K-means Method Research Articles

Penerapan Metode K-Means Dalam Pengelompokkan Buku Untuk Menentukan Minat Baca Pada Perpustakaan Daerah Kota Medan

The library is a facility that functions as an information center, a source of various knowledge, research, recreation and cultural preservation. The Medan City Regional Library is a library organized by the regional government using general funds which aims to serve the public in obtaining comprehensive information without distinguishing gender, religion, race, age, occupation and position. In 2021 the Medan city regional library has 28 thousands of titles books in several categories. Of the many books contained in the library, a system must be needed where the system is useful for both the library and the reader in maximizing grouping of books and searching books easily by the reader, therefore the K-Means Clustering method is used, where the method This is a method in data mining that processes clustering data that is grouped into one or more clusters. In this study, 100 samples of book category data were used in the Medan City library. This study groups the data categories into 3 clusters, namely the most desirable, desirable and least desirable. The results of this method process will find out the most popular book category data so that in the future it will be a consideration for the librarian to increase the collection of books at the Medan City regional library. The process of calculating the K-Means method in grouping books is only carried out until the 2nd iteration because iteration -3 gets the same value. In Cluster 1 (Most Interested) choose 6 categories of books including Category 020-Library and Information, 070-Mass Media, Journalism and Publication, 050-Psychology, 420-Indonesian, 600-Technology, 650-Management. In Cluster 2 (Desired) choose 16 categories of books including 000-General Publications and General Information, 030-Encyclopedias and Books, 040-Biography, 050-Magazines and Journals, 090-Manuscripts and Rare Books, 210-Islamic Religion, 300-Science Social, 320-Political Science, 330-Economics, 410-Indonesian, 510-Mathematics, 620-Technical Sciences, 770-Photography and Photos, 780-Music, 910-General Travel Geography, 930-Old World History. And in cluster 3 (Less Interested) 78 categories of books were selected. 10 of them 010-Bibliography, 060-Association of Organizations and Museums, 080-Quotes, 100-Philosophy and Psychology, 110-Mathematics, 120-Epistimology, 030-Parapsychology and Occultism, 040-Philosophical Thought, 060 Logical Philosophy, 070-Ethics.

Automated detection in microscopic images using segmentation

In this paper, we present a segmentation clusteringbased approach for automated object detection. This paper deals with the segmentation and classification of blood cells for the purpose of detecting leukemia (abnormal blood cells). After the image acquisition and the preprocessing step, we proceeded to the application of the k-means method. In order to show the interest of the proposed approach, we present the different cancerous regions identified with their characteristics for biomedical diagnostic aid. The proposed method is tested on image dataset and achieves 98% segmentation accuracy. These results show that our approach offers encouraging performance and best automatic leukemia detection. The proposed system is successfully implemented in Matlab, experimental results demonstrate that our approach offers encouraging performance and better quality automatic leukemia detection.

МЕТОД ПОШУКУ ЗАЛЕЖНОСТЕЙ У ВЕЛИКИХ НАБОРАХ ДАНИХ ІЗ ПРОПУСКАМИ НА ПРИКЛАДІ ДАНИХ ПРО ПОШИРЕННЯ COVID

In the article, a method of big data analysis with omissions is developed on the example of the construction of advisory rules regarding adequate state policy to reduce the spread of new Covid-19 cases. Association rules and rule generation in Big Data have a number of challenges, the main one being the presence of large numbers of vectors and multivalued datasets. That is why in the paper the system of these rules is based on a new ensemble of machine learning techniques such as associative rules, regression tree and clustering. This study used pooled data from the Government's COVID-19 Response Tracker and ECDC's Covid-19. Clustering was performed using the k-means method. Gap statistics allow finding an appropriate number of clusters, and in the case study three clusters were selected. The clusters differ in the recommendations and actions of the correspondent governments. Thus, the first countries of the cluster chose to close schools and control international travel as the main recommendations; countries in the second cluster recommended staying at home, while the main recommendations from the governments of countries in the third cluster were to stay at home and cancel public events. The same country can be attached to different clusters at different time intervals. Therefore, the clustering by countries will not be so unambiguous. That is why the time series for the detached country can be interesting and will be the subject of further study. In addition, the impact of such clustering on the spread of COVID-19, the position and duration of the peak, and the mortality rate will also be the subject of our further investigation. A regression decision-making tree was built, a set of rules was obtained from the decision tree and applied rules for generating associative dependencies. The resulting dependencies can be used for strategic planning in the healthcare system.

