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اثر سطح- مقطع تابش فراصدای متمرکز پر- شدت (هایفو) بر توزیع حرارتی بافت هدف (مقاله پژوهشی) Effect of cross-sectional area of high-intensity focused ultrasound (HIFU) on the thermal distribution of the target tissue (Research Article) 1 1 فن‌آوری هایفو به عنوان روش درمانی غیر‌تهاجمی مبتنی بر تبدیل انرژی صوتی به انرژی حرارتی به صورت کانونی در ناحیه هدف است. در این تحقیق اثر سطح- مقطع تابش بر توزیع حرارت در بافت هدف بررسی می‌شود. برای بررسی اثر سطح-  مقطع تابش، در این مطالعه از روش عنصر متناهی و نرم‌افزار شبیه‌سازی کامسول، در حالت انتشار خطی امواج فراصدا استفاده شد. در بیشینه شدت صوتی ورودی 10 وات بر سانتی‌مترمربع، معادله موج فشار با در نظر گرفتن سیال گرمالزج، در دو سطح- مقطع  1/5 و 0/8 سانتی‌مترمربع حل شد. براساس معادله انتقال حرارتی پنس، توزیع میدان حرارتی در بافت پوست، چربی زیر پوستی و ماهیچه محاسبه شدند. برای اعتبارسنجی نتایج شبیه‌سازی، همبستگی نتایج حاصل از الگوی ریاضی و نتایج تجربی با سطح اطمینان 95 درصد ارزیابی شدند (عدد p کم‌تر از 0/05). در شدت صدای ورودی 10 وات بر سانتی‌مترمربع و در فاصله کانونی 4/5 میلی‌متر از سطح پوست، با افزایش سطح- مقطع از 0/8 به 1/5 سانتی‌مترمربع، بیشینه فشار صدا از 8/6 به 20/9 مگاپاسکال و بیشینه دما از 60 به 139 درجه سلسیوس افزایش یافت. نتایج نشان دادند، توزیع فشار صدا و دُز حرارتی بافت هدف به سطح مقطع تراگذارهای صدا وابسته است. نتیجه‌گیری می‌شود برای دستیابی به پروتکل درمان منتخب، بایستی طرح درمان براساس ورودی تابش و مشخصات فیزیکی بافت شبیه‌سازی شود. 2 HIFU technology is a non-invasive treatment method based on the conversion of acoustic energy into thermal energy focused on the target area. In this study, the effect of radiation cross- section on heat distribution in the target tissue is investigated. To investigate the effect of radiation cross- section, in this study, finite element method and COMSOL simulation software were used in the linear emission mode of ultrasonic waves. At a maximum input sound intensity of 10 W/cm2, the pressure wave equation was solved by considering thermo-viscous fluid at two cross- sections of 1.5 and 0.8 cm2. Based on the Pennes heat transfer equation, the heat field distribution in skin tissue, subcutaneous fat, and muscle was calculated. To validate the simulation results, the correlation between the results of the mathematical model the experimental method with a 95% confidence level (p<0.05) was evaluated. At an input sound intensity of 10 W/ cm2 and at a focal length of 4.5 mm from the skin surface, with an increase in cross- section from 0.8 to 1.5 cm2, the maximum sound pressure from 8.6 to 20.9 MPa and the maximum temperature from 60 to 139 °C. The results showed that the sound pressure distribution and the thermal dose of the target tissue depended on the cross-sectional area of the sound transducers. It is concluded that in order to achieve the selected treatment protocol, the treatment plan should be simulated based on radiation input and physical characteristics of the tissue. 1 9 2021/03/11 1399/12/21 2021/07/12 1400/4/21 ساره مرتضوی S. Mortazavi گروه فیزیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس ایران s.mortazavi95@gmail.com منیژه مختاری دیزجی M. Mokhtari-dizaji گروه فیزیک پزشکی، دانشکده علوم پزشکی، دانشگاه تربیت مدرس ایران mokhtarm@modares.ac.ir High intensity focoused ultrasound Cross- section of radiation Sound pressure distribution Thermal distribution. امواج فراصدای متمرکز پر- شدت سطح- مقطع تابش توزیع فشار صدا طرح حرارتی. [1] A. Bataineh, O. Jenne, J. Huber, “Clinical and future applications of high intensity focused ultrasound in cancer,” Current Cancer Therapy Reviews, vol. 38, no. 5, pp. 346-353, 2012. ##[2] T.A. Leslie, J.E. Kennedy, “High-intensity focused ultrasound principles, current uses, and potential for the future,” Ultrasound Quarterly, vol. 22, no. 4, pp. 263-72, 2006. ##[3] J.E. Kennedy, “Ter Haar GR, Cranston D. High intensity focused ultrasound: Surgery of the future?,” British Journal Radiology, vol. 76, no. 909, pp. 590-599, 2003. ##[4] J.H. Park, S.D. Lim, S.H. Oh, J.H. Lee, U.C. Yeo, “High‐intensity focused ultrasound treatment for skin: Ex vivo evaluation,” Skin Research and Technology, vol. 23, no. 3, pp. 384-91, 2017.##[5] G. Haar, C. Coussios, “High intensity focused ultrasound: Past, present and future,” International Journal of Hyperthermia, vol. 23, no. 2, pp. 85-85, 2007.##[6] S. Gharloghi, M. Gholami, A. Haghparast, V. Dehlaghi, “Numerical study for optimizing parameters of high-intensity focused ultrasound-induced thermal field during liver tumor ablation: HIFU Simulator,” Iranian Journal of Medical Physics, vol. 14, pp. 15-22, 2017. ##[7] S. Haddadi, M.T. Ahmadian, “Analysis of nonlinear acoustic wave propagation in HIFU treatment using Westervelt equation,” Scientia Iranica B, vol. 25, no. 4, pp. 2087-2097, 2018.##[8] R. Samanipour, M. Maerefat, H.R. Nejad, “Numerical study of the effect of ultrasound frequency on temperature distribution in layered tissue,” Journal of Thermal of Biology, vol. 38, no. 6, pp. 287-293, 2013.##[9] A.S. Ergün, “Analytical and numerical calculations of optimum design frequency for focused ultrasound therapy and acoustic radiation force,” Ultrasonics, vol. 51, no. 7, pp. 786-794, 2011.##[10] A. Grisey, M. Heidmann, V. Letort, P. Lafitte, S. Yon, “Influence of skin and subcutaneous tissue on high-intensity focused ultrasound beam: experimental quantification and numerical modeling,” Ultrasound in Medicine and Biology, vol. 42, no. 10, pp. 2457-65, 2016.##[11] M. Qi, J. Liu, Y. Mao, X. Liu “Temperature rise induced by an annular focused transducer with a wide aperture angle in multi-layer tissue,” Chinese Physics B, vol. 27, no. 1, pp. 014301, 2018. ##[12] M.E. Ravari, M. Mokhtari-Dizaji, S.H. Momeni-Masuleh, S. Motiee, “Estimation of ultrasound pressure distribution due to 1 MHz ultrasonic transducer for ultrasonic treatment planning in hyperthermia methods,” Journal of Acoustical Engineering Society of Iran, vol. 3, pp. 46-55, 2015.##[13] H. Han, H. Lee, K. Kim, H. Kim, “Effect of high intensity focused ultrasound (HIFU) in conjunction with a nanomedicines-microbubble complex for enhanced drug delivery,” Journal of Controlled Release, vol. 28, no. 266, pp. 75-86, 2017.##[14] R. Martinez, A. Vera, L. Leija, “Finite element HIFU transducer acoustic field modeling evaluation with measurements,” In 2012 Pan American Health Care Exchanges, pp. 101-104, 2012.##[15] M.A. Solovchuk, T.W. Sheu, W.L. Lin, I. Kuo, “Thiriet, M.Simulation study on acoustic streaming and convective cooling in blood vessels during a high-intensity focused ultrasound thermal ablation,” International Journal of Heat and Mass Transfer, vol. 55, no. 4, pp. 1261-1270, 2012.##[16] T.P. Omena, A.J. Fontes-Pereira, R.M. Costa, R.J. Simões, M.A. Krüger, W.C. Albuquerque Pereira, “Why we should care about soft tissue interfaces when applying ultrasonic diathermy: An experimental and computer simulation study,” Journal of Therapeutic Ultrasound, vol. 5, no. 1, pp. 1-6, 2017.##[17] M.R. Salimpour, E. Shirani, “Heat transfer analysis of skin during thermal therapy using thermal wave equation,” Journal of Thermal of Biology, vol. 64, pp. 7-18, 2017.##[18] M. Carter, A. Sullivan, K. Byers, M. Jessel, “Optimizing ultrasonic intensity for high intensity focused ultrasound therapy,” Ecommons, Open scholarship at Cornell, pp. 1-27, 2014.##[19] R.J. Van Sloun, A. Pandharipande, M. Mischi, L. Demi, “Compressed sensing for beam formed ultrasound computed tomography,” IEEE Transaction on Biomedical Engineering, vol. 62, pp. 1-5, 2015.##[20] K. Comley, N.A. Fleck, “A micromechanical model for the Young’s modulus of adipose tissue,” International Journal of Solids and Structures, vol. 47, no. 21, pp. 2982-2990, 2010.##[21] A. Bhowmik, R. Repaka, S.C. Mishra, “Thermal analysis of the increasing subcutaneous fat thickness within the human skin-a numerical study,” Numerical Heat Transfer Application, vol. 67, no. 3, pp. 313-329, 2015. ##[22] T. Okabe, T. Fujimura, J. Okajima, S. Aiba, S. Maruyama, “Non-invasive measurement of effective thermal conductivity of human skin with a guard-heated thermistor probe,” International Journal of Heat and Mass Transfer, vol. 126, pp. 625-635, 2018.##[23] J.E. Soneson, M.R. Myers, “Thresholds for nonlinear effects in high-intensity focused ultrasound propagation and tissue heating,” IEEE Transactions on Ultrasonic, Ferroelectrics, and Frequency Control, vol. 57, pp. 2450-2459, 2010.##[24] R. Martínez, A. Vera, L. Leija, “HIFU induced heating modelling by using the finite element method,” Physica Procedia, vol. 63, pp. 127-133, 2015. ##[25] J. Kim, J. Jung, M. Kim, K. Ha, E. Lee, I. Lee, “Distribution of temperature elevation caused by moving high-intensity focused ultrasound transducer,” Japanese Journal of Applied Physics, vol. 54, no. 7S1, pp. 07HF13, 2015.##[1] A. Bataineh, O. Jenne, J. Huber, “Clinical and future applications of high intensity focused ultrasound in cancer,” Current Cancer Therapy Reviews, vol. 38, no. 5, pp. 346-353, 2012. ##[2] T.A. Leslie, J.E. Kennedy, “High-intensity focused ultrasound principles, current uses, and potential for the future,” Ultrasound Quarterly, vol. 22, no. 4, pp. 263-72, 2006. ##[3] J.E. Kennedy, “Ter Haar GR, Cranston D. High intensity focused ultrasound: Surgery of the future?,” British Journal Radiology, vol. 76, no. 909, pp. 590-599, 2003. ##[4] J.H. Park, S.D. Lim, S.H. Oh, J.H. Lee, U.C. Yeo, “High‐intensity focused ultrasound treatment for skin: Ex vivo evaluation,” Skin Research and Technology, vol. 23, no. 3, pp. 384-91, 2017.##[5] G. Haar, C. Coussios, “High intensity focused ultrasound: Past, present and future,” International Journal of Hyperthermia, vol. 23, no. 2, pp. 85-85, 2007.##[6] S. Gharloghi, M. Gholami, A. Haghparast, V. Dehlaghi, “Numerical study for optimizing parameters of high-intensity focused ultrasound-induced thermal field during liver tumor ablation: HIFU Simulator,” Iranian Journal of Medical Physics, vol. 14, pp. 15-22, 2017. ##[7] S. Haddadi, M.T. Ahmadian, “Analysis of nonlinear acoustic wave propagation in HIFU treatment using Westervelt equation,” Scientia Iranica B, vol. 25, no. 4, pp. 2087-2097, 2018.##[8] R. Samanipour, M. Maerefat, H.R. Nejad, “Numerical study of the effect of ultrasound frequency on temperature distribution in layered tissue,” Journal of Thermal of Biology, vol. 38, no. 6, pp. 287-293, 2013.##[9] A.S. Ergün, “Analytical and numerical calculations of optimum design frequency for focused ultrasound therapy and acoustic radiation force,” Ultrasonics, vol. 51, no. 7, pp. 786-794, 2011.##[10] A. Grisey, M. Heidmann, V. Letort, P. Lafitte, S. Yon, “Influence of skin and subcutaneous tissue on high-intensity focused ultrasound beam: experimental quantification and numerical modeling,” Ultrasound in Medicine and Biology, vol. 42, no. 10, pp. 2457-65, 2016.##[11] M. Qi, J. Liu, Y. Mao, X. Liu “Temperature rise induced by an annular focused transducer with a wide aperture angle in multi-layer tissue,” Chinese Physics B, vol. 27, no. 1, pp. 014301, 2018. ##[12] M.E. Ravari, M. Mokhtari-Dizaji, S.H. Momeni-Masuleh, S. Motiee, “Estimation of ultrasound pressure distribution due to 1 MHz ultrasonic transducer for ultrasonic treatment planning in hyperthermia methods,” Journal of Acoustical Engineering Society of Iran, vol. 3, pp. 46-55, 2015.