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Based on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments.

This paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future.

Piezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential.

In this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the physical, mechanical, tribological and water resistance properties of 5% NaOH-treated bamboo fiber–reinforced composites.

In this research, the epoxy/bamboo/TiO₂ hybrid composite filled with 0–8 Wt.% TiO₂ particles has been fabricated using simple hand layup techniques, and testing of the developed composite was done in accordance with the American Society for Testing and Materials (ASTM) standard.

The results of this study indicate that the addition of TiO₂ particles improved the mechanical properties of the developed epoxy/bamboo composites. Tensile properties were found to be maximum for 6 Wt.%, and impact strength was found to be maximum for 8 Wt.% TiO₂ particles-filled composite. The highest flexural properties were found at a lower TiO₂ fraction of 2 Wt.%. Adding TiO₂ filler helped to reduce the water absorption rate. The studies related to the wear and friction behavior of the composite under dry and abrasive wear conditions reveal that TiO₂ filler was beneficial in improving the wear performance of the composite.

This research paper attempts to include both TiO₂ filler and bamboo fibers to develop a novel composite material. TiO₂ micro and nanoparticles are promising filler materials; it helps to enhance the mechanical and tribological properties of the epoxy composites and in literature, there is not much work reported, where TiO₂ is used as a filler material with bamboo fiber–reinforced epoxy composites.

The present work focuses on evaluating the physical and mechanical characteristics of geopolymer concrete (GPC) by replacing the sodium silicate waste (SSW) in place of traditional river sand. The aim is to create eco-friendly concrete that mitigates the depletion of conventional river sand and conserves natural resources. Additionally, the study seeks to explore how the moisture content of filler materials affects the performance of GPC.

SSW obtained from the sodium silicate industry was used as filler material in the production of GPC, which was cured at ambient temperature. Instead of the typical conventional river sand, SSW was substituted at 25 and 50% of its weight. Three distinct moisture conditions were applied to both river sand and SSW. These conditions were classified as oven dry (OD), air dry (AD) and saturated surface dry (SSD).

As the proportion of SSW increased, there was a decrease in the slump of the GPC. The setting time was significantly affected by the higher percentage of SSW. The presence of angular-shaped SSW particles notably improved the compressive strength of GPC when replacing a portion of the river sand with SSW. When exposed to elevated temperatures, the performance of the GPC with SSW exhibited similar behavior to that of the mix containing conventional river sand, but it demonstrated a lower residual strength following exposure to elevated temperatures.

Exploring the possible utilization of SSW as a substitute for river sand in GPC, and its effects on the performance of the proposed mix. Analyzing, how varying moisture conditions affect the performance of GPC containing SSW. Evaluating the response of the GPC with SSW exposed to elevated temperatures in contrast to conventional river sand.

This contribution responds to the call of various researchers for a shift in Responsible Management Education (RME) to adopt a more human-centered and less organizational-centered approach. Service learning (SL) is introduced as a possibility to offer didactical opportunities for participants to connect real-world experiences to system thinking in various ways. We suggest an approach called a “Prism of Reflections” to pique participants' hermeneutical, technical, and emancipatory interests so they can delve deeply into local social and environmental issues and be able to connect them to broader global issues as encapsulated in the Sustainable Development Goals (SDGs). We exemplify our method by demonstrating how students reflect on their experiences working at food banks, and how they relate to concerns of sustainability, poverty, and access to food. Our research suggests that this approach offers a way to situate organizational thinking and instrumental reasoning in a larger framework that considers the aims of hermeneutics, technical and emancipatory discourses. Our findings demonstrate that there are conflicts and dissonances when connecting intersubjective real-world perceptions to emancipatory interests and technical knowledge, particularly when it comes to challenges in the realm of food.

The purpose of this review paper is to provide a review of the most recent advances in the field of manufacturing composite sandwich panels along with their advantages and limitations. The other purpose of this paper is to familiarize the researchers with the available developments in manufacturing sandwich structures.

The most recent research articles in the field of manufacturing various composite sandwich structures were reviewed. The review process started by categorizing the available sandwich manufacturing techniques into nine main categories according to the method of production and the equipment used. The review is followed by outlining some automatic production concepts toward composite sandwich automated manufacturing. A brief summary of the sandwich manufacturing techniques is given at the end of this article, with recommendations for future work.

It has been found that several composite sandwich manufacturing techniques were proposed in the literature. The diversity of the manufacturing techniques arises from the variety of the materials as well as the configurations of the final product. Additive manufacturing techniques represent the most recent trend in composite sandwich manufacturing.

This work is valuable for all researchers in the field of composite sandwich structures to keep up with the most recent advancements in this field. Furthermore, this review paper can be considered as a guideline for researchers who are intended to perform further research on composite sandwich structures.

This paper aims to investigate the theoretical and practical links between digital twin (DT) application in heritage facilities management (HFM) from a life cycle management perspective and to signpost the future development directions of DT in HFM.

This state-of-the-art review was conducted using a systematic literature review method. Inclusive and exclusive criteria were identified and used to retrieve relevant literature from renowned literature databases. Shortlisted publications were analysed using the VOSviewer software and then critically reviewed to reveal the status quo of research in the subject area.

