工业工程最新文献

{"title":"Antibiotic resistance occurrence and ecological impact in landfill leachate: A review on compound effect of antibiotics and non-antibiotics","authors":"Yongtao Xu ,&nbsp;Dan Li ,&nbsp;Ying Yuan ,&nbsp;Fei Fang ,&nbsp;Beidou Xi ,&nbsp;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}

{"title":"Assessing particle application in multi-pass overlapping scenarios with variable rate centrifugal fertilizer spreaders for precision agriculture","authors":"Shi Yinyan,&nbsp;Zhu Yangxu,&nbsp;Wang Xiaochan,&nbsp;Zhang Xiaolei,&nbsp;Zheng Enlai,&nbsp;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}

{"title":"Transformer-based audio-visual multimodal fusion for fine-grained recognition of individual sow nursing behaviour","authors":"Yuqing Yang ,&nbsp;Chengguo Xu ,&nbsp;Wenhao Hou ,&nbsp;Alan G. McElligott ,&nbsp;Kai Liu ,&nbsp;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}

{"title":"Spatio-temporal prediction of terrorist attacks based on GCN-LSTM","authors":"Yingjie Du ,&nbsp;Ning Ding ,&nbsp;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}

{"title":"Navigating challenges/opportunities in developing smart agricultural extension platforms: Multi-media data mining techniques","authors":"Josué Kpodo ,&nbsp;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}

