as proteins, lipids, nucleic acids, and carbohydrates. Spectral data were used as inputs to PCA and PLS-DA models as clustering algorithms in order to classify the different pregnancy outcomes. The output obtained by the classification model generated with PLS-DA shows two distributions; a ‘real’ and ‘null/random’ model. These models displayed poor correct classification rates, indicating no differences in the infrared signatures between the different pregnancy outcomes. Similar findings were also observed in the PCA results as no clustering pattern indicating correct classification between the experimental groups was observed in the scores plot. Although infrared spectroscopy has been well demonstrated as an analytical tool to discriminate several disorders from healthy/control samples including cancer, diabetes and many others, these findings indicate that the information about the overall biochemistry probed by infrared spectroscopy acquired from serum samples currently lacks accuracy/sensitivity to discriminate different pregnancy outcomes. Limitations, reasons for caution Proteins were not removed from samples prior to analysis, which may mask discreet changes in small molecule metabolites. As FT-IR spectra multivariate in nature power calculations are not possible. If this study had shown case-control separation then these findings would have been validated with a second set of samples. Wider implications of the findings The findings of this study suggest that FT-IR spectroscopy of serum may not be discriminatory to identify important early pregnancy outcomes, but further analytical techniques should be utilised to confirm if metabolomic analysis of blood or other biofluids would provide a novel diagnostic test. Trial registration number REC 17/NW/0646

Multi-objective optimal capacity allocation of integrated energy system with co-evolution mechanism

Abstract With the deepening of multi-energy flow coupling and the diversification of energy utilization demand, integrated energy system (IES) puts forward higher requirements for the economy and reliability of capacity allocation scheme. However, there is a complex relationship between economy and reliability, which makes it difficult for them to achieve coordinated optimization. Therefore, an economic and reliability optimal capacity allocation method of IES based on improved co-evolution algorithm is proposed. Firstly, the timing scenario reduction method of “wind power-electrical load” based on ordered clustering algorithm and K-means clustering algorithm is proposed. Aiming at minimizing the expected annual cost of construction investment, operation and maintenance, carbon emission and energy purchase cost, and constrained by equipment operation and energy flow balance, a multi-scenario economic operation model is constructed. Considering the dissatisfaction degree of comprehensive energy consumption, the energy supply reliability evaluation model based on Markov chain Monte Carlo (MCMC) method is established. On this basis, the IES multi-objective optimal capacity allocation model is established. Further, based on the orthogonal test method, the influence factors of decision variables on IES economy and reliability are calculated, and the evolution subpopulation of economy and reliability is established, and then the optimization model’s solution method based on improved co-evolution algorithm is proposed. Finally, an example simulation analysis is carried out combined with the actual data of a certain region. The simulation results show that the proposed model can cooperatively improve the economy and reliability of IES capacity allocation scheme, and provide a reference for the optimal capacity allocation of equipment in IES.

Constructing a Large-Scale Urban Land Subsidence Prediction Method Based on Neural Network Algorithm from the Perspective of Multiple Factors

