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Activity sampling is a technique to monitor onsite labourers' time utilisation, which can provide helpful information for the management level to implement suitable labour productivity improvement strategies continuously. However, there needs to be a review paper that compiles research on activity sampling studies to give readers a thorough grasp of the research trend. Hence, this paper aims to investigate the activity sampling techniques applied in earlier research from the angles of activity categories formation, data collection methods and data analysis.

The method used in this paper is a systematic review guided by the PRISMA framework. The search was conducted in Scopus and Web of Science. The inclusion and exclusion criteria were applied, selecting 70 articles published between 2011 and 2022 for data extraction and analysis. The analysis method involved a qualitative synthesis of the findings from the selected articles.

Activity sampling is broadly divided into four stages: targeting trade, determining activity categories, data collection and data analysis. This paper divides the activity categories into three levels and classifies the data collection methods into manual observation, sensor-based activity sampling and computer vision-based activity sampling. The previous studies applied activity sampling for two construction management purposes: labour productivity monitoring and ergonomic safety monitoring. This paper also further discusses the scientific research gaps and future research directions.

This review paper contributes to the body of knowledge in construction management by thoroughly understanding current state-of-the-art activity sampling techniques and research gaps.

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

This study aims to investigate into the adsorption mechanism of heavy metal ions Pb²⁺ and methylene blue (MB) dyes on an adsorbent-web formed from kapok fibres (KP) and polypropylene fibres (PP).

Initially, the KP underwent pre-treatment with NaClO₂ solution, and their morphology and structure changes were examined through scanning electron microscope images. Subsequently, the KP fibres were blended with PP fibres at a ratio of 70 / 30 and processed through a laboratory carding machine to form a fibre web, which was then secured using a heat press to fabricate the adsorbent-web. The resultant adsorbent-web was subjected to evaluation for the adsorption process.

The findings revealed that the adsorbent-web produced via the dry-laid method exhibited promising attributes, and the adsorption process conformed to the pseudo-second-order model and the Langmuir isotherm model. Parameters such as adsorbent-web porosity, solution pH, and adsorption thermodynamics exerted notable influences on the adsorption capacity of the adsorbent-web. Notably, the adsorbent-web demonstrated remarkable reusability and desorption capacity. In the fifth cycle, the adsorption capacity of adsorbent-web for MB and Pb²⁺ ions only decreased by 9.13% and 11.48%, respectively, compared to the first cycle. The desorption efficiency of the adsorbent-web exceeded 90% for MB and over 73% for Pb²⁺ ions.

This study makes a significant contribution to the practical application of KP as an adsorbent-web for treating waste from the textile dyeing industry.

Professional Identity Formation is the dynamic evolution to “think, act and feel” to become part of a professional community. This document presents the development and the study that aimed to assess the usability of a m-Learning Identity App (MLIA) focused on the formation of professional identity among undergraduate medical students.

MLIA development included four phases: Conceptual, prototype, pilot and implementation, before further deployment. The conceptual model was designed by eight faculty members from three Latin American universities. The prototype was developed and tested with stakeholders. The pilot was performed during 5 weeks before the implementation. Cross-sectional data collected during implementation from 138 medical students who completed a survey to assess the usability of MLIA are presented. During deployment, 977 posts were made on Professional Identity Formation, and examples of these posts are presented.

The prototype and pilot phases demanded improvements. The survey explored (1) Familiarity, (2) Perceived ease of use, (3) Perceived usefulness for Professional Identity Formation, (4) Satisfaction, (5) Intention to reuse (6) Digital aesthetics and (7) Safety. Results from the usability assessment suggest that students perceived MLIA as a secure space with positive aesthetics and ease of use.

Important limitations of the present study include, firstly, that it does not provide information on the effectiveness of the MLIA in shaping professional identity in medical students, it focuses exclusively on its development (conceptual model, prototype, pilot and implementation) and usability. Secondly, the study design did not consider a control group and, therefore, does not provide information on how the App compares with other strategies addressing self-reflection and sharing of meaningful experiences related to professional identity.

MLIA introduces a different approach to education, simulating a secure, easy-to-use, social media with a friendly interface in a safe environment to share academic and motivational moments, transitioning from being to becoming a professional.

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.