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Process analysis of nuclear hydrogen production via intermediate temperature SOEC electrolysis 认领 引用 被引量：1
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作者 Qing Shao Yue Lu +7 位作者 Dun Jin Ling-Hong Luo Xiu-Lin Wang Hui-Chao Yao Ruo-Yun Dai Cheng-Zhi Guan Guo-Ping Xiao Jian-Qiang Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第2期122-133,共12页
When the operating temperature of a solid oxide electrolysis cell(SOEC)is lower than the outlet temperature of a nuclear reactor,the reactor can be directly coupled with the SOEC as a high-temperature heat source.Howe... When the operating temperature of a solid oxide electrolysis cell(SOEC)is lower than the outlet temperature of a nuclear reactor,the reactor can be directly coupled with the SOEC as a high-temperature heat source.However,the key to the efficiency and return on investment of this hybrid energy system lies in the expected lifetime of the SOEC.This study assessed Ni-YSZ|YSZ|GDC|LSC fuel electrode support cells’long-term stability during electrolysis at 650℃with a current density of−0.5Acm−2over 1818 h.The average voltage degradation rate of 2.63%kh−1unfolded in two phases:an initial rapid decay(90 to 1120 h at 3.58%kh−1)and a stable decay(1120 to 1818 h at 2.14%kh−1),emphasizing SOECs’probability coupling with nuclear reactors at 650℃.Post-1818-hour electrolysis revealed nickel particle formation associated with Ni(OH)xdiffusion and re-deposition,alongside a strontium-containing layer causing interface cracking.Despite minimal strontium segregation in the EDS,XPS data indicated surface segregation of Sr.This study provides crucial insights into prolonged SOEC operation,highlighting both its potential and challenges. 展开更多
关键词 Nuclear hydrogen production SOEC Stability Intermediate temperature
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Does digital literacy promote climate disaster-adaptive production behaviors among grain-producing smallholders in China? 认领 引用 被引量：1
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作者 Qingyun Bai Jiajia Li +3 位作者 Jian Zhang Dungang Zang Kuan Zhang Qianling Shen 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第6期2214-2228,共15页
Climate disasters lead to substantial economic losses and grain yield losses,emphasizing the need for adaptation to ensure food security.As digital technologies advance,it is imperative to investigate how digital lite... Climate disasters lead to substantial economic losses and grain yield losses,emphasizing the need for adaptation to ensure food security.As digital technologies advance,it is imperative to investigate how digital literacy among grain farmers affects their adaptive production behaviors in the face of climate disasters.Drawing on survey data from 505 grain-producing smallholders in Sichuan Province,China,this study constructs a theoretical framework linking digital literacy,climate disaster risk perception,and adaptive production behaviors.Empirical analysis shows that digital literacy positively impacts the adaptive production behaviors of grain-producing smallholders.Our results are robust across various models and tests.An analysis of the mediation mechanism reveals that digital literacy contributes to climate disaster-adaptive production behaviors by improving the awareness of climate disaster risks.Heterogeneity analysis shows that the positive impact of digital literacy is more pronounced for smallholders that receive internet skills training and climate information services,and this impact intensifies as the level of agricultural infrastructure improves.The findings suggest that digital literacy plays a key role in reducing production risks,thereby contributing to increased sustainable agricultural development among smallholders. 展开更多
关键词 digital literacy climate disaster-adaptive production behaviors grain-producing smallholders China
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Screening of tyrosine phosphatase SHP2(PTPN11)inhibitors from natural products with therapeutic potential for receptor tyrosine kinase-driven cancer 认领 引用 被引量：1
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作者 Lingfeng Chen Di Ke +4 位作者 Zheng Jiang Ruixiang Luo Jie Li Lulu Zheng Guang Liang 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2026年第2期488-499,共12页
