Grandmother hypothesis

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Grandmother_hypothesis 

The grandmother hypothesis is a hypothesis to explain the existence of menopause in human life history by identifying the adaptive value of extended kin networking. It builds on the previously postulated "mother hypothesis" which states that as mothers age, the costs of reproducing become greater, and energy devoted to those activities would be better spent helping her offspring in their reproductive efforts. It suggests that by redirecting their energy onto those of their offspring, grandmothers can better ensure the survival of their genes through younger generations. By providing sustenance and support to their kin, grandmothers not only ensure that their genetic interests are met, but they also enhance their social networks which could translate into better immediate resource acquisition. This effect could extend past kin into larger community networks and benefit wider group fitness.

Background

One explanation to this was presented by G.C. Williams who was the first to posit that menopause might be an adaptation. Williams suggested that at some point it became more advantageous for women to redirect reproductive efforts into increased support of existing offspring. Since a female's dependent offspring would die as soon as she did, he argued, older mothers should stop producing new offspring and focus on those existing. In so doing, they would avoid the age-related risks associated with reproduction and thereby eliminate a potential threat to the continued survival of current offspring. The evolutionary reasoning behind this is driven by related theories.

Kin selection

Main article: Kin selection

Kin selection provides the framework for an adaptive strategy by which altruistic behavior is bestowed on closely related individuals because easily identifiable markers exist to indicate them as likely to reciprocate. Kin selection is implicit in theories regarding the successful propagation of genetic material through reproduction, as helping an individual more likely to share one's genetic material would better ensure the survival of at least a portion of it. Hamilton's rule suggests that individuals preferentially help those more related to them when costs to themselves are minimal. This is modeled mathematically as ${\displaystyle rb>c}$. Grandmothers would, therefore, be expected to forgo their own reproduction once the benefits of helping those individuals (b) multiplied by the relatedness to that individual (r) outweighed the costs of the grandmother not reproducing (c).

Evidence of kin selection emerged as correlated with climate-driven changes, around 1.8 –1.7 million years ago, in female foraging and food sharing practices. These adjustments increased juvenile dependency, forcing mothers to opt for a low-ranked, common food source (tubers) that required adult skill to harvest and process. Such demands constrained female IBIs (Inter Birth Intervals) thus providing an opportunity for selection to favor the grandmother hypothesis.

Parental investment

Main article: Parental investment

Parental investment, originally put forth by Robert Trivers, is defined as any benefit a parent confers on an offspring at a cost to its ability to invest elsewhere. This theory serves to explain the dynamic sex difference in investment toward offspring observed in most species. It is evident first in gamete size, as eggs are larger and far more energetically expensive than sperm. Females are also much more sure of their genetic relationship with their offspring, as birth serves as a very reliable marker of relatedness. This paternity uncertainty that males experience makes them less likely than females to invest, since it would be costly for males to provide sustenance to another male's offspring. This translates into the grandparental generation, as grandmothers should be much more likely than grandfathers to invest energy into the offspring of their children, and more so in the offspring of their daughters than sons.

The grandmother effect

Evolutionary theory dictates that all organisms invest heavily in reproduction in order to replicate their genes. According to parental investment, human females will invest heavily in their young because the number of mating opportunities available to them and how many offspring they are able to produce in a given amount of time is fixed by the biology of their sex. This inter birth interval (IBI) is a limiting factor in how many children a woman can have because of the extended developmental period that human children experience. Extended childhood, like the extended post-reproductive lifespan for females, is unique to humans. Because of this correlation, human grandmothers are well-poised to provide supplemental parental care to their offspring's children. Since their grandchildren still carry a portion of their genes, it is still in the grandmother’s genetic interest to ensure those children survive to reproduction.

Reproductive senescence

Main article: Evolution of aging

The mismatch between the rates of degradation of somatic cells versus gametes in human females provides an unsolved paradox. Somatic cells decline more slowly, and humans invest more in somatic longevity relative to other species. Since natural selection has a much stronger influence on younger generations, deleterious mutations during later life become harder to select out of the population.

In female placentals, the number of ovarian oocytes is fixed during embryonic development, possibly as an adaptation to reduce the accumulation of mutations, which then mature or degrade over the life course. At birth there are, typically, one million ova. However, by menopause, only approximately 400 eggs would have actually matured. In humans, the rate of follicular atresia increases at older ages (around 38-40), for reasons that are not known. In chimpanzees, our closest nonhuman, genetic relatives, there is a very similar rate of oocyte atresia until the age of 35 at which point humans experience a far accelerated rate compared to chimpanzees. However, chimpanzee females, unlike humans, usually die while still in their reproductive phase.

The aging process in humans leaves a dilemma in that females live past their ability to reproduce. The question poised to evolutionary researchers then becomes, why do human bodies live on so robustly and for so long past their reproductive potential, and could there be an adaptive benefit to abandoning one's own attempts at reproduction to assist kin?

Alloparenting

The practice of dividing parenting responsibilities among non-parents affords females a great advantage in that they can dedicate more effort and energy toward having an increased number of offspring. While this practice is observed in several species, it has been an especially successful strategy for humans who rely extensively on social networks. One observational study of the Aka foragers of Central Africa demonstrated how allomaternal investment toward an offspring increased specifically during times that the mother's investment in subsistence and economic activities increased.

If the grandmother effect were true, post-menopausal women should continue to work after the cessation of fertility and use the proceeds to preferentially provision their kin. Studies of Hadza women have provided such evidence. A modern hunter-gatherer group in Tanzania, the post-menopausal Hadza women often help their grandchildren by foraging for food staples that younger children are inefficient at acquiring successfully. Children, therefore, require the assistance of an adult to gain this crucial version of sustenance. Often, however, mothers are inhibited by the care of younger offspring and are less available to help their older children forage. In this regard, the Hadza grandmothers become vital to the care of existing grandchildren, and allow reproductive-age women to redirect energy from existing offspring into younger offspring or other reproductive efforts.

