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Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

This study aims to present an experimental approach to develop a high-strength 3D-printed recycled polymer composite reinforced with continuous metal fiber.

The continuous metal fiber composite was 3D printed using recycled and virgin acrylonitrile butadiene styrene-blended filament (RABS-B) in the ratio of 60:40 and postused continuous brass wire (CBW). The 3D printing was done using an in-nozzle impregnation technique using an FFF printer installed with a self-modified nozzle. The tensile and single-edge notch bend (SENB) test samples are fabricated to evaluate the tensile and fracture toughness properties compared with VABS and RABS-B samples.

The tensile and SENB tests revealed that RABS-B/CBW composite 3D printed with 0.7 mm layer spacing exhibited a notable improvement in Young’s modulus, ultimate tensile strength, elongation at maximum load and fracture toughness by 51.47%, 18.67% and 107.3% and 22.75% compared to VABS, respectively.

This novel approach of integrating CBW with recycled thermoplastic represents a significant leap forward in material science, delivering superior strength and unlocking the potential for advanced, sustainable composites in demanding engineering fields.

Limited research has been conducted on the in-nozzle impregnation technique for 3D printing metal fiber-reinforced recycled thermoplastic composites. Adopting this method holds the potential to create durable and high-strength sustainable composites suitable for engineering applications, thereby diminishing dependence on virgin materials.

The purpose of this paper is to compare the influence of relative density (RD) and strain rates on failure mechanism and specific energy absorption (SEA) of polyamide lattices ranging from bending to stretch-dominated structures using selective laser sintering (SLS).

Three bending and two stretch-dominated unit cells were selected based on the Maxwell stability criterion. Lattices were designed with three RD and fabricated by SLS technique using PA12 material. Quasi-static compression tests with three strain rates were carried out using Taguchi's L9 experiments. The lattice compressive behaviour was verified with the Gibson–Ashby analytical model.

It has been observed that RD and strain rates played a vital role in lattice compressive properties by controlling failure mechanisms, resulting in distinct post-yielding responses as fluctuating and stable hardening in the plateau region. Analysis of variance (ANOVA) displayed the significant impact of RD and emphasised dissimilar influences of strain rate that vary to cell topology. Bending-dominated lattices showed better compressive properties than stretch-dominated lattices. The interesting observation is that stretch-dominated lattices with over-stiff topology exhibited less compressive properties contrary to the Maxwell stability criterion, whereas strain rate has less influence on the SEA of face-centered and body-centered cubic unit cells with vertical and horizontal struts (FBCCXYZ).

This comparative study is expected to provide new prospects for designing end-user parts that undergo various impact conditions like automotive bumpers and evolving techniques like hybrid and functionally graded lattices.

To the best of the authors' knowledge, this is the first work that relates the strain rate with compressive properties and also highlights the lattice behaviour transformation from ductile to brittle while the increase of RD and strain rate analytically using the Gibson–Ashby analytical model.

Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.

In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.

The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.

By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

The primary purpose of this study is to explore the relationship between the unsolicited proposal (USP) and the performance of private participation infrastructure (PPI) projects in developing countries.

The main data set for this study was collected from the World Bank database consisting of 8,951 PPI projects that occurred in developing countries from 1996 to 2020. Hierarchical logistic regression was applied for investigating the effects of USPs on project success. Three moderators, namely, control of corruption, presence of local sponsor and project size were also included in the model to test the impact of their interactions with the USP on the performance of PPI projects. Further, to assess the impact of the effect of USPs, the average marginal effect was calculated. The framework used in this study consists of 18 control variables, three moderators and one noncontrolled independent variable (the USP).

The results of hierarchical logistic regression indicate that USPs have a significant and negative effect on the success of PPI projects occurring in developing countries. The negative effect of a USP weakens with the presence of local sponsors and stronger control of corruption in the host country. However, contrary to the authors’ expectations, the results show that project size does not significantly affect the association between USPs and the success of PPI projects. Moreover, the results of average marginal effects show that the negative impact of USP on the success of PPI projects ranges between 2.4% and 3.8%.

This study quantifies the negative impact of USP on the success of PPI projects in developing countries, which will be helpful for the practitioners to understand the associated risk with USP projects. Furthermore, it also identifies the moderating roles of control of corruption and the presence of local sponsors on the relationship between USP and the success of PPI projects.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice.

A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively.

The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain.

If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation.

While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks.

As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students.

The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

Introduction: Cost competitiveness, customer focus, and sustainability compliance are essential for new-age firms to survive and succeed in the VUCA market environment. This study examines how automobile corporations have improved cost competitiveness, productivity, and product quality.

Purpose: This study examines the importance of cost competitiveness, customer focus, and sustainability compliance for the long-term survival of organisations in VUCA markets, looking at the practical efforts made by automobile corporations to enhance cost competitiveness, productivity, and quality.

Methodology: The study utilises a comprehensive analysis of the strategies and initiatives implemented by the selected automobile companies. It involves a review of relevant literature, case studies, financial data analysis, and interviews with key industry experts, providing a holistic understanding of the actions taken by these organisations to achieve their goals.

Findings: The study reveals that cost competitiveness, customer focus, and sustainability compliance are critical factors for the long-term survival and success of organisations in the automotive industry. The analysed automobile companies have undertaken practical efforts to improve cost competitiveness, enhance productivity, and ensure high-quality products, enabling them to navigate the challenges and maintain a competitive edge.

Significance: The findings of this study contribute to a deeper understanding of the importance of cost competitiveness, customer focus, and sustainability compliance in the automotive industry. It highlights the need for organisations to constantly monitor both qualitative and quantitative profit to avoid complacency and ensure long-term efficiency. The study’s insights are relevant to businesses operating in other sectors, as they face similar challenges in the VUCA market environment.

The purpose of this study is to examine relevant demographic and socio-economic factors as they relate to progress towards intercultural communicative competence (ICC) and intercultural sensitivity for ethnic-minority Latinx middle school English learners (ELs) using a mobile-assisted funds-of-knowledge-featured writing practice.

Through the theoretical lens of funds of knowledge, this three-year study implemented a survey-based quasi-experimental design centered on the Latinx ELs’ ICC development with the implementation of an intercultural sensitivity questionnaire (Chen and Starosta, 2000). The authors first investigated the relationship between ELs’ intercultural sensitivity and associated demographic and socio-economic factors. The authors then examined the changes of ELs’ intercultural sensitivity. Over ten weeks, the intervention group completed five funds-of-knowledge-featured narrative essays using pen and paper and mobile-based writing tools alternatively.

Findings indicated that ELs’ intercultural sensitivity increased as they advanced to a higher-level grade from sixth to eighth. The embedded mobile-assisted funds-of-knowledge writing practice as intervention promoted ELs’ intercultural sensitivity in interaction engagement, respect of cultural differences, interaction enjoyment and interaction attentiveness. Among the variables, interaction enjoyment was portrayed the most. ELs who reported not speaking English at home were statistically significant in this experiment.

This study acknowledges the robust and variance of funds of knowledge as a niche to address the interculturality and hybridity of ELs’ cultural practices accumulated through Latinx ELs’ family socialization and social development using mobile-assisted writing practice. This study could provide implications for optimizing inclusive experience to promote computer-assisted language learning in a contemporary, postcolonial global world.