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Rotating machines in particular induction electrical machines are important industry instruments. In manufacturing, electrical motors are exposed to many damages, and this causes stators and rotors not to work correctly. In this paper we addressed modal analysis and an intelligent method to detect motor load condition and also the stator faults such as turn-to-turn and coil-to-coil faults using motor vibration analysis. A three-phase induction motor with a special winding was used to create the faults artificially. The vibration signal of motor in different states such as working without fault, with various faults and with various loads was acquired. Some spectral analysis was done using the spectrum and the spectrograph of vibration signals and differences due to different states of motor were observed. Suitable features such as Linear Prediction Cepstral Coefficients and Fourier Transform Filter Bank Coefficients were extracted from vibration signals and were then applied to non-supervised (SOM) and supervised (LVQ) neural networks in order to classify motor faults and its load condition. Many experiments were conducted to evaluate the effect of neural network type, type and length of feature vector, length of training signal etc. In brief, using SOM and LVQ neural networks, 20 element Filter Bank feature vectors, and 600ms of the training data, performance of 93.6% and 94.2% were obtained for load and fault detection respectively.

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Introduction:
Technological innovation is one of the key indicators for economic growth and productivity. Recent studies show that R&D investment causes technological change. However, this relationship is not always obvious and seems to vary according to the level of economic development. A large number of studies on developed countries confirm the positive relationship between research and development, innovation and productivity. However, in developing countries, this relationship is not always clear. In this regard, in order to allocate an important share of national income to research and development, developing economies need to achieve a high and sustainable economic growth rate or create an economic development policy based on new innovation. This paper investigates the threshold effect of medium-high technology exports on total factor productivity in 50 developing and developed countries over the period 2007-2020.

Methodology:
For analyzing data, panel smooth transition regression (PSTR) model is used, which was presented and expanded by Gonzalez et al. (2005) and Colletaz & Hurlin (2006) and is very suitable for heterogeneous panel data. Thus, Medium-High Technology Exports index is chosen as the transition variable. Following the study of Hammar and Bellarebi (2021), the general model shows the relationship between the logarithm of total factor productivity, the logarithm of advanced and medium exports (transition variable), the logarithm of trade openness, and the logarithm of research and development expenditures.

Results and Discussion:
The results show a nonlinear relationship between the variables under study. Based on the necessary test results, considering only one transition function with a threshold value and two regimes is sufficient for nonlinear estimation of the model. Also, the logarithm of the value of the transition variable threshold is estimated about 3.0816 and the slope parameter is estimated about 6.4226. Research and development (R&D) expenditures and trade have negative significant effects on total factor productivity in the first regime on total factor productivity that this effect by crossing the threshold (Medium-High Technology Exports) effect for the variable of R&D expenditures becomes positive and insignificant. This result is consistent with the study of Sepherdoost and Afshari (2016). In addition, the results show that the influence of trade on total factor productivity is negative and significant, but its influence is lower than before, in the second regime. This result is consistent with the study of Lotfalipour et al. (2015)

Conclusion:
Considering the role of high and medium technology exports in the relationship between research and development expenditures and total factor productivity, it can be said that developing countries in the initial stages of growth can increase their productivity by increasing the export of high technology industries, to a level of specific development, despite the very high importance of research and development in the development of high-tech industries. Only a very small part of the country's resources is spent on research and development, and the weakness of the workforce has reduced the utilization of this small amount of domestic research and development investment. So, the low contribution of research and development expenses indicates that companies do not have much desire for innovative efforts and the creation of new technology transfer capacity as a stimulus for the quantitative and qualitative growth of industrial products. This action has caused them to not provide new products and services and reduce their competitiveness in domestic and foreign markets.
The most important policy recommendation is that the governments of developing countries should develop high and medium technology exports witch through their positive effects such as productivity growth, reduction of production costs, improvement of financial development and growth of innovation and technology, it is possible to achieve favorable economic growth and to improve the productivity of all factors. Also, the development of exports with advanced and medium technology and knowledge-based production will initially attract educated and specialized unemployed people, and with the improvement of management practices, the productivity of production factors and the level of technology will increase and lead to product innovation. Therefore, considering the importance of exporting with advanced and medium technology and knowledge-based production, it is suggested that the universities move towards the third generation university, in which case the chain of knowledge to technology will be completed in the university and the university will support the industries by developing the latest technologies. It is also suggested that in order to improve their competitiveness in the international arena and to advance their development goals, developing countries allocate a greater share of their income resources to research and development and create incentives for researchers in various economic sectors, especially in industries with technological capabilities, and move more towards the knowledge-based economy and the implementation of research policies based on innovation.

