Melting heat transfer analysis of electrically conducting nanofluid flow over an exponentially shrinking/stretching porous sheet with radiative heat flux under a magnetic field

Modern magnetic nanomaterial processing operations are progressing rapidly and require increasingly sophisticated mathematical models for their optimization. Stimulated by such developments, in this paper, a theoretical and computational study of a steady magnetohydrodynamic nanofluid over an exponentially stretching/shrinking permeable sheet with melting (phase change) and radiative heat transfer is presented. Besides, wall transpiration, that is, suction and blowing (injection), is included. This study deploys Buongiorno's nanofluid model, which simulates the effects of the Brownian motion and thermophoresis. The transport equations and boundary conditions are normalized via similarity transformations and appropriate variables, and the similarity solutions are shown to depend on the transpiration parameter. The emerging dimensionless nonlinear coupled ordinary differential boundary value problem is solved numerically with the Newton-Fehlberg iteration technique. Validation with special cases from the literature is included. The increase in the magnetic field, that is, the Hartmann number, is observed to elevate nanoparticle concentration and temperature, whereas it dampens the velocity. Higher values of the melting parameter consistently decelerate the boundary layer flow and suppress temperature and nanoparticle concentration. A higher radiative parameter strongly increases temperature (and thermal boundary layer thickness) and weakly accelerates the flow. The increase in the Brownian motion reduces nanoparticle concentrations, whereas a greater thermophoretic body force strongly enhances them. The Nusselt number and Sherwood number are observed to be decreased with an increasing Hartmann number, whereas they are elevated with a stronger wall suction and melting parameter.     

The influence of national culture on responsible innovation: A case of CO2 utilisation in Indonesia

Responsible innovation (RI) offers a promising step towards a more responsible adoption of innovations, such as new and emerging technologies. However, how RI would be practised in different cultural contexts and settings is still less studied, especially in the context of developing countries. Taking the utilisation of CO₂ in Indonesia as an empirical case, the purpose of this paper is to explore this issue through examining the influence of key characteristics of Indonesia's national culture on core dimensions of RI: anticipation, reflexivity, deliberation, responsiveness and participation. The different RI dimensions are applied in practice in a focus group discussion (FGD) and a round of interviews with different stakeholders that explored CO₂ utilisation in the country. Hofstede's classification of national cultural dimensions is used to single out key focus points for attention in these RI processes in the Indonesian context. The results from the experiment illustrate that culture matters in the application of RI dimensions in practice. In the sense that in order for RI to work well, the governance of core RI processes such as participation and deliberation has to be adjusted to social norms of what is considered legitimate, desirable and good behaviour. These norms differ across societies. The findings further suggest ways of applying RI dimensions in practice.     

儿童用品TBT通报数据可视化预警研究

探索采用数据可视化技术分析儿童用品TBT通报数据,以可视化图形图像呈现通报热点并揭示趋势信息,提出对策与建议,助力为儿童用品产业升级、TBT预警数据分析和信息传播工作提供新思路,提高中小企业的国外市场准入机会。     

Sustainability of ride-hailing services in China’s mobility market: A simulation model of socio-technical system transition

As a symbol of sharing economy, ride-hailing services have spread to Asia, where various forms of services have been established according to the existing socio-technical regimes, including legal, policy, and environmental concerns. China, in particular, has accepted ride-hailing services by revising existing institutions. This study analyzes China’s acceptance of ride-hailing services in the socio-technical system context set up for legacy services. Simulation modeling, combined with transition theory and an agent-based model, is used to analyze the data. This study calculates consumer disutility based on mobility market share, reflecting consumer preference, and predicts the sustainability of ride-hailing services. The simulation results conclude that legalization and socio-technical context are of importance for sustainable mobility.     

