Afiq Aizuddin Bin Anuar

Plants are one of nature that are subjected to diseases. Even though it is a natural occurrence, plant diseases are the major problems in plantation industries. It can cause a huge loss in production. Some of plant diseases can be examined by our naked eyes and some of them cannot. The traditional way to detect plant diseases requires detailed monitoring and takes a lot of time especially in large farm. This paper presents several methods using different machine learning algorithms to detect different plant diseases on the different plants to achieve highest possible accuracy. Furthermore, an analytical review of various images processing method for recognizing plant diseases from captured images of plants is presented. Image processing techniques consist of image acquisition, image pre-processing, image segmentation, feature extraction, and classification. All these methods are explained in detail in this paper will be useful for farmers to gather information from the data given to take early action from getting worse. This paper also compares different techniques used by other researchers that consider various features and classifier in terms of their proficiency to enhance the classification methods' accuracy ratios. From the analysis that we made; it shows the most used method with highest accuracy is based on convolutional neural networks (CNN).

We present an analysis of the sensitivity of current and future LHC searches for new spin-0 particles in top-anti-top-quark ($t\bar{t}$) final states, focusing on generic axion-like particles (ALPs) that are coupled to top quarks and gluons. As a first step, we derive new limits on the effective ALP Lagrangian in terms of the Wilson coefficients $c_t$ and $c_{\tilde{G}}$ based on the results of the CMS search using $35.9$ fb$^{-1}$ of data, collected at $\sqrt{s} = 13$ TeV. We then investigate how the production of an ALP with generic couplings to gluons and top quarks can be distinguished from the production of a pseudoscalar which couples to gluons exclusively via a top-quark loop. To this end, we make use of the invariant $t\bar{t}$ mass distribution and angular correlations that are sensitive to the $t\bar{t}$ spin correlation. Using a mass of 400 GeV as an example, we find that already the data collected during Run 2 and Run 3 of the LHC provides an interesting sensitivity to the underlying nature of a possible new particle. We also analyze the prospects for data anticipated to be collected during the high-luminosity phase of the LHC. Finally, we compare the limits obtained from the $t \bar t$ searches to existing experimental bounds from LHC searches for narrow di-photon resonances, from measurements of the production of four top quarks, and from global analyses of ALP-SMEFT interference effects.

A bstract A search for new top quark interactions is performed within the framework of an effective field theory using the associated production of either one or two top quarks with a Z boson in multilepton final states. The data sample corresponds to an integrated luminosity of 138 fb − 1 of proton-proton collisions at $$ \sqrt{s} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> </mml:math> = 13 TeV collected by the CMS experiment at the LHC. Five dimension-six operators modifying the electroweak interactions of the top quark are considered. Novel machine-learning techniques are used to enhance the sensitivity to effects arising from these operators. Distributions used for the signal extraction are parameterized in terms of Wilson coefficients describing the interaction strengths of the operators. All five Wilson coefficients are simultaneously fit to data and 95% confidence level intervals are computed. All results are consistent with the SM expectations.

The CMS experiment has been designed with a 2-level trigger system. The first level is implemented using custom-designed electronics. The second level is the so-called High Level Trigger (HLT), a streamlined version of the CMS offline reconstruction software running on a computer farm. For Run II of the Large Hadron Collider, the increase in center-of-mass energy and luminosity will raise the event rate to a level challenging for the HLT algorithms. New approaches have been studied to keep the HLT output rate manageable while maintaining thresholds low enough to cover physics analyses. The strategy mainly relies on porting online the ingredients that have been successfully applied in the offline reconstruction, thus allowing to move HLT selection closer to offline cuts. Improvements in HLT electron and photon definitions will be presented, focusing in particular on: updated clustering algorithm and the energy calibration procedure, new Particle-Flow-based isolation approach and pileup mitigation techniques, and the electron-dedicated track fitting algorithm based on Gaussian Sum Filter.

The use of computer clusters for computational sciences including computational physics is vital as it provides computing power to crunch big numbers at a faster rate. In compute intensive applications that requires high resolution such as Monte Carlo simulation, the use of computer clusters in a grid form that supplies computational power to any nodes within the grid that needs computing power, has now become a necessity. In this paper, we described how the clusters running on a specific application could use resources within the grid, to run the applications to speed up the computing process.

The CMS experiment has been designed with a 2-level trigger system. The first level is implemented using custom-designed electronics. The second level is the so-called High Level Trigger (HLT), a streamlined version of the CMS offline reconstruction software running on a computer farm. For Run II of the Large Hadron Collider, the increase in center-of-mass energy and luminosity will raise the event rate to a level challenging for the HLT algorithms. New approaches have been studied to keep the HLT output rate manageable while maintaining thresholds low enough to cover physics analyses. The strategy mainly relies on porting online the ingredients that have been successfully applied in the offline reconstruction, thus allowing to move HLT selection closer to offline cuts. Improvements in HLT electron and photon definitions will be presented, focusing in particular on: updated clustering algorithm and the energy calibration procedure, new Particle-Flow-based isolation approach and pileup mitigation techniques, and the electron-dedicated track fitting algorithm based on Gaussian Sum Filter.

Measurements of the properties of the top quark can serve as stringent tests of the standard model (SM).To date, all the measurements of its pair and single production cross sections and properties indicate that it is indeed the top quark as predicted by the SM.However, moderate deviations from this expectation still cannot be ruled out.The spin density matrix of top quark pair production consists of coefficients which are affected by various discrete symmetry properties, making it a rich trove of information to search for effects beyond the SM.This talk describes the first measurement of top polarization and t t spin correlations at √ s = 13 TeV that are encoded in a systematic way into the spin density matrix, using data recorded by the CMS experiment in 2016.The measurements are found to be consistent with the SM prediction derived from Monte Carlo generators and analytic calculations at next to leading order QCD (+weak) accuracy.They are also used to derive the best constraint on the anomalous chromomagnetic moment of the top quark to date.