However, some commentators felt that the role of Hadza men, who contribute 96% of the mean daily intake of protein, was ignored; though the authors have addressed this criticism in numerous publications. Other studies also demonstrated reservations about behavioral similarities between the Hadza and our ancestors.

Maternal v. paternal grandmothers

Because grandmothers should be expected to provide preferential treatment to offspring she is most certain of her relationship to, there should be differences in the help she provides to each grandchild according to that relationship. Studies have found that not only does the maternal or paternal relationship of the grandparent affect whether or how much help a grandchild receives, but also what kind of help. Paternal grandmothers often had a detrimental effect on infant mortality. Also, maternal grandmothers concentrate on offspring survival, whereas paternal grandmothers increase birth rates. These finding are consistent with ideas of parental investment and paternity uncertainty. Equally, a grandmother could be both a maternal and paternal grandmother and thus in division of resources, a daughter’s offspring should be favored.

Other studies have focused on the genetic relationship between grandmothers and grandchildren. Such studies have found that the effects of maternal / paternal grandmothers on grandsons / granddaughters may vary based on degree of genetic relatedness, with paternal grandmothers having positive effects on granddaughters but detrimental effects on grandsons, and paternity uncertainty may be less important than chromosome inheritance.

Criticisms and alternative hypotheses

Some critics have cast doubt on the hypothesis because while it addresses how grandparental care could have maintained longer female post-reproductive lifespans, it does not provide an explanation for how it would have evolved in the first place. Some versions of the grandmother hypothesis asserted that it helped explain the longevity of human senescence. However, demographic data has shown that historically rising numbers in older people among the population correlated with lower numbers of younger people. This suggests that at some point grandmothers were not helpful toward the survival of their grandchildren, and does not explain why the first grandmother would forgo her own reproduction to help her offspring and grandchildren.

In addition, all variations on the mother, or grandmother effect, fail to explain longevity with continued spermatogenesis in males.

Another problem concerning the grandmother hypothesis is that it requires a history of female philopatry. Though some studies suggest that hunter-gatherer societies are patriarchal, mounting evidence shows that residence is fluid among hunter-gatherers and that married women in at least one patrilineal society visit their kin during times when kin-based support can be especially beneficial to a woman's reproductive success. One study does suggest, however, that maternal kin were essential to the fitness of sons as fathers in a patrilocal society.

It also fails to explain the detrimental effects of losing ovarian follicular activity. While continued post-menopausal synthesis of estrogen occurs in peripheral tissues through the adrenal pathways, these women undoubtedly face an increased risk of conditions associated with lower levels of estrogen: osteoporosis, osteoarthritis, Alzheimer's disease and coronary artery disease.

However, cross-cultural studies of menopause have found that menopausal symptoms are quite variable among different populations, and that some populations of females do not recognize, and may not even experience, these "symptoms". This high level of variability in menopausal symptoms across populations brings into question the plausibility of menopause as a sort of "culling agent" to eliminate non-reproductive females from competition with younger, fertile members of the species. This also faces the task of explaining the paradox between the typical age for menopause onset and the life expectancy of female humans.

Evolution of menopause

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Evolution_of_menopause 

Few animals have a menopause: humans are joined by just four other species in which females live substantially longer than their ability to reproduce. The others are all cetaceans: beluga whales, narwhals, orcas and short-finned pilot whales. There are various theories on the origin and process of the evolution of menopause. These attempt to suggest evolutionary benefits to the human species stemming from the cessation of women's reproductive capability before the end of their natural lifespan. Explanations can be categorized as adaptive and non-adaptive:

Non-adaptive hypotheses

The high cost of female investment in offspring may lead to physiological deteriorations that amplify susceptibility to becoming infertile. This hypothesis suggests the reproductive lifespan in humans has been optimized, but it has proven more difficult in females and thus their reproductive span is shorter. If this hypothesis were true, however, age at menopause should be negatively correlated with the amount of energy expended to maintain the reproductive organs, and the available data does not support this.

A recent increase in female longevity due to improvements in the standard of living and social care has also been suggested. It is difficult for selection, however, to favor aid to offspring from parents and grandparents. Irrespective of living standards, adaptive responses are limited by physiological mechanisms. In other words, senescence is programmed and regulated by specific genes.

Early human selection shadow

While it is fairly common for extant hunter-gatherers to live past age 50 provided that they survive childhood, fossil evidence shows that mortality in adults has decreased over the last 30,000 to 50,000 years and that it was extremely unusual for early Homo sapiens to live to age 50. This discovery has led some biologists to argue that there was no selection for or against menopause at the time at which the ancestor of all modern humans lived in Africa, suggesting that menopause is instead a random evolutionary effect of a selection shadow regarding aging in early Homo sapiens. It is also argued that since the population fraction of post-menopausal women in early Homo sapiens was so low, menopause had no evolutionary effect on mate selection or social behaviors related to mate selection.

Adaptive hypotheses

"Survival of the fittest" hypothesis

This hypothesis suggests that younger mothers and offspring under their care will fare better in a difficult and predatory environment because a younger mother will be stronger and more agile in providing protection and sustenance for herself and a nursing baby. The various biological factors associated with menopause had the effect of male members of the species investing their effort with the most viable of potential female mates.

A problem with this hypothesis is that, if true, we would expect to see menopause exhibited among many species in the animal kingdom, and another problem is that in the case of extended child development, even a female who was relatively young, still agile, and attractive when producing a child would lose future support from her male partner due to him seeking out fertile mates when she reaches menopause, while the child is still not independent. This would be counterproductive to the supposed adaptation of getting male support, as it would significantly decrease the survival for children produced over much of the female's fertile and agile life, unless children were raised in ways that did not rely on support from a male partner, which would eliminate the supposed evolutionary benefit anyway.

