Distinct Segments Research Articles

Solar-Active Carbon Nitride Film Integrated by Free Radical Copolymerization for Photocatalytic Indoor Air Purification.

The current lack of stable, scalable, and efficient coating technology dramatically limits the exploitation of solar-driven graphitic carbon nitride (CN) photocatalysts. Herein, a unique, efficient, and scalable method is reported to immobilize CN powder on various substrates ranging from Fluorine tin oxide (FTO), glass, Plexiglas, Al foil, Ti foil, and Granite stone, to even wood. The film shows an outstanding thickness of 212µm, which is the highest value ever reported. The formation process is ascribed to free radical copolymerization between the tri-s-triazine backbone and polyacrylamide, followed by cross-linking. The smooth, non-oxidizable, and well-defined continuous coating exhibits excellent adherence and durability. The distinctive sequence segments preserve the light transition within the film while enhancing the optical absorption in the solar spectrum. Under visible light illumination, the film shows outstanding photodegradation performance toward air pollutants, whether for gaseous acetaldehyde (Act) or toluene (Tol). This method is a great step forward that can open new opportunities for the commercial applications of CN powder.

Customer Electronic Word of Mouth Management Strategies Based on Computing with Words: The Case of Spanish Luxury Hotel Reviews on TripAdvisor

The rapid growth of the internet and social media has made electronic word of mouth (eWOM) a key element of modern marketing. In the hospitality industry, nowadays, effective eWOM management is essential for developing impactful strategies and fostering customer satisfaction. This paper introduces an enhanced approach to strategic customer base management based on online reviews by extending the Recency, Frequency, and Monetary (RFM) model with three novel dimensions, the Helpfulness, Promoter Score, and Stability of the customer, thereby forming the RFHPS model. It also includes the 2-tuple linguistic model, one of the most popular computing with words models, to improve precision in the RFHPS score’s computation and the findings’ interpretability. Using K-means clustering, customers are segmented across these five dimensions. The data on luxury hotels in Spain gathered from TripAdvisor demonstrate the model’s applicability. By integrating this framework into customer relationship management systems, managers can tailor marketing strategies for distinct segments, facilitating deeper customer understanding and bolstering eWOM generation.

AI-Driven Customers Segmentation Using K-Means Clustering

Effective customer segmentation is essential for businesses to enhance marketing strategies and improve customer engagement. This study applies K-Means clustering to segment customers based on demographic and behavioral data collected through structured surveys and questionnaires. The dataset includes attributes such as age, income, purchase frequency, product preferences, and brand loyalty. Data preprocessing involved normalization, imputation, and encoding to ensure quality and suitability for clustering. The optimal number of clusters was determined using the Elbow Method and Davies-Bouldin Index, resulting in three distinct segments: High-Spending, Frequent Shoppers, Price-Sensitive, Infrequent Shoppers, and Brand-Loyal, Moderate Shoppers. These clusters provide actionable insights for businesses, enabling tailored strategies such as loyalty programs, discounts, and targeted promotions. The Davies-Bouldin Index score of 1.27 and visualization using Principal Component Analysis (PCA) validated the effectiveness and distinctiveness of the segmentation. This research highlights the potential of AI-driven methods like K-Means clustering in uncovering hidden patterns in customer behavior, offering a robust alternative to traditional segmentation approaches. Future studies could explore larger datasets, integrate additional behavioral attributes, and compare the performance of other clustering algorithms to further enhance segmentation outcomes.

Distinct morphological drivers of jumping and maneuvering performance in gerbils.

