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Restrained captive domination number

  • Zainab Yasir Alrikabi EMAIL logo , Ahmed A. Omran and Hassan Jiad Al Hwaeer
Published/Copyright: January 20, 2024
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Abstract

The restrained captive domination number (RCDN), denoted by γ Rca ( G ) , is a new definition of domination number in graphs introduced in this article. If D is a captive dominating set and G [ V D ] has no isolated vertex, then D is a restrained captive dominating set (RCDS) of graph G . Furthermore, some properties, theorems, and propositions are determined. The inverse RCDN is introduced too. The RCDN in complement graphs is discussed. Finally, the RCDS of some graphs is determined. We have explained that if the graph has pendent vertex, then it has no RCDN.

1 Introduction

In this work, a graph G is a simple, finite, and undirected graph, where V ( G ) = { v 1 , v 2 , , v p } is the set of vertices and E ( G ) = { e 1 , e 2 , , e q } is the set of edges, such that | V | = p and | E | = q , the number of edge incident on a vertex v 1 is called the degree of it, and it is denoted by deg ( v 1 ) with minimum and maximum degree δ ( G ) and ( G ) , respectively [1 2 3]. Let G [ D ] be a subgraph of G induced by vertices in D . A subset D V ( G ) is a captive dominating set if G [ D ] has no isolated vertex ( D is a total dominating set), and each vertex v D is adjacent to at least one vertex in V D . The cardinality of a smallest captive dominating set of G , denoted by γ ca ( G ) , is the captive domination number of G . The symbol γ 1 ( G ) represents the minimum cardinality over all inverse dominating set of G [4].

Therefore, many studies have been presented on and from these fields, topological graphs [5,6,7,8,9], number theory graph [10], general graphs [11,12,13,14,15,16], and others. The reader can found all notions that are not mentioned in previous studies [10,17,18]. The captive domination is initiated by Ahmed and colleagues [4], and they obtain many properties that are bounded. Later, Zainab and Ahmed determined the captive domination, complement of captive domination, and inverse of captive domination of many kinds of graphs [19]. In this work, the new properties are discussed, also for some graphs, and the restrained captive domination number (RCDN) and inverse RCDN (IRCDN) are determined for some graphs, and several bounds for RCDN are stated.

Figure 1 
               Relation between dominating set, CDS, and RCDS.
Figure 1

Relation between dominating set, CDS, and RCDS.

Figure 2 
               Diagram of 
                     
                        
                        
                           D
                        
                        D
                     
                  , 
                     
                        
                        
                           
                              
                                 D
                              
                              
                                 1
                              
                           
                        
                        {D}_{1}
                     
                  , and 
                     
                        
                        
                           
                              
                                 D
                              
                              
                                 2
                              
                           
                        
                        {D}_{2}
                     
                  .
Figure 2

Diagram of D , D 1 , and D 2 .

Figure 3 
               
                  
                     
                        
                        
                           G
                           .
                        
                        G.
Figure 3

G .

Figure 4 
               
                  
                     
                        
                        
                           
                              
                                 C
                              
                              
                                 6
                              
                           
                        
                        {C}_{6}
                     
                  .
Figure 4

C 6 .

Figure 5 
               
                  
                     
                        
                        
                           
                              
                                 C
                              
                              
                                 3
                              
                           
                        
                        {C}_{3}
                     
                  .
Figure 5

C 3 .

Figure 6 
               
                  
                     
                        
                        
                           
                              
                                 F
                              
                              
                                 n
                              
                           
                        
                        {F}_{n}
                     
                  .
Figure 6

F n .

Figure 7 
               
                  
                     
                        
                        
                           
                              
                                 P
                              
                              
                                 n
                              
                           
                           ⊙
                           
                              
                                 P
                              
                              
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                        {P}_{n}\odot {P}_{m}
                     
                  .
Figure 7

P n P m .

Figure 8 
               
                  
                     
                        
                        
                           G
                           ≡
                           
                              
                                 B
                              
                              
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                        G\equiv {B}_{n,n}
                     
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Figure 8

G B n , n .

