The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This rev...The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.展开更多
Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflo...Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflow optimization.The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology,including their historical evolution,technical foundations,and practical uses.Despite notable advances,inherent architectural constraints,such as token-level sequential processing,limit their ability to perform deep abstract reasoning and holistic contextual understanding,which are critical for fine-grained diagnostic interpretation.We provide a critical perspective on current LLMs and discuss key challenges,including model reliability,bias,and explainability,highlighting the pressing need for novel approaches to advance radiology AI.Large concept models(LCMs)represent a nascent and promising paradigm in radiology AI,designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration.The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs,highlighting their potential to facilitate enhanced semantic reasoning,long-range context synthesis,and improved clinical decision-making.Critically,the core of this section is the proposal of a novel theoretical framework for LCMs,formalized and extended from our group’s foundational concept-based models-the world’s earliest articulation of this paradigm for medical AI.This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI,providing a large-scale engineering blueprint for the future development of this technology.We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice,aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI.While challenges persist,we are at the very beginning of a new era,and it is not unreasonable to hope that future advancements will overcome these hurdles,pushing the boundaries of AI in Radiology,far beyond even the most state-of-the-art models of today.展开更多
The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness dimin...The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.展开更多
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the A...The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the Arno River and its main tributaries were analyzed to assess the water pollution status.The geochemical composition of the Arno River changes from the source(dominated by a Ca-HCO_(3) facies)to the mouth(where a Na-Cl(SO4)chemistry prevails)with an increasing quality deterioration,as suggested by the Chemical Water Quality Index,due to anthropogenic contributions and seawater intrusion before flowing into the Ligurian Sea.The Ombrone and Usciana tributaries introduce anthropogenic pollutants into the Arno River,whilst Elsa tributary supplies significant contents of geogenic sulfate.The concentrations of dissolved nitrate and nitrite(up to 63 and 9 mg/L,respectively)and the respective isotopic values of𝛿15N and𝛿18O were also determined to understand origin and fate of the N-species in the Arno River Basin surface waters.The combined application of𝛿15N-NO_(3) and𝛿18O-NO_(3) and N-source apportionment modelling allowed the identification of soil organic nitrogen and sewage and domestic wastes as primary sources for dissolved NO_(3)-.The𝛿15N-NO_(2) and𝛿18O-NO_(2) values suggest that the nitrification process affects the ARB waters,thus controlling the abundances and proportion of the N-species.Our work indicates that additional efforts are needed to improve management strategies to reduce the release of nitrogenated species to the surface waters of the Arno River Basin,since little progress has been made from the early 2000s.展开更多
With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and ...With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.展开更多
Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers infl...Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
The rapid advancement of wearable electronic devices has paved the way for a more intelligent and interconnected world.However,ensuring the sustainable energy supply for these devices remains a critical challenge,part...The rapid advancement of wearable electronic devices has paved the way for a more intelligent and interconnected world.However,ensuring the sustainable energy supply for these devices remains a critical challenge,particularly for specialized populations and professionals in demanding environments,where a lack of power can pose life-threatening risks.Herein,we propose a mechanically intelligent biomechanical energy harvesting approach that adapts to complex human motion excitations,thereby improving the energy harvesting performance.Leveraging a mechanical intelligence mechanism,the energy harvester aligns with human physiological habits,selectively activating or deactivating as needed.The system can also adapt to excitations of varying directions,amplitudes,and frequencies.Furthermore,the string tension helps reduce the impact forces on the knee joint during foot strikes.A theoretical model for the biomechanical energy harvesting system is developed to describe its dynamic and electrical characteristics,and a prototype is fabricated and tested under diverse conditions.The experimental results are in good agreement with the simulation trends,validating the effectiveness of the theoretical model.A test subject running at 8 km/h for 90 seconds can successfully power a smartphone for 20 seconds,demonstrating the viability of self-powered applications.This mechanically intelligent biomechanical energy harvesting method holds a promising solution for the sustainable power supply for wearable electronic devices.展开更多
