In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid ...In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained with data collected from various sources of literature. ETC is predicted using ANFIS with volume fraction and thermal conductivities of fillers and matrixes as input parameters, respectively. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.展开更多
The issue of food waste has emerged as a critical global challenge,affecting economies,the environment,and societies worldwide.The food waste squanders valuable resources and significantly contributes to greenhouse ga...The issue of food waste has emerged as a critical global challenge,affecting economies,the environment,and societies worldwide.The food waste squanders valuable resources and significantly contributes to greenhouse gas emissions.However,recent studies have highlighted the potential of utilizing food waste as a source of bioactive compounds with significant health benefits.These compounds possess properties that could mitigate diseases and promote well-being.The multifaceted impact of food waste extends throughout the supply chain,from production and distribution to consumer behavior,resulting in substantial economic losses globally.Efforts to address this challenge require a comprehensive approach involving enhanced public awareness,efficient supply chain management,innovative packaging solutions,and responsible consumption practices.Many products from food waste include citrus fiber,pectin,polyphenols,antioxidants,lycopene,and banana peel flour.Bioactive compounds derived from food waste offer a promising avenue for value creation and environmental sustainability.Various extraction methods have been explored to isolate these compounds efficiently,presenting challenges due to their diverse nature.Valorization strategies,converting waste into high-value products,showcase economic benefits and environmental advantages.Looking ahead,the integration of artificial intelli-gence(AI)and machine learning(ML)holds promise across diverse fields,from healthcare to enhanced di-agnostics and treatment strategies.Tools such as TensorFlow and IBM Watson are revolutionizing food waste valorization by enabling real-time optimization and predictive modeling enhancing yield,and streamlining foodbioprocesses.The present study comprehensively examines food waste valorization by detailing extraction methods,addressing challenges,and proposing solutions for harnessing bioactive compounds.Its novel and holistic approach offers valuable insights into transforming food waste into sustainable resources.展开更多
文摘In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained with data collected from various sources of literature. ETC is predicted using ANFIS with volume fraction and thermal conductivities of fillers and matrixes as input parameters, respectively. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.
基金support from SERB,Department of Science and Technology,India as a JC Bose Fellowship Grant No.JBR/2020/000045。
文摘The issue of food waste has emerged as a critical global challenge,affecting economies,the environment,and societies worldwide.The food waste squanders valuable resources and significantly contributes to greenhouse gas emissions.However,recent studies have highlighted the potential of utilizing food waste as a source of bioactive compounds with significant health benefits.These compounds possess properties that could mitigate diseases and promote well-being.The multifaceted impact of food waste extends throughout the supply chain,from production and distribution to consumer behavior,resulting in substantial economic losses globally.Efforts to address this challenge require a comprehensive approach involving enhanced public awareness,efficient supply chain management,innovative packaging solutions,and responsible consumption practices.Many products from food waste include citrus fiber,pectin,polyphenols,antioxidants,lycopene,and banana peel flour.Bioactive compounds derived from food waste offer a promising avenue for value creation and environmental sustainability.Various extraction methods have been explored to isolate these compounds efficiently,presenting challenges due to their diverse nature.Valorization strategies,converting waste into high-value products,showcase economic benefits and environmental advantages.Looking ahead,the integration of artificial intelli-gence(AI)and machine learning(ML)holds promise across diverse fields,from healthcare to enhanced di-agnostics and treatment strategies.Tools such as TensorFlow and IBM Watson are revolutionizing food waste valorization by enabling real-time optimization and predictive modeling enhancing yield,and streamlining foodbioprocesses.The present study comprehensively examines food waste valorization by detailing extraction methods,addressing challenges,and proposing solutions for harnessing bioactive compounds.Its novel and holistic approach offers valuable insights into transforming food waste into sustainable resources.