##[13] H. Han, H. Lee, K. Kim, H. Kim, “Effect of high intensity focused ultrasound (HIFU) in conjunction with a nanomedicines-microbubble complex for enhanced drug delivery,” Journal of Controlled Release, vol. 28, no. 266, pp. 75-86, 2017.##[14] R. Martinez, A. Vera, L. Leija, “Finite element HIFU transducer acoustic field modeling evaluation with measurements,” In 2012 Pan American Health Care Exchanges, pp. 101-104, 2012.##[15] M.A. Solovchuk, T.W. Sheu, W.L. Lin, I. Kuo, “Thiriet, M.Simulation study on acoustic streaming and convective cooling in blood vessels during a high-intensity focused ultrasound thermal ablation,” International Journal of Heat and Mass Transfer, vol. 55, no. 4, pp. 1261-1270, 2012.##[16] T.P. Omena, A.J. Fontes-Pereira, R.M. Costa, R.J. Simões, M.A. Krüger, W.C. Albuquerque Pereira, “Why we should care about soft tissue interfaces when applying ultrasonic diathermy: An experimental and computer simulation study,” Journal of Therapeutic Ultrasound, vol. 5, no. 1, pp. 1-6, 2017.##[17] M.R. Salimpour, E. Shirani, “Heat transfer analysis of skin during thermal therapy using thermal wave equation,” Journal of Thermal of Biology, vol. 64, pp. 7-18, 2017.##[18] M. Carter, A. Sullivan, K. Byers, M. Jessel, “Optimizing ultrasonic intensity for high intensity focused ultrasound therapy,” Ecommons, Open scholarship at Cornell, pp. 1-27, 2014.##[19] R.J. Van Sloun, A. Pandharipande, M. Mischi, L. Demi, “Compressed sensing for beam formed ultrasound computed tomography,” IEEE Transaction on Biomedical Engineering, vol. 62, pp. 1-5, 2015.##[20] K. Comley, N.A. Fleck, “A micromechanical model for the Young’s modulus of adipose tissue,” International Journal of Solids and Structures, vol. 47, no. 21, pp. 2982-2990, 2010.##[21] A. Bhowmik, R. Repaka, S.C. Mishra, “Thermal analysis of the increasing subcutaneous fat thickness within the human skin-a numerical study,” Numerical Heat Transfer Application, vol. 67, no. 3, pp. 313-329, 2015. ##[22] T. Okabe, T. Fujimura, J. Okajima, S. Aiba, S. Maruyama, “Non-invasive measurement of effective thermal conductivity of human skin with a guard-heated thermistor probe,” International Journal of Heat and Mass Transfer, vol. 126, pp. 625-635, 2018.##[23] J.E. Soneson, M.R. Myers, “Thresholds for nonlinear effects in high-intensity focused ultrasound propagation and tissue heating,” IEEE Transactions on Ultrasonic, Ferroelectrics, and Frequency Control, vol. 57, pp. 2450-2459, 2010.##[24] R. Martínez, A. Vera, L. Leija, “HIFU induced heating modelling by using the finite element method,” Physica Procedia, vol. 63, pp. 127-133, 2015. ##[25] J. Kim, J. Jung, M. Kim, K. Ha, E. Lee, I. Lee, “Distribution of temperature elevation caused by moving high-intensity focused ultrasound transducer,” Japanese Journal of Applied Physics, vol. 54, no. 7S1, pp. 07HF13, 2015. ##

تحلیل ترابرد فونونی در پرووسکیت‌های (X=Cl, Br) CsCaX3 با استفاده از روش نظریه تابعی چگالی (مقاله پژوهشی) Phonon transport analysis of CsCaX3 (X=Cl, Br) perovskites using the density functional theory (Research Article) 1 1 ترابرد فونونی پرووسکیت‌های هالیدی 3CsCaCl و 3CsCaBr به‌منظور شناسایی کاربردهای بالقوه آن‌ها در زمینه‌های مختلفی از جمله خنک‌سازی و گرمابرقی بررسی شدند. مطالعات اصول اولیه مواد در فاز مکعبی‌شان در تقریب شیب تعمیم‌یافته انجام شد. مرتبه‌های دوم و سوم ثابت نیرو با رهیافت ابریافته و با کدهای فونوپای و فونو3پای برآورد شدند. ثابت شبکه مواد برابر oA 5/463 و 5/768 به‌ترتیب برای 3CsCaCl و 3CsCaBr به‌دست آمدند. از مقایسه چگالی ابر الکترونی و مشخصه‌های فونونی، اتم هالید هر دو ماده نقش مهمی در پراکندگی فونون‌ها دارد، اما اتم کلسیم نقش ناچیزی در برخوردها و سرعت گروه فونونی دارد. سرعت ترابرد فونونی دو ماده در بسامدهای میانی (بین 2 الی 4 تراهرتز)، حدود 5000، 3000 متر بر ثانیه و عمر فونونی آن‌ها در قله برخوردها برابر 0/2 و 0/1 پیکوثانیه به‌ترتیب برای 3CsCaCl و 3CsCaBr به‌دست آمد. ماده 3CsCaCl به علت داشتن ترابرد فونونی بیش‌تری در تقریباً همه بازه‌های بسامدی و با اختلاف 2000 متر بر ثانیه در بازه‌های میانی بسامدی، پیش‌بینی می‌شود در کاربردهای مورد نیاز برای رسانندگی صوتی و گرمایی بالاتر مؤثر باشد. این در حالی‌ است که ماده 3CsCaBr به‌دلیل حرکت لرزشی و برخوردهای فونونی مهمی که اتم سزیم در قفسه 6CaBr دارد، انتظار می‌رود در زمینه گرمابرقی مفید واقع شود.   2 Phonon transport of CsCaBr3 and CsCaCl3 halide perovskites was investigated to identify their potential applications in various fields including cooling and thermoelectric. Ab-initio studies of these materials were performed in their cubic phase in generalized gradient approximation. Second and third order force constants were calculated with supercell approach and phonopy and phono3py packages. Lattice constant was obtained 5.463 and 5.768 the for CsCaCl3 and CsCaBr3 respectively. From a comparison of electron density and phonon properties, it was found that the halide atom in both compounds plays an important role in phonons’ propagation, while calcium atom has a negligible role in phonon scattering and phonon group velocity. Phonon velocities of both materials at intermediate frequencies (between 2 and 4 THz) are 5000 and 3000 m/s, and their phononic lifetime is 0.2 and 0.1 ps, for CsCaCl3 and CsCaBr3, respectively. Due to the higher phonon transport of CsCaCl3 in almost frequency and with a difference of 2000 m/s in the middle frequency range, it is predicted to be more effective in applications requiring higher acoustic and thermal conductivity. However, CsCaBr3 is expected to be useful in thermoelectric applications owning to rattling motion and consequently higher phonon scattering of Cs atom in CaBr6 cages. 10 17 2021/03/112021/03/10 1399/12/20 2021/07/122021/07/22 1400/4/31 مهدی فلاح M. Fallah دانشگاه مازندران ایران mfphysics@hotmail.com حسین میلانی مقدم H. Milani Moghaddam دانشگاه مازندران ایران milani@umz.ac.ir Perovskite Phononic transport Phonon group velocity Phonon scattering Phonon lifetime. پرووسکایت ترابرد فونونی سرعت گروه فونونی پراکندگی فونونی عمر فونونی. [1] M. Maldovan, “Sound and heat revolutions in phononics,” Nature, vol. 503, no. 7475, pp. 209-217, 2013.##[2] S. Muy, J.C. Bachman, L. Giordano, H.-H. Chang, D.L. Abernathy, D. Bansal, O. Delaire, S. Hori, R. Kanno, F. Maglia, S. Lupart, P. Lamp, Y. Shao-Horn, “Tuning mobility and stability of lithium ion conductors based on lattice dynamics,” Energy & Environmental Science, vol. 11, no. 4, pp. 850–859, 2018.##[3] T.M. Brenner, D.A. Egger, A.M. Rappe, L. 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امکان‌سنجی شناسایی خودکار گوشه‌‌های اصلی دستگاه شور با استفاده از ویژگی‌های شنیداری موسیقایی (مقاله پژوهشی) The feasibility of automatic identification of principal Gushehs of Shur Dastgāh, using the musical audio features (Research Article) 1 1 هدف از انجام این پژوهش، بررسی میزان اهمیت هر یک از 21 ویژگی‌ زمانی، طیفی و سِپسترال در شناسایی گوشه‌‌های اصلی دستگاه شور بوده است. در این پژوهش، ابتدا پایگاه‌ داده‌ای متشکل از شش گوشۀ اصلیِ دستگاه شور، براساس ردیف میرزا عبدالله و به تفکیک چهار ساز تار، سه‌تار، سنتور و بَربَط و در مجموع 173 قطعه ضبط و ایجاد شد. به‌منظور بررسی میزان اهمیت هر یک از ویژگی‌های شنیداری در تفکیک گوشه‌ها از یکدیگر، ابتدا با استفاده از مقیاس فیشر، هر یک از 21 ویژگی صوتی در نرم‌افزار متلب امتیازدهی شده و سپس از بین آن‌ها، سه ویژگی برتر انتخاب شدند. در مرحله بعد، با استفاده از دسته‌بند واکافت (آنالیز) تمایز خطی، امکان جداسازی گوشه‌ها از یکدیگر، برای هر یک از سازها به‌طور جداگانه و نیز به‌صورت کلی بررسی شد. نتایج پژوهش نشان دادند که در بین 21 ویژگی استخراج‌شده، ویژگی‌های سِپسترال بالاترین امتیاز را در جداسازی گوشه‌ها از یکدیگر کسب کردند و نسبت به ویژگی‌های زمانی و طیفی وضعیت بهتری داشتند؛ ولی در کل، هیچ‌ یک از این ویژگی‌ها‌، توانایی تفکیک و شناسایی شش گوشه اصلی دستگاه شور را نداشتند. با اینکه ویژگی‌های شنیداری کارکرد قابل قبولی در شناسایی خودکار موسیقی غربی و به‌خصوص شناسایی سبک موسیقی دارند، به دلیل ماهیت منحصربه‌فرد موسیقی سنتی ایرانی، در تفکیک و شناسایی گوشه‌های موسیقی سنتی ایرانی، از کارآمدی لازم برخوردار نیستند. 2 The main aim of this study is to investigate the importance of the 21 temporal, spectral, and cepstral features in detecting the principal Gushehs of Shur. In the present study, a dataset was created, including 173 pieces of music, consisting of six principal Gushehs of Shur Dastgāh, played by four musical instruments: Tar, Setar, Santur, and Barbat, based on Mirzaabdollah Radif (Repertoire). To investigate the significance of each temporal, spectral, cepstral feature in identifying the six principal Gushehs of Shur, the 21 musical features (extracted from the literature) were scored by using the Fisher scale. Then, the LDA classifier was trained, and then three superior and best-scored features (out of 21 ones) were selected to measure the classifier capability of six Gushehs detection for each of four instruments separately and also for all instruments. Findings show that among the 21 features, cepstral features gained the highest scores in distinguishing the six Gushehs and were better off than spectral and temporal features; but in general, none of them could distinguish the Gushehs from each other. Though, despite the acceptable efficiency of musical features in automatic detection of Western music, it is not efficient in Persian traditional music. 18 27 2021/03/112021/03/102021/02/3 1399/11/15 2021/07/122021/07/222021/04/22 1400/2/2 امیر وفائیان A. Vafaeian دانشگاه خوارزمی ایران amirvafa@gmail.com حامد ساجدی H. Sajedi دانشگاه شاهد ایران sadjedi@shahed.ac.ir کیوان برنا K. Borna دانشگاه خوارزمی ایران borna@khu.ac.ir داریوش علیمحمدی D. Alimohammadi دانشگاه خوارزمی ایران webliographer@gmail.com پویا سرایی P. Sarai دانشگاه آزاد واحد تهران مرکزی ایران pouya.sarai@gmail.com Iranian traditional music Music information retrieval Automatic detection Musical feature extraction Classifier. موسیقی سنتی ایرانی بازیابی اطلاعات موسیقایی شناسایی خودکار استخراج ویژگی‌ موسیقایی دسته‌بند. [1] M.Barkeshli, “International Music Congress: Conservation of national and folk music traditions,” 1961, Music-e Iran Magazine, vol. 9, no. 12, Republished by: Mahoor Music Quarterly, vol. 6, no. 22, pp. 173-193, 2004 (In Persian).##[2] M. Kiani, “Seven scales (Dastgahs) of Iranian music,” Sooremehr Publishing Co., 2014, (In Persian). ##[3] F. Fakhraddini, “Analysis and description of Radifs (repertoires) in Iranian Music,” Moin and Music Museum of Iran Publishing Co., 2013, (In Persian).##[4] E. Gavahian, “Persian music classification using pitch profile feature,” M.Sc. Thesis, Supervisor: HosseinMarvi, Consultant: Ali Soleimani, Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, I.R. Iran, Shahrood, 2010 (In Persian).##[5] M. Abbasi Layegh, S. Haghipour, Y. Najafi Sarem, “Classification of the Radif of Mirza Abdollah a canonic repertoire of Persian music using SVM method,” Gazi University Journal of Science, Part A: Engineering and Innovation, vol. 1, no. 4, pp. 57-66, 2013. ##[6] S. Samadi, A. Pourzaki, “Electric and electronics engineering dictionary,” Daneshyar Publication, I.R. Iran, Tehran, 2009, (In Persian).##[7] J.M. Ren, M.J. Wu, J.S. Roger Jang, “Automatic Music Mood Classification Based on Timbre and Modulation Features”, IEEE Transactions on Affective Computing, vol. 6, Issue 3, pp. 236-246, 2015.##[8] Z. Fu, G. Lu, K. Ming Ting, D. Zhang, “A Survey of Audio-based Music Classification and Annotation”, IEEE Transaction on Multimedia, vol. 13, no. 2, pp. 303-319, 2011.