More attention has been paid to the monitoring, assessment, prediction, early warning and sustainable management of regional ecological environment and the changes of ecosystem state in recent years. It is an important scientific and technological task to develop quantitative methods and numerical simulation techniques for ecosystem modelling, and to construct the continental scale numerical simulator with the characteristics of multi-process coupling, multi-technology integration, and multi-objective application for stimulating research on ecosystem and global change and its resources, environment and disaster effects, based on the in-depth understanding of the components, processes, functions, patterns, and their interaction mechanism of terrestrial ecosystem. Here, we reviewed the current status and future direction of terrestrial ecosystem models, and discussed the conceptual framework of developing the simulation system of dynamic change and spatial variation in large-scale terrestrial ecosystems and its resource and environment effect, as well as basic issues on the function orientation and structure design of the simulation system, which would provide reference for constructing Chinese terrestrial ecosystem numerical simulator.
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http://dx.doi.org/10.13287/j.1001-9332.202108.040 | DOI Listing |
J Med Internet Res
January 2025
Hospital Administration, Ramaiah Memorial Hospital, Bengaluru, Karnataka, India.
Background: Monitoring vital signs in hospitalized patients is crucial for evaluating their clinical condition. While early warning scores like the modified early warning score (MEWS) are typically calculated 3 to 4 times daily through spot checks, they might not promptly identify early deterioration. Leveraging technologies that provide continuous monitoring of vital signs, combined with an early warning system, has the potential to identify clinical deterioration sooner.
View Article and Find Full Text PDFAn Acad Bras Cienc
January 2025
University of Technology, Department of Control and System Engineering, Baghdad, 10066, Iraq.
Latency in flux observation has an adverse effect on the performance of observer-based field-oriented speed control for three-phase induction motor (IM). The reduction of the convergent rate of estimation errors could improve the performance of speed-controlled IM based on flux observers. The main contribution is to design a fast convergent flux observer, which provides bounded estimation error immediately after one instant of motor startup.
View Article and Find Full Text PDFAn Acad Bras Cienc
January 2025
Universidade de Brasília, Laboratório de Criptógamas, Departamento de Botânica, Campus Universitário Darcy Ribeiro, Bloco D, 1° Andar, 70910-900 Brasília, DF, Brazil.
The exploration of extraterrestrial environments has become a focal point of scientific inquiry, driven by advancements in technology and a growing interest in the potential for life beyond Earth. This study investigates the adaptability of Antarctic nematodes, known for thriving in extreme cold and isolation, to simulated Martian (MGS-1) and Lunar (LMS-1) soils. The experiment revealed differential responses in nematode survivability to the two simulants, with Lunar soil demonstrating better adaptability compared to Martian soil.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Mechanical Engineering, Thuyloi University, Hanoi, Vietnam.
Road surface roughness is the cause of vehicle vibration, which is considered a system disturbance. Previous studies on suspension system control often ignore the influence of disturbances while designing the controller, leading to system performance degradation under severe vibration conditions. In this work, we propose a control method to improve active suspension performance that reduces vehicle vibration by eliminating the influence of road disturbances.
View Article and Find Full Text PDFPLoS One
January 2025
School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.
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