{"title":"Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques","authors":"Rabiu Aminu ,&nbsp;Samantha M. Cook ,&nbsp;David Ljungberg ,&nbsp;Oliver Hensel ,&nbsp;Abozar Nasirahmadi","doi":"10.1016/j.aiia.2025.03.008","DOIUrl":"10.1016/j.aiia.2025.03.008","url":null,"abstract":"&lt;div&gt;&lt;div&gt;To reduce damage caused by insect pests, farmers use insecticides to protect produce from crop pests. This practice leads to high synthetic chemical usage because a large portion of the applied insecticide does not reach its intended target; instead, it may affect non-target organisms and pollute the environment. One approach to mitigating this is through the selective application of insecticides to only those crop plants (or patches of plants) where the insect pests are located, avoiding non-targets and beneficials. The first step to achieve this is the identification of insects on plants and discrimination between pests and beneficial non-targets. However, detecting small-sized individual insects is challenging using image-based machine learning techniques, especially in natural field settings. This paper proposes a method based on explainable artificial intelligence feature selection and machine learning to detect pests and beneficial insects in field crops. An insect-plant dataset reflecting real field conditions was created. It comprises two pest insects—the Colorado potato beetle (CPB, &lt;em&gt;Leptinotarsa decemlineata&lt;/em&gt;) and green peach aphid (&lt;em&gt;Myzus persicae&lt;/em&gt;)—and the beneficial seven-spot ladybird (&lt;em&gt;Coccinella septempunctata&lt;/em&gt;). The specialist herbivore CPB was imaged only on potato plants (&lt;em&gt;Solanum tuberosum&lt;/em&gt;) while green peach aphids and seven-spot ladybirds were imaged on three crops: potato, faba bean (&lt;em&gt;Vicia faba)&lt;/em&gt;, and sugar beet (&lt;em&gt;Beta vulgaris&lt;/em&gt; subsp. &lt;em&gt;vulgaris&lt;/em&gt;). This increased dataset diversity, broadening the potential application of the developed method for discriminating between pests and beneficial insects in several crops. The insects were imaged in both laboratory and outdoor settings. Using the GrabCut algorithm, regions of interest in the image were identified before shape, texture, and colour features were extracted from the segmented regions. The concept of explainable artificial intelligence was adopted by incorporating permutation feature importance ranking and Shapley Additive explanations values to identify the feature set that optimized a model's performance while reducing computational complexity. The proposed explainable artificial intelligence feature selection method was compared to conventional feature selection techniques, including mutual information, chi-square coefficient, maximal information coefficient, Fisher separation criterion and variance thresholding. Results showed improved accuracy (92.62 % Random forest, 90.16 % Support vector machine, 83.61 % K-nearest neighbours, and 81.97 % Naïve Bayes) and a reduction in the number of model parameters and memory usage (7.22 &lt;em&gt;×&lt;/em&gt; 10&lt;sup&gt;7&lt;/sup&gt; Random forest, 6.23 &lt;em&gt;×&lt;/em&gt; 10&lt;sup&gt;3&lt;/sup&gt; Support vector machine, 3.64 &lt;em&gt;×&lt;/em&gt; 10&lt;sup&gt;4&lt;/sup&gt; K-nearest neighbours and 1.88 &lt;em&gt;×&lt;/em&gt; 10&lt;sup&gt;2&lt;/sup&gt; Naïve Bayes) compared to using all features. Prediction and training times were also reduced by approxima","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 3","pages":"Pages 377-394"},"PeriodicalIF":8.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835178","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":"Influence of anti-androgen cyproterone acetate on sperm characteristics, testosterone level, testicular and liver histology in male goldfish (Carassius auratus)","authors":"Hamed Abdollahpour ,&nbsp;Milad Karimzadeh ,&nbsp;Naghmeh Jafari Pastaki ,&nbsp;Hosseinali Zamani","doi":"10.1016/j.emcon.2025.100507","DOIUrl":"10.1016/j.emcon.2025.100507","url":null,"abstract":"<div><div>Cyproterone acetate (CA), a synthetic anti-androgen, was injected intraperitoneally into fish at two doses (5 and 50 mg/kg body weight) to evaluate its effects on physiological, biochemical, and reproductive parameters. A total of 180 fish were divided into three groups: Control (C), low-dose (LCA), and high-dose (HCA), with two replicates per group. Fish were observed daily for behavior, and water quality was controlled throughout the study. After 28-days of exposure, blood samples were collected to analyze plasma biochemical parameters. Sperm motility, concentration, and histological analyses of testes and liver tissues were also conducted. Results showed a significant dose-dependent decrease in cholesterol, triglycerides, and testosterone levels in the CA-treated groups, with the highest reduction observed in the HCA group (P &lt; 0.05). Sperm motility was significantly reduced in the low-dose (66.1 ± 2.2 %) and high-dose (36.5 ± 3.9 %) groups compared to the control group (79.5 ± 2.2 %). Similarly, sperm concentration and motility time were significantly reduced in the CA-treated groups (P &lt; 0.05). The gonadosomatic index (GSI) also decreased significantly in the treated groups, with the HCA group showing the lowest GSI (P &lt; 0.05), indicating impaired gonadal development. Histological analysis of the testes revealed delayed spermatogenesis, with the HCA group predominantly showing immature gonadal stages (Stage II), and a significant reduction in spermatocytes and spermatozoa. Liver histology indicated necrosis, cell death, blood congestion, and vacuolization in both the LCA and HCA groups, particularly at the high dose (P &lt; 0.05), suggesting hepatotoxicity. In conclusion, CA exerted significant endocrine-disrupting effects in fish, impairing sperm motility, gonadal development, and liver function in a dose-dependent manner. The high-dose treatment caused pronounced effects, including hepatocellular injury and a severe decline in reproductive health. These findings highlight the potential risks of cyproterone acetate as an endocrine disruptor and emphasize the importance of its effects on aquatic species, particularly in ecotoxicological assessments.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 3","pages":"Article 100507"},"PeriodicalIF":5.3,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806784","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}

{"title":"Microplastics in settled indoor dust: Implications for human exposure","authors":"Hassan Khalid Ageel,&nbsp;Stuart Harrad,&nbsp;Mohamed Abou-Elwafa Abdallah","doi":"10.1016/j.emcon.2025.100506","DOIUrl":"10.1016/j.emcon.2025.100506","url":null,"abstract":"<div><div>In recent years, microplastics (MPs) occurrence in indoor environments has raised significant concerns due to their distinctive physicochemical properties and potential toxicity. Indoor dust is well-documented as important vector for human exposure to emerging contaminants via inadvertent ingestion. Consequently, the main aim of the present study is to assess human exposure to MPs in settled indoor dust by determining concentrations, and characteristics (size, shape, and polymer type) of MPs in dust samples collected from houses (n = 30) and workplaces (n = 30) in Birmingham, UK. The average MPs concentrations were 155 ± 222 MP/mg in homes, and 125 ± 209 MP/mg in workplaces. Statistical analysis revealed the mean concentration of MPs in the studied homes was significantly higher than that in workplaces, which was attributed mainly to carpeting. In terms of morphology, various shapes of MPs (e.g., fiber, fragment, foam, foil) were identified, with fibres and fragments constituting ∼90 % of the identified MPs in all studied samples. Within the MPs particle size range (10–150 μm) investigated in the current study, the particle size fraction (50–100 μm) was predominant (&gt;49 %) in all the studied samples, followed by particles &gt;100 μm, with fibres having the largest average size (67 μm) among the detected MPs shapes. Polypropylene (PP) and Polyethylene Terephthalate (PET) were the most abundant polymer types in the studied samples, followed by Polyvinyl Chloride (PVC). Typical daily exposures of UK adults and toddlers to MPs through inadvertent ingestion of indoor dust were estimated at 56 and 103 MPs/day. The higher exposure of toddlers was exacerbated by their lower body weight with an estimated typical exposure of 9.7 MP/kg Bw/day, compared to 0.8 MP/kg Bw/day for adults. The higher ingestion exposure to MPs in toddlers raises concern over potential adverse health effects due to their incompletely developed immune and nervous systems.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 3","pages":"Article 100506"},"PeriodicalIF":5.3,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806857","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}

