Advanced Quantitative Methods Research Articles

Enhancing exam question quality in medical education through bootstrapping.

Medical schools are required to assess and evaluate their curricula and to develop exam questions with strong reliability and validity evidence, often based on data derived from statistically small samples of medical students. Achieving a large enough sample to reliably and validly evaluate courses, assessments, and exam questions would require extensive data collection over many years, which is inefficient, especially in the fast-changing educational environment of medical schools. This article demonstrates how advanced quantitative methods, such as bootstrapping, can provide reliable data by resampling a single dataset to create many simulated samples. This economic approach, among others, allows for the creation of confidence intervals and, consequently, the accurate evaluation of exam questions as well as broader course and curriculum assessments. Bootstrapping offers a robust alternative to traditional methods, improving the psychometric quality of exam questions, and contributing to fair and valid assessments in medical education.

A relay race or an ironman? A systematic review of the literature on innovation in the mining sector

As global demand for minerals and metals surges, the mining industry is faced with the triple challenge of finite resources, societal resistance, and environmental considerations. To address these challenges the mining sector is relying on innovation and technological advancements to enable exploitation of so far inaccessible deposits, minimize energy consumption, and improve sustainability. This study presents a systematic review of the literature on innovation in mining and the unique management approach being adopted to respond to the above challenges. From an initial corpus of 4059 scientific articles, a reproducible process filtered out 222 documents, published between 1996 and 2023. The analysis reveals that the literature on innovation in the mining industry, predominantly qualitative and exploratory in nature, is showing a shift towards empirical validation using advanced quantitative methods. Mining innovation appears to be shaped by the industry's inherent features such as a conservative culture, market volatility, emphasis on productivity, and profitability. While the industry somewhat risk-averse approach favours stability and specialization which could hamper innovation, its simultaneous focus on productivity encourages innovation to achieve cost reductions, enhanced processes, and implementation of new technology. However, the uniformity of ore and the position of miners in the value chain is not favorable to product innovation and the focus tends to be on —mainly supplier-led— process innovation. The review also shows the potential benefits of non-technological innovations, including marketing and organizational changes although these are infrequent. The study concludes by addressing conceptualizations of innovation and mining management and highlighting gaps in research that focuses on regions, minerals, and innovation types. It also discusses the knowledge management implications in this context.

Do fintech, natural resources and globalization matter during ecological crises? A step towards ecological sustainability

The rate of biodiversity loss and forest depletion has surged significantly, primarily driven by the substantial increase in worldwide environmental deterioration. This alarming development is nearing the critical temperature limit of 2 °C, necessitating increased scrutiny. This study therefore adopts the Load Capacity Curve framework that amalgamates these concepts, offering a guide for implementing a robust and sustainable adaptation plan in the North African region. The research utilized data spanning from 1991 to 2022 and employed advanced quantitative methods, including quantile regression (QR), augmented mean group (AMG), and the Half-Panel Jackknife (HPJ) Wald-type test, to explore causality. The empirical findings challenge the notion of a U-shaped relationship between per capita GDP and ecological load capacity, revealing instead an inverted U-shaped pattern at the 25th and 50th quantiles. Additionally, the study identifies a negative and significant association between natural resource rents and globalization with the ecological load capacity factor, indicating a detrimental effect. On the other hand, Fintech demonstrates a statistically significant and positive impact on environmental sustainability, indicating a mitigating effect. Causality tests further reveal bidirectional causality between ecological load capacity and per capita GDP, GDP squared, and Fintech, while a unidirectional positive relationship exists between globalization and ecological load capacity. Based on these findings, the study puts forth several policy recommendations to support informed decision-making.