The magnetic properties and Kondo effect in Ce_(3)TiBi_(5) with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) ...The magnetic properties and Kondo effect in Ce_(3)TiBi_(5) with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) undergoes an antiferromagnetic(AFM)transition at T_(N)∼5 K.Under high pressures within 8.9 GPa,we find that Kondo scattering contributes differently to the high-temperature resistance,R(T),depending on the applied current direction,demonstrating a significantly anisotropic Kondo effect.The complete P–T phase diagram has been constructed,in which the pressure dependence of T_(N) exhibits a dome-like shape.The AFM order remains robust under pressure,even when the coherence temperature T^(*) far exceeds 300 K.We attribute the observed anisotropic Kondo effect and the robust AFM to the underlying anisotropy in electronic hybridization under high pressure.展开更多
Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, ...Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, battery technologies are desirable energy storage devices for GLEES due to their easy modularization, rapid response, flexible installation, and short construction cycles. In general, battery energy storage technologies are expected to meet the requirements of GLEES such as peak shaving and load leveling, voltage and frequency regulation, and emergency response, which are highlighted in this perspective. Furthermore, several types of battery technologies, including lead–acid, nickel–cadmium, nickel–metal hydride, sodium–sulfur, lithium-ion, and flow batteries, are discussed in detail for the application of GLEES. Moreover, some possible developing directions to facilitate efforts in this area are presented to establish a perspective on battery technology, provide a road map for guiding future studies, and promote the commercial application of batteries for GLEES.展开更多
The effects of nitrogen(N)deposition on forest soil organic carbon(SOC)are largely unclear,likely due to the divergent responses of particulate(POC)and mineral-associated carbon(MAOC).Conventional understory inorganic...The effects of nitrogen(N)deposition on forest soil organic carbon(SOC)are largely unclear,likely due to the divergent responses of particulate(POC)and mineral-associated carbon(MAOC).Conventional understory inorganic N(UIN)additions neglect canopy processes and the impacts of organic N,potentially misevaluating N deposition effects.This study was conducted in a long-term N addition experiment established in a Moso bamboo forest,which included six treatments combining canopy and understory N additions with organic(urea glycine)and inorganic(NH_(4)NO_(3))forms at a rate of 50 kg N·ha^(-1)·yr^(-1).Litterbags were installed for a two-year decomposition experiment and collected at quarterly intervals,together with concurrent soil sampling under litterbags at 0–10 cm depth.We aimed to examine the effects of canopy vs.understory N addition and organic vs.inorganic N form on soil POC and MAOC concentrations.Our results showed that canopy N additions significantly reduced POC(ased POC-15.9%)but did not affect MAOC(P>0.05).Conversely,understory N additions significantly incre(30.9%)and decreased MAOC(and fungal diversity(FuD),-28.9%).Canopy N additions decreased POC by enhancing peroxidase activity while understory N additions promoted POC by inhibiting litter decomposition.Additionally,understory N addition-induced soil acidification decreased soil Ca^(2+)concentration,microbial carbon use efficiency,and bacterial necromass C,as well as the release of litter water-soluble compounds,thereby inhibiting MAOC.Moreover,nitrogen forms(organic vs.inorganic)had no effect on SOC fractions.Our findings underscore that canopy and understory N addition approaches differentially regulate SOC fractions by altering litter decomposition–microbial–mineral interactions,and the understory approach may overestimate soil POC gain and MAOC loss driven by atmospheric N deposition.展开更多
Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen e...Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.展开更多
AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on...AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on 189 patients with positive anti-HCV and viral RNA enrolled in a 12-month IFN protocol.We evaluated:a) the baseline prevalence of autoimmune gastritis,b) the impact of IFN treatment on development of biochemical signs of autoimmune gastritis (at 3,6 and 12 months),c) the evolution after IFN withdrawal (12 months) in terms of anti-gastric-parietal-cell antibodies (APCA),gastrin,anti-thyroid,and anti-non-organ- specific antibodies. RESULTS:APCA positivity and 3-fold gastrin levels were detected in 3 (1.6%) and 9 (5%) patients,respectively,at baseline,in 25 (13%) and 31 (16%) patients at the end of treatment (both P<0.001,vs baseline),and in 7 (4%) and 14 (7%) patients 12 months after withdrawal (P=0.002 and P=0.01 respectively,vs baseline;P=not significant vs end of treatment).The development of autoimmune gastritis was strictly associated with the presence of autoimmune thyroiditis (P=0.0001),no relationship was found with other markers of autoimmunity. CONCLUSION:In HCV patients,IFN frequently precipitates latent autoimmune gastritis,particularly in females.Following our 12-month protocol,the phenomenon generally regressed.Since APCA positivity and high gastrin levels are associated with the presence of antithyroid antibodies, development of autoimmune thyroiditis during IFN treatment may provide a surrogate preliminary indicator of possible autoimmune gastritis to limit the need for invasive examinations.展开更多