##[9] S. Abdollah-zadegan, S. Jafari, M. Dirend, “Dastgah and scale automatic detection in Iranian traditional music based on recital performance of Tar and Santur by note intelligent extraction,” 20th Annual Conference of Computer Society of Iran (CSICC), Ferdowsi University, I.R. Iran, Mashhad, 2014 (In Persian).##[10] M. Liu, C. Wan, “Feature selection for automatic classification of musical instrument sounds,” Proceedings of the first ACM/IEEE-CS joint conference on Digital libraries (JCDL), pp. 247-248, 2001.##[11] A. Wieczorkowska, A. Czyżewski, “Rough Set Based Automatic Classification of Musical Instrument Sounds,” Electronic Notes in Theoretical Computer Science, vol. 82, no. 4, pp. 298-309, 2003.##[12] P. Herrera-Boyer, G. Peeters, S. Dubnov, “Automatic Classification of Musical Instrument Sounds”, Journal of New Music Research, vol. 32, no. 1, pp. 3-21, 2003. ##[13] C. Weihs, U. Ligges, F. Mörchen, D. Müllensiefen, “Classification in Music Research,” Advances in Data Analysis and Classification, vol. 1, no. 3, pp. 255-291, 2007.##[14] J. Peivandi, “Design An Efficient System to Detect Traditionaliranian Music Style,” M.Sc. Thesis, Supervisor: HadiSoltanizadeh, Consultant: Ali Soleimani, Department of ArtificalInteligence, Faculty of Electrical and Computer Engineering, Semnan University, I.R. Iran, Semnan, 2014, (In Persian). ##[15] MM. Habibi Aghdam, H. Homayounpour, “Automatic recognition of music genre,” Signal and Data Processing Journal, vol. 7, no. 1, pp. 33-52, 2010, (In Persian).##[16] D. Talai, “Radif analysis: based on the notation of MirzaAbdollah'sradif with annotated visual description,” Ney Publication, I.R. Iran, Tehran, 2015, (In Persian). ‬‬##[17] M. R. Lotfi, “Fundamentals of Tar Playing,” Ketab -e-sal-Shayda (Periodical). Proceedings of Music for research in Iranian culture. Compiled by: M.R. Lotfi. Tehran: KetabeKhorshid, 1999, (In Persian).##[18] A. Vafaeian, “Automatic Identification for Shur Dastgāh of the Iranian Traditional Music Based on Recital Performance of Tar, Setar, Santur and Barbat,” PhD Thesis, Supervisors: Keivan Borna and Hamed Sajedi, Consultant: Dariush Alimohammadi and Pouya Saraei, Department of Information Science and Knowledge Studies, Faculty of Psychology and Education, Kharazmi, I.R. Iran, Tehran, 2019, (In Persian).##[1] M.Barkeshli, “International Music Congress: Conservation of national and folk music traditions,” 1961, Music-e Iran Magazine, vol. 9, no. 12, Republished by: Mahoor Music Quarterly, vol. 6, no. 22, pp. 173-193, 2004 (In Persian).##[2] M. Kiani, “Seven scales (Dastgahs) of Iranian music,” Sooremehr Publishing Co., 2014, (In Persian). ##[3] F. Fakhraddini, “Analysis and description of Radifs (repertoires) in Iranian Music,” Moin and Music Museum of Iran Publishing Co., 2013, (In Persian).##[4] E. Gavahian, “Persian music classification using pitch profile feature,” M.Sc. Thesis, Supervisor: HosseinMarvi, Consultant: Ali Soleimani, Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, I.R. Iran, Shahrood, 2010 (In Persian).##[5] M. Abbasi Layegh, S. Haghipour, Y. Najafi Sarem, “Classification of the Radif of Mirza Abdollah a canonic repertoire of Persian music using SVM method,” Gazi University Journal of Science, Part A: Engineering and Innovation, vol. 1, no. 4, pp. 57-66, 2013. ##[6] S. Samadi, A. Pourzaki, “Electric and electronics engineering dictionary,” Daneshyar Publication, I.R. Iran, Tehran, 2009, (In Persian).##[7] J.M. Ren, M.J. Wu, J.S. Roger Jang, “Automatic Music Mood Classification Based on Timbre and Modulation Features”, IEEE Transactions on Affective Computing, vol. 6, Issue 3, pp. 236-246, 2015.##[8] Z. Fu, G. Lu, K. Ming Ting, D. Zhang, “A Survey of Audio-based Music Classification and Annotation”, IEEE Transaction on Multimedia, vol. 13, no. 2, pp. 303-319, 2011.##[9] S. Abdollah-zadegan, S. Jafari, M. Dirend, “Dastgah and scale automatic detection in Iranian traditional music based on recital performance of Tar and Santur by note intelligent extraction,” 20th Annual Conference of Computer Society of Iran (CSICC), Ferdowsi University, I.R. Iran, Mashhad, 2014 (In Persian).##[10] M. Liu, C. Wan, “Feature selection for automatic classification of musical instrument sounds,” Proceedings of the first ACM/IEEE-CS joint conference on Digital libraries (JCDL), pp. 247-248, 2001.##[11] A. Wieczorkowska, A. Czyżewski, “Rough Set Based Automatic Classification of Musical Instrument Sounds,” Electronic Notes in Theoretical Computer Science, vol. 82, no. 4, pp. 298-309, 2003.##[12] P. Herrera-Boyer, G. Peeters, S. Dubnov, “Automatic Classification of Musical Instrument Sounds”, Journal of New Music Research, vol. 32, no. 1, pp. 3-21, 2003. ##[13] C. Weihs, U. Ligges, F. Mörchen, D. Müllensiefen, “Classification in Music Research,” Advances in Data Analysis and Classification, vol. 1, no. 3, pp. 255-291, 2007.##[14] J. Peivandi, “Design An Efficient System to Detect Traditionaliranian Music Style,” M.Sc. Thesis, Supervisor: HadiSoltanizadeh, Consultant: Ali Soleimani, Department of ArtificalInteligence, Faculty of Electrical and Computer Engineering, Semnan University, I.R. Iran, Semnan, 2014, (In Persian). ##[15] MM. Habibi Aghdam, H. Homayounpour, “Automatic recognition of music genre,” Signal and Data Processing Journal, vol. 7, no. 1, pp. 33-52, 2010, (In Persian).##[16] D. Talai, “Radif analysis: based on the notation of MirzaAbdollah'sradif with annotated visual description,” Ney Publication, I.R. Iran, Tehran, 2015, (In Persian). ‬‬##[17] M. R. Lotfi, “Fundamentals of Tar Playing,” Ketab -e-sal-Shayda (Periodical). Proceedings of Music for research in Iranian culture. Compiled by: M.R. Lotfi. Tehran: KetabeKhorshid, 1999, (In Persian).##[18] A. Vafaeian, “Automatic Identification for Shur Dastgāh of the Iranian Traditional Music Based on Recital Performance of Tar, Setar, Santur and Barbat,” PhD Thesis, Supervisors: Keivan Borna and Hamed Sajedi, Consultant: Dariush Alimohammadi and Pouya Saraei, Department of Information Science and Knowledge Studies, Faculty of Psychology and Education, Kharazmi, I.R. Iran, Tehran, 2019, (In Persian). ##

طبقه‌بندی ویژگی های گفتار مقاوم به نوفه در سامانه تعیین هویت گوینده (مقاله پژوهشی) Classification of noise-robust speech features in the speaker authentication system (Research Article) 1 1 تشخیص­ خودکار هویت ­گوینده کاربردهای وسیعی در سامانه­ های صنعتی و امنیتی دارد و وابسته به ویژگی علامت گفتار است. کاربرد ماتریس ویژگی در شناسایی بی­ درنگ گوینده بسیار مهم و وجود نوفه محیطی و پردازشی منجر به تغییر مشخصات ویژگی­ ها و تولید خطا در تعیین ­هویت است. افزایش ­دقت در تشخیص هویت به فرایند حذف ­نوفه برای تعیین صحیح ویژگی‌های انرژی، آنتروپی انرژی، نرخ عبور از صفر، مرکز ثقل طیفی، گسترش طیفی، آنتروپی طیفی، شار طیفی، و رل­ آف طیفی از علامت ­گوینده نیاز دارد. در طراحی الگوریتم‌های بی­درنگ و قابل ­اعتماد، فرایندهای مهم استخراج صحیح گفتار، شناسایی میزان­ حساسیت و سنجش میزان ­مقاومت مؤلفه‌های علامت برای حذف نوفه و بهبود کیفیت­ گفتار در بهبود علامت ­به نوفه نقش اساسی دارند. هدف اصلی این مقاله ارایه روش طبقه‌بندی ویژگی­­ های علامت گفتار جهت طراحی الگوریتم ­های بی­ درنگ تعیین­ هویت گوینده و مقاوم ­به نوفه با سنجش میزان مقاومت آن است. روش پیشنهادی حذف ­نوفه از ماسک دودویی با ویژگی­ مقاوم مشخص­ بهره­ می­ برد و نتایج تجربی آزمایش‌ها روی داده­ ها، بهبود علامت ­به نوفه 2 الی 3 دسی ­بل را نشان می‌دهد. ارزیابی ماتریس ویژگی در سامانه تشخیص هویت از ضریب کپسترال بسامد­مل و ضریب­ پیشگویی خطی و ضریب­کپستروم تشکیل­ شده که با روش فاصله­ یابی اقلیدسی در مجموعه ­داده ­های استاندارد ارزیابی شده­ است. روش پیشنهادی با وجود داده ­های نوفه­ای توانسته قدرت تشخیص تعیین هویت ­گوینده را به دقت بالای 80 درصد و سرعت بی­ درنگ افزایش دهد.   2 Automatic speaker recognition has a wide range of applications in industrial and security systems and requires the extraction of speech signal features. The use of the feature matrix is ​​very important in real-time recognition of the speaker, and the presence of environmental and processing noise leads to a violation in the characteristics of the features and the production of recognition errors. Increasing the accuracy of recognition detection requires the noise removal process to correctly determine the energy characteristics, energy entropy, zero- crossing rate, spectral centroid, spectral spread, spectral entropy, spectral flux, and spectral roll off the signal. In designing real-time and reliable algorithms, there are critical processes of correct speech extraction, sensitivity detection, and measuring the robustness of signal parameters to eliminate noise and improve speech quality, which play a key role in improving the signal-to-noise ratio. In this paper, the classification of speech signal features for designing real-time and noise-robust speaker recognition algorithms in measuring its robustness are investigated. The proposed method of noise removal uses a binary mask with a robust feature and the experimental results of the experiments on the standard data show the rate of signal improvement to the noise of approximately 2 to 3 db. The feature matrix evaluation for the authentication system consists of mel frequency coefficient, linear prediction coefficient and, cepstrum coefficient, which has been evaluated by the Euclidean distance method in another experimental standard data set. Our proposed method achieves on overall 80% real-time recognition accuracy in noisy data set.  28 39 2021/03/112021/03/102021/02/32020/09/10 1399/6/20 2021/07/122021/07/222021/04/222021/07/22 1400/4/31 محدثه میربیگی M. Mirbeygi دانشگاه شاهد ایران m.mirbeygi@gmail.com امین اله مه آبادی A. Mahabadi دانشگاه شاهد ایران mahabadi@shahed.ac.ir اکبر رنجبر A. Ranjbar دانشگاه شاهد ایران ranjbar@shahed.ac.ir Classification of speech features Speaker authentication Noise-robust Speech signal Noise. طبقه‌بندی ویژگی های گفتار تعیین هویت گوینده مقاوم به نوفه علامت گفتار نوفه. ##[1] R. Ram, A. Das, S.K. Mohapatra, “Speech enhancement using wiener filter based on voiced speech probability,” Advances in Intelligent Computing and Communication, vol. 1, pp. 161-170, 2020.##[2] G. Krisha, C. Tran, J. Yu, A.H. Tewfik, “Speech recognition with no speech or with noisy speech,” IEEE International Conference on Acoustics, Speech and Signal Processing, 2019.##[3] H.P.P. Win, P.T.T. Khine, “Speech enhancement techniques for noisy speech in real world environments,” 7th International Conference on Computer Applications (ICCA), 2019.##[4] M. Forsberg, “Why is speech recognition difficult,” Chalmers University of Technology, 2003.##[5] M.H. Soni, N. Shah, H.A. Ptil, “Time-frequency masking-based speech enhancement using generative adversarial network,” International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 5039-5043, 2018.##[6] G. Theodoros, P. Aggelos, “Introduction to audio analysis: A MATLAB approach,” Academic Press, 2014.##[7] A.S. Ajibola, N.K. Alang Rashid, “Some commonly used speech feature extraction algorithms,” IntechOpen, pp. 2-19, 2018.##[8] R. Rajeev, T. Abhishek, “Analysis of feature extraction techniques for speech recognition system,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 7C2, pp. 197-200, 2019.##[9] S. Nisar, M. Tariq, A. Adeel, M. Gogate, A. Hussain, “Cognitively inspired feature extraction and speech recognition for automated hearing loss testing,” Cognitive Computation, vol. 11, no. 4, pp. 489-502, 2019.