Role of Post-translational Modifications in Alzheimer's Disease

The global burden of Alzheimer's disease (AD) is growing. Valiant efforts to develop clinical candidates for treatment have continuously met with failure. Currently available palliative treatments are temporary and there is a constant need to search for reliable disease pathways, biomarkers and drug targets for developing diagnostic and therapeutic tools to address the unmet medical needs of AD. Challenges in drug-discovery efforts raise further questions about the strategies of current conventional diagnosis; drug design; and understanding of disease pathways, biomarkers and targets. In this context, post-translational modifications (PTMs) regulate protein trafficking, function and degradation, and their in-depth study plays a significant role in the identification of novel biomarkers and drug targets. Aberrant PTMs of disease-relevant proteins could trigger pathological pathways, leading to disease progression. Advancements in proteomics enable the generation of patterns or signatures of such modifications, and thus, provide a versatile platform to develop biomarkers based on PTMs. In addition, understanding and targeting the aberrant PTMs of various proteins provide viable avenues for addressing AD drug-discovery challenges. This review highlights numerous PTMs of proteins relevant to AD and provides an overview of their adverse effects on the protein structure, function and aggregation propensity that contribute to the disease pathology. A critical discussion offers suggestions of methods to develop PTM signatures and interfere with aberrant PTMs to develop viable diagnostic and therapeutic interventions in AD.     

Fair‐Weather Friends: Evidence of Lipoxin Dysregulation in Neurodegeneration

Lipoxins (LXs) are autacoids, specialized proresolving lipid mediators (SPMs) acting locally in a paracrine or autocrine fashion. They belong to a complex superfamily of dietary small polyunsaturated fatty acid (PUFA)–metabolites, which direct potent cellular responses to resolve inflammation and restore tissue homeostasis. Together, these SPM activities have been intensely studied in systemic inflammation and acute injury or infection, but less is known about LX signaling and activities in the central nervous system. LXs are derived from arachidonic acid, an omega‐6 PUFA. In addition to well‐established roles in systemic inflammation resolution, they have increasingly become implicated in regulating neuroinflammatory and neurodegenerative processes. In particular, chronic inflammation plays a central role in Alzheimer's disease (AD) etiology, and dysregulated LX production and activities have been reported in a variety of AD rodent models and clinical tissue samples, yet with complex and sometimes conflicting results. In addition, reduced LX production following retinal injury has been reported recently by the authors, and an intriguing direct neuronal activity promoting survival and homeostasis in retinal and cortical neurons is demonstrated. Here, the authors review and clarify this growing literature and suggest new research directions to further elaborate the role of lipoxins in neurodegeneration.     

Strain hardening behaviors and mechanisms of polyurethane under various strain rate loading

The uniaxial tension experiments are performed on thermoplastic polyurethane to investigate its mechanical behaviors and related potential mechanisms, and the loading strain rate is designing to be wide ranging from 0.0001 to 1 s⁻¹. It is found that the polyurethane presents an obvious rate-dependence, and the stress strain curves share distinct strain hardening characteristics under the investigated strain rates. Furthermore, the strain hardening ratios are sharing nearly same trends and appear to be influenced by both strain rate and the induced adiabatic heating. Besides, the ratio is also strain-dependent on previous loading history. Then, a two-dimension unit cell model is built to investigate potential equivalent mechanisms, of which the hard phase as inclusion is equivalent with crystallization zone, crosslinking sites, and so forth. The simulation results facilitate to explain the distinct strain hardening ratios, even for the matrix from the extrapolated curves under super-low strain rate loading. Finally, the analogic mechanisms of equivalent hard inclusions are proposed, which can reasonably explain the strain rate- and strain-dependence characteristics of polyurethane mechanical behaviors.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Biocontrol of Listeria monocytogenes by lactic acid bacteria isolated from brewer's grains used as feedstuff in Argentina

Brewer's grains are the most important by-product of the brewery industry and it is mostly used as a protein and energy source in animal nutrition. Listeria monocytogenes is a food-borne pathogen can cause invasive diseases such as meningoencephalitis, sepsis, abortion, and gastroenteritis in humans and several animal species. The aim of this work was to study the antilisterial activity of lactic acid bacteria (LAB) isolated from brewer's grains. The incidence of Listeria spp. in brewer's grains was 3.12%. Twenty-one LAB inhibited the growth of the eight strains of L. monocytogenes. The mean inhibition halo of cell free supernatants of LAB ranged between 11.5 and 24.5 mm. The isolation of lactic acid bacteria with antilisterial activity from brewer's grains is promising based on their capacity to produce antimicrobial compounds. The production of antimicrobial metabolites by LAB in the substrate would generate an unfavorable environment for the growth of the pathogenic bacterium under study.