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This paper is devoted to prepare micro-cone structure with variable cross-section size by Stereo Lithography Appearance (SLA)-based 3D additive manufacturing technology. It is mainly focused on analyzing the forming mechanism of equipment and factors affecting the forming quality and accuracy, investigating the influence of forming process parameters on the printing quality and optimization of the printing quality. This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The µ-SLA process is optimized based on the variable cross-section micro-cone structure printing. Multi-index analysis method was used to analyze the influence of process parameters. The process parameter influencing order is determined and validated with flawless micro array structure.

After the optimization analysis of the top diameter size, the bottom diameter size and the overall height, the influence order of the printing process parameters on the quality of the micro-cone forming is: exposure time (B), print layer thickness (A) and number of vibrations (C). The optimal scheme is A1B3C1, that is, the layer thickness of 5 µm, the exposure time of 3000 ms and the vibration of 64x. At this time, the cone structure with the bottom diameter of 50 µm and the cone angle of 5° could obtain a better surface structure.

This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The lack of knowledge has hindered the successful implementation of lean in the construction industry. This has alarmed the need for lean learning practices. Out of numerous models, the learning-to-learn sand cone model received a wider acknowledgment for learning practices. Thus, this study aims to propose a learning-to-learn sand cone model integrated lean learning framework for the construction industry.

The research adopted an interpretivism stance. A qualitative research approach was adopted for the study. Consequently, fifteen (15) semi-structured interviews and document reviews were carried out to collect data in three (3) cases selected through purposive sampling. Code-based content analysis was used to analyse the data.

Fifty-two (52) sub-activities pertaining to nine lean learners at each stage of the lean learning procedure were identified. The most significant practices in the lean learning procedure to continuously improve lean learning in the organisation were maintaining records, providing a performance update to senior management and preparing and distributing several hierarchical manuals for all levels of staff to aid in the implementation of lean approaches.

The findings of the research can be aided to successfully implement lean by following the identified sub-activities via various parties within the organisation. The proposed lean learning framework opens several research areas on lean learning in the construction industry. This is the first research to uncover a lean learning framework in the construction industry rather than at the educational institute level.

This empirical study aims to investigate the erosion wear performance of two different 3D-printed materials (acrylonitrile butadiene styrene [ABS] and polylactic acid [PLA]) with various micro textures. The two different textures (prism and square) were created over the surfaces of both materials by using the 3D-printed technique.

The erosion experiments on both materials were performed by using Ducom Erosion Jet Tester. Erosion tests were performed at four different impacting velocities (15, 30, 45 and 60 m/s) with the four different particle sizes (17, 39, 63 97 µm) at the impact angles (30°–90°) for the time duration of 5, 10, 15 and 20 min. The two different textures prism and cone were used for performing the erosion experiments. Taguchi’s orthogonal L16 (mixed level) was used to reduce the number of experiments and to determine the impact of these parameters on erosion wear performance of both 3D-printed materials.

The PLA with cone texture was found to be best (against erosion) than the ABS cone and prism textures due to their high hardness (68 HV). Also, the average signal to noise (S/N) ratio for PLA and ABS was measured as 56.4 and 44.4 dB, respectively. As the value of the S/N ratio is inversely proportional to the erosion rate, the PLA has the least erosion rate as compared to the ABS. The sequence of erosion wear influencing parameters for both materials was in the following order: velocity > erodent size > texture > impact angle > time interval.

Both PLA and ABS with different micro textures for erosion testing were studied with Taguchi’s optimization method, and the erosion mechanisms are well analyzed by using scanning electron microscopy and Image J techniques.

The author studies forms over finite fields obtained as the determinant of Hermitian matrices and use these determinatal forms to define and study the base polynomial of a square matrix over a finite field.

The authors give full proofs for the new results, quoting previous works by other authors in the proofs. In the introduction, the authors quoted related references.

The authors get a few theorems, mainly describing some monic polynomial arising as a base polynomial of a square matrix.

As far as the author knows, all the results are new, and the approach is also new.

Lean manufacturing (LM) concepts have been widely adopted in diverse industrial sectors. However, no literature review focusing on case studies describing LM implementation is available. Case studies represent the actual implementation and provide secondary data for further analysis. This study aims to review the same to understand the pathways of LM implementation. In addition, it aims to analyse other related review questions, such as how implementing LM impacts manufacturing capabilities and the maturity level of manufacturing organisations that implemented LM, to name a few.

A literature review of case studies that discuss the implementation of LM during the last decade (from 2010 to 2020) is carried out. These studies were synthesised, and content analyses were performed to reveal critical insights.

The implementation pattern of LM significantly varies across manufacturing organisations. The findings show simultaneous improvement in manufacturing capabilities. Towards the end of the last decade, organisations implemented LM with radio frequency identification, e-kanban, simulation, etc.

Reviewing the case studies documenting LM implementation to comprehend the various nuances is a novel attempt. Furthermore, potential future research directions are identified for advancing the research in the domain of LM.

This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.

A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.

On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.

This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.

In order to achieve sustainable development objectives, safeguard the ecosystem, combat global warming and preserve biodiversity for a more sustainable and secure future, the ecological footprint (EF) must be reduced. Therefore, embracing holistic methods, emphasizing renewable energy (RN) and environmental taxes (ET) is crucial. Therefore, the present study aims to capture the effect of ET and RN on EF in Germany.

