Modeling Techniques Research Articles

A review of the formability of woven fabrics for composite materials

AbstractTextile composites are advanced materials composed of preforms combined with matrix materials. The fiber structure in the preform has a significant impact on the mechanical properties of the composite. Precise control over preform dimensions and internal fiber structural uniformity, termed ‘accurate shape control’, is essential to ensure reliable and stable composite component mechanical properties. This paper reviews current research progress on fabric deformation mechanisms, focusing on experimental characterization and numerical simulation. Experimental methods for fabric deformation include tensile, compression, bending, and shear deformation, whereas numerical methods encompass macroscopic continuum, discrete, and semi‐discrete models. The insights offered in this paper will aid a greater understanding of fabric deformation mechanisms, enabling an accurate prediction of complex shape molding and effective process parameter design, ultimately facilitating the structural design and engineering applications of textile composites.Highlights Recent trends and challenges in the study of fabric deformation mechanisms are presented. The experimental methods for fabric deformation were summarized and evaluated. Representative numerical modeling techniques and simulation methods are discussed. Some recommendations on potential future research directions are detailed.

Towards characterising rocky worlds: Trends in chemical make-ups of M dwarfs versus GK dwarfs

Elemental abundances of Sun-like stars have been shown to be crucial for understanding the detailed properties of planets surrounding them. However, accurately measuring elemental abundances of M stars, the most abundant class of stars in the solar neighbourhood, is challenging due to their faintness and pervasive molecular features in optical photospheric spectra. As a result, elemental abundances of Sun-like stars have been proposed to constrain those of M stars, particularly by scaling X/H given measured Fe/H This work aims to test the robustness of this convenient practice based on two selected sets of M- and GK-dwarf stellar abundances and a set of rigorous statistical methods. We compiled the elemental abundances of a sample of up to 43 M dwarfs for ten major rock-forming elements (Fe, C, O, Mg, Si, Al, Ca, Na, Ni, and Ti) from high-resolution near-infrared stellar surveys including APOGEE, CARMENES, and Subaru. We carried out bootstrap-based linear regressions on the selected sample of M dwarfs to constrain the statistical trends of X/H versus Fe/H and then compare them with those of GK dwarfs (sampled from the GALAH database). We then applied a two-sample, multivariate Mahalanobis Distance test to assess the significance of the differences in the X/H Fe/H trends for individual elemental pairs between M and GK dwarfs. We find that the null hypothesis -- that is, no significant difference in the chemical trends of X/H versus Fe/H between M and GK dwarfs -- is strongly rejected for all elements except for Si, for which the rejection is marginal ($p$-value close to 0.05), and Na and Ni, for which the results are inconclusive. This finding suggests that assuming no difference may result in biased results, and thus inaccurate constraints on characterising rocky planets around M dwarfs by scaling the (unmeasured) chemical abundances of planet-hosting M dwarfs from the chemical trends of X/H Fe/H determined by GK dwarfs. It is therefore crucial for both the stellar and exoplanet communities to be aware of these observed differences. To better understand these differences, we advocate for dedicated modelling techniques for M-dwarf atmospheres and an increasing set of benchmark, homogeneous abundance analyses. Intermediately, our statistically constrained trends of X/H Fe/H for M dwarfs provide a new constraint on estimating M-dwarf elemental abundances given measured Fe/H and further on characterising the detailed properties of M-dwarf-hosted rocky worlds.

El Gordo needs El Anzuelo: Probing the structure of cluster members with multi-band extended arcs in JWST data

Gravitational lensing by galaxy clusters involves hundreds of galaxies over a large redshift range and increases the likelihood of rare phenomena (supernovae, microlensing, dark substructures, etc.). Characterizing the mass and light distributions of foreground and background objects often requires a combination of high-resolution data and advanced modeling techniques. We present the detailed analysis of a prominent quintuply imaged dusty star-forming galaxy ($ mainly lensed by three members of the massive galaxy cluster ACT-CL\,J0102$-$4915, also known as d =0.87$). We leverage JWST/NIRCam images, which contain lensing features that were unseen in previous HST images, using a Bayesian, multi-wavelength, differentiable and GPU-accelerated modeling framework that combines (lens modeling) and (field model and inference) software packages. For one of the deflectors, we complement lensing constraints with stellar kinematics measured from VLT/MUSE data. In our lens model, we explicitly include the mass distribution of the cluster, locally corrected by a constant shear field. We find that the two main deflectors (L1 and L2) have logarithmic mass density slopes steeper than isothermal, with $ L1 and $ L2 We argue that such steep density profiles can arise due to tidally truncated mass distributions, which we probe thanks to the cluster lensing boost and the strong asymmetry of the lensing configuration. Moreover, our three-dimensional source model captures most of the surface brightness of the lensed galaxy, revealing a clump with a maximum diameter of $400$ parsecs at the source redshift, visible at wavelengths $ rest mu m. Finally, we caution on using point-like features within extended arcs to constrain galaxy-scale lens models before securing them with extended arc modeling.

