Categorical Principal Component Analysis Research Articles

Exploring the Botanical Origins of Bee-Collected Pollen: A Comprehensive Historical and Contemporary Analysis.

Bee-collected pollen is one of the most important bee products. In order to predict its chemical composition and nutritional value botanical origin of pollen plays a crucial role. This review intended to collect all available data published about botanical origin of pollen collected all around the world. Due to enourmous amount of data and variables nonlinear principal component analysis (NLPCA), by applying Categorical Principal Component Analysis (CATPCA), was conducted in order to try to determine any specifity and/or differences among samples. Also, importance of some plant families/genera/species for bees was monitored. Based on CATPCA results families can serve in order to distinct samples from North/South America. Also, some samples from Europe (Turkey and Serbia) were characterized with presence of some specific families. Genera were excellent tool to distinguish samples from different parts of Brazil as well as Australia, Asia and Africa. Due to high and specific biodiversity pollen samples obtained from Sonoran desert (USA) completely were separated during analysis. This review presents the first attempt to summarize and classify a large number of data about botanical sources of bee-collected pollen. Keywords bee-collected pollen • botanical sources • palynological analysis • statistical analyses.

Measuring soundscape quality of urban environments using physiological indicators: Construction of physiological assessment dimensions and comparison with subjective dimensions

Residents and designers typically pursue healthy urban acoustic environments. The soundscape community has established internationally recommended standards for subjective questionnaires to evaluate soundscape quality; however, much work is still needed to standardise physiological assessments. This study measures the physiological responses and subjective evaluations of participants in 20 common sound sources (scenarios) in urban public open spaces, and discusses the potential dimensions when assessing the quality of urban soundscapes through physiological indicators. Categorical principal components analysis are employed to reduce the dimensionality of the physiological data. The results indicate that physiological Dimension 1 represents the Natural–Artificial dimension, and physiological Dimension 2 potentially represents the Regular–Irregular dimension, providing a rough estimate of regularity or predictability in soundscapes. Subjective data are used as a control for physiological data, subjective Dimension 1 can be summarised as the Calm (Pleasant)–Chaotic (Annoying) dimension, whereas subjective Dimension 2 can be summarised as Familiar (Congruent)–Unfamiliar (Incongruent). Through comparing and discussing the distributions in physiological and subjective models under different sound sources, it is found that both physiologically and subjectively, the foremost dimension (Dimension 1) represents comfort and pleasure, and natural sounds can be distinctly differentiated from noise through this dimension. Although its physiological significance is not clear, the two-dimensional physiological indicator model reveals distinctions between sound sources of various categories. These findings have implications for the planning and design of liveable and comfortable urban environments from the perspective of physiological indicators.

Exploring the Motivations and Personality Traits of Pro-Russian Collaborationists in Ukraine.

This study investigates the personality traits and motivations of collaborationists with Russian invaders in Ukraine. A focus group of individuals who knew collaborationists personally identified 14 collaborationism motives (CMs), which were used to interview 104 probation clients convicted of collaborationism. The study utilized the five-factor model developed by Costa and McCrae to evaluate individual characteristics. Categorical principal component analysis (CATPCA) reduced the 14 CMs to three dimensions: pragmatism, adaptation, and fear. The study found that individual factors promoting collaborationism included pragmatism, ideological adaptation to changing realities, and fear stemming from threats to the life and health of an individual and/or their relatives. The study provides insights into the psychology of collaborationism, which can be useful in designing resocialization programs during the probation period of collaborationists.

IDENTIFICATION OF MOBILITY STRATEGIES FOR SMALL AND URBANIZED BRAZILIAN MUNICIPALITIES

Due to intense urbanization and inadequate planning, various mobility problems have emerged in small Brazilian municipalities. To reverse this scenario, it is necessary to adopt sustainable mobility policies. Thus, this article aims to identify mobility strategies for small and urbanized Brazilian municipalities. Firstly, based on the literature, twenty strategies were identified. Then, experts and managers evaluated these strategies to identify the most suitable ones for small municipalities. Next, the data was analyzed to measure the reliability of the responses, the correlation, and the ranking of the strategies. Finally, the most appropriate strategies were identified using Categorical Principal Component Analysis. The results indicated that the most relevant strategies to be implemented in small Brazilian municipalities are related to policy issues, promoting active transportation, and encouraging public transportation. The findings can improve public policies and transportation planning, with implications for quality of life and sustainable development in small Brazilian municipalities.

