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However, the past formulations of the subloading surface model have contained several inexact equations, which have been modified repeatedly since the concept of the subloading surface was proposed in 1977 (Hashiguchi and Ueno 1977). The exact formulation is presented first in this article for the hypoelastic-based plasticity, which enjoys the distinguished superiority in the both aspects of the description of material behavior in high accuracy and of the numerical calculation in high efficiency. It is further provided for all the four basic frameworks, i.e. the infinitesimal hypoelastic-based plasticity, the infinitesimal hyperelastic-based plasticity, the hypoelastic-based plasticity and the multiplicative hyperelastic-based plasticity for finite strain. Further, the subloading-crystal plasticity model is formulated modifying the former one (Hashiguchi 2013) by incorporating the decomposition of the crystalline shear strain rate into the elastic and the plastic parts. This would be the guidebook to the subloading surface model and also the memorial monograph for the historical development of the subloading surface model.",{"EN":188},"Exact Formulation of Subloading Surface Model: Unified Constitutive Law for Irreversible Mechanical Phenomena in Solids",{"VOID":190},"[]",{"VOID":192},"Armstrong PJ, Frederick CO (1966) A mathematical representation of the multiaxial Bauschinger effect. CEGB Report RD\u002FB\u002FN 731 (or in: Mater High Temp 24:1–26 (2007))\nAsaoka A, Nakano M, Noda T (1997) Soil–water coupled behaviour of heavily overconsolidated clay near\u002Fat critical state. Soils Found 37(1):13–28\nBassani JL, Wu TY (1991) Latent hardening in single crystals II: Theory analytical characterization and predictions. Proc R Soc Lond A 435:21–41\nBelytschko T, Liu WK, Moran B (2000) Nonlinear finite elements for continua and structures. Wiley, London (see also, Belytschko T, Liu WK, Moran B, Elkhodary KI (2014) 2nd edn)\nChaboche JL, Dang-Van K, Cordier G (1979) Modelization of the strain memory effect on the cyclic hardening of 316 stainless steel. In: Transactions on 5th international conference SMiRT, Berlin, Division L. paper no. L. 11\u002F3\nde Souza EN, Peric D, Owen DJR (2008) Computational methods for plasticity. Wiley, London\nDafalias YF (1985) The plastic spin. J Appl Mech (ASME) 52:865–871\nDafalias YF (1998) Plastic spin: necessity or redundancy ? Int. J. Plasticity 14:909–931\nDafalias YF, Popov EP (1975) A model of nonlinearly hardening materials for complex loading. Acta Mech 23:173–192\nDrucker DC (1988) Conventional and unconventional plastic response and representation. Appl Meek Rev (ASME) 41:151–167\nHarder J (1999) A crystallographic model for the study of local deformation processes in polycrystals. J Plast 15:605–624\nHashiguchi K (1977) An expression of anisotropy in a plastic constitutive equation of soils. In: Constitutive equations of soils (proceedings of 9th soil mechanics and foundation engineering, special session 9), Tokyo. JSSMFE, pp 302–305\nHashiguchi K (1980) Constitutive equations of elastoplastic materials with elastic–plastic transition. J Appl Mech (ASME) 47:266–272\nHashiguchi K (1985) Subloading surface model of plasticity. In: Constitutive laws of soils (proceedings of discussion session 1A, 11th international conference on soil mechanics foundation and engineering), San Francisco, pp 127–130\nHashiguchi K (1989) Subloading surface model in unconventional plasticity. Int J Solids Struct 25:917–945\nHashiguchi K (1993) Fundamental requirements and formulation of elastoplastic constitutive equations with tangential plasticity. Int J Plast 9:525–549\nHashiguchi K (1993) Mechanical requirements and structures of cyclic plasticity models. Int J Plast 9:721–748\nHashiguchi K (1994) Loading criterion. Int J Plast 8:871–878\nHashiguchi K (1997) The extended flow rule in plasticity. Int J Plast 13:37–58\nHashiguchi K (1998) The tangential plasticity. Mech Mater 4:652–656\nHashiguchi K (2000) Fundamentals in constitutive equation: continuity and smoothness conditions and loading criterion. Soils Found 40(3):155–161\nHashiguchi K (2007) Extended overstress model for general rate of deformation including impact load. In: Proceedings of 