Young female preference hypothesis

The young female preference hypothesis proposes that changes in male preferences for younger mates allowed late-age acting fertility mutations to accumulate in females without any evolutionary penalty, giving rise to menopause. A computer model was constructed to test this hypothesis, and showed that it was feasible. However, in order for deleterious mutations that affect fertility past roughly age fifty to accumulate, human maximum lifespan had to first be extended to about its present value. As of 2016 it was unclear if there has been sufficient time since that happened for such an evolutionary process to occur.

Male-biased philopatry hypothesis

The male-biased philopatry theory proposes that if human social groups were originally based around men leaving their birth communities more frequently than women, then this leads to increased relatedness to the group in relation to female age, making inclusive fitness benefits older females receive from helping the group greater than what they would receive from continued reproduction, which in turn eventually led to the evolution of menopause. In a pattern of male-biased dispersal and local mating, the relatedness of the individuals in the group decreases with female age, leading to a decrease in kin selection with female age. This occurs because a female will stay with her father in her birth community throughout life, initially being closely related to the males and females. Females are born and stay in the group, so relatedness to the females stays about the same. However, throughout time, the older male relatives will die and any sons she gives birth to will disperse, so that local relatedness to males, and therefore the whole group, declines. The situation is reversed in species where males are philopatric and either females disperse, or mating is non-local. Under these conditions, a female's reproductive life begins away from her father and paternal relatives because she was either born into a new group from non-local mating or because she dispersed. In the case of female-biased dispersal, the female is initially equally unrelated with every individual in the group, and with non-local mating, the female is closely related to the females of the group, but not the males since her paternal relatives are in another group. As she gives birth, her sons will stay with her, increasing her relatedness to males in the group over time and thus her relatedness with the overall group. The common feature that connects these two otherwise different behaviors is male-biased philopatry, which leads to an increase in kin selection with female age.

While not conclusive, evidence does exist to support the idea that female-biased dispersal existed in pre-modern humans. The closest living relatives to humans, chimpanzees, bonobos, and both mountain gorillas and western lowland gorillas, are female-biased dispersers. Analysis of sex specific genetic material, the non-recombining portions of the Y chromosome and mitochondrial DNA, show evidence of a prevalence of female-biased dispersal as well; however, these results could also be affected by the effective breeding numbers of males and females in local populations. Evidence of female-biased dispersion in hunter-gatherers is not definitive, with some studies supporting the idea, and others suggesting there is no strong bias towards either sex. In orcas, both sexes mate non-locally with members of a different pod but return to the pod after copulation. Demographic data shows that a female's mean relatedness to the group does increase over time due to increasing relatedness to males. While less well-studied, there is evidence that short-finned pilot whales, another menopausal species, also display this behavior. However, mating behavior that increases local relatedness with female age is prevalent in non-menopausal species, making it unlikely that it is the only factor that determines if menopause will evolve in a species.

Mother hypothesis

The mother hypothesis suggests that menopause was selected for humans because of the extended development period of human offspring and high costs of reproduction so that mothers gain an advantage in reproductive fitness by redirecting their effort from new offspring with a low survival chance to existing children with a higher survival chance.

Grandmother hypothesis

Main article: Grandmother hypothesis

The Grandmother hypothesis suggests that menopause was selected for humans because it promotes the survival of grandchildren. According to this hypothesis, post-reproductive women feed and care for children, adult nursing daughters, and grandchildren whose mothers have weaned them. Human babies require large and steady supplies of glucose to feed the growing brain. In infants in the first year of life, the brain consumes 60% of all calories, so both babies and their mothers require a dependable food supply. Some evidence suggests that hunters contribute less than half the total food budget of most hunter-gatherer societies, and often much less than half, so that foraging grandmothers can contribute substantially to the survival of grandchildren at times when mothers and fathers are unable to gather enough food for all of their children. In general, selection operates most powerfully during times of famine or other privation. So although grandmothers might not be necessary during good times, many grandchildren cannot survive without them during times of famine. Post-reproductive female orcas tend to lead their pods, especially during years of food scarcity. Furthermore, the increased mortality risk of an orca due to losing a grandmother is stronger in years of food scarcity

Analysis of historical data found that the length of a female's post-reproductive lifespan was reflected in the reproductive success of her offspring and the survival of her grandchildren. Another study found comparative effects but only in the maternal grandmother—paternal grandmothers had a detrimental effect on infant mortality (probably due to paternity uncertainty). Differing assistance strategies for maternal and paternal grandmothers have also been demonstrated. Maternal grandmothers concentrate on offspring survival, whereas paternal grandmothers increase birth rates.

Some believe variations on the mother, or grandmother effect fail to explain longevity with continued spermatogenesis in males (oldest verified paternity is 94 years, 35 years beyond the oldest documented birth attributed to females). Notably, the survival time past menopause is roughly the same as the maturation time for a human child. That a mother's presence could aid in the survival of a developing child, while an unidentified father's absence might not have affected survival, could explain the paternal fertility near the end of the father's lifespan. A man with no certainty of which children are his may merely attempt to father additional children, with support of existing children present but small. Note the existence of partible paternity supporting this. Some argue that the mother and grandmother hypotheses fail to explain the detrimental effects of losing ovarian follicular activity, such as osteoporosis, osteoarthritis, Alzheimer's disease and coronary artery disease.

The theories discussed above assume that evolution directly selected for menopause. Another theory states that menopause is the byproduct of the evolutionary selection for follicular atresia, a factor that causes menopause. Menopause results from having too few ovarian follicles to produce enough estrogen to maintain the ovarian-pituitary-hypothalamic loop, which results in the cessation of menses and the beginning of menopause. Human females are born with approximately a million oocytes, and approximately 400 oocytes are lost to ovulation throughout life.

