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This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

This study aims to dissect the multifaceted impact of ISO/IEC 17025 accreditation, specifically within civil engineering testing and calibration laboratories. To achieve this, it intends to explore several key objectives: identifying the prominent benefits of accreditation to laboratory performance, understanding the advantages conferred through participation in proficiency testing schemes, assessing the role of accreditation in enhancing laboratory competitiveness, examining the primary challenges encountered during the accreditation process, investigating any discernible adverse effects of accreditation on laboratory performance and evaluating whether the financial cost of accreditation justifies the resultant profitability.

This study employs a qualitative approach through semi-structured interviews with 23 industry professionals—including technical managers, quality managers, external auditors and clients. Thematic analysis, guided by Braun and Clarke’s six-stage paradigm, was utilized to interpret the data, ensuring a comprehensive understanding of the accreditation’s impact.

Findings reveal that accreditation significantly enhances operational processes, fosters quality awareness and facilitates continuous improvement, contributing to greater client satisfaction. In addition, standardized operations and rigorous quality controls further result in enhanced performance metrics, such as staff capability and measurement accuracy. However, the study also uncovers the challenges of accreditation, including high resource costs and bureaucratic hurdles that can inhibit innovation and slow routine operations. Importantly, the research underscores that the impact of accreditation on profitability is not universal, but contingent upon various factors like sector-specific regulations and market demand. The study also highlights sector-specific variations in the role of accreditation as a marketing tool and differing perceptions of its value among clients. It further emphasizes the psychological stress of high-stakes evaluations during audits.

This study represents the first in-depth investigation into the impact of ISO/IEC 17025 accreditation on civil engineering testing and calibration laboratories, directly contributing to the enhancement of their quality and operational standards. Providing actionable insights for laboratories, it underscores the importance of weighing accreditation costs and benefits and the necessity for a tailored approach to the unique market and regulatory landscapes they operate in.

This study aims to investigate the ammonia-sensing performance of polyaniline/polyethylene oxide (PANI/PEO) and polyaniline/polyethylene oxide/zinc oxide (PANI/PEO-ZnO) composite nanofibers at room temperature.

Gas sensor structures were fabricated using microfabrication techniques. First, onto the SiO₂ wafer, gold electrodes were fabricated via thermal evaporation. PANI/PEO nanofibers were produced by the electrospinning method, and the ZnO layer was deposited by using radio frequency (RF) magnetron sputtering on the electrospun nanofibers as a sensing layer. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray diffraction were performed to characterize the analysis of nanofibers. After all, gas sensing analysis of PANI/PEO and PANI/PEO/ZnO nanofibers was conducted using an experimental setup at room temperature conditions. Furthermore, the impact of humidity (17%–90% RH) on the sensor resistance was actively investigated.

FTIR analysis confirms the presence of functional groups of PANI, PEO and ZnO in nanofiber structure. SEM micrographs demonstrate beads-free, thinner and smooth nanofibers with ZnO contribution to electrospun PANI/PEO nanofibers. Moreover, according to the gas sensing results, the PANI/PEO nanofibers exhibit 115 s and 457 s response time and recovery time, respectively. However, the PANI/PEO/ZnO nanofibers exhibit 245 s and 153 s response time and recovery time, respectively. PANI/PEO/MOx composite nanofibers ensure stability to the NH₃ gas owing to the high surface/volume ratio and decrease in the humidity dependence of gas sensors, making gas sensors more stable to the environment.

In this study, ZnO was deposited via RF magnetron sputtering techniques on PANI/PEO nanofibers as a different approach instead of in situ polymerization to investigate and enhance the sensor response and recovery time of the PANI/PEO/ZnO and PANI/PEO composite nanofibers to ammonia. These results indicated that ZnO can enhance the sensing properties of conductive polymer-based resistive sensors.

The purpose of this research was to develop and evaluate a machine learning (ML) algorithm to accurately predict bamboo compressive strength (BCS). Using a dataset of 150 bamboo samples with features such as cross-sectional area, dry weight, density, outer diameter, culm thickness and load, various ML algorithms including artificial neural network (ANN), extreme learning machine (ELM) and support vector regression (SVR) were tested. The ELM algorithm outperformed others, showing superior accuracy based on metrics like R2, MSE, RMSE, MAE and MAPE. The study highlights the efficacy of ELM in enhancing the precision and reliability of BCS predictions, establishing it as a valuable tool for assessing bamboo strength.