The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)h...The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.展开更多
AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. ...AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. METHODS:A case-control study was conducted to investigate the genetic polymorphisms of these enzymes (CYPIAI*2C and GSTP1 exon 7 Val alleles,GSTMI*2/*2 and GSTTl *2/*2 null genotypes).A total of 79 esophageal cancer cases and 130 controls were recruited. RESULTS:GSTMI*2/*2 and CYPIAI*IA/*2C genotype frequencies were higher among squamous cell carcinomas at a level dose to statistical significance(OR =1.83,95% CI 0.88-3.83,P=0.11;OR=3.03,95% CI 0.93-9.90,P=0.07, respectively).For GSTP1 polymorphism,no difference was found between controls and cases,whatever their histological status.Lower frequency of GSTT1 deletion was observed in ADC group compared to controls with a statistically significant difference(OR=13.31,95% CI 1.66-106.92,P<0.01). CONCLUSION:In SCC,our results are consistent with the strong association of this kind of tumour with tobacco exposure.In ADC,our results suggest 3 distinct hypotheses: (1)activation of exogenous procarcinogens,such as small halogenated compounds by GSTT1;(2)contribution of GSTT1 to the inflammatory response of esophageal mucosa,which is known to be a strong risk factor for ADC, possibly through leukotriene synthesis;(3)higher sensitivity to the inflammatory process associated with intracellular depletion of glutathione.展开更多
A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and rel...A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory,taking into account factors such as switching frequencies,noise intensities,and initial conditions.Results reveal that Markov switching leads to stochastic P-bifurcation,enhancing dynamic balance and reducing white-noise-induced oscillations.But frequent switching can heighten initial value dependence,harming reliability.Further,the influence of the subsystem on the switching system is not proportional to its action probabilities.Monte Carlo simulations validate the findings,offering an in-depth exploration of these dynamics.展开更多
When micro/nano-scale gradient coatings are subject to large thermal gradients or high heat fluxes,the spatial size effect cannot be ignored.It is important to understand how the size effect influences the thermal fra...When micro/nano-scale gradient coatings are subject to large thermal gradients or high heat fluxes,the spatial size effect cannot be ignored.It is important to understand how the size effect influences the thermal fracture behavior of functionally graded coating/substrate structures.This study aims at analyzing the transient thermal fracture behavior of collinear interface cracks in functionally graded coating/substrate structures based on the nonlocal dual-phase-lag heat conduction model.By means of integral transform techniques,the mixed boundary problem is transformed into a set of singular integral equations,which are solved by the Chebyshev polynomials.The effects of the nonlocal parameter,coating thickness,crack spacing,and non-homogeneous parameters on the temperature and stress intensity factors(SIFs)are examined.The numerical results show that these parameters play an essential role in controlling the thermal fracture behavior of the structures,especially at micro/nano-scales.展开更多
文摘The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.
文摘Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflow optimization.The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology,including their historical evolution,technical foundations,and practical uses.Despite notable advances,inherent architectural constraints,such as token-level sequential processing,limit their ability to perform deep abstract reasoning and holistic contextual understanding,which are critical for fine-grained diagnostic interpretation.We provide a critical perspective on current LLMs and discuss key challenges,including model reliability,bias,and explainability,highlighting the pressing need for novel approaches to advance radiology AI.Large concept models(LCMs)represent a nascent and promising paradigm in radiology AI,designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration.The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs,highlighting their potential to facilitate enhanced semantic reasoning,long-range context synthesis,and improved clinical decision-making.Critically,the core of this section is the proposal of a novel theoretical framework for LCMs,formalized and extended from our group’s foundational concept-based models-the world’s earliest articulation of this paradigm for medical AI.This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI,providing a large-scale engineering blueprint for the future development of this technology.We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice,aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI.While challenges persist,we are at the very beginning of a new era,and it is not unreasonable to hope that future advancements will overcome these hurdles,pushing the boundaries of AI in Radiology,far beyond even the most state-of-the-art models of today.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 62276109The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the Research Group Project number(ORF-2025-585).
