The effects of different storage temperatures (2, 4 and 8 ℃) and their corresponding optimal heat treatment conditions on the quality, physiological and biochemical indexes of Cucumber Fruits during storage were studied by using the quadratic regression orthogonal rotation combination design. The effects of different storage temperatures (2, 4 and 8 ℃) and their corresponding optimal heat treatment conditions on the chilling injury, hardness, weightlessness rate, polyphenol oxidase (PPO), catalase (CAT), peroxidase (POD), H₂O₂, super oxygen anion free radical (O^2-), ASA and GSH were determined. The results showed that heat treatment could inhibit chilling injury, while heat treatment combined with 4 ℃ low temperature storage could effectively inhibit the decline of fruit hardness and weight loss rate, delay the increase of peroxidase (POD) and polyphenol oxidase (PPO) activities, inhibit the increase of H₂O₂ and superoxide anion free radical O^2- and significantly inhibit the browning of cucumber, delay the decline of ascorbic acid and maintain the content of GSH, it was beneficial to adjust the balance of active oxygen system. The results showed that under the storage condition of 4 ℃, the hot water treatment condition of cucumber was 39.4 ℃ and 24.3 min, which could delay the senescence of cucumber fruit and better maintain the quality of cucumber fruit.

The purpose of this study is to determine the relationship between the exogenous variables (administrative support, career placement & employability, academic staff support, institutional factors, and information systems) as service delivery quality dimensions with satisfaction and moderating variable (academic and social integration) between endogenous variables (satisfaction and retention) among undergraduate students from Malaysian private higher education institutions. In order to accomplish the objectives proposed with hypotheses, a model reflecting the relationship between service delivery quality dimensions and satisfaction moderated by academic and social integration towards retention is applied. This empirical study focused on probability-stratified random sampling with a final sample size of 309 students. This study achieved statistically significant positive results by emphasizing academic and social integration as a moderating variable to achieve student retention by linking Perceived Performance Theory and Tinto’s Interactionist Theory from satisfaction to retention. Evaluation of the structural model on the coefficient of determination for the model’s predictive accuracy in this study produced an R² = 0.85 for satisfaction, suggesting nearly 85% of the variance in endogenous latent construct satisfaction is explained by all the service delivery quality dimensions linked to it. As for retention produced R² = 0.74, suggesting nearly 74% of the variance in endogenous latent construct retention is explained by all the service delivery quality dimensions linked together with satisfaction and academic and social integration as moderator. The model has a substantial effect with 0.76 in the Goodness-of-Fit index, indicating that the model has better explaining power.

This paper investigates the impact of financial inclusion on financial stability in BRICS countries from 2004 to 2020. Using a panel smooth transition regression model, the results reveal a U-shaped relationship between financial inclusion and financial stability. Financial inclusion reduces financial stability up to a threshold of 44.7%. Beyond this point, financial inclusion contributes to greater financial stability, through gradual transitions. Enhanced financial inclusion supports banks in stabilizing their deposit funding by facilitating access to more stable, long-term funds and alleviating the negative impacts of fluctuations in returns. Furthermore, the study examines the role of institutional quality in shaping the financial inclusion-financial stability nexus, indicating a significant positive effect, especially in the upper regime. These findings provide valuable insights for financial regulatory authorities, highlighting the importance of promoting financial inclusion in BRICS economies and adapting regulations to mitigate potential risks to global financial stability.

This study critically examines the relationship between Total Quality Management (TQM) and Service Quality (SQ) within Dubai’s housing sector, with a specific focus on the moderating influence of blockchain technology (BT) in this relationship. Employing a quantitative approach grounded in a deductive research strategy and positivist epistemology, data were gathered from a sample of industry professionals and subjected to rigorous analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that the deliberate deployment of TQM methodologies leads to significant improvements in SQ metrics, and the catalytic role of BT further enhances these service quality improvements. The study highlights the transformative potential of BT in recalibrating conventional paradigms of service delivery within the housing sector. Specifically, the analysis reveals that BT plays a pivotal moderating role in the relationship between TQM practices and SQ outcomes, thereby enriching our comprehension of the intricate interplay between these constructs. The study concludes by furnishing nuanced insights into the multifaceted dynamics shaping SQ within the housing sector, while also delineating avenues for future inquiry.

The mobile health market is expected to continue to grow that will make it harder for mobile application developer to compete. One of the most popular types of mobile health application is health and fitness applications. This application aims to modify user behavior; therefore, it requires user to use the system continuously in relatively longer period of time to effectively change user behavior. Thus, user satisfaction is essential and must be maintained to reach this goal. This study aims to define the mobile health application qualities that would influence user satisfaction level. Developer can priorities the most influential qualities when building their application. Quality dimensions would be explored by literature review and Google Play Store review and categorised using DeLone McLean IS Success Model. We identified 12 quality dimension that will furthered analysed using Kano Model. The data collecting was conducted with online form with 12 pairs of Kano two-dimensional questionnaires (n = 115). The results show that the important qualities of mobile health application are Privacy, Availability, Reliability, Ease of Use, Accuracy and Responsiveness, lack of these qualities would cause dissatisfaction from user. The developer might also consider to improve user interface and usefulness of the application to increase user satisfaction even though these qualities would not cause much of dissatisfaction

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.