Search Results (178)

The quality of mangoes is a crucial factor in both domestic and commercial markets that directly influences consumer satisfaction and economic value. Traditional methods of checking mango quality often involve destructive techniques, which lead to the loss of the fruit in the testing process. This study presents an advanced approach that could predict the quality of mangoes using advance non-destructive methods leveraging machine learning algorithms to predict quality parameters such as ripeness, sweetness and overall freshness without damaging the fruit. In this research, a dataset consisting of various mango samples was collected, with attributes including color, texture, size, weight and acidity levels. Sensors, such as pH sensors (for acidity) and e-nose sensors (for aroma and sweetness detection), were used to gather data, while a combination of machine learning models such as Decision Tree, K-Nearest Neighbors (KNN), and Automated Machine Learning (AutoMLP), Naive Bayes were applied to predict the mangoes’ quality. The accuracy of each model was measured based on its ability to classify mangoes as fresh, ripe, or rotten. The results determine that the AutoMLP model performs the best out of the traditional models, achieving an accuracy of 98.46%, making it the most suitable model for mango quality prediction. The research explains the significance of feature extraction methods, model optimization, and sensor data pretreatment in reaching a high prediction accuracy. Full article

Background/Objectives: Chronic diseases, such as diabetes mellitus, require sustained management and medication adherence to reduce the risk of related complications and mortality. However, the adherence levels are not satisfactory, which could be attributed to several factors, including cultural beliefs and socioeconomic factors. This study aimed to assess the relationship between cultural and socioeconomic factors, patient preferences, and medication adherence among diabetic patients. Methods: A mixed-methods cross-sectional design was implemented using face-to-face questionnaires and personal interviews. This study was conducted in 159 primary healthcare clinics (PHCs) in Madinah, Saudi Arabia, from 26 August 2024 to 10 February 2025. It included type 1 and type 2 diabetic patients. The Morisky Medication Adherence and General Medication Adherence Scales were used to evaluate diabetes medication adherence among the participants. Results: The included 424 diabetic patients had a predominant age range from 40 to 59 (48.1%). The majority were non-smokers (88.7%), Saudi Arabian (94.6%), and female (62.7%). The findings revealed a significant association between patient age (p < 0.001), body weight (p = 0.023), nationality (p = 0.015), educational level (p = 0.027), and the presence of comorbidities (p = 0.005) with the level of medication adherence. Conclusions: This study revealed that most diabetic patients attending PHCs in Madinah exhibited medium-to-high levels of medication adherence, with key influencing factors including age, comorbidities, education level, physician satisfaction, and health self-awareness. Full article

With the ongoing advancement of urban renewal and cultural tourism, public spaces within historic cultural districts face dual challenges of structural complexity and diverse user demands. There is an urgent need to establish a scientific, user-oriented evaluation system to enhance spatial quality and user satisfaction. This study takes the Nanhesha Historic and Cultural Quarter in Yangzhou as a case study, focusing on two primary user groups: tourists and local residents. Employing semi-structured interviews and grounded theory, it distils a demand evaluation framework comprising four dimensions—spatial structure, environmental perception, socio-cultural aspects, and facility systems—with a total of 21 indicators. Subsequently, employing the Delphi method, experts were invited to refine the indicators through two rounds of deliberation. The Kano model was then applied to classify the demand attributes of different groups, identifying five common demands and sixteen differentiated demands. These were categorised into three sensitivity levels. Further integrating the Satisfaction Increment Index (SII), Dissatisfaction Decrement Index (DDI), and sensitivity values, a two-dimensional prioritisation model was constructed. This yielded a unified three-tier priority system alongside independent ranking frameworks for each user group. Findings reveal that visitors prioritise immediate experiential attributes such as spatial accessibility, appropriate scale, and environmental cleanliness, whereas residents favour long-term usage-oriented aspects including cultural expression, convenient facilities, and climate adaptability. This research not only enriches the theoretical framework for studying public space perception in historic cultural districts but also provides actionable evaluation criteria and practical pathways for multi-stakeholder spatial optimisation design. It offers guidance for the high-quality, refined development of public spaces within historic quarters. Full article