The Scale of Motivationess: an Empirical Description of the Phenomenon and a Presentation of the Method

The aim of the article consists in establishing empirical descriptors of the state of motivation. Methods. The study was implemented according to the Nelson model, which provides for the possibility of determining the essence of the problem under the existing conditions. The empirical research used such methods as the motivation structure questionnaire, the existential feasibility scale of Lange and Orgler, the Schwartz motivational values questionnaire, the Luthans psychological capital method, the Riff psychological well-being questionnaire, the personality life tasks questionnaire (Laboratory of Social Psychology of the personality of Prof. Tytarenko), the questionnaire of Stepa’s reserve motivation, Stepa’s psychological resourcefulness questionnaire, meaningfulness of life method (an adaptation version of the Krumbo and Maholik questionnaire), Muddy’s hardiness questionnaire. The following methods of mathematical and statistical analysis were applied: multivariate, cluster, correlational, comparative, and classification analysis. Research results. The following indicators of motivationess were determined: confidence in one’s capabilities, realistic goal formulation, a clearly defined sequence of actions, effective self-organization of time, understanding of the ratio of one’s strengths and difficulties, completing a task by applying one’s efforts, interest in the process of completing the task, desire to complete the task, inspiration from one’s own experience of success, the ability to draw analytical conclusions from one’s own experience of failures, resistance to stress during task performance, proactiveness in setting the task, the desire to receive and provide informative feedback during the execution and completion of the task, readiness to learn new knowledge and skills to perform the task, sense vocation for staging and completing the task. Cronbach alpha for statements of the Motivationess scale is within 0.91–0.92. The value of intercorrelations of motivationess indicators is 0.30–0.78 (p < .001). The classification analysis showed that low, medium, and high levels of motivationess were correctly determined by 90%, 100%, and 100%, respectively. The structure of motivationess is two-factor, cumulative multi-factor analysis explained 59% of the variance of the data in the group. A higher level of motivationess is characterized by its procedural, dynamic indicators. The k-means method showed the correctness of distinguishing two types of motivationess - low and high; the type of motivationess is determined by its dynamic indicators. Conclusions. Motivationess is characterized by us as a dynamic motivational state of existential discourse, which is actualized about a certain life task, is manifested in the ability of the individual to correlate his resources with the challenges of life conditions in a dialogic interaction, and is experienced by him as inspiration. The motivation scale received the necessary indicators of its reliability. The text of the Motivationess scale is included in the appendix to the article.

USING CLUSTERING ALGORITHMS FOR COMPANY PERSONNEL SEGMENTATION

This research thoroughly examines the importance and potential of applying clustering algorithms in the context of human resource management. It is noted that personnel segmentation is a strategic component for companies of any size and industry, as it allows for more effective adaptation of management strategies to the needs and characteristics of different groups of employees. It is emphasized that clustering algorithms can automate the segmentation process, providing objective and consistent results. Various types of clustering algorithms are described in detail, including their principles of operation and application peculiarities in the context of personnel management. In particular, it is highlighted how clustering can be based on different criteria such as skills, professional experience, motivation, and others, and how these clusters can be used to improve hiring processes, career planning, performance evaluation, and personnel development. Successful case examples of implementing clustering algorithms in human resource management practices are also provided, demonstrating their positive impact on company efficiency and strategic development. Finally, the need for further research in this area is emphasized to refine personnel segmentation methodologies and expand their application in various business sectors. This research conducted a cluster analysis of a dataset with marketing data using three different clustering methods: k-means, DBSCAN, and agglomerative clustering. The results of the evaluation showed that DBSCAN demonstrated the best performance both with the full and reduced dataset, indicating its effectiveness in identifying densely populated clusters with irregular distributions of points. In contrast, the k-means method proved to be less effective for data with irregular distributions and outliers. Agglomerative clustering yielded moderate results but was found to be vulnerable to outliers and demanding in terms of computational resources. Overall, the study allows us to conclude on the effectiveness of different clustering methods in identifying data structures in marketing research.

