Multilevel Framework Research Articles

Working from everywhere: The future of work and inclusive organizational behavior (IOB)

SummaryThe rapid digitalization of work, accelerated by the COVID‐19 pandemic, has fundamentally transformed organizational behavior and necessitated a reconceptualization of inclusion in virtual spaces. This editorial synthesizes six groundbreaking studies that advance our understanding of inclusive organizational behavior (IOB) in digital contexts. Drawing on these contributions, we propose a novel, multilevel framework for digital IOB that integrates individual, group, and organizational factors shaping inclusive experiences in virtual and hybrid work arrangements. Key themes emerging from the studies include (1) reconceptualizing inclusion for digital contexts, (2) multilevel dynamics of digital inclusion, and (3) technology as both enabler and barrier to inclusion. Our framework conceptualizes digital IOB as arising from complex interactions between individual, group, and organizational factors, mediated and moderated by technological infrastructures and practices. We discuss theoretical and practical implications, highlighting the need for holistic, systemic approaches to fostering digital inclusion and outlining promising avenues for future research in this critical area.

Sustainability requires the integration of farmer knowledge, scientific advancements, and comprehensive innovation.

This viewpoint paper emphasises the need to diversify food production methods to simultaneously combat hunger and reduce environmental problems. The recommendations of the UN Food System Summit 2021 relate primarily to (i) the conservation of natural ecosystems, (ii) the sustainable management of existing agricultural land while increasing productivity and (iii) the restoration of already degraded land. Europe in particular faces unique challenges, such as reducing pollution and promoting organic farming up to 25 percent of the agricultural land area while maintaining food production. Ongoing efforts aim to create a transparent, fair and multi-level regulatory framework to support the Green Deal. The implementation of the Corporate Sustainability Reporting Directive (CSRD), which will sooner or later affect a larger proportion of European farmers, should support the transition. Science and innovation play a central role in this, as they are the cornerstones on which sustainable food systems are built. It is imperative that farmers actively participate in the co-design processes and utilise their wealth of experience and creativity to drive these innovations forward. A crucial aspect of the transition to sustainability is changing consumption patterns to limit food waste and reduce meat consumption. While this transition is essential, it is not without its formidable challenges. Diversification of agriculture, encompassing a spectrum of established techniques, is touted as a promising approach to achieving sustainability without sacrificing productivity. Furthermore, integrating truly sustainable agricultural practices with cutting-edge innovations, including new genomic techniques, has the potential to be a transformative solution.

Cognitive Health Benefits of Everyday Physical Activity in a Diverse Sample of Middle-Aged Adults.

Physical activity has been associated with a wide range of health benefits including long-term benefits for cognitive and brain health. Whether episodes of everyday physical activity are associated with immediate cognitive benefits remains unknown. The purpose of the current study was to examine whether episodes of physical activity, occurring over the course of participants' daily lives, are associated with short-term improvements in cognitive health. Participants completed a 9-day ecological momentary assessment protocol involving 5 daily assessments of self-reported physical activity and ambulatory cognitive assessments of processing speed and visuospatial working memory. Data were analyzed in a multilevel modeling framework to explore changes in performance on each task associated with physical activity during the period leading up to the assessment as well as individual differences in average frequency of physical activity. Results of MLMs indicated that engaging in physical activity during the period (~3.5hr) leading up to an assessment was associated with improvements in processing speed equivalent to 4 years of cognitive aging. Such improvements were observed for both light and moderate-to-vigorous physical activity levels. No association was observed for visuospatial working memory accuracy; however, response time during the working memory task reliably mirrored the association observed for processing speed. The short-term benefits were observed, particularly, for individuals with an overall higher frequency of reported physical activity. Our findings suggest that engaging in everyday physical activity of any intensity level may have short-term, acute benefits for cognitive health and point to new potential targets for intervention. NCT03240406.

Intersectional employee voice inequalities and culture care theory: the case of migrant palliative care nurses inSaudi Arabia.

