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This study aims to investigate the morphing concepts able to manipulate the dynamics of the downstream unsteadiness in the separated shear layers and, in the wake, be able to modify the upstream shock–boundary layer interaction (SBLI) around an A320 morphing prototype to control these instabilities, with emphasis to the attenuation or even suppression of the transonic buffet. The modification of the aerodynamic performances according to a large parametric study carried out at Reynolds number of 4.5 × 10⁶, Mach number of 0.78 and various angles of attack in the range of (0, 2.4)° according to two morphing concepts (travelling waves and trailing edge vibration) are discussed, and the final benefits in aerodynamic performance increase are evaluated.

This article examines through high fidelity (Hi-Fi) numerical simulation the effects of the trailing edge (TE) actuation and of travelling waves along a specific area of the suction side starting from practically the most downstream position of the shock wave motion according to the buffet and extending up to nearly the TE. The present paper studies through spectral analysis the coherent structures development in the near wake and the comparison of the aerodynamic forces to the non-actuated case. Thus, the physical mechanisms of the morphing leading to the increase of the lift-to-drag ratio and the drag and noise sources reduction are identified.

This study investigates the influence of shear-layer and near-wake vortices on the SBLI around an A320 aerofoil and attenuation of the related instabilities thanks to novel morphing: travelling waves generated along the suction side and trailing-edge vibration. A drag reduction of 14% and a lift-to-drag increase in the order of 8% are obtained. The morphing has shown a lift increase in the range of (1.8, 2.5)% for angle of attack of 1.8° and 2.4°, where a significant lift increase of 7.7% is obtained for the angle of incidence of 0° with a drag reduction of 3.66% yielding an aerodynamic efficiency of 11.8%.

This paper presents results of morphing A320 aerofoil, with a chord of 70cm and subjected to two actuation kinds, original in the state of the art at M = 0.78 and Re = 4.5 million. These Hi-Fi simulations are rather rare; a majority of existing ones concern smaller dimensions. This study showed for the first time a modified buffet mode, displaying periodic high-lift “plateaus” interspersed by shorter lift-decrease intervals. Through trailing-edge vibration, this pattern is modified towards a sinusoidal-like buffet, with a considerable amplitude decrease. Lock-in of buffet frequency to the actuation is obtained, leading to this amplitude reduction and a drastic aerodynamic performance increase.

Fused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing process.

By conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing parameters.

As indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R² over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 °C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the experiments.

The developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing technologies.

Stochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

The construction industry sector is developing rapidly, especially with the increasing pace of the Fourth Industrial Revolution in this sector. Construction projects can benefit from greater integration and collaboration between their technologies and processes to reap the advantages and keep pace with the recent significant technological and managerial techniques developments. Therefore, this study aims to delve into and investigate building information modeling (BIM) and Lean Construction (L.C.) with a concentration on the potential BIM–lean interactions synergy and integration in the Jordanian construction industry.

This study takes exploratory nature, followed by the deductive research approach, and is designed to be a mono-quantitative research methodology. Moreover, the sampling technique is non-probability convenience sampling, and the research strategy is implemented through a questionnaire used and analyzed by using Statistical Package for Social Science to conduct descriptive and inferential statistical analysis and verify the reliability and validity through proper tests.

The BIM–lean interactions synergy and integration findings revealed that eliminating waste (time, cost, resources), promoting continuous improvement (Kaizen) and standardizing as lean construction principles are the most significant and agreeable toward achieving BIM–lean interactions synergy. On the other hand, “High 3D Visualization Modelling” was the most significant BIM function, followed by “Rapid and Auto-Generation of Documents and Multiple Design Alternatives” and “Maintenance of Information and Design Model Integrity.” Moreover, based on the relative importance index (RII) values, “Lack of Technical Expertise in BIM-LEAN” is the most significant challenge with a 0.89 value of RII, followed by “Lack of Government Direction and Standard Guidelines” with a 0.88 value of RII and “Financial considerations” with a 0.83 value of RII.

This study will help provide a new detailed overview that investigates the effects and expected benefits of integrating BIM processes and technological functionalities with lean construction principles within a synergetic environment. Moreover, the study will increase the awareness of using new technologies and management approaches in the architectural, engineering and construction industry, seeking to achieve integration between these technologies to reach ideal results in terms of the outputs of construction operations.

The purpose of this paper is to analyse the influence of economic growth, digitalisation, eco-innovation, energy consumption and patents on environmental technologies on the volume of greenhouse gas emissions (GHG) recorded in European countries for a period of nine years (2010–2018).