An empirical investigation of student online learning continuance intention in the post-COVID-19 pandemic era

Purpose The purpose of this paper is to investigate students’ viewpoint regarding continuation of using online learning in the post-COVID-19 pandemic world. While during the pandemic years predominantly all formal learning was forced to move online, in the postpandemic environment traditional postsecondary education institutions generally resumed in-person (or face-to-face) learning. Nonetheless, it is possible that some students would like to continue using online learning after using such a system during COVID-19 restrictions. Therefore, it is important for postsecondary institutions to understand students’ views on continuing with online learning so that these institutions can better adapt their offerings to learners’ preferences. Design/methodology/approach This study uses a cross-sectional online survey-based approach grounded on an innovative theoretical framework blending the unified theory of acceptance and use of technology 2 into the expectation-confirmation model of information systems continuance. Data were collected from 247 students in Canada in Fall 2022 and were analyzed with partial least squares structural equation modeling techniques. Findings Perceptions of usefulness and of monetary benefits relative to costs together with developing positive habits regarding online learning are the most significant beliefs motivating students to want to continue with online learning. Furthermore, positive disconfirmation of initial expectations and satisfaction relying on previous use together with a favorable attitude with respect to online learning strongly influence the intention to continue with online education. Originality/value The study opens the door for similar research in other cultural contexts (e.g. with a different individualistic-collectivistic pattern) and for other domains that moved totally online during the COVID-19 pandemic (e.g. primary health care) to maximize people satisfaction while minimizing societal costs.

Creating Fuzzy Models from Limited Data

The design of experiments is a methodological approach in which measurement experiments are carefully planned to obtain highly informative data. This paper addresses the challenge of constructing mathematical models for complex nonlinear processes when the available measurement data have low information content. This problem often arises when data are collected without the guidance of an experimental modeling expert. We examine two practical examples to illustrate this issue: a textile wastewater decolorization process and atmospheric corrosion of structural metal materials. In both cases, the measured data were insufficient to construct highly accurate models. It is, therefore, necessary to make a trade-off between model complexity and accuracy by adapting modeling techniques to work effectively with the limited data available. The main aim of the paper is, therefore, to focus on simple but effective techniques that allow as much information as possible to be extracted from low-quality measurements and to maximize the usefulness of the model for its intended purpose.

Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

An ensemble learning paradigm for subsurface stratigraphy from sparse measurements and augmented training images

The performance of computer vision-based techniques for stratigraphic modeling relies heavily on qualified training images to capture the complex stratigraphic connectivity. In geotechnical engineering, only limited training images are available for a specific site. Stochastic simulation modelling based on limited training data may be biased as the collected images that reflect prior geological knowledge may not encompass all potential stratigraphic patterns. Therefore, it is crucial to establish a high-quality, domain-specific training image database for effective stratigraphic modelling. In this study, an ensemble learning paradigm is proposed to tackle this issue and develop subsurface geological cross-sections from sparse data by reconstruction and redistribution of stratigraphic statistics revealed from limited training images. A domain-specific training image database is first established using generative adversarial networks (GAN) that enable the generation of arbitrary sized image samples from a single training image. Subsequently, multiple qualified image samples that are compatible with site-specific data are adaptively selected and utilized for the ensemble learning of geological cross-sections. The performance of the proposed framework is demonstrated using real geological cross-sections collected from a reclamation site and a tunnelling project in Hong Kong. The results indicate that the proposed method can effectively generate diverse image samples that encompass stratigraphic features beyond those reflected in a single training image. More importantly, the ensemble learning framework can capture the complex spatial stratigraphic connectivity of soil layers with enhanced prediction accuracy.

Impact of Cosmic Rays on Radiation Exposures and Scientific Activities at the Atacama Large Millimeter/Submillimeter Array (ALMA) Sites.