This narrative literature review examines intersectional employee voice inequalities in a non-Western, high power distance context to develop a multilevel conceptual framework. The authors use Leininger's (1997, 2002) culture care model to explore multilevel influences on intersectional voice inequalities. The article applies insights from a review of 31 studies to the specific challenges of migrant palliative care (PC) nurses in Saudi Arabia. The themes identified in the review indicate how better transcultural communications might mitigate voice inequalities that influence migrant employee wellbeing and intentions to quit which result from cultural incongruities. The impact of national culture differences and intersectional inequalities on employee voice has largely been ignored in academic research. This paper offers unique insights drawing on culture care theory into intersectional voice challenges from a non-Western perspective in the underresearched setting of Saudi Arabia which is mid-way through a national transformation program. It starkly contrasts policy ambitions for advancing healthcare with discriminatory practices based on conservative attitudes which stifle migrant worker voices.

Leadership in federally qualified health centers: examining recruitment and retention in rural Oklahoma.

Healthcare workforce shortages, particularly in rural areas, present a global challenge. The purpose of this study is to explore the leadership dynamics within federally qualified health centers (FQHC) in rural Oklahoma, focusing on recruitment, retention, job satisfaction and development practices. Eighteen managers with five or more years of tenure from Oklahoma FQHCs were interviewed. Leadership's role in influencing job satisfaction, recruitment, retention and development practices was analyzed using a multilevel ecological framework through qualitative content analysis with NVivo. The analysis includes ten key themes including the critical role of leadership in addressing recruitment and retention challenges, the importance of aligning organizational culture, rural culture, access challenges, trainings and values with workforce development initiatives, and the impact of leadership practices on job satisfaction. This study uniquely examines leadership strategies in rural FQHCs, integrating ecological considerations for cultural, logistical and community-specific factors. It emphasizes the pivotal role leadership plays in shaping workforce development. As rural healthcare evolves, refining these approaches is crucial for addressing workforce challenges, improving healthcare access and ensuring that rural FQHCs remain sustainable, driving positive outcomes for healthcare professionals and communities.

The emergence of team compassion: Theoretical implications and practical interventions

With the recent experiences involving COVID-19, there is a growing need for organisations to better understand compassion in addressing employees’ suffering and boosting their well-being. Particularly, as teamwork is becoming ubiquitous, organisational scholars have identified positive benefits of compassion at the team level such as improving communication, decreasing interpersonal conflicts and boosting team effectiveness. Using a multilevel theoretical framework in reviewing compassion research, this article advances our understanding of team-level compassion by elucidating the processes through which individual-level compassion gives rise to team-level compassion. First, we delineate composition and compilation models of the emergence of team compassion and review empirical studies with respect to the two models. Second, we explain three social mechanisms in teams – social learning, emotional contagion and reciprocity – that shape the emergence of team compassion. Finally, we discuss interventions that can facilitate the emergence of team compassion and offer practical guidance for managers seeking to foster team compassion. JEL Classification: D23, I31

Exploring circular economy strategies of SMEs in the Italian tanning cluster of Arzignano: A multilevel perspective of reactive and proactive strategies

While the Circular Economy predominantly affects large companies and multinationals, its impact on Small and Medium-sized Enterprises (SMEs) is an emerging research area. This research aimed to unfold the Circular Economy strategies implemented by SMEs within an industrial cluster context. Ten companies from the Italian tanning cluster of Arzignano were selected as case studies. Thematic analysis, pattern-matching, and multi-value Qualitative Comparative Analysis techniques were used to analyze primary and secondary data. Main findings revealed that CE strategies taken by the studied companies were characterized by the economic and efficiency benefits provided, such as resource-saving, waste management, energy use reduction, and materials selection for further recycling. SMEs' management is targeting compliance with environmental regulations/certifications, market pressures, and cost-benefit assessments, evidencing an adaptive behavior to external exigencies. Novel findings came from the meso level, such as a ‘symbiotic circularity’ strategy taken by two SMEs and their value chain for co-production, and the chain actions related to recycling, remanufacturing, and reusing wasted products by partner companies. A cascade chain of interdependent actions toward Circular Economy practices was also identified within the cluster level. Reactive and proactive Circular Economy strategies were identified in a multilevel framework.

Health and wellbeing of survivors of sexual violence and abuse attending sexual assault referral centres in England: the MESARCH mixed-methods evaluation.