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رIntroduction
The tourism sector plays a pivotal role in national economic development because it promotes the development of related industries such as transportation. The boosting effect of tourism on economic growth is more obvious in developing countries with abundant tourism resources (Dieke, 2003). However, tourism development undergoes great dynamic changes due to complex and volatile external environments, such as global climate change and social disturbances with a high degree of uncertainty (Nguyen et al., 2020; Scott et al., 2019). thus, the tourism economy has become very fragile and has a weak ability to withstand risks from various sources (Wang et al., 2022). Therefore, this study attempts to examine the Indirect impact of tourism on economic vulnerability and other factors affecting economic vulnerability in selected developing countries over the period 1995-2021 by using a panel smooth transition regression model.
Methodology
In this study, the nonlinear threshold effect of tourism on economic vulnerability in selected developing countries is examined using a PSTR model. For this purpose, following Gonzalez et al. (2005) and Colletaz & Hurlin (2006), a PSTR model with two regimes and a transition function is defined. according to the study of Colletaz & Hurlin (2006), can be chosen among the explanatory variables, the lag of the dependent variable, or any other variable outside the model that is theoretically related to the model under study and causes a nonlinear relationship.
qit  represents the transition variable and, according Gonzalez et al. (2005) suggest that, in practice, considering one or two thresholds, m = 1  or m = 2 , is sufficient to account for parameter variability. For m =1 , the model implies that the two extreme regimes are associated with low and high values of transition variable with a single monotonic transition of the coefficients from β0  to β0+β1 as transition variable increases, with the change centered around location parameters. When →∞  , transition function the model becomes an indicator function Iqit>c1 , defined as IA=1  when event A occurs and 0 otherwise. In this case, the PSTR model in (1) reduces to the two-regime panel threshold model of Hansen (1999). For m = 2, the transition function has its minimum at c1+c22  and reaches 1 at both low and high values of qit . In this case, the transition function (2) becomes constant for any value of m when γ → 0 . In this case, the model collapses into a fixed effects homogeneous or linear panel regression model. Accordingly, in the PSTR model, based on the observations of the transition variable and the slope parameter, the estimated coefficients are continuous and bounded between F = 1 and F = 0.
As mentioned earlier, another salient feature of the PSTR model is that it provides a parametric approach to cross-country heterogeneity and time instability of the slope coefficients, allowing the parameters to change smoothly as a function of the threshold variable yit . More precisely, the income elasticity for the i ^th country at time t is defined by the weighted average of the parameters β0  and β1 .
 It is worth noting that the estimation of the parameters of the PSTR model consists in eliminating the individual effects by removing the individual means and then applying nonlinear least squares (NLS) to the transformed model (see for details, Gonzalez et al., 2005). This method is equivalent to maximum likelihood (ML) estimation in the case of normal errors.
Following Gonzalez et al. (2005), Colletaz & Hurlin (2006), and Jude (2010), the estimation steps of a PSTR model are as follows: First, the linearity test against PSTR is performed using Wald Tests (LMw ) coefficients, Fisher Tests (LMF ) coefficients and LRT Tests (LR ) coefficient statistics according to Colletaz & Hurlin (2006). Once we have rejected the linearity hypothesis, we can verify that nonlinearity no longer exists. Then it is a matter of testing whether there is a transition function or whether there are at least two transition functions.
Results and Discussion:
The results show that in the first regime, trade openness has a negative effect on economic vulnerability, which has decreased and turned positive after crossing the threshold location in the second regime. Government expenditure has a positive effect on economic vulnerability, and after crossing the threshold location and entering the second regime, its effect gradually decreased and became positive. Inflation coefficients in the regime had a negative and insignificant effect on economic vulnerability, which after crossing the threshold location and entering the second regime, its effect gradually decreased and became positive, but it was significant at the 10 percent level.
Also, the results show that before the threshold location and at low levels of tourism income, the logarithm of financial development has a negative and significant effect on economic vulnerability, and after the threshold location and entering the second regime, this effect is still negative and increases. The coefficients of the logarithm of total unemployment have a negative effect on economic vulnerability in the first regime and before the threshold location. By crossing the threshold location and entering the second regime, this effect decreases and becomes positive.
Conclusion
In this study, the threshold and Indirect effect of tourism on economic vulnerability in selected developing countries during 1995-2021 was investigated. For this purpose, the PSTR model provided and developed by Gonzalez et al. (2005) and Colletaz & Hurlin (2006) was used. The estimation results suggested a nonlinear relationship between trade openness, financial development, government spending, total unemployment, inflation and economic vulnerability. Moreover, considering a threshold with two regimes or a transition function is sufficient to investigate nonlinear behaviors. The results show that the threshold of the transition variable is equal to 3.1378 and the slope parameter is equal to 33.8978, which include only one transition function and only one threshold.
Considering the positive impact of tourism on financial development and government spending, it can be said that the development of tourism income can indirectly reduce the economic vulnerability of developing countries by increasing financial development and national income and adjusting industrial structures, while this mediating effect at the level Social does not appear. Therefore, it is suggested that considering that in developing countries where the overall economic strength of a country is weak, with low economic development, the development of international tourism should be cautious. The main task should be to create infrastructure and stimulate domestic consumption. Investment should be focused on industries such as manufacturing and financial development to increase the growth of GDP and improve people's quality of life. Physical needs are the most important factor to maintain economic stability and prevent economic vulnerability. For these countries, attention should be paid to domestic tourism by strengthening the construction of tourism service facilities, adjusting the structure of the tourism industry and ensuring the sustainable development of international tourism, while accelerating the development of domestic tourism.
From an institutional perspective, creating active employment policies to create preferential employment conditions for low-income people can further ensure the positive impact of low-level international tourism on economic vulnerability. Finally, regardless of the level of economic development, one should have a clear understanding of the performance of the tourism industry based on the state of the country. This is possible by correctly positioning the tourism industry and not exaggerating the role of tourism and not giving up on its development due to some negative factors. Economic vulnerability can be effectively reduced only by combining tourism with other industries and focusing on overall economic development.