The existing neural network model in urban land-subsidence prediction is over-reliant on historical subsidence data. It cannot accurately capture or predict the fluctuation in the sequence deformation, while the improper selection of training samples directly affects its final prediction accuracy for large-scale urban land subsidence. In response to the shortcomings of previous urban land-subsidence predictions, a subsidence prediction method based on a neural network algorithm was constructed in this study, from a multi-factorial perspective. Furthermore, the scientific selection of a large range of training samples was controlled using a K-shape clustering algorithm in order to produce this high-precision urban land subsidence prediction method. Specifically, the main urban area of Kunming city was taken as the research object, LiCSBAS technology was adopted to obtain the information on the land-subsidence deformation in the main urban area of Kunming city from 2018–2021, and the relationship between the land subsidence and its influencing factors was revealed through a grey correlation analysis. Hydrogeology, geological structure, fault, groundwater, high-speed railways, and high-rise buildings were selected as the influencing factors. Reliable subsidence training samples were obtained by using the time-series clustering K-shape algorithm. Particle swarm optimization–back propagation (PSO-BP) was constructed from a multi-factorial perspective. Additionally, after the neural network algorithm was employed to predict the urban land subsidence, the fluctuation in the urban land-subsidence sequence deformation was predicted with the LSTM neural network from a multi-factorial perspective. Finally, the large-scale urban land-subsidence prediction was performed. The results demonstrate that the maximum subsidence rate in the main urban area of Kunming reached −30.591 mm⋅a−1 between 2018 and 2021. Moreover, there were four main significant subsidence areas in the whole region, with uneven distribution characteristics along Dianchi: within the range of 200–600 m from large commercial areas and high-rise buildings, within the range of 400–1200 m from the under-construction subway, and within the annual average. The land subsidence tended to occur within the range of 109–117 mm of annual average rainfall. Furthermore, the development of faults destroys the stability of the soil structure and further aggravates the land subsidence. Hydrogeology, geological structure, and groundwater also influence the land subsidence in the main urban area of Kunming. The reliability of the training sample selection can be improved by clustering the subsidence data with the K-shape algorithm, and the constructed multi-factorial PSO-BP method can effectively predict the subsidence rate with a mean squared error (MSE) of 4.820 mm. The prediction accuracy was slightly improved compared to the non-clustered prediction. We used the constructed multi-factorial long short-term memory (LSTM) model to predict the next ten periods of any time-series subsidence data in the three types of cluster data (Cluster 1, Cluster 2, and Cluster 3). The root mean square errors (RMSE) were 0.445, 1.475, and 1.468 mm; the absolute error ranges were 0.007–1.030, 0–3.001, and 0.401–3.679 mm; the errors (mean absolute error, MAE) were 0.319, 1.214, and 1.167 mm, respectively. Their prediction accuracy was significantly improved, and the predictions met the measurement specifications. Overall, the prediction method proposed from the multi-factorial perspective improves large-scale, high-accuracy urban land-subsidence prediction.

Ordinal synchronization and typical states in high-frequency digital markets

In this paper we study Algorithmic High-Frequency Financial Markets as dynamical networks. After an individual analysis of 24 stocks of the US market during a trading year of fully automated transactions by means of ordinal pattern series, we define an information-theoretic measure of pairwise synchronization for time series which allows us to study this subset of the US market as a dynamical network. We apply to the resulting network a couple of clustering algorithms in order to detect collective market states, characterized by their degree of centralized or decentralized synchronicity. This collective analysis has shown to reproduce, classify and explain the anomalous behavior previously observed at the individual level. We also find two whole coherent seasons of highly centralized and decentralized synchronicity, respectively. Finally, we model these states dynamics through a simple Markov model.

A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids

Community Microgrid offers effective energy harvesting from distributed energy resources and efficient energy consumption by employing an energy management system (EMS). Therefore, the collaborative microgrids are essentially required to apply an EMS, underlying an operative control strategy in order to provide an efficient system. An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, the revenue of trading spinning and non-spinning reserve capacity can effectively be managed by EMS. Consequently, the importance of optimization is explicit in microgrid applications. In this paper, the most common control strategies in the microgrid community with potential pros and cons are analyzed. Moreover, a comprehensive review of single objective and multi-objective optimization methods is performed by considering the practical and technical constraints, uncertainty, and intermittency of renewable energies sources. The Pareto-optimal solution as the most popular multi-objective optimization approach is investigated for the advanced optimization algorithms. Eventually, feature selection and neural network-based clustering algorithms in order to analyze the Pareto-optimal set are introduced.

An automatic phytopathometry system for chlorosis and necrosis severity evaluation of asian soybean rust infection

