Zirconium and its alloys are considered to be materials for artificial joints because of their excellent biocompatibility. In this study, we proposed the introduction of high-purity iron beads as external deoxidisers to inhibit the oxidation of Zr2.5Nb during thermal nitriding and investigated the biotribological properties of this alloy after deoxidation. Zr2.5Nb samples were subjected to deoxidation thermal nitriding at 900°C and 1000°C for 4 h. The main phase on the surface was ZrN, which was accompanied by a minor phase of unsaturated zirconium oxides (ZrO0.33, ZrO0.27). The thickness of the ZrN ceramic layer increased from 5.26 ± 0.37 μm to 7.78 ± 0.19 μm. During electrochemical friction–corrosion test, the open-circuit potential (OCP) and coefficient of friction (COF) values for the sample prepared at 900°C were −809.8 mV and 0.3015, and those for the sample prepared at 1000°C were −682.3 mV and 0.3168. The samples that underwent deoxidation thermal nitriding exhibited better friction–corrosion resistance and a lower friction coefficient than the original sample. Additionally, the volume wear loss was reduced by 50.53% and 62.27%, also demonstrating the superior biotribological properties achieved through deoxidation thermal nitriding.
{"title":"Thermal Nitridation Deoxygenation and Biotribological Properties of Zr2.5Nb","authors":"Liuwang Zhang, Jiangchuan Xu, Hao Liu, Yong Luo","doi":"10.1049/bsb2.70005","DOIUrl":"https://doi.org/10.1049/bsb2.70005","url":null,"abstract":"<p>Zirconium and its alloys are considered to be materials for artificial joints because of their excellent biocompatibility. In this study, we proposed the introduction of high-purity iron beads as external deoxidisers to inhibit the oxidation of Zr2.5Nb during thermal nitriding and investigated the biotribological properties of this alloy after deoxidation. Zr2.5Nb samples were subjected to deoxidation thermal nitriding at 900°C and 1000°C for 4 h. The main phase on the surface was ZrN, which was accompanied by a minor phase of unsaturated zirconium oxides (ZrO<sub>0.33</sub>, ZrO<sub>0.27</sub>). The thickness of the ZrN ceramic layer increased from 5.26 ± 0.37 μm to 7.78 ± 0.19 μm. During electrochemical friction–corrosion test, the open-circuit potential (OCP) and coefficient of friction (COF) values for the sample prepared at 900°C were −809.8 mV and 0.3015, and those for the sample prepared at 1000°C were −682.3 mV and 0.3168. The samples that underwent deoxidation thermal nitriding exhibited better friction–corrosion resistance and a lower friction coefficient than the original sample. Additionally, the volume wear loss was reduced by 50.53% and 62.27%, also demonstrating the superior biotribological properties achieved through deoxidation thermal nitriding.</p>","PeriodicalId":52235,"journal":{"name":"Biosurface and Biotribology","volume":"11 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bsb2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tactile perception is essential for humans to recognise objects. This study systematically investigated the tribological behaviour of the finger and physiological response of the brain related to the width recognition of tactile perception using subjective evaluation, friction and electroencephalography methods. The results show that the texture feeling, recognition accuracy of the texture and proportion of deformation friction increased with the texture width. The average width recognition threshold of the fine texture was 45.4 μm. The load index, maximum amplitude of the vibration signal, entropy, longest vertical line and P300 amplitude were positively correlated with the texture width. P300 latency was negatively correlated with the texture width. When the texture width exceeded the width recognition thresholds of tactile perception, the main frequency of the vibration signals increased to the optimal perceptual range of the Pacinian corpuscle. The nonlinear features of the vibration signal increased, and the vibration system transitioned from a homogenous state to a disrupted state. Moreover, the activation intensity and area of the brain and the speed of tactile recognition increased. The study demonstrated that the mechanical stimuli of friction and vibration generated in the touching of fine textures having various widths affected the subjective evaluation and brain response.