Theoretically, animals with longer hindlimbs are better jumpers, while those with shorter hindlimbs are better maneuverers. Yet experimental evidence of this relationship in mammals is lacking. We compared jump force and maneuverability in a lab population of Mongolian gerbils (Meriones unguiculatus). We hypothesized that gerbils with long legs (ankle to knee) and thighs (knee to hip) would produce the greatest jump forces, while gerbils with short legs and thighs would be able to run most rapidly around turns. Consistent with these hypotheses, gerbils with longer legs produced greater jump forces after accounting for sex and body mass: a 1-mm greater leg length provided 1 body-weight-unit greater jump force on average. Furthermore, gerbils with shorter thighs were more maneuverable: a 1-mm greater thigh length reduced turn speed by 5%. Rather than a trade-off, however, there was no significant correlation between jump force and turn speed. There was also no correlation between jump force and total hindlimb length, and a weak positive correlation between corner-turning speed and total hindlimb length. These experiments revealed how distinct hindlimb segments contributed in different ways to each performance measure: legs to jumping and thighs to maneuvering. Understanding how variations in limb morphology contribute to overall gerbil locomotor performance may have important impacts on fitness in natural habitats.

Perceived stress across population segments characterized by differing stressor profiles-A latent class analysis.

We aimed to 1) identify distinct segments within the general population characterized by various combinations of stressors (stressor profiles) and to 2) examine the socio-demographic composition of these segments and their associations with perceived stress levels. Segmentation was carried out by latent class analysis of nine self-reported stressors in a representative sample of Danish adults (N = 32,417) aged 16+ years. Perceived stress level was measured by the Perceived Stress Scale (PSS). Seven classes were identified: Class 1 was labeled Low Stressor Burden (64% of the population) and the remaining six classes, which had different stressor combinations, were labeled: 2) Burdened by Financial, Work, and Housing Stressors (10%); 3) Burdened by Disease and Death among Close Relatives (9%); 4) Burdened by Poor Social Support and Strained Relationships (8%); 5) Burdened by Own Disease (6%); 6) Complex Stressor Burden Involving Financial, Work, and Housing Stressors (2%); and 7) Complex Stressor Burden Involving Own Disease and Disease and Death among Close Relatives (2%). Being female notably increased the likelihood of belonging to Classes 2, 3, 5, and 7. Higher age increased the likelihood of belonging to Class 3. Low educational attainment increased the likelihood of belonging to Classes 5 and 6. A significant difference was observed in perceived stress levels between the seven latent classes. Average PSS varied from 9.0 in Class 1 to 24.2 in Class 7 and 25.0 in Class 6. Latent class analysis allowed us to identify seven population segments with various stressor combinations. Six of the segments had elevated perceived stress levels but differed in terms of socioeconomic composition and stressor combinations. These insights may inform a strategy aimed at improving mental health in the general population by targeting efforts to particular population segments, notably segments experiencing challenging life situations.

CLUSTER ANALYSIS OF RISK PERCEPTION TO IMPLEMENT TOURIST HEALTH SAFETY IN VIETNAM

Perceived risk refers to tourists' objective assessment of the negative consequences associated with travel. The COVID-19 pandemic has significantly impacted all areas of human activity, influencing personal behavior and organizational practices. In post-COVID-19, travellers' perceptions of disease risk and behaviour have evolved. This study aimed to evaluate tourists' risk perceptions while travelling, categorizing them into distinct groups to identify differences among them. The research uses a mixed-method approach with validated surveys to measure travelers' risk perceptions via Likert scales. Data was collected from representative samples at destinations. K-means clustering identifies distinct segments, with the optimal number of clusters determined using the Elbow Method. Using cluster analysis, tourists were divided into three groups: Cluster 1 (32.18%), Cluster 2 (16.56%), and Cluster 3 (51.26%). The perceived risks were ranked in descending order as safety and hygiene risks, health risks, time risks, and emergency response support concerns. The analysis highlights that tourists prioritize safety, hygiene, health risks, and time risks when considering future travel, emphasizing the need for improved safety measures and effective tourism marketing strategies to restore traveler confidence. This study offers original insights through cluster analysis, showcasing the diverse risk perceptions among different social clusters and customer segmentation.