##[10] K. Gajanan, K. Birajdar, D. Mukesh, “Speech/music classification using visual and spectral chromagram features,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 1, pp. 329-347, 2020.##[11] T. Drugman, Y. Usuke, Y. Kida, “Voice activity detection: Merging source and filter-based information,” IEEE SIGNAL PROCESSING LETTERS, vol. 23, no. 2, pp. 252-256, 2015.##[12] Y. Shi, J. BAI, P. Xue, D. Shi, “Fusion feature extraction based on auditory and energy for noise-robust speech recognition,” IEEE Access, vol. 7, pp. 81911-81922, 2019.##[13] D. Deshwal, P. Sangwan, D. Kumar, “Feature Extraction Methods in Language Identification: A Survey,” Wireless Personal Communications, vol. 107, no. 4, pp. 2071-103, 2019.##[14] M.T. Hosain, A.A. Arif, A.I.Pritom, M.R. Rahman, M. Zahidul Islam, “Development of a tangent based robust speech feature extraction model,” International Conference on Cyber Security and Computer Science, pp. 414-425, 2020.##[15] N. Saleem, M.I. Khattak, “Deep neural networks for speech enhancement in complex noisy environments,” IJIMAI journal, vol. 6, no.1, pp. 84-90, 2019.##[16] S. Lakshmikanth, “Noise cancellation in speech signal processing-A review,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 3, no. 1, pp. 5175-5186, 2014.##[17] C. Shraddha, M.L. Chayadevi, M.A. Anusuya, “Noise cancellation and noise reduction techniques: A review,” 1st International Conference on Advances in Information Technology (ICAIT), IEEE, pp. 159-166, 2019.##[18] NOIZEUS, https://ecs.utdallas.edu/loizou/speech/noizeus/.##[19] ILCDR, http://www.imm.dtu.dk.##[20] Z. Heydari, A. Mahabadi, A. Ranjbar, “The effect of energy and interpolation combination on the accuracy of indoor localization based on the speech signal time difference of the arrival,” Journal of Acoustical Engineering Society of Iran, vol. 8, no. 2, pp. 60-78, 2021. ##

بررسی نواقص در هدایت حرارتی و طیف فونونی گرافن (مقاله پژوهشی) Investigation of defects in the thermal conductivity and phonon spectra of graphene (Research Article) 1 1 گرافن یکی از نانو‌ساختارهای کربنی است که با توجه به خواص حرارتی بالای آن امروزه در صنایع بسیاری مورد توجه قرار گرفته است. در این‌کار با توجه به هدایت حرارتی بسیار بالا و وجود نقص‌های متفاوت در طول سنتز گرافن به بررسی هم‌زمان اثر نقص‌های جای‌خالی و استون- والز تا تراکم نقص بالا روی هدایت حرارتی گرافن پرداخته شده است. در این‌کار از شبیه‌سازی دینامیک مولکولی غیر‌تعادلی معکوس با استفاده از پتانسیل ایربو که یک پتانسیل تجربی است، استفاده شده است. این پتانسیل بر‌هم‌کنش‌های غیر‌پیوندی را از طریق تطبیق رفتار بر‌هم‌کنش‌های بین‌مولکولی توصیف می‌کند، که در این‌جا برای برهم‌کنش کربن- کربن گرافن استفاده می‌شود. هم‌چنین به بررسی طیف فونونی و هم‌پوشانی روی هدایت حرارتی نانو‌روبان گرافن پرداخته شده است. نتایج نشان می‌دهد که هدایت حرارتی نانو‌روبان گرافن حاوی هر دو نقص حداکثر به میزان 46 درصد برای تراکم نقص کم و حداقل میزان 25 درصد برای حداکثر تراکم کاهش می‌یابد، یعنی با افزایش تراکم نقص میزان کاهش هدایت حرارتی نسبت به نانو‌روبان بدون نقص کاهش کم‌تری دارد. 2 Graphene is one of the carbon nanostructures that has been considered in many industries today due to its high thermal properties. In this work, due to the high thermal conductivity and the presence of various defects during the synthesis of graphene, the simultaneous investigation of the effects of the vacancy defects and Stone-Wales to high-density defects on the thermal conductivity of graphene has been studied. This work has been used to simulate a reverse non-equilibrium molecular dynamics simulation with using Airebo potential was used to model the interactions between  all atoms, which is an empirical potential. This describes the potential of non-binding interactions by matching the behavior of inter-molecular interactions, which is here for carbon-carbon interactions, graphene is used. It also studies the phonon spectra and overlaps on the thermal conductivity of graphene nanoribbons. The results show that the thermal conductivity of graphene nanoribbons reduces both defects by a maximum of 46% for low deficit density and a minimum of 25% for maximum density, that is, decreasing the density of the defect in the thermal conductivity reduction relative to the defective nano-tubes. 40 47 2021/03/112021/03/102021/02/32020/09/102020/08/1 1399/5/11 2021/07/122021/07/222021/04/222021/07/222021/07/12 1400/4/21 حمدالله صالحی H. Salehi دانشگاه شهید چمران اهواز ایران salehi_h@scu.ac.ir مریم عزیزی M. Azizi دانشگاه شهید چمران اهواز ایران maryam.aziziphysics@gmail.com Graphene nanoribbons Dynamic molecular simulation Vacancy defect Stone-Wales defect Thermal conductivity. نانو‌روبان گرافن شبیه‌سازی دینامیک مولکولی نقص جای‌خالی نقص استون-‌ والز هدایت حرارتی. ##[1] K.S. Novoselov, A.K. Geim, S.V. Morozov, D. Jiang, Y. Zhang, S.V. Dubonos, I.V. Grigorieva, A.A. Firsov, “Electric field effect in atomically thin carbon films,” Science, vol. 306, no. 5696, pp. 666-669, 2004.##[2] W. Hao, Y. Wang, H. Zhao, J. Zhu, S. Li, “Strong dependence of the vertical charge carrier mobility on the π-π stacking distance in molecule/graphene heterojunctions,” Physical Chemistry Chemical Physic, 2020.##[3] M.D. Stoller, S. Park, Y. Zhu, J. An, R.S. Ruoff, “Graphene-based ultracapacitors,” Nano Letters, vol. 8, no. 10, pp. 3498-3502, 2008.##[4] R.R. Nair, P. Blake, A.N. Grigorenko, K.S. Novoselov, T.J. Booth, T. Stauber, N.M. Peres, A.K. Geim, “Fine structure constant defines visual transparency of graphene,” Science, vol. 320, no. 5881, pp. 1308-1308, 2008.##[5] C. Lee, X. Wei, J.W. Kysar, J. Hone, “Measurement of the elastic properties and intrinsic strength of monolayer graphene,” science, vol. 321, no. 5887, pp. 385-388, 2008.##[6] K. Cao, S. Feng, Y. Han, L. Gao, T. Hue Ly, Z. Xu, Y. Lu, “Elastic straining of free-standing monolayer graphene,” Nature Communications, vol. 11, no. 1, pp. 284, 2020.##[7] A.A. Balandin, S. Ghosh, W. Bao, I. Calizo, D. Teweldebrhan, F. Miao, C.N. Lau, “Superior thermal conductivity of single-layer graphene,” Nano Letters, vol. 8, no. 3, pp. 902-907, 2008.##[8] D.G. Papageorgiou, L.A. Kinloch, R.J. Young, “Mechanical properties of graphene and graphene-based nanocomposites,” Progress in Materials Science, vol. 90, pp. 75-12, 2017.##[9] W. Yu, L. Sisi, Y. Haiyan, L. Jie, “Progress in the functional modification of graphene/graphene oxide: A review,” RSC Advances, vol. 10, no. 26, pp. 15328-1534, 2020.##[10] S. Ghosh, W. Bao, D.L. Nika, S. Subrina, E.P. Pokatilov, C.N. Lau, A.A. Balandin, “Dimensional crossover of thermal transport in few-layer graphene,” Nature Materials, vol. 9, no. 7, pp. 555-558, 2010.##[11] D. Zhang, B. Hu, D. Guan, Z. Luo, “Essential roles of defects in pure graphene/Cu2O photocatalyst,” Catalysis Communications, vol. 76, pp. 7-12, 2016.##[12] J. Ma, D. Alfe, A. Michaelides, E. Wang, “Stone-Wales defects in graphene and other planar s p 2-bonded materials,” Physical Review B, vol. 80, no. 3, pp. 033407, 2009.##[13] F. Banhart, J. Kotakoski, A.V. Krasheninnikov, “Structural defects in graphene,” ACS Nano, vol. 5, no. 1, pp. 26-41, 2010.##[14] S. Ebrahimi, M. Azizi, “The effect of high concentrations and orientations of Stone–Wales defects on the thermal conductivity of graphene nanoribbons,” Molecular Simulation, vol. 44, no. 3, pp. 236-242, 2018.##[15] D. Liu, “Investigation on thermal conductivity of graphene/Si heterostructure with different defect ratios and sizes,” Physics letters A, vol. 384, pp. 126070, 2020.##[16] T.Y. Ng, J.J. Yeo, Z. Liu, “A molecular dynamics study of the thermal conductivity of nanoporous silica aerogel, obtained through negative pressure rupturing,” Journal of Non-Crystalline Solids, vol. 358, no. 11, pp. 1350-1355, 2012.##[17] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of Computational Physics, vol. 117, no. 1, pp. 1-19, 1995.##[18] D. Brenner, J. Harrison, C. White, R. Colton, “Molecular dynamics simulations of the nanometer-scale mechanical properties of compressed Buckminsterfullerene,” Thin Solid Films, vol. 206, no. 1-2, pp. 220-223, 1991.##[19] S. Nosé, “A unified formulation of the constant temperature molecular dynamics methods,” The Journal of Chemical Physics, vol. 81, no. 1, pp. 511-519, 1984.##[20] M. Noshin, A.I. Khan, I.A. Navid, H.A. Uddin, S. Subrina, “Impact of vacancies on the thermal conductivity of graphene nanoribbons: A molecular dynamics simulation study,” Aip Advances, vol. 7, no. 1, pp. 1-7, 2017.##[21] T.Y. Ng, J.J. Yeo, Z. Liu, “A molecular dynamics study of the thermal conductivity of graphene nanoribbons containing dispersed Stone–Thrower–Wales defects,” Carbon, vol. 50, no. 13, pp. 4887-4893, 2012.##[22] N. Chandra, S. Namilae, C. 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تعیین مؤلفه‌های بهینه امواج فراآوایی بر روند اسپرم‌زایی بیضه موش نوزاد (مقاله پژوهشی) Determination of optimal parameters of ultrasonic waves on permatogenesis process of neonate mouse testis (Research Article) 1 1 امواج فراآوا به‌عنوان یک نوآوری با هدف بازسازی بافت و بهبود عملکرد یاخته‌ای در مهندسی بافت بسیار مورد توجه قرار گرفته‌اند. در این مطالعه مؤلفه‌های بهینه تابش امواج فراآوا با شدت پایین روی بافت بیضه موش نوزاد در جهت بهبود روند اسپرم‌زایی و کمک به درمان ناباروری با علل مردانه، در دو گروه کنترل و آزمایشی مورد ارزیابی قرار گرفته‌اند، در فاز اول از آزمایش به‌منظور جلوگیری از آثار حرارتی کنترل دما صورت گرفت و در فاز دوم و سوم به ترتیب میزان شدت بهینه و بهره ‌کاری بهینه تابش امواج فراآوا مورد ارزیابی قرار گرفت با توجه به ارزیابی‌های بافت‌شناسی توسط رنگ‌آمیزی هماتوکسیلین- ائوزین انجام‌شده مشخص شد که امواج فراآوا با بسامد 1 مگاهرتز، شدت 0/5 وات بر سانتی‌مترمربع و بهره ‌کاری ٤٠ درصد نسبت به دیگر شدت‌ها و بهره‌های کاری مورد بررسی تأثیرات بهتری روی حفظ ساختار بافت بیضه طی چهارده روز کشت را دارند. هم‌چنین امواج فراآوا به‌صورت تپ (پالس) با شدت پایین دارای تأثیرات مطلوب روی پیشرفت روند اسپرم‌زایی در بافت بیضه موش نوزاد هستند. نتایج حاصل از ارزیابی بافت‌شناسی به‌صورت کمّی نشان داد که افزایش معنا‌داری در میزان مساحت بافت بیضه تحت تأثیر تابش امواج فراآوا نسبت به گروه کنترل وجود دارد. (0/05p<). 2 Ultrasonic waves are rapidly emerging as an innovative regeneration technique, improving cell performance in tissue engineering. In this study, the optimal parameters of low intensity pulsed ultrasonic stimulation (LIPUS) waves on the testicular tissue of neonatal mice in the control and experimental groups were evaluated to improve the process of spermatogenesis and aid treatment of infertility with male causes. In the first phase of the study, the temperature was controlled to prevent thermal effects, and in the second and third phases, the optimal intensity and the optimal duty cycle of ultrasonic waves were evaluated, respectively. Histological H&E evaluations indicate that ultrasonic waves with an intensity of 0.5 W/cm2 and 40% duty cycle during 14 days of organ culture have a better effect on maintaining the structure of testicular tissue compared to other intensities and duty cycles. Additionally, LIPUS waves had a propitious influence on the enhancement of spermatogenesis in the testicular tissue of neonatal mice. Quantitative histological findings showed a significant increase in the area of ​​testicular tissue stimulated by LIPUS compared to the control group (p<0.05). 