From provider to partner? Main elements of the relationship between schools and small‐ and medium‐sized firms in vocational education work placements in the Basque and Navarre regions (Spain)

AbstractThis paper seeks to contribute to the knowledge of the relationship established between firms and vocational education and training (VET) schools around work placements in companies (both standard and extended, so‐called ‘dual’, models) in Spain. An ad hoc survey of 332 small and medium enterprises (SMEs) from two industrial regions was conducted and a categorical principal components analysis was applied to identify the main dimensions or components of those relationships. The main elements found are related to (i) staff selection and social responsibility; (ii) trust, reciprocity and knowledge sharing with schools; and (iii) the organizational culture of the companies. The results obtained illustrate an evolution from a ‘provider‐client’ scheme towards a more strategic partnership between schools and firms, thus contributing new aspects to the literature available on the Spanish case.

Exploring user adoption of online hotel booking platforms: A text mining approach

The growing importance of online hotel booking platforms has revolutionized the hospitality industry, generating new business opportunities and intense competition among service providers This study analyzes user-generated content from online reviews to understand what drives user adoption of online hotel booking platforms. Using topic modeling, categorical principal component analysis, and relative importance ranking, the study identifies key factors like comparison of hotel options and online booking features as crucial for user adoption. Conversely, negative hotel experiences contribute to dissatisfaction. The results offer valuable insights for the hospitality industry, emphasizing the dominance of online booking and price comparison in customer adoption.

Knowledge and Attitudes of Parents and Caregivers in New Delhi to Childhood Hearing Loss and Hearing Services.

Parental and caregiver support is crucial for addressing childhood hearing loss in low and middle-income countries, where a significant burden of disabling hearing loss exists among children. This study aimed to assess the knowledge and attitudes of parents and caregivers regarding childhood hearing loss and available hearing services in both urban and rural settings in Delhi, India. A total of 314 participants were recruited and interviewed using a culturally adapted questionnaire consisting of 26 items, covering topics like biomedical and non-biomedical beliefs, knowledge of otitis media-related hearing loss, identification, and intervention. Statistical analysis of data was performed using Mann Whitney U tests and categorical principal component analysis (PCA). The study found that parents generally had a higher percentage of correct responses compared to caregivers in both urban and rural areas. Categorical PCA revealed variations in responses between fathers, mothers, and caregivers. In the urban group, mothers exhibited higher knowledge and more positive attitudes, while fathers showed lower involvement. In the rural group, while mothers demonstrated relatively higher awareness on all categories compared to fathers, these differences were highly relevant on questions concerning noise exposure, maternal medicine during pregnancy, and communication milestones. The findings emphasize the importance of considering regional and cultural factors when designing programs. Additionally, involving fathers in awareness campaigns and support networks is crucial to achieve comprehensive coverage in addressing childhood hearing loss. In conclusion, this study contributes valuable insights into the preparedness of parents and caregivers for audiology services and the need for contextually appropriate strategies to enhance childhood hearing health services in India.

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet problem through a strategic amalgamation of Hybrid Feature Selection methods—Categorical Analysis, Mutual Information, and Principal Component Analysis—and a robust ensemble of machine learning techniques. We uniquely combine these feature selection tools to refine the input space, enhancing the detection capabilities of the ensemble learners. Extra Trees, as the ensemble technique of choice, exhibits exemplary performance, culminating in a near-perfect 99.99% accuracy rate in botnet classification across varied datasets. Our model not only surpasses previous benchmarks but also demonstrates exceptional adaptability to new botnet phenomena, ensuring persistent accuracy in a landscape of evolving threats. Detailed comparative analyses manifest our model's superiority, consistently achieving over 99% True Positive Rates and an unprecedented False Positive Rate close to 0.00%, thereby setting a new precedent for reliability in botnet detection. This research signifies a transformative step in cybersecurity, offering unprecedented precision and resilience against botnet infiltrations, and providing an indispensable blueprint for the development of next-generation security frameworks.