2 RCDN

Definition 2.1. Consider G be a graph, a subset D V ( G ) is a restrained captive dominating set (RCDS) in G if D is a CDS and every vertex in V D is adjacent to at least one vertex in V D .

Let CDS be a captive dominating set and CDN be a captive domination number.

Definition 2.2. A subset D V ( G ) is a minimal RCDS (MRCDS) if it has no proper RCDS.

Definition 2.3. The set D is a RCDN if:

D = min { D i ; D i MRCDS } , and is denoted by γ Rca ( G ) . (Figures 1 and 2)

Example 2.4. In Figure 3.

D = { v , w } is the MCDS of G; thus, γ ca ( G ) = 2 .

D 1 = { u , w } is the MRCDS of G; thus, γ Rca ( G ) = 2 .

Observation 2.5: If G is of order n with RCDS and RCDN, then:

  1. The order of G is n 4 .

  2. deg ( v ) 2 , for all v V ( G ) .

  3. δ ( G ) 2 , ( G ) 2 .

  4. If v V D , then N ( v ) D and N ( v ) V D .

  5. Induced subgraphs G [ D ] and G [ V D ] have no isolated vertex.

  6. γ ( G ) γ t ( G ) γ ca ( G ) γ Rca ( G ) .

Lemma 2.6

[3] If G is a graph in which the degree of each vertex is at least 2, then G contains a cycle.

Proposition 2.7

If a graph G has RCDN, then G contains a cycle. But the converse is not necessarily true.

Proof

Since every graph G has RCDN and the degree of each vertex is at least 2 by Observation 2.5. (2), then G contains a cycle by Lemma 2.6.

The converse is not necessarily true. For the example, take C 6 (Figure 4). Every vertex is in C 6 of degree 2 but C 6 has no RCDN.

Corollary 2.8

Any graph G of order n has a pendent vertex, and then, G has no RCDN.

Corollary 2.9

If G is a cycle graph, C n has RCDN, and n 0 ( mod 4 ) , then G [ V D ] is an induced subgraph that has components of perfect matching graph.

Theorem 2.10

The RCDN of the K n , K n , m , W n , C n , and P n is as follows:

  1. γ Rca ( K n ) = 2 , n 4 .

  2. γ Rca ( K n , m ) = 2 , n , m 2 .

  3. γ Rca ( W n ) = 2 .

  4. γ Rca ( C n ) = n 2 if n 0 ( mod 4 ) .

  5. γ Rca ( P n ) has no RCDN.

Proof

  1. Since the set D is CDS by Observation 2.10. [4] and degree of every vertex in K n is n 1 , this means that every vertex in V D is adjacent to at least one vertex in V D , and the result is obtained.

  2. In a graph K n , m , there are two sets of vertices, namely, { v 1 , v 2 , , v n } is first set and { u 1 , u 2 , , u m } is second set in K n , m , such that every vertex in the first set is adjacent to all vertices in the second set. To create a RCDS, one of each set must belong to D , since the set D is CDS by Observation 2.10 [4] and every vertex in V D is adjacent to at least one vertex in V D , and the result is obtained.

  3. Let D = { v , u } , where the vertex v is dominating vertex, since D is CDS by Observation 2.10. [4] and an induced subgraph in V D is a path P n ; therefore, every vertex in V D is adjacent to at least one vertex in V D , and then, γ Rca ( W n ) = 2 .

  4. If n 0 ( mod 4 ) , then it is axiomatic that the vertices of set D = v 2 + 4 i , v 3 + 4 i , i = 0,1 , , n 4 1 represent the RCDS such that all adjacent vertices in the set D have maximum neighborhood and G [ V D ] has no isolated vertex. So, D is the minimum RCDS. Thus, γ Rca ( C n ) = n 2 .

  5. P n has no RCDN by Corollary 2.8.

Remark 2.11: If G has RCDN, then G has CDN, but the converse is not true.

Proof

By definition, the result is obtained.

The converse is not true. For the example, take C 3 (Figure 5). A graph C 3 has CDN equal 2 , but it has no RCDN.