Evaluation of CBCT reconstructed tooth models at different thresholds and voxels and their accuracy in fusion with IOS data: an in vitro validation study

BackgroundThis study aims to evaluate the impact of different thresholds and voxel sizes on the accuracy of Cone-beam computed tomography (CBCT) tooth reconstruction and to assess the accuracy of fused CBCT and intraoral scanning (IOS) tooth models using curvature continuity algorithms under varying thresholds and voxel conditions.MethodsThirty-two isolated teeth were digitized using IOS and CBCT at two voxel sizes and five threshold settings. Crown-root fusion was performed using a curvature continuity algorithm. Volume, surface area, and crown width of tooth models were compared to laser scanning models, and RMS error was measured. Data were analyzed using Wilcoxon signed-rank test, paired t-test, and one-way ANOVA.ResultsVolume amplification errors of CBCT with 0.15 mm and 0.3 mm voxels ranged from 1.22 to 19.07%, surface area errors from 0.18 to 7.78%, crown linearity errors ranged from 2.47 to 7.69%, root linearity errors ranged from − 1.02 to 2.26% and RMS from 0.0691 mm to 0.2408 mm. Crown-root fusion of IOS and CBCT data reduced volume error to -0.90–5.10%, surface area error to -0.66–4.15%, and RMS to 0.0359 mm to 0.0945 mm.ConclusionsVoxel size and threshold settings significantly affect the accuracy of CBCT reconstruction and crown-root fusion. Smaller voxel sizes yield higher reconstruction precision, and different voxel sizes and tooth regions correspond to distinct optimal segmentation thresholds. The validated semi-automated crown-root fusion algorithm significantly enhances overall model accuracy, offering new possibilities for clinical applications.

A new species of Arctodiaptomus Kiefer, 1932 (Copepoda: Diaptomidae) from the Kumaun Himalaya of India

A new species of the freshwater copepod genus Arctodiaptomus is described from a high-altitude lake of Kumaun Himalaya. The undescribed species differs from their congeners by the presence of a strong comb-shape denticulated spine at the antepenultimate segment of male right antennule. Basis of the male fifth right leg possess a butterfly shape hyaline membrane on the inner lateral side; 2-segmented endopod with board distinct proximal segment. Endopodite of the female fifth leg is elongated round apex without any septum. The identified diaptomid tends to be restricted in the Himalayan region; it seems apparently isolated from the purported main area of the origin.

The Effect of Earnings Volatility on Capital Structure: A Case of Oligopolistic Banking Sector

The article investigates the impact of earnings volatility on the South African bank’s capital structure using a sample of registered banks from 2012 to 2021. None of the research on this relationship has examined the oligopolistic market of the banking sector, which stands as a structurally distinctive economic segment. Using the generalized method of moments (GMM) model, the results show a negative and significant effect of earnings volatility on leverage. These findings help finance professionals understand the oligopolistic banking sector’s leverage dynamics and earnings volatility within the context of unexpected economic shocks. Thus, the central banks should require banks to diversify in a bid to minimize earnings variance, thereby providing them with financing flexibility. Bank executives and regulators will find the findings pertinent, providing them with perspectives for making well-informed decisions regarding the capital structure based on the degree of earnings variability.

Perceived value, service quality and service convenience on behavioral intentions in fitness centers: a comparison between customers with and without personal training

PurposeThis study aimed to propose and test a model that describes the different aspects and mechanisms that influence the behavioral intentions of fitness center customers by examining the relationship between service convenience, service quality and perceived value, comparing customers with and without PT service. This study is highlighted by the need for fitness centers to remain competitive and profitable and the fact that PT services are one of the top ten global fitness trends.Design/methodology/approachData were collected from 412 fitness center customers. Partial least square structural equation modeling (PLS-SEM) was used to test each proposed hypothesis.FindingsNine of the ten hypotheses tested, between service convenience, service quality, perceived value and behavioral intentions, were supported. The results from bootstrapping supported the indirect effects of service quality and perceived value in the relationship between service convenience, service quality and behavioral intentions. The presence of the PT service affects the majority of the relationships in the model.Research limitations/implicationsCustomers with PT services may have different expectations and perceptions based on the cost associated with the service. Managers may consider adapting their service strategies to these distinct customer segments, offering differentiated service levels or targeted convenience improvements.Originality/valueThe variables under study integrate a comprehensive model for fitness centers while innovating by incorporating PT services as a moderating variable, providing a differentiated view of how customer segments perceive and respond to fitness center offerings.