Visitation patterns in a peri-urban natural park: Comparing mountain bikers, runners, walkers and hikers

Urban natural parks are popular destinations for activities such as short walks, longer hikes, mountain biking and running. But how does visitation for these activities vary, and how can information about visitation patterns facilitate management by enhancing social and environmental benefits? Employing a case study approach, we compared the four activities in Serra de Collserola Natural Park, a large popular park close to Barcelona in Spain. When 1788 visitors were surveyed and data analysed using Categorical Principal Components Analysis and Chi-squared tests, clear differences were found in sociodemographics (age, gender, occupation, educational level and where people lived), visitation patterns (frequency, means of transport, group size and composition, when visited, length of visit, and trail use), motivations (active, relaxation, nature, landscapes, socializing, time with family, etc.), and environmental and social impacts, including conflicts among user groups and crowding. This diversity in visitation highlights ways in which management could be better tailored to specific cohorts of visitors. For this specific park it highlighted issues with concentrated use (more resources needed on weekends), how equity of use could be enhanced (promoting running and biking for women), and how conflict could be reduced (by spatial segregation for faster and slower activities). Future surveys could examine emerging issues associated with changes in use since COVID-19, facilities for aging populations, and the spread of e-bikes among others.

Unpacking the invisible complex realities: intersections of gender and marital status in determining the intrinsic vulnerability of smallholder farmers to climate change in Northwestern Ethiopia

ABSTRACT Studies in Ethiopia have shown how climate-induced impacts are experienced differently by male and female farmers, with women being generally more vulnerable. Yet, less is known about how vulnerability to climate-induced risks differs between men and women of different marital statuses Therefore, we applied an intersectionality framework to examine how the level of intrinsic vulnerability is mediated through gender and marital status. Categorical Principal Component Analysis (CAPCA) was used to construct a household heads’ intrinsic vulnerability index. Drawing on a systematic random sample of 1602 respondents and based on logistic regression analysis, the study shows that, while a man’s marital status is a less important factor in determining his level of vulnerability to climate change, it is a key factor in the case of women. Compared to other women, female divorcees and widows are highly likely to be vulnerable to climate change in northwestern parts of Ethiopia. Integrating information about marital relationships, especially the neglected widows and divorcees into the design of interventions may help to better target potential beneficiaries among smallholder farming communities. Our study makes a unique contribution to the intersectionality literature in understanding the diversified experiences of vulnerable rural communities which are susceptible to climate-induced shocks.

Can CATPCA be utilized for spatial modeling? a case of the generation susceptibility of gully head in a watershed

Many spatial modeling methods have emerged; however, they require dependent variables, cannot reflect the relationship between categorical variables and numerical variables, and are limited by the interference of data collinearity. Categorical principal components analysis (CATPCA) has the potential to overcome these issues. Therefore, in order to investigate the suitability of CATPCA for spatial modeling, we conducted a case study based on the generation susceptibility of gully heads was determined, including 2310 gully head and 23 variables. CATPCA was first used in the spatial modeling of gully head generation. The first six principal components retained 76.4% of data trends. The area of the training and validation sensitivity curves were 75.4% and 75.7%, respectively, which reflected good levels. CATPCA can simulate gully head spatial differences, and thus, has great potential for spatial modeling applications. Among the 23 factors, elevation, distance to residential areas, human footprint, lithology, and soil type were identified as the main controlling factors affecting the generation susceptibility of gully heads, with high correlations between them. Low altitudes, close proximity to residential areas, high human footprint, and poor vegetation were associated with high susceptibility. The findings of this study provide a better understanding of the applicability of CATPCA for spatial modeling. CATPCA is a novel solution for spatial modeling strategies that can improve understanding and has great potential for various spatial modeling applications in the future.

