LIFE: International Journal of Health and Life-Sciences

HOSPITAL IMAGE DYNAMICS IN SERVICE RECOVERY: MODERATION OF EMOTIONAL INTELLIGENCE

Thanuset Chokpiriyawat, Kampanat Siriyota — Tue, 18 Jun 2024 00:00:00 +0000

This conceptual paper contributes to the academic literature by elucidating the interplay between brand image dimensions, service recovery strategies, customer satisfaction, and word-of-mouth communication and revisiting intentions in the context of private hospitals in Thailand. The findings of this study provide a foundation for future research endeavors by offering insights into the potential moderating role of emotional intelligence in shaping service recovery outcomes. Moreover, the alignment of our results with theoretical frameworks such as Expectancy-Confirmation Theory (ECT) and Social Exchange Theory (SET) suggests avenues for exploring similar phenomena in different industries. Practically, our findings offer actionable implications for private hospitals to enhance customer retention by strategically managing brand image dimensions and incorporating emotional intelligence considerations into service recovery efforts. Future research could delve deeper into the nuanced dynamics of these relationships across diverse cultural and organizational contexts, thereby enriching our understanding and offering practical guidelines for service-oriented businesses globally."

APPLYING HYPERGRAPHS TO STUDIES IN QUANTITATIVE BIOLOGY

Samuel Barton, Adelle Coster, Diane Donovan, James Lefevre — Tue, 18 Jun 2024 00:00:00 +0000

The objective of this research is to demonstrate hypergraph versatility and applicability for modeling diverse biological systems. The inherent structure of hypergraphs allows for encoding of higher-order feature interactions, providing a flexible framework for efficient models that can enhance our understanding of physical phenomena and one that can be generalized across various datasets. By adopting innovative methods including centrality measure and populations of models rather than singular instances, biases and overfitting tendencies are mitigated, again presenting promise for application across a broad spectrum of biological systems. Furthermore, emphasis is placed on the significance of probabilistic distribution analysis in elucidating threshold selection and feature relevance while maintaining high levels of accuracy. Our results demonstrate the advantages of hypergraph models on two different datasets; with the first on gene expression and the identification of outlier genes and the second on classifying starch grains. There is significant scope in the application of the hypergraph to a wider class of biological systems, with the potential to improve understanding of the biological processes.

PSYCHOLOGICAL DYNAMICS AND COPING MECHANISMS OF WOMEN INVOLVED IN ISLAMIC POLYGAMOUS MARRIAGES

Gintarė Sereikaitė-Motiejūnė — Sat, 15 Jun 2024 00:00:00 +0000

This study explores the coping mechanisms and religious beliefs employed by women in polygamous relationships to manage emotional distress, and the extent to which this approach challenges or reinforces patriarchal structures within these relationships. Data collected a closed Facebook group for Muslim women only reveal that women in polygamous relationships often experience psychological and emotional distress. To cope, they commonly resort to religious faith, acceptance of fate, and compromise with co-wives. While these strategies help manage emotional distress, they can potentially reinforce patriarchal structures within these relationships. The research provides a nuanced perspective, arguing that these coping mechanisms can be seen both as a form of resistance against and an acceptance of patriarchal norms. The study also underscores the need for further research into factors such as stress levels, coping skills, and marital factors that can impact behavioral, emotional, and cognitive outcomes in polygamous marriages. To conclude, in polygamous relationships, women often use avoidance and religious justification as coping mechanisms. While these strategies provide temporary relief or spiritual comfort, they don't address the long-term psychological harm or inherent gender inequalities.

IMPACT OF THE LS-FACTOR ESTIMATION METHODS ON MEAN ANNUAL SOIL LOSS

Sat, 15 Jun 2024 00:00:00 +0000

Soil erosion was assessed in Anambra State using three different LS factor estimation methods. It showed that soil loss from the LS factor based on the slope in percent gives a good result with little underestimation with a PBIAS of -9.04 % while that based on slopes in radians performed best with a slight overestimation with a PBIAS of 2.81 %. The one based on degrees’ slope performed the least with a high underestimation with a PBIAS of -46.43 %. The result from field measurement yielded 27.76 t ha^-1yr^-1. The coefficient of variations was 241.47 %, 192.01 %, and 157.97 %, respectively, for slope in percent, radians and degree. Soil erosion is a highly variable phenomenon which was reflected by the high coefficient of variations. It shows that modelling soil erosion in the State with the LS factor estimation based on slopes in radians and percent yields better results. We believe this finding will be useful to authorities and scientists interested in soil erosion studies in the State. It is recommended that a similar study be extended to other terrains with moderate slopes.

EFFICIENT DETECTION OF MULTICLASS EYE DISEASES USING DEEP LEARNING MODELS: A COMPARATIVE STUDY

Gözde Arslan, Çağatay Berke Erdaş — Sat, 15 Jun 2024 00:00:00 +0000

Eye diseases pose a significant health threat, impacting human life adversely. Conditions like cataracts, diabetic retinopathy, and glaucoma lead to irreversible and serious health issues. Age, genetics, and environmental factors play a crucial role in eye health. Accurate diagnosis is essential for effective treatment, placing a heightened responsibility on clinicians. Advanced technology and deep learning enable the detection and identification of eye diseases. This research aims to utilize prominent Convolutional Neural Network models, including DenseNet, EfficientNet, Xception, VGG, and ResNet, to detect eye diseases. Technical term abbreviations are explained, and the dataset comprises 4217 retinal fundus images, including 1038 cataracts, 1098 diabetic retinopathy, 1007 glaucoma, and 1074 healthy individuals. Model performance is evaluated through metrics like accuracy, recall, precision, F1-score, and Matthews's correlation coefficient using 10-fold cross-validation. Among the models tested, EfficientNet demonstrates the best results with 87.84% accuracy, 92.84% recall, 94.41% precision, 93.53% F1-score, and 83.87% Matthews's correlation coefficient. Consequently, EfficientNet proves to be the most effective architecture for classifying eye diseases in this study.