1. Introduction and Preliminaries
It is a common problem for scientists in many fields, such as economics, systems engineering, medicine, artificial intelligence, and others, to build complicated systems that involve uncertainty. Though widely used, “mathematical” approaches for handling such circumstances, conventional probability, fuzzy set [
1], and rough set [
2] theories, are unable to, due to parameter constraints, consistently generate satisfactory answers. Molodtsov [
3] looked into soft set theory, a novel method for handling uncertainty in a way that surpasses the limitations of previous methods. The method uses soft sets, which lead to a universal set’s parameterized collection of subsets. Soft set theory, in contrast to earlier methods, does not impose any specific limitations on object lighting, and parameters can be selected in a number of ways, such as words, phrases, integers, and mappings. Because of this, the theory is highly flexible and simple to use in practical settings. Molodtsov demonstrated the versatility and wide applicability of soft set theory by extending it to a range of subjects, such as probability studies and game theory. Other scholars provide a variety of practical uses for soft sets and their expansions (see [
4,
5,
6,
7,
8,
9,
10]).
General topology is a well-known and significant area of mathematics that deals with the application of set theory concepts and topological structures. Soft topology is a new topic of topology study established in 2011 [
11], combining soft sets and topology concepts. The main concepts they introduced were soft open sets, soft neighborhoods, soft closure of a soft set, soft separation axioms, and soft regular and soft normal spaces. A few examples of classical topological ideas that have been extended and developed in soft set settings are presented in [
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24]. The notion of soft mapping with details was first presented in [
25], and then, soft continuity for soft mappings was defined in [
26]. Since then, researchers have focused on the study of soft continuity concepts (see [
27,
28,
29,
30,
31,
32]).
“Soft topology” is a useful extension of classical topology. There are several advantages of soft topology over standard topology, including the following: (i) Open set identification accuracy is increased by the soft topological structure. In contrast to soft topology, which permits the inclusion of intermediate degrees of openness, general topology works with binary ideas. This helps to provide more accurate information about the topological space’s characteristics. (ii) Classical topology is one instance of the broad area of soft topology. Since soft topology relaxes the rigid restrictions of classical topology, it broadens the range of topological structures and is therefore a valuable tool for comprehending a greater variety of complicated and varied settings. (iii) Since soft topology is the most efficient means of expressing uncertainty and imprecision, researchers have used it in computer science, image processing, fuzzy logic, and decision-making. (iv) When dealing with ambiguous or unclear data, soft topology is a useful mathematical modeling tool. It works especially well for encoding and assessing imprecise data, which is frequently encountered in real-world applications.
A lot of study has been done on soft continuity in soft topology and other math fields. Soft continuity is widely used in many fields, such as science, engineering, business, and economics. This encouraged us to write this work.
This work introduces and investigates three weak types of soft faint continuity: soft faint semi-continuity, soft faint pre-continuity, and soft faint -continuity.
Let  be a set of parameters and let O be a non-empty set. A soft set over O relative to  is a function .  represents the collection of all soft sets over O relative to . If  such that  for any  (resp.  and  for each ), then K is represented by  (resp. ).  and  will denote  and , respectively. If , then K is a soft point over O relative to  and represented as  if  and  for any .  represents the collection of all soft points over O with respect to . If  and , then  is said to belong to K (notation: ) if . Let . Then, K is a soft subset of H, denoted by , if  for each . The soft union (resp. intersection, difference) of K and H is denoted by  (resp. , ) and defined by  (resp. , ) for each . For any sub-collection , the soft union (resp. intersection) of the members of  is denoted by  (resp. ) and defined by  (resp. ) for each . Let  and  be two families of soft sets, and ,  be two functions. Then, a soft mapping  is defined as follows: for each  and ,  if ,  if , and . A sub-collection  is called a soft topology on O relative to , and the triplet  is called a soft topological space if ,  for any , and  for any . Let  be a soft topological space and let . Then, K is called a soft open set in  if  and K is called a soft closed set in  if .
In this paper, we will follow the terminology and concepts from [
15,
33], and we will denote a topological space as TS and a soft topological space as STS.
