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Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rainwater. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan Island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima in 2019. The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increase approximately 17–18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions.

Abstract Despite a variety of studies on the tropical cyclone (TC) response to climate change, few of them have examined the projected damages of future TCs. Here we quantify the impact of anthropogenic warming on TC-induced damages in the late twenty-first century along the coasts of Southeast China based on convection-permitting TC simulations and machine-learning-based damage models. We found that if the area’s 10 super typhoons between 2013 and 2019 were to occur at the end of the century under the high emissions RCP8.5 scenario, they would have on average a 12% ± 4% increase in landfall intensity, 25% ± 23% increase in precipitation, and 128% ± 70% increase in economic losses, compared to historical simulations. We also found a significant increase in the full risk profile. The estimated typhoon loss with a 50-year return period for Zhejiang, Fujian, Guangdong, and Hainan (four most typhoon-prone provinces among the seven provinces in the region) would increase by 71%, 170%, 20%, and 85%, respectively, towards the end of the century even under the lower emissions RCP4.5 pathway. Our findings imply the need to design effective local hazard mitigation measures to reduce future typhoon risks.

With the rapid development of the construction industry, tower cranes are increasingly used in coastal engineering. However, due to the complexity of their operating environment, tower cranes are vulnerable to typhoons, thunderstorms, and other extreme natural disasters. Therefore, it is becoming increasingly important to carry out safety warnings for the tower crane structure under the action of strong winds. In this paper, a real-time monitoring system for tower responses based on the Internet of things (IoT), which realizes long-term monitoring of the whole process of tower crane operation, was built. Based on the long-term monitoring data and the machine learning algorithm, two tower response prediction models were established. During the transit of super typhoon In-fa, the maximum displacement of the tower structure was predicted in advance, based on the measured wind speed data at the site, which is in good agreement with the displacement data monitored by the IoT. The results show that under strong winds, the non-working tower has a response lag, resulting in the fact that its maximum displacement does not correspond to the maximum wind speed moment at the site. This is mainly due to the weathercock effect of the tower in the non-working condition. The prediction model proposed in this paper can provide timely and effective safety warnings for the tower structure. It also can provide useful engineering references and scientific structural safety warning suggestions for the same type of tower cranes that do not have IoT monitoring systems installed.

Abstract With the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

Introduction/aim: The supraphysiologic chloride concentration of normal saline may contribute to acute kidney injury (AKI). Balanced crystalloids can decrease chloride concentration and AKI in critically ill patients. We aim to test the hypothesis that, in patients with predicted severe acute pancreatitis (pSAP), compared with saline, fluid therapy with balanced crystalloids will decrease plasma chloride concentration.Methods/Design: This is a multicenter, stepped-wedge, cluster-randomized, controlled trial. All eligible patients presenting to the 11 participating sites across China during the study period will be recruited. All sites will use saline for the first month and sequentially change to balanced crystalloids at the pre-determined and randomly allocated time point. The primary endpoint is the plasma chloride concentration on day 3 of enrollment. Secondary endpoints will include major adverse kidney events on hospital discharge or day 30 (MAKE 30) and free and alive days to day 30 for intensive care admission, invasive ventilation, vasopressors, and renal replacement therapy. Additional endpoints include daily serum chloride and sequential organ failure assessment (SOFA) score over the first seven days of enrollment.Discussion: This study will provide data to define the impact of normal saline vs. balanced crystalloids on plasma chloride concentration and clinical outcomes in pSAP patients. It will also provide the necessary data to power future large-scale randomized trials relating to fluid therapy.Ethics and Dissemination: This study was approved by the ethics committee of Jinling Hospital, Nanjing University (2020NZKY-015-01) and all the participating sites. The results of this trial will be disseminated in peer-reviewed journals and at scientific conferences.Trial registration: The trial has been registered at the Chinese Clinical Trials Registry (ChiCTR2100044432).

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.

