Rigorous Validation Process Research Articles

Evaluation of the GATIS score for predicting prognosis in rectal neuroendocrine neoplasms

The GATIS score, developed by Zeng et al , represents a significant advancement in predicting the prognosis of patients with rectal neuroendocrine neoplasms (R-NENs). This study, which included 1408 patients from 17 major medical centres in China over 12 years, introduces a novel prognostic model based on the tumour grade, T stage, tumour size, age, and the prognostic nutritional index. Compared with traditional methods such as the World Health Organization classification and TNM staging systems, the GATIS score has superior predictive power for overall survival and progression-free survival. With a C-index of 0.915 in the training set and 0.812 in the external validation set, the GATIS score’s robustness and reliability are evident. The study’s use of a large, multi-centre cohort and rigorous validation processes underscore its significance. The GATIS score offers clinicians a powerful tool to accurately predict patient outcomes, guide treatment decisions, and improve follow-up strategies. This development represents a crucial step forwards in the management of R-NENs, addressing the complexity and variability of these tumours and setting a new benchmark for future research and clinical practice.

Systematic review of risk prediction tools for primary cutaneous melanoma outcomes and validation of sentinel lymph node positivity prediction in a UK tertiary cohort.

It is difficult for clinicians to make predictions for cancer progression or outcomes based on AJCC staging for individual patients. Models individualising risk prediction for clinical outcomes are developed using patient level data, advanced statistical techniques, and artificial intelligence. A systematic search identified cutaneous melanoma prognostic prediction tools published between January 1985-March 2023. Population comparisons of key clinico-pathological variables, external prediction of receiver operating characteristics and calibration analysis are applied to an unselected group of patients undergoing sentinel lymph node biopsy in a UK University hospital setting (n = 1564). Twenty-nine models were identified which predicted survival, disease recurrence or sentinel lymph node positivity (Internal validation n = 19 and external validation n = 14). 3 out of 7 tools for sentinel node positivity were contemporaneous with available characteristics for external validation. External validation of models by Lo et al. Friedman et al. & Bertolli et al. highlighted good discriminative performance (AUC 68.1% (64.5-71.8%), 77.1% (66.8-85.7%) & 68.6% (63.3-74.1%) respectively) but were sub-optimally calibrated for the UK patient cohort (Calibration intercept & slope Friedman: -4.01 & 32.92, Lo: -1.17 & 0.44, Bertolli: -2.75 & 4.88). This work highlights the complexity of predictive modelling and the rigorous validation process necessary to ensure accurate predictions. Our search highlights a tendency to focus on discriminative performance over calibration, and the possibility for inconsistent predictions when tools are applied to populations with differing characteristics.

Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Enhancing preparedness by developing robust implementation

Abstract In EUVABECO, a goal is defined as a public health objective that motivates a vaccination practice. A vaccination practice encompasses specific methods or approaches used in vaccination efforts, while a tool refers to a tangible resource or software developed to support the implementation of these practices. EUVABECO is undertaking a series of structured activities from January 2024 until mid-2026. These include mapping innovative practices, deriving requirements for tools and validation plans, developing implementation plans, and facilitating their adoption by European MS. The project will also evaluate, validate, and verify these plans to ensure their efficacy and sustainability. EUVABECO’s methodology is informed by the Systems Development Lifecycle (SDLC) framework, utilizing the V-model to ensure a strong linkage between the development and testing phases, thereby guaranteeing a cohesive workflow. Each implementation plan developed will feature comprehensive policy recommendations and a workplan that covers the necessary organizational, technical, legal, and ethical aspects required for the tool’s deployment. Additionally, these plans will include tailored strategies to enhance the tool’s functionality during outbreaks, criteria for verifying implementation effectiveness, and financial considerations critical for supporting adoption. The implementation plans will undergo a rigorous validation process to assess their suitability, transferability, and sustainability across MS. The ultimate goal is to disseminate validated plans whilst ensuring a parallel multistakeholder engagement approach to ensure MS are actively involved in key project phases, thus, facilitating rapid uptake of said plans across EU MS and a transformation of the vaccination ecosystem across Europe.