文摘The personalized fine-tuning of large languagemodels(LLMs)on edge devices is severely constrained by limited computation resources.Although split federated learning alleviates on-device burdens,its effectiveness diminishes in few-shot reasoning scenarios due to the low data efficiency of conventional supervised fine-tuning,which leads to excessive communication overhead.To address this,we propose Language-Empowered Split Fine-Tuning(LESFT),a framework that integrates split architectures with a contrastive-inspired fine-tuning paradigm.LESFT simultaneously learns frommultiple logically equivalent but linguistically diverse reasoning chains,providing richer supervisory signals and improving data efficiency.This process-oriented training allows more effective reasoning adaptation with fewer samples.Extensive experiments demonstrate that LESFT consistently outperforms strong baselines such as SplitLoRA in task accuracy.LESFT consistently outperforms strong baselines on GSM8K,CommonsenseQA,and AQUA_RAT,with the largest gains observed on Qwen2.5-3B.These results indicate that LESFT can effectively adapt large language models for reasoning tasks under the computational and communication constraints of edge environments.
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
文摘The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the Arno River and its main tributaries were analyzed to assess the water pollution status.The geochemical composition of the Arno River changes from the source(dominated by a Ca-HCO_(3) facies)to the mouth(where a Na-Cl(SO4)chemistry prevails)with an increasing quality deterioration,as suggested by the Chemical Water Quality Index,due to anthropogenic contributions and seawater intrusion before flowing into the Ligurian Sea.The Ombrone and Usciana tributaries introduce anthropogenic pollutants into the Arno River,whilst Elsa tributary supplies significant contents of geogenic sulfate.The concentrations of dissolved nitrate and nitrite(up to 63 and 9 mg/L,respectively)and the respective isotopic values of𝛿15N and𝛿18O were also determined to understand origin and fate of the N-species in the Arno River Basin surface waters.The combined application of𝛿15N-NO_(3) and𝛿18O-NO_(3) and N-source apportionment modelling allowed the identification of soil organic nitrogen and sewage and domestic wastes as primary sources for dissolved NO_(3)-.The𝛿15N-NO_(2) and𝛿18O-NO_(2) values suggest that the nitrification process affects the ARB waters,thus controlling the abundances and proportion of the N-species.Our work indicates that additional efforts are needed to improve management strategies to reduce the release of nitrogenated species to the surface waters of the Arno River Basin,since little progress has been made from the early 2000s.
文摘With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.
基金supported by the Discovery Grants program of the Natural Sciences and Engineering Research Council of Canada(No.RGPIN-2021-03553)the Canadian Research Chair in dendroecology and dendroclimatology(CRC-2021-00368)+3 种基金the Ministère des Ressources Naturelles et des Forèts(MRNF,Contract no.142332177-D)the Natural Sciences and Engineering Research Council of Canada(Alliance Grant No.ALLRP 557148-20,obtained in partnership with the MRNF and Resolute Forest Products)the Fonds de recherche du Qu ebec–Nature et technologies(Partnership Research Program on the Contribution of the Forestry Sector to Climate Change MitigationGrant No.2022-0FC-309064)。
文摘Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金Project supported by the National Natural Science Foundation of China(Nos.12202262,12172127,12032015,and 12121002)the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(No.2023QNRC001)the Hunan Province Science and Technology Innovation Program of China(Nos.2025JJ20012 and 2025RC4022)。
文摘The rapid advancement of wearable electronic devices has paved the way for a more intelligent and interconnected world.However,ensuring the sustainable energy supply for these devices remains a critical challenge,particularly for specialized populations and professionals in demanding environments,where a lack of power can pose life-threatening risks.Herein,we propose a mechanically intelligent biomechanical energy harvesting approach that adapts to complex human motion excitations,thereby improving the energy harvesting performance.Leveraging a mechanical intelligence mechanism,the energy harvester aligns with human physiological habits,selectively activating or deactivating as needed.The system can also adapt to excitations of varying directions,amplitudes,and frequencies.Furthermore,the string tension helps reduce the impact forces on the knee joint during foot strikes.A theoretical model for the biomechanical energy harvesting system is developed to describe its dynamic and electrical characteristics,and a prototype is fabricated and tested under diverse conditions.The experimental results are in good agreement with the simulation trends,validating the effectiveness of the theoretical model.A test subject running at 8 km/h for 90 seconds can successfully power a smartphone for 20 seconds,demonstrating the viability of self-powered applications.This mechanically intelligent biomechanical energy harvesting method holds a promising solution for the sustainable power supply for wearable electronic devices.
