Multilevel Design Research Articles

Structural Influences on Methamphetamine Use Among Black Sexual Minority Men (HISTORY Study): Protocol for a Longitudinal Cohort Study.

Sexual minority men are disproportionately affected by methamphetamine use, with recent studies suggesting an increase in use specifically among Black sexual minority men. Black sexual minority men face unique structural barriers to achieving optimal health. Given its harmful effects, and in light of existing health disparities, an increase in methamphetamine use among Black sexual minority men poses a significant public health concern. The Health Impacts and Struggles to Overcome the Racial Discrimination of Yesterday (HISTORY) study is investigating the potential impacts of exposure to the census tract-level structural racism and discrimination (SRD) on methamphetamine use among Black sexual minority men in Atlanta, Georgia, and will identify intervention targets to improve prevention and treatment of methamphetamine use in this population. This study uses a mixed methods and multilevel design over a 5-year period and incorporates participatory approaches. Individual-level quantitative data will be collected from a community-based cohort of Black sexual minority men (N=300) via periodic assessment surveys, ecological momentary assessments, and medical record abstractions. Census tract-level measures of SRD will be constructed using publicly available administrative data. Qualitative data collection will include longitudinal, repeated in-depth interviews with a subset (n=40) of study participants. Finally, using a participatory group model-building process, we will build on our qualitative and quantitative data to generate causal maps of SRD and methamphetamine use among Black sexual minority men, which in turn will be translated into actionable recommendations for structural intervention. Enrollment in the HISTORY study commenced in March 2023 and is anticipated to be completed by November 2024. The HISTORY study will serve as a crucial background upon which future structural interventions can be built, to mitigate the effects of methamphetamine use and SRD among Black sexual minority men. DERR1-10.2196/63761.

Concurrent conversion of waste cooking oil using Al metal-organic framework and subcritical methanol through esterification/transesterification process

Recently, some scientists have focused on discovering techniques for manufacturing biodiesel on a significant, efficient, and ecologically sustainable scale. Choosing a catalyst is a notable challenge due to the instability and cost feasibility issues connected with newly designed homogeneous and heterogeneous catalysts. This paper describes the synthesis and utilization of aluminum-based metal-organic frameworks (MOFs) as catalysts for the generation of biodiesel from waste cooking oil (WCO). The processes involved in this study are esterification and transesterification, which are carried out using subcritical methanol. The catalyst analysis reveals that the MOFs possess a filamentous structure, with a crystallite size ranging from 300 to 500 nm. Furthermore, these MOFs exhibit thermal stability up to 470 °C. The catalyst underwent examination for biodiesel production from waste cooking oil (WCO), and the resulting biodiesel samples met the standards set by the American Society for Testing and Materials (ASTM). The experiment was devised and fine-tuned to incorporate the three independent variables using a multilevel factorial design, response surface methodology, and a three-way analysis of variance (ANOVA). The FAME conversion peaked at 91.96 %, as determined through statistical analysis of the optimization findings. The corresponding values for the variables were X1: 423.15 K, X2: 1800s, and X3: 28 mol/mol. Based on ANOVA statistics, the experimental and mathematical verification demonstrates a fair correlation between the actual and calculated catalyst performance. The temperature and molar ratio of methanol: WCO significantly affected the FAME conversion.

Virtual Instruments for Peak-Overlapping Studies to Determine Low- and High-Concentration Components with Ion Chromatography: Potassium and Sodium.

