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This study reviews scholarly work in sustainable green logistics and remanufacturing (SGLR) and their subdisciplines, in combination with bibliometric, thematic and content analyses that provide a viewpoint on categorization and a future research agenda. This paper provides insight into current research trends in the subjects of interest by examining the most essential and most referenced articles promoting sustainability and climate-neutral logistics.

For the literature review, the authors extracted and sifted 2180 research and review papers for the period 2008–2023 from the Scopus database. The authors performed bibliometric and content analyses using multiple software programs such as Gephi, VOSviewer and R programming.

The SGLR papers can be grouped into seven clusters: (1) The circular economy facets; (2) Decarbonization of operations to nurture a climate-neutral business; (3) Green sustainable supply chain management; (4) Drivers and barriers of reverse logistics and the circular economy; (5) Business models for sustainable logistics and the circular economy; (6) Transportation problems in sustainable green logistics and (7) Digitalization of logistics and supply chain management.

In this review, fundamental ideas are established, research gaps are identified and multiple future research subjects are proposed. These propositions are categorized into three main research streams, i.e. (1) Digitalization of SGLR, (2) Enhancing scopes, sectors and industries in the context of SGLR and (3) Developing more efficient and effective climate-neutral and climate change-related solutions and promoting more environmental-related and sustainability research concerning SGLR. In addition, two conceptual models concerning SGLR and climate-neutral strategies are developed and presented for managers and practitioners to consider when adopting green and sustainability principles in supply chains. This review also highlights the need for academics to go beyond frameworks and build new techniques and instruments for monitoring SGLR performance in the real world.

This study provides an overview of the evolution of SGLR; it also clarifies concepts, environmental concerns and climate change practices, particularly those directed to supply chain management.

The purpose of this paper is to carry out a hydrodynamic and thermal analysis of turbulent forced-convection flows of pure water, pure ethylene glycol and water-ethylene glycol mixture, as base fluids dispersed by Al₂O₃ nano-sized solid particles, through a constant temperature-surfaced rectangular cross-section channel with detached and attached obstacles, using a computational fluid dynamics (CFD) technique. Effects of various base fluids and different Al₂O₃ nano-sized solid particle solid volume fractions with Reynolds numbers ranging from 5,000 to 50,000 were analyzed. The contour plots of dynamic pressure, stream-function, velocity-magnitude, axial velocity, transverse velocity, turbulent intensity, turbulent kinetic energy, turbulent viscosity and temperature fields, the axial velocity profiles, the local and average Nusselt numbers, as well as the local and average coefficients of skin friction, were obtained and investigated numerically.

The fluid flow and temperature fields were simulated using the Commercial CFD Software FLUENT. The same package included a preprocessor GAMBIT which was used to create the mesh needed for the solver. The RANS equations, along with the standard k-epsilon turbulence model and the energy equation were used to control the channel flow model. All the equations were discretized by the finite volume method using a two-dimensional formulation, using the semi-implicit method for pressure-linked equations pressure-velocity coupling algorithm. With regard to the flow characteristics, the interpolation QUICK scheme was applied, and a second-order upwind scheme was used for the pressure terms. The under-relaxation was changed between the values 0.3 and 1.0 to control the update of the computed variables at each iteration. Moreover, various grid systems were tested to analyze the effect of the grid size on the numerical solution. Then, the solutions are said to be converging when the normalized residuals are smaller than 10-12 and 10-9 for the energy equation and the other variables, respectively. The equations were iterated by the solver till it reached the needed residuals or when it stabilized at a fixed value.

The result analysis showed that the pure ethylene glycol with Al₂O₃ nanoparticles showed a significant heat transfer enhancement, in terms of local and average Nusselt numbers, compared with other pure or mixed fluid-based nanofluids, with low-pressure losses in terms of local and average skin friction coefficients.

The present research ended up at interesting results which constitute a valuable contribution to the improvement of the knowledge basis of professional work through research related to turbulent flow forced-convection within channels supplied with obstacles, and especially inside heat exchangers and solar flat plate collectors.

This study aims to comprehend the application of analytics in the supply chain during the ongoing COVID-19 crisis and identify the emerging themes.

The author downloaded a list of research articles on the application of analytics to the supply chain from SCOPUS, conducted a systematic literature review for exploratory analysis and proposed a framework. Notably, the author used the topic modeling technique to identify research themes published during the ongoing COVID-19 crisis and thereby underscore some future research directions.