{"title":"Recognition of Fine Textures Using Friction and EEG Methods","authors":"Shousheng Zhang, Wei Tang","doi":"10.1049/bsb2.70006","DOIUrl":"https://doi.org/10.1049/bsb2.70006","url":null,"abstract":"<p>Tactile perception is essential for humans to recognise objects. This study systematically investigated the tribological behaviour of the finger and physiological response of the brain related to the width recognition of tactile perception using subjective evaluation, friction and electroencephalography methods. The results show that the texture feeling, recognition accuracy of the texture and proportion of deformation friction increased with the texture width. The average width recognition threshold of the fine texture was 45.4 μm. The load index, maximum amplitude of the vibration signal, entropy, longest vertical line and P300 amplitude were positively correlated with the texture width. P300 latency was negatively correlated with the texture width. When the texture width exceeded the width recognition thresholds of tactile perception, the main frequency of the vibration signals increased to the optimal perceptual range of the Pacinian corpuscle. The nonlinear features of the vibration signal increased, and the vibration system transitioned from a homogenous state to a disrupted state. Moreover, the activation intensity and area of the brain and the speed of tactile recognition increased. The study demonstrated that the mechanical stimuli of friction and vibration generated in the touching of fine textures having various widths affected the subjective evaluation and brain response.</p>","PeriodicalId":52235,"journal":{"name":"Biosurface and Biotribology","volume":"11 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bsb2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the groundwater quality of an aquifer located in medium-populated area of the Ticino Valley with strong agricultural vocation. Two monitoring campaigns were carried out according to the phases of rice cultivation (pre- and post-flooding) on the subsurface and surface irrigation network, Ticino River and wastewater effluents, highlighting a diffuse contamination. The isotopic analyses evidenced mixing phenomena, with both contributions from local rainfall and irrigation network. Combining chemical and microbiological approaches, the anthropogenic impact was evaluated by analysing a selection of traditional and emerging pollutants, such as pesticides, antibiotics and hormones, and assessing the extent of enterobacterial contamination and potential antibiotic resistance genes. Most of the investigated contaminants were found in concentrations from 0.1 ng/L to 632 ng/L, with the exception of Glyphosate and AMPA up to 5 and 20 μg/L, respectively. Even at these low concentrations, contamination of water resources is a serious issue because long-term exposure to such pollutants may cause detrimental effects. The most frequently detected pesticide was the fungicide Tricyclazole, while glucocorticoid Dexamethasone was the most frequent steroid hormone. Noteworthy is the ubiquity of Trimethoprim and a recurrent presence of fluoroquinolones. The occurrence of antibiotics at most sites, although at very low levels, is of environmental and public health concern, as they exert a selective pressure on bacterial populations, allowing the development of antibiotic resistant microbes, as highlighted by microbiological investigations. Indeed, a high microbial load was found in both campaigns, in particular in those sampling sites close to wastewater treatment plants, with the β-lactams and quinolones classes of antibiotics as the most affected by the phenomenon of resistance.
{"title":"Occurrence of micropollutants and enterobacteria in Ticino Valley: an insight of water contamination in an agricultural area with highly anthropogenic impact","authors":"Aurora Piazza , Francesca Merlo , Aseel AbuAlshaar , Francesca Piscopiello , Federica Maraschi , Alice Bernini , Melissa Spalla , Michela Sturini , Roberta Migliavacca , Giorgio Pilla , Antonella Profumo","doi":"10.1016/j.emcon.2025.100509","DOIUrl":"10.1016/j.emcon.2025.100509","url":null,"abstract":"<div><div>This study investigates the groundwater quality of an aquifer located in medium-populated area of the Ticino Valley with strong agricultural vocation. Two monitoring campaigns were carried out according to the phases of rice cultivation (pre- and post-flooding) on the subsurface and surface irrigation network, Ticino River and wastewater effluents, highlighting a diffuse contamination. The isotopic analyses evidenced mixing phenomena, with both contributions from local rainfall and irrigation network. Combining chemical and microbiological approaches, the anthropogenic impact was evaluated by analysing a selection of traditional and emerging pollutants, such as pesticides, antibiotics and hormones, and assessing the extent of enterobacterial contamination and potential antibiotic resistance genes. Most of the investigated contaminants were found in concentrations from 0.1 ng/L to 632 ng/L, with the exception of Glyphosate and AMPA up to 5 and 20 μg/L, respectively. Even at these low concentrations, contamination of water resources is a serious issue because long-term exposure to such pollutants may cause detrimental effects. The most frequently detected pesticide was the fungicide Tricyclazole, while glucocorticoid Dexamethasone was the most frequent steroid hormone. Noteworthy is the ubiquity of Trimethoprim and a recurrent presence of fluoroquinolones. The occurrence of antibiotics at most sites, although at very low levels, is of environmental and public health concern, as they exert a selective pressure on bacterial populations, allowing the development of antibiotic resistant microbes, as highlighted by microbiological investigations. Indeed, a high microbial load was found in both campaigns, in particular in those sampling sites close to wastewater treatment plants, with the β-lactams and quinolones classes of antibiotics as the most affected by the phenomenon of resistance.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 3","pages":"Article 100509"},"PeriodicalIF":5.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-12DOI: 10.1016/j.aiia.2025.03.009
