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The primary focus of this study is to tackle a critical industry issue concerning energy inefficiency. This is achieved through an investigation into enhancing heat transfer in solar radiation phenomena on a curved surface. The problem formulation of governing equations includes the combined effects of thermal relaxation, Newtonian heating, radiation mechanism, and Darcy-Forchheimer to enhance the uniqueness of the model. This research employs the Cattaneo–Christov heat theory model to investigate the thermal flux via utilizing the above-mentioned phenomenon with a purpose of advancing thermal technology. A mixture of silicon dioxide (SiO_2)\ and Molybdenum disulfide (MoS_2) is considered for the nanoparticle’s thermal propagation in base solvent propylene glycol. The simulation of the modeled equations is solved using the Shifted Legendre collocation scheme (SLCS). The findings show that, the solar radiation effects boosted the heating performance of the hybrid nanofluid. Furthermore, the heat transmission progress increases against the curvature and thermal relaxation parameter.

Shifted Legendre collocation scheme (SLCS) is utilized to solve the simulation of the modeled equations.

The findings show that, the solar radiation effects boosted the heating performance of the hybrid nanofluid. The heat transmission progress increase against the curvature and thermal relaxation parameter.

This research employs the Cattaneo–Christov heat theory model to investigate the thermal flux via utilizing the above-mentioned phenomenon with a purpose of advancing thermal technology.

This study aims to introduce an image-based method to determine the processing window for a given alloy system using laser powder bed fusion equipment based on achieving the desired melting mode across multiple materials for powder-free specimens. The method uses a convolutional neural network trained to classify different track morphologies across different alloy systems to select appropriate printing settings. This method is intended for the development of new alloy systems, where the powder feedstock may be unavailable, or prohibitively expensive to manufacture.

A convolutional neural network is designed from scratch to identify the 4 key melting modes that are observed in laser powder bed fusion additive manufacturing across different alloy systems. To increase the prediction accuracy and generalisation accuracy across different materials, the network is trained using a novel hybrid data set that combines fully unsupervised learning with semi-supervised learning.

This study demonstrates that our convolutional network with a novel hybrid training approach can be generalised across different materials, and k-fold validation shows that the model retains good accuracy with changing training conditions. The model can predict the processing maps for the different alloys with an accuracy of up to 96% in some cases. It is also shown that powder-free single-track experiments are a useful indicator for predicting the final print quality of a component.

The “invariant information clustering” (IIC) approach is applied to process optimisation for additive manufacturing, and a novel hybrid data set construction approach that accounts for uncertainty in the ground truth data, enables the trained convolutional model to perform across a range of different materials and most importantly, generalise to materials outside of the training data set. Compared to the traditional cross-sectioning approach, this method considers the whole length of the single track when determining the melting mode.

This research is about the possibilities of using high-density polyethylene (HDPE)-based composites consisting of long glass and short carbon fibres because HDPE is one of the more preferred thermoplastics day by day due to its sustainability, cost-effectiveness and availability in the relevant markets. HDPE has become an increasingly preferred material in the marine industry in recent years due to its high resistance to marine environmental conditions (high resistance to UV, surface-fouling marine organisms and corrosive effects of salty and low-pH water). In the highly competitive boat building industry, additive manufacturing offers new opportunities such as rapid prototyping and design freedom. This study aims to investigate the possibilities of using a material suitable for the marine environment and an additive manufacturing (AM) method offering new possibilities, especially for small craft with complex forms.

A total of six new HDPE-based composites consisting of long glass and short carbon fibres at 10, 15 and 20% by weight have been proposed for the first time in this study for the use in boat building industry, proposing the application of these new composite materials with AM method, which the industry is not yet fully adopted, is also an innovative aspect of the study. The performances of the materials in AM’s material extrusion (MEX) method were evaluated using the results obtained from mechanical (tensile, compression, shear and impact) and thermal (melt flow index [MFI], thermogravimetric analysis [TGA] and thermomechanical analysis [TMA]) tests. In addition, the structure of the composites was examined with scanning electron microscopy and micro computed tomography visually, and the rheological properties of the composites were also determined by the related tests. As an industrial case study, a ship propeller was manufactured from the composites produced with CF15, which was thought to give the best performance in marine use, and this propeller was tested under water flow.

