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This study aims to investigate the formation and characteristics of Taylor bubbles resulting from short-time gas injection in liquid-conveying pipelines. Understanding these characteristics is crucial for optimizing pipeline efficiency and enhancing production safety.

The authors conducted short-time gas injection experiments in a vertical rectangular pipe, focusing on Taylor bubble formation time and stable length. Computational fluid dynamics simulations using large eddy simulation and volume of fluid models were used to complement the experiments.

Results reveal that the stable length of Taylor bubbles is significantly influenced by gas injection velocity and duration. Specifically, high injection velocity and duration lead to increased bubble aggregation and recirculation region capture, extending the stable length. Additionally, a higher injection velocity accelerates reaching the critical local gas volume fraction, thereby reducing formation time. The developed fitting formulas for stable length and formation time show good agreement with experimental data, with average errors of 6.5% and 7.39%, respectively. The predicted values of the formulas in glycerol-water and ethanol solutions are also in good agreement with the simulation results.

This research provides new insights into Taylor bubble dynamics under short-time gas injection, offering predictive formulas for bubble formation time and stable length. These findings are valuable for optimizing industrial pipeline designs and mitigating potential safety issues.

The present study scrutinizes the relative performance of various near-wall treatments coupled with two-equation RANS models to explore the turbulence transport mechanism in terms of the kinetic energy budget in a plane wall jet and the significance of the near-wall molecular and turbulent shear, to select the best combination among the models which reveals wall jet characteristics most efficiently.

A two-dimensional steady incompressible plane wall jet in a quiescent surrounding is simulated using ANSYS-Fluent solver. Three near-wall treatments, namely the Standard Wall Function (SWF), Enhanced Wall Treatment (EWT) and Menter-Lechner (ML) treatment coupled with Realisable, RNG and Standard k-e models and also the Standard and Shear-Stress Transport (SST) k-ω models are employed for this investigation.

The ML treatment slightly overestimated the budget components on an outer scale, whereas the k-ω models strikingly underestimated them. In the buffer layer at the inner scale, the SWF highly over-predicts turbulent production and dissipation and k-ω models over-predict dissipation. Appreciably accurate inner and outer scale k-budgets are observed with the EWT schemes. With a sufficiently resolved near-wall mesh, the Realisable model with EWT exhibits the mean flow, turbulence characteristics and turbulence energy transport even better than the SST k-ω model.

Three distinct near-wall strategies are chosen for comparative performance analysis, focusing not only on the mean flow and turbulence characteristics but the turbulence energy budget as well, for finding the best combination, having potential as a viable and low-cost alternative to LES and DNS for wall jet simulation in industrial application.

This paper aims to improve the refractive index sensor performance for analytes with large refractive index by adopting the technology of microstructured fiber (MF) and surface plasmon resonance (SPR).

The structure adopts an MF with a hexagonal lattice cladding structure composed of all-circular air holes, and three defect regions are introduced. The liquid analyte that needs to be tested is filled in the defect area. The surface plasmon polarition mode is generated and coupled with the core mode, thus forming a refractive index sensing channel. When the resonance conditions are satisfied, the resonance wavelength will be changed with the refractive index of the liquid analyte. All parameters that may affect the performance of the sensor are numerical simulated, and the structure is optimized through a large number of calculations.

The results demonstrate that the maximum dynamic sensitivity (S_R) can reach to 24,260 nm/RIU, and the average sensitivity (S_R-AV) can reach to 18,046 nm/RIU when the refractive index range is from 1.42 to 1.47. Besides, the sensitivity linearity (R²) is approximately 0.965, and its resolution is 4.1 × 10^–6 RIU. The comparison with some literature results shown that the proposed sensor has certain advantages over the sensors reported in these literatures.

This work proposed an SPR-based refractive index sensor with a simple MF structure. It has a certain reference significance for the design and optimization of SPR-based MF sensors. Moreover, owing to its simple structure, high refractive index sensitivity and linear sensing performance, this sensor will play an important role in the detection of high refractive index liquid analytes.

Responding to the continuing separation of participants and researchers in LIS participatory research, a new methodology is proposed: action partnership research design (APRD). It is asserted that APRD can mitigate or remove the hierarchical structures often inherent in the research process, thus allowing for equal contribution from all.