Let  be a TS,  be an STS, , and . Then, the closure of X in , the interior of X in , the soft closure of H in , and the soft interior of H in  will be denoted by , , , and , respectively, and the family of all closed sets in  (resp. soft closed sets in ) will be denoted by  (resp. ).
We will use the following definitions and notations in the sequel.
Definition 1.  Let  be a TS and let . Then, A is called a semi-open [34] (resp. pre-open [35], β-open [36]) set in  if  (resp. , ). The collection of all semi-open sets (resp. pre-open sets, β-open sets) in  will be denoted by  (resp. , ).  Definition 2.  A function  is called semi-continuous (S-C) [34] (resp. pre-continuous (P-C) [35], β-continuous (β-C) [36]) if  (resp. , ) for every .  Definition 3.  A function  is called faintly continuous (F-C) [37] (resp. faintly semi-continuous (F-S-C) [38], faintly pre-continuous (F-P-C) [38], faintly β-continuous (F-β-C) [38]) if  (resp. , , ) for every .  Definition 4  ([
38])
. A function  is called quasi-θ-continuous if  for every . Definition 5.  Let  be an STS and let . Then:
- (a) 
- H is called a soft semi-open [39] (resp. soft pre-open [40], soft β-open [40]) set in  if  (resp. , ). The collection of all soft semi-open sets (resp. soft pre-open sets, soft β-open sets) in  will be denoted by  (resp. , ). 
- (b) 
- H is called a soft semi-closed [39] (resp. soft pre-closed [40], soft β-closed [40]) set in  if  (resp. , ). The collection of all soft semi-open sets (resp. soft pre-open sets, soft β-open sets) in  will be denoted by  (resp. , ). 
 Definition 6.  A soft function  is said to be called soft semi-continuous (soft S-C) [41] (resp. soft pre-continuous (soft P-C) [42], soft β-continuous (soft β-C) [43]) if  (resp. , ) for every .  Definition 7  ([
44])
. Let  be an STS and let . Then, G is called a soft θ-open set in  if for every  G, there exists H∈φ such that . The family of all soft θ-open sets in  is denoted by .It is well known that  and  in general.
 Definition 8  ([
15])
. A soft function  is said to be soft faintly continuous (soft F-C) if, for every  SP(O, and  with , we find  such that  and . Definition 9.  Let  be an STS and let . Then, we obtain the following:
- (a) 
- . 
- (b) 
- . 
- (c) 
- . 
- (d) 
- . 
- (e) 
- . 
- (f) 
- . 
   2. Soft Faint Semi-Continuity
Definition 10.  A soft function  is said to be soft faintly semi-continuous (soft F-S-C, for simplicity) if, for every  SP(O, and  with , we find  such that  and .
 Theorem 1.  For a soft function , the following are equivalent:
- (a) 
-  is soft F-S-C. 
- (b) 
-  is soft S-C. 
- (c) 
-  for every . 
- (d) 
-  for every . 
- (e) 
-  for every . 
- (f) 
-  for every . 
 Proof.  - (a)
- ⟶ (b): Let  and let . Then, , and, by (a), we find  such that  and . Hence, . Therefore, . 
- (b)
- ⟶ (c): Clear. 
- (c)
- ⟶ (d): Let . Then, , and, by (c), . Hence, . 
- (d)
- ⟶ (e): Let . Then,  and, by (d), . Thus, . Since , then . 
- (e)
- ⟶ (f): Let  - . Then, by (e),  - . However,
             - 
            and
             
- Thus,  and hence, . 
- (f)
- ⟶ (a): Let  and  such that . Then, . So, by (f),  and, thus, . Therefore, , and, hence, . We set . Then,  such that  and . Therefore,  is soft F-S-C. 
□
 Theorem 2.  Let  and  be two collections of TSs. We consider the functions  and , where w is a bijection. Then,  is soft F-S-C iff  is F-S-C for all .
 Proof.  Necessity. Let 
 be soft F-S-C. Let 
. Let 
. Then, according to Theorem 2.21 of [
45], 
. So, 
. Since 
 is injective, 
. Therefore, by Theorem 4.10 of [
46], 
. This shows that 
 is F-S-C.