Community pharmacists’ readiness to take action to improve working conditions

ObjectiveThe objective of this study was to assess pharmacists’ readiness to take actions resulting in change at the individual level or systemic level. Design/Setting and Participants/Outcome measuresAn online survey was distributed via pharmacy-related social media pages and several state pharmacy association newsletters and e-mail listservs. Fifteen items assessed participants’ readiness to pursue change using a 5-point scale based on the transtheoretical model of behavior change (1 = Do not plan on doing, 2 = Plan to take in the next 6 months, 3 = Plan to take in the next month, 4 = Took in the past 6 months, 5 = Took more than 6 months ago). Categorical principal components analysis (CATPCA) was used as a data reduction technique to identify underlying components of the 15 items assessing readiness to improve working conditions. Component scores for each component were calculated and used as dependent variables in multivariable linear regression models to test associations between the components and sociodemographic and work-related predictors. ResultsCATPCA revealed a 12-item scale with 2 components accounting for 39.97% of the total variance. Pharmacists working in independent pharmacies had a significantly higher willingness than chain pharmacists to pursue actions with potential to result in systemic-level changes, such as contacting or joining an organization to attempt to improve their current working conditions (regression coefficient = 0.623, P < 0.001). Pharmacists working in independent pharmacies had a significantly lower willingness than chain pharmacists to pursue actions resulting in individual-level changes, such as changing their current job or quitting (regression coefficient = −0.485, P < 0.001). ConclusionPharmacists working in national chains may be less willing than independent pharmacists to pursue systemic-level changes owing to fear of discipline from employers. Future research should examine the effect of interventions intended to increase pharmacists’ motivation and readiness to pursue changes to improve working conditions.

Validation of the test for finding word retrieval deficits (WoFi) in detecting Alzheimer's disease in a naturalistic clinical setting

BackgroundDetecting impaired naming capacity contributes to the detection of mild (MildND) and major (MajorND) neurocognitive disorder due to Alzheimer’s disease (AD). The Test for Finding Word retrieval deficits (WoFi) is a new, 50-item, auditory stimuli-based instrument.ObjectiveThe study aimed to adapt WoFi to the Greek language, to develop a short version of WoFi (WoFi-brief), to compare the item frequency and the utility of both instruments with the naming subtest of the widely used Addenbrooke’s cognitive examination III (ACEIIINaming) in detecting MildND and MajorND due to AD.MethodsThis cross-sectional, validation study included 99 individuals without neurocognitive disorder, as well as 114 and 49 patients with MildND and MajorND due to AD, respectively. The analyses included categorical principal components analysis using Cramer’s V, assessment of the frequency of test items based on corpora of television subtitles, comparison analyses, Kernel Fisher discriminant analysis models, proportional odds logistic regression (POLR) models and stratified repeated random subsampling used to recursive partitioning to training and validation set (70/30 ratio).ResultsWoFi and WoFi-brief, which consists of 16 items, have comparable item frequency and utility and outperform ACEIIINaming. According to the results of the discriminant analysis, the misclassification error was 30.9%, 33.6% and 42.4% for WoFi, WoFi-brief and ACEIIINaming, respectively. In the validation regression model including WoFi the mean misclassification error was 33%, while in those including WoFi-brief and ACEIIINaming it was 31% and 34%, respectively.ConclusionsWoFi and WoFi-brief are more effective in detecting MildND and MajorND due to AD than ACEIIINaming.

Typology of Irrigation Technology Adopters in Oil Palm Production: A Categorical Principal Components and Fuzzy Logic Approach

Oil palm is the second most cultivated oilseed crop in the world after soybeans, with more than 23 million hectares cultivated worldwide; it has become crucial for the economy of many countries. In Colombia, it is one of the most developed agricultural sectors, and every year the sector promotes the development of technologies that lead to greater sustainability of agricultural and food systems and address the challenges and opportunities of agribusiness. In this research, the central focus was the adoption of irrigation technologies, which is limited despite significant efforts and investments in physical and human capital. On many occasions, the typology of farmers has been associated with low technology implementation. Thus, linking the typology of farmers according to certain commonalities or differences is an essential step in exploring the factors that explain the adoption. In addition, the ranking also helps in the understanding of existing adoption constraints, as well as finding opportunities for change. This study aimed to determine the socioeconomic and demographic typology of those who adopt irrigation technologies. The analysis was performed using categorical principal component analysis to reduce dimensionality and fuzzy cluster analysis to classify the groups. As a result, four groups of producers that differ in terms of their demographic and socioeconomic characteristics were obtained, where the groups “population with female leadership” and “diversified population” were the adopters of irrigation technologies. The most outstanding characteristics of these two groups were the profitability of the harvest and the age of the producers. Determining the typology of farmers is a fundamental step in expanding the technology adoption process through agricultural extension services, which represent a way of reaching producers directly. In addition, these results allow decision makers to participate in this dynamic reflectively and intentionally (such as governments, researchers, and technology transferors).