It is evaluated that the composites proposed in this study can be used in marine industry in line with the analyses and test results. The performance of the propeller produced as a case application also confirms this view. The printability of HDPE-based composites, reinforced by both glass and carbon fibre, is much better than that of pure HDPE, and the composites are suitable for use AM’s MEX method in boat building industry. As the fibre contents in the proposed composites increase, the strength values increase and the impact resistance and hardness decrease. The CF15 composite, which meets each of those mechanical and physical values at an average level, is a recommended option for marine applications.

This study has two basic originalities: (1) On the basis of HDPE, which is widely used in the marine industry, to produce composites that will overcome the deficiencies of this material in practice and to present them to relevant industry by improving their properties; (2) at the same time, to discuss for the first time the use of new HDPE-based materials in AM, whose printability has also been improved through composite, to help dissemination of AM technologies in marine industry in general and in the boat building industry in particular.

The aim of this study is to investigate the printing parameters of fused deposition modeling (FDM), a material extrusion-based method, and to examine the mechanical and thermal properties of their polylactic acid (PLA) components reinforced with copper, bronze, and carbon fiber micro particles.

Tensile test samples were created by extruding composite filament materials using FDM-based 3D printer. Taguchi method was used to design experiments where layer thickness, infill density, and nozzle temperature were the printing variables. Analysis of variance (ANOVA) was applied to determine the effect of these variables on tensile strength.

The results of this study showed that the reinforcement of metal particles in PLA material reduces strength and increases elongation. The highest tensile strength was obtained when the layer thickness, infill density, and nozzle temperature were set to 100 µm, 60%, and 230 °C, respectively. As a result of thermal analysis, cooper-PLA showed the highest thermal resistance among metal-based PLA samples.

It is very important to examine the mechanical and thermal quality of parts fabricated in FDM with metal-PLA composites. In the literature, the mechanical properties of metal-reinforced composite PLA parts have been examined using different factors and levels. However, the fabrication of parts using the FDM method with four different metal-added PLA materials has not been examined before. Another unique aspect of the study is that both mechanical and thermal properties of composite materials will be examined.

This study aims to fabricate the acrylic-based polymeric composite coating with a hydrophobic surface associated with natural oil polyol (NOP) and polydimethylsiloxane with the incorporation of 3 Wt.% SiO₂ nanoparticle (SiO₂np) against the corrosive NaCl media.

The structural properties of the formulated polymeric composite coatings were investigated by using Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, water contact angle (WCA) and cross-hatch (X-Hatch) tests. The WCA measurement was used to study the surface wettability of the formulated polymeric composite coatings. The corrosion protection performance of the nanocomposite coated on the mild steel substrate was studied by immersing the samples in 3.5 Wt.% NaCl solution for 30 days using electrochemical impedance spectroscopy.

The enhanced polymeric composite coating system performed with an excellent increase in the WCA up to 111.1° which is good hydrophobic nature and very high coating resistance in the range of 10¹⁰ Ω attributed to the superiority of SiO₂np.

The incorporation of SiO₂np into the polymeric coating could enhance the surface roughness and hydrophobic properties that could increase corrosion protection. This approach is a novel attempt of using NOP along with the addition of SiO₂np.

This article delves into a theoretical framework evaluating the outcomes of justice perception (JP) on post-complaint behaviors. In addition, this article intends to facilitate service strategists to tailor service recovery efforts to attain a competitive gain.

We explored the complex relationships between JP and positive word-of-mouth (PWOM), evaluating the role of recovery disconfirmation (RD) as a mediator and also exploring factors that work as moderators. Data were collected in three different studies (Study 1: survey in banking service, Study 2a: experiment in hotel service and Study 2b: experiment in banking service) and were evaluated through the regression-based analysis.

Findings show that the link between JP and PWOM is mediated by RD. Moreover, the attitude toward complaining, failure severity and cognitive reappraisal function as moderators in affecting the behavioral consequences of JP.

In contrast to existing research discussing similar topics, in this article, exclusive focus is given on the moderation and mediation impacts for hypotheses evaluation. Thus, the outcomes from our studies provide a strategic outline for managing service failures by enhancing customer interactions, creating opportunities for competitive gain.

The successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.

Based on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.

The study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.

This study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

The purpose of this research is to explore the transformative impact of AI-augmented tools on design pedagogy. It aims to understand how artificial intelligence technologies are being integrated into educational settings, particularly in creative design courses, and to assess the potential advancements these tools can bring to the field.