The Problem of Scale Contamination and Construct Validity in Mental Health, Social Science and Spirituality-Related Research

This article explores the pervasive issue of scale contamination and its impact on construct validity in mental health, social science, and spirituality-related research. Scale contamination occurs when measurement tools designed to assess one specific domain inadvertently incorporate elements from unrelated constructs, leading to confusion regarding what the scale is actually measuring. The blending of distinct psychological domains, such as depression, anxiety and well-being, undermines measurement precision, leading to skewed research conclusions and misguided clinical practices. The failure to detect or address contamination can perpetuate errors in treatment efficacy, particularly in areas such as mental health and spirituality, where precise measurement is crucial. This article emphasizes the importance of ensuring construct validity, which refers to the accurate measurement of specific concepts such as moral injury or posttraumatic stress disorder, and it highlights the risk of conflating distinct constructs. The widespread use of contaminated scales, often adopted without critical evaluation, poses a significant threat to the integrity of research outcomes. In order to address these challenges, this study advocates for rigorous scale development and validation processes, encouraging researchers, peer reviewers and journal editors to scrutinize the psychometric properties of scales. By ensuring that scales measure only the intended constructs, future research can produce more accurate and meaningful results, leading to improved clinical interventions and policy decisions.

Development and validation of an instrument to evaluate the perspective of using the electronic health record in a hospital setting

BackgroundEvaluating healthcare information systems, such as the Electronic Health Records (EHR), is both challenging and essential, especially in resource-limited countries. This study aims to psychometrically develop and validate an instrument (questionnaire) to assess the factors influencing the successful adoption of the EHR system by healthcare professionals in Moroccan university hospitals.MethodsThe questionnaire validation process occurred in two main stages. Initially, data collected from a pilot sample of 164 participants underwent analysis using exploratory factor analysis (EFA) to evaluate the validity and reliability of the retained factor structure. Subsequently, the validity of the overall measurement model was confirmed using confirmatory factor analysis (CFA) in a sample of 368 healthcare professionals.ResultsThe structure of the modified HOT-fit model, comprising seven constructs (System Quality, Information Quality, Information technology Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance), was confirmed through confirmatory factor analysis. Absolute, incremental, and parsimonious fit indices all indicated an appropriate level of acceptability, affirming the robustness of the measurement model. Additionally, the instrument demonstrated adequate reliability and convergent validity, with composite reliability values ranging from 0.75 to 0.89 and average variance extracted (AVE) values ranging from 0.51 to 0.63. Furthermore, the square roots of AVE values exceeded the correlations between different pairs of constructs, and the heterotrait-monotrait ratio of correlations (HTMT) was below 0.85, confirming suitable discriminant validity.ConclusionsThe resulting instrument, due to its rigorous development and validation process, can serve as a reliable and valid tool for assessing the success of information technologies in similar contexts.

An improved analytical method for rarefied aerothermodynamics on a complex geometry in free-molecular flows

A numerical method for high altitude aerothermodynamic analysis code for Very Low Earth Orbit (VLEO) satellite and spacecraft, named as HACS, is presented in this article. HACS is a computational tool that addresses the challenges by integrating the free-molecular gas kinetic model with novel particle sampling method and robust particle tracing algorithm to predict the shadow region on surfaces. To ensure the reliability and accuracy of the HACS, a rigorous verification and validation process has been systematically conducted by comparing the aerothermodynamic properties of forces, moments, and heat flux with the direct simulation Monte-Carlo (DSMC) results. The HACS is applied to the realistic geometries of VLEO satellite, the Apollo re-entry module, and spacecraft including fin and wing shapes. Results demonstrate that HACS provides the aerothermodynamic properties within the time frame of 60 seconds for the complex geometries and the accuracy of the HACS is guaranteed above 120 km altitude. The maximum relative error of the aerothermodynamic properties is less than 5% compared to the DSMC results at the lowest altitude of about 120 km, and has smaller relative errors as the altitude increases.