Application of the Time-Domain Multichromophoric Fluorescence Resonant Energy Transfer Method in the NISE Programme.

We present the implementation of the time-domain multichromophoric fluorescence resonant energy transfer (TC-MCFRET) approach in the numerical integration of the Schrödinger equation (NISE) program. This method enables the efficient simulation of incoherent energy transfer between distinct segments within large and complex molecular systems, such as photosynthetic complexes. Our approach incorporates a segmentation protocol to divide these systems into manageable components and a modified thermal correction to ensure detailed balance. The implementation allows us to calculate the energy transfer rate in the NISE program systematically and easily. To validate our method, we applied it to a range of test cases, including parallel linear aggregates and biologically relevant systems like the B850 rings from LH2 and the Fenna-Matthews-Olson complex. Our results show excellent agreement with previous studies, demonstrating the accuracy and efficiency of our TD-MCFRET method. We anticipate that this approach will be widely applicable to the calculation of energy transfer rates in other large molecular systems and will pave the way for future simulations of multidimensional electronic spectra.

Decoding Consumer Minds in the Age of Online Accommodation Reviews: A Client Profiling Approach

In the era of online accommodation reviews, understanding the consumer mind is essential for the hospitality industry. This study seeks to profile consumers based on their reservation decisions made after reviewing online feedback and to explore the complex relationship between consumer perceptions and their decision-making processes. To lay a solid foundation for this research, a thorough bibliometric analysis was conducted to map the existing literature and identify key trends in the field. Data were collected using a non-probability convenience sampling method through an online survey targeting Romanian residents. Performing a hierarchical cluster analysis, followed by a K-means cluster analysis, distinct consumer segments with varying levels of trust and responsiveness were identified. The four primary clusters are Young Risk-Averse Planners, Trust-Oriented Quality Seekers, Skeptical Detail Seekers and Independent Value Seekers. Each segment displayed unique preferences regarding the types of reviews they value and their influence on booking decisions. These findings highlight the need for hotel managers and marketers to develop tailored strategies that cater to the diverse needs of consumers, enhancing service delivery and promoting sustainable tourism practices. This research provides valuable insights into the dynamics of online reviews and stresses the importance of understanding consumer perceptions in navigating the complexities of today’s hospitality industry.

Novel Building Detection and Location Intelligence in Aerial Satellite Imagery

The accurate detection and extraction of building information from aerial imagery is of paramount importance in urban planning, land use analysis, and disaster management. This study presents a comprehensive investigation into the development of a robust and efficient methodology for building detection in satellite imagery utilizing state-of-the-art deep learning techniques. We conducted a comparative analysis of three distinct semantic segmentation models based on the U-Net architecture: a baseline U-Net trained from scratch, a U-Net incorporating a pre-trained ResNet34 encoder, and a U-Net with custom architectural enhancements. Our methodological approach encompassed data augmentation strategies, transfer learning techniques, and ensemble methods to optimize model performance. The Inria Aerial Image Labelling Dataset served as the primary source for model training and validation. We explored the efficacy of various loss functions, including dice loss, focal loss, and weighted cross-entropy, to address class imbalance and enhance segmentation accuracy. Model performance was rigorously evaluated using a comprehensive set of metrics, including pixel-wise accuracy, Intersection over Union (IoU), and F1-score. Our highest-performing individual model achieved a dice score of 92 percent on the validation set, while the implementation of ensemble techniques further improved detection accuracy to 93 percent on the heldout test set. Post-processing algorithms, incorporating traditional computer vision methods, were applied to refine building polygon delineation. This research demonstrates the efficacy of deep learning-based segmentation approaches for building detection in aerial imagery and offers valuable insights into potential applications across various domains, including urban planning, construction monitoring, and disaster response. Future research directions may explore building classification, change detection analysis, and model optimization for real-time applications in dynamic urban environments.

