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The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.

It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.

A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.

We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.

With its presence, which can create inefficiencies, uncertainties and risks, information asymmetry poses a significant challenge to successfully managing the agri-food supply chain (AFSC). Understanding the variables that influence information asymmetry is crucial for devising more effective strategies to mitigate it. This study, therefore, explores the variables that influence information asymmetry in the AFSC.

A qualitative analysis was conducted, relying on semi-structured interviews with 17 experts representing different actors in the AFSC (e.g. seed producers, retailers, etc.) in Germany. The collected data was analysed using the GABEK® method.

The study confirms that the influencing variables derived from the existing theory, such as price performance, digitalisation, environmental, process and quality measures, contribute to information asymmetry. It further reveals new variables that associate with information asymmetry, including documentation requirements, increasing regulation, consumer behaviour, incorrect data within the company as well as crises, political conflicts and supplier–buyer conflicts. Furthermore, the study shows that supply chain actors counteract asymmetry by focusing on social behaviour and monitoring suppliers through key performance indicators, employees and social aspects.

The study was limited to the universal influence of the variables on information asymmetry in the AFSC, making the magnitude of the influence and its context-specific nature unexplained.

This study is one of the very few that examines information asymmetry across the AFSC from the perspective of different actors, providing a more overarching and deeper understanding of information asymmetry.

This paper aims to study numerically the non-Newtonian solution of carboxymethyl cellulose in water along with copper oxide nanoparticles, which flow turbulently through twisted smooth and finned tubes.

The twisted-tape inserts of rectangular and triangular sections are investigated under constant wall heat flux and the nanoparticle concentration varies between 0% and 1.5%. Computational fluid dynamics simulation is first validated by experimental information from two test cases, showing that the numerical results are in good agreement with previous studies. Here, the impact of nanoparticle concentration, tube twist and fins shape on the heat transfer and pressure loss of the system is measured. It is accomplished using longitudinal rectangular and triangular fins in a wide range of prominent parameters.

The results show that first, both the Nusselt number and friction factor increase with the rise in the concentration of nanoparticles and twist of the tube. Second, the trend is repeated by adding fins, but it is more intense in the triangular cases. The tube twist increases the Nusselt number up to 9%, 20% and 46% corresponding to smooth tube, rectangular and triangular fins, respectively. The most twisted tube with triangular fins and the highest value of concentration acquires the largest performance evaluation criterion at 1.3, 30% more efficient than the plain tube with 0% nanoparticle concentration.

This study explores an innovative approach to enhancing heat transfer in a non-Newtonian nanofluid flowing through an oval tube. The use of twisted-tape inserts with rectangular and triangular sections in this specific configuration represents a novel method to improve fluid flow characteristics and heat transfer efficiency. This study stands out for its originality in combining non-Newtonian fluid dynamics, nanofluid properties and geometric considerations to optimize heat transfer performance. The results of this work can be dramatically considered in advanced heat exchange applications.

This paper aims to model and analyze Jeffery Hamel’s channel flow with the magnetohydrodynamics second-grade hybrid nanofluid. Considering the importance of studying the velocity slip and temperature jump in the boundary conditions of the flow, which leads to results close to reality, this paper intends to analyze the mentioned topic in the convergent and divergent channels that have significant applications.

The examination is conducted on a EG-H_2 O <30%–70%> base fluid that contains hybrid nanoparticles (i.e. SWCNT-MWCNT). To ensure comprehensive results, this study also considers the effects of thermal radiation, thermal sink/source, rotating convergent-divergent channels and magnetic fields. Initially, the governing equations are formulated in cylindrical coordinates and then simplified to ordinary differential equations through appropriate transformations. These equations are solved using the Explicit Runge–Kutta numerical method, and the results are compared with previous studies for validation.

After the validation, the effect of the governing parameters on the temperature and velocity of the second-grade hybrid nanofluid has been investigated by means of various and comprehensive contours. In the following, the issue of entropy generation and its related graphical results for this problem is presented. The mentioned contours and graphs accurately display the influence of problem parameters, including velocity slip and temperature jump. Besides, when thermal radiation is introduced (Rd = +0.1 and Rd = +0.2), entropy generation in convergent-divergent channels decreases by 7% and 14%, respectively, compared to conditions without thermal radiation (Rd = 0). Conversely, increasing the thermal sink/source from 0 to 4 leads to an 8% increase in entropy generation at Q = 2 and a 17% increase at Q = 4 in both types of channels. The details of the analysis of contours and the entropy generation results are fully mentioned in the body of the paper.