Src homology 2 domain-containing phosphatase 2(SHP2)is a pivotal regulator linking receptor tyrosine kinase(RTK)signaling.Abnormal SHP2 activity has been associated with tumorigenesis and metastasis.Although some SHP2... Src homology 2 domain-containing phosphatase 2(SHP2)is a pivotal regulator linking receptor tyrosine kinase(RTK)signaling.Abnormal SHP2 activity has been associated with tumorigenesis and metastasis.Although some SHP2-targeting modulators have entered clinical trials,U.S.Food and Drug Administraction(FDA)-approved SHP2 targeting drugs are still not available.Herein,we describe cooperative biochemical inhibition experiments that facilitate the identification of both catalytic and allosteric SHP2 inhibitors using an in-house natural product(NP)library.Based on this screening methodology,structurally diverse sets of NPs were characterized,among which dihydrotanshinone I(DHT)potently inhibited the wild-type SHP2 protein tyrosine phosphatase(PTP)domain and gain-of-function SHP2 variants.Trichostatin A(TSA)bound to the“tunnel”binding site,acting as an allosteric inhibitor.This study illustrates an optimized screening methodology and tactics to identify novel SHP2 modulators from NPs and provides a foundation for further NP-based drug development for the treatment of RTK-driven cancer. 展开更多
关键词 SHP2 High-throughput screening Lead compound Natural products Allosteric inhibitor
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Modulation of mitochondrial dysfunction:Mechanisms and strategies for the use of natural products to treat stroke 认领 引用 被引量：3
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作者 Na Qin Rujuan Liu +3 位作者 Rong Deng Liuliu Shi Lei Wang Ting Zhu 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第6期2215-2226,共12页
Modulations of mitochondrial dysfunction,which involve a series of dynamic processes such as mitochondrial biogenesis,mitochondrial fusion and fission,mitochondrial transport,mitochondrial autophagy,mitochondrial apop... Modulations of mitochondrial dysfunction,which involve a series of dynamic processes such as mitochondrial biogenesis,mitochondrial fusion and fission,mitochondrial transport,mitochondrial autophagy,mitochondrial apoptosis,and oxidative stress,play an important role in the onset and progression of stroke.With a better understanding of the critical role of mitochondrial dysfunction modulations in post-stroke neurological injury,these modulations have emerged as a potential target for stroke prevention and treatment.Additionally,since effective treatments for stroke are extremely limited and natural products currently offer some outstanding advantages,we focused on the findings and mechanisms of action related to the use of natural products for targeting mitochondrial dysfunction in the treatment of stroke.Natural products achieve neuroprotective through multi-target regulation of mitochondrial dysfunction encompassing the following processes:(1)Mitochondrial biogenesis:Cordyceps and hydroxysafflor yellow A activate the peroxisome proliferator-activated receptor gamma coactivator 1-alphauclear respiratory factor pathway,promote mitochondrial DNA replication and respiratory chain protein synthesis,and thereby restore energy supply in the ischemic penumbra.(2)Mitochondrial dynamics balance:Ginsenoside Rb3 promotes Opa1-mediated neural stem cell migration and diffusion for recovery of damaged brain tissue.(3)Mitochondrial autophagy:Gypenoside XVII selectively eliminates damaged mitochondria via the phosphatase and tensin homolog-induced kinase 1/Parkin pathway and blocks reactive oxygen species and the NOD-like receptor protein 3 inflammasome cascade,thereby alleviating blood-brain barrier damage.(4)Anti-apoptotic mechanisms:Ginkgolide K inhibits Bax mitochondrial translocation and downregulates caspase-3/9 activity,reducing neuronal programmed death induced by ischemia-reperfusion.(5)Oxidative stress regulation:Scutellarin exerts antioxidant properties and improves neurological function by modulating the extracellular signal-regulated kinase 5-Kruppel-like factor 2-endothelial nitric oxide synthase signaling pathway.(6)Intercellular mitochondrial transport:Neuroprotective effects of Chrysophanol are associated with accelerated mitochondrial transfer from astrocytes to neurons.Existing studies have confirmed that natural products exhibit neuroprotective effects through multidimensional interventions targeting mitochondrial dysfunction in both ischemic and hemorrhagic stroke models.However,their clinical translation still faces challenges,such as the difficulty in standardization due to component complexity,insufficient cross-regional clinical data,and the lack of long-term safety evaluations.Future research should aim to integrate new technologies,such as single-cell sequencing and organoid models,to deeply explore the mitochondria-targeting mechanisms of natural products and validate their efficacy through multicenter clinical trials,providing theoretical support and translational pathways for the development of novel anti-stroke drugs. 展开更多
关键词 apoptosis autophagy hemorrhagic stroke ischemic stroke mitochondrial biogenesis mitochondrial dynamics mitochondrial dysfunction modulations mitochondrial transport natural products oxidative stress
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Harnessing CRISPR-Cas9 for Lactobacillus improvement in silage production:current knowledge and future perspectives 认领 引用 被引量：1
