Spatial and temporal dynamics of phytoplankton biomass and water turbidity can provide crucial information about the function, health and vulnerability of lagoon ecosystems (coral reefs, sea grasses, etc.). A statisti...
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Spatial and temporal dynamics of phytoplankton biomass and water turbidity can provide crucial information about the function, health and vulnerability of lagoon ecosystems (coral reefs, sea grasses, etc.). A statistical algorithm is proposed to estimate chlorophyll-a concentration ([chl-a]) in optically complex waters of the New Caledonian lagoon from MODIS-derived remote-sensing reflectance (R-rs). The algorithm is developed via supervised learning on match-ups gathered from 2002 to 2010. The best performance is obtained by combining two models, selected according to the ratio of R-rs in spectral bands centered on 488 and 555 nm: a log-linear model for low [chl-a] (AFLC) and a support vector machine (SVM) model or a classic model (OC3) for high [chl-a]. The log-linear model is developed based on SVM regression analysis. This approach outperforms the classical OC3 approach, especially in shallow waters, with a root mean squared error 30% lower. The proposed algorithm enables more accurate assessments of [chl-a] and its variability in this typical oligo- to meso-trophic tropical lagoon, from shallow coastal waters and nearby reefs to deeper waters and in the open ocean.
This research introduces an Artificial Intelligence (AI) based model designed to concurrently optimize energy supply management, biocide dosing, and maintenance scheduling for heat exchangers. This optimization consid...
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This research introduces an Artificial Intelligence (AI) based model designed to concurrently optimize energy supply management, biocide dosing, and maintenance scheduling for heat exchangers. This optimization considers energetic, technical, economic, and environmental considerations. The impact of biofilm on heat exchangers is assessed, revealing a 41% reduction in thermal efficiency and a 113% increase in flow frictional resistance of the fluid compared to the initial state. Consequently, the pump's power consumption, required to maintain hydraulic conditions, rises by 9%. The newly developed AI model detects the point at which the heat exchanger's performance begins to decline due to accumulating dirt, marking day 44 of experimentation as the threshold to commence the antifouling biocide dosing. Leveraging this AI model to monitor heat exchanger efficiency represents an innovative approach to optimizing antifouling biocide dosing and reduce the environmental impact stemming from industrial plants.
We propose a statistical algorithm to assess chlorophyll-a concentration ([chl-a]) using remote sensing reflectance (Rrs) derived from MODerate Resolution Imaging Spectroradiometer (MODIS) data. This algorithm is a co...
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ISBN:
(纸本)9781628413281
We propose a statistical algorithm to assess chlorophyll-a concentration ([chl-a]) using remote sensing reflectance (Rrs) derived from MODerate Resolution Imaging Spectroradiometer (MODIS) data. This algorithm is a combination of two models: one for low [chl-a] (oligotrophic waters) and one for high [chl-a]. A satellite pixel is classified as low or high [chla] according to the Rrs ratio (488 and 555 nm channels). If a pixel is considered as a low [chl-a] pixel, a log-linear model is applied;otherwise, a more sophisticated model (Support Vector Machine) is applied. The log-linear model was developed thanks to supervised learning on Rrs and [chl-a] data from SeaBASS and more than 15 campaigns accomplished from 2002 to 2010 around New Caledonia. Several models to assess high [chl-a] were also tested with statistical methods. This novel approach outperforms the standard reflectance ratio approach. Compared with algorithms such as the current NASA OC3, Root Mean Square Error is 30% lower in New Caledonian waters.
Harsh environments such as alternating wet and dry conditions and cyclic loading cause erosion in steel bars, leading to severe damage to structures. Steel bars are also prone to bending under long-term fatigue loads....
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Harsh environments such as alternating wet and dry conditions and cyclic loading cause erosion in steel bars, leading to severe damage to structures. Steel bars are also prone to bending under long-term fatigue loads. This paper establishes the bending performance test of corroded reinforced concrete (RC) beam under fatigue load based on the mathematical-statistical algorithm. It investigates the influence of heavy load on the fatigue performance of damaged RC beams, the influence of corrosive environment on the fatigue performance of RC beams, the degree of corrosion of steel bars in concrete beams under fatigue loading, and the distribution of chloride ions. The study results found that the stress ratio has a significant effect on the maximum crack width of the specimen beam. The greater is the stress ratio, the longer the fatigue life. This directly affects the performance of the specimen beam under fatigue loading. For this reason, we should pay special attention to the impact of corrosion on the fatigue performance of a structure during the design of steel bars.
Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelli...
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Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications.
The rapid development of urbanization brings a large number of people to the city, and the traffic demand generated by people's travel has exceeded the traffic supply capacity of urban roads, resulting in the over...
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Investigating oxide defect-induced random telegraph noise signals within stress-induced leakage current is a key approach for understanding the degradation of thin oxides in deeply-scaled devices. However, experimenta...
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The increasing apprehension for health, safety and quality of life in modern society has resulted in the widespread use of biosensors. Biosensors are characterised by their high sensitivity, real-time monitoring, and ...
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The increasing apprehension for health, safety and quality of life in modern society has resulted in the widespread use of biosensors. Biosensors are characterised by their high sensitivity, real-time monitoring, and easy integration, making them indispensable for environmental monitoring on-site, as well as invasive and non-invasive health monitoring. Signal processing and analysis are crucial to biosensor applications, with an important role being played by chemometrics in this regard. This review presents a review of recent research findings in the fields of environmental and health monitoring. In addition, it investigates the role that chemometrics plays in the processing and analysis of biosensor data. The research comprises conventional statistical techniques, including principal component analysis and wavelet transform, as well as modern techniques of artificial intelligence, such as machine learning with neural networks. Through the examination of various algorithm strengths and weaknesses, significant recommendations are offered for biosensor applications. Furthermore, the assessment delivers focused proposals for surmounting signal processing difficulties in biosensors. Additionally, the review contains a concise analysis and reflection on the issue of multiple detection and analysis. The review intends to give essential guidance to future researchers in selecting efficient and sensible methods of data processing for their studies. image
the application of computer technology in e-commerce not only drives the innovative development of computer technology itself, but also promotes the digital process of e-commerce, and promotes the innovation and devel...
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ISBN:
(数字)9781510652118
ISBN:
(纸本)9781510652118;9781510652101
the application of computer technology in e-commerce not only drives the innovative development of computer technology itself, but also promotes the digital process of e-commerce, and promotes the innovation and development of computer technology in China to a certain extent. The most important part of computer technology is software technology. Computer software technology includes database technology, operating system technology, algorithm technology and data structure, information security technology, software programming technology, software testing technology and so on. Software programming technology in e-commerce is mainly to install software programs, system management and program writing. This paper mainly analyzes the specific application of algorithm, data structure and software engineering in e-commerce.
This paper mainly analyzes the fitting and precision of software reliability growth model based on polynomial regression model. Firstly, the software reliability testing workload and software reliability modeling fram...
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ISBN:
(数字)9781510652118
ISBN:
(纸本)9781510652118;9781510652101
This paper mainly analyzes the fitting and precision of software reliability growth model based on polynomial regression model. Firstly, the software reliability testing workload and software reliability modeling framework are proposed. On this basis, the deformation software workload function and modeling function are introduced into the modeling framework. A new software reliability growth model is established. At the same time, Harry uses the framework to analyze and address the core issues in the modeling process. Based on the software reliability model parameters and two groups of actual effect data, the model framework is used to establish the most suitable growth model. Thus, the reliability of software system is guaranteed, and the integration and development of software and polynomial regression model are further increased.
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