Assessing Rural Tourism Experiences: What Can We Learn from the Azores Region?

This research paper focused on tourists’ experiences after engaging in a rural tourism experience, the activities developed during that period, and the relevant factors they considered when planning the holidays. We collected data in the Azores, a region known to implement rural tourism. Therefore, the collected data were analyzed using mainly summary statistics, graphical representations, and exploratory data analysis methods, including the nonlinear (categorical) principal component analysis (CatPCA). The CatPCA’s objective is the same as that of the principal component analysis (PCA). We found that the landscape, adventure-related activities, and local gastronomy were some of the reasons tourists choose the Azores as a destination. The rural living experience and the local population’s welcoming traits were mentioned as factors that make tourists want to return to the Azores on holidays. Moreover, rural tourism in the Azores offers a unique opportunity to connect with the region’s rich cultural heritage. The islands have a long history shaped by centuries of settlement and the blending of various cultures, resulting in a distinct Azorean identity.

Temporal instability assessment of motorcyclist-injury severities using categorical principal component analysis and random parameter approach with heterogeneity in means

Motorcyclists are considered as one of the most vulnerable road participants that often suffer higher injury severities. Furthermore, contributing factors of motorcyclist-injury severities may vary over time, which requires further investigation. In this study, with Michigan crash data from 2015 to 2018, categorical principal component analysis (CatPCA) is firstly conducted to assess the similarities/differences among yearly samples. Then, a random parameter logit model with heterogeneity in means is employed for each analysis year. Marginal effects are also estimated to quantify the temporal instability of the influencing factors. The results reveal that some determinants of motorcyclist-injury severities are temporally unstable across the studied years, such as middle-aged motorcyclist, helmet worn, signal control, clear weather, two-vehicle crashes, and disabling damage. However, some factors have relatively stable effects on motorcyclist-injury severities in most of the year periods, such as alcohol impaired, totally or partially ejected from the motorcycle, stopped on the roadway, and posted speed limits higher of 50 mph. The findings can help decision makers to propose cost-effective motorcycle safety improvements and policies.

How Digitalization, Work-Family Balance, and Work Efficiency Can Influence Employees' Preferences for Teleworking in the Future

During Covid-19 pandemic many employees found themselves in the new position of teleworkers. Proof of the last years, teleworking remains an alternative to the classic system, reason why in this paper we aim to examine, based on an online questionnaire, the perceptions of Romanian employees about the very complex aspects that teleworking involves: the new ICT tools and technologies adoption, impact on work efficiency, work-family balance, and employees physical and mental health. The paper is based on an empirical analysis of data, and the authors have used as research methods: testing the independence of groups (Mann-Whitney U and Kruskal Wallis) and the correlation between variables, categorical principal components analysis (CATPCA) and logistic regression model. Our findings show that several factors, such as flexible working hours, family time, and autonomy have a positive influence on the decision to telework, while IT security risks, interruptions and virtual meetings were not perceived by respondents as negatively influencing their decision to telework. However, the consequences for mental health, such as mental stress, lack of socialisation and the difficulty of separating working time from family time, lead respondents to prefer another working system. The implications of the research are relevant for policy makers, employers, and employees as they reveal which categories of employees are more likely to telework and why.