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作者 Jing Ma Jiao Zhang Xusheng Guo 《Journal of Animal Science and Biotechnology》 SCIE CAS CSCD 2026年第1期127-146,共20页
High-quality silage is the cornerstone to sustainable livestock development and animal food production.As the core fermentation bacteria of silage,Lactobacillus directly regulates silage fermentation by producing lact... High-quality silage is the cornerstone to sustainable livestock development and animal food production.As the core fermentation bacteria of silage,Lactobacillus directly regulates silage fermentation by producing lactic acid,enzymes,and other bioactive molecules.However,traditional screening methods for functional strains are labor-intensive and time-consuming.Recent advances in synthetic biology,particularly the development of CRISPR-Cas genome editing technology,offer a revolutionary approach to designing Lactobacillus strains with customized traits.This review systematically reviewed the importance of silage in sustainable agricultural development and the limitations of current silage preparation and promotion.It also discussed the application of strain engineering approaches in optimizing the phenotypic performance of Lactobacillus for better silage.Building on this,we reviewed the research progress of CRISPR-Cas9 gene editing in Lactobacillus and discussed how to leverage its high efficiency and precision to optimize the strain's traits for improved silage quality and functionality.CRISPR-Cas9 toolkits are expected to achieve directed evolution of strain performance,ultimately yielding next-generation silage microbial inoculants with multiple functions,adaptability to multiple substrates,and eco-friendly characteristics.The use of such innovative biotechnologies would facilitate resource-efficient utilization,promote animal performance and health for sustainable development in livestock production. 展开更多
关键词 Gene editing Lactobacillus engineering Silage Sustainable livestock production
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Learning Laws for Deep Convolutional Neural Networks With Guaranteed Convergence 认领 引用 被引量：1
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作者 Sitan Li Chien Chern Cheah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期170-185,共16页
Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empir... Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empirical achievements.In this paper,the first filter learning framework with convergence-guaranteed learning laws for end-to-end learning of deep CNNs is proposed.Novel update laws with convergence analysis are formulated based on the mathematical representation of each layer in convolutional neural networks.The proposed learning laws enable concurrent updates of weights across all layers of the deep convolutional neural network and the analysis shows that the training errors converge to certain bounds which are dependent on the approximation errors.Case studies are conducted on benchmark datasets and the results show that the proposed concurrent filter learning framework guarantees the convergence and offers more consistent and reliable results during training with a trade-off in performance compared to stochastic gradient descent methods.This framework represents a significant step towards enhancing the reliability and effectiveness of deep convolutional neural network by developing a theoretical analysis which allows practical implementation of the learning laws with automatic tuning of the learning rate to guarantee the convergence during training. 展开更多
关键词 Convergence convolution neural networks(CNNs) end-to-end learning online learning
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Inhibitory mechanism of CMP-LSOPC nanoparticles on advanced glycation end products release during digestion 认领 引用 被引量：1
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作者 Wenjun Li Mengyao Niu +7 位作者 Chenxu Bao Zihao Gong Xiaozhi Ming Haiyin Zheng Ping Wu Cui Chengc Wei Li Qian Wu 《Food Science and Human Wellness》 SCIE CAS CSCD 2026年第5期2229-2243,共15页
Advanced glycation end products(AGEs)are harmful molecules formed through non-enzymatic reactions between proteins,lipids,and reducing sugars,contributing to diseases such as diabetes,Alzheimer's disease,and cardi... Advanced glycation end products(AGEs)are harmful molecules formed through non-enzymatic reactions between proteins,lipids,and reducing sugars,contributing to diseases such as diabetes,Alzheimer's disease,and cardiovascular conditions.This study investigates the inhibitory mechanisms of CMP-LSOPC nanoparticles(NPs)on AGEs release during gastrointestinal digestion.CMP-LSOPC NPs were synthesized by complexing carboxymethyl pachymaran(CMP)with lotus seedpod oligomeric procyanidins(LSOPC),and its structure confirmed via Fourier transform infrared(FTIR),ultraviolet-visible(UV-Vis),scanning electron microscopy(SEM),and differential scanning calorimetry(DSC)analyses.In simulated gastrointestinal conditions,CMP-LSOPC NPs exhibited a significant reduction in AGE formation,achieving up to lowering AGE release by 48.5%compared to LSOPC alone.Furthermore,associated mechanisms are explored,including CMP-LSOPC NPs improving the stability and antioxidant activity of LSOPC,inhibiting the activity of related hydrolase enzymes in the gastrointestinal environment.The CMP-LSOPC NPs exhibited 4.1%higher LSOPC content during the gastric phase compared to LSOPC alone,indicating that CMP-LSOPC NPs with better stability.The antioxidant activity,measured through DPPH,ABTS+,and hydroxyl radical scavenging assays,demonstrated that CMP-LSOPC NPs enhanced antioxidant capacity,with a 35%increase in DPPH radical scavenging and 29%increase in ABTS+radical scavenging compared to LSOPC alone.Enzyme inhibition assays showed a protective effect,with a 22%decrease in trypsin activity and 19%reduction in pepsin activity.Meanwhile,mass spectrometry revealed the presence of more long-chain glycopeptides in the CMP-LSOPC NPs group,which may exert beneficial influence on adiminishing the absorption of harmful AGEs.However,the potential risks of accumulating long glycated peptides in the colon should not be overlooked.Overall,CMP-LSOPC NPs effectively inhibited AGE release,which may offer a promising strategy for reducing the dietary risk of AGEs. 展开更多
关键词 Advanced glycation end products Gastrointestinal digestion Enzyme inhibition Lotus seedpod oligomeric procyanidins Carboxymethyl pachymaran
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A Bridge Transformer Network With Deep Graph Convolution for Hyperspectral Image Classification 认领 引用
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作者 Yuquan Gan Siyu Wu +3 位作者 Chang Su Nan Xiang Zhijie Xu Yushan Pan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期464-482,共19页
Transformers have been widely applied to hyperspectral image classification,leveraging their self-attention mechanism for powerful global modelling.However,two key challenges remain as follows:excessive memory and com... Transformers have been widely applied to hyperspectral image classification,leveraging their self-attention mechanism for powerful global modelling.However,two key challenges remain as follows:excessive memory and computational costs from calculating correlations between all tokens(especially as image size or spectral bands increase)and limited ability to model local boundary information due to lacking explicit enhancement mechanisms.This paper proposes a novel method,bridge transformer network fused with deep graph convolution(BTDGC),to address these issues.The framework includes three components as follows:a double random masking mechanism(DRMM)that forces the model to infer masked features from context during training,a bridge transformer(BT)module with bridge tokens for cross-region feature interaction and a Deep Graph Convolutional Pooling(DGCP)module that preserves spatial topology while aggregating hierarchical information.Experiments on standard hyperspectral datasets show BTDGC outperforms mainstream methods in classification accuracy and robustness,effectively balancing global modelling and local boundary representation.The code is available at http://gffzz188fe103f8f1460aspq66kqqxx0cv6ob9.ffgz.tsg.suse.edu.cn/jenny3489/BTDGC. 展开更多
关键词 convolution graph convolutional network masking mechanism transforms
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Construction of an S-scheme g-C3N4/TiO2 heterostructure for tetracycline degradation and hydrogen production 认领 引用 被引量：1
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作者 AN Mingze ZHANG Bingbing +6 位作者 YANG Zhao PU Hao CHEN Weijie XUE Bin WANG Sheng DING Xiaoyan SHI Lulu 《无机化学学报》 SCIE CAS CSCD 北大核心 2026年第4期843-860,共18页
To overcome the limitations of traditional photocatalysts,such as inefficient separation of charge carriers and poor visible-light absorption,S-scheme g-C3N4/TiO2 heterojunction photocatalysts were synthesize... To overcome the limitations of traditional photocatalysts,such as inefficient separation of charge carriers and poor visible-light absorption,S-scheme g-C3N4/TiO2 heterojunction photocatalysts were synthesized via a combined method of thermal polymerization,hydrothermal synthesis,and calcination.The crystal structures,morphological features,and optical properties of the composites were systematically characterized,and their photocatalytic performance was evaluated through tetracycline(TC)degradation and hydrogen evolution experiments.Trapping experiments and electron paramagnetic resonance(EPR)measurements were conducted to elucidate the reaction mechanisms.The results demonstrate that the S-scheme heterojunction effectively extends the visible-light absorption range and facilitates the efficient separation of photogenerated electron-hole pairs.Under optimal conditions,the composite achieved a TC degradation rate of 94.5%and a hydrogen evolution rate of 329.1μmol·h-1·g-1 after 8 h of irradiation,both values being significantly higher than those of pristine g-C3N4 or TiO2.Moreover,the S-scheme g-C3N4/TiO2 heterojunction retained high photocatalytic activity over five consecutive cycles,confirming its excellent stability.Mechanistic investigations revealed that the S-scheme heterojunction maintained strong redox capacities,with superoxide radicals(·O2-),hydroxyl radicals(·OH),electrons(e-),and holes(h+)serving as the primary active species responsible for TC degradation and H2 production. 展开更多