Proposition 2.12

If G has CDN and it has at least two dominating vertices u and v one of them in V D , then G has RCDN.

Proof

Suppose the vertex v V D; since v is dominating vertex, which means it is adjacent to every vertex in V D , then G has RCDN.

Proposition 2.13

If a graph G G 1 G 2 G n , and a graph G has components G i , i = 1 , 2 , , n , then γ Rca ( G ) = γ Rca ( G 1 ) + γ Rca ( G 2 ) + + γ Rca ( G n ) .

Proof

It is clear that every component in G has distinct RCDS with RCDN ( G i ) , i = 1 , 2 , , n . So, γ Rca ( G ) = γ Rca ( G 1 ) + γ Rca ( G 2 ) + + γ Rca ( G n ) .

Observation 2.14: Consider G be a graph, with ( G ) = n 1 . Then, G ̅ has no RCDS.

Remark 2.15: The graphs W n , ̅ S n , ̅ and K n ̅ have no RCDN.

Proof

Since W n ̅ , S n ̅ , and K n ̅ have no captive domination number by Remark 2.16 [4] and Observation 2.6 [4], then it has no RCDN.

Proposition 2.16

γ Rca ( C n ̅ ) = 2 if n 6 .

Proof

Since C n ̅ = E ( K n ) E ( C n ) , this means that every vertex in C n ̅ has a regular degree equal to n 3 , the set D is a CDS by Proposition 2.19 [4], and since C n ̅ [ V D ] has no isolated vertex, then γ Rca ( C n ̅ ) = 2 .

Proposition 2.17

γ Rca ( P n ̅ ) = 2 , for n 6 .

Proof

Since P n ̅ = E ( K n ) E ( P n ) , this means that there exist two vertices of degree n 2 and n 2 of vertices of degree n 3 and the set D is a CDS by Proposition 2.18 [4], and since C n ̅ [ V D ] has no isolated vertex, then γ Rca ( P n ̅ ) = 2 .

3 IRCDN

Definition 3.1: Consider G be a graph, and let D V ( G ) be a MRCDN in G if V ( G ) D , contains a RCDS D 1 with respect to set D , then D 1 is an inverse RCDS (IRCDS) in G , and the minimum cardinality of an IRCDS of G , denoted by γ Rca 1 ( G ) , is called an IRCDN.

Proposition 3.2

γ Rca 1 ( C n ) = n 2 , n 4 if and only if n 0 ( mod 4 ) .

Proof

If n 0 ( mod 4 ) , which is obvious by taking the remaining vertices, by Theorem 2.10 (4), then γ Rca 1 ( C n ) = n 2 . Conversely, since γ Rca 1 ( C n ) = n 2 , this means n 0,2 ( mod 4 ) , so C n has no RCDN in this modulus ( n 2 ( mod 4 ) ) . Therefore, n 0 ( mod 4 ) .

Observation 3.3

  1. γ Rca 1 ( K n ) = 2 , for n 4 .

  2. γ Rca 1 ( K n , m ) = 2 , for n , m 2 .

Proposition 3.4

γ Rca 1 ( W n ) = 2 n 4 if n 0 , 3 ( mod 4 ) 2 n 4 1 if n 2 ( mod 4 ) 2 n 4 2 if n 1 ( mod 4 ) .

Proof

Let v 1 , v 2 , , v n be the vertices of graph W n and D V ( G ) such that:

D 1 = { v 2 + 4 i , v 3 + 4 i , i = 0 , 1 , . . . , n 4 1 } if n 0 ( mod 4 ) { v 2 + 4 i , v 3 + 4 i , i = 0 , 1 , . . . , n 4 2 } if n 1 ( mod 4 ) { v 2 + 4 i , v 3 + 4 i , i = 0 , 1 , . , n 4 2 } { v n 2 } if n 2 ( mod 4 ) { v 2 + 4 i , v 3 + 4 i , i = 0 , 1 , , n 4 2 } { v n 1 , v n 2 } if n 3 ( mod 4 ) .

