ali maleki

Stress is the physiological and psychological response of the body to external or internal pressures that the brain perceives as a threat, affecting both physical and mental performance. Low stress levels can enhance performance, but high stress levels can cause psychological and bodily harm. One effective method for estimating stress is mapping the features of biological signals to quantitative values for stress. In recent years, efforts have been made to continuously detect stress using biological signals and applying complex methods to estimate stress. This article introduces a simple and effective method for estimating stress. The existence of a monotonic increasing relationship between stress and biological signal features is an assumption of this study. Accordingly, EMG and ECG signals recorded under stressful conditions were preprocessed. Then, features were extracted and normalized from each time window. Subsequently, by calculating the 2-norm of the features and applying a scaling factor based on the individual's relative heart rate, a continuous value termed estimated stress was obtained. The qualitative evaluation of the results with self-reported values and stress levels at different stages of the experiment confirms the effectiveness of this method. The simplicity, understandability, and low computational cost are characteristics of the proposed method. The proposed method can be implemented in low-cost gadgets and can transfer stress estimation from the laboratory to daily life.

A facile synthesis of Fe3O4-HNTs-polypyrrole (FHP) nanocomposite was performed by in situ polymerization of polypyrrole (PPy) with two different percentages on the synthesized Fe3O4-HNTs by co-precipitation method. To confirm the FHP nanocomposite structure, various analytical techniques were employed, including FTIR, SEM, TGA, BET, and VSM analysis. The TGA analysis provided the quantity of polymer incorporated into the structure. Metal ions adsorption efficiency was experimentally investigated based on factors such as pH, adsorbent dosage, contact time, and initial contamination concentration. The adsorption efficiency of Cd (II) and Cu (II) using 30 mg of adsorbent in 60 min was determined to be 77.2 % and 82.8 %, with maximum adsorption capacities of 74.7 mg/g and 66.3 mg/g, respectively. The FHP nanocomposite, which has multiple reactive sites, was created by utilizing cost-effective materials that are readily available. Furthermore, the adsorption mechanism can be accurately characterized by pseudo-first-order kinetic model, suggesting physical adsorption, along with the Langmuir and Dubinin-Raduskevich (D-R) isotherm models, pointing towards the adsorption of a single layer of pollutants on the surface of the nanocomposite and micropore filling theory. Hence, the FHP nanocomposite shows promise as an adsorption system for effectively removing metal ions due to its exceptional efficiency in removal.

In this study, a hybrid fuzzy regression method for trapezoidal fuzzy data is proposed. Initially, a new definition of a weighted fuzzy arithmetic for trapezoidal fuzzy data is introduced. Subsequently, a method for hybrid fuzzy least squares linear regression based on this definition is developed. Additionally, methods for bivariate and multivariate regression models are obtained. Finally, reliability measures for the hybrid regression model using the new definition of weighted fuzzy arithmetic for trapezoidal fuzzy data are calculated.

Autism spectrum disorders are a type of developmental disorder that primarily disrupt social interactions and communications. Autism has no treatment, but early diagnosis of it is crucial to reduce these effects. The incidence of autism is represented in repetitive patterns of children’s motion. When walking, these children tighten their muscles and cannot control and maintain their body position. Autism is not only a mental health disorder but also a movement disorder.

This study aims to identify autistic children based on data recorded from their gait patterns using a Kinect sensor. The database used in this study comprises walking information, such as joint positions and angles between joints, of 50 autistic and 50 healthy children. Two groups of features were extracted from the Kinect data in this study. The first one was statistical features of joints’ position and angles between joints. The second group was the features based on medical knowledge about autistic children’s behaviors. Then, extracted features were evaluated through statistical tests, and optimal features were selected. Finally, these selected features were classified by naïve Bayes, support vector machine, k‑nearest neighbors, and ensemble classifier.