We developed the LabVIEW-based virtual instruments (VIs) to bridge a gap in commercial software and to enable systematic peak-overlapping studies to recognise the concentration levels enabling reliable simultaneous determination of major and minor constituents in samples with wide concentration proportions. The VIs were applied to a case study of the ion chromatographic determination of potassium as minor and sodium as a major ion with an IonPac CS12A column and 50 μL injection loop. Two successive studies based on multilevel two-factorial response surface experimental designs, (1) a model peak-overlapping study based on single-ion injections, and (2) an accuracy and precision study, provided guidelines for real sample analyses. By adjusting sample dilutions so that the sodium mass concentration was set to 340 mg/L, the simultaneous determination of potassium in the presence of sodium was possible in samples with sodium over potassium concentration ratios between 14 and 341. The relative expanded uncertainty associated with potassium ion determination was between 0.52 and 4.4%, and the relative bias was between -3.8 and 1.9%. We analysed Ringer's physiologic solutions, standard sea, trisodium citrate anticoagulant, and buffered citrate anticoagulant solutions. We confirmed that the VI-supported peak-overlapping studies contributed to the quality of results by enabling the evidence-based choices of concentration levels adjusted by a dilution.

The ABCs of mental health at the university: a multi-level intervention design for promoting mental well-being.

There is an escalating concern for the mental health of university students being recognized as a high-risk group for psychological distress. Despite research emphasizing the need to integrate mental well-being into higher education, existing interventions primarily focus on challenges and support services, leaving a gap in practical insights for promoting mental well-being at the university as a whole. This paper aims to cover the theoretical and methodological foundations for the design and development of a complex multi-level intervention called the ABCs of mental health at the university (ABC-uni). Following the MRC framework for complex interventions, the design and development of a novel complex intervention is described (Phase I). Using the socio-ecological model and incorporating principles from health promotion charters, capacity building, organizational change models, and the principles of the ABCs of mental health a program theory for mental health promotion at the university is proposed. Following this theoretical foundation a logic model outlines the ABC-uni intervention components at structural, community, and individual levels. The components include staff training, campaign materials, a photovoice project, integration of mental well-being literacy into introductory programs, a credit-bearing course, and an online module. Preferably, most of these components are developed and carried out collaboratively with students at the university. As proposed in the logic model expected outcomes encompass heightened awareness, supportive environments, commitment, and accountability, aiming to enhance mental well-being across the entire university. The design and development of the intervention components occur at a Danish public university. This section addresses the strengths and limitations of the design of the ABC-uni intervention. Future research will cover the feasibility phase of each components of the ABC-uni intervention (phase II). The conceptual framework and program theory outlining mental health promotion at the university, along with the detailed description of the intervention components, provides valuable insights for fostering mental well-being in the university community.

ATVis: Understanding and diagnosing adversarial training processes through visual analytics

Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions. Despite enhancing robustness, adversarial training often results in a trade-off with standard accuracy on normal data, a phenomenon that remains a contentious issue. In addition, the opaque nature of deep neural network models renders it more difficult to inspect and diagnose how adversarial training processes evolve. This paper introduces ATVis, a visual analytics framework for examining and diagnosing adversarial training processes. Through multi-level visualization design, ATVis enables the examination of model robustness from various granularity, facilitating a detailed understanding of the dynamics in the training epochs. The framework reveals the complex relationship between adversarial robustness and standard accuracy, which further offers insights into the mechanisms that drive the trade-offs observed in adversarial training. The effectiveness of the framework is demonstrated through case studies.

PROGRESS: A Patient-centered Engagement Infrastructure and Multi-level Approach to Enrich Diversity, Equity, and Inclusion in a National Randomized Online Behavioral Pain Treatment Study