This study delves into the investigation of cosmic-ray radiation exposure levels for workers and their impact on the signal correlation subsystems at the Atacama Large Millimeter/submillimeter Array (ALMA) observatory sites. The analysis presents a detailed examination of secondary cosmic ray spectra and flux at the ALMA sites, encompassing the operational period from 2010 to the present day, with a particular focus on the consequences of extreme solar flares. In terms of radiation exposure for ALMA employees, the annual exposure at the highest site (AOS) reaches approximately 4.8 mSv. This value exceeds the exposure level of a typical nuclear fuel cycle worker or those working at high-altitude Antarctica stations. The exposure is approximately 2.7 times lower at the ALMA Operations Support Facility (OSF). Furthermore, the additional ambient dose equivalent resulting from solar events, while low for events similar to those observed since the 1950s, can reach up to approximately 1 mSv when considering more ancient solar events based on environmental archives. Our analysis includes radiation effects measurements in the Baseline Correlator at the AOS and, more generally, underscores the significance of employing accurate modeling and simulation techniques to assess the effects of galactic cosmic rays and extreme solar events on the integrated circuits utilized or planned in the ALMA correlation subsystem.

Comparing 5D BIM costs: stilt housing against conventional housing for flood management

Purpose Sustainable and resilient infrastructure projects aim to reduce flooding impacts and improve community adaptability. For instance, flood-resistant stilts elevate structures, mitigating flood damage. Comprehensive consideration is crucial when adding elements to housing projects, incurring costs for all involved parties. This study aims to assess the viability of concrete stilts for cost-effective flood mitigation in Malaysian terrace housing. Design/methodology/approach The study evaluates cost implications through a comparative 5D building information modeling (BIM) cost analysis of stilted and conventional (standard) housing models. This assesses the percentage increase in total cost. Furthermore, a survey of construction professionals was undertaken. The study used online convenience and stratified sampling techniques. Out of the 222 emails that were sent, 27 construction professionals located in Johor, Sabah and Selangor within Malaysia participated in the research survey. Their perspectives on stilt housing prospects and factors for costing such structures were analyzed through a descriptive analysis using SPSS. Findings The case study models revealed that the incorporation of stilts could lead to a 21.64% increase in the overall cost per unit. This cost increase was primarily attributed to the additional reinforcement required. However, the survey findings highlighted that a majority of construction professionals perceived the cost increment to fall within the range of 10%–20%. Consequently, it becomes imperative to meticulously consider cost factors such as foundational requirements, staircases, and the extended construction duration to effectively curtail expenditures. The prospect of heightened costs potentially posing a threat to profit margins and discouraging developers necessitates careful financial management. Notwithstanding these challenges, the survey's insights underscored that professionals in the construction industry indeed recognize the potential of stilt technology in the realm of flood mitigation and management, particularly within housing projects. Practical implications This research has significant practical implications. It provides a precise financial contrast between housing categories using 5D BIM and incorporates construction experts’ viewpoints on raised housing. Enhanced design considerations for raised housing can make it economically viable, offering a cost-effective, nature-based approach to flood mitigation. This approach can bring substantial benefits to residents by reducing flood-related damages and enhancing community resilience. Originality/value One of the notable aspects of this research is its originality. It uses a dual quantitative methodology involving modeling and survey techniques to address its objectives effectively. This approach contributes significantly to the relatively limited body of research focused on stilt housing and the application of 5D BIM. By combining these methodologies, the study explores a relatively uncharted area, making a valuable contribution to the field.

Consilience of healthcare legislation, complexity science & computational analysis

Our US Health Care System (HCS) has evolved from simple to complex and needs reform. Thus far, all legislative initiatives have failed to result in establishing a friendly, cost-effective, quality healthcare system. The question becomes, can Complexity Science (CS) and computational analytic platform modeling be used to help create better Health Care Policies (HCP) and reform our all too complex HCS? Modeling has been used in many diverse disciplines but has yet to be utilized in preemptive evaluation of major US legislative HCPs. Review of US Health Care Policy History (HCPH) viewed in the context of a Complex Adaptive System(CAS) reveals how, unanticipated historical events, politics, social, and personal leadership have tangentially shaped our US HCPs. Future construction of HCPs with the help of CS, and preemptive computational modeling (CM) techniques, will hopefully yield stronger conclusions in legislative HCP construction and advance our US HCS into one with dynamic resilience.

