Search Results (2,572)

This paper presents an initial analysis of the factors influencing tourism success at the NUTS 2 regional level across the Eurozone from 2010 to 2019. Utilizing an extensive dataset that includes economic, demographic, and tourism-specific indicators, we employ unsupervised machine learning techniques, primarily K-means clustering and Principal Component Analysis (PCA), to unearth underlying patterns and relationships. Our study reveals distinct clusters of regions characterized by varying degrees of economic prosperity, infrastructure development, and tourism activity. Through K-means clustering, we identified optimal groupings of regions that share similar characteristics in terms of GDP per capita, unemployment rates, tourist arrivals, and overnight stays, among other metrics. Subsequent PCA provided deeper insights into the most influential factors driving these clusters, offering a reduced-dimensional perspective that highlights the primary axes of variation. The findings underscore significant disparities in tourism success across the Eurozone, with economic robustness and strategic infrastructural investments emerging as key drivers. Regions with higher GDP per capita and lower unemployment rates tend to exhibit higher tourism metrics, suggesting that economic health is a substantial contributor to regional tourism appeal and capacity. This paper contributes to the literature by demonstrating how machine learning can be applied to regional tourism data to better understand and strategize for tourism development. The insights garnered from this study are poised to assist policy-makers and tourism planners in crafting targeted interventions aimed at enhancing tourism competitiveness in underperforming regions. Full article

Human behavior significantly contributes to severe road injuries, underscoring a critical road safety challenge. This study addresses the complex task of predicting dangerous driving behaviors through a comprehensive analysis of over 356,000 trips, enhancing existing knowledge in the field and promoting sustainability and road safety. The research uses advanced machine learning algorithms (e.g., Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and K-Nearest Neighbors) to categorize driving behaviors into ‘Dangerous’ and ‘Non-Dangerous’. Feature selection techniques are applied to enhance the understanding of influential driving behaviors, while k-means clustering establishes reliable safety thresholds. Findings indicate that Gradient Boosting and Multilayer Perceptron excel, achieving recall rates of approximately 67% to 68% for both harsh acceleration and braking events. This study identifies critical thresholds for harsh events: (a) 48.82 harsh accelerations and (b) 45.40 harsh brakings per 100 km, providing new benchmarks for assessing driving risks. The application of machine learning algorithms, feature selection, and k-means clustering offers a promising approach for improving road safety and reducing socio-economic costs through sustainable practices. By adopting these techniques and the identified thresholds for harsh events, authorities and organizations can develop effective strategies to detect and mitigate dangerous driving behaviors. Full article

Those responsible for elite and youth athletes are increasingly aware of the need to balance the quest for superior performance with the need to protect the physical and psychological wellbeing of athletes. As a result, regular assessment of risks to mental health is a common feature in sports organisations. In the present study, the Brazil Mood Scale (BRAMS) was administered to 898 athletes (387 female, 511 male, age range: 12–44 years) at a leading sports club in Rio de Janeiro using either “past week” or “right now” response timeframes. Using seeded k-means cluster analysis, six distinct mood profile clusters were identified, referred to as the iceberg, surface, submerged, shark fin, inverse iceberg, and inverse Everest profiles. The latter three profiles, which are associated with varying degrees of increased risk to mental health, were reported by 238 athletes (26.5%). The prevalence of these three mood clusters varied according to the response timeframe (past week > right now) and the sex of the athletes (female > male). The prevalence of the iceberg profile varied by athlete sex (male > female), and age (12–17 years > 18+ years). Findings supported use of the BRAMS as a screening tool for the risk of psychological issues among athletes in Brazilian sports organisations. Full article

Color is one of the first criteria to assess the quality of cheese. However, very limited data are available on the color heterogeneity of the rind and its relationship with microbial community structure. In this study, the color of a wide range of smear-ripened Munster cheeses from various origins was monitored during storage by photographic imaging and data analysis in the CIELAB color space using luminance, chroma, and hue angle as descriptors. Different levels of inter- and intra-cheese heterogeneity were observed. The most heterogeneous Munster cheeses were the darkest with orange-red colors. The most homogeneous were the brightest with yellow-orange. K-means clustering revealed three clusters distinguished by their color heterogeneity. Color analysis coupled with metabarcoding showed that rinds with heterogeneous color exhibited higher microbial diversity associated with important changes in their microbial community structure during storage. In addition, intra-cheese community structure fluctuations were associated with heterogeneity in rind color. The species Glutamicibacter arilaitensis and Psychrobacter nivimaris/piscatorii were found to be positively associated with the presence of undesirable brown patches. This study highlights the close relationship between the heterogeneity of the cheese rind and its microbiota. Full article