The highest classification accuracy for medical knowledge‑based features was 87% with 86% sensitivity and 88% specificity using an ensemble classifier; for statistical features, 84% of accuracy was obtained with 86% sensitivity and 82% specificity using naïve Bayes.

The dimension of the resulted feature vector based on autistic children’s medical knowledge was 16, with an accuracy of 87%, showing the superiority of these features compared to 42 statistical features.

Autism is classified as a developmental disorder and primarily disrupts social interactions and communication. This disorder has no definitive treatment, making early diagnosis crucial for mitigating its effects. The purpose of this study is to identify autistic individuals based on the recorded information of their walking pattern by Kinect sensor.

In this research, the machine learning method was employed to identify autistic individuals based on recorded joint position data during walking, recorded by the Kinect sensor. First, a group of statistical features was extracted from the Kinect data, which included joint positions and the angles between them. Then, the extracted features were evaluated using the statistical test of analysis of variance, and the optimal features were selected. Finally, classification was performed by decision tree classifier.

In this research, the classification of healthy and autistic individuals was done by the decision tree classification and 42 optimal features selected based on statistical analysis, and the accuracy of classification was 85%. The sensitivity and specificity obtained in this classification are 88 and 82%, respectively.

According to the classification results, this research was able to achieve acceptable accuracy by using the low dimension feature vector obtained by statistical analysis. This research, shows autistic individuals can be classified from healthy people only by having the position of several joints. It is suggested researches in future, using this method for measurement the recovery rate or control autism in patient after performing treatment methods.

Toxicological studies on silver nanoparticles (Ag-NPs) have great importance due to their extensive biomedical applications. Because of high antioxidant property of Ziziphora clinopodioides, the aim of this study was to investigate the protective effect of this plant against Ag-NPs-induced toxicity.

In this experimental study, 30 male rats weighing 200±20 g were divided into 5 groups of 6 which included the control group, the group receiving Ag-NPs with a dose of 200 ppm, the groups receiving the hydroalcoholic extract of Ziziphora clinopodioides with doses of 20, 60, and 180 mg/kg along with Ag-NPs (200 ppm). The compounds were administered orally once a day for 30 days. At the end of the treatment period, blood samples were collected and serum activity of alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, and serum concentration of urea and creatinine were evaluated. Data were analyzed by one-way analysis of variance and Tukey’s post hoc test.

The results indicated that administration of Ag-NPs significantly increased the serum activity of alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, and serum concentration of urea and creatinine compared to the control group (p<0.05). These changes were improved during treatment with the extract of Ziziphora clinopodioides in different doses compared to the group receiving Ag-NPs (p<0.05).

Based on the results of the present study, the Ziziphora clinopodioides may be effective in preventing the toxicity of Ag-NPs.

Rice is recognized as one of the main cereals in the world, feeding two-thirds of the global population, especially in Asian countries. Accurate assessment of the percentage of filled grains (PFG) is crucial for the efficiency and quality of rice harvesting. Traditional methods of measuring PFG are practical and based on personal judgment. This study introduces an innovative and non-destructive approach based on an acoustic sensor alongside deep learning models to predict PFG based on the acoustic spectrum of rice grains. Using an advanced deep learning architecture, the Deep Spectrum, which works directly on raw spectral data, eliminates the need for preprocessing and enhances prediction accuracy. A modified impedance tube was used to measure the acoustic spectrum, which was then analyzed using the Deep Spectrum model to predict PFG. Results indicated that this approach significantly improves the quantitative analysis of spectral data and provides a reliable prediction of rice grain filling. The prediction accuracy of the Deep Spectrum model was significantly higher compared to traditional methods, with a low root mean square error of prediction (RMSEP) of 0.24 ± 0.05 and a coefficient of determination (R²) of 0.95 ± 0.02. This prediction is vital for assessing rice quality, breeding, and genetic research. This study introduces new perspectives and methods in the field of grain quality prediction and classification using acoustic spectrum analysis and deep learning, which could be beneficial for future research in this area.