The development of soybean cultivars resistant to Phakopsora pachyrhizi (also known as Asian Soybean Rust (ASR)) may be achieved through genetic breeding. In the stage of selecting the resistant genotypes, one factor that can be used is to observe visually the severity of the rust symptoms upon leaflets of sampled soybean plants. By being able to quantify the exact amounts of regions with chlorosis, necrosis, and unaffected of each sample an automatic method is developed to grade rust severity and to classify the samples accordingly. The proposed approach uses input images of soybean leaflets, in a range from healthy to different levels of ASR severity, sampled from controlled experiments in a farm in southeast Brazil. Images are then processed by channel transformation (RGB to CIELab), channel distribution analysis, and a clustering algorithm in order to segment the regions in three main areas. Those regions represent the main visual symptoms of the ASR severity, but still with ill-defined transitions between them. A post-processing superpixel algorithm is leveraged upon those results to arrive at a first rust intervals classification. A necrosis’ neighborhood analysis is proposed to compute the intermediate areas and to include them in a final rust index. Results are given upon four different experiments: 1) 200 acquired image samples taken directly from the field; 2) 67 detached leaflets at different evolved periods of infection; 3) 50 images from a previous open database, and 4) 10 images with the standards for diagrammatic scales. By grading rust severity, and separating chlorosis, and necrosis indexes this solution contributes to efforts in managing ASR in crops, and in breeding programs.

A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

Fuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust segmentation noise suppression. Fuzzy Local Information C-means Clustering (FLICM) is a widely used robust segmentation algorithm, which combines spatial information with the membership degree of adjacent pixels. In order to further improve the robustness of FLICM algorithm, non-local information is embedded into FLICM algorithm and a fuzzy C-means clustering algorithm has local and non-local information (FLICMLNLI) is obtained. When calculating distance from pixel to cluster center, FLICMLNLI algorithm considers two distances from current pixel and its neighborhood pixels to cluster center. However, the algorithm gives the same weight to two different distances, which incorrectly magnifies the importance of neighborhood information in calculating the distance, resulting in unsatisfactory image segmentation effects and loss of image details. In order to solve this problem, we raise an improved self-learning weighted fuzzy algorithm, which directly obtains different weights in distance calculation through continuous iterative self-learning, then the distance metric with the weights obtained from self-learning is embedded in the objective function of the fuzzy clustering algorithm in order to improve the segmentation performance and robustness of the algorithm. A large number of experiments on different types of images show that the algorithm can not only suppress the noise but also retain the details in the image, the effect of segmenting complex noise images is better, and it provides better image segmentation results than the existing latest fuzzy clustering algorithms.

Unsupervised learning of Swiss population spatial distribution.

The paper deals with the analysis of spatial distribution of Swiss population using fractal concepts and unsupervised learning algorithms. The research methodology is based on the development of a high dimensional feature space by calculating local growth curves, widely used in fractal dimension estimation and on the application of clustering algorithms in order to reveal the patterns of spatial population distribution. The notion “unsupervised” also means, that only some general criteria—density, dimensionality, homogeneity, are used to construct an input feature space, without adding any supervised/expert knowledge. The approach is very powerful and provides a comprehensive local information about density and homogeneity/fractality of spatially distributed point patterns.

Attitude survey in the ESG investment fund market

Nowadays the so-called ESG investment funds are becoming increasingly popular in the world. There was a huge increase in the number of sustainable funds managed in Hungary too in 2020, so in the present paper I will examine the attitude of the Hungarian population toward this financial instrument. My goal is to explore the main obstacles and the potential drivers in the development of the market and to compare the results with relevant studies. I will use a decision tree and hierarchical clustering algorithms in order to find the characteristics of the target market. As a result, I will shed light on the fact that the main restraining factors are the lack of information and recommendations from financial providers. According to my research Hungarians are sensitive to environmental factors and the good performance of ESG funds compared to traditional ones can be an important aspect in their investment decisions. In defining the target group, the individual’s demographics proved to be less influential, rather the basic attitude and perceptions toward ESG investments are the important factors.

Clustering Uncertain Data Objects Using Jeffreys-Divergence and Maximum Bipartite Matching Based Similarity Measure

In recent years, uncertain data clustering has become the subject of active research in many fields, for example, pattern recognition, and machine learning. Nowadays, researchers have committed themselves to substitute the traditional distance or similarity measures with new metrics in the existing centralized clustering algorithms in order to tackle uncertainty in data. However, in order to perform uncertain data clustering, representation plays an imperative role. In this paper, a Monte-Carlo integration is adopted and modified to express uncertain data in a probabilistic form. Then three similarity measures are used to determine the closeness between two probability distributions including one novel measure. These similarity measures are derived from the notion of Kullback-Leibler divergence and Jeffreys divergence. Finally, density-based spatial clustering of applications with noise and k-medoids algorithms are modified and implemented on one synthetic database and three real-world uncertain databases. The obtained outcomes confirm that the proposed clustering technique defeats some of the existing algorithms.