Identification of Students with Similar Performances in Micro-Learning Programming Courses with Automatically Evaluated Student Assignments

The identification of heterogeneous and homogeneous groups of students using clustering analysis in learning analytics is still rare. The paper describes a study in which the students’ performance data stored in the micro-learning platform Priscilla are analyzed using learning analytics methods. This study aims to identify the groups of students with similar performances in micro-learning courses focused on learning programming and uncover possible changes in the number and composition of the identified groups of students. The CRISP-DM methodology was used to navigate through the complexity of the knowledge discovery process. Six different datasets representing different types of graded activities or term periods were prepared and analyzed for that purpose. The clustering analysis using the K-Means method found two clusters in all cases. Subsequently, performance metrics, the internal composition, and transfers of the students between clusters identified in different datasets were analyzed. As a result, this study confirms that analyzing student performance data from a micro-learning platform using learning analytics methods can reveal distinct groups of students with different academic performances, and these groups change over time. These findings align with teachers’ assumptions that the micro-learning platform with automated evaluation of programming assignments highlights how the students perceive the role of learning tools during learning programming in different term periods. Simultaneously, this study acknowledges that clustering, as an exploratory method, provides a solid basis for further research and can identify distinct groups of students with similar characteristics.

FORMATION OF AN OPTIMAL PORTFOLIO OF LQ45 SHARES USING MARKOWITZ METHOD

Currently, there are still many investors who do not realize that stock management strategies are important. Therefore, as a practitioner, through this research, it is hoped that authors can help overcome this problem so that it can calculate maximum profits in-stock selection. This research aims at understanding stock management strategies by forming a Markowitz portfolio with the help of the K-Means grouping method. The population in this research included stock companies listed on the Indonesian Stock Exchange. The sample selection method used was the targeted sampling method. The sample data used was daily stock returns from LQ45 stock companies. The research results showed that based on data processing, stock grouping using the k-Means method and the Markowitz method was proven to produce maximum profits and low risk. Therefore, the method used in this research can be useful in the world of finance, especially to help investors.

Integrating street view imagery and taxi trajectory for identifying urban function of street space

ABSTRACT Street space is a crucial component of public space, serving as a site for a variety of human activities. However, prior studies have primarily focused on the traffic function of street space, neglecting other functional types, such as residential and commercial. To address this gap, this study proposes a classification method for street space by integrating taxi trajectory and street view imagery. The dynamic travel features of the residents are extracted from taxi trajectory data and the multi-level physical environment features of the streets are constructed based on street view imagery data. Then, the street space with same urban functions is assigned to the same cluster based on the dynamic travel features of the residents, the multi-level physical environment features of streets and K-means method. The proposed method is empirically applied to the Futian District of Shenzhen to demonstrate its validity, and the street space is successfully classified into five types, namely public, traffic, commercial, residential, and mixed commercial and residential. This work contributes to the evaluation of street space quality, facilitating scientific planning and efficient governance of street space.

