In traditional speech processing, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech processing applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech processing where these filters have proven Effective.

In the developing countries the central governments play mainly the major role in society. In these societies the distribution of wealth is not fairly that leads to economic hardship and social exclusion as the base of corruption. For this reason considering the financial, controlling and monitoring indicators are deniable. And it should change the controlling indicators according to economic development in order to fight against financial fraud as one of the economic picky component. In this manner recognition of Effective parameters on financial fraud is absolutely essential. In this article by utilization of the related studies and interviewing with experts, the Effective parameters on financial fraud were identified. Afterwards by using of collected data from 388 questionnaires besides Topsis fuzzy approach, these parameters were prioritized. According to the results the main causes of this problem are: the dependence of central bank, lack of efficient controlling and monitoring system besides other causes, which have been presented by a preventive methods and suggestions.

Introduction: In this survey to analyze the effect of Aquaculture Farms on quality of Gamasiab Rive, we selected five sampling station along the river for sampling. Water quality parameters were determined and then compared with standard limits.Materials and Methods: Sampling was done in three months August and September 2007 and March 2008 in the morning and evening because of the effect of Feeding in water quality. The water sample s were gathered in polyethylene bottles and DO, pH, EC were determined at the station then samples were transferred to the lab for determining other parameters such as BOD5 , NH4+, NO3-, NO2- and PO43-.Results: The results showed that only EC has no significant difference in stations but other parameters have that. For DO, pH and BOD5 parameters only between B1 station and control station (A) have not showed significant difference. NH4+ and NO3- have showed significant difference between control station (A) and the others because of inputting the nutrition particles of aquaculture facility and villages' wastewaters in the river.Also, between Aquaculture Farms there are significant differences in some parameters like NH4+ and NO3-. For instance Ghazal Danesh farm with producing 100 tone fish entered more pollution to the river comparing with Ghazal Zaghros farm with producing 190 tone fish.Discussion and Conclusions: Comparing the water quality parameters in Gamasiab River with water quality classification table in physical, chemical and biological parameters showed that this river water quality is in a-meso saprobe until b-meso saprobe. So, at present this river can do self-purification of pollution but because of the policy of developing Aquaculture Industry in Iran, especially Hamedan province and no controlling and monitoring on pollutant sources, this river would have serious problems such as purification in the future.

Increase of environmental pollution and restricted emission legislations have forced companies to produce automobiles with lower air pollutants. In this respect, discharge of blowby gases into the environment has been prohibited and their recirculation into the combustion chamber is proposed as an alternative solution. In addition, using EGR technique to control and reduce nitrogen oxides in internal combustion engines has been quite Effective. An important common feature of these two methods is the fact that improper EGR/blowby distribution leads to the increase in other pollutants and the significant engine power reduction. Therefore, the study of important factors in maldistribution of the injected gases is of great practical importance. Besides the injection position that has significant role on distribution of injected gases, it seems that other parameters such as engine speed, injection velocity and angle may affect the distribution of injected gases. In this numerical study, a new technique is used to determine the effect of these parameters on distribution of injected EGR or blowby gases into the EF7 intake manifold. Numerical calculations are performed for three injection velocities, five injection angles and three different engine speeds. It was found that recirculated gases distribution is slightly influenced by the injection angle and injection velocity, while the engine speed is the most influential factor.

The purpose of the present study was to investigate the effect of generative learning teaching based to pattern cognitive Load on the memorization math of 7th grade students of in the academic year of 2021- 2022. The research method is quasi-experimental with pre-test and post-test with four groups: two control group and two experimental groups. The statistical population of the study consisted of all 7th grade male and female students of District 2 of Mashhad city that 112 person sample was selected by single stage cluster sampling method. The subjects in the experimental group involved in generative learning based to cognitive Load in 16 sessions and the control group did not receive any interventions. The research instrument consisted of a 20-questions researcher-made test. To analyze the obtained data, Multivariate analysis of covariance and SPSS version 24 software were used. The results showed that generative learning teaching based to approach cognitive Load have a positive and significant effect on 7th grade students' memorization math. Based on the findings, it can be concluded that one of the Effective training on math memorization students is the use of generative learning on the based load cognitive pattern.