48 54 2021/03/112021/03/102021/02/32020/09/102020/08/12021/03/15 1399/12/25 2021/07/122021/07/222021/04/222021/07/222021/07/122021/07/29 1400/5/7 شقایق محمدی S. Mohamadi دانشگاه تربیت مدرس ایران shaghayeghmohamadi94@gmail.com منصوره موحدین M. Movahedin دانشگاه تربیت مدرس ایران movahed.m@modares.ac.ir منیژه مختاری دیزجی M. Mokhtari Dizaji دانشگاه تربیت مدرس ایران Manijhem@yahoo.com Ultrasonic waves Testicular tissue Spermatogenesis. امواج ‌‌‌‌فراآوا بافت بیضه اسپرم‌زایی. ##[1] S. Al-Daghreer, M. Doschak, A.J. Sloan, P.W. Major, G. Heo, C. Scurtescu, “Long term effect of low intensity pulsed ultrasound on a human tooth slice organ culture,” Archives of Oral Biology, vol. 57, no. 6, pp.760-768, 2012.##[2] K.W. Jang, “The effect of low-intensity pulsed ultrasound on chondrocyte migration and its potential for the repair of articular cartilage,” Biomedical Engineering, vol. 17, pp. 70-73, 2011.##[3] E. Tanaka, S. Kuroda, S. Horiuchi, A. Tabata, T.J. Aobe, E.Bialy, “Low-intensity pulsed ultrasound in dentofacial tissue engineering,ˮ Annals of Biomedical Engineering, vol. 43, no. 4, pp. 871-86, 2015.##[4] M. Mohaqiq, M. Movahedin, M.M. Dizaji, Z.J.C. Mazaheri, “Upregulation of integrin-α6 and integrin-β1 gene expressions in mouse spermatogonial stem cells after continues and pulsed low intensity ultrasound stimulation,” Cell Journal (Yakhteh), vol. 19, no. 4, pp. 634, 2018.##[5] A. Azimi, Z. Mazaheri, M. Movahedin, “Production of embryonic stem like-cells from neonatal mouse testis after exposure to low-intensityultrasound,” Pathobiology Research. vol. 19, no. 3, pp. 49-60, 2017.##[6] N.M. Gharenaz, M. Movahedin, Z. Mazaheri, “Three-dimensional culture of mouse spermatogonial stem cells using a decellularised testicular scaffold,” Cell Journal (Yakhteh), vol. 21, no. 4, pp. 410, 2020.##[7] L. Monfared, “Effects of mobile Phone radiation on the histological and anatomical parameters of testis and serum levels of testosterone in mice,” Scientific Journal of Ilam University of Medical, vol. 24, no. 2, pp. 110-8, 2016. ##[8] D.L.J. Miller, “Biology. A review of the ultrasonic bioeffects of microsonation, gas-body activation, and related cavitation-like phenomena,” Ultrasound in Medicine & Biology, vol. 13, no. 2, pp. 443-70, 1987.##[9] Y. Wu, Q. Gao, S. Zhu, Q. Wu, R. Zhu, H. Zhong, “Low-intensity pulsed ultrasound regulates proliferation and differentiation of neural stem cells through notch signaling pathway,” Biochemical and Biophysical Research Communications, vol. 526, no. 3, pp. 793-8, 2020.##[10] S. Zhou, A. Schmelz, T. Seufferlein, Y. Li, J. Zhao, M.G. Bachem, “Molecular mechanisms of low intensity pulsed ultrasound in human skin fibroblasts,” Journal of Biological Chemistry, vol. 279, no. 52, pp. 54463-9, 2004.##[11] D. Hamrahi, M.B. Shiran, E.M. Baghban, H. Gourabi, L. Rouhi, “Effect of low-intensity ultrasound on osteogenic differentiation of rat bone-marrow mesenchymal stem cell: An in vitro study,” Laser in Medicine, vol. 5, no. 2, pp. 6-11, 2008.##[12] Y. Monma, T. Shindo, K. Eguchi, R. Kurosawa, Y. Kagaya, Y. Ikumi, “Low- intensity pulsed ultrasound ameliorates cardiac diastolic dysfunction in mice-A possible novel therapy for HFpEF,” Cardiovascular Research, vol. 117, no. 1, pp. 1325–1338, 2019.##[13] A. Harrison, S. Lin, N. Pounder, Y.J.U. Mikuni-Takagaki, “Mode & mechanism of low intensity pulsed ultrasound (LIPUS) in fracture repair,” Ultrasonics, vol. 70, no. 1, pp. 45-52, 2016. ##

پیاده‌سازی و ارزیابی قیاس صوتی کرل به‌منظور پیش‌بینی نوفه دوردست برای یک هندسه مربعی (مقاله پژوهشی) Implementation and evaluation of Curle’s acoustic analogy in order to far-field noise prediction for a square geometry (Research Article) 1 1 در این مقاله با هدف پیاده‌سازی، ارزیابی و اعتبار‌سنجی قیاس صوتی کرل در پیش‌بینی نوفه دوردست، جریان اطراف هندسه مربعی با اعداد رینولدز 46000 و 69000، در نرم‌افزار متن‌باز اوپن‌فوم شبیه‌سازی شده است. برای حل جریان تراکم‌ناپذیر از روش حل معادلات غیر‌دائم متوسط‌گیری‌شده به روش رینولدز با الگوی تلاطم کا‌- ‌‌امگا و رهیافت شبیه‌سازی گردابه‌های بزرگ استفاده شده است. به‌منظور پیش‌بینی مقادیر تراز فشار صدا در دوردست با استفاده از قیاس صوتی کرل، داده‌برداری مقادیر نوسانات فشار روی سطح هندسه مربعی با استفاده از ابزار کاوشگر انجام شده است. در این مطالعه متوسط ضرایب آیرودینامیکی (ضرایب پسا و برا)، متوسط ضریب فشار و عدد استروهال بررسی شده و مشخص گردید که نوسانات نیروهای برآ، پسا و تلاطم جریان از عوامل تأثیر‌گذار بر تراز فشار صدا هستند. تراز فشار صدا در دور‌دست با رهیافت گردابه‌های بزرگ به خوبی پیش‌بینی شده است که در موقعیت دو شنونده مورد نظر برای اعداد رینولدز 46000 و 69000 به‌ترتیب مقدار 90/62 و 97/8 دسی‌بل است. در نهایت انطباق مقادیر تراز فشار صدا در دوردست از نرم‌افزار اوپن‌فوم با نتایج تجربی، نشان‌دهنده کارآیی و دقت روش ترکیبی ارائه‌شده در این مقاله و صحت قیاس صوتی کرل در نرم‌افزار اوپن‌فوم است.   2 In this paper, we aim to implement, evaluate and validate Curle’s acoustic analogy in predicting far-field noise emitted due to the turbulent flow around a two-dimensional square geometry. Flow is considered to be incompressible with two Reynolds numbers of 46000 and 69000. Open- source software OpenFOAM is utilized for flow simulation using both k-w and large eddy simulation turbulence models. To predict far-field sound pressure levels, the Curle acoustic analogy implemented within the software and pressure fluctuations data over the surface of a square geometry have been used as one of the inputs to the Curle analogy. The average aerodynamic coefficients, mean pressure coefficients, and Strouhal number were determined and it was found that the fluctuations of  lift and drag forces, as well as the turbulence intencity are the main factors affecting the far field sound pressure level. The far-field sound pressure is well predicted by a combination of large eddy simulation and Curle’s acoustic analogy, which at two different observer locations and two Reynolds numbers of 46000 and 69000, are 90.62 and 97.8 (dB), respectively. Finally, the agreement of numerically predicted far-field sound pressure level with the experimental results indicates the efficiency of Curle’s acoustic analogy incorporated within the OpenFoam. 55 69 2021/03/112021/03/102021/02/32020/09/102020/08/12021/03/152021/01/29 1399/11/10 2021/07/122021/07/222021/04/222021/07/222021/07/122021/07/292021/07/29 1400/5/7 میلاد ذبیحی نژاد M. Zabihi Nejad دانشگاه یزد ایران miladzabihi@stu.yazd.ac.ir علی اکبر دهقان A. Dehghan دانشگاه یزد ایران adehghan@yazd.ac.ir محمد فرمانی M. Farmani دانشگاه یزد ایران mfarmani@stu.yazd.ac.ir Numerical investigation Large eddy simulation K-W turbulence models Curle’s acoustic analogy OpenFOAM software. بررسی عددی شبیه‌سازی گردابه‌های بزرگ الگوی تلاطم کا- امگا قیاس صوتی کرل نرم‌افزار اوپن‌فوم. ##[1] S. Becker, M. Escobar, C. Hahn, I. Ali, M. Kaltenbacher, B. Basel, M. Grunewald, “Experimental and numerical investigation of the flow induced noise from a forward facing step,” in: 11th AIAA/CEAS Aeroacoustics Conference, pp. 3006, 2005.##[2] J.F. Dorneanu, A. Mueller, P. Rambaud, E.T.A.V.D. Weide, A. Hirschberg, “Tonal and silent wake modes of a square rod at incidence,” Acta Acustica united with Acustica, vol. 102, no. 3, pp. 419-422, 2016.##[3] E. Latorre Iglesias, D.J. Thompson, M.G. Smith, “Experimental study of the aerodynamic noise radiated by cylinders with different cross-sections and yaw angles,” Journal of Sound and Vibration, vol. 361, pp. 108-12, 2016.##[4] J.C. Cai, J. Pan, A. Kryzhanovskyi, S.J. E, “A numerical study of transient flow around a cylinder and aerodynamic sound radiation,” Thermophysics and Aeromechanics, vol. 3, no. 25, pp. 346-331, 2018.##[5] F. Margnat, “Hybrid prediction of the aerodynamic noise radiated by a rectangular cylinder at incidence,” Computers & Fluids, vol. 109 , pp. 13-26, 2015.##[6] W.S.H. Fujita, H. Furutani, H. Suzuki, “Experimental investigations and prediction of arerodynamic sound generated from square cylinders,” 4th AIAA/CEAS Aeroacoustics Conference ,pp .2369-2375, 1998.##[7] T.F.B.F.V. Hutcheson, “Noise radiation from single and multiple rod configurations,” International Journal of Aeroacoustics, vol. 11, no. 3, pp. 291-333, 2012.##[8] A. Mueller, “Large eddy simulation of cross-flow around a square rod at incidence with application to tonal noise prediction,” University of Twente, 2012.##[9] M. Mohamadrezaei, A.A. Dehghan, A. Movahedi, “Comparison of different methods of numerical simulation of flow and sound fields around a square cylinder at various incidence angles,” Modares Mechanical Engineering, vol. 17, no. 5, pp. 147-158, 2017, (in Persian).##[10] M. Mohammadrezaei, A.A. Dehghan, A. Movahedi, “Numerical investigation of flow induced sound around a square cylinder at various incidence angles,” Journal Of Applied and Computational Sciences in Mechanics, vol. 28, no. 2, pp. 97-114, 2017, (in Persian).##[11] C.J. Doolan, “Flow and noise simulation of the nasa tandem cylinder experiment using openfoam,” In15th AIAA/CEAS Aeroacoustics Conference, pp. 3157, 2009.##[12] S.R.L. Samion, M.S.M. Ali, A. Abu, C.J. Doolan, R.Z.-Y. Porteous, “Aerodynamic sound from a square cylinder with a downstream wedge,” Aerospace Science and Technology, vol. 53, pp. 85-94, 2016.##[13] D.C. Wilcox, “Reassessment of the scale-determining equation for advanced turbulence models,” AIAA Journal, vol. 26, no. 11, pp. 1299-1310, 1988.##[14] C.H.L.X. Jiang, “Numerical techniques for direct and large eddy simulations,” Florida: CRC Press, 4th Edittion, pp. 19-24, 2016.##[15] M. Lesieur, “Turbulence in Fluids,” 4th Edition, Germany: Springer Science & Buisiness Media, pp. 419-452, 2008.##[16] J.C.Y.P. Wang, H.C. Lee, “Accurate simulations of surface pressure fluctuations and flow-induced noise near bluff body at low mach numbers,” The Seventh International Colloquium on Bluff Body Aerodynamics and Applications (BBAA7), 2012.##[17] M.J. Lighthill, “On sound generated aerodynamically I. General theory,” Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, vol. 211, no. 1107, pp. 564-587, 1952.##[18] C. N, “The influence of solid boundaries upon aerodynamic sound,” Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, vol. 231, no. 1187, pp. 505-514,1955.##[19] C.J. Greenshields, “OpenFOAM user guide,” OpenFOAM Foundation Ltd, vol. 3, no. 1, pp. 2888, 2015.##[20] C. Wagner, T. Hüttl, P. Sagaut, “Large-eddy simulation for acoustics,” Cambridge University Press, 2007. ##