The maximum number of vertices that can be restrained captive dominated by two vertices is four. So, we can choose the middle vertices from any four sequent vertices. Then, there are four cases, which are as follows:

Case 1: If n 0 ( mod 4 ) , let D 1 1 = { v 2 + 4 i , v 3 + 4 i , i = 0,1 , . . , n 4 2 } be IRCDS; all adjacent vertices in this set have maximum neighborhood. So, D 1 1 is the minimum RCDS and γ Rca 1 ( W n ) = 2 n 4 .

Case 2: If n 1 ( mod 4 ) , let D 2 1 = { v 2 + 4 i , v 3 + 4 i , i = 0 , 1 , . . , n 4 2 } , then it is clear that by the same way in Case 1, the set D 2 1 is the minimum RCDS. Thus, γ Rca 1 ( W n ) = 2 n 4 1 .

Case 3: If n 2 ( mod 4 ) , let D 3 1 = { v 2 + 4 i , v 3 + 4 i , i = 0 , 1 , . . , n 4 2 } , then by the same way in Case 1 and Case 2, D 3 1 is the minimum RCDS to vertices { v n , v 1 , , v n 2 } . So the remaining vertex v n 1 is not dominated by any vertex in D 3 1 ; therefore, we need take a vertex v n 2 in D 3 1 ; then, D 1 = D 3 1 { v n 2 } . Thus, γ Rca 1 ( W n ) = 2 n 4 2 .

Case 4: If n 3 ( mod 4 ) , let D 4 1 = { v 2 + 4 i , v 3 + 4 i , i = 0 , 1 , . . , n 4 2 } , then D 4 1 is MRCDS to vertices { v 1 , v 2 , , v n 3 } , so the remaining vertices in W n that are not dominated by D 4 1 are { v n 1 , v n 2 } ; then, D 1 = D 4 1 { v n 1 , v n 2 } . Thus , γ Rca 1 ( W n ) = 2 n 4 .

From all the aforementioned cases, we obtain the results.

4 RCDN and IRCDN of some graphs and its complements

Proposition 4.1

If G be a fan graph denoted by G F n P 1 P m , then γ Rca ( G ) = 2 .

Proof

Consider G be a fan graph of order 1 + m such that 1 be the order of a path P 1 and m be the order of a path P m . The vertex set of the first path is { v 1 } and the vertex set of the second path is { u 1 , u 2 , , u m } ; since the vertex v 1 is dominating vertex, which means that dominates on all vertices in G , then D = { v 1 , u 1 } be minimum and RCDS, so γ Rca ( G ) = 2 .

Proposition 4.2

If G be a fan graph denoted by G F n P 1 P m , then

γ Rca 1 ( G ) = 2 m 4 if m 0,2,3 ( mod 4 ) 2 m 4 1 if m 1 ( mod 4 ) , and the graph G has no inverse RCDS when m = 1 , 2 .

Proof

Let G be a fan graph of order 1 + m such that P 1 be a path of order 1 and P m be a path of order m (as shown in the Figure 6), then depending on m , there are four cases as follows:

Case 1. If m = 1 , then the graph P 1 P 1 K 2 , so one can be concluded that there is no CDS; therefore, there is no RCDS in this case.

Case 2. If m = 2 , then the graph P 1 P 3 K 3 , so one can be concluded that there is no RCDS; therefore, there is no IRCDS in this case.

Case 3. If m 0,2,3 ( mod 4 ) , then γ Rca 1 ( G ) γ Rca 1 ( F n ) = γ ca ( P m ) = 2 m 4 by Theorem 2.12 [4].

Case 4. If m 1 ( mod 4 ) , then γ Rca 1 ( G ) γ Rca 1 ( F n ) = γ ca ( P n ) 1 = 2 m 4 1 .

From all the aforementioned cases, we obtain the results.

Proposition 4.3

If G be a graph denoted by G F n ̅ P 1 P m , ̅ then G has no RCDN.

Proof

Since a graph G has isolated vertex, then G has no RCDN by Observation 2.5 (2) and by Definition 2.1.

Proposition 4.4

If G be a corona graph denoted by G P n P m , then

γ Rca ( G ) = 2 if n = 1 , m 3 n if n 2 , m 2 , the graph G has no RCDS when n = 1 , m = 1 , 2 and n 2 , m = 1 .

