工业工程最新文献

{"title":"[Prediction method of paroxysmal atrial fibrillation based on multimodal feature fusion].","authors":"Yongjian Li, Lei Liu, Meng Chen, Yixue Li, Yuchen Wang, Shoushui Wei","doi":"10.7507/1001-5515.202403039","DOIUrl":"https://doi.org/10.7507/1001-5515.202403039","url":null,"abstract":"<p><p>The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"42 1","pages":"42-48"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504749","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}

{"title":"Targeted and non-targeted LC-MS analysis of microcystins in Clarias gariepinus from fishponds","authors":"Odo J. Bassey ,&nbsp;Munyaradzi Mujuru ,&nbsp;Mulalo I. Mutoti ,&nbsp;Adeeyo Adeyemi ,&nbsp;Farai Dondofema ,&nbsp;Jabulani Ray Gumbo","doi":"10.1016/j.emcon.2025.100484","DOIUrl":"10.1016/j.emcon.2025.100484","url":null,"abstract":"<div><div>Cyanotoxins produced by cyanobacteria are formidable threats to aquatic ecosystems and public health worldwide. The potential health risks associated with cyanotoxins from contaminated fishponds are becoming a growing concern, as cyanotoxin production has steadily increased over time in these aquatic environments. Therefore, this study aims to utilize targeted and non-targeted Liquid Chromatography Mass Spectrometer (LC-MS) analytical methods to detect cyanotoxins in catfish (<em>Clarias gariepinus</em>) tissue harvested from fishponds. For detecting cyanotoxins in fish tissue utilizing the non-targeted approach, high-resolution MS/MS spectra data obtained from the analysis were converted to mzML format, analyzed with the Global Natural Product Social (GNPS) Library and CANOPUS annotations for LEVEL 3 metabolite identification, and visualized as a molecular network in Cytoscape. Regarding the targeted method, the toxin identification and quantification were achieved by comparing samples spiked with known concentrations of MC-RR and YR to an authentic toxin standard. The results of the target analysis showed that microcystin variant MC-RR was not detected in the fish tissue. The MC-YR variant was detected in the intestines and gills of <em>Clarias gariepinus</em> at concentrations of 13.2–10.6 μg/g and 1.5–13.9 μg/g, respectively. The muscle tissues across all fish ponds showed MC-YR concentrations between 10.5 and 16.06 μg/g. The highest concentration of MC-YR was found in the liver tissue in pond 6 (20.9 μg/g). The untargeted LC-MS method led to the identification of a larger number of cyanometabolites in the fish tissue, such as aeruginosins, anabaenopeptins, microginins. Non-toxic secondary metabolites like octadecadienoic acid, while phosphocholine (PC), ethanesulfonic acid, pheophorbide A, microcolins, cholic acid, phenylalanine, amyl amine and phosphocholine (PC), triglyceride (TG), phosphocholine (PC) and sulfonic acid derieved from cyanobacteria, fish and anthropogenic sources were also detected in the fish tissues. The non-targeted analysis facilitates the identification of both unexpected and unknown compounds.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 2","pages":"Article 100484"},"PeriodicalIF":5.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure assessment of 113 exogenous chemicals simultaneously in serum samples from children in north Shandong, China, and their association with sex, age, and body mass index","authors":"Fenghong Zhang ,&nbsp;Danyang Wang ,&nbsp;Mengxue Zhi,&nbsp;Jianshe Wang","doi":"10.1016/j.emcon.2025.100483","DOIUrl":"10.1016/j.emcon.2025.100483","url":null,"abstract":"<div><div>Children are particularly vulnerable to adverse effects from exposure to environmental chemicals, necessitating comprehensive assessment to mitigate health risks. In this study, we analyzed 477 serum samples from children aged 2–6 years in North Shandong, China, using liquid chromatography-mass spectrometry (LC-MS) to create an exposure profile of 184 exogenous chemicals. These chemicals encompass pesticides, pharmaceuticals, industrial and consumer chemicals, and food additives. Of these, 113 exogenous chemicals were identified above the limit of detection in the serum of at least one child participant, and 37 were detected in more than 30 % of the children. Notably, 17 of the 24 selected perfluoroalkyl and polyfluoroalkyl substances (PFAS) were detected. PFOA, PFBA, and PFHxS exhibited the highest concentrations, with geometric means of 38.11 ng/mL, 17.39 ng/mL, and 7.35 ng/mL, respectively. The elevated levels of short-chain PFBA suggests increased production and environmental release in recent years. Analysis of sex-based differences revealed significant differences in the serum levels of 11 chemicals, with nine compounds displaying higher