This study thoroughly investigates the fundamental mechanisms influencing mass transfer in nanofluids and assesses how each mechanism either weakens or intensifies this process based on previous research. Additionally, it examines key operating parameters that enhance absorption efficiency, including nanoparticle concentration, the nature and synthesis of nanoparticles, the surface-area-to-volume ratio, as well as the viscosity, density of the nanofluid, and the use of surfactants. By detailing these factors, the study provides a clear understanding of how they impact mass transfer performance, emphasizing that neglecting these considerations significantly affects the final yield.

A key challenge in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems is to effectively recognize frequencies within a short time window. To address this challenge, the specific characteristics of the data are needed to select the frequency recognition method. These characteristics include factors, such as the number of stimulation targets and the presence of harmonic frequencies, resulting in optimizing the performance and accuracy of SSVEP-based BCI systems. The current study aimed to examine the effect of data characteristics on frequency recognition accuracy. In this analytical study, five commonly used frequency recognition methods were examined, used to various datasets containing different numbers of frequencies, including sub-data with and without frequency harmonics. The increase in the number of frequencies in the Multivariate Linear Regression (MLR) method has led to a decrease in frequency recognition accuracy by 9%. Additionally, the presence of harmonic frequencies resulted in an 8% decrease in accuracy for the MLR method.  Frequency recognition using the MLR method reduces the effect of the number of different frequencies and harmonics of the stimulation frequencies on the frequency recognition accuracy.

Efficient microwave absorbing materials are critical for a variety of applications ranging from military aircraft to communications equipment. Addressing the challenges in reducing S-band (2-8 GHz) waves, this research focused on the development of high-performance microwave absorber materials and investigating the potential of graphene in combination with magnetic elements. Cobalt ferrite, known for its promising magnetic properties, forms the basis of these composites. However, the inherent limitations of pure CoFe2O4 led to the investigation of new approaches, including the incorporation of graphene derivatives such as reduced graphene oxide (rGO) and conductive polymers such as polyaniline. This synthesis included combining iron salts, cobalt sources and rGO, optimizing specific chemical reactions to produce rGO/CoFe2O4 and rGO/CoFe2O4/PANI composites. Structural analysis through different techniques showed the presence of distinct functional groups, the morphology of the composites, their thermal and magnetic behaviors. Analysis of microwave absorption highlighted the effectiveness of the composite, especially at 7 GHz, in minimizing the reflection of electromagnetic waves. Also, the presence of polyaniline significantly increased the electrical permeability of the composite and contributed to the effective absorption of microwaves due to the appropriate matching of the composite resistance. In conclusion, the synthesized rGO/CoFe2O4/PANI composite showed exceptional potential as an efficient microwave absorber material in the S-band range. This innovative ternary composite paves the way for advanced applications in diverse fields where attenuation of electromagnetic waves is important.

Unwanted electromagnetic wave interference poses significant challenges in military and industrial contexts, prompting extensive research into electromagnetic interference shielding materials, particularly radar absorbing coatings. Materials with inherent dielectric or magnetic properties, such as magnetite and polymers, have proven effective in wave absorption, alongside investigations into metals and carbon-based materials. Nevertheless, reduced graphene oxide (RGO) presents a drawback due to its suboptimal impedance matching. To address this limitation, researchers have explored the enhancement of impedance matching by incorporating RGO with magnetic materials. Emerging as promising candidates are ternary and quaternary nanocomposites, including conducting polymers, offering the potential to effectively absorb microwave and radio waves. This study focuses on RGO/Fe3O4 nanoporous composites incorporating polyaniline. These composites exhibit remarkable capabilities in the absorption of electromagnetic waves, coupled with a straightforward synthesis process. The study makes a significant contribution to the realm of electromagnetic interference shielding materials, underscoring the considerable promise held by RGO/Fe3O4/PANI composites.

