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This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

The purpose of this paper is to discuss a production planning and control model known as the Lean construction management (LCM) model, which applies a number of visual tools in a systematic way to the planning and control process. The application of the visual tools in this way facilitates the flow of information, thus improving transparency between the interfaces of planning, execution and control.

Design Science research is adopted for this investigation, which analyses the original development of the model and reports on its testing and refinement over different types of projects. The research is divided into three parts, each part focussing on a different stage of development and construction project type.

The main findings are related to the benefits of visual management in the construction planning and control process, such as maintaining consistency between different planning levels, so that feasible execution plans are created; control becomes more focussed on prevention rather than correction, and creates opportunities for collaborative problem solving. Moreover, the physical display of the visual tools in a discrete planning area on-site encourages a regular exchange between participants on actual work progress as it unfolds, leading to more timely reaction to the problems at hand.

The problem of a lack of transparency in construction planning and control leads to communication issues on-site, poor process orientation and high levels of waste. LCM improves process transparency by making information related to system-wide processes more readily available to project participants. This enables them to foresee problems in a timely manner and to take necessary measures to resolve them or to adapt the process to current circumstances. The LCM model proposes a new way of applying visual tools and controls systematically to improve transparency in construction planning and control.

This paper aims to investigate the current state of knowledge in key reporting aspects in relation to sustainability reporting in general and to reflect on their relevance to Global Reporting Initiative (GRI) in particular. In doing so, the major gaps in that knowledge are identified, and the paper proceeds to suggest further research avenues.

The authors conduct a review of papers published in leading journals concerning sustainability reporting to analyse the progress in the literature regarding three important reporting topics: materiality, comparability and assurance.

The review conducted in this study shows that there is still work to be done to ensure high-quality and consistent sustainability reporting. Key takeaways from the review of the extant literature are as follows: there is ongoing debate about the nature of sustainability reporting materiality, and single versus double materiality. Clearer guidance and better contextualisation are seen as essential for comparability, and, as GRI suggests, there is an important link to materiality that needs to be considered. Finally, assurance has not been mandatory under the GRI, but the current development at EU level might lead to the GRI principles being incorporated in the primary assurance standards.

In this paper, the authors review and synthesise the previous literature on GRI reporting dealing with three key reporting aspects.

The authors extract some takeaways from the literature on materiality, comparability and assurance that will all be key challenges for GRI in the future.

This paper provides an updated review of the literature on GRI reporting dealing with three key reporting aspects.

The purpose of this study is to critically review consumer knowledge in marketing and propose a future research agenda. Despite the many works that have examined this variable, given its strong influence on behaviour, it has generally been studied in association with other constructs, and no studies have focused on it in a specific way. Its definition, measurement and approaches to its role and usefulness are superficial and underdeveloped. After structuring and analysing the existing literature, the authors establish, (I) which aspects are of little use to the discipline, and (II) which research lines have the most potential and should be developed and studied in greater depth, to advance and complete the existing consumer knowledge framework.

A search was undertaken for documents in the main databases in which the term “consumer knowledge” appears in a marketing or consumer context, and a critical and reflexive approach was taken to analyse the main contributions and to structure them by content blocks.

Five main content blocks were identified. A set of research gaps were detected, mainly related to the lax conceptualisation of the topic, measurement problems and the scarcity of more useful works connected with business management, and several research lines are proposed that complement the existing framework to make it more complete and operational.

This paper offers a critical review and proposes a research agenda for one of the most used but little studied variables in the field of marketing, which may help academics and professionals in the discipline to continue developing useful theories and models.

El objetivo de este trabajo es revisar críticamente el conocimiento del consumidor en marketing y proponer una agenda de investigación futura. A pesar de los numerosos trabajos que han examinado esta variable, dada su fuerte influencia en el comportamiento, generalmente se ha estudiado en asociación con otros constructos, y ningún estudio se ha centrado en ella de manera específica. Su definición, medición y aproximaciones sobre su papel y utilidad son superficiales y poco desarrollados. Después de estructurar y analizar la literatura existente, establecemos (I) qué aspectos tienen poco uso para la disciplina y (II) qué líneas de investigación tienen más potencial y deben ser desarrolladas y estudiadas con mayor profundidad; para avanzar y completar el marco existente sobre conocimiento del consumidor.

Se realizó una búsqueda de documentos en las principales bases de datos en las que aparece el término “conocimiento del consumidor” en un contexto de marketing o consumo, y se adoptó un enfoque crítico y reflexivo para analizar las principales contribuciones y estructurarlas por bloques de contenido.

Se identificaron cinco bloques principales de contenido. Se detectó un conjunto de huecos de investigación, principalmente relacionados con la laxa conceptualización del tema, problemas de medición y la escasez de trabajos más útiles conectados con la gestión empresarial; y se proponen varias líneas de investigación que complementan el marco existente para hacerlo más completo y operativo.

Este documento ofrece una revisión crítica y propone una agenda de investigación para una de las variables más utilizadas pero poco estudiadas en el campo del marketing, lo que puede ayudar a académicos y profesionales en la disciplina a continuar desarrollando teorías y modelos útiles.

本文旨在对市场营销中的消费者知识进行批判性审视, 并提出未来的研究议程。虽然已有许多研究检验了该变量, 但由于其对行为产生强大影响, 通常会与其他结构变量一起研究, 而没有以特定方式专注于该变量。对其定义、测量以及其作用和用途的方法仍旧存在研究空白。通过对现有文献进行结构化分析后, 确定了以下两个方面：（I）哪些方面对该学科意义不大, （II）哪些研究方向最具研究潜力, 并且应该进一步深入发展和研究, 以推进和完善现有的消费者知识框架。

通过主要数据库检索市场营销或消费者背景下涉及“消费者知识”一词的文献, 采取批判性和反思性方法来分析其主要贡献, 并通过内容块对其进行结构化。

识别了五个主要内容块, 并发现存在一定程度的研究空白, 主要涉及该主题的概念松散化、测量问题以及与商业管理相关的有效研究的稀缺性。此外, 本文提出了几个研究线索, 这些线索为现有框架补充了信息, 使其更加完整且具备更强的操作性。

本文对市场营销领域中广泛使用但研究较少的变量进行了批判性评述, 并提出了相关研究议程。这一工作有助于学术界和专业人士继续发展实用的理论和模型。