concentrations in girls than in boys. Notably, long-chain PFAS, including PFUnDA, PFDA, and PFTrDA, were present at higher concentrations in girls, while short-chain PFHpA and PFBS were higher in boys. Additionally, serum levels of diphenyl phosphate and fipronil sulfone declined slightly with age, indicating heightened exposure risk during early childhood. Positive associations between monoethyl phthalate and fipronil sulfone concentrations with BMI categories were observed, suggesting a potential obesogenic effect of these compounds. This study provides critical insights into the profiles of exogenous chemicals in young children and highlights the need for targeted risk assessment of environmental pollutants impacting pediatric health.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 2","pages":"Article 100483"},"PeriodicalIF":5.3,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-dimensional assessment of heavy metal contamination in soil affected by urbanization at the urban-rural interface of Chengdu","authors":"Rong Liao ,&nbsp;Zeming Shi ,&nbsp;Ke Cheng ,&nbsp;Na Zhang ,&nbsp;Ge Jin ,&nbsp;Dewei Wang ,&nbsp;Kun Lin ,&nbsp;Lvhang Yang ,&nbsp;Kailiang Zhang ,&nbsp;Junji Zhang","doi":"10.1016/j.emcon.2025.100482","DOIUrl":"10.1016/j.emcon.2025.100482","url":null,"abstract":"<div><div>Urbanization, particularly the transformation of agricultural land into urban areas, significantly impacts soil quality, especially concerning the concentration and distribution of heavy metals. This study investigates the spatial distribution of six heavy metals (As, Cd, Cr, Pb, Ni, and Zn) in soils at the urban-rural interface of Chengdu, focusing on areas transitioning from agricultural land to urbanized land and back to agricultural land. The study analyzes the three-dimensional spatial distribution of heavy metals and assesses the impact of urbanization on soil contamination. It was found that the top 1-m soil layer exhibited higher concentrations of heavy metals compared to the bedrock and deeper soil layers. A combination of geochemical assessments, including the Ratio of Secondary Phase to Primary Phase (RSP) and the Risk Assessment Code (RAC), was used to evaluate the ecological risks posed by these metals. The findings indicate that Cd is the most hazardous contaminant, with contamination levels being particularly high in older urban areas (N2, Shuangliu) and at river confluences (N6, Huayang). Soils in the old urban area not only had a higher total amount of heavy metals but also had a higher proportion of its heavy metal exchangeable form. These areas face significant ecological risks, and the study suggests that targeted soil remediation strategies should be developed, with a focus on urban soil reclamation and risk mitigation.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 2","pages":"Article 100482"},"PeriodicalIF":5.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toxicity due to release of microplastic fibres from disposable face masks on marine diatom Chaetoceros sp. and the role of EPS in combating the toxic effects","authors":"B.Y. Neethudas ,&nbsp;Camil Rex M. ,&nbsp;P.K. Suresh ,&nbsp;Amitava Mukherjee","doi":"10.1016/j.emcon.2025.100481","DOIUrl":"10.1016/j.emcon.2025.100481","url":null,"abstract":"<div><div>Due to the COVID-19 pandemic, disposable face masks have become a significant source of microplastic pollution in marine ecosystems. Diatoms, as primary producers are often used as model organism for aquatic toxicity assessments. Only a limited number of studies have examined the toxicity of mask leachate (ML) on diatoms. However, the toxicity mechanism of ML released at different time intervals is underexplored. Furthermore, the role of extracellular polymeric substances (EPS) in modulating ML toxicity is also poorly understood. To address these gaps, we investigated the toxicity of ML from three time intervals (1-day, 14-day, and 21-day) on the marine diatom <em>Chaetoceros</em> sp., finding that toxicity increased with time: 21-day ML &gt; 14-day ML &gt; 1-day ML. To assess the toxicity, we have estimated chlorophyll pigment levels, reactive oxygen species, and malondialdehyde levels. Furthermore, the presence of heavy metals in the ML was analyzed using Inductively Coupled Plasma Mass Spectrometry. Our results suggest that increased ROS production is a crucial mechanism of toxicity, while EPS reduces toxic effects compared to pristine ML. The interaction of EPS with ML was analyzed using Fourier-Transform Infrared Spectroscopy and 3D-Excitation Emission Matrix spectroscopy. Pearson correlation and heatmap were used to assess the correlations between toxicity endpoints. This study provides critical insights into the environmental impact of ML on marine diatoms and highlights the role of EPS in mitigating ML toxicity.