We aimed to provide a realistic portrayal of customer sentiment and product competitiveness, as well as to inspire businesses to optimise their products and enhance their services. This paper uses 119,190 pairs of real composite review data as a corpus to examine customer sentiment analysis and product competitiveness. The research is conducted by combining TF-IDF text mining with a time-phase division through the k-means clustering method. The study identified ‘quality’, ‘taste’, ‘appearance packaging’, ‘logistics’, ‘prices’, ‘service’, ‘evaluations’, and ‘customer loyalty’ as the commodity dimensions that customers are most concerned about. These dimensions should therefore serve as the primary entry point for improving the commodity and understanding customers. A review of customer feedback reveals that the composite reviews can be divided into three time stages. Furthermore, the sentiment expressed by customers can become increasingly negative over time. The product competitiveness based on the composite review can be characterised by four regional quadrants, such as ‘Advantage Area’, ‘Struggle Area’, ‘Opportunity Area’, and ‘Waiting Area’, and merchants can target these areas to improve product competitiveness according to the dimensional distribution. In the future, it will also be possible to take customer demographics into account in order to gain a deeper understanding of the customer base. Full article

The traditional k-means algorithm is widely used in large-scale data clustering because of its easy implementation and efficient process, but it also suffers from the disadvantages of local optimality and poor robustness. In this study, a Csk-means algorithm based on contour similarity is proposed to overcome the drawbacks of the traditional k-means algorithm. For the traditional k-means algorithm, which results in local optimality due to the influence of outliers or noisy data and random selection of the initial clustering centers, the Csk-means algorithm overcomes both drawbacks by combining data lattice transformation and dissimilar interpolation. In particular, the Csk-means algorithm employs Fisher optimal partitioning of the similarity vectors between samples for the process of determining the number of clusters. To improve the robustness of the k-means algorithm to the shape of the clusters, the Csk-means algorithm utilizes contour similarity to compute the similarity between samples during the clustering process. Experimental results show that the Csk-means algorithm provides better clustering results than the traditional k-means algorithm and other comparative algorithms. Full article

As one of the largest frequency regulation markets, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market allows extensive access of Battery Energy Storage Systems (BESSs). The designed signal regulation D (RegD) is friendly for use with BESSs with a fast ramp rate but limited energy. Designing operating strategies and optimizing the sizing of BESSs in this market are significantly influenced by the regulation signal. To represent the inherent randomness of the RegD signal and reduce the computational burden, typical frequency regulation scenarios with lower resolution are often generated. However, due to the rapid changes and energy neutrality of the RegD signal, generating accurate and representative scenarios presents challenges for the methods based on shape similarity. This paper proposes a novel probability-based method for generating typical regulation scenarios. The method relies on the joint probability distribution of two features with a 15-min resolution, extracted from the RegD signal with a 2 s resolution. The two features can effectively portray the characteristic of RegD signal and its influence on BESS operation. Multiple regulation scenarios are generated based on the joint probability distributions of these features at first, with the final typical scenarios chosen based on their probability distribution similarity to the actual distribution. Utilizing regulation data from the PJM market in 2020, this paper validates and analyzes the performance of the generated typical scenarios in comparison to existing methods, specifically K-means clustering and the forward scenarios reduction method. Full article

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions. Full article

More collisions occur at the exit ramps of expressways due to frequent lane-changing behavior and interweaving between vehicles. Young drivers with shorter driving mileage and driving experience, radical driving styles, and worse behavior prediction are likelier to be involved in collisions at the exit ramps. This paper focuses on the correlation analysis between young drivers’ characteristics and their visual and physiological attributes at expressway exit ramps. First, the driver’s gender, driving experience, and mileage are classified. Then, seven expressway exit models are established using the UC/Win road modeling software. The driver’s driving plane vision is divided into four areas using the K-means clustering algorithm. In addition, the driver’s visual and heart rate attributes were analyzed at 500 m, 300 m, 200 m, and 100 m away from an expressway exit. The results show that the visual attributes, gender, and driving mileage of young drivers strongly correlate with the fixation times and average saccade amplitude. In contrast, the driving experience has almost no correlation with the fixation behavior of young drivers. Young drivers’ driving experience and mileage strongly correlate with cardiac physiological attributes, but there is virtually no correlation with gender. The practical implications of these results should be helpful to highway planners and designers. Full article

