Mathematical modelling for pseudorapidity distribution of hadron-hadron collisions

El-Bakry, Mahmoud Y.; El‐Dahshan, El‐Sayed A.; El-Bakry, Salah Yaseen

doi:10.1140/epjp/i2015-15017-5

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…(a) SSF is rarely thermophilic, although some exceptions can be found in recent literature, 20,21 in an attempt to overcome the problem of self-heating observed in organic matrices, 22 which often negatively affects the development of the desired mesophilic strain. On the contrary, composting is inherently thermophilic, as these temperatures are necessary for the sanitation of a product that is mainly applied to land as soil amendment.…”

Section: Howevermentioning

confidence: 99%

“…Down-stream is the sequence of operations needed to obtain an end product from fermentation, and must be assessed at a representative scale, 7 while purification and characterization are usually performed at a lower scale, with the objective of having a clear picture of what has been obtained from SSF. 20,21 (b) Type and use of bioproduct? As commented above, it is evident that the complexity, cost, and feasibility of downstream from exhaust SSF solids strongly depends on the bioproduct considered.…”

Section: Solid-state Fermentation Challengesmentioning

confidence: 99%

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A Perspective of Solid-State Fermentation As Emergent Technology for Organic Waste Management in the Framework of Circular Bioeconomy

Sánchez

2024

ACS Sustainable Resour. Manage.

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Among the different technologies to treat and valorize organic waste, solid-state fermentation (SSF) is gaining relevance in recent years as it permits the recovery of valuable biomaterials from waste, changing the paradigm: “from waste to raw material source”. SSF is very similar to composting, as it implies the aerobic biodegradation of organic solid waste, but with the objective of obtaining bioproducts in some point of the process, although a compost-like end product may be also produced. Also, SSF can be coupled with anaerobic digestion, as solid digested materials can act as substrates for SSF, with a double benefit: renewable bioenergy and bioproducts. Among these bioproducts, SSF has moved from classical biotechnological compounds (enzymes or antibiotics) to more complex biomaterials, such as biopesticides, bioplastics, biosurfactants, or biostimulants, among others, which are now an emergent field of research and one of the main objectives of SSF. In summary, it is evident that SSF will have a predominant role in the framework of circular bioeconomy and in novel biorefineries for biowaste and wastewater sludge valorization, although some challenges still need further research (scale-up and down-stream).

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Section: Howevermentioning

confidence: 99%

Section: Solid-state Fermentation Challengesmentioning

confidence: 99%

A Perspective of Solid-State Fermentation As Emergent Technology for Organic Waste Management in the Framework of Circular Bioeconomy

Sánchez

2024

ACS Sustainable Resour. Manage.

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“…Our group [13][14][15][16] studied the applications of artificial intelligence and the evolutionary computation techniques such as neural network, adaptive fuzzy inference system, genetic programming, genetic algorithm, hybrid technique model and many others to solve many complex (nonlinear) problems in high-energy physics and showed best fitting with the corresponding experimental data in comparison with the conventional techniques.…”

Section: застосування генетичного алгоритму-алгоритму нейронної мереж...mentioning

confidence: 99%

The Application of the Genetic Algorithm-Back Propagation Neural Network Algorithm in the High-Energy Physics

El-Bakry,

El-Dahshan,

Radi

et al. 2016

East Eur. J. Phys.

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Multiparticle production mechanism is one of the most phenomena that the high-energy physics concerns. In this work, the evolutionary genetic algorithm (GA) is used to optimize the parameters of the back-propagation neural networks (BPNN). The hybrid evolutionary-neuro model (GA-BPNN) was trained to simulate the rapidity distribution 1/N(dN/dY) of positive and negative pions p-Au, p-Ag and p-Xe for p-Ar, p-Xe interactions at lab momentum Plab =100 GeV/c. Also, for total charged, positive and negative pions for interactions at Plab = 200 GeV/c. Finally, total charged particles for p- Pb collision at center-of-mass energy sqrt(s) = 5.02 TeV are simulated. An efficient ANN network with different connection parameters (weights and biases) have been designed by the GA to calculate and predict the rapidity distribution as a function of the lab momentum Plab, mass number (A) and the number of particles per unit solid angle (Y). Our simulated results have been compared with the experimental data and the matching has been clearly found. It is indicated that the developed GA-BPNN model for rapidity distribution was more successful.

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“…In recent years, the artificial neural networks (ANNs) [36] approach has been widely used for prediction of physics as classification and regression problems [37][38][39][40][41][42]. However, this approach has been found to be more efficient compared to statistical algorithms.…”

Section: Introductionmentioning

confidence: 99%

Proton--proton and proton--antiproton differential elastic cross sections modeling at high and ultra-high energies using a hybrid computing paradigm

El‐Dahshan¹

2017

Turk J Phys

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This work presents a hybrid computing technique for modeling the differential elastic cross section of both proton-proton "pp" and proton-antiproton "pp(bar)" collisions from high to ultra-high energy regions (from 13.9 GeV to 14 TeV) as a function of the center-of-mass energy "s" squared and four momentum transfer squared "t". We proposed a genetic algorithm (GA) and support vector regression (SVR) hybrid techniques to calculate and predict the "differential elastic cross section" of both "pp" and "pp(bar)". Our proposed GA-SVR hybrid model shows a good match to the available experimental data, as well as predicting the latest and future "TOTEM" experiments for differential elastic cross sections that are not utilized in the training phase. An extrapolation of the differential cross section to |t| → 0 gives the total cross section through the optical theorem. The model calculations and predictions for "pp" and "pp(bar)" total cross-sections over a wide collision energy ranging from ISR to LHC are given. All the results for the total cross-sections matched well with the last and the more recent measurements ( √ s = 7 TeV and 8 TeV), as well as the results of other models for the future "TOTEM" measurements at ( √ s = 10 TeV and 14 TeV).

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Mathematical modelling for pseudorapidity distribution of hadron-hadron collisions

Cited by 3 publications

References 34 publications

A Perspective of Solid-State Fermentation As Emergent Technology for Organic Waste Management in the Framework of Circular Bioeconomy

A Perspective of Solid-State Fermentation As Emergent Technology for Organic Waste Management in the Framework of Circular Bioeconomy

The Application of the Genetic Algorithm-Back Propagation Neural Network Algorithm in the High-Energy Physics

Proton--proton and proton--antiproton differential elastic cross sections modeling at high and ultra-high energies using a hybrid computing paradigm

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