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 2","pages":"Article 100481"},"PeriodicalIF":5.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based classification, detection, and segmentation of tomato leaf diseases: A state-of-the-art review","authors":"Aritra Das ,&nbsp;Fahad Pathan ,&nbsp;Jamin Rahman Jim ,&nbsp;Md Mohsin Kabir ,&nbsp;M.F. Mridha","doi":"10.1016/j.aiia.2025.02.006","DOIUrl":"10.1016/j.aiia.2025.02.006","url":null,"abstract":"<div><div>The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity, efficiency and quality. Misdiagnosis by the farmers poses the risk of inadequate treatments, harming both tomato plants and agroecosystems. Precision of disease diagnosis is essential, necessitating a swift and accurate response to misdiagnosis for early identification. Tropical regions are ideal for tomato plants, but there are inherent concerns, such as weather-related problems. Plant diseases largely cause financial losses in crop production. The slow detection periods of conventional approaches are insufficient for the timely detection of tomato diseases. Deep learning has emerged as a promising avenue for early disease identification. This study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and weaknesses. The study delves into various diagnostic procedures, including image pre-processing, localization and segmentation. In conclusion, applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 192-220"},"PeriodicalIF":8.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520260","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}

{"title":"Using UAV-based multispectral images and CGS-YOLO algorithm to distinguish maize seeding from weed","authors":"Boyi Tang ,&nbsp;Jingping Zhou ,&nbsp;Chunjiang Zhao ,&nbsp;Yuchun Pan ,&nbsp;Yao Lu ,&nbsp;Chang Liu ,&nbsp;Kai Ma ,&nbsp;Xuguang Sun ,&nbsp;Ruifang Zhang ,&nbsp;Xiaohe Gu","doi":"10.1016/j.aiia.2025.02.007","DOIUrl":"10.1016/j.aiia.2025.02.007","url":null,"abstract":"<div><div>Accurate recognition of maize seedlings on the plot scale under the disturbance of weeds is crucial for early seedling replenishment and weed removal. Currently, UAV-based maize seedling recognition depends primarily on RGB images. The main purpose of this study is to compare the performances of multispectral images and RGB images of unmanned aerial vehicle (UAV) on maize seeding recognition using deep learning algorithms. Additionally, we aim to assess the disturbance of different weed coverage on the recognition of maize seeding. Firstly, principal component analysis was used in multispectral image transformation. Secondly, by introducing the CARAFE sampling operator and a small target detection layer (SLAY), we extracted the contextual information of each pixel to retain weak features in the maize seedling image. Thirdly, the global attention mechanism (GAM) was employed to capture the features of maize seedlings using the dual attention mechanism of spatial and channel information. The CGS-YOLO algorithm was constructed and formed. Finally, we compared the performance of the improved algorithm with a series of deep learning algorithms, including YOLO v3, v5, v6 and v8. The results show that after PCA transformation, the recognition mAP of maize seedlings reaches 82.6 %, representing 3.1 percentage points improvement compared to RGB images. Compared with YOLOv8, YOLOv6, YOLOv5, and YOLOv3, the CGS-YOLO algorithm has improved mAP by 3.8, 4.2, 4.5 and 6.6 percentage points, respectively. With the increase of weed coverage, the recognition effect of maize seedlings gradually decreased. When weed coverage was more than 70 %, the mAP difference becomes significant, but CGS-YOLO still maintains a recognition mAP of 72 %. Therefore, in maize seedings recognition, UAV-based multispectral images perform better than RGB images. The application of CGS-YOLO deep learning algorithm with UAV multi-spectral images proves beneficial in the recognition of maize seedlings under weed disturbance.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 162-181"},"PeriodicalIF":8.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512498","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}