基金supported by the National Key Research and Development Program of Chinathe National Natural Science Foundation of China (Grant Nos.2024YFA1408000,12474097,and2023YFA1406001)+2 种基金the Guangdong Provincial Quantum Science Strategic Initiative (Grant No.GDZX2201001)the Center for Computational Science and Engineering at Southern University of Science and Technology,the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen(for J.L.Z.and Y.L.)the Chinese funding sources applied via HPSTAR。
文摘The magnetic properties and Kondo effect in Ce_(3)TiBi_(5) with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) undergoes an antiferromagnetic(AFM)transition at T_(N)∼5 K.Under high pressures within 8.9 GPa,we find that Kondo scattering contributes differently to the high-temperature resistance,R(T),depending on the applied current direction,demonstrating a significantly anisotropic Kondo effect.The complete P–T phase diagram has been constructed,in which the pressure dependence of T_(N) exhibits a dome-like shape.The AFM order remains robust under pressure,even when the coherence temperature T^(*) far exceeds 300 K.We attribute the observed anisotropic Kondo effect and the robust AFM to the underlying anisotropy in electronic hybridization under high pressure.
文摘Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, battery technologies are desirable energy storage devices for GLEES due to their easy modularization, rapid response, flexible installation, and short construction cycles. In general, battery energy storage technologies are expected to meet the requirements of GLEES such as peak shaving and load leveling, voltage and frequency regulation, and emergency response, which are highlighted in this perspective. Furthermore, several types of battery technologies, including lead–acid, nickel–cadmium, nickel–metal hydride, sodium–sulfur, lithium-ion, and flow batteries, are discussed in detail for the application of GLEES. Moreover, some possible developing directions to facilitate efforts in this area are presented to establish a perspective on battery technology, provide a road map for guiding future studies, and promote the commercial application of batteries for GLEES.
文摘The effects of nitrogen(N)deposition on forest soil organic carbon(SOC)are largely unclear,likely due to the divergent responses of particulate(POC)and mineral-associated carbon(MAOC).Conventional understory inorganic N(UIN)additions neglect canopy processes and the impacts of organic N,potentially misevaluating N deposition effects.This study was conducted in a long-term N addition experiment established in a Moso bamboo forest,which included six treatments combining canopy and understory N additions with organic(urea glycine)and inorganic(NH_(4)NO_(3))forms at a rate of 50 kg N·ha^(-1)·yr^(-1).Litterbags were installed for a two-year decomposition experiment and collected at quarterly intervals,together with concurrent soil sampling under litterbags at 0–10 cm depth.We aimed to examine the effects of canopy vs.understory N addition and organic vs.inorganic N form on soil POC and MAOC concentrations.Our results showed that canopy N additions significantly reduced POC(ased POC-15.9%)but did not affect MAOC(P>0.05).Conversely,understory N additions significantly incre(30.9%)and decreased MAOC(and fungal diversity(FuD),-28.9%).Canopy N additions decreased POC by enhancing peroxidase activity while understory N additions promoted POC by inhibiting litter decomposition.Additionally,understory N addition-induced soil acidification decreased soil Ca^(2+)concentration,microbial carbon use efficiency,and bacterial necromass C,as well as the release of litter water-soluble compounds,thereby inhibiting MAOC.Moreover,nitrogen forms(organic vs.inorganic)had no effect on SOC fractions.Our findings underscore that canopy and understory N addition approaches differentially regulate SOC fractions by altering litter decomposition–microbial–mineral interactions,and the understory approach may overestimate soil POC gain and MAOC loss driven by atmospheric N deposition.
基金Supported by the National Natural Science Foundation of China(No.52273056)the Science and Technology Development Program of Jilin Province,China(No.YDZJ202501ZYTS305)。
文摘Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.
