Bioenergy with carbon capture and storageBioenergy with carbon capture and storage (BECCS) is the process of extracting bioenergy from biomass and capturing and storing the carbon, thereby removing it from the atmosphere. BECCS can be a "negative emissions technology" (NET). The carbon in the biomass comes from the greenhouse gas carbon dioxide (CO2) which is extracted from the atmosphere by the biomass when it grows. Energy ("bioenergy") is extracted in useful forms (electricity, heat, biofuels, etc.
Failure of electronic componentsElectronic components have a wide range of failure modes. These can be classified in various ways, such as by time or cause. Failures can be caused by excess temperature, excess current or voltage, ionizing radiation, mechanical shock, stress or impact, and many other causes. In semiconductor devices, problems in the device package may cause failures due to contamination, mechanical stress of the device, or open or short circuits. Failures most commonly occur near the beginning and near the ending of the lifetime of the parts, resulting in the bathtub curve graph of failure rates.
Cognitive biasA cognitive bias is a systematic pattern of deviation from norm or rationality in judgment. Individuals create their own "subjective reality" from their perception of the input. An individual's construction of reality, not the objective input, may dictate their behavior in the world. Thus, cognitive biases may sometimes lead to perceptual distortion, inaccurate judgment, illogical interpretation, and irrationality. While cognitive biases may initially appear to be negative, some are adaptive.
Greenhouse gasGreenhouse gases are those gases in the atmosphere that raise the surface temperature of planets such as the Earth. What distinguishes them from other gases is that they absorb the wavelengths of radiation that a planet emits, resulting in the greenhouse effect. The Earth is warmed by sunlight, causing its surface to radiate heat, which is then mostly absorbed by water vapor (), carbon dioxide (), methane (), nitrous oxide (), and ozone (). Without greenhouse gases, the average temperature of Earth's surface would be about , rather than the present average of .
Model aircraftA model aircraft is a small unmanned aircraft. Many are replicas of real aircraft. Model aircraft are divided into two basic groups: flying and non-flying. Non-flying models are also termed static, display, or shelf models. Aircraft manufacturers and researchers make wind tunnel models for testing aerodynamic properties, for basic research, or for the development of new designs. Sometimes only part of the aircraft is modelled.
Scale modelA scale model is a physical model which is geometrically similar to an object (known as the prototype). Scale models are generally smaller than large prototypes such as vehicles, buildings, or people; but may be larger than small prototypes such as anatomical structures or subatomic particles. Models built to the same scale as the prototype are called mockups. Scale models are used as tools in engineering design and testing, promotion and sales, filmmaking special effects, military strategy, and hobbies such as rail transport modeling, wargaming and racing; and as toys.
Neutron radiationNeutron radiation is a form of ionizing radiation that presents as free neutrons. Typical phenomena are nuclear fission or nuclear fusion causing the release of free neutrons, which then react with nuclei of other atoms to form new nuclides—which, in turn, may trigger further neutron radiation. Free neutrons are unstable, decaying into a proton, an electron, plus an electron antineutrino. Free neutrons have a mean lifetime of 887 seconds (14 minutes, 47 seconds). Neutron radiation is distinct from alpha, beta and gamma radiation.
Bias–variance tradeoffIn statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set: The bias error is an error from erroneous assumptions in the learning algorithm.
