## 4 Results
  
\begin{tabular}{l l l l l l l l} \hline \hline
**Flight Phase** & **Time (s)** & \(\mathbf{we_{1}}\) & \(\mathbf{we_{2}}\) & \(\mathbf{we_{3}}\) & \(\mathbf{we_{4}}\) & \(\mathbf{we_{5}}\) \\ \hline \multirow{9}{*}{\(F_{1}\)} & \(t_{1}\) & 0 & 0.964442249 & 0.390430326 & 0 & 0 \\  & \(t_{2}\) & 0.002702187 & 1 & 0.36404086 & 0.0000953 & 0.16444297 \\  & \(t_{3}\) & 0.016580289 & 1 & 0.225480532 & 0.0000813 & 0.403093601 \\  & \(\Also, according to Formula (8), the direction of the goals changed as follows:  
Aircraft 1: \(we_{F_{1}}(C_{1})=0.3279\); \(we_{F_{2}}(C_{1})=0.2945\); \(we_{F_{3}}(C_{1})=0.2276\). Aircraft 2: \(we_{F_{1}}(C_{1})=0.3627\); \(we_{F_{2}}(C_{1})=0.3633\); \(we_{F_{3}}(C_{1})=0.2797\). Noted that a smaller value indicates a lower psychophysiological load on the flight instructor. The expert model for assessing the mental health of the flight instructor is designated as \(M_{mh}\). The assessment of the mental health of the flight instructor was carried out based on the proposed criteria. Each criterion represents a question to which instructor \(C_{1}\) responded on a ten-point scale: \(K_{1}=4\); \(K_{2}=3\); \(K_{3}=5\); \(K_{4}=4\); \(K_{5}=5\); \(K_{6}=6\); \(K_{7}=2\). After obtaining these expert assessments, the scores were summed up using Formula (9): \(p(C_{1})=29\). Then, the obtained data underwent phase classification using a quadratic Z-spline, converted the numerical values into fuzzy interpretation via Formula (10). As a result of this process, \(\mu_{mh}(C_{1})=0.77\). The obtained value of \(\mu_{mh}(C_{1})\) was then compared with the corresponding range of the linguistic variable. In this case, the value 0.77 falls within the interval (0.64; 0.84), which corresponds to the linguistic level \(e_{2}\)--\"Above-average level of mental health of the flight instructor\". The hybrid model for determining the overall psychophysiological workload of the flight instructor during duty is \(H_{psfi}\). The aggregated predicted assessment of the individual psychophysiological workload of the flight instructor in the working environment \(we(C)\) considers the noise level and determines the linguistic conclusion DM \(E\), which characterizes the level of their mental health. To obtain the overall quantitative indicator of the flight instructor's psychological workload, fuzzy logical inference was applied based on the S-shaped membership function (12). The evaluation process was carried out for the two aircraft in different flight phases, which allowed consideration of dynamic changes in workload conditions to provide a more accurate interpretation of the obtained results, as follows:  
For aircraft 1: \(\tau_{2F_{1}}(C_{1})=0.281\); \(\tau_{2F_{2}}(C_{1})=0.2767\); \(\tau_{2F_{3}}(C_{1})=0.2675\);  
For aircraft 2: \(\tau_{2F_{1}}(C_{1})=0.2852\); \(\tau_{2F_{2}}(C_{1})=0.2852\); \(\tau_{2F_{3}}(C_{1})=0.2748\). After that, an integrated quantitative indicator of the psychophysiological load of the flight instructor was determined, considering all phases of flight for the studied aircraft according to Formula (16), as follows:  
For aircraft 1: \(\tau(C)=\frac{1}{3}(0.281+0.2767+0.2675)=0.2751\);  
For aircraft 2: \(\tau(C)=\frac{1}{3}(0.2852+0.2852+0.2748)=0.2817\). Next, \(\Lambda\)--the linguistic level of the flight instructor's psychophysiological workload in the working environment of the aircraft--was determined. For this, the obtained value of \(\tau(C)\) was compared with the term-set variable, and the results obtained were as follows:  
For aircraft 1: \(\Lambda_{3}\) = \"Moderate level of psychophysiological workload of the flight instructor\";  
For aircraft 2: \(\Lambda_{3}\) = \"Moderate level of psychophysiological workload of the flight instructor\". Thus, an example of assessing the psychophysiological workload level of a flight instructor during the performance of official duties is demonstrated.