Two empirical methods were integrated into the theoretical approach developed based on the analysis of the current scientific framework. Multiple linear regression, an extended version of the OLS model, and a non-causal analysis as a robustness method, Dumitrescu–Hurlin, were used to achieve the proposed research objective.

Digitalisation described by the number of individual Internet users and patents on environmental technologies determines the amount of GHG in Europe, and economic growth continues to have a significant effect on the amount of emissions, as well as the consumption of renewable energy. European countries are not framed in well-established patterns, but the economic growth, digitalisation, eco-innovation and renewable energy have an impact on the amount of GHG in one way or another. In many European countries, the amount of GHGs is decreasing as a result of economic growth, changes in the energy field and digitalisation. The positive influence of economic growth on climate neutrality depends on its degree of sustainability, while patents have the same conditional effect of their translation into environmentally efficient technologies.

This study has a number of limitations which derive, first of all, from the lack of digitalisation indicators. The missing data restricted the inclusion in the analysis of variables relevant to the description of the European digitalisation process, also obtaining conclusive results on the effects of digitalisation on GHG emissions.

A similar analysis of the relationship among the amount of greenhouse gas emissions and economic growth, digitalisation, eco-innovation and renewable energy is less common in the literature. Also, the results can be inspirational in the sphere of macroeconomic policy.

The advancement of enterprise social networks (ESNs) facilitates information sharing but also presents the challenge of managing information boundaries. This study aims to explore the factors that influence the information-control behavior of ESN users when continuously sharing information.

This study specifies the information-control behaviors in the “wall posts” channel and applies communication privacy management (CPM) theory to analyze the effects of the individual-specific factor (disposition to value information), context-specific factors (work-relatedness and information richness) and risk-benefit ratio (public benefit and public risk). Data on actual information-control behaviors extracted from ESN logs are examined using multilevel mixed-effects logistic regression analysis.

The study's findings show the direct effects of the individual-specific factor, context-specific factors and risk-benefit ratio, highlighting interactions between the individual motivation factor and ESN context factors.

This study reshapes the relationship of CPM theory boundary rules in the ESN context, extending information-control research and providing insights into ESNs' information-control practices.

This paper aims to explore the epistemic connections between three instructional coaches and a first-year in-service teacher during remote planning and debrief meetings. Prior evidence suggests that remote instructional coaching leads to better teaching practices and identifies the instructional coaching moves used to prompt teacher reflection.

In this study, the authors utilized quantitative ethnography and epistemic network analysis (ENA) approaches to explore the epistemic frames of three remote university-based instructional coaches as they supported a first-year in-service teacher.

Quantitative ENA findings shed light on the network connections between instructional coaches and teachers, as well as the epistemic frames observed during planning and debrief meetings. Additionally, the authors provide qualitative findings that complement and reinforce the quantitative results.

All data collection occurred via Zoom, and the class was in a hybrid modality, with some students attending class in person and some attending remotely via Zoom. This unique context could have impacted the epistemic connections surrounding technology and logistics.

This study provides a practical codebook for use in future studies that explores instructional coaching. Findings from this study can be used to inform instructional coaching decisions.

The ENA findings helped deepen the authors' understanding of how instructional coaches can support a first-year in-service teacher during planning and debrief meetings in several ways. Additionally, this study presents a unique context given the COVID-19 pandemic and the remote model of instructional coaching.

The purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.

This research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.

The methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.

The research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.

The societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.

This research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.

This study aims to propose the reuse of PA12 (powder) in another AM process, binder jettiinng, which is less sensitive to the chemical and mechanical degradation of the powder after multiple cycles in the laser system.

The experimental process for evaluating the reuse of SLS powders in a subsequent binder jetting process consists of four phases: powder characterization, bonding analysis, mixture testing and mixture characteristics. Analyses were carried out using techniques such as Fourier Transform Infrared Spectroscopy, scanning electron microscopy, thermogravimetric analysis and stress–strain tests for tension and compression. The surface roughness, color, hardness and density of the new mixture were also determined to find physical characteristics. A Taguchi design L8 was used to search for a mixture with the best mechanical strength.

The results indicated that the integration of waste powder PA12 with calcium sulfate hemihydrate (CSH) generates appropriate particle distribution with rounded particles of PA12 that improve powder flowability. The micropores observed with less than 60 µm, facilitated binder and infiltrant penetration on 3D parts. The 60/40 (CSH-PA12) mixture with epoxy resin postprocessing was found to be the best-bonded mixture in mechanical testing, rugosity and hardness results. The new CSH-PA12 mixture resulted lighter and stronger than the CSH powder commonly used in binder jetting technology.

This study adds value to the polymer powder bed fusion process by using its waste in a circular process. The novel reuse of PA12 waste in an established process was achieved in an accessible and economical manner.