Surgery through different mechanisms causes immunosuppression in the postoperative period.  This study aimed to investigate the effects of preoperative administration of propranolol on blood levels of interleukin-2 (IL-2), interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), and hematological parameters, such as white blood cells (WBCs) and lymphocytes.  Forty-five Wistar male rats were divided into three groups. Group 1 (normal control) was injected with normal saline. Groups 2 and 3 were injected subcutaneously with 4 mg/kg of the propranolol (P4) and 8 mg/kg of the propranolol (P8), respectively. Blood samples were collected (before, immediately, 6, 24, and 72 hours after surgery). Then, the IL-2, IFN-γ, TNF-α, WBCs, and lymphocytes levels were determined at different time points. The data were analyzed by one-way ANOVA and the Pearson-test at a significant level of P≤0.05.  The results showed a higher level of IL-2 in the P8 and P4 groups with a significant difference compared to the control group (P≤0.05). TNF-α was decreased significantly in the P8 compared to the P4 and control groups (P≤0.05). The P4 has shown a lower level of IFN- γ compared to the P8 and control groups with a significant difference (P≤0.05). It appears that propranolol has considerable immunomodulatory effects on immune responses. Therefore, perioperative use of propranolol may improve immune system function.

Heating the soil temperature can improve seed germination and plant growth. Using renewable energy for greenhouse heating is recommended due to environmental concerns and the possibility of fossil fuel scarcity. This study analyzed the heat transfer relationships of pipes and solar collectors using Python software to determine the most optimal mode. This research aims to heat a specific type of greenhouse (with a surface area of 10 m2 out of 500 m2 in Shahtekord, Iran) using root-zone installations. The results indicate that the diameter and depth of the pipe placement from the root have a significant impact on the amount of heat supplied from the collector side. Increasing the pipe diameter results in higher system heat requirements while decreasing the depth of the pipes leads to a decrease in the supplied heat from the collector side. After analyzing these parameters, it was determined that the optimal distance for placing the pipe from the root is 30 cm. Also, in December, which is the coldest month, 16 collectors are required to meet the necessary heat supply.

The COVID-19 disease has serious economic consequences in all countries of the world. In this paper, the impact of the pandemic on gross domestic product (GDP) and the value-added of Iranian economic sectors is simulated based on input-output analysis. The delay caused by the pandemic will not necessarily be compensated in the coming years, so it is important to understand the long-term impact of COVID-19 on the economy. Using Iran’s economy input-output tables produced by Iranian authorities, it is possible to measure the economy’s response to the COVID-19 shock. The nine shock scenarios are considered based on the Oxford Government Response Tracker to make the assumptions as close to reality as possible. The median scenario implies that Iran’s output and GDP will decline by 2.6% and 1.7%, respectively, in 2021 and 2022. The scenarios range from the optimistic one with fewer restrictions, resulting in a 2.8% GDP decline, to the pessimistic scenario with major restrictions and barriers leading toward a GDP decrease of 7%. The huge value-added losses were in the “libraries, museums, and other cultural activities” sector, and the largest increase was in the “health care” sector.

The development of startup ecosystems has become a global trend. These ecosystems have proven their importance to governments by having a significant impact on the creation of technology companies and the economic growth of the country. In Iran, too, for many years, efforts have been made under various headings to develop the startup ecosystems. In this article, based on the Narrative Policy Framework (NPF), the existing policy narratives of startup ecosystem development policy in Iran between 2013-20 years have been analyzed. After analyzing these data, it was found that the three main narratives of catch-up, wealth creation and infiltration, have formed the discussions around the development of this ecosystem. In the first part of the analysis, which was related to representing the structuring of narratives, it was found that the narrative of catch-up and wealth creation during their coalition with each other, marked the birth and initial development of the startup ecosystem in Iran. The infiltration narration entered the policy arena sometime later as a negative force. Finally, the strengths and weaknesses of the narrations were examined.