13th international symposium on plasticity and its current applications, pp 37–39\nHashiguchi K (2008) Extension of the formulation for cyclic stagnation of isotropic-hardening of metals by the subloading surface concept. In: Proceedings of international symposium on plasticity, pp 346–348\nHashiguchi K (2013) General description of elastoplastic deformation\u002Fsliding phenomena of solids in high accuracy and numerical efficiency: subloading surface concept. Arch Comput Methods Eng 20:361–417\nHashiguchi K (2013) Elastoplasticity theory, Lecture notes in applied and computational mechanics, 2nd edn. Springer, Berlin\nHashiguchi K (2015) Formulation of subloading-damage model. In: Proceedings of JSME, Kyushu Branch\nHashiguchi K, Chen Z-P (1998) Elastoplastic constitutive equations of soils with the subloading surface and the rotational hardening. Int J Numer Anal Methods Geomech 22:197–227\nHashiguchi K, Mase T (2011) Physical interpretation and quantitative prediction of cyclic mobility by the subloading surface model. Japan Geotech J 6:225–241\nHashiguchi K, Oka M (2014) Subloading-damage model. In: Proceedings of 63rd National Congress of Theoretical and Applied Mechanics, Japan OS16-01-01\nHashiguchi K, Okamura K (2014) Subloading phase-transformation model. In: Proceedings of 27th JSME Computational Mechanics Division conference OS17-1707\nHashiguchi K, Okayasu T, Saitoh K (2005) Rate-dependent inelastic constitutive equation: the extension of elastoplasticity. Int J Plast 21:463–491\nHashiguchi K, Ozaki S (2008) Constitutive equation for friction with transition from static to kinetic friction and recovery of static friction. Int J Plast 24:2102–2124\nHashiguchi K, Ozaki S, Okayasu T (2005) Unconventional friction theory based on the subloading surface concept. Int J Solids Struct 42:1705–1727\nHashiguchi K, Protasov A (2004) Localized necking analysis by the subloading surface model with tangential-strain rate and anisotropy. Int J Plast 20:1909–1930\nHashiguchi K, Saitoh K, Okayasu T, Tsutsumi S (2002) Evaluation of typical conventional and unconventional plasticity models for prediction of softening behavior of soils. Geotechnique 52:561–573\nHashiguchi K, Tsutsumi S (2001) Elastoplastic constitutive equation with tangential stress rate effect. Int J Plast 17:117–145\nHashiguchi K, Tsutsumi S (2003) Shear band formation analysis in soils by the subloading surface model with tangential stress rate effect. Int J Plast 19:1651–1677\nHashiguchi K, Tsutsumi S (2006) Gradient plasticity with the tangential subloading surface model and the prediction of shear band thickness of granular materials. Int J Plast 22:767–797\nHashiguchi K, Ueno M (1977) Elastoplastic constitutive laws of granular materials. In: Murayama S, Schofield AN (eds) Constitutive equations of soils (proceedings 9th international conference soil mechanics found engineering, special session 9), Tokyo, JSSMFE, pp 73–82\nHashiguchi K, Ueno M, Ozaki T (2012) Elastoplastic model of metals with smooth elastic–plastic transition. Acta Mech 223:985–1013\nHashiguchi K, Yamakawa Y (2012) Introduction to finite strain theory for continuum elasto-plasticity. Wiley series in computational mechanics. Wiley, London\nHashiguchi K, Yoshimaru T (1995) A generalized formulation of the concept of nonhardening region. Int J Plast 11:347–365\nHiguchi R, Okamura K, Ohta F, Hashiguchi K (2014) Extension of subloading surface model for accurate prediction of elastoplastic deformation behavior of metals with cyclic softening. Bull JAME Ser A. doi:10.1299\u002Ftransjsme.2014smm0082\nHill R (1967) On the classical constitutive relations for elastic\u002Fplastic solids. In: Recent progress in applied mechanics, pp 241–249\nHill R (1983) On the intrinsic eigenstates in plasticity with generalized variables. Math Proc Camb Philos Soc 93:177–189\nJaumann G (1911) Geschlossenes System physicalisher und chemischer Differentialgesetze. Sitzber. Akad. Wiss. Wien (IIa) 120:385–530\nKhojastehpour M, Hashiguchi K (2004) The plane strain bifurcation analysis of soils by the tangential-subloading surface model. Int J Solids Struct 41:5541–5563\nKhojastehpour M, Hashiguchi K (2004) Axisymmetric bifurcation analysis in soils by the tangential-subloading surface model. J Mech Phys Solids 52:2235–2262\nKhojastehpour M, Murakami Y, Hashiguchi K (2006) Antisymmetric bifurcation in a circular cylinder with tangential plasticity. Mech Mater 38:1061–1071\nMandel J (1972) Director vectors and constitutive equations for plastic and viscoplastic media. In: Sawczuk A (ed) Problems of plasticity (proceedings of international symposium on foundation of plasticity), Noordhoff, pp 135–141\nMasing G (1926) Eigenspannungen und Verfestigung beim Messing. 