Background/Objectives: Chronic medical conditions are comorbid with psychological disorders, often attributed to the weight of managing persistent demands associated with debilitating illness. Lifestyle adjustments, physical pain, and costs of health care can impose impairment of functioning, exacerbated by the onset of a chronic disease. While cause-and-effect directionality is difficult to ascertain, it is widely assumed that psychological stress can exacerbate the ability of patients to manage chronic medical conditions. Methods: The current study examined a novel model comprising five psychological factors which might explain variations in patients’ level of adherence, satisfaction, and quality of life. The sample consisted primarily of 124 low-income, female Hispanic patients, who were patients diagnosed with rheumatic and endocrine medical diagnoses. Results: Psychological distress and the lingering psychological effects of the COVID-19 pandemic were negatively associated with patient adherence, satisfaction, and quality of life, and that patients’ reports of the working alliances with their doctors moderated (i.e., significantly lessened) the negative association between the lingering impact of the COVID-19 pandemic and their satisfaction with care. Patients’ self-efficacy, resilience, and working alliance were all positively and significantly associated with adherence, satisfaction, and QOL. The association between working alliance and satisfaction represents a very large effect (r = 0.77, p < 0.001). Path analysis found a direct effect between psychological distress (stand. est. = 0.28, p = 0.05) and treatment adherence and a direct effect between COVID-19 impact and adherence (stand. est. = −0.19, p = 0.05). Conclusions: This study provides evidence of the role that both psychological stress and psychological strengths play in the experience of receiving medical care for low-income patients with rheumatic and endocrine conditions. Psychological stress inhibits adherence, and the physician–patient working alliance moderates the negative correlation between COVID impact and treatment satisfaction. Full article

This study investigates urban life satisfaction and residents’ settlement intention as emergent outcomes of interconnected urban systems and examines the moderating role of urban regeneration budget execution as a systemic policy input. Drawing on the bottom-up spillover perspective and policy feedback theory, this study posits that satisfaction with core aspects of urban living—such as housing, transportation, and public safety—reflects the functioning of multiple interrelated urban subsystems, which accumulate into a global sense of well-being that influences settlement intention. Furthermore, when urban regeneration budgets are visibly and fully executed, they operate as institutional feedback mechanisms, leading residents to attribute their life satisfaction to effective system performance and reinforcing their desire to stay. Using survey data from Incheon Metropolitan City and Gyeonggi Province in South Korea, the study employs stereotype logistic regression to test the proposed model. The findings reveal that urban life satisfaction significantly predicts stronger settlement intention, and this effect is amplified in municipalities with higher levels of budget execution. These results contribute to theoretical understanding by linking subjective well-being with institutional performance and offer practical guidance for South Korean local governments seeking to strengthen community resilience through transparent and outcome-driven urban policy delivery. Full article

This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit attention–implicit utility discrepancy, we extend the traditional IPA–Kano model by incorporating an attention dimension, thereby constructing a three-dimensional optimization framework with eight decision spaces. This enhanced framework enables the following: (1) fine-grained classification of customer requirements by distinguishing between an attribute’s perceived salience and its actual impact on satisfaction; (2) strategic resource allocation, differentiating between quality enhancement priorities and cognitive expectation management to maximize innovation impact under resource constraints. To validate the model, we conducted a case study on wearable watches for the elderly, analyzing 12,527 online reviews to extract 41 functional attributes. Among these, 14 were identified as improvement priorities, 9 as maintenance attributes, and 7 as low-priority features. Additionally, six cognitive management strategies were formulated to address attention–utility mismatches. Comparative validation involving domain experts and consumer interviews confirmed that the proposed IPAA–Kano model, leveraging deep learning, outperforms the traditional IPA–Kano model in classification accuracy and decision relevance. By integrating deep learning with optimization-based decision models, this research offers a practical and systematic methodology for translating customer attention and satisfaction data into actionable innovation strategies, thus providing a robust, data-driven approach to resource-efficient product development and technological innovation. Full article

Public open spaces have recently attracted significant attention in the national development programs aimed at improving urban livability and quality of life in Saudi Arabia. While many studies have examined the design quality of public open spaces in the country, a contextualized evaluation index that takes into account users’ preferences and the nation’s social context is still lacking. This gap calls for additional field studies to better understand users’ needs and their interactions with the current urban design practices of public open spaces. This study provides deeper insights into the design quality of public open spaces in Saudi Arabia. The study first identified 16 attributes of design quality of public open spaces, and then assessed a case study, Alrabie Park in Al-Khobar city, based on field observation and a survey of users’ satisfaction levels in relation to these quality attributes The findings revealed that the average of users’ satisfaction was 3.76 out of 5.0, indicating a neutral to satisfied response. Key strengths were noted in accessibility and users’ comfort, while areas needing improvement included environmental quality and amenities and services. The study recommends the development of a national evaluation index for public open spaces to create inclusive, safe, and vibrant environments that reflect Saudi Arabia’s urban and socio-cultural context. It also emphasizes the importance of community engagement in this regard to ensure that the design of public spaces aligns well with the users’ needs and helps to create sustainable urban spaces in the city. Full article

