In an era of electronics,recovering the precious metal such as gold from ever increasing piles of electronic-wastes and metal-ion infested soil has become one of the prime concerns for researchers worldwide.Biological...In an era of electronics,recovering the precious metal such as gold from ever increasing piles of electronic-wastes and metal-ion infested soil has become one of the prime concerns for researchers worldwide.Biological mining is an attractive,economical and nonhazardous to recover gold from the low-grade auriferous ore containing waste or soil.This review represents the recent major biological gold retrieval methods used to bio-mine gold.The biomining methods discussed in this review include,bioleaching,bio-oxidation,bio-precipitation,bio-flotation,bio-flocculation,bio-sorption,bio-reduction,bioelectrometallurgical technologies and bio accumulation.The mechanism of gold biorecovery by microbes is explained in detail to explore its intracellular mechanistic,which help it withstand high concentrations of gold without causing any fatal consequences.Major challenges and future opportunities associated with each method and how they will dictate the fate of gold bio-metallurgy from metal wastes or metal infested soil bioremediation in the coming future are also discussed.With the help of concurrent advancements in high-throughput technologies,the gold bio-exploratory methods will speed up our ways to ensure maximum gold retrieval out of such low-grade ores containing sources,while keeping the gold mining clean and more sustainable.展开更多
Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and varia...Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.展开更多
The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them.However,due to the complexity and diversity of this system,precise predictive models are cur...The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them.However,due to the complexity and diversity of this system,precise predictive models are currently unavailable.This study introduces an effective and precise methodology for assessing flexural strength using Monte Carlo Model Averaging(MCMA),a statistical technique that combines the strengths of model averaging(MA)and Monte Carlo simulation.To construct the MCMA model,input variables were derived by analyzing the experimental results,and a database of 433 bending test specimens was compiled.The MCMA model incorporated four different machine learning models,namely decision tree(DT),linear regression(LR),adaptive boosting(AdaBoost),and multilayer perceptron(MLP).Comparative analyses revealed that the MCMA model outperformed baseline models(DT,AdaBoost,LR,and MLP)across all employed metrics.The impact of three different categories on flexural capacity was explored through boxplot analysis.Furthermore,a comparison between the MCMA model and the strut and tie model highlighted the superior performance of the MCMA model.The impact of input variables on the flexural strength prediction was further examined through Shapley Additive exPlanations based feature importance and global interpretation,as well as parametric study.展开更多
The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study pro...The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals.A one-dimensional(1D)convolutional neural network(CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored structure.Raw admittance data set is augmented with white noise to simulate realistic measurement conditions.Stratified K-fold cross-validation technique is employed for training and testing the network.The experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%.Comparing with established 1D CNN models reveals superior performance of the proposed method,with significantly lower testing error.The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features,overcoming limitations associated with traditional piezoelectric admittance approaches.By eliminating the need for signal preprocessing,this method holds promise for real-time damage monitoring of plate structures.展开更多
The rapid industrial and economic development runs on fossil fuel and other energy sources.Limited oil reserves,environmental issues,and high transportation costs lead towards carbon unbiased renewable and sustainable...The rapid industrial and economic development runs on fossil fuel and other energy sources.Limited oil reserves,environmental issues,and high transportation costs lead towards carbon unbiased renewable and sustainable fuel.Compared to other carbon-based fuels,biodiesel is attracted worldwide as a biofuel for the reduction of global dependence on fossil fuels and the greenhouse effect.During biodiesel production,approximately 10%of glycerol is formed in the transesterification process in a biodiesel plant.The ditching of crude glycerol is important as it contains salt,free fatty acids,and methanol that cause contamination of soil and creates environmental challenges for researchers.However,the excessive cost of crude glycerol refining and market capacity encourage the biodiesel industries for developing a new idea for utilising and produced extra sources of income and treat biodiesel waste.This review focuses on the significance of crude glycerol in the value-added utilisation and conversion to bioethanol by a fermentation process and describes the opportunities of glycerol in various applications.展开更多
基金supported by the University Malaysia Pahang, Malaysiafinancial support provided by university under Research Grant No.RDU190332Ministry of Education of Malaysia under the research Grant of RDU 190121.
