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Summary 1. We used high‐frequency in situ dissolved oxygen measurements to investigate the seasonal variability and factors regulating metabolism in a subtropical alpine lake in Taiwan between May 2004 and October 2005, specifically exploring how the typhoon season (from June or July to October) affects lake metabolism. 2. Gross primary production (GPP) and ecosystem respiration (R) both peaked in early summer and mid‐autumn but dropped during the typhoon season and winter. Yuan‐Yang Lake is a net heterotrophic ecosystem (annual mean net ecosystem production −39.6 μmole O2 m−3). 3. Compared to the summer peaks, seasonal averages of GPP and R decreased by approximately 50% and 25%, respectively, during the typhoon season. Ecosystem respiration was more resistant to external disturbances than GPP and showed strong daily variation during typhoon seasons. 4. Changes in the quality and quantity of dissolved organic carbon controlled the temporal dynamics and metabolic regulation. External disturbances (typhoons) caused increased allochthony, increasing DOC and water colour and influencing lake metabolism. 5. Seasonal winter mixing and typhoon‐induced water mixing in summer and autumn play a key role in determining the extent to which the lake is a seasonal carbon sink or source to the atmosphere.

We present a research tool that supports marine ecologists' research by allowing analysis of long-term and continuous fish monitoring video content. The analysis can be used for instance to discover ecological phenomena such as changes in fish abundance and species composition over time and area. Two characteristics set our system apart from traditional ecological data collecting and processing methods. First, the continuous video recording results in enormous data volumes of monitoring data. Currently around a year of video recordings (containing over the 4 million fish observations) have been processed. Second, different from traditional manual recording and analysing the ecological data, the whole recording, analysing and presentation of results is automated in this system. On one hand, it saves the effort of manually examining every video, which is infeasible. On the other hand, no automatic video analysis method is perfect, so the user interface provides marine ecologists with multiple options to verify the data. Marine ecologists can examine the underlying videos, check results of automatic video analysis at different certainty levels computed by our system, and compare results generated by multiple versions of automatic video analysis software to verify the data in our system. This research tool enables marine ecologists for the first time to analyse long-term and continuous underwater video records.

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.

Lattice coding over a Gaussian wiretap channel, where an eavesdropper listens to transmissions between a transmitter and a legitimate receiver, is considered. A new lattice invariant called the secrecy gain [1] is used as a code design criterion for wiretap lattice codes since it was shown to characterize the confusion that a chosen lattice can cause at the eavesdropper: the higher the secrecy gain of the lattice, the more confusion. In this paper, a formula for the secrecy gain of unimodular lattices is derived. Secrecy gains of extremal odd unimodular lattices as well as unimodular lattices in dimension n, 16 ≤ n ≤ 23 are computed, covering the 4 extremal odd unimodular lattices and all the 111 non-extremal unimodular lattices (both odd and even) providing thus a classification of the best wiretap lattice codes coming from unimodular lattices in dimension n, 8 < n ≤ 23. Finally, to permit lattice encoding via Construction A, the corresponding error correction codes are determined.

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.

Abstract:The application of a stationary ultra-capacitor energy storage system (ESS) in urban rail transit allows for the recuperation of vehicle braking energy for increasing energy savings as well as for a better vehicle voltage profile. This paper aims to obtain the best energy savings and voltage profile by optimizing the location and size of ultra-capacitors. This paper firstly raises the optimization objective functions from the perspectives of energy savings, regenerative braking cancellation and installation cost, respectively. Then, proper mathematical models of the DC (direct current) traction power supply system are established to simulate the electrical load-flow of the traction supply network, and the optimization objections are evaluated in the example of a Chinese metro line. Ultimately, a methodology for optimal ultra-capacitor energy storage system locating and sizing is put forward based on the improved genetic algorithm. The optimized result shows that certain preferable and compromised schemes of ESSs' location and size can be obtained, acting as a compromise between satisfying better energy savings, voltage profile and lower installation cost.