The construction industry is moving toward digitalization, and technologies support various construction processes. In the automated construction progress monitoring domain, several modern progress measurement techniques have been introduced. However, a hesitant attitude has been observed toward its adoption. Researchers have highlighted lack of theoretical understanding of effectual implementation is one of the significant reasons. This study aims to analyze general technological parameters related to automated monitoring technologies and devise a theoretical-based conceptual framework explaining the aspects affecting the adequate operation of automated monitoring. The study has been executed by following a systematic inline process for the identification of effective parameters, which include a structured literature review, semi-structured interviews, pilot survey, questionnaire survey, and structural equation modeling (SEM)-based mathematical model. A refined conceptual framework has been devised with 21 effective parameters under five significant categories, i.e., "Target Object," "Technical," "External Interference," "Occlusions," and "Sensing." A knowledge framework has been established by adopting the SEM technique, which is designed on the characteristics-based theme. This conceptual framework provides the theoretical base for practitioners toward the conceptual understanding of automated monitoring processes related to technological parameters that affect the outcomes. This study is unique as it focused on the general criteria or parameters that affect the performance or outcomes of the digital monitoring process and is easily understandable by the user or operator.
The emergence of a pandemic affecting the respiratory system has resulted in a significant demand for face masks. Masks have always been mentioned as an effective tool against environmental threats. This includes the use of double masks by large sections of the public, as can be seen during the current global spread of COVID-19. However, there is limited knowledge available on the performance of the various commonly available double face masks used. In this study, we have compared the performance of single and double face configurations. Three types of 3-ply face mask (FM), three types of fabric face mask (CM) and nine configurations of these face masks were investigated based on morphology, filtration efficiencies, breathing resistance and carbon dioxide (CO2) content. The filtration efficiencies of the double face masks are enhanced when combined with a fabric mask, and were found to improve by 500% compared to those of a fabric mask. The lowest breathing resistance among the double masking was observed in the combination of FM1 and fabric face masks. From the results, it was found that all combinations of double face mask are suitable to use since the measurements indicate that the breathing resistance and CO2 contents are still lower than the acceptable value set by the Malaysian Standard (MS 2323:2010) and European Standard (EN 149:2001 + A1:2009). The performance of double masks is comparable with that of N95 respirators. The double masking combination can be used as an alternative method to reduce inhalation exposure to airborne contaminants.
Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.
A physiological-based emotion recognition system (ERS) with a unimodal approach such as an electrocardiogram (ECG) is not as popular compared to a multimodal approach. However, a single modality has the advantage of lower development and computational cost. Therefore, this study focuses on a unimodal ECG-based ERS. The ECG-based ERS has the potential to become the next mass-adopted consumer application due to the wide availability of wearable and mobile ECG devices in the market. Currently, ECG-inclusive affective datasets are limited, and many of the existing datasets have small sample sizes. Hence, ECG-based ERS studies are stunted by the lack of quality data. A novel multi-filtering augmentation technique is proposed here to increase the sample size of the ECG data. This technique augments the ECG signals by cleaning the data in different ways. Three small ECG datasets labelled according to emotion state are used in this study. The benefit of the proposed augmentation techniques is measured using the classification accuracy of five machine learning algorithms; k-nearest neighbours (KNN), support vector machine, decision tree, random forest and multilayer perceptron. The results show that with the proposed technique, there is a significant improvement in performance for all the datasets and classifiers. KNN classifier improved the most with the augmented data and the reported classification accuracies of over 90%.