بررسی ویژگی‌های فونونی و ترمودینامیکی تلورایدجیوه در فاز بلندروی با استفاده از شیوۀ شبه‌پتانسیل (مقاله پژوهشی) Investigation of thermodynamic and phononic properties of HgTe in Zinc Blende phase using pseudopotential method (Research Article) 1 1 در این مقاله خواص فونونی و ترمودینامیکی ترکیب تلورایدجیوه (HgTe) در فاز بلند روی مورد بررسی قرار می‌گیرد. محاسبات با استفاده از روش شبه‌پتانسیل در چارچوب نظریۀ تابعی چگالی و با استفاده از نرم‌افزار کوانتوم اسپرسو صورت گرفته است. نتایج ساختار نواری برای این ترکیب نشان می‌دهند که انتهای نوار رسانش و بالای نوار ظرفیت در نقطۀ Г به هم می‌رسند و این موجب به وجود آمدن گاف نواری صفر می‌شود. نمودار پاشندگی فونونی نشان می‌دهد که این ترکیب دارای گافی بین 1cm-‌80 تا 1cm- 127 است. هم‌چنین بررسی ویژگی‌های گرمایی نشان می‌دهندکه ظرفیت گرمایی این ترکیب در فاز بلند روی در دماهای پایین طبق تجربه متناسب با توان سوم دما می‌باشد و در دماهای بالا به یک مقدار اشباع رسیده که به دما وابسته نیست که این امر با واقعیات تجربی سازگار است. 2 In this paper, the phononic and thermodynamic properties of mercury telluride (HgTe) in Zinc Blende phase have been studied. The calculations have been performed with the pseudopotantial method by using Quntume Espresso package that is based on density functional theory. The result of the band structure indicates that the bottom of the conduction band touches the top of the valence band at Г point, this giving rise to zero bandgaps. The phonon spectrum shows that this compound has a frequancy gap between 80 cm-1 and 127 cm-1. Also, the study of thermal properties shows that the heat capacity of this compound in the Zinc Blende phase in low temperatures according to experience is proportional to the third power of temperature and high temperatures reach a value of saturation that is not temperature-dependent, which is consistent with experimental facts. 70 77 2021/03/112021/03/102021/02/32020/09/102020/08/12021/03/152021/01/292020/08/19 1399/5/29 2021/07/122021/07/222021/04/222021/07/222021/07/122021/07/292021/07/292021/07/12 1400/4/21 زینب ایزدی Z. Izadi دانشگاه شهید چمران اهواز ایران zeinab.izadi65@gmail.com حمدالله صالحی H. Salehi دانشگاه شهید چمران اهواز ایران salehi_h@scu.ac.ir Mercury telluride Phononic properties Thermodynamic properties. تلورایدجیوه خواص فونونی خواص ترمودینامیکی. ##[1] V. Dinesh, S. Shriya, R. khenata, “Structural phase transition and elastic properties of mercury chalcogenides,” Journal Materials Chmictry and Physics, vol. 135, pp. 365-384, 2012.##[2] S. Mnasri, S. Abdi-Ben Nasrallah, N. Sfina, N. Bouarissa, “Electronic, lattice vibration and mechanical properties of CdTe, ZnTe, MnTe, MgTe, HgTe and their ternary alloys,” Semoconductor Sience and Tecnologhy, vol. 24, no. 9, pp. 095008, 2009.##[3] R. Franco, P. Mori-Sanchez, J.M. Recio, “Theoretical compressibilities of high-pressure ZnTe polymorphs,” Physical Review B, vol. 68, pp. 195208, 2003.##[4] H. Salehi, Z. Izadi, “An investigation of structural, electronic and optical properties of HgTe in cinnabar phase,” Iranian Journal of Applied Physics, vol. 7, no. 1, pp. 53-64, 2017.##[5] M. Cardona, R.K. Kremer, R. Lauck, G. Siegle, A. Muñoz, A.H. Romero, “Electronic, vibrational, and thermodynamic properties of metacinnabar β-HgS, HgSe, and HgTe,” Physical Review B, vol. 80, no. 19, pp. 195204, 2009. ##[6] N. Svane, E. Christensen, M. Cardona, A.N. Chantis, M. Van Schilfgaarde, T. Kotani, “Quasiparticle band tructures of B-HgS ,HgSe and HgTe,” Physical Review B, vol. 84, pp. 205205, 2011.##[7] S. Rath, S. Sahu, “Electronic structure of HgTe nanocrystals: An observation of p-d weakening,” Surface Science, vol. 600, pp. 110-115, 2006.##[8] A. Ranga Rao, V. Dutta, “Electric-field-induced self-assembled one-dimensional nanostructures in CdX and HgX (X= Se, Te) thin films,” Solar Energy Materials and Solar Cells, vol. 95, pp. 1050-1056, 2011.##[9] M.N. Secuk, M. Aycibin, B. Erdinc, S.E. Gulebaglan, E.K. Dogan, H. Akkus “Ab initio calculation of structural, electronic, optical, dynamic and thermodynamic properties of HgTe and HgSe,” American Journal of Condensed Matter Physics, vol. 4, no. 1, pp. 13-19, 2014.##[10] F.E. Haj Hassan, B.I. Shafaay, H. Meradij, G. Sabti, H. Belkhir, M. Korek, “Ab initio study of fundamental properties of HgSe, HgTe and their HgSexTe1-x alloys,” Physics of Solar Cells, vol. 84, no. 6, pp. 065601, 2011.##[11] H. Zhao,X. Chen, J. Lu, H. Shu, W. Lu, “Ab initio investigation of the structural and electronic properties of amorphous HgTe,” Journal of Physics Condensed Matter, vol. 26, no. 4, pp. 045503, 2014.##[12] T. Ouyang, M. Hu, “First-principles study on lattice thermal conductivity of hermoelectrics HgTe in different phases,” Journal of Applied Physics, vol. 117, no. 24, pp. 245101, 2015.##[13] http://www.quantum-espresso.org.##[14] H. Aimin, Y. Xiaocui, Y. Ruomeng, G. Chunxiai, L. Riping, T. Yongjun, “Study of structural stabilities and optical properties of HgTe under high pressure,” Journal of Physics and Chemistry of Solids, vol. 70, pp. 433-438, 2009.##[15] G. Grosso, “Solid State Physics,” University of Pisa, second printing, pp. 312-315, 2003.##[16] S.W. Koch, “Quantum theory of the optical and electronic properties of semiconductors,” World Scientific Publishing Company Incorporated, 1994.##[17] J. Tan, G. Ji, X. Chen, L. Zhang, Y. Wen, “The high-pressure phase transitions and vibrational properties of zinc-blende XTe (X= Zn, Cd, Hg): Performance of local-density-approximation density functional theory,” Computational Materials Science, vol. 48, pp. 796-801, 2010.##[18] K. Charles, “Introduction to solid state physics,” 2004. ##

بازشناختی چندترازی درد جهت واکافت صدای گریه نوزاد با به کارگیری ویژگی‌های بُعد شکستال و رگرسیون منطقی با طبقه‌بند بیشینه محتمل (مقاله پژوهشی) Multilevel pain recognition to analyze infant crying sound using fractal dimension features and logistic regression with a maximum likelihood classifier (Research Article) 1 1 در تقابل با دیگر شیوه‌های آشکارسازی خودکار درد در نوزادان، تشخیص درد از طریق واکافت صدا برای درک وضعیت نوزاد چندان مورد توجه نبوده است. هر چند استخراج ویژگی­ های مناسب از صداهای نوزاد در زمان گریه کردن به نتایج مطلوب طبقه ­بندی منجر می‌شود، اما این مسئله مستلزم وجود دانش کافی در مورد ویژگی­ های به‌دست‌آمده و نیز انتخاب مؤثر صفات است. در این مقاله، چندین توصیف گر برای استخراج اطلاعات تفکیک‌پذیر در کنار رأی‌گیری وزنی در انتخاب ­ویژگی پیشنهاد شده‌اند. هم‌چنین، شیوه بهبودیافته درست­ نمایی رگرسیون منطقی جهت طبقه­ بندی درد به‌کار گرفته شد. شیوه پیشنهادی توسط مجموعه ای از صداهای ضبط‌شده نوزادان مورد ارزیابی‌ قرارگرفت. با استفاده از انواع مختلفی از ویژگی­ ها، بازشناختی کلی معادل 96/6 درصد در طبقه­ بندی پنج تراز مختلف از حس درد رقم خورده است. نتایج حاکی از آن هستند که طبقه ­بندی بهینه‌سازی‌شده نسبت به راه­کارهای مشابه از نظر دقت تشخیص دارای اثربخشی چشم‌گیری است. چالش­ هایی چون عدم­ قطعیت و بروز خطاهای طبقه ­بندی بالا که اغلب با اعمال داده ­های دیده نشده رخ می­دهند، به‌دلیل توانایی انطباق­ پذیری شیوه پیشنهادی حل ‌شده‌اند. تجزیه‌ و تحلیل مقایسه ­ای نشان از این واقعیت دارد که بر مبنای شیوه پیشنهادی در ترکیب با و یژگی­های ادراکی، پیشرفت چشم‌گیری در عملکرد تشخیص چندترازی حاصل شده است. 2 In contrast to other automatic pain detection methods for infants, the diagnosis of pain through sound analysis to understand the infant's condition has not received much attention. Although extracting appropriate features from infant sounds when crying leads to desirable classification results, this requires sufficient knowledge of the features acquired and an effective selection of attributes. In this paper, several descriptors are proposed for extracting discriminative information and weighting voting in feature selection. Also, the improved logistic regression method with maximum likelihood to classify the type of pain was employed. The proposed method was evaluated by an infants sound dataset. A total recognition of 96.6% has been achieved in classifying five different levels of pain sense using different types of features. The results indicate that the optimized classification is significantly more effective than similar solutions in terms of diagnostic accuracy. Challenges such as uncertainty and the high classification errors, which often occur with the application of unseen data, have been addressed due to the adaptation ability of the proposed method. The comparative analysis illustrates the fact that by using the suggested method combined with perceptual features, significant progress has been made in the performance of multilevel diagnosis. 78 90 2021/03/112021/03/102021/02/32020/09/102020/08/12021/03/152021/01/292020/08/192020/09/19 1399/6/29 2021/07/122021/07/222021/04/222021/07/222021/07/122021/07/292021/07/292021/07/122021/09/22 1400/6/31 خسرو رضائی K. Rezaee دانشگاه میبد ایران kh.rezaee@meybod.ac.ir فردین قادری F. Ghaderi دانشگاه میبد ایران fardin76ghadery@gmail.com فاطمه نقوی F. Naghavi دانشگاه اشرفی اصفهانی ایران Naghavi.f@gmail.com Pain recognition Infant cry Sound processing Feature extraction Weighting voting Optimized classifier. بازشناختی درد گریه نوزاد پردازش صدا استخراج ویژگی رای‌گیری وزنی طبقه بند بهینه. [1] P.S. Douglas, H. Hiscock, “The unsettled baby: Crying out for an integrated multidisciplinary primary care approach,” The Medical Journal of Australia, pp. 533-536, 2010.##[2] C.-Y. Chang, C.-W. Chang, S. Kathiravan, C. Lin and S.-T. Chen, “DAG-SVM based infant cry classification system using sequential forward floating feature selection,” In Multidimensional Systems and Signal Processing, pp. 1-16, 2016.##[3] LA. Bănică, H. Cucu, A. Buzo, D. Burileanu, C. Burileanu, “Automatic methods for infant cry classification,” International Conference on Communications (COMM) Proceedings IEEE, pp. 51-54, 2016.##[4] S. Orlandi, C.A. Reyes Garcia, A. Bandini, G. Donzelli, C. Manfredi, “Application of pattern recognition techniques to the classification of full-term and preterm infant cry,” Journal of Voice, vol. 30, no. 6, pp. 656-663, 2016.##[5] S.J. Sheinkopf, J.M. Iverson, M.L. Rinaldi, B.M. Lester, “Atypical cry acoustics in 6- month-old infants at risk for autism spectrum disorder,” Autism Research, vol. 5, pp. 331– 339, 2012.##[6] L. Liu, W. Li, X. Wu, B. X. Zhou, “Infant cry language analysis and recognition: An experimental approach,” IEEE/CAA Journal of Automatica Sinica, vol. 6, pp. 778-788, May 2019.##[7] H. Yoo, E.H. Buder, D.D. Bowman, G.M. Bidelman, D.K. Oller, “Acoustic correlates and adult perceptions of distress in infant speech-like vocalizations and cries,” Frontiers in psychology, vol. 10, pp. 1154, 2019.##[8] F.S. Matikolaie, C. Tadj, “On the use of long-term features in a newborn cry diagnostic system,” Biomedical Signal Processing and Control, vol. 59, pp. 101889, 2020.##[9] G. Várallyay, “Future prospects of the application of the infant cry in the medicine,” Periodica Polytechnica Electrical Engineering, vol. 50, no. 1–2, pp. 47-62, 2006.##[10] J. Saraswathy, M. Hariharan, S. Yaacob, W. Khairunizam, “Automatic classification of infant cry: A review,” Proceedings of International Conference Biomedical Engineering, pp. 543-548, 2012.##[11] S. Jeyaraman, H. Muthusamy, W. Khairunizam, S. Jeyaraman, T. Nadarajaw, S. Yaacob, S. Nisha, “A review: Survey on automatic infant cry analysis and classification,” Health and Technology, vol. 8, no. 5, pp. 391-404, 2018.##[12] L.W. Jer, M. Hariharan, V. Vijean, H. Yazid, L.C. Chin, “Infant cry classification using dual tree complex wavelet transform features,” Advance Science Letters., vol. 24, no. 3, pp. 1741-1744, 2018.##[13] J. Saraswathy, M. Hariharan, W. Khairunizam, J. Sarojini, N. Thiyagar, Y. Sazali, S. Nisha, “Time-frequency analysis in infant cry classification using quadratic time frequency distributions,” Biocybernetics and Biomedical Engineering., vol. 38, no. 3, pp. 634-645, 2018.##[14] B.X. Fang, J.T. Cen, T. Yuan, G.D. Yin, J. Gu, S.Q. Zhang, Z.C. Li, Y.F. Liang, X.L. Zeng, “Etiology of newborn hearing impairment in Guangdong province: 10-year experience with screening, diagnosis, and follow-up,” World Journal of Pediatrics, vol. 16, pp. 305-313, 2020. ##[15] J. Saraswathy, M. Hariharan, W. Khairunizam, J. Sarojini, S. Yaacob, “Time-frequency analysis-based method for application of infant cry classification,” International Journal of Medical Engineering and Informatics, vol. 12, no. 2, pp. 119-134, 2020.##[16] Y. Kheddache, C. Tadj, “Identification of diseases in newborns using advanced acoustic features of cry signals,” Biomedical Signal Processing and Control, vol. 50, pp. 35-44, 2019.