There are many studies on convergent and divergent channels, but this study comprehensively investigates the effects of velocity slip and temperature jump and certainly, this geometry with the specifications presented in this paper has not been explored before. Among the other distinctive features of this paper compared to previous works, the authors can mention the presentation of velocity and temperature results in the form of contours, which makes the physical analysis of the problem simpler.

Predicting dimensional and geometrical errors in 3D printing parts during the design stage can significantly enhance the product’s quality. This study aims to predict the form deviation and process capability in additive manufacturing (AM) specimens considering layer thickness, laser power and scan speed parameters in the laser powder bed fusion (LPBF) method. Various machine learning (ML) techniques are implemented to estimate the form deviation and process capability with the highest accuracy in 3D-printed cylindrical parts as a case study.

The workflow started by simulating the LPBF AM process using a finite element modeling approach. Then, different ML algorithms like artificial neural networks are used to predict the form deviation. The process capability value is forecasted using some classification ML models and process capability indices (PCIs) for cylindrical parts. Finally, concentricity tolerance classification is performed for cylindrical parts, which can ensure quality control issues in the production stage.

Results present an accuracy of about 93% for predicting form deviations and 95% accuracy for predicting PCI C_pm in PCI classification based on random forest model as an ML algorithm.

The noteworthy point of the research is accessing the form deviation due to AM and process capability evaluation in the AM process before the production stage, which has not been studied before based on the author’s knowledge. So that the product quality is evaluated based on the shape deviation and its tolerances in the AM process digital chain.

In many societies, environmental problems have had some negative impacts on both social and economic features with issues like capital structure seriously affected. In this write-up, an attempt has been made to pinpoint the influence of the combined effects of integrated reporting and financial leverage on the value of a firm. In most emerging markets, investment is heavily dependent on foreign capital inflow which is mainly in the form of financial leverage. It is, therefore, necessary to know how this shapes the net worth of firms in which they are invested. Shareholders' fund is a major factor in determining the level of investment and economic stability of a nation and consequently improvements in sustainable development. Hence, the moderating role of financial leverage in the integrated reporting-shareholders’ funds relationship aims to warrant this research.

All listed firms on the Nigerian Stock Exchange (NSE) as of 31st December 2021 were affected by this research. Filtering resulted in the use of 77 companies as a sample for the study covering a period of 12 years (2010–2021) with a total of 788 observations. Analyses of data were done through line graphs to show the trend and flow of disclosures between 2010 and 2021. Furthermore, linear regression was also applied to help determine the multiple effects of financial leverage and integrated reporting on shareholders' funds.

The outcomes showed that economic disclosure was 100% throughout the period of observation as opposed to environmental and social disclosures which, fluctuate throughout the period with an average of slightly over 55%. It was also discovered that a low but significant financial leverage moderated the interaction of integrated reporting on shareholders' funds.

Stakeholders and policymakers should, therefore, put in place rules, regulations, standards, structures and administrative networks to enable firms to comply with local rules, guidelines and upgraded standards of international worth on sustainability issues.

This research explores the problem of the effects of integrated reporting on investment capital as it affects developing economies. Results from this study could go a long way in narrowing the lack of interest in sustainability issues by prospective investors coupled with the low level of environmental and social reporting by firms in African economies that are mostly underdeveloped.

This study aims to investigate the impact of market competitiveness on investment efficiency, and the moderating role of ownership and regulatory structures.

In this study, the Herfindahl–Hirschman Index (HHI), Lerner Index (LI) and industry-adjusted Lerner Index (LIIA) were used to measure market competitiveness. The research population consisted of companies listed on Tehran Stock Exchange (TSE). Using a systematic elimination, 199 companies were selected within eight years during 2014–2021.

The results showed that market competitiveness (based on the LI, LIIA and HHI) positively affected investment efficiency. Moreover, institutional ownership and managerial ownership affected the relationship between market competitiveness (based on all proxies of market competitiveness) and investment efficiency. Blockholders’ ownership also moderated the relationship between market competitiveness (based on LIIA and HHI) and investment efficiency. The hypothesis testing had robustness based on additional analyses.

In recent years, competitive environment and the ownership structure of companies have changed to a certain degree, paving the way for the private sector to enter many areas of activity especially in emerging Asian markets. Moreover, investment drivers and investment efficiency in developed markets may not be generalized to emerging Asian markets. Therefore, the present findings can show the significance of this research to fill the existing gap in the literature and provide insights into ownership and regulatory structures as a governance mechanism in market competitiveness and investment efficiency.

