Startseite Study of energy loss, range, and stopping time for proton in germanium and copper materials
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Study of energy loss, range, and stopping time for proton in germanium and copper materials

  • Ahlam Habeeb Hussien EMAIL logo und Rashid Owaid Kadhim
Veröffentlicht/Copyright: 1. März 2024
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Abstract

In this study, the energy loss, range, and stopping time were calculated using the Bethe theory for protons in germanium and copper materials over an energy range of 0.01–1,000 MeV. The mathematical equations were programmed and implemented using MATLAB2013 to obtain the results for the relative mass stopping power and range, illustrated in tables and graphs. The computed results from the Bethe and Ziegler equations were compared with the experimental results from Pstar. The results showed a difference between the experimental and theoretical results at the beginning of the range used in the Bethe equation because it is a quantized equation that is not suitable for low energies. Hence, a correction term was proposed for the Bethe equation to correct the results at low energies, which produced negative values. On the other hand, the Ziegler equation showed good agreement along the range used as it is a semi-empirical equation. The range and stopping time of the incident particle increased with increasing incident particle energy in the mentioned materials.

1 Introduction

Ionizing radiation interacts with matter and energy loss as it moves through it. The study of this subject is of utmost importance for radiation measurements. Radiation detection is based on the energy deposited in the interacting material. The radiation measurement is performed by detectors, capable of interpreting the measurement results, to study the radiation interactions and their potential consequences [1].

Understanding how heavy charged particles gain energy is crucial in various scientific and technological fields. This knowledge helps researchers develop efficient particle accelerators and understand the behavior of cosmic rays in space. There are several ways heavy charged particles can gain energy. These include interactions with electric and magnetic fields and collisions with other particles [2].

Charged particles are often used for radiation therapy because they have a well-defined penetration depth in tissue, the depth being dependent on the incident energy of the particles, its physical characteristics, and the nature of the irradiated tissue. The main reason for using charge particles as protons and heavy ions in radiation therapy lies in their physical selectivity, i.e., the ability to deliver some amount of energy to the target. Beams of charged particles have a unique dose distribution, exhibiting a relatively flat entrance dose region (plateau) followed by a sharp dose peak, the Bragg peak, in which the particles lose rest of their energy [3].

Protons can be used in the production of protective armor due to their high energy and penetrating capabilities. Charged subatomic particles have shown potential in various applications within the field of engineering [4].

2 Stopping power due to ionization–excitation for p and α

The loss energy of charged particles can be calculated using Bethe equation (quantum mechanics) [5]:

(1) d E d x ( mass stopping power ) = k z 2 z 1 2 A β 2 L Bethe ,

where

(2) L Bethe = Ln 2 m e c 2 β 2 1 β 2 β 2 Ln I ,

k = 4 π r 0 2 m e c 2 = 0.307075 ,

(3) d E d x = 0.307075 z 2 z 1 2 A β 2 L Bethe ,

where r 0 is the classical radius of electron e 2 m e c 2 = 2,0818 × 10 15 m , m e is the rest mass of electron 9.11 × 10 31 kg , c is the velocity of light in the vacuum 3 × 10 8 m / s , v is the velocity of light in the target material, z 2 is the atomic number of target material, z 1 is the atomic number of projectile, A is the atomic mass of medium, β is the relative velocity β = v / c , I is thethe ionization potential of the medium in eV

The formalism of Bethe–Bloch theory of energy loss, including various modifications, has been described extensively in several investigations [6]:

d E d x = 4 π Z 2 Z 1 2 e 4 m e v 2 N ln 2 m e c 2 v 2 1 β 2 β 2 ln I + L Bloch ,

where

L Bloch = L 0 + Z 1 L 1 + Z 1 2 L 2 ,

where L 0 is the Born correction; Z 1 L 1 is the Barkas effect, which comes due to the polarization; and Z 1 2 L 2 is the Bloch effect.

3 Ziegler formula

Using interpolation formulae of various degrees of complexity, the gap between the high- and low-energy theories has been closed. Varelas and Biersack suggested a model of complexity [7]:

(4) d E d x = S Low S High S Low + S High ,

where S Low (low-energy stopping) and S High (high-energy stopping)

From E = (1–10) keV

(5) d E d x = A 1 E 1 / 2 .

From E = (10–999 keV)

(6) d E d x = s Low 1 + S High 1 ,

where S Low = A 2 E 0.5 .

S High = A 3 E ln 1 + A 4 E + ( A 5 E ) .