The basic wind pressure or the reference wind pressure for structural design varies greatly across complex terrain. Since only a few meteorological stations can provide adequate extreme wind speed records, it is very difficult to appropriately determine the basic wind pressure for a specific site without a long history of meteorological records. To solve this problem, a mesoscale CFD model was developed and optimized based on geographic information data for Taizhou and suitable turbulence models were selected for CFD simulation. The mean extreme wind speed and the corresponding direction at five main weather stations with long observation histories in Taizhou were used as the verification conditions to perform the CFD simulation of the extreme wind field. The maximum wind speeds of the rural areas, cities, and streets of Taizhou were obtained from the results of the mesoscale CFD simulations. Then, the 50-year return period reference wind pressures were calculated and could be used for the wind-resistant structural design of buildings for sites without a long history of meteorological records. The reliability of the results was verified by comparing the simulation results with the observation data at five main stations with a long history.

Although widely used in various fields due to its powerful capability of signal processing, empirical mode decomposition has to decompose signals separately, which limits its application for multivariate data such as the structural monitoring data recorded by multiple sensors. In order to avoid this shortcoming, a multivariate extension of empirical mode decomposition is proposed to deal with the multidimensional signals synchronously by employing a real-valued projection on hyperspheres. This study presents a hybrid modal identification method combining the multivariate empirical mode decomposition with stochastic subspace identification and fast Bayesian FFT methods to more conveniently and accurately identify structural dynamic parameters from multi-sensor vibration measurements. Deployed as a preprocessing tool, the multivariate signals are decomposed into several aligned intrinsic mode functions, which contain only a dominant component in the frequency domain. Then, the modal parameters can be identified by advanced fast Bayesian FFT and stochastic subspace identification directly. The combined method is first validated by a numerical illustration of a frame structure and then is applied in a shaking table test and a full-scale measurement under nonstationary earthquake excitation. Compared with the finite element method, the peak–pick, the half-power bandwidth methods, and Hilbert–Huang transform method, the results show that this hybrid method is more robust and reliable in the modal parameters identification. The main contribution of this paper is to develop a more effective integrated approach for accurate modal identification with the output-only multi-dimensional nonstationary signal.

In services-oriented computing networks, packets in the process of routing to a data center must wait for a sufficient amount of data before service aggregation to reduce the network transmission load. However, packets must be uploaded to the data center as soon as possible to reduce delay. With the exponential growth in the number of IoT connected devices, the wait time for packets is longer at routers due to massive amounts of data, which causes a large queuing delay. If this queuing time can be utilized for service aggregation in a service-oriented computing network, the network performance will be substantially improved. Therefore, a queuing delay utilization scheme for on-path service aggregation (SAQD) is proposed in this paper. This scheme has the following innovations: 1) SAQD fully utilizes the queuing delay of packets for service aggregation, which can effectively reduce the transmission volume and communication overhead. Based on the proposed service aggregation algorithm, packets are divided into forwarding packets and aggregating packets, and the service aggregation of aggregating packets is completed by utilizing the transmission time of forwarding packets to ensure that the transmission volume and communication overhead are effectively reduced without additional latency. 2) SAQD can effectively alleviate the traffic pressure of the data center and balance the workload of routers. By the service aggregation and intranet cache of routers, some requests for the data center can be handled by routers, which reduces the traffic pressure of the data center, especially in the peak period. Compared with conventional schemes, the experimental results demonstrate that SAQD reduces the workload of the data center by 55.8%-66.26% and provides users with a better quality of experience by reducing the request response delay by 31.33%~51.41%.

Cyber-physical-social (CPS) systems integrate Big Data Collectors (BDCs), Service Organizers (SOs) and users to build a unified data-centric computing framework. In CPS systems, BDCs leverage a vast variety of sensing devices to collect cyber-physical-social data, and report these data to SOs to orchestrate various services provided to users, thus offering a great potential for solving complex network tasks that are far beyond the capabilities of existing networks. However, due to the lack of an economic model to describe such complex data interactions, their applications are limited. So, a game-based economic model is proposed in this paper to make smart price decisions in CPS systems. Specifically, it has the following innovations: (a) The economic model gives a dynamic game income matrix which can accurately describe the revenue changes of BDCs in the game, so as to help BDCs select appropriate game parameters and strategies, and make BDCs competitive in the game. (b) The economic model can help SOs to make optimized data purchase price and service selling price based on data collection cost and competitor price analysis, so that SOs can have a better Quality of Service (QoS) and users attraction, and maximize the profit. Experimental results demonstrate that the proposed model can help BDCs and SOs find the most suitable game strategy and price adjustment principle, which has great significance in applications.