 Sufficiency. Let 
 be F-S-C for all 
. Let 
. Then, by Theorem 2.21 of [
45], 
 for all 
. For every 
, 
 is F-S-C, and, so, 
. Hence, for each 
, 
. Therefore, by Theorem 4.10 of [
46], 
. This shows that 
 is soft F-S-C.    □
  Corollary 1.  We consider the functions  and , where w is a bijection. Then,  is F-S-C iff  is soft F-S-C.
 Proof.  For each  and , we set  and . Then,  and . Theorem 2 ends the proof.    □
 Theorem 3.  Let  and  be two collections of TSs. We consider the functions  and , where w is a bijection. Then,  is soft S-C iff  is S-C for all .
 Proof.  Necessity. Let 
 be soft S-C. Let 
. Let 
. Then, 
. So, 
. Since 
 is injective, 
. Therefore, by Theorem 4.10 of [
46], 
. This shows that 
 is S-C.
 Sufficiency. Let 
 is S-C for all 
. Let 
. Then, 
 for all 
. For every 
, 
 is S-C, and, so, 
. Hence, for each 
, 
. Therefore, by Theorem 4.10 of [
46], 
. This shows that 
 is soft S-C.    □
  Corollary 2.  We consider the functions  and , where w is a bijection. Then,  is S-C iff  is soft S-C.
 Proof.  For each  and , we set  and . Then,  and . Theorem 3 ends the proof.    □
 Theorem 4.  Every soft F-C function is soft F-S-C.
 Proof.  Let  be soft F-C. Let . Then, . Consequently,  is soft F-S-C.    □
 Theorem 5.  Every soft S-C function is soft F-S-C.
 Proof.  Let  be soft S-C. Let . Then, . So, . Consequently,  is soft F-S-C.    □
 The converse of Theorem 4 need not be true in general.
Example 1.  Let , , , and , . We consider the identities functions  and . Since , then . Hence,  is S-C, and, by Theorem 4.1 (a) of [38], it is F-S-C. On the other hand, since  while , then  is not F-C. Therefore, by Corollary 1 and Corollary 1 of [15],  is soft F-S-C but not soft F-C.  The converse of Theorem 5 need not be true in general.
Example 2.  Let , , , , and . We define  and  by , , and  for all . Since , then  is F-C. On the other hand, since  while , then  is not S-C.
Therefore, by Corollaries 1 and 2,  is soft F-C but not soft S-C. Finally, by Theorem 4,  is soft F-S-C.
 Theorem 6.  Let  be soft regular. The following are equivalent for a soft function:
- (a) 
-  is soft S-C. 
- (b) 
-  is soft F-S-C. 
 Proof.  - (a)
- ⟶ (b): Follows from Theorem 5. 
- (b)
- ⟶ (c): Let . Since  is soft regular, then . So,  and, by (b), . This shows that  is soft S-C. 
□
 Theorem 7.  Let  be a soft function. If   is soft F-S-C, then  is soft F-S-C.
 Proof.  Let 
. Since 
, by Theorem 10 of [
15], 
. Since 
 is soft F-S-C, by Theorem 1b, 
. By Lemma 2 of [
15], 
, and, hence, 
. Thus, again, by Theorem 1b, 
 is soft F-S-C.    □
 Lemma 1.  Let  be an STS. If  and  is a non-empty subset of O such that , then .
 Proof.  Let . Let  such that . We choose  such that . Since , then . Since  and , then . Hence, . We choose . Since  and , then . Hence, . It follows that .    □
 Proposition 1.  Let  be an STS. If  and  is a non-empty subset of O such that , then .
 Proof.  Since , there exists  such that , and, so, . On the other hand, by Lemma 1, . Thus, we have  and . Hence, .    □
 Theorem 8.  If  is a soft F-S-C function and  such that , then  is soft F-S-C.
 Proof.  Let . Since  is soft F-S-C, by Theorem 1b, . So, by Proposition 1, . Hence, again, by Theorem 1b,  is soft F-S-C.    □
 Definition 11.  A soft function  is called soft quasi-θ-continuous if  for every .
 Theorem 9.  A soft function  is soft quasi-θ-continuous iff  for every .
 Proof.  Straightforward.    □
 Theorem 10.  Let  and  be two collections of TSs. Let  and  be two functions, with r being a bijection. Then, ⟶ is soft quasi-θ-continuous iff  is quasi-θ-continuous for all .