Potentıals And Lımıtatıons Of Socıal Area Analysıs: The Case of Bağcılar Dıstrıct, Istanbul

This paper explores the interplay between social differentiation and urban space in Istanbul’s Bağcılar district, utilizing Social Area Analysis (SAA) to examine micro-level urban dynamics. Initial research involved a literature and META-Analysis on SAA, introduced by Shevky and Bell (1955) and evolved through advanced quantitative methods by Ostendorf and Musterd (2012). This study integrates macro-level perspectives, analyzing economic changes, socio cultural shifts, and government policies from 1923 to present. These dimensions are essential for understanding the spatial and social transformations in Istanbul. The micro-level analysis employs SAA components such as social rank, urbanism, and social differentiation, with data derived from the Turkish Statistical Institute and the Household Survey of Istanbul Transportation Planning. The analysis highlights how Bağcılar’s rapid growth since the 1950s, fueled by rural migration and industrial development, has led to profound socio-economic changes. The findings will be contextualized within Bağcılar’s specific characteristics illustrating the district's unique urban development and its implications for social stratification

Foreign direct investment, information and communication technology, and economic growth: The case of North African Countries

This paper examines the role of information and communication technology (ICT) in modulating the effects of foreign direct investment (FDI) on economic growth in North Africa (Morocco, Algeria, Tunisia, and Egypt) over the period 1995 to 2021. Using advanced quantitative methods, including quantile regression, and comparing with Two-Stage Least Squares (2SLS) regression and Generalized Method of Moments (GMM), the study assesses the impact of FDI and ICT on growth, controlling for country fixed effects. The results indicate that FDI has a significant positive impact on growth, particularly when interacting with transmission channels, notably the telephone (mobile and fixed). However, the interaction with the internet varies according to quantile levels, showing positive effects at low quantiles and negative effects at high quantiles. The study highlights the importance of investment policies, digital inclusion, and research to optimize the benefits of FDI in the region.

Inter-cropping patterns and nutrient management effects on maize growth, yield and quality

ContextIntercropping, a sustainable agricultural practice, is pivotal in enhancing land use efficiency, soil fertility, and overall crop productivity. This study zeroes in on the dynamics of Maize-Cowpea intercropping in the Alluvial Zone of West Bengal, a region characterized by its fertile soil and diverse agro-ecological conditions. The practice of intercropping, particularly in regions like West Bengal, holds immense potential in revolutionizing traditional farming methods by maximizing yield per unit area while ensuring sustainability. The study aims to unravel the complexities and benefits of this practice, especially in the context of the monsoon dependent Kharif season, which is crucial for agricultural productivity in the region. ObjectivesThe study aims to evaluate the effects of various Maize-Cowpea intercropping patterns and nutrient management strategies on maize growth, yield, and quality during the Kharif seasons of 2019 and 2020. It hypothesizes that intercropping, combined with optimal nutrient management, can significantly enhance maize performance. MethodsEmploying a split-plot design, the study analysed 25 treatment combinations of intercropping and nitrogen fertilization under sub-tropical sub-humid conditions. Advanced quantitative methods, including regression analysis and principal component analysis (PCA), were used to assess the interactions between intercropping systems and nutrient management practices. ResultsThe findings revealed that while sole maize cultivation showed better growth and yield, Maize-Cowpea intercropping, especially in a 1:1 row ratio, significantly increased the total system yield by 13.6%. This method also improved grain quality, notably enhancing crude protein content and nutrient percentage. A combination of 75% recommended dose of nitrogen (RDN) through chemical means with 25% RDN through organic sources, including seaweed, was particularly effective in boosting maize performance. ConclusionsThe study confirms the superior performance of Kharif maize in an intercropping setup, particularly when integrated with cowpea. It demonstrates the effectiveness of specific nutrient management strategies in enhancing both quantitative and qualitative traits of maize. ImplicationsThis research significantly contributes to the field of sustainable agriculture, particularly in the context of field crops research and practice. The findings offer valuable insights for enhancing crop productivity and sustainability in similar agro-ecological regions globally. The study underscores the benefits of integrating legumes with cereals in intercropping systems, paving the way for innovative agricultural practices that can meet the dual challenges of food security and environmental sustainability. The implications of this research are far-reaching, providing a robust framework for future agricultural strategies in regions facing similar climatic and soil conditions.