关键词 S-scheme heterojunction g-C3N4/TiO2 photocatalysis tetracycline degradation photocatalytic hydrogen production
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Critical review on anti-fatigue effects of natural dietary products:present insights and future trends 认领 引用
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作者 Syam P.K.Jeepipalli Nidesha Randeni +1 位作者 Bin Du Baojun Xu 《Food Science and Human Wellness》 SCIE CAS CSCD 2026年第2期453-472,共20页
Fatigue impacts both mental and physical health,significantly reducing quality of life and daily productivity.Natural bioactive compounds have emerged as promising agents to combat fatigue due to their multifaceted bi... Fatigue impacts both mental and physical health,significantly reducing quality of life and daily productivity.Natural bioactive compounds have emerged as promising agents to combat fatigue due to their multifaceted biological activities and minimal side effects.Key mechanisms through which these compounds exert anti-fatigue effects include enhancing energy metabolism,reducing oxidative stress,supporting mitochondrial integrity,modulating the immune response,and regulating neurotransmitter balance.Plant-derived metabolites such as flavonoids,ginsenosides,saponins,and polysaccharides,as well as animal-based peptides and microbial-derived substances,have demonstrated significant potential in alleviating fatigue symptoms in both clinical and preclinical studies.Additionally,fermented products like kefir,fermented rice bran,and yogurt enhance endurance performance,reduce lactate buildup,and improve glycogen storage,further contributing to fatigue mitigation.As consumer interest in natural alternatives grows,future research should prioritize improving the bioavailability,stability,and targeted delivery of these compounds.This review consolidates recent advances in the understanding of anti-fatigue mechanisms of natural products and highlights emerging directions for their development as functional foods and therapeutic agents. 展开更多
关键词 Anti-fatigue activity Plant metabolites Animal products Microbial products Fermented products Amino acid
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Modularized Graph Convolutional Network 认领 引用
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作者 Tiantian He Zhixuan Duan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期737-739,共3页
Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighb... Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighbor aggregation are fixed, leading to the limited capability of capturing diverse relationships among nodes for representation learning. Conventional GCNs always learn node representations in the graph according to the weights computed from the graph Laplacian, consequently overlooking the similarity and group cohesiveness of node features. 展开更多
关键词 graph convolution networks gcns capturing diverse relationships nodes representation learning modularized graph convolution network neighbor aggregation graph neural network modularized graph convolution network mgcn graph convolutional network
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Improving spikelet production efficiency is crucial for further unleashing yield potential in rice 认领 引用
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作者 Weiyang Zhang Meijie Jia +6 位作者 Shengkai Yang Xiaohan Zhong Haotian Chen Ying Liu Zhiqing Wang Jianhua Zhang Jianchang Yang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第6期2627-2629,共3页
Highlights Improving spikelet production efficiency(SPE)can further enhance the grain yield and harvest index in rice.Higher SPE can enhance post-anthesis photoassimilate production in the shoots of rice.Higher SPE ca... Highlights Improving spikelet production efficiency(SPE)can further enhance the grain yield and harvest index in rice.Higher SPE can enhance post-anthesis photoassimilate production in the shoots of rice.Higher SPE can promote the post-anthesis remobilization of non-structural carbohydrate(NSC)from rice stems.Rice grain yield is a complex agronomic trait determined by four key components:panicles per plant,spikelets per panicle,filled-grain rate,and grain weight. 展开更多