##[17] H. Farsaie Alaie, L. Abou-Abbas, C. Tadj, “Cry-based infant pathology classification using gmms,” Speech Communication, vol. 77, no. C, pp. 28-52, 2016.##[18] A. Zabidi, I.M. Yassin, N. Ismail, M.M.A.M. Hamzah, Z.I. Rizman, H.Z. Abidin, “Detection of asphyxia in infants using deep learning convolutional neural network (CNN) trained on Mel frequency cepstrum coefficient (MFCC) features extracted from cry sounds,” Journal of Fundamental and Applied Sciences, vol. 9, no. 3S, pp. 768-778, 2017.##[19] E. Franti, I. Ispas, M. Dascalu, “Testing the universal baby language hypothesis - automatic infant speech recognition with cnns,” 2018 41st International Conference on Telecommunications and Signal Processing (TSP) 2018, pp. 1-4, 2018.##[20] D. Ferretti, M. Severini, E. Principi, A. Cenci, S. Squartini, “Infant cry detection in adverse acoustic environments by using deep neural networks,” Proceeding of 26th European Signal Processing Conference (EUSIPCO), pp. 997-1001, 2018.##[21] C.Y. Chang, J.J. Li, “Application of deep learning for recognizing infant cries,” 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1-2, 2016.##[22] C.-Y. Chang, F.R. Chen, “Application of deep learning for infant vomiting and crying detection,” 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2018.##[23] R. Torres, D. Battaglino, L. Lepauloux, “Baby cry sound detection: A comparison of hand crafted features and deep learning approach,” International Conference on Engineering Applications of Neural Networks, pp. 168-179, 2017.##[24] K.K. Paliwal, J.G. Lyons, K.K. Wójcicki, “Preference for 20-40 ms window duration in speech analysis,” International Conference on Signal Processing and Communication Systems, pp. 1-4, 2010.##[25] D. Poeppel, “The analysis of speech in different temporal integration windows: Cerebral lateralization as ‘Asymmetric sampling in time,” Speech Communication, vol. 41, no. 1, pp. 245-255, 2003.##[26] N. Tavasoli, K. Rezaee, M. Momenzadeh, M. Sehhati, “An ensemble soft weighted gene selection-based approach and cancer classification using modified metaheuristic learning,” Journal of Computational Design and Engineering, vol. 8, no. 4, pp. 1172-1189, 2021.##[27] K. Rezaee, A. Badiei, S. Meshgini, “A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images,” In 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), pp. 234-241, 2020.##[28] R.V. Craiu, T. Duchesne, D. Fortin, S. Baillargeon, “Conditional logistic regression with longitudinal follow-up and individual-level random coefficients: A stable and efficient two-step estimation method,” Journal of Computational and Graphical Statistics, vol. 20, no. 3, pp.767-784, 2011.##[29] A.J. Bekker, M. Shalhon, H. Greenspan, J. Goldberger, “Multi-view probabilistic classification of breast microcalcifications,” IEEE Transactions on Medical Imaging, vol. 35, pp. 645-653, 2016.##[30] G. Veres, “Donate-a-cry Corpus,” 2015 [Online]. Available: https://github.com/gveres/donateacry-corpus.##[1] P.S. Douglas, H. Hiscock, “The unsettled baby: Crying out for an integrated multidisciplinary primary care approach,” The Medical Journal of Australia, pp. 533-536, 2010.##[2] C.-Y. Chang, C.-W. Chang, S. Kathiravan, C. Lin and S.-T. Chen, “DAG-SVM based infant cry classification system using sequential forward floating feature selection,” In Multidimensional Systems and Signal Processing, pp. 1-16, 2016.##[3] LA. Bănică, H. Cucu, A. Buzo, D. Burileanu, C. Burileanu, “Automatic methods for infant cry classification,” International Conference on Communications (COMM) Proceedings IEEE, pp. 51-54, 2016.##[4] S. Orlandi, C.A. Reyes Garcia, A. Bandini, G. Donzelli, C. Manfredi, “Application of pattern recognition techniques to the classification of full-term and preterm infant cry,” Journal of Voice, vol. 30, no. 6, pp. 656-663, 2016.##[5] S.J. Sheinkopf, J.M. Iverson, M.L. Rinaldi, B.M. Lester, “Atypical cry acoustics in 6- month-old infants at risk for autism spectrum disorder,” Autism Research, vol. 5, pp. 331– 339, 2012.##[6] L. Liu, W. Li, X. Wu, B. X. Zhou, “Infant cry language analysis and recognition: An experimental approach,” IEEE/CAA Journal of Automatica Sinica, vol. 6, pp. 778-788, May 2019.##[7] H. Yoo, E.H. Buder, D.D. Bowman, G.M. Bidelman, D.K. Oller, “Acoustic correlates and adult perceptions of distress in infant speech-like vocalizations and cries,” Frontiers in psychology, vol. 10, pp. 1154, 2019.##[8] F.S. Matikolaie, C. Tadj, “On the use of long-term features in a newborn cry diagnostic system,” Biomedical Signal Processing and Control, vol. 59, pp. 101889, 2020.##[9] G. Várallyay, “Future prospects of the application of the infant cry in the medicine,” Periodica Polytechnica Electrical Engineering, vol. 50, no. 1–2, pp. 47-62, 2006.##[10] J. Saraswathy, M. Hariharan, S. Yaacob, W. Khairunizam, “Automatic classification of infant cry: A review,” Proceedings of International Conference Biomedical Engineering, pp. 543-548, 2012.##[11] S. Jeyaraman, H. Muthusamy, W. Khairunizam, S. Jeyaraman, T. Nadarajaw, S. Yaacob, S. Nisha, “A review: Survey on automatic infant cry analysis and classification,” Health and Technology, vol. 8, no. 5, pp. 391-404, 2018.##[12] L.W. Jer, M. Hariharan, V. Vijean, H. Yazid, L.C. Chin, “Infant cry classification using dual tree complex wavelet transform features,” Advance Science Letters., vol. 24, no. 3, pp. 1741-1744, 2018.##[13] J. Saraswathy, M. Hariharan, W. Khairunizam, J. Sarojini, N. Thiyagar, Y. Sazali, S. Nisha, “Time-frequency analysis in infant cry classification using quadratic time frequency distributions,” Biocybernetics and Biomedical Engineering., vol. 38, no. 3, pp. 634-645, 2018.##[14] B.X. Fang, J.T. Cen, T. Yuan, G.D. Yin, J. Gu, S.Q. Zhang, Z.C. Li, Y.F. Liang, X.L. Zeng, “Etiology of newborn hearing impairment in Guangdong province: 10-year experience with screening, diagnosis, and follow-up,” World Journal of Pediatrics, vol. 16, pp. 305-313, 2020. ##[15] J. Saraswathy, M. Hariharan, W. Khairunizam, J. Sarojini, S. Yaacob, “Time-frequency analysis-based method for application of infant cry classification,” International Journal of Medical Engineering and Informatics, vol. 12, no. 2, pp. 119-134, 2020.##[16] Y. Kheddache, C. Tadj, “Identification of diseases in newborns using advanced acoustic features of cry signals,” Biomedical Signal Processing and Control, vol. 50, pp. 35-44, 2019.##[17] H. Farsaie Alaie, L. Abou-Abbas, C. Tadj, “Cry-based infant pathology classification using gmms,” Speech Communication, vol. 77, no. C, pp. 28-52, 2016.##[18] A. Zabidi, I.M. Yassin, N. Ismail, M.M.A.M. Hamzah, Z.I. Rizman, H.Z. Abidin, “Detection of asphyxia in infants using deep learning convolutional neural network (CNN) trained on Mel frequency cepstrum coefficient (MFCC) features extracted from cry sounds,” Journal of Fundamental and Applied Sciences, vol. 9, no. 3S, pp. 768-778, 2017.##[19] E. Franti, I. Ispas, M. Dascalu, “Testing the universal baby language hypothesis - automatic infant speech recognition with cnns,” 2018 41st International Conference on Telecommunications and Signal Processing (TSP) 2018, pp. 1-4, 2018.##[20] D. Ferretti, M. Severini, E. Principi, A. Cenci, S. Squartini, “Infant cry detection in adverse acoustic environments by using deep neural networks,” Proceeding of 26th European Signal Processing Conference (EUSIPCO), pp. 997-1001, 2018.##[21] C.Y. Chang, J.J. Li, “Application of deep learning for recognizing infant cries,” 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1-2, 2016.##[22] C.-Y. Chang, F.R. Chen, “Application of deep learning for infant vomiting and crying detection,” 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2018.##[23] R. Torres, D. Battaglino, L. Lepauloux, “Baby cry sound detection: A comparison of hand crafted features and deep learning approach,” International Conference on Engineering Applications of Neural Networks, pp. 168-179, 2017.##[24] K.K. Paliwal, J.G. Lyons, K.K. Wójcicki, “Preference for 20-40 ms window duration in speech analysis,” International Conference on Signal Processing and Communication Systems, pp. 1-4, 2010.##[25] D. Poeppel, “The analysis of speech in different temporal integration windows: Cerebral lateralization as ‘Asymmetric sampling in time,” Speech Communication, vol. 41, no. 1, pp. 245-255, 2003.##[26] N. Tavasoli, K. Rezaee, M. Momenzadeh, M. Sehhati, “An ensemble soft weighted gene selection-based approach and cancer classification using modified metaheuristic learning,” Journal of Computational Design and Engineering, vol. 8, no. 4, pp. 1172-1189, 2021.##[27] K. Rezaee, A. Badiei, S. Meshgini, “A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images,” In 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), pp. 234-241, 2020.##[28] R.V. Craiu, T. Duchesne, D. Fortin, S. Baillargeon, “Conditional logistic regression with longitudinal follow-up and individual-level random coefficients: A stable and efficient two-step estimation method,” Journal of Computational and Graphical Statistics, vol. 20, no. 3, pp.767-784, 2011.##[29] A.J. Bekker, M. Shalhon, H. Greenspan, J. Goldberger, “Multi-view probabilistic classification of breast microcalcifications,” IEEE Transactions on Medical Imaging, vol. 35, pp. 645-653, 2016.##[30] G. Veres, “Donate-a-cry Corpus,” 2015 [Online]. Available: https://github.com/gveres/donateacry-corpus. ##

مقایسه تجربی افت تراگسیل صدا در فوم‌های آلومینیوم خالص و کامپوزیت آلومینیوم با 3 درصد وزنی نانولوله کربنی و نانوصفحه گرافنی (مقاله پژوهشی) Experimental comparison of sound transmission loss in pure aluminum and aluminum composite foams with 3wt% carbon nanotube and graphene nanoplate (Research Article) 1 1 در سال‌های اخیر، اهمیت مهار نوفه به‌دلیل آلودگی نوفه‌ای به طور چشمگیری افزایش یافته است. در این پژوهش به منظور بهبود عملکرد صوتی فوم آلومینیوم خالص، 3 درصد وزنی نانولوله کربنی و 3 درصد وزنی نانوصفحه گرافنی به فوم آلومینیوم خالص افزوده شدند، فوم‌ها با 60 درصد تخلخل و اندازه قطر حفرات 500 میکرون ساخته شدند. سپس، افت تراگسیل صدا در فوم‌های آلومینیوم- 3 درصد وزنی نانولوله کربنی و نانوصفحه گرافنی با فوم آلومینیوم خالص مقایسه شدند. این مقاله روی افت تراگسیل صدا (اِس‌تی‌اِل) در فوم‌های آلومینیوم، آلومینیوم- 3 درصد وزنی نانولوله کربنی و آلومینیوم- 3 درصد وزنی نانوصفحه گرافنی متمرکز شده است. نتایج نشان دادند که افزودن 3 درصد وزنی نانولوله کربنی و 3 درصد وزنی نانوصفحه گرافنی به ترتیب حدود 20 و 7 دسی‌بل افت تراگسیل صدا در مقایسه با فوم آلومینیوم خالص ایجاد می‌نماید. عملکرد بهتر نانولوله‌ها در مقایسه با نانوصفحات به دلیل بروز پدیده گرماصوتی در نانولوله و در نتیجه تبدیل انرژی صوتی به حرارتی است. 2 In recent years, the importance of noise control has dramatically increased due to noise pollution. In this study, in order to improve the acoustic performance of pure aluminum foam, 3wt% carbon nanotube (CNT) and 3wt% graphene nanoplate (GNP) were added to the pure aluminum foam, foams were made with 60% porosity by 500 microns in diameter size. Then the transmission loss in Al-3wt%CNT and Al-3wt%GNP foams were compared to that of pure aluminum foam. The paper is focused on the sound transmission loss (STL) of the pure aluminum, Al-3wt%CNT and Al-3wt%GNP foams. The results showed that the addition of 3wt%CNT and 3wt%GNP respectively had about 20 and 7 dB in sound transmission loss in comparison to the pure aluminum foam. A better performance of nanotubes in comparison to nanoplates is due to the occurrence of Thermoacoustic phenomenon in the nanotubes and as a result of conversion of acoustic energy into heat energy. 