 Proof.  Necessity. Let 
 be soft quasi-
-continuous. Let 
. Let 
. Then, according to Theorem 2.21 of [
45], 
. So, 
. Since 
 is injective, 
. Therefore, by Theorem 2.21 of [
45], 
. This shows that 
 is quasi-
-continuous.
 Sufficiency.  is quasi-
-continuous for all 
. Let 
. Then, by Theorem 2.21 of [
45], 
 for all 
. For every 
, 
 is quasi-
-continuous, and, so, 
. Hence, for each 
, 
. Therefore, by Theorem 2.21 of [
45], 
. This shows that 
 is soft quasi-
-continuous.    □
  Corollary 3.  Let  be a function between two TSs, and let  be a bijective function. Then,  is quasi-θ-continuous iff  is soft quasi-θ-continuous.
 Proof.  For each  and , we set  and . Then,  and . Theorem 10 ends the proof.    □
 Theorem 11.  If  is soft θ-continuous, then  is soft quasi-θ-continuous.
 Proof.  The proof follows from Theorem 9 and Corollary 5.30 of [
44].    □
 It is not difficult to check that the soft function defined in Example 2 is soft quasi--continuous but not soft -continuous.
Theorem 12.  If  is soft F-S-C and  is soft quasi-θ-continuous, then  is soft F-S-C.
 Proof.  Let . Then,  and, hence, . Therefore,  is soft F-S-C.    □
 The following result follows from Theorems 11 and 12:
Corollary 4.  If  is soft F-S-C and  is soft θ-continuous, then  is soft F-S-C.
 Corollary 5.  If  is soft F-S-C and  is soft continuous, then  is soft F-S-C.
 Proof.  It follows from Corollary 4 and the fact that soft continuous functions are soft -continuous.    □
 The subsequent illustrations demonstrate that, according to Corollary 5, the composition in reverse order may not possess the same quality.
Example 3.  Let , , , , and . Let , , and  be the identity functions. Then,  is soft continuous and  is soft S-C (and, hence, soft F-S-C), while  is not soft F-S-C.
   3. Soft Faint Pre-Continuity
Definition 12.  A soft function  is called soft faintly pre-continuous (soft F-P-C, for short) if, for each  SP(O, and  such that , we find  such that  and .
 Theorem 13.  For a soft function , the following are equivalent:
- (a) 
-  is soft F-P-C. 
- (b) 
-  is soft P-C. 
- (c) 
-  for every . 
- (d) 
-  for every . 
- (e) 
-  for every . 
- (f) 
-  for every . 
 Proof.  - (a)
- ⟶ (b): Let  and let . Then, , and, by (a), we find  such that  and . Hence, . Therefore, . 
- (b)
- ⟶ (c): Clear. 
- (c)
- ⟶ (d): Let . Then, , and, by (c), . Hence, . 
- (d)
- ⟶ (e): Let . Then,  and, by (d), . Thus, . Since , then . 
- (e)
- ⟶ (f): Let  - . Then, by (e),  - . However,
             - 
            and
             
- Thus,  and, hence, . 
- (f)
- ⟶ (a): Let  and  such that . Then, . So, by (f),  and, thus, . Therefore, , and, hence, . We set . Then,  such that  and . Therefore,  is soft F-P-C. 
□
 Theorem 14.  Let  and  be two collections of TSs. We consider the functions  and , where w is a bijection. Then,  is soft F-P-C iff  is F-P-C for all .
 Proof.  Necessity. Let 
 be soft F-P-C. Let 
. Let 
. Then, according to Theorem 2.21 of [
45], 
. So, 
. Since 
 is injective, 
. Therefore, by Theorem 5.8 of [
47], 
. This shows that 
 is F-P-C.
 Sufficiency. Let 
 is F-P-C for all 
. Let 
. Then, by Theorem 2.21 of [
45], 
 for all 
. For every 
, 
 is F-P-C, and, so, 
. Hence, for each 
, 
. Therefore, by Theorem 5.8 of [
47], 
. This shows that 
 is soft F-P-C.    □
  Corollary 6.  We consider the functions  and , where w a bijection. Then,  is S-P-C iff  is soft S-P-C.
