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In recent years, many studies have focused on the application of advanced technology as a way to improve management of construction safety management. A Wireless Sensor Network (WSN), one of the key technologies in Internet of Things (IoT) development, enables objects and devices to sense and communicate environmental conditions; Building Information Modeling (BIM), a revolutionary technology in construction, integrates database and geometry into a digital model which provides a visualized way in all construction lifecycle management. This paper integrates BIM and WSN into a unique system which enables the construction site to visually monitor the safety status via a spatial, colored interface and remove any hazardous gas automatically. Many wireless sensor nodes were placed on an underground construction site and to collect hazardous gas level and environmental condition (temperature and humidity) data, and in any region where an abnormal status is detected, the BIM model will alert the region and an alarm and ventilator on site will start automatically for warning and removing the hazard. The proposed system can greatly enhance the efficiency in construction safety management and provide an important reference information in rescue tasks. Finally, a case study demonstrates the applicability of the proposed system and the practical benefits, limitations, conclusions, and suggestions are summarized for further applications.

This paper proposes a method, called autoencoder with probabilistic random forest (AE-PRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the autoencoder to extract features of low-dimensionality from credit card transaction data features of high-dimensionality. It then relies on the random forest, an ensemble learning mechanism using the bootstrap aggregating (bagging) concept, with probabilistic classification to classify data as fraudulent or normal. The credit card fraud detection (CCFD) dataset is applied to AE-PRF for performance evaluation and comparison. The CCFD dataset contains large numbers of credit card transactions of European cardholders; it is highly imbalanced since its normal transactions far outnumber fraudulent transactions. Data resampling schemes like the synthetic minority oversampling technique (SMOTE), adaptive synthetic (ADASYN), and Tomek link (T-Link) are applied to the CCFD dataset to balance the numbers of normal and fraudulent transactions for improving AE-PRF performance. Experimental results show that the performance of AE-PRF does not vary much whether resampling schemes are applied to the dataset or not. This indicates that AE-PRF is naturally suitable for dealing with imbalanced datasets. When compared with related methods, AE-PRF has relatively excellent performance in terms of accuracy, the true positive rate, the true negative rate, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve.

Scarlet fever has resurged in China starting in 2011, and the environment is one of the potential reasons. Nationwide data on 655,039 scarlet fever cases and six air pollutants were retrieved. Exposure risks were evaluated by multivariate distributed lag nonlinear models and a meta-regression model. We show that the average incidence in 2011–2018 was twice that in 2004–2010 [RR = 2.30 (4.40 vs. 1.91), 95% CI: 2.29–2.31; p < 0.001] and generally lower in the summer and winter holiday (p = 0.005). A low to moderate correlation was seen between scarlet fever and monthly NO 2 (r = 0.21) and O 3 (r = 0.11). A 10 μg/m 3 increase of NO 2 and O 3 was significantly associated with scarlet fever, with a cumulative RR of 1.06 (95% CI: 1.02–1.10) and 1.04 (95% CI: 1.01–1.07), respectively, at a lag of 0 to 15 months. In conclusion, long-term exposure to ambient NO 2 and O 3 may be associated with an increased risk of scarlet fever incidence, but direct causality is not established. The reason for a re-emergence of scarlet fever in China remains unclear. Here the authors show that the number of scarlet fever cases surged in 2011 peaking in 2018, this correlates with an increase in NO 2 and O 3 but does not necessarily imply causation.

Earthquakes as a natural hazard have caused substantial economic losses and human life loss in many countries. Taiwan, which is located on the western Circum-Pacific seismic belt, encountered this problem in the form of the Meishan, Hsinchu-Taichung, and Chi-Chi earthquakes a few years ago. In this paper, the researchers propose a novel robot-event integrated system capable of doing the automated inspection and emergency response due to a significant earthquake. As the household’s earthquake warning receiving device picks up an alert, its built-in wireless communications system will send a signal to the robot. The robot will then commence the inspection of the indoor area via real-time image recognition and tracking. Upon detecting fallen people, it will approach them, regulating their movements via the robot operating system monitoring interface. The robot is designed to operate in a house that remains standing with acceptable damage in which the furniture might be falling and injure the occupants after an earthquake hit. The indoor experiment was conducted to verify the robot system and operation with a designed condition such as fallen and non-fallen people as the detected object. The precision of the robot arm was tested in the case of the delivery of supplies to the fallen people while waiting for the rescuers to arrive. The tests indicated that the proposed smart robot has prospective implementation in real-world applications with more research and development. The smart robot integrated with an earthquake early warning system is a promising approach to the temporary care of people affected by earthquakes.

Atomic-scale spin entity in a two-dimensional topological insulator lays the foundation to manufacture magnetic topological materials with single atomic thickness. Here, we have successfully fabricated Fe monomer, dimer and trimer doped in the monolayer stanene/Cu(111) through a low-temperature growth and systematically investigated Kondo effect by combining scanning tunneling microscopy/spectroscopy (STM/STS) with density functional theory (DFT) and numerical renormalization group (NRG) method. Given high spatial and energy resolution, tunneling conductance (dI/dU) spectra have resolved zero-bias Kondo resonance and resultant magnetic-field-dependent Zeeman splitting, yielding an effective spin Seff = 3/2 with an easy-plane magnetic anisotropy on the self-assembled Fe atomic dopants. Reduced Kondo temperature along with attenuated Kondo intensity from Fe monomer to trimer have been further identified as a manifestation of Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between Sn-separated Fe atoms. Such magnetic Fe atom assembly in turn constitutes important cornerstones for tailoring topological band structures and developing magnetic phase transition in the single-atom-layer stanene.