To achieve this aim, the novel Fourier-based Autoregressive Distributive Lag (ADL) cointegration and the time and frequency-based connections among the variables are investigated in this work throughout the 1994–2021 time span using the wavelet analytic methods, including wavelet power spectrum (WPS) and wavelet coherence (WC) methods, respectively.

The study’s results express that (1) RN, ET and EF are cointegrated in the long run; (2) EF and RN have volatility; (3) RN use in Germany prevents environmental deterioration and (4) ET decreases EF.

The research findings imply that Germany needs rigorous environmental restrictions and enforcement of alternate energy sources for energy use plans and sustainable production objectives.

To the best of our knowledge, the effect of RN and ET on EF in Germany has not been comprehensively explored by using newly developed econometrics techniques and a single dataset. Therefore, the study provides important policy implementations for the German government and is also likely to open debate on the concept.

The purpose of this study is to experimentally determine whether mechanical properties of concrete can be improved by using olive pomace aggregates (OPA) as a substitute for natural sand. Two types of OPA were tested by replacing an equivalent amount of natural sand. The first type was OPA mixed with olive mill wastewater (OMW), and the second type was OPA not mixed with OMW. For each type, two series of concrete were produced using OPA in both dry and saturated states. The percentage of partial substitution of natural sand by OPA varied from 0% to 15%.

The addition of OPA leads to a reduction in the dry density of hardened concrete, causing a 5.69% decrease in density when compared to the reference concrete. After 28 days, ultrasonic pulse velocity tests indicated that the resulting material is of good quality, with a velocity of 4.45 km/s. To understand the mechanism of resistance development, microstructural analysis was conducted to observe the arrangement of OPA and calcium silicate hydrates within the cementitious matrix. The analysis revealed that there is a low level of adhesion between the cement matrix and OPA at interfacial transition zone level, which was subsequently validated by further microstructural analysis.

The laboratory mechanical tests indicated that the OPCD_OPW (5) sample, containing 5% of OPA, in a dry state and mixed with OMW, demonstrated the best mechanical performance compared to the reference concrete. After 28 days of curing, this sample exhibited a compressive strength (R_c) of 25 MPa. Furthermore, it demonstrated a tensile strength of 4.61 MPa and a dynamic modulus of elasticity of 44.39 GPa, with rebound values of 27 MPa. The slump of the specimens ranged from 5 cm to 9 cm, falling within the acceptable range of consistency (Class S2). Based on these findings, the OPCD_OPW (5) formulation is considered optimal for use in concrete production.

This research paper provides a valuable contribution to the management of OPA and OMW (OPA_OMW) generated from the olive processing industry, which is known to have significant negative environmental impacts. The paper presents an intriguing approach to recycling these materials for use in civil engineering applications.

This paper aims to (1) identify the different performance drivers (lead indicators) and outcome measures (lag indicators) investigated in the literature concerning the four balanced scorecard (BSC) perspectives in operations management (OM) contexts and (2) understand how performance drivers and outcome measures (and substantiated perspectives) are related.

We undertake a systematic literature review of the BSC literature in OM journals. From the final sample of 40 articles, performance drivers and outcome measures have been identified, and the relationships amongst them have been synthesised according to the system dynamics approach.

Findings show (1) the most relevant performance drivers and outcome measures within each BSC perspective, (2) their relationships, (3) how the perspectives are linked through the performance drivers and outcome measures and (4) how the different measures relate systemically. Accordingly, four causal loops amongst identified measures have been built, which – jointly considered – allowed for the creation of a dynamic strategy map for OM.

This study is the first one that provides a comprehensive and holistic view of how the different performance drivers and outcome measures within and between the four BSC perspectives in OM relate systemically, increasing the knowledge and understanding of scholars and practitioners.

In response to mounting global concerns about climate change and scarcity of natural resources, manufacturers have been pressured to develop strategies and enhance their sustainability performance. The integration of sustainable lean manufacturing (SLM) during value chain processes could balance environmental, social and economic concerns into their decision-making, which not only ensures responsible practices but also drives efficiency and success. This paper aims to identify, measure and prioritize metrics to develop a performance measurement system that assesses the multi-dimensional performance of SLM.

Strategic decision-making has some conflicting criteria and objectives to be considered simultaneously. The Multi-Criteria Decision Making provides a foundation for selecting, sorting and prioritizing these strategies with the determination of drivers and indicator weight.

The performance model enables the decision-makers to consistently evaluate the level of sustainability through a multidimensional framework, which could support the assessment of the existing sustainability of a manufacturing process and analyze opportunities for improvement. This study divided the performance into five drivers: Quality, Operational, Finance, Environment, Safety and People and selected 17 KPIs for assessing the multi-dimensional performance of SLM organizations. The research results revealed an organization's perspective transition from strategies focused on operational and economic performance to a more sustainable ideal with greater importance for social and environmental directions.

This framework will be facilitated by the selection of the most significant drivers and the development of strategic plans for the successful adoption of sustainable manufacturing. The practices support implementation, pursue competitive advantages and sustain manufacturing, meeting strategic requirements of suitable and lean performance. With the limited resources of the organizations, the framework proposed will guide the priorities and actions to be taken toward the SLM.