Modeling Framework For Analysis Research Articles

Intracortical brain-heart interplay: An EEG model source study of sympathovagal changes.

The interplay between cerebral and cardiovascular activity, known as the functional brain-heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain-heart axis have been proposed to estimate BHI non-invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies. This study proposes an analytical modeling framework for estimating the BHI at the source-brain level. This analysis relies on the low-resolution electromagnetic tomography sources localization from scalp electrophysiological recordings. BHI is then quantified as the functional correlation between the intracortical sources and cardiovascular dynamics. Using this approach, we aimed to evaluate the reliability of BHI estimates derived from source-localized EEG signals as compared with prior findings from neuroimaging methods. The proposed approach is validated using an experimental dataset gathered from 32 healthy individuals who underwent standard sympathovagal elicitation using a cold pressor test. Additional resting state data from 34 healthy individuals has been analysed to assess robustness and reproducibility of the methodology. Experimental results not only confirmed previous findings on activation of brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, and anterior and mid-cingulate cortices) but also provided insights into the anatomical bases of brain-heart axis. In particular, we show that the bidirectional activity of electrophysiological pathways of functional brain-heart communication increases during cold pressure with respect to resting state, mainly targeting neural oscillations in the , , and bands. The proposed approach offers new perspectives for the investigation of functional BHI that could also shed light on various pathophysiological conditions.

An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data

Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&D personnel input, R&D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies’ green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality.

Exploring the gap in people’s travel behavior between urban villages and commercial housing: The role of built environment

As urban expansion accelerates, travel distance and vehicle miles traveled continue to grow. Simultaneously, agricultural land is gradually giving way to urban construction, and ancient communities are transforming into urban villages as a result of being surrounded by urban land. Given the widespread distribution of urban villages in Chinese cities, which accommodate a large number of low-income groups, paying attention to the travel behavior of these groups is beneficial for promoting the fair development ofsociety. Therefore, this study utilized resident survey data from urban villages and commercial housing communities of Zhuhai in 2018 and built a structural equation modeling analysis framework to investigate the differences in the influence of the built environment (BE) on the travel behavior of these two types of housing. The results showed that the BE of urban villages had a significantly different impact on travel behavior compared to commercial housing. This discovery can help authorities better understand the impact of the BE of urban villages on travel distance, transit choices, and walking/cycling decisions. Moreover, this research is conducive to proposing targeted measures for sustainable transportation development in urban villages.

Enhanced cross laminated timber (ECLT) for ballistic resistant design

Cross-laminated timber (CLT), an engineered wood product, has grown in popularity in the United States over the last decade due to its ease of assembly, aesthetic appeal, and potential to serve as a sustainable and renewable alternative building material. The improved robustness of CLT over light-frame timber structures has increased its potential in force-protected infrastructure that must resist blast waves and ballistic projectile penetration, but softwood CLT is typically not sufficient to fulfill standard criteria for ballistic threats. To address these threats, enhanced CLT (ECLT) composite panels were manufactured with layers of various reinforcing materials (e.g., steel, FRP) placed between wood laminations. These panels were tested against various ballistic threats during two sets of laboratory tests in order to provide a basis for future research. The first set of testing subjected laboratory-produced Southern Yellow Pine (SYP) ECLT to 84 shots of 0.50-inch (1.27 cm) steel sphere projectiles to evaluate the performance of various reinforcing materials, layups, and fastening methods. A subsequent set of tests subjected commercially produced ECLT of steel-reinforced Spruce-Pine-Fir (South) (SPF-S) to 36 shots of NATO ammunition projectiles traveling at realistic, standard striking velocities. Except for a panel using four layers of a thin and finely woven steel hardware cloth, steel reinforcement was effective in stopping projectiles from all three types of NATO ammunition fired. Ultimately considering the results of these two sets of tests, an initial cost-benefit structure was developed and discussed to compare ECLT configurations holistically, a multi-material wood analytical model framework was established and compared to experimental results, and the first ballistic experimental database for ECLT was created.