Monitoring the performance of reinforced concrete structures, particularly in terms of strength reduction, presents significant challenges due to the practical limitations of traditional detection methods. This study introduces an innovative framework that incorporates a non-destructive technique using electromagnetic waves (EM-waves) transmitted via Radio Frequency Identification (RFID) technology, combined with two-dimensional (2-D) Fourier transform, fractal dimension analysis, and deep learning techniques to predict reductions in structural strength. Experiments were conducted on three reinforced concrete beam (RCB) specimens exhibiting various levels of reinforcement corrosion. From these, a dataset of 1,800 EMwave images was generated and classified into “normal” and “reduced strength” categories. These categories were used to train and validate a Convolutional Neural Network (CNN), which demonstrated robust performance, achieving a high accuracy of 0.91 and an F1-score of 0.93 in classifying instances of reduced structural strength. This approach offers a promising solution for detecting strength reduction in reinforced concrete infrastructures, enhancing both safety and maintenance efficiency. First published online 5 November 2024

Smoking, sex, air pollution, lifestyle, and diet may act independently or in concert with each other to contribute to the different outcomes of lung cancer (LC). This study aims to explore their associations with the carcinogenesis of LC, which will be useful for formulating further preventive strategies. This retrospective, longitudinal follow-up cohort study was carried out by connecting to the MJ Health Database, Taiwan Cancer Registry database, and Taiwan cause of death database from 2000 to 2015. The studied subjects were persons attending the health check-ups, distributed throughout the main island of Taiwan. Cox proportional hazards regression models were used to investigate the risk factors associated with LC development and mortality after stratifying by smoking status, with a special emphasis on ambient two-year average PM2.5 exposure, using a satellite-based spatiotemporal model at a resolution of 1 km2, and on dietary habit including consumption of fruits and vegetables. After a median follow-up of 12.3 years, 736 people developed LC, and 401 people died of LC-related causes. For never smokers, the risk of developing LC (aHR: 1.32, 95%CI: 1.12–1.56) and dying from LC-related causes (aHR: 1.28, 95%CI: 1.01–1.63) rises significantly with every 10 μg/m3 increment of PM2.5 exposure, but not for ever smokers. Daily consumption of more than two servings of vegetables and fruits is associated with lowering LC risk in ever smokers (aHR: 0.68, 95%CI: 0.47–0.97), and preventing PM2.5 exposure is associated with lowering LC risk for never smokers.

This study developed an integrated global-local approach for locating damage on building structures. A damage detection approach with a novel embedded frequency response function damage index (NEFDI) was proposed and embedded in the Imote2.NET-based wireless structural health monitoring (SHM) system to locate global damage. Local damage is then identified using an electromechanical impedance- (EMI-) based damage detection method. The electromechanical impedance was measured using a single-chip impedance measurement device which has the advantages of small size, low cost, and portability. The feasibility of the proposed damage detection scheme was studied with reference to a numerical example of a six-storey shear plane frame structure and a small-scale experimental steel frame. Numerical and experimental analysis using the integrated global-local SHM approach reveals that, after NEFDI indicates the approximate location of a damaged area, the EMI-based damage detection approach can then identify the detailed damage location in the structure of the building.

This work presents a digital image processing approach with a unique hive triangle pattern by integrating subpixel analysis for noncontact measurement of structural dynamic response data. Feasibility of proposed approach is demonstrated based on numerical simulation of a photography experiment. According to those results, the measured time-history displacement of simulated image correlates well with the numerical solution. A small three-story frame is then mounted on a small shaker table, and a linear variation differential transformation (LVDT) is set on the second floor. Experimental results indicate that the relative error between data from LVDT and analyzed data from digital image correlation is below 0.007%, 0.0205 in terms of frequency and displacement, respectively. Additionally, the appropriate image block affects the estimation accuracy of the measurement system. Importantly, the proposed approach for evaluating pattern center and size is highly promising for use in assigning the adaptive block for a digital image correlation method.

Water that penetrates through cracks in concrete can corrode steel bars. There is a need for reliable and practical seepage sensing technology to prevent failure and determine the necessary maintenance for a concrete structure. Therefore, we propose a modified plasma-assisted electrochemical exfoliated graphite (MPGE) nanosheet smart tag. We conducted a comparative study of standard and modified RFID smart tags with sensor technology for seepage detection in concrete. The performance of both smart tags was tested and verified for seepage sensing in concrete, characterized by sensor code and frequency values. Seepage was simulated by cracking the concrete samples, immersing them for a designated time, and repeating the immersing phase with increasing durations. The test showed that the modified smart tag with 3% MPGE and an additional crosslinking agent provided the best sensitivity compared with the other nanosheet compositions. The presence of 3D segregated structures on the smart tag’s sensing area successfully enhanced the sensitivity performance of seepage detection in concrete structures and is expected to benefit structural health monitoring as a novel non-destructive test method.