Wang Dai , Kebiao Mao , Zhonghua Guo , Zhihao Qin , Jiancheng Shi , Sayed M. Bateni , Liurui Xiao
The rapid advancement of artificial intelligence in domains such as natural language processing has catalyzed AI research across various fields. This study introduces a novel strategy, the AutoKeras-Knowledge Distillation (AK-KD), which integrates knowledge distillation technology for joint optimization of large and small models in the retrieval of surface temperature and emissivity using thermal infrared remote sensing. The approach addresses the challenges of limited accuracy in surface temperature retrieval by employing a high-performance large model developed through AutoKeras as the teacher model, which subsequently enhances a less accurate small model through knowledge distillation. The resultant student model is interactively integrated with the large model to further improve specificity and generalization capabilities. Theoretical derivations and practical applications validate that the AK-KD strategy significantly enhances the accuracy of temperature and emissivity retrieval. For instance, a large model trained with simulated ASTER data achieved a Pearson Correlation Coefficient (PCC) of 0.999 and a Mean Absolute Error (MAE) of 0.348 K in surface temperature retrieval. In practical applications, this model demonstrated a PCC of 0.967 and an MAE of 0.685 K. Although the large model exhibits high average accuracy, its precision in complex terrains is comparatively lower. To ameliorate this, the large model, serving as a teacher, enhances the small model's local accuracy. Specifically, in surface temperature retrieval, the small model's PCC improved from an average of 0.978 to 0.979, and the MAE decreased from 1.065 K to 0.724 K. In emissivity retrieval, the PCC rose from an average of 0.827 to 0.898, and the MAE reduced from 0.0076 to 0.0054. This research not only provides robust technological support for further development of thermal infrared remote sensing in temperature and emissivity retrieval but also offers important references and key technological insights for the universal model construction of other geophysical parameter retrievals.
{"title":"Joint optimization of AI large and small models for surface temperature and emissivity retrieval using knowledge distillation","authors":"Wang Dai , Kebiao Mao , Zhonghua Guo , Zhihao Qin , Jiancheng Shi , Sayed M. Bateni , Liurui Xiao","doi":"10.1016/j.aiia.2025.03.009","DOIUrl":"10.1016/j.aiia.2025.03.009","url":null,"abstract":"<div><div>The rapid advancement of artificial intelligence in domains such as natural language processing has catalyzed AI research across various fields. This study introduces a novel strategy, the AutoKeras-Knowledge Distillation (AK-KD), which integrates knowledge distillation technology for joint optimization of large and small models in the retrieval of surface temperature and emissivity using thermal infrared remote sensing. The approach addresses the challenges of limited accuracy in surface temperature retrieval by employing a high-performance large model developed through AutoKeras as the teacher model, which subsequently enhances a less accurate small model through knowledge distillation. The resultant student model is interactively integrated with the large model to further improve specificity and generalization capabilities. Theoretical derivations and practical applications validate that the AK-KD strategy significantly enhances the accuracy of temperature and emissivity retrieval. For instance, a large model trained with simulated ASTER data achieved a Pearson Correlation Coefficient (PCC) of 0.999 and a Mean Absolute Error (MAE) of 0.348 K in surface temperature retrieval. In practical applications, this model demonstrated a PCC of 0.967 and an MAE of 0.685 K. Although the large model exhibits high average accuracy, its precision in complex terrains is comparatively lower. To ameliorate this, the large model, serving as a teacher, enhances the small model's local accuracy. Specifically, in surface temperature retrieval, the small model's PCC improved from an average of 0.978 to 0.979, and the MAE decreased from 1.065 K to 0.724 K. In emissivity retrieval, the PCC rose from an average of 0.827 to 0.898, and the MAE reduced from 0.0076 to 0.0054. This research not only provides robust technological support for further development of thermal infrared remote sensing in temperature and emissivity retrieval but also offers important references and key technological insights for the universal model construction of other geophysical parameter retrievals.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 407-425"},"PeriodicalIF":8.2,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1016/j.aiia.2025.04.002
