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.

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 research analyzed the psychometric properties of the Ambivalent Classism Inventory (ICA) in Peru. Methodology: A critical review of literature related to poverty, inequality, and structural gaps was conducted, involving 882 participants aged 14 to 89 years (M = 24.61, SD = 9.07). Results: Exploratory-confirmatory factor analyses were satisfactory, finding a similar factorial structure to the original scale and the adaptation (hostile classism, protective paternalism, and complementary class differentiation). Regarding items, there was a reduction, leaving only 12; however, comparing alternative models, the three-factor structure with 12 reagents showed adequate fit (χ² = 214.588, df = 51, p < 0.001; CFI = 0.996; RMSEA = 0.060; SRMR = 0.033), allowing for invariance testing. Practical Implications: The scale allows for investigating attitude profiles of individuals with privileged social class. Contribution: The instrument is a valuable contribution, considering that the nation has a high poverty rate, leading to economic, political, and social inequality among the population.

Introduction: In contemporary healthcare education, the integration of technology has emerged as an essential factor in enhancing the efficiency and efficacy of training methodologies. Particularly within the domain of cardiopulmonary resuscitation (CPR) training, the adoption of technology-driven approaches holds considerable potential for enriching the skills and proficiencies of healthcare practitioners. Through the utilization of innovative technologies, such as simulation software and leveraging smartphones as primary tools, CPR training programs can be customized to provide immersive, interactive, and authentic learning experiences. This study aims to validate a comprehensive CPR training module tailored explicitly for healthcare professionals, to integrate it into smartphones as a medium for delivering CPR training. Methods: Two validity tests, namely content validity and face validity were conducted to evaluate the validity of the Smart-CPR training module. A self-constructed measurement scale was utilized to assess four parameters: consistency, representativeness, clarity, and relevancy. Content validity employed the content validity ratio, with scores ranging between 1 and −1, indicating the level of consensus among experts regarding the significance of each item. Face validity was assessed using two indices: the item face validity index and the scale face validity index. Ratings of 3 or 4 were given a score of 1, while ratings of 1 or 2 received a score of 0. Result: The content validity shows that CVI values for ‘consistency’ and ‘representativeness’ were 0.99 for the module and questionnaire, and 0.96 and 0.97, respectively. ‘Clarity’ scored 0.99 for the module and 0.96 for the questionnaire, while ‘relevance’ achieved 0.99 for both. All 44 items exceeded the 0.83 threshold for face validity. The Lawshe’s content validity ratio (CVR) and content validity index (CVI) value were used to evaluate the content validity of both the CRSTP module and questionnaire, with CVR values result ranging from 0.80 to 0.99 across dimensions. These findings demonstrate robust content validity. Additionally, high CVI scores, mostly exceeding 0.95, suggest favorable outcomes and indicate no need for revisions. In face validity method, all 44 items surpassed the minimum threshold of 0.83, signifying a favourable outcome. Thus, all items were deemed acceptable. Conclusion: The Smart-CPR training module and questionnaires were meticulously developed to meet both face and content validity standards. All 44 items demonstrated appropriate levels of validity, ensuring they effectively enhance and maintain CPR competency among healthcare providers and potentially benefit the broader community. The positive results of the Smart-CPR training module confirm the high validity of the CPR competency assessment. Content validity, evaluated by experts, received a perfect score, demonstrating agreement on the relevance of each module component. Similarly, face validity, assessed by healthcare professionals, also received a flawless score, indicating consensus on the module’s clarity and relevance. These findings validate the module’s effectiveness in teaching CPR techniques to a diverse audience and ensuring compliance with established standards. With such strong validity, digitizing the module becomes more straightforward, facilitating easier sharing and use across digital platforms. Ultimately, the module’s high validity facilitates its integration into digital platforms, thereby enhancing CPR education and improving outcomes during real emergencies.

This study constructs and empirically validates a Creative Activity Chain (CCA) structure model tailored for innovation in sustainable infrastructure development. In today’s competitive environment, fostering innovation is crucial for maintaining the relevance and effectiveness of infrastructure projects. The research underscores that a significant portion of a project’s long-term value is established during its initial concept and planning stages, highlighting the critical role of creativity in infrastructure development. The CCA model is developed through theoretical frameworks and empirical data, encompassing three key dimensions: creative subject chain, creative action chain, and creative operation chain. The model’s validity is tested with data from five large infrastructure development firms in China, involving 768 R&D staff as respondents. Rigorous statistical methods, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM), and regression analysis, confirm the model’s robustness. The findings reveal significant positive correlations between the creative activity chain’s dimensions and the successful development of sustainable infrastructure projects. Additionally, the study examines the mediating effect of link strength within the creative activity chain, demonstrating its substantial impact on project outcomes. Implications for management include promoting diverse creative teams, systematic process management, and leveraging varied operational tools to enhance creativity in infrastructure development. This research contributes to the literature by introducing an integrated model for managing creative activities in sustainable infrastructure development, offering practical insights for improving innovation processes.