Customer Selection in the Conversion Program of LPG Stove to Induction Stove

The oversupply condition is due to differences in the current electricity supply which is higher than actual electricity demand. One option to increase demand is by converting LPG stoves into induction stoves. To achieve the conversion goal successfully, a customer selection process must be carried out to determine the conversion targets, aiming for a significant increase in electricity demand with the lowest investment costs. The main problem is the absence of a standard for determining conversion targets. The combination of clustering techniques and Multiple Criteria Decision Making (MCDM) analysis provides the best ideal solution to overcome these problems. The research included several main criteria for selecting customers that support the success factors of implementing a conversion program. The initial analysis involved clustering customers based on coordinates using the k-means method. By using the clustered customer data, the TOPSIS (Technique for Ordering Preference by Similarity to an Ideal Solution) method identified the best cluster based on general criteria. Additionally, using detailed customer data, the TOPSIS method also identified the ranking of customer priorities for each selected cluster. The recommended priority target customers are those with the greatest potential for additional kWh, the best level of electricity reliability, shortest distance to the cluster centroid, and supplied by feeders and distribution transformers with the largest reserve margin. The results indicated the difference in revenue improvement compared to the current method was US$ 16,291.31 per month. Investment costs were also reduced by US$ 166,084.80 compared to the current method. The other results were a reduction in operational costs, improvement in average reliability according to the SAIDI SAIFI, improvement in the average operation-hours, and an improvement in the distribution transformer average reserve margins.

A Parameter Assessment of Teaching Quality Indicators Based on Data Class Mining Fuzzy K-Mean Type Clustering

This paper proposes a data-based mining and hesitant fuzzy C-canopy-K mean clustering degree algorithm and uses it in the parameter assessment model of teaching quality indicators. Simulation and training are carried out through data class mining, and information input, followed by combining the hesitant fuzzy K-mean classification assessment method, which involves a hesitant fuzzy type evaluation system, a neural network identification and prediction system, and an application system for module identity verification. The simulation results show that the results of the six simulation conditions are consistent with the actual results, with only slight differences in some amplitudes, and a high degree of consistency in the overall trend, the change rule, and the average peak value. Through the prediction model processing in this paper, the teaching quality index parameter assessment has high accuracy and can reach more than 95.0%, in addition, the development of the law also fits very well. a, b, c, d four kinds of teaching quality parameter assessment of the average calculation of the assessment speed increased by 52.5%. In addition, the assessment test after the integrated design of the module shows that the system can effectively identify the four clustering identification processes that can be seen as excellent, good, medium, and poor; at the same time, the test data show that the system class effectively for teaching quality indicator parameter assessment.

Análisis de los patrones espaciales del desarrollo y pobreza en Costa Rica

This paper aims to analyze the relationships between the different dimensions of poverty and development from a multidimensional and spatial perspective. Methodologically, this research is composed of two phases. The first, a descriptive statistics analysis of the Multidimensional Poverty Index and the Human Development one, was carried out, departing from the definitions of territoriality used in Costa Rica (rural-urban and by planning regions) with county-level data. In second place, a cluster analysis, with K-means method, was developed with a database of 32 variables for development and poverty at the country level. About the results, we discovered that the typologies of rural and urban areas are significant but can be self-explanatory; the category of planning regions reveals relevant patterns in the center-periphery dynamics, particularly due to asymmetries between the center, the Pacific coast and, the Atlantic coast and borders. The cluster analysis confirms these patterns, showing that in terms of development, the country seems to be divided into 3 regions, while in terms of poverty is possible to identify 4. Concluding with some public and economic policy recommendations.

Glycative stress inhibits hypertrophy and impairs cell membrane integrity in overloaded mouse skeletal muscle.