Effective application of biosensor analytical techniques in drug testing

Abstract This study explores biosensor technology, focusing on its application in drug detection through advanced quantitative analysis methods: partial least squares (PLS) and probabilistic principal component analysis (PPCA). We developed a rapid quantitative calibration model using azure A, B, and C—metabolites of pefloxacin mesylate and methylene blue— demonstrated through surface-enhanced Raman spectroscopy. The findings highlight the superior accuracy of PLS and PPCA in predicting drug concentrations, with pefloxacin mesylate detection deviations maintained between 0.24%-0.98% and 0.35%-1.02%, respectively. PLS proved to be slightly more effective. This study confirms the potential of biosensor technology in ensuring drug safety, offering substantial support for public health protection and regulatory compliance.

Triangulating brain alterations in anorexia nervosa: a multimodal investigation of magnetic resonance spectroscopy, morphometry and blood-based biomarkers

The acute state of anorexia nervosa (AN) is associated with widespread reductions in cortical gray matter (GM) thickness and white matter (WM) volume, suspected changes in myelin content and elevated levels of the neuronal damage marker neurofilament light (NF-L), but the underlying mechanisms remain largely unclear. To gain a deeper understanding of brain changes in AN, we applied a multimodal approach combining advanced neuroimaging methods with analysis of blood-derived biomarkers. In addition to standard measures of cortical GM thickness and WM volume, we analyzed tissue-specific profiles of brain metabolites using multivoxel proton magnetic resonance spectroscopy, T1 relaxation time as a proxy of myelin content leveraging advanced quantitative MRI methods and serum NF-L concentrations in a sample of 30 female, predominately adolescent patients with AN and 30 age-matched female healthy control participants. In patients with AN, we found a reduction in GM cortical thickness and GM total N-acetyl aspartate. The latter predicted higher NF-L levels, which were elevated in AN. Furthermore, GM total choline was elevated. In WM, there were no group differences in either imaging markers, choline levels or N-acetyl aspartate levels. The current study provides evidence for neuronal damage processes as well as for increased membrane lipid catabolism and turnover in GM in acute AN but no evidence for WM pathology. Our results illustrate the potential of multimodal research including tissue-specific proton magnetic resonance spectroscopy analyses to shed light on brain changes in psychiatric and neurological conditions, which may ultimately lead to better treatments.

Predicting the Severity of Runway Excursions from Aviation Safety Reports

Despite all technological advances, runway excursions (REs) remain among the most frequent aviation accidents. Several factors have been shown to contribute to RE incidents. Although different studies have addressed the prediction of REs using quantitative data, the present study contributes to RE research by applying advanced quantitative analysis methods using text mining of voluntarily submitted reports to predict aircraft damage as the outcome of a runway excursion event. The study also intended to unveil relevant predictors from the voluntary safety reports data. The random forest model presented better results than the naïve Bayes and the gradient boosting approaches. The positive predictive value of the random forest method was measured at 74.90%, which is in contrast to 63.58% of the naïve Bayes method and 72.56% of the gradient boosting method. Although several generalizable topics were identified in the reports (such as runway and touchdown zone characteristics, flight control during approach, aircraft malfunction, weather factors, and runway surface and braking action conditions), no defined predictors for runway excursion events resulting in aircraft damage could be established.

Multiparametric MRI of Solid Renal Masses: Principles and Applications of Advanced Quantitative and Functional Methods for Tumor Diagnosis and Characterization.

Solid renal masses (SRMs) are increasingly detected and encompass both benign and malignant masses, with renal cell carcinoma (RCC) being the most common malignant SRM. Most patients with SRMs will undergo management without a priori pathologic confirmation. There is an unmet need to noninvasively diagnose and characterize RCCs, as significant variability in clinical behavior is observed and a wide range of differing management options exist. Cross-sectional imaging modalities, including magnetic resonance imaging (MRI), are increasingly used for SRM characterization. Multiparametric (mp) MRI techniques can provide insight into tumor biology by probing different physiologic/pathophysiologic processes noninvasively. These include sequences that probe tissue microstructure, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and T1 relaxometry; oxygen metabolism (blood oxygen level dependent [BOLD-MRI]); as well as vascular flow and perfusion (dynamic contrast-enhanced MRI [DCE-MRI] and arterial spin labeling [ASL]). In this review, we will discuss each mpMRI method in terms of its principles, roles, and discuss the results of human studies for SRM assessment. Future validation of these methods may help to enable a personalized management approach for patients with SRM in the emerging era of precision medicine. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: 2.