Estimates of hospitalisations and deaths in patients with COVID-19 associated with undiagnosed diabetes during the first phase of the pandemic in eight low-income and middle-income countries: a modelling study

Patients with COVID-19 that had diagnosed chronic diseases - including diabetes - may experience higher rates of hospitalisation and mortality relative to the general population. However, the burden of undiagnosed co-morbidities during the pandemic has not been adequately studied. We developed a model to estimate the hospitalisation and mortality burden of patients with COVID-19 that had undiagnosed type 1 and type 2 diabetes (UD). The retrospective analytical modelling framework was informed by country-level demographic, epidemiological and COVID-19 data and parameters. Eight low-and middle-income countries (LMICs) were studied: Brazil, China, India, Indonesia, Mexico, Nigeria, Pakistan, and South Africa. The modelling period consisted of the first phase of the pandemic - starting from the date when a country identified its first COVID case to the date when the country reached 1% coverage with one dose of a COVID-19 vaccine. The end date ranged from Jan 20, 2021 for China to June 2, 2021 for Nigeria. Additionally, we estimated the change in burden under a scenario in which all individuals with UD had been diagnosed prior to the pandemic. Based on our modelling estimates, across the eight countries, 6.7 (95% uncertainty interval: 3.4-11.3) million COVID-19 hospitalised patients had UD of which 1.9 (0.9-3.4) million died. These represented 21.1% (13.4%-30.1%) of all COVID-19 hospitalisations and 30.5% (14.3%-55.5%) of all COVID-19 deaths in these countries. Based on modelling estimates, if these populations had been diagnosed for diabetes prior to the COVID-19 pandemic, 1.7% (-3.0% to 5.9%) of COVID-19 hospitalisations and 5.0% (-0.9% to 14.1%) of COVID-19 deaths could have been prevented, and 1.8 (-0.3 to 5.0) million quality-adjusted life years gained. Our findings suggest that undiagnosed diabetes contributed substantially to COVID-19 hospitalisations and deaths in many LMICs. This work was supported, in part, by the Bill & Melinda Gates Foundation [INV-029062] and FIND.

An integrated analytical modeling framework for determining site-specific soil screening levels for PFAS

Soils at many contaminated sites have accumulated a significant amount of per- and polyfluoroalkyl substances (PFAS) and may require remediation to mitigate leaching to groundwater. USEPA’s current approaches for determining soil screening levels (SSLs) were developed for non-PFAS contaminants. Because many PFAS are interfacially-active with unique leaching behaviors in soils, the current non-PFAS-specific soil screening models may not be applicable. Following USEPA’s general methodology, we develop a new modeling framework representing PFAS-specific transport processes for determining site-specific SSLs for PFAS-contaminated sites. We couple a process-based analytical model for PFAS leaching in the vadose zone and a dilution factor model for groundwater in an integrated framework. We apply the new modeling framework to two typical types of contaminated sites. Comparisons with the standard USEPA SSL approach suggest that accounting for the PFAS-specific transport processes may significantly increase the SSL for some PFAS. For the range of soil properties and groundwater recharge rates examined, while SSLs determined with the new model are less than a factor of 2 different from the standard-model values for less interfacially-active shorter-chain PFAS, they are up to two orders of magnitudes greater for more interfacially-active longer-chain PFAS. The new analytical modeling framework provides an effective tool for deriving more accurate site-specific SSLs and improving site characterization and remedial efforts at PFAS-contaminated sites.

On the Target Detection Performance of a Molecular Communication Network with Multiple Mobile Nanomachines.

A network of nanomachines (NMs) can be used to build a target detection system for a variety of promising applications. They have the potential to detect toxic chemicals, infectious bacteria, and biomarkers of dangerous diseases such as cancer within the human body. Many diseases and health disorders can be detected early and efficiently treated in the future by utilizing these systems. To fully grasp the potential of these systems, mathematical analysis is required. This paper describes an analytical framework for modeling and analyzing the performance of target detection systems composed of multiple mobile nanomachines of varying sizes with passive/ absorbing boundaries. We consider both direct contact detection, in which NMs must physically contact the target to detect it, and indirect sensing, in which NMs must detect the marker molecules emitted by the target. The detection performance of such systems is calculated for degradable and non-degradable targets, as well as mobile and stationary targets. The derived expressions provide various insights, such as the effect of NM density and target degradation on detection probability.