The purpose of this paper was to investigate the influence of long-term corrosion on the deterioration of wind resistance of a steel transmission tower during its service life. An analytical model for predicting the long-term corrosion depth of carbon steel was established, and the corrosion depth of carbon steel in the Ningbo area was predicted based on the local atmospheric environment data. With the help of typhoon full-track simulation and wind field simulation technology, a joint probability distribution model of multidirectional extreme wind speeds was constructed using the t-Copula function to determine the typhoon climate of the transmission tower site. Finite element models of the ZM4 cathead transmission tower under 30/60/90 corrosion years were then established, respectively, according to the predicted corrosion depth of carbon steel in Ningbo. Three damage modes, i.e., minor damage, moderate damage and severe damage, corresponding to the transmission tower under wind loads, were defined, and pushover analyses were used to determine the limit values of each damage mode so as to obtain the typhoon-induced fragility curves of the transmission tower within 30/60/90 corrosion years. The results show that the increase in corrosion age leads to a deterioration in the nominal mechanical properties of the transmission tower components, making the damage probability to the transmission tower increase. Under the typhoon wind loads of a 50-year return period in the most unfavorable wind direction in Ningbo, the probability of moderate damage of the tower is within 10% and the probability of minor damage is controlled between 10% and 40%.

The purpose of this paper was to provide a new approach to achieve quantitative and accurate typhoon loss assessment of disaster-bearing bodies at township-level resolution. Based on the policy insurance data of Ningbo city, this paper took rural housing as the target disaster-bearing body and analyzed the aggregated data of disaster losses such as payout amount and insured loss rate of rural housing in Ningbo area under the influence of 25 typhoons during 2014–2019. The intensity data of disaster-causing factors such as the maximum average wind speed in Ningbo area under the influence of 25 typhoons were simulated and generated with the wind field engineering model, and a township-level high-resolution rural housing typhoon loss assessment model was established using a RBF artificial neural network. It was found that the insured loss rate of rural housing under wind damage was higher in the townships of southern Ningbo than in the townships of northern Ningbo, and the townships with larger insured loss rates were concentrated in mountainous or coastal areas that are prone to secondary disasters under the attack of the typhoon’s peripheral spiral wind and rain belt. The RBF neural network can effectively establish a typhoon loss assessment model from the causal factors to the losses of the disaster-bearing bodies, and the RBF neural network has a faster convergence speed and a smaller overall prediction error than the commonly used BP neural network.

With the emergence of Internet of Things, the number of connected devices has been dramatically increasing, causing severe spectrum shortage problem. To fully explore the spectrum resources, big data, and cloud computing can be employed by cognitive radio networks, to make efficient use of various sensing results from different sensing sources. However, the massive growth of sensing data brings tremendous load pressure on the data center, resulting in long service response time and poor Quality of Experience. Edge computing and fog computing deal with these issues by placing computation resources at the network edge. However, compared with the data center, the capabilities at edge servers are limited. Therefore, a services routing-based caching scheme (SRCS) is proposed, which can greatly lighten the load on the data center and maintain the advantages of global intelligent computing of traditional cloud computing. Specifically, SRCS first introduces the concept of transmitting service flow. At the edge layer, data are converted to service flow by network hardware and software, thus achieving the network architecture centered on service computing. Then, SRCS proposes a service routing based on service similarity, transmits similar services through the same path, and service data are fused on the path to minimize transmission load. Moreover, SRCS caches services in content routers (CRs). When the service is requested again, CRs are used as service providers to return data, thus achieving the nearest access to the content. Both theoretical analysis and experiment results demonstrate that comparing existing schemes, SRCS improves service response time by 13.67%–51.15%, reduces transmitting data amount by 23.62%–30.3%, and makes energy consumption more balanced.