Hao Fu , Xueguan Zhao , Haoran Tan , Shengyu Zheng , Changyuan Zhai , Liping Chen
To address the low recognition accuracy of open-field vegetables under light occlusion, this study focused on cabbage and developed an online target recognition model based on deep learning. Using Yolov8n as the base network, a method was proposed to mitigate the impact of light occlusion on the accuracy of online cabbage recognition. A combination of cabbage image filters was designed to eliminate the effects of light occlusion. A filter parameter adaptive learning module for cabbage image filter parameters was constructed. The image filter combination and adaptive learning module were embedded into the Yolov8n object detection network. This integration enabled precise real-time recognition of cabbage under light occlusion conditions. Experimental results showed recognition accuracies of 97.5 % on the normal lighting dataset, 93.1 % on the light occlusion dataset, and 95.0 % on the mixed dataset. For images with a light occlusion degree greater than 0.4, the recognition accuracy improved by 9.9 % and 13.7 % compared to Yolov5n and Yolov8n models. The model achieved recognition accuracies of 99.3 % on the Chinese cabbage dataset and 98.3 % on the broccoli dataset. The model was deployed on an Nvidia Jetson Orin NX edge computing device, achieving an image processing speed of 26.32 frames per second. Field trials showed recognition accuracies of 96.0 % under normal lighting conditions and 91.2 % under light occlusion. The proposed online cabbage recognition model enables real-time recognition and localization of cabbage in complex open-field environments, offering technical support for target-oriented spraying.
{"title":"Effective methods for mitigate the impact of light occlusion on the accuracy of online cabbage recognition in open fields","authors":"Hao Fu , Xueguan Zhao , Haoran Tan , Shengyu Zheng , Changyuan Zhai , Liping Chen","doi":"10.1016/j.aiia.2025.04.002","DOIUrl":"10.1016/j.aiia.2025.04.002","url":null,"abstract":"<div><div>To address the low recognition accuracy of open-field vegetables under light occlusion, this study focused on cabbage and developed an online target recognition model based on deep learning. Using Yolov8n as the base network, a method was proposed to mitigate the impact of light occlusion on the accuracy of online cabbage recognition. A combination of cabbage image filters was designed to eliminate the effects of light occlusion. A filter parameter adaptive learning module for cabbage image filter parameters was constructed. The image filter combination and adaptive learning module were embedded into the Yolov8n object detection network. This integration enabled precise real-time recognition of cabbage under light occlusion conditions. Experimental results showed recognition accuracies of 97.5 % on the normal lighting dataset, 93.1 % on the light occlusion dataset, and 95.0 % on the mixed dataset. For images with a light occlusion degree greater than 0.4, the recognition accuracy improved by 9.9 % and 13.7 % compared to Yolov5n and Yolov8n models. The model achieved recognition accuracies of 99.3 % on the Chinese cabbage dataset and 98.3 % on the broccoli dataset. The model was deployed on an Nvidia Jetson Orin NX edge computing device, achieving an image processing speed of 26.32 frames per second. Field trials showed recognition accuracies of 96.0 % under normal lighting conditions and 91.2 % under light occlusion. The proposed online cabbage recognition model enables real-time recognition and localization of cabbage in complex open-field environments, offering technical support for target-oriented spraying.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 449-458"},"PeriodicalIF":8.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-10DOI: 10.1016/j.emcon.2025.100508
Yongtao Xu , Dan Li , Ying Yuan , Fei Fang , Beidou Xi , Wenbing Tan
Landfilling remains one of the primary methods for managing municipal solid waste (MSW), processing approximately 350 million tons of waste annually. Among the various components of landfill waste, pharmaceuticals and personal care products (PPCPs), including both antibiotics and non-antibiotic compounds, pose significant environmental challenges. Landfill leachate is a highly complex medium, consisting of diverse contaminants such as non-antibiotic pharmaceuticals (average concentration ∼1.74 μg/L), antibiotics (average concentration ∼527 ng/L), heavy metals, dissolved organic matter, and micro/nano-plastics (concentration range 0.64–2.16 mg/L). This unique mixture can alter the native microbial community structure, profoundly impacting antibiotic resistance, potentially disrupting soil and groundwater ecosystems, and threatening ecological balance. Existing research has extensively investigated the composition, physicochemical properties, environmental behavior, and microbial community structure of landfill leachate, leading to significant advancements in the field. However, due to the complexity and large volume of landfill leachate, current risk assessment approaches predominantly rely on conventional pollutant indicators, and most treatment strategies are designed for general contaminants. There is a lack of systematic descriptions that integrate pollutants with their direct impacts on microbial communities. This review focuses on the current pollution status, spatiotemporal trends, interactions, and migration risks of antibiotics and non-antibiotic pollutants in landfill leachate. In particular, we explore the in situ emergence of antibiotic resistance in landfill leachate (rather than the horizontal transfer of antibiotic resistance genes) and examine the influence of various leachate components on antibiotic resistance. By emphasizing the importance of understanding the combined effects of antibiotics and non-antibiotic pollutants in landfill environments, this review highlights the necessity of long-term ecological risk assessments for antibiotic-induced resistance as an emerging contaminant.