{"title":"Enzyme–and GSH–responsive gelatin coated magnetic multi-shell hollow mesoporous organosilicon nanoparticles for avermectin controlled release","authors":"Jiazhen Gao,&nbsp;Pengyu Luo,&nbsp;Siqiang Shen,&nbsp;Ying Liu,&nbsp;Xiaoyun Li,&nbsp;Xiaoying Wang","doi":"10.1186/s42825-025-00191-z","DOIUrl":"10.1186/s42825-025-00191-z","url":null,"abstract":"<div><p>To enhance the utilization of pesticides and reduce environmental risks, we constructed the magnetic recyclable and dual stimulus-responsive microspheres to achieve on-demand pesticide release. Magnetic multi-shell hollow mesoporous organosilicon nanoparticles (mMSN) were prepared by one-step hydrothermal method and loaded with pesticide avermectin (A@mMSN), afterward A@mMSN was coated with gelatin through emulsification and chemical cross-linking to prepare A@mMSN@G microspheres (21.5 ± 9.7 μm). After being absorbed by the pests, the gelatin layer was hydrolyzed with the neutral protease, and the disulfide bonds within mMSN framework were decomposed by glutathione (GSH), endowing A@mMSN@G microspheres with enzyme and GSH responsiveness to achieve sustained avermectin release till 7 days (about 3.5 times that of the commercial avermectin emulsion). Importantly, the A@mMSN@G microspheres containing Fe₃O₄ nanoparticles could be easily magnetically collected from soil with a recovery ratio of 63.7%, to reduce the environmental risks. With excellent biosafety, A@mMSN@G microspheres showed outstanding pest control effects till two weeks and the growth of cabbage was not affected by it. Therefore, based on the recyclability and dual stimulus-responsive controllable release, the fabricated A@mMSN@G microspheres have broad application potential in pesticide delivery.</p><h3>Graphical abstract</h3><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":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://JLSE.SpringerOpen.com/counter/pdf/10.1186/s42825-025-00191-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761665","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":"Research Progress in Effect of Repetitive Noxious Stimuli in Neonatal Period on Neural Development.","authors":"Yan Li, Wen-Yu Zhang, Zhi Xiao, Xing-Feng Liu","doi":"10.3881/j.issn.1000-503X.16266","DOIUrl":"https://doi.org/10.3881/j.issn.1000-503X.16266","url":null,"abstract":"<p><p>The establishment and development of neonatal intensive care unit(NICU)have significantly increased the survival rate of premature infants.However,the diagnosis,treatment,and surgeries performed in NICU may expose neonates to more noxious stimuli.As the neonatal period is crucial for brain development,these noxious stimuli may cause irreversible damage to the neonatal nervous system.Existing clinical studies have shown that repetitive noxious stimuli during the neonatal period can lead to poor brain development,persistent hyperalgesia,and various sequelae.However,the underlying mechanisms remain unclear,and effective treatment methods are lacking.This article summarizes the effects of repetitive noxious stimuli during the neonatal period on neural development and the complications,aiming to provide a basis for the neonatal analgesia management and the prevention and treatment of related sequelae.</p>","PeriodicalId":6919,"journal":{"name":"中国医学科学院学报","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770998","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}