Quickly and efficiently transmitting data to sink via intelligent routing is an important issue in wireless sensor networks. In previous scenarios, there has existed the phenomenon of “energy hole,”which results in difficulties in synchronous optimization of energy and delay. Thus, a smart High-Speed Backbone Path (HSBP) construction approach is proposed in this paper. In the HSBP approach, several High-speed Backbone Paths (HBPs) are established at different locations of the network, and the duty cycles of nodes on the HBPs are increased to 1; therefore, the data are forwarded by HBPs without the existence of sleeping delay, which greatly reduces transmission latency. Furthermore, the HBPs are built in regions with adequate residual energy, and they are switched periodically; thus, more nodes can be utilized to equalize the energy consumption. A comprehensive performance analysis demonstrates that the HSBP approach has obvious advantages in improving network performance compared with previous studies; it reduces transmission delay by 48.10% and improves energy utilization by 38.21% while guaranteeing the same network lifetime.

Northern leaf blight (NLB) is a serious disease in maize which leads to significant yield losses. Automatic and accurate methods of quantifying disease are crucial for disease identification and quantitative assessment of severity. Leaf images collected with natural backgrounds pose a great challenge to the segmentation of disease lesions. To address these problems, we propose an image segmentation method based on YOLACT++ with an attention module for segmenting disease lesions of maize leaves in natural conditions in order to improve the accuracy and real-time ability of lesion segmentation. The attention module is equipped on the output of the ResNet-101 backbone and the output of the FPN. The experimental results demonstrate that the proposed method improves segmentation accuracy compared with the state-of-the-art disease lesion-segmentation methods. The proposed method achieved 98.71% maize leaf lesion segmentation precision, a comprehensive evaluation index of 98.36%, and a mean Intersection over Union of 84.91%; the average processing time of a single image was about 31.5 ms. The results show that the proposed method allows for the automatic and accurate quantitative assessment of crop disease severity in natural conditions.

An omnidirectional cantilever-type eddy current tuned mass damper (ECTMD) for lattice towers is introduced to suppress bidirectional vibration of lattice towers, in a form of a cantilever beam with a tip magnet mass. The damping of the ECTMD can be easily changed by tuning the amount of the current. A typhoon-fashion wind environment is simulated for the wind tunnel test. Test results show that there exists an optimal damping of the ECTMD along with an optimal frequency ratio. The scaled aeroelastic model is tested under various wind conditions, and a good effectiveness of ECTMD is observed. The wind directions, perpendicular and parallel to the cross-arm, are the most critical for the design of ECTMD, as the vibration mitigation in either of these two cases is relatively weaker. Finally, a simplified model is established for theoretical analysis in the frequency domain, whereby the variance responses of the tower with and without ECTMD are computed. The numerical results agree well with the experimental results, which corroborates the feasibility of using the proposed omnidirectional cantilever-type ECTMD in suppressing the vibrations of the tower in both along-wind and across-wind directions.

Traditional flatness detection method with 2-m ruler and feeler gauge can only manually measure to obtain data at sparse locations. Terrestrial Laser Scanning (TLS) has been widely used in the detection task of small-scale components. To overcome the time-consuming and laborious problem of traditional method and extend the application of TLS-based method, an efficient, full-coverage and intelligent TLS-based flatness detection method applicable to large-scale floors is presented in this paper to help with the acceptance of emery floor. An algorithm is designed to find the optimal position of ruler above the ground profile and measure the deviation between ruler and ground. The results of two comparison experiments demonstrates that the TLS-based method proposed in this paper has a high detection accuracy. In the detection task of a super-flat emery floor, floor plan labeled with pass rates can be easily obtained. Color maps marked with unqualified areas can be used as a guide for concrete polishing. By comparing row detection and column detection results, the pass rate is significantly directional in some conditions. [ABSTRACT FROM AUTHOR]