Reproductive conflict hypothesis

In social vertebrates, the sharing of resources among the group places limits on how many offspring can be produced and supported by members of the group. This creates a situation in which each female must compete with others of the group to ensure they are the one that reproduces. The reproductive conflict hypothesis proposes that this female reproductive conflict favors the cessation of female reproductive potential in older age to avoid reproductive conflict, increasing the older female's fitness through inclusive benefits. Female-biased dispersal or non-local mating leads to an increase in relatedness to the social group with female age. In the human case of female-biased dispersal, when a young female enters a new group, she is not related to any individual and she reproduces to produce an offspring with a relatedness of 0.5. An older female could also choose to reproduce, producing an offspring with a relatedness of 0.5, or she could refrain from reproducing and allow another pair to reproduce. Because her relatedness to males in the group is high, there is a fair probability that the offspring will be her grandchild with a relatedness of 0.25. The younger female experiences no cost to her inclusive fitness from using the resources necessary to successfully rear offspring since she is not related to members of the group, but there is a cost for the older female. As a result, the younger female has the advantage in reproductive competition. Although a female orca born into a social group is related to some members of the group, the whale case of non-local mating leads to similar outcomes because the younger female relatedness to the group as a whole is less than the relatedness of the older female. This behavior makes more likely the cessation of reproduction late in life to avoid reproductive conflict with younger females.

Research using both human and orca demographic data has been published that supports the role of reproductive conflict in the evolution of menopause. Analysis of demographic data from pre-industrial Finnish populations found significant reductions in offspring survivorship when mothers-in-laws and daughters-in-laws had overlapping births, supporting the idea that avoiding reproductive conflict is beneficial to offspring survivorship. Humans, more so than other primates, rely on food sharing for survival, so the large survivorship reduction values could be caused by a straining of community resources. Avoiding such straining is a possible explanation for why the reproductive overlap seen in humans is much lower than other primates. Food sharing is also prevalent among another menopausal species, orcas. Reproductive conflict has also been observed in orcas, with increased calf mortality seen when reproductive overlap between a younger and older generational female occurred.

Aerobraking

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Aerobraking

An artist's conception of aerobraking with the Mars Reconnaissance Orbiter

An example of Aerobraking
   Mars Reconnaissance Orbiter ·   Mars

Aerobraking is a spaceflight maneuver that reduces the high point of an elliptical orbit (apoapsis) by flying the vehicle through the atmosphere at the low point of the orbit (periapsis). The resulting drag slows the spacecraft. Aerobraking is used when a spacecraft requires a low orbit after arriving at a body with an atmosphere, as it requires less fuel than using propulsion to slow down.

Method

When an interplanetary vehicle arrives at its destination, it must reduce its velocity to achieve orbit or to land. To reach a low, near-circular orbit around a body with substantial gravity (as is required for many scientific studies), the required velocity changes can be on the order of kilometers per second. Using propulsion, the rocket equation dictates that a large fraction of the spacecraft mass must consist of fuel. This reduces the science payload and/or requires a large and expensive rocket. Provided the target body has an atmosphere, aerobraking can be used to reduce fuel requirements. The use of a relatively small burn allows the spacecraft to enter an elongated elliptic orbit. Aerobraking then shortens the orbit into a circle. If the atmosphere is thick enough, a single pass can be sufficient to adjust the orbit. However, aerobraking typically requires multiple orbits higher in the atmosphere. This reduces the effects of frictional heating, unpredictable turbulence effects, atmospheric composition, and temperature. Aerobraking done this way allows sufficient time after each pass to measure the velocity change and make corrections for the next pass. Achieving the final orbit may take over six months for Mars, and may require hundreds of passes through the atmosphere. After the last pass, if the spacecraft shall stay in orbit, it must be given more kinetic energy via rocket engines in order to raise the periapsis above the atmosphere. If the craft shall land, it must loose kinetic energy, also via rocket engines.

The kinetic energy dissipated by aerobraking is converted to heat, meaning that spacecraft must dissipate this heat. The spacecraft must have sufficient surface area and structural strength to produce and survive the required drag, The temperatures and pressures associated with aerobraking are not as severe as those of atmospheric reentry or aerocapture. Simulations of the Mars Reconnaissance Orbiter aerobraking use a force limit of 0.35 N per square meter with a spacecraft cross section of about 37 m², equate to a maximum drag force of about 7.4 N, and a maximum expected temperature as 170 °C. The force density (i.e. pressure), roughly 0.2 N per square meter, that was exerted on the Mars Observer during aerobraking is comparable to the aerodynamic resistance of moving at 0.6 m/s (2.16 km/h) at sea level on Earth, approximately the amount experienced when walking slowly. 

Regarding spacecraft navigation, Moriba Jah was the first to demonstrate the ability to process Inertial Measurement Unit (IMU) data collected on board the spacecraft, during aerobraking, using an unscented Kalman Filter to statistically infer the spacecraft's trajectory independent of ground-based measurement data. Jah did this using actual IMU data from Mars Odyssey and Mars Reconnaissance Orbiter. Moreover, this was the first use of an unscented Kalman Filter to determine the orbit of an anthropogenic space object about another planet. This method, which could be used to automate aerobraking navigation, is called Inertial Measurements for Aeroassisted Navigation (IMAN) and Jah won a NASA Space Act Award for this work.

Many spacecraft use solar panels to power their operations. The panels can be used to refine aerobraking to reduce the number of required orbits. The panels rotate according to an AI-powered algorithm to increase/reduce drag and can reduce arrival times from months to weeks.

Related methods

Aerocapture is a related but more extreme method in which no initial orbit-injection burn is performed. Instead, the spacecraft plunges deeply into the atmosphere without an initial insertion burn, and emerges from this single pass in the atmosphere with an apoapsis near that of the desired orbit. Several small correction burns are then used to raise the periapsis and perform final adjustments. This method was originally planned for the Mars Odyssey orbiter, but the significant design impacts proved too costly.

