Richard’s equation was approximated by finite-difference numerical scheme to model water infiltration profile in variably unsaturated soil^[1]. The published data of Philip’s semi-analytical solution was used to validate the simulated results from the numerical scheme. A discrepancy was found between the simulated and the published semi-analytical results. Morris method as a global sensitivity tool was used as an alternative to local sensitivity analysis to assess the results discrepancy. Morris method with different sampling strategies were tested, of which Manhattan distance method has resulted a better sensitivity measures and also a better scan of input space than Euclidean method. Moreover, Morris method at p = 2 , r = 2 and Manhattan distance sampling strategy, with only 2 extra simulation runs than local sensitivity analysis, was able to produce reliable sensitivity measures (μ^*, σ). The sensitivity analysis results were cross-validated by Sobol’ variance-based method with 150,000 simulation runs. The global sensitivity tool has identified three important parameters, of which spatial discretization size was the sole reason of the discrepancy observed. In addition, a high proportion of total output variance contributed by parameters β and θ_s is suggesting a greater significant digits to reduce its input uncertainty range.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

The purpose of this paper is to introduce a new dimension of organizational collective engagement (OCE), namely spiritual engagement. This dimension proposes spiritual engagement, which is considered to increase the bundle of engagement as a whole at the organizational level. We collected data from 107 employees who worked in various agencies in Indonesia. We tested the validity and reliability of the proposed indicators of OCE and spiritual engagement using exploratory and confirmatory factor analysis. This study enhances the literature in the field of human resource development, especially in OCE, with the Islamic dimension of spiritual engagement. The findings reveal that there are 10 valid and reliable indicators that can be used to measure the concept of OCE among employees in Indonesia. OCE with four dimensions (physical, emotional, cognitive, and spiritual) can be an effort to increase organizational effectiveness through the collective engagement of all employees. Since this research is limited to Indonesia, further studies are needed in institutions around the world so that the consistency of the results can be justified.

The experiments were carried out to validate an analytical method and to examine the impact of various decontaminating solutions on the removal of acephate residues from okra. Acephate analysis was performed using HPLC-UV, and sample extraction was done using the QuEChERS method. Method validation encompassed assessing specificity, linearity, precision, accuracy, as well as limits of detection (LOD) and quantification (LOQ). The method exhibited excellent linearity with R² values ≥ 0.99. LOD and LOQ were determined at 0.5 µg mL⁻¹ and 2 µg mL⁻¹, respectively. The results indicated average recoveries ranging from 80.2% to 83.3% with a % RSD below 5%. The decontamination procedures include rinsing with running tap water, soaking in lukewarm water, 2% CH₃COOH, 1% NaCl, 5% NaHCO₃, 0.01% KMnO₄, and in commercially available decontamination products such as nimwash, veggie clean, and arka herbiwash for a duration 10 minutes. Among all the treatments, soaking in nimwash solution showed remarkable effectiveness (96.75% removal), followed by veggie clean (94.97% removal) and arka herbiwash (95.80% removal). Washing okra samples in running tap water was found to be the least effective compared to other treatments.

Objective: This study aimed to examine the psychometric properties of the 21-item Depression, Anxiety, and Stress Scale (DASS-21) in a sample of Moroccan students. Method: A total of 208 Moroccan students participated in this study. The dimensionality of the DASS-21 scale was assessed using exploratory factor analysis. Construct validity was assessed using the Stress Perception (PSS-10), State Anxiety (SAI), and Depression (CESD-10) scales. Results: Correlation analyses between Depression, Anxiety, and Stress subscales showed significant results. The exploratory factor analysis results confirmed the DASS’s three-dimensional structure. Furthermore, correlation analyses revealed positive correlations between the DASS-18 sub-dimensions and the three scales for Stress (PSS-10), Anxiety (SAI), and Depression (CESD-10). Conclusion: In line with previous work, the results of this study suggest that the DASS-18 reflect adequate psychometric properties, making it an appropriate tool for use in the university context.