 Proof.  For each  and , we set  and . Then,  and . Theorem 14 ends the proof.    □
 Theorem 15.  Let  and  be two collections of TSs. We consider the functions  and , where w is a bijection. Then,  is soft P-C iff  is P-C for all .
 Proof.  Necessity. Let 
 be soft P-C. Let 
. Let 
. Then, 
. So, 
. Since 
 is injective, 
. Therefore, by Theorem 5.8 of [
47], 
. This shows that 
 is P-C.
 Sufficiency.  is P-C for all 
. Let 
. Then, 
 for all 
. For every 
, 
 is P-C, and, so, 
. Hence, for each 
, 
. Therefore, by Theorem 5.8 of [
47], 
. This shows that 
 is soft P-C.    □
  Corollary 7.  We consider the functions  and , where w is a bijection. Then,  is P-C iff  is soft P-C.
 Proof.  For each  and , we set  and . Then,  and . Theorem 15 ends the proof.    □
 Theorem 16.  Every soft F-C function is soft F-P-C.
 Proof.  Let  be soft F-C. Let . Then, . Consequently,  is soft F-P-C.    □
 Theorem 17.  Every soft P-C function is soft F-P-C.
 Proof.  Let  be soft P-C. Let . Then, . So, . Consequently,  is soft F-P-C.    □
 The converse of Theorem 16 need not be true in general.
Example 4.  Let , , and ℑ the usual topology on X. We define the functions  and  by Then,  is soft F-P-C but not soft F-C.
 The converse of Theorem 17 need not be true in general.
Example 5.  Let , , , , and . We define  and  by , , and  for all . Since , then  is F-P-C. On the other hand, since  while , then  is not P-C.
Therefore, by Corollaries 6 and 7,  is soft F-P-C but not soft P-C.
 Theorem 18.  Let  be soft regular. The following are equivalent for a soft function 
          :
- (a) 
-  is soft P-C. 
- (b) 
-  is soft F-P-C. 
 Proof.  - (a)
- ⟶ (b): Follows from Theorem 17. 
- (b)
- ⟶ (c): Let . Since  is soft regular, then . So,  and, by (b), . This shows that  is soft P-C. 
□
 The following two examples show that “soft F-S-C” and “soft F-P-C” are independent concepts:
Example 6.  Let  and . Let ℑ and ℵ be the indiscrete and the discrete topologies on X, respectively. Let  and  be the identity functions. Then,  is soft F-P-C but it is not soft F-S-C.
 Example 7.  Let , , , ℑ be the usual typology on X, and . We define  and  as follows: Then,  is soft F-S-C but it is not soft F-P-C.
 Theorem 19.  Let  be a soft function. If   is soft F-P-C, then  is soft F-P-C.
 Proof.  Let 
. Since 
, by Theorem 10 of [
15], 
. Since 
 is soft F-P-C, by Theorem 13b, 
. By Lemma 2 of [
15], 
, and, hence, 
. Thus, again, by Theorem 13b, 
 is soft F-P-C.    □
 Proposition 2.  Let  be an STS. If  and  is a non-empty subset of O such that , then .
 Proof.  Since  and , there exist  such that  and . We have  and .    □
 Claim 1.   and, hence, .
 Proof of Claim 1.  Let . To see that , let  such that . We choose  such that . Since  and , . We choose . Since  and , . Since , then . Therefore, .    □
 Theorem 20.  If  is a soft F-P-C function and  such that , then  is soft F-P-C.
 Proof.  Let . Since  is soft F-P-C, by Theorem 13b, . So, by Proposition 2, . Hence, again, by Theorem 13b,  is soft F-P-C.    □
 Theorem 21.  If  is soft F-P-C and  is soft quasi-θ-continuous, then  is soft F-P-C.
 Proof.  Let . Then,  and, hence, . Therefore,  is soft F-P-C.    □
 The following result follows from Theorems 11 and 21.
Corollary 8.  If  is soft F-P-C and  is soft θ-continuous, then  is soft F-P-C.
 Corollary 9.  If  is soft F-P-C and  is soft continuous, then  is soft F-P-C.
 Proof.  It follows from Corollary 8 and the fact that soft continuous functions are soft -continuous.    □
 The subsequent illustrations demonstrate that, according to Corollary 9, the composition in reverse order may not possess the same quality.