Twenty percent of individuals experience chronic pain worldwide posing significant challenges to those living with it. Pain research is crucial for developing and characterizing effective strategies to reduce the burden of chronic pain. Traditional research approaches often yield homogeneous study samples that poorly generalize and have unknown applicability across diverse patient populations. The Pain Relief with Online Groups that Empower Skills-based Symptom Reduction (PROGRESS) study aims to address disparities in pain research engagement and patient outcomes through the intentional inclusion of people with varied backgrounds and experiences of pain, and through a multilevel design informed by diverse stakeholder recommendations. The composition of three advisory boards (Patient Engagement and Diversity Board, Local Patient Advisory Board, and the National Patient Advisory Panel) prioritized diversity in patient/expert advisor background, geographic location, race, and ethnicity. Our engagement approach aligns with the Foundational Expectations for Partnerships in Research by Patient-Centered Outcomes Research Institute (PCORI), which emphasizes diverse representation, early and ongoing engagement, dedicated funds for advisor compensation, collaborative decision making, meaningful participation, and continuous assessment. The first 24 months of study advisor engagement has yielded multiple recruitment strategies resulting in a study population enriched with a breadth of identities within PROGRESS (e.g., inclusive patient-facing materials). Lessons learned underscore the importance of investing time in building patient and stakeholder relationships, trust, and embracing diverse viewpoints amongst the study team. PROGRESS demonstrates the potential of diverse patient-centered engagement to support evidence-based outcomes and practices that are more inclusive, equitable, and representative of the broader population. PerspectiveThe PROGRESS study demonstrates how diverse patient engagement and inclusive advisory boards enhance research outcomes. By aligning with PCORI standards and employing innovative recruitment strategies, it highlights the vital role of stakeholder relationships and diverse perspectives. Key lessons learned emphasize adaptive strategies and continuous feedback for advancing equitable pain research.

Single- and Multilevel Perspectives on Covariate Selection in Randomized Intervention Studies on Student Achievement

Well-chosen covariates boost the design sensitivity of individually and cluster-randomized trials. We provide guidance on covariate selection generating an extensive compilation of single- and multilevel design parameters on student achievement. Embedded in psychometric heuristics, we analyzed (a) covariate types of varying bandwidth-fidelity, namely domain-identical (IP), cross-domain (CP), and fluid intelligence (Gf) pretests, as well as sociodemographic characteristics (SC); (b) covariate combinations quantifying incremental validities of CP, Gf, and/or SC beyond IP; and (c) covariate time lags of 1–7 years, testing validity degradation in IP, CP, and Gf. Estimates from six German samples (1868 ≤ N ≤ 10,543) covering various outcome domains across grades 1–12 were meta-analyzed and included in precision simulations. Results varied widely by grade level, domain, and hierarchical level. In general, IP outperformed CP, which slightly outperformed Gf and SC. Benefits from coupling IP with CP, Gf, and/or SC were small. IP appeared most affected by temporal validity decay. Findings are applied in illustrative scenarios of study planning and enriched by comprehensive Online Supplemental Material (OSM) accessible via the Open Science Framework (OSF; https://osf.io/nhx4w).

Biomimetic Multichannel Silk Nerve Conduits With Multicellular Spatiotemporal Distributions for Spinal Cord Injury Repair.

Bioengineered nerve conduits have shown great promise for spinal cord injury (SCI) repair, while their practical values are limited by poor regenerative efficacy and lack of multi-level structural design. Here, inspired by the ingenious anatomy of natural spinal cords, a biomimetic multichannel silk nerve conduit (namely BNC@MSCs/SCs) with multicellular spatiotemporal distributions for effective SCI repair is presented. The biomimetic silk nerve conduit (BNC) with hierarchical channels and aligned pore structures is prepared via a modified directional freeze-casting strategy. Such hierarchical structures provide appropriate space for the mesenchymal stem cells (MSCs) and Schwann cells (SCs) settled in specific channels, which contributes to the generation of BNC@MSCs/SCs resembling the cellular spatiotemporal distributions of natural spinal cords. The in vitro results reveal the facilitated SC migration and MSC differentiation in such BNC@MSCs/SCs multicellular system, which further promotes the tube formation and cell migration of endothelial cells as well as M2 polarization of macrophages. Moreover, BNC@MSCs/SCs can effectively promote the tissue repair and function recovery in SCI rats by attenuating glial scar formation while promoting neuron regeneration and myelin sheath reconstruction. Thus, it is believed that the biomimetic multichannel silk nerve conduits with multicellular spatiotemporal distributions are valuable for SCI repair and other neural tissue regeneration.