B-316 Evaluation of an FDA-cleared Chromogranin A Assay on an Automated Analyzer

Abstract Background Chromogranin A (CgA) is a hydrophilic and acidic protein, present in the chromaffin granules of neuroendocrine cells. It is the preferred tumor marker for the monitoring of neuroendocrine tumors (NETS), which are often found in the lungs, appendix, pancreas, and gastrointestinal tract. Previous CgA assays were performed by manual ELISA testing, and were not FDA-approved, leading to a longer, more rigorous validation process and longer run times. The introduction of an automated, FDA-cleared CgA assay allows the laboratory to process patient samples more quickly and reliably, shortening a multiple hour process to minutes. The objective of this study was to evaluate the performance of an automated CgA assay recently cleared by the FDA. Methods We validated the the B·R·A·H·M·S™ Chromogranin A II (CGAII) assay on the automated B·R·A·H·M·S KRYPTOR Compact PLUS (Thermo Fisher Scientific Inc.). The CGAII assay is an immunofluorescent assay, which uses Time-Resolved Amplified Cryptate Emission (TRACE) to quantitate CGA in human serum. Assay performance characteristics were evaluated including measuring range (MR) and maximum dilution, precision, method comparison, stability, reference interval, stability, and carryover. Results The MR for CGAII was established with triplicate analysis of serum at 8 levels, ranging from 25.8 - 2,700 ng/mL. Linearity was demonstrated with a slope of 0.907, intercept of 1.948 and observed error of 2.54 ng/mL. Recovery at each point ranged from 83.3-110.3%. Carryover (<5.0 ng/mL) was not observed at a concentration approximately 2 times the upper limit of the MR. A 1:25 or 1:500 automatic dilution was determined to be acceptable (% bias <16%). Intraday precision coefficient of variation (CV) ranged from 4.1-1.7% (SDs from 2.32-6.64 ng/mL) at concentrations of 56.7 and 393.0 ng/mL. The interday CVs ranged from 6.0-7.1% (SDs from 3.43 - 28.84 ng/mL) at concentrations of 57.5 and 403.8 ng/mL. Twenty samples with concentrations ranging from 56.4-2,907 ng/mL were compared to a reference laboratory utilizing a similar methodology. The correlation coefficient was 0.9934, with a slope of 1.014, and intercept of -32.95. An additional twenty samples with concentrations ranging from 67.0-2,446 ng/mL were compared to a CgA ELISA kit (CGA-ELISA-NG, CISBIO) to determine the bias between the two methodologies. The correlation coefficient was 0.9902, with a slope of 0.845, and intercept of -15.31, and a bias of -19.9%. The serum samples were stable for 3 days when stored at 2-8°C. Recovered CgA concentrations fell outside of the 95% confidence interval after day 3 at 2-8°C. The manufacturer provided reference interval of <187 ng/mL was verified by testing 20 healthy adult samples, all of which fell within this range. Conclusions This assay has been validated and shown to be an accurate and precise method for monitoring CgA. Further, the method was validated on an automated analyzer to allow expeditious turnaround time in a core laboratory.

The effect of university students' self-efficacy on problem-solving disposition: The chained dual mediating role of metacognition disposition and critical thinking disposition

Developing problem solving in university students is a crucial topic in education today. Based on existing theories and research, this study proposed nine hypotheses as well as constructed a chained dual-mediation model to investigate the pathways through which college students' self-efficacy affects problem-solving disposition. The obtained influence path model fully considered the relationship between self-efficacy and problem-solving disposition, and investigated the mediating roles of metacognition disposition and critical thinking disposition between the two. The study used scales to collect data from 346 university students from East China, including their self-efficacy, metacognition disposition, critical thinking disposition, and problem-solving disposition. The study underwent a rigorous validation process, and the scales were confirmed to have satisfactory reliability, structural validity, convergent validity, and discriminant validity suitable for conducting structural equation modelling. The structural equation modelling supported eight of the proposed nine hypotheses and confirmed the chained dual mediating role of metacognition disposition and critical thinking disposition between self-efficacy and problem-solving disposition. The study concluded that these four variables are significantly and positively correlated and that university students' self-efficacy can have an effect on problem solving disposition through metacognition disposition and critical thinking disposition. Therefore, to better develop university students' problem-solving disposition, educators need to not only help them develop positive self-efficacy beliefs, but also promote the development of their metacognition disposition and critical thinking disposition.