Glycative stress, characterized by the formation and accumulation of advanced glycation end products (AGEs) associated with protein glycation reactions, has been implicated in inducing a decline of muscle function. Although the inverse correlation between glycative stress and muscle mass and strength has been demonstrated, the underlying molecular mechanisms are not fully understood. This study aimed to elucidate how glycative stress affects the skeletal muscle, particularly the adaptive muscle response to hypertrophic stimuli and its molecular mechanism. Male C57BL/6NCr mice were randomly divided into the following two groups: the bovine serum albumin (BSA)-treated and AGE-treated groups. Mice in the AGE-treated group were intraperitoneally administered AGEs (0.5mg/g) once daily, whereas those in the BSA-treated group received an equal amount of BSA (0.5mg/g) as the vehicle control. After 7days of continuous administration, the right leg plantaris muscle of mice in each group underwent functional overload treatment by synergist ablation for 7days to induce muscle hypertrophy. In in vitro studies, cultured C2C12 myocytes were treated with AGEs (1mg/mL) to examine cell adhesion and cell membrane permeability. Continuous AGE administration increased the levels of fluorescent AGEs, Nε-(carboxymethyl) lysine, and methylglyoxal-derived hydroimidazolone-1 in both plasma and skeletal muscle. Plantaris muscle weight, muscle fibre cross-sectional area, protein synthesis rate, and the number of myonuclei increased with functional overload in both groups; however, the increase was significantly reduced by AGE treatment. Some muscles of AGE-treated mice were destroyed by functional overload. Proteomic analysis was performed to explore the mechanisms of muscle hypertrophy suppression and myofibre destruction by AGEs. When principal component analysis was performed on 4659 data obtained by proteomic analysis, AGE treatment was observed to affect protein expression only in functionally overloaded muscles. Enrichment analysis of the 436 proteins extracted using the K-means method further identified a group of proteins involved in cell adhesion. Consistent with this finding, dystrophin-glycoprotein complex proteins and cell adhesion-related proteins were confirmed to increase with functional overload; however, this was attenuated by AGE treatment. Additionally, the treatment of C2C12 muscle cells with AGEs inhibited their ability to adhere and increased cell membrane permeability. This study indicates that glycative stress may be a novel pathogenic factor in skeletal muscle dysfunctions by causing loss of membrane integrity and preventing muscle mass gain.

Penerapan Algoritma K-Means untuk Klasterisasi Produksi Budidaya Perikanan Provinsi Sulawesi Utara

Capture fisheries production is decreasing due to natural resources or weather conditions, so other resources are needed to support fisheries production. One alternative is to increase the production of aquaculture commodities through seawater, freshwater, or brackish water cultivation. Various potentials for developing aquaculture have been developed in various regions, including the North Sulawesi region. Grouping aquaculture commodity production according to container type is very important to maintain and increase aquaculture production. This research aims to cluster aquaculture production in the North Sulawesi area using the K-Means method using Euclidean, Manhattan, and Minkowsky distances. The results of the research obtained three clusters, namely the first cluster, the region with the highest production of pond container types, namely the Sitaro Islands, and for second cluster consisting of 13 regions that have variations in production ranging from low to high for the types of floating net containers and ponds, while third cluster is the region Bitung with moderate production for pond types. It is hoped that this research can help related agencies to create policies to increase the production potential of aquaculture.

A Morphing-Based Future Scenario Generation Method for Stochastic Power System Analysis

As multiple wind and solar photovoltaic farms are integrated into power systems, precise scenario generation becomes challenging due to the interdependence of power generation and future climate change. Future climate data derived from obsolete climate models, featuring diminished accuracy, less-refined spatial resolution, and a limited range of climate scenarios compared to more recent models, are still in use. In this paper, a morphing-based approach is proposed for generating future scenarios, incorporating the interdependence of power generation among multiple wind and photovoltaic farms using copula theory. The K-means method was employed for scenario generation. The results of our study indicate that the average annual variations in dry-bulb temperature (DBT), global horizontal irradiance (GHI), and wind speed (WS) are projected to increase by approximately 0.4 to 1.9 °C, 7.5 to 20.4 W/m2, and 0.3 to 1.7 m/s, respectively, in the forthcoming scenarios of the four considered Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). It seems that accumulated maximum wind electricity output (WEO) and solar electricity output (SEO) will increase from 0.9% to 7.3% and 1.1% to 6.8%, respectively, in 2050.