The success rate of a type of technology is manifested in its adoption and continued use. In this sense, the condition development, user behavior, and the factors affecting it should be identified. However, this issue has been almost neglected and most of the studies only examined the factors influencing the adoption and use of technology at a particular time. Over the last two decades, scientific research has notably focused on the adoption of theories of emerging technologies within information and industrial systems. Innovation intermediaries are organizations or bodies that act as representatives or agents in every aspect of the innovation process between two parties and are recognized as important actors that can facilitate the innovation process. The combination of these two with the embargoes governing Iran’s industrial environment, imposed with different intentions, has led this research to investigate the effect of innovation Intermediaries on the adoption of emerging technologies in the automotive industry with the moderating role of embargo restrictions.
 
This research is applied in terms of purpose and descriptive survey in terms of method. Its statistical population includes senior managers (CEO, board members, research and development managers, sales managers, and quality control managers) of Iran’s automotive industry. This paper used the probability sampling method with the available population. Considering the statistical population, 130 people were selected as a sample using Cochran’s formula. The main instrument for gathering information was a questionnaire. The reliability and validity of the research instrument were measured using composite reliability tests, factor loadings, Cronbach's alpha, convergent, and divergent validity. The data were analyzed using SmartPLS 3 and SPSS 24. A key advantage of this approach is its ability to achieve reliable results with a smaller sample size compared to Lisrel modeling. Through SmartPLS modeling, standard regression coefficients can be obtained for paths, coefficients of determination for internal variables, and index sizes for the conceptual model. It is worth mentioning that to test the hypotheses, the t-test was used to determine the validity of the relationship between the constructs.
 
The hypotheses showed that the innovation intermediaries have a significant effect on understanding the ease of use of emerging technologies, perceived usefulness, willingness to adopt emerging technologies, legal support in the adoption of emerging technologies, security concerns in the adoption of emerging technologies, cost savings of emerging technologies in the automotive industry. The F2 test results revealed that sanction restrictions act as a moderating factor in the relationship between innovation intermediaries and the adoption of emerging technologies in Iran's automotive industry.
 
This study strongly showed that innovation intermediaries could provide an effective contribution to innovation processes and the adoption of emerging technologies in automotive companies. The present study has filled a research gap by empirically verifying the relationship between innovation intermediaries and the adoption of emerging technologies in the automotive industry. Innovation intermediaries play an important role in filling the gap between emerging technologies and their successful adoption by businesses and consumers. They act as catalysts, connecting different stakeholders, providing expertise, and fostering collaboration, which in turn paves the way for the adoption of emerging technologies.

This research aims to examine the conflicts within political cultures in Iran. Employing a descriptive method, including content analysis and qualitative analysis, the study delves into the issue of the multiplicity of political cultures and the resulting diversity in beliefs and political orientations in Iran. The central question is how, given the diversity and multiplicity of political cultures in Iran after the Islamic Revolution, strategies can be developed to harmonize and consolidate political culture to contribute to Iran's political and economic development. The results indicate that open dialogue among intellectuals and carriers of political culture in a space free from political and economic dominance can be the most suitable solution for harmonizing and unifying elements of political culture, such as political values, beliefs, and emotional stances of the people, in the direction of political and economic development. Additionally, within the framework of critical theory, fostering free dialogue among citizens and civil institutions, creating a platform for public persuasion, avoiding exclusionary approaches toward elements of other cultures, promoting a spirit of tolerance and coexistence, and respecting the foundations and principles of political cultures are recommended. A conscious amalgamation of Islamic, Iranian, and progressive political cultures is proposed through informed and persuasive dialogue.