关键词 grain yield non structural carbohydrate panicles per plant spikelet production post anthesis remobilization agronomic trait post anthesis photoassimilate production spikelet production efficiency
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Advances in natural product discovery:strategies,technologies,and insights 认领 引用
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作者 Buddha Bahadur Basnet Zhen-Yi Zhou +2 位作者 Rajesh Basnet Bin Wei Hong Wang 《Natural Products and Bioprospecting》 CSCD 2026年第1期39-79,共41页
Natural products(NPs)and their analogues have long underpinned therapies in humans,animals,and plants health,yet,discovering truly novel scaffolds remains a formidable challenge,even with the enormous diversity offere... Natural products(NPs)and their analogues have long underpinned therapies in humans,animals,and plants health,yet,discovering truly novel scaffolds remains a formidable challenge,even with the enormous diversity offered.Over the last two decades,breakthroughs in bioinformatics,cheminformatics,advanced analytical methods,synthetic biology toolkits,and optimized microbial culture have surmounted many of the bottlenecks that stalled NP research in the 1990s and 2000s.Researchers now deploy innovative extraction and purification protocols alongside high-throughput dereplication tools to fish trace metabolites out of complex matrices.These combined approaches not only enable the discovery and rigorous characterization of biosynthesized metabolites,bio-transformed ana-logues and new chemical entities but also allow precise tuning of biosynthetic gene clusters(BGCs)and culture conditions-modulation and optimization,dramatically improving yield,scalability,and cost-efficiency.Several of these newly unearthed compounds exhibit unique bioactivities that directly inspire drug-development programs against metabolic disorders,cancer drug resistance,and infectious diseases.In this review,we present an up-to-date,concise roadmap of natural product discovery(NPD),majorly covering strategies for awakening silent BGCs,genome mining,and late-stage diversification systems,and we discuss the current limitations and perspectives of rational NPD. 展开更多
关键词 Natural products Culturing modulation Unexplored reservoirs Genome mining Natural product diversification
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Development status and trends of oil production engineering technology in the context of interdisciplinary integration 认领 引用
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作者 LIU He JIN Xu +2 位作者 YANG Qinghai WANG Xiaoqi MENG Siwei 《Petroleum Exploration and Development》 SCIE 2026年第3期844-863,共20页
This paper systematically reviews the development stages and current status of key oil production engineering domains,including injection-production engineering,artificial lift,reservoir stimulation,and workover opera... This paper systematically reviews the development stages and current status of key oil production engineering domains,including injection-production engineering,artificial lift,reservoir stimulation,and workover operations.The major challenges for oil production engineering are identified in four aspects:intelligent terminal equipment and process integration,extreme-environment operations,and collaborative operational constraints;AI-driven data and modeling complexities,and advanced structural and functional materials requirements;and the need for geology-engineering integration in reservoir characterization,operational efficiency and green development.Centered on multidisciplinary integration,the concept of the Oil Production Engineering Agent is introduced as a miniaturized,intelligent,and integrated hardware-software system designed for extreme downhole environments and complex conditions,incorporating power supply,communication,sensing,computation,and actuation modules to enable environmental perception,autonomous decision-making and adaptive control.The characteristics of various agent types,including those for injection-production,lift,fracturing and workover,are analyzed,with key research directions identified in miniaturized self-powered energy management,reliable communication in high-interference environments,highly integrated multi-parameter sensing with long-term drift self-calibration,and high-reliability microsystem integration manufacturing.AI-driven decision optimization remains the core feature,requiring advances in data acquisition,governance,and fusion architectures,alongside algorithmic improvements in model performance and deployment compatibility.Additionally,advanced structural and functional materials support agent construction and extreme-environment adaptability,while geology-engineering integration continues to expand the functional scope of oil production engineering. 展开更多
关键词 oil production engineering interdisciplinary integration oil production engineering agents intelligent transformation low-carbon transition