This paper analyzes eleven Central and Eastern European countries after the financial crisis of 2008 and the COVID-19 pandemic of 2020. It investigates the heterogeneity in the labor market among the selected countries based on the youth inactivity, secondary education attainment, and income share of the bottom fifty percent of the population. A hierarchical cluster analysis with Ward’s method and k-means clustering generated diverse cluster solutions. A comparative analysis of the four-cluster solutions for 2008 and 2020 showed multiple changes in the cluster composition. The joint groupings of geographically and historically close countries, such as the Baltics, the former Czechoslovakia, and the former Yugoslav republics of Croatia and Slovenia, were identified for 2008. Lithuania emerged as a singleton in 2020. The youth inactivity, educational levels, and income inequality reveal the status of the youth in Central and Eastern Europe during these crises. Full article

Objectives: From a cross-cultural perspective, aging well may encompass pertinent challenges in terms of adjustment, sexual well-being, and satisfaction with life in the late years. Considering the paucity of empirical data concerning cultural diversity of experiencing aging, this study aims to help fill this gap by assessing the specific patterns of sexual satisfaction, adjustment to aging (AtA), and life satisfaction with life (SwL) of older adults in Portugal and Spain. Methods: This cross-national study included 326 older adults, age 65 and older, from Portugal and Spain. Five instruments were applied: (a) Adjustment to Aging Scale (ATAS); (b) Satisfaction with Life Scale (SwLS); (c) New Sexual Satisfaction Scale-Short (NSSS-S); (d) Mini-Mental State Exam; and (e) Sociodemographic, Health and Lifestyle questionnaire. K-means cluster analysis was employed to identify and characterize the clusters considering adjustments to aging, sexual satisfaction, and life satisfaction. One-way ANOVAs were conducted to analyze differences in sexual well-being among clusters. Results: Findings indicated three clusters, which explained 77.7% (R-sq = 0.777) of the total variance: Cluster 1: “Most skilled” (n = 26, 8.0%), Cluster 2: “Least adjusted” (n = 115, 35.3%), and Cluster 3: “Aging strivers” (n = 185, 56.7%). Participants in Cluster 1 were mostly Portuguese, with high levels of AtA, sexual satisfaction, and SWL. Conversely, Cluster 2 included mostly Portuguese participants with moderate sexual satisfaction and lower levels of AtA and SwL. Participants from Cluster 3 were mostly Spanish, with moderate levels of AtA and reduced sexual satisfaction and SwL. Conclusions: This study innovates by exploring the elaborate interplay among sexual satisfaction, AtA, and SwL in a cross-cultural perspective, with implications for tailoring interventions, service planning, development, and evaluation of culturally diverse older populations. Full article

China has identified the synergistic reduction of pollution and carbon emissions as a crit ical component of its environmental protection and climate mitigation efforts. An assessment of this synergy can provide clarity on the strategic management of both air pollution and carbon emissions. Due to the extensive regional differences in China, the spatial effects of influencing factors on this synergy exhibit variation across different provinces. In this study, the reduction indexes of PM_2.5 and CO₂ were calculated based on their reduction bases, reduction efforts, and reduction stabilities across provinces. Then, the synergistic reduction effect was assessed using an exponential function with the PM_2.5 reduction index as the base and the CO₂ reduction index as the exponent. Next, the MGWR model was applied in order to analyze the influencing factors of the synergistic reduction effect, considering natural settings, socioeconomic conditions, and external emission impacts. Finally, the k-means clustering method was utilized to classify provinces into different categories based on the degree of impact of each influencing factor. The results indicated that air circulation, vegetation, tertiary industry ratio, and emission reduction efficiency are major impact indicators that have a positive effect. The topography and emissions from neighboring provinces have a statistically significant negative impact. The spatial influences of different factors exhibit a distribution trend characterized by a high-high cluster and a low-low cluster. A total of 31 provinces are divided into three categories, and suggestions on the corresponding category are proposed, to provide a scientific reference to the synergistic reduction of PM_2.5 and CO₂. Full article

