With the increasing climatic extremes, the frequency and severity of urban flood events have intensified worldwide. In this study, image-based automated monitoring of flood formation and analyses of water level fluctuation were proposed as value-added intelligent sensing applications to turn a passive monitoring camera into a visual sensor. Combined with the proposed visual sensing method, traditional hydrological monitoring cameras have the ability to sense and analyze the local situation of flood events. This can solve the current problem that image-based flood monitoring heavily relies on continuous manned monitoring. Conventional sensing networks can only offer one-dimensional physical parameters measured by gauge sensors, whereas visual sensors can acquire dynamic image information of monitored sites and provide disaster prevention agencies with actual field information for decision-making to relieve flood hazards. The visual sensing method established in this study provides spatiotemporal information that can be used for automated remote analysis for monitoring urban floods. This paper focuses on the determination of flood formation based on image-processing techniques. The experimental results suggest that the visual sensing approach may be a reliable way for determining the water fluctuation and measuring its elevation and flood intrusion with respect to real-world coordinates. The performance of the proposed method has been confirmed; it has the capability to monitor and analyze the flood status, and therefore, it can serve as an active flood warning system.

Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective.

Prolonged exposure to high-stress environments can lead to mental illnesses such as anxiety disorders, depression, and posttraumatic stress disorder. Here, a wearable device utilizing photoplethysmography (PPG) technology is developed to noninvasively measure physiological signals and analyze heart rate variability (HRV) parameters. Traditional normative HRV databases typically do not account for responses induced by specific stressors such as cognitive tasks. Therefore, machine learning is used to build a more dynamic stress assessment model. Machine learning can capture complex nonlinear relationships among HRV parameters during stress-inducing tasks, adapts to individual stress response variations, and provides real-time stress level predictions. Furthermore, machine learning models can integrate temporal patterns in HRV data to achieve nuanced stress level assessment. This study examines the feasibility of PPG signals and validates the developed stress model. The RR intervals derived from PPG signals were highly positively correlated with those from electrocardiography signals (correlation coefficient = 0.9920, R-squared = 0.9837); this confirms the usability of PPG signals for HRV analysis. The stress model is constructed via the open-source Swell dataset. In the experiments, participants complete the Depression Anxiety Stress Scales-21-Chinese (DASS-21-C) questionnaire to quantify levels of depression, anxiety, and stress over a week. Baseline and stress-state PPG data are collected, converted into HRV values, and input into the model for stress quantification. The Stroop test is used to elicit stress responses. After the experiment, the DASS-21-C stress scores were compared with the model's baseline, stress state, and combined scores. The highest correlation was observed between the model's baseline score and the DASS-21-C stress score (correlation coefficient = 0.92, R-squared = 0.8457), supporting the model's psychological significance in quantifying everyday stress. HRV parameter changes across experimental phases are discussed as well as sex differences in stress responses. In the future, this device may be applied in clinical scenarios for further validation and could be integrated with additional physiological indicators for broader application in daily health management and stress warning systems.

¹⁸F-9-Fluoropropyl-(+)-dihydrotetrabenazine [¹⁸F-FP-(+)-DTBZ] positron emission tomography (PET) has been shown to detect dopaminergic neuron loss associated with Parkinson’s disease (PD) in human and neurotoxin-induced animal models. A polyphenol compound, magnolol, was recently proposed as having a potentially restorative effect in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)- or 6-hydroxydopamine-treated animal models. In this study, ¹⁸F-FP-(+)-DTBZ PET was used to determine the therapeutic efficacy of magnolol in an MPTP–PD mouse model that was prepared by giving an intraperitoneally (i.p.) daily dose of 25 mg/kg MPTP to male C57BL/6 mice for 5 consecutive days. Twenty-minute static ¹⁸F-FP-(+)-DTBZ PET scans were performed before MPTP treatment and 5 days after the termination of MPTP treatment to set up the baseline control. Half of the MPTP-treated mice then received a daily dose of magnolol (10 mg/kg dissolved in corn oil, i.p.) for 6 days. ¹⁸F-FP-(+)-DTBZ PET imaging was performed the day after the final treatment. All ¹⁸F-FP-(+)-DTBZ PET images were analysed and the specific uptake ratio (SUr) was calculated. Ex vivo autoradiography (ARG) and corresponding immunohistochemistry (IHC) studies were conducted to confirm the distribution of dopaminergic terminals in the striatum. The striatal SUr ratios of ¹⁸F-FP-(+)-DTBZ PET images for the Sham, the MPTP, and the MPTP + Magnolol-treated groups were 1.25 ± 0.05, 0.75 ± 0.06, and 1.00 ± 0.11, respectively (n = 4 for each group). The ex vivo ¹⁸F-FP-(+)-DTBZ ARG and IHC results correlated favourably with the PET imaging results. ¹⁸F-FP-(+)-DTBZ PET imaging suggested that magnolol post-treatment may reverse the neuronal damage in the MPTP-lesioned PD mice. In vivo imaging of the striatal vesicular monoamine transporter type 2 (VMAT2) distribution using ¹⁸F-FP-(+)-DTBZ animal PET is a useful method to evaluate the efficacy of therapeutic drugs i.e., magnolol, for the management of PD.