Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor ...Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor lighting conditions in underground mining environments,dust disturbances,platform vibrations during operation,and limited resources for large-scale computing equipment.These factors contribute to extended computation times and unsatisfactory detection accuracy.To address these challenges,this paper proposes an ore waste detection algorithm based on an improved version of YOLOv5.To enhance feature extraction capabilities,the RepLKNet module is incorporated into the YOLOv5 backbone and neck networks.This module enhances the deformation information of feature extraction with the maximum effective Receptive Field to increase the model's accuracy.The Normalizationbased Attention Module(NAM)was introduced to enhance the attention mechanism by focusing on the most relevant features.This improves accuracy in detecting objects against noisy or unclear backgrounds,thereby further enhancing detection performance while reducing model parameters.Additionally,the loss function is optimized to constrain angular deviation using the SIOU loss function,which prevents the training frame from drifting during training and enhances convergence speed.To validate the performance of the proposed method,we tested it using a self-constructed dataset comprising 1,328 images obtained from the crushing station at Jinchuan Group's No.2 mine.The results indicate that,compared to YOLOv5s on the self-constructed dataset,the proposed algorithm achieves an 18.3%improvement in mAP(0.5),a 54%reduction in FLOPs,and a 52.53%decrease in model parameters.The effectiveness and superiority of the proposed algorithm are demonstrated through case studies and comparative analyses.展开更多
Rapid industrial growth,urbanization,and agricultural activities have led to the discharge of large volumes of pollutants into coastal environments,raising levels of metals such as arsenic(As),cadmium(Cd),and mercury(...Rapid industrial growth,urbanization,and agricultural activities have led to the discharge of large volumes of pollutants into coastal environments,raising levels of metals such as arsenic(As),cadmium(Cd),and mercury(Hg)in water and sediments.Bivalve molluscs,such as Meretrix lyrata and Saccostrea glomerata can accumulate high amounts of toxic heavy metals in their tissues that pose potential risks to human health.They are frequently used as bioindicators due to their filter-feeding behavior and high accumulation potential.This study evaluates heavy metal accumulation in bivalve molluscs from Northeastern Vietnam,including Quang Ninh Province and Hai Phong City.In this study,a systematic literature review was conducted,combined with a bibliometric analysis,to synthesize and evaluate data on heavy metal accumulation in bivalve molluscs from Northeastern Vietnam.The analysis results showed bio-concentration factors exceeding 1,000 for As,Cd,and Hg in certain species,particularly in samples from Quang Ninh Province.Meanwhile,sediment accumulation factors(BSAF)were lower,suggesting that waterborne pathways predominantly contribute to heavy metal uptake.These findings highlight significant food safety risks due to toxic metal accumulation in seafood resources,emphasizing the urgent need for continuous monitoring and the establishment of local safety standards.The study provides important scientific evidence to support marine environmental management and public health protection.展开更多
Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating ef...Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating effects of CC,including rising sea levels,typhoons,flooding,and droughts.In this context,this article aims to assess the vulnerability of households'livelihoods in Quang Nam Province by applying the Livelihood Vulnerability Index(LVI)developed by Hahn et al.,along with the Intergovernmental Panel on Climate Change framework(LVI-IPCC).The study utilises five sources of household capital—human,social,physical,natural,and financial—to construct its indices.The data for this article is based on a survey of 200 households.The research methodology combines both quantitative and qualitative methods,including questionnaire interviews,in-depth interviews,and focus group discussions.The research period spans from 2021 to 2023.The study results revealed that the household LVI was 0.371,while the LVI-IPCC was 0.086,highlighting the critical need for access to food and clean water,which scored 0.458 and 0.351,respectively.The research underscores how CC significantly affects the livelihoods of coastal communities,particularly in sectors such as fishing,aquaculture,and agriculture.The study concludes that CC poses significant challenges to the livelihoods of coastal communities in Quang Nam Province and that adaptation measures are necessary to support these communities.The research highlights the importance of livelihood diversification,job transformation,and improving knowledge and skills to enhance the resilience of coastal communities to CC.展开更多
基金supported by the Department of science and technology of Shaanxi Province(NO.2023-ZDLGY-24).