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prediction and early diagnosis of eye diseases are critical for effective treatment and prevention of vision loss. The identification of eye diseases has recently been the subject of much advanced research. Vision problems can significantly affect a person’s quality of life, limiting their ability to perform daily activities, impacting their independence, and leading to emotional and psychological distress. Lack of timely and accurate identification of the cause of vision problems can lead to significant challenges and consequences. Delayed diagnosis prolongs the period of impaired vision and its associated negative impact on an individual’s well-being. Deep learning techniques have emerged as powerful tools for analyzing medical images, including retinal images and predicting various eye diseases. This review provides an analysis of deep learning techniques commonly used for eye disease prediction. The techniques discussed include Convolutional Neural Networks (CNNs), Transfer Learning, Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Attention Mechanisms, and Explainable Deep Learning. The application of these techniques in eye disease prediction is explored, highlighting their strengths and potential contributions. The review emphasizes the importance of collaborative efforts between deep learning researchers and healthcare professionals to ensure the safe and effective integration of these techniques. The analysis highlights the promise of deep learning in advancing the field of eye disease prediction and its potential to improve patient outcomes.",{"EN":286},"Analysis of Deep Learning Techniques for Prediction of Eye Diseases: A Systematic 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However, computational aspect of conventional RDO can often get computationally intensive as neighborhood assessments of every solution are required to compute the performance variance and ensure feasibility. Surrogate assisted optimization is one of the efficient approaches in order to mitigate this issue of computational expense. However, the performance of a surrogate model plays a key factor in determining the optima in multi-modal and highly non-linear landscapes, in presence of uncertainties. In other words, the approximation accuracy of the model is principal in yielding the actual optima and thus, avoiding any misguide to the decision maker on the basis of false or, local optimum points. Therefore, an extensive survey has been carried out by employing most of the well-known surrogate models in the framework of RDO. It is worth mentioning that the numerical study has revealed consistent performance of a model out of all the surrogates utilized. 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IEEE Trans Evol Comput 13:1054–1074\nDeLand S (2012) Solving large-scale optimization problems with MATLAB: A hydroelectric flow example",{"VOID":937},"10.1007\u002Fs11831-017-9240-5","2024-06-26T10:00:45.500+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11831-017-9240-5",[941,956,971],{"id":942,"sortIndex":21,"researcher":20,"roles":943,"affiliations":944,"properties":953,"displayName":955,"givenName":20,"familyName":20},"80b6a8d5-f0e0-46de-9d66-8b080768d689",[204],[945],{"id":946,"sortIndex":21,"affiliation":947,"properties":20},"e475d29b-d52c-479f-b585-c547500d4e10",{"id":946,"createTime":20,"updateTime":20,"relativeEntities":948,"slug":20,"properties":949,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":952,"statistic":20},[],{"title":950},{"VI":951},"Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India",[],{"title":954},{"VI":955},"Tanmoy