This study experimentally created a dataset of 150 bamboo samples to predict BCS using ML algorithms. Key predictive features included cross-sectional area, dry weight, density, outer diameter, culm thickness and load. The performance of various ML algorithms, including ANN, ELM and SVR, was evaluated. ELM demonstrated superior performance based on metrics such as coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), establishing its robustness in predicting BCS accurately.

The study found that the ELM algorithm outperformed other ML algorithms, including ANN and SVR, in predicting BCS. ELM achieved the highest accuracy based on key metrics such as R2, MSE, RMSE, MAE and MAPE. These results indicate that ELM is a highly effective and reliable tool for predicting the compressive strength of bamboo, thereby enhancing the precision and dependability of BCS evaluations.

This study is original in its application of the ELM algorithm to predict BCS using experimentally derived data. By comparing ELM with other ML algorithms like ANN and SVR, the research establishes ELM’s superior performance and reliability. The findings demonstrate the significant potential of ELM in material strength prediction, offering a novel and robust approach to evaluating bamboo’s compressive properties. This contributes valuable insights into the field of material science and engineering, particularly in the context of sustainable construction materials.

The growing demand for housing and infrastructure, as well as the requirement for affordable housing, has been a significant factor, necessitating investigation for sustainable approaches and implementation of alternative construction innovations. Hence, this study aims to identify and assess the drivers for implementing modular construction systems (MCS) in developing countries.

The study adopts a quantitative research approach to seek respondents’ opinions on the factors that can drive the implementation of MCS in developing countries. Accordingly, a structured questionnaire was used as an instrument of data collection based on five Likert scales. The data was analysed using the mean score, one sample t-test, Kruskal–Wallis, factor analysis (FA) and Pearson correlation analysis.

Results show that 15 of the 16 major identified drivers were statistically significant towards implementing MCS, which indicates that the drivers are crucial for implementing MCS in developing countries. However, the Kruskal–Wallis test reveals that the respondents have varying opinions on the identified drivers. FA categorised the drivers into four categories, namely, “management and sustainability”, “key performance”, “know-how and logistics” and “regulations and policies”. A strong relationship among the four categories of drivers was established using Pearson correlation, which indicated that all the drivers’ categories are essential for implementing MCS in developing countries.

This study identified and assessed the drivers towards implementing MCS in developing countries. The study concludes that the identified drivers are essential for implementing MCS in developing countries. Also, the study considers the government the most placed player in driving the implementation of MCS in developing countries.

Renewable energy alternatives and nanoscale materials have gained huge attention in recent years due to the problems associated with fossil fuels. The recyclable battery is one of the recent developments to address the energy requirement issues. In this work, the development of nanoscale materials is focused on using green synthesis methods to address the energy requirements of hybrid electric vehicles.

The current research focuses on developing metal oxide nanoscale materials (NANO-SMs). The Zno-Aloe vera NANO-SM is prepared using the green synthesis method. The developed nanoscale materials are characterized using analysis methods like FESEM, TEM, XRD and FTIR.

The average size of ZnO-Aloe vera mono-crystalline was recorded as 60–70 nm/Hexagonal shape. The nanoscale materials are used for the detection of LPG gases. The sensitivity observed was 48%. The response time and recovery time were recorded as 8–10 s and 230–250 s, respectively. The average size of SnO₂-green papaya leaves poly-crystalline was recorded as 10–20 nm/powder form.

Nanoscale materials are developed using green synthesis methods for hybrid vehicle applications. The nanoscale materials are used for the detection of harmful gases in hybrid vehicles.

The circular design process in contemporary fashion design, from two-dimensional (2D) sketching and pattern making to three-dimensional (3D) prototypes, can be facilitated by virtual prototyping. Virtual pressure representations on avatars provide visual and quantitative information regarding garment fit and comfort, which are particularly important for active wear. The purpose of this study is to investigate the benefits of using avatars in active poses from 3D body scans and the use of digital 3D tools for the design process and the prediction of fit of active wear.

This research initially explores virtual fit of cycling wear in active poses and compares the actual pressure values from humans with virtual pressure maps on custom avatars made from body scans in cycling poses across a range of sizes.