Charting fields and spaces quantitatively: from multiple correspondence analysis to categorical principal components analysis

Multiple correspondence analysis (MCA) has started to gain popularity within sociology as a method of mapping ‘fields’ and ‘social spaces’ in the style of Pierre Bourdieu, its capacity to document multidimensional geometric relationships within data being a snug fit for the relational mode of thought he championed. There is a risk, however, of over-relying on MCA when the data suggest alternative methods and, as a result, drawing unsound conclusions. As a case in point, I take a recent analysis of political attitudes in the UK using MCA that drew bold inferences about the relationship with social class and reanalyse the same data with categorical principal components analysis (CatPCA). The results suggest the opposite conclusion to what was originally argued. I thus urge greater methodological flexibility and openness among those wishing to chart fields and social spaces and, more specifically, I make a case for CatPCA as a tool of geometric data analysis.

Farm Household Typology Based on Soil Quality and Influenced by Socio-Economic Characteristics and Fertility Management Practices in Eastern Kenya

The smallholder farming systems in Sub-Saharan Africa (SSA) are highly diverse and heterogeneous in terms of biophysical and socio-economic characteristics. This study was conducted in upper Eastern Kenya (UEK) to categorize farm households and determine the influence of socio-economic characteristics (SeC) and soil fertility management practices (SFMP) on soil fertility across farms. Conditioned Latin hypercube sampling (cLHS) was performed to determine 69 soil sampling sites within Meru and Tharaka Nithi counties. From each household (whose field soil sample was obtained), data relating to resource endowment and soil fertility management were collected through a household questionnaire survey. Standard laboratory procedures were used to analyse soil samples. Data reduction was performed using categorical principal component analysis (CATPCA) (for SeC and SFMP) and standard principal component analysis (PCA) (for soil properties). Two-step cluster analysis identified three distinct farm categories or farm types (FT), namely, low fertility farms (FT1), moderately fertile farms (FT2), and fertile farms (FT3). The correlation of clusters against soil properties was significant across pH, soil organic carbon (SOC), cation exchange capacity (CEC), available P, plant available K, and exchangeable bases. FT1 had low SOC, pH, CEC and available P (soil characteristics), low usage of fertilizer and manure (soil fertility management), and smaller household size, lower income, and smaller farm size (socio-economic). FT2 had lower SOC (compared to FT3) and available P. In terms of soil fertility management, FT2 had higher cases of fallowing and composting with moderate fertilizer usage. Households in this category had moderate income, family size, and land size (socio-economic). FT3 had relatively high SOC, pH, CEC, and mineral nutrients. This farm type was characterized by high fertilizer use (soil fertility management) as well as larger household size, higher income, and larger farm size (socio-economic). The results indicate the importance of nutrient management in enhancing soil quality. Delineation and characterization of farms based on the various parameters including resource endowment reveal imbalanced farm resource flows, suggesting a need for locally tailored interventions suited for location-specific conditions to facilitate improved targeting of soil fertility-enhancing technologies and sustainable crop production regimes. While fertilizer is one of the most critical inputs for enhancing agricultural production, it is a major contributor to nitrous oxide emissions from agriculture and can have negative environmental effects on soil biota and water sources. Farmers’ knowledge on the use of fertilizer is thus necessary in developing strategies (such as integrated approach) to promote its efficient use and minimize its detrimental impacts.

Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction

Accurate supply temperature prediction plays a vital role in achieving meticulous management of heating station. However, there are relatively few studies on ultra-short-term supply temperature prediction at present. This paper comprehensively applied 4 feature selection methods and 3 prediction algorithms to estimate hourly secondary supply temperature. Taking a floor radiant heating system as the case, the correlation analysis (CA) based on the back propagation neural network (BPNN) model and the support vector regression (SVR) model shows that outdoor temperature, supply and return temperatures are the main input feature categories. This paper novelty proposed the categorical principal component analysis (CPCA) method, compared with the traditional principal component analysis (PCA), this method can reduce the root mean square error (RMSE) of BPNN model and SVR model by an average of 18.6% and 19.7%, respectively. The comparison of 4 historical input lengths for the long and short-term memory (LSTM) model shows that historical 12 h can fully consider the influence of building thermal inertia and heating system thermal delay for floor radiant. Further comprehensive comparison shows that the BPNN model based on correlation analysis has the best performance.