Another related technique is that of aerogravity assist, in which the spacecraft flies through the upper atmosphere and uses aerodynamic lift instead of drag at the point of closest approach. If correctly oriented, this can increase the deflection angle above that of a pure gravity assist, resulting in a larger delta-v.

Spacecraft missions

Animation of 2001 Mars Odyssey's trajectory around Mars from 24 October 2001 to 24 October 2002
   2001 Mars Odyssey ·   Mars

Animation of ExoMars Trace Gas Orbiter's trajectory around Mars
   Mars ·    ExoMars Trace Gas Orbiter

Although the theory of aerobraking is well developed, using the technique is difficult because a very detailed knowledge of the character of the target planet's atmosphere is needed in order to plan the maneuver correctly. Currently, the deceleration is monitored during each maneuver and plans are modified accordingly. Since no spacecraft can yet aerobrake safely on its own, this requires constant attention from both human controllers and the Deep Space Network. This is particularly true near the end of the process, when the drag passes are relatively close together (only about 2 hours apart for Mars). NASA has used aerobraking four times to modify a spacecraft's orbit to one with lower energy, reduced apoapsis altitude, and smaller orbit.

On 19 March 1991, aerobraking was demonstrated by the Hiten spacecraft. This was the first aerobraking maneuver by a deep space probe. Hiten (a.k.a. MUSES-A) was launched by the Institute of Space and Astronautical Science (ISAS) of Japan. Hiten flew by the Earth at an altitude of 125.5 km over the Pacific at 11.0 km/s. Atmospheric drag lowered the velocity by 1.712 m/s and the apogee altitude by 8665 km. Another aerobraking maneuver was conducted on 30 March.

In May 1993, aerobraking was used during the extended Venusian mission of the Magellan spacecraft. It was used to circularize the orbit of the spacecraft in order to increase the precision of the measurement of the gravity field. The entire gravity field was mapped from the circular orbit during a 243-day cycle of the extended mission. During the termination phase of the mission, a "windmill experiment" was performed: Atmospheric molecular pressure exerts a torque via the windmill-sail-like oriented solar cell wings, the necessary counter-torque to keep the probe from spinning is measured.

In 1997, the Mars Global Surveyor (MGS) orbiter was the first spacecraft to use aerobraking as the main planned technique of orbit adjustment. The MGS used the data gathered from the Magellan mission to Venus to plan its aerobraking technique. The spacecraft used its solar panels as "wings" to control its passage through the tenuous upper atmosphere of Mars and lower the apoapsis of its orbit over the course of many months. Unfortunately, a structural failure shortly after launch severely damaged one of the MGS's solar panels and necessitated a higher aerobraking altitude (and hence one third the force) than originally planned, significantly extending the time required to attain the desired orbit. More recently, aerobraking was used by the Mars Odyssey and Mars Reconnaissance Orbiter spacecraft, in both cases without incident.

In 2014, an aerobraking experiment was successfully performed on a test basis near the end of the mission of the ESA probe Venus Express.

In 2017–2018, the ESA ExoMars Trace Gas Orbiter performed aerobraking at Mars to reduce the apocentre of the orbit, being the first operational aerobraking for a European mission.

Aerobraking in fiction

In Robert A. Heinlein's 1948 novel Space Cadet, aerobraking is used to save fuel while slowing the spacecraft Aes Triplex for an unplanned extended mission and landing on Venus, during a transit from the Asteroid Belt to Earth.

The spacecraft Cosmonaut Alexei Leonov in Arthur C. Clarke's 1982 novel 2010: Odyssey Two and its 1984 film adaptation uses aerobraking in the upper layers of Jupiter's atmosphere to establish itself at the L₁ Lagrangian point of the Jupiter – Io system.

In the 2004 TV series Space Odyssey: Voyage to the Planets the crew of the international spacecraft Pegasus perform an aerobraking manoeuvre in Jupiter's upper atmosphere to slow them down enough to enter Jovian orbit.

In the fourth episode of Stargate Universe, the Ancient ship Destiny suffers an almost complete loss of power and must use aerobraking to change course. The 2009 episode ends in a cliffhanger with Destiny headed directly toward a star.

In the space simulation sandbox game Kerbal Space Program, this is a common method of reducing a craft's orbital speed. It is sometimes humorously referred to as "aerobreaking", because the high drag sometimes causes large crafts to split in several parts.

In Kim Stanley Robinson's Mars trilogy, the Ares spaceship carrying the first hundred humans to arrive on Mars uses aerobraking to enter into orbit around the planet. Later in the books, as an effort to thicken the atmosphere, scientists bring an asteroid into aerobraking in order to vaporize it and release its contents into the atmosphere.

In the 2014 film Interstellar, astronaut pilot Cooper uses aerobraking to save fuel and slow the spacecraft Ranger upon exiting the wormhole to arrive in orbit above the first planet.

Aerodynamic braking

See also: Air brake (aeronautics)

Aerodynamic braking is a method used in landing aircraft to assist the wheel brakes in stopping the plane. It is often used for short runway landings or when conditions are wet, icy or slippery. Aerodynamic braking is performed immediately after the rear wheels (main mounts) touch down, but before the nose wheel drops. The pilot begins to pull back on the stick, applying elevator pressure to hold the nose high. The nose-high attitude exposes more of the craft's surface-area to the flow of air, which produces greater drag, helping to slow the plane. The raised elevators also cause air to push down on the rear of the craft, forcing the rear wheels harder against the ground, which aids the wheel brakes by helping to prevent skidding. The pilot will usually continue to hold back on the stick even after the elevators lose their authority, and the nose wheel drops, to keep added pressure on the rear wheels.

