Multi-indicator assessment of water environment in government environmental auditing

This study focused on a multi-indicator assessment methodology for governmental environmental auditing of water protection programs. The environmental status of Wuli Lake in China was assessed using the global indicators （driver-status-response） developed by the Commission on Sustainable Development, and four additional indicators proposed by the author： water quality, pollution load, aquatic ecosystem status, and lake sediment deposition. Various hydrological, chemical, biological and environmental parameters were used to estimate the values of the indicators for assessment of environmental status of the lake based on time series data sets for twenty years. The indicators proposed can be customized to meeting the needs for particular assessment of water protection programs. This method can be used to evaluate the performance of national environmental protection programs and provide technical support for environmental auditors.

The Four Dimensional Connotation Definition of Scientific Understanding of the Ocean and Human Practical Achievements

By analyzing the sustainable development process of strengthening marine environmental protection and global reporting and assessment of marine environmental conditions since the Human Environment Conference, this paper summarizes the scientific connotation of “scientific understanding of the ocean” reflected in the United Nations Global Marine Environmental Assessment Report, proposes the etymological definition and specific coverage, representative global and regional practical experience of scientific understanding of the ocean, and further analyzes and defines the human activities and cognitive evolution process of “scientific understanding of the ocean”. It marks the leap in human cognition in four dimensions: observation and evaluation, intervention and regulation, disciplinary knowledge system, and supporting guarantee system. It condenses the connotation definitions and human practical achievements of each dimension, and puts forward countermeasures and suggestions to strengthen marine environmental protection and sustainable development.

Life prediction of ZPW-2000A track circuit equipment based on SVDD and gray prediction

Evaluation of the health state and prediction of the remaining life of the track circuit are important for the safe operation of the equipment of railway signal system.Based on support vector data description(SVDD)and gray prediction,this paper illustrates a method of life prediction for ZPW-2000A track circuit,which combines entropy weight method,SVDD,Mahalanobis distance and negative conversion function to set up a health state assessment model.The model transforms multiple factors affecting the health state into a health index named H to reflect the health state of the equipment.According to H,the life prediction model of ZPW-2000A track circuit equipment is established by means of gray prediction so as to predict the trend of health state of the equipment.The certification of the example shows that the method can visually reflect the health state and effectively predict the remaining life of the equipment.It also provides a theoretical basis to further improve the maintenance and management for ZPW-2000A track circuit.

Vision-based multi-level synthetical evaluation of seismic damage for RC structural components: a multi-task learning approach

Recent studies for computer vision and deep learning-based,post-earthquake inspections on RC structures mainly perform well for specific tasks,while the trained models must be fine-tuned and re-trained when facing new tasks and datasets,which is inevitably time-consuming.This study proposes a multi-task learning approach that simultaneously accomplishes the semantic segmentation of seven-type structural components,three-type seismic damage,and four-type deterioration states.The proposed method contains a CNN-based encoder-decoder backbone subnetwork with skip-connection modules and a multi-head,task-specific recognition subnetwork.The backbone subnetwork is designed to extract multi-level features of post-earthquake RC structures.The multi-head,task-specific recognition subnetwork consists of three individual self-attention pipelines,each of which utilizes extracted multi-level features from the backbone network as a mutual guidance for the individual segmentation task.A synthetical loss function is designed with real-time adaptive coefficients to balance multi-task losses and focus on the most unstably fluctuating one.Ablation experiments and comparative studies are further conducted to demonstrate their effectiveness and necessity.The results show that the proposed method can simultaneously recognize different structural components,seismic damage,and deterioration states,and that the overall performance of the three-task learning models gains general improvement when compared to all single-task and dual-task models.

A Safe and Reliable Routing Mechanism of LEO Satellite Based on SDN

Satellite networks have high requirements for security and data processing speed.In order to improve the reliability of the network,software-defined network(SDN)technology is introduced and a central controller is set in the network.Due to the characteristics of global perspective,control data separation,and centralized control of SDN,the idea of SDN is introduced to the design of the satellite network model.As a result,satellite nodes are only responsible for data transmission,while the maintenance of the links and the calculation of routes are implemented by the controller.For the massive LEO satellite network based on SDN,a state evaluation decision routing mechanism is proposed.The designed mechanism monitors the status of the entire network effectively and reduces the on-board load on the satellite network.The best routing decision is made under the comprehensive consideration of the current and historical status of each inter-satellite link between Low Earth Orbit(LEO)satellite network nodes.The calculation and storage requirements are controlled within a reasonable range.Based on the curve parameter transmission fuzzy encryption algorithm,a safe and reliable condition assessment decision routing mechanism(CADRM)is designed.It ensures that the personal information of the LEO satellite network can be transmitted safely and effectively.The experimental simulation results show the improvement of network throughput,the reduction of packet loss rate and the enhancing of network reliability.

Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

Application of Big Data Technology in Evaluation of Operating Status of High-pressure Hydrogenation Heat Exchanger

The high-pressure hydrogenation heat exchanger is an impoltmlt equipment of the refinery, but it is exposed tothe problem of leakage caused by ammonium salt corrosion. Therefore, it is very important to evaluate the operating statusof flae hydrogenation heat exchanger. To improve flae method for evaluating the operating status of hydrogenation heat ex-chmagers by using flae traditional method, flais paper proposes a new method for evaluating the operation of hydrogenationheat exchangers based on big data. To address flae noisy data common in flae industry, this paper proposes an automatednoisy interval detection algorithm. To deal with flae problem that the sensor parameters have voluminous and mtrelateddimensions, flais paper proposes a key parameter detection algorithm based on flae Pearson correlation coefficient. Finally,this paper presents a system-based health scoring algorithm based on PCA （Principal Component Analysis） to assist site op-erators in assessing the healfla of hydrogenation heat exchangers. The evaluation of flae operating status of flae hydrorefiningheat exchange device based on big data technology will help the operators to more accurately grasp the status of flae indus-trial system mad have positive guiding significance for the early warning offlae failure.

Differences in cognitive profiles between traumatic brain injury and stroke： A comparison of the Montreal Cognitive Assessment and Mini-Mental State Examination

Purpose： To investigate the profiles of cognitive impairment through Montreal Cognitive Assessment （MoCA） and Mini-Mental State Examination （MMSE） in patients with chronic traumatic brain injury （TBI） or stroke and to evaluate the sensitivity of the two scales in patients with TBI. Methods： In this cohort study, a total of 230 patients were evaluated, including TBI group （n = 103） and stroke group （n - 127）. The cognitive functions of two groups were evaluated by designated specialists using MoCA （Beijing version） and MMSE （Chinese version）. Results： Compared with the patients with stroke, the patients with TBI received significantly lower score in orientation subtest and recall subtest in both tests. MoCA abnormal rates in the TBI group and stroke group were 94.17% and 86.61% respectively, while MMSE abnormal rates were 69.90% and 57.48%, respectively. In the TBI group, 87.10% patients with normal MMSE score had abnormal MoCA score and in the stroke group, about 70.37% patients with normal MMSE score had abnormal MoCA score. The diagnostic consistency of two scales in the TBI group and the stroke group were 72% and 69%, re.spectively. Conclusion： In our rehabilitation center, patients with TBI may have mare extensive and severe cognitive impairments than patients with stroke, prominently in orientation and recall domain. In screening post- TBI cognitive impairment, MoCA tends to be more sensitive than MIV[SE.

Feasibility of Assessing Health State by Detecting Redox State of Human Body Based on Chinese Medicine Constitution

This article discussed the feasibility of assessing health state by detecting redox state of human body. Firstly, the balance of redox state is the basis of homeostasis, and the balance ability of redox can reflect health state of human body. Secondly, the redox state of human body is a sensitive index of multiple risk factors of health such as age, external environment and psychological factors. It participates in the occurrence and development of multiple diseases involving metabolic diseases and nervous system diseases, and can serve as a cut-in point for treatment of these diseases. Detecting the redox state of high risk people is significantly important for early detection and treatment of disease. The blood plasma and urine could be selected to detect, which is convenient. It is pointed that the indexes not only involve oxidation product and antioxidant enzyme but also redox couple. Chinese medicine constitution reflects the state of body itself and the ability of adapting to external environment, which is consistent with the connotation of health. It is found that there are nine basic types of constitution in Chinese population, which provides a theoretical basis of health preservation, preventive treatment of disease and personalized treatment. With the combination of redox state detection and the Chinese medicine constitution theory, the heath state can be systemically assessed by conducting large-scale epidemiological survey with classified detection on redox state of human body.