Terrain and Infrastructure Monitoring Across the Electromagnetic Spectrum

Geotechnical engineers often participate in projects that involve Building Information Modeling, or BIM, digital-twin engineering design, and even just classical geotechnical studies. Therefore, it's helpful to know about the latest advanced quantitative methods that can best characterize topography and changes to structural surfaces. An array of new, remote sensing tools utilizes the most transmissive portions of the electromagnetic spectrum, EM, to map surfaces and surface change at millimeter-to-centimeter accuracy.

Technological forecasting based on estimation of word embedding matrix using LSTM networks

There are a vast number of quantitative and qualitative technological forecasting methods. In the last decade, advanced quantitative technological forecasting methods based on the various applications of data science approaches have been proposed. Text mining is one of the key approaches used to examine large datasets consisting of scientific publications and patent documents with the aim of offering foresight for a selected area. However, the existing related studies either perform a qualitative approach by analysing the recent data to identify the emerging topics or use extrapolation techniques to predict the future values of some statistical terms or the future frequency of some important keywords. In this study, different from such related studies, we propose a deep learning-based framework to predict future co-similarity matrix representing the possible new and disappearing interactions between the words in the future. For this purpose, word vectors are generated using a word embedding technique and the temporal changes of the associations between the words are modelled using Long Short-Term Memory networks for the future estimation of the word embedding matrix. The text mining area is chosen as a case study. The clusters of the terms extracted from the predicted word embedding matrices were analysed and potentially emerging areas were identified for different prediction horizon lengths. The accuracy of the proposed model was analysed based on a set of evaluation metrics that measure the amount of overlapping between the actual and predicted word maps. The quantitative analysis showed that the proposed system can successfully identify the emerging and disappearing areas and can be used as a decision-making tool for the future projection of other areas.

A Latent Variable Analysis of Psychomotor and Neurocognitive Performance After Acute Cannabis Smoking.

This paper evaluated a novel, tablet-based neurocognitive and psychomotor test battery for detecting impairment from acute cannabis smoking using advanced quantitative methods. The study was conducted in a state with legal, recreational cannabis use and included participants who use cannabis occasionally or daily, and a no use comparison group. Participants completed a tablet-based test assessing reaction time, decision making, working memory and spatial-motor performance. The test was completed before and after participants smoked cannabis (or after a rest period in the case of controls). An Exploratory Factor Analysis approach was implemented to reduce dimensionality and evaluate correlations across the four assessed domains. Linear regression models were utilized to quantify associations between factor scores and cannabis use groups (daily vs. occasional vs. no use). Seven factors were identified explaining 56.7% of the variance among the 18 measures. Regression models of the change in factors after cannabis smoking indicated those who use cannabis daily demonstrated poorer performance on a latent factor termed Displaced and Delayed (standardized coefficient 0.567, 95% CI: 0.178, 0.955; P = 0.005) compared to those with no use. Those who use cannabis occasionally exhibited a decline in performance on a latent factor termed Recall and Reaction (standardized coefficient 0.714, 95% CI: 0.092, 1.336; P = 0.025) compared to no use. This analysis demonstrates an innovative, quantitative approach to study how cannabis consumption affects neurocognitive and psychomotor performance. Results demonstrated that acute cannabis use is associated with changes in neurocognitive and psychomotor performance, with differences based on the pattern of occasional or daily use.

