diff --git a/figures/source.jpg b/figures/source.jpg
index 90e6aed..e5f3bc3 100644
Binary files a/figures/source.jpg and b/figures/source.jpg differ
diff --git a/paper_eng_short.tex b/paper_eng_short.tex
index 5890797..e3a30aa 100644
--- a/paper_eng_short.tex
+++ b/paper_eng_short.tex
@@ -124,7 +124,7 @@ According to the diagram, the knowledge base consists of several levels of condi
 According to figure \ref{fig:kb-structure}, the rule base will be represented as a hierarchical structure with two levels of rules. Previously, the authors obtained a structural model of the metadata $M$ of the integrated IS \cite{Kamaletdinova-2024}.
 Thus, the first level will be represented as rules consisting of linguistic terms and will depend on changes in the meta-model. The second level of rules will be dynamically formed based on the results obtained at the first level.
 
-Let $INP=\{INP_1,INP_2, ..., INP_z\}, z \in N$ be the set of linguistic terms representing the input data of the metadata model $M$, and $OUT= \allowbreak \{OUT_1, OUT_2, ...,\allowbreak OUT_w\}, w \in N$ be the set of linguistic terms representing the key processes of the metadata model $M$. Hence, the rule describing the first level will have a set-theoretic representation as follows: 
+Let $INP=\{INP_1,INP_2, ..., INP_z\}, z \in N$ be the set of linguistic terms representing the input data of the metadata model $M$, and $OUT= \allowbreak \{OUT_1, ...,\allowbreak OUT_w\}, w \in N$ be the set of linguistic terms representing the key processes of the metadata model $M$. Hence, the rule describing the first level will have a set-theoretic representation as follows: 
 \begin{equation} 
 		P(INP) \rightarrow \{INP^{OUT_s}\}, OUT_s,
 \end{equation}
@@ -191,7 +191,7 @@ First, on the basis of the metamodel, the first level of the rule base is formed
 
 The algorithm presented in Figure \ref{fig:algorithm} consists of the following steps: 
 \begin{itemize}
-		 \item Input data, represented as key-value data tuples from data storage of IS (e.g. $inp1 = 7$) of different types (integer, string, date, and boolean variables), are transformed into linguistic terms represented as $INP=\{INP_1, ..., INP_z\},$ $z \in N$, figure \ref{fig:sources}.
+		 \item Input data, represented as key-value data tuples from data storage of IS (e.g. $inp1 = 7$) of different types (integer, string, date, and boolean variables), are transformed into linguistic terms represented as $INP=\{INP_1, ..., INP_z\},$ $z \in N$, figure \ref{fig:sources}, for detail see \cite{Kamaletdinova-2024-2}.
 	
 		\item Using the first level rule base (abstract level rule base) and the transformed input data ($INP$), a logical inference is performed, represented as $\{\{INP^{OUT_s}\}, OUT_s\}, s \in N$.