The review results show that DT has been mainly adopted to support decision-making on conservation approach and method selection, performance monitoring and prediction, maintenance strategies design and development, and energy evaluation and management. Although many researchers attempted to develop DT models for part of a heritage building at component or system level and test the models using real-life cases, their works were constrained by availability of empirical data. Furthermore, data capture approaches, data acquisition methods and modelling with multi-source data are found to be the existing challenges of DT application in HFM.

In a broader sense, this study contributes to the field of engineering, construction and architectural management by providing an overview of how DT has been applied to support management activities throughout the building life cycle. For the HFM practice, a DT-cum-heritage building information modelling (HBIM) framework was developed to illustrate how DT can be integrated with HBIM to facilitate future DT application in HFM. The overall implication of this study is that it reveals the potential of heritage DT in facilitating HFM in the urban development context.

The rapid growth of 3D printing has transformed the cost-effective production of prototypes and functional items, primarily using extrusion technology with thermoplastics. This study aims to focus on optimizing mechanical properties, precisely highlighting the crucial role of mechanical compressive strength in ensuring the functionality and durability of 3D-printed components, especially in industrial and engineering applications.

Using the Box−Behnken experimental design, the research investigated the influence of layer thickness, wall perimeter and infill level on mechanical resistance through compression. Parameters such as maximum force, printing time and mass utilization are considered for assessing and enhancing mechanical properties.

The layer thickness was identified as the most influential parameter over the compression time, followed by the degree of infill. The number of surface layers significantly influences both maximum strength and total mass. Optimization strategies suggest reducing infill percentage while maintaining moderate to high values for surface layers and layer thickness, enabling the production of lightweight components with adequate mechanical strength and reduced printing time. Experimental validation confirms the effectiveness of these strategies, with generated regression equations serving as a valuable predictive tool for similar parameters.

This research offers valuable insights for industries using 3D printing in creating prototypes and functional parts. By identifying optimal parameters such as layer thickness, surface layers and infill levels, the study helps manufacturers achieve stronger, lighter and more cost-efficient components. For industrial and engineering applications, adopting the outlined optimization strategies can result in components with enhanced mechanical strength and durability, while also reducing material costs and printing times. Practitioners can use the developed regression equations as predictive tools to fine-tune their production processes and achieve desired mechanical properties more effectively.

This research contributes to the ongoing evolution of additive manufacturing, providing insights into optimizing structural rigidity through polylactic acid (PLA) selection, Box−Behnken design and overall process optimization. These findings advance the understanding of fused deposition modeling (FDM) technology and offer practical implications for more efficient and economical 3D printing processes in industrial and engineering applications.

The purpose of this study is to further investigate the potential benefits brought about by the development of modern technology in the steel construction industry. Specifically, the study focuses on the optimization of tapered members for pre-engineered steel structures, aligning with Eurocode 3 standards. By emphasizing the effectiveness of material utilization in construction, this research aims to enhance the structural performance and safety of buildings. Moreover, it recognizes the pivotal role played by such advancements in promoting economic growth through the reduction of material waste, optimization of cost-efficiency and support for sustainable construction practices.

Structural performance at initial analysis and final analysis of the selected critical frame were carried out using Dlubal RSTAB 8.18. The structural frame stability and sway imperfections were checked based on MS EN1993-1-1:2005 (EC3). To assess the structural stability of the portal frame using MS EN 1993-1-1:2005 (EC3), cross-sectional resistance and member buckling resistance were verified based on Clause 6.2.4 – Compression, Clause 6.2.5 – Bending Moment, Clause 6.2.6 – Shear, Clause 6.2.8 – Bending and Shear, Clause 6.2.9 – Bending and Axial Force and Clause 6.3.4 – General Method for Lateral and Lateral Torsional Buckling of Structural Components.

In this study, the cross sections of the web-tapered rafter and column were classified under Class 4. These involved the consideration of elastic shear resistance and effective area on the critical steel sections. The application of the General Method on the verification of the resistance to lateral and lateral torsional buckling for structural components required the extraction of some parameters using structural analysis software. From the results, there was only 5.90% of mass difference compared with the previous case study.

By classifying the web-tapered cross sections of the rafter and column under Class 4, the study accounts for important factors such as elastic shear resistance and effective area on critical steel sections.

Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).

This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.

The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.

This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.

This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

This study aims to investigate the efficacy of Ag@GO/Na₂SiO₃ nanocomposite in eliminating As from aqueous solutions. Employing response surface methodology, the research systematically examines the adsorption process.

Various experimental parameters including sample pH, contact time, As concentration and adsorbent dosage are optimized to enhance the As removal process.

Under optimized conditions, the initial As concentration, contact time, pH and adsorbent dosage are determined to be 32 ppm, 50 mins, 6.5 and 0.4 grams, respectively. While the projected removal of As stands at 97.6% under these conditions, practical application achieves a 93% removal rate. Pareto analysis identifies the order of significance among factors as follows: adsorbent dosage > contact time > pH > As concentration.

This study highlights the potential Ag@GO/Na₂SiO₃ as a promising adsorbent for efficiently removing industrial As from aqueous solutions, and it is likely to have a good sufficiency in the filtration of water and wastewater treatment plans to remove some chemical pollution, including paints and heavy metals.

The simplicity of the nanocomposite preparation method without the need for advanced equipment and the cheapness of the raw materials and its potential ability to remove As are the prominent advantages of this research.