Characterisation of energy poverty in Mexico using energy justice and econophysics

The exploitation of limited energy resources can generate socio-economic inequalities calling for a combination of justice with the socio-technical study of modern energy systems to advance its understanding and remediation. Within this context, this paper uses energy justice and econophysics as theoretical and methodological frameworks to discuss issues of energy poverty in Mexico. The results emerging from this research illustrate that, according to data from 2014, around 61 % of Mexican households suffered from energy poverty due to issues of accessibility or affordability of modern energy services and fuels (with 11.54 % of households facing both types of energy poverty). This paper provides a novel approach that combines advanced quantitative methods based on econophysics, with conceptual frameworks from social sciences like energy justice to discuss issues of energy poverty in Mexico. Furthermore, this research performs an approximation to energy consumption in Mexican households based on monetary expenditure, the heat value of fuels and unit prices during 2014. These methods contribute to the understanding and characterisation of household energy needs and energy consumption in Mexico, reducing the existing gap in the academic literature on the analysis and critical thinking of energy poverty in Global South contexts (specifically in Latin America).

THE IMPACT OF THE CONSUMPTION OF VARIOUS DRUGS ON STUDENTS’ ACADEMIC ACHIEVEMENT IN MOGADISHU, SOMALIA: EXPLORATORY FACTOR ANALYSIS

The aim of this study was to use exploratory factor analysis to investigate the impact of consumption of various drugs (smokeless tobacco as known as Tabu in Somalia, Tramadol, and cannabis) on students’ academic achievement in light of the students’ lack of discipline, poor communication skills, participation in-class activities, and GPA at secondary schools in Mogadishu, Somalia. This study applied factor analysis to advanced quantitative research methods. This study also concentrated on target respondents 266 and featured teaching and non-teaching staff who work in public and private schools. To collect and analyze the data, a questionnaire and SPSS Version 20 were used. Cronbach’s Alpha The.908 indicated that the data’s dependability was correct. The correlation of the variables in this article was (r =.865*). The KMO value of the data .869 appears to be convenient for factor analysis in terms of sample size. Also, Barlett’s Test is found to be scientifically significant. Factor analysis The performed scree plot curve, the total variance explained, rotated components matrix, and anti-image values varied from 0.859 to 0.844. The main findings show that drug misuse, including cannabis, tramadol, and smokeless tobacco, has a negative effect on a student’s academic achievement in terms of conduct, poor communication, participation in-class activities, and GPA in secondary schools in Mogadishu. We recommend the government of Somalia cracks down on traffickers who bring smokeless tobacco and other drugs that harm Somali students and produce rules that protect them against drug abuse. We also recommend that the Somali community, particularly teachers, school principals, and religious leaders, raise awareness about how to prevent and treat drug abuse, particularly with tabu and tramadol. KEYWORDS: drug abuse, smokeless tobacco, tramadol, cannabis, students academic achievement

A lightweight keypoint matching framework for insect wing morphometric landmark detection

Geometric morphometrics has become an important approach in insect morphology studies because it capitalizes on advanced quantitative methods to analyze shape. Shape could be digitized as a set of landmarks from specimen images. However, the existing tools mostly require manual landmark digitization, and previous works on automatic landmark detection methods do not focus on implementation for end-users. Motivated by that, we propose a novel approach for automatic landmark detection, based on visual features of landmarks and keypoint matching techniques. While still archiving comparable accuracy to that of the state-of-the-art method, our framework requires less initial annotated data to build prediction model and runs faster. It is lightweight also in terms of implementation, in which a four-step workflow is provided with user-friendly graphical interfaces to produce correct landmark coordinates both by model prediction and manual correction. The utility iMorph is freely available at https://github.com/ha-usth/InsectWingLandmark, currently supporting Windows, MacOS, and Linux.

The association between childhood adiposity and appetite assessed using the Child Eating Behavior Questionnaire and Baby Eating Behavior Questionnaire: A systematic review and meta‐analysis