Sociodemographic aspects influence consumer perception in a food hall. This study evaluates the attributes that determine the gastronomic experience and examines how sociodemographic aspects (age, education level, income, consumption) affect the perception of restaurant attributes, satisfaction, and loyalty. Using a valid sample of 420 participants, exploratory factor analysis and multiple regression were applied. The results show that education level and income significantly affect satisfaction (β = −0.173; p = 0.006 and β = 0.195; p = 0.015, respectively) and loyalty dimensions, including revisit intention (β = −0.179; p = 0.004 and β = 0.269; p = 0.001), recommendation (β = −0.171; p = 0.005 and β = 0.295; p = 0.001), and intention to say positive things (β = −0.120; p = 0.051 and β = 0.215; p = 0.006). Unlike prior studies focused on traditional restaurants, this research offers new empirical evidence within food halls as hybrid gastronomic spaces. The findings provide practical insights for food hall managers and urban tourism developers by emphasizing the importance of segmenting marketing strategies according to education, income, and visit frequency to enhance customer satisfaction, loyalty, and destination attractiveness. Full article

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article

This study compares the views of mathematics teachers in private and public schools on mathematics education during the COVID-19 pandemic from their perspectives after the pandemic, focusing on factors influencing secondary and high school mathematics education. In this study, the survey method was used to collect data. The survey method was employed to obtain information for this investigation. During the 2023–2024 school year, 644 math teachers took part in the study. Of these, 260 were from private schools, and 384 were from public schools. There are 10,323 teachers in public schools and 694 in private schools, and this sample size is more than the 371 participants that are needed for statistical purposes (based on Cochran’s formula at a 95% confidence level and a 5% margin of error). A scale consisting of thirteen 5-point Likert-type questions was developed by researchers for data collection, and mathematical modeling techniques were employed. Factor analysis using SPSS 24.00 revealed four key factors influencing teachers’ responses: teachers’ professional development and support, job satisfaction, students’ engagement, and teaching experience. The results highlight significant disparities between public and private schools in mathematics education, attributed to various factors to be elucidated further in the subsequent discussion. Full article

Ecological empathy is a complex and multifaceted attribute, a highly relevant human factor in health care. This study aimed to determine the association between individual resilience and empathy in dental students and to assess whether resilience predicted empathy and its dimensions. The study employed a quantitative and descriptive cross-sectional design. The sample, consisting of 397 students from the first to the seventh year of dental surgery, was measured on empathy and resilience. Multiple regression analysis, confirmatory factor analysis, invariance analysis, and methods were employed to ensure the reliability of our measurements. It was established that the measurements of resilience and empathy were valid and reliable in the studied population. Ecological resilience increased the levels of the perspective adoption dimension, and adaptive resilience raised the compassionate threshold. The engineering resilience dimension was not associated with any of the empathy dimensions. While resilience may not fully explain empathy variability, it remains a personal attribute related to empathy. Further investigation of the role of resilience in empathy and its components is required since improving healthcare professionals’ empathy has positive implications for the quality of care and patient satisfaction. Full article

(1) Background: With the deep integration of e-commerce and video technology, live-streaming marketing has emerged globally and maintained rapid growth. However, most of the current research on live-streaming e-commerce marketing focuses on merchants’ sales strategies and consumers’ purchase intentions, and there is relatively little research related to the risks of live-streaming e-commerce marketing. Nevertheless, with the development of live-streaming e-commerce marketing and its integration with technologies such as artificial intelligence and virtual reality (VR), live-streaming e-commerce marketing still faces challenges such as unclear subject responsibility, difficulty in verifying the authenticity of marketing information, and uneven product quality. It also harbors problems such as the ethical misbehavior of AI anchors and the excessive beautification of products by VR technology. (2) Methods: This study systematically analyzes the scenarios of live-streaming marketing to elucidate the mechanisms of risk formation. Utilizing fault tree analysis (FTA) and risk checklist methods, risks are identified based on the three core elements of live-streaming marketing: “people–products–scenes”. Subsequently, the Delphi method is employed to refine the initial risk indicator system, resulting in the construction of a comprehensive risk indicator system comprising three first-level indicators, six second-level indicators, and 16 third-level indicators. A hesitant fuzzy multi-attribute group decision-making method (HFMGDM) is then applied to calculate the weights of the risk indicators and comprehensively assess the live-streaming marketing risks in live broadcast rooms of three prominent celebrity anchors in China. Furthermore, a detailed analysis is conducted on the risks associated with the six secondary indicators. Based on the risk evaluation results, targeted recommendations are proposed. This study aims to enhance consumers’ awareness of risk prevention when conducting live-streaming transactions and pay attention to related risks, thereby safeguarding consumer rights and fostering the healthy and sustainable development of the live-streaming marketing industry. (3) Conclusions: The results show that the top five risk indicators in terms of weight ranking are: Ethical Risk of the AI Anchor (A4), VR Technology Promotion Risk (F3), Anchor Reputation (A1), Product Quality (D1), and Logistics Distribution Service Quality (D2). The comprehensive live-streaming marketing risk of each live broadcast room is Y > L > D. Based on the analysis results, targeted recommendations are provided for anchors, MCN institutions, merchants, supply chains, and live-streaming platforms to improve consumer satisfaction and promote sustainable development of the live-streaming marketing industry. Full article