Proof

Consider G be a graph of order n + nm such that P n is a path of order n and P m is a path of order m , then depending on n and m , there are five cases as follows:

Case 1. If n = 1 , m = 1 then the graph P n P m K 2 , so one can be concluded that there is no RCDS in this case.

Case 2. If n = 1 , m = 2 , then the graph P n P m K 3 , so one can be concluded that there is no RCDS in this case.

Case 3. If n 2 , m = 1 , then G [ V D ] has isolated vertices, so one can be concluded that there is no RCDS in this case by Observation 2.5 (5).

Case 4. If n = 1 , m 3 , then the graph P n P m F n , then γ Rca ( G ) = 2 .

Case 5. If n 2 , m 2 , then let v 1 , v 2 , , v n be the vertices of path P n and let u 1 , u 2 , , u m be the vertices of path P m ; since every vertex in P n joins with vertices of copy path p m from u 1 to u m and G [ V D ] has no isolated vertices, then the minimum dominating set D = { v 1 , v 2 , , v n } is RCDS and γ Rca ( G ) = n .

For all the aforementioned cases, we obtain the results.

Proposition 4.5

If G be a corona graph denoted by G p n p m , then

γ 1 Rca ( G ) = n 2 m 4 if n 1 , m 0 , 2 , 3 ( mod 4 ) n 2 m 4 1 if n 1 , m 1 ( mod 4 ) , and the graph G has no IRCDS when n = 1 , m = 1 , 2 and when n 2 , m = 1 .

Proof

Let G be a graph of order n + nm such that P n is a path of order n and P m is a path of order m (as shown in Figure 7) then depending on n and m then there are five cases as follows:

Case 1: If n = 1 , m = 1 , then the graph P 1 P 1 K 2 , so one can be concluded that there is no inverse RCDS in this case.

Case 2: If n = 1 , m = 2 , then the graph P 1 P 2 K 3 , so one can be concluded that there is no IRCDS in this case.

Case 3: If n 2 , m = 1 , then the graph G has pendent vertices, so there is no RCDS; therefore, there is no IRCDN in this case by Corollary 2.8.

Case 4: If n 1 , m 0 , 2 , 3 ( mod 4 ) , then there are two subcases as follows:

  1. If n = 1 , m 0,2,3 ( mod 4 ) , then P 1 P m F n , so γ Rca 1 ( G ) = 2 m 4 by Proposition 4.2.

  2. If n > 1 , m 0 , 2 , 3 ( mod 4 ) , then since there are n copies of F n connected by a path and every copy of F n , γ Rca 1 ( F n ) = 2 m 4 by Proposition 4.2, then γ Rca 1 ( G ) = n 2 m 4 .

Case 5: If n 1 , m 1 ( mod 4 ) , then there are two subcases as follows:

  1. If n = 1 , m 1 ( mod 4 ) , then P 1 P m F n , so γ Rca 1 ( G ) = 2 m 4 1 by Proposition 4.2.

  2. If n > 1 , m 1 ( mod 4 ) , by the same way in Case 4, then γ Rca 1 ( G ) = n 2 m 4 1 .

From all the aforementioned cases, we obtain the results.

Proposition 4.11

If G B n , n be a barbell graph of order 2 n , then γ Rca ( G ) = 2 .

Proof

The vertex set of B n , n is { v i :1 i 2 n }, the vertex set of the first complete graph K n is { v 1 , v 2 , , v n } , and the vertex set of the second complete graph K n is { v n + 1 , v n + 2 , , v 2 n } as shown in Figure 8.

From the definition of a barbell graph, there are two copies of a complete graph, each of which can be dominated by one vertex, and to keep the totality of a dominating set, two adjacent vertices are taken. Thus, the set D = { v n , v n + 1 } is a minimum RCDS, and γ Rca ( G ) = 2 .

Proposition 4.12

If G B n , n , n > 2 be the barbell graph of order 2n, then γ Rca 1 ( G ) = 4 .