文摘AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on 189 patients with positive anti-HCV and viral RNA enrolled in a 12-month IFN protocol.We evaluated:a) the baseline prevalence of autoimmune gastritis,b) the impact of IFN treatment on development of biochemical signs of autoimmune gastritis (at 3,6 and 12 months),c) the evolution after IFN withdrawal (12 months) in terms of anti-gastric-parietal-cell antibodies (APCA),gastrin,anti-thyroid,and anti-non-organ- specific antibodies. RESULTS:APCA positivity and 3-fold gastrin levels were detected in 3 (1.6%) and 9 (5%) patients,respectively,at baseline,in 25 (13%) and 31 (16%) patients at the end of treatment (both P<0.001,vs baseline),and in 7 (4%) and 14 (7%) patients 12 months after withdrawal (P=0.002 and P=0.01 respectively,vs baseline;P=not significant vs end of treatment).The development of autoimmune gastritis was strictly associated with the presence of autoimmune thyroiditis (P=0.0001),no relationship was found with other markers of autoimmunity. CONCLUSION:In HCV patients,IFN frequently precipitates latent autoimmune gastritis,particularly in females.Following our 12-month protocol,the phenomenon generally regressed.Since APCA positivity and high gastrin levels are associated with the presence of antithyroid antibodies, development of autoimmune thyroiditis during IFN treatment may provide a surrogate preliminary indicator of possible autoimmune gastritis to limit the need for invasive examinations.
文摘The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.
基金Supported by the Grants From Ligue Nationale Contre le Cancer,Comités Départementaux de la Manche,de l'Orne et du Calvados and from Université de Metz
文摘AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. METHODS:A case-control study was conducted to investigate the genetic polymorphisms of these enzymes (CYPIAI*2C and GSTP1 exon 7 Val alleles,GSTMI*2/*2 and GSTTl *2/*2 null genotypes).A total of 79 esophageal cancer cases and 130 controls were recruited. RESULTS:GSTMI*2/*2 and CYPIAI*IA/*2C genotype frequencies were higher among squamous cell carcinomas at a level dose to statistical significance(OR =1.83,95% CI 0.88-3.83,P=0.11;OR=3.03,95% CI 0.93-9.90,P=0.07, respectively).For GSTP1 polymorphism,no difference was found between controls and cases,whatever their histological status.Lower frequency of GSTT1 deletion was observed in ADC group compared to controls with a statistically significant difference(OR=13.31,95% CI 1.66-106.92,P<0.01). CONCLUSION:In SCC,our results are consistent with the strong association of this kind of tumour with tobacco exposure.In ADC,our results suggest 3 distinct hypotheses: (1)activation of exogenous procarcinogens,such as small halogenated compounds by GSTT1;(2)contribution of GSTT1 to the inflammatory response of esophageal mucosa,which is known to be a strong risk factor for ADC, possibly through leukotriene synthesis;(3)higher sensitivity to the inflammatory process associated with intracellular depletion of glutathione.
基金Project supported by the National Natural Science Foundation of China(Grant No.12472033)。
文摘A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory,taking into account factors such as switching frequencies,noise intensities,and initial conditions.Results reveal that Markov switching leads to stochastic P-bifurcation,enhancing dynamic balance and reducing white-noise-induced oscillations.But frequent switching can heighten initial value dependence,harming reliability.Further,the influence of the subsystem on the switching system is not proportional to its action probabilities.Monte Carlo simulations validate the findings,offering an in-depth exploration of these dynamics.
基金Project supported by the Natural Science Foundation of Shandong Province of China(No.ZR2024MA085)the Fundamental Research Funds for Central Universities of China(No.27RA2515008)。
文摘When micro/nano-scale gradient coatings are subject to large thermal gradients or high heat fluxes,the spatial size effect cannot be ignored.It is important to understand how the size effect influences the thermal fracture behavior of functionally graded coating/substrate structures.This study aims at analyzing the transient thermal fracture behavior of collinear interface cracks in functionally graded coating/substrate structures based on the nonlocal dual-phase-lag heat conduction model.By means of integral transform techniques,the mixed boundary problem is transformed into a set of singular integral equations,which are solved by the Chebyshev polynomials.The effects of the nonlocal parameter,coating thickness,crack spacing,and non-homogeneous parameters on the temperature and stress intensity factors(SIFs)are examined.The numerical results show that these parameters play an essential role in controlling the thermal fracture behavior of the structures,especially at micro/nano-scales.