91 99 2021/03/112021/03/102021/02/32020/09/102020/08/12021/03/152021/01/292020/08/192020/09/192021/07/21 1400/4/30 2021/07/122021/07/222021/04/222021/07/222021/07/122021/07/292021/07/292021/07/122021/09/222021/09/22 1400/6/31 عباس بحرینی A. Bahreini دانشکده فنی مهندسی، دانشگاه بین‌المللی امام خمینی (ره) ایران baahreini@gmail.com محمد تلافی نوغانی M. Talafi Noghani دانشکده فنی مهندسی، دانشگاه بین‌المللی امام خمینی (ره) ایران noghani@eng.ikiu.ac.ir مرتضی ثقفی یزدی M. Saghafi Yazdi دانشکده فنی مهندسی، دانشگاه بین‌المللی امام خمینی (ره) ایران msaghafi@eng.ikiu.ac.ir Sound transmission loss Composite aluminum-carbon nanotube foam Composite aluminum-graphene nanoplate foam Thermoacoustic phenomenon. افت تراگسیل صدا فوم کامپوزیتی آلومینیوم- نانولوله کربنی فوم کامپوزیتی آلومینیوم- نانوصفحه گرافنی پدیده گرماصوتی. ##[1] M.A. Kuczmarski, J.C. Johnston, “Acoustic absorption in porous materials,” NASA/TM-216995-2011, 2011.##[2] D.M. Inamdar, P. Kachare, S.Y. Salunkhe, “Analysis of sound absorption and damping coefficient for different configurations of aluminum metal foam,” Techno-Societal, pp. 975-986, 2020.##[3] A. Bahreini, A. Alizadeh, H.R. Baharvandi, “Investigation of the multilayer 5083 aluminum foam absorption properties for marine applications,” 6th International Conference and 11th Congress Iranian Metallurgical and Materials Engineering Society and Iranian Foundrymen Society, 2017, (In Persian).##[4] Y.H. Ko, H.T. Son, J.I. Cho, C.S. Kang, I.H. 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Baharvandi, “Production of A5083/SiC composite foam by space holder method and investigation of effective parameters on foam sound (In Persian),” 6th International Conference and 11th Congress Iranian Metallurgical and Materials Engineering Society and Iranian Foundrymen Society, 2017.##[25] R.P. Gilbert, M.J. Ou, “Acoustic wave propagation in a composite of two different poroelastic materials with a very rough periodic interface: A homogenization approach,” International Journal for Multiscale Computational Engineering, vol. 4, no. 1, 2003.##[26] X. Li, K. Yu, R. Zhao, J. Han, H. Song, “Sound transmission loss of composite and sandwich panels in thermal environment. Composites Part B: Engineering, vol. 133, pp. 1-14, 2018. ##[27] L. Cao, Q. Fu, Y. Si, B. Ding, J. Yu, “Porous materials for sound absorption,” Composites Communications, vol. 10, pp. 25-35, 2018. ##[28] A. Bahreini, M.T. Noughani, M. 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تولید انرژی برقی از امواج صوتی با استفاده از نانومولد تریبوبرقی (مقاله پژوهشی) Electric energy generation from acoustic waves using triboelectric nanogenerator (Research Article) 1 1 نانومولدهای تریبوبرقی نسل جدیدی از تراگذارهای انرژی مکانیکی به انرژی برقی هستند که به دلیل عملکرد بالا توجه بسیاری را به خود جلب کرده‌‌اند. در این پژوهش نوع جدیدی از نانومولدهای تریبوبرقی با ساختاری منحصر به فرد ساخته شده است. این ساختار می‌تواند نوفه صوتی موجود در محیط را به انرژی برقی تبدیل کند. ساختار این نانومولد توسط میکروسکوپ الکترونی روبشی مورد بررسی قرار گرفت و وجود نانوالیاف‌های پلی‌‌وینیلیدن فلوراید تایید شد. بعلاوه، این نانومولد قابلیت استفاده به عنوان یک میکروفون خود توان را دارد. نتایج آزمون‌‌های صوتی نشان دادند که با قرار دادن این نانومولد در یک میدان فشار صوتی با شدت 90 د‌‌سی‌‌بل و بسامد 90 هرتز، می‌تواند ولتاژ مدار باز اوج به اوج 70 ولتی تولید کند.   2 Triboelectric nanogenerators are a new generation of transducers of mechanical energy to electric energy which have attracted great attention because of their high performance. In this research a new kind of triboelectric nanogenerators with a unique structure is fabricated. It can transform environment acoustic noise into electric energy. The structure of this nanogenerator is investigated with scanning electron microscopy and the existence of poly vinylidene fluoride nanofibers is verified. Furthermore, the nanogenerator has potential of use as a selfpowered microphone. Results of acoustic tests showed that putting the nanogenerator in a acoustic pressure field with 90 dB intensity and 90 Hz frequency can generate open circuit voltage of 70V peak to peak. 100 104 2021/03/112021/03/102021/02/32020/09/102020/08/12021/03/152021/01/292020/08/192020/09/192021/07/212021/06/15 1400/3/25 2021/07/122021/07/222021/04/222021/07/222021/07/122021/07/292021/07/292021/07/122021/09/222021/09/222021/09/22 1400/6/31 اکبر قاسمی یک لنگی A. Ghasemi Yeklangi دانشگاه تربیت مدرس ایران akbarghasemi2@gmail.com سیامک اسماعیل زاده خادم S. Esmaeelzadeh Khadem دانشگاه تربیت مدرس ایران khadem@modares.ac.ir Triboelectric Nanogenerator Acoustic waves Electrospinning Nanofibers Poly vinylidene fluride. نانومولد تریبوبرقی امواج صوتی برقاریسی نانوالیاف پلی‌‌وینیلیدن‌‌فلوراید. ##[1] G. Zhu, B. Peng, J. Chen, Q. Jing, Z.L. Wang, “Triboelectric nanogenerators as a new energy technology: From fundamentals, devices, to applications,” Nano Energy, vol. 14, pp. 126-138, 2015.##[2] M. Yuan, C. Li, H. Liu, Q. Xu, Y. Xie, “A 3D-printed acoustic triboelectric nanogenerator for quarter-wavelength acoustic energy harvesting and self-powered edge sensing,” Nano Energy, vol. 85, pp. 105962, 2021.##[3] F. Wang, Z. Wang, Y. Zhou, C. Fu, F. Chen, Y. Zhang, H. Lu, Y. Wu, L. Chen, H. Zhang, “Windmill-inspired hybridized triboelectric nanogenerators integrated with power management circuit for harvesting wind and acoustic energy,” Nano Energy, vol. 78, pp. 105244, 2020.##[4] N. Cui, L. Gu, J. Liu, S. Bai, J. Qiu, J. Fu, X. Kou, H. Liu, Y. Qin, Z.L. Wang, “High performance sound driven triboelectric nanogenerator for harvesting noise energy,” Nano Energy, 2015.##[5] X. Fan, J. Chen, J. Yang, P. Bai, Z. Li, Z.L. Wang, “Ultrathin, rollable, paper-based triboelectric nanogenerator for acoustic energy harvesting and self-powered sound recording,” ACSNano, vol. 9, pp. 4236-4243, 2015.##[6] M. Kanik, M.G. Say, B. Daglar, A.F. Yavuz, M.H. Dolas, M.M. El-Ashry, M. Bayindir, “A motion- and sound-activated, 3d-printed, chalcogenide-based triboelectric nanogenerator,” Advanced Materials, vol. 27, pp. 2367-2376, 2015.##[7] A.F. Yu, M. Song, Y. Zhang, Y. Zhang, L. Chen, J. Zhai, Z.L. Wang, “Self-powered acoustic source locator in underwater environment based on organic film triboelectric nanogenerator,” Nano Research, vol. 8, pp. 765-773, 2015.##[8] S. Garain, S. Jana, T.K. Sinha, D. Mandal, “Design of in situ poled Ce3+-doped electrospun PVDF/graphene composite nanofibers for fabrication of nanopressure sensor and ultrasensitive acoustic nanogenerator,” ACS Applied Materials and Interfaces, vol. 8, pp. 4532-4540, 2016.##[9] J. Liu, N. Cui, L. Gu, X. Chen, S. Bai, Y. Zheng, C. Hu, Y. Qin, “A three-dimensional integrated nanogenerator for effectively harvesting sound energy from the environment,” Nanoscale, vol. 8, pp. 4938-4944, 2016.##[10] W. Qiu, Y. Feng, N. Luo, S. Chen, D. Wang, “Sandwich-like sound-driven triboelectric nanogenerator for energy harvesting and electrochromic based on Cu foam,” Nano Energy, vol. 70, p. 104543, 2020.##[11] C. Chen, Z. Wen, J. Shi, X. Jian, P. Li, J.T.W. Yeow, X. Sun, “Micro triboelectric ultrasonic device for acoustic energy transfer and signal communication,” Nature Communications, vol. 11, pp. 1-9, 2020. ##

انتشار امواج صدا با بسامدهای مختلف در خلیج‌فارس (یادداشت فنی) Sound waves propagation with different frequencies in the Persian Gulf (Technical Note) 1 1 انتشار امواج صدا در آب‌های کم‌عمق به دلیل ماهیت خاص شرایط سطح و بستر توده‌های آبی، پیچیده و ناشناخته می‌باشد. در سال‌های اخیر استفاده از طیف وسیعی از نرم‌افزارهای کاربردی جهت شناخت این اثرگذاری بر انتشار امواج صدا جهت بهبود عملکرد سامانه‌های سوناری افزایش یافته است. در محیط‌های کم‌عمق و مناطق ساحلی تداخل ایجاد شده ناشی از بازتاب‌های سطح و بستر دریا بسیار مهم و تاثیرگذار هستند. روش‌های متعددی برای الگوسازی انتشار امواج صدا توسعه داده شده‌اند. روش نظری پرتو، مدهای بهنجار (نرمال)، انتگرال‌گیری عدد موج و معادلات سهموی از جمله روش‌های الگوسازی انتشار امواج صدا می‌باشند. در طرح حاضر، از روش معادلات سهموی با در نظر گرفتن بسامد‌های 500، 1000 و 10.000 هرتز برای فرستنده جهت الگوسازی انتشار امواج صدا در خلیج فارس استفاده شده است. نتایج نشان دادند که اثرات بستر عامل اصلی تنکش (تضعیف) انرژی امواج صدا است. هم‌چنین بیش‌ترین نفوذ امواج صدا در لایه‌های بستر در بسامد‌های پایین و کم‌تر از 1 کیلوهرتز رخ داده و با افزایش عمق فرستنده، برهم‌کنش پرتوهای صدا با بستر نیز افزایش یافته و به تبع آن اتلاف بیش‌تری در مسیر انتقال ثبت شده است. 2 The propagation of sound waves in shallow water is complex and unknown due to the special conditions of the surface and bed of water bodies. In recent years, the use of a wide range of application software to recognize this effect on the propagation of sound waves to improve the performance of sonar systems has increased. In shallow water and coastal areas, the interference caused by surface and seabed reflections is very important and effective. Several methods have been developed to model the propagation of sound waves. In the present research, the method of parabolic equations by considering the frequencies of 500, 1000 and 10000 Hz for the transmitter has been used to model the propagation of sound waves in the Persian Gulf. The results showed that the effects of the seabed are the main factor attenuating the energy of sound waves. Also, the highest penetration of sound waves in the bed layers occurred at frequencies lower than 1 kHz. The interaction of sound beams with the seabed is also increased, with increasing the depth of the transmitter and as a result more loss in the transmission path is recorded. 105 113 2021/03/112021/03/102021/02/32020/09/102020/08/12021/03/152021/01/292020/08/192020/09/192021/07/212021/06/152019/08/12 1398/5/21 2021/07/122021/07/222021/04/222021/07/222021/07/122021/07/292021/07/292021/07/122021/09/222021/09/222021/09/222020/10/4 1399/7/13 مهیار مجیدی نیک M. Majidy Nik دانشگاه علوم و فنون دریایی خرمشهر ایران m.majidynik@yahoo.com Sound waves propagation Parabolic equations Seabed loss Actup model Persian Gulf. انتشار امواج صدا معادلات سهموی اتلاف بستر خلیج فارس. ##[1] M. Akbarinasab, “Effect of Horizontal and Vertical Heterogeneities on the propagation of sound waves in the Oman Sea,” PhD Thesis, Khorramshahr University of Marine Sciences and Technology, 2012, (In Persian).##[2] M. Bahadori, “Introduction to Underwater Acoustics and Sonar Technology,” Space Thrusters Research Institute, Engineering Research Institute, 2013, (In Persian).##[3] A. Khodayari, M. Bakhoday, A. Valinejad, “Introduction to sonar and modeling of sound wave propagation in seawater,” Agricultural Jihad, Engineering Research Institute, 2010, (In Persian).##[4] H. Roosta Nohadani, A. Mohseni Arasteh, “Effect of Internal Waves on Sound Channel Temporal Stability and Acoustic Wave Emission in the Oman Sea,” Journal of applied mathematics, Islamic Azad University of Lahijan, vol. 7, no. 2, 2010, (In Persian).##[5] G. Kumar, P. Balasubramanian, “IWAVE: An ocean simulation model for internal waves,” Current Science, vol. 89, no. 11, pp. 1927-1932, 2005.##[6] R. Reynolds, “Overview of physical oceanographic measurments taken during the Mt. Mitchell Cruise to the ROPME sea area,” Brookhaven National Laboratory, 1993.##[7] J. Urick, “Principles of underwater sound,” McGraw-Hill, 1983. ##