A deep learning and clustering‐based topic consistency modeling framework for matching health information supply and demand

AbstractImproving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply‐side and demand‐side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease‐related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy‐making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.

The Hidden Economic Consequences of Migraine to the UK Government: Burden-of-Disease Analysis Using a Fiscal Framework.

Background: Migraine is a highly prevalent and incapacitating neurological disorder associated with the highest global disability burden in people aged 15 to 49 years. Europe has the fourth-highest prevalence of migraine, after North America, South America, and Central America, and above Asia and Africa. Migraine leads to relatively modest direct healthcare expenditure but has substantial indirect costs due to reduced productivity. Methods: The economic burden of migraine was estimated in comparison with the general population of the United Kingdom (UK) using an analytical fiscal modeling framework applying the government cost perspective. Published measures of migraine's impact on labor participation were applied to rates of economic activity/inactivity of the general population. The model estimates lifetime changes to earnings from employment, direct and indirect taxes paid, and financial support requirements over the life course. Incremental differences between those affected and unaffected by migraine are reported as net fiscal consequences to public accounts. Fiscal costs are reported as the discounted average per capita over a 20-year time horizon and for the entire annual UK cohort with prevalent migraine. Results: People affected by migraine are more likely to be absent from work, unemployed, and disabled, and to retire early. A 44-year-old individual affected by migraine was associated with £19 823 in excess fiscal costs to the UK government, £1379 per year living with the condition, compared with someone not affected by the disease. Annually, migraine was estimated to represent £12.20 billion to the public economy, approximately £130.63 per migraine episode. The model predicted annual productivity losses in the health and social care workforce to be £2.05 billion and total annual productivity losses to be over £5.81 billion. Conclusions: This fiscal analysis monetizes the occupational consequences of migraine to the UK government, both in terms of lost tax revenue and transfer payments. The findings are substantial and useful to characterize disease severity and to inform the body of evidence considered by decision makers appraising the cost-effectiveness of health technologies.

The Hidden Economic Consequences of Migraine to the UK Government: Burden-of-Disease Analysis Using a Fiscal Framework

Background: Migraine is a highly prevalent and incapacitating neurological disorder associated with the highest global disability burden in people aged 15 to 49 years. Europe has the fourth-highest prevalence of migraine, after North America, South America, and Central America, and above Asia and Africa. Migraine leads to relatively modest direct healthcare expenditure but has substantial indirect costs due to reduced productivity. Methods: The economic burden of migraine was estimated in comparison with the general population of the United Kingdom (UK) using an analytical fiscal modeling framework applying the government cost perspective. Published measures of migraine’s impact on labor participation were applied to rates of economic activity/inactivity of the general population. The model estimates lifetime changes to earnings from employment, direct and indirect taxes paid, and financial support requirements over the life course. Incremental differences between those affected and unaffected by migraine are reported as net fiscal consequences to public accounts. Fiscal costs are reported as the discounted average per capita over a 20-year time horizon and for the entire annual UK cohort with prevalent migraine. Results: People affected by migraine are more likely to be absent from work, unemployed, and disabled, and to retire early. A 44-year-old individual affected by migraine was associated with £19 823 in excess fiscal costs to the UK government, £1379 per year living with the condition, compared with someone not affected by the disease. Annually, migraine was estimated to represent £12.20 billion to the public economy, approximately £130.63 per migraine episode. The model predicted annual productivity losses in the health and social care workforce to be £2.05 billion and total annual productivity losses to be over £5.81 billion. Conclusions: This fiscal analysis monetizes the occupational consequences of migraine to the UK government, both in terms of lost tax revenue and transfer payments. The findings are substantial and useful to characterize disease severity and to inform the body of evidence considered by decision makers appraising the cost-effectiveness of health technologies.

Modeling the Impact of Phonon Scattering with Strain Effects on the Electrical Properties of MoS2 Field-Effect Transistors.