Study on laminar combustion characteristics and the optimization of the coupling mechanism in a mixture of propanol and gasoline

When both isopropanol and n-propanol are incorporated, the utilization of propanol as a fuel substitute (or a gasoline additive) presents promising potential for enhancing the combustion efficiency and thermal performance in compact, turbocharged, direct-injection gasoline engines upon blending. However, the complexity of the laminar combustion behavior of propanol-blended gasoline has yet to be fully investigated, as current coupling mechanisms are insufficiently sophisticated to precisely mirror the complex experimental conditions.This study establishes a testbed specifically designed for measuring laminar burning velocity (LBV) using the heat flux method. This setup is employed to measure the LBV of pure n-heptane and isooctane, as well as the LBV of the gasoline surrogate fuel TRF with two distinct blend ratios. Additionally, it measures the LBV of propanol and its blends with TRF. The research findings reveal that isooctane demonstrates a heightened sensitivity to fuel preheating temperature, whereas the toluene proportion in TRF fuels exerts the most pronounced influence on combustion behavior. At an equivalence ratio of 1.1, the LBV of n-propanol differs from that of its isomer, isopropanol, by 4.65 cm/s. Notably, the LBV exhibits a discernible upward trend, corresponding to the increasing proportion of toluene in the blended fuel. Furthermore, there is a pronounced distinction in LBV among the propanol isomers, with blended TRF occupying an intermediate position between pure propanol and TRF. After the enhancement of the mechanism based on experimental benchmarks of LBV, a rigorous validation process demonstrated a substantial improvement in the alignment between simulated outcomes and empirical LBV measurements.

A streamlined approach to scale adaptation: Enhancing validity and feasibility in educational measurement

In educational research, the adaptation of measurement scales to different contexts is necessary but often lacks thorough validation, leading to unreliable and invalid results that compromise research integrity and the effectiveness of educational policies. In this paper, I highlight these issues and advocate for elevated research standards and rigorous validation processes. I propose a practical validation approach that balances feasibility and rigor, including initial content validity checks, pilot testing with small samples, simplified statistical validation, and cross-validation when possible. While practical, this method has limitations, such as potential biases and incomplete psychometric assessments. Despite these, it enhances the reliability and validity of adapted scales, contributing to more robust and trustworthy educational research. By prioritizing meticulous validation, I believe researchers can uphold their ethical responsibilities and advance educational practices and policies grounded in robust evidence.

Development of a scale for measuring psychosocial vulnerability in disaster-prone areas