Predictive modeling of specific fuel consumption in compression ignition engines using neural networks: a comparative analysis across diesel and polymer-based fuels

The study utilized neural network modeling to forecast the fuel consumption in compression ignition engines fueled by diesel, HDPE PO, PP PO, and LDPE PO. Using empirical data, a neural network model was constructed and used to estimate specific fuel consumption (SFC). Employing orthogonal arrays and parameter adjustments ensured accurate prediction of SFC, which was validated through experimentation. The multilayer perception network, coupled with traditional backpropagation, facilitates the nonlinear mapping of inputs to outcomes. In the LM10TP architecture, the key metrics from the training set included an impressive R-squared value of 1, indicating a perfect fit with a root mean square error (RMSE) of 0.0012 and a mean square error (MSE) of 1.5143E-06. Similarly, the validation set exhibited robust performance metrics with an R-squared value of 0.9999, an RMSE of 0.0011, and an MSE of 1.2185E-06. These metrics underscore the efficacy of neural networks in both the training and validation phases, affirming their utility as reliable predictive tools for SFC. Overall, this study highlights the effectiveness of neural network modeling for accurately predicting fuel consumption in compression ignition engines across diesel and polymer-based fuels. By leveraging empirical data and sophisticated modeling techniques, this study contributes to advancing predictive capabilities in the field, offering valuable insights for optimizing engine performance and fuel efficiency.

Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods

BackgroundIn randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual’s risk of experiencing an outcome, then measuring treatment effect by subgroup. Given the limited availability of externally validated outcome risk prediction models, internal models (created using the same dataset in which heterogeneity of treatment analyses also will be performed) are commonly developed for subgroup identification. We aim to compare different methods for generating internally developed outcome risk prediction models for subject partitioning in HTE analysis.MethodsThree approaches were selected for generating subgroups for the 2,441 participants from the United States enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial. An extant proportional hazards-based outcomes predictive risk model developed on the overall ASPREE cohort of 19,114 participants was identified and was used to partition United States’ participants by risk of experiencing a composite outcome of death, dementia, or persistent physical disability. Next, two supervised non-parametric machine learning outcome classifiers, decision trees and random forests, were used to develop multivariable risk prediction models and partition participants into subgroups with varied risks of experiencing the composite outcome. Then, we assessed how the partitioning from the proportional hazard model compared to those generated by the machine learning models in an HTE analysis of the 5-year absolute risk reduction (ARR) and hazard ratio for aspirin vs. placebo in each subgroup. Cochran’s Q test was used to detect if ARR varied significantly by subgroup.ResultsThe proportional hazard model was used to generate 5 subgroups using the quintiles of the estimated risk scores; the decision tree model was used to generate 6 subgroups (6 automatically determined tree leaves); and the random forest model was used to generate 5 subgroups using the quintiles of the prediction probability as risk scores. Using the semi-parametric proportional hazards model, the ARR at 5 years was 15.1% (95% CI 4.0–26.3%) for participants with the highest 20% of predicted risk. Using the random forest model, the ARR at 5 years was 13.7% (95% CI 3.1–24.4%) for participants with the highest 20% of predicted risk. The highest outcome risk group in the decision tree model also exhibited a risk reduction, but the confidence interval was wider (5-year ARR = 17.0%, 95% CI= -5.4–39.4%). Cochran’s Q test indicated ARR varied significantly only by subgroups created using the proportional hazards model. The hazard ratio for aspirin vs. placebo therapy did not significantly vary by subgroup in any of the models. The highest risk groups for the proportional hazards model and random forest model contained 230 participants each, while the highest risk group in the decision tree model contained 41 participants.ConclusionsThe choice of technique for internally developed models for outcome risk subgroups influences HTE analyses. The rationale for the use of a particular subgroup determination model in HTE analyses needs to be explicitly defined based on desired levels of explainability (with features importance), uncertainty of prediction, chances of overfitting, and assumptions regarding the underlying data structure. Replication of these analyses using data from other mid-size clinical trials may help to establish guidance for selecting an outcomes risk prediction modelling technique for HTE analyses.

Cultural identity, does it matter? A study on Konark Sun Temple, India

ABSTRACT It's always a challenge for destination marketers to identify experiences which create value that exacts visitors committed behavioural responses in the market. This study investigates the role of cultural identity in influencing the economic value, emotional value, and destination loyalty intentions of the visitors coming to Konark Sun Temple, India. Data analysis and hypothesis testing were done with the help of the structural equation modelling technique. Results reveal that cultural identity has a higher effect on economic value and moderate effects on emotional value and destination loyalty intention. The findings of the study also contribute to broad theoretical and managerial applications.