Customer Segmentation With K-Means Clustering Suzuki Mobil Bandung Customer Case Study

In the city of Bandung, it is recorded that until February 2023 as many as 500 thousand private cars crowded the streets in the city of Bandung. People's needs for private cars are met through the purchase of new cars from dealers or used purchases. As a dealer, the main task is to meet car sales targets every month and year. Suzuki dealers, especially in Bandung, do not have solid information about what type of car is most liked by the people of Bandung, what is the background of the customers and what marketing efforts are most optimal to increase sales. Suzuki car sales data for the June-October 2023 period was analyzed as many as 165 sales from various types of cars, customer domicile, customer's proffesion and marketing efforts carried out until the purchase occurred and the choice of payment method. In this paper, a clustering analysis of the K-means method with 4 clusters with car type, customer domicile location, marketing effort, customer profession, transmission type and payment method is made. Analysis performed with IBM SPSS v.29 program.The type of Carry passenger vehicle is the choice of many Suzuki customers in Bandung and Suzuki customers mostly come from the people of Bandung and around Bandung who work as entrepreneurs and traders. Suzuki Bandung needs to maintain and improve Canvansing as an effort to acquire customers as can be seen from the analysis of customer segmentation data in this paper

Differentiation of innovation ecosystems of the countries being the Global Innovation Index leaders in the global competitive context

Innovations have become pivotal for the growth and competitiveness of national economies. Generating innovations necessitates a comprehensive ecosystem as a set of conducive conditions. With competition intensifying and focusing on innovation, countries increasingly prioritize the enhancement of their innovation ecosystems. The foundation for this lies in international comparisons, particularly among countries that are global leaders, as it aids in identifying their specific characteristics and advantages. The aim of the study is to differentiate the innovation ecosystems of world-leading countries by highlighting the indicators in which they differ the most.The paper covered the top 15 countries according to the Global Innovation Index, each characterized by 23 indicators in their innovation ecosystems. In the first stage, using mathematical processing (the k-means method), the countries were divided into six clusters. Then, to find the parameters that differentiate the obtained clusters, a classification analysis was conducted (the “decision tree” method), resulting in 11 indicators that, in various pairwise combinations, most differentiate the analyzed countries. These indicators reflect the features and most important advantages (or weaknesses) of each innovation ecosystem and are also priorities for increasing the parameters of these ecosystems to improve the position of countries. It is advisable to use these indicators to form state innovation policy.

Penerapan Algoritma K-Means Untuk Mengelompokkan Kepadatan Penduduk Di Provinsi DKI Jakarta

DKI Jakarta Province is an attraction for immigrants. If the population increases, if it cannot be resolved and managed well, it will result in bad things such as increasing the number of unemployed and affecting economic growth. Population data is used to help group regions based on population density in DKI Jakarta Province in 2019-2022 using the K-Means clustering method. From the results of the research, it provides a solution for the government to pay attention to population groups with the aim of preventing population density because it causes bad effects, so that community welfare is more guaranteed, so grouping (clustering) of provinces in DKI Jakarta is needed to provide information for people who wish to live in the Province DKI Jakarta. The research proves that the test results carried out clustering iterations of population density data were obtained in three iterations. For the results obtained by calculations using the K-Means method and using the rapidminer application, the results obtained were of the same value, namely the cluster with the highest population density of three districts/cities, namely South Jakarta, East Jakarta and West Jakarta whose population density continues to increase.

Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery

Crop Residue Cover (CRC) is crucial for enhancing soil quality and mitigating erosion in agricultural fields. Accurately estimating CRC in near real-time presents challenges due to the limitations of traditional and remote sensing methods. This study addresses the challenge of accurately estimating CRC using unsupervised algorithms on high-resolution Unmanned Aerial System (UAS) imagery. We employ two methods to perform CRC estimation: (1) K-means unsupervised algorithm and (2) Principal Component Analysis (PCA) along with the Otsu thresholding technique. The advantages of these methods lie in their independence from human intervention for any supervised training stage. Additionally, these methods are rapid and suitable for near real-time estimation of CRC as a decision-making support in agricultural management. Our analysis reveals that the K-means method, with an R2=0.79, achieves superior accuracy in CRC estimation over the PCA-Otsu method with an R2=0.46. The accuracy of CRC estimation for both corn and soybean crops is significantly higher in winter than in spring, attributable to the more weathered state of crop residue. Furthermore, CRC estimations in corn fields exhibit a stronger correlation, likely due to the larger size of corn residue which enhances detectability in images. Nevertheless, the variance in CRC estimation accuracy between corn and soybean fields is minimal. Furthermore, CRC estimation achieves the highest correlation in no-till fields, while the lowest correlation is observed in conventionally tilled fields, a difference likely due to the soil disturbance during plowing in conventional tillage practices.