One million people in England and Wales experience sexual violence and abuse each year, with nearly half experiencing serious sexual offences; around 30,000 survivors access sexual assault referral centres. This research was commissioned by National Institute for Health and Care Research to evaluate access, interventions and care pathways for survivors, especially those provided through sexual assault referral centres. The sexual assault referral centres care pathway was investigated through six sub-studies. There were two Cochrane Reviews (4274 participants). Seventy-two providers and 5 survivors were interviewed at eight sites; the children and young people study involved 12 participants from two sexual assault referral centres. A cohort study involving three-wave data collection over 1 year (21 sites; 2602 service users screened, 337 recruited) used a multilevel modelling framework to explore risk factors for burden of post-traumatic stress disorder symptoms at baseline and change at 1 year. We analysed costs and outcomes and conducted a narrative analysis (41 survivors). We worked closely with survivors and prioritised the safety/welfare of participants and researchers. Cochrane Reviews identified large effects from psychosocial interventions for post-traumatic stress disorder and depression. Sexual assault referral centres delivered a high-quality frontline service for survivors but groups experiencing domestic abuse and some ethnic and cultural minorities were under-represented. The qualitative research emphasised inter-agency collaboration for survivor benefit. The cohort study identified a risk 'triad' of adverse childhood experiences, poor mental health and economic deprivation, which was associated with baseline trauma burden. There were important improvements in trauma symptoms a year later. These improvements were unrelated to different sexual assault referral centre models. Costs and other outcomes were also similar across models. Harmful policing and justice practices/procedures were identified by 25% of participants. In this context, trauma-competent interviewing techniques, regular/timely updates and conveying case decisions with care signalled good practice. The cohort study lacked a comparison group, reducing confidence in the finding that access to sexual assault referral centres explained the reduction observed in post-traumatic stress disorder. Barriers to access call for concerted efforts to implement trauma-informed universal health services. The risk 'triad' underscores the value of holistic approaches to care at sexual assault referral centres and timely follow-on care. Poor mental health was the main barrier to service access beyond sexual assault referral centres. The persistence of trauma symptoms a year after accessing sexual assault referral centres signals urgent need for tackling counselling wait-lists, expanding support options and commitment to lifelong care. Multidisciplinary evaluation of sexual assault referral centres for better health provides a foundation for advancing trauma-informed practices in the context of sexual violence and abuse. This study is registered as ISRCTN30846825 https://doi.org/10.1186/ISRCTN30846825. This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 16/117/04) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 35. See the NIHR Funding and Awards website for further award information.

Subsyndromes and symptom clusters: Multilevel factor analysis of behavioral and psychological symptoms of dementia with intensive longitudinal data.

Behavioral and psychological symptoms in dementia (BPSD) are dynamic phenomena with a high amount of intraindividual variability. We applied a multilevel framework to identify subsyndromes (between-person factors) that represent clinically relevant profiles of BPSD and identify symptom clusters (within-person factors) that represent contextually driven daily symptom experiences. This study used an intensive longitudinal design in which 68 co-residing family caregivers to persons living with dementia were recruited to proxy report on their care recipient's daily symptom experiences of 23 different BPSD for eight consecutive days (n=443 diaries). A multilevel exploratory/confirmatory factor analysis was used to account for nested data and separate within-person variances from between-level factor estimates. Exploratory factor analysis identified a 4-between 3-within factor structure based on fit statistics and clinical interpretability. This study offers major methodological and conceptual advancements for management of BPSD within Alzheimer's disease and related dementias by introducing two related but distinct concepts of subsyndromes and symptom clusters. Because behavioral and psychological symptoms of dementia (BPSD) are dynamic temporal phenomenon, this introduces measurement error into aggregate group-level estimates when trying to create subsyndromes. We propose a multilevel analysis to provide a more valid and reliable estimation by separating out variance due to within-person daily fluctuations. Using a multilevel exploratory factor analysis with intensive longitudinal data, we identified distinct and meaningful groups of BPSD. The four factors at the between-person level represented subsyndromes that are based on how BPSD co-occurred among persons with Alzheimer's disease (AD). These subsyndromes are clinically relevant because they share features of established clinical phenomena and may have similar neurobiological etiologies. We also found three within-person factors representing distinct symptom clusters. They are based on how BPSD clustered together on a given day for an individual with AD and related dementias. These clusters may have shared environmental triggers.