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Prevention and treatment of knee osteoarthritis by natural products:potential mechanisms based on articular cartilage targets 认领 引用
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作者 Qitao Ye Kangyu Zhou +5 位作者 Qingqi Gao Chengyan Li Yunjie Sheng Li Zou Chaoying Tong Dan Shou 《Chinese Journal of Natural Medicines》 SCIE CAS CSCD 2026年第2期129-144,共16页
Knee osteoarthritis(KOA)is a prevalent chronic degenerative joint disorder characterized by an imbalance between articular cartilage degradation and synthesis,a central mechanism in KOA pathogenesis.Given the absence ... Knee osteoarthritis(KOA)is a prevalent chronic degenerative joint disorder characterized by an imbalance between articular cartilage degradation and synthesis,a central mechanism in KOA pathogenesis.Given the absence of disease-modifying therapies,there is a critical need to elucidate the underlying pathological processes,establish reliable biomarkers for early detection and prognosis,and identify safer,more effective therapeutic agents.In recent years,natural products have attracted considerable interest due to their low toxicity,cost-effectiveness,and distinct biological activities,demonstrating significant potential in KOA management.These compounds can impede KOA progression through multiple mechanisms,including promoting cartilage matrix synthesis,mitigating inflammation,reducing oxidative stress,suppressing chondrocyte apoptosis,and modulating autophagy,thereby supporting their translational application.This review summarizes biomarkers relevant to early diagnosis and phenotypic stratification in KOA,with a focus on elucidating the pharmacological actions and molecular mechanisms of natural products,such as flavonoids,alkaloids,saponins,terpenes,and traditional Chinese medicine(TCM)formulas,in KOA intervention,aiming to provide evidence-based strategies for improved disease management. 展开更多
关键词 Knee osteoarthritis Cartilage Natural products Pharmacotherapies Biomarkers
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Plant-derived natural products improve disease:focus on gut-brain axis 认领 引用
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作者 Chen Song Wenjing Lang +2 位作者 Li Meng Wenxiang Ping Jingping Ge 《Food Science and Human Wellness》 SCIE CAS CSCD 2026年第4期1567-1580,共14页
Dysbiosis of the gut microbiota may lead to a wide range of metabolic,neurological,intestinal and cardiovascular disorders and even to tumorigenesis.Evidence suggests that the gut-brain axis(GBA)plays a crucial role i... Dysbiosis of the gut microbiota may lead to a wide range of metabolic,neurological,intestinal and cardiovascular disorders and even to tumorigenesis.Evidence suggests that the gut-brain axis(GBA)plays a crucial role in the treatment of these diseases.Many plant-derived natural actives modulate the gut microbiota and its metabolites,gut hormones and neurotransmitters through a variety of mechanisms,and these actions contribute to the alleviation of irritable bowel syndrome,type 2 diabetes mellitus,cancer,and brain disorders.This review focuses on how natural products act through the GBA to protect the central nervous system,regulate metabolism and promote apoptosis in cancer cells.The role of gut microbiota metabolites and neurotransmitter release in modulating inflammation-related factors,promoting or reducing oxidative stress,and activating or inhibiting related signaling pathways is also discussed.This comprehensive overview of the complex GBA mechanisms deepens the understanding of how natural products can target the GBA to treat disease,providing valuable insights into the development and utilization of these natural interventions. 展开更多
关键词 Natural products Gut-brain axis Gut microbiota Disease
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Unconventional oil production forecasting based on PiAM meta-learning 认领 引用
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作者 Yu-Long Zhao Qing-Yu Xiao +4 位作者 Xing-Jie Zeng Bin Xiong Shuai Wang Song Zhao Yun-Sheng Wei 《Petroleum Science》 SCIE EI CAS CSCD 2026年第3期1335-1347,共13页
Accurate production forecasting serves as a critical determinant for optimizing extraction strategies,guiding long-term field management in reservoir development.Both conventional methods and deep learning techniques ... Accurate production forecasting serves as a critical determinant for optimizing extraction strategies,guiding long-term field management in reservoir development.Both conventional methods and deep learning techniques face significant challenges in production forecasting due to the increasing complexities of reservoir extraction.Firstly,traditional production forecasting methods often fail to fully captu re the complex reservoir behavior.Finally,these approaches demonstrate suboptimal perfo rmance in wells with limited data.These problems can lead to a decrease