Intrusion Detection System Using K-Means Based on Cuckoo Search Optimization

Recently, data protection is very important with the technological and digital revolution, as a vast amount of data is generated from different networks. It was found that the Intrusion Detection System (IDS) is probably the best option because of its ability to differentiate between threats that occur inside or outside a public internet. Cluster analysis is a common method of data mining and is characterized as the grouping of similar data. One of the clustering algorithms for clustering numerical data is K-Means. The K-Means Algorithm features are simple to implement and large amounts of data can be handled efficiently. Natural optimization algorithms have recently been combined with clustering algorithms in order to reach the best global solution. Algorithm for optimization search in Cuckoo is a recent meta algorithm for heuristic optimization. The intelligent behavior of the cuckoo is based on this algorithm. Cuckoo Search Optimization (CSO) and the K-Means clustering algorithm are combined in this paper to achieve the optimal solution globally. Different data sets are evaluated and results are compared with those of the clustering algorithms based on optimization.

Spatial-Temporal-DBSCAN-Based User Clustering and Power Allocation for Sum Rate Maximization in Millimeter-Wave NOMA Systems

The combination of millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) systems exploits the capability to serve multiple user devices simultaneously in one resource block. User clustering, power allocation (PA), and hybrid beamforming problems in mmWave-NOMA systems can utilize the network setting’s potential to enhance the system performance. Based on similar characteristics of the spatial distributions of users in real life, we propose a novel spatial-temporal density-based spatial clustering of applications with noise (ST-DBSCAN)-based unsupervised user clustering in order to enhance the system sum-rate. ST-DBSCAN is a state-of-the-art density-based clustering algorithm for solving spatial and non-spatial problems. Moreover, instead of symmetric PA, we propose an inter-cluster PA algorithm. Next, we apply boundary-compressed particle swarm optimization in order to reduce inter-cluster interference and enhance system performance. The simulation results reveal that our proposed solution improves the sum-rate of mmWave-NOMA-based systems when compared with that of mmWave-OMA-based systems. In addition, we compare our proposed algorithm with other benchmark user clustering algorithms in order to investigate the performance of our ST-DBSCAN-based user clustering algorithm. The results also illustrate that our proposed approach outperforms the state-of-the-art user clustering algorithms in mmWave-NOMA systems.

The endless quarantine: the impact of the COVID-19 outbreak on healthcare workers after three months of mandatory social isolation in Argentina

ObjectivesAt the end of 2019 the SARS-CoV-2 outbreak spread around the globe with a late arrival to South America. The objective of this study was to evaluate the impact of the long period of mandatory social isolation that took place in Argentina on the general psychological well-being of healthcare workers due to the COVID-19 pandemic. MethodsA survey was conducted during June 2020, in healthcare workers. Pittsburgh Sleep Quality Index, Insomnia Severity Index, Sleepiness-Wakefulness Inability and Fatigue Test, and Goldberg depression and anxiety scale, were used to analyze the effects of the SARS-Cov 2 outbreak after three months of mandatory social isolation. Analyses were performed by logistic regression and a clustering algorithm in order to classify subjects in the function of their outcome's severity. ResultsFrom 1059 surveys, the majority reported symptoms of depression (81.0%), anxiety (76.5%), poor sleep quality (84.7%), and insomnia (73.7%) with 58.9% suffering from nightmares. Logistic regression showed that being in contact with COVID-19 patients, age, gender and the consumption of sleep medication during the mandatory social isolation were relevant predictors for insomnia, anxiety, and depression. Clustering analysis classified healthcare workers in three groups with healthy/mild, moderate, and severe outcomes. The most vulnerable group was composed mainly of younger people, female, non-medical staff, or physicians in training. ConclusionAn extremely high proportion of Argentinian healthcare workers suffered from sleep problems, anxiety, and depression symptoms. The clustering algorithm successfully separates vulnerable from non-vulnerable populations suggesting the need to carry out future studies involving resilience and vulnerability factors.