Despite the growth of fruit production, the challenge of postharvest fruit loss particularly in tropical and subtropical fruits due to spoilage, decay, and natural deterioration remains a critical issue, impacting the global food supply chain by reducing both the quantity and quality of fruits postharvest. Edible coatings have emerged as a sustainable solution to extending the shelf life of fruits and decreasing postharvest losses. The precise composition and application of these coatings are crucial in determining their effectiveness in preventing microbial growth and preserving the sensory attributes of fruits. Furthermore, the integration of nanotechnology into edible coatings has the potential to enhance their functionalities, including improved barrier properties, the controlled release of active substances, and increased antimicrobial capabilities. Recent advancements highlighting the impact of edible coatings are underscored in this review, showcasing how they help in prolonging shelf life, preserving quality, and minimizing postharvest losses of subtropical fresh fruits worldwide. The utilization of edible coatings presents challenges in terms of production, storage, and large-scale application, all while ensuring consumer acceptance, food safety, nutritional value, and extended shelf life. Edible coatings based on polysaccharides and proteins encounter difficulties due to inadequate water and gas barrier properties, necessitating the incorporation of plasticizers, emulsifiers, and other additives to enhance their mechanical and thermal durability. Moreover, high levels of biopolymers and active components like essential oils and plant extracts could potentially impact the taste of the produce, directly influencing consumer satisfaction. Therefore, ongoing research and innovation in this field show great potential for reducing postharvest losses and strengthening food security. This paper presents a comprehensive overview of the latest advancements in the application of edible coatings and their influence on extending the postharvest longevity of main subtropical fruits, emphasizing the importance of maintaining the quality of fresh and fresh-cut subtropical fruits, prolonging their shelf life, and protecting them from deterioration through innovative techniques. Full article

Background: This study aimed to assess patient satisfaction with the quality of healthcare services at selected public primary healthcare facilities in Almaty during the COVID-19 pandemic and to identify associated demographic and facility-related factors. Methods: A cross-sectional, quantitative study was conducted over a period of 6 months, from 30 June to 31 December 2021, through a web-based survey. An adapted questionnaire was employed to survey the respondents. In total, 1035 respondents participated in the study. To examine the relationship between demographic and facility characteristics and patient satisfaction, we utilized the proportional odds model for ordinal logistic regression. Results: A total of eight primary healthcare organizations from the public sector in Almaty participated in the survey. The analysis identified significant demographic predictors of patient satisfaction, such as marital status, social status, self-perceived health, and the use of online consultations. Among the facility-related factors, only the availability of a cross-ventilation system emerged as a significant predictor. Conclusions: This study provides evidence for the factors influencing patient satisfaction with primary healthcare services in Almaty during the COVID-19 pandemic. Both demographic characteristics and facility-level attributes were found to significantly affect satisfaction levels. These findings underscore the need for targeted structural and organizational improvements in primary healthcare settings, especially during public health emergencies. Addressing these gaps through infrastructural upgrades, enhanced preparedness, and the integration of patient-centered care models can help to bolster trust and resilience within Kazakhstan’s healthcare system. Full article

Personal Comfort Systems (PCS) have emerged as a flexible alternative to address the diversity of environmental perceptions in office environments. Unlike conventional HVAC systems, PCSs allow users to improve their satisfaction and comfort by exercising individualized control over their immediate environment without interfering with others around them. This study evaluated the use of machine learning models generated by H2O AutoML to predict the use of three PCSs in four office buildings with effective occupancy. These were a thermal wristband, a desk fan, and an adjustable lamp. Data collected through environmental sensors, perception surveys, and spatial and personal attributes were used. Synthetic data augmentation and automated variable selection were also used to optimize the models’ performance. The predictive models had a robust performance, with R² values in the test set of 0.86 for the wristband, 0.84 for the fan, and 0.52 for the lamp. The most influential variables included the BMI, CO₂ level, and thermal satisfaction, highlighting the importance of physiological and subjective factors. The results confirm that the models allow anticipating the use of PCS with high precision in most cases, laying the foundations for the future implementation of user-oriented adaptive systems. This preliminary approach contributes to the design of healthier, more personalized, and more energy-efficient work environments. Full article