Aerodynamic braking is a common braking technique during landing, which can also help to protect the wheel brakes and tires from excess wear, or from locking up and sending the craft sliding out of control. It is often used by private pilots, commercial planes, fighter aircraft, and was used by the Space Shuttles during landings.

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  An F-22 Raptor landing at Elmendorf AFB, demonstrating aerodynamic braking.

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  Aerodynamic braking in Space Shuttle landings.

Generative artificial intelligence

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Generative_artificial_intelligence

Théâtre d'Opéra Spatial, an image generated by Midjourney

Generative artificial intelligence (also generative AI or GenAI) is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

In the early 2020s, advances in transformer-based deep neural networks enabled a number of generative AI systems notable for accepting natural language prompts as input. These include large language model chatbots such as ChatGPT, Bing Chat, Bard, and LLaMA, and text-to-image artificial intelligence art systems such as Stable Diffusion, Midjourney, and DALL-E.

Generative AI has uses across a wide range of industries, including art, writing, script writing, software development, product design, healthcare, finance, gaming, marketing, and fashion.Investment in generative AI surged during the early 2020s, with large companies such as Microsoft, Google, and Baidu as well as numerous smaller firms developing generative AI models. However, there are also concerns about the potential misuse of generative AI, including cybercrime or creating fake news or deepfakes which can be used to deceive or manipulate people.

History

Main article: History of artificial intelligence

The academic discipline of artificial intelligence was founded at a research workshop at Dartmouth College in 1956, and has experienced several waves of advancement and optimism in the decades since. Since its founding, researchers in the field have raised philosophical and ethical arguments about the nature of the human mind and the consequences of creating artificial beings with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity. These concepts of automated art date back at least to the automata of ancient Greek civilization, where inventors such as Daedalus and Hero of Alexandria were described as having designed machines capable of writing text, generating sounds, and playing music. The tradition of creative automatons has flourished throughout history, such as Maillardet's automaton, created in the early 1800s.

Since the founding of AI in the 1950s, artists and researchers have used artificial intelligence to create artistic works. By the early 1970s, Harold Cohen was creating and exhibiting generative AI works created by AARON, the computer program Cohen created to generate paintings.

Markov chains have long been used to model natural languages since their development by Russian mathematician Andrey Markov in the early 20th century. Markov published his first paper on the topic in 1906, and analyzed the pattern of vowels and consonants in the novel Eugeny Onegin using Markov chains. Once a Markov chain is learned on a text corpus, it can then be used as a probabilistic text generator.

The field of machine learning often uses statistical models, including generative models, to model and predict data. Beginning in the late 2000s, the emergence of deep learning drove progress and research in image classification, speech recognition, natural language processing and other tasks. Neural networks in this era were typically trained as discriminative models, due to the difficulty of generative modeling.

In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative, rather than discriminative, models of complex data such as images. These deep generative models were the first able to output not only class labels for images, but to output entire images.

In 2017, the Transformer network enabled advancements in generative models, leading to the first generative pre-trained transformer (GPT) in 2018. This was followed in 2019 by GPT-2 which demonstrated the ability to generalize unsupervised to many different tasks as a Foundation model.

In 2021, the release of DALL-E, a transformer-based pixel generative model, followed by Midjourney and Stable Diffusion marked the emergence of practical high-quality artificial intelligence art from natural language prompts.

In March 2023, GPT-4 was released. A team from Microsoft Research argued that "it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system".

Modalities

A generative AI system is constructed by applying unsupervised or self-supervised machine learning to a data set. The capabilities of a generative AI system depend on the modality or type of the data set used.

Generative AI can be either unimodal or multimodal; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input. For example, one version of OpenAI's GPT-4 accepts both text and image inputs.

Text

A user conversing with a Character.ai simulation of Ludwig Wittgenstein

Generative AI systems trained on words or word tokens include GPT-3, LaMDA, LLaMA, BLOOM, GPT-4, and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks. Data sets include BookCorpus, Wikipedia, and others (see List of text corpora).

Code

In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs. Examples include OpenAI Codex.

Images

See also: Artificial intelligence art

Stable Diffusion, prompt a photograph of an astronaut riding a horse

Producing high-quality visual art is a prominent application of generative AI. Many such artistic works have received public awards and recognition.

Generative AI systems trained on sets of images with text captions include Imagen, DALL-E, Midjourney, Adobe Firefly, Stable Diffusion and others (see Artificial intelligence art, Generative art, and Synthetic media). They are commonly used for text-to-image generation and neural style transfer. Datasets include LAION-5B and others (See Datasets in computer vision).

Music

Generative AI systems such as MusicLM and MusicGen can be trained on the audio waveforms of recorded music along with text annotations, in order to generate new musical samples based on text descriptions such as a calming violin melody backed by a distorted guitar riff.

Video

Generative AI trained on annotated video can generate temporally-coherent video clips. Examples include Gen1 and Gen2 by RunwayML and Make-A-Video by Meta Platforms.

Molecules

Generative AI systems can be trained on sequences of amino acids or molecular representations such as SMILES representing DNA or proteins. These systems, such as AlphaFold, are used for protein structure prediction and drug discovery. Datasets include various biological datasets.

Robotics

Generative AI can also be trained on the motions of a robotic system to generate new trajectories for motion planning or navigation. For example, UniPi from Google Research uses prompts like "pick up blue bowl" or "wipe plate with yellow sponge" to control movements of a robot arm. Multimodal "vision-language-action" models such as Google's RT-2 can perform rudimentary reasoning in response to user prompts and visual input, such as picking up a toy dinosaur when given the prompt pick up the extinct animal at a table filled with toy animals and other objects.

Planning

The terms generative AI planning or generative planning were used in the 1980s and 1990s to refer to AI planning systems, especially computer-aided process planning, used to generate sequences of actions to reach a specified goal.

Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use, process plans for manufacturing and decision plans such as in prototype autonomous spacecraft.

Software and hardware

Generative AI models are used to power chatbot products such as ChatGPT, programming tools such as GitHub Copilot, text-to-image products such as Midjourney, and text-to-video products such as Runway Gen-2. Generative AI features have been integrated into a variety of existing commercially-available products such as Microsoft Office, Google Photos, and Adobe Photoshop. Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA language model.

Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a version with 7 billion parameters) can run on a Raspberry Pi 4 and one version of Stable Diffusion can run on an iPhone 11.

Larger models with tens of billions of parameters can run on laptop or desktop computers. To achieve an acceptable speed, models of this size may require accelerators such as the GPU chips produced by Nvidia and AMD or the Neural Engine included in Apple silicon products. For example, the 65 billion parameter version of LLaMA can be configured to run on a desktop PC.

Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs (such as Nvidia's H100) or AI accelerator chips (such as Google's TPU). These very large models are typically accessed as cloud services over the Internet.

In 2022, the United States New Export Controls on Advanced Computing and Semiconductors to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI. Chips such as the Nvidia A800 and the Biren Technology BR104 were developed to meet the requirements of the sanctions.

Concerns

See also: Ethics of artificial intelligence and Existential risk from artificial general intelligence

The development of generative AI has raised concerns from governments, businesses, and individuals, resulting in protests, legal actions, calls to pause AI experiments, and actions by multiple governments. In a July 2023 briefing of the United Nations Security Council, Secretary-General António Guterres stated "Generative AI has enormous potential for good and evil at scale", that AI may "turbocharge global development" and contribute between $10 and $15 trillion to the global economy by 2030, but that its malicious use "could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale".

Job losses

A picketer at the 2023 Writers Guild of America strike. While not a top priority, one of the WGA's 2023 requests was "regulations around the use of (generative) AI".

Main articles: Workplace impact of artificial intelligence and Technological unemployment

From the early days of the development of AI, there have been arguments put forward by ELIZA creator Joseph Weizenbaum and others about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculations and qualitative, value-based judgements. In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost. In July 2023, developments in generative AI contributed to the 2023 Hollywood labor disputes. Fran Drescher, president of the Screen Actors Guild, declared that "artificial intelligence poses an existential threat to creative professions" during the 2023 SAG-AFTRA strike.

Deepfakes

Main article: Deepfake

Deepfakes (a portmanteau of "deep learning" and "fake") are AI-generated media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks. Deepfakes have garnered widespread attention and concerns for their uses in deepfake celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud. This has elicited responses from both industry and government to detect and limit their use.

Cybercrime

Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including phishing scams. Deepfake video and audio have been used to create disinformation and fraud. Former Google fraud czar Shuman Ghosemajumder has predicted that while deepfake videos initially created a stir in the media, they would soon become commonplace, and as a result, more dangerous. Cybercriminals have created large language models focused on fraud, including WormGPT and FraudGPT.

Misuse in journalism

In January 2023, Futurism.com broke the story that CNET had been using an undisclosed internal AI tool to write at least 77 of its stories; after the news broke, CNET posted corrections to 41 of the stories.

In April 2023, German tabloid Die Aktuelle published a fake AI-generated interview with former racing driver Michael Schumacher, who had not made any public appearances since 2013 after sustaining a brain injury in a skiing accident. The story included two possible disclosures: the cover included the line "deceptively real", and the interview included an acknowledgement at the end that it was AI-generated. The editor-in-chief was fired shortly thereafter amid the controversy.

Regulation

Main article: Regulation of artificial intelligence

In the European Union, the proposed Artificial Intelligence Act includes requirements to disclose copyrighted material used to train generative AI systems, and to label any AI-generated output as such.

In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the White House in July 2023 to watermark AI-generated content.

In China, the Interim Measures for the Management of Generative AI Services introduced by the Cyberspace Administration of China regulates any public-facing generative AI. It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data collection, and a guideline that generative AI must "adhere to socialist core values".

Martian surface

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Martian_surface 

Mars sample return missions have been proposed that would return material from the surface of Mars back to Earth

The study of surface characteristics (or surface properties and processes) is a broad category of Mars science that examines the nature of the materials making up the Martian surface. The study evolved from telescopic and remote-sensing techniques developed by astronomers to study planetary surfaces. However, it has increasingly become a subdiscipline of geology as automated spacecraft bring ever-improving resolution and instrument capabilities. By using characteristics such as color, albedo, and thermal inertia and analytical tools such as reflectance spectroscopy and radar, scientists are able to study the chemistry and physical makeup (e.g., grain sizes, surface roughness, and rock abundances) of the Martian surface. The resulting data help scientists understand the planet's mineral composition and the nature of geological processes operating on the surface. Mars’ surface layer represents a tiny fraction of the total volume of the planet, yet plays a significant role in the planet's geologic history. Understanding physical surface properties is also very important in determining safe landing sites for spacecraft.

Albedo and Color

Like all planets, Mars reflects a portion of the light it receives from the sun. The fraction of sunlight reflected is a quantity called albedo, which ranges from 0 for a body that reflects no sunlight to 1.0 for a body that reflects all sunlight. Different parts of a planet's surface (and atmosphere) have different albedo values depending on the chemical and physical nature of the surface.

Mollweide projection of albedo features on Mars from Hubble Space Telescope. Bright ochre areas in left, center, and right are Tharsis, Arabia, and Elysium, respectively. The dark region at top center left is Acidalium Planitia. Syrtis Major is the dark area projecting upward in the center right. Note orographic clouds over Olympus and Elysium Montes (left and right, respectively).