{"title":"The deep detoxification of tetrabromobisphenol a in a hybrid reactor system: Iron-based trimetallic and aerobic activated sludge methods","authors":"Chaoyi Zhou ,&nbsp;Weilong Xing ,&nbsp;Zhen Wang ,&nbsp;Wen Gu ,&nbsp;Fenglin Li ,&nbsp;Mengyuan Liang ,&nbsp;Shuai Sun ,&nbsp;Deling Fan ,&nbsp;Lei Wang","doi":"10.1016/j.emcon.2025.100480","DOIUrl":"10.1016/j.emcon.2025.100480","url":null,"abstract":"<div><div>Tetrabromobisphenol A (TBBPA) is prevalent in various environmental media and biological matrices, posing considerable ecological and health risks due to its endocrine-disrupting, immunotoxic, neurotoxic, and carcinogenic properties. In this study, we developed an advanced mineralization process for the efficient mineralization of TBBPA, utilizing a hybrid system that combines zero-valent iron (ZVI) technology with an aerobic activated sludge method. A trimetallic material, s-Fe⁰-Cu-Pd, was synthesized by stepwise deposition of copper and palladium onto ZVI to improve its catalytic efficiency in degrading TBBPA. Optimal conditions for TBBPA degradation, including Cu and Pd loading ratios, initial pH, trimetallic dosage, and TBBPA concentration, were systematically investigated. The s-Fe⁰-Cu-Pd catalyst demonstrated superior performance compared to conventional ZVI and bimetallic systems, achieving 97.93 % degradation of TBBPA within 60 min, with BPA identified as the primary degradation product. Subsequent aerobic activated sludge treatment facilitated the complete degradation of intermediate products, achieving a BPA degradation rate of 100 % within 10 h. Electrochemical analyses (CV, EIS, and LSV) and DFT calculations demonstrated enhanced redox activity and electron transfer efficiency of the s-Fe⁰-Cu-Pd. Comprehensive characterization (SEM, XRD, XPS, ESR) and macrogenomic analysis were employed to elucidate the chemical and biological degradation mechanisms and to propose potential degradation pathways. This study represents the first integration of ZVI-based trimetallic catalysts with aerobic activated sludge to enhance TBBPA degradation efficiency, offering a sustainable solution for mitigating the ecological risks associated with TBBPA contamination.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 2","pages":"Article 100480"},"PeriodicalIF":5.3,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thanks to our academic editors and peer reviewers","authors":"","doi":"10.1016/S1674-2370(25)00012-2","DOIUrl":"10.1016/S1674-2370(25)00012-2","url":null,"abstract":"","PeriodicalId":23628,"journal":{"name":"Water science and engineering","volume":"18 1","pages":"Page I"},"PeriodicalIF":3.7,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419759","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}

{"title":"Topological data analysis with digital microscope leather images for animal species classification","authors":"Takuya Ehiro,&nbsp;Takeshi Onji","doi":"10.1186/s42825-024-00187-1","DOIUrl":"10.1186/s42825-024-00187-1","url":null,"abstract":"<div><p>This study presents a method for classifying cow and horse leather using a small number of digital microscope images and topological data analysis. In this method, hair pore coordinates in the images are used as essential information for classification. First, the coordinates were semiautomatically extracted using conventional image processing methods and persistent homology (PH) computation. Binary images with white pixels corresponding to the coordinates were generated, and their PHs were computed using filtration based on the Manhattan distance. In addition to the pairwise distance between the two pores, zeroth- and first-order lifetimes were used as explanatory variables to construct the classifier. Among the three explanatory variables, the zeroth-order lifetime resulted in the highest classification accuracy (86%) for the test data. Furthermore, we constructed logistic regression (LR) and random forest (RF) models using the zeroth-order lifetime computed from all images and conducted model interpretation. In both LR and RF, information on a zeroth-order lifetime of less than 10 was used as an important explanatory variable. Additionally, the inverse analysis of birth–death pairs suggested that the zeroth-order lifetime contains topological information distinct from the conventional pairwise distance. Our proposed method is designed to be robust in data-limited situations because it only uses hair pore coordinates as explanatory variables and does not require other information, such as hair pore density or pore size. This study demonstrates that accurate classifiers can be obtained using topological features related to hair pore arrangement.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":640,"journal":{"name":"Journal of Leather Science and Engineering","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://JLSE.SpringerOpen.com/counter/pdf/10.1186/s42825-024-00187-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404136","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}