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Topic Review
Gravity Wave
In fluid dynamics, gravity waves are waves generated in a fluid medium or at the interface between two media when the force of gravity or buoyancy tries to restore equilibrium. An example of such an interface is that between the atmosphere and the ocean, which gives rise to wind waves. A gravity wave results when fluid is displaced from a position of equilibrium. The restoration of the fluid to equilibrium will produce a movement of the fluid back and forth, called a wave orbit. Gravity waves on an air–sea interface of the ocean are called surface gravity waves or surface waves, while gravity waves that are within the body of the water (such as between parts of different densities) are called internal waves. Wind-generated waves on the water surface are examples of gravity waves, as are tsunamis and ocean tides. Wind-generated gravity waves on the free surface of the Earth's ponds, lakes, seas and oceans have a period of between 0.3 and 30 seconds (3Hz to 30mHz). Shorter waves are also affected by surface tension and are called gravity–capillary waves and (if hardly influenced by gravity) capillary waves. Alternatively, so-called infragravity waves, which are due to subharmonic nonlinear wave interaction with the wind waves, have periods longer than the accompanying wind-generated waves.
  • 333
  • 21 Nov 2022
Topic Review
Bioclimatic Building Design
Bioclimatic building design emerges as a holistic approach to sustainable architecture that integrates the built environment with natural elements. Bioclimatic building design’s capacity to significantly reduce energy consumption, enhance occupant well-being, and shape sustainable behavior has been well documented in existing research. 
  • 295
  • 21 Feb 2024
Topic Review
Methodologies for Wind Field Reconstruction in the U-SPACE
The main methodologies used to reconstruct wind fields in the U-SPACE have been analyzed. The SESAR U-SPACE program aims to develop an Unmanned Traffic Management system with a progressive introduction of procedures and services designed to support secure access to the air space for a large number of drones. Some of these techniques were originally developed for reconstruction at high altitudes, but successively adapted to treat different heights. A common approach to all techniques is to approximate the probabilistic distribution of wind speed over time with some parametric models, apply spatial interpolation to the parameters and then read the predicted value.
  • 273
  • 23 Nov 2023
Topic Review
Anaerobic Co-Digestion of Primary Sludge and Biowastes
Primary sludge is a valuable substrate for anaerobic digestion as it contains a higher percentage of fatty acids and lipids compared to secondary sludge, although its carbon-to-nitrogen ratio is relatively low due to its inherent deficiency of carbon. This limiting factor of C/N ratio can be overwhelmed by the co-digestion of primary sludge with organic fractions such as agricultural byproducts and municipal solid wastes. The operating principle of this practice is based on the fact that organic fractions such as agricultural byproducts contain a high percentage of carbon and a low percentage of nitrogen, so the co-digestion of primary sludge with different organic fractions, such as animal manure, agricultural residues, organic fractions of municipal waste, or vegetable residues, may improve the balance of nutrients, provide buffering capacity, adjust the C/N ratio, reduce the concentration of ammonia, and hence its inhibitory effects, and overall promote the process of methanogenesis.
  • 272
  • 26 Feb 2024
Topic Review
Recent Advances in Predictability and Prediction Using PDE-Based, AI-Powered, and Idealized Generalized Lorenz Models
In recent years, substantial progress has been achieved in the domain of atmospheric predictability and forecasting, harnessing both traditional partial differential equation (PDE)-based methods and advanced artificial intelligence (AI) technologies. Drawing on our recent comprehensive review of predictability studies, this investigation explores the potential for extending the two-week predictability limit initially proposed in the 1960s. While PDE-physics-based systems have consistently provided valuable insights, the emergence of AI-powered models, particularly those employing deep learning and transformer-based architectures, has paved the way for extending prediction horizons. These AI models have demonstrated performance that is comparable to, or even surpasses, traditional methods in short-term forecasts (3-14 days) and hold significant promise for addressing the challenges of subseasonal prediction. The synergy of AI and traditional approaches also underscores the potential for cost-effective, long-range weather predictions, with reports indicating promising predictions beyond 30 days. Furthermore, the development of generalized Lorenz models, incorporating time-varying parameters, has deepened our understanding of the coexistence of chaotic and regular behaviors with distinct predictability, challenging the conventional perception of weather systems as purely chaotic. This dual nature introduces fresh perspectives on long-term predictability and regional dependencies, such as seasonal variations and blocking patterns. In addition to reviewing recent advancements, this study proposes future research directions aimed at enhancing predictive accuracy and further exploring the limits of predictability in the realms of both weather and climate modeling.
  • 249
  • 03 Sep 2024
Topic Review
New Ways to Modelling and Predicting Ionosphere Variables
The new way of thinking science from Newtonian determinism to nonlinear unpredictability and the dawn of advanced computer science and technology can be summarized in the words of the theoretical physicist Michel Baranger, who, in 2000, said in a conference: “Twenty-first-century theoretical physics is coming out of the chaos revolution; it will be about complexity and its principal tool will be the computer.”. This can be extended to natural sciences in general. Modelling and predicting ionosphere variables have been considered since many decades as a paramount objective of research by scientists and engineers. The new approach to natural sciences influenced also ionosphere research. Ionosphere as a part of the solar–terrestrial environment is recognised to be a complex chaotic system, and its study under this new way of thinking should become an important area of ionospheric research, particularly with the addition of machine learning techniques.
  • 223
  • 19 Dec 2023
Topic Review
Semantic Segmentation Networks for Forest Applications
Deforestation remains one of the key concerning activities around the world due to commodity-driven extraction, agricultural land expansion, and urbanization. The effective and efficient monitoring of national forests using remote sensing technology is important for the early detection and mitigation of deforestation activities. Deep learning techniques have been vastly researched and applied to various remote sensing tasks, whereby fully convolutional neural networks have been commonly studied with various input band combinations for satellite imagery applications, but very little research has focused on deep networks with high-resolution representations, such as HRNet.
  • 222
  • 08 Jan 2024
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