This study conducts a thorough literature review and meta-analysis to explore the adoption of blockchain technology (BCT) in supply chain management (SCM). It aims to identify the potential benefits, challenges, and critical factors influencing the implementation of this technology in supply chains.

A systematic analysis of 157 highly cited publications is performed, offering insights into research trends, citations, industries, research methods, and contextual aspects. Thematic analysis is employed to uncover key findings regarding enablers, barriers, drivers, challenges, benefits, and drawbacks associated with BCT adoption in supply chains.

The analysis highlights the complexities and opportunities involved in adopting BCT in SCM. A proposed model aligns with five dimensions, including inter-organizational, intra-organizational, technological, legal, and to assist businesses in harnessing the potential of BCT, overcoming obstacles, and managing challenges. This model provides practical recommendations for navigating the intricacies of BCT implementation while balancing associated challenges and risks.

Organizations operating in supply chains can leverage the insights gained from this investigation to position themselves at the forefront of BCT adoption. By implementing the proposed model, they can unlock benefits such as increased transparency, efficiency, trust, and cost reduction.

The novelty of this paper lies in its extensive review of publications on Blockchain Technology adoption in supply chains. It offers insights into various aspects such as enablers, barriers, drivers, challenges, benefits, and drawbacks. Additionally, the paper presents a comprehensive model specifically designed for successful adoption of Blockchain Technology in supply chains. This model addresses multiple dimensions including inter-organizational, intra-organizational, technological, legal, and financial.

The use of polysaccharides increases solubility and consistency and causes functions such as viscosity? Moisture and food emulsifier stabilizer. This study aims to enrich the formulation of low-fat mozzarella cheese using microcoated vitamin D3 (VD3).

This study investigates the addition of hydrocolloids to low-fat mozzarella cheese to enhance its properties and nutritional value. Tests were conducted on cheese samples with 0.05% and 0.25% hydrocolloid concentrations at various stages: before production and at three and six months’ postproduction. The samples were evaluated for elasticity, pH and solubility to select the best one, which was then fortified with VD3. The vitamin was microencapsulated using alginate and whey protein to shield it from light and oxygen, optimizing the formula using the response surface method. The fortified cheese was tested for VD3 content over its shelf life.

Results indicated that all hydrocolloids tested improved moisture and meltability of the cheese while higher protein levels increased stretchability two to threefold. Rice starch hydrocolloid at 0.05% concentration was chosen due to superior sensory scores and minimal oil separation. This study concluded that VD3 levels remained stable during the cheese’s shelf life, suggesting that this approach could enhance the nutritional value of low-fat cheese without compromising its quality. Therefore, after examining the obtained results and comparing the regression models, the results indicated that the Quadratic model was chosen to investigate the effect of independent variables on the response rate, which had a statistically significant difference with other models (p = 0.0019). Also the results of the area under the curve and using the encapsulation efficiency equation, the percentage of microencapsulated vitamin was obtained, and according to the simulation results, the encapsulation efficiency was reported as 89.02%.

Developing innovative functional dairy products fortified with VD3 could improve the vitamin D status in deficient populations. Therefore, these designs can be applied at industrial scales for functional cheese production.

This study aims to explore the possibility of using magnetic biochar composite (MBCC) derived from Heglig tree bark (HTB) powder (agricultural solid waste) and cobalt ferrite (CoFe₂O₄, CFO) for oil spill removal from seawater surface.

One-pot co-precipitation route was used to synthesize MBCC. The prepared materials were characterized by X-ray diffraction, scanning electron microscopy-energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy. The densities of the prepared materials were also estimated. Crude, diesel engine and gasoline engine oils were used as seawater pollutant models. The gravimetric oil removal (GOR) method was used for removing oil spills from seawater using MBCC as a sorbent material.

The obtained results revealed that the prepared materials (CFO and MBCC) were able to remove the crude oil and its derivatives from the seawater surface. Besides, when the absorbent amount was 0.01 g, the highest GOR values for crude oil (31.96 ± 1.02 g/g) and diesel engine oil (14.83 ± 0.83 g/g) were obtained using MBCC as an absorbent. For gasoline engine oil, the highest GOR (27.84 ± 0.46 g/g) was attained when CFO was used as an absorbent.

Oil spill removal using MBCC derived from cobalt ferrite and HTB. Using tree bark as biomass (eco-friendly, readily available and low-cost) for magnetic biochar preparation also is a promising method for minimizing agricultural solid wastes (e.g. HTB) and obtaining value-added-products.