From E = (1,000–1,00,000) keV

(7) d E d x = A 6 β 2 ln A 7 β 2 1 β 2 β 2 i = 0 4 A i = 8 ( ln E ) I ,

where A 1A i are the fitting constants represented in the study by Andreson and Ziegler [8].

4 Range

The range of heavy charged particles in matter refers to the distance these particles can travel before losing a significant amount of their energy. This distance is influenced by various factors, such as the particle’s energy, the density of the material it is passing through, and the type of interactions the particle undergoes with the atoms of the material [9]:

(8) R ( E 0 ) = 0 E 0 d E d x 1 d E .

If alpha particle energy is equal to 5.5 MeV, they have described that the range with energies between 4–7 MeV can be expressed by the following equation [10]:

(9) R air = E 3 / 2 .

The range relies on the density in the medium. The range of alpha particle in solids and liquids may be approximated by comparison with ranges in air based on equation (4), which is explained by:

(10) R ( cm ) = 0.00032 A 1 2 ρ R air ,

where R ( cm ) is the total range in cm of alpha particle in an absorber, A is the atomic weight of the solid or liquid, and ρ is the density of the medium.

5 Stopping time

It determines how long it takes for the particle to come to a complete stop and lose its kinetic energy while interacting with the material. The stopping time is influenced by factors such as the type and energy of the charged particle, the properties of the material, and the mechanisms of interaction between the particle and the material [1]:

(11) v = 2 E m e = c 2 E m e c 2 = ( 3 × 10 8 ) E 931 ( MeV / amu ) m A ,

where m A is the mass of particle in (amu) and E is the energy of particle in MeV. Then, the stopping time t is calculated from the range R as:

(12) t = R v = R kc m e c 2 2 E = R K ( 3 × 10 8 m / s ) 932 MeV / amu 2 m A E .

If the particle were uniformly decelerated, then (v) would be given by v/2 and k would be 0.5. However, charged particles generally lose energy at a greater rate near the end of their range, and k should be a somewhat higher fraction. By assuming K = 0.60, the stopping time can be estimated as:

(13) t 1.2 × 10 7 R m A E .

And this time can be expressed in terms of the stopping power by using the chain of differentiation [11]:

(14) d E d t = d E ρ d x ρ d x d t = 1 ρ d E d x ( ρ v ) = ρ v d E ρ d x ,

where v = dx/dt denotes the velocity of the particle [12].

(15) t = E d E d t = E ρ v d E ρ d x .

6 Results and discussion

As shown in Figure 1, the loss of energy caused by the collision of a proton with germanium atoms due to atomic ionization was calculated using the Bethe equation (3), as a function of energy in MeV cm2/mg. The results were compared with the practical program Pstar [13] at the energy range of 0.01–1,000 MeV. The Bethe equation yielded results that differed from the practical results of the Pstar program, In low energies, the values are negative due to logarithm in the equation. These negative values have no physical meaning as they indicate that the energy of the charged particle increases as it passes through the absorbing medium. This contradicts reality, as a moving charged particle within an absorbing medium loses its energy until it reaches a state of rest. In this case, the negative values are ignored. Therefore, in this research, a semi-empirical equation is proposed to be added to the Bethe equation for the purpose of correcting the results at low energies. This equation is referred to as equation (16) as a personal work. but it began to approach the results at a range of 0.2 MeV with a slight difference. However, at an energy range of 0.4 MeV, the results of the Bethe equation approached the practical results and reached a level of agreement. This is because the Bethe equation is valid for high-energy quantities.

Figure 1 
               Stopping power for proton in copper.
Figure 1

Stopping power for proton in copper.

As for the Ziegler equation, it was compatible with the practical program from the beginning to the end of the range because it is a semi-empirical equation.

As shown in Figure 2, in this study, the energy loss as a function of the energy range for copper was calculated using the same equations. The results were compared with the Pstar program, and it was found that the equation yielded results that differed from the practical program at the beginning of the energy range up to 0.4 MeV. It started to approach after this range but still showed a difference compared to the experimental results. At 0.6 MeV, it approached and provided promising results, reaching a level of agreement.

Figure 2 
               Stopping power for proton in germanium.
Figure 2

Stopping power for proton in germanium.

To eliminate negative values that do not yield accurate results at lower energies, the same correction mentioned above was applied to the Beth equation to align the results with the practical outcomes of the P star program. This addition was included as a personal contribution to the results of this research:

(16) d E d x ( mass stopping power ) = 0.307075 z 2 z 1 2 A β 2 L Bethe + s ,

S = a · E b ,

a = 0.01974, b = −1.475.

