Search results

This paper aims to compare the aging behavior of water-based coatings and solvent-based coatings in sulfuric acid environments and to discuss the related mechanism.

A sulfuric acid solution with a concentration of 5 Wt.% was selected for immersion test at 23°C. The failure behavior of the coating was studied by combining the transformation rules of the macroscopic morphology and basic properties with the results of electrochemical impedance spectrum analysis.

The results showed that the surface smoothness of the water-based coating was lower than that of the solvent-based coating. The glossiness, thickness and hardness of the water-based coating exhibited more significant changes. The electrochemical test also indicated that the water-based coating was infiltrated by a large number of corrosive media, which may have induced corrosion under the coating. In contrast, the solvent-based coating showed good shielding properties, but the adhesion was seriously affected by the corrosive medium.

This work clarified the difference of failure behavior and mechanism between water-based coatings and solvent-based coatings in acidic environment and provided a theoretical basis for the selection and mechanism research of anticorrosive coatings.

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

This study aims to thoroughly examine the milling process applied to fused filament fabrication (FFF) parts. The primary objective is to identify the key variables in creating smooth surfaces on FFF specimens and establish trends about specific parameters.

In this study, PLA and ABS samples fabricated by FFF are subjected to side milling in several experiments. Achievable surface quality is studied in relation to material properties, milling parameters, tooling and macrostructure. The surface finish is quantified using profile measurements of the processed surfaces. The study classifies the created chips into categories that can be used as criteria for the anticipated quality. Spectral analysis is used to examine the various surface formation modes. Thermal monitoring is used to track chip formation and surface temperature changes during the milling process.

This study reveals that effective heat dissipation through proper chip formation is vital for maintaining high surface quality. Recommended methodology demands using a tool with a substantial flute volume, using high positive rake and clearance angles and optimizing the feed-per-tooth and cutting speed. Disregarding these guidelines may cause the surface temperature to surpass the material’s glass transition, resulting in inferior quality characterized by viscous folding. For FFF thermoplastics, optimal milling can bring the average surface roughness down to the micron level.

This research contributes to the field by providing valuable guidance for achieving superior results in milling FFF parts. This study includes a concise summary of the theoretically relevant insights, presents verification of the key factors by qualitative analysis and offers optimal milling parameters for 3D-printed thermoplastics based on systematic experiments.

This study aims to comprehensively analyze the intersection of technology management and innovation management amidst the fourth industrial revolution, uncovering evolving trends and influential contributors.

Using the Bibliometrix R-package, this pioneering research conducts a bibliometric analysis to delve into innovation and technology management literature, quantifying scholarly output and identifying thematic breakthroughs.

The study reveals quantitative insights into the progression of innovation and technology management research, offering guidance on evolving trends, thematic breakthroughs and influential contributors.

The findings offer valuable insights for practitioners and managers, guiding them through emerging trends and recommending a dual focus on fundamental principles and emerging areas for strategic decision-making.

By fostering active engagement with evolving trends, this research contributes to the ongoing technology and innovation management discourse, potentially leading to societal benefits and advancements.

This study pioneers an in-depth bibliometric analysis at the intersection of innovation and technology management, offering unique insights and quantitative assessments of scholarly output and thematic trends, thus adding significant value to the existing literature.

The personalities of leaders have been shown to impact the culture of their organizations and are also expected to have a more distal impact on the firm’s financial performance. However, the authors also expect that leader gender is an important intervening variable such that exhibiting various personality dimensions may result in unique cultural and performance-based outcomes for women and men leaders. Thus, the authors seek to examine first the impact of leader personality on organizational performance, as driven through organizational culture as a mediating mechanism. In doing so, the authors propose the expected impact of specific personality dimensions on certain types of organizational cultures, and those cultures’ subsequent impact on the organization’s performance. The authors then extend to consider the moderating effects of leader gender on the relationship between leader personality and organization. To support their propositions, the authors draw from upper echelons and implicit leadership theories. The authors encourage researchers to consider the proposition within a sample of the largest publicly traded US companies (i.e., Fortune 500) at an important era in history such that for the first time, 10% of these companies are led by women. In doing so, the authors hope to understand the leadership dynamics at the highest echelons of corporate governance and provide actionable insights for companies aiming to optimize their leadership composition and drive sustainable performance.

This paper introduces the problematic of the SBP modeling. Our objective is to provide a conceptual analysis related to the concept of SBP. This facilitates, on the one hand, easier understanding by business analysts and end-users, and one the other hand, the integration of the new specific concepts relating to the SBP/BPM-KM domains into the BPMN meta-model (OMG, 2013).

We propose a rigorous characterization of SBP (Sensitive Business Processes) (which distinguishes it from classic, structured and conventional BPs). Secondly, we propose a multidimensional classification of SBP modeling aspects and requirements to develop expressive, comprehensive and rigorous models. Besides, we present an in-depth study of the different modeling approaches and languages, in order to analyze their expressiveness and their abil-ity to perfectly and explicitly represent the new specific requirements of SBP modeling. In this study, we choose the better one positioned nowadays, BPMN 2.0, as the best suited standard for SBP representation. Finally, we propose a semantically rich conceptualization of a SBP organized in core ontology.

We defined a rigorous conceptual specification for this type of BP, organized in a multi-perspective formal ontology, the Core Ontology of Sensitive Business Processes (COSBP). This reference ontology will be used to define a generic BP meta-model (BPM4KI) further specifying SBPs. The objective is to obtain an enriched consensus modeling covering all generic concepts, semantic relationships and properties needed for the exploitation of SBPs, known as core modeling.

