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In the current study, we investigated the relationships among academic stress, psychological capital (PsyCap), and well-being and the effect of the demographic variables of gender, majors, and academic years on academic stress and PsyCap. We used the structural equation model to investigate the mediating role of PsyCap between academic stress and well-being. The participants (N = 827) included students from a midsized university in the Eastern Province of Saudi Arabia (636 females, 191 males) who comprised 450 science and 377 humanities majors from all academic years. To collect data, we utilized the Academic Stress Inventory, the Psychological Capital Questionnaire, and the Warwick-Edinburgh Mental Well-Being Questionnaire. The structural equation model analysis results revealed that PsyCap was a full mediator between academic stress and wellbeing. Gender did not affect on PsyCap or academic stress. In addition, the academic majors affected PsyCap: Students in the humanities majors had better PsyCap than those in the science majors. Furthermore, the academic years affected academic stress; specifically, academic stress decreased as students progressed through their academic years. Therefore, science majors in their first academic year were the most in need of a PsyCap-based intervention.

Objective Chordoma is a low-grade malignant tumor that originates from the remnant tissue of the embryonic notochord. Postoperative or definitive radiotherapy (RT) has been used to enhance local control. This study aims to assess the outcomes of the expanded endoscopic endonasal approach (EEA) for maximal removal of clival chordomas followed by RT for visualized residual or tumor recurrence. Materials and Methods A retrospective review was performed on consecutive patients with clival chordoma who underwent endoscopic endonasal resection in the otorhinolaryngology and neurosurgery departments, between 2016 and 2021. We included all patients with pathologically confirmed clival chordoma who were treated using the EEA. Patients who underwent combined external and endoscopic approaches or transcranial surgery were excluded. Results Seventeen patients were included in this study. Most of them had tumors located in the middle clivus. Regarding RT, the majority of patients underwent postoperative RT. Almost half of them underwent CyberKnife (CK) RT. None of them had severe toxicities (grade 3 or higher). Three patients died, resulting in a mortality rate of 17.6% none of them related to radiation side effect. The 2-year overall survival was 82.4% (mean standard error [SE] = 1.765, 95% confidence interval [CI] = 1.505–2.024), and the progression-free survival (PFS) was 76.5% (mean SE = 3.403, 95% CI = 2.791–4.016). No distal metastasis was reported in our series. Conclusion This series demonstrates that expanded endoscopic endonasal approach (EEA) for the resection of skull base chordomas, followed by CyberKnife radiosurgery, presents a viable alternative to proton beam therapy; however, further research is necessary to directly compare these modalities. [ABSTRACT FROM AUTHOR]

Count regression models, particularly negative binomial regression (NBR), are widely used in various fields, including biometrics, ecology, and insurance. Over-dispersion is likely when dealing with count data, and NBR has gained attention as an effective tool to address this challenge. However, multicollinearity among covariates and the presence of outliers can lead to inflated confidence intervals and inaccurate predictions in the model. This study proposes a comprehensive approach integrating robust and regularization techniques to handle the simultaneous impact of multicollinearity and outliers in the negative binomial regression model (NBRM). We investigate the estimators' performance through extensive simulation studies and provide analytical comparisons. The simulation results and the theoretical comparisons demonstrate the superiority of the proposed robust hybrid KL estimator (M-NBKLE) with predictive accuracy and stability when multicollinearity and outliers exist. We illustrate the application of our methodology by analyzing a forestry dataset. Our findings complement and reinforce the simulation and theoretical results. [ABSTRACT FROM AUTHOR]

Fractional cointegration in time series data has been explored by several authors, but panel data applications have been largely neglected. A previous study of ours discovered that the Chen and Hurvich fractional cointegration test for time series was fairly robust to a moderate degree of heterogeneity across sections of the six tests considered. Therefore, this paper advances a customized version of the Chen and Hurvich methodology to detect cointegrating connections in panels with unobserved fixed effects. Specifically, we develop a test statistic that accommodates variation in the long-term cointegrating vectors and fractional cointegration parameters across observational units. The behavior of our proposed test is examined through extensive Monte Carlo experiments under various data-generating processes and circumstances. The findings reveal that our modified test performs quite well comparatively and can successfully identify fractional cointegrating relationships in panels, even in the presence of idiosyncratic disturbances unique to each cross-sectional unit. Furthermore, the proposed modified test procedure established the presence of long-run equilibrium between the exchange rate and labor wage of 36 countries' agricultural markets. [ABSTRACT FROM AUTHOR]