As shown in Figures 3 and 4, the range of alpha particles in copper and germanium elements has been calculated using equation (8) after substituting equation (16) into it. The energy range increases linearly with energy as ionization and excitation. It is noted that the range is very small within the energy range of 0.01–20 MeV, indicating that the charged particles lose their energy significantly. The energy loss is attributed to inelastic collisions between the heavy charged particles and target electron atoms, leading to ionization and excitation of the medium atoms, resulting in the greatest energy loss. When the energy is greater than 20 MeV, the range increases significantly, indicating that relative effects become dominant. In this case, the interaction time between the incident charged particles and target electron atoms decreases, and thus, there is not enough time for the charged particles to lose energy. Electronic collisions become predominant, thereby increasing the range.

Figure 3 
               Range proton in copper.
Figure 3

Range proton in copper.

Figure 4 
               Range proton in germanium.
Figure 4

Range proton in germanium.

As shown in Figure 5, the graph of particle energy versus stopping time using equation (12) shows that the stopping time increases linearly with the stopping power dependent on the atomic number. At low energies, nuclear stopping time prevails due to elastic collisions of the charged particle with target nuclei, resulting in a relatively short stopping time. At high energies, the stopping time increases linearly with energy as ionization, excitation, and electronic stopping become prevalent. It is noted that the stopping time is similar for the elements copper and germanium due to their proximity in atomic numbers (Tables 1 and 2).

Figure 5 
               Stopping time for proton in copper and germanium materials.
Figure 5

Stopping time for proton in copper and germanium materials.

Table 1

Stopping power values for copper and germanium materials

E (MeV) Copper (cu) Germanium (Ge)
Pstar Bethe p.w (equation (16)) Ziegler Pstar Bethe p.w (equation (16)) Ziegler
0.01 0.110 −17.398 0.135 0.1129 0.145 −17.290 0.153 0.1474
0.04 0.183 −2.065 0.192 0.1945 0.240 −2.120 0.206 0.2410
0.06 0.199 −0.931 0.211 0.2171 0.254 −0.984 0.248 0.2545
0.08 0.206 −0.461 0.238 0.2278 0.253 −0.510 0.259 0.2531
0.10 0.209 −0.222 0.245 0.2315 0.246 −0.266 0.273 0.2464
0.15 0.209 0.030 0.254 0.2275 0.227 −0.006 0.289 0.2270
0.20 0.205 0.118 0.240 0.2181 0.212 0.087 0.299 0.2114
0.40 0.179 0.173 0.229 0.1819 0.172 0.154 0.200 0.1719
0.60 0.154 0.160 0.202 0.1554 0.147 0.146 0.187 0.1471
0.80 0.133 0.144 0.171 0.1357 0.126 0.132 0.159 0.1292
1.00 0.118 0.130 0.149 0.1197 0.111 0.120 0.140 0.1138
2.00 0.080 0.088 0.095 0.0805 0.074 0.082 0.089 0.0765
4.00 0.051 0.056 0.058 0.0516 0.048 0.052 0.055 0.0491
6.00 0.039 0.042 0.043 0.0391 0.037 0.039 0.041 0.0373
8.00 0.032 0.034 0.035 0.0319 0.030 0.032 0.033 0.0304
10.00 0.027 0.028 0.029 0.0271 0.026 0.027 0.028 0.0259
20.00 0.016 0.017 0.017 0.0162 0.015 0.016 0.016 0.0155
40.00 0.010 0.010 0.010 0.0096 0.009 0.009 0.009 0.0092
60.00 0.007 0.007 0.007 0.0071 0.007 0.007 0.007 0.0068
80.00 0.006 0.006 0.006 0.0057 0.005 0.006 0.006 0.0055
100.00 0.005 0.005 0.005 0.0049 0.005 0.005 0.005 0.0047
150.00 0.004 0.004 0.004 0.0037 0.003 0.004 0.004 0.0035
200.00 0.003 0.003 0.003 0.0031 0.003 0.003 0.003 0.0029
250.00 0.003 0.003 0.003 0.0027 0.003 0.003 0.003 0.0026
300.00 0.002 0.002 0.002 0.0024 0.002 0.002 0.002 0.0023
350.00 0.002 0.002 0.002 0.0022 0.002 0.002 0.002 0.0021
400.00 0.002 0.002 0.002 0.0021 0.002 0.002 0.002 0.0020
450.00 0.002 0.002 0.002 0.0020 0.002 0.002 0.002 0.0019
500.00 0.002 0.002 0.002 0.0019 0.002 0.002 0.002 0.0018
600.00 0.002 0.002 0.002 0.0018 0.002 0.002 0.002 0.0017
700.00 0.002 0.002 0.002 0.0017 0.002 0.002 0.002 0.0016
800.00 0.002 0.002 0.002 0.0016 0.002 0.002 0.002 0.0016
900.00 0.002 0.002 0.002 0.0016 0.002 0.002 0.002 0.0015
1000.00 0.002 0.002 0.002 0.0016 0.001 0.001 0.001 0.0015
Table 2