{"title":"Antibiotic resistance occurrence and ecological impact in landfill leachate: A review on compound effect of antibiotics and non-antibiotics","authors":"Yongtao Xu , Dan Li , Ying Yuan , Fei Fang , Beidou Xi , Wenbing Tan","doi":"10.1016/j.emcon.2025.100508","DOIUrl":"10.1016/j.emcon.2025.100508","url":null,"abstract":"<div><div>Landfilling remains one of the primary methods for managing municipal solid waste (MSW), processing approximately 350 million tons of waste annually. Among the various components of landfill waste, pharmaceuticals and personal care products (PPCPs), including both antibiotics and non-antibiotic compounds, pose significant environmental challenges. Landfill leachate is a highly complex medium, consisting of diverse contaminants such as non-antibiotic pharmaceuticals (average concentration ∼1.74 μg/L), antibiotics (average concentration ∼527 ng/L), heavy metals, dissolved organic matter, and micro/nano-plastics (concentration range 0.64–2.16 mg/L). This unique mixture can alter the native microbial community structure, profoundly impacting antibiotic resistance, potentially disrupting soil and groundwater ecosystems, and threatening ecological balance. Existing research has extensively investigated the composition, physicochemical properties, environmental behavior, and microbial community structure of landfill leachate, leading to significant advancements in the field. However, due to the complexity and large volume of landfill leachate, current risk assessment approaches predominantly rely on conventional pollutant indicators, and most treatment strategies are designed for general contaminants. There is a lack of systematic descriptions that integrate pollutants with their direct impacts on microbial communities. This review focuses on the current pollution status, spatiotemporal trends, interactions, and migration risks of antibiotics and non-antibiotic pollutants in landfill leachate. In particular, we explore the in situ emergence of antibiotic resistance in landfill leachate (rather than the horizontal transfer of antibiotic resistance genes) and examine the influence of various leachate components on antibiotic resistance. By emphasizing the importance of understanding the combined effects of antibiotics and non-antibiotic pollutants in landfill environments, this review highlights the necessity of long-term ecological risk assessments for antibiotic-induced resistance as an emerging contaminant.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 3","pages":"Article 100508"},"PeriodicalIF":5.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-10DOI: 10.1016/j.aiia.2025.04.003
Shi Yinyan, Zhu Yangxu, Wang Xiaochan, Zhang Xiaolei, Zheng Enlai, Zhang Yongnian
Environmental impacts and economic demands are driving the development of variable rate fertilization (VRF) technology for precision agriculture. Despite the advantages of a simple structure, low cost and high efficiency, uneven fertilizer-spreading uniformity is becoming a key factor restricting the application of centrifugal fertilizer spreaders. Accordingly, the particle application characteristics and variation laws for centrifugal VRF spreaders with multi-pass overlapped spreading needs to be urgently explored, in order to improve their distribution uniformity and working accuracy. In this study, the working performance of a self-developed centrifugal VRF spreader, based on real-time growth information of rice and wheat, was investigated and tested through the test methods of using the collection trays prescribed in ISO 5690 and ASAE S341.2. The coefficient of variation (CV) was calculated by weighing the fertilizer mass in standard pans, in order to evaluate the distribution uniformity of spreading patterns. The results showed that the effective application widths were 21.05, 22.58 and 23.67 m for application rates of 225, 300 and 375 kg/ha, respectively. The actual fertilizer application rates of multi-pass overlapped spreading were generally higher than the target rates, as well as the particle distribution CVs within the effective spreading widths were 11.51, 9.25 and 11.28 % for the respective target rates. Field test results for multi-pass overlapped spreading showed that the average difference between the actual and target application was 4.54 %, as well as the average particle distribution CV within the operating width was 11.94 %, which met the operation requirements of particle transverse distribution for centrifugal fertilizer spreaders. The results and findings of this study provide a theoretical reference for technical innovation and development of centrifugal VRF spreaders and are of great practical and social significance for accelerating their application in implementing precision agriculture.