Example 8.  Let  and . Let ℑ, ℵ, and ℘ be the usual, indiscrete, and discrete topologies on X, respectively. Let , , and  be the identity functions. Then,  is soft continuous and  is soft P-C (and, hence, soft F-P-C) while  is not soft F-P-C.
   4. Soft Faint -Continuity
Definition 13.  A soft function  is called soft faintly β-continuous (soft F-β-C, for short) if, for each  SP(O, and  such that , we find  such that  and .
 Theorem 22.  For a soft function , the following are equivalent:
- (a) 
-  is soft F-β-C. 
- (b) 
-  is soft β-C. 
- (c) 
-  for every . 
- (d) 
-  for every . 
- (e) 
-  for every . 
- (f) 
-  for every . 
 Proof.  - (a)
- ⟶ (b): Let  and let . Then, , and, by (a), we find  such that  and . Hence, . Therefore, . 
- (b)
- ⟶ (c): Clear. 
- (c)
- ⟶ (d): Let . Then, , and, by (c), . Hence, . 
- (d)
- ⟶ (e): Let . Then,  and, by (d), . Thus, . Since , then . 
- (e)
- ⟶ (f): Let  - . Then, by (e),  - . However,
             - 
            and
             
- Thus,  and, hence, . 
- (f)
- ⟶ (a): Let  and  such that . Then, . So, by (f),  and, thus, . Therefore, , and, hence, . We set . Then,  such that  and . Therefore,  is soft F--C. 
□
 Theorem 23.  Let  and  be two collections of TSs. We consider the functions  and , where w is a bijection. Then,  is soft F-β-C iff  is F-β-C for all .
 Proof.  Necessity. Let 
 be soft F-
-C. Let 
. Let 
. Then, according to Theorem 2.21 of [
45], 
. So, 
. Since 
 is injective, 
. Therefore, by Theorem 1 of [
13], 
. This shows that 
 is F-
-C.
 Sufficiency. Let 
 is F-
-C for all 
. Let 
. Then, by Theorem 2.21 of [
45], 
 for all 
. For every 
, 
 is F-
-C, and, so, 
. Hence, for each 
, 
. Therefore, by Theorem 1 of [
13], 
. This shows that 
 is soft F-
-C.    □
  Corollary 10.  We consider the functions  and , where w is a bijection. Then,  is F-β-C iff  is soft F-β-C.
 Proof.  For each  and , we set  and . Then,  and . Theorem 23 ends the proof.    □
 Theorem 24.  Let  and  be two collections of TSs. We consider the functions  and , where w is a bijection. Then,  is soft β-C iff  is β-C for all .
 Proof.  Necessity. Let 
 be soft 
-C. Let 
. Let 
. Then, 
. So, 
. Since 
 is injective, 
. Therefore, by Theorem 1 of [
13], 
. This shows that 
 is 
-C.
 Sufficiency.  is 
-C for all 
. Let 
. Then, 
 for all 
. For every 
, 
 is 
-C, and, so, 
. Hence, for each 
, 
. Therefore, by Theorem 1 of [
13], 
. This shows that 
 is soft 
-C.    □
  Corollary 11.  We consider the functions  and , where w is a bijection. Then,  is β-C iff  is soft β-C.
 Proof.  For each  and , we set  and . Then,  and . Theorem 24 ends the proof.    □
 Theorem 25.  Every soft F-C function is soft F-β-C.
 Proof.  Let  be soft F-C. Let . Then, . Consequently,  is soft F--C.    □
 Theorem 26.  Every soft β-C function is soft F-β-C.
 Proof.  Let  be soft -C. Let . Then, . So, . Consequently,  is soft F--C.    □
 Theorem 27.  Every soft F-S-C function is soft F-β-C.
 Proof.  Let  be soft F-S-C. Let . Then, . Consequently,  is soft F--C.    □
 Theorem 28.  Every soft F-P-C function is soft F-β-C.
 Proof.  Let  be soft F-P-C. Let . Then, . Consequently,  is soft F--C.    □
 The converse of Theorem 25 need not be true in general.
Example 9.  Let , , and ℑ the usual topology on X. We define the functions  and  by Then,  is soft F-β-C but not soft F-C.
