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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.

Regulations mandate testing concrete’s compressive strength after the concrete has cured for 28 days. In the ideal situation, cured strength equals the target strength. Advanced estimation of concrete’s compressive strength can facilitate quality management, improve safety, and present economic advantages in sustainable use. Basic statistical methods cannot effectively predict concrete’s strength or its non-linear relationships with the proportions of its constituent materials. In this study, a baseline model for predicting concrete’s compressive strength was constructed using a state-of-the-art machine-learning method. Most related studies have used sets of concrete mix design results concerning concrete specimens for laboratory-produced concrete specimens as training sets and have obtained simple models through regression; however, these models have been unsuitable for onsite prediction of the compressive strength of concrete with the various mix designs. Control over mix proportions is high in laboratories, resulting in low variation; onsite manual operation and environmental factors cause significant variations in assessment data. In this study, machine-learning techniques and a newly developed metaheuristic optimization algorithm were applied to big long-term data from 75 concrete plants to construct the optimal machine-learning model. Our self-developed forensic-based investigation algorithm was employed to fine-tune the hyperparameters of the extreme gradient boosting model and to improve the model’s generalizability. The lowest mean absolute percentage error (MAPE) obtained using this model was 9.29%, which was smaller than the lowest MAPE achieved using the conventional simple regression with the water-to-binder (W/B) ratio (12.73%). The traditional method tends to overestimate the actual compressive strength. Finally, a convenient expert system was developed that facilitates the use of the proposed model by onsite engineers for quality management. This system expedites the judgment of whether a mixed design is reasonable, reducing production costs while maintaining the safety of concrete structures. It can be widely applied in practice and function as an effective decision-making tool.

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.

Kijanose is one of the most highly functionalized deoxysugars found in nature and a challenging synthetic target. We found that the ring opening of trisubstituted, 2-oxazolidinone-fused aziridines is regio- and stereoselective, and the azide adduct has the same stereochemistry as that of kijanose after converting the azido to a nitro group. Therefore, both α- and β-methyl l-kijanosides were prepared from ethyl l-lactate in 14% total yield.

Kijanose is one of the most highly functionalized deoxysugars found in nature and a challenging synthetic target. We found that the ring opening of trisubstituted, 2-oxazolidinone-fused aziridines is regio- and stereoselective, and the azide adduct has the same stereochemistry as that of kijanose after converting the azido to a nitro group. Therefore, both α- and β-methyl l-kijanosides were prepared from ethyl l-lactate in 14% total yield.

Nowadays, Intelligent Transportation Systems (ITS) technologies are exploring a wide range of services such as freeway management, crash prevention & safety, driver assistance, and infotainment of drivers and/or passengers. In this paper, an agile urban parking recommendation service for vehicular intelligent guiding system is designed to facilitate city citizens with fully efficient, real-time and precise parking lot guiding suggestions for the sustainability of the future green city. The system offers drivers a friendly parking lot recommendation sequence and saves driver's time circling around by the accurate prediction of the successful parking probability in each parking lot. The proposed cost model constructs an optimal recommendation sequence considering successful parking probability and time to reach the parking lot. Through the collection and analysis of realistic records from parking lots in Taipei city, a prediction algorithm is developed to estimate the successful parking probability by using current available space counts. Extensive experiments are conducted to demonstrate the effectiveness of the prediction algorithm.

Silicon nanowire-based biosensors have been identified as a promising biosensing technology. However, it suffers from the interface signal process to be applied into applications. In this paper, we utilize a readout circuit to directly transfer the signal obtained from a Si nanowire based sensing device into digital format. Based on the experimental results, cTnI and NT-proBNP, the biomarkers for acute myocardial infraction diagnosis, can be detected by the developed bio-sensing system in different concentration (320fM, 32pM, 3.2nM and 32nM). As a consequence, this work demonstrates the potential to translate Si nanowire biosensors device into applied bio-sensing systems.

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 co-segmentation techniques on the CMU-Cornell iCoseg dataset that has variability in object deformations and poses.