IntroductionDisaster vulnerability is a complex concept influenced by both natural forces and societal factors, including psychosocial aspects that shape an individual's resilience to natural hazards. Despite technological advancements, assessing psychosocial vulnerability remains challenging, necessitating specialized scales. This study attempted to develop a psychosocial vulnerability assessment scale, recognizing the intricate interplay of physical, psychological, and social factors. MethodsThe study was conducted in two disaster-prone wards in Cuttack, India, susceptible to cyclones, earthquakes, and floods. The scale development process involved two stages: instrument design and judgmental evidence. During the instrument design phase, qualitative data on psychosocial vulnerabilities were gathered through Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs). These insights led to the generation of 44 items, which were subsequently refined to 35 items through expert consultation. The judgmental evidence phase included content validity assessment, internal consistency analysis, and test-retest reliability estimation. Exploratory Factor Analysis (EFA) identified the instrument's factor structure, with items retaining factor loadings exceeding 0.4. Data collection involved 400 individuals selected through multistage sampling, with strict adherence to ethical considerations. ResultData collected in the initial instrument design stage revealed key psychosocial vulnerability themes. These themes informed the creation of a 35-item scale categorized into three domains: Physical, social, and psychological vulnerability. The instrument underwent rigorous validation processes, demonstrating strong content validity agreement among experts. Internal consistency analysis revealed good reliability, as indicated by the Kuder-Richardson coefficient (KR20 = 0.904). Test-retest reliability further established the stability of responses over time, with an ICC of 0.82. EFA confirmed the scale's three-factor structure, explaining 60 % of the variance. After removing three items with low factor loadings, the scale proved reliable and valid, effectively categorizing vulnerabilities into these distinct domains. ConclusionThe scale developed in this study offers a comprehensive tool for understanding the unique challenges people living in disaster-prone areas face. It addresses the often-overlooked psychosocial components of vulnerability, filling a critical gap in existing research and measurement scales. Its application helps in planning psychosocial interventions and improving disaster risk reduction strategies.

Predicting multidimensionality of mathematical creativity among students: Do mathematics self-efficacy, attitude to mathematics and motivation to mathematics matter?

Mathematical creativity is the focus of most curriculum in that students with this ability are prone to solving problems in society from a multidimensional perspective. However, studies of this nature, especially in Africa, are limited, and this poses a challenge to policy development. To cover this gap, the study sought to model the predictive effect of mathematics self-efficacy, motivation for mathematics, and attitude towards mathematics on mathematical creativity from a multidimensional perspective. The study adopted a cross-sectional survey using 654 mathematics students for the study. Two instruments–the mathematics self-efficacy, attitude towards mathematics, and mathematical motivation scales and the mathematical creativity test were used for data analysis after they had undergone a qualitative and quantitatively rigorous validation process. This was done using exploratory and confirmatory factor analysis with the average variance extracted and Fornell-Larcker criterion for convergent and divergent analysis, respectively, while Cronbach’s alpha was used to determine the stability of the scales. Analysis was performed using hierarchical regression and the result showed that mathematics self-efficacy and attitude towards mathematics, when taken individually, are the only strong predictors of mathematics creativity from the perspective of fluency, flexibility, and originality components. Collectively, the three predictors were significant in contributing to the variance in mathematics creatively from the three dimensions assessed. However, mathematics self-efficacy was the strongest predictor, followed by attitude towards mathematics. The implications of the study were discussed for policy and curriculum redesign in mathematics education.

Social Media Disorder Scale: Structure, Reliability and Validity in Indian Context

India is a country that has seen a notable increase in the amount of time its citizens spend on the Internet. One of the primary drivers of internet usage is the widespread adoption of social media platforms. When there is compulsive and unhealthy use of those platforms, it is termed Social Media Disorder (SMD), which has far-reaching implications. However, a valid tool to measure this issue still needs to be created in India. The study aimed to determine the reliability and validity of the Social Media Disorder Scale (SMDS), initially developed in the Netherlands. The developers used the diagnostic criteria of DSM-5 Internet Gaming Disorder since both have the same overarching construct of internet addiction. The nine-dimensional 27-item scale was put through a rigorous validation process while being administered among 552 adolescents in five districts and two states of India. The data collection lasted for three months between March - May, 2024. Construct validity of SMDS was assessed with Exploratory Factor Analysis (first subsample n=280) and Confirmatory Factor Analysis (second subsample n=272) using SPSS and AMOS software, respectively. The nine dimensions and 27 items were retained, yielding an internal consistency Cronbach’s alpha of 0.945. This comprehensive validation process has ensured and provided strong evidence that the scale can be utilized in the Indian context to assess SMD.