Chatterjee",{"id":957,"sortIndex":313,"researcher":20,"roles":958,"affiliations":959,"properties":968,"displayName":970,"givenName":20,"familyName":20},"6b0bfc76-c06c-418f-8b14-b130247b8e93",[204],[960],{"id":961,"sortIndex":21,"affiliation":962,"properties":20},"b969b3ff-1a8d-4c3e-8b31-5c0dbaf3a854",{"id":961,"createTime":20,"updateTime":20,"relativeEntities":963,"slug":20,"properties":964,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":967,"statistic":20},[],{"title":965},{"VI":966},"Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, USA",[],{"title":969},{"VI":970},"Souvik Chakraborty",{"id":972,"sortIndex":112,"researcher":20,"roles":973,"affiliations":974,"properties":981,"displayName":983,"givenName":20,"familyName":20},"bd5d228e-089d-4845-b3c8-4631064fd60a",[204],[975],{"id":946,"sortIndex":21,"affiliation":976,"properties":20},{"id":946,"createTime":20,"updateTime":20,"relativeEntities":977,"slug":20,"properties":978,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":980,"statistic":20},[],{"title":979},{"VI":951},[],{"title":982},{"VI":983},"Rajib Chowdhury",{"url":939,"publisher":985,"properties":1031},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":986,"slug":10,"properties":987,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":991,"manageAffiliations":1000,"indexDatabases":1011,"url":88,"thumbnailPath":20,"statistic":1026,"gsStatistic":20,"type":173,"analyzePriority":20},[],{"issn":988,"title":989,"eissn":990},{"VOID":13},{"EN":15},{"VOID":17},[992,996],{"id":24,"createTime":20,"updateTime":20,"relativeEntities":993,"label":994,"description":995,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":27},{},{"id":30,"createTime":20,"updateTime":20,"relativeEntities":997,"label":998,"description":999,"parentId":20,"standard":20,"scholarHubFieldId":20},[],{"EN":33},{},[1001,1006],{"id":37,"createTime":20,"updateTime":20,"relativeEntities":1002,"slug":20,"properties":1003,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1005,"statistic":20},[],{"title":1004},{"EN":41},[],{"id":44,"createTime":20,"updateTime":20,"relativeEntities":1007,"slug":20,"properties":1008,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1010,"statistic":20},[],{"title":1009},{"EN":48},[50],[1012,1019],{"id":53,"indexDatabase":1013,"url":66,"indexYears":20,"academicFieldIds":1018,"indexDatabaseRanking":20},{"id":55,"createTime":20,"updateTime":20,"relativeEntities":1014,"label":1015,"description":1016,"key":62,"publicationTags":1017,"standard":20},[],{"EN":58,"VI":58},{"EN":60,"VI":61},[64,65],[68,69,69],{"id":71,"indexDatabase":1020,"url":82,"indexYears":83,"academicFieldIds":1025,"indexDatabaseRanking":87},{"id":73,"createTime":20,"updateTime":20,"relativeEntities":1021,"label":1022,"description":1023,"key":79,"publicationTags":1024,"standard":20},[],{"EN":76,"VI":76},{"EN":76,"VI":78},[81],[85,86],{"impactFactor":21,"impactFactorByYear":1027,"i10Index":103,"i10IndexLast5Year":104,"totalPublication":105,"totalPublicationByYear":1028,"totalCitation":127,"totalCitationByYear":1029,"totalCitationPerPublication":148,"totalCitationPerPublicationByYear":1030,"hindexLast5Year":130,"hindex":130},{"2012":91,"2013":92,"2014":93,"2015":94,"2016":95,"2017":96,"2018":97,"2019":98,"2020":99,"2021":100,"2022":101,"2023":102},{"1994":107,"1995":108,"1996":108,"1997":109,"1998":109,"1999":107,"2000":110,"2001":111,"2002":109,"2003":112,"2004":107,"2005":111,"2006":111,"2007":113,"2008":111,"2009":109,"2010":114,"2011":115,"2012":116,"2013":117,"2014":118,"2015":113,"2016":119,"2017":120,"2018":121,"2019":122,"2020":123,"2021":124,"2022":124,"2023":125,"2024":126},{"1995":119,"1996":129,"1997":130,"2001":131,"2002":132,"2003":133,"2005":134,"2006":135,"2009":115,"2010":110,"2011":136,"2012":137,"2014":138,"2015":139,"2016":140,"2017":141,"2018":142,"2019":143,"2020":144,"2021":145,"2022":146,"2023":147,"2024":108},{"1995":150,"1996":151,"1997":152,"2001":153,"2002":154,"2003":155,"2005":156,"2006":157,"2009":158,"2010":159,"2011":160,"2012":161,"2014":162,"2015":163,"2016":164,"2017":165,"2018":166,"2019":167,"2020":168,"2021":169,"2022":170,"2023":171,"2024":172},{"pages":1032,"volume":1034},{"VOID":1033},"245-274",{"VOID":1035},"26","2017-07-13",2017,"2026-07-16T13:04:07.097+00:00",[64,87],{"id":1041,"createTime":1042,"updateTime":1043,"relativeEntities":1044,"slug":1045,"properties":1046,"entityType":195,"verifyStatus":196,"verifyTime":1057,"verifyNote":198,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1058,"fullTextUrl":20,"authors":1059,"publicationType":217,"publisherRelationship":1124,"citationCount":21,"citationInfo":1176,"publishDate":1179,"publishYear":1177,"citationAnalyzeStatus":19,"lastCitationAnalyze":1180,"indexDatabases":1181,"openAccess":20,"references":20,"isForceReanalyzing":275},"886de108-e011-4255-8105-6743672e0ee0","2023-12-25T17:27:09.189+00:00","2026-07-07T05:59:13.879+00:00",[],"State-of-the-Art-on-numerical-simulation-of-fiber-reinforced-thermoplastic-forming-processes",{"abstract":1047,"title":1049,"gsPaper":1051,"references":1053,"doi":1055},{"EN":1048},"Short