The author found that artificial intelligence, machine learning, internet of thing and blockchain are trending topics. Additionally, the author identified five themes by topic modeling, including the theme “Social Media information in Supply chain.”

The results were derived from a data set extracted from SCOPUS. Thus, the author excluded all studies not listed in SCOPUS from the analysis. Future research with articles indexed in other databases should be investigated to get a more holistic perspective of specific themes.

This study provides a deeper understanding and proposes a framework for applications of analytics in the supply chain that researchers could use for future research and industry practitioners to implement in their organizations to make a more sustainable and resilient supply chain.

This study provides exploratory information from published articles on the use of analytics in the supply chain during the COVID-19 crisis and generates themes that help understand the emerging and underpinned area of research.

Convection is one of the main heat transfer mechanisms in both high to low temperature media. The accurate convection heat transfer coefficient (HTC) value is required for exact prediction of heat transfer. As convection HTC depends on many variables including fluid properties, flow hydrodynamics, surface geometry and operating and boundary conditions, among others, its accurate estimation is often too hard. Homogeneous dispersion of nanoparticles in a base fluid (nanofluids) that found high popularities during the past two decades has also increased the level of this complexity. Therefore, this study aims to show the application of least-square support vector machines (LS-SVM) for prediction of convection heat transfer coefficient of nanofluids through circular pipes as an accurate alternative way and draw a clear path for future researches in the field.

The proposed LS-SVM model is developed using a relatively huge databank, including 253 experimental data sets. The predictive performance of this intelligent approach is validated using both experimental data and empirical correlations in the literature.

The results show that the LS-SVM paradigm with a radial basis kernel outperforms all other considered approaches. It presents an absolute average relative deviation of 2.47% and the regression coefficient (R²) of 0.99935 for the estimation of the experimental databank. The proposed smart paradigm expedites the procedure of estimation of convection HTC of nanofluid flow inside circular pipes.

Therefore, the focus of the current study is concentrated on the estimation of convection HTC of nanofluid flow through circular pipes using the LS-SVM. Indeed, this estimation is done using operating conditions and some simply measured characteristics of nanoparticle, base fluid and nanofluid.

This study aims to (1) investigate the influence of firms’ social media utilization on performance through supply chain agility, (2) examine the mediating role of supply chain agility and (3) explore the indirect effect of social media utilization on operational performance via supply chain agility as knowledge transfer increases.

A survey of 298 Chinese manufacturing firms was conducted to assess the proposed relationships, employing moderated mediation analysis with Andrew Hayes (2017) PROCESS macro.

Social media utilization indirectly enhances operational performance through supply chain agility, supporting our mediation hypothesis (H1). Additionally, knowledge transfer moderates the positive impact of social media utilization on supply chain agility (H2). The moderated mediation analysis reveals that the mediating effect of supply chain agility on operational performance is stronger at higher levels of knowledge transfer (H3), shedding light on the intricate relationships between these variables and providing insights for businesses seeking to leverage social media and knowledge transfer to enhance supply chain resilience and operational performance.

This study empirically investigates the role of social media utilization in supply chains within the digital age. We explore how social media enhances supply chain agility and knowledge transfer, highlighting its transformative potential for real-time communication, responsiveness and collaboration across networks. By integrating dynamic capability theory with contemporary digital practices, we demonstrate how leveraging digital platforms alongside traditional supply chain processes can significantly improve manufacturing efficiency. This research bridges existing gaps in the literature and provides valuable insights for businesses navigating complex, rapidly changing environments in the era of digital transformation.

The purpose of this paper is to investigate the effect of shock waves and strain hardening effect of laser and shot peening on precipitation‐hardened aluminium alloy AA 6082‐T651.

The hardened layer was evaluated by means of surface integrity with optical microscopy, scanning electron microscope (SEM), energy dispersive spectroscopy, analysis of microhardness and residual stress profiles. Corrosion anodic polarization tests in a 3.5 per cent NaCl water solution were carried out to express a pitting potential and the degree of pitting attack, which was verified on SEM and with 3D metrology.

Research results indicated significant differences between two treatment techniques which had an important influence on the final condition of the surface layer. Potentiodynamic polarization tests inferred that laser peening enabled shift of the pitting potential to more positive values, which ensures higher corrosion resistance.

Results confirmed that the higher corrosion resistance of the laser‐peened specimens against pitting corrosion depends on the modification of the surface, due to ablation during plasma generation. Despite increased surface roughness, laser‐peened specimen exhibits beneficial increase of the pitting/breakdown potential and in reduction of pitting attack degree at the specimen surface.