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Topic Review
Start School Later Movement
The movement to start school later consists of efforts by health care professionals, sleep scientists, educators, economists, legislators, parents, students, and other concerned citizens to restore a later start to the school day. Based on a growing body of evidence that starting middle and high schools too early in the morning is unhealthy, counterproductive, and incompatible with adolescent sleep needs and patterns. During the second half of the 20th century, many public schools in the United States began shifting instructional time earlier than the more conventional bell time, thought to be about 9 a.m. Today it is common for American schools to begin the instructional day in the 7 a.m. hour and end about seven hours later, around 2 p.m. Most sleep research suggests that morning classes should begin no earlier than 8:30 a.m. for middle and high school students. Advocates of a return to later school start times argue that sleep and school hours should be viewed as a public health issue, citing evidence linking early school start times to widespread sleep deprivation among teenagers as well as a wide array of acute and chronic physical, psychological, and educational problems. Not only do students consistently get significantly more sleep on school nights when their schools move to later start times, but later school hours have been consistently linked with improved school performance, reduced impulsiveness, and greater motivation, as well as with lower rates of depression, tardiness, truancy, and morning automobile accidents. Recent (2011) studies suggest that early school start times disproportionately hurt economically disadvantaged students and may even negatively impact future earning potential of students, offsetting any financial savings to the school system attributed to earlier hours.
  • 2.7K
  • 06 Oct 2022
Topic Review
Dowel-Type Joints in Timber Structures
Dowel-type joints are one of the most common connectors. They basically consist of one or more cylindrical steel dowels inserted into aligned holes of different elements. The dowels transmit loads between the elements, being subjected to opposite compressive forces on the contact area with each element. This causes the dowels to work under bending moments and shear forces. There is an almost infinite number of possible configurations for dowel-type joints.
  • 2.6K
  • 09 Oct 2021
Topic Review
Agricultural Extension for Smallholder Farmers
The creation of commercialization opportunities for smallholder farmers has taken primacy on the development agenda of many developing countries. Invariably, most of the smallholders are less productive than commercial farmers and continue to lag in commercialization. Apart from the various multifaceted challenges which smallholder farmers face, limited access to agricultural extension services stands as the underlying constraint to their sustainability. 
  • 2.7K
  • 02 Jul 2021
Topic Review
Pathophysiology of Osteoporosis
Osteoporosis refers to excessive bone loss as reflected by the deterioration of bone mass and microarchitecture, which compromises bone strength. It is a complex multifactorial endocrine disease. Its pathogenesis relies on the presence of several endogenous and exogenous risk factors, which skew the physiological bone remodelling to a more catabolic process that results in net bone loss.
  • 2.6K
  • 15 Nov 2022
Biography
Paracelsus
Paracelsus (/ˌpærəˈsɛlsəs/; 1493/4[1] – 24 September 1541), born Theophrastus von Hohenheim (full name Philippus Aureolus Theophrastus Bombastus von Hohenheim[2]), was a Swiss[3] physician, alchemist, and astrologer of the German Renaissance.[4][5] He was a pioneer in several aspects of the "medical revolution" of the Renaissance, emphasizing the value of observation in combination with
  • 2.6K
  • 25 Nov 2022
Topic Review
Cereal–Legume Intercropping
With the current objective of moving away from monoculture and the development of the "ecological intensification" of agrosystems, the cereal-legume intercropping takes advantage of the symbiotic relationships that the legume develops with soil micro-organisms (rhizobiums). Legumes are capable of fixing atmospheric nitrogen thanks to the nodules of its roots, and thus provide to this crop a part of its nitrogen needs. The choice of species and the proportion of grains to be sown are determined by the objectives of intercropping. For human food, simple mixtures are favoured (e.g. wheat/pea, barley/bean, triticale/pea). For fodder production, the number of species can be higher.
  • 2.6K
  • 27 Oct 2020
Topic Review
IB and ND in Poultry
Infectious bronchitis (IB) and Newcastle disease (ND) are among the most important viral diseases of poultry with substantial global economic impact . Infectious bronchitis is caused by the IB virus (IBV), a member of the Gammacoronavirus genus, family Coronaviridae, and subfamily Orthocoronavirinae. IBV is commonly referred to as avian coronavirus and it belongs in the same family and subfamily as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is currently ravaging the world, although the latter is in a different genus—Betacoronavirus. Newcastle disease (ND) is caused by ND virus (NDV), which belongs to the genus Avulavirus in the family Paramyxoviridae. Both viruses have genomes made up of single stranded RNA (ssRNA). 
  • 2.6K
  • 21 Jan 2021
Topic Review
Evolution of Digital Twins
A digital twin can be described as a digital replica of a real-world entity. It simulates the physical state and maybe the biological state and behavior of the real-world entity based on input data. It helps in predicting, optimizing, and improving decision making. It has revolutionized the industrial world, particularly the manufacturing industry, construction and healthcare sector, smart cities, and energy industry. 
  • 2.6K
  • 13 May 2021
Topic Review
3p Deletion Syndrome
3p deletion syndrome is a condition that results from a chromosomal change in which a small piece of chromosome 3 is deleted in each cell. The deletion occurs at the end of the short (p) arm of the chromosome. This chromosomal change often leads to intellectual disability, developmental delay, and abnormal physical features.
  • 2.6K
  • 23 Dec 2020
Topic Review
Machine Learning for Plant Breeding/Biotechnology
Classical univariate and multivariate statistics are the most common methods used for data analysis in plant breeding and biotechnology studies. Evaluation of genetic diversity, classification of plant genotypes, analysis of yield components, yield stability analysis, assessment of biotic and abiotic stresses, prediction of parental combinations in hybrid breeding programs, and analysis of in vitro-based biotechnological experiments are mainly performed by classical statistical methods. Despite successful applications, these classical statistical methods have low efficiency in analyzing data obtained from plant studies, as the genotype, environment, and their interaction (G × E) result in nondeterministic and nonlinear nature of plant characteristics. Large-scale data flow, including phenomics, metabolomics, genomics, and big data, must be analyzed for efficient interpretation of results affected by G × E. Nonlinear nonparametric machine learning techniques are more efficient than classical statistical models in handling large amounts of complex and nondeterministic information with "multiple-independent variables versus multiple-dependent variables" nature. Neural networks, partial least square regression, random forest, and support vector machines are some of the most fascinating machine learning models that have been widely applied to analyze nonlinear and complex data in both classical plant breeding and in vitro-based biotechnological studies. High interpretive power of machine learning algorithms has made them popular in the analysis of plant complex multifactorial characteristics. The classification of different plant genotypes with morphological and molecular markers, modeling and predicting important quantitative characteristics of plants, the interpretation of complex and nonlinear relationships of plant characteristics, and predicting and optimizing of in vitro breeding methods are the examples of applications of machine learning in conventional plant breeding and in vitro-based biotechnological studies. Precision agriculture is possible through accurate measurement of plant characteristics using imaging techniques and then efficient analysis of reliable extracted data using machine learning algorithms. Perfect interpretation of high-throughput phenotyping data is applicable through coupled machine learning-image processing. This entry shows how nonlinear machine learning algorithms can be used in different branches of classical plant breeding and in vitro-based methods. An idea is provided at the end of the entry that shows how coupled image processing-machine learning (especially deep CNN) could be used to identify the ploidy level of plants. It could be used in laboratories without flowcytometry equipment and/or in plant species without an established chromosome counting protocol.
  • 2.6K
  • 16 Feb 2021
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