Using low-cost feedstocks such as rendered animal fats in biodiesel production willreducebiodieselexpenditures. One of the low-cost feedstocksfor biodiesel production could be the fat extracted from poultry feathers producedin slaughterhouses abundantly. This paper describes a new and environmentally friendly process for developing biodiesel production technology from feather waste produced in poultry industry. In this research the crude oil of poultry feather fat was extracted by soxhlet method using hexane as a solvent. The data resulted from gas chromatography (GC) revealed these percentages for fatty acid compositions: myristic acid (3%), palmitic acid (30%), stearic acid (22%), oleic acid (8.1%), linoleic acid (3%) and arachidonic acid (7%).In this experimental research, the effects of some parameters such as alcohol to oil molar ratio (4: 1, 6: 1, 8: 1), catalyst concentration (0.75, 1 and 1.25% w/w) and the transesterification reaction time (40, 60 and80min) on the percentage offatty acids conversioninto methyl ester (biodiesel) are studied. The results show increasing catalyst concentration up to 1% causes the oil to biodiesel conversion percentage having an upward trend and then adownward trend byincreasing catalyst concentration up to 1.25%. With increasing molar ratio from 4: 1 to 6: 1 and then 8: 1, oil to biodiesel conversion percentage increased16% and2%, respectively. Ultimatelythe optimum point defined by response surface method (RSM) forproducing biodiesel from feather fat is calculated catalyst concentration of 1 wt%, 7.24: 1 molar ratio and in 75 minutes resulting in conversion percentage of97.62%.

Facilities built in areas affected by earthquake activity, such as tunnels, which have always been an integral part of human life, must withstand both dynamic and static loading. It has led to the need for practical studies on the effects of earthquakes on underground structures and the factors affecting their destruction. For this purpose, in this research, at first different patterns of tunnel’s excavation were investigated and by using Plaxis 2D software and based on Tabas earthquake in Iran, sensitivity analysis on geotechnical parameters of the soil surrounding tunnel such as cohesion, friction angle, unit weight and modulus of elasticity was carried out, and the parameters whose changes have the greatest and least effects on the bending moment changes on the tunnel lining are introduced. The results show that tunnel excavation patterns significantly affect the bending moment, axial forces, displacements, and surface settlement of the tunnel. Often, by dividing tunnel excavation area to small parts, the values of bending moment, axial forces, displacements, and surface settlement of the tunnel decreases in static analysis. Also, outputs of sensitivity analysis on geotechnical parameters of the soil surrounding tunnel showed that modulus of elasticity of the soil surrounding tunnel has the most effect and cohesion changes have the least effect on bending moment induced on tunnel lining..

Biohydrogen production is a green method that utilizes organic materials in activated sludge as a substrate. The process involves microorganisms contracting the volume of the sludge content to produce hydrogen, CO2, CH4, and etc. However, the efficiency of this process is low. In this study, bioaugmentation was carried out by inoculating a 10% Escherichia coli suspension adapted in whey and activated sludge medium. The effects of parameters such as pH, temperature, and stirring, the concentration of whey as carbon source and nitrate as nitrogen source on hydrogen production were screened using the Plackett-Burman method with Minitab 21 software. Among the selected parameters, pH, temperature, concentration of whey and nitrate were found to be the most Effective parameters in hydrogen production and were further optimized using Response surface methodology. Stirring wasn’t statistically significant. The optimum conditions for hydrogen production were pH=5.4, temperature=39 ˚C, whey concentration=30 g/L, and nitrate concentration=3.6 g/L. Under these conditions with a 10% inoculation, the total volume of gas production was extended to 1.61 L per liters of activated sludge with 0.046 mole H2 per liters of activated sludge. Comparing the bioaugmentation method with other method showed that the total time of the process decreased by 8 hours. Additionally, hydrogen production started after 10 hours of incubation and reached its maximum value in 16 hours, resulting in a 59% increase in productivity in less than 16 hours.