in prediction accuracy.To address these challenges,this paper introduces the Patching-iTransformer method and applies meta learning.The method improves prediction accuracy and overcomes the problem of few samples in production forecasting.Specifically,we implement a patching mechanism that segments the input time series,thereby converting the univariate time series into a two-dimensional representation.This architectural enhancement significantly strengthens the model's capability to capture latent interdependencies among variables.Currently,we develop a PiAM meta-learning algorithm with domain-specific adaptation for oil field applications by quantitatively assessing individual well contributions to reservoir exploitation.We use time series data from real wells to evaluate the accuracy of multiple wells under the PiAM model.The experimental results demonstrate that Patching-iTransformer achieved better performance improvements than the iTransformer method.R2 increased by 0.297,RMSE decreased by11.64% and MAE decreased by 3.49%.PiAM meta-learning method demonstrated superior performance over the Patching-iTransformer model,showing a 0.535-point improvement in the R2 coefficient along with a reduction of 27.54% in RMSE and a decrease of 28.22% in MAE. 展开更多
关键词 Production forecasting Meta learning Transformer Time series
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Efficient Video Emotion Recognition via Multi-Scale Region-Aware Convolution and Temporal Interaction Sampling 认领 引用
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作者 Xiaorui Zhang Chunlin Yuan +1 位作者 Wei Sun Ting Wang 《Computers, Materials & Continua》 SCIE EI 2026年第2期2036-2054,共19页
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-... Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition. 展开更多
关键词 Multi-scale region-aware convolution temporal interaction sampling video emotion recognition
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Dual Channel Graph Convolutional Networks via Personalized PageRank 认领 引用
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作者 Longlong Lin Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期221-223,共3页
Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representat... Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications. 展开更多
关键词 convolutional node feature similarity graph convolutional framework learning graph representations neural networks gnns networks graph personalized
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Pharmacological mechanisms of natural products with antidepressant effects:A focus on the programmed cell death regulation 认领 引用
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作者 Guangheng Zhang Shimeng Lv +9 位作者 Shengchuan Bao Weijie Zhao Yunhao Yi Haonan Gao Xia Zhong Xiangyu Li Fengzhao Liu Yitong Lu Siyuan Sun Jing Teng 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2026年第1期120-141,共22页
Depression is a prevalent mental disorder characterized by persistent disinterest and a depressed mood,with severe cases potentially leading to suicide.In recent years,the incidence of depression has steadily increase... Depression is a prevalent mental disorder characterized by persistent disinterest and a depressed mood,with severe cases potentially leading to suicide.In recent years,the incidence of depression has steadily increased,making it the second-largest global health burden.The pathogenesis of depression involves a series of complex pathological mechanisms,although the key underlying causes remain unclear.Programmed cell death(PCD),including apoptosis,autophagy,pyroptosis,ferroptosis,and necroptosis,involves highly organized gene expression processes that may influence the occurrence and development of depression by regulating cellular fate.Furthermore,numerous studies have shown that natural products can modulate PCDs through various signaling pathways,presenting significant potential for managing depression.Natural products offer benefits such as cost-effectiveness,fewer side effects,and other advantages,making them viable supplements or alternatives to traditional antidepressant drugs.To explore this potential,we reviewed studies demonstrating the antidepressant effects of natural products through multi-target modulation of PCDs.In addition,we discussed the toxicity and clinical applications of these natural products.This study highlights that diverse core biological pathways and targets are involved in determining the fate of depression-associated brain cells,including the PI3K/Akt signaling pathway,caspase-8,GSDMD,and others.In conclusion,the multi-target mechanisms of PCD regulation by natural products may provide a promising foundation for the future development of novel antidepressant medications. 展开更多
关键词 Depression Pharmacological mechanism Programmed cell death Natural products Antidepressant
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