Breeding is an effective method for the varietal development of pineapple. However, due to open pollination, it is necessary to conduct authentic identification of the hybrid offspring. In this study, we identified the authenticity of offspring and analyzed the genetic diversity within the offspring F₁ hybrids resulting from crosses between ‘Josapine’ and ‘MD2’ by single nucleotide polymorphism (SNP) markers. From the resequencing data, 26 homozygous loci that differentiate between the parents have been identified. Then, genotyping was performed on both the parents and 36 offspring to select SNP markers that are suitable for authentic identification. The genotyping results revealed that 2 sets of SNP primers, namely SNP4010 and SNP22550, successfully identified 395 authentic hybrids out of 451 hybrid offspring. We randomly selected two true hybrids and four pseudohybrids for sequencing validation, and the results have shown that two true hybrids had double peaks with A/G, while pseudohybrids had single peaks with base A or G. Further study showed that the identification based on SNP molecular markers remained consistent with the morphological identification results in the field, with a true hybridization rate of 87.58%. K-means clustering and UPGMA tree analysis revealed that the hybrid offspring could be categorized into two groups. Among them, 68.5% of offspring aggregated with MD2, while 31.95% were grouped with Josapine. The successful application of SNP marker to identify pineapple F₁ hybrid populations provides a theoretical foundation and practical reference for the future development of rapid SNP marker-based methods for pineapple hybrid authenticity and purity testing. Full article

This study investigates optimal training intervals for small hydro power regression models, crucial for accurate forecasts in diverse conditions, particularly focusing on Portugal’s small hydro portfolio. Utilizing a regression model based on kernel density estimation, historical hourly production values, and calendar variables, forecasts are generated. Various approaches, including dynamic time warping (DTW), “K-Means Alike,” and traditional K-means clustering, are assessed for determining the most effective historical training periods. Results highlight the “K-Means Alike” approach, which, with a 2-month training period, outperforms conventional methods, offering enhanced accuracy while minimizing computational resources. Despite promising results, DTW exhibits increased computational demands without consistent performance superiority. Full article

Despite the steady rise of electric vehicles (EVs) in other countries, the Philippines has yet to capitalize on its proliferation due to several mixed concerns. Status, socio-demographic characteristics, and availability have been the main concerns with purchasing EVs in the country. Consumer segmentation and analysis for EV acceptance and utility in the Philippines were determined in this study due to the need for understanding consumer preferences and market segmentation towards EVs in the Philippines. A total of 311 valid responses coming from EV owners were collected through purposive and snowball sampling approaches. The data were collected via face-to-face distribution and online distribution of a questionnaire covering demographic characteristics for market segmentation. Demographic data such as gender, age, residence type, car ownership, and income were used to identify consumer segments using the K-means clustering approach. Jupyter Notebook v7.1.3 was used for the overall analysis, and the number of clusters was optimized, ensuring precise segmentation. The results indicated a strong correlation between car ownership and the ability to purchase EVs, where K-means clustering effectively identified consumer groups. The groupings also included “Not Capable at All” to “Highly Capable” individuals based on their likelihood to purchase EVs. Based on the results, the core-value customers of EVs are male, older than 55 years old, live in urban areas, own a vehicle and car insurance, and have a monthly income of more than PHP 130,000. Following those are high-value customers, considered target users expected to use EVs frequently. It could be posited that customers are frequent purchasers of products and services. Based on the results, high-value customers are male, aged 36–45 years old, live in urban areas, own a car, have car insurance, and have a monthly income of PHP 100,001–130,000. Both of these should be highly considered by EV industries, as these characteristics would be the driving market of EVs in the Philippines. The constructed segmentation provided valuable insights for the EV industry, academic institutions, and policymakers, offering a foundation for targeted marketing strategies and promoting EV adoption in the Philippines. Moreover, the sustainable marketing strategies developed could be adopted and extended among other developing countries wanting to adopt EVs for utility. Future works are also suggested based on the study limitations for researchers to consider as study extensions, such as a holistic approach to EV adoption that considers environmental, social, and economic factors, as well as policies and promotion development. Full article