Obesity ReviewsVolume 23, Issue 4 e13437 CORRIGENDUMFree Access The association between childhood adiposity and appetite assessed using the Child Eating Behavior Questionnaire and Baby Eating Behavior Questionnaire: A systematic review and meta-analysis This article corrects the following: The association between childhood adiposity and appetite assessed using the Child Eating Behavior Questionnaire and Baby Eating Behavior Questionnaire: A systematic review and meta-analysis Alice Kininmonth, Andrea Smith, Susan Carnell, Silje Steinsbekk, Alison Fildes, Clare Llewellyn, Volume 22Issue 5Obesity Reviews First Published online: February 7, 2021 First published: 11 March 2022 https://doi.org/10.1111/obr.13437AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat In Kininmonth et al,1 the following errors were published in the Acknowledgment and Conflict of Interest sections. In the Acknowledgment section, funding statements should have been added. The statements are provided below: CHL is a fellow of MQ: Transforming Mental Health (MQF17/4), and AK was supported by an Economic and Social Research Council Advanced Quantitative Methods Studentship (Project reference: 1948633). The funders had no role in any element of this research. For the Conflict of Interest section, there should have no conflict of interest to be declared and it should have read: CONFLICT OF INTEREST No conflict of interest. The online version has been corrected. REFERENCE 1Kininmonth A, Smith A, Carnell S, Steinsbekk S, Fildes A, Llewellyn C. The association between childhood adiposity and appetite assessed using the Child Eating Behavior Questionnaire and Baby Eating Behavior Questionnaire: A systematic review and meta-analysis. Obes Rev. 2021; 22(5):e13169. Wiley Online LibraryPubMedWeb of Science®Google Scholar Volume23, Issue4April 2022e13437 ReferencesRelatedInformation

Spatial Incorporation of Multiple Immigrant Groups in Gateway Cities: Comparative Analysis of Sydney, Barcelona, and Prague

Immigrant incorporation in cities is often explained using the theories of spatial and segmented assimilation, which differ, among other things, in their approach to the spatial aspects of incorporation. However, empirical evidence about immigrant spatial incorporation from both theories is ambiguous, since most case studies focus on a single location and a limited number of immigrant groups, which hampers their external validity. Nonetheless, the level of immigrant spatial incorporation depends heavily on the characteristics of immigrant groups, the destination context, and the interplay between them. Therefore, we compared the spatial incorporation of 143 immigrant groups in Sydney, 54 in Barcelona, and 95 in Prague. These three cities differ greatly in their immigration histories, since each represents a different stage of the migration cycle. By employing both traditional and advanced quantitative methods, we show that immigrant spatial incorporation patterns vary considerably across groups and destination cities, with cultural proximity and the maturity of the immigration system among the key determinants. These findings imply that geographical context has a substantial potential to affect the immigrant spatial incorporation.

Causal Mediation Analysis in Single Case Experimental Designs: Introduction to the Special Issue.

This special issue of Evaluation and the Health Professions is dedicated to methods for causal mediation analysis in Single Case Experimental Designs (SCEDs). Mediation analysis is used to identify intermediate variables that transmit the effect of the independent variable on the outcome. Until recently, mediation analysis was mostly confined to between-subjects designs and panel studies with few exceptions. Consequently, most of the developments in causal mediation analysis have also been restricted to such designs. In applied health research, SCEDs have been used to evaluate total effects of treatments on outcomes of interest. Providing researchers with the methods for evaluating causal indirect effects for individual participants can lead to important improvements in diagnosis, treatment, and prevention. This special issue includes articles that describe advanced quantitative methods for testing mediators in SCEDs, propose and test approaches that allow for relaxing statistical assumptions that may not hold in real data, and illustrate mediation analysis for a single participant in real and simulated SCEDs data.

Color trend prediction method based on genetic algorithm and extreme learning machine

AbstractPredicting the trends of future color is a crucial task in the study of color application, which is also of great importance to the business economy and has received increasing attention in the design field in recent years. More advanced quantitative computational methods are expected to make the prediction more accurate, and thus help designers to avoid the interference of subjective factors when carrying out the design. In this article, a hybrid model based on genetic algorithm and extreme learning machine is developed to predict color trends. The accuracy is improved through the optimal solution of hidden bias and input weights of the extreme learning machine searched by a genetic algorithm. By using a real historical dataset of smartphone appearance color, the prediction results of the genetic algorithm‐extreme learning machine model are compared with those of several commonly used models in the past. The accuracy of the model is evaluated in terms of absolute error and mean absolute error values. The results show that the method proposed in this article can maintain higher accuracy when predicting the trends of smartphone appearance color.