Cross-cultural adaptation and validation of the Arabic convergence insufficiency symptom survey (Arab-CISS) in Saudi Arabia

Background: Convergence insufficiency (CI) is a common binocular vision disorder characterized by difficulty maintaining close visual focus. The Convergence Insufficiency Symptom Survey (CISS) is widely used to assess the presence and severity of CI symptoms, yet its applicability across different linguistic and cultural groups requires validation. This study aimed to translate, validate, and culturally adapt the CISS for Arabic-speaking populations, focusing on a sample from Saudi Arabia. Methods: Following established guidelines for questionnaire adaptation, the CISS was translated into Arabic and back-translated to ensure accuracy. The Arab-CISS underwent rigorous validation processes, including expert review and pre-testing. A sample of 104 participants from Qassim University, aged 18–40 years, was recruited with a Best-Corrected Visual Acuity (BCVA) of 0.1 (20/25) or better in both eyes. Internal consistency was evaluated using Cronbach’s alpha, and test–retest reliability was assessed through the Intraclass Correlation Coefficient (ICC). Results: The Arab-CISS demonstrated excellent internal consistency (Cronbach’s alpha = 0.88) and test–retest reliability (ICC = 0.93, 95% confidence interval (CI): 0.69–0.97). The Bland–Altman analysis supported these findings, showing minimal mean difference between test and retest scores (−0.1 ± 1.51), indicating stable and consistent responses over time without significant bias. Conclusion: The Arab-CISS is a reliable and valid instrument for assessing CI symptoms among Arabic-speaking individuals. Its introduction is a significant step toward improving the diagnosis and management of CI in Arabic-speaking populations.

Enhanced enchondroma detection from x-ray images using deep learning: A step towards accurate and cost-effective diagnosis.

This study investigates the automated detection of enchondromas, benign cartilage tumors, from x-ray images using deep learning techniques. Enchondromas pose diagnostic challenges due to their potential for malignant transformation and overlapping radiographic features with other conditions. Leveraging a data set comprising 1645 x-ray images from 1173 patients, a deep-learning model implemented with Detectron2 achieved an accuracy of 0.9899 in detecting enchondromas. The study employed rigorous validation processes and compared its findings with the existing literature, highlighting the superior performance of the deep learning approach. Results indicate the potential of machine learning in improving diagnostic accuracy and reducing healthcare costs associated with advanced imaging modalities. The study underscores the significance of early and accurate detection of enchondromas for effective patient management and suggests avenues for further research in musculoskeletal tumor detection.

A novel intelligent stereo vision approach for blast-induced fragmentation size distribution: Case study at Golgohar open-pit mine, Iran

Rock fragmentation assessment is a critical aspect of evaluating blasting operations in the mining industry, offering insights for optimising procedures and subsequent mining processes. Traditional methods, such as sieving, while accurate, are costly and time-consuming. Digital image analysis methods have emerged as alternatives, yet they face challenges in accurately handling fragments of varying sizes. Recent advancements in computer vision, particularly deep learning techniques, offer promising solutions to automate and refine fragmentation analysis. This paper introduces a novel intelligent stereo vision approach utilising YOLOv8, an advanced deep learning model, for blast-induced fragmentation size distribution assessment at the Golgohar open-pit mine in Iran. By training YOLOv8 with an automated self-labelled dataset and evaluating its performance against real mine site data, this research aims to enhance the accuracy and efficiency of blast fragmentation analysis. The proposed method leverages stereo images and machine learning to provide precise measurements, leading to improved optimisation of blasting parameters and potential economic benefits compared to traditional methods. Through rigorous validation processes and case studies across diverse scenarios, the proposed approach demonstrates robustness and reliability in assessing blast fragmentation, offering a practical and efficient solution for the mining industry’s evolving needs.

