Effects of lag time in forest restoration and management

The increased speed of global change and associated high severity disturbances,in conjunction with the increasing suite of societal expectations on forests,suggest that the timeliness of interventions to encourage the adaptive capacity of ecosystems and to reduce negative impacts in regards to provision of ecosystem services is increasingly relevant.To address this issue,we expand the concept of lag time as used in ecological discussions into a forest management context.In this context,lag times have earlier starting and later ending points and can be separated into different components.These components include the delay till detection,decision making,and implementation,followed by ecological lag time and the time till ecosystem services are provided at acceptable levels.The first three components are influenced by the availability of information,the lack of which can extend lag times.Also,the lengths of components are not simply additive but they interact.For example,treatment preparation due to a quicker detection can lead to shorter decision and implementation lag times.We highlight the benefits of addressing the various components of lag time in forestry operations.Especially when considering adaptive capacity in times of global change,our analysis suggests that all aspects of the forestry sector are challenged to consider how to optimize lag times.Last,we propose that such issues need to be considered with any management action and are especially relevant in discussions whether the best strategy after disturbances or in the light of global change is to adopt a passive approach and let natural ecosystem processes play out on their own or whether active management is better suited to ensure a more rapid and fitting ecosystem response to facilitate the continued provision of ecosystem services.

Research on Decision Support System (DSS) of Atmospheric Environment Management in Anhui Province Based on Air Quality Forecasting

With the atmospheric stereoscopic monitoring, air quality forecasting and decision of environment management as the main line, and comprehensive management system as the guidance, five platforms including infrastructure, technological support, monitoring and early monitoring, decision support and information services were established. These platforms have 15 subsystems, including stereoscopic monitoring network, visual business consultation, high-performance computing environment, comprehensive management of atmospheric data, emission inventories of pollu-tion sources, evaluation tools of atmospheric models, monitoring and management of air pollution, forecasting and early warning of air quality, diag-nostic analysis of atmospheric environment, tracking of air pollution sources, emergency management of air pollution, conformity management of air quality, comprehensive display of information, releasing of information to external networks, and releasing of information by mobile networks. The decision support system （DSS） of atmospheric environment management could realize an integration business system of 11 air quality forecast - heavy pollution weather warning - diagnosis of pollution causes （dynamic analysis of pollution sources） -air quality conformity planning （air pollu-tion emergency management） -evaluation of forecasting and warning results （evaluation pf management measures） -air quality forecasting＂ and provide the technical support for the prevention and control of atmosphere pollution in Anhui province.

Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study

Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.

The Design of Intelligent Corporate E-commerce System

The paper shows how to design and implement an inte ll igent e-commerce system for medium or small enterprises. The corporate intranet is connected with Internet, which constitutes the hardware of the system. The s ystem adopts modern voice-identification technology for user authentication to enable secure access to key databases and thus enhance the security of system da tabases. The software system consists of four modules: 1. Corporate website and advertisement information system, intending for updatin g online advertisement, collecting and analyzing market research information. 2. Employee technology training and assessment system, including a variety of co urses containing technology, technique and management and so on. Login the training system, employees are able to study courses of technology or technique or others. The training system adopts modern multimedia E-Learning te chnology and in fact it is an innovative education method, which includes self- study, tutorship and lecture. It is able to help employees enjoy their learning. Employees can take simulative exams, and managers can test employees onlin e periodically. 3. Corporate decision-making management system, calculating and analyzing the o peration, predicting future development, facilitating communication between mana gers and employees. It provides valuable foundation for decisions, which enables managers to make strategic decisions and scientific management. 4. E-commerce online transaction system, which facilitates material purchase an d product sale. By using intelligent e-commerce system, corporations will enhance the employee’ s skills and management level, reduce production cost, promote product sale , and thus strengthen corporation’s market competitiveness effectively.

Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.

Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.