Neighborhood Socioeconomic Context Predicts Pediatric Asthma Exacerbation.

Causes of asthma exacerbation in children have been studied extensively at the individual level, but contributions of neighborhood-level factors are less explored. We test which distinctive residential characteristics produce variation in uncontrolled asthma among pediatric patients. We extracted electronic medical record data from pediatric patients living in Southern California and used multilevel modeling techniques to isolate which neighborhood characteristics drive inequitable asthma control. Above and beyond the individual-level factors known to predict inadequate disease control, neighborhoods with greater concentration of non-Hispanic black residents (odds ratios [OR] = 1.02; 95% confidence interval [CI]: 0.99-1.03; P < .05), higher proportions of female-headed households (OR = 1.01; 95% CI: 0.99-1.01; P < .05), and higher levels of ambient air pollution (OR = 1.05; 95% CI: 1.01-1.10; P < .001) associate with greater odds of asthma exacerbation. The interplay between community characteristics and asthma management during childhood is complex, and place-based initiatives are needed to narrow the gap in asthma exacerbation.

Mapping Avian Habitat Suitability Using Linear and Non-Linear Techniques in the Case of Wetland Landscapes

Habitat quality is crucial for wildlife management that impacts the conservation of sensitive landscapes such as wetlands. With advancements in GIS, habitat modelling now effectively predicts species occurrences and habitat suitability. This study aims to model and map habitat suitability for case bird species of Kentish plover in Tuzla Lagoon using multiple techniques. Kentish plover nesting data were collected from 293 nests, and reproductive success measures such as lay date, egg volume, and nest fate were analysed. Spatial habitat modelling techniques, including regression, co-kriging, artificial neural networks, and decision trees, were used with IKONOS imagery and ground data. The overall prediction accuracies were poor for lay date across all techniques, with the decision tree being the most accurate, while egg volume was best predicted by co-kriging, egg success by linear regression, and nest fate by both binomial logistic regression and ANN with 75% accuracy.

The influence of knowledge storage on knowledge management processes in higher education institutions

ABSTRACT Knowledge storage is one of the essential processes in the knowledge management cycle for an efficient knowledge base to access and transfer required knowledge. Nevertheless, scientific studies lack thorough research on its influence on other knowledge management processes and frequently neglect to investigate seniority’s role in storing knowledge. Therefore, to fill this research gap, a survey was employed to collect data from 220 academics, and PLS-structural equation modelling techniques and one-way ANOVA were employed for data analysis. The findings indicated a significant and positive influence of knowledge storage on the knowledge processes analysed in the study and an insignificant difference in average knowledge storage level between groups of academics belonging to different work experience categories. The results of this study are of high importance for higher education institutions interested in improving the knowledge potential in their organisations.

Longitudinal analysis of cortisol changes during pubertal development in indigenous Qom girls.

Pubertal research has primarily focused on hypothalamic-pituitary-gonadal axis (HPG) regulation of puberty, though the hypothalamic-pituitary-adrenal axis (HPA) is increasingly considered critical. Heightened HPA function proxied by increasing cortisol levels may play a role in accelerated pubertal timing. However, the extent to which cortisol varies across ages and its relation to pubertal changes in linear growth are less well substantiated. We explored relationships between age, linear growth, adiposity, C-peptide (proxy for insulin), and cortisol across puberty, and we tested whether higher cortisol levels are associated with earlier ages at menarche and peak height velocity. We utilize longitudinal data (n = 777 urine samples) from Qom females ages 7-14 (n = 46) and test our pre-registered analysis using Bayesian longitudinal mixed effects models and joint modeling techniques. We find limited evidence supporting the overarching hypothesis that HPA upregulation is associated with pubertal maturation or timing. We find some evidence that HPA upregulation, as proxied by cortisol, may be more clearly related to differences in relative linear growth at early-mid puberty, as measured by height-for-age z-scores. Transdisciplinary perspectives on puberty, including the assumption that stressors acting via cortisol accelerate pubertal development, are discussed.