The role of leadership in group cohesion: insights from sport club volunteering

Purpose This study aims to address the challenge many organizational leaders face in fostering workforce cohesion. Focusing on the context of sports club volunteering, this study investigates how leaders can enhance group cohesion among volunteers. The study findings provide valuable insights applicable across various work settings. Design/methodology/approach Through a multilevel design, this study examined the role of autonomy–supportive and structuring leadership in shaping social and task cohesion within volunteer teams at the group level. In total, 557 volunteers nested within 52 nonprofit sports clubs situated in the Flemish region of Belgium were involved in this study, providing a robust foundation for our analysis. Findings This research revealed that regular volunteers form “true groups”, exhibiting substantial between-group variance in social and task cohesion and a strong within-group consensus. Additionally, the findings underscored the significance of autonomy–supportive leadership in fostering cohesion, demonstrating a positive relation with social and task cohesion at the group level. Originality/value This study demonstrates that volunteer teams, like work teams in the for-profit sector, actively engage in interpersonal exchanges within their organization, which help shape their collective sense of unity and alignment with common objectives. Leaders can enhance these interactions by fostering an autonomy–supportive environment where members feel empowered to share their perspectives and ideas. Additionally, the findings suggest that the nature of the task and the specific context can influence which leadership style is most effective, with the provision of structure also playing a role. With these insights, leaders in diverse organizational settings can effectively nurture the development of cohesive groups.

Optic Nerve Sheath Ultrasound Image Segmentation Based on CBC-YOLOv5s

The diameter of the optic nerve sheath is an important indicator for assessing the intracranial pressure in critically ill patients. The methods for measuring the optic nerve sheath diameter are generally divided into invasive and non-invasive methods. Compared to the invasive methods, the non-invasive methods are safer and have thus gained popularity. Among the non-invasive methods, using deep learning to process the ultrasound images of the eyes of critically ill patients and promptly output the diameter of the optic nerve sheath offers significant advantages. This paper proposes a CBC-YOLOv5s optic nerve sheath ultrasound image segmentation method that integrates both local and global features. First, it introduces the CBC-Backbone feature extraction network, which consists of dual-layer C3 Swin-Transformer (C3STR) and dual-layer Bottleneck Transformer (BoT3) modules. The C3STR backbone’s multi-layer convolution and residual connections focus on the local features of the optic nerve sheath, while the Window Transformer Attention (WTA) mechanism in the C3STR module and the Multi-Head Self-Attention (MHSA) in the BoT3 module enhance the model’s understanding of the global features of the optic nerve sheath. The extracted local and global features are fully integrated in the Spatial Pyramid Pooling Fusion (SPPF) module. Additionally, the CBC-Neck feature pyramid is proposed, which includes a single-layer C3STR module and three-layer CReToNeXt (CRTN) module. During upsampling feature fusion, the C3STR module is used to enhance the local and global awareness of the fused features. During downsampling feature fusion, the CRTN module’s multi-level residual design helps the network to better capture the global features of the optic nerve sheath within the fused features. The introduction of these modules achieves the thorough integration of the local and global features, enabling the model to efficiently and accurately identify the optic nerve sheath boundaries, even when the ocular ultrasound images are blurry or the boundaries are unclear. The Z2HOSPITAL-5000 dataset collected from Zhejiang University Second Hospital was used for the experiments. Compared to the widely used YOLOv5s and U-Net algorithms, the proposed method shows improved performance on the blurry test set. Specifically, the proposed method achieves precision, recall, and Intersection over Union (IoU) values that are 4.1%, 2.1%, and 4.5% higher than those of YOLOv5s. When compared to U-Net, the precision, recall, and IoU are improved by 9.2%, 21%, and 19.7%, respectively.