High‐Performance Liquid Chromatography–Based Standardization of Stigmasterol in Moringa oleifera: Method Development and Validation Through Design of Experiment Approach

ABSTRACTThe primary objective of this investigation was to develop and validate a robust reversed‐phase high‐performance liquid chromatography (RP‐HPLC) method for quantifying stigmasterol in both Moringa oleifera powder and tablets. The analysis was conducted using a Box–Behnken design. Stigmasterol separation was achieved with a Phenomenex Luna C‐18 column (4.6 × 150 mm2, 5 µm) and a mobile phase consisting of methanol and 0.1% formic acid in an 80:20% v/v ratio. The separation was performed at a flow rate of 1 mL/min, and stigmasterol was detected at a wavelength of 210 nm using a photodiode array detector. Method validation followed the guidelines set forth by the International Council for Harmonisation (ICH) Q2 (R1). This rigorous validation process evaluated critical parameters, including linearity, accuracy, system suitability, precision, and robustness. The results for each parameter were well within the acceptable range, confirming the reliability and efficacy of the established RP‐HPLC method. The findings of this study highlight the potential of the RP‐HPLC method as a dependable tool for the routine analysis of stigmasterol in herbal formulations.

Spatial Mapping for Multi-Hazard Land Management in Sparsely Vegetated Watersheds Using Machine Learning Algorithms

This study breaks new ground by developing a multi-hazard vulnerability map for the Tensift watershed and the Haouz plain in the Moroccan High Atlas area. The unique juxtaposition of flat and mountainous terrain in this area increases sensitivity to natural hazards, making it an ideal location for this research. Previous extreme events in this region have underscored the urgent need for proactive mitigation strategies, especially as these hazards increasingly intersect with human activities, including agriculture and infrastructure development. In this study six advanced machine learning (ML) models were used to comprehensively assess the combined probability of three significant natural hazards: flooding, gully erosion, and landslides. These models rely on causal factors derived from reputable sources, including geology, topography, meteorology, human activities, and hydrology. The research's rigorous validation process, which includes metrics such as specificity, precision, sensitivity, and accuracy, underlines the robust performance of all six models. The validation process involved comparing the model's predictions with actual hazard occurrences over a specific period. According to the outcomes in terms of the area under curve (AUC), the XGBoost model emerged as the most predictive, with remarkable AUC values of 93.41% for landslides, 91.07% for gully erosion and 93.78% for flooding. Based on the overall findings of this study, a multi-hazard risk map was created using the relationship between flood risk, gully erosion, and landslides in a geographic information system (GIS) architecture. The innovative approach presented in this work, which combined ML algorithms with geographical data, demonstrates the power of these tools in sustainable land management and the protection of communities and their assets in the Moroccan High Atlas and regions with similar topographical, geological, and meteorological conditions that are vulnerable to the aforementioned risks.

Exploring Engineering Philosophy from an Epistemological Perspective

This study explores engineering philosophy from an epistemological perspective, aiming to improve students' critical thinking skills, foster interdisciplinary understanding, promote ethical reflection, and deepen engagement with the philosophical foundations of technology. The study uses a comparative analysis and critical review of the literature on the philosophy of engineering. The results are as follows: First, the essence of engineering knowledge lies in practical application, problem-solving orientation, and interdisciplinary integration. Second, epistemological methods in engineering emphasize the importance of balancing intuition and analysis, critical thinking, rigorous experimental design, and verification and validation processes. Third, while experience and expertise are certainly crucial in shaping engineering knowledge, it is necessary to critically examine the potential biases and limitations inherent in this process. Fourth, the epistemological challenges of interdisciplinary engineering necessitate humility, open-mindedness, and interdisciplinary integration. Finally, the ethical and social dimensions of engineering knowledge are essential considerations for responsible and sustainable engineering practices. Integrating ethical reflection into decision-making and embracing social responsibility and community involvement can enable engineers to contribute to social advancement while upholding professional integrity and ethical standards. Future research suggestions include conducting a meta-epistemological analysis to study the fundamental assumptions, methodologies, and conceptual frameworks employed in engineering philosophy research from an epistemological perspective. It can explore the intersection of epistemology, ethics, and social theory in engineering practice and education. Finally, conducting historical and comparative studies of engineering epistemology in diverse cultural, historical, and technological contexts can offer meaningful insights.