Bioinstrumentation Ppt || Spectroscopy || Spectrophotometry || E. M. Waves
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Bioinstrumentation is a subject encompassing different techniques and concepts in the field of Biotechnology and it's application in day to day life. The techniques introduced: - Spectrophotometry - Spectroscopy - Electromagnetic Waves By Mohammed Valikarimwala FY BSc Biotechnology Fergusson college, Pune
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Spectroscopy involves analyzing the interaction of electromagnetic radiation with matter. Different regions of the electromagnetic spectrum are used to study different types of molecular motion and structure. UV-visible spectroscopy analyzes electronic transitions that occur when molecules absorb ultraviolet or visible light. The wavelength and intensity of absorbed light provides information about functional groups and molecular structure. Key concepts in UV-visible spectroscopy include chromophores, which are functional groups that absorb light in characteristic regions, and Beer's law, which states that absorbance is proportional to concentration.
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In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
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Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
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Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
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原版纸张【微信：741003700 】【(carleton毕业证书)卡尔顿大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
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6:个人职称评审加20分
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Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
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Bioinstrumentation Ppt || Spectroscopy || Spectrophotometry || E. M. Waves
- 2. Bioinstrumentation is an application of biomedical engineering, which focuses on the devices and mechanics used to measure, evaluate, and treat biological systems. It focuses on the use of multiple sensors to monitor physiological characteristics of a human or animal.
- 3. Fitness Trackers Genetic testing Drug delivery/aiding machines Biomedical optics Botany Agriculture
- 5. Spectrometry deals with the measurement of a specific spectrum.
- 7. It is a well known fact that light is composed of electromagnetic radiation which is often treated as a wave phenomenon which has a defined wavelength. In a wave, wavelength is defined by the distance in a wave where the shape of the wave repeats and is usually determined by the distance between corresponding points of the same phase. E = h*c / λ v=c / λ E = h. ν **Note – frequency and wavelength are inversely proportional to each other.
- 9. The entire electromagnetic spectrum, from the lowest to the highest frequency (longest to shortest wavelength), includes all radio waves (e.g., commercial radio and television, microwaves, radar), infrared radiation, visible light, ultraviolet radiation, X-rays, and gamma rays. Nearly all frequencies and wavelengths of electromagnetic radiation can be used for spectroscopy.
- 10. Ultraviolet–visible spectroscopy or ultraviolet–visible spectrophotometry refers to absorption spectroscopy or reflectance spectroscopy in the ultraviolet-visible spectral region. This means it uses light in the visible and adjacent ranges.
- 11. Ultraviolet (UV) spectroscopy works on a principle similar to that of colorimetry, except it uses ultraviolet light. UV spectroscopy is also called electronic spectroscopy, because the results depend on the electrons in the chemical bonds of the sample compound. Researchers use UV spectrometers to study chemical bonding and to determine the concentrations of substances (nucleic acids for example) that do not interact with visible light. UV Spectrometers
- 12. Chemists use infrared (IR) spectrometers to measure the response of a sample to infrared light. The device sends a range of IR wavelengths through the sample to record the absorbance. IR spectroscopy is also called vibrational or rotational spectroscopy because the vibrational and rotational frequencies of atoms bonded to each other, are the same as the frequencies of IR radiation. IR spectrometers are used to identify unknown compounds or to confirm their identity since the IR spectrum of a substance serves as a unique "fingerprint."
- 15. A cuvette is a piece of laboratory equipment that is intended to hold samples for spectroscopic analysis. Cuvettes are made from glass, plastic, or optical-grade quartz. Plastic cuvettes have the advantage of being less expensive and disposable and are often used in fast spectroscopic assays.