{"title":"Assessing particle application in multi-pass overlapping scenarios with variable rate centrifugal fertilizer spreaders for precision agriculture","authors":"Shi Yinyan, Zhu Yangxu, Wang Xiaochan, Zhang Xiaolei, Zheng Enlai, Zhang Yongnian","doi":"10.1016/j.aiia.2025.04.003","DOIUrl":"10.1016/j.aiia.2025.04.003","url":null,"abstract":"<div><div>Environmental impacts and economic demands are driving the development of variable rate fertilization (VRF) technology for precision agriculture. Despite the advantages of a simple structure, low cost and high efficiency, uneven fertilizer-spreading uniformity is becoming a key factor restricting the application of centrifugal fertilizer spreaders. Accordingly, the particle application characteristics and variation laws for centrifugal VRF spreaders with multi-pass overlapped spreading needs to be urgently explored, in order to improve their distribution uniformity and working accuracy. In this study, the working performance of a self-developed centrifugal VRF spreader, based on real-time growth information of rice and wheat, was investigated and tested through the test methods of using the collection trays prescribed in ISO 5690 and ASAE S341.2. The coefficient of variation (CV) was calculated by weighing the fertilizer mass in standard pans, in order to evaluate the distribution uniformity of spreading patterns. The results showed that the effective application widths were 21.05, 22.58 and 23.67 m for application rates of 225, 300 and 375 kg/ha, respectively. The actual fertilizer application rates of multi-pass overlapped spreading were generally higher than the target rates, as well as the particle distribution CVs within the effective spreading widths were 11.51, 9.25 and 11.28 % for the respective target rates. Field test results for multi-pass overlapped spreading showed that the average difference between the actual and target application was 4.54 %, as well as the average particle distribution CV within the operating width was 11.94 %, which met the operation requirements of particle transverse distribution for centrifugal fertilizer spreaders. The results and findings of this study provide a theoretical reference for technical innovation and development of centrifugal VRF spreaders and are of great practical and social significance for accelerating their application in implementing precision agriculture.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 395-406"},"PeriodicalIF":8.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-08DOI: 10.1016/j.aiia.2025.03.006
Yuqing Yang , Chengguo Xu , Wenhao Hou , Alan G. McElligott , Kai Liu , Yueju Xue
Nursing behaviour and the calling-to-nurse sound are crucial indicators for assessing sow maternal behaviour and nursing status. However, accurately identifying these behaviours for individual sows in complex indoor pig housing is challenging due to factors such as variable lighting, rail obstructions, and interference from other sows' calls. Multimodal fusion, which integrates audio and visual data, has proven to be an effective approach for improving accuracy and robustness in complex scenarios. In this study, we designed an audio-visual data acquisition system that includes a camera for synchronised audio and video capture, along with a custom-developed sound source localisation system that leverages a sound sensor to track sound direction. Specifically, we proposed a novel transformer-based audio-visual multimodal fusion (TMF) framework for recognising fine-grained sow nursing behaviour with or without the calling-to-nurse sound. Initially, a unimodal self-attention enhancement (USE) module was employed to augment video and audio features with global contextual information. Subsequently, we developed an audio-visual interaction enhancement (AVIE) module to compress relevant information and reduce noise using the information bottleneck principle. Moreover, we presented an adaptive dynamic decision fusion strategy to optimise the model's performance by focusing on the most relevant features in each modality. Finally, we comprehensively identified fine-grained nursing behaviours by integrating audio and fused information, while incorporating angle information from the real-time sound source localisation system to accurately determine whether the sound cues originate from the target sow. Our results demonstrate that the proposed method achieves an accuracy of 98.42 % for general sow nursing behaviour and 94.37 % for fine-grained nursing behaviour, including nursing with and without the calling-to-nurse sound, and non-nursing behaviours. This fine-grained nursing information can provide a more nuanced understanding of the sow's health and lactation willingness, thereby enhancing management practices in pig farming.
