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The purpose of this paper is to present investigations on the significant influence of the tube material and fin density on fluid elastic instability and vortex shedding in a parallel triangular finned tube array subjected to water cross flow.

The experiment was conducted on finned tube arrays with a fin height of 6 mm and fin density of 3 fins per inch (fpi) and 9 fpi. A dedicated setup has been developed to examine fluid elastic instability and vortex shedding. Nine parallel triangular tube arrays with a pitch to tube diameter ratio of 1.78 were considered. The plain tube arrays, coarse finned tube arrays and fine finned tube arrays each of steel, copper and aluminium materials were tested. Plain tube arrays were tested to compare the results of the finned tube arrays having an effective tube diameter same as that of the plain tube.

A significant effect of fin density and tube material with a variable mass damping parameter was observed on the instability threshold. In the parallel triangular finned tube array subjected to water cross flow, a delay in the instability threshold was observed with an increase in fin density. For steel and aluminium tube arrays, the natural frequency is 9.77 Hz and 10.38 Hz, which is close to each other, whereas natural frequency of the copper tubes is 7.40 Hz. The Connors’ stability constant K for steel and aluminium tube arrays is 4.78 and 4.87, respectively, whereas it is 5.76 for copper tube arrays, which increases considerably compared to aluminum and steel tube arrays. The existence of vortex shedding is confirmed by comparing experimental results with Owen’s hypothesis and the Strouhal number and Reynolds number relationship.

This paper’s results contribute to understand the effect of tube materials and fin density on fluid elastic instability threshold of finned tube arrays subjected to water cross flow.

This study aims to investigate if engineered compression variations using moisture-responsive knitted fabric design can improve breast support in seamless knitted sports bras.

An experimental approach was used to integrate a novel moisture-responsive fabric panel into a seamless knitted bra, and the resulting compression variability in dry versus wet conditions were compared with those of a control bra. Air permeability and elongation testing of between breasts fabric panels was conducted in dry and wet conditions, followed by three-dimensional body scanning of eight human participants wearing the two bras in similar conditions. Questionnaires were used to evaluate perceived comfort and breast support of both bras in both conditions.

Air permeability test results showed that the novel panel had the highest variance between dry and wet conditions, confirming its moisture-responsive design, and increased its elongation coefficient in both wale and course directions in wet condition. There were significant main effects of bra type and body location on breast compression measurements. Breast circumferences in the novel bra were significantly larger than in the control bra condition. The significant two-way interaction between bra type and moisture condition showed that the control bra lost compressive power in wet condition, whereas the novel bra became more compressive when wet. Changes in compression were confirmed by participants’ perception of tighter straps and drier breast comfort.

These findings add to the limited scientific knowledge of moisture adaptive bra design using engineered knitted fabrics via advanced manufacturing technologies, with possible applications beyond sports bras, such as bras for breast surgery recovering patients.

This study aims on a broad review of Concrete's Rheological Properties. The Concrete is a commonly used engineering material because of its exquisite mechanical interpretation, but the addition of constituent amounts has significant effects on the concrete’s fresh properties. The workability of the concrete mixture is a short-term property, but it is anticipated to affect the concrete’s long-term property.

In this review, the concrete and workability definition; concrete’s rheology models like Bingham model, thixotropy model, H-B model and modified Bingham model; obtained rheological parameters of concrete; the effect of constituent’s rheological properties, which includes cement and aggregates; and the concrete’s rheological properties such as consistency, mobility, compatibility, workability and stability were studied in detail.

Also, this review study has detailed the constituents and concrete’s rheological properties effects. Moreover, it exhibits the relationship between yield stress and plastic viscosity in concrete’s rheological behavior. Hence, several methods have been reviewed, and performance has been noted. In that, the abrasion resistance concrete has attained the maximum compressive strength of 73.6 Mpa; the thixotropy approach has gained the lowest plastic viscosity at 22 Pa.s; and the model coaxial cylinder has recorded the lowest stress rate at 8 Pa.

This paper especially describes the possible strategies to constrain improper prediction of concrete’s rheological properties that make the workability and rheological behavior prediction simpler and more accurate. From this, future guidelines can afford for prediction of concrete rheological behavior by implementing novel enhancing numerical techniques and exploring the finest process to evaluate the workability.

This study aims to deal with both the development and mechanical investigations of unsaturated polyester matrix (UPR) composites containing recycled polyethylene terephthalate (PET) fibers as new fillers.

UPR/PET fibers composites have been developed as mats by incorporating 5, 8, 13 and 18 parts per hundred of rubber (phr) of 6-, 10- and 15-mm length PET fibers from the recycling of postconsumer bottles. The mechanical and physical properties of the composites were investigated as a function of fiber content and length. A significant increase in stress at break and in ultimate stress (sr) were observed for composites reinforced with 5 and 8 phr of 15-mm length PET fibers. The Izod impact strength of UPR/mat PET fiber composites as a function of fiber rate and length showed that the 5 and 8 phr composites for the 15-mm length PET fiber have the optimal mechanical properties 13.55 and 10.50 Kj/m², respectively. The morphological study showed that the strong adhesion resulting from the affinity of the PET fiber for the UPR matrix. The ductile fracture of materials reinforced with 5 and 8 phr is confirmed by the fiber deformation and fracture surface roughness.