Segmentation of the general public according to differences in knowledge and beliefs about radiation-cluster analysis by attitude, knowledge, belief and anxiety.

Following the 2011 Fukushima Daiichi Nuclear Power Plant accident, public behaviors have been marked by excessive avoidance and stigma, driven by fear and uncertainty regarding radiation exposure and its health implications. Despite extensive media dissemination of information on radiation, the precise nature of the public's knowledge, beliefs and the resultant behavioral responses remain unclear. This study aimed to segment the population based on their attitudes, knowledge, beliefs and anxiety levels about radiation, correlating these factors with their cognitive and behavioral responses to radiation exposure. Surveying 2400 individuals, we identified seven distinct segments that illustrated a spectrum of health concerns, even among those well-informed about radiation. Notably, individuals with higher health anxiety were found to reject discriminatory prejudices linked to radiation, yet they tended to distrust official information, potentially as a psychological mechanism to justify their avoidance behaviors. These findings underscore the need for tailored communication strategies that address the complex landscape of radiation-related perceptions and misinformation.

Effects of Movement Restrictions on Consumer Consumption

This study addresses the impact of movement restrictions, particularly during pandemics, on global consumer behavior, with a focus on grocery shopping. Despite the widespread effects, research on these changes remains limited, prompting the need to investigate hidden purchase patterns contributing to economic growth. Employing secondary data analysis, the study explores the effects of movement restrictions on customer consumption patterns, using K-means cluster analysis to identify distinct consumer segments. The findings highlight significant impacts on purchasing power, item prices, and consumption behavior, with a notable increase in item prices during the post-restriction period followed by a subsequent decline influenced by economic factors such as financial uncertainty and shifting priorities. This research contributes by shedding light on the dynamic nature of consumer behavior during and after movement restrictions, offering valuable insights for policymakers and businesses navigating the post-restriction economic landscape and informing strategies for economic recovery and growth.

An Interactive Virtual Reality Approach to Understanding Cultural Heritage Through Storyliving: A Case Study of Seoul City Wall (Hanyangdoseong) in South Korea

In this study, we developed and evaluated a nonlinear narrative virtual reality approach to enhancing the understanding of cultural heritage. The system was designed to convey the historical and cultural context of Seoul City Wall (Hanyangdoseong) while engaging users in a personalized experience. A novel “storyliving” structure was used, in which users choose the progress of the narrative. To achieve this, the methodology employed three key components: evaluating criteria to assess Hanyangdoseong’s sense-of-place, clustering to create distinct scenario segments, and sticker mapping to identify spatial preferences. Our approach maximizes user immersion through selective pathways and branching stories. Initial validation results suggest that this virtual reality system can greatly deepen users’ understanding of Hanyangdoseong and demonstrate the system’s potential as an innovative tool for cultural heritage education. This study contributes to the digital transformation of cultural heritage and provides important direction for the development of virtual reality content with richer and more personalized educational experiences.

Fault localization using sliding adaptive data segmentation and improved k-nearest neighbour with application to railway track circuits

This paper addresses the challenges of disorganized sampling data, high misjudgement rates in manual fault localization, lengthy analysis periods, and inefficiencies in jointless track circuits (JTCs), it introduces a novel fault localization method for JTC compensated capacitors, which is based on sliding adaptive data segmentation (SADS) and fast dynamic time warping (FDTW), to enhance the K-nearest neighbour (KNN) algorithm. The approach models the track circuit to understand voltage signal changes and establishes a universal fault discrimination model, this is followed by applying the SADS approach, which breaks down the measured induced voltage signal into distinct segments reflecting the characteristics of compensation capacitor faults. The method then employs FDTW to refine the traditional K-nearest neighbour algorithm for effective fault localization. Experimental results show that the segmented dataset achieves accuracy rates of 97.8% on simulated datasets and 97.1% on measurement datasets, offering a powerful tool for locating compensation capacitor faults in rail circuits.