Challenges for expanding inventories of climate possibilities through indigenous and local knowledges in rural Zambia

This article explores the integration (or marginality) of indigenous and local knowledge (IKL) in donor-driven community climate adaptation (CDCA) projects and the extent to which this helps expand inventories of adaptation possibilities for host communities and strengthen climate adaptation resilience in Zambia. Through multi-level qualitative research design, this study reveals that, even where climate interventions are intentional about being inclusive of community knowledge, they are likely to promote policy-centric knowledges and interventions that invisibilises ILK. Empirical evidence shows the application of CDCA expresses top-down assumptions of livelihood resilience and embeds uncritical views of what community is, including what might be socially and culturally appropriate forms of adaptation. CDCA implementation strategy is exclusionary and misaligned with ILK, affecting possibilities of knowledge intersection. This article elucidates how climate adaptation that marginalises ILK fails to expand inventories of climate adaptation possibilities for communities supposed to be adapting and proposes how this gap could be bridged. (This article is published in the thematic collection ‘African ecologies: the value and politics of indigenous knowledges’, edited by Adriaan van Klinken, Simon Manda, Damaris Parsitau and Abel Ugba.)

A multi-level multi-product supply chain network design of vegetables products considering costs of quality: A case study.

Effective logistics management is crucial for the distribution of perishable agricultural products to ensure they reach customers in high-quality condition. This research examines an integrated, multi-echelon supply chain for perishable agricultural goods. The supply chain consists of four stages: supply, processing, storage, and customers. This study investigates the quality-related costs associated with product perishability to maximize supply chain profitability. Key factors considered include the network design, location of processing and distribution centers, the ability to process raw products to minimize post-harvest quality degradation, the option to sell the excess produce to a secondary market due to unpredictable yields, and the decision not to fulfill demand from distant customers where significant quality loss and price drops would be involved, instead diverting those products to the aforementioned secondary market. Quantitative methods and linear mathematical programming are employed to model and validate the proposed supply chain using actual data from a real-world case study on vegetable supply chains. The main contribution of this research is the incorporation of quality costs into the objective function, which allows the supply chain to prioritize meeting nearby customers' demands with minimal quality loss over serving distant customers where high quality loss is unavoidable. Additionally, deploying a faster transportation fleet can significantly improve the overall profitability of the perishable product supply chain.

Accelerating joint species distribution modelling with Hmsc-HPC by GPU porting.

Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this study, we aim to enhance JSDM scalability by leveraging high-performance computing (HPC) resources for an existing fitting method. Our work focuses on the Hmsc R-package, a widely used JSDM framework that supports the integration of various dataset types into a single comprehensive model. We developed a GPU-compatible implementation of its model-fitting algorithm using Python and the TensorFlow library. Despite these changes, our enhanced framework retains the original user interface of the Hmsc R-package. We evaluated the performance of the proposed implementation across various model configurations and dataset sizes. Our results show a significant increase in model fitting speed for most models compared to the baseline Hmsc R-package. For the largest datasets, we achieved speed-ups of over 1000 times, demonstrating the substantial potential of GPU porting for previously CPU-bound JSDM software. This advancement opens promising opportunities for better utilizing the rapidly accumulating new biodiversity data resources for inference and prediction.

Migraine heritability and beyond: A scoping review of twin studies.

This scoping review aimed to summarize current knowledge from twin studies on migraine. Migraine heritability, genetic correlations with migraine comorbid disorders, and the use of discordant twin pairs in migraine research are described. Further, the review considers the unused potential of twin studies in migraine research and reflects on future directions. Twin studies can be used to understand how heritable and environmental factors influence human traits and disorders. The classical twin design compares the resemblance of a trait in monozygotic twins to that in dizygotic twins. The classical twin design can be extended to estimate the genetic correlation between disorders, model causality, and describe differences within discordant twin pairs. Studies focusing on migraine and using a twin study design were included. The search was performed on the PubMed-MEDLINE database using the search terms "migraine" AND "twin" OR "twins." It was done in May 2023, rerun in November 2023, and managed with the Covidence software. The search identified 52 twin studies on migraine. In 24 papers, the heritability of migraine was estimated with a classical twin design. Heritability estimates ranged from 0.36 to 0.48 for studies with adults, both men and women, and unspecified migraine. Migraine heritability was predominantly estimated with twin cohorts of North European ancestry, and only two studies examined migraine subtypes. A multilevel classical twin design was used in 11 studies to examine the co-occurrence between migraine and comorbid disorders. The differences within migraine discordant twin pairs were examined in nine studies. The heritability of migraine was estimated with a classical twin design in twin cohorts from seven different countries, with remarkably similar results across studies. Future studies should include migraine subtypes and twin cohorts of non-North European ancestry to better reflect the global population. Beyond heritability estimations, the twin method is a valuable tool for understanding causality and describing differences within discordant twin pairs. Despite more than 80 years of twin studies in migraine research, the twin design has a large unused potential to advance our understanding of migraine.