Molybdenum disulfide (MoS2) has distinctive electronic and mechanical properties which make it a highly prospective material for use as a channel in upcoming nanoelectronic devices. An analytical modeling framework was used to investigate the I-V characteristics of field-effect transistors based on MoS2. The study begins by developing a ballistic current equation using a circuit model with two contacts. The transmission probability, which considers both the acoustic and optical mean free path, is then derived. Next, the effect of phonon scattering on the device was examined by including transmission probabilities into the ballistic current equation. According to the findings, the presence of phonon scattering caused a decrease of 43.7% in the ballistic current of the device at room temperature when L = 10 nm. The influence of phonon scattering became more prominent as the temperature increased. In addition, this study also considers the impact of strain on the device. It is reported that applying compressive strain could increase the phonon scattering current by 13.3% at L = 10 nm at room temperature, as evaluated in terms of the electrons' effective masses. However, the phonon scattering current decreased by 13.3% under the same condition due to the existence of tensile strain. Moreover, incorporating a high-k dielectric to mitigate the impact of scattering resulted in an even greater improvement in device performance. Specifically, at L = 6 nm, the ballistic current was surpassed by 58.4%. Furthermore, the study achieved SS = 68.2 mV/dec using Al2O3 and an on-off ratio of 7.75 × 104 using HfO2. Finally, the analytical results were validated with previous works, showing comparable agreement with the existing literature.

An analytical framework for modeling ride pooling efficiency and minimum fleet size

Ride pooling (RP) is a transport mode using on-demand buses to combine the trips of multiple users into one vehicle. Its required fleet size and carbon emissions are quantified by the system’s efficiency. Due to the complex interplay between street network, buses, users and dispatch algorithm, efficiency case studies are available but bottom-up predictions are not. Here we close this gap using probabilistic and geometric arguments in an analytical model framework. Its modular design allows for adaptation to specific usage scenarios and provides an over-arching view of them. In a showcase on Euclidean spaces, our model quantifies how RP outperforms private cars as user demand increases. Its predicted power-law scaling is verified using a custom simulation framework, which further reveals improved scaling on real street networks and graphs with hierarchical structures. Henceforth, our work may help to identify street networks well-suited for RP, and predict key performance indicators analytically.

Contract selection and piracy surveillance for video platforms in the age of social media

AbstractPirated video clips, a new form of piracy, have spread rapidly in recent years with the vigorous development of short‐video social media platforms such as TikTok and Douyin. This has led to a huge loss of revenue for the film and TV drama industry where the negative economic impact trickles down from legitimate video platforms to content creators. As video platforms fight back against copyright infringement, they need to decide how much effort to invest in detecting and taking down pirated video clips that emerge on social media platforms. In addition, they need to decide whether a lump sum or unit price contract should be used to secure the playing rights for a video from the content creator. A content creator has no incentive to engage in piracy surveillance together with the video platform after signing a lump sum contract but has an incentive to do so after signing a unit price contract because he/she would receive royalties each time someone watches the copyrighted video. Using an analytical model framework, we find that the video platform should use a unit price contract when production of the video is less costly for the same quality (e.g., music‐based, horror, and romance movies) but a lump sum contract when production is costly (e.g., Sci‐fi, animation, and fantasy genres). We show that in equilibrium, the total surveillance effort between the video platform and the content creator is smaller and the video platform charges customers less to watch the copyrighted video under the lump sum contract compared to the unit price contract. As a result, the lump sum contract not only achieves higher consumer surplus compared to the unit price contract but also leads to a higher percentage of customers legally accessing the video program. Finally, the content creator's revenue is the same under both contracts, but the content creator may prefer the lump sum contract because it attracts more (legal or illegal) viewers and the increased visibility could be beneficial in the long run.