Clinical characteristics and ultrasound biomicroscopic evaluation of anterior segment dysgenesis: a retrospective cross-sectional study.

Anterior segment dysgenesis (ASD) encompasses a range of congenital disorders affecting the development of the eye's anterior segment, often leading to significant visual impairment and glaucoma. Although numerous studies have focused on the genetic basis of ASD, few have comprehensively compared the clinical features across ASD subtypes. This study aims to address this gap by describing and comparing the clinical characteristics and ultrasound biomicroscopy (UBM) imaging features of various ASD subtypes in a Chinese tertiary medical center. A retrospective review of medical records was conducted for hospitalized patients diagnosed with ASD in the Eye & ENT Hospital, Fudan University between January 2013 and December 2022. Patients were enrolled consecutively. Demographic data, clinical manifestations, and UBM images of various ASD subtypes were analyzed. A total of 1,031 patients were included in the analysis. Primary congenital glaucoma (PCG) was the most prevalent, affecting 396 patients (625 eyes), whereas congenital ectropion uveae (CEU) was the rarest, affecting 8 patients (13 eyes). All subtypes exhibited a spectrum of characteristic clinical manifestations. UBM imaging revealed distinct anterior segment abnormalities characteristic of each subtype. Trabeculodysgenesis was implicated in glaucoma pathogenesis across multiple ASDs, including PCG, aniridia, Axenfeld-Rieger syndrome, Peters anomaly, CEU, iris coloboma, and microcornea, whereas persistent fetal vasculature and congenital fibrovascular pupillary membrane exhibited completely closed angles in glaucomatous eyes. Our study provides comprehensive insights into the clinical features and UBM characteristics of ASD subtypes, enhancing diagnostic accuracy and guiding appropriate interventions.

Five user types of autonomous driving in Hungary

Abstract One of the most socially impactful innovations of the near future will be the proliferation of self-driving vehicles, which will have a major impact not only on the passengers in the vehicle but on all road users and even on society as a whole, transforming cityscapes. This study aims to contribute to the social acceptance of self-driving vehicles. As society is not unified in its attitude towards self-driving vehicles, the authors believe that successful social acceptance requires different messages to be delivered to different types of consumers. This research segmented consumers based on their acceptance of self-driving technology, thereby providing a basis for targeted communication in the future. Cluster analyses were used on a sample of 517 Hungarian consumers to identify five segments based on attitudes towards self-driving vehicles. The analysis identified five distinct segments of consumers: (1) tradition-loving dismissers, (2) open-minded adventurers, (3) uncertain optimists, (4) distrustful sceptics, and (5) abstentious observers. These segments can be targeted with differentiated communication strategies. This paper contributes to the literature on self-driving technology acceptance by providing a detailed segmentation of the consumer market, highlighting the importance of targeted communication to enhance technology adoption. It offers a novel approach by focusing on specific consumer segments rather than society in general. By identifying the needs and characteristics of different consumer segments, marketers can develop more effective communication strategies to promote the acceptance of self-driving technology. Using a more targeted marketing approach instead of mass-marketing may result in a smoother spread of innovation and maximise social welfare benefits from technological advancements.

Customer Segmentation using Machine Learning

In the competitive landscape of financial services, understanding customer behavior is paramount for effective marketing strategies and personalized services. This study explores customer segmentation techniques applied to credit card holders utilizing a rich dataset comprising demographic, transactional, and behavioral information. Through advanced data analytics, including clustering algorithms and machine learning models, we unveil distinct customer segments based on spending habits, payment behavior, credit utilization, and other relevant features. Our findings reveal several distinct segments within the credit card holder population, each exhibiting unique characteristics and preferences. By delineating these segments, financial institutions can tailor their marketing campaigns, product offerings, and customer service initiatives to better meet the diverse needs of their clientele. Moreover, the segmentation analysis provides insights into risk assessment, fraud detection, and customer retention strategies, thus enhancing overall business performance and customer satisfaction.