Proton range values in copper and germanium materials

E (MeV) Copper (Cu) Germanium (Ge)
Pstar p.w (equation (10)) Pstar p.w (equation (10))
0.01 0.00000604 0.0000063 0.00001420 0.0000176
0.04 0.00002196 0.0000374 0.00003688 0.0000399
0.06 0.00003182 0.00003993 0.00005174 0.00004045
0.08 0.00004154 0.00004727 0.00006688 0.00004811
0.10 0.00006137 0.00005463 0.00008261 0.00007859
0.15 0.00007594 0.00004693 0.00012513 0.00013070
0.2 0.00010135 0.00009415 0.00017206 0.00020650
0.4 0.00021334 0.00022322 0.00039933 0.00042168
0.6 0.00034484 0.00042533 0.00068653 0.00118454
0.8 0.00049832 0.00057201 0.00103183 0.00187155
1.0 0.00067354 0.00085822 0.00143227 0.00206863
2.0 0.00183632 0.00208472 0.00419689 0.00503390
4.0 0.00539686 0.00668442 0.01310141 0.01418599
6.0 0.01041031 0.01254725 0.02602887 0.03608388
8.0 0.01674888 0.02014438 0.04267209 0.05281177
10 0.02434978 0.03027909 0.06280533 0.08382179
20 0.07948430 0.11222846 0.21191710 0.31255483
40 0.26558296 0.23786305 0.72575870 0.84094428
60 0.54035874 0.64376269 1.49407846 1.69287086
80 0.89405830 1.01713452 2.48963731 2.83270664
100 1.31950673 1.45032493 3.69207994 4.03913641
150 2.66031390 2.76344238 7.49814952 7.69615176
200 4.34080717 4.36619377 12.29459660 12.1597939
250 6.30941704 6.22572490 15.01480385 17.33856435
300 8.52354260 8.31932532 24.26350851 23.16921480
350 10.94730942 10.63000584 31.21391562 29.60443053
400 13.46300448 13.14439794 38.71206514 36.60698043
450 16.31838565 15.85160382 46.63212435 44.14651423
500 19.18721973 18.74250431 54.95928942 52.19763519
600 25.39237668 25.04527476 72.68689859 69.75077044
700 31.92825112 32.00156342 91.48778682 89.12394553
800 38.77802691 39.57112449 111.0288675 110.2050764
900 45.85201794 47.72115017 131.4951887 132.9027939
1,000 53.12780269 56.42418729 152.3686158 157.1406411

7 Conclusion

  1. The Bethe equation is invalid at low energy ranges, but after proposing a semi-empirical correction equation that is added to the Bethe equation, the results become suitable for the used energy range. This knowledge can be used in various fields, such as designing solid materials used for radiation protection or nuclear packaging materials. It helps improve the efficiency of particle direction and reduces their negative impact on surrounding materials.

  2. The range of the proton in the charged particle increases with the increase in the energy range.

  3. The stopping time of a charged particle is directly proportional to the distance traveled and increases with an increase in the atomic number. It determines how long it takes for the particle to come to a complete stop and lose its kinetic energy while interacting with the material. The stopping time is influenced by factors such as the type and energy of the charged particle, the properties of the material, and the mechanisms of interaction between the particle and the material.

  1. Conflict of interest: Authors state no conflict of interest.

  2. Data availability statement: The most datasets generated and/or analysed in this study are comprised in this submitted manuscript. The other datasets are available on reasonable request from the corresponding author with the attached information.

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Received: 2023-11-22
Revised: 2023-12-18
Accepted: 2024-01-04
Published Online: 2024-03-01

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
Heruntergeladen am 4.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/eng-2022-0576/html
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