Modeling and measuring strategic alignment

PurposeThis paper presents an analytical framework for modeling and measuring strategic alignment. The resource-product-market (RPM) model is introduced as a means of representing the alignment of the firm's internal resources with its product lines and external markets. A strategic alignment index is defined to measure the degree of alignment represented by a model.Design/methodology/approachThe RPM model is derived as an extension of prior research on diversification indexes. The strategic alignment index is mathematically defined and the properties of the model are characterized using graph theory. The approach is illustrated for two example firms.FindingsThe RPM model is flexible and can be used with different types and measures of resources, products and markets. The model represents strategy in a structural manner addressing a vertical type of alignment. The index ranges continuously from 0 to 1.0, providing a useful scale for measurement and comparison.Practical implicationsPractitioners may use RPM modeling to assess the current alignment of their respective firms and to identify strategic alternatives which increase alignment through a taxonomy of 13 strategic moves. The results of applying the model to ten firms are summarized.Originality/valueThe paper contributes to the literature by providing a new method for modeling firm strategy which integrates resource and industry views, thereby enabling a measurement of their alignment. The paper is also novel in the application of graph theory to management.

Bandgap formation and low-frequency structural vibration suppression for stiffened plate-type metastructure with general boundary conditions

Metastructures with unique mechanical properties have shown attractive potential application in vibration and noise reduction. Typically, most of the metastructures deal with the vibration bandgap properties of infinite structures without considering specific boundary condition and dynamic behaviors, which cannot be directly applied to the engineering structures. In this research, we design a Stiffened Plate-type Metastructure (SPM) composed of a plate with periodic stiffeners and cantilever beam-type resonators subjected to general boundary conditions for low-frequency vibration suppression. The effects of boundary conditions and the number and orientation of the stiffeners on Locally Resonant (LR) type bandgap properties in SPM are further investigated. An analytical modeling framework is developed to predict the bandgap formations and vibration behaviors of SPMs in finite-size configuration. The governing equations of the SPM reinforced by various arrangements of stiffeners are derived based on the first-order shear deformation theory and Hamilton’s principle, and a Fourier series combined with auxiliary functions is employed to satisfy the arbitrary boundary conditions. Finite element analysis and experimental investigations of vibration behaviors for the SPM are carried out to validate the accuracy and reliability of the present analytical model. For practical designs of the SPMs with specific boundary conditions, it is found that there exist optimal numbers of stiffeners and resonators which can produce the significant LR-type bandgap behaviors. Furthermore, various arrangements of stiffeners and resonators are explored for different boundary conditions by breaking the requirement of spatially periodicity. It is shown that for the designed SPM, the vibration modes of its host structure should be considered to widen the frequency range in which the resonators transfer and store energy, and hence improve the performance of low-frequency vibration suppression. The present work can provide a significant theoretical guidance for the engineering application of metamaterial stiffened structures.

Xcore: an R package for inference of gene expression regulators

BackgroundElucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is a common question asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types.ResultsGiven the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that gene expression modeling can be done using ChIP-seq “signatures” directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore, an R package that allows TF activity modeling based on ChIP-seq signatures and the user's gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChIP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using transforming growth factor beta induced epithelial-mesenchymal transition time-courses, rinderpest infection time-courses, and embryonic stem cells differentiated to cardiomyocytes time-course profiled with Cap Analysis Gene Expression.Conclusionsxcore provides a simple analytical framework for gene expression modeling using linear models that can be easily incorporated into differential expression analysis pipelines. Taking advantage of public ChIP-seq databases, xcore can identify meaningful molecular signatures and relevant ChIP-seq experiments.

A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.

A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications. This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient's risk of an adverse outcome and compare its accuracy with and without patient subgroup information. The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility. In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), significantly replicated (Rand Index=0.92, 0.94, 0.89; P<.001, <.001, <.01), and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.6%, 99.34%, 99.86%). In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement (0.059, 0.11) but not as measured by the C-statistic or integrated discrimination improvement. Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond.