Protective Factors Associated With Lower Likelihood of Injection Drug Use and Experiencing Overdose Among Incarcerated Women

PurposePrior studies evaluated protective factors individually as they relate to fewer drug use risk behaviors and related consequences. This is the first study to examine protective factors as part of a multilevel framework along a risk continuum among women involved in the criminal legal system who use drugs. This study describes factors within the socio-ecological framework that are protective against engaging in injection drug use and experiencing nonfatal overdose. MethodData were collected from 900 women with a history of opioid use disorder who were incarcerated and enrolled in the National Institutes of Health/National Institute on Drug Abuse–funded Justice Community Opioid Innovation Network cooperative. Analysis focused on the relationship among individual, interpersonal, and community- or institutional-level protective factors associated with not injecting drugs and not experiencing an overdose in the 90 days before incarceration using multinomial logistic regression. FindingsFindings from this study suggest that, even among a sample of women who use drugs, there are a number of factors associated with being less likely to report higher-risk injection behavior and/or overdose experiences at the individual level (age, religiosity, and less polysubstance use), interpersonal level (not having a partner who injects drugs), and community or institutional level (fewer months incarcerated, less treatment utilization, and less enacted stigma by health care workers). ConclusionsFindings from this study underscore the importance of being able to target prevention interventions to women at different stages of substance use severity and to capitalize on protective factors for those at lower-risk levels to reduce the trajectory of risk to injection practices and overdose experiences.

LOTUS: Protocol for a double-blind placebo controlled randomized trial of hemp-derived cannabidiol for the treatment of cannabis use disorder.

As cannabis legalization continues to spread across the United States, average Δ9-tetrahydrocannabinol concentrations in recreational products have significantly increased, and no prior study has evaluated effective treatments to reduce cannabis use among high potency cannabis users. Some research has found that the non-intoxicating cannabinoid cannabidiol reduces cannabis use and cannabis use disorder-related symptoms, such as affective disturbance and withdrawal. Results of these studies are promising but limited to synthetic or isolated forms of cannabidiol. Conduct a placebo-controlled randomized control trial comparing the effects of hemp-derived cannabidiol on reducing Δ9-tetrahydrocannabinol use in concentrate users with cannabis use disorder. Design. Double-blind, three-arm randomized placebo-controlled trial. Setting. University in the Denver-Boulder, CO, USA area. Study population. Community members who are heavy, stable cannabis concentrate users that meet criteria for at least moderate cannabis use disorder and are seeking to decrease or stop cannabis use. Data. Self-report demographics, substance use, and mental health characteristics, blood and urine based biomarkers and anthropometrics. Outcomes. Affective, physiological, and physical withdrawal symptoms, Δ9-tetrahydrocannabinol use. Analysis. Three-group ANOVAs and χ2 tests will be used to compare baseline variables between groups. Characteristics that differ between groups will be evaluated as potential covariates in subsequent analyses. A multilevel modeling framework will be used for primary outcome analysis to account for the repeated observations nested within participants over time. Pairwise post-hoc simple effects tests will be conducted to confirm patterns of differences. ClinicalTrials.gov NCT06107062.

Syn-COM: A Multi-Level Predictive Synergy Framework for Innovative Drug Combinations.

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

Reconceptualising graduate resilience – an integrated multi-level framework for future research

This paper draws on Bourdieusian social theory to reconceptualise graduate resilience in post-industrial societies to provide a fresh perspective on a concept that has gained increasing prominence in recent years. Through a review of sociological critiques of resilience, this paper argues that graduate resilience is a complex social phenomenon shaped by a range of factors, including material and social resources. In response, we propose an integrated multi-level framework that identifies different stages of graduate resilience, in the context of early transitions into the labour market, and how these stages are shaped at the micro, meso, and macro levels. This framework places resilience in the context of neo-liberalism and highlights structural barriers that hinder the building and signalling of graduate resilience. We argue that the framework enables current representations and understandings of graduate resilience within research, policy, and practice to be problematised and provides a critical starting point for advancing understanding.