Proof

By proposition 4.11, let = { v n , v n + 1 } , so it is obvious that the set D is restrained captive dominating and it is the minimum, and since there exists another dominating set in V D and it is minimum, let S = { v 1 , v 2 } { v n + 2 , v n + 3 } , this set is inverse RCDN, then γ Rca 1 ( G ) = 4 .

5 Conclusion

New definitions, properties, and boundaries of a new parameter have been discovered along the process of arriving at the results described in this article. Specially identified above are several graphs’ inverse and complement.

  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: 2023-05-19
Revised: 2023-08-02
Accepted: 2023-08-08
Published Online: 2024-01-20

© 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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  51. Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
  52. Optimizing the performance of concrete tiles using nano-papyrus and carbon fibers
  53. Special Issue: AESMT-5 - Part II
  54. Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM
  55. The complex of Weyl module in free characteristic in the event of a partition (7,5,3)
  56. Restrained captive domination number
  57. Experimental study of improving hot mix asphalt reinforced with carbon fibers
  58. Asphalt binder modified with recycled tyre rubber
  59. Thermal performance of radiant floor cooling with phase change material for energy-efficient buildings
  60. Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks
  61. A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models
  62. Evaluation of mechanically stabilized earth retaining walls for different soil–structure interaction methods: A review
  63. Assessment of heat transfer in a triangular duct with different configurations of ribs using computational fluid dynamics
  64. Sulfate removal from wastewater by using waste material as an adsorbent
  65. Experimental investigation on strengthening lap joints subjected to bending in glulam timber beams using CFRP sheets
  66. A study of the vibrations of a rotor bearing suspended by a hybrid spring system of shape memory alloys
  67. Stability analysis of Hub dam under rapid drawdown
  68. Developing ANFIS-FMEA model for assessment and prioritization of potential trouble factors in Iraqi building projects
  69. Numerical and experimental comparison study of piled raft foundation
  70. Effect of asphalt modified with waste engine oil on the durability properties of hot asphalt mixtures with reclaimed asphalt pavement
  71. Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
  72. Numerical study on discharge capacity of piano key side weir with various ratios of the crest length to the width
  73. The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm
  74. Numerical and experimental study of the impact on aerodynamic characteristics of the NACA0012 airfoil
  75. Effect of nano-TiO2 on physical and rheological properties of asphalt cement
  76. Performance evolution of novel palm leaf powder used for enhancing hot mix asphalt
  77. Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study
  78. Flexural behavior of RC beams externally reinforced with CFRP composites using various strategies
  79. Influence of fiber types on the properties of the artificial cold-bonded lightweight aggregates
  80. Experimental investigation of RC beams strengthened with externally bonded BFRP composites
  81. Generalized RKM methods for solving fifth-order quasi-linear fractional partial differential equation
  82. An experimental and numerical study investigating sediment transport position in the bed of sewer pipes in Karbala
  83. Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach
  84. Implementation for the cases (5, 4) and (5, 4)/(2, 0)
  85. Center group actions and related concepts
  86. Experimental investigation of the effect of horizontal construction joints on the behavior of deep beams
  87. Deletion of a vertex in even sum domination
  88. Deep learning techniques in concrete powder mix designing
  89. Effect of loading type in concrete deep beam with strut reinforcement
  90. Studying the effect of using CFRP warping on strength of husk rice concrete columns
  91. Parametric analysis of the influence of climatic factors on the formation of traditional buildings in the city of Al Najaf
  92. Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq
  93. Hybrid approach for cost estimation of sustainable building projects using artificial neural networks
  94. Assessment of indirect tensile stress and tensile–strength ratio and creep compliance in HMA mixes with micro-silica and PMB
  95. Density functional theory to study stopping power of proton in water, lung, bladder, and intestine
  96. A review of single flow, flow boiling, and coating microchannel studies
  97. Effect of GFRP bar length on the flexural behavior of hybrid concrete beams strengthened with NSM bars
  98. Exploring the impact of parameters on flow boiling heat transfer in microchannels and coated microtubes: A comprehensive review
  99. Crumb rubber modification for enhanced rutting resistance in asphalt mixtures
  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  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
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