Similar fit results were achieved visually in both the standing and cycling poses. However, the comparisons showed no correlation between the actual and virtual pressure data. Of the 32 cases representing different combinations of the parameters of this research (four sizes, two garment types, four active poses), the differences were significant. The results suggest that, rather than providing a direct correlation with pressure values on the body, the main value of avatar data is in providing comparative visual support for fit evaluation.

The approach taken in this research, which considers the active pose and the size range, potentially contributes to the improvement of virtual fit technology, and its more effective use in apparel product development and fit evaluation.

A crisis period can reduce employees’ willingness to take risks, which are important predictors of organizational performance. Built upon the broaden-and-build theory of positive emotions, this study aims to examine the role of leadership’s behavioral change in promoting the willingness to take risks, as mediated by liking, at two different levels of task complexity.

An online survey has been answered by 281 employees of businesses with various scales, in different sectors in Indonesia. Indonesia was selected as it is among the countries with a high power distance culture. Hence, employees are expected to favor more directive leadership changes in the postpandemic context.

Drawing upon the broaden-and-build theory of positive emotions, this study found that leaders’ behavioral change toward a more directive approach promotes a willingness to take risks, as the employees like this change regardless of the task’s complexity level.

In an organization where the employees’ willingness to take risks is critical, the leaders should not simply adopt empowering leadership, as suggested by previous studies. However, leaders need to ensure that any change in leadership behavior during the transition period, either toward a more directive or empowering style, is favorable for the employees, regardless of the level of complexity of the tasks.

This study contributes to the literature by demonstrating that Indonesian employees’ willingness to take risks increases only when the employees like the change in the style of leadership to a more directive one regardless of the level of task complexity. In addition, Indonesian employees have not perceived any substantial change in their leaders’ behavior after the pandemic, and they remain neutral about this type of leadership.

The purpose of this study is to improve the magnetron quality in Company T by identifying the nonconforming defect, adjusting the factors affecting the leakage of the magnetron tube core, and determining the optimal parameter values of these factors.

A case study method is used to present the quality improvement of magnetron tube core. The define, measure, analyze, improve, and control framework is applied in the case study as well as several Six Sigma tools.

The results show that Ag–W thickness, Ag–W installation state and furnace entry interval are significant factors on the leakage of magnetron tube core, and the optimum settings for these factors are 0.055 mm, offset by 1 mm from the outer edge and 5 cm, respectively.

The main limitation of this study is that it was carried out on a small number of production processes. The authors would like to analyze more case studies on the improvements of after-sales quality and supplier quality.

This research could be used in magnetron manufacturing process as a tool for managers and engineers to improve product quality, which can also be extended to similar manufacturing systems.

In this case study, the Six Sigma approach has been applied for the first time to solve magnetron manufacturing problems by improving the quality of magnetron production process. It can help the quality engineers be more familiar with the deployment of Six Sigma and effective tools.

Researchers and practitioners have recently been interested in corporate sustainability performance (CSP). However, knowledge on measuring CSP is limited. Many CSP-measurements are eclectic, without guidance for contextual applications. This paper aims to develop a conceptual framework that categorizes, explains and evaluates measurements based on their accuracy and precision and provides a guideline for their context-specific application.

The authors conducted a systematic literature review of an initial sample of 1,415 papers.

The final sample of 74 papers suggested four measurement categories: isolated indicators, indicator frameworks, Sustainability Balanced Scorecards (SBSC) and Sustainability Performance Measurement Systems (SPMS). The analysis reveals that isolated indicators are inaccurate and imprecise, limiting their application to organizations with delimited, specific measurements of parts of CSP due to the risk of a GIGO-effect (i.e. low-quality input will always produce low-quality output). CSP-indicator frameworks are imprecise but accurate, making them applicable to organizations that handle a more significant amount of CSP data. They have a risk of greensplashing, i.e. many indicators not connected to the industry, organization or strategy. In contrast, SBSCs are precise but inaccurate and valuable for organizations desiring a comprehensive strategic management tool with limited capacity to handle sustainability issues. They pose a risk of the streetlight effect, where organisations do not measure relevant indicators but what is easy to measure.

The ideal CSP-measurement was identified as SPMSs, which are both precise and accurate. SPMSs are useful for organizations with complex, comprehensive, connected and tailored indicators but are methodologically challenging.