Building on the bonded design (BD) methodology and informed by a scoping literature review conducted by the same authors, APRD is a human-centered research approach with the goal of empowering and valuing community partnerships. APRD originates from research investigating the use of participatory design methods to foster collaboration between two potentially disparate groups, firstly with adult researchers/designers and elementary school children, and secondly with university faculty and IT professionals.

To achieve this goal, in addition to BD techniques, APRD draws inspiration from elements of indigenous and decolonization research methodologies, particularly those with an emphasis on destabilizing power hierarchies and involving research participants as full partners.

APRD, which emerged from findings from previous participatory design studies, especially those of BD, is based on the premise of partnership, recognizing that each member of a design team, whether researcher or participant/user, has unique expertise to contribute. By considering participants/users as full research partners, APRD aims to flatten the hierarchies exhibited in some LIS participatory research methodologies, where participants are treated more like research subjects than partners.

Equipment failure is a critical factor in construction accidents, often leading to severe consequences. Therefore, this study addresses two significant gaps in construction safety research: (1) effectively using historical data to investigate equipment failure and (2) understanding the classification of equipment failure according to Occupational Safety and Health Administration (OSHA) standards.

Our research utilized a multi-stage methodology. We curated data from the OSHA database, distinguishing accidents involving equipment failures. Then we developed a framework using generative artificial intelligence (AI) and large language models (LLMs) to minimize manual processing. This framework employed a two-step prompting strategy: (1) classifying narratives that describe equipment failures and (2) analyzing these cases to extract specific failure details (e.g. names, types, categories). To ensure accuracy, we conducted a manual analysis of a subset of reports to establish ground truth and tested two different LLMs within our approach, comparing their performance against this ground truth.

The tested LLMs demonstrated 95% accuracy in determining if narratives describe equipment failures and 73% accuracy in extracting equipment names, enabling automated categorical identifications. These findings highlight LLMs’ promising identification accuracy compared to manual methods.

The research’s focus on equipment data not only validates the research framework but also highlights its potential for broader application across various accident categories beyond construction, extending into any domain with accessible accident narratives. Given that such data are essential for regulatory bodies like OSHA, the framework’s adoption could significantly enhance safety analysis and reporting, contributing to more robust safety protocols industry-wide.

Using the developed approach, the research enables us to use accident narratives, a reliable source of accident data, in accident analysis. It provides deeper insights than traditional data types, enabling a more detailed understanding of accidents at an unprecedented level. This enhanced understanding can significantly inform and improve worker safety training, education and safety policies, with the potential for broader applications across various safety-critical domains.

This research presents a novel approach to analyzing construction accident reports using AI and LLMs, significantly reducing manual processing time while maintaining high accuracy. By identifying equipment failures more efficiently, our work lays the groundwork for developing targeted safety protocols, contributing to overall safety improvements in construction practices and advancing data-driven analysis processes.

This paper aims to explore the psychological, emotional and equity implications of compulsory acquisition, evaluate the adequacy of compensation in mitigating those consequences and assess the sustainability of cash compensation for future generations.

A case study approach was operationalised to investigate the experiences of 40 project-affected persons (PAPs) four years after a compulsory acquisition project in Ghana’s New Akrade-Mpakadan region for the construction of a railway line. These perspectives were analysed through descriptive statistics and thematic analyses using the NVivo software. Figures and a holistic framework were adopted to report the identified issues.

Overall, only 25% of PAPs received formal communication prior to the acquisition, and only 10.3% have been fully compensated four years later. Despite the acquiring body initiating the marking of properties and compensation assessment in 2019, no payments were made until 2021. This induced emotional responses of distress, loss, uncertainty, stress and sadness, which was further exacerbated by feelings of hopelessness because there was no platform to voice concerns or pursue arbitration. Although PAPs were only offered compensation in the form of single monetary payments, a third would have preferred re-settlement.

By exploring the emotional and psychological effects of compulsory land acquisition, the study adds a new dimension to understanding its consequences. This may spark more interest, debate and discourse amongst researchers and policymakers and lead to the creation or enhancement of existing policy and legal measures to address the needs of PAPs in compulsory acquisition projects in developing countries.