One of the most important tasks of any political system is to draw efficient strategies for drawing the geometry of interactions inside the country and facing political units in the field of international relations. For such a confrontation, the strategy of the second step of the Islamic revolution, based on the statement of the supreme leader of the revolution, is known as resistance. Examining the jurisprudential foundations of resistance in the Islamic Republic can determine the way of interaction with countries, institutions and international organizations. Therefore, the main problem of the research is, what is the position of the foundations of resistance desired by Ayatollah Khamenei in the system of Shia jurisprudence and which of the jurisprudential chapters can it be explained? The most important achievement of the present research is the explanation of the jurisprudential foundations of resistance as a determining component in the political and economic equations of the Islamic society and overcoming the challenges caused by the power seeking of the global domination system. The present research pursues this hypothesis that the concept of resistance intended by Ayatollah Khamenei can be explained with different chapters of jurisprudence such as defensive jihad, taqiyyah, amrba-maaruf, and the like. This research, with a descriptive and analytical method, during discussions and topics such as confronting tyranny inside the country and negation of domination, independence, achieving modern Islamic civilization in the field of international relations, deals with the jurisprudential explanation of the foundations of resistance in order to be able to provide a reasonable and comprehensive explanation of it. Muslims and intellectuals of the Islamic world.

Election is a peaceful method for legislating and selecting leaders, and one of the features of democratic governments. Islam, through religious texts, including Qur'anic verses and the narrations of the Imams (as), has outlined its own ethical principles for elections. This research employs an academic approach, utilizing a descriptive-analytical method for the examination of Qur'anic and narrative texts. It aims to theoretically analyze the features and criteria introduced by the Holy Qur'an and the narrations of the Infallible (as) in the context of elections. Additionally, it explores the distinctive attributes of Islamic political ethics concerning elections within the framework of a religious democracy, drawing comparisons with modern democracies. The main finding of this research is the theoretical analysis of Islamic electoral ethics through the analysis of Qur'anic and narrational texts and in line with the political ethics of elections. This analysis not only clarify the theoretical benefits of Islamic elevated ethics but also serve as a guide for Islamic governments in their political development programs. Defining Islamic political ethics and its distinctions from modern political ethics are among the other findings of this research.

Germination is one of the most important stages of crop plant growth and proper germination in a wide range of environmental conditions is necessary for plant establishment. In this research, 20 varieties of Iranian barley and 24 varieties of European barley were used in three salinity levels (100, 200 and 300 mM sodium chloride) and a control level (distilled water) in order to investigate the tolerance of salinity stress in the germination stage, as a factorial test based on a completely randomized design with three replications in the mushroom cultivation chamber with controllable temperature and light and sterile conditions in the Nosoud agricultural service center by a protein marker in the biotechnology laboratory of the agriculture and natural resources of Razi university. The results of analysis of variance of traits related to germination showed that the effect of salinity, cultivar and salinity × cultivar were significant for all traits. By increasing the salinity level from 0 to 300 mM, all traits and indices decreased except the average germination time. The germination percentage had a positive and significant correlation with the total seedling length, shoot length, seed germination index, average germination rate and germination rate index. Stepwise regression analysis of different traits with protein markers showed that there were 13 bands associated with different traits. The most significant correlation with traits was related to the 60 kDa marker which was related four indices of seed germ, average germination speed, shoot length and cleoptile length. After the validation studies and confirmation of the results, the identified markers can be used in marker-assisted selection for related traits.

The sound of the human fetal heart can be heard from the fourteenth week of pregnancy in thin women and in the twentieth week in all pregnant women. If this sound is not heard, the fetus may die; Therefore, in these cases, the doctor uses a Sony Kid device to check the health of the fetus. In this article, an attempt has been made to briefly examine the overall structure and proper functioning of this device and to show the benefits of this device in diagnosing the health and survival of the fetus.

Negative early-life experiences (e.g. having an aggressive father) can leave long-lastingimpacts on the behavior. However, it is not clear if they influence learning and memory. 

