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Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.

In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.

The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.

This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.

This study aims to investigate the effects of nano or inorganic fillers on unsaturated polyester’s (UPE) thermal, mechanical, and physical properties. UPE reinforced with nanoparticles shows better properties than the pure polymer itself. Nano or inorganic fillers are used in the polymeric matrix to improve thermal, mechanical and physical properties.

To improve thermal, mechanical and physical properties, UPE resin was modified with silica (S), boron nitride (BN) and S/BN hybrid nanoparticles at different ratios. Viscosity and solids content measurement, Fourier transform infrared spectroscopy, contact angle measurement, scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and thermal conductivity coefficient tests were performed on the samples.

In the SEM analysis, the UPE sample showed a smooth appearance, while all samples containing additives showed phase separation and overall heterogeneous distribution. TGA results demonstrated that the thermal stability of the resin increased in the presence of S and BN additives. According to the results, it was observed that the presence of S and BN additives in the UPE resin and the use of certain ratios improved the resin properties.

As a result of the literature search, to the best of the authors’ knowledge, no study was found in which BN nanoparticles were included in the UPE resin together with S.

The purpose of this study is to extract and characterize a novel natural dye from the leaves of Lannea coromandelica and the extraction with finding ways of dyeing cotton fabric using three mordants.

The colouring agents were extracted from the leaves of Lannea coromandelica using an aqueous extraction method. The extract was characterized using analysis methods of pH, gas chromatography-mass spectrometry (GC-MS), Fourier transform infrared (FTIR), ultraviolet-visible (UV-vis) and cyclic voltammetry measurement. The extract was applied to cotton fabric samples using a non-mordant and three mordants under the two mordanting methods. The dyeing performance of the extracted colouring agent was evaluated using colour fastness properties, colour strength (K/S) and colour space (CIE Lab).

The aqueous dye extract showed reddish-brown colour, and its pH was 5.94. The GC-MS analysis revealed that the dye extract from the leaves of Lannea coromandelica contained active chemical compounds. The UV-vis and FTIR analyses found that groups influenced the reddish-brown colour of the dye extraction. The cyclic voltammetry measurements discovered the electrochemical properties of the dye extraction. The mordanted fabric samples showed better colour fastness properties than the non-mordanted fabric sample. The K/S and CIE Lab results indicate that the cotton fabric samples dyed with mordants showed more significant dye affinities than non-mordanted fabric samples.

Researchers have never discovered that the Lannea coromandelica leaf extract is a natural dye for cotton fabric dyeing. The findings of this study showed that natural dyes extracted from Lannea coromandelica leaf could be an efficient colouring agent for use in cotton fabric.

This study investigates how firms’ awareness of sustainability affects the revision of their internationalization strategy. Adopting a resource-based view (RBV) approach, the authors argue that sustainable-oriented firms have a higher propensity to de-internationalize (i.e., to go back to their home country) when confronted with the need to relocate foreign manufacturing subsidiaries, as the shortening of value chains would allow the reduction of transportation emissions and enhanced corporate image as green-oriented entities. Furthermore, the authors explore the role exerted by a stringent regulatory setting in the home country on the likelihood of de-internationalization. The empirical test conducted on a sample of relocations performed across European nations in 2002–2014 reveals that multinational enterprises (MNEs) – regardless of their sustainability orientation – have a higher probability to de-internationalize when their home countries have strict institutional contexts in place.

This research is located at the intersection of communication, memetics, and the study of the imaginary. As a presupposition, we put forward the existence of a communicational imaginary, in which the contemporary person functions through their competencies in social networks, by meeting the demands of the public and the private, managing the obsessiveness of the sharing of intimacy and the exorbitant number of images. Considering memes as a significant aspect of this communicational imaginary, we seek to understand the dynamics and path of memes in the midst of this plethora of images. From the concept of iconophagy, we deal with the exacerbated multiplication of the images and the path of memes starting from a marginalized environment until it is integrated into social roles and a rational level of thought. Thus, it is the general objective of this research to understand the dynamics and the path of memes amidst the plethora of images in the context of communicational imagery and to investigate the multiplication of memes as representative of the myriad images in contemporary imagery.

The aim of the paper is to analyze how actors in foodservice companies organize for inbound open innovation (OI).

This paper conducted a case analysis of a large and successful foodservice company operating in the Dutch market. Furthermore, drawing on 18 interviews and archive data, we identified the main organizational practices involved in the implementation of inbound innovation activities and the ways they are embraced are defined.

The results provide a holistic view of the main organizational practices a foodservice company implemented at different organizational levels, to exploit external knowledge coming from third parties and to promote the sharing and recombination of knowledge resources within the organization. The identified organizational practices reveal the main interaction patterns between relevant internal actors and other external parties in the company network, as well as between actors on different hierarchical organizational levels which allows processing relevant innovation information and make relevant decisions about it.

Implications are provided in terms of both theory and practice. This paper helps foodservice companies to create an internal organizational environment that supports the exploitation of customer knowledge.

There are few studies on how companies organize themselves for OI in general, and especially in the foodservice sector.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.