On the nonlinear post-critical responses of VAT sandwich beams with variable stiffness composite skins under axial compression

In this article, the nonlinear post-critical problem of Variable Angle Tow (VAT) sandwich beams with variable stiffness composite skins under axial compression is studied for the first time. The present sandwich beam model is constructed from a modified version of extended high-order sandwich panel theory (EHSAPT), in which the first-order shear deformation theory with von Kármán’s strain–displacement relation is utilized for the skin layer, whereas the high-order theory with linear strain–displacement is applied to characterize the deformation of the core layer. Based on the principal of virtual work, a system of differential equations with variable coefficients is firstly formulated for the postbuckling problem of VAT sandwich beams with variable stiffness composite skins under plane-strain state. Meanwhile, a more efficient Rayleigh–Ritz approach combined with variational formulas is employed to derive the nonlinear algebraic equations in weak forms, and thereafter a sophisticated path-tracing technique is applied to determine the postbuckling equilibrium path. Both super-critical and sub-critical postbuckling states of VAT sandwich beams can be readily captured under the present analytical model framework. The accuracy and effectiveness of the developed Rayleigh–Ritz procedure are validated by comparing against the results available in literature. Effects of variation in skin stiffness on both the critical instability and post-critical behaviours of VAT sandwich beams are discussed in numerical examples. It is found that the change in skin stiffness plays a determinative role in the formation of nonperiodic wrinkling patterns of VAT sandwich beams. In addition, the change in skin stiffness may cause global and local instability loads to be close to each other, and accordingly, the possibility of encountering interactive buckling is increased. The results presented herein may be beneficial for the design of VAT sandwich structures under compression.

Recycling channel selection and financing strategy for capital-constrained retailers in a two-period, closed-loop supply chain

This article studies a two-period, closed-loop supply chain (CLSC) with manufacturer or retailer recycling. It establishes a model analysis framework to analyze pricing optimization strategies and the channel-mode selection of electric vehicle batteries and considers manufacturer recycling and retailer recycling scenarios. When a retailer recycles, it needs to invest capital to build its recycling channel and so suffers from capital constraints. For this reason, retailers consider bank loans or trade-credit financing from manufacturers. This work explores a two-stage, CLSC pricing strategy that considers consumers’ preferences for remanufactured products and recycling rates, and it investigates financing channels for capital-constrained retailers. It analyzes optimal equilibrium strategies in three modes and compares the recycling and financing modes. Through numerical examples, it analyzes the effects of value preference rates and recycling rates of remanufactured products on supply chain profits, product demand, and model selection. The results show that the recycling rate can effectively incentivize the demand for new products in the first period. For any remanufactured product recycling cost, the manufacturer’s profit is most significant in the retailer-recycling bank financing model, and the retailer’s profit is largest in the manufacturer recycling model. As the consumer preference rate for remanufactured products increases, the demand for new products in the second stage decreases and the demand for remanufactured products increases. The retailer recycling trade-credit financing from the manufacturer model gives the largest profit to the retailer when the preference rate is significant or when the preference rate is low with a moderate recycling rate.

Different dimensions of durability in the luxury fashion industry: An analysis framework to conduct a literature review

As luxury fashion brands need to address the environmental impact of their products (both internally and externally), a framework to achieve a common and accessible language becomes paramount to avoid confusing claims. Tools, such as Life Cycle Assessment and Eco-Design, help companies assess and reduce the negative effects of their products on the environment; nonetheless, more work is needed. The desire to create durable goods is a direct reaction to the current throw-away culture perpetuated by planned obsolescence during the production phase of many fast-fashion brands. However, without a proper understanding of the term ‘durability’ and its different dimensions, a common language cannot be adopted by the various actors in the value chain (both up- and down-stream). Particularly, the characteristics of terms such as ‘resilience’ are mistakenly confused with those of ‘durability.’ Here, we review the literature from 2011 to date for determining clear definitions for both terms. A self-developed analysis framework was used for the systematic review of the literature, slightly adapting the methodology from an established process. The different levels (A, B, and C) of the framework reveal, in a structured manner, what a ‘durable’ or ‘resilient’ product is (at every stage of its lifecycle). We show that two dimensions should be considered for both concepts: ‘intrinsic’ (also referred to as ‘physical’ in the existing literature) and ‘extrinsic’ (often referred to as ‘emotional’). Most authors fail to acknowledge the existence of both terms or use them inconsistently. Consequently, a thorough assessment of product durability and resilience is lacking, as studies have focused solely on one of the two dimensions or on a single lifecycle stage (most often the design phase) of a product. Thus, beyond its original aim, our model analysis framework can also help evaluate the environmental impact of luxury fashion products at each stage of their lifecycle.