CFD and RSM Assist in Reducing the LPG Consumption of Burners for Agarwood Oil Production in Thailand

ABSTRACT This research aimed to optimize LPG consumption in Thai commercial burners used in agarwood oil production, specifically focusing on the KB-10, S-10, and EB-10 burners. Enhancing burner efficiency is crucial due to rising energy costs and environmental concerns. A dual approach using Response Surface Methodology (RSM) and Computational Fluid Dynamics (CFD) was employed in order to achieve the best burner. Central Composite Design (CCD) within the RSM framework determined the optimal swirl and inclination angles combinations. Modified burners with variant combination angles, as suggested by RSM, were numerically investigated. CFD simulations with Ansys Fluent, utilizing the k-ε turbulence and discrete ordinates (DO) radiation models, provided detailed insights into the combustion and heat transfer processes of those modified burners. The modified burners, SB10–1, SS10–3, and SEB10–7, demonstrated significant improvements. Laboratory and field tests indicated energy savings of 16.15%, 23.74%, and 20.75%, respectively. Statistical analysis confirmed these savings with p-values < .05. The 95% confidence intervals for LPG savings were 31.31 ± 0.8%, 33.42 ± 0.7%, and 31.84 ± 0.6%, corresponding to payback periods of approximately 2.99, 4.21, and 8.95 months. Additionally, CO and NOx emissions were kept below 350 ppm and 100 ppm, respectively. The novelty of this study lies in the combined use of RSM and CFD to optimize burner performance, leading to significant energy savings and emission reductions. These findings highlight the potential of these methods for burner optimization, offering both economic and environmental benefits. This research underscores the importance of integrating advanced modeling techniques to achieve sustainable and cost-effective industrial processes.

Investigating the motivating factors that influence the adoption of blended learning for teachers’ professional development

Blended learning (BL), a teaching method merging online and face-to-face learning, is lauded for its potential to enrich educational outcomes and tackle challenges entrenched in conventional teaching practices. In countries like Pakistan, where equitable access to quality professional development remains an obstacle, BL is a promising avenue to surmount training barriers. While BL adoption has evolved swiftly, research into its integration within teacher training remains limited. Notably, no comprehensive model exists describing the motivational factors influencing teachers' perceptions and intentions regarding the blended mode of teacher training. This study aims to identify the motivational elements that motivate schoolteachers in teacher training institutions in Pakistan to incorporate blended learning into their programs. The motivational factors identified in BL literature have been employed to craft a motivation model grounded in their causal relationship. This quantitative study examines the interplay between multiple motivational factors and their impact on BL adoption within teacher training and the BL environment. Surveying 350 schoolteachers (participants) from teacher training institutions, we employed Structural Equation Modeling (SEM) techniques with Smart PLS 4.0 for data analysis. Results reveal that extrinsic and intrinsic motivational factors significantly influence teachers' motivation to adopt BL for training. Notably, "overall training quality" and "educational environment" were non-influential. Overall, the findings underscore that considering a blend of extrinsic and intrinsic factors can wield a 65 % influence on BL adoption. The study's results provide practical guidance for educational leaders, curriculum designers, and faculty members aiming to cultivate a unified blended learning environment for teacher professional development. These insights also underscore the importance of incorporating essential motivational factors into forthcoming blended learning training programs.

Modeling of Parkinson's Disease Progression and Implications for Detection of Disease Modification in Treatment Trials.

Objectively measuring Parkinson's disease (PD) signs and symptoms over time is critical for the successful development of treatments aimed at halting the disease progression of people with PD. To create a clinical trial simulation tool that characterizes the natural history of PD progression and enables a data-driven design of randomized controlled studies testing potential disease-modifying treatments (DMT) in early-stage PD. Data from the Parkinson's Progression Markers Initiative (PPMI) were analyzed with nonlinear mixed-effect modeling techniques to characterize the progression of MDS-UPDRS part I (non-motor aspects of experiences of daily living), part II (motor aspects of experiences of daily living), and part III (motor signs). A clinical trial simulation tool was built from these disease models and used to predict probability of success as a function of trial design. MDS-UPDRS part III progresses approximately 3 times faster than MDS-UPDRS part II and I, with an increase of 3 versus 1 points/year. Higher amounts of symptomatic therapy is associated with slower progression of MDS-UPDRS part II and III. The modeling framework predicts that a DMT effect on MDS-UPDRS part III could precede effect on part II by approximately 2 to 3 years. Our clinical trial simulation tool predicted that in a two-year randomized controlled trial, MDS-UPDRS part III could be used to evaluate a potential novel DMT, while part II would require longer trials of a minimum duration of 3 to 5 years underscoring the need for innovative trial design approaches including novel patient-centric measures.