If you conduct a scientific experiment or undertake a piece of research, you’ll usually need to write up a corresponding project or lab report, to summarize the objective of your task, the methods you followed, the results you obtained, and the conclusions you drew from your work. Here we provide a sample of great templates for producing such reports, which include layout guidelines to help guide you through the process.
The purpose of this lab was to illustrate the validity of the law of conservation of energy along with the determination of the spring constant of a given spring. For the first part the spring constantk was to be found from a given spring. Through the suspension of various known metal masses on a vertically suspended spring, the spring constant was determined. Two methods were used: the algebraic rearrangement of Hooke's Law and a slope analysis of a linear regression on a Force (N) against Stretch Length (m) scatter plot. The spring constant k was determined to be 26.438 ± 1.063. For the second part of the lab, the aim was to validate the law of conservation of energy through the oscillation of a vertically suspended spring. Data was collected using a Vernier Motion Detector 2 machine and the various energies (kinetic energy, gravitational potential energy and spring potential energy) were collected and summed up. The sum of these energies yielded a fairly constant energy total (2.287 J ± 0.025 J) which supports the authenticity of the law of conservation of energy. While there were some uncertainties due to the lab setup, human error and equipment error it did not affect the validity of the methods during experimentation. Overall, the spring constant k of a given spring was determined and the law of conservation of energy was validated through the calculation of total energy during a suspended mass' oscillation.
Gene regulatory networks have an important role to study the behaviour of genes. By analysing
these Gene Regulatory Networks we can get the detailed information i.e. the occurrence of diseases by
changing behaviour of GRNs. Many different approaches are used (i.e. qualitative modelling and hybrid
modelling) and various tools (i.e. GenoTech, GINsim) have been developed to model and simulate gene
regulatory networks. GenoTech allows the user to specify a GRN on Graphical User Interface (GUI) according
to the asynchronous multivalued logical functions of René Thomas, and to simulate and/or analyse its
qualitative dynamical behaviour. René
Thomas discrete modelling of gene regulatory network (GRN) is a
well known approach to study the dynamics of genes. It deals with some parameters which reflect the possible
targets of trajectories. Those parameters are priory unknown. These unknown parameters are fetched using
another model checking tool SMBioNet. SMBioNet produces all the possible parameters satisfying the given
Computational Logic Tree (CTL) formula as input. This approach involving logical parameters and conditions
also known as qualitative modelling of GRN. However, this approach neglects the time delays for a gene to
pass from one level of expression to another one i.e. inhibition to activation and vice versa. To find out these
time delays, another modelling tool HyTech is used to perform hybrid modelling of GRN.
We have developed a Java based tool called GenNet http://asanian.com/gennet to facilitate the
model checking user by providing a unique GUI layout for both qualitative and quantitative modelling of GRNs.
As we discussed, three separate modelling tools are used for complete modelling and analysis of a GRN. This
process is much lengthy and takes too much time. GenNet assists the modelling users by providing some extra
features i.e. CTL editor, parameters filtering and input/output files management.
GenNet takes a GRN network as input and does all the rest of computations i.e. CTL verification,
K-parameters generation, parameter implication to GRN, state graph, hybrid modelling and parameter
filtration automatically. GenNet serves the user by computing the results within seconds that were taking hours
and days of manual computation
Muons compose the penetrating component of Cosmic Rays. At sea level, they constitute the largest part of Secondary Cosmic Rays, giving an average flux of ≈ 100 m−2s−1sr−1. The aim of our experiment is to estimate, from muon decay, the mean lifetime and the mass of invisible products. Our experimental setup includes four detectors: three of them are plastic scintillators and compose the trigger system, while the last one is a liquid scintillator which measures the particles energy. All these scintillators are read by photomultipliers. Trigger and pulse thresholds are computed by logical and temporal modules in a VME crate. The Data Acquisition System has been verified to work properly. It is composed of two fADCs modules, one I/O Register, one Motorola computer and a Farm. The liquid scintillator has been calibrated in energy using both passing muons and 60CO gamma source. Thanks to the charge-energy conversion factor we estimated electron energy spectrum. In particular we selected a sample of decay events by estimating muon mean lifetime τμ = 2.19 ± 0.34 μs; then we finally extrapolated an upper limit for invisible products mass mν < 5.99 ± 0.73 MeV/c2.
In this experiment, we attempt to better understand how materials properties are tested. We tested a number of simple beams of different materials under a stress. The bending of the materials allowed for us to calculate the Poisson's Ratio and elastic moduli for each material. From this, we were able to not only compare materials but also methods of measuring elasticity. Despite some error in our results, which can be explained by the scale of our measurements in relation to the stiffness of certain materials, we find both strain gauges and equations of cantilever to be appropriate measurement techniques for measuring the elastic modulus of simple beams.
Computer vision systems can be applied to a wide variety of tasks, but some of the most interesting are those related with security and surveillance. Within this group, our application for Video Surveillance for Road Traffic Monitoring can be placed. We propose a solution based on machine learning and video analysis techniques that involves the whole process: database evaluation, background estimation, foreground segmentation, video stabilization and object tracking. As a result of this, our system will be able to monitorize some basic parameters of traffic flow as vehicles counting or speed estimation.
C. Carmona, A. Flores, A. Hernández, A. Imbernon, A. Mosella
#9774 Nano Ninjas is a rookie FTC Team consisting of fifteen girls in seventh and eighth grade and is a neighborhood team located in Portland, OR. This is our Engineering Notebook capturing every moment of of FTC journey.
Read more about our amazing project in our story on the Overleaf blog.
This is a big, detailed report at 300+ pages, so give it a few seconds to load! :-)