文摘In an era of electronics,recovering the precious metal such as gold from ever increasing piles of electronic-wastes and metal-ion infested soil has become one of the prime concerns for researchers worldwide.Biological mining is an attractive,economical and nonhazardous to recover gold from the low-grade auriferous ore containing waste or soil.This review represents the recent major biological gold retrieval methods used to bio-mine gold.The biomining methods discussed in this review include,bioleaching,bio-oxidation,bio-precipitation,bio-flotation,bio-flocculation,bio-sorption,bio-reduction,bioelectrometallurgical technologies and bio accumulation.The mechanism of gold biorecovery by microbes is explained in detail to explore its intracellular mechanistic,which help it withstand high concentrations of gold without causing any fatal consequences.Major challenges and future opportunities associated with each method and how they will dictate the fate of gold bio-metallurgy from metal wastes or metal infested soil bioremediation in the coming future are also discussed.With the help of concurrent advancements in high-throughput technologies,the gold bio-exploratory methods will speed up our ways to ensure maximum gold retrieval out of such low-grade ores containing sources,while keeping the gold mining clean and more sustainable.
基金Acknowledgements This research was supported by the Research Program funded by Seoul National University of Science and Technology(SeoulTech).
文摘Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.
文摘The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them.However,due to the complexity and diversity of this system,precise predictive models are currently unavailable.This study introduces an effective and precise methodology for assessing flexural strength using Monte Carlo Model Averaging(MCMA),a statistical technique that combines the strengths of model averaging(MA)and Monte Carlo simulation.To construct the MCMA model,input variables were derived by analyzing the experimental results,and a database of 433 bending test specimens was compiled.The MCMA model incorporated four different machine learning models,namely decision tree(DT),linear regression(LR),adaptive boosting(AdaBoost),and multilayer perceptron(MLP).Comparative analyses revealed that the MCMA model outperformed baseline models(DT,AdaBoost,LR,and MLP)across all employed metrics.The impact of three different categories on flexural capacity was explored through boxplot analysis.Furthermore,a comparison between the MCMA model and the strut and tie model highlighted the superior performance of the MCMA model.The impact of input variables on the flexural strength prediction was further examined through Shapley Additive exPlanations based feature importance and global interpretation,as well as parametric study.
文摘The conventional admittance approach utilizing statistical evaluation metrics offers limited information about the damage location,especially when damage introduces nonlinearities in admittance features.This study proposes a novel automated damage localization method for plate-like structures based on deep learning of raw admittance signals.A one-dimensional(1D)convolutional neural network(CNN)-based model is designed to automate processing of raw admittance response and prediction of damage probabilities across multiple locations in a monitored structure.Raw admittance data set is augmented with white noise to simulate realistic measurement conditions.Stratified K-fold cross-validation technique is employed for training and testing the network.The experimental validation of the proposed method shows that the proposed method can accurately identify the state and damage location in the plate with an average accuracy of 98%.Comparing with established 1D CNN models reveals superior performance of the proposed method,with significantly lower testing error.The proposed method exhibits the ability to directly handle raw electromechanical admittance responses and extract optimal features,overcoming limitations associated with traditional piezoelectric admittance approaches.By eliminating the need for signal preprocessing,this method holds promise for real-time damage monitoring of plate structures.
基金Universiti Malaysia Pahang for providing financial support through higher education Malaysia’s ministry under the fundamental research grant scheme number FRGS/1/2019/STG05/UMP/01/1(UMP internal Reference no RDU1901123).
文摘The rapid industrial and economic development runs on fossil fuel and other energy sources.Limited oil reserves,environmental issues,and high transportation costs lead towards carbon unbiased renewable and sustainable fuel.Compared to other carbon-based fuels,biodiesel is attracted worldwide as a biofuel for the reduction of global dependence on fossil fuels and the greenhouse effect.During biodiesel production,approximately 10%of glycerol is formed in the transesterification process in a biodiesel plant.The ditching of crude glycerol is important as it contains salt,free fatty acids,and methanol that cause contamination of soil and creates environmental challenges for researchers.However,the excessive cost of crude glycerol refining and market capacity encourage the biodiesel industries for developing a new idea for utilising and produced extra sources of income and treat biodiesel waste.This review focuses on the significance of crude glycerol in the value-added utilisation and conversion to bioethanol by a fermentation process and describes the opportunities of glycerol in various applications.