The research adopts a case-study approach, examining three distinct courses within a creative technology curriculum. This methodology involves an in-depth investigation of the role and impact of AI in each course, focusing on how these technologies are incorporated into different creative disciplines such as production design, fine arts, and digital artistry.

The research findings highlight that the integration of AI with creative disciplines is not just a passing trend but signals the onset of a new era in technological empowerment in creative education. This amalgamation is found to potentially redefine the boundaries of creative education, enhancing various aspects of the learning process. However, the study also emphasizes the irreplaceable value of human mentorship in cultivating creativity and advancing analytical thinking.

The limitations of this research might include the scope of the case studies, which are limited to three courses in a specific curriculum. This limitation could affect the generalizability of the findings. The implications of this research are significant for educational institutions, as it suggests the need for a balanced interaction between AI's computational abilities and the intrinsic qualities of human creativity, ensuring that the core essence of artistry is preserved in the age of AI.

The originality of this paper lies in its specific focus on the intersection of AI and creative education, a relatively unexplored area in design pedagogy. The value of this research is in its contribution to understanding how AI can be harmoniously integrated with traditional creative teaching methods. It offers insights for educational institutions preparing for this technological transformation, highlighting the importance of maintaining a balance between technological advancements and humanistic aspects of creative education.

This research investigation aims to explore the factors that affect purchasing behaviour among consumers in India in the context of green electronic products with the aid of the extended theory of planned behaviour (TPB) by incorporating two additional constructs, namely environmental concern and ethical obligation.

The current research adopted a quantitative methodology, utilising a survey (questionnaire) to gather data from respondents residing in Delhi-NCR, India. A convenience sampling technique was used to select these respondents. Both online and offline modes were used to collect the data. Moreover, to evaluate the theoretical foundation of the investigation, the researchers employed PLS-SEM (partial least square structural equation modelling) on 346 useable samples.

The outcome reveals that attitude, subjective norm and perceived behaviour control (PBC) are significant predictors, supplementary constructs such as environmental concern are not significant predictors, whereas ethical obligation is a substantial predictor of purchase intention. Consumer purchase behaviour, in turn, is influenced by consumer purchase intention.

The outcome of this research could help the country’s legislators formulate policies and programs related to current environmental problems faced by our planet. Marketers could understand the factors that drive consumers' purchasing behaviour towards green electronic products and design effective marketing strategies accordingly. Thus, by enhancing the company’s green image, marketers of green products may potentially increase future purchases, all of which contribute to solving worldwide environmental problems.

This research confirms the usefulness of TPB in understanding Indian consumers' purchasing intention and behaviour towards green electronic products. It further extends the TPB by showing that new components, environmental concerns and ethical obligations influence Indian consumers' purchasing intentions and behaviour. Moreover, prior studies mostly used both additional constructs for green products. Current research used specific types of green products, i.e. green electronic products, as very few studies are available on these products that apply TPB, which makes this study novel. In contrast to previous research that primarily examined purchase intention, this study takes a further step by investigating the connection between intentions and consumers' purchase behaviour. It establishes intention as a precursor to behaviour, specifically towards green electronic products. Additionally, this study introduces new constructs and explores the Indian context, significantly contributing to the existing literature.

This study aims to investigate the dynamic interaction between interest rates and commercial property financing offered by Islamic banks in Malaysia.

The authors use the autoregressive distributed lag (ARDL) cointegration methodology to analyse the short- and long-run effect of the interest rates and rental rates on commercial property financing of Islamic banks in Malaysia between 2010: Q1 and 2018: Q2.

The findings reveal that changes in interest rates affect Islamic commercial property financing. This indicates that Islamic banks still rely on interest rates as a benchmark without fully implementing Islamic rental rates. This corroborates the subsequent finding, where overnight policy rates influence commercial property financing.

Despite the authors’ attempt to provide insights into Islamic commercial property financing, the study is limited to secondary data; further research can use survey information to obtain other details that are not included in this study. Similarly, this study does not cover the operation and financial lease debate in Musharakah Mutanaqisah. Future studies can examine the challenges faced by the financial institution towards implementing rental rates in other emerging and developing countries using a different methodology.

This study is the first to investigate the dynamic changes in overnight policy rates, average lending rates and rental rates on Islamic commercial property financing in Malaysia using ARDL techniques. The authors uncover the research and institutional implications of Islamic commercial property financing rates and provide policy and future research directions coupled with the proposed modified rental rate to be developed.