This study concluded that the PET fiber enhances the properties of composites, a good correlation was observed between the results of the mechanical tests and the structural analysis revealing that for the lower concentrations, the PET fibers are well dispersed into the resin, but entanglements are evidenced when the fiber content increases.

It can be shown from scanning electron microscopy micrographs that the fabrication technique produced composites with good interfacial adhesion between PET fibers and UPR matrix.

The purpose of this paper is to prepare a multifunctional nanocomposite that is slow-release and resistant to seawater corrosion and biofouling corrosion and to explore the synergistic effect between the two corrosion inhibitors.

The morphology, structure and release properties of CAP@HNTs, BTA@HNTs and CAP/BTA@HNTs were investigated by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, specific surface area analysis and UV spectrophotometry. The corrosion resistance and antimicrobial properties were investigated by electrochemical measurements and bioinhibition rate tests, and the synergistic effect between the two corrosion inhibitors was explored by X-ray photoelectron spectroscopy.

The CAP/BTA@HNTs are responsive to acidic environments and have significantly improved antibacterial and corrosion resistance compared with CAP@HNTs and BTA@HNTs. CAP and BTA have a positive synergistic effect on anticorrosion and antifouling.

Two types of inhibitors, anticorrosion and antifouling, were loaded into the same nanocontainer to prepare a slow-releasable and multifunctional nanocomposite with higher resistance to seawater corrosion and biocorrosion and to explore the synergistic effect of CAP and BTA on corrosion resistance.

There is a growing urgent concern in involving higher education institutions (HEIs) towards an international effort in implementing a more suitable role as conductors of sustainable development. This paper/study aims to present the application of light emiting diode (LED) technology in an HEI assuming technology innovation as part of a larger institutional innovation management strategy addressing multiple dimensions in sustainability.

Investments in LED technology are analyzed for their impact on consumption results and quantitative comparisons between 2008 and 2022 are impaired with detailed information on the types of luminaires and the amount of installed energy.

The collected data shows a clear economic advantage of using LED technology, and the results subsidize institutional planning, considering not only ongoing technological innovation, but also educational components and community involvement in the organization sustainability strategy.

The study is limited to a specific HEI and further comparative research should be carried out.

A holistic approach on sustainability objectives encourages further investment in environmental-friendly technology, example to other HEIs.

The strategic vision of innovation is confirmed with the involvement of the community, at various levels, such as the academic community, local community, scientific international community.

This study addresses the lack of examples in the literature of structural planning and management systems that see sustainability as a strategy built in HEIs. The elaboration of an environmental sustainability plan places environmental sustainability at the confluence of themes such as education, investigation, use of natural resources, waste separation. For each topic are listed measures, actions, environment improvements, institution improvements and their results.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Smart farming technologies (SFTs) can increase yields and reduce the environmental impacts of farming by improving the efficient use of inputs. This paper is to estimate farmers' preference and willingness to pay (WTP) for a well-defined SFT, smart drip irrigation (SDI) technology.

This study conducted a discrete choice experiment (DCE) among 1,300 maize farmers in North China to understand their WTP for various functions of SDI using mixed logit (MIXL) models.

The results show that farmers have a strong preference for SDI in general and its specific functions of smart sensing and smart control. However, farmers do not have a preference for the function of region-level agronomic planning. Farmers' preferences for different functions of SDI are heterogeneous. Their preference was significantly associated with their education, experience of being village cadres and using computers, household income and holding of land and machines. Further analysis show that farmers' WTP for functions facilitated by hardware is close to the estimated prices, whereas their WTP for functions wholly or partially facilitated by software is substantially lower than the estimated prices.

Findings from the empirical study lead to policy implications for enhancing the design of SFTs by integrating software and hardware and optimizing agricultural extension strategies for SFTs with digital techniques such as videos.

This study provides initial insights into understanding farmers' preferences and WTP for specific functions of SFTs with a DCE.

This study aims to develop a safe, wearable clothing system that combines visibility-enhancing and emergency–accident-responding functions for two-wheeled vehicle (TWV) users' safety assistance.

First, the wearable system (WS) allowing users to control turn signals, brake lights and emergency flasher only with head movements was developed. Second, multiconnected systems were developed between WSs and a smartphone application (AS), providing accident occurrence recognition, driving photo capture–storage and emergency notification functions. Third, usability testing in each function was performed to assess the operability of the systems.

The intuitive interface, which uses head movement as gesture commands, was effectively operated for controlling turn signals, brake lights and emergency flasher when driving, despite differences in user physique and boarding structure among TWVs. In addition, using Bluetooth low energy and Wi-Fi protocols simultaneously can establish automatic accident recognition–notification and driving photo capture–storage–display functions by linking two WSs with one AS.

This study presents a case using relatively accessible technologies within the fashion industry to improve users' safety and provide fundamental data for convergence education for smart fashion products, highlighting the significance of this study in this convergence era.

The WSs and the AS of a TWV user visually evoke the attention of other drivers and pedestrians, reducing the risk of accidents; social contribution regarding public safety will be possible by allowing the system to autonomously inform emergencies and receive emergency medical treatment quickly when the accident occurred.

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.