Theoretical constraints on masses of heavy particles in Left-Right Symmetric Models

Left-Right symmetric models with general $g_L \neq g_R$ gauge couplings which include bidoublet and triplet scalar multiplets are studied. Possible scalar mass spectra are outlined by imposing Tree-Unitarity, and Vacuum Stability criteria and also using the bounds on neutral scalar masses $M_{\rm H^{ FCNC}}$ which assure the absence of Flavour Changing Neutral Currents (FCNC). We are focusing on mass spectra relevant for the LHC analysis, i.e., the scalar masses are around TeV scale. As all non-standard heavy particle masses are related to the vacuum expectation value (VEV) of the right-handed triplet ($v_R$), the combined effects of relevant Higgs potential parameters and $M_{\rm H^{ FCNC}}$ regulate the lower limits of heavy gauge boson masses. The complete set of Renormalization Group Evolutions for all couplings are provided at the 1-loop level, including the mixing effects in the Yukawa sector. Most of the scalar couplings suffer from the Landau poles at the intermediate scale $Q \sim 10^{6.5}$ GeV, which in general coincides with violation of the Tree-Unitarity bounds.Comment: 9 pages, 5 figures, pdflatex, Matches published versio

Left-Right Symmetry and the Charged Higgs Bosons at the LHC

The charged Higgs boson sector of the Minimal Manifest Left-Right Symmetric model (MLRSM) is investigated in the context of LHC discovery search for new physics beyond Standard Model. We discuss and summarise the main processes within MLRSM where heavy charged Higgs bosons can be produced at the LHC. We explore the scenarios where the amplified signals due to relatively light charged scalars dominate against heavy neutral $Z_2$ and charged gauge $W_2$ as well as heavy neutral Higgs bosons signals which are dumped due to large vacuum expectation value $v_R$ of the right-handed scalar triplet. In particular, production processes with one and two doubly charged Higgs bosons are considered. We further incorporate the decays of those scalars leading to multi lepton signals at the LHC. Branching ratios for heavy neutrino $N_R$, $W_2$ and $Z_2$ decay into charged Higgs bosons are calculated. These effects are substantial enough and cannot be neglected. The tri- and four-lepton final states for different benchmark points are analysed. Kinematic cuts are chosen in order to strength the leptonic signals and decrease the Standard Model (SM) background. The results are presented using di-lepton invariant mass and lepton-lepton separation distributions for the same sign (SSDL) and opposite sign (OSDL) di-leptons as well as the charge asymmetry are also discussed. We have found that for considered MLRSM processes tri-lepton and four-lepton signals are most important for their detection when compared to the SM background. Both of the signals can be detected at 14 TeV collisions at the LHC with integrated luminosity at the level of $300 fb^{-1}$ with doubly charged Higgs bosons up to approximately 600 GeV. Finally, possible extra contribution of the charged MLRSM scalar particles to the measured Higgs to di-photon ($H_0^0 \to \gamma \gamma$) decay is computed and pointed out.Comment: FCNC analysis is incorporated while fitting the scalar spectrum. Light doubly charged scalars are still compatible with FCNC. Accepted in JHEP. New References and figures are added. The fitted scalar spectrum is given in detail in appendi

Multi-photon signal in supersymmetry comprising non-pointing photon(s) at the LHC

We study a distinct supersymmetric signal of multi-photons in association with jets and missing transverse energy. At least one of these photons has the origin in displaced vertex, thus delayed and non-pointing. We consider a supersymmetric scenario in which the gravitino is the lightest supersymmetric particle (LSP) (with a mass $\sim 1~{keV}$) and the lightest neutralino is the next-to-lightest supersymmetric particle (NLSP). The NLSP decays dominantly into a photon and a gravitino within the detector with a decay length ranging from $c\tau_{\tilde{\chi}}\sim$ 50-100 cm. In addition, we assume that the second lightest neutralino and the lightest neutralino are nearly degenerate and this leads to a prompt radiative decay of the next-to-lightest neutralino into a photon and a lightest neutralino with a large branching ratio. Such degenerate neutralinos can be realised in various representations of the $SU(5)$, $SO(10)$, and $E(6)$ Grand Unified Theories (GUTs). The non-pointing photons can be reconstructed at the electromagnetic calorimeter of the ATLAS inner-detector, which have been designed with good timing and directional resolution. We find that with a centre-of-mass energy $E_{cm}=14 ~{TeV}$ at an integrated luminosity of 100 $fb^{-1}$ one may see evidence of hundreds of tri-photon events and a few four-photons events at the LHC, in addition to several thousands di-photon events. We also predict the event rates even at the early phase of LHC run.Comment: 10 pages; 6 figure