Typology of Acquiring Life Skills and Mental Health in International Students in Japan during COVID-19

Life Skills (LS) are essential in the COVID era because people acquire LS (learn different abilities) to adapt to problems that arise in their daily lives at different times. Therefore, the purpose of this study is to categorize and clarify the characteristics of LS acquisition and mental health among international students during the COVID-19 pandemic. This study surveyed 407(The final analysis included 134 undergraduates: M = 69, F = 65; Mean age=19.55) international students enrolled at two universities in the Kansai region of Japan, regardless of their nationality or subject major, from October to November 2021. Utilizing the University Student Edition of the Daily Life Skills Scale (English version) and three scales measuring Loneliness, Satisfaction, and Anxiety. Firstly, the target participants were classified into three clusters using the K-means method, denoted as "Maladaptive Type," "Adaptive Type," and "Expansive Type." Secondly, the results of correlation coefficients between three mental health questions and LS indicated significant correlations among Satisfaction, Anxiety, and Interpersonal and Personal skills. Significant correlations of 0.20 or more were observed among Satisfaction, Anxiety, Interpersonal (Satisfaction (r = 0.25, p < 0.01), Anxiety: (r=-0.28, p < 0.01)), and Personal skills (Satisfaction (r = 0.39, p < 0.01), Anxiety: (r=-0.47, p < 0.01)), as well as between Personal skills and Loneliness (r=-0.28, p < 0.01). Thirdly, while focusing on exercise frequency, exercise intensity, and total exercise time in the exercise habits of international students, an examination was conducted regarding the relationship with mental health and LS acquisition. The results indicated no significant relationships, and the study concluded that exercise habits did not affect the relationship between mental health and LS acquisition among international students during the COVID-19. In the future, to enhance support for international students, educational institutions are expected to promote LS acquisition to help students adapt to the new lifestyle resulting from the pandemic.

Patient characteristics correlate with diagnostic performance of photodynamic diagnostic assisted transurethral resection of bladder tumors: A retrospective, single-center study

BackgroundIdentification of patient subclasses that correlate with the diagnostic performance of photodynamic diagnostic (PDD)-assisted transurethral resection of bladder tumors (TURBT) may improve outcomes. MethodsData were extracted from patients that underwent PDD-assisted TURBT at the University of Tsukuba Hospital between 2018 and 2023. Sensitivity and specificity were evaluated based on PDD findings (excluding WL findings) and pathology results. Cluster analysis using uniform manifold approximation and projection and k-means methods was performed, focusing on patients with malignant lesions. ResultsA total of 267 patients and 2082 specimens were extracted. Sensitivity was lowest with regard to BCG treatment (53.7 %), followed by flat lesions (57.2 %), urine cytology class ≥ III (62.9 %), and recurrent tumors (64.5 %). In the cluster analysis of 231 patients with malignant lesions, two showed lower sensitivity: Cluster 3 (62.4 %), consisting of patients with recurrent tumors and post-BCG treatment, and Cluster 4 (55.7 %), consisting of patients with primary tumors and urine cytology class ≥ III. Clusters 1 and 2, consisting of patients without BCG treatment and patients with lower urine cytology classes, exhibited higher sensitivities (94.4 % and 87.7 %). Among all clusters, Cluster 4 had the highest proportion of specimens which were negative for both PDD and white light (WL) findings but actually had malignant lesions (20.8 %). ConclusionsPDD-assisted TURBT sensitivity was lower in subclasses after BCG treatment or with cytology class III or higher. Random biopsy for PDD/WL double-negative lesions may improve diagnostic accuracy in these subclasses.