Resilience of socio-technical transportation systems: A demand-driven community detection in human mobility structures

Existing scholarship on transportation resilience analysis has primarily focused on engineering resilience, often overlooking the intricate socio-technical dimensions. This oversight underscores the necessity for a more comprehensive understanding of the dynamic interplay between social, including travel behaviors, and technical infrastructure components within transportation systems. This article delves into the impact of “social shocks” on transportation systems, which are defined as disturbances affecting the social subsystem without yet affecting the technical subsystem. Drawing inspiration from C.S. Holling’s ecological resilience, which signifies a system’s ability to cope with change by adapting its structure and functionality, we propose a multi-level resilience assessment framework. It encompasses four mobility-related indicators: entropy (measuring network-level complexity), stationarity (assessing community compositional changes at the cluster level), and two node-level metrics — within-module degree and weighted participation coefficient — capturing location connectivity. These indicators proxy for evaluating the mobility structure and node functionality within the social subsystem. In a case study, we analyze historical smart card data to examine the mobility pattern’s structural changes within Hong Kong, a rail-oriented metropolis, during a prolonged and city-wide protest. The framework and associated indicators provide an alternative perspective for transit planners and operators, allowing them to assess both the overall system and individual stations, moving beyond traditional assessments of service supply and patronage changes.

Testing an Organizational Implementation Process Model Related to Teachers' Implementation-Related Attitudes and Behaviors: a Multilevel Mediation Analysis.

The effectiveness of school-based universal prevention programs is frequently diminished due to low-quality implementation. Organizational factors support high-quality implementation because of their broad influence across implementers. Conceptually, implementation leadership (i.e., behaviors that prioritize, reward, and support evidence-based practice [EBP] implementation) works to embed a favorable implementation climate (i.e., implementers' collective perceptions that their organization prioritizes, rewards, and support EBP implementation) leading to improved implementation citizenship behavior and attitudes toward EBP. This organizational implementation process model has some empirical support but has not been tested in a multilevel framework or related to hypothesized attitudinal and behavioral outcomes. The sample included 319 teachers across 39 US public elementary schools; all were implementing Schoolwide Positive Behavior Interventions and Supports. Multilevel mediation (level 1 = teacher, level 2 = school) was used to test the indirect association of implementation leadership on implementation-related attitudes and behaviors via implementation climate across two time points (fall and spring). At the school level, the organizational implementation process model was validated related to implementation citizenship behavior, but not attitudes toward EBP. At the teacher level, the process model was validated related to both outcomes, and there was a significant direct effect of implementation leadership on attitudes toward EBP. Developing strong leaders for implementation seems key to achieving high-quality EBP implementation. Implications for schools, principal training, and research are discussed.

Cooperative coevolution for non-separable large-scale black-box optimization: Convergence analyses and distributed accelerations

Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimization framework, on non-separable functions. First, we reveal empirical reasons of when decomposition-based methods are preferred or not in practice on some non-separable large-scale problems, which have not been clearly pointed out in many previous CC papers. Then, we formalize CC to a continuous-game model via simplification, but without losing its essential property. Different from previous evolutionary game theory for CC, our new model provides a much simpler but useful viewpoint to analyze its convergence, since only the pure Nash equilibrium concept is needed and more general fitness landscapes can be explicitly considered. Based on convergence analyses, we propose a hierarchical decomposition strategy for better generalization, as for any decomposition, there is a risk of getting trapped into a suboptimal Nash equilibrium. Finally, we use powerful distributed computing to accelerate it under the recent multi-level learning framework, which combines the fine-tuning ability from decomposition with the invariance property of CMA-ES. Experiments on a set of high-dimensional test functions validate both its search performance and scalability (w.r.t. CPU cores) on a clustering computing platform with 400 CPU cores.

Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities

In response to global climate challenges, urban low-carbon transformation has become a critical strategy for sustainable development. This study constructs a theoretical model for urban low-carbon transformation using the multi-level perspective framework. We focused on three batches of low-carbon pilot cities in China and employed fuzzy set qualitative comparative analysis to investigate the transformation pathways and impact mechanisms during the periods 2010–2012, 2012–2017, and 2017–2019. The results indicate that none of the six antecedent conditions is necessary for urban low-carbon transformation. Initially, the transformation is primarily driven by a pathway led by low-carbon industries. In the mid-stage, two pathways emerge: one dominated by the combination of low-carbon industries and research and development (R&amp;D) human capital and another led by low-carbon consumption awareness and economic development levels. In the later stage, the influencing factors involve a combination across micro, meso, and macro levels, reflecting an increasingly diversified and intricate configuration. The regional industrial structure consistently plays a dominant role, while awareness of low-carbon consumption has grown over time. This study not only enhances our understanding of the underlying mechanisms but also provides practical policy recommendations for local governments to tailor their strategies for effective low-carbon transformation.

Med-MGF: multi-level graph-based framework for handling medical data imbalance and representation

BackgroundModeling patient data, particularly electronic health records (EHR), is one of the major focuses of machine learning studies in healthcare, as these records provide clinicians with valuable information that can potentially assist them in disease diagnosis and decision-making.MethodsIn this study, we present a multi-level graph-based framework called MedMGF, which models both patient medical profiles extracted from EHR data and their relationship network of health profiles in a single architecture. The medical profiles consist of several layers of data embedding derived from interval records obtained during hospitalization, and the patient-patient network is created by measuring the similarities between these profiles. We also propose a modification to the Focal Loss (FL) function to improve classification performance in imbalanced datasets without the need to imputate the data. MedMGF’s performance was evaluated against several Graphical Convolutional Network (GCN) baseline models implemented with Binary Cross Entropy (BCE), FL, class balancing parameter α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}, and Synthetic Minority Oversampling Technique (SMOTE).ResultsOur proposed framework achieved high classification performance (AUC: 0.8098, ACC: 0.7503, SEN: 0.8750, SPE: 0.7445, NPV: 0.9923, PPV: 0.1367) on an extreme imbalanced pediatric sepsis dataset (n=3,014, imbalance ratio of 0.047). It yielded a classification improvement of 3.81% for AUC, 15% for SEN compared to the baseline GCN+α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}FL (AUC: 0.7717, ACC: 0.8144, SEN: 0.7250, SPE: 0.8185, PPV: 0.1559, NPV: 0.9847), and an improvement of 5.88% in AUC and 22.5% compared to GCN+FL+SMOTE (AUC: 0.7510, ACC: 0.8431, SEN: 0.6500, SPE: 0.8520, PPV: 0.1688, NPV: 0.9814). It also showed a classification improvement of 3.86% for AUC, 15% for SEN compared to the baseline GCN+α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}BCE (AUC: 0.7712, ACC: 0.8133, SEN: 0.7250, SPE: 0.8173, PPV: 0.1551, NPV: 0.9847), and an improvement of 14.33% in AUC and 27.5% in comparison to GCN+BCE+SMOTE (AUC: 0.6665, ACC: 0.7271, SEN: 0.6000, SPE: 0.7329, PPV: 0.0941, NPV: 0.9754).ConclusionWhen compared to all baseline models, MedMGF achieved the highest SEN and AUC results, demonstrating the potential for several healthcare applications.

Process-driven solvent screening for efficient extractive distillation using interpolative rational functions

When designing extractive distillation processes, using selectivity and capacity at infinite dilution alone is hard to identify the real optimal solvent with minimal process cost. To overcome this problem, a new process-driven solvent screening approach is developed. As simple and reliable surrogate models, rational functions (algebraic fractions such that the numerator and the denominator are polynomials) and multivariate polynomials (a subset of rational functions) are trained to interpolate vapor–liquid equilibria with thermodynamic consistency. The surrogate models can directly be embedded into superstructure-based extractive distillation process design to obtain optimal solutions within a few seconds. This enables to evaluate the real process performance of numerous solvents efficiently. Incorporating the accelerated process design strategy, a multi-level solvent screening framework is proposed and exemplified for the separation of a close-boiling mixture ethylbenzene/styrene. The solvent C2H2Br4 ultimately enables a cost reduction of 27.9 % compared to the industrially used benchmark solvent sulfolane.

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.