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Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the mechanical properties of concrete that is directly related to the safety of the structures. Therefore, predicting the compressive strength can facilitate the early planning of material quality management. A series of deep learning (DL) models that suit computer vision tasks, namely the convolutional neural networks (CNNs), are used to predict the compressive strength of ready-mixed concrete. To demonstrate the efficacy of computer vision-based prediction, its effectiveness using imaging numerical data was compared with that of the deep neural networks (DNNs) technique that uses conventional numerical data. Various DL prediction models were compared and the best ones were identified with the relevant concrete datasets. The best DL models were then optimized by fine-tuning their hyperparameters using a newly developed bio-inspired metaheuristic algorithm, called jellyfish search optimizer, to enhance the accuracy and reliability. Analytical experiments indicate that the computer vision-based CNNs outperform the numerical data-based DNNs in all evaluation metrics except the training time. Thus, the bio-inspired optimization of computer vision-based convolutional neural networks is potentially a promising approach to predict the compressive strength of ready-mixed concrete.

A general method for synthesizing optically active, primary, secondary, and tertiary organofluorides was developed. This chiral pool synthesis utilized the skeleton of arabinose to generate diastereomerically pure 2-oxazolidinone-fused aziridines, which underwent ring opening with a fluoride anion. The adducts, polyoxygenated organofluorides, were useful precursors to various fluorinated compounds, such as fluorinated amino acids.

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Plant microbial fuel cells (PMFCs) are an emergent green-energy technology that continuously converts solar energy into electricity. Placing PMFCs on the roofs of urban buildings can help to create green urban environments even as they generate power. The power generation performance of PMFCs is affected by a range of environmental factors, so their power generation capacity is difficult to estimate. To develop an artificial intelligence model to forecast PMFC power generation accurately, relevant results obtained using shallow and deep learning techniques are compared for the first time. Once deep learning techniques had been identified as superior for this purpose, they were used with a bio-inspired optimization algorithm to dynamically setting the model hyperparameters. The developed model can also be applied to estimate the power generation capacity of PMFC devices in the future. The model was trained using data collected from sensors in a site experiment that was carried out using PMFCs embedded with Chinese pennisetumin (Pennisetum alopecuroides), narrowleaf cattail (Typha angustifolia), dwarf rotala (Rotala rotundifolia), and no plant as a control group. The original data of device parameters, environmental parameters, and the measured power generation of PMFCs in numerical form were applied to train shallow learning and time-series deep learning models. Meanwhile, the state-of-the-art sliding window technique was used to establish a numerical matrix, which was converted into a 2D image-like format to represent inputs for deep convolutional neural network (CNN) models. The accuracy in predicting the power generation capacity of PMFC devices showed that EfficientNet, an advanced type of CNN, was the best model among the shallow and deep learning techniques. These analytical results demonstrate the superior performance of deep CNNs in learning image features and their consequent suitability for constructing PMFC power generation forecasting models. To enhance the generalization performance of CNN, a

The goal of image co-segmentation is to segment the same or similar objects from a set of images. Unlike traditional methods, we propose a matching based algorithm to achieve this goal. Our method contains two phases. In the first phase, we use a matching algorithm to jointly estimate initial foreground labels of the input images. In the next phase, the labels of each image are used to extract its foreground regions via graph cuts. In contrast to other co-segmentation algorithms, our approach decomposes the co-segmentation problem into the two simpler phases, thus preventing the need to construct a complicated co-segmentation graph model which may cause troublesome optimization. The experimental results show the competitive performance of the proposed method in comparison with other famous image cosegmentation techniques on the CMU-Cornell iCoseg dataset that has variability in object deformations and poses.

Due to the COVID-19 pandemic in Taiwan, many construction sites must limit the number of people on the jobsite or conduct work independently to avoid the spread of COVID-19. The quality of construction may be in doubt with unclear job handover, especially when workers have COVID-19 infection that should be isolated immediately. On top of that, first-level subcontractor self-inspections are crucial parts of construction process management, and neglecting inspection processes can lead to construction errors and poor quality. To improve current quality inspection methods for private projects, a literature analysis was conducted to identify construction quality management issues that are faced in private housing projects. In-depth interviews with small and medium-sized subcontractors of private housing projects were performed to understand the quality management methods that they use in practice. Next, improvement measures for quality management were formulated and a simplified checklist for private project subcontractors, based on the practical feedback obtained, was created. Finally, the AppSheet platform was used to develop an inspection application for construction, and a subcontractor was invited to confirm its feasibility. The paperless design avoids redundant human contact, and the results of this study greatly facilitate construction practice, particularly during the pandemic. The main contribution of this study is its investigation of the procedures that are used by private project subcontractors to inspect their work for quality management; its results can serve as a reference for academics in evaluating construction quality management levels and improving the management of work by subcontractors to promote safety and health.