fiber reinforced composites have gained increasing technological importance due to their versatility that lends them to a wide range of applications. These composites are useful because they include a reinforcing phase in which high tensile strengths can be reached, and a matrix that allows to hold the reinforcement and to transfer applied stress to it. It is a well-known fact, that such materials can have excellent mechanical, thermal and electrical properties that make them widely used in industry. During the manufacture process, fibers adopt a preferential orientation that can vary significantly across the geometry. Once the suspension is cooled or cured to make a solid composite, the fiber orientation becomes a key feature of the final product since it affects the elastic modulus, the thermal and electrical conductivities, and the strength of the composite material. In this work we analyzed the state-of-the-art and the recent developments in the numerical modeling of short fiber suspensions involved in industrial flows.",{"EN":1050},"State-of-the-Art on numerical simulation of fiber-reinforced thermoplastic forming processes",{"VOID":1052},"[\"9819218606962617193\"]",{"VOID":1054},"T. Abdul-Karem, D.M. Binding and M. Sindelar (1993), “Contraction and expansion flows of non-Newtonian fluids”,Compos. Manuf.,4, 109.\nS.G. Advani and C.L. Tucker III (1987), “The use of tensors to describe and predict fiber orientation in short fiber composites”,J. Rheol. 31, 751.\nS.G. Advani and C.L. Tucker III (1990), “Closure approximations for three-dimensional structure tensors”,J. Rheol.,34, 367.\nA. Ahmed and A.N. Alexandrou (1994), “Unsteady flow of semi-concentrated suspensions using finite deformation tensors”,J. Non-Newtonian Fluid Mech. 55, 115.\nA. Aik-Kadi and M. 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Fluids,2, 1839.",{"VOID":1056},"10.1007\u002FBF02736650","2024-05-16T09:49:48.380+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02736650",[1060,1075,1090,1107],{"id":1061,"sortIndex":21,"researcher":20,"roles":1062,"affiliations":1063,"properties":1072,"displayName":1074,"givenName":20,"familyName":20},"36537755-2a70-4df4-a52c-ec9d6dd06c1f",[204],[1064],{"id":1065,"sortIndex":21,"affiliation":1066,"properties":20},"a6f2964c-9c75-4f1c-9b0b-8e4cb74703ea",{"id":1065,"createTime":20,"updateTime":20,"relativeEntities":1067,"slug":20,"properties":1068,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":1071,"statistic":20},[],{"title":1069},{"VI":1070},"Department of Chemical and Petroleum Engineering, The University of Calgary, Calgary, Canada",[],{"title":1073},{"VI":1074},"J. 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International Food Policy Research Institute (IFPRI)\nFerentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145(September 2017), 311–318. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2018.01.009\nLecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324\nKrizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp 1097–1105\nJia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding categories and subject descriptors. 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Wiley, New York",{"doi":393},{"id":2067,"createTime":2068,"updateTime":2069,"relativeEntities":2070,"slug":2071,"properties":2072,"entityType":195,"verifyStatus":196,"verifyTime":2081,"verifyNote":198,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":2082,"fullTextUrl":20,"authors":2083,"publicationType":217,"publisherRelationship":2133,"citationCount":21,"citationInfo":2183,"publishDate":2186,"publishYear":2184,"citationAnalyzeStatus":19,"lastCitationAnalyze":2069,"indexDatabases":2187,"openAccess":20,"references":2188,"isForceReanalyzing":275},"c9a2f917-6694-43d1-8051-8d28ef49fd8e","2024-01-27T17:36:41.786+00:00","2026-04-10T23:51:02.239+00:00",[],"Recent-Advancements-in-Helmholtz-Resonator-Based-Low-Frequency-Acoustic-Absorbers-A-Critical-Review",{"abstract":2073,"title":2075,"gsPaper":2077,"doi":2079},{"EN":2074},"Helmholtz resonator (HR) is an elementary resonating structure predominantly used for acoustic wave manipulation. The sound absorption capabilities of HR are well examined and widely accepted, and it has extensive applications in engineering acoustics. Perhaps, low-frequency sound mitigation is a major technological challenge wherein, HR based absorbers play a pivotal role. In this review, the recent advancements in various HR based sound absorbers are considered in general and low-frequency absorbers in particular for a detailed comparison and critical evaluation. Since the majority of the reported investigations have numerical predictions to corroborate the experimental findings, a detailed review of analytical and computational methods is necessary. Initially, finite element computations of a conventional HR are performed to assess the efficacy of trusted simulation techniques such as thermo-viscous, narrow-region and poro-acoustics models. Then, the structural aspects and noise absorption characteristics of various alterations of conventional HR configurations are critically examined using an analytical approach. Thereafter, a detailed appraisal of the low frequency sound attenuation properties of different HR combinations such as arrays of resonators, hybrid models, and acoustic metamaterials is performed. Moreover, a non-dimensional performance parameter is introduced for uniform comparison among available absorbers and to identify suitable candidates for efficient low-frequency acoustic attenuation. Finally, different optimization approaches including forward and inverse design strategies for selecting appropriate sub-wavelength HR designs for targeted low-frequency noise mitigation are also provided. The development of effective strategies for the creation of HR structures amenable to the real-life industrial environment that provide low-frequency acoustic attenuation is discussed as a future direction.",{"EN":2076},"Recent Advancements in Helmholtz Resonator Based Low-Frequency Acoustic Absorbers: A Critical Review",{"VOID":2078},"[\"17266448962787246919\"]",{"VOID":2080},"10.1007\u002Fs11831-023-10038-7","2024-05-01T05:53:07.950+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11831-023-10038-7",[2084,2101,2118],{"id":2085,"sortIndex":21,"researcher":20,"roles":2086,"affiliations":2087,"properties":2096,"displayName":2098,"givenName":20,"familyName":20},"b06a113a-b185-4a47-9bb1-15cb24bbdc6d",[204],[2088],{"id":2089,"sortIndex":21,"affiliation":2090,"properties":20},"e1a37923-30e1-4277-81cc-04b5181db7a5",{"id":2089,"createTime":20,"updateTime":20,"relativeEntities":2091,"slug":20,"properties":2092,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":2095,"statistic":20},[],{"title":2093},{"VI":2094},"Department of Mechanical Engineering, College of Engineering Trivandrum (Government of Kerala), Thiruvananthapuram, India",[],{"title":2097,"gsAuthor":2099},{"VI":2098},"K. 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Appl Acoust 131:87–102","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0003682X17303146",{"doi":2233},"10.1016\u002Fj.apacoust.2017.10.004",{"id":2235,"text":2236,"url":2237,"identifiers":2238},"b6e7ec7b-f89e-4e4d-a7c3-22e91221abcf","Cambonie T, Gourdon E (2018) Innovative origami-based solutions for enhanced quarter-wavelength resonators. J Sound Vib 434:379–403","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0022460X18304735",{"doi":2239},"10.1016\u002Fj.jsv.2018.07.029",{"id":20,"text":2241,"url":20,"identifiers":2242},"Maa D-Y (1975) Theory and design of microperforated panel sound-absorbing constructions. Sci Sinica 18(1):55–71",{},{"id":389,"text":2244,"url":391,"identifiers":2245},"Maa D-Y (1987) Microperforated-panel wideband absorbers. Noise Control Eng J 29(3):77",{"doi":393},{"id":389,"text":2247,"url":391,"identifiers":2248},"Maa D-Y (1998) Potential of microperforated panel absorber. J Acoust Soc Am 104(5):2861–2866",{"doi":393},{"id":389,"text":2250,"url":391,"identifiers":2251},"Herrin D, Liu J, Seybert A (2011) Properties and applications of microperforated panels. Sound Vib 45(7):6–9",{"doi":393},{"id":2253,"text":2254,"url":2255,"identifiers":2256},"03708db3-065b-4d29-9fdd-789095e494c1","Arjunan A, Baroutaji A, Latif A (2021) Acoustic behaviour of 3d printed titanium perforated panels. Results Eng 11:100252","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2590123021000530",{"doi":2257},"10.1016\u002Fj.rineng.2021.100252",{"id":389,"text":2259,"url":391,"identifiers":2260},"Arjunan A (2019) Acoustic absorption of passive destructive interference cavities. Mater Today Commun 19:68–75",{"doi":393},{"id":389,"text":2262,"url":391,"identifiers":2263},"Setaki F, Tenpierik M, Turrin M, van Timmeren A (2014) Acoustic absorbers by additive manufacturing. 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Sci Rep 6(1):1–8",{"doi":393},{"id":389,"text":2607,"url":391,"identifiers":2608},"Huang S, Fang X, Wang X, Assouar B, Cheng Q, Li Y (2018) Acoustic perfect absorbers via spiral metasurfaces with embedded apertures. Appl Phys Lett 113(23):233501",{"doi":393},{"id":2610,"text":2611,"url":2612,"identifiers":2613},"6c7f2599-8e1b-4661-abd1-de28a578028b","Basirjafari S (2020) Innovative solution to enhance the Helmholtz resonator sound absorber in low-frequency noise by nature inspiration. J Environ Health Sci Eng 18(2):873–882","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs40201-020-00512-w",{"doi":2614},"10.1007\u002Fs40201-020-00512-w",{"id":389,"text":2616,"url":391,"identifiers":2617},"Herrero-Durá I, Cebrecos A, Picó R, Romero-García V, García-Raffi LM, Sánchez-Morcillo VJ (2020) Sound absorption and diffusion by 2D arrays of Helmholtz resonators. 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MDPI",{"doi":393},{"id":2692,"createTime":2693,"updateTime":2694,"relativeEntities":2695,"slug":2696,"properties":2697,"entityType":195,"verifyStatus":196,"verifyTime":2706,"verifyNote":198,"languages":20,"translateLanguages":20,"viewCount":112,"primaryUrl":2707,"fullTextUrl":20,"authors":2708,"publicationType":217,"publisherRelationship":2790,"citationCount":21,"citationInfo":2841,"publishDate":2844,"publishYear":2842,"citationAnalyzeStatus":19,"lastCitationAnalyze":2694,"indexDatabases":2845,"openAccess":20,"references":2846,"isForceReanalyzing":275},"2b63aab7-3430-4da5-8009-4221d5f8bf95","2023-12-29T15:40:30.552+00:00","2026-03-28T17:47:31.119+00:00",[],"A-Review-on-SAR-Image-and-its-Despeckling",{"abstract":2698,"title":2700,"gsPaper":2702,"doi":2704},{"EN":2699},"The method of speckle reduction is widely used in synthetic aperture radar (SAR) imagery over the last three decades. The SAR images are inherently speckled in nature. Speckle noise is a granular pattern distribution, usually modeled as a multiplicative noise that affects the SAR images, as well as all coherent images. Other SAR related problems are also discussed in this paper. Therefore, despeckling approaches are needed to improve the quality of SAR images. However, there is a trade-off between speckle reduction and the preservation of fine details in the despeckled SAR image. The reduction of the speckle noise without losing the fine details of the SAR image is a diffucult task. However, many despeckling methods have been discussed to reduce the speckle noise from the SAR images. Each method has their own norms, advantages and disadvantages. This article contains a review of some major work in the field of SAR image despeckling. Often, scientists and scholars have faced the struggle to understand the pattern distribution of the speckle noise in SAR images. Hence, a brief details about radar, SAR imaging, speckle noise in SAR images and the prevalent approaches of SAR image despeckling are reviewed here. The advantages and disadvantages of SAR image despeckling approaches are also analysed and discussed.",{"EN":2701},"A Review on SAR Image and its Despeckling",{"VOID":2703},"[\"775535620485571713\"]",{"VOID":2705},"10.1007\u002Fs11831-021-09548-z","2024-04-29T15:46:05.094+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11831-021-09548-z",[2709,2726,2743,2758,2775],{"id":2710,"sortIndex":21,"researcher":20,"roles":2711,"affiliations":2712,"properties":2721,"displayName":2723,"givenName":20,"familyName":20},"25874d64-d00b-4f4b-9a03-565d634a73b7",[204],[2713],{"id":2714,"sortIndex":21,"affiliation":2715,"properties":20},"0ec8bb08-a290-43b2-9452-747a35293651",{"id":2714,"createTime":20,"updateTime":20,"relativeEntities":2716,"slug":20,"properties":2717,"entityType":20,"verifyStatus":20,"verifyTime":20,"verifyNote":20,"languages":20,"translateLanguages":20,"viewCount":20,"url":20,"parentIds":2720,"statistic":20},[],{"title":2718},{"VI":2719},"Department of CSE, 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New York; Montreal : McGraw-Hill,[xv], 1634, A26 p",{"doi":393},{"id":20,"text":2866,"url":20,"identifiers":2867},"Abbreviations and acronyms. Navy dot MIL. United States Navy. Retrieved 9 August 2017",{},{"id":389,"text":2869,"url":391,"identifiers":2870},"Small and Short-Range Radar Systems. CRC Net Base. Retrieved 9 August 2017",{"doi":393},{"id":20,"text":2872,"url":2873,"identifiers":2874},"Real Aperture Radar. Available at: http:\u002F\u002Fwtlab.iis.u-tokyo.ac.jp\u002F~wataru\u002Flecture\u002Frsgis\u002Frsnote\u002Fcp4\u002Fcp4-2.htm","http:\u002F\u002Fwtlab.iis.u-tokyo.ac.jp\u002F~wataru\u002Flecture\u002Frsgis\u002Frsnote\u002Fcp4\u002Fcp4-2.htm",{},{"id":20,"text":2876,"url":2877,"identifiers":2878},"Microwave Remote Sensing, Synthetic Aperture Radar (SAR). 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IEEE Geosci Remote Sens Mag. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMGRS.2013.2248301","https:\u002F\u002Fdoi.org\u002F10.1109\u002Fmgrs.2013.2248301",{"mag":3285,"openalex":3286,"doi":3287},"2079299474","W2079299474","10.1109\u002Fmgrs.2013.2248301",{"id":3289,"createTime":3290,"updateTime":3291,"relativeEntities":3292,"slug":3293,"properties":3294,"entityType":195,"verifyStatus":196,"verifyTime":3305,"verifyNote":198,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":3306,"fullTextUrl":20,"authors":3307,"publicationType":217,"publisherRelationship":3372,"citationCount":20,"citationInfo":20,"publishDate":3424,"publishYear":3425,"citationAnalyzeStatus":19,"lastCitationAnalyze":3291,"indexDatabases":3426,"openAccess":20,"references":20,"isForceReanalyzing":275},"647b6270-ac1d-44eb-80f9-c69327aa247a","2024-01-19T05:24:46.981+00:00","2026-03-18T20:03:03.578+00:00",[],"Parallelization-Strategies-for-Computational-Fluid-Dynamics-Software-State-of-the-Art-Review",{"abstract":3295,"title":3297,"gsPaper":3299,"references":3301,"doi":3303},{"EN":3296},"Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics used to analyze fluid flow situation. This analysis is based on simulations carried out on computing machines. For complex configurations, the grid points are so large that the computational time required to obtain the results are very high. Parallel computing is adopted to reduce the computational time of CFD by utilizing the available resource of computing. Parallel computing tools like OpenMP, MPI, CUDA, combination of these and few others are used to achieve parallelization of CFD software. This article provides a comprehensive state of the art review of important CFD areas and parallelization strategies for the related software. Issues related to the computational time complexities and parallelization of CFD software are highlighted. Benefits and issues of using various parallel computing tools for parallelization of CFD software are briefed. Open areas of CFD where parallelization is not much attempted are identified and parallel computing tools which can be useful for parallelization of CFD software are spotlighted. Few suggestions for future work in parallel computing of CFD software are also provided.",{"EN":3298},"Parallelization Strategies for Computational Fluid Dynamics Software: State of the Art Review",{"VOID":3300},"[\"3731090453244554407\"]",{"VOID":3302},"Accary G, Bessonov O, Fougère D, Meradji S, Morvan D (2007) Optimized parallel approach for 3d modelling of forest fire behaviour. Parallel computing technologies. Springer, Berlin, pp 96–102\nAlOnazi A, Keyes D, Lastovetsky A, Rychkov V (2015) Design and optimization of openfoam-based CFD applications for hybrid and heterogeneous HPC platforms. arXiv:1505.07630\nAmritkar A, Deb S, Tafti D (2014) Efficient parallel CFD-DEM simulations using OpenMP. 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