A performance-based ballistic design framework for RC panels and a probabilistic model for crater quantification

Ballistic design is crucial for widely utilised concrete structures in today’s unpredictable world. In the past, extensive experimental and numerical studies have investigated concrete panels, resulting in design guidelines and empirical formulations for local damage parameters. However, with the advent of performance-based design, notably in seismic design, the ballistic design of concrete structures lacks a comprehensive design philosophy. Additionally, deterministic empirical formulations for local damage parameters, particularly in the case of reinforced concrete (RC) panels’ crater formation, necessitate a probabilistic treatment. Consequently, this paper addresses the need for a well-defined ballistic design philosophy for concrete structures and introduces a probabilistic approach for crater quantification. Firstly, a novel multi-level and multi-parameter performance-based design framework for RC panels is proposed, centred on damage states, namely depth of penetration and crater area. This framework establishes an innovative design philosophy featuring four damage levels for each state. Secondly, using dimensionless explanatory functions, a Bayesian inference model is developed to estimate the crater diameter in RC panels under hard projectile impact, accounting for the associated uncertainties. LS-Dyna is used for numerical modelling of RC panels and rigid projectiles. The numerical models are validated with the experimental data and are utilised to develop the probabilistic model. This study reveals the profound influence of projectile and concrete properties in addition to the incidence angle of the projectile on crater formation. The developed probabilistic model is validated with experimental results demonstrating its reliability and accuracy. Consequently, a reliable design formula for estimating crater diameter for RC panels under hard projectile impact is proposed in addition to the novel ballistic design framework.

Torrefaction of durian peel in air and N2 atmospheres: Impact on chemical properties and optimization of energy yield using multilevel factorial design

This study investigated the torrefaction of durian peel using air and nitrogen as carrier gases. A multilevel factorial design coupled with response surface methodology (RSM) and ANOVA analysis was employed to analyze the impact of torrefaction parameters on chemical properties and energy yield. Durian peel, an agricultural waste product, was torrefied at temperatures ranging from 200 to 320 °C for residence times between 0 and 30 min. Results showed that air torrefaction significantly enhanced thermal decomposition, reducing mass yield from 94.65 % to 30.63 % as the temperature increased from 200 °C to 300 °C with a 30-min holding time. Air torrefaction also increased the higher heating value (HHV) from 19.02 MJ/kg to 35.26 MJ/kg at 300 °C, compared to nitrogen, which achieved a maximum HHV of 32.49 MJ/kg. ANOVA analysis revealed that torrefaction temperature and carrier gas significantly affect energy yield and chemical properties. Air torrefaction positively affected HHV while reducing mass yield compared to nitrogen. Low-temperature air torrefaction showed enhanced energy yield improvement. These findings provided insights for optimizing torrefaction processes enhancing utilization wasted durian peel as a sustainable bioenergy resource.