{"title":"Transformer-based audio-visual multimodal fusion for fine-grained recognition of individual sow nursing behaviour","authors":"Yuqing Yang , Chengguo Xu , Wenhao Hou , Alan G. McElligott , Kai Liu , Yueju Xue","doi":"10.1016/j.aiia.2025.03.006","DOIUrl":"10.1016/j.aiia.2025.03.006","url":null,"abstract":"<div><div>Nursing behaviour and the calling-to-nurse sound are crucial indicators for assessing sow maternal behaviour and nursing status. However, accurately identifying these behaviours for individual sows in complex indoor pig housing is challenging due to factors such as variable lighting, rail obstructions, and interference from other sows' calls. Multimodal fusion, which integrates audio and visual data, has proven to be an effective approach for improving accuracy and robustness in complex scenarios. In this study, we designed an audio-visual data acquisition system that includes a camera for synchronised audio and video capture, along with a custom-developed sound source localisation system that leverages a sound sensor to track sound direction. Specifically, we proposed a novel transformer-based audio-visual multimodal fusion (TMF) framework for recognising fine-grained sow nursing behaviour with or without the calling-to-nurse sound. Initially, a unimodal self-attention enhancement (USE) module was employed to augment video and audio features with global contextual information. Subsequently, we developed an audio-visual interaction enhancement (AVIE) module to compress relevant information and reduce noise using the information bottleneck principle. Moreover, we presented an adaptive dynamic decision fusion strategy to optimise the model's performance by focusing on the most relevant features in each modality. Finally, we comprehensively identified fine-grained nursing behaviours by integrating audio and fused information, while incorporating angle information from the real-time sound source localisation system to accurately determine whether the sound cues originate from the target sow. Our results demonstrate that the proposed method achieves an accuracy of 98.42 % for general sow nursing behaviour and 94.37 % for fine-grained nursing behaviour, including nursing with and without the calling-to-nurse sound, and non-nursing behaviours. This fine-grained nursing information can provide a more nuanced understanding of the sow's health and lactation willingness, thereby enhancing management practices in pig farming.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 363-376"},"PeriodicalIF":8.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-07DOI: 10.1016/j.jnlssr.2025.02.005
Yingjie Du , Ning Ding , Hongyu Lv
Terrorist attacks represent a significant threat to national order, social stability, and economic security. Accurate prediction of such attacks is a critical task for casualty reduction, enhanced decision-making, and optimal resource distribution in counter-terrorism efforts. This paper introduces an innovative spatio-temporal fusion framework that combines graph convolutional network (GCN) with long short-term memory (LSTM) models. By capturing and merging spatio-temporal features from relevant events, the proposed GCN-LSTM model achieves remarkable accuracy in predicting terrorist attacks. The experimental results demonstrate outstanding performance, with the model attaining minimal RMSE and MAE values of 0.037 and 0.031, respectively, surpassing all baseline models (LSTM, GCN, and CNN-LSTM-Transformer). Through its effective interpretation of complex spatio-temporal patterns underlying terrorist attacks, our model substantially enhances the predictive accuracy across diverse time horizons. These findings carry crucial implications for enhancing counter-terrorism strategies.