文摘Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor lighting conditions in underground mining environments,dust disturbances,platform vibrations during operation,and limited resources for large-scale computing equipment.These factors contribute to extended computation times and unsatisfactory detection accuracy.To address these challenges,this paper proposes an ore waste detection algorithm based on an improved version of YOLOv5.To enhance feature extraction capabilities,the RepLKNet module is incorporated into the YOLOv5 backbone and neck networks.This module enhances the deformation information of feature extraction with the maximum effective Receptive Field to increase the model's accuracy.The Normalizationbased Attention Module(NAM)was introduced to enhance the attention mechanism by focusing on the most relevant features.This improves accuracy in detecting objects against noisy or unclear backgrounds,thereby further enhancing detection performance while reducing model parameters.Additionally,the loss function is optimized to constrain angular deviation using the SIOU loss function,which prevents the training frame from drifting during training and enhances convergence speed.To validate the performance of the proposed method,we tested it using a self-constructed dataset comprising 1,328 images obtained from the crushing station at Jinchuan Group's No.2 mine.The results indicate that,compared to YOLOv5s on the self-constructed dataset,the proposed algorithm achieves an 18.3%improvement in mAP(0.5),a 54%reduction in FLOPs,and a 52.53%decrease in model parameters.The effectiveness and superiority of the proposed algorithm are demonstrated through case studies and comparative analyses.
文摘Rapid industrial growth,urbanization,and agricultural activities have led to the discharge of large volumes of pollutants into coastal environments,raising levels of metals such as arsenic(As),cadmium(Cd),and mercury(Hg)in water and sediments.Bivalve molluscs,such as Meretrix lyrata and Saccostrea glomerata can accumulate high amounts of toxic heavy metals in their tissues that pose potential risks to human health.They are frequently used as bioindicators due to their filter-feeding behavior and high accumulation potential.This study evaluates heavy metal accumulation in bivalve molluscs from Northeastern Vietnam,including Quang Ninh Province and Hai Phong City.In this study,a systematic literature review was conducted,combined with a bibliometric analysis,to synthesize and evaluate data on heavy metal accumulation in bivalve molluscs from Northeastern Vietnam.The analysis results showed bio-concentration factors exceeding 1,000 for As,Cd,and Hg in certain species,particularly in samples from Quang Ninh Province.Meanwhile,sediment accumulation factors(BSAF)were lower,suggesting that waterborne pathways predominantly contribute to heavy metal uptake.These findings highlight significant food safety risks due to toxic metal accumulation in seafood resources,emphasizing the urgent need for continuous monitoring and the establishment of local safety standards.The study provides important scientific evidence to support marine environmental management and public health protection.
基金supported by the"Vietnam Sea for the Goals of National Defence and National Development"project managed by the Office of the Vietnam Academy of Social Sciences。
文摘Vietnam,with its twenty-eight coastal provinces,is one of the nations most profoundly affected by the adverse impacts of climate change(CC).These provinces face severe challenges as they contend with the escalating effects of CC,including rising sea levels,typhoons,flooding,and droughts.In this context,this article aims to assess the vulnerability of households'livelihoods in Quang Nam Province by applying the Livelihood Vulnerability Index(LVI)developed by Hahn et al.,along with the Intergovernmental Panel on Climate Change framework(LVI-IPCC).The study utilises five sources of household capital—human,social,physical,natural,and financial—to construct its indices.The data for this article is based on a survey of 200 households.The research methodology combines both quantitative and qualitative methods,including questionnaire interviews,in-depth interviews,and focus group discussions.The research period spans from 2021 to 2023.The study results revealed that the household LVI was 0.371,while the LVI-IPCC was 0.086,highlighting the critical need for access to food and clean water,which scored 0.458 and 0.351,respectively.The research underscores how CC significantly affects the livelihoods of coastal communities,particularly in sectors such as fishing,aquaculture,and agriculture.The study concludes that CC poses significant challenges to the livelihoods of coastal communities in Quang Nam Province and that adaptation measures are necessary to support these communities.The research highlights the importance of livelihood diversification,job transformation,and improving knowledge and skills to enhance the resilience of coastal communities to CC.