Background: Heart failure, a major public health concern, significantly contributes to hospital admissions. This study evaluates the impact of malnutrition on both patient and hospital outcomes in heart failure admissions, with a specific focus on variations in outcomes based on the severity of malnutrition. Methods: Utilizing the National Inpatient Sample (NIS) database, this retrospective cohort study included adult patients admitted with a principal diagnosis of heart failure. Malnutrition was identified using the well-validated ICD 10 codes. We compared outcomes between patients with and without malnutrition, focusing on mortality, length of stay (LOS), hospital charges, cardiac arrest, and cardiogenic shock. Results: Out of 1,110,085 heart failure patients, 36,522 (3.29%) were malnourished. Malnourished patients exhibited significantly higher adjusted in-hospital mortality rates (aOR 3.32; 95% CI 3.03–3.64), longer LOS (mean increase of 4.67 days; p < 0.001), and higher hospital charges (mean increase of USD 77,416.9; p < 0.01). Increased rates of cardiac arrest (aOR 2.39; 95% CI 1.99–2.86; p < 0.001) and cardiogenic shock (aOR 3.74; 95% CI 3.40–4.12; p < 0.001) were also noted in malnourished patients. Severely malnourished patients faced worse outcomes compared to those with mild to moderate malnutrition. Conclusions: Heart failure patients with malnutrition experience higher mortality rates, longer hospital stays, increased hospitalization charges, and greater complication rates, including cardiac arrest and cardiogenic shock, compared to non-malnourished patients. Outcomes deteriorate with the increasing severity of malnutrition. Timely and individualized nutritional interventions may significantly improve outcomes for heart failure admissions. [ABSTRACT FROM AUTHOR]

Education is essential and increasingly crucial for the development of almost all countries worldwide. As educational data has become increasingly available, scholars have shown a growing interest in exploring the correlation between students’ academic achievements and other factors that may impact their performance using machine learning algorithms. This research paper introduces a novel hybrid classifier that aims to predict the academic performance of students by using a combination of different single algorithms. The proposed hybrid classifier (PHC) is compared to six available classification algorithms (random forest (RF), C4.5, classification and regression trees (CART), support vector machines (SVM), naive Bayes (NB) and K-nearest neighbors (KNN)) using recall, precision, F1-score, and accuracy evaluation measures. Our experimental results reveal that the PHC classifier consistently outperforms the individual classifiers across multiple evaluation metrics. Specifically, the PHC classifier achieved an accuracy rate of 92.40%, surpassing the RF, C4.5, and CART classifiers, which were the next best performers. In terms of precision and F1 score, the PHC also demonstrated superior performance, indicating its robustness in correctly identifying positive instances and providing balanced accuracy. While the C4.5 classifier performed comparably to the PHC classifier concerning the recall metric, the hybrid model’s overall performance highlights its effectiveness in leveraging the complementary strengths of the included classifiers. The suggested hybrid model has the potential to enhance students’ academic performance and success more effectively and efficiently. It could benefit students, educators, and academic institutions. Additionally, it provides practical insights for educators and institutions striving to improve student achievement using predictive analysis. [ABSTRACT FROM AUTHOR]

This research demonstrated whether the analytic choice of using a multiple regression or structural equation modeling methodology affected the results of faculty research productivity in Saudi Arabia. This study not only showed the differences between multiple regression and structural equation modeling results but also the disparity of results within each type of analysis. The results indicated that using either a multiple regression or structural equation modeling methodology delivered different results in terms of significant predictors and the model's overall explained variance. Further, differential outcomes produced by the various structural equation modeling models employed illustrate how the incorrect specification of formative (i.e., causal) indicators can result in worse data-fitting models. Implications for selecting analytic procedures are discussed. [ABSTRACT FROM AUTHOR]