In this study, we investigated the influences that the presence of an aggressive father had on the level of passive avoidance learning and spatial memory. We also studied the changes in the dopamine receptor D2 (DRD2) and peroxisome proliferator-activated receptor gamma coactivator 1-α (PGC-1α) gene expression in the hippocampus. Then, we evaluated if a DRD2 antagonist (sulpiride, 0.125, 0.25, or 0.5 μg/rat) could modulate these changes. 

We found that the subjects exposed to early-life stress made by aggressive fathers had impaired passive avoidance learning and spatial memory compared to subjects with normal fathers. Treatment with sulpiride improved passive avoidance learning and spatial memory in rats with aggressive fathers. The rats with aggressive fathers also had higher expression of the DRD2 gene in their hippocampus than those with normal fathers, while the PGC-1α gene expression was not different among groups. Treatment with sulpiride (0.125, 0.25, or 0.5 μg/rat) reduced the DRD2 gene expression in those with aggressive fathers to the normal level compared to those with normal fathers. 

These data suggest that having and living in a shared place with an aggressive father, even without any physical contact, can detrimentally affect passive avoidance learning and spatial memory which is accompanied by the increased expression of the DRD2 gene. Also, sulpiride as a dopaminergic antagonist could reverse this process.

Making economic decisions and allocating resources optimally without the presence of valid and reliable data is not possible. Capital flows towards superior economic activities when capitalist decisions rely on timely, relevant and reliable information. In this regard, auditing plays a vital role in determining the validity of information; in other words, given the accountability requiring the presence of valid and reliable data, it can be stated that auditing is one of the fundamental accountability processes. In the current research, an optimum prediction method for independent auditor's report types is selected and two approaches of the J48 algorithm and random forest are compared. This research has been conducted on 84 corporates during 2008-2017. In order to train, test and investigate the research variables, Weka software was used. The dependent variable is the auditor's report type. Results indicated that the accuracy of the J48 algorithm has been 72.61% and 60.42% in training and test sections, respectively and the accuracy of the random forest has been 94.57% and 63.09% in training and test sections, respectively; so, the random forest model is more effective.

Since gait disorder is the most common complication among patients with multiple sclerosis. This study aims to examine the effect of hippotherapy on the onset activity of selected lower limb muscles in patients with multiple sclerosis (MS) during the gait initiation protocol with a perturbation response approach.

A total of 20 MS patients with EDSS score <4.5 were selected by simple convenience sampling method and were randomly divided into two groups of exercise n=10 and control n=10. Ten healthy and matched individuals were selected and placed in the healthy control group. The MS patients in the training group underwent hippotherapy for 4 sessions per week. Before and after the exercises, the electrical activity of the selected muscles of the lower extremities was recorded during the gait initiation protocol.

Data analysis showed a significant difference between groups (P<0.05). Hippotherapy exercises were effective in the onset activity time of the lower limb muscles and reduced the reaction time of the muscles.

According to the results of the present study and the effect of hippotherapy on improving reaction time, hippotherapy can be used as a rehabilitation method in MS patients.

Eggs are a widely consumed material that can endanger people's health due to their high perishability. Therefore, detecting its freshness is very important for the food industry. Several nondestructive methods have been proposed to measure the internal quality of eggs, and in this research, an acoustic system was used to detect egg freshness. The samples were kept at room temperature for 16 days. A 45 cm long inclined surface with a 4 mm high step in the middle was used in the mentioned system. The sound resulting from the rolling of the samples and the impact caused by passing the step were recorded by a microphone. Then, the fractal dimension of sound signals was calculated using Higuchi and Katz methods. A destructive test was also done considering the Haugh unit and the eggs were divided into two groups AA and A+B in terms of freshness. The results of the three classification methods of artificial neural network (ANN), support vector machine (SVM) and linear diagnostic analysis (LDA) showed that the SVM method has better efficiency compared to the other two methods. The average classification accuracy of test data in the SVM method for Katz fractal dimension and window length of 25 milliseconds was obtained as 78.07%.