Processes of heavy quark pair (lepton pair) and two gluon (two photon) production in the high energy quark (electron) proton peripheral collisions

We considered the three jet production processes in the region of the incident lepton, photon, quark or gluon fragmentation. The fourth jet is created by the recoil proton. The kinematics of jet production is discussed in jets production in the fragmentation region. The non-trivial relation between the momenta of the recoil proton and the polar angle of its emission was derived. Based on this formalism the differential cross sections of QCD processes $gp \to (ggg) p; \,\,\,qp \to (q \bar{Q} Q) p; \,\,\, gp \to (g Q \bar{Q})p$ were obtained, including the distribution on transverse momentum component of jets fragments. It was shown that the role of the contribution of "non-Abelian" nature may become dominant in a particular kinematics of the final particles. The kinematics, in which the initial particle changes the direction of movement to the opposite one, was considered in the case of heavy quark-antiquark pair production. Different distributions, including spectral, azimuthal and polar angle distribution on the fragments of jets can be arranged using our results. We present besides the behavior of the ratio of non-Abelian contribution to the cross section to the total contribution. We show that it dominates for large values of the transverse momenta of jets component (gluons or quarks). Some historical introduction to the cross-sections of peripheral processes, including 2$\gamma$ creation mechanism production, including the result Brodsky-Kinoshita-Terazawa, is given.Comment: 25 pages, 4 figures, 5 table

On computing joint invariants of vector fields

A constructive version of the Frobenius integrability theorem -- that can be programmed effectively -- is given. This is used in computing invariants of groups of low ranks and recover examples from a recent paper of Boyko, Patera and Popoyvich \cite{BPP}

Calculation of the total cross section of the process e++e−→Σ0+Σˉ0e^+ + e^- \to \Sigma^0 + \bar{\Sigma}^0 in the vicinity of charmonium ψ(3770)\psi (3770) including the DD-meson loop and three gluon contributions

For the study of the structure of baryons it is necessary the investigate the production of a baryon pair in $e^+e^-$ annihilation. The baryon-antibaryon pair production at the electron-positron linear collider makes it possible to investigate in detail the basic structure of the Standard Model. The creation of baryon-antibaryon pairs in electron-positron annihilation provides an increasingly powerful tool at higher center-of-mass energies. We present phenomenological results for $\Sigma^0 \bar {\Sigma}^0$ production in $e^+e^-$ interaction at the BESIII and BABAR Colliders. In the present work, we investigate a hyperon pair produced in the reaction $e^+e^- \to \Sigma^0 \bar{\Sigma}^0$. We calculate the total cross section of the process $e^+e^- \to \Sigma^0 \bar {\Sigma}^0$ taking into account the contributions of the $D$-meson loop and three gluon loops as well as the interference of all diagrams to the Born approximation. For these contributions large relative phases are generated with respect to the pure electromagnetic mechanism. For the large momentum transferred region we obtain as a by product a fit of the electromagnetic form factor of the $\Sigma$ hyperon. The obtained results are in satisfactory agreement with experimental data.Comment: 26 pages, 12 figure

झींगा एवं मछ्ली – खास्थ्य प्रबंधन के बदलते समीकरण

कृपया पूरा लेखा पढ

Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency

Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics