{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:18:38Z","timestamp":1758845918901,"version":"3.44.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62133008","No.62076150"],"award-info":[{"award-number":["No.62133008","No.62076150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Research and Development Program of Shandong Province","doi-asserted-by":"crossref","award":["No.2021CXGC011205","No.2021CXGC011205"],"award-info":[{"award-number":["No.2021CXGC011205","No.2021CXGC011205"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s40747-025-02059-5","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T07:48:52Z","timestamp":1755589732000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel mechanism-guided residual network for accurate modelling of scroll expander under noisy and sparse data conditions"],"prefix":"10.1007","volume":"11","author":[{"given":"Xiaoshuang","family":"Lv","sequence":"first","affiliation":[]},{"given":"Xin","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chengdong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"2059_CR1","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1016\/j.rser.2016.09.137","volume":"69","author":"S Sen","year":"2017","unstructured":"Sen S, Ganguly S (2017) Opportunities, barriers and issues with renewable energy development-A discussion. Renew Sustain Energy Rev 69:1170\u20131181. https:\/\/doi.org\/10.1016\/j.rser.2016.09.137","journal-title":"Renew Sustain Energy Rev"},{"key":"2059_CR2","doi-asserted-by":"publisher","first-page":"5449","DOI":"10.1007\/s40747-023-01034-2","volume":"9","author":"S Lin","year":"2023","unstructured":"Lin S, Lo H, Gul M (2023) An assessment model for national sustainable development based on the hybrid dea and modified topsis techniques. Complex Intell Syst 9:5449\u20135466. https:\/\/doi.org\/10.1007\/s40747-023-01034-2","journal-title":"Complex Intell Syst"},{"key":"2059_CR3","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.applthermaleng.2018.06.001","volume":"141","author":"J Yang","year":"2018","unstructured":"Yang J, Sun Z, Yu B, Chen J (2018) Modeling and optimization criteria of scroll expander integrated into organic Rankine cycle for comparison of R1233zd(E) as an alternative to R245fa. Appl Therm Eng 141:386\u2013393. https:\/\/doi.org\/10.1016\/j.applthermaleng.2018.06.001","journal-title":"Appl Therm Eng"},{"key":"2059_CR4","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1016\/j.egypro.2017.03.530","volume":"105","author":"Y Lu","year":"2017","unstructured":"Lu Y, Roskilly AP, Smallbone A, Yu X, Wang Y (2017) Design and parametric study of an Organic Rankine Cycle using a scroll expander for engine waste heat recovery. Energy Proc 105:1420\u20131425. https:\/\/doi.org\/10.1016\/j.egypro.2017.03.530","journal-title":"Energy Proc"},{"key":"2059_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.115929","volume":"187","author":"C Campana","year":"2019","unstructured":"Campana C, Cioccolanti L, Renzi M, Caresana F (2019) Experimental analysis of a small-scale scroll expander for low-temperature waste heat recovery in Organic Rankine Cycle. Energy 187:115929. https:\/\/doi.org\/10.1016\/j.energy.2019.115929","journal-title":"Energy"},{"key":"2059_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.applthermaleng.2021.117722","volume":"201","author":"Y Du","year":"2022","unstructured":"Du Y, Tian G, Pekris M (2022) A comprehensive review of micro-scale expanders for carbon dioxide related power and refrigeration cycles. Appl Therm Eng 201:117722. https:\/\/doi.org\/10.1016\/j.applthermaleng.2021.117722","journal-title":"Appl Therm Eng"},{"issue":"2","key":"2059_CR7","doi-asserted-by":"publisher","first-page":"433","DOI":"10.3390\/en17020433","volume":"17","author":"H Yang","year":"2024","unstructured":"Yang H, Xu Y, Zhong X, Zeng J, Yang F (2024) Experimental investigation on the performance of the scroll expander under various driving cycles. Energies 17(2):433. https:\/\/doi.org\/10.3390\/en17020433","journal-title":"Energies"},{"key":"2059_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2023.106896","volume":"63","author":"J Sun","year":"2023","unstructured":"Sun J, Peng B, Zhu B, Li Y (2023) Research on the performance characteristics of an oil-free scroll expander that is applied to a micro-scale compressed air energy storage system. J Energy Storage 63:106896. https:\/\/doi.org\/10.1016\/j.est.2023.106896","journal-title":"J Energy Storage"},{"issue":"1","key":"2059_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.11322\/tjsrae.8.53","volume":"8","author":"T Hirano","year":"2011","unstructured":"Hirano T, Hagimoto K, Maeda M (2011) Study on scroll profile for scroll fluid machines. Trans Jpn Soc Refrig Air Condit Eng 8(1):53\u201364. https:\/\/doi.org\/10.11322\/tjsrae.8.53","journal-title":"Trans Jpn Soc Refrig Air Condit Eng"},{"issue":"7","key":"2059_CR10","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1016\/j.ijrefrig.2013.01.005","volume":"36","author":"IH Bell","year":"2013","unstructured":"Bell IH, Groll EA, Braun JE, Horton WT (2013) A computationally efficient hybrid leakage model for positive displacement compressors and expanders. Int J Refrig 36(7):1965\u20131973. https:\/\/doi.org\/10.1016\/j.ijrefrig.2013.01.005","journal-title":"Int J Refrig"},{"issue":"1","key":"2059_CR11","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1109\/TMECH.2009.2036608","volume":"16","author":"J Wang","year":"2011","unstructured":"Wang J, Yang L, Luo X, Mangan S, Derby JW (2011) Mathematical modeling study of scroll air motors and energy efficiency analysis-part I. IEEE\/ASME Trans Mechatron 16(1):112\u2013121. https:\/\/doi.org\/10.1109\/TMECH.2009.2036608","journal-title":"IEEE\/ASME Trans Mechatron"},{"issue":"1","key":"2059_CR12","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/TMECH.2009.2036607","volume":"16","author":"J Wang","year":"2011","unstructured":"Wang J, Luo X, Yang L, Shpanin LM, Jia N, Mangan S, Derby JW (2011) Mathematical modeling study of scroll air motors and energy efficiency analysis-part II. IEEE\/ASME Trans Mechatron 16(1):122\u2013132. https:\/\/doi.org\/10.1109\/TMECH.2009.2036607","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"2059_CR13","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.apenergy.2016.08.025","volume":"186","author":"Z Ma","year":"2017","unstructured":"Ma Z, Bao H, Roskilly AP (2017) Dynamic modelling and experimental validation of scroll expander for small scale power generation system. Appl Energy 186:262\u2013281. https:\/\/doi.org\/10.1016\/j.apenergy.2016.08.025","journal-title":"Appl Energy"},{"key":"2059_CR14","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.applthermaleng.2014.05.094","volume":"75","author":"P Song","year":"2015","unstructured":"Song P, Wei M, Shi L, Danish SN, Ma C (2015) A review of scroll expanders for organic Rankine cycle systems. Appl Therm Eng 75:54\u201364. https:\/\/doi.org\/10.1016\/j.applthermaleng.2014.05.094","journal-title":"Appl Therm Eng"},{"key":"2059_CR15","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.apenergy.2018.09.025","volume":"230","author":"D Ziviani","year":"2018","unstructured":"Ziviani D, James NA, Accorsi FA, Braun JE, Groll EA (2018) Experimental and numerical analyses of a 5 kWe oil-free open-drive scroll expander for small-scale organic Rankine cycle (ORC) applications. Appl Energy 230:1140\u20131156. https:\/\/doi.org\/10.1016\/j.apenergy.2018.09.025","journal-title":"Appl Energy"},{"key":"2059_CR16","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.ijrefrig.2016.11.011","volume":"74","author":"A Zendehboudi","year":"2017","unstructured":"Zendehboudi A, Li X, Wang B (2017) Utilization of ANN and ANFIS models to predict variable speed scroll compressor with vapor injection. Int J Refrig 74:475\u2013487. https:\/\/doi.org\/10.1016\/j.ijrefrig.2016.11.011","journal-title":"Int J Refrig"},{"key":"2059_CR17","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.enconman.2018.02.062","volume":"164","author":"F Yang","year":"2018","unstructured":"Yang F, Cho H, Zhang H, Zhang J, Wu Y (2018) Artificial neural network (ANN) based prediction and optimization of an organic Rankine cycle (ORC) for diesel engine waste heat recovery. Energy Convers Manag 164:15\u201326. https:\/\/doi.org\/10.1016\/j.enconman.2018.02.062","journal-title":"Energy Convers Manag"},{"key":"2059_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.112700","volume":"210","author":"W Wang","year":"2020","unstructured":"Wang W, Deng S, Zhao D, Zhao L, Lin S, Chen M (2020) Application of machine learning into organic Rankine cycle for prediction and optimization of thermal and exergy efficiency. Energy Convers Manag 210:112700. https:\/\/doi.org\/10.1016\/j.enconman.2020.112700","journal-title":"Energy Convers Manag"},{"key":"2059_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.124027","volume":"254","author":"Z Tian","year":"2022","unstructured":"Tian Z, Gan W, Zou X, Zhang Y, Gao W (2022) Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm. Energy 254:124027. https:\/\/doi.org\/10.1016\/j.energy.2022.124027","journal-title":"Energy"},{"key":"2059_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.applthermaleng.2023.120120","volume":"224","author":"Y Feng","year":"2023","unstructured":"Feng Y, Xu K, Zhang Q, Hung T, He Z, Xi H, Rasheed N (2023) Experimental investigation and machine learning optimization of a small-scale organic Rankine cycle. Appl Therm Eng 224:120120. https:\/\/doi.org\/10.1016\/j.applthermaleng.2023.120120","journal-title":"Appl Therm Eng"},{"key":"2059_CR21","doi-asserted-by":"publisher","unstructured":"Zheng T, Li K, Ma X, Qu C, Zhang C (2019) Modeling of scroll expander based on long short-term memory neural network. In: 2019 Chinese Automation Congress (CAC), pp 732\u2013736. https:\/\/doi.org\/10.1109\/CAC48633.2019.8996157","DOI":"10.1109\/CAC48633.2019.8996157"},{"key":"2059_CR22","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.ijrefrig.2022.01.012","volume":"136","author":"Y Li","year":"2022","unstructured":"Li Y, Pan X, Liao X, Xing Z (2022) A data-driven energy management strategy based on performance prediction for cascade refrigeration systems. Int J Refrig 136:114\u2013123. https:\/\/doi.org\/10.1016\/j.ijrefrig.2022.01.012","journal-title":"Int J Refrig"},{"key":"2059_CR23","doi-asserted-by":"publisher","first-page":"5881","DOI":"10.1007\/s40747-023-01061-z","volume":"9","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Zhang Y, Wen Y, Ren Y (2023) Data-driven XGBoost model for maximum stress prediction of additive manufactured lattice structures. Complex Intell Syst 9:5881\u20135892. https:\/\/doi.org\/10.1007\/s40747-023-01061-z","journal-title":"Complex Intell Syst"},{"key":"2059_CR24","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.egypro.2018.08.140","volume":"148","author":"E Fanelli","year":"2018","unstructured":"Fanelli E, Pinto G, Cornacchia G, Braccio G (2018) Parameters identification for scroll expander semi-empirical model by using genetic algorithm. Energy Proc 148:736\u2013743. https:\/\/doi.org\/10.1016\/j.egypro.2018.08.140","journal-title":"Energy Proc"},{"key":"2059_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117723","volume":"202","author":"C Kutlu","year":"2020","unstructured":"Kutlu C, Erdinc MT, Li J, Su Y, Pei G, Gao G, Riffat S (2020) Evaluate the validity of the empirical correlations of clearance and friction coefficients to improve a scroll expander semi-empirical model. Energy 202:117723. https:\/\/doi.org\/10.1016\/j.energy.2020.117723","journal-title":"Energy"},{"key":"2059_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.126356","volume":"266","author":"W Wang","year":"2023","unstructured":"Wang W, Huo J, Tao Y, Lei B, Wu Y, Ma C (2023) Semi-empirical modelling and analysis of single screw expanders considering inlet and exhaust pressure losses. Energy 266:126356. https:\/\/doi.org\/10.1016\/j.energy.2022.126356","journal-title":"Energy"},{"key":"2059_CR27","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.apenergy.2019.04.070","volume":"249","author":"M Bianchi","year":"2019","unstructured":"Bianchi M, Branchini L, De Pascale A, Melino F, Ottaviano S, Peretto A, Torricelli N (2019) Application and comparison of semi-empirical models for performance prediction of a kW-size reciprocating piston expander. Appl Energy 249:143\u2013156. https:\/\/doi.org\/10.1016\/j.apenergy.2019.04.070","journal-title":"Appl Energy"},{"key":"2059_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115672","volume":"278","author":"J Oh","year":"2020","unstructured":"Oh J, Jeong H, Kim J, Lee H (2020) Numerical and experimental investigation on thermal-hydraulic characteristics of a scroll expander for organic Rankine cycle. Appl Energy 278:115672. https:\/\/doi.org\/10.1016\/j.apenergy.2020.115672","journal-title":"Appl Energy"},{"key":"2059_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.applthermaleng.2021.116784","volume":"190","author":"R Moradi","year":"2021","unstructured":"Moradi R, Villarini M, Cioccolanti L (2021) Experimental modeling of a lubricated, open drive scroll expander for micro-scale organic Rankine cycle systems. Appl Therm Eng 190:116784. https:\/\/doi.org\/10.1016\/j.applthermaleng.2021.116784","journal-title":"Appl Therm Eng"},{"key":"2059_CR30","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ijrefrig.2023.09.005","volume":"155","author":"X Ma","year":"2023","unstructured":"Ma X, Lv X, Li C, Li K (2023) Accurate modelling of the scroll expander via a mechanism-incorporated data-driven method. Int J Refrig 155:32\u201346. https:\/\/doi.org\/10.1016\/j.ijrefrig.2023.09.005","journal-title":"Int J Refrig"},{"key":"2059_CR31","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi M, Perdikaris P, Karniadakis GE (2019) Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686\u2013707. https:\/\/doi.org\/10.1016\/j.jcp.2018.10.045","journal-title":"J Comput Phys"},{"key":"2059_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-023-01299-7","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Liu Y, Ning N, Wang L, Zhang Z, Gao X, Lu N (2023) Integrating knowledge representation into traffic prediction: a spatial-temporal graph neural network with adaptive fusion features. Complex Intell Syst. https:\/\/doi.org\/10.1007\/s40747-023-01299-7","journal-title":"Complex Intell Syst"},{"key":"2059_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.adapen.2020.100004","volume":"1","author":"Y Chen","year":"2021","unstructured":"Chen Y, Zhang D (2021) Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory. Adv Appl Energy 1:100004. https:\/\/doi.org\/10.1016\/j.adapen.2020.100004","journal-title":"Adv Appl Energy"},{"key":"2059_CR34","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.camwa.2023.05.014","volume":"143","author":"H Guo","year":"2023","unstructured":"Guo H, Zhuang X, Alajlan N, Rabczuk T (2023) Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning. Comput Math Appl 143:303\u2013317. https:\/\/doi.org\/10.1016\/j.camwa.2023.05.014","journal-title":"Comput Math Appl"},{"issue":"3","key":"2059_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447814","volume":"2","author":"X Jia","year":"2021","unstructured":"Jia X, Willard J, Karpatne A, Read JS, Zwart JA, Steinbach M, Kumar V (2021) Physics-guided machine learning for scientific discovery: an application in simulating lake temperature profiles. ACM\/IMS Trans Data Sci 2(3):1\u201326. https:\/\/doi.org\/10.1145\/3447814","journal-title":"ACM\/IMS Trans Data Sci"},{"key":"2059_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120240","volume":"225","author":"X Luo","year":"2021","unstructured":"Luo X, Zhang D, Zhu X (2021) Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge. Energy 225:120240. https:\/\/doi.org\/10.1016\/j.energy.2021.120240","journal-title":"Energy"},{"key":"2059_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.121830","volume":"351","author":"H Ren","year":"2023","unstructured":"Ren H, Xu C, Lyu Y, Ma Z, Sun Y (2023) A thermodynamic-law-integrated deep learning method for high-dimensional sensor fault detection in diverse complex HVAC systems. Appl Energy 351:121830. https:\/\/doi.org\/10.1016\/j.apenergy.2023.121830","journal-title":"Appl Energy"},{"key":"2059_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2023.106054","volume":"68","author":"G Jing","year":"2023","unstructured":"Jing G, Ning C, Qin J, Ding X, Duan P, Liu H, Sang H (2023) Physics-guided framework of neural network for fast full-field temperature prediction of indoor environment. J Build Eng 68:106054. https:\/\/doi.org\/10.1016\/j.jobe.2023.106054","journal-title":"J Build Eng"},{"key":"2059_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61185\/SMIJ.2022.11101","volume":"1","author":"W Abd El-khalik","year":"2022","unstructured":"Abd El-khalik W (2022) A machine learning approach for improved thermal comfort prediction in sustainable built environments. Sustain Mach Intell J 1:1\u20138. https:\/\/doi.org\/10.61185\/SMIJ.2022.11101","journal-title":"Sustain Mach Intell J"},{"key":"2059_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61356\/SMIJ.2024.11103","volume":"6","author":"A Alzoubi","year":"2024","unstructured":"Alzoubi A, Alaiad A, Alkhattib K, Alkhatib AJ, Aqoulah AA, Hayajnah O et al (2024) Detection of depression from Arabic tweets using machine learning. Sustain Mach Intell J 6:1\u20137. https:\/\/doi.org\/10.61356\/SMIJ.2024.11103","journal-title":"Sustain Mach Intell J"},{"key":"2059_CR41","doi-asserted-by":"publisher","first-page":"42","DOI":"10.61356\/j.mawa.2023.16261","volume":"1","author":"M Mohamed","year":"2023","unstructured":"Mohamed M (2023) Toward smart logistics: hybrization of intelligence techniques of machine learning and multi-criteria decision-making in logistics 5.0. Multicrit Algorithm Appl 1:42\u201357. https:\/\/doi.org\/10.61356\/j.mawa.2023.16261","journal-title":"Multicrit Algorithm Appl"},{"key":"2059_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61185\/SMIJ.2023.55105","volume":"5","author":"M Ismail","year":"2023","unstructured":"Ismail M (2023) Towards sustainable equine welfare: comparative analysis of machine learning techniques in predicting horse survival. Sustain Mach Intell J 5:1\u20138. https:\/\/doi.org\/10.61185\/SMIJ.2023.55105","journal-title":"Sustain Mach Intell J"},{"key":"2059_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61356\/SMIJ.2024.77103","volume":"7","author":"SA Walli","year":"2024","unstructured":"Walli SA, Sallam K (2024) Machine learning for intrusion detection: a reproducible baseline is all you need. Sustain Mach Intell J 7:1\u201329. https:\/\/doi.org\/10.61356\/SMIJ.2024.77103","journal-title":"Sustain Mach Intell J"},{"key":"2059_CR44","doi-asserted-by":"publisher","first-page":"80","DOI":"10.61356\/j.mawa.2024.26961","volume":"2","author":"AM Ali","year":"2024","unstructured":"Ali AM, Broumi S (2024) Machine learning with multi-criteria decision making model for thyroid disease prediction and analysis. Multicrit Algorithm Appl 2:80\u201388. https:\/\/doi.org\/10.61356\/j.mawa.2024.26961","journal-title":"Multicrit Algorithm Appl"},{"key":"2059_CR45","doi-asserted-by":"publisher","first-page":"65","DOI":"10.61356\/j.mawa.2024.26861","volume":"2","author":"MG Mahdi","year":"2024","unstructured":"Mahdi MG, Sleem A, Elhenawy I (2024) Deep learning algorithms for Arabic optical character recognition: a survey. Multicrit Algorithm Appl 2:65\u201379. https:\/\/doi.org\/10.61356\/j.mawa.2024.26861","journal-title":"Multicrit Algorithm Appl"},{"key":"2059_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61356\/SMIJ.2024.66101","volume":"6","author":"M Mohamed","year":"2024","unstructured":"Mohamed M, Gharib M (2024) PAM: cultivate a novel LSTM predictive analysis model for the behavior of cryptocurrencies. Sustain Mach Intell J 6:1\u201310. https:\/\/doi.org\/10.61356\/SMIJ.2024.66101","journal-title":"Sustain Mach Intell J"},{"key":"2059_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruct.2024.117889","volume":"331","author":"X Xu","year":"2024","unstructured":"Xu X, Liu C (2024) Physics-guided deep learning for damage detection in cfrp composite structures. Compos Struct 331:117889. https:\/\/doi.org\/10.1016\/j.compstruct.2024.117889","journal-title":"Compos Struct"},{"key":"2059_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2023.115829","volume":"246","author":"J Zhang","year":"2024","unstructured":"Zhang J, Srivatsa P, Ahmadzai FH, Liu Y, Song X, Karpatne A, Kong ZJ, Johnson BN (2024) Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning. Biosens Bioelectron 246:115829. https:\/\/doi.org\/10.1016\/j.bios.2023.115829","journal-title":"Biosens Bioelectron"},{"issue":"1","key":"2059_CR49","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1093\/bib\/bbab530","volume":"23","author":"F Ge","year":"2022","unstructured":"Ge F, Zhang Y, Xu J, Muhammad A, Song J, Yu D-J (2022) Prediction of disease-associated nssnps by integrating multi-scale resnet models with deep feature fusion. Brief Bioinform 23(1):530. https:\/\/doi.org\/10.1093\/bib\/bbab530","journal-title":"Brief Bioinform"},{"key":"2059_CR50","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1016\/j.applthermaleng.2018.06.045","volume":"141","author":"S Emhardt","year":"2018","unstructured":"Emhardt S, Tian G, Chew J (2018) A review of scroll expander geometries and their performance. Appl Therm Eng 141:1020\u20131034. https:\/\/doi.org\/10.1016\/j.applthermaleng.2018.06.045","journal-title":"Appl Therm Eng"},{"issue":"14","key":"2059_CR51","doi-asserted-by":"publisher","first-page":"3094","DOI":"10.1016\/j.applthermaleng.2009.04.013","volume":"29","author":"V Lemort","year":"2009","unstructured":"Lemort V, Quoilin S, Cuevas C, Lebrun J (2009) Testing and modeling a scroll expander integrated into an Organic Rankine Cycle. Appl Therm Eng 29(14):3094\u20133102. https:\/\/doi.org\/10.1016\/j.applthermaleng.2009.04.013","journal-title":"Appl Therm Eng"},{"issue":"14","key":"2059_CR52","doi-asserted-by":"publisher","first-page":"2073","DOI":"10.1016\/j.applthermaleng.2010.05.015","volume":"30","author":"G Liu","year":"2010","unstructured":"Liu G, Zhao Y, Li L, Shu P (2010) Simulation and experiment research on wide ranging working process of scroll expander driven by compressed air. Appl Therm Eng 30(14):2073\u20132079. https:\/\/doi.org\/10.1016\/j.applthermaleng.2010.05.015","journal-title":"Appl Therm Eng"},{"issue":"4","key":"2059_CR53","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1080\/10789669.2013.784644","volume":"19","author":"Z Zhang","year":"2013","unstructured":"Zhang Z, Ma Y, Li M, Zhao L (2013) Recent advances of energy recovery expanders in the transcritical CO2 refrigeration cycle. HVAC &R Research 19(4):376\u2013384. https:\/\/doi.org\/10.1080\/10789669.2013.784644","journal-title":"HVAC &R Research"},{"key":"2059_CR54","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2059_CR55","doi-asserted-by":"publisher","unstructured":"Tammina S (2019) Transfer learning using VGG-16 with deep convolutional neural network for classifying images, 9:9420. https:\/\/doi.org\/10.29322\/IJSRP.9.10.2019.p9420","DOI":"10.29322\/IJSRP.9.10.2019.p9420"},{"key":"2059_CR56","doi-asserted-by":"publisher","unstructured":"Wang H, Hu D (2005) omparison of SVM and LS-SVM for regression. In: 2005 International Conference on Neural Networks and Brain 1:279\u2013283. https:\/\/doi.org\/10.1109\/ICNNB.2005.1614615","DOI":"10.1109\/ICNNB.2005.1614615"},{"issue":"8","key":"2059_CR57","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1016\/S0893-6080(97)00014-2","volume":"10","author":"H Dai","year":"1997","unstructured":"Dai H, MacBeth C (1997) Effects of learning parameters on learning procedure and performance of a BPNN. Neural Netw 10(8):1505\u20131521. https:\/\/doi.org\/10.1016\/S0893-6080(97)00014-2","journal-title":"Neural Netw"},{"key":"2059_CR58","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neucom.2019.10.068","volume":"385","author":"Y Wang","year":"2020","unstructured":"Wang Y, Zhang X, Lu M, Wang H, Choe Y (2020) Attention augmentation with multi-residual in bidirectional LSTM. Neurocomputing 385:340\u2013347. https:\/\/doi.org\/10.1016\/j.neucom.2019.10.068","journal-title":"Neurocomputing"},{"key":"2059_CR59","doi-asserted-by":"publisher","unstructured":"Nosouhian S, Nosouhian F, Khoshouei AK (2021) A review of recurrent neural network architecture for sequence learning: comparison between lstm and gru. https:\/\/doi.org\/10.20944\/preprints202107.0252.v1","DOI":"10.20944\/preprints202107.0252.v1"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02059-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02059-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02059-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T13:31:48Z","timestamp":1758807108000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02059-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"references-count":59,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2059"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02059-5","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,8,19]]},"assertion":[{"value":"25 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"418"}}