 The converse of Theorem 26 need not be true in general.
Example 10.  Let , , and . We define  and  by , , , , and  for all . Then,  is soft F-β-C but not soft β-C.
 The soft function in Example 6 is soft F--C but not soft F-S-C. Furthermore, the soft function in Example 7 is soft F--C but not soft F-P-C. Therefore, Theorems 27 and 28 are not reversible, in general.
Theorem 29.  Let  be soft regular. The following are equivalent for a soft function:
- (a) 
-  is soft β-C. 
- (b) 
-  is soft F-β-C. 
 Proof.  - (a)
- ⟶ (b): Follows from Theorem 26. 
- (b)
- ⟶ (c): Let . Since  is soft regular, then . So,  and, by (b), . This shows that  is soft -C. 
□
 Theorem 30.  Let  be a soft function. If   is soft F-β-C, then  is soft F-β-C.
 Proof.  Let 
. Since 
, by Theorem 10 of [
15], 
. Since 
 is soft F-
-C, by Theorem 22b, 
. By Lemma 2 of [
15], 
, and, hence, 
. Thus, again, by Theorem 22b, 
 is soft F-
-C.    □
 Theorem 31.  If  is soft F-β-C and  is soft quasi-θ-continuous, then  is soft F-β-C.
 Proof.  Let . Then,  and, hence, . Therefore,  is soft F--C.    □
 The following result follows from Theorems 11 and 31.
Corollary 12.  If  is soft F-β-C and  is soft θ-continuous, then  is soft F-β-C.
 Corollary 13.  If  is soft F-β-C and  is soft continuous, then  is soft F-β-C.
 Proof.  It follows from Corollary 12 and the fact that soft continuous functions are soft -continuous.    □
 The subsequent illustrations demonstrate that, according to Corollary 13, the composition in reverse order may not possess the same quality.
Example 11.  Let  and . Let ℑ, ℵ, and ℘ be the usual, indiscrete, and discrete topologies on X, respectively. Let , , and  be the identity functions. Then,  is soft continuous and  is soft β-C (and, hence, soft F-β-C), while  is not soft F-β-C.
   5. Conclusions
Many of the things we deal with daily include ambiguity. Soft set theory and its associated notions are one of the most significant theories for addressing uncertainty. One of the most significant frameworks to come out of soft set theory is soft topology. One of the most crucial ideas in soft topology is that of soft continuity, which is the subject of this paper.
In this paper, three generalizations of soft continuity—soft faint semi-continuity, soft faint pre-continuity, and soft faint -continuity—were defined and explored. We characterized each of them (Theorems  1, 13, and 22). We also investigated the correspondence between each of them and their analog concept in general topology (Theorems 2, 14, and 23, and Corollaries 1, 6, and 10). Moreover, we proved that soft faint semi-continuity is strictly weaker than both soft faint continuity (Theorem 4 and Example 1) and soft semi-continuity (Theorem 5 and Example 2), and we proved that soft faint pre-continuity is strictly weaker than both soft faint continuity (Theorem 16 and Example 4) and soft pre-continuity (Theorem 17 and Example 5). We also proved that the concepts soft faint semi-continuity and soft faint pre-continuity are independent (Examples 6 and 7). In addition, we proved that soft faint -continuity is a strictly weaker form of each of soft -continuity (Theorem 26 and Example 10), soft faint semi-continuity (Theorem 27 and Example 6), and soft faint pre-continuity (Theorem 28 and Example 7). Soft regularity, on the codomain of a soft function, is given as a sufficient condition for the equivalence between soft faint semi-continuity (resp. soft faint pre-continuity, soft faint -continuity) and soft semi-continuity (resp. soft pre-continuity, soft -continuity). In addition to these, we provided several results on soft restriction (Theorems 8 and 20), soft composition (Theorems 12, 21, and 31 and Corollaries 4, 5, 8, 9, 12, and 13), and soft graph (Theorems 7, 19, and 30).
Future research might look into the following topics: (1) defining soft weakly quasi-continuous functions; (2) defining soft almost weakly continuous functions; (3) finding a use for these new concepts in a “decision making problem”; and (4) extending the concept of 
-Menger spaces [
48] to include STSs.