Shape optimization of non-matching isogeometric shells with moving intersections

While shape optimization using isogeometric shells exhibits appealing features by integrating design geometries and analysis models, challenges arise when addressing computer-aided design (CAD) geometries comprised of multiple non-uniform rational B-splines (NURBS) patches, which are common in practice. The intractability stems from surface intersections within these CAD models. In this paper, we develop an approach for shape optimization of non-matching isogeometric shells incorporating intersection movement. Separately parametrized NURBS surfaces are modeled using Kirchhoff–Love shell theory and coupled using a penalty-based formulation. The optimization scheme allows shell patches to move without preserving relative location with other members during the shape optimization. This flexibility is achieved through an implicit state function, and analytical sensitivities are derived for the relative movement of shell patches. The introduction of differentiable intersections expands the design space and overcomes challenges associated with large mesh distortion, particularly when optimal shapes involve significant movement of patch intersections in physical space. Throughout optimization iterations, all members within the shell structures maintain the NURBS geometry representation, enabling efficient integration of analysis and design models. The optimization approach leverages the multilevel design concept by selecting a refined model for accurate analysis from a coarse design model while maintaining the same geometry. We adopt several example problems to verify the effectiveness of the proposed scheme and demonstrate its applicability to the optimization of the internal stiffeners of an aircraft wing.

An analytical framework for the embodiment of structural inequities.

The goal of this article is to describe a conceptual multilevel model that provides evidence of embodiment of a societal stressor on the health of the individuals and illustrate with simulated data how omitting components in the analysis model fails to properly capture how context influences health. We describe a two-level model with variables at each level: stress at the group level and appraisal at the individual level. These factors are assumed to influence the blood pressure of individuals. Importantly, the person-level predictor is responsible for bringing the group-level predictor to the individual level by a cross-level interaction between stress and appraisal and/or a mediated effect of stress. When combined, the model components may be partitioned into a pure direct effect, a pure indirect effect, pure interaction effect, and an interaction-in-mediation effect. Data were generated in accordance with the model with each component accounting for some proportion of variance in blood pressure. To the extent these components operate in the process of embodiment, a proposition we argue is reasonable, failure to specify the analytic model with all components leads to failure to characterize embodiment and misattribution of the effect and mechanism. To fully quantify embodiment of a societal stressor on a health outcome, studies should use multilevel designs and estimate cross-level interactions and mediated effects. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Assessment of Solvent Extraction using Sonication to Recover Tryptophan from Kappaphycus alvarezii (Doty) Doty ex Silva: Experimental and Modelling

The application of Kappaphycus alvarezii in the food system has attracted researchers due to its bioactive compounds, including tryptophan. The present study was conducted to extract tryptophan from K. alvarezii with the aid of sonication. A reduced multilevel factorial design was conducted to evaluate the effect of solvent, ultrasound power, duty-pulse cycle, time and temperature on the recovery of tryptophan. Analysis of variance suggested that the type of solvent and combination setting of power and duty-pulse cycle significantly influenced the extraction (p < 0.05). In contrast, extraction time and temperature did not alter the extraction (p > 0.05). The optimum was using ethanol, ultrasound power 80 %, pulse duty-cycle 0.8 s–1, extraction time 10 min, and temperature 25 °C and tryptophan concentration was 56.41 ± 2.42 mg L–1. Subsequently, a COnducto-like Screening MOdel for Real Solvent (COSMO-RS) was performed to clarify the impact of solvent affinity and polarity on the tryptophan extraction from K. alvarezii.

A Multilevel Triadic-Experience Model of Travel App-Induced Attitude

ABSTRACT The literature on tourist experience commonly puts the current en-route experience at the center stage through single-level analysis. This research steers away from that tradition to improvise a triple-experience model through multiple inquiries. Data were collected from a survey to assess how prior and current travel app usage experiences could impact app-induced site attitude and revisit intention via the app, based on a multilevel design with survey data collected from 21 sites. The cross-level moderation of others’ experiences was also explored. This research draws on the functional theory of attitude and the theory of normative conduct to acknowledge the knowledge and social functions of attitude change. It contributes to the literature by bridging the divide between the online app and offline travel evaluations. It builds a theoretical synthesis between the aforementioned theoretical lens to demonstrate how the experiences of others through travel apps can impact an individual’s experience, ultimately affecting attitude toward app-induced tourism destinations.