{"title":"Spatio-temporal prediction of terrorist attacks based on GCN-LSTM","authors":"Yingjie Du , Ning Ding , Hongyu Lv","doi":"10.1016/j.jnlssr.2025.02.005","DOIUrl":"10.1016/j.jnlssr.2025.02.005","url":null,"abstract":"<div><div>Terrorist attacks represent a significant threat to national order, social stability, and economic security. Accurate prediction of such attacks is a critical task for casualty reduction, enhanced decision-making, and optimal resource distribution in counter-terrorism efforts. This paper introduces an innovative spatio-temporal fusion framework that combines graph convolutional network (GCN) with long short-term memory (LSTM) models. By capturing and merging spatio-temporal features from relevant events, the proposed GCN-LSTM model achieves remarkable accuracy in predicting terrorist attacks. The experimental results demonstrate outstanding performance, with the model attaining minimal RMSE and MAE values of 0.037 and 0.031, respectively, surpassing all baseline models (LSTM, GCN, and CNN-LSTM-Transformer). Through its effective interpretation of complex spatio-temporal patterns underlying terrorist attacks, our model substantially enhances the predictive accuracy across diverse time horizons. These findings carry crucial implications for enhancing counter-terrorism strategies.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 2","pages":"Pages 186-195"},"PeriodicalIF":3.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-05DOI: 10.1016/j.aiia.2025.04.001
Josué Kpodo , A. Pouyan Nejadhashemi
Agricultural Extension (AE) research faces significant challenges in producing relevant and practical knowledge due to rapid advancements in artificial intelligence (AI). AE struggles to keep pace with these advancements, complicating the development of actionable information. One major challenge is the absence of intelligent platforms that enable efficient information retrieval and quick decision-making. Investigations have shown a shortage of AI-assisted solutions that effectively use AE materials across various media formats while preserving scientific accuracy and contextual relevance. Although mainstream AI systems can potentially reduce decision-making risks, their usage remains limited. This limitation arises primarily from the lack of standardized datasets and concerns regarding user data privacy. For AE datasets to be standardized, they must satisfy four key criteria: inclusion of critical domain-specific knowledge, expert curation, consistent structure, and acceptance by peers. Addressing data privacy issues involves adhering to open-access principles and enforcing strict data encryption and anonymization standards. To address these gaps, a conceptual framework is introduced. This framework extends beyond typical user-oriented platforms and comprises five core modules. It features a neurosymbolic pipeline integrating large language models with physically based agricultural modeling software, further enhanced by Reinforcement Learning from Human Feedback. Notable aspects of the framework include a dedicated human-in-the-loop process and a governance structure consisting of three primary bodies focused on data standardization, ethics and security, and accountability and transparency. Overall, this work represents a significant advancement in agricultural knowledge systems, potentially transforming how AE services deliver critical information to farmers and other stakeholders.
{"title":"Navigating challenges/opportunities in developing smart agricultural extension platforms: Multi-media data mining techniques","authors":"Josué Kpodo , A. Pouyan Nejadhashemi","doi":"10.1016/j.aiia.2025.04.001","DOIUrl":"10.1016/j.aiia.2025.04.001","url":null,"abstract":"<div><div>Agricultural Extension (AE) research faces significant challenges in producing relevant and practical knowledge due to rapid advancements in artificial intelligence (AI). AE struggles to keep pace with these advancements, complicating the development of actionable information. One major challenge is the absence of intelligent platforms that enable efficient information retrieval and quick decision-making. Investigations have shown a shortage of AI-assisted solutions that effectively use AE materials across various media formats while preserving scientific accuracy and contextual relevance. Although mainstream AI systems can potentially reduce decision-making risks, their usage remains limited. This limitation arises primarily from the lack of standardized datasets and concerns regarding user data privacy. For AE datasets to be standardized, they must satisfy four key criteria: inclusion of critical domain-specific knowledge, expert curation, consistent structure, and acceptance by peers. Addressing data privacy issues involves adhering to open-access principles and enforcing strict data encryption and anonymization standards. To address these gaps, a conceptual framework is introduced. This framework extends beyond typical user-oriented platforms and comprises five core modules. It features a neurosymbolic pipeline integrating large language models with physically based agricultural modeling software, further enhanced by Reinforcement Learning from Human Feedback. Notable aspects of the framework include a dedicated human-in-the-loop process and a governance structure consisting of three primary bodies focused on data standardization, ethics and security, and accountability and transparency. Overall, this work represents a significant advancement in agricultural knowledge systems, potentially transforming how AE services deliver critical information to farmers and other stakeholders.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 426-448"},"PeriodicalIF":8.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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