The financial consequences of eminent domain are well documented, but this study explored the psychological, emotional and equity implications of the practice under conditions of weak regulatory frameworks. The adequacy of single lump-sum compensations was also explored to highlight preferred alternatives to ensure fairness for generations unborn.

The present study aims to design and simulate various types of deoxyribonucleic acid (DNA) origami-based nanopores and explore their stability under different temperatures and constraints. To create DNA origami nanopores, both one-layer and two-layer structures can be utilized.

One of the key applications of DNA origami structures involves the creation of nanopores, which have garnered significant interest for their diverse applications across multiple scientific disciplines. DNA origami nanopores can be studied individually and in combination with other structures. The structural stability of these nanopores across various temperature conditions is crucial for enabling the passage of diverse payloads.

Comparing these DNA origami structures can provide valuable insights into the performance of these nanopores under different conditions. The results indicate that two-layer nanopores exhibit better structural stability under various temperatures compared to one-layer nanopores. Additionally, small structural changes in two-layer nanopores enable them to maintain stability even at high temperatures.

In this paper, various DNA origami-based nanopores were designed and simulated, focusing specifically on one-layer and two-layer configurations. The two-layer nanopore consistently exhibited superior stability across both free and restrained scenarios, undergoing fewer structural changes compared to the one-layer nanopore. As temperatures increased, the two-layer nanopore remained less susceptible to deformation, maintaining closer to its original shape. Moreover, in the free scenario, the geometric shape of the two-layer nanopore demonstrated fewer variations than the one-layer nanopore.

The textile sector is one of the sectors where competition is intense and requires the production of high-value-added products. This study aims to conduct patent analysis to find the technology status, recent trends, applications and technological evaluations of protective textile technologies in practice.

More than 36,840 patent documents related to protective textile technologies are available for researchers, patent examiners and patent researchers. Patent analysis is conducted to report the technology status, recent trends and applications of protective textile technologies. This analysis provides insights into the possible future directions of protective textile technologies in practice. Additionally, association rule mining (ARM) is performed to find the hidden patterns among protective textile technologies.

The development of protective textile technologies is revealed by the technology evaluation in this study. In addition, the sub-technology classes affecting protective textile technologies are examined using the cooperative patent classification (CPC) codes of the patent documents. Technology status and recent trends of protective textile technologies are provided in detail. The results of this study show that (1) protective textile technologies are constantly being developed, (2) the working areas of medical protective textiles are increasing, (3) there are frequent studies on fabric structures for saving lives within the framework of human needs and (4) there are four technology classes, namely A41D, Y10T, B32B and A62B impacting the other technology classes related to textile technologies such as D10B, Y10T, F41H, A62D, D04H, Y10S and D10B.

To have a competitive advantage in the marketplace, evaluation of textile technologies is critical in developing “functionalized” and “technologized” textile products. In particular, evaluating technologies in developing protective textile products is extremely important to meet customer demands and present competitive products in the market. Examining these patents for technology developers, decision-makers and policymakers is an urgent and necessary job. However, studies examining the development of protective textile technologies with patent analysis are very limited in the literature. To fill this gap, technology status, recent trends and applications of protective textile technologies are reported based on patent analysis and ARM in this study.

The purpose of this study is to investigate how ethical leadership impacts employees’ innovative work behavior among public employees through the mediating role of group cohesiveness. This work further offers deeper insight into the moderating mechanism of openness to experience in the relationship between ethical leadership and employees’ innovative work behavior.

Three time-lagged sets of data (n = 532) were collected among Vietnamese public employees. The partial least squares – structural equation modeling method was applied to test the research hypotheses.

Ethical leadership positively relates to employees’ innovative work behavior. Furthermore, group cohesiveness plays a mediating role in the link between ethical leadership and employees’ innovative work behavior. The moderating impact of openness to experience between ethical leadership and employees’ innovative work behavior is supported.

This inquiry is probably the first attempt to explore the mechanism linking ethical leadership and employees’ innovative work behavior through the mediator of group cohesiveness. Additionally, this study extends the current knowledge by investigating the moderating role of openness to experience in ethical leadership and employees’ innovative work behavior nexus.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.