No topography is visible on Mars from Earth-based telescopes. The bright areas and dark markings on pre-spaceflight-era maps of Mars are all albedo features. (See Classical albedo features on Mars.) They have little relation to topography. Dark markings are most distinct in a broad belt from 0° to 40° S latitude. However, the most prominent dark marking, Syrtis Major Planum, is in the northern hemisphere, outside this belt. The classical albedo feature Mare Acidalium (Acidalia Planitia) is another prominent dark area that lies north of the main belt. Bright areas, excluding the polar caps and transient clouds, include Hellas, Tharsis, and Arabia Terra. The bright areas are now known to be locations where fine dust covers the surface. The dark markings represent areas that the wind has swept clean of dust, leaving behind a lag of dark, rocky material. The dark color is consistent with the presence of mafic rocks, such as basalt.

The albedo of a surface usually varies with the wavelength of light hitting it. Mars reflects little light at the blue end of the spectrum but much at red and higher wavelengths. This is why Mars has the familiar reddish-orange color to the naked eye. But detailed observations reveal a subtle range of colors on Mars' surface. Color variations provide clues to the composition of surface materials. The bright areas are reddish-ochre in color, and the dark areas appear dark gray. A third type of area, intermediate in color and albedo, is also present and thought to represent regions containing a mixture of the material from the bright and dark areas. The dark gray areas can be further subdivided into those that are more reddish and those less reddish in hue.

Reflectance Spectroscopy

Reflectance spectroscopy is a technique that measures the amount of sunlight absorbed or reflected by the Martian surface at specific wavelengths. The spectra represent mixtures of spectra from individual minerals on the surface along with contributions from absorption lines in the solar spectrum and the Martian atmosphere. By separating out (“deconvolving”) each of these contributions, scientists can compare the resulting spectra to laboratory spectra of known minerals to determine the probable identity and abundance of individual minerals on the surface.

Using this technique, scientists have long known that the bright ochre areas probably contain abundant ferric iron (Fe³⁺) oxides typical of weathered iron-bearing materials (e.g., rust). Spectra of the dark areas are consistent with the presence of ferrous iron (Fe²⁺) in mafic minerals and show absorption bands suggestive of pyroxene, a group of minerals that is very common in basalt. Spectra of the redder dark areas are consistent with mafic materials covered with thin alteration coatings.

Thermal Inertia

Global thermal inertia based on data from Thermal Emission Spectrometer (TES) on Mars Global Surveyor spacecraft.

Thermal inertia measurement is a remote-sensing technique that allows scientists to distinguish fine-grained from coarse-grained areas on the Martian surface. Thermal inertia is a measure of how fast or slow something heats up or cools off. For example, metals have very low thermal inertia. An aluminum cookie sheet taken out of an oven is cool to the touch in less than a minute; while a ceramic plate (high thermal inertia) taken from the same oven takes much longer to cool off.

Scientists can estimate the thermal inertia on the Martian surface by measuring variations in surface temperature with respect to time of day and fitting this data to numerical temperature models. The thermal inertia of a material is directly related to its thermal conductivity, density, and specific heat capacity. Rocky materials do not vary much in density and specific heat, so variations in thermal inertia are mainly due to variations in thermal conductivity. Solid rock surfaces, such as outcroppings, have high thermal conductivities and inertias. Dust and small granular material in the regolith have low thermal inertias because the void spaces between grains restrict thermal conductivity to the contact point between grains.

Thermal inertia values for most of the Martian surface are inversely related to albedo. Thus, high albedo areas have low thermal inertias indicating surfaces that are covered with dust and other fine granular material. The dark gray, low albedo surfaces have high thermal inertias more typical of consolidated rock. However, thermal inertia values are not high enough to indicate widespread outcroppings are common on Mars. Even the rockier areas appear to be mixed with a significant amount of loose material. Data from the Infrared Thermal Mapping (IRTM) experiment on the Viking orbiters identified areas of high thermal inertia throughout the interior of Valles Marineris and the chaotic terrain, suggesting that these areas contain a relatively large number of blocks and boulders.

Radar Investigations

Radar studies provide a wealth of data on elevations, slopes, textures, and material properties of the Martian surface. Mars is an inviting target for Earth-based radar investigations because of its relative proximity to Earth and its favorable orbital and rotational characteristics that allow good coverage over wide areas of the planet's surface. Radar echoes from Mars were first obtained in the early 1960s, and the technique has been vital in finding safe terrain for Mars landers.

Radargram of north pole layered deposits from SHARAD shallow ground-penetrating radar on Mars Reconnaissance Orbiter.

Dispersion of the returned radar echoes from Mars shows that a lot of variation exists in surface roughness and slope across the planet's surface. Wide areas of the planet, particularly in Syria and Sinai Plana, are relatively smooth and flat. Meridiani Planum, the landing site of the Mars Exploration Rover Opportunity, is one of the flattest and smoothest (at decimeter-scale) locations ever investigated by radar—a fact borne out by surface images at the landing site. Other areas show high levels of roughness in radar that are not discernible in images taken from orbit. The average surface abundance of centimeter- to meter-scale rocks is much greater on Mars than the other terrestrial planets. Tharsis and Elysium, in particular, show a high degree of small-scale surface roughness associated with volcanoes. This extremely rough terrain is suggestive of young, ʻaʻā lava flows. A 200-km-long band of low to zero radar albedo ("stealth" region) cuts across the southwest Tharsis. The region corresponds to the location of the Medusa Fossae Formation, which consists of thick layers of unconsolidated materials, perhaps volcanic ash or loess.

Ground-penetrating radar instruments on the Mars Express orbiter (MARSIS) and the Mars Reconnaissance Orbiter (SHARAD) are currently providing stunning echo-return data on subsurface materials and structures to depths of up to 5 km. Results have shown that the polar layered deposits are composed of almost pure ice, with no more than 10% dust by volume and that fretted valleys in Deuteronilus Mensae contain thick glaciers covered by a mantle of rocky debris.