Evolutionary framework with reinforcement learning-based mutation adaptation

Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of state-of-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.</p

A simple and effective approach for tackling the permutation flow shop scheduling problem

In this research, a new approach for tackling the permutation flow shop scheduling problem (PFSSP) is proposed. This algorithm is based on the steps of the elitism continuous genetic algorithm improved by two strategies and used the largest rank value (LRV) rule to transform the continuous values into discrete ones for enabling of solving the combinatorial PFSSP. The first strategy is combining the arithmetic crossover with the uniform crossover to give the algorithm a high capability on exploitation in addition to reducing stuck into local minima. The second one is re-initializing an individual selected randomly from the population to increase the exploration for avoiding stuck into local minima. Afterward, those two strategies are combined with the proposed algorithm to produce an improved one known as the improved efficient genetic algorithm (IEGA). To increase the exploitation capability of the IEGA, it is hybridized a local search strategy in a version abbreviated as HIEGA. HIEGA and IEGA are validated on three common benchmarks and compared with a number of well-known robust evolutionary and meta-heuristic algorithms to check their efficacy. The experimental results show that HIEGA and IEGA are competitive with others for the datasets incorporated in the comparison, such as Carlier, Reeves, and Heller.</p

An improved artificial jellyfish search optimizer for parameter identification of photovoltaic models

The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population. To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks. The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final results and convergence rate. In addition, to assess the performance of the proposed approach under different operation conditions for the solar cells, two additional PV modules (multi-crystalline and thin-film) are investigated, and the demonstrated scenarios highlight the utility of the proposed MJSO-based methodology.</p

An improved jellyfish algorithm for multilevel thresholding of magnetic resonance brain image segmentations

Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based onmultilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer).We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is achieved by applying two novel strategies: Rankingbased updating and an adaptive method. Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions. We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution; we allow a small amount of exploration to avoid descents into local minima. The two strategies are integrated with the JSA to produce an improved JSA (IJSA) that optimally thresholds brain MR images. To compare the performances of the IJSA and JSA, seven brain MR images were segmented at threshold levels of 3, 4, 5, 6, 7, 8, 10, 15, 20, 25, and 30. IJSA was compared with several other recent image segmentation algorithms, including the improved and standard marine predator algorithms, the modified salp and standard salp swarm algorithms, the equilibrium optimizer, and the standard JSA in terms of fitness, the Structured Similarity Index Metric (SSIM), the peak signal-to-noise ratio (PSNR), the standard deviation (SD), and the Features Similarity IndexMetric (FSIM). The experimental outcomes and theWilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM, the PSNR, the objective values, and the SD; in terms of the SSIM, IJSA was competitive with the others.</p

Patchy Amphiphilic Dendrimers Bind Adenovirus and Control Its Host Interactions and in Vivo Distribution

The surface of proteins is heterogeneous with sophisticated but precise hydrophobic and hydrophilic patches, which is essential for their diverse biological functions. To emulate such distinct surface patterns on macromolecules, we used rigid spherical synthetic dendrimers (polyphenylene dendrimers) to provide controlled amphiphilic surface patches with molecular precision. We identified an,. I optimal spatial arrangement of these patches on certain dendrimers that enabled their interaction with human adenovirus 5 (Ads). Patchy dendrimers bound to the surface of Ads formed a synthetic polymer corona that greatly altered various host interactions of Ads as well as in vivo distribution. The dendrimer corona (1) improved the ability of Ad5-derived gene transfer vectors to transduce cells deficient for the primary Ad5 cell membrane receptor and (2) modulated the binding of Ads to blood coagulation factor X, one of the most critical virus host interactions in the bloodstream. It significantly enhanced the transduction efficiency of Ad5 while also protecting it from neutralization by natural antibodies and the complement system in human whole blood. Ads with a synthetic dendrimer corona revealed profoundly altered in vivo distribution, improved transduction of heart, and dampened vector sequestration by liver and spleen. We propose the design of bioactive polymers that bind protein surfaces solely based on their amphiphilic surface patches and protect against a naturally occurring protein corona, which is highly attractive to improve Ad5-based in vivo gene therapy applications