This paper introduces the problem of conceptual analysis of SBPs for (crucial) knowledge identification and management. These processes are highly complex and knowledge-intensive. The originality of this contribution lies in the multi-dimensional approach we have adopted for SBP modeling as well as the definition of a Core Ontology of Sensitive Business Processes (COSBP) which is very useful to extend the BPMN notation for knowledge management.

There is an increasing shift toward cashless societies worldwide, with electronic payment networks at the forefront of facilitating this transition. The purpose of this research is to explore the critical role of domestic payment networks and to propose recommendations for the effective implementation of such networks.

This research paper uses a multiple-case study design, informed by global best practices in domestic payment systems. Using thematic and content analysis methodologies, this research rigorously analyzes secondary data sources to investigate the strategic importance of domestic payment networks to national economies and the motivations driving their developments.

This paper illuminates the role of domestic payment networks in advancing cost-effective transactions, enhancing financial inclusion and safeguarding national sovereignty. It highlights the growing trend among nations to prioritize the development of their own payment systems. The research further explores the strategic initiatives undertaken by governments to prefer domestic over multinational networks, thereby maintaining control over their financial systems and safeguarding economic interests. Additionally, the study addresses the challenges these networks face, providing a thorough analysis that serves as insight for policymakers and financial institutions aiming to develop and improve their domestic payment infrastructures amidst current and future challenges.

This study contributes to the existing literature on domestic payment networks by studying their significance within the global financial ecosystem, particularly highlighting their role in advancing financial inclusion and ensuring national financial sovereignty. This research paper uses competition state theory as a foundation for its arguments and provides policy and practical recommendations for policymakers and financial institutions. Through this synthesis, the research aims to facilitate the enhancement and strategic development of domestic payment infrastructures globally.

This study aims to evaluate the relationship between the estimated levels of diet advanced glycation end products (dAGEs) intake and obesity in university students.

This cross-sectional study included 301 university students aged 18–30 years. dAGEs was estimated using a food frequency questionnaire, for 549 routinely consumed food items and were reported by dividing total energy intake. Dietary intake and sociodemographic data were collected using validated questionnaires, and the anthropometric characteristics were measured. The relationship between anthropometric measurements and dAGEs intake was examined by binary logistic regression.

A total of 43.2% of the participants had high levels of dAGEs. A significant decreasing trend was found in the percentage of carbohydrate intake compared to the increasing trend in dAGEs consumption (p = 0.005). The percentage of fat intake and meat consumption were significantly higher in participants with the highest consumption, compared with the lowest consumption of dAGEs (p = 0.006). According to the dAGEs classification of participants, body mass index, waist circumference and energy intake were found to be significantly related in all model groups. Accordingly, the increase in body mass index, waist circumference and energy intake were determined as a risk factor in those with high dAGEs intakes.

The findings of this study emphasized that higher intake of dAGEs was associated with an increased risk of obesity parameters in college students.

The purpose of this paper is to solve the diverse and complex problems of flat-knitting sports upper process design, improve the design ability of upper organization, and realize three-dimensional simulation function.

Firstly, the matrix is used to establish the corresponding pattern diagram and organizational diagram model, and the relationship between the two is established by color coding as a bridge to completed the transformation of the flat-knitted sports upper process design model. Secondly, the spatial coordinates of the loop type value points are obtained through the establishment of loop mesh model, the index of two-dimensional and three-dimensional models of uppers and the establishment of spatial transformation relationship. Finally, using Visual Studio as a development tool, use the C# language to implement this series of processes.

Digitizing the fabric into a matrix model, combined with matrix transformation, can quickly realize the design of the flat-knitting process. Taking the knitting diagram of the upper process as the starting point, the loop geometry model corresponding to the element information is established, and the three-dimensional simulation effect of the flat-knitted upper based on the loop structure is realized under the premise of ensuring that it can be knitted.

This paper proposes a design and modeling method for flat-knitted uppers. Taking the upper design process and 3D simulation effect as an example, the feasibility of the method is verified, which improves the efficiency of the development of the flat-knitted upper product and lays the foundation for the high-end customization of the flat-knitted upper.

Powder bed additive manufacturing has recently seen substantial growth, yet consistently producing high-quality parts remains challenging. Recoating streaking is a common anomaly that impairs print quality. Several data-driven models for automatically detecting this anomaly have been proposed, each with varying effectiveness. However, comprehensive comparisons among them are lacking. Additionally, these models are often tailored to specific data sets. This research addresses this gap by implementing and comparing these anomaly detection models for recoating streaking in a reproducible way. This study aims to offer a clearer, more objective evaluation of their performance, strengths and weaknesses. Furthermore, this study proposes an improvement to the Line Profiles detection model to broaden its applicability, and a novel preprocessing step was introduced to enhance the models’ performances.

All found anomaly detection models have been implemented along with several preprocessing steps. Additionally, a new universal benchmarking data set has been constructed. Finally, all implemented models have been evaluated on this benchmarking data set and the effect of the different preprocessing steps was studied.

This comparison shows that the improved Line Profiles model established it as the most efficient detection approach in this study’s benchmark data set. Furthermore, while most state-of-the-art neural networks perform very well off the shelf, this comparison shows that specialised detection models outperform all others with the correct preprocessing.

This comparison gives new insights into different recoater streaking (RCS) detection models, showcasing each one with its strengths and weaknesses. Furthermore, the improved Line Profiles model delivers compelling performance in detecting RCS.